Citation Thakore P Raut P

Bhattacharjee J Loss of Relict Oak

Forests along Coastal Louisiana A

Multiyear Analysis Using Google

Earth Engine Forests 2022 13 1132

httpsdoiorg103390f13071132

Academic Editor Daniel Moya

Received 16 May 2022

Accepted 10 July 2022

Published 18 July 2022

Publisherrsquos Note MDPI stays neutral

with regard to jurisdictional claims in

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Copyright copy 2022 by the authors

Licensee MDPI Basel Switzerland

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Article

Loss of Relict Oak Forests along Coastal Louisiana A MultiyearAnalysis Using Google Earth EnginePaurava Thakore 1 Parusha Raut 2 and Joydeep Bhattacharjee 1

1 Plant Ecology Lab Department of Biology University of Louisiana at Monroe 700 University AvenueMonroe LA 71209 USA joydeepulmedu

2 Department of Computer Science University of Louisiana at Monroe 700 University AvenueMonroe LA 71209 USA rautpwarhawksulmedu

Correspondence thakorepwarhawksulmedu

Abstract Coastal forests along the southeastern Gulf of Mexico are known to be diminishing atan alarming rate The live-oak dominant chenier forests of southeast Louisiana are amongst thoseexhibiting the steepest declines The remnant stands have experienced numerous hurricanes andintense storm events in recent years calling into question the current status and immediate futureof this imperiled natural resource Despite their noted ecological and physiographic importancethere is a lack within national geographic data repositories of accurate representations of forest lossand wetland extent for this region Supervised machine learning algorithms in the Google EarthEngine were used to classify and process high-resolution National Agricultural Image Product (NAIP)datasets to create accurate (gt90) tree cover maps of the Louisiana Chenier Plains in Cameron andVermilion Parishes Data from three different years (2003 2007 and 2019) were used to map 2302 km2

along the southwestern coast of Louisiana According to the analyses there was a 3573 loss offorest cover in this region between 2003 and 2019 A majority of the land-use change was from treecover to saltmarsh with losses in pastoral land also documented We found variable rates of losswith respect to elevation Forest cover losses corresponded strongly to rises in mean sea level Thesefindings deliver a baseline understanding of the rate of forest loss in this region highlighting thereduction and potentially the eventual extirpation of this imperiled ecosystem

Keywords Google Earth Engine vegetation decline coastal forests NAIP forest loss live oak meansea-level

1 Introduction

Coastal forests are exposed to a unique suite of disturbances Rising sea levels hu-man encroachment salinification of soils and exposure to extreme weather all of whichare intricately tied to global change are leading to irreversible changes in these environ-ments [1ndash3] Cheniers and their associated plains are products of sediment outwash andfluvial sedimentation from major river systems along coastal regions They are foundglobally across South America Europe North America Africa Australia and Asia [4ndash6]Our study focuses on cheniers along the TexasndashLouisiana Coastal Marsh Ecoregion formedfrom the Mississippi River within the last 3000 years [78] These cheniers once containeda rich diversity of closed-canopy hardwood species the dominant being the coastal liveoak (Quercus virginiana Mill) [9] Colloquially the term lsquochecircnersquo translates to ldquoplace ofoaksrdquo in Acadian French which correctly recognizes the stature and prevalence of thiswide-spreading tree as a keystone member of the ecosystem Agrarian settlement andinfrastructure development has driven land-use change to non-forest means from theearly 1900s to the present day [10] In this study we used remotely sensed imagery tocensus forest area and assess rates of loss in tree cover from degradative natural andanthropogenic influences

Forests 2022 13 1132 httpsdoiorg103390f13071132 httpswwwmdpicomjournalforests

Forests 2022 13 1132 2 of 22

The National Land Cover National Wetland Inventory Coastal Change AnalysisProgram and Global Forest Cover datasets (PALSAR-2) have failed to identify theseforests due to the coarse nature of classifying imagery [11] As we continue to see rapiddeclines in forest areas along coastlines it is appropriate to recognize and inventoryareas not previously considered in the analysis of forest loss in the southeastern UnitedStates (US) A lack of inclusion of this land-cover type has led to an underrepresentationof over 2790 ha of forested land area Existing data for southeastern Louisiana theFloridian Gulf Coastal forests and coastal forests in North and South Carolina all provideestimations of areal forest and marsh extent from the mid-1980s to the present day [12ndash14]In comparison this area is data-poor regarding land use and change limiting the ability ofgovernmental agencies or non-governmental organizations to draft policies that includethis forest ecosystem Forest change modelling can be done using a variety of methodsfrom spectral to spatial dependent on the imaging sensor characteristics (multispectralhyperspectral SAR etc) Multi-band imagery can be utilized to create novel vegetationindices that can map forest change successfully at variety of scales (30 mndash1 km pixelsize) [15] Sub-pixel mapping allows investigators to create geospatial models that aremore highly granular than the original pixels by using auxiliary datasets Sub-pixelmethodologies such as spectral unmixing or sub-pixel swaps present opportunities tocreate more refined maps Valjarevic et al [16] used historic topographic maps to createsocio-economic associations between forest cover population abundance and average ratesof deforestation Spatially based methodologies can be done using clustering groupingor organizing of pixels into fixed arrays with regard to the similarity or lack thereofto neighboring pixels Cluster mapping such as Fuzzy K-Means fusion algorithms orvarious machine-learning algorithms (RF CNN SVM) can perform very accurate (gt90)classification of a variety of remote sensing products [1718]

The preceding studies show the importance of ldquobig datardquo and remote sensing infunctional ecological studies Big datasets can present difficulties for analyses as theirvolume prevents traditional tools from accessing underlying information of value [19]Large time-series datasets (ie satellite imagery) are a powerful way to exemplify changesat the global or local scale for any vegetated land use [20] Very high resolution (VHR)imagery (lt2 m) extends the benefit by providing the ability to clearly demarcate change andassess fine patterns of variation in land use and land-cover [21] The VHR National ImageAgricultural Product (NAIP) used in this study is voluminous a coterminous orthoimageof the US would be over 65TB [22] For researchers without access to super-computerscloud-based computing increases accessibility to these data One can access a wide varietyof data sources or iteratively test algorithms in the creation of a processing workflowsomething that could take days or weeks if processed locally [2324] In this study weused Google Earth Engine (GEE) to tackle this large-scale mapping and analysis intuitivelystitching and classifying custom extents of desired study areas

The geomorphology of Louisianarsquos coastline has been in a state of intense flux overthe last century impacting biotic components which rely on stable stratigraphy and hy-drology [25] Much of the research done on this dilemma has focused on dynamics relatedto land loss or on the marsh aspects of the ecosystem [2627] Neyland and Meyer [28]carried out the first study on the composition extent and ecology of the Louisiana ChenierPlain woodlands in 1997 yet did not give estimates of the relative abundance of remnantforests The forests of the Louisiana Chenier Plain were decimated before being properlyinventoried historic plant communities and abundances are largely unknown [29] Gos-selink [9] estimates a historic extent of 46500 ha forested land-cover between cheniersspoil banks and bottomland swamp forests Our results show a significant change in thearea and location of forest cover today

Largely climactic factors and sea level have determined the fate of coastal forestsand by extension the whole region [3031] Extreme weather is also a growing concernSince the year 2000 the chenier woodlands have suffered the wrath of hurricanes RitaGustav Laura and Delta These storms were all category 2 or greater when making landfall

Forests 2022 13 1132 3 of 22

on the coast Each hurricane brought the dual threats of storm surge which can causeinundation up to 4m above ground level and structural damage from wind-gusts up to177 kmh [32] There is an eminent need to quantify the impacts on the vegetation ofcoastal forests as tropical storm events are likely to increase in frequency and intensity inthe near future [33] Our observations help establish baseline data on the current extent ofthese unique and vulnerable forest ecosystems within the region making clear their statusand the vulnerability this ecosystem

2 Materials and Methods21 Study Area

The study area encompassed the coastline of Cameron and Vermilion Parishes insouthwestern Louisiana (Figure 1) A varying buffer of 6 to 14 km from the coast wasmaintained to capture pertinent physiographic features The area is classified as a low-profile microtidal storm-dominated coast that experiences both erosive and accumulativesediment loads [8] It is distinctive in that the land experiences subsidence in additionto above-average rates of mean sea level (MSL) rise [34] Broadly this area is dividedinto two geomorphic regions the Chenier Plains and the marginal deltaic plains of SELouisiana The chenier plain ridges are laterally accreted sections of sand and shellsdeposited episodically Accretion heights vary between 0ndash4 m with lengths ranging from10ndash90 km and widths less than 1 km across arrayed in a linear-to-concave manner [20]These ridges form complexes defined as two or more parallel or sub-parallel sectionsthat stretch for lengths well beyond 400 km Chenier complexes can directly about thecoast while the most landward are located up to 15 km away The two soil complexesof the region are Hackberry and Mermenteau [35] Hackberry soils form the ridges andare deep poorly drained and moderately permeable composed of sand and shells TheMermenteau soil complex forms the edaphic base of the saltmarshes The complex is verydeep with reduced permeability and is primarily composed of clayey over loamy sedimentContextually these ridges form the only topography above MSL within this region and arethus visually striking when seen at distance

Figure 1 A map indicating the distribution of chenier ridges shown in black bars along the coast ofVermillion and Cameron Parishes Adapted from McBride (2007) with permission from Elsevier Thelines and numbering represent divisions created for data analysis

The extant vegetation communities form a closed-canopy hardwood forest composedof live oak (Quercus virginiana Mill) southern hackberry (Celtis laevigata (Kunth) SprengAmerican elm (Ulmus americana L) black willow (Salix nigra Marshall) honey locust(Gleditsia triacanthos L) and persimmon (Diospyros virginiana L) among others as wellas now the increasingly common invasive Chinese tallow (Triadica sebifera L) Shrubssuch as American holly (Ilex opaca Aiton) coastal mallow (Kosteletzkya pentacarpos L)common hawthorn (Crataegus viridis L) toothache-tree (Zanthoxylum americanum Mill)and chinaberry (Melia azedarach L) among many more are also frequently encountered [22]

Forests 2022 13 1132 4 of 22

The intact community forms a dense stand that prohibits the establishment of shade-intolerant species These communities have been described as ldquocathedral-likerdquo due to thetall and wide branching stature of the live oaks The leaves of live oaks are dark greenin hue and maintain foliage year-round making their foliage a strong visual indicator inaerial imagery

Recent vegetation surveys show a shift to a community more akin to an open saltmarshswamp with thick stands of southern amaranth (Amaranthus australis (AGray) JDSauer)roseau cane (Phragmites australis (Cav) Steud) narrow-leaved cattail (Typha angustifolia L)and other wetland obligate species interspersed with dead or dying trees (unpublished dataBhattacharjee J) This newly emerging ecotype represents a breakdown of the traditionalsegregation of upland forest and lowland haline swamp This change is broadly representedby the encroachment of hydrophytic vegetation becoming dominant in the understory Thischange is identifiable when seen in VHR imagery although the spectral characteristics ofthe scene can appear differently Much of the structural complexity of the forest has beenlost The physiognomy appears as a brushy single layer with limited overstorey mirroringthe surrounding saltmarsh to some degree Some areas have become entirely dominated bydense herbaceous vegetation preventing the establishment of native upper-story speciesThe site has always experienced anthropogenic influence in community composition thenative Attakapas people contributed to the original diversity of vegetation by introducingand maintaining an abundance of food-bearing plants within the landscape [3637] Thestudy site does not have any mangrove swamps which can be found further east and southalong the Gulf Coast

22 Data Sources

The National Agricultural Image Program of the United States Department of Agriculture(USDA) houses aerial orthoimage sets for the contiguous United States in 375 times 375 quadranglesThe 2003 and 2007 imagery for Louisiana were collected at 2 m and 1 m Ground SampleDistance (GSD) respectively in red green and blue bands The 2019 imagery has 05 mGSD with the addition of a near-infrared band In total 234 NAIP image tiles were used foranalysis Finer scale imagery was not downsampled to coarser resolution Additionally allbands were used in the classification scheme to provide the greatest discriminatory accuracyAn effort was made to classify imagery from around a similar date to capture equivalentphenophases although image quality was given precedence over temporal constraintsThe aerial images used in the data analysis pipeline for the years 2003 2007 and 2019were collected on 82403 81007 and 9519 respectively These particular years werechosen as they represented the highest quality imagery within the study period It shouldbe noted the NAIP is not an annual dataset collections vary according to agreements withstate governments and are often performed on a biennial or triennial cycle Limiting factorsin many of the interior year image sets were cloud cover or high local variance in scenereflectance which were prohibitive for the manner of land cover classification used inthe study Intense glare from surrounding water bodies caused significant reduction inquality of less optimal image correction after the fact It became difficult to distinguishingbetween many similar vegetation types as even moderate local variance can introducenoise degrading algorithm effectiveness Imagery from 2013 had begun to be processed butwas abandoned after initial development due to scene variability and poor classificationperformance Only image sets with less than 5 cloud cover were used

The Coastal National Elevation Database (CoNED) Digital Elevation Model (DEM)was used for elevation analysis The CoNED is generated by the United States GeologicalSurvey (USGS) using LiDAR and has a 3 m GSD It is housed in the National Map Datarepository The raster was downsampled to 1 m to match the resolution of the GEEclassification output Mean sea level (MSL) salinity and temperature values were extractedfrom two USGS stream gages (08017118 07387050) located at opposite ends of the studyarea providing data for the entirety of our study period The data were extracted fromthe USGS StreamStats interactive web map The National Hydrography Dataset (NHD)

Forests 2022 13 1132 5 of 22

a vector repository of waterbodies (NHDWaterbody NHDArea) and wetland features(NHDWaterbody FCode 46600 46601 46602) was used for feature input in the creation ofpost-processing masks The NHD is also a product of the USGS

23 Classification and Processing Algorithms

Google Earth Engine offers a suite of supervised and unsupervised Machine LearningClassifiers for image classification Through iterative testing of all available options theClassification and Regression Tree (CART) algorithm was chosen for this study CART isa decision-tree-based supervised learning model able to handle nonlinear relationshipsamong features [38] Because class balancing is recommended for this algorithm a stableratio of tree-to-pasture and tree-to-marsh training sets were created Parametrizationwas not modified from default properties The primary consideration in choosing thisalgorithm was robust classification for forest cover Visually we observed that many othercontenders had higher incidence rates of non-forest-based classification of tree cover whenconducting initial testing leading to a general under-representation of forest cover Weuse a reductionist approach for accuracy in this study where pixel values for forest coverwere reduced rather than added which makes underrepresentation problematic Thepost-processing vectors allowed extremely fine detail in the modification of the originalCART output

The study area was divided into four sections (A B C and D) of equal approximatelength as seen in Figure 1 Sections C and D were further divided into three subsectionsdue to image variability in the 2003 dataset and were merged post-classification Thisfacilitated a more targeted training dataset given local variation while also expeditingprocessing within the workflow We used six total land-cover classes in the classificationschememdashtree pasture pond river saltmarsh and impervious area The classes werecomprised of manually delineated Point FeatureCollections within GEE and were givenspecific unique IDs Tree cover was interpreted as the visual presence of dark foliagebranching or scrubbiness As such these selections include trees located on spoil banklevees (Figure 2B) swamp forests (Figure 2C) or residential areas in addition to the live-oak communities found on the main chenier ridges (Figure 2AD) The rationale was toinclude any type of vegetation that promotes beneficial land use and habitat Pasture wasinterpreted as any area in which there are grazing activities or managed lands on whichregular mowing occurs such as yards or parks The Point FeatureCollections were createdusing 2019 NAIP imagery and were manually validated before use in each preceding yearto ensure classes remained accurate Modifications were made when class values changedbetween years The CART classification was subject to a 3-pixel square image reducerthereby homogenizing values to reduce the ldquosalt and pepper effectrdquo common in pixelwiseclassifications [39] The data were then exported to Google Drive for further processing inQuantum GIS (QGIS) A detailed workflow for the procedure is given in Figure 3

24 Post-Processing of GEE Outputs

Two-layer masks created from the NHD were used to refine the classified output Themasks were originally ascertained and modified in a vector format and were convertedto a raster-type format when used for modifying the GEE outputs The two layers whichwere in the same projection and scale were employed to perform mathematical operationson a per-pixel basis Raster calculations can be applied to overlapping areas according totheir underlying geospatial properties affecting how data is represented [40] As the NHDis incomplete regarding wetlands and pond features care was taken to digitize areas ofthe 2003 NAIP imagery that were wholly saltmarsh or water [41] An assumption wasmade that any saltmarsh or water feature present in 2003 would maintain its non-forestedland use during the study period This allowed a set standard for the modification ofGEE outputs The saltmarsh mask was set so that any pixel registering as a tree wouldbe converted into saltmarsh without the modification of any other land-cover class Thewater mask was absolute in terms of pixel modification All pixels overlapping the mask

Forests 2022 13 1132 6 of 22

were converted regardless of their class Tree cover vectors were then extracted from eachyearrsquos classification All cover area calculations including different elevation productswere performed on this vectorized form due to the superior computational ability forcalculating absolute area of georeferenced polygons in a vector format Polygons were alsomanually scanned over the respective years of NAIP imagery and were either deleted oradjusted following a set standard for Quality Assurance (QA) to ensure best representation

Forests 2022 13 x FOR PEER REVIEW 6 of 23

Figure 2 Panels (AndashD) show diagnostic types of chenier forests Panels (AC) show a chenier rem‐

nant forest Panel (B) is a spoil‐bank forest from the days of oil exploration The southern terminus

of (D) shows a swamp forest which could inhabit less haline marsh communities found in the north‐

ern reaches of the Chenier Plains The black‐outlined areas are the respective yearsrsquo forest extent

Figure 2 Panels (AndashD) show diagnostic types of chenier forests Panels (AC) show a chenier remnantforest Panel (B) is a spoil-bank forest from the days of oil exploration The southern terminus of(D) shows a swamp forest which could inhabit less haline marsh communities found in the northernreaches of the Chenier Plains The black-outlined areas are the respective yearsrsquo forest extent

25 Data Analysis and Statistics

Because post-processing and QA significantly modified the original CART classifi-cation baseline statistics on algorithm confidence would not be representative Insteadproducerrsquos accuracy (PA) was used as the primary determinant of classification success(Equation (1) where reference sites denote created validation points)

Producerrsquos Accuracy = of correctly classified reference sites

of total reference sitestimes100 (1)

Forests 2022 13 1132 7 of 22Forests 2022 13 x FOR PEER REVIEW 7 of 23

Figure 3 Process workflow used in the study The section bracketed in blue was processed using

Google Earth Engine and the section bracketed in green was carried out using QGIS Blue blocks

represent raw inputs into the workflow green blocks are the final outputs

24 Post‐Processing of GEE Outputs

Two‐layer masks created from the NHD were used to refine the classified output

The masks were originally ascertained and modified in a vector format and were con‐

verted to a raster‐type format when used for modifying the GEE outputs The two layers

which were in the same projection and scale were employed to perform mathematical

operations on a per‐pixel basis Raster calculations can be applied to overlapping areas

according to their underlying geospatial properties affecting how data is represented [40]

As the NHD is incomplete regarding wetlands and pond features care was taken to dig‐

itize areas of the 2003 NAIP imagery that were wholly saltmarsh or water [41] An as‐

sumption was made that any saltmarsh or water feature present in 2003 would maintain

its non‐forested land use during the study period This allowed a set standard for the

modification of GEE outputs The saltmarsh mask was set so that any pixel registering as

a tree would be converted into saltmarsh without the modification of any other land‐

cover class The water mask was absolute in terms of pixel modification All pixels over‐

lapping the mask were converted regardless of their class Tree cover vectors were then

extracted from each yearrsquos classification All cover area calculations including different

Figure 3 Process workflow used in the study The section bracketed in blue was processed usingGoogle Earth Engine and the section bracketed in green was carried out using QGIS Blue blocksrepresent raw inputs into the workflow green blocks are the final outputs

The creation of points for sections A B C and D to assess accuracy was carried outusing three tools in the QGIS Processing Toolbox First a lsquoDifferencersquo was run to extracttree cover in an original yearrsquos dataset but not in the succeeding year A lsquoDissolversquo was runon this product to create one large MultiPolygon object Finally lsquoRandom Pointsrsquo inside thePolygon function was used to create 200 point features that were manually validated Threecolumns indicating original year accuracy (YN) whether land-use change was correctlyattributed (YN) and what it had changed to (six classes) were populated for each pointThe last metric had absolute accuracy irrespective of the yearrsquos classification as it wasassigned visually Validation was carried out against the respective yearrsquos NAIP datasetBecause we did not assess land-use change in 2019 only accuracy was computed for thatA Cohenrsquos Kappa value was used to assess interrater reliability using Equation (2) wherePr(a) represents the actual observed agreement and Pr(e) represents chance agreement Theequations were derived from and expanded on by McHugh [42]

κ =Pr(a)minus Pr(e)

1 minus Pr(e)(2)

Forests 2022 13 1132 8 of 22

The CoNED DEM was subdivided into 0ndash1 1ndash2 2ndash3 and 3+ meter classes using theRaster Calculator tool in QGIS The class values were then vectorized to run a geospatialdifference iteratively with each yearrsquos tree cover vectors The difference was validatedand then used for area calculations regarding differential rates of loss for elevation gra-dients Gage height and temperature values were extracted from both stream gages andwere combined into a site average We used gage height as a proxy for MSL due to thespatial proximity to the coast and study sites as well as for considering the response tofreshwater influence which is an important hydrological component of the Chenier PlainsAverage values for the three wettest months (July August and September) were used inthe regressions and analysis

3 Results31 Forest Loss Transition and Classification Accuracy

From 2003 to 2019 an overall decline of 3573 was seen for forest cover and 2238for pasture The annualized rate of loss for forest cover was approximately 2 higherwithin the period 2003ndash2007 losing 42 of tree cover per year assuming a constant change(Table 1) Losses were greatest in the western two reaches of the Chenier Plains averagingover 213 more than sections C or D The largest amount of tree cover was located withinsection D with significant contributions from spoil banks Chenier Au Tigre (locationminus9220665 2956885) had the largest total tree cover within the study area albeit spreadover a large expanse Annual loss rates were more constant in sections containing primarilychenier ridge forests in contrast to swamp or spoil bank forests Section D has a largesaltmarsh (1ndash10 km) bisecting the chenier from the coast which contributed to a largerland and forest area There was a cumulative gain of 231 for the saltmarshes adding2528 km2 In the 2003ndash2007 period the only gains in land cover type were in saltmarshwhich was maintained into 2019 (Figure 4)

We surveyed a total of 2123 points across the study area Producerrsquos accuracy was916 816 and 918 for the years 2003 2007 and 2019 respectively The CohenrsquosKappa indexes of 091 082 and 093 for 2003 2007 and 2019 respectively showed strongagreement between raters across all years We considered any value above 085 to be a validassessment The year 2007 scored lower in both PA and Kappa values likely due to thenature of the base imagery however it still fell into an acceptable range Extreme contrastand saturation in the 2007 NAIP imagery caused larger aggregations of tree cover pixels asgrasses and trees have a similar hue accentuated by the pixel reduction post-classificationValidation points that were very close to tree cover but not categorized as such were notconsidered accurate which contributed to this source of error The 2019 classifications weregeneralized as being more correct from the onset with little need for QA

Changes in land-cover were not evenly distributed across different sections as canbe seen in Table 1 Typically cheniers are more proximally located to the coast withinthe eastern reaches with depositional bars spreading the length One noted outlier in theassessment of change was observed from 2003 to 2007 in section B where the loss of pastureaccounted for over 656 The quantitative measurements for changes in land cover werealso ascertained from these samples We observed changes of 606 282 34 and 76for saltmarsh pasture water and impervious area respectively

Observed results deviated from hypothesized values specifically in the large propor-tion of conversion to pasture A lone tree existing one year after transitioning to an openfield was common across all areas It was assumed the percentage of conversion fromtree to saltmarsh would be even greater However impervious area transitions were alsohigh given that considerable forest cover directly adjoins roads and houses in this areaThe underlying structure can then be seen following the mortality of vegetation Changeto water was also observed for several reasons in areas along waterbodies a loss of treecover exposed the water while severe areas of deforestation prompted a change to a morewetland hydrology which also required water replacement pixels

Forests 2022 13 1132 9 of 22

Table 1 The amount and percentage loss of three land-cover typesmdashtree marsh and pasturemdashovereach section and the total area spanning two parishes Calcasieu and Vermillion on the westernLouisiana Gulf Coast Sections A B C and D represent areas delineated for this study within thetwo parishes (for details see Figure 1)

Landcover 2003(ha)

2007(ha)

2019(ha)

Net Change ()(2003ndash2019)

TreeA 46274 37639 17988 minus6113B 64389 38634 27486 minus5731C 99623 82791 65050 minus3470D 231775 215192 173580 minus2511

Total 442061 374256 284104 minus3573

MarshA 1182244 1148060 1238636 477B 1113045 1084738 1395453 2537C 2051730 2277785 2204412 744D 6598632 6839319 6359952 minus362

Total 10945651 11349903 11198453 231

PastureA 510146 460323 324348 minus3642B 136395 89019 77021 minus4353C 696209 754500 653027 minus620D 1152047 1036039 881960 minus2344

Total 2494796 2339881 1936355 minus2238

Forests 2022 13 x FOR PEER REVIEW 9 of 23

adding 2528 km2 In the 2003ndash2007 period the only gains in land cover type were in salt‐

marsh which was maintained into 2019 (Figure 4)

Table 1 The amount and percentage loss of three land‐cover typesmdashtree marsh and pasturemdashover

each section and the total area spanning two parishes Calcasieu and Vermillion on the western

Louisiana Gulf Coast Sections A B C and D represent areas delineated for this study within the

two parishes (for details see Figure 1)

Landcover 2003

(ha)

2007

(ha)

2019

(ha)

Net Change ()

(2003ndash2019)

Tree

A 46274 37639 17988 minus6113

B 64389 38634 27486 minus5731

C 99623 82791 65050 minus3470

D 231775 215192 173580 minus2511

Total 442061 374256 284104 minus3573

Marsh

A 1182244 1148060 1238636 477

B 1113045 1084738 1395453 2537

C 2051730 2277785 2204412 744

D 6598632 6839319 6359952 minus362

Total 10945651 11349903 11198453 231

Pasture

A 510146 460323 324348 minus3642

B 136395 89019 77021 minus4353

C 696209 754500 653027 minus620

D 1152047 1036039 881960 minus2344

Total 2494796 2339881 1936355 minus2238

0

20000

40000

60000

80000

100000

120000

0

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

Land area ‐MarshPasture (ha)

Land area ‐Forest Cover (ha)

Year

Land‐cover dynamics over time

West East Marsh Pasture

Figure 4 A representation of the changes in three respective land-cover types (forest cover pasturemarsh) over the period 2003ndash2019 For forest cover the three western sections are represented as Westand D is represented as East Light green indicates marsh yellow represents pasture dark green andhatched green show forest cover

Forests 2022 13 1132 10 of 22

32 Rates of Loss with Respect to Elevation

A net decrease in forest cover was seen across all elevation categories (Table 2 Figure 5)The largest losses were in the 1ndash2 m category with a net reduction of 593 from 2003 to2019 There was a temporal shift in the rate of change with the greatest amount of lossbetween 2003ndash2007 contributed by section D Values within the 2ndash3 m category remainedsteady from 2007 to 2019 with a reduction of only 076 The large majority (749) ofthe remaining forest was seen to be located within the 0ndash1 m band The losses were notevenly distributed within this band buffered to some degree by section D A comparisonof sections A vs C shows a decrease of 493 with losses centered around the period2007ndash2019 One issue to note is that the GSD for the DEM was 3 m which is wider thansome spoil banks While the local elevation may in fact rise to 2ndash3 m the value was reduceddue to the effect of averaging The losses in the 3 m+ band are discussed below a noteshould be made of the limited representation Overall the losses varied both spatially andtemporally across the region largely separated into eastern and western reaches

Table 2 Differences in the amount of forest area within each elevation band and the associatedpercentage loss of forest cover between years Sections A B C and D represent areas delineated forthis study within the two parishes (for details see Figure 1)

Sites and Forest Cover (ha)Percentage

LossYearmdash2003

Elevation (m) A B C D Total

0ndash1 13083 25525 66334 187236 2921781ndash2 27168 36374 25583 39048 1281722ndash3 1934 2188 6481 4640 15242gt3 361 170 005 343 879

Yearmdash2007A B C D Total 2003ndash2007

0ndash1 18371 16502 56349 166776 257997 minus11701ndash2 17834 20210 21289 4205 63538 minus50432ndash3 1228 1783 4118 5446 12575 minus1750gt3 048 208 002 404 662 minus2474

Yearmdash2019A B C D Total 2003ndash2019

0ndash1 7161 8227 37762 142102 195253 minus33171ndash2 10283 12913 24656 4401 52254 minus59232ndash3 1506 932 5789 4253 12480 minus1812gt3 045 141 002 402 590 minus3288

33 Interaction with Water Level

Water plays a critical role in the natural function of this ecosystem As such we consid-ered the interplay of temperature and MSL over a decadal time scale and the local rates oftemperature and gage height from both USGS stations (Figure 6) In Figure 6 forest extentis superimposed for each yearrsquos classification to show visual correspondence between thetwo drivers Temperature and rise in sea-level were negatively correlated until an inflectionpoint around 2010 after which there was a relatively stronger correlation increasing to amaximum of 025 The correlative increase lends evidence to the correspondence betweenclimate and water pressure in coastal areas An increase in the average MSL has been ob-served for the site The two stream gages showed an annualized MSL rise of 136 mmyearfor Vermilion Bay and 59 mmyear for the Calcasieu River There was a weak but positivetrend for temperature to increase slightly from the onset of the study period Time provedto be a good indicator for increase in water level and for decrease in forest cover (Figure 6)The lack of tree-cover samples prohibited many statistical comparisons but visually thecorrespondence seems evident

Forests 2022 13 1132 11 of 22

Forests 2022 13 x FOR PEER REVIEW 11 of 23

Table 2 Differences in the amount of forest area within each elevation band and the associated

percentage loss of forest cover between years Sections A B C and D represent areas delineated for

this study within the two parishes (for details see Figure 1)

Sites and Forest Cover (Ha)

Percentage Loss Yearmdash2003

Elevation (m) A B C D Total

0ndash1 13083 25525 66334 187236 292178

1ndash2 27168 36374 25583 39048 128172

2ndash3 1934 2188 6481 4640 15242

gt3 361 170 005 343 879

Yearmdash2007

A B C D Total 2003ndash2007

0ndash1 18371 16502 56349 166776 257997 minus1170

1ndash2 17834 20210 21289 4205 63538 minus5043

2ndash3 1228 1783 4118 5446 12575 minus1750

gt3 048 208 002 404 662 minus2474

Yearmdash2019

A B C D Total 2003ndash2019

0ndash1 7161 8227 37762 142102 195253 minus3317

1ndash2 10283 12913 24656 4401 52254 minus5923

2ndash3 1506 932 5789 4253 12480 minus1812

gt3 045 141 002 402 590 minus3288

0

1

2

3

4

5

6

7

8

9

10

0

500

1000

1500

2000

2500

3000

3500

Land area 3mdash4m ‐(ha)

Land area 0mdash3m ‐(ha)

Year

Forest cover in elevational bands over time

0‐1m

1‐2m

2‐3m

3‐4m

Figure 5 Visual depictions of two forms of loss one from hurricanes on Hackberry Beach and theother documenting whittling reduction The left column refers to 2003 NAIP imagery while the rightuses 2007 NAIP imagery Black shaded areas indicate current year forest extent and the red hashedareas indicate loss

Forests 2022 13 x FOR PEER REVIEW 12 of 23

Figure 5 Visual depictions of two forms of loss one from hurricanes on Hackberry Beach and the

other documenting whittling reduction The left column refers to 2003 NAIP imagery while the right

uses 2007 NAIP imagery Black shaded areas indicate current year forest extent and the red hashed

areas indicate loss

33 Interaction with Water Level

Water plays a critical role in the natural function of this ecosystem As such we con‐

sidered the interplay of temperature and MSL over a decadal time scale and the local

rates of temperature and gage height from both USGS stations (Figure 6) In Figure 6

forest extent is superimposed for each yearrsquos classification to show visual correspondence

between the two drivers Temperature and rise in sea‐level were negatively correlated

until an inflection point around 2010 after which there was a relatively stronger correla‐

tion increasing to a maximum of 025 The correlative increase lends evidence to the cor‐

respondence between climate and water pressure in coastal areas An increase in the av‐

erage MSL has been observed for the site The two stream gages showed an annualized

MSL rise of 136 mmyear for Vermilion Bay and 59 mmyear for the Calcasieu River

There was a weak but positive trend for temperature to increase slightly from the onset of

the study period Time proved to be a good indicator for increase in water level and for

decrease in forest cover (Figure 6) The lack of tree‐cover samples prohibited many statis‐

tical comparisons but visually the correspondence seems evident

Figure 6 Trends in mean water levels and water temperature over 19 years in Vermillion Parish

Data obtained from the USGS Station 07387050 Vermilion Bay (minus921355 296744) with gage datum

165 m above NAVD88 and from the USGS Station 08017118 Calcasieu River at Cameron LA

(minus933488 298155) with gage datum 107m above NAVD88

Rsup2 = 09453

Rsup2 = 01283

Rsup2 = 06529

08

085

09

095

1

105

11

115

12

125

1995 2000 2005 2010 2015 2020 2025

15

20

25

30

35

40

45

50

Average

water level (m)

Year

Forest cover loss Average

water temp

Loss of tree cover (sqkm) Average water temp (deg C) Average waterlevel (m)

Figure 6 Trends in mean water levels and water temperature over 19 years in Vermillion Parish Dataobtained from the USGS Station 07387050 Vermilion Bay (minus921355 296744) with gage datum 165 mabove NAVD88 and from the USGS Station 08017118 Calcasieu River at Cameron LA (minus933488298155) with gage datum 107m above NAVD88

Forests 2022 13 1132 12 of 22

4 Discussion41 Classification Reliability and Constraints

NAIP imagery proved efficacious for fine-scale analysis in this region shown by thestrong average producerrsquos accuracy and Kappa coefficient This is a positive indicatorthat variability between years or areas did not influence classification accuracies to alarge extent and that the final classification is a reliable interpretation [42] Many otherstudies conducting land-cover classifications using NAIP imagery have also shown robustaccuracies [43ndash45] However there were noted issues seen in certain parts of the workflowA problem preeminent in the initial classification was a limitation of spectral resolution Thisproblem became pronounced in areas with similar shades of green or between dark bluesand black hues Without the use of ancillary datasets the classification accuracy was poorbetween dark water and shadows of trees with considerable overlap (misclassificationsof approximately 50) Mixed land use can also prove problematic in NAIP land-coverclassifications [46] This became especially pronounced in transitional forestmarsh areasin which there was no dominant spectral signature for a particular land use Local variationbetween adjoining image sets highlighted another shortcoming of NAIP imagery Theviewing geometry illumination and time between the capture of neighboring imagetiles created edge effects including artificially high or low pixel values and refractionproblems on water [47] Each yearrsquos dataset was collected by an external contractor causingdifferences in sensor instrumentation and parametrization between years By 2019 many ofthese problems had been mitigated and images were of much higher quality

Compared to a large swath sensor such as Landsat-8 or Sentinel-2 an innate non-uniformity poses problems for pixel-wise classifications of VHR imagery Typically datafrom satellite imagery have been the primary choice for analyzing coastal forests marshesand forests at large [48ndash50] The spaceborne sensors obtain information over eight bandsallowing much finer spectral discrimination of certain classes of objects [51] Even amongstdiverse forest types space-borne Sentinel-2 and ASTER imagery has been used to mapdistinct species clusters of broadleaf and deciduous plant species The large spatial scaleenables the use of these created geospatial products to analyze the efficacy of governmentalreforestation programs in remote areas of the world [52] The usage of normalized differ-ence indices including the Normalized Difference Vegetation Index (NDVI) EnhancedVegetation Index (EVI) and Enhanced Water Index (EWI) can also provide greater confi-dence in the separation of water and marsh features from deciduous broadleaf forests [53]However a spatial resolution of 10m is still too coarse for the detection of remnant andlandscape trees especially those alongside homogenous flat features such as maintainedgrass or water bodies The most practical solution would be to assess this land with ahigher resolution dataset such as unmanned aerial systems (UAS) imagery which cancapture a much finer GSD in a plethora of bands if multispectral or hyperspectral sensorsare used [54] In fact scientists are now pushing the boundaries of the scale of analysisconducted through remote sensing Broad groupings (eg clades or forest types) have beenthe historic norm in the classification of imagery for use in land management A greatermixture of classes allows more accurate measurements for those interested in landscape-scale ecological processes However VHR can be used to map stands at the species levelwhich allows forest managers an unparalleled insight into the dynamics of recruitmentdisease or resource availability [55]

Google Earth Engine allowed us to utilize NAIP imagery at scale which had for sometime been a limiting factor in processing this data source locally Even so some limitationshindered the functional utility of GEE for detailed land-cover classification In our relativelyuniform environment we discriminated between many shades of green When viewinggeometries cause variations in hue different classes begin to overlap within the samespectral regions Solutions using Geographic Object-Based Image Analysis (GEOBIA) cancircumvent this issue yet this segmentation algorithm is currently not supported by GEEMany consider GEOBIA the optimal tool for segmenting landscapes and for the productionof land-cover classifications from this source of imagery [3856] Its successful integration

Forests 2022 13 1132 13 of 22

could substantially elevate the use and utility of mapping within GEE Additionally theallocation of increased processing bandwidth on GEE servers may help overcome somelimits in classification Greater bandwidth allows larger and more diverse training setsin addition to more complex parameterizations of machine learning classifiers If oneis proceeding on local workstations the analysis of NAIP imagery requires an intimateknowledge of data structures and process optimization Custom architectures processingpipelines and simpler binary classifications can allow an expansion of the study area tocover coterminous areas on a larger scale [16]

42 Forest Loss Contextually

We mapped forest cover change dynamics between 2003ndash2019 on the southwest coastof Louisiana 1580 hectares of tree cover 13 of the terrestrial area was converted todifferent land use The total loss of forest cover in the study period was 3573 over sixteenyears which is indicative of a larger rate of forest loss than at sites in a similar physio-geographic region yet is certainly not a global outlier [18] These findings validate thesentiments of people in the region and underscore a major irreversible trend in the area Ourestimate should be considered conservative due to the way we approached the definition ofldquoforest coverrdquo within the landscape Abandoned pastoral land exhibiting dense scrubbinessand shrub cover was demarcated as forest cover even though the target species (live oak)was probably not present Spoil banks which are the byproduct of historic oil explorationin the region indicating possible mortality were also often considered a component offorest cover if the imagery could not indicate the absence of high vegetation The primarymethod of identifying whether or not forest cover was present was the observation of ashadow adjoining the vegetation indicating a vertical structure

The Big Bend region of Florida one of the longest undisturbed coastlines in thecontinental US offers some insight into the vegetation dynamics this region has beenexperiencing It has been the focus of numerous ecological studies has a similar climateand is exposed to storms with similar regularity Raabe and Stumpf [57] found that fromthe early to late 1900s over 82 km2 of forest land was converted to marsh and 66 km2 wasin a transitional state to marsh McCarthy et al [58] document an annual forest cover lossof 744 from 2010ndash2017 an acceleration in the trend within that area A new study bythe same team found rates of loss totaling approximately 10 km2 a year 800 larger thanpreviously stated in the literature [59] Rates of annual mortality ranged from 4ndash16 inSoutheastern Louisiana from 1987 to 1997 [60] In comparison the annualized rate of lossfound in our study never exceeded 5 Plots located in South Carolina that have beenless significantly impacted by MSL rise show rates of loss closer to 1ndash2 in line withour observed rate of loss from 2007ndash2019 However we cannot assume that percentagesof forest loss across the country have been constant within the last century therefore it iscritical to carry out intensive geospatial studies at regular intervals

In the adjoining southeastern part of the state of Louisiana in the Atchafalaya andTerrebonne basins forested wetlands occupy 121871 hectares or 41 of all vegetatedland use [12] In comparison the terrestrial forested land use in our study area was 21in 2019 The wide variation can be interpreted in several ways each with importantimplications Historic rates of deforestation differ in the two regions with a greateremphasis on the preservation of wilderness areas in the Atchafalaya basin [61] TheAtchafalaya and Terrebonne basins also have productive land used in the growth of timberwhich promotes long-term forest cover The regeneration of forests in our study region alsodiffers substantially Chenier ecosystems and traditional alluvial bottomland hardwoodforests are compositionally different in vegetation and soil even when accounting for theirunique hydrologic regimes Communities of live oak may be less resilient to the types offorcings that cause changes in community composition within these ecosystems furtheredby proximity to the sea If stocking density rates are similar to the adjoining basin as theywere in the past we would see an areal extent well over 50000 hectares

Forests 2022 13 1132 14 of 22

The analysis vis-agrave-vis elevation shows weak trends in the manners of loss on eleva-tional gradients Logically in the paradigm of coastal retreat there should be an aggregationof forest resources towards the highest bands of topography in a region Our analysis doesnot follow this chain of thought with the largest losses observed in the 1ndash2 m categoryrather than the lowest 0ndash1 m (Table 2 2007 C 2019 D) While we do see a large loss bypercentage in the highest elevation band it is important to note that a small sample causesinherent variance Forest cover on the highest reaches of the chenier ridges is typically lessthan 1 of total forest cover for that region The reduction can be attributed to the loss of23 features in section A accounting for 33 hectares bordering the Sabine River (Figure 1minus9386286 2973355) The loss of forest cover was driven by intense weather Vegetationin the area was completely destroyed and substantial amounts of sedimentation could beobserved as evident in the 2007 NAIP imagery The only gain in forest cover was seen inthe 2ndash3 m elevation band in the period 2007 in an area that had a high density of pastoralland that was abandoned Forest retreat is often discussed as one method of bufferingforest loss along coasts [62] Unfortunately the geomorphology of this area does not allowgreat spatial movement as the current extent is already located within the highest bandsof elevation

43 Land Use Conversion and Driving Factors

The leading cause of land-use conversion from tree cover was to a saltmarsh ecotype(606) often following mortality and replacement of vegetative biomass The loss offorest cover can largely be typified into two categories The ingression of saltwater causingsalinification within the soil matrix paired with increased flooding caused mortality ofoverstorey vegetation over a multi-year timescale [6364] Hurricanes or other storm eventscaused rapid mortality through wind-throws severe defoliation saltwater inundationduring a storm surge or canopy damage Following intense weather events between2003 and 2007 the vast majority of cheniers located within a 1km buffer of the coast wereunable to support tree cover as seen in Figure 7AB Clearing for industry or developmentalso caused immediate shifts in forest composition as can be seen in Figure 8AB Thischange was more pronounced on reaches A and B especially on Hackberry Beach Thisarea was the site of landfall for Hurricane Rita which caused massive devastation to othercomponents of the ecosystem [6566] Singular storm events have a transformative impacton vegetation communities and the weight of this driver on annual rates of forest lossis a topic that should be explored further Wayward shifts in species composition are atrend that has been observed globally especially in forests that border some source of waterTrees like Juniperus virginiana L that have distinctive water-use patterns in comparison toother coastal tree species are susceptible to salinification at much faster rates as evidencedthrough isotope analyses [67] As with many ecological systems the entangled effectsof climate and vegetation require diverse means of looking at change Isotope analysesdendrochronology field sensors and remote sensing data are needed to characterize land-use change at the stand and community levels

Large forest remnants such as Cow Island (minus9283581 2973842) Tiger Island (minus92784392972832) and Grand Chenier Ridge (minus9281175 2972986) among others have diminishedmore slowly By 2019 these stands had lost an average of 15 of their maximum extentThe land use changed to both marsh and managed pasture largely dependent on thesurrounding land use The conversion to managed pasture (282) accounted for a largerthan expected amount of land-cover change Many of these changes were in areas wheretree cover may have been more ldquoornamentalrdquo in nature Trees beside houses or alongsideroads are some of the largest specimens of live-oak to be found However these trees areoften solitary and perhaps more vulnerable to wind-borne disturbance The concern is thattheir loss due to abiotic drivers may not be easily mitigated leading to a permanent declineOur data support evidence on the whittling of forest remnants and the eventual fate ofthese trees becoming lsquorelict forestsrsquo unable to recruit new generations in the short term andin the long-term total disappearance of these cheniers along the Louisiana Gulf Coast [68]

Forests 2022 13 1132 15 of 22Forests 2022 13 x FOR PEER REVIEW 16 of 23

Figure 7 Visual depictions of two forms of loss one from hurricanes on Hackberry Beach (AB) and

the other documenting whittling reduction (CD) The left column displays 2003 NAIP imagery and

the right is 2007 NAIP imagery Black shaded areas indicate current year forest extent red hashed

areas indicate a loss

Figure 7 Visual depictions of two forms of loss one from hurricanes on Hackberry Beach (AB) andthe other documenting whittling reduction (CD) The left column displays 2003 NAIP imagery andthe right is 2007 NAIP imagery Black shaded areas indicate current year forest extent red hashedareas indicate a loss

Beyond loss in forest cover there is a noticeable trend towards an altered hydrologywhere pastoral areas and intact forest remnants are associated with pockets of standingwater An increase in the correlative factor between temperature and water levels could bea worrying trend a direct conversion of tree cover to water at 34 could be an unforeseenoutcome Chenier ridges in their geomorphic context should not support non-ephemeralponding It is not documented how increased tidal influence which is one factor of risingMSL influences the hydrology of this ecosystem and its associated communities Albeitin a more inland context Magolan and Halls [69] documented a transition of over 40 ofland to emergent marsh from closed-canopy cover from 1949 to 2018 due to hydrologicinfluence Recent rates of relative sea rise have been documented as high as 119 mmyearcompared to background Holocene rates of 17 mmyear [70] This has resulted in a rate oflateral coastal forest retreat between 2ndash14 times greater than pre-industrial levels [26] Therate of shoreline retreat in this region increased from ~61 to ~94 myear based on the 100-and 30-year moving averages amounting to over 10000 hectares of land loss every yearfrom 1800 to 2005 [27]

Forests 2022 13 1132 16 of 22Forests 2022 13 x FOR PEER REVIEW 17 of 23

Figure 8 A depiction of clearing for industry in (AB) and a depiction of forest whittling from (CD)

The black extent represents the current forest area red hashed lines indicate a loss The left panels

show 2007 NAIP imagery while the right are 2019 NAIP imagery

Large forest remnants such as Cow Island (minus9283581 2973842) Tiger Island

(minus9278439 2972832) and Grand Chenier Ridge (minus9281175 2972986) among others have

diminished more slowly By 2019 these stands had lost an average of 15 of their maxi‐

mum extent The land use changed to both marsh and managed pasture largely depend‐

ent on the surrounding land use The conversion to managed pasture (282) accounted

for a larger than expected amount of land‐cover change Many of these changes were in

areas where tree cover may have been more ldquoornamentalrdquo in nature Trees beside houses

or alongside roads are some of the largest specimens of live‐oak to be found However

these trees are often solitary and perhaps more vulnerable to wind‐borne disturbance The

concern is that their loss due to abiotic drivers may not be easily mitigated leading to a

permanent decline Our data support evidence on the whittling of forest remnants and

the eventual fate of these trees becoming lsquorelict forestsrsquo unable to recruit new generations

in the short term and in the long‐term total disappearance of these cheniers along the

Louisiana Gulf Coast [68]

Beyond loss in forest cover there is a noticeable trend towards an altered hydrology

where pastoral areas and intact forest remnants are associated with pockets of standing

water An increase in the correlative factor between temperature and water levels could

be a worrying trend a direct conversion of tree cover to water at 34 could be an unfore‐

seen outcome Chenier ridges in their geomorphic context should not support non‐ephem‐

eral ponding It is not documented how increased tidal influence which is one factor of

Figure 8 A depiction of clearing for industry in (AB) and a depiction of forest whittling from (CD)The black extent represents the current forest area red hashed lines indicate a loss The left panelsshow 2007 NAIP imagery while the right are 2019 NAIP imagery

44 Future of Chenier Forests

The chenier complexes of Louisiana do not have the natural benefaction of maintaininglarge original tracts untouched by axe or dozer The extent map through the years (Figure 9)exemplifies the scattered nature of forest remnants The largest single continuous chenierforest (Grand Chenier Ridgemdash185 ha) encompasses less than 1 of the total forest areaExposed forest edges pose a problem for the continued integrity of the stand as adaptedspecies fail to compete in these new microclimates [71] Slow-growing saplings of desirabletree species (live oak hackberry) are subject to unfavorable growing conditions fromweather and light availability Vegetation surveys following aspect and inundation alongthe Florida coast have all but confirmed the eventual extirpation of overstory vegetationthrough poor recruitment of native species [31] Chinese tallow (Triadica sebifera) a non-native tree is likely to fill empty niche space This aggressive invader is problematicthroughout the Gulf Coastal Plain from Texas to Florida [72] The tree has a wide meansof dispersal being primarily distributed through frugivory via avian populations A highrate of germination compounded with quick growth and a sexual maturity of less thanthree years make it a difficult species to control Its invasive capability is seen in nearlyall forest types in the region although it is more pervasive in coastal forests than inlandModeling studies show a high likelihood of Chinese tallow becoming the dominant specieswithin this ecosystem and in surrounding areas [73] Future publications from this lab will

Forests 2022 13 1132 17 of 22

provide more in-depth data and analysis on the state of existing stands and recruitmenttrends within remnants of the Chenier Plains via field sampling

Louisiana has enacted a historic response to the problem of land-loss following a publicoutcry after devastating storms in 2005 The Coastal Protection and Restoration Authority(CPRA) have been tasked with enacting a management plan to combat coastal erosion TheComprehensive Master Plan details proposals to create marshes build sediment diversionsform new hydrologic structures and promote barrier island restoration through builtand natural means As of 2017 over 135 projects had been completed impacting over14568 ha [74] An update to this document is due to be released in 2023 The projecthas an overall operating budget of over 50 billion US dollars with equal shares dedicatedto restoration and risk reduction The primary funding source is a settlement associatedwith the Deepwater Horizon Oil Spill in addition to various initiatives at the state andfederal levels However there are no current plans to supplement vegetation or performany geoengineering work on the cheniers for afforestation

Coastal wetlands rank among the most important ecosystems in terms of the valuationof ecosystem services [75] Provisioning services such as water filtration or storm-surgeprotection are commonly considered when discussing coastal areas Beyond these com-monalities chenier forests provide vital wildlife habitats being the first encountered sourceof vertical structure for migratory neo-tropical birds and migrating monarch butterflies inthe Mississippi alluvial valley flyway [76] This en-route habitat is of critical importancedue to its closeness to the Gulf of Mexico During favorable weather conditions 10 ofbirds migrating through the Gulf use these forests as a stopover spot rising dramatically to80 during adverse weather conditions [29] With losses mounting many species may notbe able to seek refuge during storms The area also has a unique suite of amphibians andreptiles being home to 13 obligate chenier forest species of herpetofauna [77]

Lu et al [78] used the eddy covariance method (ECM) to calculate strong carbon sinkeffects for a chenier ecosystem driven by forest vegetation Net energy production valueswere significantly higher than surrounding marshland signifying the importance of densevegetative cover on a per-unit basis Counter to this there is also strong evidence thatdegraded forest ecosystems act as net carbon sources A study in North Carolina also usingECM found that a seasonally inundated tide-influenced forest was a net carbon sourceannually and only functioned as a sink for 3 months of the growing year [79] Similarly astudy in the northeastern part of the state showed net negative rates of carbon sequestrationfor an ephemerally flooded forest [80] Unique environments demand individual attentiona characterization of this ecosystem for nitrogen or carbon flux with respect to a similarsystem may not be functionally sound [81]

Forests 2022 13 1132 18 of 22Forests 2022 13 x FOR PEER REVIEW 19 of 23

Figure 9 The total land area classified in GEE for the years 2003 2007 and 2019 Light green repre‐

sents the marsh area tan represents pasture The water layer has been removed for purposes of

clarity The dark black outline is the outline of the forest cover for the respective year

Louisiana has enacted a historic response to the problem of land‐loss following a

public outcry after devastating storms in 2005 The Coastal Protection and Restoration

Authority (CPRA) have been tasked with enacting a management plan to combat coastal

erosion The Comprehensive Master Plan details proposals to create marshes build sedi‐

ment diversions form new hydrologic structures and promote barrier island restoration

through built and natural means As of 2017 over 135 projects had been completed im‐

pacting over 14568 ha [74] An update to this document is due to be released in 2023 The

project has an overall operating budget of over 50 billion US dollars with equal shares

dedicated to restoration and risk reduction The primary funding source is a settlement

associated with the Deepwater Horizon Oil Spill in addition to various initiatives at the

state and federal levels However there are no current plans to supplement vegetation or

perform any geoengineering work on the cheniers for afforestation

Coastal wetlands rank among the most important ecosystems in terms of the valua‐

tion of ecosystem services [75] Provisioning services such as water filtration or storm‐

surge protection are commonly considered when discussing coastal areas Beyond these

commonalities chenier forests provide vital wildlife habitats being the first encountered

source of vertical structure for migratory neo‐tropical birds and migrating monarch but‐

terflies in the Mississippi alluvial valley flyway [76] This en‐route habitat is of critical

importance due to its closeness to the Gulf of Mexico During favorable weather condi‐

tions 10 of birds migrating through the Gulf use these forests as a stopover spot rising

Figure 9 The total land area classified in GEE for the years 2003 2007 and 2019 Light greenrepresents the marsh area tan represents pasture The water layer has been removed for purposes ofclarity The dark black outline is the outline of the forest cover for the respective year

5 Conclusions

The cheniers of southwestern Louisiana were found to be declining in size at a concern-ing rate Many of the driving factors are ecosystem-wide trends that have been increasingin intensity within the last century The results of this study suggest that coastal forestsare an ecosystem in peril and the ecology of such systems should be a closely monitoredsubject The 365 loss of forest cover is an alarming but not wholly unexpected result ofmany decades of intense perturbation and human exploitation A dynamic of continuedforest loss is evident Unless significant investment is made in engineering solutions tomitigate storm effects and land subsidence we will continue to see the decline of chenierforests in Louisiana Through targeted mitigation governmental programs such as theWetland or Conservation Reserve Program may be able to supplement existing forestsas well as the incomes of local people who have lost their livelihoods In this study wehave begun the process of accurate areal estimates of forest extent and have shed light onecosystem processes in the region As this community continues to diminish we see theforest not by the trees but rather the lack of them

Forests 2022 13 1132 19 of 22

Author Contributions Conceptualization PT methodology PT software PT and PR validationPT and PR formal analysis PT PR and PT writingmdashoriginal draft preparation PT and JBwritingmdashreview and editing PT and JB visualization PT and JB supervision JB All authorshave read and agreed to the published version of the manuscript

Funding This research received no external funding

Data Availability Statement The data will be published in and stored on the Harvard Dataverse athttpsdataverseharvardedudatasetxhtmlpersistentId=doi107910DVN3J7SNF (accessed on1 May 2022)

Acknowledgments The authors would like to recognize the hard work of undergraduate studentsParusha Raut and Subash Sapkota in manually validating thousands of points and creating numeroustraining sets We also extend thanks to our institution and numerous people within the lab who leantboth mental and emotional fortitude

Conflicts of Interest The authors declare no conflict of interest

References1 Michener WK Blood ER Bildstein KL Brinson MM Gardner LR Climate change hurricanes and tropical storms and

rising sea level in coastal wetlands Ecol Appl 1997 7 770ndash801 [CrossRef]2 Donoghue JF Sea level history of the northern Gulf of Mexico coast and sea level rise scenarios for the near future Clim Chang

2011 107 17ndash33 [CrossRef]3 Stanturf JA Goodrick SL Outcalt KW Disturbance and coastal forests A strategic approach to forest management in

hurricane impact zones For Ecol Manag 2007 250 119ndash135 [CrossRef]4 Augustinus P Cheniers and chenier plains A general introduction Mar Geol 1989 90 219ndash229 [CrossRef]5 Fu Z Wang P Sun J Lu Z Yang H Liu J Li T Composition seasonal variation and salinization characteristics of soil

salinity in the Chenier Island of the Yellow River Delta Glob Ecol Conserv 2020 24 e01318 [CrossRef]6 Marques IG Campelo F Rivaes R Albuquerque A Ferreira MT Rodriacuteguez-Gonzaacutelez PM Tree rings reveal long-term

changes in growth resilience in Southern European riparian forests Dendrochronologia 2018 55 167ndash176 [CrossRef]7 Daigle JJ Griffith GE Omernik JM Faulkner PL McCulloh RP Handley LR Smith LM Chapman SS Ecoregions of

Louisiana (Color Poster with Map Descriptive Text Summary Tables and Photographs) US Geological Survey Reston VI USA 20068 McBride RA Taylor MJ Byrnes MR Coastal morphodynamics and Chenier-Plain evolution in southwestern Louisiana USA

A geomorphic model Geomorphology 2007 88 367ndash422 [CrossRef]9 Gosselink JG An Ecological Characterization Study of the Chenier Plain Coastal Ecosystem of Louisiana and Texas Narrative Report

National Coastal Ecosystems Team Office of Biological Services Fish and Wildlife Service US Department of the InteriorWashington DC USA 1979 Volume 1

10 Blum MD Roberts HH The Mississippi delta region Past present and future Annu Rev Earth Planet Sci 2012 40 655ndash683[CrossRef]

11 Hansen MC Potapov PV Moore R Hancher M Turubanova SA Tyukavina A Townshend J High-resolution globalmaps of 21st-century forest cover change Science 2013 342 850ndash853 [CrossRef]

12 Doyle TW Krauss KW Conner WH From AS Predicting the retreat and migration of tidal forests along the northern Gulfof Mexico under sea-level rise For Ecol Manag 2010 259 770ndash777 [CrossRef]

13 Thomas N Simard M Castantildeeda-Moya E Byrd K Windham-Myers L Bevington A Twilley R High-resolution mappingof biomass and distribution of marsh and forested wetlands in southeastern coastal Louisiana Int J Appl Earth Obs Geoinf 201980 257ndash267 [CrossRef]

14 Desantis LR Bhotika S Williams K Putz FE Sea-level rise and drought interactions accelerate forest decline on the GulfCoast of Florida USA Glob Chang Biol 2007 13 2349ndash2360 [CrossRef]

15 Boyd DS Foody G Ripple W Evaluation of approaches for forest cover estimation in the Pacific Northwest USA usingremote sensing Appl Geogr 2002 22 375ndash392 [CrossRef]

16 Valjarevic A Djekic T Stevanovic V Ivanovic R Jandzikovic B GIS numerical and remote sensing analyses of forest changesin the Toplica region for the period of 1953ndash2013 Appl Geogr 2018 92 131ndash139 [CrossRef]

17 He T Sun YJ Xu JD Wang X Hu C Enhanced land usecover classification using support vector machines and fuzzyk-means clustering algorithms J Appl Remote Sens 2014 8 083636 [CrossRef]

18 Michez A Pieacutegay H Lisein J Claessens H Lejeune P Classification of riparian forest species and health condition usingmulti-temporal and hyperspatial imagery from unmanned aerial systems Environ Monit Assess 2016 188 146 [CrossRef][PubMed]

19 Hampton SE Strasser C Tewksbury J Gram W Budden A Batcheller A Duke C Porter J Big data and the future ofecology Front Ecol Environ 2013 11 156ndash162 [CrossRef]

20 Curtis PG Slay CM Harris NL Tyukavina A Hansen MC Classifying drivers of global forest loss Science 2018 3611108ndash1111 [CrossRef]

Forests 2022 13 1132 3 of 22

on the coast Each hurricane brought the dual threats of storm surge which can causeinundation up to 4m above ground level and structural damage from wind-gusts up to177 kmh [32] There is an eminent need to quantify the impacts on the vegetation ofcoastal forests as tropical storm events are likely to increase in frequency and intensity inthe near future [33] Our observations help establish baseline data on the current extent ofthese unique and vulnerable forest ecosystems within the region making clear their statusand the vulnerability this ecosystem

2 Materials and Methods21 Study Area

The study area encompassed the coastline of Cameron and Vermilion Parishes insouthwestern Louisiana (Figure 1) A varying buffer of 6 to 14 km from the coast wasmaintained to capture pertinent physiographic features The area is classified as a low-profile microtidal storm-dominated coast that experiences both erosive and accumulativesediment loads [8] It is distinctive in that the land experiences subsidence in additionto above-average rates of mean sea level (MSL) rise [34] Broadly this area is dividedinto two geomorphic regions the Chenier Plains and the marginal deltaic plains of SELouisiana The chenier plain ridges are laterally accreted sections of sand and shellsdeposited episodically Accretion heights vary between 0ndash4 m with lengths ranging from10ndash90 km and widths less than 1 km across arrayed in a linear-to-concave manner [20]These ridges form complexes defined as two or more parallel or sub-parallel sectionsthat stretch for lengths well beyond 400 km Chenier complexes can directly about thecoast while the most landward are located up to 15 km away The two soil complexesof the region are Hackberry and Mermenteau [35] Hackberry soils form the ridges andare deep poorly drained and moderately permeable composed of sand and shells TheMermenteau soil complex forms the edaphic base of the saltmarshes The complex is verydeep with reduced permeability and is primarily composed of clayey over loamy sedimentContextually these ridges form the only topography above MSL within this region and arethus visually striking when seen at distance

Figure 1 A map indicating the distribution of chenier ridges shown in black bars along the coast ofVermillion and Cameron Parishes Adapted from McBride (2007) with permission from Elsevier Thelines and numbering represent divisions created for data analysis

The extant vegetation communities form a closed-canopy hardwood forest composedof live oak (Quercus virginiana Mill) southern hackberry (Celtis laevigata (Kunth) SprengAmerican elm (Ulmus americana L) black willow (Salix nigra Marshall) honey locust(Gleditsia triacanthos L) and persimmon (Diospyros virginiana L) among others as wellas now the increasingly common invasive Chinese tallow (Triadica sebifera L) Shrubssuch as American holly (Ilex opaca Aiton) coastal mallow (Kosteletzkya pentacarpos L)common hawthorn (Crataegus viridis L) toothache-tree (Zanthoxylum americanum Mill)and chinaberry (Melia azedarach L) among many more are also frequently encountered [22]

Forests 2022 13 1132 4 of 22

The intact community forms a dense stand that prohibits the establishment of shade-intolerant species These communities have been described as ldquocathedral-likerdquo due to thetall and wide branching stature of the live oaks The leaves of live oaks are dark greenin hue and maintain foliage year-round making their foliage a strong visual indicator inaerial imagery

Recent vegetation surveys show a shift to a community more akin to an open saltmarshswamp with thick stands of southern amaranth (Amaranthus australis (AGray) JDSauer)roseau cane (Phragmites australis (Cav) Steud) narrow-leaved cattail (Typha angustifolia L)and other wetland obligate species interspersed with dead or dying trees (unpublished dataBhattacharjee J) This newly emerging ecotype represents a breakdown of the traditionalsegregation of upland forest and lowland haline swamp This change is broadly representedby the encroachment of hydrophytic vegetation becoming dominant in the understory Thischange is identifiable when seen in VHR imagery although the spectral characteristics ofthe scene can appear differently Much of the structural complexity of the forest has beenlost The physiognomy appears as a brushy single layer with limited overstorey mirroringthe surrounding saltmarsh to some degree Some areas have become entirely dominated bydense herbaceous vegetation preventing the establishment of native upper-story speciesThe site has always experienced anthropogenic influence in community composition thenative Attakapas people contributed to the original diversity of vegetation by introducingand maintaining an abundance of food-bearing plants within the landscape [3637] Thestudy site does not have any mangrove swamps which can be found further east and southalong the Gulf Coast

22 Data Sources

The National Agricultural Image Program of the United States Department of Agriculture(USDA) houses aerial orthoimage sets for the contiguous United States in 375 times 375 quadranglesThe 2003 and 2007 imagery for Louisiana were collected at 2 m and 1 m Ground SampleDistance (GSD) respectively in red green and blue bands The 2019 imagery has 05 mGSD with the addition of a near-infrared band In total 234 NAIP image tiles were used foranalysis Finer scale imagery was not downsampled to coarser resolution Additionally allbands were used in the classification scheme to provide the greatest discriminatory accuracyAn effort was made to classify imagery from around a similar date to capture equivalentphenophases although image quality was given precedence over temporal constraintsThe aerial images used in the data analysis pipeline for the years 2003 2007 and 2019were collected on 82403 81007 and 9519 respectively These particular years werechosen as they represented the highest quality imagery within the study period It shouldbe noted the NAIP is not an annual dataset collections vary according to agreements withstate governments and are often performed on a biennial or triennial cycle Limiting factorsin many of the interior year image sets were cloud cover or high local variance in scenereflectance which were prohibitive for the manner of land cover classification used inthe study Intense glare from surrounding water bodies caused significant reduction inquality of less optimal image correction after the fact It became difficult to distinguishingbetween many similar vegetation types as even moderate local variance can introducenoise degrading algorithm effectiveness Imagery from 2013 had begun to be processed butwas abandoned after initial development due to scene variability and poor classificationperformance Only image sets with less than 5 cloud cover were used

The Coastal National Elevation Database (CoNED) Digital Elevation Model (DEM)was used for elevation analysis The CoNED is generated by the United States GeologicalSurvey (USGS) using LiDAR and has a 3 m GSD It is housed in the National Map Datarepository The raster was downsampled to 1 m to match the resolution of the GEEclassification output Mean sea level (MSL) salinity and temperature values were extractedfrom two USGS stream gages (08017118 07387050) located at opposite ends of the studyarea providing data for the entirety of our study period The data were extracted fromthe USGS StreamStats interactive web map The National Hydrography Dataset (NHD)

Forests 2022 13 1132 5 of 22

a vector repository of waterbodies (NHDWaterbody NHDArea) and wetland features(NHDWaterbody FCode 46600 46601 46602) was used for feature input in the creation ofpost-processing masks The NHD is also a product of the USGS

23 Classification and Processing Algorithms

Google Earth Engine offers a suite of supervised and unsupervised Machine LearningClassifiers for image classification Through iterative testing of all available options theClassification and Regression Tree (CART) algorithm was chosen for this study CART isa decision-tree-based supervised learning model able to handle nonlinear relationshipsamong features [38] Because class balancing is recommended for this algorithm a stableratio of tree-to-pasture and tree-to-marsh training sets were created Parametrizationwas not modified from default properties The primary consideration in choosing thisalgorithm was robust classification for forest cover Visually we observed that many othercontenders had higher incidence rates of non-forest-based classification of tree cover whenconducting initial testing leading to a general under-representation of forest cover Weuse a reductionist approach for accuracy in this study where pixel values for forest coverwere reduced rather than added which makes underrepresentation problematic Thepost-processing vectors allowed extremely fine detail in the modification of the originalCART output

The study area was divided into four sections (A B C and D) of equal approximatelength as seen in Figure 1 Sections C and D were further divided into three subsectionsdue to image variability in the 2003 dataset and were merged post-classification Thisfacilitated a more targeted training dataset given local variation while also expeditingprocessing within the workflow We used six total land-cover classes in the classificationschememdashtree pasture pond river saltmarsh and impervious area The classes werecomprised of manually delineated Point FeatureCollections within GEE and were givenspecific unique IDs Tree cover was interpreted as the visual presence of dark foliagebranching or scrubbiness As such these selections include trees located on spoil banklevees (Figure 2B) swamp forests (Figure 2C) or residential areas in addition to the live-oak communities found on the main chenier ridges (Figure 2AD) The rationale was toinclude any type of vegetation that promotes beneficial land use and habitat Pasture wasinterpreted as any area in which there are grazing activities or managed lands on whichregular mowing occurs such as yards or parks The Point FeatureCollections were createdusing 2019 NAIP imagery and were manually validated before use in each preceding yearto ensure classes remained accurate Modifications were made when class values changedbetween years The CART classification was subject to a 3-pixel square image reducerthereby homogenizing values to reduce the ldquosalt and pepper effectrdquo common in pixelwiseclassifications [39] The data were then exported to Google Drive for further processing inQuantum GIS (QGIS) A detailed workflow for the procedure is given in Figure 3

24 Post-Processing of GEE Outputs

Two-layer masks created from the NHD were used to refine the classified output Themasks were originally ascertained and modified in a vector format and were convertedto a raster-type format when used for modifying the GEE outputs The two layers whichwere in the same projection and scale were employed to perform mathematical operationson a per-pixel basis Raster calculations can be applied to overlapping areas according totheir underlying geospatial properties affecting how data is represented [40] As the NHDis incomplete regarding wetlands and pond features care was taken to digitize areas ofthe 2003 NAIP imagery that were wholly saltmarsh or water [41] An assumption wasmade that any saltmarsh or water feature present in 2003 would maintain its non-forestedland use during the study period This allowed a set standard for the modification ofGEE outputs The saltmarsh mask was set so that any pixel registering as a tree wouldbe converted into saltmarsh without the modification of any other land-cover class Thewater mask was absolute in terms of pixel modification All pixels overlapping the mask

Forests 2022 13 1132 6 of 22

were converted regardless of their class Tree cover vectors were then extracted from eachyearrsquos classification All cover area calculations including different elevation productswere performed on this vectorized form due to the superior computational ability forcalculating absolute area of georeferenced polygons in a vector format Polygons were alsomanually scanned over the respective years of NAIP imagery and were either deleted oradjusted following a set standard for Quality Assurance (QA) to ensure best representation

Forests 2022 13 x FOR PEER REVIEW 6 of 23

Figure 2 Panels (AndashD) show diagnostic types of chenier forests Panels (AC) show a chenier rem‐

nant forest Panel (B) is a spoil‐bank forest from the days of oil exploration The southern terminus

of (D) shows a swamp forest which could inhabit less haline marsh communities found in the north‐

ern reaches of the Chenier Plains The black‐outlined areas are the respective yearsrsquo forest extent

Figure 2 Panels (AndashD) show diagnostic types of chenier forests Panels (AC) show a chenier remnantforest Panel (B) is a spoil-bank forest from the days of oil exploration The southern terminus of(D) shows a swamp forest which could inhabit less haline marsh communities found in the northernreaches of the Chenier Plains The black-outlined areas are the respective yearsrsquo forest extent

25 Data Analysis and Statistics

Because post-processing and QA significantly modified the original CART classifi-cation baseline statistics on algorithm confidence would not be representative Insteadproducerrsquos accuracy (PA) was used as the primary determinant of classification success(Equation (1) where reference sites denote created validation points)

Producerrsquos Accuracy = of correctly classified reference sites

of total reference sitestimes100 (1)

Forests 2022 13 1132 7 of 22Forests 2022 13 x FOR PEER REVIEW 7 of 23

Figure 3 Process workflow used in the study The section bracketed in blue was processed using

Google Earth Engine and the section bracketed in green was carried out using QGIS Blue blocks

represent raw inputs into the workflow green blocks are the final outputs

24 Post‐Processing of GEE Outputs

Two‐layer masks created from the NHD were used to refine the classified output

The masks were originally ascertained and modified in a vector format and were con‐

verted to a raster‐type format when used for modifying the GEE outputs The two layers

which were in the same projection and scale were employed to perform mathematical

operations on a per‐pixel basis Raster calculations can be applied to overlapping areas

according to their underlying geospatial properties affecting how data is represented [40]

As the NHD is incomplete regarding wetlands and pond features care was taken to dig‐

itize areas of the 2003 NAIP imagery that were wholly saltmarsh or water [41] An as‐

sumption was made that any saltmarsh or water feature present in 2003 would maintain

its non‐forested land use during the study period This allowed a set standard for the

modification of GEE outputs The saltmarsh mask was set so that any pixel registering as

a tree would be converted into saltmarsh without the modification of any other land‐

cover class The water mask was absolute in terms of pixel modification All pixels over‐

lapping the mask were converted regardless of their class Tree cover vectors were then

extracted from each yearrsquos classification All cover area calculations including different

Figure 3 Process workflow used in the study The section bracketed in blue was processed usingGoogle Earth Engine and the section bracketed in green was carried out using QGIS Blue blocksrepresent raw inputs into the workflow green blocks are the final outputs

The creation of points for sections A B C and D to assess accuracy was carried outusing three tools in the QGIS Processing Toolbox First a lsquoDifferencersquo was run to extracttree cover in an original yearrsquos dataset but not in the succeeding year A lsquoDissolversquo was runon this product to create one large MultiPolygon object Finally lsquoRandom Pointsrsquo inside thePolygon function was used to create 200 point features that were manually validated Threecolumns indicating original year accuracy (YN) whether land-use change was correctlyattributed (YN) and what it had changed to (six classes) were populated for each pointThe last metric had absolute accuracy irrespective of the yearrsquos classification as it wasassigned visually Validation was carried out against the respective yearrsquos NAIP datasetBecause we did not assess land-use change in 2019 only accuracy was computed for thatA Cohenrsquos Kappa value was used to assess interrater reliability using Equation (2) wherePr(a) represents the actual observed agreement and Pr(e) represents chance agreement Theequations were derived from and expanded on by McHugh [42]

κ =Pr(a)minus Pr(e)

1 minus Pr(e)(2)

Forests 2022 13 1132 8 of 22

The CoNED DEM was subdivided into 0ndash1 1ndash2 2ndash3 and 3+ meter classes using theRaster Calculator tool in QGIS The class values were then vectorized to run a geospatialdifference iteratively with each yearrsquos tree cover vectors The difference was validatedand then used for area calculations regarding differential rates of loss for elevation gra-dients Gage height and temperature values were extracted from both stream gages andwere combined into a site average We used gage height as a proxy for MSL due to thespatial proximity to the coast and study sites as well as for considering the response tofreshwater influence which is an important hydrological component of the Chenier PlainsAverage values for the three wettest months (July August and September) were used inthe regressions and analysis

3 Results31 Forest Loss Transition and Classification Accuracy

From 2003 to 2019 an overall decline of 3573 was seen for forest cover and 2238for pasture The annualized rate of loss for forest cover was approximately 2 higherwithin the period 2003ndash2007 losing 42 of tree cover per year assuming a constant change(Table 1) Losses were greatest in the western two reaches of the Chenier Plains averagingover 213 more than sections C or D The largest amount of tree cover was located withinsection D with significant contributions from spoil banks Chenier Au Tigre (locationminus9220665 2956885) had the largest total tree cover within the study area albeit spreadover a large expanse Annual loss rates were more constant in sections containing primarilychenier ridge forests in contrast to swamp or spoil bank forests Section D has a largesaltmarsh (1ndash10 km) bisecting the chenier from the coast which contributed to a largerland and forest area There was a cumulative gain of 231 for the saltmarshes adding2528 km2 In the 2003ndash2007 period the only gains in land cover type were in saltmarshwhich was maintained into 2019 (Figure 4)

We surveyed a total of 2123 points across the study area Producerrsquos accuracy was916 816 and 918 for the years 2003 2007 and 2019 respectively The CohenrsquosKappa indexes of 091 082 and 093 for 2003 2007 and 2019 respectively showed strongagreement between raters across all years We considered any value above 085 to be a validassessment The year 2007 scored lower in both PA and Kappa values likely due to thenature of the base imagery however it still fell into an acceptable range Extreme contrastand saturation in the 2007 NAIP imagery caused larger aggregations of tree cover pixels asgrasses and trees have a similar hue accentuated by the pixel reduction post-classificationValidation points that were very close to tree cover but not categorized as such were notconsidered accurate which contributed to this source of error The 2019 classifications weregeneralized as being more correct from the onset with little need for QA

Changes in land-cover were not evenly distributed across different sections as canbe seen in Table 1 Typically cheniers are more proximally located to the coast withinthe eastern reaches with depositional bars spreading the length One noted outlier in theassessment of change was observed from 2003 to 2007 in section B where the loss of pastureaccounted for over 656 The quantitative measurements for changes in land cover werealso ascertained from these samples We observed changes of 606 282 34 and 76for saltmarsh pasture water and impervious area respectively

Observed results deviated from hypothesized values specifically in the large propor-tion of conversion to pasture A lone tree existing one year after transitioning to an openfield was common across all areas It was assumed the percentage of conversion fromtree to saltmarsh would be even greater However impervious area transitions were alsohigh given that considerable forest cover directly adjoins roads and houses in this areaThe underlying structure can then be seen following the mortality of vegetation Changeto water was also observed for several reasons in areas along waterbodies a loss of treecover exposed the water while severe areas of deforestation prompted a change to a morewetland hydrology which also required water replacement pixels

Forests 2022 13 1132 9 of 22

Table 1 The amount and percentage loss of three land-cover typesmdashtree marsh and pasturemdashovereach section and the total area spanning two parishes Calcasieu and Vermillion on the westernLouisiana Gulf Coast Sections A B C and D represent areas delineated for this study within thetwo parishes (for details see Figure 1)

Landcover 2003(ha)

2007(ha)

2019(ha)

Net Change ()(2003ndash2019)

TreeA 46274 37639 17988 minus6113B 64389 38634 27486 minus5731C 99623 82791 65050 minus3470D 231775 215192 173580 minus2511

Total 442061 374256 284104 minus3573

MarshA 1182244 1148060 1238636 477B 1113045 1084738 1395453 2537C 2051730 2277785 2204412 744D 6598632 6839319 6359952 minus362

Total 10945651 11349903 11198453 231

PastureA 510146 460323 324348 minus3642B 136395 89019 77021 minus4353C 696209 754500 653027 minus620D 1152047 1036039 881960 minus2344

Total 2494796 2339881 1936355 minus2238

Forests 2022 13 x FOR PEER REVIEW 9 of 23

adding 2528 km2 In the 2003ndash2007 period the only gains in land cover type were in salt‐

marsh which was maintained into 2019 (Figure 4)

Table 1 The amount and percentage loss of three land‐cover typesmdashtree marsh and pasturemdashover

each section and the total area spanning two parishes Calcasieu and Vermillion on the western

Louisiana Gulf Coast Sections A B C and D represent areas delineated for this study within the

two parishes (for details see Figure 1)

Landcover 2003

(ha)

2007

(ha)

2019

(ha)

Net Change ()

(2003ndash2019)

Tree

A 46274 37639 17988 minus6113

B 64389 38634 27486 minus5731

C 99623 82791 65050 minus3470

D 231775 215192 173580 minus2511

Total 442061 374256 284104 minus3573

Marsh

A 1182244 1148060 1238636 477

B 1113045 1084738 1395453 2537

C 2051730 2277785 2204412 744

D 6598632 6839319 6359952 minus362

Total 10945651 11349903 11198453 231

Pasture

A 510146 460323 324348 minus3642

B 136395 89019 77021 minus4353

C 696209 754500 653027 minus620

D 1152047 1036039 881960 minus2344

Total 2494796 2339881 1936355 minus2238

0

20000

40000

60000

80000

100000

120000

0

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

Land area ‐MarshPasture (ha)

Land area ‐Forest Cover (ha)

Year

Land‐cover dynamics over time

West East Marsh Pasture

Figure 4 A representation of the changes in three respective land-cover types (forest cover pasturemarsh) over the period 2003ndash2019 For forest cover the three western sections are represented as Westand D is represented as East Light green indicates marsh yellow represents pasture dark green andhatched green show forest cover

Forests 2022 13 1132 10 of 22

32 Rates of Loss with Respect to Elevation

A net decrease in forest cover was seen across all elevation categories (Table 2 Figure 5)The largest losses were in the 1ndash2 m category with a net reduction of 593 from 2003 to2019 There was a temporal shift in the rate of change with the greatest amount of lossbetween 2003ndash2007 contributed by section D Values within the 2ndash3 m category remainedsteady from 2007 to 2019 with a reduction of only 076 The large majority (749) ofthe remaining forest was seen to be located within the 0ndash1 m band The losses were notevenly distributed within this band buffered to some degree by section D A comparisonof sections A vs C shows a decrease of 493 with losses centered around the period2007ndash2019 One issue to note is that the GSD for the DEM was 3 m which is wider thansome spoil banks While the local elevation may in fact rise to 2ndash3 m the value was reduceddue to the effect of averaging The losses in the 3 m+ band are discussed below a noteshould be made of the limited representation Overall the losses varied both spatially andtemporally across the region largely separated into eastern and western reaches

Table 2 Differences in the amount of forest area within each elevation band and the associatedpercentage loss of forest cover between years Sections A B C and D represent areas delineated forthis study within the two parishes (for details see Figure 1)

Sites and Forest Cover (ha)Percentage

LossYearmdash2003

Elevation (m) A B C D Total

0ndash1 13083 25525 66334 187236 2921781ndash2 27168 36374 25583 39048 1281722ndash3 1934 2188 6481 4640 15242gt3 361 170 005 343 879

Yearmdash2007A B C D Total 2003ndash2007

0ndash1 18371 16502 56349 166776 257997 minus11701ndash2 17834 20210 21289 4205 63538 minus50432ndash3 1228 1783 4118 5446 12575 minus1750gt3 048 208 002 404 662 minus2474

Yearmdash2019A B C D Total 2003ndash2019

0ndash1 7161 8227 37762 142102 195253 minus33171ndash2 10283 12913 24656 4401 52254 minus59232ndash3 1506 932 5789 4253 12480 minus1812gt3 045 141 002 402 590 minus3288

33 Interaction with Water Level

Water plays a critical role in the natural function of this ecosystem As such we consid-ered the interplay of temperature and MSL over a decadal time scale and the local rates oftemperature and gage height from both USGS stations (Figure 6) In Figure 6 forest extentis superimposed for each yearrsquos classification to show visual correspondence between thetwo drivers Temperature and rise in sea-level were negatively correlated until an inflectionpoint around 2010 after which there was a relatively stronger correlation increasing to amaximum of 025 The correlative increase lends evidence to the correspondence betweenclimate and water pressure in coastal areas An increase in the average MSL has been ob-served for the site The two stream gages showed an annualized MSL rise of 136 mmyearfor Vermilion Bay and 59 mmyear for the Calcasieu River There was a weak but positivetrend for temperature to increase slightly from the onset of the study period Time provedto be a good indicator for increase in water level and for decrease in forest cover (Figure 6)The lack of tree-cover samples prohibited many statistical comparisons but visually thecorrespondence seems evident

Forests 2022 13 1132 11 of 22

Forests 2022 13 x FOR PEER REVIEW 11 of 23

Table 2 Differences in the amount of forest area within each elevation band and the associated

percentage loss of forest cover between years Sections A B C and D represent areas delineated for

this study within the two parishes (for details see Figure 1)

Sites and Forest Cover (Ha)

Percentage Loss Yearmdash2003

Elevation (m) A B C D Total

0ndash1 13083 25525 66334 187236 292178

1ndash2 27168 36374 25583 39048 128172

2ndash3 1934 2188 6481 4640 15242

gt3 361 170 005 343 879

Yearmdash2007

A B C D Total 2003ndash2007

0ndash1 18371 16502 56349 166776 257997 minus1170

1ndash2 17834 20210 21289 4205 63538 minus5043

2ndash3 1228 1783 4118 5446 12575 minus1750

gt3 048 208 002 404 662 minus2474

Yearmdash2019

A B C D Total 2003ndash2019

0ndash1 7161 8227 37762 142102 195253 minus3317

1ndash2 10283 12913 24656 4401 52254 minus5923

2ndash3 1506 932 5789 4253 12480 minus1812

gt3 045 141 002 402 590 minus3288

0

1

2

3

4

5

6

7

8

9

10

0

500

1000

1500

2000

2500

3000

3500

Land area 3mdash4m ‐(ha)

Land area 0mdash3m ‐(ha)

Year

Forest cover in elevational bands over time

0‐1m

1‐2m

2‐3m

3‐4m

Figure 5 Visual depictions of two forms of loss one from hurricanes on Hackberry Beach and theother documenting whittling reduction The left column refers to 2003 NAIP imagery while the rightuses 2007 NAIP imagery Black shaded areas indicate current year forest extent and the red hashedareas indicate loss

Forests 2022 13 x FOR PEER REVIEW 12 of 23

Figure 5 Visual depictions of two forms of loss one from hurricanes on Hackberry Beach and the

other documenting whittling reduction The left column refers to 2003 NAIP imagery while the right

uses 2007 NAIP imagery Black shaded areas indicate current year forest extent and the red hashed

areas indicate loss

33 Interaction with Water Level

Water plays a critical role in the natural function of this ecosystem As such we con‐

sidered the interplay of temperature and MSL over a decadal time scale and the local

rates of temperature and gage height from both USGS stations (Figure 6) In Figure 6

forest extent is superimposed for each yearrsquos classification to show visual correspondence

between the two drivers Temperature and rise in sea‐level were negatively correlated

until an inflection point around 2010 after which there was a relatively stronger correla‐

tion increasing to a maximum of 025 The correlative increase lends evidence to the cor‐

respondence between climate and water pressure in coastal areas An increase in the av‐

erage MSL has been observed for the site The two stream gages showed an annualized

MSL rise of 136 mmyear for Vermilion Bay and 59 mmyear for the Calcasieu River

There was a weak but positive trend for temperature to increase slightly from the onset of

the study period Time proved to be a good indicator for increase in water level and for

decrease in forest cover (Figure 6) The lack of tree‐cover samples prohibited many statis‐

tical comparisons but visually the correspondence seems evident

Figure 6 Trends in mean water levels and water temperature over 19 years in Vermillion Parish

Data obtained from the USGS Station 07387050 Vermilion Bay (minus921355 296744) with gage datum

165 m above NAVD88 and from the USGS Station 08017118 Calcasieu River at Cameron LA

(minus933488 298155) with gage datum 107m above NAVD88

Rsup2 = 09453

Rsup2 = 01283

Rsup2 = 06529

08

085

09

095

1

105

11

115

12

125

1995 2000 2005 2010 2015 2020 2025

15

20

25

30

35

40

45

50

Average

water level (m)

Year

Forest cover loss Average

water temp

Loss of tree cover (sqkm) Average water temp (deg C) Average waterlevel (m)

Figure 6 Trends in mean water levels and water temperature over 19 years in Vermillion Parish Dataobtained from the USGS Station 07387050 Vermilion Bay (minus921355 296744) with gage datum 165 mabove NAVD88 and from the USGS Station 08017118 Calcasieu River at Cameron LA (minus933488298155) with gage datum 107m above NAVD88

Forests 2022 13 1132 12 of 22

4 Discussion41 Classification Reliability and Constraints

NAIP imagery proved efficacious for fine-scale analysis in this region shown by thestrong average producerrsquos accuracy and Kappa coefficient This is a positive indicatorthat variability between years or areas did not influence classification accuracies to alarge extent and that the final classification is a reliable interpretation [42] Many otherstudies conducting land-cover classifications using NAIP imagery have also shown robustaccuracies [43ndash45] However there were noted issues seen in certain parts of the workflowA problem preeminent in the initial classification was a limitation of spectral resolution Thisproblem became pronounced in areas with similar shades of green or between dark bluesand black hues Without the use of ancillary datasets the classification accuracy was poorbetween dark water and shadows of trees with considerable overlap (misclassificationsof approximately 50) Mixed land use can also prove problematic in NAIP land-coverclassifications [46] This became especially pronounced in transitional forestmarsh areasin which there was no dominant spectral signature for a particular land use Local variationbetween adjoining image sets highlighted another shortcoming of NAIP imagery Theviewing geometry illumination and time between the capture of neighboring imagetiles created edge effects including artificially high or low pixel values and refractionproblems on water [47] Each yearrsquos dataset was collected by an external contractor causingdifferences in sensor instrumentation and parametrization between years By 2019 many ofthese problems had been mitigated and images were of much higher quality

Compared to a large swath sensor such as Landsat-8 or Sentinel-2 an innate non-uniformity poses problems for pixel-wise classifications of VHR imagery Typically datafrom satellite imagery have been the primary choice for analyzing coastal forests marshesand forests at large [48ndash50] The spaceborne sensors obtain information over eight bandsallowing much finer spectral discrimination of certain classes of objects [51] Even amongstdiverse forest types space-borne Sentinel-2 and ASTER imagery has been used to mapdistinct species clusters of broadleaf and deciduous plant species The large spatial scaleenables the use of these created geospatial products to analyze the efficacy of governmentalreforestation programs in remote areas of the world [52] The usage of normalized differ-ence indices including the Normalized Difference Vegetation Index (NDVI) EnhancedVegetation Index (EVI) and Enhanced Water Index (EWI) can also provide greater confi-dence in the separation of water and marsh features from deciduous broadleaf forests [53]However a spatial resolution of 10m is still too coarse for the detection of remnant andlandscape trees especially those alongside homogenous flat features such as maintainedgrass or water bodies The most practical solution would be to assess this land with ahigher resolution dataset such as unmanned aerial systems (UAS) imagery which cancapture a much finer GSD in a plethora of bands if multispectral or hyperspectral sensorsare used [54] In fact scientists are now pushing the boundaries of the scale of analysisconducted through remote sensing Broad groupings (eg clades or forest types) have beenthe historic norm in the classification of imagery for use in land management A greatermixture of classes allows more accurate measurements for those interested in landscape-scale ecological processes However VHR can be used to map stands at the species levelwhich allows forest managers an unparalleled insight into the dynamics of recruitmentdisease or resource availability [55]

Google Earth Engine allowed us to utilize NAIP imagery at scale which had for sometime been a limiting factor in processing this data source locally Even so some limitationshindered the functional utility of GEE for detailed land-cover classification In our relativelyuniform environment we discriminated between many shades of green When viewinggeometries cause variations in hue different classes begin to overlap within the samespectral regions Solutions using Geographic Object-Based Image Analysis (GEOBIA) cancircumvent this issue yet this segmentation algorithm is currently not supported by GEEMany consider GEOBIA the optimal tool for segmenting landscapes and for the productionof land-cover classifications from this source of imagery [3856] Its successful integration

Forests 2022 13 1132 13 of 22

could substantially elevate the use and utility of mapping within GEE Additionally theallocation of increased processing bandwidth on GEE servers may help overcome somelimits in classification Greater bandwidth allows larger and more diverse training setsin addition to more complex parameterizations of machine learning classifiers If oneis proceeding on local workstations the analysis of NAIP imagery requires an intimateknowledge of data structures and process optimization Custom architectures processingpipelines and simpler binary classifications can allow an expansion of the study area tocover coterminous areas on a larger scale [16]

42 Forest Loss Contextually

We mapped forest cover change dynamics between 2003ndash2019 on the southwest coastof Louisiana 1580 hectares of tree cover 13 of the terrestrial area was converted todifferent land use The total loss of forest cover in the study period was 3573 over sixteenyears which is indicative of a larger rate of forest loss than at sites in a similar physio-geographic region yet is certainly not a global outlier [18] These findings validate thesentiments of people in the region and underscore a major irreversible trend in the area Ourestimate should be considered conservative due to the way we approached the definition ofldquoforest coverrdquo within the landscape Abandoned pastoral land exhibiting dense scrubbinessand shrub cover was demarcated as forest cover even though the target species (live oak)was probably not present Spoil banks which are the byproduct of historic oil explorationin the region indicating possible mortality were also often considered a component offorest cover if the imagery could not indicate the absence of high vegetation The primarymethod of identifying whether or not forest cover was present was the observation of ashadow adjoining the vegetation indicating a vertical structure

The Big Bend region of Florida one of the longest undisturbed coastlines in thecontinental US offers some insight into the vegetation dynamics this region has beenexperiencing It has been the focus of numerous ecological studies has a similar climateand is exposed to storms with similar regularity Raabe and Stumpf [57] found that fromthe early to late 1900s over 82 km2 of forest land was converted to marsh and 66 km2 wasin a transitional state to marsh McCarthy et al [58] document an annual forest cover lossof 744 from 2010ndash2017 an acceleration in the trend within that area A new study bythe same team found rates of loss totaling approximately 10 km2 a year 800 larger thanpreviously stated in the literature [59] Rates of annual mortality ranged from 4ndash16 inSoutheastern Louisiana from 1987 to 1997 [60] In comparison the annualized rate of lossfound in our study never exceeded 5 Plots located in South Carolina that have beenless significantly impacted by MSL rise show rates of loss closer to 1ndash2 in line withour observed rate of loss from 2007ndash2019 However we cannot assume that percentagesof forest loss across the country have been constant within the last century therefore it iscritical to carry out intensive geospatial studies at regular intervals

In the adjoining southeastern part of the state of Louisiana in the Atchafalaya andTerrebonne basins forested wetlands occupy 121871 hectares or 41 of all vegetatedland use [12] In comparison the terrestrial forested land use in our study area was 21in 2019 The wide variation can be interpreted in several ways each with importantimplications Historic rates of deforestation differ in the two regions with a greateremphasis on the preservation of wilderness areas in the Atchafalaya basin [61] TheAtchafalaya and Terrebonne basins also have productive land used in the growth of timberwhich promotes long-term forest cover The regeneration of forests in our study region alsodiffers substantially Chenier ecosystems and traditional alluvial bottomland hardwoodforests are compositionally different in vegetation and soil even when accounting for theirunique hydrologic regimes Communities of live oak may be less resilient to the types offorcings that cause changes in community composition within these ecosystems furtheredby proximity to the sea If stocking density rates are similar to the adjoining basin as theywere in the past we would see an areal extent well over 50000 hectares

Forests 2022 13 1132 14 of 22

The analysis vis-agrave-vis elevation shows weak trends in the manners of loss on eleva-tional gradients Logically in the paradigm of coastal retreat there should be an aggregationof forest resources towards the highest bands of topography in a region Our analysis doesnot follow this chain of thought with the largest losses observed in the 1ndash2 m categoryrather than the lowest 0ndash1 m (Table 2 2007 C 2019 D) While we do see a large loss bypercentage in the highest elevation band it is important to note that a small sample causesinherent variance Forest cover on the highest reaches of the chenier ridges is typically lessthan 1 of total forest cover for that region The reduction can be attributed to the loss of23 features in section A accounting for 33 hectares bordering the Sabine River (Figure 1minus9386286 2973355) The loss of forest cover was driven by intense weather Vegetationin the area was completely destroyed and substantial amounts of sedimentation could beobserved as evident in the 2007 NAIP imagery The only gain in forest cover was seen inthe 2ndash3 m elevation band in the period 2007 in an area that had a high density of pastoralland that was abandoned Forest retreat is often discussed as one method of bufferingforest loss along coasts [62] Unfortunately the geomorphology of this area does not allowgreat spatial movement as the current extent is already located within the highest bandsof elevation

43 Land Use Conversion and Driving Factors

The leading cause of land-use conversion from tree cover was to a saltmarsh ecotype(606) often following mortality and replacement of vegetative biomass The loss offorest cover can largely be typified into two categories The ingression of saltwater causingsalinification within the soil matrix paired with increased flooding caused mortality ofoverstorey vegetation over a multi-year timescale [6364] Hurricanes or other storm eventscaused rapid mortality through wind-throws severe defoliation saltwater inundationduring a storm surge or canopy damage Following intense weather events between2003 and 2007 the vast majority of cheniers located within a 1km buffer of the coast wereunable to support tree cover as seen in Figure 7AB Clearing for industry or developmentalso caused immediate shifts in forest composition as can be seen in Figure 8AB Thischange was more pronounced on reaches A and B especially on Hackberry Beach Thisarea was the site of landfall for Hurricane Rita which caused massive devastation to othercomponents of the ecosystem [6566] Singular storm events have a transformative impacton vegetation communities and the weight of this driver on annual rates of forest lossis a topic that should be explored further Wayward shifts in species composition are atrend that has been observed globally especially in forests that border some source of waterTrees like Juniperus virginiana L that have distinctive water-use patterns in comparison toother coastal tree species are susceptible to salinification at much faster rates as evidencedthrough isotope analyses [67] As with many ecological systems the entangled effectsof climate and vegetation require diverse means of looking at change Isotope analysesdendrochronology field sensors and remote sensing data are needed to characterize land-use change at the stand and community levels

Large forest remnants such as Cow Island (minus9283581 2973842) Tiger Island (minus92784392972832) and Grand Chenier Ridge (minus9281175 2972986) among others have diminishedmore slowly By 2019 these stands had lost an average of 15 of their maximum extentThe land use changed to both marsh and managed pasture largely dependent on thesurrounding land use The conversion to managed pasture (282) accounted for a largerthan expected amount of land-cover change Many of these changes were in areas wheretree cover may have been more ldquoornamentalrdquo in nature Trees beside houses or alongsideroads are some of the largest specimens of live-oak to be found However these trees areoften solitary and perhaps more vulnerable to wind-borne disturbance The concern is thattheir loss due to abiotic drivers may not be easily mitigated leading to a permanent declineOur data support evidence on the whittling of forest remnants and the eventual fate ofthese trees becoming lsquorelict forestsrsquo unable to recruit new generations in the short term andin the long-term total disappearance of these cheniers along the Louisiana Gulf Coast [68]

Forests 2022 13 1132 15 of 22Forests 2022 13 x FOR PEER REVIEW 16 of 23

Figure 7 Visual depictions of two forms of loss one from hurricanes on Hackberry Beach (AB) and

the other documenting whittling reduction (CD) The left column displays 2003 NAIP imagery and

the right is 2007 NAIP imagery Black shaded areas indicate current year forest extent red hashed

areas indicate a loss

Figure 7 Visual depictions of two forms of loss one from hurricanes on Hackberry Beach (AB) andthe other documenting whittling reduction (CD) The left column displays 2003 NAIP imagery andthe right is 2007 NAIP imagery Black shaded areas indicate current year forest extent red hashedareas indicate a loss

Beyond loss in forest cover there is a noticeable trend towards an altered hydrologywhere pastoral areas and intact forest remnants are associated with pockets of standingwater An increase in the correlative factor between temperature and water levels could bea worrying trend a direct conversion of tree cover to water at 34 could be an unforeseenoutcome Chenier ridges in their geomorphic context should not support non-ephemeralponding It is not documented how increased tidal influence which is one factor of risingMSL influences the hydrology of this ecosystem and its associated communities Albeitin a more inland context Magolan and Halls [69] documented a transition of over 40 ofland to emergent marsh from closed-canopy cover from 1949 to 2018 due to hydrologicinfluence Recent rates of relative sea rise have been documented as high as 119 mmyearcompared to background Holocene rates of 17 mmyear [70] This has resulted in a rate oflateral coastal forest retreat between 2ndash14 times greater than pre-industrial levels [26] Therate of shoreline retreat in this region increased from ~61 to ~94 myear based on the 100-and 30-year moving averages amounting to over 10000 hectares of land loss every yearfrom 1800 to 2005 [27]

Forests 2022 13 1132 16 of 22Forests 2022 13 x FOR PEER REVIEW 17 of 23

Figure 8 A depiction of clearing for industry in (AB) and a depiction of forest whittling from (CD)

The black extent represents the current forest area red hashed lines indicate a loss The left panels

show 2007 NAIP imagery while the right are 2019 NAIP imagery

Large forest remnants such as Cow Island (minus9283581 2973842) Tiger Island

(minus9278439 2972832) and Grand Chenier Ridge (minus9281175 2972986) among others have

diminished more slowly By 2019 these stands had lost an average of 15 of their maxi‐

mum extent The land use changed to both marsh and managed pasture largely depend‐

ent on the surrounding land use The conversion to managed pasture (282) accounted

for a larger than expected amount of land‐cover change Many of these changes were in

areas where tree cover may have been more ldquoornamentalrdquo in nature Trees beside houses

or alongside roads are some of the largest specimens of live‐oak to be found However

these trees are often solitary and perhaps more vulnerable to wind‐borne disturbance The

concern is that their loss due to abiotic drivers may not be easily mitigated leading to a

permanent decline Our data support evidence on the whittling of forest remnants and

the eventual fate of these trees becoming lsquorelict forestsrsquo unable to recruit new generations

in the short term and in the long‐term total disappearance of these cheniers along the

Louisiana Gulf Coast [68]

Beyond loss in forest cover there is a noticeable trend towards an altered hydrology

where pastoral areas and intact forest remnants are associated with pockets of standing

water An increase in the correlative factor between temperature and water levels could

be a worrying trend a direct conversion of tree cover to water at 34 could be an unfore‐

seen outcome Chenier ridges in their geomorphic context should not support non‐ephem‐

eral ponding It is not documented how increased tidal influence which is one factor of

Figure 8 A depiction of clearing for industry in (AB) and a depiction of forest whittling from (CD)The black extent represents the current forest area red hashed lines indicate a loss The left panelsshow 2007 NAIP imagery while the right are 2019 NAIP imagery

44 Future of Chenier Forests

The chenier complexes of Louisiana do not have the natural benefaction of maintaininglarge original tracts untouched by axe or dozer The extent map through the years (Figure 9)exemplifies the scattered nature of forest remnants The largest single continuous chenierforest (Grand Chenier Ridgemdash185 ha) encompasses less than 1 of the total forest areaExposed forest edges pose a problem for the continued integrity of the stand as adaptedspecies fail to compete in these new microclimates [71] Slow-growing saplings of desirabletree species (live oak hackberry) are subject to unfavorable growing conditions fromweather and light availability Vegetation surveys following aspect and inundation alongthe Florida coast have all but confirmed the eventual extirpation of overstory vegetationthrough poor recruitment of native species [31] Chinese tallow (Triadica sebifera) a non-native tree is likely to fill empty niche space This aggressive invader is problematicthroughout the Gulf Coastal Plain from Texas to Florida [72] The tree has a wide meansof dispersal being primarily distributed through frugivory via avian populations A highrate of germination compounded with quick growth and a sexual maturity of less thanthree years make it a difficult species to control Its invasive capability is seen in nearlyall forest types in the region although it is more pervasive in coastal forests than inlandModeling studies show a high likelihood of Chinese tallow becoming the dominant specieswithin this ecosystem and in surrounding areas [73] Future publications from this lab will

Forests 2022 13 1132 17 of 22

provide more in-depth data and analysis on the state of existing stands and recruitmenttrends within remnants of the Chenier Plains via field sampling

Louisiana has enacted a historic response to the problem of land-loss following a publicoutcry after devastating storms in 2005 The Coastal Protection and Restoration Authority(CPRA) have been tasked with enacting a management plan to combat coastal erosion TheComprehensive Master Plan details proposals to create marshes build sediment diversionsform new hydrologic structures and promote barrier island restoration through builtand natural means As of 2017 over 135 projects had been completed impacting over14568 ha [74] An update to this document is due to be released in 2023 The projecthas an overall operating budget of over 50 billion US dollars with equal shares dedicatedto restoration and risk reduction The primary funding source is a settlement associatedwith the Deepwater Horizon Oil Spill in addition to various initiatives at the state andfederal levels However there are no current plans to supplement vegetation or performany geoengineering work on the cheniers for afforestation

Coastal wetlands rank among the most important ecosystems in terms of the valuationof ecosystem services [75] Provisioning services such as water filtration or storm-surgeprotection are commonly considered when discussing coastal areas Beyond these com-monalities chenier forests provide vital wildlife habitats being the first encountered sourceof vertical structure for migratory neo-tropical birds and migrating monarch butterflies inthe Mississippi alluvial valley flyway [76] This en-route habitat is of critical importancedue to its closeness to the Gulf of Mexico During favorable weather conditions 10 ofbirds migrating through the Gulf use these forests as a stopover spot rising dramatically to80 during adverse weather conditions [29] With losses mounting many species may notbe able to seek refuge during storms The area also has a unique suite of amphibians andreptiles being home to 13 obligate chenier forest species of herpetofauna [77]

Lu et al [78] used the eddy covariance method (ECM) to calculate strong carbon sinkeffects for a chenier ecosystem driven by forest vegetation Net energy production valueswere significantly higher than surrounding marshland signifying the importance of densevegetative cover on a per-unit basis Counter to this there is also strong evidence thatdegraded forest ecosystems act as net carbon sources A study in North Carolina also usingECM found that a seasonally inundated tide-influenced forest was a net carbon sourceannually and only functioned as a sink for 3 months of the growing year [79] Similarly astudy in the northeastern part of the state showed net negative rates of carbon sequestrationfor an ephemerally flooded forest [80] Unique environments demand individual attentiona characterization of this ecosystem for nitrogen or carbon flux with respect to a similarsystem may not be functionally sound [81]

Forests 2022 13 1132 18 of 22Forests 2022 13 x FOR PEER REVIEW 19 of 23

Figure 9 The total land area classified in GEE for the years 2003 2007 and 2019 Light green repre‐

sents the marsh area tan represents pasture The water layer has been removed for purposes of

clarity The dark black outline is the outline of the forest cover for the respective year

Louisiana has enacted a historic response to the problem of land‐loss following a

public outcry after devastating storms in 2005 The Coastal Protection and Restoration

Authority (CPRA) have been tasked with enacting a management plan to combat coastal

erosion The Comprehensive Master Plan details proposals to create marshes build sedi‐

ment diversions form new hydrologic structures and promote barrier island restoration

through built and natural means As of 2017 over 135 projects had been completed im‐

pacting over 14568 ha [74] An update to this document is due to be released in 2023 The

project has an overall operating budget of over 50 billion US dollars with equal shares

dedicated to restoration and risk reduction The primary funding source is a settlement

associated with the Deepwater Horizon Oil Spill in addition to various initiatives at the

state and federal levels However there are no current plans to supplement vegetation or

perform any geoengineering work on the cheniers for afforestation

Coastal wetlands rank among the most important ecosystems in terms of the valua‐

tion of ecosystem services [75] Provisioning services such as water filtration or storm‐

surge protection are commonly considered when discussing coastal areas Beyond these

commonalities chenier forests provide vital wildlife habitats being the first encountered

source of vertical structure for migratory neo‐tropical birds and migrating monarch but‐

terflies in the Mississippi alluvial valley flyway [76] This en‐route habitat is of critical

importance due to its closeness to the Gulf of Mexico During favorable weather condi‐

tions 10 of birds migrating through the Gulf use these forests as a stopover spot rising

Figure 9 The total land area classified in GEE for the years 2003 2007 and 2019 Light greenrepresents the marsh area tan represents pasture The water layer has been removed for purposes ofclarity The dark black outline is the outline of the forest cover for the respective year

5 Conclusions

The cheniers of southwestern Louisiana were found to be declining in size at a concern-ing rate Many of the driving factors are ecosystem-wide trends that have been increasingin intensity within the last century The results of this study suggest that coastal forestsare an ecosystem in peril and the ecology of such systems should be a closely monitoredsubject The 365 loss of forest cover is an alarming but not wholly unexpected result ofmany decades of intense perturbation and human exploitation A dynamic of continuedforest loss is evident Unless significant investment is made in engineering solutions tomitigate storm effects and land subsidence we will continue to see the decline of chenierforests in Louisiana Through targeted mitigation governmental programs such as theWetland or Conservation Reserve Program may be able to supplement existing forestsas well as the incomes of local people who have lost their livelihoods In this study wehave begun the process of accurate areal estimates of forest extent and have shed light onecosystem processes in the region As this community continues to diminish we see theforest not by the trees but rather the lack of them

Forests 2022 13 1132 19 of 22

Author Contributions Conceptualization PT methodology PT software PT and PR validationPT and PR formal analysis PT PR and PT writingmdashoriginal draft preparation PT and JBwritingmdashreview and editing PT and JB visualization PT and JB supervision JB All authorshave read and agreed to the published version of the manuscript

Funding This research received no external funding

Data Availability Statement The data will be published in and stored on the Harvard Dataverse athttpsdataverseharvardedudatasetxhtmlpersistentId=doi107910DVN3J7SNF (accessed on1 May 2022)

Acknowledgments The authors would like to recognize the hard work of undergraduate studentsParusha Raut and Subash Sapkota in manually validating thousands of points and creating numeroustraining sets We also extend thanks to our institution and numerous people within the lab who leantboth mental and emotional fortitude

Conflicts of Interest The authors declare no conflict of interest

References1 Michener WK Blood ER Bildstein KL Brinson MM Gardner LR Climate change hurricanes and tropical storms and

rising sea level in coastal wetlands Ecol Appl 1997 7 770ndash801 [CrossRef]2 Donoghue JF Sea level history of the northern Gulf of Mexico coast and sea level rise scenarios for the near future Clim Chang

2011 107 17ndash33 [CrossRef]3 Stanturf JA Goodrick SL Outcalt KW Disturbance and coastal forests A strategic approach to forest management in

hurricane impact zones For Ecol Manag 2007 250 119ndash135 [CrossRef]4 Augustinus P Cheniers and chenier plains A general introduction Mar Geol 1989 90 219ndash229 [CrossRef]5 Fu Z Wang P Sun J Lu Z Yang H Liu J Li T Composition seasonal variation and salinization characteristics of soil

salinity in the Chenier Island of the Yellow River Delta Glob Ecol Conserv 2020 24 e01318 [CrossRef]6 Marques IG Campelo F Rivaes R Albuquerque A Ferreira MT Rodriacuteguez-Gonzaacutelez PM Tree rings reveal long-term

changes in growth resilience in Southern European riparian forests Dendrochronologia 2018 55 167ndash176 [CrossRef]7 Daigle JJ Griffith GE Omernik JM Faulkner PL McCulloh RP Handley LR Smith LM Chapman SS Ecoregions of

Louisiana (Color Poster with Map Descriptive Text Summary Tables and Photographs) US Geological Survey Reston VI USA 20068 McBride RA Taylor MJ Byrnes MR Coastal morphodynamics and Chenier-Plain evolution in southwestern Louisiana USA

A geomorphic model Geomorphology 2007 88 367ndash422 [CrossRef]9 Gosselink JG An Ecological Characterization Study of the Chenier Plain Coastal Ecosystem of Louisiana and Texas Narrative Report

National Coastal Ecosystems Team Office of Biological Services Fish and Wildlife Service US Department of the InteriorWashington DC USA 1979 Volume 1

10 Blum MD Roberts HH The Mississippi delta region Past present and future Annu Rev Earth Planet Sci 2012 40 655ndash683[CrossRef]

11 Hansen MC Potapov PV Moore R Hancher M Turubanova SA Tyukavina A Townshend J High-resolution globalmaps of 21st-century forest cover change Science 2013 342 850ndash853 [CrossRef]

12 Doyle TW Krauss KW Conner WH From AS Predicting the retreat and migration of tidal forests along the northern Gulfof Mexico under sea-level rise For Ecol Manag 2010 259 770ndash777 [CrossRef]

13 Thomas N Simard M Castantildeeda-Moya E Byrd K Windham-Myers L Bevington A Twilley R High-resolution mappingof biomass and distribution of marsh and forested wetlands in southeastern coastal Louisiana Int J Appl Earth Obs Geoinf 201980 257ndash267 [CrossRef]

14 Desantis LR Bhotika S Williams K Putz FE Sea-level rise and drought interactions accelerate forest decline on the GulfCoast of Florida USA Glob Chang Biol 2007 13 2349ndash2360 [CrossRef]

15 Boyd DS Foody G Ripple W Evaluation of approaches for forest cover estimation in the Pacific Northwest USA usingremote sensing Appl Geogr 2002 22 375ndash392 [CrossRef]

16 Valjarevic A Djekic T Stevanovic V Ivanovic R Jandzikovic B GIS numerical and remote sensing analyses of forest changesin the Toplica region for the period of 1953ndash2013 Appl Geogr 2018 92 131ndash139 [CrossRef]

17 He T Sun YJ Xu JD Wang X Hu C Enhanced land usecover classification using support vector machines and fuzzyk-means clustering algorithms J Appl Remote Sens 2014 8 083636 [CrossRef]

18 Michez A Pieacutegay H Lisein J Claessens H Lejeune P Classification of riparian forest species and health condition usingmulti-temporal and hyperspatial imagery from unmanned aerial systems Environ Monit Assess 2016 188 146 [CrossRef][PubMed]

19 Hampton SE Strasser C Tewksbury J Gram W Budden A Batcheller A Duke C Porter J Big data and the future ofecology Front Ecol Environ 2013 11 156ndash162 [CrossRef]

20 Curtis PG Slay CM Harris NL Tyukavina A Hansen MC Classifying drivers of global forest loss Science 2018 3611108ndash1111 [CrossRef]

Forests 2022 13 1132 4 of 22

The intact community forms a dense stand that prohibits the establishment of shade-intolerant species These communities have been described as ldquocathedral-likerdquo due to thetall and wide branching stature of the live oaks The leaves of live oaks are dark greenin hue and maintain foliage year-round making their foliage a strong visual indicator inaerial imagery

Recent vegetation surveys show a shift to a community more akin to an open saltmarshswamp with thick stands of southern amaranth (Amaranthus australis (AGray) JDSauer)roseau cane (Phragmites australis (Cav) Steud) narrow-leaved cattail (Typha angustifolia L)and other wetland obligate species interspersed with dead or dying trees (unpublished dataBhattacharjee J) This newly emerging ecotype represents a breakdown of the traditionalsegregation of upland forest and lowland haline swamp This change is broadly representedby the encroachment of hydrophytic vegetation becoming dominant in the understory Thischange is identifiable when seen in VHR imagery although the spectral characteristics ofthe scene can appear differently Much of the structural complexity of the forest has beenlost The physiognomy appears as a brushy single layer with limited overstorey mirroringthe surrounding saltmarsh to some degree Some areas have become entirely dominated bydense herbaceous vegetation preventing the establishment of native upper-story speciesThe site has always experienced anthropogenic influence in community composition thenative Attakapas people contributed to the original diversity of vegetation by introducingand maintaining an abundance of food-bearing plants within the landscape [3637] Thestudy site does not have any mangrove swamps which can be found further east and southalong the Gulf Coast

22 Data Sources

The National Agricultural Image Program of the United States Department of Agriculture(USDA) houses aerial orthoimage sets for the contiguous United States in 375 times 375 quadranglesThe 2003 and 2007 imagery for Louisiana were collected at 2 m and 1 m Ground SampleDistance (GSD) respectively in red green and blue bands The 2019 imagery has 05 mGSD with the addition of a near-infrared band In total 234 NAIP image tiles were used foranalysis Finer scale imagery was not downsampled to coarser resolution Additionally allbands were used in the classification scheme to provide the greatest discriminatory accuracyAn effort was made to classify imagery from around a similar date to capture equivalentphenophases although image quality was given precedence over temporal constraintsThe aerial images used in the data analysis pipeline for the years 2003 2007 and 2019were collected on 82403 81007 and 9519 respectively These particular years werechosen as they represented the highest quality imagery within the study period It shouldbe noted the NAIP is not an annual dataset collections vary according to agreements withstate governments and are often performed on a biennial or triennial cycle Limiting factorsin many of the interior year image sets were cloud cover or high local variance in scenereflectance which were prohibitive for the manner of land cover classification used inthe study Intense glare from surrounding water bodies caused significant reduction inquality of less optimal image correction after the fact It became difficult to distinguishingbetween many similar vegetation types as even moderate local variance can introducenoise degrading algorithm effectiveness Imagery from 2013 had begun to be processed butwas abandoned after initial development due to scene variability and poor classificationperformance Only image sets with less than 5 cloud cover were used

The Coastal National Elevation Database (CoNED) Digital Elevation Model (DEM)was used for elevation analysis The CoNED is generated by the United States GeologicalSurvey (USGS) using LiDAR and has a 3 m GSD It is housed in the National Map Datarepository The raster was downsampled to 1 m to match the resolution of the GEEclassification output Mean sea level (MSL) salinity and temperature values were extractedfrom two USGS stream gages (08017118 07387050) located at opposite ends of the studyarea providing data for the entirety of our study period The data were extracted fromthe USGS StreamStats interactive web map The National Hydrography Dataset (NHD)

Forests 2022 13 1132 5 of 22

a vector repository of waterbodies (NHDWaterbody NHDArea) and wetland features(NHDWaterbody FCode 46600 46601 46602) was used for feature input in the creation ofpost-processing masks The NHD is also a product of the USGS

23 Classification and Processing Algorithms

Google Earth Engine offers a suite of supervised and unsupervised Machine LearningClassifiers for image classification Through iterative testing of all available options theClassification and Regression Tree (CART) algorithm was chosen for this study CART isa decision-tree-based supervised learning model able to handle nonlinear relationshipsamong features [38] Because class balancing is recommended for this algorithm a stableratio of tree-to-pasture and tree-to-marsh training sets were created Parametrizationwas not modified from default properties The primary consideration in choosing thisalgorithm was robust classification for forest cover Visually we observed that many othercontenders had higher incidence rates of non-forest-based classification of tree cover whenconducting initial testing leading to a general under-representation of forest cover Weuse a reductionist approach for accuracy in this study where pixel values for forest coverwere reduced rather than added which makes underrepresentation problematic Thepost-processing vectors allowed extremely fine detail in the modification of the originalCART output

The study area was divided into four sections (A B C and D) of equal approximatelength as seen in Figure 1 Sections C and D were further divided into three subsectionsdue to image variability in the 2003 dataset and were merged post-classification Thisfacilitated a more targeted training dataset given local variation while also expeditingprocessing within the workflow We used six total land-cover classes in the classificationschememdashtree pasture pond river saltmarsh and impervious area The classes werecomprised of manually delineated Point FeatureCollections within GEE and were givenspecific unique IDs Tree cover was interpreted as the visual presence of dark foliagebranching or scrubbiness As such these selections include trees located on spoil banklevees (Figure 2B) swamp forests (Figure 2C) or residential areas in addition to the live-oak communities found on the main chenier ridges (Figure 2AD) The rationale was toinclude any type of vegetation that promotes beneficial land use and habitat Pasture wasinterpreted as any area in which there are grazing activities or managed lands on whichregular mowing occurs such as yards or parks The Point FeatureCollections were createdusing 2019 NAIP imagery and were manually validated before use in each preceding yearto ensure classes remained accurate Modifications were made when class values changedbetween years The CART classification was subject to a 3-pixel square image reducerthereby homogenizing values to reduce the ldquosalt and pepper effectrdquo common in pixelwiseclassifications [39] The data were then exported to Google Drive for further processing inQuantum GIS (QGIS) A detailed workflow for the procedure is given in Figure 3

24 Post-Processing of GEE Outputs

Two-layer masks created from the NHD were used to refine the classified output Themasks were originally ascertained and modified in a vector format and were convertedto a raster-type format when used for modifying the GEE outputs The two layers whichwere in the same projection and scale were employed to perform mathematical operationson a per-pixel basis Raster calculations can be applied to overlapping areas according totheir underlying geospatial properties affecting how data is represented [40] As the NHDis incomplete regarding wetlands and pond features care was taken to digitize areas ofthe 2003 NAIP imagery that were wholly saltmarsh or water [41] An assumption wasmade that any saltmarsh or water feature present in 2003 would maintain its non-forestedland use during the study period This allowed a set standard for the modification ofGEE outputs The saltmarsh mask was set so that any pixel registering as a tree wouldbe converted into saltmarsh without the modification of any other land-cover class Thewater mask was absolute in terms of pixel modification All pixels overlapping the mask

Forests 2022 13 1132 6 of 22

were converted regardless of their class Tree cover vectors were then extracted from eachyearrsquos classification All cover area calculations including different elevation productswere performed on this vectorized form due to the superior computational ability forcalculating absolute area of georeferenced polygons in a vector format Polygons were alsomanually scanned over the respective years of NAIP imagery and were either deleted oradjusted following a set standard for Quality Assurance (QA) to ensure best representation

Forests 2022 13 x FOR PEER REVIEW 6 of 23

Figure 2 Panels (AndashD) show diagnostic types of chenier forests Panels (AC) show a chenier rem‐

nant forest Panel (B) is a spoil‐bank forest from the days of oil exploration The southern terminus

of (D) shows a swamp forest which could inhabit less haline marsh communities found in the north‐

ern reaches of the Chenier Plains The black‐outlined areas are the respective yearsrsquo forest extent

Figure 2 Panels (AndashD) show diagnostic types of chenier forests Panels (AC) show a chenier remnantforest Panel (B) is a spoil-bank forest from the days of oil exploration The southern terminus of(D) shows a swamp forest which could inhabit less haline marsh communities found in the northernreaches of the Chenier Plains The black-outlined areas are the respective yearsrsquo forest extent

25 Data Analysis and Statistics

Because post-processing and QA significantly modified the original CART classifi-cation baseline statistics on algorithm confidence would not be representative Insteadproducerrsquos accuracy (PA) was used as the primary determinant of classification success(Equation (1) where reference sites denote created validation points)

Producerrsquos Accuracy = of correctly classified reference sites

of total reference sitestimes100 (1)

Forests 2022 13 1132 7 of 22Forests 2022 13 x FOR PEER REVIEW 7 of 23

Figure 3 Process workflow used in the study The section bracketed in blue was processed using

Google Earth Engine and the section bracketed in green was carried out using QGIS Blue blocks

represent raw inputs into the workflow green blocks are the final outputs

24 Post‐Processing of GEE Outputs

Two‐layer masks created from the NHD were used to refine the classified output

The masks were originally ascertained and modified in a vector format and were con‐

verted to a raster‐type format when used for modifying the GEE outputs The two layers

which were in the same projection and scale were employed to perform mathematical

operations on a per‐pixel basis Raster calculations can be applied to overlapping areas

according to their underlying geospatial properties affecting how data is represented [40]

As the NHD is incomplete regarding wetlands and pond features care was taken to dig‐

itize areas of the 2003 NAIP imagery that were wholly saltmarsh or water [41] An as‐

sumption was made that any saltmarsh or water feature present in 2003 would maintain

its non‐forested land use during the study period This allowed a set standard for the

modification of GEE outputs The saltmarsh mask was set so that any pixel registering as

a tree would be converted into saltmarsh without the modification of any other land‐

cover class The water mask was absolute in terms of pixel modification All pixels over‐

lapping the mask were converted regardless of their class Tree cover vectors were then

extracted from each yearrsquos classification All cover area calculations including different

Figure 3 Process workflow used in the study The section bracketed in blue was processed usingGoogle Earth Engine and the section bracketed in green was carried out using QGIS Blue blocksrepresent raw inputs into the workflow green blocks are the final outputs

The creation of points for sections A B C and D to assess accuracy was carried outusing three tools in the QGIS Processing Toolbox First a lsquoDifferencersquo was run to extracttree cover in an original yearrsquos dataset but not in the succeeding year A lsquoDissolversquo was runon this product to create one large MultiPolygon object Finally lsquoRandom Pointsrsquo inside thePolygon function was used to create 200 point features that were manually validated Threecolumns indicating original year accuracy (YN) whether land-use change was correctlyattributed (YN) and what it had changed to (six classes) were populated for each pointThe last metric had absolute accuracy irrespective of the yearrsquos classification as it wasassigned visually Validation was carried out against the respective yearrsquos NAIP datasetBecause we did not assess land-use change in 2019 only accuracy was computed for thatA Cohenrsquos Kappa value was used to assess interrater reliability using Equation (2) wherePr(a) represents the actual observed agreement and Pr(e) represents chance agreement Theequations were derived from and expanded on by McHugh [42]

κ =Pr(a)minus Pr(e)

1 minus Pr(e)(2)

Forests 2022 13 1132 8 of 22

The CoNED DEM was subdivided into 0ndash1 1ndash2 2ndash3 and 3+ meter classes using theRaster Calculator tool in QGIS The class values were then vectorized to run a geospatialdifference iteratively with each yearrsquos tree cover vectors The difference was validatedand then used for area calculations regarding differential rates of loss for elevation gra-dients Gage height and temperature values were extracted from both stream gages andwere combined into a site average We used gage height as a proxy for MSL due to thespatial proximity to the coast and study sites as well as for considering the response tofreshwater influence which is an important hydrological component of the Chenier PlainsAverage values for the three wettest months (July August and September) were used inthe regressions and analysis

3 Results31 Forest Loss Transition and Classification Accuracy

From 2003 to 2019 an overall decline of 3573 was seen for forest cover and 2238for pasture The annualized rate of loss for forest cover was approximately 2 higherwithin the period 2003ndash2007 losing 42 of tree cover per year assuming a constant change(Table 1) Losses were greatest in the western two reaches of the Chenier Plains averagingover 213 more than sections C or D The largest amount of tree cover was located withinsection D with significant contributions from spoil banks Chenier Au Tigre (locationminus9220665 2956885) had the largest total tree cover within the study area albeit spreadover a large expanse Annual loss rates were more constant in sections containing primarilychenier ridge forests in contrast to swamp or spoil bank forests Section D has a largesaltmarsh (1ndash10 km) bisecting the chenier from the coast which contributed to a largerland and forest area There was a cumulative gain of 231 for the saltmarshes adding2528 km2 In the 2003ndash2007 period the only gains in land cover type were in saltmarshwhich was maintained into 2019 (Figure 4)

We surveyed a total of 2123 points across the study area Producerrsquos accuracy was916 816 and 918 for the years 2003 2007 and 2019 respectively The CohenrsquosKappa indexes of 091 082 and 093 for 2003 2007 and 2019 respectively showed strongagreement between raters across all years We considered any value above 085 to be a validassessment The year 2007 scored lower in both PA and Kappa values likely due to thenature of the base imagery however it still fell into an acceptable range Extreme contrastand saturation in the 2007 NAIP imagery caused larger aggregations of tree cover pixels asgrasses and trees have a similar hue accentuated by the pixel reduction post-classificationValidation points that were very close to tree cover but not categorized as such were notconsidered accurate which contributed to this source of error The 2019 classifications weregeneralized as being more correct from the onset with little need for QA

Changes in land-cover were not evenly distributed across different sections as canbe seen in Table 1 Typically cheniers are more proximally located to the coast withinthe eastern reaches with depositional bars spreading the length One noted outlier in theassessment of change was observed from 2003 to 2007 in section B where the loss of pastureaccounted for over 656 The quantitative measurements for changes in land cover werealso ascertained from these samples We observed changes of 606 282 34 and 76for saltmarsh pasture water and impervious area respectively

Observed results deviated from hypothesized values specifically in the large propor-tion of conversion to pasture A lone tree existing one year after transitioning to an openfield was common across all areas It was assumed the percentage of conversion fromtree to saltmarsh would be even greater However impervious area transitions were alsohigh given that considerable forest cover directly adjoins roads and houses in this areaThe underlying structure can then be seen following the mortality of vegetation Changeto water was also observed for several reasons in areas along waterbodies a loss of treecover exposed the water while severe areas of deforestation prompted a change to a morewetland hydrology which also required water replacement pixels

Forests 2022 13 1132 9 of 22

Table 1 The amount and percentage loss of three land-cover typesmdashtree marsh and pasturemdashovereach section and the total area spanning two parishes Calcasieu and Vermillion on the westernLouisiana Gulf Coast Sections A B C and D represent areas delineated for this study within thetwo parishes (for details see Figure 1)

Landcover 2003(ha)

2007(ha)

2019(ha)

Net Change ()(2003ndash2019)

TreeA 46274 37639 17988 minus6113B 64389 38634 27486 minus5731C 99623 82791 65050 minus3470D 231775 215192 173580 minus2511

Total 442061 374256 284104 minus3573

MarshA 1182244 1148060 1238636 477B 1113045 1084738 1395453 2537C 2051730 2277785 2204412 744D 6598632 6839319 6359952 minus362

Total 10945651 11349903 11198453 231

PastureA 510146 460323 324348 minus3642B 136395 89019 77021 minus4353C 696209 754500 653027 minus620D 1152047 1036039 881960 minus2344

Total 2494796 2339881 1936355 minus2238

Forests 2022 13 x FOR PEER REVIEW 9 of 23

adding 2528 km2 In the 2003ndash2007 period the only gains in land cover type were in salt‐

marsh which was maintained into 2019 (Figure 4)

Table 1 The amount and percentage loss of three land‐cover typesmdashtree marsh and pasturemdashover

each section and the total area spanning two parishes Calcasieu and Vermillion on the western

Louisiana Gulf Coast Sections A B C and D represent areas delineated for this study within the

two parishes (for details see Figure 1)

Landcover 2003

(ha)

2007

(ha)

2019

(ha)

Net Change ()

(2003ndash2019)

Tree

A 46274 37639 17988 minus6113

B 64389 38634 27486 minus5731

C 99623 82791 65050 minus3470

D 231775 215192 173580 minus2511

Total 442061 374256 284104 minus3573

Marsh

A 1182244 1148060 1238636 477

B 1113045 1084738 1395453 2537

C 2051730 2277785 2204412 744

D 6598632 6839319 6359952 minus362

Total 10945651 11349903 11198453 231

Pasture

A 510146 460323 324348 minus3642

B 136395 89019 77021 minus4353

C 696209 754500 653027 minus620

D 1152047 1036039 881960 minus2344

Total 2494796 2339881 1936355 minus2238

0

20000

40000

60000

80000

100000

120000

0

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

Land area ‐MarshPasture (ha)

Land area ‐Forest Cover (ha)

Year

Land‐cover dynamics over time

West East Marsh Pasture

Figure 4 A representation of the changes in three respective land-cover types (forest cover pasturemarsh) over the period 2003ndash2019 For forest cover the three western sections are represented as Westand D is represented as East Light green indicates marsh yellow represents pasture dark green andhatched green show forest cover

Forests 2022 13 1132 10 of 22

32 Rates of Loss with Respect to Elevation

A net decrease in forest cover was seen across all elevation categories (Table 2 Figure 5)The largest losses were in the 1ndash2 m category with a net reduction of 593 from 2003 to2019 There was a temporal shift in the rate of change with the greatest amount of lossbetween 2003ndash2007 contributed by section D Values within the 2ndash3 m category remainedsteady from 2007 to 2019 with a reduction of only 076 The large majority (749) ofthe remaining forest was seen to be located within the 0ndash1 m band The losses were notevenly distributed within this band buffered to some degree by section D A comparisonof sections A vs C shows a decrease of 493 with losses centered around the period2007ndash2019 One issue to note is that the GSD for the DEM was 3 m which is wider thansome spoil banks While the local elevation may in fact rise to 2ndash3 m the value was reduceddue to the effect of averaging The losses in the 3 m+ band are discussed below a noteshould be made of the limited representation Overall the losses varied both spatially andtemporally across the region largely separated into eastern and western reaches

Table 2 Differences in the amount of forest area within each elevation band and the associatedpercentage loss of forest cover between years Sections A B C and D represent areas delineated forthis study within the two parishes (for details see Figure 1)

Sites and Forest Cover (ha)Percentage

LossYearmdash2003

Elevation (m) A B C D Total

0ndash1 13083 25525 66334 187236 2921781ndash2 27168 36374 25583 39048 1281722ndash3 1934 2188 6481 4640 15242gt3 361 170 005 343 879

Yearmdash2007A B C D Total 2003ndash2007

0ndash1 18371 16502 56349 166776 257997 minus11701ndash2 17834 20210 21289 4205 63538 minus50432ndash3 1228 1783 4118 5446 12575 minus1750gt3 048 208 002 404 662 minus2474

Yearmdash2019A B C D Total 2003ndash2019

0ndash1 7161 8227 37762 142102 195253 minus33171ndash2 10283 12913 24656 4401 52254 minus59232ndash3 1506 932 5789 4253 12480 minus1812gt3 045 141 002 402 590 minus3288

33 Interaction with Water Level

Water plays a critical role in the natural function of this ecosystem As such we consid-ered the interplay of temperature and MSL over a decadal time scale and the local rates oftemperature and gage height from both USGS stations (Figure 6) In Figure 6 forest extentis superimposed for each yearrsquos classification to show visual correspondence between thetwo drivers Temperature and rise in sea-level were negatively correlated until an inflectionpoint around 2010 after which there was a relatively stronger correlation increasing to amaximum of 025 The correlative increase lends evidence to the correspondence betweenclimate and water pressure in coastal areas An increase in the average MSL has been ob-served for the site The two stream gages showed an annualized MSL rise of 136 mmyearfor Vermilion Bay and 59 mmyear for the Calcasieu River There was a weak but positivetrend for temperature to increase slightly from the onset of the study period Time provedto be a good indicator for increase in water level and for decrease in forest cover (Figure 6)The lack of tree-cover samples prohibited many statistical comparisons but visually thecorrespondence seems evident

Forests 2022 13 1132 11 of 22

Forests 2022 13 x FOR PEER REVIEW 11 of 23

Table 2 Differences in the amount of forest area within each elevation band and the associated

percentage loss of forest cover between years Sections A B C and D represent areas delineated for

this study within the two parishes (for details see Figure 1)

Sites and Forest Cover (Ha)

Percentage Loss Yearmdash2003

Elevation (m) A B C D Total

0ndash1 13083 25525 66334 187236 292178

1ndash2 27168 36374 25583 39048 128172

2ndash3 1934 2188 6481 4640 15242

gt3 361 170 005 343 879

Yearmdash2007

A B C D Total 2003ndash2007

0ndash1 18371 16502 56349 166776 257997 minus1170

1ndash2 17834 20210 21289 4205 63538 minus5043

2ndash3 1228 1783 4118 5446 12575 minus1750

gt3 048 208 002 404 662 minus2474

Yearmdash2019

A B C D Total 2003ndash2019

0ndash1 7161 8227 37762 142102 195253 minus3317

1ndash2 10283 12913 24656 4401 52254 minus5923

2ndash3 1506 932 5789 4253 12480 minus1812

gt3 045 141 002 402 590 minus3288

0

1

2

3

4

5

6

7

8

9

10

0

500

1000

1500

2000

2500

3000

3500

Land area 3mdash4m ‐(ha)

Land area 0mdash3m ‐(ha)

Year

Forest cover in elevational bands over time

0‐1m

1‐2m

2‐3m

3‐4m

Figure 5 Visual depictions of two forms of loss one from hurricanes on Hackberry Beach and theother documenting whittling reduction The left column refers to 2003 NAIP imagery while the rightuses 2007 NAIP imagery Black shaded areas indicate current year forest extent and the red hashedareas indicate loss

Forests 2022 13 x FOR PEER REVIEW 12 of 23

Figure 5 Visual depictions of two forms of loss one from hurricanes on Hackberry Beach and the

other documenting whittling reduction The left column refers to 2003 NAIP imagery while the right

uses 2007 NAIP imagery Black shaded areas indicate current year forest extent and the red hashed

areas indicate loss

33 Interaction with Water Level

Water plays a critical role in the natural function of this ecosystem As such we con‐

sidered the interplay of temperature and MSL over a decadal time scale and the local

rates of temperature and gage height from both USGS stations (Figure 6) In Figure 6

forest extent is superimposed for each yearrsquos classification to show visual correspondence

between the two drivers Temperature and rise in sea‐level were negatively correlated

until an inflection point around 2010 after which there was a relatively stronger correla‐

tion increasing to a maximum of 025 The correlative increase lends evidence to the cor‐

respondence between climate and water pressure in coastal areas An increase in the av‐

erage MSL has been observed for the site The two stream gages showed an annualized

MSL rise of 136 mmyear for Vermilion Bay and 59 mmyear for the Calcasieu River

There was a weak but positive trend for temperature to increase slightly from the onset of

the study period Time proved to be a good indicator for increase in water level and for

decrease in forest cover (Figure 6) The lack of tree‐cover samples prohibited many statis‐

tical comparisons but visually the correspondence seems evident

Figure 6 Trends in mean water levels and water temperature over 19 years in Vermillion Parish

Data obtained from the USGS Station 07387050 Vermilion Bay (minus921355 296744) with gage datum

165 m above NAVD88 and from the USGS Station 08017118 Calcasieu River at Cameron LA

(minus933488 298155) with gage datum 107m above NAVD88

Rsup2 = 09453

Rsup2 = 01283

Rsup2 = 06529

08

085

09

095

1

105

11

115

12

125

1995 2000 2005 2010 2015 2020 2025

15

20

25

30

35

40

45

50

Average

water level (m)

Year

Forest cover loss Average

water temp

Loss of tree cover (sqkm) Average water temp (deg C) Average waterlevel (m)

Figure 6 Trends in mean water levels and water temperature over 19 years in Vermillion Parish Dataobtained from the USGS Station 07387050 Vermilion Bay (minus921355 296744) with gage datum 165 mabove NAVD88 and from the USGS Station 08017118 Calcasieu River at Cameron LA (minus933488298155) with gage datum 107m above NAVD88

Forests 2022 13 1132 12 of 22

4 Discussion41 Classification Reliability and Constraints

NAIP imagery proved efficacious for fine-scale analysis in this region shown by thestrong average producerrsquos accuracy and Kappa coefficient This is a positive indicatorthat variability between years or areas did not influence classification accuracies to alarge extent and that the final classification is a reliable interpretation [42] Many otherstudies conducting land-cover classifications using NAIP imagery have also shown robustaccuracies [43ndash45] However there were noted issues seen in certain parts of the workflowA problem preeminent in the initial classification was a limitation of spectral resolution Thisproblem became pronounced in areas with similar shades of green or between dark bluesand black hues Without the use of ancillary datasets the classification accuracy was poorbetween dark water and shadows of trees with considerable overlap (misclassificationsof approximately 50) Mixed land use can also prove problematic in NAIP land-coverclassifications [46] This became especially pronounced in transitional forestmarsh areasin which there was no dominant spectral signature for a particular land use Local variationbetween adjoining image sets highlighted another shortcoming of NAIP imagery Theviewing geometry illumination and time between the capture of neighboring imagetiles created edge effects including artificially high or low pixel values and refractionproblems on water [47] Each yearrsquos dataset was collected by an external contractor causingdifferences in sensor instrumentation and parametrization between years By 2019 many ofthese problems had been mitigated and images were of much higher quality

Compared to a large swath sensor such as Landsat-8 or Sentinel-2 an innate non-uniformity poses problems for pixel-wise classifications of VHR imagery Typically datafrom satellite imagery have been the primary choice for analyzing coastal forests marshesand forests at large [48ndash50] The spaceborne sensors obtain information over eight bandsallowing much finer spectral discrimination of certain classes of objects [51] Even amongstdiverse forest types space-borne Sentinel-2 and ASTER imagery has been used to mapdistinct species clusters of broadleaf and deciduous plant species The large spatial scaleenables the use of these created geospatial products to analyze the efficacy of governmentalreforestation programs in remote areas of the world [52] The usage of normalized differ-ence indices including the Normalized Difference Vegetation Index (NDVI) EnhancedVegetation Index (EVI) and Enhanced Water Index (EWI) can also provide greater confi-dence in the separation of water and marsh features from deciduous broadleaf forests [53]However a spatial resolution of 10m is still too coarse for the detection of remnant andlandscape trees especially those alongside homogenous flat features such as maintainedgrass or water bodies The most practical solution would be to assess this land with ahigher resolution dataset such as unmanned aerial systems (UAS) imagery which cancapture a much finer GSD in a plethora of bands if multispectral or hyperspectral sensorsare used [54] In fact scientists are now pushing the boundaries of the scale of analysisconducted through remote sensing Broad groupings (eg clades or forest types) have beenthe historic norm in the classification of imagery for use in land management A greatermixture of classes allows more accurate measurements for those interested in landscape-scale ecological processes However VHR can be used to map stands at the species levelwhich allows forest managers an unparalleled insight into the dynamics of recruitmentdisease or resource availability [55]

Google Earth Engine allowed us to utilize NAIP imagery at scale which had for sometime been a limiting factor in processing this data source locally Even so some limitationshindered the functional utility of GEE for detailed land-cover classification In our relativelyuniform environment we discriminated between many shades of green When viewinggeometries cause variations in hue different classes begin to overlap within the samespectral regions Solutions using Geographic Object-Based Image Analysis (GEOBIA) cancircumvent this issue yet this segmentation algorithm is currently not supported by GEEMany consider GEOBIA the optimal tool for segmenting landscapes and for the productionof land-cover classifications from this source of imagery [3856] Its successful integration

Forests 2022 13 1132 13 of 22

could substantially elevate the use and utility of mapping within GEE Additionally theallocation of increased processing bandwidth on GEE servers may help overcome somelimits in classification Greater bandwidth allows larger and more diverse training setsin addition to more complex parameterizations of machine learning classifiers If oneis proceeding on local workstations the analysis of NAIP imagery requires an intimateknowledge of data structures and process optimization Custom architectures processingpipelines and simpler binary classifications can allow an expansion of the study area tocover coterminous areas on a larger scale [16]

42 Forest Loss Contextually

We mapped forest cover change dynamics between 2003ndash2019 on the southwest coastof Louisiana 1580 hectares of tree cover 13 of the terrestrial area was converted todifferent land use The total loss of forest cover in the study period was 3573 over sixteenyears which is indicative of a larger rate of forest loss than at sites in a similar physio-geographic region yet is certainly not a global outlier [18] These findings validate thesentiments of people in the region and underscore a major irreversible trend in the area Ourestimate should be considered conservative due to the way we approached the definition ofldquoforest coverrdquo within the landscape Abandoned pastoral land exhibiting dense scrubbinessand shrub cover was demarcated as forest cover even though the target species (live oak)was probably not present Spoil banks which are the byproduct of historic oil explorationin the region indicating possible mortality were also often considered a component offorest cover if the imagery could not indicate the absence of high vegetation The primarymethod of identifying whether or not forest cover was present was the observation of ashadow adjoining the vegetation indicating a vertical structure

The Big Bend region of Florida one of the longest undisturbed coastlines in thecontinental US offers some insight into the vegetation dynamics this region has beenexperiencing It has been the focus of numerous ecological studies has a similar climateand is exposed to storms with similar regularity Raabe and Stumpf [57] found that fromthe early to late 1900s over 82 km2 of forest land was converted to marsh and 66 km2 wasin a transitional state to marsh McCarthy et al [58] document an annual forest cover lossof 744 from 2010ndash2017 an acceleration in the trend within that area A new study bythe same team found rates of loss totaling approximately 10 km2 a year 800 larger thanpreviously stated in the literature [59] Rates of annual mortality ranged from 4ndash16 inSoutheastern Louisiana from 1987 to 1997 [60] In comparison the annualized rate of lossfound in our study never exceeded 5 Plots located in South Carolina that have beenless significantly impacted by MSL rise show rates of loss closer to 1ndash2 in line withour observed rate of loss from 2007ndash2019 However we cannot assume that percentagesof forest loss across the country have been constant within the last century therefore it iscritical to carry out intensive geospatial studies at regular intervals

In the adjoining southeastern part of the state of Louisiana in the Atchafalaya andTerrebonne basins forested wetlands occupy 121871 hectares or 41 of all vegetatedland use [12] In comparison the terrestrial forested land use in our study area was 21in 2019 The wide variation can be interpreted in several ways each with importantimplications Historic rates of deforestation differ in the two regions with a greateremphasis on the preservation of wilderness areas in the Atchafalaya basin [61] TheAtchafalaya and Terrebonne basins also have productive land used in the growth of timberwhich promotes long-term forest cover The regeneration of forests in our study region alsodiffers substantially Chenier ecosystems and traditional alluvial bottomland hardwoodforests are compositionally different in vegetation and soil even when accounting for theirunique hydrologic regimes Communities of live oak may be less resilient to the types offorcings that cause changes in community composition within these ecosystems furtheredby proximity to the sea If stocking density rates are similar to the adjoining basin as theywere in the past we would see an areal extent well over 50000 hectares

Forests 2022 13 1132 14 of 22

The analysis vis-agrave-vis elevation shows weak trends in the manners of loss on eleva-tional gradients Logically in the paradigm of coastal retreat there should be an aggregationof forest resources towards the highest bands of topography in a region Our analysis doesnot follow this chain of thought with the largest losses observed in the 1ndash2 m categoryrather than the lowest 0ndash1 m (Table 2 2007 C 2019 D) While we do see a large loss bypercentage in the highest elevation band it is important to note that a small sample causesinherent variance Forest cover on the highest reaches of the chenier ridges is typically lessthan 1 of total forest cover for that region The reduction can be attributed to the loss of23 features in section A accounting for 33 hectares bordering the Sabine River (Figure 1minus9386286 2973355) The loss of forest cover was driven by intense weather Vegetationin the area was completely destroyed and substantial amounts of sedimentation could beobserved as evident in the 2007 NAIP imagery The only gain in forest cover was seen inthe 2ndash3 m elevation band in the period 2007 in an area that had a high density of pastoralland that was abandoned Forest retreat is often discussed as one method of bufferingforest loss along coasts [62] Unfortunately the geomorphology of this area does not allowgreat spatial movement as the current extent is already located within the highest bandsof elevation

43 Land Use Conversion and Driving Factors

The leading cause of land-use conversion from tree cover was to a saltmarsh ecotype(606) often following mortality and replacement of vegetative biomass The loss offorest cover can largely be typified into two categories The ingression of saltwater causingsalinification within the soil matrix paired with increased flooding caused mortality ofoverstorey vegetation over a multi-year timescale [6364] Hurricanes or other storm eventscaused rapid mortality through wind-throws severe defoliation saltwater inundationduring a storm surge or canopy damage Following intense weather events between2003 and 2007 the vast majority of cheniers located within a 1km buffer of the coast wereunable to support tree cover as seen in Figure 7AB Clearing for industry or developmentalso caused immediate shifts in forest composition as can be seen in Figure 8AB Thischange was more pronounced on reaches A and B especially on Hackberry Beach Thisarea was the site of landfall for Hurricane Rita which caused massive devastation to othercomponents of the ecosystem [6566] Singular storm events have a transformative impacton vegetation communities and the weight of this driver on annual rates of forest lossis a topic that should be explored further Wayward shifts in species composition are atrend that has been observed globally especially in forests that border some source of waterTrees like Juniperus virginiana L that have distinctive water-use patterns in comparison toother coastal tree species are susceptible to salinification at much faster rates as evidencedthrough isotope analyses [67] As with many ecological systems the entangled effectsof climate and vegetation require diverse means of looking at change Isotope analysesdendrochronology field sensors and remote sensing data are needed to characterize land-use change at the stand and community levels

Large forest remnants such as Cow Island (minus9283581 2973842) Tiger Island (minus92784392972832) and Grand Chenier Ridge (minus9281175 2972986) among others have diminishedmore slowly By 2019 these stands had lost an average of 15 of their maximum extentThe land use changed to both marsh and managed pasture largely dependent on thesurrounding land use The conversion to managed pasture (282) accounted for a largerthan expected amount of land-cover change Many of these changes were in areas wheretree cover may have been more ldquoornamentalrdquo in nature Trees beside houses or alongsideroads are some of the largest specimens of live-oak to be found However these trees areoften solitary and perhaps more vulnerable to wind-borne disturbance The concern is thattheir loss due to abiotic drivers may not be easily mitigated leading to a permanent declineOur data support evidence on the whittling of forest remnants and the eventual fate ofthese trees becoming lsquorelict forestsrsquo unable to recruit new generations in the short term andin the long-term total disappearance of these cheniers along the Louisiana Gulf Coast [68]

Forests 2022 13 1132 15 of 22Forests 2022 13 x FOR PEER REVIEW 16 of 23

Figure 7 Visual depictions of two forms of loss one from hurricanes on Hackberry Beach (AB) and

the other documenting whittling reduction (CD) The left column displays 2003 NAIP imagery and

the right is 2007 NAIP imagery Black shaded areas indicate current year forest extent red hashed

areas indicate a loss

Figure 7 Visual depictions of two forms of loss one from hurricanes on Hackberry Beach (AB) andthe other documenting whittling reduction (CD) The left column displays 2003 NAIP imagery andthe right is 2007 NAIP imagery Black shaded areas indicate current year forest extent red hashedareas indicate a loss

Beyond loss in forest cover there is a noticeable trend towards an altered hydrologywhere pastoral areas and intact forest remnants are associated with pockets of standingwater An increase in the correlative factor between temperature and water levels could bea worrying trend a direct conversion of tree cover to water at 34 could be an unforeseenoutcome Chenier ridges in their geomorphic context should not support non-ephemeralponding It is not documented how increased tidal influence which is one factor of risingMSL influences the hydrology of this ecosystem and its associated communities Albeitin a more inland context Magolan and Halls [69] documented a transition of over 40 ofland to emergent marsh from closed-canopy cover from 1949 to 2018 due to hydrologicinfluence Recent rates of relative sea rise have been documented as high as 119 mmyearcompared to background Holocene rates of 17 mmyear [70] This has resulted in a rate oflateral coastal forest retreat between 2ndash14 times greater than pre-industrial levels [26] Therate of shoreline retreat in this region increased from ~61 to ~94 myear based on the 100-and 30-year moving averages amounting to over 10000 hectares of land loss every yearfrom 1800 to 2005 [27]

Forests 2022 13 1132 16 of 22Forests 2022 13 x FOR PEER REVIEW 17 of 23

Figure 8 A depiction of clearing for industry in (AB) and a depiction of forest whittling from (CD)

The black extent represents the current forest area red hashed lines indicate a loss The left panels

show 2007 NAIP imagery while the right are 2019 NAIP imagery

Large forest remnants such as Cow Island (minus9283581 2973842) Tiger Island

(minus9278439 2972832) and Grand Chenier Ridge (minus9281175 2972986) among others have

diminished more slowly By 2019 these stands had lost an average of 15 of their maxi‐

mum extent The land use changed to both marsh and managed pasture largely depend‐

ent on the surrounding land use The conversion to managed pasture (282) accounted

for a larger than expected amount of land‐cover change Many of these changes were in

areas where tree cover may have been more ldquoornamentalrdquo in nature Trees beside houses

or alongside roads are some of the largest specimens of live‐oak to be found However

these trees are often solitary and perhaps more vulnerable to wind‐borne disturbance The

concern is that their loss due to abiotic drivers may not be easily mitigated leading to a

permanent decline Our data support evidence on the whittling of forest remnants and

the eventual fate of these trees becoming lsquorelict forestsrsquo unable to recruit new generations

in the short term and in the long‐term total disappearance of these cheniers along the

Louisiana Gulf Coast [68]

Beyond loss in forest cover there is a noticeable trend towards an altered hydrology

where pastoral areas and intact forest remnants are associated with pockets of standing

water An increase in the correlative factor between temperature and water levels could

be a worrying trend a direct conversion of tree cover to water at 34 could be an unfore‐

seen outcome Chenier ridges in their geomorphic context should not support non‐ephem‐

eral ponding It is not documented how increased tidal influence which is one factor of

Figure 8 A depiction of clearing for industry in (AB) and a depiction of forest whittling from (CD)The black extent represents the current forest area red hashed lines indicate a loss The left panelsshow 2007 NAIP imagery while the right are 2019 NAIP imagery

44 Future of Chenier Forests

The chenier complexes of Louisiana do not have the natural benefaction of maintaininglarge original tracts untouched by axe or dozer The extent map through the years (Figure 9)exemplifies the scattered nature of forest remnants The largest single continuous chenierforest (Grand Chenier Ridgemdash185 ha) encompasses less than 1 of the total forest areaExposed forest edges pose a problem for the continued integrity of the stand as adaptedspecies fail to compete in these new microclimates [71] Slow-growing saplings of desirabletree species (live oak hackberry) are subject to unfavorable growing conditions fromweather and light availability Vegetation surveys following aspect and inundation alongthe Florida coast have all but confirmed the eventual extirpation of overstory vegetationthrough poor recruitment of native species [31] Chinese tallow (Triadica sebifera) a non-native tree is likely to fill empty niche space This aggressive invader is problematicthroughout the Gulf Coastal Plain from Texas to Florida [72] The tree has a wide meansof dispersal being primarily distributed through frugivory via avian populations A highrate of germination compounded with quick growth and a sexual maturity of less thanthree years make it a difficult species to control Its invasive capability is seen in nearlyall forest types in the region although it is more pervasive in coastal forests than inlandModeling studies show a high likelihood of Chinese tallow becoming the dominant specieswithin this ecosystem and in surrounding areas [73] Future publications from this lab will

Forests 2022 13 1132 17 of 22

provide more in-depth data and analysis on the state of existing stands and recruitmenttrends within remnants of the Chenier Plains via field sampling

Louisiana has enacted a historic response to the problem of land-loss following a publicoutcry after devastating storms in 2005 The Coastal Protection and Restoration Authority(CPRA) have been tasked with enacting a management plan to combat coastal erosion TheComprehensive Master Plan details proposals to create marshes build sediment diversionsform new hydrologic structures and promote barrier island restoration through builtand natural means As of 2017 over 135 projects had been completed impacting over14568 ha [74] An update to this document is due to be released in 2023 The projecthas an overall operating budget of over 50 billion US dollars with equal shares dedicatedto restoration and risk reduction The primary funding source is a settlement associatedwith the Deepwater Horizon Oil Spill in addition to various initiatives at the state andfederal levels However there are no current plans to supplement vegetation or performany geoengineering work on the cheniers for afforestation

Coastal wetlands rank among the most important ecosystems in terms of the valuationof ecosystem services [75] Provisioning services such as water filtration or storm-surgeprotection are commonly considered when discussing coastal areas Beyond these com-monalities chenier forests provide vital wildlife habitats being the first encountered sourceof vertical structure for migratory neo-tropical birds and migrating monarch butterflies inthe Mississippi alluvial valley flyway [76] This en-route habitat is of critical importancedue to its closeness to the Gulf of Mexico During favorable weather conditions 10 ofbirds migrating through the Gulf use these forests as a stopover spot rising dramatically to80 during adverse weather conditions [29] With losses mounting many species may notbe able to seek refuge during storms The area also has a unique suite of amphibians andreptiles being home to 13 obligate chenier forest species of herpetofauna [77]

Lu et al [78] used the eddy covariance method (ECM) to calculate strong carbon sinkeffects for a chenier ecosystem driven by forest vegetation Net energy production valueswere significantly higher than surrounding marshland signifying the importance of densevegetative cover on a per-unit basis Counter to this there is also strong evidence thatdegraded forest ecosystems act as net carbon sources A study in North Carolina also usingECM found that a seasonally inundated tide-influenced forest was a net carbon sourceannually and only functioned as a sink for 3 months of the growing year [79] Similarly astudy in the northeastern part of the state showed net negative rates of carbon sequestrationfor an ephemerally flooded forest [80] Unique environments demand individual attentiona characterization of this ecosystem for nitrogen or carbon flux with respect to a similarsystem may not be functionally sound [81]

Forests 2022 13 1132 18 of 22Forests 2022 13 x FOR PEER REVIEW 19 of 23

Figure 9 The total land area classified in GEE for the years 2003 2007 and 2019 Light green repre‐

sents the marsh area tan represents pasture The water layer has been removed for purposes of

clarity The dark black outline is the outline of the forest cover for the respective year

Louisiana has enacted a historic response to the problem of land‐loss following a

public outcry after devastating storms in 2005 The Coastal Protection and Restoration

Authority (CPRA) have been tasked with enacting a management plan to combat coastal

erosion The Comprehensive Master Plan details proposals to create marshes build sedi‐

ment diversions form new hydrologic structures and promote barrier island restoration

through built and natural means As of 2017 over 135 projects had been completed im‐

pacting over 14568 ha [74] An update to this document is due to be released in 2023 The

project has an overall operating budget of over 50 billion US dollars with equal shares

dedicated to restoration and risk reduction The primary funding source is a settlement

associated with the Deepwater Horizon Oil Spill in addition to various initiatives at the

state and federal levels However there are no current plans to supplement vegetation or

perform any geoengineering work on the cheniers for afforestation

Coastal wetlands rank among the most important ecosystems in terms of the valua‐

tion of ecosystem services [75] Provisioning services such as water filtration or storm‐

surge protection are commonly considered when discussing coastal areas Beyond these

commonalities chenier forests provide vital wildlife habitats being the first encountered

source of vertical structure for migratory neo‐tropical birds and migrating monarch but‐

terflies in the Mississippi alluvial valley flyway [76] This en‐route habitat is of critical

importance due to its closeness to the Gulf of Mexico During favorable weather condi‐

tions 10 of birds migrating through the Gulf use these forests as a stopover spot rising

Figure 9 The total land area classified in GEE for the years 2003 2007 and 2019 Light greenrepresents the marsh area tan represents pasture The water layer has been removed for purposes ofclarity The dark black outline is the outline of the forest cover for the respective year

5 Conclusions

The cheniers of southwestern Louisiana were found to be declining in size at a concern-ing rate Many of the driving factors are ecosystem-wide trends that have been increasingin intensity within the last century The results of this study suggest that coastal forestsare an ecosystem in peril and the ecology of such systems should be a closely monitoredsubject The 365 loss of forest cover is an alarming but not wholly unexpected result ofmany decades of intense perturbation and human exploitation A dynamic of continuedforest loss is evident Unless significant investment is made in engineering solutions tomitigate storm effects and land subsidence we will continue to see the decline of chenierforests in Louisiana Through targeted mitigation governmental programs such as theWetland or Conservation Reserve Program may be able to supplement existing forestsas well as the incomes of local people who have lost their livelihoods In this study wehave begun the process of accurate areal estimates of forest extent and have shed light onecosystem processes in the region As this community continues to diminish we see theforest not by the trees but rather the lack of them

Forests 2022 13 1132 19 of 22

Author Contributions Conceptualization PT methodology PT software PT and PR validationPT and PR formal analysis PT PR and PT writingmdashoriginal draft preparation PT and JBwritingmdashreview and editing PT and JB visualization PT and JB supervision JB All authorshave read and agreed to the published version of the manuscript

Funding This research received no external funding

Data Availability Statement The data will be published in and stored on the Harvard Dataverse athttpsdataverseharvardedudatasetxhtmlpersistentId=doi107910DVN3J7SNF (accessed on1 May 2022)

Acknowledgments The authors would like to recognize the hard work of undergraduate studentsParusha Raut and Subash Sapkota in manually validating thousands of points and creating numeroustraining sets We also extend thanks to our institution and numerous people within the lab who leantboth mental and emotional fortitude

Conflicts of Interest The authors declare no conflict of interest

References1 Michener WK Blood ER Bildstein KL Brinson MM Gardner LR Climate change hurricanes and tropical storms and

rising sea level in coastal wetlands Ecol Appl 1997 7 770ndash801 [CrossRef]2 Donoghue JF Sea level history of the northern Gulf of Mexico coast and sea level rise scenarios for the near future Clim Chang

2011 107 17ndash33 [CrossRef]3 Stanturf JA Goodrick SL Outcalt KW Disturbance and coastal forests A strategic approach to forest management in

hurricane impact zones For Ecol Manag 2007 250 119ndash135 [CrossRef]4 Augustinus P Cheniers and chenier plains A general introduction Mar Geol 1989 90 219ndash229 [CrossRef]5 Fu Z Wang P Sun J Lu Z Yang H Liu J Li T Composition seasonal variation and salinization characteristics of soil

salinity in the Chenier Island of the Yellow River Delta Glob Ecol Conserv 2020 24 e01318 [CrossRef]6 Marques IG Campelo F Rivaes R Albuquerque A Ferreira MT Rodriacuteguez-Gonzaacutelez PM Tree rings reveal long-term

changes in growth resilience in Southern European riparian forests Dendrochronologia 2018 55 167ndash176 [CrossRef]7 Daigle JJ Griffith GE Omernik JM Faulkner PL McCulloh RP Handley LR Smith LM Chapman SS Ecoregions of

Louisiana (Color Poster with Map Descriptive Text Summary Tables and Photographs) US Geological Survey Reston VI USA 20068 McBride RA Taylor MJ Byrnes MR Coastal morphodynamics and Chenier-Plain evolution in southwestern Louisiana USA

A geomorphic model Geomorphology 2007 88 367ndash422 [CrossRef]9 Gosselink JG An Ecological Characterization Study of the Chenier Plain Coastal Ecosystem of Louisiana and Texas Narrative Report

National Coastal Ecosystems Team Office of Biological Services Fish and Wildlife Service US Department of the InteriorWashington DC USA 1979 Volume 1

10 Blum MD Roberts HH The Mississippi delta region Past present and future Annu Rev Earth Planet Sci 2012 40 655ndash683[CrossRef]

11 Hansen MC Potapov PV Moore R Hancher M Turubanova SA Tyukavina A Townshend J High-resolution globalmaps of 21st-century forest cover change Science 2013 342 850ndash853 [CrossRef]

12 Doyle TW Krauss KW Conner WH From AS Predicting the retreat and migration of tidal forests along the northern Gulfof Mexico under sea-level rise For Ecol Manag 2010 259 770ndash777 [CrossRef]

13 Thomas N Simard M Castantildeeda-Moya E Byrd K Windham-Myers L Bevington A Twilley R High-resolution mappingof biomass and distribution of marsh and forested wetlands in southeastern coastal Louisiana Int J Appl Earth Obs Geoinf 201980 257ndash267 [CrossRef]

14 Desantis LR Bhotika S Williams K Putz FE Sea-level rise and drought interactions accelerate forest decline on the GulfCoast of Florida USA Glob Chang Biol 2007 13 2349ndash2360 [CrossRef]

15 Boyd DS Foody G Ripple W Evaluation of approaches for forest cover estimation in the Pacific Northwest USA usingremote sensing Appl Geogr 2002 22 375ndash392 [CrossRef]

16 Valjarevic A Djekic T Stevanovic V Ivanovic R Jandzikovic B GIS numerical and remote sensing analyses of forest changesin the Toplica region for the period of 1953ndash2013 Appl Geogr 2018 92 131ndash139 [CrossRef]

17 He T Sun YJ Xu JD Wang X Hu C Enhanced land usecover classification using support vector machines and fuzzyk-means clustering algorithms J Appl Remote Sens 2014 8 083636 [CrossRef]

18 Michez A Pieacutegay H Lisein J Claessens H Lejeune P Classification of riparian forest species and health condition usingmulti-temporal and hyperspatial imagery from unmanned aerial systems Environ Monit Assess 2016 188 146 [CrossRef][PubMed]

19 Hampton SE Strasser C Tewksbury J Gram W Budden A Batcheller A Duke C Porter J Big data and the future ofecology Front Ecol Environ 2013 11 156ndash162 [CrossRef]

20 Curtis PG Slay CM Harris NL Tyukavina A Hansen MC Classifying drivers of global forest loss Science 2018 3611108ndash1111 [CrossRef]

Forests 2022 13 1132 5 of 22

a vector repository of waterbodies (NHDWaterbody NHDArea) and wetland features(NHDWaterbody FCode 46600 46601 46602) was used for feature input in the creation ofpost-processing masks The NHD is also a product of the USGS

23 Classification and Processing Algorithms

Google Earth Engine offers a suite of supervised and unsupervised Machine LearningClassifiers for image classification Through iterative testing of all available options theClassification and Regression Tree (CART) algorithm was chosen for this study CART isa decision-tree-based supervised learning model able to handle nonlinear relationshipsamong features [38] Because class balancing is recommended for this algorithm a stableratio of tree-to-pasture and tree-to-marsh training sets were created Parametrizationwas not modified from default properties The primary consideration in choosing thisalgorithm was robust classification for forest cover Visually we observed that many othercontenders had higher incidence rates of non-forest-based classification of tree cover whenconducting initial testing leading to a general under-representation of forest cover Weuse a reductionist approach for accuracy in this study where pixel values for forest coverwere reduced rather than added which makes underrepresentation problematic Thepost-processing vectors allowed extremely fine detail in the modification of the originalCART output

The study area was divided into four sections (A B C and D) of equal approximatelength as seen in Figure 1 Sections C and D were further divided into three subsectionsdue to image variability in the 2003 dataset and were merged post-classification Thisfacilitated a more targeted training dataset given local variation while also expeditingprocessing within the workflow We used six total land-cover classes in the classificationschememdashtree pasture pond river saltmarsh and impervious area The classes werecomprised of manually delineated Point FeatureCollections within GEE and were givenspecific unique IDs Tree cover was interpreted as the visual presence of dark foliagebranching or scrubbiness As such these selections include trees located on spoil banklevees (Figure 2B) swamp forests (Figure 2C) or residential areas in addition to the live-oak communities found on the main chenier ridges (Figure 2AD) The rationale was toinclude any type of vegetation that promotes beneficial land use and habitat Pasture wasinterpreted as any area in which there are grazing activities or managed lands on whichregular mowing occurs such as yards or parks The Point FeatureCollections were createdusing 2019 NAIP imagery and were manually validated before use in each preceding yearto ensure classes remained accurate Modifications were made when class values changedbetween years The CART classification was subject to a 3-pixel square image reducerthereby homogenizing values to reduce the ldquosalt and pepper effectrdquo common in pixelwiseclassifications [39] The data were then exported to Google Drive for further processing inQuantum GIS (QGIS) A detailed workflow for the procedure is given in Figure 3

24 Post-Processing of GEE Outputs

Two-layer masks created from the NHD were used to refine the classified output Themasks were originally ascertained and modified in a vector format and were convertedto a raster-type format when used for modifying the GEE outputs The two layers whichwere in the same projection and scale were employed to perform mathematical operationson a per-pixel basis Raster calculations can be applied to overlapping areas according totheir underlying geospatial properties affecting how data is represented [40] As the NHDis incomplete regarding wetlands and pond features care was taken to digitize areas ofthe 2003 NAIP imagery that were wholly saltmarsh or water [41] An assumption wasmade that any saltmarsh or water feature present in 2003 would maintain its non-forestedland use during the study period This allowed a set standard for the modification ofGEE outputs The saltmarsh mask was set so that any pixel registering as a tree wouldbe converted into saltmarsh without the modification of any other land-cover class Thewater mask was absolute in terms of pixel modification All pixels overlapping the mask

Forests 2022 13 1132 6 of 22

were converted regardless of their class Tree cover vectors were then extracted from eachyearrsquos classification All cover area calculations including different elevation productswere performed on this vectorized form due to the superior computational ability forcalculating absolute area of georeferenced polygons in a vector format Polygons were alsomanually scanned over the respective years of NAIP imagery and were either deleted oradjusted following a set standard for Quality Assurance (QA) to ensure best representation

Forests 2022 13 x FOR PEER REVIEW 6 of 23

Figure 2 Panels (AndashD) show diagnostic types of chenier forests Panels (AC) show a chenier rem‐

nant forest Panel (B) is a spoil‐bank forest from the days of oil exploration The southern terminus

of (D) shows a swamp forest which could inhabit less haline marsh communities found in the north‐

ern reaches of the Chenier Plains The black‐outlined areas are the respective yearsrsquo forest extent

Figure 2 Panels (AndashD) show diagnostic types of chenier forests Panels (AC) show a chenier remnantforest Panel (B) is a spoil-bank forest from the days of oil exploration The southern terminus of(D) shows a swamp forest which could inhabit less haline marsh communities found in the northernreaches of the Chenier Plains The black-outlined areas are the respective yearsrsquo forest extent

25 Data Analysis and Statistics

Because post-processing and QA significantly modified the original CART classifi-cation baseline statistics on algorithm confidence would not be representative Insteadproducerrsquos accuracy (PA) was used as the primary determinant of classification success(Equation (1) where reference sites denote created validation points)

Producerrsquos Accuracy = of correctly classified reference sites

of total reference sitestimes100 (1)

Forests 2022 13 1132 7 of 22Forests 2022 13 x FOR PEER REVIEW 7 of 23

Figure 3 Process workflow used in the study The section bracketed in blue was processed using

Google Earth Engine and the section bracketed in green was carried out using QGIS Blue blocks

represent raw inputs into the workflow green blocks are the final outputs

24 Post‐Processing of GEE Outputs

Two‐layer masks created from the NHD were used to refine the classified output

The masks were originally ascertained and modified in a vector format and were con‐

verted to a raster‐type format when used for modifying the GEE outputs The two layers

which were in the same projection and scale were employed to perform mathematical

operations on a per‐pixel basis Raster calculations can be applied to overlapping areas

according to their underlying geospatial properties affecting how data is represented [40]

As the NHD is incomplete regarding wetlands and pond features care was taken to dig‐

itize areas of the 2003 NAIP imagery that were wholly saltmarsh or water [41] An as‐

sumption was made that any saltmarsh or water feature present in 2003 would maintain

its non‐forested land use during the study period This allowed a set standard for the

modification of GEE outputs The saltmarsh mask was set so that any pixel registering as

a tree would be converted into saltmarsh without the modification of any other land‐

cover class The water mask was absolute in terms of pixel modification All pixels over‐

lapping the mask were converted regardless of their class Tree cover vectors were then

extracted from each yearrsquos classification All cover area calculations including different

Figure 3 Process workflow used in the study The section bracketed in blue was processed usingGoogle Earth Engine and the section bracketed in green was carried out using QGIS Blue blocksrepresent raw inputs into the workflow green blocks are the final outputs

The creation of points for sections A B C and D to assess accuracy was carried outusing three tools in the QGIS Processing Toolbox First a lsquoDifferencersquo was run to extracttree cover in an original yearrsquos dataset but not in the succeeding year A lsquoDissolversquo was runon this product to create one large MultiPolygon object Finally lsquoRandom Pointsrsquo inside thePolygon function was used to create 200 point features that were manually validated Threecolumns indicating original year accuracy (YN) whether land-use change was correctlyattributed (YN) and what it had changed to (six classes) were populated for each pointThe last metric had absolute accuracy irrespective of the yearrsquos classification as it wasassigned visually Validation was carried out against the respective yearrsquos NAIP datasetBecause we did not assess land-use change in 2019 only accuracy was computed for thatA Cohenrsquos Kappa value was used to assess interrater reliability using Equation (2) wherePr(a) represents the actual observed agreement and Pr(e) represents chance agreement Theequations were derived from and expanded on by McHugh [42]

κ =Pr(a)minus Pr(e)

1 minus Pr(e)(2)

Forests 2022 13 1132 8 of 22

The CoNED DEM was subdivided into 0ndash1 1ndash2 2ndash3 and 3+ meter classes using theRaster Calculator tool in QGIS The class values were then vectorized to run a geospatialdifference iteratively with each yearrsquos tree cover vectors The difference was validatedand then used for area calculations regarding differential rates of loss for elevation gra-dients Gage height and temperature values were extracted from both stream gages andwere combined into a site average We used gage height as a proxy for MSL due to thespatial proximity to the coast and study sites as well as for considering the response tofreshwater influence which is an important hydrological component of the Chenier PlainsAverage values for the three wettest months (July August and September) were used inthe regressions and analysis

3 Results31 Forest Loss Transition and Classification Accuracy

From 2003 to 2019 an overall decline of 3573 was seen for forest cover and 2238for pasture The annualized rate of loss for forest cover was approximately 2 higherwithin the period 2003ndash2007 losing 42 of tree cover per year assuming a constant change(Table 1) Losses were greatest in the western two reaches of the Chenier Plains averagingover 213 more than sections C or D The largest amount of tree cover was located withinsection D with significant contributions from spoil banks Chenier Au Tigre (locationminus9220665 2956885) had the largest total tree cover within the study area albeit spreadover a large expanse Annual loss rates were more constant in sections containing primarilychenier ridge forests in contrast to swamp or spoil bank forests Section D has a largesaltmarsh (1ndash10 km) bisecting the chenier from the coast which contributed to a largerland and forest area There was a cumulative gain of 231 for the saltmarshes adding2528 km2 In the 2003ndash2007 period the only gains in land cover type were in saltmarshwhich was maintained into 2019 (Figure 4)

We surveyed a total of 2123 points across the study area Producerrsquos accuracy was916 816 and 918 for the years 2003 2007 and 2019 respectively The CohenrsquosKappa indexes of 091 082 and 093 for 2003 2007 and 2019 respectively showed strongagreement between raters across all years We considered any value above 085 to be a validassessment The year 2007 scored lower in both PA and Kappa values likely due to thenature of the base imagery however it still fell into an acceptable range Extreme contrastand saturation in the 2007 NAIP imagery caused larger aggregations of tree cover pixels asgrasses and trees have a similar hue accentuated by the pixel reduction post-classificationValidation points that were very close to tree cover but not categorized as such were notconsidered accurate which contributed to this source of error The 2019 classifications weregeneralized as being more correct from the onset with little need for QA

Changes in land-cover were not evenly distributed across different sections as canbe seen in Table 1 Typically cheniers are more proximally located to the coast withinthe eastern reaches with depositional bars spreading the length One noted outlier in theassessment of change was observed from 2003 to 2007 in section B where the loss of pastureaccounted for over 656 The quantitative measurements for changes in land cover werealso ascertained from these samples We observed changes of 606 282 34 and 76for saltmarsh pasture water and impervious area respectively

Observed results deviated from hypothesized values specifically in the large propor-tion of conversion to pasture A lone tree existing one year after transitioning to an openfield was common across all areas It was assumed the percentage of conversion fromtree to saltmarsh would be even greater However impervious area transitions were alsohigh given that considerable forest cover directly adjoins roads and houses in this areaThe underlying structure can then be seen following the mortality of vegetation Changeto water was also observed for several reasons in areas along waterbodies a loss of treecover exposed the water while severe areas of deforestation prompted a change to a morewetland hydrology which also required water replacement pixels

Forests 2022 13 1132 9 of 22

Table 1 The amount and percentage loss of three land-cover typesmdashtree marsh and pasturemdashovereach section and the total area spanning two parishes Calcasieu and Vermillion on the westernLouisiana Gulf Coast Sections A B C and D represent areas delineated for this study within thetwo parishes (for details see Figure 1)

Landcover 2003(ha)

2007(ha)

2019(ha)

Net Change ()(2003ndash2019)

TreeA 46274 37639 17988 minus6113B 64389 38634 27486 minus5731C 99623 82791 65050 minus3470D 231775 215192 173580 minus2511

Total 442061 374256 284104 minus3573

MarshA 1182244 1148060 1238636 477B 1113045 1084738 1395453 2537C 2051730 2277785 2204412 744D 6598632 6839319 6359952 minus362

Total 10945651 11349903 11198453 231

PastureA 510146 460323 324348 minus3642B 136395 89019 77021 minus4353C 696209 754500 653027 minus620D 1152047 1036039 881960 minus2344

Total 2494796 2339881 1936355 minus2238

Forests 2022 13 x FOR PEER REVIEW 9 of 23

adding 2528 km2 In the 2003ndash2007 period the only gains in land cover type were in salt‐

marsh which was maintained into 2019 (Figure 4)

Table 1 The amount and percentage loss of three land‐cover typesmdashtree marsh and pasturemdashover

each section and the total area spanning two parishes Calcasieu and Vermillion on the western

Louisiana Gulf Coast Sections A B C and D represent areas delineated for this study within the

two parishes (for details see Figure 1)

Landcover 2003

(ha)

2007

(ha)

2019

(ha)

Net Change ()

(2003ndash2019)

Tree

A 46274 37639 17988 minus6113

B 64389 38634 27486 minus5731

C 99623 82791 65050 minus3470

D 231775 215192 173580 minus2511

Total 442061 374256 284104 minus3573

Marsh

A 1182244 1148060 1238636 477

B 1113045 1084738 1395453 2537

C 2051730 2277785 2204412 744

D 6598632 6839319 6359952 minus362

Total 10945651 11349903 11198453 231

Pasture

A 510146 460323 324348 minus3642

B 136395 89019 77021 minus4353

C 696209 754500 653027 minus620

D 1152047 1036039 881960 minus2344

Total 2494796 2339881 1936355 minus2238

0

20000

40000

60000

80000

100000

120000

0

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

Land area ‐MarshPasture (ha)

Land area ‐Forest Cover (ha)

Year

Land‐cover dynamics over time

West East Marsh Pasture

Figure 4 A representation of the changes in three respective land-cover types (forest cover pasturemarsh) over the period 2003ndash2019 For forest cover the three western sections are represented as Westand D is represented as East Light green indicates marsh yellow represents pasture dark green andhatched green show forest cover

Forests 2022 13 1132 10 of 22

32 Rates of Loss with Respect to Elevation

A net decrease in forest cover was seen across all elevation categories (Table 2 Figure 5)The largest losses were in the 1ndash2 m category with a net reduction of 593 from 2003 to2019 There was a temporal shift in the rate of change with the greatest amount of lossbetween 2003ndash2007 contributed by section D Values within the 2ndash3 m category remainedsteady from 2007 to 2019 with a reduction of only 076 The large majority (749) ofthe remaining forest was seen to be located within the 0ndash1 m band The losses were notevenly distributed within this band buffered to some degree by section D A comparisonof sections A vs C shows a decrease of 493 with losses centered around the period2007ndash2019 One issue to note is that the GSD for the DEM was 3 m which is wider thansome spoil banks While the local elevation may in fact rise to 2ndash3 m the value was reduceddue to the effect of averaging The losses in the 3 m+ band are discussed below a noteshould be made of the limited representation Overall the losses varied both spatially andtemporally across the region largely separated into eastern and western reaches

Table 2 Differences in the amount of forest area within each elevation band and the associatedpercentage loss of forest cover between years Sections A B C and D represent areas delineated forthis study within the two parishes (for details see Figure 1)

Sites and Forest Cover (ha)Percentage

LossYearmdash2003

Elevation (m) A B C D Total

0ndash1 13083 25525 66334 187236 2921781ndash2 27168 36374 25583 39048 1281722ndash3 1934 2188 6481 4640 15242gt3 361 170 005 343 879

Yearmdash2007A B C D Total 2003ndash2007

0ndash1 18371 16502 56349 166776 257997 minus11701ndash2 17834 20210 21289 4205 63538 minus50432ndash3 1228 1783 4118 5446 12575 minus1750gt3 048 208 002 404 662 minus2474

Yearmdash2019A B C D Total 2003ndash2019

0ndash1 7161 8227 37762 142102 195253 minus33171ndash2 10283 12913 24656 4401 52254 minus59232ndash3 1506 932 5789 4253 12480 minus1812gt3 045 141 002 402 590 minus3288

33 Interaction with Water Level

Water plays a critical role in the natural function of this ecosystem As such we consid-ered the interplay of temperature and MSL over a decadal time scale and the local rates oftemperature and gage height from both USGS stations (Figure 6) In Figure 6 forest extentis superimposed for each yearrsquos classification to show visual correspondence between thetwo drivers Temperature and rise in sea-level were negatively correlated until an inflectionpoint around 2010 after which there was a relatively stronger correlation increasing to amaximum of 025 The correlative increase lends evidence to the correspondence betweenclimate and water pressure in coastal areas An increase in the average MSL has been ob-served for the site The two stream gages showed an annualized MSL rise of 136 mmyearfor Vermilion Bay and 59 mmyear for the Calcasieu River There was a weak but positivetrend for temperature to increase slightly from the onset of the study period Time provedto be a good indicator for increase in water level and for decrease in forest cover (Figure 6)The lack of tree-cover samples prohibited many statistical comparisons but visually thecorrespondence seems evident

Forests 2022 13 1132 11 of 22

Forests 2022 13 x FOR PEER REVIEW 11 of 23

Table 2 Differences in the amount of forest area within each elevation band and the associated

percentage loss of forest cover between years Sections A B C and D represent areas delineated for

this study within the two parishes (for details see Figure 1)

Sites and Forest Cover (Ha)

Percentage Loss Yearmdash2003

Elevation (m) A B C D Total

0ndash1 13083 25525 66334 187236 292178

1ndash2 27168 36374 25583 39048 128172

2ndash3 1934 2188 6481 4640 15242

gt3 361 170 005 343 879

Yearmdash2007

A B C D Total 2003ndash2007

0ndash1 18371 16502 56349 166776 257997 minus1170

1ndash2 17834 20210 21289 4205 63538 minus5043

2ndash3 1228 1783 4118 5446 12575 minus1750

gt3 048 208 002 404 662 minus2474

Yearmdash2019

A B C D Total 2003ndash2019

0ndash1 7161 8227 37762 142102 195253 minus3317

1ndash2 10283 12913 24656 4401 52254 minus5923

2ndash3 1506 932 5789 4253 12480 minus1812

gt3 045 141 002 402 590 minus3288

0

1

2

3

4

5

6

7

8

9

10

0

500

1000

1500

2000

2500

3000

3500

Land area 3mdash4m ‐(ha)

Land area 0mdash3m ‐(ha)

Year

Forest cover in elevational bands over time

0‐1m

1‐2m

2‐3m

3‐4m

Figure 5 Visual depictions of two forms of loss one from hurricanes on Hackberry Beach and theother documenting whittling reduction The left column refers to 2003 NAIP imagery while the rightuses 2007 NAIP imagery Black shaded areas indicate current year forest extent and the red hashedareas indicate loss

Forests 2022 13 x FOR PEER REVIEW 12 of 23

Figure 5 Visual depictions of two forms of loss one from hurricanes on Hackberry Beach and the

other documenting whittling reduction The left column refers to 2003 NAIP imagery while the right

uses 2007 NAIP imagery Black shaded areas indicate current year forest extent and the red hashed

areas indicate loss

33 Interaction with Water Level

Water plays a critical role in the natural function of this ecosystem As such we con‐

sidered the interplay of temperature and MSL over a decadal time scale and the local

rates of temperature and gage height from both USGS stations (Figure 6) In Figure 6

forest extent is superimposed for each yearrsquos classification to show visual correspondence

between the two drivers Temperature and rise in sea‐level were negatively correlated

until an inflection point around 2010 after which there was a relatively stronger correla‐

tion increasing to a maximum of 025 The correlative increase lends evidence to the cor‐

respondence between climate and water pressure in coastal areas An increase in the av‐

erage MSL has been observed for the site The two stream gages showed an annualized

MSL rise of 136 mmyear for Vermilion Bay and 59 mmyear for the Calcasieu River

There was a weak but positive trend for temperature to increase slightly from the onset of

the study period Time proved to be a good indicator for increase in water level and for

decrease in forest cover (Figure 6) The lack of tree‐cover samples prohibited many statis‐

tical comparisons but visually the correspondence seems evident

Figure 6 Trends in mean water levels and water temperature over 19 years in Vermillion Parish

Data obtained from the USGS Station 07387050 Vermilion Bay (minus921355 296744) with gage datum

165 m above NAVD88 and from the USGS Station 08017118 Calcasieu River at Cameron LA

(minus933488 298155) with gage datum 107m above NAVD88

Rsup2 = 09453

Rsup2 = 01283

Rsup2 = 06529

08

085

09

095

1

105

11

115

12

125

1995 2000 2005 2010 2015 2020 2025

15

20

25

30

35

40

45

50

Average

water level (m)

Year

Forest cover loss Average

water temp

Loss of tree cover (sqkm) Average water temp (deg C) Average waterlevel (m)

Figure 6 Trends in mean water levels and water temperature over 19 years in Vermillion Parish Dataobtained from the USGS Station 07387050 Vermilion Bay (minus921355 296744) with gage datum 165 mabove NAVD88 and from the USGS Station 08017118 Calcasieu River at Cameron LA (minus933488298155) with gage datum 107m above NAVD88

Forests 2022 13 1132 12 of 22

4 Discussion41 Classification Reliability and Constraints

NAIP imagery proved efficacious for fine-scale analysis in this region shown by thestrong average producerrsquos accuracy and Kappa coefficient This is a positive indicatorthat variability between years or areas did not influence classification accuracies to alarge extent and that the final classification is a reliable interpretation [42] Many otherstudies conducting land-cover classifications using NAIP imagery have also shown robustaccuracies [43ndash45] However there were noted issues seen in certain parts of the workflowA problem preeminent in the initial classification was a limitation of spectral resolution Thisproblem became pronounced in areas with similar shades of green or between dark bluesand black hues Without the use of ancillary datasets the classification accuracy was poorbetween dark water and shadows of trees with considerable overlap (misclassificationsof approximately 50) Mixed land use can also prove problematic in NAIP land-coverclassifications [46] This became especially pronounced in transitional forestmarsh areasin which there was no dominant spectral signature for a particular land use Local variationbetween adjoining image sets highlighted another shortcoming of NAIP imagery Theviewing geometry illumination and time between the capture of neighboring imagetiles created edge effects including artificially high or low pixel values and refractionproblems on water [47] Each yearrsquos dataset was collected by an external contractor causingdifferences in sensor instrumentation and parametrization between years By 2019 many ofthese problems had been mitigated and images were of much higher quality

Compared to a large swath sensor such as Landsat-8 or Sentinel-2 an innate non-uniformity poses problems for pixel-wise classifications of VHR imagery Typically datafrom satellite imagery have been the primary choice for analyzing coastal forests marshesand forests at large [48ndash50] The spaceborne sensors obtain information over eight bandsallowing much finer spectral discrimination of certain classes of objects [51] Even amongstdiverse forest types space-borne Sentinel-2 and ASTER imagery has been used to mapdistinct species clusters of broadleaf and deciduous plant species The large spatial scaleenables the use of these created geospatial products to analyze the efficacy of governmentalreforestation programs in remote areas of the world [52] The usage of normalized differ-ence indices including the Normalized Difference Vegetation Index (NDVI) EnhancedVegetation Index (EVI) and Enhanced Water Index (EWI) can also provide greater confi-dence in the separation of water and marsh features from deciduous broadleaf forests [53]However a spatial resolution of 10m is still too coarse for the detection of remnant andlandscape trees especially those alongside homogenous flat features such as maintainedgrass or water bodies The most practical solution would be to assess this land with ahigher resolution dataset such as unmanned aerial systems (UAS) imagery which cancapture a much finer GSD in a plethora of bands if multispectral or hyperspectral sensorsare used [54] In fact scientists are now pushing the boundaries of the scale of analysisconducted through remote sensing Broad groupings (eg clades or forest types) have beenthe historic norm in the classification of imagery for use in land management A greatermixture of classes allows more accurate measurements for those interested in landscape-scale ecological processes However VHR can be used to map stands at the species levelwhich allows forest managers an unparalleled insight into the dynamics of recruitmentdisease or resource availability [55]

Google Earth Engine allowed us to utilize NAIP imagery at scale which had for sometime been a limiting factor in processing this data source locally Even so some limitationshindered the functional utility of GEE for detailed land-cover classification In our relativelyuniform environment we discriminated between many shades of green When viewinggeometries cause variations in hue different classes begin to overlap within the samespectral regions Solutions using Geographic Object-Based Image Analysis (GEOBIA) cancircumvent this issue yet this segmentation algorithm is currently not supported by GEEMany consider GEOBIA the optimal tool for segmenting landscapes and for the productionof land-cover classifications from this source of imagery [3856] Its successful integration

Forests 2022 13 1132 13 of 22

could substantially elevate the use and utility of mapping within GEE Additionally theallocation of increased processing bandwidth on GEE servers may help overcome somelimits in classification Greater bandwidth allows larger and more diverse training setsin addition to more complex parameterizations of machine learning classifiers If oneis proceeding on local workstations the analysis of NAIP imagery requires an intimateknowledge of data structures and process optimization Custom architectures processingpipelines and simpler binary classifications can allow an expansion of the study area tocover coterminous areas on a larger scale [16]

42 Forest Loss Contextually

We mapped forest cover change dynamics between 2003ndash2019 on the southwest coastof Louisiana 1580 hectares of tree cover 13 of the terrestrial area was converted todifferent land use The total loss of forest cover in the study period was 3573 over sixteenyears which is indicative of a larger rate of forest loss than at sites in a similar physio-geographic region yet is certainly not a global outlier [18] These findings validate thesentiments of people in the region and underscore a major irreversible trend in the area Ourestimate should be considered conservative due to the way we approached the definition ofldquoforest coverrdquo within the landscape Abandoned pastoral land exhibiting dense scrubbinessand shrub cover was demarcated as forest cover even though the target species (live oak)was probably not present Spoil banks which are the byproduct of historic oil explorationin the region indicating possible mortality were also often considered a component offorest cover if the imagery could not indicate the absence of high vegetation The primarymethod of identifying whether or not forest cover was present was the observation of ashadow adjoining the vegetation indicating a vertical structure

The Big Bend region of Florida one of the longest undisturbed coastlines in thecontinental US offers some insight into the vegetation dynamics this region has beenexperiencing It has been the focus of numerous ecological studies has a similar climateand is exposed to storms with similar regularity Raabe and Stumpf [57] found that fromthe early to late 1900s over 82 km2 of forest land was converted to marsh and 66 km2 wasin a transitional state to marsh McCarthy et al [58] document an annual forest cover lossof 744 from 2010ndash2017 an acceleration in the trend within that area A new study bythe same team found rates of loss totaling approximately 10 km2 a year 800 larger thanpreviously stated in the literature [59] Rates of annual mortality ranged from 4ndash16 inSoutheastern Louisiana from 1987 to 1997 [60] In comparison the annualized rate of lossfound in our study never exceeded 5 Plots located in South Carolina that have beenless significantly impacted by MSL rise show rates of loss closer to 1ndash2 in line withour observed rate of loss from 2007ndash2019 However we cannot assume that percentagesof forest loss across the country have been constant within the last century therefore it iscritical to carry out intensive geospatial studies at regular intervals

In the adjoining southeastern part of the state of Louisiana in the Atchafalaya andTerrebonne basins forested wetlands occupy 121871 hectares or 41 of all vegetatedland use [12] In comparison the terrestrial forested land use in our study area was 21in 2019 The wide variation can be interpreted in several ways each with importantimplications Historic rates of deforestation differ in the two regions with a greateremphasis on the preservation of wilderness areas in the Atchafalaya basin [61] TheAtchafalaya and Terrebonne basins also have productive land used in the growth of timberwhich promotes long-term forest cover The regeneration of forests in our study region alsodiffers substantially Chenier ecosystems and traditional alluvial bottomland hardwoodforests are compositionally different in vegetation and soil even when accounting for theirunique hydrologic regimes Communities of live oak may be less resilient to the types offorcings that cause changes in community composition within these ecosystems furtheredby proximity to the sea If stocking density rates are similar to the adjoining basin as theywere in the past we would see an areal extent well over 50000 hectares

Forests 2022 13 1132 14 of 22

The analysis vis-agrave-vis elevation shows weak trends in the manners of loss on eleva-tional gradients Logically in the paradigm of coastal retreat there should be an aggregationof forest resources towards the highest bands of topography in a region Our analysis doesnot follow this chain of thought with the largest losses observed in the 1ndash2 m categoryrather than the lowest 0ndash1 m (Table 2 2007 C 2019 D) While we do see a large loss bypercentage in the highest elevation band it is important to note that a small sample causesinherent variance Forest cover on the highest reaches of the chenier ridges is typically lessthan 1 of total forest cover for that region The reduction can be attributed to the loss of23 features in section A accounting for 33 hectares bordering the Sabine River (Figure 1minus9386286 2973355) The loss of forest cover was driven by intense weather Vegetationin the area was completely destroyed and substantial amounts of sedimentation could beobserved as evident in the 2007 NAIP imagery The only gain in forest cover was seen inthe 2ndash3 m elevation band in the period 2007 in an area that had a high density of pastoralland that was abandoned Forest retreat is often discussed as one method of bufferingforest loss along coasts [62] Unfortunately the geomorphology of this area does not allowgreat spatial movement as the current extent is already located within the highest bandsof elevation

43 Land Use Conversion and Driving Factors

The leading cause of land-use conversion from tree cover was to a saltmarsh ecotype(606) often following mortality and replacement of vegetative biomass The loss offorest cover can largely be typified into two categories The ingression of saltwater causingsalinification within the soil matrix paired with increased flooding caused mortality ofoverstorey vegetation over a multi-year timescale [6364] Hurricanes or other storm eventscaused rapid mortality through wind-throws severe defoliation saltwater inundationduring a storm surge or canopy damage Following intense weather events between2003 and 2007 the vast majority of cheniers located within a 1km buffer of the coast wereunable to support tree cover as seen in Figure 7AB Clearing for industry or developmentalso caused immediate shifts in forest composition as can be seen in Figure 8AB Thischange was more pronounced on reaches A and B especially on Hackberry Beach Thisarea was the site of landfall for Hurricane Rita which caused massive devastation to othercomponents of the ecosystem [6566] Singular storm events have a transformative impacton vegetation communities and the weight of this driver on annual rates of forest lossis a topic that should be explored further Wayward shifts in species composition are atrend that has been observed globally especially in forests that border some source of waterTrees like Juniperus virginiana L that have distinctive water-use patterns in comparison toother coastal tree species are susceptible to salinification at much faster rates as evidencedthrough isotope analyses [67] As with many ecological systems the entangled effectsof climate and vegetation require diverse means of looking at change Isotope analysesdendrochronology field sensors and remote sensing data are needed to characterize land-use change at the stand and community levels

Large forest remnants such as Cow Island (minus9283581 2973842) Tiger Island (minus92784392972832) and Grand Chenier Ridge (minus9281175 2972986) among others have diminishedmore slowly By 2019 these stands had lost an average of 15 of their maximum extentThe land use changed to both marsh and managed pasture largely dependent on thesurrounding land use The conversion to managed pasture (282) accounted for a largerthan expected amount of land-cover change Many of these changes were in areas wheretree cover may have been more ldquoornamentalrdquo in nature Trees beside houses or alongsideroads are some of the largest specimens of live-oak to be found However these trees areoften solitary and perhaps more vulnerable to wind-borne disturbance The concern is thattheir loss due to abiotic drivers may not be easily mitigated leading to a permanent declineOur data support evidence on the whittling of forest remnants and the eventual fate ofthese trees becoming lsquorelict forestsrsquo unable to recruit new generations in the short term andin the long-term total disappearance of these cheniers along the Louisiana Gulf Coast [68]

Forests 2022 13 1132 15 of 22Forests 2022 13 x FOR PEER REVIEW 16 of 23

Figure 7 Visual depictions of two forms of loss one from hurricanes on Hackberry Beach (AB) and

the other documenting whittling reduction (CD) The left column displays 2003 NAIP imagery and

the right is 2007 NAIP imagery Black shaded areas indicate current year forest extent red hashed

areas indicate a loss

Figure 7 Visual depictions of two forms of loss one from hurricanes on Hackberry Beach (AB) andthe other documenting whittling reduction (CD) The left column displays 2003 NAIP imagery andthe right is 2007 NAIP imagery Black shaded areas indicate current year forest extent red hashedareas indicate a loss

Beyond loss in forest cover there is a noticeable trend towards an altered hydrologywhere pastoral areas and intact forest remnants are associated with pockets of standingwater An increase in the correlative factor between temperature and water levels could bea worrying trend a direct conversion of tree cover to water at 34 could be an unforeseenoutcome Chenier ridges in their geomorphic context should not support non-ephemeralponding It is not documented how increased tidal influence which is one factor of risingMSL influences the hydrology of this ecosystem and its associated communities Albeitin a more inland context Magolan and Halls [69] documented a transition of over 40 ofland to emergent marsh from closed-canopy cover from 1949 to 2018 due to hydrologicinfluence Recent rates of relative sea rise have been documented as high as 119 mmyearcompared to background Holocene rates of 17 mmyear [70] This has resulted in a rate oflateral coastal forest retreat between 2ndash14 times greater than pre-industrial levels [26] Therate of shoreline retreat in this region increased from ~61 to ~94 myear based on the 100-and 30-year moving averages amounting to over 10000 hectares of land loss every yearfrom 1800 to 2005 [27]

Forests 2022 13 1132 16 of 22Forests 2022 13 x FOR PEER REVIEW 17 of 23

Figure 8 A depiction of clearing for industry in (AB) and a depiction of forest whittling from (CD)

The black extent represents the current forest area red hashed lines indicate a loss The left panels

show 2007 NAIP imagery while the right are 2019 NAIP imagery

Large forest remnants such as Cow Island (minus9283581 2973842) Tiger Island

(minus9278439 2972832) and Grand Chenier Ridge (minus9281175 2972986) among others have

diminished more slowly By 2019 these stands had lost an average of 15 of their maxi‐

mum extent The land use changed to both marsh and managed pasture largely depend‐

ent on the surrounding land use The conversion to managed pasture (282) accounted

for a larger than expected amount of land‐cover change Many of these changes were in

areas where tree cover may have been more ldquoornamentalrdquo in nature Trees beside houses

or alongside roads are some of the largest specimens of live‐oak to be found However

these trees are often solitary and perhaps more vulnerable to wind‐borne disturbance The

concern is that their loss due to abiotic drivers may not be easily mitigated leading to a

permanent decline Our data support evidence on the whittling of forest remnants and

the eventual fate of these trees becoming lsquorelict forestsrsquo unable to recruit new generations

in the short term and in the long‐term total disappearance of these cheniers along the

Louisiana Gulf Coast [68]

Beyond loss in forest cover there is a noticeable trend towards an altered hydrology

where pastoral areas and intact forest remnants are associated with pockets of standing

water An increase in the correlative factor between temperature and water levels could

be a worrying trend a direct conversion of tree cover to water at 34 could be an unfore‐

seen outcome Chenier ridges in their geomorphic context should not support non‐ephem‐

eral ponding It is not documented how increased tidal influence which is one factor of

Figure 8 A depiction of clearing for industry in (AB) and a depiction of forest whittling from (CD)The black extent represents the current forest area red hashed lines indicate a loss The left panelsshow 2007 NAIP imagery while the right are 2019 NAIP imagery

44 Future of Chenier Forests

The chenier complexes of Louisiana do not have the natural benefaction of maintaininglarge original tracts untouched by axe or dozer The extent map through the years (Figure 9)exemplifies the scattered nature of forest remnants The largest single continuous chenierforest (Grand Chenier Ridgemdash185 ha) encompasses less than 1 of the total forest areaExposed forest edges pose a problem for the continued integrity of the stand as adaptedspecies fail to compete in these new microclimates [71] Slow-growing saplings of desirabletree species (live oak hackberry) are subject to unfavorable growing conditions fromweather and light availability Vegetation surveys following aspect and inundation alongthe Florida coast have all but confirmed the eventual extirpation of overstory vegetationthrough poor recruitment of native species [31] Chinese tallow (Triadica sebifera) a non-native tree is likely to fill empty niche space This aggressive invader is problematicthroughout the Gulf Coastal Plain from Texas to Florida [72] The tree has a wide meansof dispersal being primarily distributed through frugivory via avian populations A highrate of germination compounded with quick growth and a sexual maturity of less thanthree years make it a difficult species to control Its invasive capability is seen in nearlyall forest types in the region although it is more pervasive in coastal forests than inlandModeling studies show a high likelihood of Chinese tallow becoming the dominant specieswithin this ecosystem and in surrounding areas [73] Future publications from this lab will

Forests 2022 13 1132 17 of 22

provide more in-depth data and analysis on the state of existing stands and recruitmenttrends within remnants of the Chenier Plains via field sampling

Louisiana has enacted a historic response to the problem of land-loss following a publicoutcry after devastating storms in 2005 The Coastal Protection and Restoration Authority(CPRA) have been tasked with enacting a management plan to combat coastal erosion TheComprehensive Master Plan details proposals to create marshes build sediment diversionsform new hydrologic structures and promote barrier island restoration through builtand natural means As of 2017 over 135 projects had been completed impacting over14568 ha [74] An update to this document is due to be released in 2023 The projecthas an overall operating budget of over 50 billion US dollars with equal shares dedicatedto restoration and risk reduction The primary funding source is a settlement associatedwith the Deepwater Horizon Oil Spill in addition to various initiatives at the state andfederal levels However there are no current plans to supplement vegetation or performany geoengineering work on the cheniers for afforestation

Coastal wetlands rank among the most important ecosystems in terms of the valuationof ecosystem services [75] Provisioning services such as water filtration or storm-surgeprotection are commonly considered when discussing coastal areas Beyond these com-monalities chenier forests provide vital wildlife habitats being the first encountered sourceof vertical structure for migratory neo-tropical birds and migrating monarch butterflies inthe Mississippi alluvial valley flyway [76] This en-route habitat is of critical importancedue to its closeness to the Gulf of Mexico During favorable weather conditions 10 ofbirds migrating through the Gulf use these forests as a stopover spot rising dramatically to80 during adverse weather conditions [29] With losses mounting many species may notbe able to seek refuge during storms The area also has a unique suite of amphibians andreptiles being home to 13 obligate chenier forest species of herpetofauna [77]

Lu et al [78] used the eddy covariance method (ECM) to calculate strong carbon sinkeffects for a chenier ecosystem driven by forest vegetation Net energy production valueswere significantly higher than surrounding marshland signifying the importance of densevegetative cover on a per-unit basis Counter to this there is also strong evidence thatdegraded forest ecosystems act as net carbon sources A study in North Carolina also usingECM found that a seasonally inundated tide-influenced forest was a net carbon sourceannually and only functioned as a sink for 3 months of the growing year [79] Similarly astudy in the northeastern part of the state showed net negative rates of carbon sequestrationfor an ephemerally flooded forest [80] Unique environments demand individual attentiona characterization of this ecosystem for nitrogen or carbon flux with respect to a similarsystem may not be functionally sound [81]

Forests 2022 13 1132 18 of 22Forests 2022 13 x FOR PEER REVIEW 19 of 23

Figure 9 The total land area classified in GEE for the years 2003 2007 and 2019 Light green repre‐

sents the marsh area tan represents pasture The water layer has been removed for purposes of

clarity The dark black outline is the outline of the forest cover for the respective year

Louisiana has enacted a historic response to the problem of land‐loss following a

public outcry after devastating storms in 2005 The Coastal Protection and Restoration

Authority (CPRA) have been tasked with enacting a management plan to combat coastal

erosion The Comprehensive Master Plan details proposals to create marshes build sedi‐

ment diversions form new hydrologic structures and promote barrier island restoration

through built and natural means As of 2017 over 135 projects had been completed im‐

pacting over 14568 ha [74] An update to this document is due to be released in 2023 The

project has an overall operating budget of over 50 billion US dollars with equal shares

dedicated to restoration and risk reduction The primary funding source is a settlement

associated with the Deepwater Horizon Oil Spill in addition to various initiatives at the

state and federal levels However there are no current plans to supplement vegetation or

perform any geoengineering work on the cheniers for afforestation

Coastal wetlands rank among the most important ecosystems in terms of the valua‐

tion of ecosystem services [75] Provisioning services such as water filtration or storm‐

surge protection are commonly considered when discussing coastal areas Beyond these

commonalities chenier forests provide vital wildlife habitats being the first encountered

source of vertical structure for migratory neo‐tropical birds and migrating monarch but‐

terflies in the Mississippi alluvial valley flyway [76] This en‐route habitat is of critical

importance due to its closeness to the Gulf of Mexico During favorable weather condi‐

tions 10 of birds migrating through the Gulf use these forests as a stopover spot rising

Figure 9 The total land area classified in GEE for the years 2003 2007 and 2019 Light greenrepresents the marsh area tan represents pasture The water layer has been removed for purposes ofclarity The dark black outline is the outline of the forest cover for the respective year

5 Conclusions

The cheniers of southwestern Louisiana were found to be declining in size at a concern-ing rate Many of the driving factors are ecosystem-wide trends that have been increasingin intensity within the last century The results of this study suggest that coastal forestsare an ecosystem in peril and the ecology of such systems should be a closely monitoredsubject The 365 loss of forest cover is an alarming but not wholly unexpected result ofmany decades of intense perturbation and human exploitation A dynamic of continuedforest loss is evident Unless significant investment is made in engineering solutions tomitigate storm effects and land subsidence we will continue to see the decline of chenierforests in Louisiana Through targeted mitigation governmental programs such as theWetland or Conservation Reserve Program may be able to supplement existing forestsas well as the incomes of local people who have lost their livelihoods In this study wehave begun the process of accurate areal estimates of forest extent and have shed light onecosystem processes in the region As this community continues to diminish we see theforest not by the trees but rather the lack of them

Forests 2022 13 1132 19 of 22

Author Contributions Conceptualization PT methodology PT software PT and PR validationPT and PR formal analysis PT PR and PT writingmdashoriginal draft preparation PT and JBwritingmdashreview and editing PT and JB visualization PT and JB supervision JB All authorshave read and agreed to the published version of the manuscript

Funding This research received no external funding

Data Availability Statement The data will be published in and stored on the Harvard Dataverse athttpsdataverseharvardedudatasetxhtmlpersistentId=doi107910DVN3J7SNF (accessed on1 May 2022)

Acknowledgments The authors would like to recognize the hard work of undergraduate studentsParusha Raut and Subash Sapkota in manually validating thousands of points and creating numeroustraining sets We also extend thanks to our institution and numerous people within the lab who leantboth mental and emotional fortitude

Conflicts of Interest The authors declare no conflict of interest

References1 Michener WK Blood ER Bildstein KL Brinson MM Gardner LR Climate change hurricanes and tropical storms and

rising sea level in coastal wetlands Ecol Appl 1997 7 770ndash801 [CrossRef]2 Donoghue JF Sea level history of the northern Gulf of Mexico coast and sea level rise scenarios for the near future Clim Chang

2011 107 17ndash33 [CrossRef]3 Stanturf JA Goodrick SL Outcalt KW Disturbance and coastal forests A strategic approach to forest management in

hurricane impact zones For Ecol Manag 2007 250 119ndash135 [CrossRef]4 Augustinus P Cheniers and chenier plains A general introduction Mar Geol 1989 90 219ndash229 [CrossRef]5 Fu Z Wang P Sun J Lu Z Yang H Liu J Li T Composition seasonal variation and salinization characteristics of soil

salinity in the Chenier Island of the Yellow River Delta Glob Ecol Conserv 2020 24 e01318 [CrossRef]6 Marques IG Campelo F Rivaes R Albuquerque A Ferreira MT Rodriacuteguez-Gonzaacutelez PM Tree rings reveal long-term

changes in growth resilience in Southern European riparian forests Dendrochronologia 2018 55 167ndash176 [CrossRef]7 Daigle JJ Griffith GE Omernik JM Faulkner PL McCulloh RP Handley LR Smith LM Chapman SS Ecoregions of

Louisiana (Color Poster with Map Descriptive Text Summary Tables and Photographs) US Geological Survey Reston VI USA 20068 McBride RA Taylor MJ Byrnes MR Coastal morphodynamics and Chenier-Plain evolution in southwestern Louisiana USA

A geomorphic model Geomorphology 2007 88 367ndash422 [CrossRef]9 Gosselink JG An Ecological Characterization Study of the Chenier Plain Coastal Ecosystem of Louisiana and Texas Narrative Report

National Coastal Ecosystems Team Office of Biological Services Fish and Wildlife Service US Department of the InteriorWashington DC USA 1979 Volume 1

10 Blum MD Roberts HH The Mississippi delta region Past present and future Annu Rev Earth Planet Sci 2012 40 655ndash683[CrossRef]

11 Hansen MC Potapov PV Moore R Hancher M Turubanova SA Tyukavina A Townshend J High-resolution globalmaps of 21st-century forest cover change Science 2013 342 850ndash853 [CrossRef]

12 Doyle TW Krauss KW Conner WH From AS Predicting the retreat and migration of tidal forests along the northern Gulfof Mexico under sea-level rise For Ecol Manag 2010 259 770ndash777 [CrossRef]

13 Thomas N Simard M Castantildeeda-Moya E Byrd K Windham-Myers L Bevington A Twilley R High-resolution mappingof biomass and distribution of marsh and forested wetlands in southeastern coastal Louisiana Int J Appl Earth Obs Geoinf 201980 257ndash267 [CrossRef]

14 Desantis LR Bhotika S Williams K Putz FE Sea-level rise and drought interactions accelerate forest decline on the GulfCoast of Florida USA Glob Chang Biol 2007 13 2349ndash2360 [CrossRef]

15 Boyd DS Foody G Ripple W Evaluation of approaches for forest cover estimation in the Pacific Northwest USA usingremote sensing Appl Geogr 2002 22 375ndash392 [CrossRef]

16 Valjarevic A Djekic T Stevanovic V Ivanovic R Jandzikovic B GIS numerical and remote sensing analyses of forest changesin the Toplica region for the period of 1953ndash2013 Appl Geogr 2018 92 131ndash139 [CrossRef]

17 He T Sun YJ Xu JD Wang X Hu C Enhanced land usecover classification using support vector machines and fuzzyk-means clustering algorithms J Appl Remote Sens 2014 8 083636 [CrossRef]

18 Michez A Pieacutegay H Lisein J Claessens H Lejeune P Classification of riparian forest species and health condition usingmulti-temporal and hyperspatial imagery from unmanned aerial systems Environ Monit Assess 2016 188 146 [CrossRef][PubMed]

19 Hampton SE Strasser C Tewksbury J Gram W Budden A Batcheller A Duke C Porter J Big data and the future ofecology Front Ecol Environ 2013 11 156ndash162 [CrossRef]

20 Curtis PG Slay CM Harris NL Tyukavina A Hansen MC Classifying drivers of global forest loss Science 2018 3611108ndash1111 [CrossRef]

Forests 2022 13 1132 6 of 22

were converted regardless of their class Tree cover vectors were then extracted from eachyearrsquos classification All cover area calculations including different elevation productswere performed on this vectorized form due to the superior computational ability forcalculating absolute area of georeferenced polygons in a vector format Polygons were alsomanually scanned over the respective years of NAIP imagery and were either deleted oradjusted following a set standard for Quality Assurance (QA) to ensure best representation

Forests 2022 13 x FOR PEER REVIEW 6 of 23

Figure 2 Panels (AndashD) show diagnostic types of chenier forests Panels (AC) show a chenier rem‐

nant forest Panel (B) is a spoil‐bank forest from the days of oil exploration The southern terminus

of (D) shows a swamp forest which could inhabit less haline marsh communities found in the north‐

ern reaches of the Chenier Plains The black‐outlined areas are the respective yearsrsquo forest extent

Figure 2 Panels (AndashD) show diagnostic types of chenier forests Panels (AC) show a chenier remnantforest Panel (B) is a spoil-bank forest from the days of oil exploration The southern terminus of(D) shows a swamp forest which could inhabit less haline marsh communities found in the northernreaches of the Chenier Plains The black-outlined areas are the respective yearsrsquo forest extent

25 Data Analysis and Statistics

Because post-processing and QA significantly modified the original CART classifi-cation baseline statistics on algorithm confidence would not be representative Insteadproducerrsquos accuracy (PA) was used as the primary determinant of classification success(Equation (1) where reference sites denote created validation points)

Producerrsquos Accuracy = of correctly classified reference sites

of total reference sitestimes100 (1)

Forests 2022 13 1132 7 of 22Forests 2022 13 x FOR PEER REVIEW 7 of 23

Figure 3 Process workflow used in the study The section bracketed in blue was processed using

Google Earth Engine and the section bracketed in green was carried out using QGIS Blue blocks

represent raw inputs into the workflow green blocks are the final outputs

24 Post‐Processing of GEE Outputs

Two‐layer masks created from the NHD were used to refine the classified output

The masks were originally ascertained and modified in a vector format and were con‐

verted to a raster‐type format when used for modifying the GEE outputs The two layers

which were in the same projection and scale were employed to perform mathematical

operations on a per‐pixel basis Raster calculations can be applied to overlapping areas

according to their underlying geospatial properties affecting how data is represented [40]

As the NHD is incomplete regarding wetlands and pond features care was taken to dig‐

itize areas of the 2003 NAIP imagery that were wholly saltmarsh or water [41] An as‐

sumption was made that any saltmarsh or water feature present in 2003 would maintain

its non‐forested land use during the study period This allowed a set standard for the

modification of GEE outputs The saltmarsh mask was set so that any pixel registering as

a tree would be converted into saltmarsh without the modification of any other land‐

cover class The water mask was absolute in terms of pixel modification All pixels over‐

lapping the mask were converted regardless of their class Tree cover vectors were then

extracted from each yearrsquos classification All cover area calculations including different

Figure 3 Process workflow used in the study The section bracketed in blue was processed usingGoogle Earth Engine and the section bracketed in green was carried out using QGIS Blue blocksrepresent raw inputs into the workflow green blocks are the final outputs

The creation of points for sections A B C and D to assess accuracy was carried outusing three tools in the QGIS Processing Toolbox First a lsquoDifferencersquo was run to extracttree cover in an original yearrsquos dataset but not in the succeeding year A lsquoDissolversquo was runon this product to create one large MultiPolygon object Finally lsquoRandom Pointsrsquo inside thePolygon function was used to create 200 point features that were manually validated Threecolumns indicating original year accuracy (YN) whether land-use change was correctlyattributed (YN) and what it had changed to (six classes) were populated for each pointThe last metric had absolute accuracy irrespective of the yearrsquos classification as it wasassigned visually Validation was carried out against the respective yearrsquos NAIP datasetBecause we did not assess land-use change in 2019 only accuracy was computed for thatA Cohenrsquos Kappa value was used to assess interrater reliability using Equation (2) wherePr(a) represents the actual observed agreement and Pr(e) represents chance agreement Theequations were derived from and expanded on by McHugh [42]

κ =Pr(a)minus Pr(e)

1 minus Pr(e)(2)

Forests 2022 13 1132 8 of 22

The CoNED DEM was subdivided into 0ndash1 1ndash2 2ndash3 and 3+ meter classes using theRaster Calculator tool in QGIS The class values were then vectorized to run a geospatialdifference iteratively with each yearrsquos tree cover vectors The difference was validatedand then used for area calculations regarding differential rates of loss for elevation gra-dients Gage height and temperature values were extracted from both stream gages andwere combined into a site average We used gage height as a proxy for MSL due to thespatial proximity to the coast and study sites as well as for considering the response tofreshwater influence which is an important hydrological component of the Chenier PlainsAverage values for the three wettest months (July August and September) were used inthe regressions and analysis

3 Results31 Forest Loss Transition and Classification Accuracy

From 2003 to 2019 an overall decline of 3573 was seen for forest cover and 2238for pasture The annualized rate of loss for forest cover was approximately 2 higherwithin the period 2003ndash2007 losing 42 of tree cover per year assuming a constant change(Table 1) Losses were greatest in the western two reaches of the Chenier Plains averagingover 213 more than sections C or D The largest amount of tree cover was located withinsection D with significant contributions from spoil banks Chenier Au Tigre (locationminus9220665 2956885) had the largest total tree cover within the study area albeit spreadover a large expanse Annual loss rates were more constant in sections containing primarilychenier ridge forests in contrast to swamp or spoil bank forests Section D has a largesaltmarsh (1ndash10 km) bisecting the chenier from the coast which contributed to a largerland and forest area There was a cumulative gain of 231 for the saltmarshes adding2528 km2 In the 2003ndash2007 period the only gains in land cover type were in saltmarshwhich was maintained into 2019 (Figure 4)

We surveyed a total of 2123 points across the study area Producerrsquos accuracy was916 816 and 918 for the years 2003 2007 and 2019 respectively The CohenrsquosKappa indexes of 091 082 and 093 for 2003 2007 and 2019 respectively showed strongagreement between raters across all years We considered any value above 085 to be a validassessment The year 2007 scored lower in both PA and Kappa values likely due to thenature of the base imagery however it still fell into an acceptable range Extreme contrastand saturation in the 2007 NAIP imagery caused larger aggregations of tree cover pixels asgrasses and trees have a similar hue accentuated by the pixel reduction post-classificationValidation points that were very close to tree cover but not categorized as such were notconsidered accurate which contributed to this source of error The 2019 classifications weregeneralized as being more correct from the onset with little need for QA

Changes in land-cover were not evenly distributed across different sections as canbe seen in Table 1 Typically cheniers are more proximally located to the coast withinthe eastern reaches with depositional bars spreading the length One noted outlier in theassessment of change was observed from 2003 to 2007 in section B where the loss of pastureaccounted for over 656 The quantitative measurements for changes in land cover werealso ascertained from these samples We observed changes of 606 282 34 and 76for saltmarsh pasture water and impervious area respectively

Observed results deviated from hypothesized values specifically in the large propor-tion of conversion to pasture A lone tree existing one year after transitioning to an openfield was common across all areas It was assumed the percentage of conversion fromtree to saltmarsh would be even greater However impervious area transitions were alsohigh given that considerable forest cover directly adjoins roads and houses in this areaThe underlying structure can then be seen following the mortality of vegetation Changeto water was also observed for several reasons in areas along waterbodies a loss of treecover exposed the water while severe areas of deforestation prompted a change to a morewetland hydrology which also required water replacement pixels

Forests 2022 13 1132 9 of 22

Table 1 The amount and percentage loss of three land-cover typesmdashtree marsh and pasturemdashovereach section and the total area spanning two parishes Calcasieu and Vermillion on the westernLouisiana Gulf Coast Sections A B C and D represent areas delineated for this study within thetwo parishes (for details see Figure 1)

Landcover 2003(ha)

2007(ha)

2019(ha)

Net Change ()(2003ndash2019)

TreeA 46274 37639 17988 minus6113B 64389 38634 27486 minus5731C 99623 82791 65050 minus3470D 231775 215192 173580 minus2511

Total 442061 374256 284104 minus3573

MarshA 1182244 1148060 1238636 477B 1113045 1084738 1395453 2537C 2051730 2277785 2204412 744D 6598632 6839319 6359952 minus362

Total 10945651 11349903 11198453 231

PastureA 510146 460323 324348 minus3642B 136395 89019 77021 minus4353C 696209 754500 653027 minus620D 1152047 1036039 881960 minus2344

Total 2494796 2339881 1936355 minus2238

Forests 2022 13 x FOR PEER REVIEW 9 of 23

adding 2528 km2 In the 2003ndash2007 period the only gains in land cover type were in salt‐

marsh which was maintained into 2019 (Figure 4)

Table 1 The amount and percentage loss of three land‐cover typesmdashtree marsh and pasturemdashover

each section and the total area spanning two parishes Calcasieu and Vermillion on the western

Louisiana Gulf Coast Sections A B C and D represent areas delineated for this study within the

two parishes (for details see Figure 1)

Landcover 2003

(ha)

2007

(ha)

2019

(ha)

Net Change ()

(2003ndash2019)

Tree

A 46274 37639 17988 minus6113

B 64389 38634 27486 minus5731

C 99623 82791 65050 minus3470

D 231775 215192 173580 minus2511

Total 442061 374256 284104 minus3573

Marsh

A 1182244 1148060 1238636 477

B 1113045 1084738 1395453 2537

C 2051730 2277785 2204412 744

D 6598632 6839319 6359952 minus362

Total 10945651 11349903 11198453 231

Pasture

A 510146 460323 324348 minus3642

B 136395 89019 77021 minus4353

C 696209 754500 653027 minus620

D 1152047 1036039 881960 minus2344

Total 2494796 2339881 1936355 minus2238

0

20000

40000

60000

80000

100000

120000

0

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

Land area ‐MarshPasture (ha)

Land area ‐Forest Cover (ha)

Year

Land‐cover dynamics over time

West East Marsh Pasture

Figure 4 A representation of the changes in three respective land-cover types (forest cover pasturemarsh) over the period 2003ndash2019 For forest cover the three western sections are represented as Westand D is represented as East Light green indicates marsh yellow represents pasture dark green andhatched green show forest cover

Forests 2022 13 1132 10 of 22

32 Rates of Loss with Respect to Elevation

A net decrease in forest cover was seen across all elevation categories (Table 2 Figure 5)The largest losses were in the 1ndash2 m category with a net reduction of 593 from 2003 to2019 There was a temporal shift in the rate of change with the greatest amount of lossbetween 2003ndash2007 contributed by section D Values within the 2ndash3 m category remainedsteady from 2007 to 2019 with a reduction of only 076 The large majority (749) ofthe remaining forest was seen to be located within the 0ndash1 m band The losses were notevenly distributed within this band buffered to some degree by section D A comparisonof sections A vs C shows a decrease of 493 with losses centered around the period2007ndash2019 One issue to note is that the GSD for the DEM was 3 m which is wider thansome spoil banks While the local elevation may in fact rise to 2ndash3 m the value was reduceddue to the effect of averaging The losses in the 3 m+ band are discussed below a noteshould be made of the limited representation Overall the losses varied both spatially andtemporally across the region largely separated into eastern and western reaches

Table 2 Differences in the amount of forest area within each elevation band and the associatedpercentage loss of forest cover between years Sections A B C and D represent areas delineated forthis study within the two parishes (for details see Figure 1)

Sites and Forest Cover (ha)Percentage

LossYearmdash2003

Elevation (m) A B C D Total

0ndash1 13083 25525 66334 187236 2921781ndash2 27168 36374 25583 39048 1281722ndash3 1934 2188 6481 4640 15242gt3 361 170 005 343 879

Yearmdash2007A B C D Total 2003ndash2007

0ndash1 18371 16502 56349 166776 257997 minus11701ndash2 17834 20210 21289 4205 63538 minus50432ndash3 1228 1783 4118 5446 12575 minus1750gt3 048 208 002 404 662 minus2474

Yearmdash2019A B C D Total 2003ndash2019

0ndash1 7161 8227 37762 142102 195253 minus33171ndash2 10283 12913 24656 4401 52254 minus59232ndash3 1506 932 5789 4253 12480 minus1812gt3 045 141 002 402 590 minus3288

33 Interaction with Water Level

Water plays a critical role in the natural function of this ecosystem As such we consid-ered the interplay of temperature and MSL over a decadal time scale and the local rates oftemperature and gage height from both USGS stations (Figure 6) In Figure 6 forest extentis superimposed for each yearrsquos classification to show visual correspondence between thetwo drivers Temperature and rise in sea-level were negatively correlated until an inflectionpoint around 2010 after which there was a relatively stronger correlation increasing to amaximum of 025 The correlative increase lends evidence to the correspondence betweenclimate and water pressure in coastal areas An increase in the average MSL has been ob-served for the site The two stream gages showed an annualized MSL rise of 136 mmyearfor Vermilion Bay and 59 mmyear for the Calcasieu River There was a weak but positivetrend for temperature to increase slightly from the onset of the study period Time provedto be a good indicator for increase in water level and for decrease in forest cover (Figure 6)The lack of tree-cover samples prohibited many statistical comparisons but visually thecorrespondence seems evident

Forests 2022 13 1132 11 of 22

Forests 2022 13 x FOR PEER REVIEW 11 of 23

Table 2 Differences in the amount of forest area within each elevation band and the associated

percentage loss of forest cover between years Sections A B C and D represent areas delineated for

this study within the two parishes (for details see Figure 1)

Sites and Forest Cover (Ha)

Percentage Loss Yearmdash2003

Elevation (m) A B C D Total

0ndash1 13083 25525 66334 187236 292178

1ndash2 27168 36374 25583 39048 128172

2ndash3 1934 2188 6481 4640 15242

gt3 361 170 005 343 879

Yearmdash2007

A B C D Total 2003ndash2007

0ndash1 18371 16502 56349 166776 257997 minus1170

1ndash2 17834 20210 21289 4205 63538 minus5043

2ndash3 1228 1783 4118 5446 12575 minus1750

gt3 048 208 002 404 662 minus2474

Yearmdash2019

A B C D Total 2003ndash2019

0ndash1 7161 8227 37762 142102 195253 minus3317

1ndash2 10283 12913 24656 4401 52254 minus5923

2ndash3 1506 932 5789 4253 12480 minus1812

gt3 045 141 002 402 590 minus3288

0

1

2

3

4

5

6

7

8

9

10

0

500

1000

1500

2000

2500

3000

3500

Land area 3mdash4m ‐(ha)

Land area 0mdash3m ‐(ha)

Year

Forest cover in elevational bands over time

0‐1m

1‐2m

2‐3m

3‐4m

Figure 5 Visual depictions of two forms of loss one from hurricanes on Hackberry Beach and theother documenting whittling reduction The left column refers to 2003 NAIP imagery while the rightuses 2007 NAIP imagery Black shaded areas indicate current year forest extent and the red hashedareas indicate loss

Forests 2022 13 x FOR PEER REVIEW 12 of 23

Figure 5 Visual depictions of two forms of loss one from hurricanes on Hackberry Beach and the

other documenting whittling reduction The left column refers to 2003 NAIP imagery while the right

uses 2007 NAIP imagery Black shaded areas indicate current year forest extent and the red hashed

areas indicate loss

33 Interaction with Water Level

Water plays a critical role in the natural function of this ecosystem As such we con‐

sidered the interplay of temperature and MSL over a decadal time scale and the local

rates of temperature and gage height from both USGS stations (Figure 6) In Figure 6

forest extent is superimposed for each yearrsquos classification to show visual correspondence

between the two drivers Temperature and rise in sea‐level were negatively correlated

until an inflection point around 2010 after which there was a relatively stronger correla‐

tion increasing to a maximum of 025 The correlative increase lends evidence to the cor‐

respondence between climate and water pressure in coastal areas An increase in the av‐

erage MSL has been observed for the site The two stream gages showed an annualized

MSL rise of 136 mmyear for Vermilion Bay and 59 mmyear for the Calcasieu River

There was a weak but positive trend for temperature to increase slightly from the onset of

the study period Time proved to be a good indicator for increase in water level and for

decrease in forest cover (Figure 6) The lack of tree‐cover samples prohibited many statis‐

tical comparisons but visually the correspondence seems evident

Figure 6 Trends in mean water levels and water temperature over 19 years in Vermillion Parish

Data obtained from the USGS Station 07387050 Vermilion Bay (minus921355 296744) with gage datum

165 m above NAVD88 and from the USGS Station 08017118 Calcasieu River at Cameron LA

(minus933488 298155) with gage datum 107m above NAVD88

Rsup2 = 09453

Rsup2 = 01283

Rsup2 = 06529

08

085

09

095

1

105

11

115

12

125

1995 2000 2005 2010 2015 2020 2025

15

20

25

30

35

40

45

50

Average

water level (m)

Year

Forest cover loss Average

water temp

Loss of tree cover (sqkm) Average water temp (deg C) Average waterlevel (m)

Figure 6 Trends in mean water levels and water temperature over 19 years in Vermillion Parish Dataobtained from the USGS Station 07387050 Vermilion Bay (minus921355 296744) with gage datum 165 mabove NAVD88 and from the USGS Station 08017118 Calcasieu River at Cameron LA (minus933488298155) with gage datum 107m above NAVD88

Forests 2022 13 1132 12 of 22

4 Discussion41 Classification Reliability and Constraints

NAIP imagery proved efficacious for fine-scale analysis in this region shown by thestrong average producerrsquos accuracy and Kappa coefficient This is a positive indicatorthat variability between years or areas did not influence classification accuracies to alarge extent and that the final classification is a reliable interpretation [42] Many otherstudies conducting land-cover classifications using NAIP imagery have also shown robustaccuracies [43ndash45] However there were noted issues seen in certain parts of the workflowA problem preeminent in the initial classification was a limitation of spectral resolution Thisproblem became pronounced in areas with similar shades of green or between dark bluesand black hues Without the use of ancillary datasets the classification accuracy was poorbetween dark water and shadows of trees with considerable overlap (misclassificationsof approximately 50) Mixed land use can also prove problematic in NAIP land-coverclassifications [46] This became especially pronounced in transitional forestmarsh areasin which there was no dominant spectral signature for a particular land use Local variationbetween adjoining image sets highlighted another shortcoming of NAIP imagery Theviewing geometry illumination and time between the capture of neighboring imagetiles created edge effects including artificially high or low pixel values and refractionproblems on water [47] Each yearrsquos dataset was collected by an external contractor causingdifferences in sensor instrumentation and parametrization between years By 2019 many ofthese problems had been mitigated and images were of much higher quality

Compared to a large swath sensor such as Landsat-8 or Sentinel-2 an innate non-uniformity poses problems for pixel-wise classifications of VHR imagery Typically datafrom satellite imagery have been the primary choice for analyzing coastal forests marshesand forests at large [48ndash50] The spaceborne sensors obtain information over eight bandsallowing much finer spectral discrimination of certain classes of objects [51] Even amongstdiverse forest types space-borne Sentinel-2 and ASTER imagery has been used to mapdistinct species clusters of broadleaf and deciduous plant species The large spatial scaleenables the use of these created geospatial products to analyze the efficacy of governmentalreforestation programs in remote areas of the world [52] The usage of normalized differ-ence indices including the Normalized Difference Vegetation Index (NDVI) EnhancedVegetation Index (EVI) and Enhanced Water Index (EWI) can also provide greater confi-dence in the separation of water and marsh features from deciduous broadleaf forests [53]However a spatial resolution of 10m is still too coarse for the detection of remnant andlandscape trees especially those alongside homogenous flat features such as maintainedgrass or water bodies The most practical solution would be to assess this land with ahigher resolution dataset such as unmanned aerial systems (UAS) imagery which cancapture a much finer GSD in a plethora of bands if multispectral or hyperspectral sensorsare used [54] In fact scientists are now pushing the boundaries of the scale of analysisconducted through remote sensing Broad groupings (eg clades or forest types) have beenthe historic norm in the classification of imagery for use in land management A greatermixture of classes allows more accurate measurements for those interested in landscape-scale ecological processes However VHR can be used to map stands at the species levelwhich allows forest managers an unparalleled insight into the dynamics of recruitmentdisease or resource availability [55]

Google Earth Engine allowed us to utilize NAIP imagery at scale which had for sometime been a limiting factor in processing this data source locally Even so some limitationshindered the functional utility of GEE for detailed land-cover classification In our relativelyuniform environment we discriminated between many shades of green When viewinggeometries cause variations in hue different classes begin to overlap within the samespectral regions Solutions using Geographic Object-Based Image Analysis (GEOBIA) cancircumvent this issue yet this segmentation algorithm is currently not supported by GEEMany consider GEOBIA the optimal tool for segmenting landscapes and for the productionof land-cover classifications from this source of imagery [3856] Its successful integration

Forests 2022 13 1132 13 of 22

could substantially elevate the use and utility of mapping within GEE Additionally theallocation of increased processing bandwidth on GEE servers may help overcome somelimits in classification Greater bandwidth allows larger and more diverse training setsin addition to more complex parameterizations of machine learning classifiers If oneis proceeding on local workstations the analysis of NAIP imagery requires an intimateknowledge of data structures and process optimization Custom architectures processingpipelines and simpler binary classifications can allow an expansion of the study area tocover coterminous areas on a larger scale [16]

42 Forest Loss Contextually

We mapped forest cover change dynamics between 2003ndash2019 on the southwest coastof Louisiana 1580 hectares of tree cover 13 of the terrestrial area was converted todifferent land use The total loss of forest cover in the study period was 3573 over sixteenyears which is indicative of a larger rate of forest loss than at sites in a similar physio-geographic region yet is certainly not a global outlier [18] These findings validate thesentiments of people in the region and underscore a major irreversible trend in the area Ourestimate should be considered conservative due to the way we approached the definition ofldquoforest coverrdquo within the landscape Abandoned pastoral land exhibiting dense scrubbinessand shrub cover was demarcated as forest cover even though the target species (live oak)was probably not present Spoil banks which are the byproduct of historic oil explorationin the region indicating possible mortality were also often considered a component offorest cover if the imagery could not indicate the absence of high vegetation The primarymethod of identifying whether or not forest cover was present was the observation of ashadow adjoining the vegetation indicating a vertical structure

The Big Bend region of Florida one of the longest undisturbed coastlines in thecontinental US offers some insight into the vegetation dynamics this region has beenexperiencing It has been the focus of numerous ecological studies has a similar climateand is exposed to storms with similar regularity Raabe and Stumpf [57] found that fromthe early to late 1900s over 82 km2 of forest land was converted to marsh and 66 km2 wasin a transitional state to marsh McCarthy et al [58] document an annual forest cover lossof 744 from 2010ndash2017 an acceleration in the trend within that area A new study bythe same team found rates of loss totaling approximately 10 km2 a year 800 larger thanpreviously stated in the literature [59] Rates of annual mortality ranged from 4ndash16 inSoutheastern Louisiana from 1987 to 1997 [60] In comparison the annualized rate of lossfound in our study never exceeded 5 Plots located in South Carolina that have beenless significantly impacted by MSL rise show rates of loss closer to 1ndash2 in line withour observed rate of loss from 2007ndash2019 However we cannot assume that percentagesof forest loss across the country have been constant within the last century therefore it iscritical to carry out intensive geospatial studies at regular intervals

In the adjoining southeastern part of the state of Louisiana in the Atchafalaya andTerrebonne basins forested wetlands occupy 121871 hectares or 41 of all vegetatedland use [12] In comparison the terrestrial forested land use in our study area was 21in 2019 The wide variation can be interpreted in several ways each with importantimplications Historic rates of deforestation differ in the two regions with a greateremphasis on the preservation of wilderness areas in the Atchafalaya basin [61] TheAtchafalaya and Terrebonne basins also have productive land used in the growth of timberwhich promotes long-term forest cover The regeneration of forests in our study region alsodiffers substantially Chenier ecosystems and traditional alluvial bottomland hardwoodforests are compositionally different in vegetation and soil even when accounting for theirunique hydrologic regimes Communities of live oak may be less resilient to the types offorcings that cause changes in community composition within these ecosystems furtheredby proximity to the sea If stocking density rates are similar to the adjoining basin as theywere in the past we would see an areal extent well over 50000 hectares

Forests 2022 13 1132 14 of 22

The analysis vis-agrave-vis elevation shows weak trends in the manners of loss on eleva-tional gradients Logically in the paradigm of coastal retreat there should be an aggregationof forest resources towards the highest bands of topography in a region Our analysis doesnot follow this chain of thought with the largest losses observed in the 1ndash2 m categoryrather than the lowest 0ndash1 m (Table 2 2007 C 2019 D) While we do see a large loss bypercentage in the highest elevation band it is important to note that a small sample causesinherent variance Forest cover on the highest reaches of the chenier ridges is typically lessthan 1 of total forest cover for that region The reduction can be attributed to the loss of23 features in section A accounting for 33 hectares bordering the Sabine River (Figure 1minus9386286 2973355) The loss of forest cover was driven by intense weather Vegetationin the area was completely destroyed and substantial amounts of sedimentation could beobserved as evident in the 2007 NAIP imagery The only gain in forest cover was seen inthe 2ndash3 m elevation band in the period 2007 in an area that had a high density of pastoralland that was abandoned Forest retreat is often discussed as one method of bufferingforest loss along coasts [62] Unfortunately the geomorphology of this area does not allowgreat spatial movement as the current extent is already located within the highest bandsof elevation

43 Land Use Conversion and Driving Factors

The leading cause of land-use conversion from tree cover was to a saltmarsh ecotype(606) often following mortality and replacement of vegetative biomass The loss offorest cover can largely be typified into two categories The ingression of saltwater causingsalinification within the soil matrix paired with increased flooding caused mortality ofoverstorey vegetation over a multi-year timescale [6364] Hurricanes or other storm eventscaused rapid mortality through wind-throws severe defoliation saltwater inundationduring a storm surge or canopy damage Following intense weather events between2003 and 2007 the vast majority of cheniers located within a 1km buffer of the coast wereunable to support tree cover as seen in Figure 7AB Clearing for industry or developmentalso caused immediate shifts in forest composition as can be seen in Figure 8AB Thischange was more pronounced on reaches A and B especially on Hackberry Beach Thisarea was the site of landfall for Hurricane Rita which caused massive devastation to othercomponents of the ecosystem [6566] Singular storm events have a transformative impacton vegetation communities and the weight of this driver on annual rates of forest lossis a topic that should be explored further Wayward shifts in species composition are atrend that has been observed globally especially in forests that border some source of waterTrees like Juniperus virginiana L that have distinctive water-use patterns in comparison toother coastal tree species are susceptible to salinification at much faster rates as evidencedthrough isotope analyses [67] As with many ecological systems the entangled effectsof climate and vegetation require diverse means of looking at change Isotope analysesdendrochronology field sensors and remote sensing data are needed to characterize land-use change at the stand and community levels

Large forest remnants such as Cow Island (minus9283581 2973842) Tiger Island (minus92784392972832) and Grand Chenier Ridge (minus9281175 2972986) among others have diminishedmore slowly By 2019 these stands had lost an average of 15 of their maximum extentThe land use changed to both marsh and managed pasture largely dependent on thesurrounding land use The conversion to managed pasture (282) accounted for a largerthan expected amount of land-cover change Many of these changes were in areas wheretree cover may have been more ldquoornamentalrdquo in nature Trees beside houses or alongsideroads are some of the largest specimens of live-oak to be found However these trees areoften solitary and perhaps more vulnerable to wind-borne disturbance The concern is thattheir loss due to abiotic drivers may not be easily mitigated leading to a permanent declineOur data support evidence on the whittling of forest remnants and the eventual fate ofthese trees becoming lsquorelict forestsrsquo unable to recruit new generations in the short term andin the long-term total disappearance of these cheniers along the Louisiana Gulf Coast [68]

Forests 2022 13 1132 15 of 22Forests 2022 13 x FOR PEER REVIEW 16 of 23

Figure 7 Visual depictions of two forms of loss one from hurricanes on Hackberry Beach (AB) and

the other documenting whittling reduction (CD) The left column displays 2003 NAIP imagery and

the right is 2007 NAIP imagery Black shaded areas indicate current year forest extent red hashed

areas indicate a loss

Figure 7 Visual depictions of two forms of loss one from hurricanes on Hackberry Beach (AB) andthe other documenting whittling reduction (CD) The left column displays 2003 NAIP imagery andthe right is 2007 NAIP imagery Black shaded areas indicate current year forest extent red hashedareas indicate a loss

Beyond loss in forest cover there is a noticeable trend towards an altered hydrologywhere pastoral areas and intact forest remnants are associated with pockets of standingwater An increase in the correlative factor between temperature and water levels could bea worrying trend a direct conversion of tree cover to water at 34 could be an unforeseenoutcome Chenier ridges in their geomorphic context should not support non-ephemeralponding It is not documented how increased tidal influence which is one factor of risingMSL influences the hydrology of this ecosystem and its associated communities Albeitin a more inland context Magolan and Halls [69] documented a transition of over 40 ofland to emergent marsh from closed-canopy cover from 1949 to 2018 due to hydrologicinfluence Recent rates of relative sea rise have been documented as high as 119 mmyearcompared to background Holocene rates of 17 mmyear [70] This has resulted in a rate oflateral coastal forest retreat between 2ndash14 times greater than pre-industrial levels [26] Therate of shoreline retreat in this region increased from ~61 to ~94 myear based on the 100-and 30-year moving averages amounting to over 10000 hectares of land loss every yearfrom 1800 to 2005 [27]

Forests 2022 13 1132 16 of 22Forests 2022 13 x FOR PEER REVIEW 17 of 23

Figure 8 A depiction of clearing for industry in (AB) and a depiction of forest whittling from (CD)

The black extent represents the current forest area red hashed lines indicate a loss The left panels

show 2007 NAIP imagery while the right are 2019 NAIP imagery

Large forest remnants such as Cow Island (minus9283581 2973842) Tiger Island

(minus9278439 2972832) and Grand Chenier Ridge (minus9281175 2972986) among others have

diminished more slowly By 2019 these stands had lost an average of 15 of their maxi‐

mum extent The land use changed to both marsh and managed pasture largely depend‐

ent on the surrounding land use The conversion to managed pasture (282) accounted

for a larger than expected amount of land‐cover change Many of these changes were in

areas where tree cover may have been more ldquoornamentalrdquo in nature Trees beside houses

or alongside roads are some of the largest specimens of live‐oak to be found However

these trees are often solitary and perhaps more vulnerable to wind‐borne disturbance The

concern is that their loss due to abiotic drivers may not be easily mitigated leading to a

permanent decline Our data support evidence on the whittling of forest remnants and

the eventual fate of these trees becoming lsquorelict forestsrsquo unable to recruit new generations

in the short term and in the long‐term total disappearance of these cheniers along the

Louisiana Gulf Coast [68]

Beyond loss in forest cover there is a noticeable trend towards an altered hydrology

where pastoral areas and intact forest remnants are associated with pockets of standing

water An increase in the correlative factor between temperature and water levels could

be a worrying trend a direct conversion of tree cover to water at 34 could be an unfore‐

seen outcome Chenier ridges in their geomorphic context should not support non‐ephem‐

eral ponding It is not documented how increased tidal influence which is one factor of

Figure 8 A depiction of clearing for industry in (AB) and a depiction of forest whittling from (CD)The black extent represents the current forest area red hashed lines indicate a loss The left panelsshow 2007 NAIP imagery while the right are 2019 NAIP imagery

44 Future of Chenier Forests

The chenier complexes of Louisiana do not have the natural benefaction of maintaininglarge original tracts untouched by axe or dozer The extent map through the years (Figure 9)exemplifies the scattered nature of forest remnants The largest single continuous chenierforest (Grand Chenier Ridgemdash185 ha) encompasses less than 1 of the total forest areaExposed forest edges pose a problem for the continued integrity of the stand as adaptedspecies fail to compete in these new microclimates [71] Slow-growing saplings of desirabletree species (live oak hackberry) are subject to unfavorable growing conditions fromweather and light availability Vegetation surveys following aspect and inundation alongthe Florida coast have all but confirmed the eventual extirpation of overstory vegetationthrough poor recruitment of native species [31] Chinese tallow (Triadica sebifera) a non-native tree is likely to fill empty niche space This aggressive invader is problematicthroughout the Gulf Coastal Plain from Texas to Florida [72] The tree has a wide meansof dispersal being primarily distributed through frugivory via avian populations A highrate of germination compounded with quick growth and a sexual maturity of less thanthree years make it a difficult species to control Its invasive capability is seen in nearlyall forest types in the region although it is more pervasive in coastal forests than inlandModeling studies show a high likelihood of Chinese tallow becoming the dominant specieswithin this ecosystem and in surrounding areas [73] Future publications from this lab will

Forests 2022 13 1132 17 of 22

provide more in-depth data and analysis on the state of existing stands and recruitmenttrends within remnants of the Chenier Plains via field sampling

Louisiana has enacted a historic response to the problem of land-loss following a publicoutcry after devastating storms in 2005 The Coastal Protection and Restoration Authority(CPRA) have been tasked with enacting a management plan to combat coastal erosion TheComprehensive Master Plan details proposals to create marshes build sediment diversionsform new hydrologic structures and promote barrier island restoration through builtand natural means As of 2017 over 135 projects had been completed impacting over14568 ha [74] An update to this document is due to be released in 2023 The projecthas an overall operating budget of over 50 billion US dollars with equal shares dedicatedto restoration and risk reduction The primary funding source is a settlement associatedwith the Deepwater Horizon Oil Spill in addition to various initiatives at the state andfederal levels However there are no current plans to supplement vegetation or performany geoengineering work on the cheniers for afforestation

Coastal wetlands rank among the most important ecosystems in terms of the valuationof ecosystem services [75] Provisioning services such as water filtration or storm-surgeprotection are commonly considered when discussing coastal areas Beyond these com-monalities chenier forests provide vital wildlife habitats being the first encountered sourceof vertical structure for migratory neo-tropical birds and migrating monarch butterflies inthe Mississippi alluvial valley flyway [76] This en-route habitat is of critical importancedue to its closeness to the Gulf of Mexico During favorable weather conditions 10 ofbirds migrating through the Gulf use these forests as a stopover spot rising dramatically to80 during adverse weather conditions [29] With losses mounting many species may notbe able to seek refuge during storms The area also has a unique suite of amphibians andreptiles being home to 13 obligate chenier forest species of herpetofauna [77]

Lu et al [78] used the eddy covariance method (ECM) to calculate strong carbon sinkeffects for a chenier ecosystem driven by forest vegetation Net energy production valueswere significantly higher than surrounding marshland signifying the importance of densevegetative cover on a per-unit basis Counter to this there is also strong evidence thatdegraded forest ecosystems act as net carbon sources A study in North Carolina also usingECM found that a seasonally inundated tide-influenced forest was a net carbon sourceannually and only functioned as a sink for 3 months of the growing year [79] Similarly astudy in the northeastern part of the state showed net negative rates of carbon sequestrationfor an ephemerally flooded forest [80] Unique environments demand individual attentiona characterization of this ecosystem for nitrogen or carbon flux with respect to a similarsystem may not be functionally sound [81]

Forests 2022 13 1132 18 of 22Forests 2022 13 x FOR PEER REVIEW 19 of 23

Figure 9 The total land area classified in GEE for the years 2003 2007 and 2019 Light green repre‐

sents the marsh area tan represents pasture The water layer has been removed for purposes of

clarity The dark black outline is the outline of the forest cover for the respective year

Louisiana has enacted a historic response to the problem of land‐loss following a

public outcry after devastating storms in 2005 The Coastal Protection and Restoration

Authority (CPRA) have been tasked with enacting a management plan to combat coastal

erosion The Comprehensive Master Plan details proposals to create marshes build sedi‐

ment diversions form new hydrologic structures and promote barrier island restoration

through built and natural means As of 2017 over 135 projects had been completed im‐

pacting over 14568 ha [74] An update to this document is due to be released in 2023 The

project has an overall operating budget of over 50 billion US dollars with equal shares

dedicated to restoration and risk reduction The primary funding source is a settlement

associated with the Deepwater Horizon Oil Spill in addition to various initiatives at the

state and federal levels However there are no current plans to supplement vegetation or

perform any geoengineering work on the cheniers for afforestation

Coastal wetlands rank among the most important ecosystems in terms of the valua‐

tion of ecosystem services [75] Provisioning services such as water filtration or storm‐

surge protection are commonly considered when discussing coastal areas Beyond these

commonalities chenier forests provide vital wildlife habitats being the first encountered

source of vertical structure for migratory neo‐tropical birds and migrating monarch but‐

terflies in the Mississippi alluvial valley flyway [76] This en‐route habitat is of critical

importance due to its closeness to the Gulf of Mexico During favorable weather condi‐

tions 10 of birds migrating through the Gulf use these forests as a stopover spot rising

Figure 9 The total land area classified in GEE for the years 2003 2007 and 2019 Light greenrepresents the marsh area tan represents pasture The water layer has been removed for purposes ofclarity The dark black outline is the outline of the forest cover for the respective year

5 Conclusions

The cheniers of southwestern Louisiana were found to be declining in size at a concern-ing rate Many of the driving factors are ecosystem-wide trends that have been increasingin intensity within the last century The results of this study suggest that coastal forestsare an ecosystem in peril and the ecology of such systems should be a closely monitoredsubject The 365 loss of forest cover is an alarming but not wholly unexpected result ofmany decades of intense perturbation and human exploitation A dynamic of continuedforest loss is evident Unless significant investment is made in engineering solutions tomitigate storm effects and land subsidence we will continue to see the decline of chenierforests in Louisiana Through targeted mitigation governmental programs such as theWetland or Conservation Reserve Program may be able to supplement existing forestsas well as the incomes of local people who have lost their livelihoods In this study wehave begun the process of accurate areal estimates of forest extent and have shed light onecosystem processes in the region As this community continues to diminish we see theforest not by the trees but rather the lack of them

Forests 2022 13 1132 19 of 22

Author Contributions Conceptualization PT methodology PT software PT and PR validationPT and PR formal analysis PT PR and PT writingmdashoriginal draft preparation PT and JBwritingmdashreview and editing PT and JB visualization PT and JB supervision JB All authorshave read and agreed to the published version of the manuscript

Funding This research received no external funding

Data Availability Statement The data will be published in and stored on the Harvard Dataverse athttpsdataverseharvardedudatasetxhtmlpersistentId=doi107910DVN3J7SNF (accessed on1 May 2022)

Acknowledgments The authors would like to recognize the hard work of undergraduate studentsParusha Raut and Subash Sapkota in manually validating thousands of points and creating numeroustraining sets We also extend thanks to our institution and numerous people within the lab who leantboth mental and emotional fortitude

Conflicts of Interest The authors declare no conflict of interest

References1 Michener WK Blood ER Bildstein KL Brinson MM Gardner LR Climate change hurricanes and tropical storms and

rising sea level in coastal wetlands Ecol Appl 1997 7 770ndash801 [CrossRef]2 Donoghue JF Sea level history of the northern Gulf of Mexico coast and sea level rise scenarios for the near future Clim Chang

2011 107 17ndash33 [CrossRef]3 Stanturf JA Goodrick SL Outcalt KW Disturbance and coastal forests A strategic approach to forest management in

hurricane impact zones For Ecol Manag 2007 250 119ndash135 [CrossRef]4 Augustinus P Cheniers and chenier plains A general introduction Mar Geol 1989 90 219ndash229 [CrossRef]5 Fu Z Wang P Sun J Lu Z Yang H Liu J Li T Composition seasonal variation and salinization characteristics of soil

salinity in the Chenier Island of the Yellow River Delta Glob Ecol Conserv 2020 24 e01318 [CrossRef]6 Marques IG Campelo F Rivaes R Albuquerque A Ferreira MT Rodriacuteguez-Gonzaacutelez PM Tree rings reveal long-term

changes in growth resilience in Southern European riparian forests Dendrochronologia 2018 55 167ndash176 [CrossRef]7 Daigle JJ Griffith GE Omernik JM Faulkner PL McCulloh RP Handley LR Smith LM Chapman SS Ecoregions of

Louisiana (Color Poster with Map Descriptive Text Summary Tables and Photographs) US Geological Survey Reston VI USA 20068 McBride RA Taylor MJ Byrnes MR Coastal morphodynamics and Chenier-Plain evolution in southwestern Louisiana USA

A geomorphic model Geomorphology 2007 88 367ndash422 [CrossRef]9 Gosselink JG An Ecological Characterization Study of the Chenier Plain Coastal Ecosystem of Louisiana and Texas Narrative Report

National Coastal Ecosystems Team Office of Biological Services Fish and Wildlife Service US Department of the InteriorWashington DC USA 1979 Volume 1

10 Blum MD Roberts HH The Mississippi delta region Past present and future Annu Rev Earth Planet Sci 2012 40 655ndash683[CrossRef]

11 Hansen MC Potapov PV Moore R Hancher M Turubanova SA Tyukavina A Townshend J High-resolution globalmaps of 21st-century forest cover change Science 2013 342 850ndash853 [CrossRef]

12 Doyle TW Krauss KW Conner WH From AS Predicting the retreat and migration of tidal forests along the northern Gulfof Mexico under sea-level rise For Ecol Manag 2010 259 770ndash777 [CrossRef]

13 Thomas N Simard M Castantildeeda-Moya E Byrd K Windham-Myers L Bevington A Twilley R High-resolution mappingof biomass and distribution of marsh and forested wetlands in southeastern coastal Louisiana Int J Appl Earth Obs Geoinf 201980 257ndash267 [CrossRef]

14 Desantis LR Bhotika S Williams K Putz FE Sea-level rise and drought interactions accelerate forest decline on the GulfCoast of Florida USA Glob Chang Biol 2007 13 2349ndash2360 [CrossRef]

15 Boyd DS Foody G Ripple W Evaluation of approaches for forest cover estimation in the Pacific Northwest USA usingremote sensing Appl Geogr 2002 22 375ndash392 [CrossRef]

16 Valjarevic A Djekic T Stevanovic V Ivanovic R Jandzikovic B GIS numerical and remote sensing analyses of forest changesin the Toplica region for the period of 1953ndash2013 Appl Geogr 2018 92 131ndash139 [CrossRef]

17 He T Sun YJ Xu JD Wang X Hu C Enhanced land usecover classification using support vector machines and fuzzyk-means clustering algorithms J Appl Remote Sens 2014 8 083636 [CrossRef]

18 Michez A Pieacutegay H Lisein J Claessens H Lejeune P Classification of riparian forest species and health condition usingmulti-temporal and hyperspatial imagery from unmanned aerial systems Environ Monit Assess 2016 188 146 [CrossRef][PubMed]

19 Hampton SE Strasser C Tewksbury J Gram W Budden A Batcheller A Duke C Porter J Big data and the future ofecology Front Ecol Environ 2013 11 156ndash162 [CrossRef]

20 Curtis PG Slay CM Harris NL Tyukavina A Hansen MC Classifying drivers of global forest loss Science 2018 3611108ndash1111 [CrossRef]

Forests 2022 13 1132 7 of 22Forests 2022 13 x FOR PEER REVIEW 7 of 23

Figure 3 Process workflow used in the study The section bracketed in blue was processed using

Google Earth Engine and the section bracketed in green was carried out using QGIS Blue blocks

represent raw inputs into the workflow green blocks are the final outputs

24 Post‐Processing of GEE Outputs

Two‐layer masks created from the NHD were used to refine the classified output

The masks were originally ascertained and modified in a vector format and were con‐

verted to a raster‐type format when used for modifying the GEE outputs The two layers

which were in the same projection and scale were employed to perform mathematical

operations on a per‐pixel basis Raster calculations can be applied to overlapping areas

according to their underlying geospatial properties affecting how data is represented [40]

As the NHD is incomplete regarding wetlands and pond features care was taken to dig‐

itize areas of the 2003 NAIP imagery that were wholly saltmarsh or water [41] An as‐

sumption was made that any saltmarsh or water feature present in 2003 would maintain

its non‐forested land use during the study period This allowed a set standard for the

modification of GEE outputs The saltmarsh mask was set so that any pixel registering as

a tree would be converted into saltmarsh without the modification of any other land‐

cover class The water mask was absolute in terms of pixel modification All pixels over‐

lapping the mask were converted regardless of their class Tree cover vectors were then

extracted from each yearrsquos classification All cover area calculations including different

Figure 3 Process workflow used in the study The section bracketed in blue was processed usingGoogle Earth Engine and the section bracketed in green was carried out using QGIS Blue blocksrepresent raw inputs into the workflow green blocks are the final outputs

The creation of points for sections A B C and D to assess accuracy was carried outusing three tools in the QGIS Processing Toolbox First a lsquoDifferencersquo was run to extracttree cover in an original yearrsquos dataset but not in the succeeding year A lsquoDissolversquo was runon this product to create one large MultiPolygon object Finally lsquoRandom Pointsrsquo inside thePolygon function was used to create 200 point features that were manually validated Threecolumns indicating original year accuracy (YN) whether land-use change was correctlyattributed (YN) and what it had changed to (six classes) were populated for each pointThe last metric had absolute accuracy irrespective of the yearrsquos classification as it wasassigned visually Validation was carried out against the respective yearrsquos NAIP datasetBecause we did not assess land-use change in 2019 only accuracy was computed for thatA Cohenrsquos Kappa value was used to assess interrater reliability using Equation (2) wherePr(a) represents the actual observed agreement and Pr(e) represents chance agreement Theequations were derived from and expanded on by McHugh [42]

κ =Pr(a)minus Pr(e)

1 minus Pr(e)(2)

Forests 2022 13 1132 8 of 22

The CoNED DEM was subdivided into 0ndash1 1ndash2 2ndash3 and 3+ meter classes using theRaster Calculator tool in QGIS The class values were then vectorized to run a geospatialdifference iteratively with each yearrsquos tree cover vectors The difference was validatedand then used for area calculations regarding differential rates of loss for elevation gra-dients Gage height and temperature values were extracted from both stream gages andwere combined into a site average We used gage height as a proxy for MSL due to thespatial proximity to the coast and study sites as well as for considering the response tofreshwater influence which is an important hydrological component of the Chenier PlainsAverage values for the three wettest months (July August and September) were used inthe regressions and analysis

3 Results31 Forest Loss Transition and Classification Accuracy

From 2003 to 2019 an overall decline of 3573 was seen for forest cover and 2238for pasture The annualized rate of loss for forest cover was approximately 2 higherwithin the period 2003ndash2007 losing 42 of tree cover per year assuming a constant change(Table 1) Losses were greatest in the western two reaches of the Chenier Plains averagingover 213 more than sections C or D The largest amount of tree cover was located withinsection D with significant contributions from spoil banks Chenier Au Tigre (locationminus9220665 2956885) had the largest total tree cover within the study area albeit spreadover a large expanse Annual loss rates were more constant in sections containing primarilychenier ridge forests in contrast to swamp or spoil bank forests Section D has a largesaltmarsh (1ndash10 km) bisecting the chenier from the coast which contributed to a largerland and forest area There was a cumulative gain of 231 for the saltmarshes adding2528 km2 In the 2003ndash2007 period the only gains in land cover type were in saltmarshwhich was maintained into 2019 (Figure 4)

We surveyed a total of 2123 points across the study area Producerrsquos accuracy was916 816 and 918 for the years 2003 2007 and 2019 respectively The CohenrsquosKappa indexes of 091 082 and 093 for 2003 2007 and 2019 respectively showed strongagreement between raters across all years We considered any value above 085 to be a validassessment The year 2007 scored lower in both PA and Kappa values likely due to thenature of the base imagery however it still fell into an acceptable range Extreme contrastand saturation in the 2007 NAIP imagery caused larger aggregations of tree cover pixels asgrasses and trees have a similar hue accentuated by the pixel reduction post-classificationValidation points that were very close to tree cover but not categorized as such were notconsidered accurate which contributed to this source of error The 2019 classifications weregeneralized as being more correct from the onset with little need for QA

Changes in land-cover were not evenly distributed across different sections as canbe seen in Table 1 Typically cheniers are more proximally located to the coast withinthe eastern reaches with depositional bars spreading the length One noted outlier in theassessment of change was observed from 2003 to 2007 in section B where the loss of pastureaccounted for over 656 The quantitative measurements for changes in land cover werealso ascertained from these samples We observed changes of 606 282 34 and 76for saltmarsh pasture water and impervious area respectively

Observed results deviated from hypothesized values specifically in the large propor-tion of conversion to pasture A lone tree existing one year after transitioning to an openfield was common across all areas It was assumed the percentage of conversion fromtree to saltmarsh would be even greater However impervious area transitions were alsohigh given that considerable forest cover directly adjoins roads and houses in this areaThe underlying structure can then be seen following the mortality of vegetation Changeto water was also observed for several reasons in areas along waterbodies a loss of treecover exposed the water while severe areas of deforestation prompted a change to a morewetland hydrology which also required water replacement pixels

Forests 2022 13 1132 9 of 22

Table 1 The amount and percentage loss of three land-cover typesmdashtree marsh and pasturemdashovereach section and the total area spanning two parishes Calcasieu and Vermillion on the westernLouisiana Gulf Coast Sections A B C and D represent areas delineated for this study within thetwo parishes (for details see Figure 1)

Landcover 2003(ha)

2007(ha)

2019(ha)

Net Change ()(2003ndash2019)

TreeA 46274 37639 17988 minus6113B 64389 38634 27486 minus5731C 99623 82791 65050 minus3470D 231775 215192 173580 minus2511

Total 442061 374256 284104 minus3573

MarshA 1182244 1148060 1238636 477B 1113045 1084738 1395453 2537C 2051730 2277785 2204412 744D 6598632 6839319 6359952 minus362

Total 10945651 11349903 11198453 231

PastureA 510146 460323 324348 minus3642B 136395 89019 77021 minus4353C 696209 754500 653027 minus620D 1152047 1036039 881960 minus2344

Total 2494796 2339881 1936355 minus2238

Forests 2022 13 x FOR PEER REVIEW 9 of 23

adding 2528 km2 In the 2003ndash2007 period the only gains in land cover type were in salt‐

marsh which was maintained into 2019 (Figure 4)

Table 1 The amount and percentage loss of three land‐cover typesmdashtree marsh and pasturemdashover

each section and the total area spanning two parishes Calcasieu and Vermillion on the western

Louisiana Gulf Coast Sections A B C and D represent areas delineated for this study within the

two parishes (for details see Figure 1)

Landcover 2003

(ha)

2007

(ha)

2019

(ha)

Net Change ()

(2003ndash2019)

Tree

A 46274 37639 17988 minus6113

B 64389 38634 27486 minus5731

C 99623 82791 65050 minus3470

D 231775 215192 173580 minus2511

Total 442061 374256 284104 minus3573

Marsh

A 1182244 1148060 1238636 477

B 1113045 1084738 1395453 2537

C 2051730 2277785 2204412 744

D 6598632 6839319 6359952 minus362

Total 10945651 11349903 11198453 231

Pasture

A 510146 460323 324348 minus3642

B 136395 89019 77021 minus4353

C 696209 754500 653027 minus620

D 1152047 1036039 881960 minus2344

Total 2494796 2339881 1936355 minus2238

0

20000

40000

60000

80000

100000

120000

0

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

Land area ‐MarshPasture (ha)

Land area ‐Forest Cover (ha)

Year

Land‐cover dynamics over time

West East Marsh Pasture

Figure 4 A representation of the changes in three respective land-cover types (forest cover pasturemarsh) over the period 2003ndash2019 For forest cover the three western sections are represented as Westand D is represented as East Light green indicates marsh yellow represents pasture dark green andhatched green show forest cover

Forests 2022 13 1132 10 of 22

32 Rates of Loss with Respect to Elevation

A net decrease in forest cover was seen across all elevation categories (Table 2 Figure 5)The largest losses were in the 1ndash2 m category with a net reduction of 593 from 2003 to2019 There was a temporal shift in the rate of change with the greatest amount of lossbetween 2003ndash2007 contributed by section D Values within the 2ndash3 m category remainedsteady from 2007 to 2019 with a reduction of only 076 The large majority (749) ofthe remaining forest was seen to be located within the 0ndash1 m band The losses were notevenly distributed within this band buffered to some degree by section D A comparisonof sections A vs C shows a decrease of 493 with losses centered around the period2007ndash2019 One issue to note is that the GSD for the DEM was 3 m which is wider thansome spoil banks While the local elevation may in fact rise to 2ndash3 m the value was reduceddue to the effect of averaging The losses in the 3 m+ band are discussed below a noteshould be made of the limited representation Overall the losses varied both spatially andtemporally across the region largely separated into eastern and western reaches

Table 2 Differences in the amount of forest area within each elevation band and the associatedpercentage loss of forest cover between years Sections A B C and D represent areas delineated forthis study within the two parishes (for details see Figure 1)

Sites and Forest Cover (ha)Percentage

LossYearmdash2003

Elevation (m) A B C D Total

0ndash1 13083 25525 66334 187236 2921781ndash2 27168 36374 25583 39048 1281722ndash3 1934 2188 6481 4640 15242gt3 361 170 005 343 879

Yearmdash2007A B C D Total 2003ndash2007

0ndash1 18371 16502 56349 166776 257997 minus11701ndash2 17834 20210 21289 4205 63538 minus50432ndash3 1228 1783 4118 5446 12575 minus1750gt3 048 208 002 404 662 minus2474

Yearmdash2019A B C D Total 2003ndash2019

0ndash1 7161 8227 37762 142102 195253 minus33171ndash2 10283 12913 24656 4401 52254 minus59232ndash3 1506 932 5789 4253 12480 minus1812gt3 045 141 002 402 590 minus3288

33 Interaction with Water Level

Water plays a critical role in the natural function of this ecosystem As such we consid-ered the interplay of temperature and MSL over a decadal time scale and the local rates oftemperature and gage height from both USGS stations (Figure 6) In Figure 6 forest extentis superimposed for each yearrsquos classification to show visual correspondence between thetwo drivers Temperature and rise in sea-level were negatively correlated until an inflectionpoint around 2010 after which there was a relatively stronger correlation increasing to amaximum of 025 The correlative increase lends evidence to the correspondence betweenclimate and water pressure in coastal areas An increase in the average MSL has been ob-served for the site The two stream gages showed an annualized MSL rise of 136 mmyearfor Vermilion Bay and 59 mmyear for the Calcasieu River There was a weak but positivetrend for temperature to increase slightly from the onset of the study period Time provedto be a good indicator for increase in water level and for decrease in forest cover (Figure 6)The lack of tree-cover samples prohibited many statistical comparisons but visually thecorrespondence seems evident

Forests 2022 13 1132 11 of 22

Forests 2022 13 x FOR PEER REVIEW 11 of 23

Table 2 Differences in the amount of forest area within each elevation band and the associated

percentage loss of forest cover between years Sections A B C and D represent areas delineated for

this study within the two parishes (for details see Figure 1)

Sites and Forest Cover (Ha)

Percentage Loss Yearmdash2003

Elevation (m) A B C D Total

0ndash1 13083 25525 66334 187236 292178

1ndash2 27168 36374 25583 39048 128172

2ndash3 1934 2188 6481 4640 15242

gt3 361 170 005 343 879

Yearmdash2007

A B C D Total 2003ndash2007

0ndash1 18371 16502 56349 166776 257997 minus1170

1ndash2 17834 20210 21289 4205 63538 minus5043

2ndash3 1228 1783 4118 5446 12575 minus1750

gt3 048 208 002 404 662 minus2474

Yearmdash2019

A B C D Total 2003ndash2019

0ndash1 7161 8227 37762 142102 195253 minus3317

1ndash2 10283 12913 24656 4401 52254 minus5923

2ndash3 1506 932 5789 4253 12480 minus1812

gt3 045 141 002 402 590 minus3288

0

1

2

3

4

5

6

7

8

9

10

0

500

1000

1500

2000

2500

3000

3500

Land area 3mdash4m ‐(ha)

Land area 0mdash3m ‐(ha)

Year

Forest cover in elevational bands over time

0‐1m

1‐2m

2‐3m

3‐4m

Figure 5 Visual depictions of two forms of loss one from hurricanes on Hackberry Beach and theother documenting whittling reduction The left column refers to 2003 NAIP imagery while the rightuses 2007 NAIP imagery Black shaded areas indicate current year forest extent and the red hashedareas indicate loss

Forests 2022 13 x FOR PEER REVIEW 12 of 23

Figure 5 Visual depictions of two forms of loss one from hurricanes on Hackberry Beach and the

other documenting whittling reduction The left column refers to 2003 NAIP imagery while the right

uses 2007 NAIP imagery Black shaded areas indicate current year forest extent and the red hashed

areas indicate loss

33 Interaction with Water Level

Water plays a critical role in the natural function of this ecosystem As such we con‐

sidered the interplay of temperature and MSL over a decadal time scale and the local

rates of temperature and gage height from both USGS stations (Figure 6) In Figure 6

forest extent is superimposed for each yearrsquos classification to show visual correspondence

between the two drivers Temperature and rise in sea‐level were negatively correlated

until an inflection point around 2010 after which there was a relatively stronger correla‐

tion increasing to a maximum of 025 The correlative increase lends evidence to the cor‐

respondence between climate and water pressure in coastal areas An increase in the av‐

erage MSL has been observed for the site The two stream gages showed an annualized

MSL rise of 136 mmyear for Vermilion Bay and 59 mmyear for the Calcasieu River

There was a weak but positive trend for temperature to increase slightly from the onset of

the study period Time proved to be a good indicator for increase in water level and for

decrease in forest cover (Figure 6) The lack of tree‐cover samples prohibited many statis‐

tical comparisons but visually the correspondence seems evident

Figure 6 Trends in mean water levels and water temperature over 19 years in Vermillion Parish

Data obtained from the USGS Station 07387050 Vermilion Bay (minus921355 296744) with gage datum

165 m above NAVD88 and from the USGS Station 08017118 Calcasieu River at Cameron LA

(minus933488 298155) with gage datum 107m above NAVD88

Rsup2 = 09453

Rsup2 = 01283

Rsup2 = 06529

08

085

09

095

1

105

11

115

12

125

1995 2000 2005 2010 2015 2020 2025

15

20

25

30

35

40

45

50

Average

water level (m)

Year

Forest cover loss Average

water temp

Loss of tree cover (sqkm) Average water temp (deg C) Average waterlevel (m)

Figure 6 Trends in mean water levels and water temperature over 19 years in Vermillion Parish Dataobtained from the USGS Station 07387050 Vermilion Bay (minus921355 296744) with gage datum 165 mabove NAVD88 and from the USGS Station 08017118 Calcasieu River at Cameron LA (minus933488298155) with gage datum 107m above NAVD88

Forests 2022 13 1132 12 of 22

4 Discussion41 Classification Reliability and Constraints

NAIP imagery proved efficacious for fine-scale analysis in this region shown by thestrong average producerrsquos accuracy and Kappa coefficient This is a positive indicatorthat variability between years or areas did not influence classification accuracies to alarge extent and that the final classification is a reliable interpretation [42] Many otherstudies conducting land-cover classifications using NAIP imagery have also shown robustaccuracies [43ndash45] However there were noted issues seen in certain parts of the workflowA problem preeminent in the initial classification was a limitation of spectral resolution Thisproblem became pronounced in areas with similar shades of green or between dark bluesand black hues Without the use of ancillary datasets the classification accuracy was poorbetween dark water and shadows of trees with considerable overlap (misclassificationsof approximately 50) Mixed land use can also prove problematic in NAIP land-coverclassifications [46] This became especially pronounced in transitional forestmarsh areasin which there was no dominant spectral signature for a particular land use Local variationbetween adjoining image sets highlighted another shortcoming of NAIP imagery Theviewing geometry illumination and time between the capture of neighboring imagetiles created edge effects including artificially high or low pixel values and refractionproblems on water [47] Each yearrsquos dataset was collected by an external contractor causingdifferences in sensor instrumentation and parametrization between years By 2019 many ofthese problems had been mitigated and images were of much higher quality

Compared to a large swath sensor such as Landsat-8 or Sentinel-2 an innate non-uniformity poses problems for pixel-wise classifications of VHR imagery Typically datafrom satellite imagery have been the primary choice for analyzing coastal forests marshesand forests at large [48ndash50] The spaceborne sensors obtain information over eight bandsallowing much finer spectral discrimination of certain classes of objects [51] Even amongstdiverse forest types space-borne Sentinel-2 and ASTER imagery has been used to mapdistinct species clusters of broadleaf and deciduous plant species The large spatial scaleenables the use of these created geospatial products to analyze the efficacy of governmentalreforestation programs in remote areas of the world [52] The usage of normalized differ-ence indices including the Normalized Difference Vegetation Index (NDVI) EnhancedVegetation Index (EVI) and Enhanced Water Index (EWI) can also provide greater confi-dence in the separation of water and marsh features from deciduous broadleaf forests [53]However a spatial resolution of 10m is still too coarse for the detection of remnant andlandscape trees especially those alongside homogenous flat features such as maintainedgrass or water bodies The most practical solution would be to assess this land with ahigher resolution dataset such as unmanned aerial systems (UAS) imagery which cancapture a much finer GSD in a plethora of bands if multispectral or hyperspectral sensorsare used [54] In fact scientists are now pushing the boundaries of the scale of analysisconducted through remote sensing Broad groupings (eg clades or forest types) have beenthe historic norm in the classification of imagery for use in land management A greatermixture of classes allows more accurate measurements for those interested in landscape-scale ecological processes However VHR can be used to map stands at the species levelwhich allows forest managers an unparalleled insight into the dynamics of recruitmentdisease or resource availability [55]

Google Earth Engine allowed us to utilize NAIP imagery at scale which had for sometime been a limiting factor in processing this data source locally Even so some limitationshindered the functional utility of GEE for detailed land-cover classification In our relativelyuniform environment we discriminated between many shades of green When viewinggeometries cause variations in hue different classes begin to overlap within the samespectral regions Solutions using Geographic Object-Based Image Analysis (GEOBIA) cancircumvent this issue yet this segmentation algorithm is currently not supported by GEEMany consider GEOBIA the optimal tool for segmenting landscapes and for the productionof land-cover classifications from this source of imagery [3856] Its successful integration

Forests 2022 13 1132 13 of 22

could substantially elevate the use and utility of mapping within GEE Additionally theallocation of increased processing bandwidth on GEE servers may help overcome somelimits in classification Greater bandwidth allows larger and more diverse training setsin addition to more complex parameterizations of machine learning classifiers If oneis proceeding on local workstations the analysis of NAIP imagery requires an intimateknowledge of data structures and process optimization Custom architectures processingpipelines and simpler binary classifications can allow an expansion of the study area tocover coterminous areas on a larger scale [16]

42 Forest Loss Contextually

We mapped forest cover change dynamics between 2003ndash2019 on the southwest coastof Louisiana 1580 hectares of tree cover 13 of the terrestrial area was converted todifferent land use The total loss of forest cover in the study period was 3573 over sixteenyears which is indicative of a larger rate of forest loss than at sites in a similar physio-geographic region yet is certainly not a global outlier [18] These findings validate thesentiments of people in the region and underscore a major irreversible trend in the area Ourestimate should be considered conservative due to the way we approached the definition ofldquoforest coverrdquo within the landscape Abandoned pastoral land exhibiting dense scrubbinessand shrub cover was demarcated as forest cover even though the target species (live oak)was probably not present Spoil banks which are the byproduct of historic oil explorationin the region indicating possible mortality were also often considered a component offorest cover if the imagery could not indicate the absence of high vegetation The primarymethod of identifying whether or not forest cover was present was the observation of ashadow adjoining the vegetation indicating a vertical structure

The Big Bend region of Florida one of the longest undisturbed coastlines in thecontinental US offers some insight into the vegetation dynamics this region has beenexperiencing It has been the focus of numerous ecological studies has a similar climateand is exposed to storms with similar regularity Raabe and Stumpf [57] found that fromthe early to late 1900s over 82 km2 of forest land was converted to marsh and 66 km2 wasin a transitional state to marsh McCarthy et al [58] document an annual forest cover lossof 744 from 2010ndash2017 an acceleration in the trend within that area A new study bythe same team found rates of loss totaling approximately 10 km2 a year 800 larger thanpreviously stated in the literature [59] Rates of annual mortality ranged from 4ndash16 inSoutheastern Louisiana from 1987 to 1997 [60] In comparison the annualized rate of lossfound in our study never exceeded 5 Plots located in South Carolina that have beenless significantly impacted by MSL rise show rates of loss closer to 1ndash2 in line withour observed rate of loss from 2007ndash2019 However we cannot assume that percentagesof forest loss across the country have been constant within the last century therefore it iscritical to carry out intensive geospatial studies at regular intervals

In the adjoining southeastern part of the state of Louisiana in the Atchafalaya andTerrebonne basins forested wetlands occupy 121871 hectares or 41 of all vegetatedland use [12] In comparison the terrestrial forested land use in our study area was 21in 2019 The wide variation can be interpreted in several ways each with importantimplications Historic rates of deforestation differ in the two regions with a greateremphasis on the preservation of wilderness areas in the Atchafalaya basin [61] TheAtchafalaya and Terrebonne basins also have productive land used in the growth of timberwhich promotes long-term forest cover The regeneration of forests in our study region alsodiffers substantially Chenier ecosystems and traditional alluvial bottomland hardwoodforests are compositionally different in vegetation and soil even when accounting for theirunique hydrologic regimes Communities of live oak may be less resilient to the types offorcings that cause changes in community composition within these ecosystems furtheredby proximity to the sea If stocking density rates are similar to the adjoining basin as theywere in the past we would see an areal extent well over 50000 hectares

Forests 2022 13 1132 14 of 22

The analysis vis-agrave-vis elevation shows weak trends in the manners of loss on eleva-tional gradients Logically in the paradigm of coastal retreat there should be an aggregationof forest resources towards the highest bands of topography in a region Our analysis doesnot follow this chain of thought with the largest losses observed in the 1ndash2 m categoryrather than the lowest 0ndash1 m (Table 2 2007 C 2019 D) While we do see a large loss bypercentage in the highest elevation band it is important to note that a small sample causesinherent variance Forest cover on the highest reaches of the chenier ridges is typically lessthan 1 of total forest cover for that region The reduction can be attributed to the loss of23 features in section A accounting for 33 hectares bordering the Sabine River (Figure 1minus9386286 2973355) The loss of forest cover was driven by intense weather Vegetationin the area was completely destroyed and substantial amounts of sedimentation could beobserved as evident in the 2007 NAIP imagery The only gain in forest cover was seen inthe 2ndash3 m elevation band in the period 2007 in an area that had a high density of pastoralland that was abandoned Forest retreat is often discussed as one method of bufferingforest loss along coasts [62] Unfortunately the geomorphology of this area does not allowgreat spatial movement as the current extent is already located within the highest bandsof elevation

43 Land Use Conversion and Driving Factors

The leading cause of land-use conversion from tree cover was to a saltmarsh ecotype(606) often following mortality and replacement of vegetative biomass The loss offorest cover can largely be typified into two categories The ingression of saltwater causingsalinification within the soil matrix paired with increased flooding caused mortality ofoverstorey vegetation over a multi-year timescale [6364] Hurricanes or other storm eventscaused rapid mortality through wind-throws severe defoliation saltwater inundationduring a storm surge or canopy damage Following intense weather events between2003 and 2007 the vast majority of cheniers located within a 1km buffer of the coast wereunable to support tree cover as seen in Figure 7AB Clearing for industry or developmentalso caused immediate shifts in forest composition as can be seen in Figure 8AB Thischange was more pronounced on reaches A and B especially on Hackberry Beach Thisarea was the site of landfall for Hurricane Rita which caused massive devastation to othercomponents of the ecosystem [6566] Singular storm events have a transformative impacton vegetation communities and the weight of this driver on annual rates of forest lossis a topic that should be explored further Wayward shifts in species composition are atrend that has been observed globally especially in forests that border some source of waterTrees like Juniperus virginiana L that have distinctive water-use patterns in comparison toother coastal tree species are susceptible to salinification at much faster rates as evidencedthrough isotope analyses [67] As with many ecological systems the entangled effectsof climate and vegetation require diverse means of looking at change Isotope analysesdendrochronology field sensors and remote sensing data are needed to characterize land-use change at the stand and community levels

Large forest remnants such as Cow Island (minus9283581 2973842) Tiger Island (minus92784392972832) and Grand Chenier Ridge (minus9281175 2972986) among others have diminishedmore slowly By 2019 these stands had lost an average of 15 of their maximum extentThe land use changed to both marsh and managed pasture largely dependent on thesurrounding land use The conversion to managed pasture (282) accounted for a largerthan expected amount of land-cover change Many of these changes were in areas wheretree cover may have been more ldquoornamentalrdquo in nature Trees beside houses or alongsideroads are some of the largest specimens of live-oak to be found However these trees areoften solitary and perhaps more vulnerable to wind-borne disturbance The concern is thattheir loss due to abiotic drivers may not be easily mitigated leading to a permanent declineOur data support evidence on the whittling of forest remnants and the eventual fate ofthese trees becoming lsquorelict forestsrsquo unable to recruit new generations in the short term andin the long-term total disappearance of these cheniers along the Louisiana Gulf Coast [68]

Forests 2022 13 1132 15 of 22Forests 2022 13 x FOR PEER REVIEW 16 of 23

Figure 7 Visual depictions of two forms of loss one from hurricanes on Hackberry Beach (AB) and

the other documenting whittling reduction (CD) The left column displays 2003 NAIP imagery and

the right is 2007 NAIP imagery Black shaded areas indicate current year forest extent red hashed

areas indicate a loss

Figure 7 Visual depictions of two forms of loss one from hurricanes on Hackberry Beach (AB) andthe other documenting whittling reduction (CD) The left column displays 2003 NAIP imagery andthe right is 2007 NAIP imagery Black shaded areas indicate current year forest extent red hashedareas indicate a loss

Beyond loss in forest cover there is a noticeable trend towards an altered hydrologywhere pastoral areas and intact forest remnants are associated with pockets of standingwater An increase in the correlative factor between temperature and water levels could bea worrying trend a direct conversion of tree cover to water at 34 could be an unforeseenoutcome Chenier ridges in their geomorphic context should not support non-ephemeralponding It is not documented how increased tidal influence which is one factor of risingMSL influences the hydrology of this ecosystem and its associated communities Albeitin a more inland context Magolan and Halls [69] documented a transition of over 40 ofland to emergent marsh from closed-canopy cover from 1949 to 2018 due to hydrologicinfluence Recent rates of relative sea rise have been documented as high as 119 mmyearcompared to background Holocene rates of 17 mmyear [70] This has resulted in a rate oflateral coastal forest retreat between 2ndash14 times greater than pre-industrial levels [26] Therate of shoreline retreat in this region increased from ~61 to ~94 myear based on the 100-and 30-year moving averages amounting to over 10000 hectares of land loss every yearfrom 1800 to 2005 [27]

Forests 2022 13 1132 16 of 22Forests 2022 13 x FOR PEER REVIEW 17 of 23

Figure 8 A depiction of clearing for industry in (AB) and a depiction of forest whittling from (CD)

The black extent represents the current forest area red hashed lines indicate a loss The left panels

show 2007 NAIP imagery while the right are 2019 NAIP imagery

Large forest remnants such as Cow Island (minus9283581 2973842) Tiger Island

(minus9278439 2972832) and Grand Chenier Ridge (minus9281175 2972986) among others have

diminished more slowly By 2019 these stands had lost an average of 15 of their maxi‐

mum extent The land use changed to both marsh and managed pasture largely depend‐

ent on the surrounding land use The conversion to managed pasture (282) accounted

for a larger than expected amount of land‐cover change Many of these changes were in

areas where tree cover may have been more ldquoornamentalrdquo in nature Trees beside houses

or alongside roads are some of the largest specimens of live‐oak to be found However

these trees are often solitary and perhaps more vulnerable to wind‐borne disturbance The

concern is that their loss due to abiotic drivers may not be easily mitigated leading to a

permanent decline Our data support evidence on the whittling of forest remnants and

the eventual fate of these trees becoming lsquorelict forestsrsquo unable to recruit new generations

in the short term and in the long‐term total disappearance of these cheniers along the

Louisiana Gulf Coast [68]

Beyond loss in forest cover there is a noticeable trend towards an altered hydrology

where pastoral areas and intact forest remnants are associated with pockets of standing

water An increase in the correlative factor between temperature and water levels could

be a worrying trend a direct conversion of tree cover to water at 34 could be an unfore‐

seen outcome Chenier ridges in their geomorphic context should not support non‐ephem‐

eral ponding It is not documented how increased tidal influence which is one factor of

Figure 8 A depiction of clearing for industry in (AB) and a depiction of forest whittling from (CD)The black extent represents the current forest area red hashed lines indicate a loss The left panelsshow 2007 NAIP imagery while the right are 2019 NAIP imagery

44 Future of Chenier Forests

The chenier complexes of Louisiana do not have the natural benefaction of maintaininglarge original tracts untouched by axe or dozer The extent map through the years (Figure 9)exemplifies the scattered nature of forest remnants The largest single continuous chenierforest (Grand Chenier Ridgemdash185 ha) encompasses less than 1 of the total forest areaExposed forest edges pose a problem for the continued integrity of the stand as adaptedspecies fail to compete in these new microclimates [71] Slow-growing saplings of desirabletree species (live oak hackberry) are subject to unfavorable growing conditions fromweather and light availability Vegetation surveys following aspect and inundation alongthe Florida coast have all but confirmed the eventual extirpation of overstory vegetationthrough poor recruitment of native species [31] Chinese tallow (Triadica sebifera) a non-native tree is likely to fill empty niche space This aggressive invader is problematicthroughout the Gulf Coastal Plain from Texas to Florida [72] The tree has a wide meansof dispersal being primarily distributed through frugivory via avian populations A highrate of germination compounded with quick growth and a sexual maturity of less thanthree years make it a difficult species to control Its invasive capability is seen in nearlyall forest types in the region although it is more pervasive in coastal forests than inlandModeling studies show a high likelihood of Chinese tallow becoming the dominant specieswithin this ecosystem and in surrounding areas [73] Future publications from this lab will

Forests 2022 13 1132 17 of 22

provide more in-depth data and analysis on the state of existing stands and recruitmenttrends within remnants of the Chenier Plains via field sampling

Louisiana has enacted a historic response to the problem of land-loss following a publicoutcry after devastating storms in 2005 The Coastal Protection and Restoration Authority(CPRA) have been tasked with enacting a management plan to combat coastal erosion TheComprehensive Master Plan details proposals to create marshes build sediment diversionsform new hydrologic structures and promote barrier island restoration through builtand natural means As of 2017 over 135 projects had been completed impacting over14568 ha [74] An update to this document is due to be released in 2023 The projecthas an overall operating budget of over 50 billion US dollars with equal shares dedicatedto restoration and risk reduction The primary funding source is a settlement associatedwith the Deepwater Horizon Oil Spill in addition to various initiatives at the state andfederal levels However there are no current plans to supplement vegetation or performany geoengineering work on the cheniers for afforestation

Coastal wetlands rank among the most important ecosystems in terms of the valuationof ecosystem services [75] Provisioning services such as water filtration or storm-surgeprotection are commonly considered when discussing coastal areas Beyond these com-monalities chenier forests provide vital wildlife habitats being the first encountered sourceof vertical structure for migratory neo-tropical birds and migrating monarch butterflies inthe Mississippi alluvial valley flyway [76] This en-route habitat is of critical importancedue to its closeness to the Gulf of Mexico During favorable weather conditions 10 ofbirds migrating through the Gulf use these forests as a stopover spot rising dramatically to80 during adverse weather conditions [29] With losses mounting many species may notbe able to seek refuge during storms The area also has a unique suite of amphibians andreptiles being home to 13 obligate chenier forest species of herpetofauna [77]

Lu et al [78] used the eddy covariance method (ECM) to calculate strong carbon sinkeffects for a chenier ecosystem driven by forest vegetation Net energy production valueswere significantly higher than surrounding marshland signifying the importance of densevegetative cover on a per-unit basis Counter to this there is also strong evidence thatdegraded forest ecosystems act as net carbon sources A study in North Carolina also usingECM found that a seasonally inundated tide-influenced forest was a net carbon sourceannually and only functioned as a sink for 3 months of the growing year [79] Similarly astudy in the northeastern part of the state showed net negative rates of carbon sequestrationfor an ephemerally flooded forest [80] Unique environments demand individual attentiona characterization of this ecosystem for nitrogen or carbon flux with respect to a similarsystem may not be functionally sound [81]

Forests 2022 13 1132 18 of 22Forests 2022 13 x FOR PEER REVIEW 19 of 23

Figure 9 The total land area classified in GEE for the years 2003 2007 and 2019 Light green repre‐

sents the marsh area tan represents pasture The water layer has been removed for purposes of

clarity The dark black outline is the outline of the forest cover for the respective year

Louisiana has enacted a historic response to the problem of land‐loss following a

public outcry after devastating storms in 2005 The Coastal Protection and Restoration

Authority (CPRA) have been tasked with enacting a management plan to combat coastal

erosion The Comprehensive Master Plan details proposals to create marshes build sedi‐

ment diversions form new hydrologic structures and promote barrier island restoration

through built and natural means As of 2017 over 135 projects had been completed im‐

pacting over 14568 ha [74] An update to this document is due to be released in 2023 The

project has an overall operating budget of over 50 billion US dollars with equal shares

dedicated to restoration and risk reduction The primary funding source is a settlement

associated with the Deepwater Horizon Oil Spill in addition to various initiatives at the

state and federal levels However there are no current plans to supplement vegetation or

perform any geoengineering work on the cheniers for afforestation

Coastal wetlands rank among the most important ecosystems in terms of the valua‐

tion of ecosystem services [75] Provisioning services such as water filtration or storm‐

surge protection are commonly considered when discussing coastal areas Beyond these

commonalities chenier forests provide vital wildlife habitats being the first encountered

source of vertical structure for migratory neo‐tropical birds and migrating monarch but‐

terflies in the Mississippi alluvial valley flyway [76] This en‐route habitat is of critical

importance due to its closeness to the Gulf of Mexico During favorable weather condi‐

tions 10 of birds migrating through the Gulf use these forests as a stopover spot rising

Figure 9 The total land area classified in GEE for the years 2003 2007 and 2019 Light greenrepresents the marsh area tan represents pasture The water layer has been removed for purposes ofclarity The dark black outline is the outline of the forest cover for the respective year

5 Conclusions

The cheniers of southwestern Louisiana were found to be declining in size at a concern-ing rate Many of the driving factors are ecosystem-wide trends that have been increasingin intensity within the last century The results of this study suggest that coastal forestsare an ecosystem in peril and the ecology of such systems should be a closely monitoredsubject The 365 loss of forest cover is an alarming but not wholly unexpected result ofmany decades of intense perturbation and human exploitation A dynamic of continuedforest loss is evident Unless significant investment is made in engineering solutions tomitigate storm effects and land subsidence we will continue to see the decline of chenierforests in Louisiana Through targeted mitigation governmental programs such as theWetland or Conservation Reserve Program may be able to supplement existing forestsas well as the incomes of local people who have lost their livelihoods In this study wehave begun the process of accurate areal estimates of forest extent and have shed light onecosystem processes in the region As this community continues to diminish we see theforest not by the trees but rather the lack of them

Forests 2022 13 1132 19 of 22

Author Contributions Conceptualization PT methodology PT software PT and PR validationPT and PR formal analysis PT PR and PT writingmdashoriginal draft preparation PT and JBwritingmdashreview and editing PT and JB visualization PT and JB supervision JB All authorshave read and agreed to the published version of the manuscript

Funding This research received no external funding

Data Availability Statement The data will be published in and stored on the Harvard Dataverse athttpsdataverseharvardedudatasetxhtmlpersistentId=doi107910DVN3J7SNF (accessed on1 May 2022)

Acknowledgments The authors would like to recognize the hard work of undergraduate studentsParusha Raut and Subash Sapkota in manually validating thousands of points and creating numeroustraining sets We also extend thanks to our institution and numerous people within the lab who leantboth mental and emotional fortitude

Conflicts of Interest The authors declare no conflict of interest

References1 Michener WK Blood ER Bildstein KL Brinson MM Gardner LR Climate change hurricanes and tropical storms and

rising sea level in coastal wetlands Ecol Appl 1997 7 770ndash801 [CrossRef]2 Donoghue JF Sea level history of the northern Gulf of Mexico coast and sea level rise scenarios for the near future Clim Chang

2011 107 17ndash33 [CrossRef]3 Stanturf JA Goodrick SL Outcalt KW Disturbance and coastal forests A strategic approach to forest management in

hurricane impact zones For Ecol Manag 2007 250 119ndash135 [CrossRef]4 Augustinus P Cheniers and chenier plains A general introduction Mar Geol 1989 90 219ndash229 [CrossRef]5 Fu Z Wang P Sun J Lu Z Yang H Liu J Li T Composition seasonal variation and salinization characteristics of soil

salinity in the Chenier Island of the Yellow River Delta Glob Ecol Conserv 2020 24 e01318 [CrossRef]6 Marques IG Campelo F Rivaes R Albuquerque A Ferreira MT Rodriacuteguez-Gonzaacutelez PM Tree rings reveal long-term

changes in growth resilience in Southern European riparian forests Dendrochronologia 2018 55 167ndash176 [CrossRef]7 Daigle JJ Griffith GE Omernik JM Faulkner PL McCulloh RP Handley LR Smith LM Chapman SS Ecoregions of

Louisiana (Color Poster with Map Descriptive Text Summary Tables and Photographs) US Geological Survey Reston VI USA 20068 McBride RA Taylor MJ Byrnes MR Coastal morphodynamics and Chenier-Plain evolution in southwestern Louisiana USA

A geomorphic model Geomorphology 2007 88 367ndash422 [CrossRef]9 Gosselink JG An Ecological Characterization Study of the Chenier Plain Coastal Ecosystem of Louisiana and Texas Narrative Report

National Coastal Ecosystems Team Office of Biological Services Fish and Wildlife Service US Department of the InteriorWashington DC USA 1979 Volume 1

10 Blum MD Roberts HH The Mississippi delta region Past present and future Annu Rev Earth Planet Sci 2012 40 655ndash683[CrossRef]

11 Hansen MC Potapov PV Moore R Hancher M Turubanova SA Tyukavina A Townshend J High-resolution globalmaps of 21st-century forest cover change Science 2013 342 850ndash853 [CrossRef]

12 Doyle TW Krauss KW Conner WH From AS Predicting the retreat and migration of tidal forests along the northern Gulfof Mexico under sea-level rise For Ecol Manag 2010 259 770ndash777 [CrossRef]

13 Thomas N Simard M Castantildeeda-Moya E Byrd K Windham-Myers L Bevington A Twilley R High-resolution mappingof biomass and distribution of marsh and forested wetlands in southeastern coastal Louisiana Int J Appl Earth Obs Geoinf 201980 257ndash267 [CrossRef]

14 Desantis LR Bhotika S Williams K Putz FE Sea-level rise and drought interactions accelerate forest decline on the GulfCoast of Florida USA Glob Chang Biol 2007 13 2349ndash2360 [CrossRef]

15 Boyd DS Foody G Ripple W Evaluation of approaches for forest cover estimation in the Pacific Northwest USA usingremote sensing Appl Geogr 2002 22 375ndash392 [CrossRef]

16 Valjarevic A Djekic T Stevanovic V Ivanovic R Jandzikovic B GIS numerical and remote sensing analyses of forest changesin the Toplica region for the period of 1953ndash2013 Appl Geogr 2018 92 131ndash139 [CrossRef]

17 He T Sun YJ Xu JD Wang X Hu C Enhanced land usecover classification using support vector machines and fuzzyk-means clustering algorithms J Appl Remote Sens 2014 8 083636 [CrossRef]

18 Michez A Pieacutegay H Lisein J Claessens H Lejeune P Classification of riparian forest species and health condition usingmulti-temporal and hyperspatial imagery from unmanned aerial systems Environ Monit Assess 2016 188 146 [CrossRef][PubMed]

19 Hampton SE Strasser C Tewksbury J Gram W Budden A Batcheller A Duke C Porter J Big data and the future ofecology Front Ecol Environ 2013 11 156ndash162 [CrossRef]

20 Curtis PG Slay CM Harris NL Tyukavina A Hansen MC Classifying drivers of global forest loss Science 2018 3611108ndash1111 [CrossRef]

Forests 2022 13 1132 8 of 22

The CoNED DEM was subdivided into 0ndash1 1ndash2 2ndash3 and 3+ meter classes using theRaster Calculator tool in QGIS The class values were then vectorized to run a geospatialdifference iteratively with each yearrsquos tree cover vectors The difference was validatedand then used for area calculations regarding differential rates of loss for elevation gra-dients Gage height and temperature values were extracted from both stream gages andwere combined into a site average We used gage height as a proxy for MSL due to thespatial proximity to the coast and study sites as well as for considering the response tofreshwater influence which is an important hydrological component of the Chenier PlainsAverage values for the three wettest months (July August and September) were used inthe regressions and analysis

3 Results31 Forest Loss Transition and Classification Accuracy

From 2003 to 2019 an overall decline of 3573 was seen for forest cover and 2238for pasture The annualized rate of loss for forest cover was approximately 2 higherwithin the period 2003ndash2007 losing 42 of tree cover per year assuming a constant change(Table 1) Losses were greatest in the western two reaches of the Chenier Plains averagingover 213 more than sections C or D The largest amount of tree cover was located withinsection D with significant contributions from spoil banks Chenier Au Tigre (locationminus9220665 2956885) had the largest total tree cover within the study area albeit spreadover a large expanse Annual loss rates were more constant in sections containing primarilychenier ridge forests in contrast to swamp or spoil bank forests Section D has a largesaltmarsh (1ndash10 km) bisecting the chenier from the coast which contributed to a largerland and forest area There was a cumulative gain of 231 for the saltmarshes adding2528 km2 In the 2003ndash2007 period the only gains in land cover type were in saltmarshwhich was maintained into 2019 (Figure 4)

We surveyed a total of 2123 points across the study area Producerrsquos accuracy was916 816 and 918 for the years 2003 2007 and 2019 respectively The CohenrsquosKappa indexes of 091 082 and 093 for 2003 2007 and 2019 respectively showed strongagreement between raters across all years We considered any value above 085 to be a validassessment The year 2007 scored lower in both PA and Kappa values likely due to thenature of the base imagery however it still fell into an acceptable range Extreme contrastand saturation in the 2007 NAIP imagery caused larger aggregations of tree cover pixels asgrasses and trees have a similar hue accentuated by the pixel reduction post-classificationValidation points that were very close to tree cover but not categorized as such were notconsidered accurate which contributed to this source of error The 2019 classifications weregeneralized as being more correct from the onset with little need for QA

Changes in land-cover were not evenly distributed across different sections as canbe seen in Table 1 Typically cheniers are more proximally located to the coast withinthe eastern reaches with depositional bars spreading the length One noted outlier in theassessment of change was observed from 2003 to 2007 in section B where the loss of pastureaccounted for over 656 The quantitative measurements for changes in land cover werealso ascertained from these samples We observed changes of 606 282 34 and 76for saltmarsh pasture water and impervious area respectively

Observed results deviated from hypothesized values specifically in the large propor-tion of conversion to pasture A lone tree existing one year after transitioning to an openfield was common across all areas It was assumed the percentage of conversion fromtree to saltmarsh would be even greater However impervious area transitions were alsohigh given that considerable forest cover directly adjoins roads and houses in this areaThe underlying structure can then be seen following the mortality of vegetation Changeto water was also observed for several reasons in areas along waterbodies a loss of treecover exposed the water while severe areas of deforestation prompted a change to a morewetland hydrology which also required water replacement pixels

Forests 2022 13 1132 9 of 22

Table 1 The amount and percentage loss of three land-cover typesmdashtree marsh and pasturemdashovereach section and the total area spanning two parishes Calcasieu and Vermillion on the westernLouisiana Gulf Coast Sections A B C and D represent areas delineated for this study within thetwo parishes (for details see Figure 1)

Landcover 2003(ha)

2007(ha)

2019(ha)

Net Change ()(2003ndash2019)

TreeA 46274 37639 17988 minus6113B 64389 38634 27486 minus5731C 99623 82791 65050 minus3470D 231775 215192 173580 minus2511

Total 442061 374256 284104 minus3573

MarshA 1182244 1148060 1238636 477B 1113045 1084738 1395453 2537C 2051730 2277785 2204412 744D 6598632 6839319 6359952 minus362

Total 10945651 11349903 11198453 231

PastureA 510146 460323 324348 minus3642B 136395 89019 77021 minus4353C 696209 754500 653027 minus620D 1152047 1036039 881960 minus2344

Total 2494796 2339881 1936355 minus2238

Forests 2022 13 x FOR PEER REVIEW 9 of 23

adding 2528 km2 In the 2003ndash2007 period the only gains in land cover type were in salt‐

marsh which was maintained into 2019 (Figure 4)

Table 1 The amount and percentage loss of three land‐cover typesmdashtree marsh and pasturemdashover

each section and the total area spanning two parishes Calcasieu and Vermillion on the western

Louisiana Gulf Coast Sections A B C and D represent areas delineated for this study within the

two parishes (for details see Figure 1)

Landcover 2003

(ha)

2007

(ha)

2019

(ha)

Net Change ()

(2003ndash2019)

Tree

A 46274 37639 17988 minus6113

B 64389 38634 27486 minus5731

C 99623 82791 65050 minus3470

D 231775 215192 173580 minus2511

Total 442061 374256 284104 minus3573

Marsh

A 1182244 1148060 1238636 477

B 1113045 1084738 1395453 2537

C 2051730 2277785 2204412 744

D 6598632 6839319 6359952 minus362

Total 10945651 11349903 11198453 231

Pasture

A 510146 460323 324348 minus3642

B 136395 89019 77021 minus4353

C 696209 754500 653027 minus620

D 1152047 1036039 881960 minus2344

Total 2494796 2339881 1936355 minus2238

0

20000

40000

60000

80000

100000

120000

0

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

Land area ‐MarshPasture (ha)

Land area ‐Forest Cover (ha)

Year

Land‐cover dynamics over time

West East Marsh Pasture

Figure 4 A representation of the changes in three respective land-cover types (forest cover pasturemarsh) over the period 2003ndash2019 For forest cover the three western sections are represented as Westand D is represented as East Light green indicates marsh yellow represents pasture dark green andhatched green show forest cover

Forests 2022 13 1132 10 of 22

32 Rates of Loss with Respect to Elevation

A net decrease in forest cover was seen across all elevation categories (Table 2 Figure 5)The largest losses were in the 1ndash2 m category with a net reduction of 593 from 2003 to2019 There was a temporal shift in the rate of change with the greatest amount of lossbetween 2003ndash2007 contributed by section D Values within the 2ndash3 m category remainedsteady from 2007 to 2019 with a reduction of only 076 The large majority (749) ofthe remaining forest was seen to be located within the 0ndash1 m band The losses were notevenly distributed within this band buffered to some degree by section D A comparisonof sections A vs C shows a decrease of 493 with losses centered around the period2007ndash2019 One issue to note is that the GSD for the DEM was 3 m which is wider thansome spoil banks While the local elevation may in fact rise to 2ndash3 m the value was reduceddue to the effect of averaging The losses in the 3 m+ band are discussed below a noteshould be made of the limited representation Overall the losses varied both spatially andtemporally across the region largely separated into eastern and western reaches

Table 2 Differences in the amount of forest area within each elevation band and the associatedpercentage loss of forest cover between years Sections A B C and D represent areas delineated forthis study within the two parishes (for details see Figure 1)

Sites and Forest Cover (ha)Percentage

LossYearmdash2003

Elevation (m) A B C D Total

0ndash1 13083 25525 66334 187236 2921781ndash2 27168 36374 25583 39048 1281722ndash3 1934 2188 6481 4640 15242gt3 361 170 005 343 879

Yearmdash2007A B C D Total 2003ndash2007

0ndash1 18371 16502 56349 166776 257997 minus11701ndash2 17834 20210 21289 4205 63538 minus50432ndash3 1228 1783 4118 5446 12575 minus1750gt3 048 208 002 404 662 minus2474

Yearmdash2019A B C D Total 2003ndash2019

0ndash1 7161 8227 37762 142102 195253 minus33171ndash2 10283 12913 24656 4401 52254 minus59232ndash3 1506 932 5789 4253 12480 minus1812gt3 045 141 002 402 590 minus3288

33 Interaction with Water Level

Water plays a critical role in the natural function of this ecosystem As such we consid-ered the interplay of temperature and MSL over a decadal time scale and the local rates oftemperature and gage height from both USGS stations (Figure 6) In Figure 6 forest extentis superimposed for each yearrsquos classification to show visual correspondence between thetwo drivers Temperature and rise in sea-level were negatively correlated until an inflectionpoint around 2010 after which there was a relatively stronger correlation increasing to amaximum of 025 The correlative increase lends evidence to the correspondence betweenclimate and water pressure in coastal areas An increase in the average MSL has been ob-served for the site The two stream gages showed an annualized MSL rise of 136 mmyearfor Vermilion Bay and 59 mmyear for the Calcasieu River There was a weak but positivetrend for temperature to increase slightly from the onset of the study period Time provedto be a good indicator for increase in water level and for decrease in forest cover (Figure 6)The lack of tree-cover samples prohibited many statistical comparisons but visually thecorrespondence seems evident

Forests 2022 13 1132 11 of 22

Forests 2022 13 x FOR PEER REVIEW 11 of 23

Table 2 Differences in the amount of forest area within each elevation band and the associated

percentage loss of forest cover between years Sections A B C and D represent areas delineated for

this study within the two parishes (for details see Figure 1)

Sites and Forest Cover (Ha)

Percentage Loss Yearmdash2003

Elevation (m) A B C D Total

0ndash1 13083 25525 66334 187236 292178

1ndash2 27168 36374 25583 39048 128172

2ndash3 1934 2188 6481 4640 15242

gt3 361 170 005 343 879

Yearmdash2007

A B C D Total 2003ndash2007

0ndash1 18371 16502 56349 166776 257997 minus1170

1ndash2 17834 20210 21289 4205 63538 minus5043

2ndash3 1228 1783 4118 5446 12575 minus1750

gt3 048 208 002 404 662 minus2474

Yearmdash2019

A B C D Total 2003ndash2019

0ndash1 7161 8227 37762 142102 195253 minus3317

1ndash2 10283 12913 24656 4401 52254 minus5923

2ndash3 1506 932 5789 4253 12480 minus1812

gt3 045 141 002 402 590 minus3288

0

1

2

3

4

5

6

7

8

9

10

0

500

1000

1500

2000

2500

3000

3500

Land area 3mdash4m ‐(ha)

Land area 0mdash3m ‐(ha)

Year

Forest cover in elevational bands over time

0‐1m

1‐2m

2‐3m

3‐4m

Figure 5 Visual depictions of two forms of loss one from hurricanes on Hackberry Beach and theother documenting whittling reduction The left column refers to 2003 NAIP imagery while the rightuses 2007 NAIP imagery Black shaded areas indicate current year forest extent and the red hashedareas indicate loss

Forests 2022 13 x FOR PEER REVIEW 12 of 23

Figure 5 Visual depictions of two forms of loss one from hurricanes on Hackberry Beach and the

other documenting whittling reduction The left column refers to 2003 NAIP imagery while the right

uses 2007 NAIP imagery Black shaded areas indicate current year forest extent and the red hashed

areas indicate loss

33 Interaction with Water Level

Water plays a critical role in the natural function of this ecosystem As such we con‐

sidered the interplay of temperature and MSL over a decadal time scale and the local

rates of temperature and gage height from both USGS stations (Figure 6) In Figure 6

forest extent is superimposed for each yearrsquos classification to show visual correspondence

between the two drivers Temperature and rise in sea‐level were negatively correlated

until an inflection point around 2010 after which there was a relatively stronger correla‐

tion increasing to a maximum of 025 The correlative increase lends evidence to the cor‐

respondence between climate and water pressure in coastal areas An increase in the av‐

erage MSL has been observed for the site The two stream gages showed an annualized

MSL rise of 136 mmyear for Vermilion Bay and 59 mmyear for the Calcasieu River

There was a weak but positive trend for temperature to increase slightly from the onset of

the study period Time proved to be a good indicator for increase in water level and for

decrease in forest cover (Figure 6) The lack of tree‐cover samples prohibited many statis‐

tical comparisons but visually the correspondence seems evident

Figure 6 Trends in mean water levels and water temperature over 19 years in Vermillion Parish

Data obtained from the USGS Station 07387050 Vermilion Bay (minus921355 296744) with gage datum

165 m above NAVD88 and from the USGS Station 08017118 Calcasieu River at Cameron LA

(minus933488 298155) with gage datum 107m above NAVD88

Rsup2 = 09453

Rsup2 = 01283

Rsup2 = 06529

08

085

09

095

1

105

11

115

12

125

1995 2000 2005 2010 2015 2020 2025

15

20

25

30

35

40

45

50

Average

water level (m)

Year

Forest cover loss Average

water temp

Loss of tree cover (sqkm) Average water temp (deg C) Average waterlevel (m)

Figure 6 Trends in mean water levels and water temperature over 19 years in Vermillion Parish Dataobtained from the USGS Station 07387050 Vermilion Bay (minus921355 296744) with gage datum 165 mabove NAVD88 and from the USGS Station 08017118 Calcasieu River at Cameron LA (minus933488298155) with gage datum 107m above NAVD88

Forests 2022 13 1132 12 of 22

4 Discussion41 Classification Reliability and Constraints

NAIP imagery proved efficacious for fine-scale analysis in this region shown by thestrong average producerrsquos accuracy and Kappa coefficient This is a positive indicatorthat variability between years or areas did not influence classification accuracies to alarge extent and that the final classification is a reliable interpretation [42] Many otherstudies conducting land-cover classifications using NAIP imagery have also shown robustaccuracies [43ndash45] However there were noted issues seen in certain parts of the workflowA problem preeminent in the initial classification was a limitation of spectral resolution Thisproblem became pronounced in areas with similar shades of green or between dark bluesand black hues Without the use of ancillary datasets the classification accuracy was poorbetween dark water and shadows of trees with considerable overlap (misclassificationsof approximately 50) Mixed land use can also prove problematic in NAIP land-coverclassifications [46] This became especially pronounced in transitional forestmarsh areasin which there was no dominant spectral signature for a particular land use Local variationbetween adjoining image sets highlighted another shortcoming of NAIP imagery Theviewing geometry illumination and time between the capture of neighboring imagetiles created edge effects including artificially high or low pixel values and refractionproblems on water [47] Each yearrsquos dataset was collected by an external contractor causingdifferences in sensor instrumentation and parametrization between years By 2019 many ofthese problems had been mitigated and images were of much higher quality

Compared to a large swath sensor such as Landsat-8 or Sentinel-2 an innate non-uniformity poses problems for pixel-wise classifications of VHR imagery Typically datafrom satellite imagery have been the primary choice for analyzing coastal forests marshesand forests at large [48ndash50] The spaceborne sensors obtain information over eight bandsallowing much finer spectral discrimination of certain classes of objects [51] Even amongstdiverse forest types space-borne Sentinel-2 and ASTER imagery has been used to mapdistinct species clusters of broadleaf and deciduous plant species The large spatial scaleenables the use of these created geospatial products to analyze the efficacy of governmentalreforestation programs in remote areas of the world [52] The usage of normalized differ-ence indices including the Normalized Difference Vegetation Index (NDVI) EnhancedVegetation Index (EVI) and Enhanced Water Index (EWI) can also provide greater confi-dence in the separation of water and marsh features from deciduous broadleaf forests [53]However a spatial resolution of 10m is still too coarse for the detection of remnant andlandscape trees especially those alongside homogenous flat features such as maintainedgrass or water bodies The most practical solution would be to assess this land with ahigher resolution dataset such as unmanned aerial systems (UAS) imagery which cancapture a much finer GSD in a plethora of bands if multispectral or hyperspectral sensorsare used [54] In fact scientists are now pushing the boundaries of the scale of analysisconducted through remote sensing Broad groupings (eg clades or forest types) have beenthe historic norm in the classification of imagery for use in land management A greatermixture of classes allows more accurate measurements for those interested in landscape-scale ecological processes However VHR can be used to map stands at the species levelwhich allows forest managers an unparalleled insight into the dynamics of recruitmentdisease or resource availability [55]

Google Earth Engine allowed us to utilize NAIP imagery at scale which had for sometime been a limiting factor in processing this data source locally Even so some limitationshindered the functional utility of GEE for detailed land-cover classification In our relativelyuniform environment we discriminated between many shades of green When viewinggeometries cause variations in hue different classes begin to overlap within the samespectral regions Solutions using Geographic Object-Based Image Analysis (GEOBIA) cancircumvent this issue yet this segmentation algorithm is currently not supported by GEEMany consider GEOBIA the optimal tool for segmenting landscapes and for the productionof land-cover classifications from this source of imagery [3856] Its successful integration

Forests 2022 13 1132 13 of 22

could substantially elevate the use and utility of mapping within GEE Additionally theallocation of increased processing bandwidth on GEE servers may help overcome somelimits in classification Greater bandwidth allows larger and more diverse training setsin addition to more complex parameterizations of machine learning classifiers If oneis proceeding on local workstations the analysis of NAIP imagery requires an intimateknowledge of data structures and process optimization Custom architectures processingpipelines and simpler binary classifications can allow an expansion of the study area tocover coterminous areas on a larger scale [16]

42 Forest Loss Contextually

We mapped forest cover change dynamics between 2003ndash2019 on the southwest coastof Louisiana 1580 hectares of tree cover 13 of the terrestrial area was converted todifferent land use The total loss of forest cover in the study period was 3573 over sixteenyears which is indicative of a larger rate of forest loss than at sites in a similar physio-geographic region yet is certainly not a global outlier [18] These findings validate thesentiments of people in the region and underscore a major irreversible trend in the area Ourestimate should be considered conservative due to the way we approached the definition ofldquoforest coverrdquo within the landscape Abandoned pastoral land exhibiting dense scrubbinessand shrub cover was demarcated as forest cover even though the target species (live oak)was probably not present Spoil banks which are the byproduct of historic oil explorationin the region indicating possible mortality were also often considered a component offorest cover if the imagery could not indicate the absence of high vegetation The primarymethod of identifying whether or not forest cover was present was the observation of ashadow adjoining the vegetation indicating a vertical structure

The Big Bend region of Florida one of the longest undisturbed coastlines in thecontinental US offers some insight into the vegetation dynamics this region has beenexperiencing It has been the focus of numerous ecological studies has a similar climateand is exposed to storms with similar regularity Raabe and Stumpf [57] found that fromthe early to late 1900s over 82 km2 of forest land was converted to marsh and 66 km2 wasin a transitional state to marsh McCarthy et al [58] document an annual forest cover lossof 744 from 2010ndash2017 an acceleration in the trend within that area A new study bythe same team found rates of loss totaling approximately 10 km2 a year 800 larger thanpreviously stated in the literature [59] Rates of annual mortality ranged from 4ndash16 inSoutheastern Louisiana from 1987 to 1997 [60] In comparison the annualized rate of lossfound in our study never exceeded 5 Plots located in South Carolina that have beenless significantly impacted by MSL rise show rates of loss closer to 1ndash2 in line withour observed rate of loss from 2007ndash2019 However we cannot assume that percentagesof forest loss across the country have been constant within the last century therefore it iscritical to carry out intensive geospatial studies at regular intervals

In the adjoining southeastern part of the state of Louisiana in the Atchafalaya andTerrebonne basins forested wetlands occupy 121871 hectares or 41 of all vegetatedland use [12] In comparison the terrestrial forested land use in our study area was 21in 2019 The wide variation can be interpreted in several ways each with importantimplications Historic rates of deforestation differ in the two regions with a greateremphasis on the preservation of wilderness areas in the Atchafalaya basin [61] TheAtchafalaya and Terrebonne basins also have productive land used in the growth of timberwhich promotes long-term forest cover The regeneration of forests in our study region alsodiffers substantially Chenier ecosystems and traditional alluvial bottomland hardwoodforests are compositionally different in vegetation and soil even when accounting for theirunique hydrologic regimes Communities of live oak may be less resilient to the types offorcings that cause changes in community composition within these ecosystems furtheredby proximity to the sea If stocking density rates are similar to the adjoining basin as theywere in the past we would see an areal extent well over 50000 hectares

Forests 2022 13 1132 14 of 22

The analysis vis-agrave-vis elevation shows weak trends in the manners of loss on eleva-tional gradients Logically in the paradigm of coastal retreat there should be an aggregationof forest resources towards the highest bands of topography in a region Our analysis doesnot follow this chain of thought with the largest losses observed in the 1ndash2 m categoryrather than the lowest 0ndash1 m (Table 2 2007 C 2019 D) While we do see a large loss bypercentage in the highest elevation band it is important to note that a small sample causesinherent variance Forest cover on the highest reaches of the chenier ridges is typically lessthan 1 of total forest cover for that region The reduction can be attributed to the loss of23 features in section A accounting for 33 hectares bordering the Sabine River (Figure 1minus9386286 2973355) The loss of forest cover was driven by intense weather Vegetationin the area was completely destroyed and substantial amounts of sedimentation could beobserved as evident in the 2007 NAIP imagery The only gain in forest cover was seen inthe 2ndash3 m elevation band in the period 2007 in an area that had a high density of pastoralland that was abandoned Forest retreat is often discussed as one method of bufferingforest loss along coasts [62] Unfortunately the geomorphology of this area does not allowgreat spatial movement as the current extent is already located within the highest bandsof elevation

43 Land Use Conversion and Driving Factors

The leading cause of land-use conversion from tree cover was to a saltmarsh ecotype(606) often following mortality and replacement of vegetative biomass The loss offorest cover can largely be typified into two categories The ingression of saltwater causingsalinification within the soil matrix paired with increased flooding caused mortality ofoverstorey vegetation over a multi-year timescale [6364] Hurricanes or other storm eventscaused rapid mortality through wind-throws severe defoliation saltwater inundationduring a storm surge or canopy damage Following intense weather events between2003 and 2007 the vast majority of cheniers located within a 1km buffer of the coast wereunable to support tree cover as seen in Figure 7AB Clearing for industry or developmentalso caused immediate shifts in forest composition as can be seen in Figure 8AB Thischange was more pronounced on reaches A and B especially on Hackberry Beach Thisarea was the site of landfall for Hurricane Rita which caused massive devastation to othercomponents of the ecosystem [6566] Singular storm events have a transformative impacton vegetation communities and the weight of this driver on annual rates of forest lossis a topic that should be explored further Wayward shifts in species composition are atrend that has been observed globally especially in forests that border some source of waterTrees like Juniperus virginiana L that have distinctive water-use patterns in comparison toother coastal tree species are susceptible to salinification at much faster rates as evidencedthrough isotope analyses [67] As with many ecological systems the entangled effectsof climate and vegetation require diverse means of looking at change Isotope analysesdendrochronology field sensors and remote sensing data are needed to characterize land-use change at the stand and community levels

Large forest remnants such as Cow Island (minus9283581 2973842) Tiger Island (minus92784392972832) and Grand Chenier Ridge (minus9281175 2972986) among others have diminishedmore slowly By 2019 these stands had lost an average of 15 of their maximum extentThe land use changed to both marsh and managed pasture largely dependent on thesurrounding land use The conversion to managed pasture (282) accounted for a largerthan expected amount of land-cover change Many of these changes were in areas wheretree cover may have been more ldquoornamentalrdquo in nature Trees beside houses or alongsideroads are some of the largest specimens of live-oak to be found However these trees areoften solitary and perhaps more vulnerable to wind-borne disturbance The concern is thattheir loss due to abiotic drivers may not be easily mitigated leading to a permanent declineOur data support evidence on the whittling of forest remnants and the eventual fate ofthese trees becoming lsquorelict forestsrsquo unable to recruit new generations in the short term andin the long-term total disappearance of these cheniers along the Louisiana Gulf Coast [68]

Forests 2022 13 1132 15 of 22Forests 2022 13 x FOR PEER REVIEW 16 of 23

Figure 7 Visual depictions of two forms of loss one from hurricanes on Hackberry Beach (AB) and

the other documenting whittling reduction (CD) The left column displays 2003 NAIP imagery and

the right is 2007 NAIP imagery Black shaded areas indicate current year forest extent red hashed

areas indicate a loss

Figure 7 Visual depictions of two forms of loss one from hurricanes on Hackberry Beach (AB) andthe other documenting whittling reduction (CD) The left column displays 2003 NAIP imagery andthe right is 2007 NAIP imagery Black shaded areas indicate current year forest extent red hashedareas indicate a loss

Beyond loss in forest cover there is a noticeable trend towards an altered hydrologywhere pastoral areas and intact forest remnants are associated with pockets of standingwater An increase in the correlative factor between temperature and water levels could bea worrying trend a direct conversion of tree cover to water at 34 could be an unforeseenoutcome Chenier ridges in their geomorphic context should not support non-ephemeralponding It is not documented how increased tidal influence which is one factor of risingMSL influences the hydrology of this ecosystem and its associated communities Albeitin a more inland context Magolan and Halls [69] documented a transition of over 40 ofland to emergent marsh from closed-canopy cover from 1949 to 2018 due to hydrologicinfluence Recent rates of relative sea rise have been documented as high as 119 mmyearcompared to background Holocene rates of 17 mmyear [70] This has resulted in a rate oflateral coastal forest retreat between 2ndash14 times greater than pre-industrial levels [26] Therate of shoreline retreat in this region increased from ~61 to ~94 myear based on the 100-and 30-year moving averages amounting to over 10000 hectares of land loss every yearfrom 1800 to 2005 [27]

Forests 2022 13 1132 16 of 22Forests 2022 13 x FOR PEER REVIEW 17 of 23

Figure 8 A depiction of clearing for industry in (AB) and a depiction of forest whittling from (CD)

The black extent represents the current forest area red hashed lines indicate a loss The left panels

show 2007 NAIP imagery while the right are 2019 NAIP imagery

Large forest remnants such as Cow Island (minus9283581 2973842) Tiger Island

(minus9278439 2972832) and Grand Chenier Ridge (minus9281175 2972986) among others have

diminished more slowly By 2019 these stands had lost an average of 15 of their maxi‐

mum extent The land use changed to both marsh and managed pasture largely depend‐

ent on the surrounding land use The conversion to managed pasture (282) accounted

for a larger than expected amount of land‐cover change Many of these changes were in

areas where tree cover may have been more ldquoornamentalrdquo in nature Trees beside houses

or alongside roads are some of the largest specimens of live‐oak to be found However

these trees are often solitary and perhaps more vulnerable to wind‐borne disturbance The

concern is that their loss due to abiotic drivers may not be easily mitigated leading to a

permanent decline Our data support evidence on the whittling of forest remnants and

the eventual fate of these trees becoming lsquorelict forestsrsquo unable to recruit new generations

in the short term and in the long‐term total disappearance of these cheniers along the

Louisiana Gulf Coast [68]

Beyond loss in forest cover there is a noticeable trend towards an altered hydrology

where pastoral areas and intact forest remnants are associated with pockets of standing

water An increase in the correlative factor between temperature and water levels could

be a worrying trend a direct conversion of tree cover to water at 34 could be an unfore‐

seen outcome Chenier ridges in their geomorphic context should not support non‐ephem‐

eral ponding It is not documented how increased tidal influence which is one factor of

Figure 8 A depiction of clearing for industry in (AB) and a depiction of forest whittling from (CD)The black extent represents the current forest area red hashed lines indicate a loss The left panelsshow 2007 NAIP imagery while the right are 2019 NAIP imagery

44 Future of Chenier Forests

The chenier complexes of Louisiana do not have the natural benefaction of maintaininglarge original tracts untouched by axe or dozer The extent map through the years (Figure 9)exemplifies the scattered nature of forest remnants The largest single continuous chenierforest (Grand Chenier Ridgemdash185 ha) encompasses less than 1 of the total forest areaExposed forest edges pose a problem for the continued integrity of the stand as adaptedspecies fail to compete in these new microclimates [71] Slow-growing saplings of desirabletree species (live oak hackberry) are subject to unfavorable growing conditions fromweather and light availability Vegetation surveys following aspect and inundation alongthe Florida coast have all but confirmed the eventual extirpation of overstory vegetationthrough poor recruitment of native species [31] Chinese tallow (Triadica sebifera) a non-native tree is likely to fill empty niche space This aggressive invader is problematicthroughout the Gulf Coastal Plain from Texas to Florida [72] The tree has a wide meansof dispersal being primarily distributed through frugivory via avian populations A highrate of germination compounded with quick growth and a sexual maturity of less thanthree years make it a difficult species to control Its invasive capability is seen in nearlyall forest types in the region although it is more pervasive in coastal forests than inlandModeling studies show a high likelihood of Chinese tallow becoming the dominant specieswithin this ecosystem and in surrounding areas [73] Future publications from this lab will

Forests 2022 13 1132 17 of 22

provide more in-depth data and analysis on the state of existing stands and recruitmenttrends within remnants of the Chenier Plains via field sampling

Louisiana has enacted a historic response to the problem of land-loss following a publicoutcry after devastating storms in 2005 The Coastal Protection and Restoration Authority(CPRA) have been tasked with enacting a management plan to combat coastal erosion TheComprehensive Master Plan details proposals to create marshes build sediment diversionsform new hydrologic structures and promote barrier island restoration through builtand natural means As of 2017 over 135 projects had been completed impacting over14568 ha [74] An update to this document is due to be released in 2023 The projecthas an overall operating budget of over 50 billion US dollars with equal shares dedicatedto restoration and risk reduction The primary funding source is a settlement associatedwith the Deepwater Horizon Oil Spill in addition to various initiatives at the state andfederal levels However there are no current plans to supplement vegetation or performany geoengineering work on the cheniers for afforestation

Coastal wetlands rank among the most important ecosystems in terms of the valuationof ecosystem services [75] Provisioning services such as water filtration or storm-surgeprotection are commonly considered when discussing coastal areas Beyond these com-monalities chenier forests provide vital wildlife habitats being the first encountered sourceof vertical structure for migratory neo-tropical birds and migrating monarch butterflies inthe Mississippi alluvial valley flyway [76] This en-route habitat is of critical importancedue to its closeness to the Gulf of Mexico During favorable weather conditions 10 ofbirds migrating through the Gulf use these forests as a stopover spot rising dramatically to80 during adverse weather conditions [29] With losses mounting many species may notbe able to seek refuge during storms The area also has a unique suite of amphibians andreptiles being home to 13 obligate chenier forest species of herpetofauna [77]

Lu et al [78] used the eddy covariance method (ECM) to calculate strong carbon sinkeffects for a chenier ecosystem driven by forest vegetation Net energy production valueswere significantly higher than surrounding marshland signifying the importance of densevegetative cover on a per-unit basis Counter to this there is also strong evidence thatdegraded forest ecosystems act as net carbon sources A study in North Carolina also usingECM found that a seasonally inundated tide-influenced forest was a net carbon sourceannually and only functioned as a sink for 3 months of the growing year [79] Similarly astudy in the northeastern part of the state showed net negative rates of carbon sequestrationfor an ephemerally flooded forest [80] Unique environments demand individual attentiona characterization of this ecosystem for nitrogen or carbon flux with respect to a similarsystem may not be functionally sound [81]

Forests 2022 13 1132 18 of 22Forests 2022 13 x FOR PEER REVIEW 19 of 23

Figure 9 The total land area classified in GEE for the years 2003 2007 and 2019 Light green repre‐

sents the marsh area tan represents pasture The water layer has been removed for purposes of

clarity The dark black outline is the outline of the forest cover for the respective year

Louisiana has enacted a historic response to the problem of land‐loss following a

public outcry after devastating storms in 2005 The Coastal Protection and Restoration

Authority (CPRA) have been tasked with enacting a management plan to combat coastal

erosion The Comprehensive Master Plan details proposals to create marshes build sedi‐

ment diversions form new hydrologic structures and promote barrier island restoration

through built and natural means As of 2017 over 135 projects had been completed im‐

pacting over 14568 ha [74] An update to this document is due to be released in 2023 The

project has an overall operating budget of over 50 billion US dollars with equal shares

dedicated to restoration and risk reduction The primary funding source is a settlement

associated with the Deepwater Horizon Oil Spill in addition to various initiatives at the

state and federal levels However there are no current plans to supplement vegetation or

perform any geoengineering work on the cheniers for afforestation

Coastal wetlands rank among the most important ecosystems in terms of the valua‐

tion of ecosystem services [75] Provisioning services such as water filtration or storm‐

surge protection are commonly considered when discussing coastal areas Beyond these

commonalities chenier forests provide vital wildlife habitats being the first encountered

source of vertical structure for migratory neo‐tropical birds and migrating monarch but‐

terflies in the Mississippi alluvial valley flyway [76] This en‐route habitat is of critical

importance due to its closeness to the Gulf of Mexico During favorable weather condi‐

tions 10 of birds migrating through the Gulf use these forests as a stopover spot rising

Figure 9 The total land area classified in GEE for the years 2003 2007 and 2019 Light greenrepresents the marsh area tan represents pasture The water layer has been removed for purposes ofclarity The dark black outline is the outline of the forest cover for the respective year

5 Conclusions

The cheniers of southwestern Louisiana were found to be declining in size at a concern-ing rate Many of the driving factors are ecosystem-wide trends that have been increasingin intensity within the last century The results of this study suggest that coastal forestsare an ecosystem in peril and the ecology of such systems should be a closely monitoredsubject The 365 loss of forest cover is an alarming but not wholly unexpected result ofmany decades of intense perturbation and human exploitation A dynamic of continuedforest loss is evident Unless significant investment is made in engineering solutions tomitigate storm effects and land subsidence we will continue to see the decline of chenierforests in Louisiana Through targeted mitigation governmental programs such as theWetland or Conservation Reserve Program may be able to supplement existing forestsas well as the incomes of local people who have lost their livelihoods In this study wehave begun the process of accurate areal estimates of forest extent and have shed light onecosystem processes in the region As this community continues to diminish we see theforest not by the trees but rather the lack of them

Forests 2022 13 1132 19 of 22

Author Contributions Conceptualization PT methodology PT software PT and PR validationPT and PR formal analysis PT PR and PT writingmdashoriginal draft preparation PT and JBwritingmdashreview and editing PT and JB visualization PT and JB supervision JB All authorshave read and agreed to the published version of the manuscript

Funding This research received no external funding

Data Availability Statement The data will be published in and stored on the Harvard Dataverse athttpsdataverseharvardedudatasetxhtmlpersistentId=doi107910DVN3J7SNF (accessed on1 May 2022)

Acknowledgments The authors would like to recognize the hard work of undergraduate studentsParusha Raut and Subash Sapkota in manually validating thousands of points and creating numeroustraining sets We also extend thanks to our institution and numerous people within the lab who leantboth mental and emotional fortitude

Conflicts of Interest The authors declare no conflict of interest

References1 Michener WK Blood ER Bildstein KL Brinson MM Gardner LR Climate change hurricanes and tropical storms and

rising sea level in coastal wetlands Ecol Appl 1997 7 770ndash801 [CrossRef]2 Donoghue JF Sea level history of the northern Gulf of Mexico coast and sea level rise scenarios for the near future Clim Chang

2011 107 17ndash33 [CrossRef]3 Stanturf JA Goodrick SL Outcalt KW Disturbance and coastal forests A strategic approach to forest management in

hurricane impact zones For Ecol Manag 2007 250 119ndash135 [CrossRef]4 Augustinus P Cheniers and chenier plains A general introduction Mar Geol 1989 90 219ndash229 [CrossRef]5 Fu Z Wang P Sun J Lu Z Yang H Liu J Li T Composition seasonal variation and salinization characteristics of soil

salinity in the Chenier Island of the Yellow River Delta Glob Ecol Conserv 2020 24 e01318 [CrossRef]6 Marques IG Campelo F Rivaes R Albuquerque A Ferreira MT Rodriacuteguez-Gonzaacutelez PM Tree rings reveal long-term

changes in growth resilience in Southern European riparian forests Dendrochronologia 2018 55 167ndash176 [CrossRef]7 Daigle JJ Griffith GE Omernik JM Faulkner PL McCulloh RP Handley LR Smith LM Chapman SS Ecoregions of

Louisiana (Color Poster with Map Descriptive Text Summary Tables and Photographs) US Geological Survey Reston VI USA 20068 McBride RA Taylor MJ Byrnes MR Coastal morphodynamics and Chenier-Plain evolution in southwestern Louisiana USA

A geomorphic model Geomorphology 2007 88 367ndash422 [CrossRef]9 Gosselink JG An Ecological Characterization Study of the Chenier Plain Coastal Ecosystem of Louisiana and Texas Narrative Report

National Coastal Ecosystems Team Office of Biological Services Fish and Wildlife Service US Department of the InteriorWashington DC USA 1979 Volume 1

10 Blum MD Roberts HH The Mississippi delta region Past present and future Annu Rev Earth Planet Sci 2012 40 655ndash683[CrossRef]

11 Hansen MC Potapov PV Moore R Hancher M Turubanova SA Tyukavina A Townshend J High-resolution globalmaps of 21st-century forest cover change Science 2013 342 850ndash853 [CrossRef]

12 Doyle TW Krauss KW Conner WH From AS Predicting the retreat and migration of tidal forests along the northern Gulfof Mexico under sea-level rise For Ecol Manag 2010 259 770ndash777 [CrossRef]

13 Thomas N Simard M Castantildeeda-Moya E Byrd K Windham-Myers L Bevington A Twilley R High-resolution mappingof biomass and distribution of marsh and forested wetlands in southeastern coastal Louisiana Int J Appl Earth Obs Geoinf 201980 257ndash267 [CrossRef]

14 Desantis LR Bhotika S Williams K Putz FE Sea-level rise and drought interactions accelerate forest decline on the GulfCoast of Florida USA Glob Chang Biol 2007 13 2349ndash2360 [CrossRef]

15 Boyd DS Foody G Ripple W Evaluation of approaches for forest cover estimation in the Pacific Northwest USA usingremote sensing Appl Geogr 2002 22 375ndash392 [CrossRef]

16 Valjarevic A Djekic T Stevanovic V Ivanovic R Jandzikovic B GIS numerical and remote sensing analyses of forest changesin the Toplica region for the period of 1953ndash2013 Appl Geogr 2018 92 131ndash139 [CrossRef]

17 He T Sun YJ Xu JD Wang X Hu C Enhanced land usecover classification using support vector machines and fuzzyk-means clustering algorithms J Appl Remote Sens 2014 8 083636 [CrossRef]

18 Michez A Pieacutegay H Lisein J Claessens H Lejeune P Classification of riparian forest species and health condition usingmulti-temporal and hyperspatial imagery from unmanned aerial systems Environ Monit Assess 2016 188 146 [CrossRef][PubMed]

19 Hampton SE Strasser C Tewksbury J Gram W Budden A Batcheller A Duke C Porter J Big data and the future ofecology Front Ecol Environ 2013 11 156ndash162 [CrossRef]

20 Curtis PG Slay CM Harris NL Tyukavina A Hansen MC Classifying drivers of global forest loss Science 2018 3611108ndash1111 [CrossRef]

Forests 2022 13 1132 9 of 22

Table 1 The amount and percentage loss of three land-cover typesmdashtree marsh and pasturemdashovereach section and the total area spanning two parishes Calcasieu and Vermillion on the westernLouisiana Gulf Coast Sections A B C and D represent areas delineated for this study within thetwo parishes (for details see Figure 1)

Landcover 2003(ha)

2007(ha)

2019(ha)

Net Change ()(2003ndash2019)

TreeA 46274 37639 17988 minus6113B 64389 38634 27486 minus5731C 99623 82791 65050 minus3470D 231775 215192 173580 minus2511

Total 442061 374256 284104 minus3573

MarshA 1182244 1148060 1238636 477B 1113045 1084738 1395453 2537C 2051730 2277785 2204412 744D 6598632 6839319 6359952 minus362

Total 10945651 11349903 11198453 231

PastureA 510146 460323 324348 minus3642B 136395 89019 77021 minus4353C 696209 754500 653027 minus620D 1152047 1036039 881960 minus2344

Total 2494796 2339881 1936355 minus2238

Forests 2022 13 x FOR PEER REVIEW 9 of 23

adding 2528 km2 In the 2003ndash2007 period the only gains in land cover type were in salt‐

marsh which was maintained into 2019 (Figure 4)

Table 1 The amount and percentage loss of three land‐cover typesmdashtree marsh and pasturemdashover

each section and the total area spanning two parishes Calcasieu and Vermillion on the western

Louisiana Gulf Coast Sections A B C and D represent areas delineated for this study within the

two parishes (for details see Figure 1)

Landcover 2003

(ha)

2007

(ha)

2019

(ha)

Net Change ()

(2003ndash2019)

Tree

A 46274 37639 17988 minus6113

B 64389 38634 27486 minus5731

C 99623 82791 65050 minus3470

D 231775 215192 173580 minus2511

Total 442061 374256 284104 minus3573

Marsh

A 1182244 1148060 1238636 477

B 1113045 1084738 1395453 2537

C 2051730 2277785 2204412 744

D 6598632 6839319 6359952 minus362

Total 10945651 11349903 11198453 231

Pasture

A 510146 460323 324348 minus3642

B 136395 89019 77021 minus4353

C 696209 754500 653027 minus620

D 1152047 1036039 881960 minus2344

Total 2494796 2339881 1936355 minus2238

0

20000

40000

60000

80000

100000

120000

0

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

Land area ‐MarshPasture (ha)

Land area ‐Forest Cover (ha)

Year

Land‐cover dynamics over time

West East Marsh Pasture

Figure 4 A representation of the changes in three respective land-cover types (forest cover pasturemarsh) over the period 2003ndash2019 For forest cover the three western sections are represented as Westand D is represented as East Light green indicates marsh yellow represents pasture dark green andhatched green show forest cover

Forests 2022 13 1132 10 of 22

32 Rates of Loss with Respect to Elevation

A net decrease in forest cover was seen across all elevation categories (Table 2 Figure 5)The largest losses were in the 1ndash2 m category with a net reduction of 593 from 2003 to2019 There was a temporal shift in the rate of change with the greatest amount of lossbetween 2003ndash2007 contributed by section D Values within the 2ndash3 m category remainedsteady from 2007 to 2019 with a reduction of only 076 The large majority (749) ofthe remaining forest was seen to be located within the 0ndash1 m band The losses were notevenly distributed within this band buffered to some degree by section D A comparisonof sections A vs C shows a decrease of 493 with losses centered around the period2007ndash2019 One issue to note is that the GSD for the DEM was 3 m which is wider thansome spoil banks While the local elevation may in fact rise to 2ndash3 m the value was reduceddue to the effect of averaging The losses in the 3 m+ band are discussed below a noteshould be made of the limited representation Overall the losses varied both spatially andtemporally across the region largely separated into eastern and western reaches

Table 2 Differences in the amount of forest area within each elevation band and the associatedpercentage loss of forest cover between years Sections A B C and D represent areas delineated forthis study within the two parishes (for details see Figure 1)

Sites and Forest Cover (ha)Percentage

LossYearmdash2003

Elevation (m) A B C D Total

0ndash1 13083 25525 66334 187236 2921781ndash2 27168 36374 25583 39048 1281722ndash3 1934 2188 6481 4640 15242gt3 361 170 005 343 879

Yearmdash2007A B C D Total 2003ndash2007

0ndash1 18371 16502 56349 166776 257997 minus11701ndash2 17834 20210 21289 4205 63538 minus50432ndash3 1228 1783 4118 5446 12575 minus1750gt3 048 208 002 404 662 minus2474

Yearmdash2019A B C D Total 2003ndash2019

0ndash1 7161 8227 37762 142102 195253 minus33171ndash2 10283 12913 24656 4401 52254 minus59232ndash3 1506 932 5789 4253 12480 minus1812gt3 045 141 002 402 590 minus3288

33 Interaction with Water Level

Water plays a critical role in the natural function of this ecosystem As such we consid-ered the interplay of temperature and MSL over a decadal time scale and the local rates oftemperature and gage height from both USGS stations (Figure 6) In Figure 6 forest extentis superimposed for each yearrsquos classification to show visual correspondence between thetwo drivers Temperature and rise in sea-level were negatively correlated until an inflectionpoint around 2010 after which there was a relatively stronger correlation increasing to amaximum of 025 The correlative increase lends evidence to the correspondence betweenclimate and water pressure in coastal areas An increase in the average MSL has been ob-served for the site The two stream gages showed an annualized MSL rise of 136 mmyearfor Vermilion Bay and 59 mmyear for the Calcasieu River There was a weak but positivetrend for temperature to increase slightly from the onset of the study period Time provedto be a good indicator for increase in water level and for decrease in forest cover (Figure 6)The lack of tree-cover samples prohibited many statistical comparisons but visually thecorrespondence seems evident

Forests 2022 13 1132 11 of 22

Forests 2022 13 x FOR PEER REVIEW 11 of 23

Table 2 Differences in the amount of forest area within each elevation band and the associated

percentage loss of forest cover between years Sections A B C and D represent areas delineated for

this study within the two parishes (for details see Figure 1)

Sites and Forest Cover (Ha)

Percentage Loss Yearmdash2003

Elevation (m) A B C D Total

0ndash1 13083 25525 66334 187236 292178

1ndash2 27168 36374 25583 39048 128172

2ndash3 1934 2188 6481 4640 15242

gt3 361 170 005 343 879

Yearmdash2007

A B C D Total 2003ndash2007

0ndash1 18371 16502 56349 166776 257997 minus1170

1ndash2 17834 20210 21289 4205 63538 minus5043

2ndash3 1228 1783 4118 5446 12575 minus1750

gt3 048 208 002 404 662 minus2474

Yearmdash2019

A B C D Total 2003ndash2019

0ndash1 7161 8227 37762 142102 195253 minus3317

1ndash2 10283 12913 24656 4401 52254 minus5923

2ndash3 1506 932 5789 4253 12480 minus1812

gt3 045 141 002 402 590 minus3288

0

1

2

3

4

5

6

7

8

9

10

0

500

1000

1500

2000

2500

3000

3500

Land area 3mdash4m ‐(ha)

Land area 0mdash3m ‐(ha)

Year

Forest cover in elevational bands over time

0‐1m

1‐2m

2‐3m

3‐4m

Figure 5 Visual depictions of two forms of loss one from hurricanes on Hackberry Beach and theother documenting whittling reduction The left column refers to 2003 NAIP imagery while the rightuses 2007 NAIP imagery Black shaded areas indicate current year forest extent and the red hashedareas indicate loss

Forests 2022 13 x FOR PEER REVIEW 12 of 23

Figure 5 Visual depictions of two forms of loss one from hurricanes on Hackberry Beach and the

other documenting whittling reduction The left column refers to 2003 NAIP imagery while the right

uses 2007 NAIP imagery Black shaded areas indicate current year forest extent and the red hashed

areas indicate loss

33 Interaction with Water Level

Water plays a critical role in the natural function of this ecosystem As such we con‐

sidered the interplay of temperature and MSL over a decadal time scale and the local

rates of temperature and gage height from both USGS stations (Figure 6) In Figure 6

forest extent is superimposed for each yearrsquos classification to show visual correspondence

between the two drivers Temperature and rise in sea‐level were negatively correlated

until an inflection point around 2010 after which there was a relatively stronger correla‐

tion increasing to a maximum of 025 The correlative increase lends evidence to the cor‐

respondence between climate and water pressure in coastal areas An increase in the av‐

erage MSL has been observed for the site The two stream gages showed an annualized

MSL rise of 136 mmyear for Vermilion Bay and 59 mmyear for the Calcasieu River

There was a weak but positive trend for temperature to increase slightly from the onset of

the study period Time proved to be a good indicator for increase in water level and for

decrease in forest cover (Figure 6) The lack of tree‐cover samples prohibited many statis‐

tical comparisons but visually the correspondence seems evident

Figure 6 Trends in mean water levels and water temperature over 19 years in Vermillion Parish

Data obtained from the USGS Station 07387050 Vermilion Bay (minus921355 296744) with gage datum

165 m above NAVD88 and from the USGS Station 08017118 Calcasieu River at Cameron LA

(minus933488 298155) with gage datum 107m above NAVD88

Rsup2 = 09453

Rsup2 = 01283

Rsup2 = 06529

08

085

09

095

1

105

11

115

12

125

1995 2000 2005 2010 2015 2020 2025

15

20

25

30

35

40

45

50

Average

water level (m)

Year

Forest cover loss Average

water temp

Loss of tree cover (sqkm) Average water temp (deg C) Average waterlevel (m)

Figure 6 Trends in mean water levels and water temperature over 19 years in Vermillion Parish Dataobtained from the USGS Station 07387050 Vermilion Bay (minus921355 296744) with gage datum 165 mabove NAVD88 and from the USGS Station 08017118 Calcasieu River at Cameron LA (minus933488298155) with gage datum 107m above NAVD88

Forests 2022 13 1132 12 of 22

4 Discussion41 Classification Reliability and Constraints

NAIP imagery proved efficacious for fine-scale analysis in this region shown by thestrong average producerrsquos accuracy and Kappa coefficient This is a positive indicatorthat variability between years or areas did not influence classification accuracies to alarge extent and that the final classification is a reliable interpretation [42] Many otherstudies conducting land-cover classifications using NAIP imagery have also shown robustaccuracies [43ndash45] However there were noted issues seen in certain parts of the workflowA problem preeminent in the initial classification was a limitation of spectral resolution Thisproblem became pronounced in areas with similar shades of green or between dark bluesand black hues Without the use of ancillary datasets the classification accuracy was poorbetween dark water and shadows of trees with considerable overlap (misclassificationsof approximately 50) Mixed land use can also prove problematic in NAIP land-coverclassifications [46] This became especially pronounced in transitional forestmarsh areasin which there was no dominant spectral signature for a particular land use Local variationbetween adjoining image sets highlighted another shortcoming of NAIP imagery Theviewing geometry illumination and time between the capture of neighboring imagetiles created edge effects including artificially high or low pixel values and refractionproblems on water [47] Each yearrsquos dataset was collected by an external contractor causingdifferences in sensor instrumentation and parametrization between years By 2019 many ofthese problems had been mitigated and images were of much higher quality

Compared to a large swath sensor such as Landsat-8 or Sentinel-2 an innate non-uniformity poses problems for pixel-wise classifications of VHR imagery Typically datafrom satellite imagery have been the primary choice for analyzing coastal forests marshesand forests at large [48ndash50] The spaceborne sensors obtain information over eight bandsallowing much finer spectral discrimination of certain classes of objects [51] Even amongstdiverse forest types space-borne Sentinel-2 and ASTER imagery has been used to mapdistinct species clusters of broadleaf and deciduous plant species The large spatial scaleenables the use of these created geospatial products to analyze the efficacy of governmentalreforestation programs in remote areas of the world [52] The usage of normalized differ-ence indices including the Normalized Difference Vegetation Index (NDVI) EnhancedVegetation Index (EVI) and Enhanced Water Index (EWI) can also provide greater confi-dence in the separation of water and marsh features from deciduous broadleaf forests [53]However a spatial resolution of 10m is still too coarse for the detection of remnant andlandscape trees especially those alongside homogenous flat features such as maintainedgrass or water bodies The most practical solution would be to assess this land with ahigher resolution dataset such as unmanned aerial systems (UAS) imagery which cancapture a much finer GSD in a plethora of bands if multispectral or hyperspectral sensorsare used [54] In fact scientists are now pushing the boundaries of the scale of analysisconducted through remote sensing Broad groupings (eg clades or forest types) have beenthe historic norm in the classification of imagery for use in land management A greatermixture of classes allows more accurate measurements for those interested in landscape-scale ecological processes However VHR can be used to map stands at the species levelwhich allows forest managers an unparalleled insight into the dynamics of recruitmentdisease or resource availability [55]

Google Earth Engine allowed us to utilize NAIP imagery at scale which had for sometime been a limiting factor in processing this data source locally Even so some limitationshindered the functional utility of GEE for detailed land-cover classification In our relativelyuniform environment we discriminated between many shades of green When viewinggeometries cause variations in hue different classes begin to overlap within the samespectral regions Solutions using Geographic Object-Based Image Analysis (GEOBIA) cancircumvent this issue yet this segmentation algorithm is currently not supported by GEEMany consider GEOBIA the optimal tool for segmenting landscapes and for the productionof land-cover classifications from this source of imagery [3856] Its successful integration

Forests 2022 13 1132 13 of 22

could substantially elevate the use and utility of mapping within GEE Additionally theallocation of increased processing bandwidth on GEE servers may help overcome somelimits in classification Greater bandwidth allows larger and more diverse training setsin addition to more complex parameterizations of machine learning classifiers If oneis proceeding on local workstations the analysis of NAIP imagery requires an intimateknowledge of data structures and process optimization Custom architectures processingpipelines and simpler binary classifications can allow an expansion of the study area tocover coterminous areas on a larger scale [16]

42 Forest Loss Contextually

We mapped forest cover change dynamics between 2003ndash2019 on the southwest coastof Louisiana 1580 hectares of tree cover 13 of the terrestrial area was converted todifferent land use The total loss of forest cover in the study period was 3573 over sixteenyears which is indicative of a larger rate of forest loss than at sites in a similar physio-geographic region yet is certainly not a global outlier [18] These findings validate thesentiments of people in the region and underscore a major irreversible trend in the area Ourestimate should be considered conservative due to the way we approached the definition ofldquoforest coverrdquo within the landscape Abandoned pastoral land exhibiting dense scrubbinessand shrub cover was demarcated as forest cover even though the target species (live oak)was probably not present Spoil banks which are the byproduct of historic oil explorationin the region indicating possible mortality were also often considered a component offorest cover if the imagery could not indicate the absence of high vegetation The primarymethod of identifying whether or not forest cover was present was the observation of ashadow adjoining the vegetation indicating a vertical structure

The Big Bend region of Florida one of the longest undisturbed coastlines in thecontinental US offers some insight into the vegetation dynamics this region has beenexperiencing It has been the focus of numerous ecological studies has a similar climateand is exposed to storms with similar regularity Raabe and Stumpf [57] found that fromthe early to late 1900s over 82 km2 of forest land was converted to marsh and 66 km2 wasin a transitional state to marsh McCarthy et al [58] document an annual forest cover lossof 744 from 2010ndash2017 an acceleration in the trend within that area A new study bythe same team found rates of loss totaling approximately 10 km2 a year 800 larger thanpreviously stated in the literature [59] Rates of annual mortality ranged from 4ndash16 inSoutheastern Louisiana from 1987 to 1997 [60] In comparison the annualized rate of lossfound in our study never exceeded 5 Plots located in South Carolina that have beenless significantly impacted by MSL rise show rates of loss closer to 1ndash2 in line withour observed rate of loss from 2007ndash2019 However we cannot assume that percentagesof forest loss across the country have been constant within the last century therefore it iscritical to carry out intensive geospatial studies at regular intervals

In the adjoining southeastern part of the state of Louisiana in the Atchafalaya andTerrebonne basins forested wetlands occupy 121871 hectares or 41 of all vegetatedland use [12] In comparison the terrestrial forested land use in our study area was 21in 2019 The wide variation can be interpreted in several ways each with importantimplications Historic rates of deforestation differ in the two regions with a greateremphasis on the preservation of wilderness areas in the Atchafalaya basin [61] TheAtchafalaya and Terrebonne basins also have productive land used in the growth of timberwhich promotes long-term forest cover The regeneration of forests in our study region alsodiffers substantially Chenier ecosystems and traditional alluvial bottomland hardwoodforests are compositionally different in vegetation and soil even when accounting for theirunique hydrologic regimes Communities of live oak may be less resilient to the types offorcings that cause changes in community composition within these ecosystems furtheredby proximity to the sea If stocking density rates are similar to the adjoining basin as theywere in the past we would see an areal extent well over 50000 hectares

Forests 2022 13 1132 14 of 22

The analysis vis-agrave-vis elevation shows weak trends in the manners of loss on eleva-tional gradients Logically in the paradigm of coastal retreat there should be an aggregationof forest resources towards the highest bands of topography in a region Our analysis doesnot follow this chain of thought with the largest losses observed in the 1ndash2 m categoryrather than the lowest 0ndash1 m (Table 2 2007 C 2019 D) While we do see a large loss bypercentage in the highest elevation band it is important to note that a small sample causesinherent variance Forest cover on the highest reaches of the chenier ridges is typically lessthan 1 of total forest cover for that region The reduction can be attributed to the loss of23 features in section A accounting for 33 hectares bordering the Sabine River (Figure 1minus9386286 2973355) The loss of forest cover was driven by intense weather Vegetationin the area was completely destroyed and substantial amounts of sedimentation could beobserved as evident in the 2007 NAIP imagery The only gain in forest cover was seen inthe 2ndash3 m elevation band in the period 2007 in an area that had a high density of pastoralland that was abandoned Forest retreat is often discussed as one method of bufferingforest loss along coasts [62] Unfortunately the geomorphology of this area does not allowgreat spatial movement as the current extent is already located within the highest bandsof elevation

43 Land Use Conversion and Driving Factors

The leading cause of land-use conversion from tree cover was to a saltmarsh ecotype(606) often following mortality and replacement of vegetative biomass The loss offorest cover can largely be typified into two categories The ingression of saltwater causingsalinification within the soil matrix paired with increased flooding caused mortality ofoverstorey vegetation over a multi-year timescale [6364] Hurricanes or other storm eventscaused rapid mortality through wind-throws severe defoliation saltwater inundationduring a storm surge or canopy damage Following intense weather events between2003 and 2007 the vast majority of cheniers located within a 1km buffer of the coast wereunable to support tree cover as seen in Figure 7AB Clearing for industry or developmentalso caused immediate shifts in forest composition as can be seen in Figure 8AB Thischange was more pronounced on reaches A and B especially on Hackberry Beach Thisarea was the site of landfall for Hurricane Rita which caused massive devastation to othercomponents of the ecosystem [6566] Singular storm events have a transformative impacton vegetation communities and the weight of this driver on annual rates of forest lossis a topic that should be explored further Wayward shifts in species composition are atrend that has been observed globally especially in forests that border some source of waterTrees like Juniperus virginiana L that have distinctive water-use patterns in comparison toother coastal tree species are susceptible to salinification at much faster rates as evidencedthrough isotope analyses [67] As with many ecological systems the entangled effectsof climate and vegetation require diverse means of looking at change Isotope analysesdendrochronology field sensors and remote sensing data are needed to characterize land-use change at the stand and community levels

Large forest remnants such as Cow Island (minus9283581 2973842) Tiger Island (minus92784392972832) and Grand Chenier Ridge (minus9281175 2972986) among others have diminishedmore slowly By 2019 these stands had lost an average of 15 of their maximum extentThe land use changed to both marsh and managed pasture largely dependent on thesurrounding land use The conversion to managed pasture (282) accounted for a largerthan expected amount of land-cover change Many of these changes were in areas wheretree cover may have been more ldquoornamentalrdquo in nature Trees beside houses or alongsideroads are some of the largest specimens of live-oak to be found However these trees areoften solitary and perhaps more vulnerable to wind-borne disturbance The concern is thattheir loss due to abiotic drivers may not be easily mitigated leading to a permanent declineOur data support evidence on the whittling of forest remnants and the eventual fate ofthese trees becoming lsquorelict forestsrsquo unable to recruit new generations in the short term andin the long-term total disappearance of these cheniers along the Louisiana Gulf Coast [68]

Forests 2022 13 1132 15 of 22Forests 2022 13 x FOR PEER REVIEW 16 of 23

Figure 7 Visual depictions of two forms of loss one from hurricanes on Hackberry Beach (AB) and

the other documenting whittling reduction (CD) The left column displays 2003 NAIP imagery and

the right is 2007 NAIP imagery Black shaded areas indicate current year forest extent red hashed

areas indicate a loss

Figure 7 Visual depictions of two forms of loss one from hurricanes on Hackberry Beach (AB) andthe other documenting whittling reduction (CD) The left column displays 2003 NAIP imagery andthe right is 2007 NAIP imagery Black shaded areas indicate current year forest extent red hashedareas indicate a loss

Beyond loss in forest cover there is a noticeable trend towards an altered hydrologywhere pastoral areas and intact forest remnants are associated with pockets of standingwater An increase in the correlative factor between temperature and water levels could bea worrying trend a direct conversion of tree cover to water at 34 could be an unforeseenoutcome Chenier ridges in their geomorphic context should not support non-ephemeralponding It is not documented how increased tidal influence which is one factor of risingMSL influences the hydrology of this ecosystem and its associated communities Albeitin a more inland context Magolan and Halls [69] documented a transition of over 40 ofland to emergent marsh from closed-canopy cover from 1949 to 2018 due to hydrologicinfluence Recent rates of relative sea rise have been documented as high as 119 mmyearcompared to background Holocene rates of 17 mmyear [70] This has resulted in a rate oflateral coastal forest retreat between 2ndash14 times greater than pre-industrial levels [26] Therate of shoreline retreat in this region increased from ~61 to ~94 myear based on the 100-and 30-year moving averages amounting to over 10000 hectares of land loss every yearfrom 1800 to 2005 [27]

Forests 2022 13 1132 16 of 22Forests 2022 13 x FOR PEER REVIEW 17 of 23

Figure 8 A depiction of clearing for industry in (AB) and a depiction of forest whittling from (CD)

The black extent represents the current forest area red hashed lines indicate a loss The left panels

show 2007 NAIP imagery while the right are 2019 NAIP imagery

Large forest remnants such as Cow Island (minus9283581 2973842) Tiger Island

(minus9278439 2972832) and Grand Chenier Ridge (minus9281175 2972986) among others have

diminished more slowly By 2019 these stands had lost an average of 15 of their maxi‐

mum extent The land use changed to both marsh and managed pasture largely depend‐

ent on the surrounding land use The conversion to managed pasture (282) accounted

for a larger than expected amount of land‐cover change Many of these changes were in

areas where tree cover may have been more ldquoornamentalrdquo in nature Trees beside houses

or alongside roads are some of the largest specimens of live‐oak to be found However

these trees are often solitary and perhaps more vulnerable to wind‐borne disturbance The

concern is that their loss due to abiotic drivers may not be easily mitigated leading to a

permanent decline Our data support evidence on the whittling of forest remnants and

the eventual fate of these trees becoming lsquorelict forestsrsquo unable to recruit new generations

in the short term and in the long‐term total disappearance of these cheniers along the

Louisiana Gulf Coast [68]

Beyond loss in forest cover there is a noticeable trend towards an altered hydrology

where pastoral areas and intact forest remnants are associated with pockets of standing

water An increase in the correlative factor between temperature and water levels could

be a worrying trend a direct conversion of tree cover to water at 34 could be an unfore‐

seen outcome Chenier ridges in their geomorphic context should not support non‐ephem‐

eral ponding It is not documented how increased tidal influence which is one factor of

Figure 8 A depiction of clearing for industry in (AB) and a depiction of forest whittling from (CD)The black extent represents the current forest area red hashed lines indicate a loss The left panelsshow 2007 NAIP imagery while the right are 2019 NAIP imagery

44 Future of Chenier Forests

The chenier complexes of Louisiana do not have the natural benefaction of maintaininglarge original tracts untouched by axe or dozer The extent map through the years (Figure 9)exemplifies the scattered nature of forest remnants The largest single continuous chenierforest (Grand Chenier Ridgemdash185 ha) encompasses less than 1 of the total forest areaExposed forest edges pose a problem for the continued integrity of the stand as adaptedspecies fail to compete in these new microclimates [71] Slow-growing saplings of desirabletree species (live oak hackberry) are subject to unfavorable growing conditions fromweather and light availability Vegetation surveys following aspect and inundation alongthe Florida coast have all but confirmed the eventual extirpation of overstory vegetationthrough poor recruitment of native species [31] Chinese tallow (Triadica sebifera) a non-native tree is likely to fill empty niche space This aggressive invader is problematicthroughout the Gulf Coastal Plain from Texas to Florida [72] The tree has a wide meansof dispersal being primarily distributed through frugivory via avian populations A highrate of germination compounded with quick growth and a sexual maturity of less thanthree years make it a difficult species to control Its invasive capability is seen in nearlyall forest types in the region although it is more pervasive in coastal forests than inlandModeling studies show a high likelihood of Chinese tallow becoming the dominant specieswithin this ecosystem and in surrounding areas [73] Future publications from this lab will

Forests 2022 13 1132 17 of 22

provide more in-depth data and analysis on the state of existing stands and recruitmenttrends within remnants of the Chenier Plains via field sampling

Louisiana has enacted a historic response to the problem of land-loss following a publicoutcry after devastating storms in 2005 The Coastal Protection and Restoration Authority(CPRA) have been tasked with enacting a management plan to combat coastal erosion TheComprehensive Master Plan details proposals to create marshes build sediment diversionsform new hydrologic structures and promote barrier island restoration through builtand natural means As of 2017 over 135 projects had been completed impacting over14568 ha [74] An update to this document is due to be released in 2023 The projecthas an overall operating budget of over 50 billion US dollars with equal shares dedicatedto restoration and risk reduction The primary funding source is a settlement associatedwith the Deepwater Horizon Oil Spill in addition to various initiatives at the state andfederal levels However there are no current plans to supplement vegetation or performany geoengineering work on the cheniers for afforestation

Coastal wetlands rank among the most important ecosystems in terms of the valuationof ecosystem services [75] Provisioning services such as water filtration or storm-surgeprotection are commonly considered when discussing coastal areas Beyond these com-monalities chenier forests provide vital wildlife habitats being the first encountered sourceof vertical structure for migratory neo-tropical birds and migrating monarch butterflies inthe Mississippi alluvial valley flyway [76] This en-route habitat is of critical importancedue to its closeness to the Gulf of Mexico During favorable weather conditions 10 ofbirds migrating through the Gulf use these forests as a stopover spot rising dramatically to80 during adverse weather conditions [29] With losses mounting many species may notbe able to seek refuge during storms The area also has a unique suite of amphibians andreptiles being home to 13 obligate chenier forest species of herpetofauna [77]

Lu et al [78] used the eddy covariance method (ECM) to calculate strong carbon sinkeffects for a chenier ecosystem driven by forest vegetation Net energy production valueswere significantly higher than surrounding marshland signifying the importance of densevegetative cover on a per-unit basis Counter to this there is also strong evidence thatdegraded forest ecosystems act as net carbon sources A study in North Carolina also usingECM found that a seasonally inundated tide-influenced forest was a net carbon sourceannually and only functioned as a sink for 3 months of the growing year [79] Similarly astudy in the northeastern part of the state showed net negative rates of carbon sequestrationfor an ephemerally flooded forest [80] Unique environments demand individual attentiona characterization of this ecosystem for nitrogen or carbon flux with respect to a similarsystem may not be functionally sound [81]

Forests 2022 13 1132 18 of 22Forests 2022 13 x FOR PEER REVIEW 19 of 23

Figure 9 The total land area classified in GEE for the years 2003 2007 and 2019 Light green repre‐

sents the marsh area tan represents pasture The water layer has been removed for purposes of

clarity The dark black outline is the outline of the forest cover for the respective year

Louisiana has enacted a historic response to the problem of land‐loss following a

public outcry after devastating storms in 2005 The Coastal Protection and Restoration

Authority (CPRA) have been tasked with enacting a management plan to combat coastal

erosion The Comprehensive Master Plan details proposals to create marshes build sedi‐

ment diversions form new hydrologic structures and promote barrier island restoration

through built and natural means As of 2017 over 135 projects had been completed im‐

pacting over 14568 ha [74] An update to this document is due to be released in 2023 The

project has an overall operating budget of over 50 billion US dollars with equal shares

dedicated to restoration and risk reduction The primary funding source is a settlement

associated with the Deepwater Horizon Oil Spill in addition to various initiatives at the

state and federal levels However there are no current plans to supplement vegetation or

perform any geoengineering work on the cheniers for afforestation

Coastal wetlands rank among the most important ecosystems in terms of the valua‐

tion of ecosystem services [75] Provisioning services such as water filtration or storm‐

surge protection are commonly considered when discussing coastal areas Beyond these

commonalities chenier forests provide vital wildlife habitats being the first encountered

source of vertical structure for migratory neo‐tropical birds and migrating monarch but‐

terflies in the Mississippi alluvial valley flyway [76] This en‐route habitat is of critical

importance due to its closeness to the Gulf of Mexico During favorable weather condi‐

tions 10 of birds migrating through the Gulf use these forests as a stopover spot rising

Figure 9 The total land area classified in GEE for the years 2003 2007 and 2019 Light greenrepresents the marsh area tan represents pasture The water layer has been removed for purposes ofclarity The dark black outline is the outline of the forest cover for the respective year

5 Conclusions

The cheniers of southwestern Louisiana were found to be declining in size at a concern-ing rate Many of the driving factors are ecosystem-wide trends that have been increasingin intensity within the last century The results of this study suggest that coastal forestsare an ecosystem in peril and the ecology of such systems should be a closely monitoredsubject The 365 loss of forest cover is an alarming but not wholly unexpected result ofmany decades of intense perturbation and human exploitation A dynamic of continuedforest loss is evident Unless significant investment is made in engineering solutions tomitigate storm effects and land subsidence we will continue to see the decline of chenierforests in Louisiana Through targeted mitigation governmental programs such as theWetland or Conservation Reserve Program may be able to supplement existing forestsas well as the incomes of local people who have lost their livelihoods In this study wehave begun the process of accurate areal estimates of forest extent and have shed light onecosystem processes in the region As this community continues to diminish we see theforest not by the trees but rather the lack of them

Forests 2022 13 1132 19 of 22

Author Contributions Conceptualization PT methodology PT software PT and PR validationPT and PR formal analysis PT PR and PT writingmdashoriginal draft preparation PT and JBwritingmdashreview and editing PT and JB visualization PT and JB supervision JB All authorshave read and agreed to the published version of the manuscript

Funding This research received no external funding

Data Availability Statement The data will be published in and stored on the Harvard Dataverse athttpsdataverseharvardedudatasetxhtmlpersistentId=doi107910DVN3J7SNF (accessed on1 May 2022)

Acknowledgments The authors would like to recognize the hard work of undergraduate studentsParusha Raut and Subash Sapkota in manually validating thousands of points and creating numeroustraining sets We also extend thanks to our institution and numerous people within the lab who leantboth mental and emotional fortitude

Conflicts of Interest The authors declare no conflict of interest

References1 Michener WK Blood ER Bildstein KL Brinson MM Gardner LR Climate change hurricanes and tropical storms and

rising sea level in coastal wetlands Ecol Appl 1997 7 770ndash801 [CrossRef]2 Donoghue JF Sea level history of the northern Gulf of Mexico coast and sea level rise scenarios for the near future Clim Chang

2011 107 17ndash33 [CrossRef]3 Stanturf JA Goodrick SL Outcalt KW Disturbance and coastal forests A strategic approach to forest management in

hurricane impact zones For Ecol Manag 2007 250 119ndash135 [CrossRef]4 Augustinus P Cheniers and chenier plains A general introduction Mar Geol 1989 90 219ndash229 [CrossRef]5 Fu Z Wang P Sun J Lu Z Yang H Liu J Li T Composition seasonal variation and salinization characteristics of soil

salinity in the Chenier Island of the Yellow River Delta Glob Ecol Conserv 2020 24 e01318 [CrossRef]6 Marques IG Campelo F Rivaes R Albuquerque A Ferreira MT Rodriacuteguez-Gonzaacutelez PM Tree rings reveal long-term

changes in growth resilience in Southern European riparian forests Dendrochronologia 2018 55 167ndash176 [CrossRef]7 Daigle JJ Griffith GE Omernik JM Faulkner PL McCulloh RP Handley LR Smith LM Chapman SS Ecoregions of

Louisiana (Color Poster with Map Descriptive Text Summary Tables and Photographs) US Geological Survey Reston VI USA 20068 McBride RA Taylor MJ Byrnes MR Coastal morphodynamics and Chenier-Plain evolution in southwestern Louisiana USA

A geomorphic model Geomorphology 2007 88 367ndash422 [CrossRef]9 Gosselink JG An Ecological Characterization Study of the Chenier Plain Coastal Ecosystem of Louisiana and Texas Narrative Report

National Coastal Ecosystems Team Office of Biological Services Fish and Wildlife Service US Department of the InteriorWashington DC USA 1979 Volume 1

10 Blum MD Roberts HH The Mississippi delta region Past present and future Annu Rev Earth Planet Sci 2012 40 655ndash683[CrossRef]

11 Hansen MC Potapov PV Moore R Hancher M Turubanova SA Tyukavina A Townshend J High-resolution globalmaps of 21st-century forest cover change Science 2013 342 850ndash853 [CrossRef]

12 Doyle TW Krauss KW Conner WH From AS Predicting the retreat and migration of tidal forests along the northern Gulfof Mexico under sea-level rise For Ecol Manag 2010 259 770ndash777 [CrossRef]

13 Thomas N Simard M Castantildeeda-Moya E Byrd K Windham-Myers L Bevington A Twilley R High-resolution mappingof biomass and distribution of marsh and forested wetlands in southeastern coastal Louisiana Int J Appl Earth Obs Geoinf 201980 257ndash267 [CrossRef]

14 Desantis LR Bhotika S Williams K Putz FE Sea-level rise and drought interactions accelerate forest decline on the GulfCoast of Florida USA Glob Chang Biol 2007 13 2349ndash2360 [CrossRef]

15 Boyd DS Foody G Ripple W Evaluation of approaches for forest cover estimation in the Pacific Northwest USA usingremote sensing Appl Geogr 2002 22 375ndash392 [CrossRef]

16 Valjarevic A Djekic T Stevanovic V Ivanovic R Jandzikovic B GIS numerical and remote sensing analyses of forest changesin the Toplica region for the period of 1953ndash2013 Appl Geogr 2018 92 131ndash139 [CrossRef]

17 He T Sun YJ Xu JD Wang X Hu C Enhanced land usecover classification using support vector machines and fuzzyk-means clustering algorithms J Appl Remote Sens 2014 8 083636 [CrossRef]

18 Michez A Pieacutegay H Lisein J Claessens H Lejeune P Classification of riparian forest species and health condition usingmulti-temporal and hyperspatial imagery from unmanned aerial systems Environ Monit Assess 2016 188 146 [CrossRef][PubMed]

19 Hampton SE Strasser C Tewksbury J Gram W Budden A Batcheller A Duke C Porter J Big data and the future ofecology Front Ecol Environ 2013 11 156ndash162 [CrossRef]

20 Curtis PG Slay CM Harris NL Tyukavina A Hansen MC Classifying drivers of global forest loss Science 2018 3611108ndash1111 [CrossRef]

Forests 2022 13 1132 10 of 22

32 Rates of Loss with Respect to Elevation

A net decrease in forest cover was seen across all elevation categories (Table 2 Figure 5)The largest losses were in the 1ndash2 m category with a net reduction of 593 from 2003 to2019 There was a temporal shift in the rate of change with the greatest amount of lossbetween 2003ndash2007 contributed by section D Values within the 2ndash3 m category remainedsteady from 2007 to 2019 with a reduction of only 076 The large majority (749) ofthe remaining forest was seen to be located within the 0ndash1 m band The losses were notevenly distributed within this band buffered to some degree by section D A comparisonof sections A vs C shows a decrease of 493 with losses centered around the period2007ndash2019 One issue to note is that the GSD for the DEM was 3 m which is wider thansome spoil banks While the local elevation may in fact rise to 2ndash3 m the value was reduceddue to the effect of averaging The losses in the 3 m+ band are discussed below a noteshould be made of the limited representation Overall the losses varied both spatially andtemporally across the region largely separated into eastern and western reaches

Table 2 Differences in the amount of forest area within each elevation band and the associatedpercentage loss of forest cover between years Sections A B C and D represent areas delineated forthis study within the two parishes (for details see Figure 1)

Sites and Forest Cover (ha)Percentage

LossYearmdash2003

Elevation (m) A B C D Total

0ndash1 13083 25525 66334 187236 2921781ndash2 27168 36374 25583 39048 1281722ndash3 1934 2188 6481 4640 15242gt3 361 170 005 343 879

Yearmdash2007A B C D Total 2003ndash2007

0ndash1 18371 16502 56349 166776 257997 minus11701ndash2 17834 20210 21289 4205 63538 minus50432ndash3 1228 1783 4118 5446 12575 minus1750gt3 048 208 002 404 662 minus2474

Yearmdash2019A B C D Total 2003ndash2019

0ndash1 7161 8227 37762 142102 195253 minus33171ndash2 10283 12913 24656 4401 52254 minus59232ndash3 1506 932 5789 4253 12480 minus1812gt3 045 141 002 402 590 minus3288

33 Interaction with Water Level

Water plays a critical role in the natural function of this ecosystem As such we consid-ered the interplay of temperature and MSL over a decadal time scale and the local rates oftemperature and gage height from both USGS stations (Figure 6) In Figure 6 forest extentis superimposed for each yearrsquos classification to show visual correspondence between thetwo drivers Temperature and rise in sea-level were negatively correlated until an inflectionpoint around 2010 after which there was a relatively stronger correlation increasing to amaximum of 025 The correlative increase lends evidence to the correspondence betweenclimate and water pressure in coastal areas An increase in the average MSL has been ob-served for the site The two stream gages showed an annualized MSL rise of 136 mmyearfor Vermilion Bay and 59 mmyear for the Calcasieu River There was a weak but positivetrend for temperature to increase slightly from the onset of the study period Time provedto be a good indicator for increase in water level and for decrease in forest cover (Figure 6)The lack of tree-cover samples prohibited many statistical comparisons but visually thecorrespondence seems evident

Forests 2022 13 1132 11 of 22

Forests 2022 13 x FOR PEER REVIEW 11 of 23

Table 2 Differences in the amount of forest area within each elevation band and the associated

percentage loss of forest cover between years Sections A B C and D represent areas delineated for

this study within the two parishes (for details see Figure 1)

Sites and Forest Cover (Ha)

Percentage Loss Yearmdash2003

Elevation (m) A B C D Total

0ndash1 13083 25525 66334 187236 292178

1ndash2 27168 36374 25583 39048 128172

2ndash3 1934 2188 6481 4640 15242

gt3 361 170 005 343 879

Yearmdash2007

A B C D Total 2003ndash2007

0ndash1 18371 16502 56349 166776 257997 minus1170

1ndash2 17834 20210 21289 4205 63538 minus5043

2ndash3 1228 1783 4118 5446 12575 minus1750

gt3 048 208 002 404 662 minus2474

Yearmdash2019

A B C D Total 2003ndash2019

0ndash1 7161 8227 37762 142102 195253 minus3317

1ndash2 10283 12913 24656 4401 52254 minus5923

2ndash3 1506 932 5789 4253 12480 minus1812

gt3 045 141 002 402 590 minus3288

0

1

2

3

4

5

6

7

8

9

10

0

500

1000

1500

2000

2500

3000

3500

Land area 3mdash4m ‐(ha)

Land area 0mdash3m ‐(ha)

Year

Forest cover in elevational bands over time

0‐1m

1‐2m

2‐3m

3‐4m

Figure 5 Visual depictions of two forms of loss one from hurricanes on Hackberry Beach and theother documenting whittling reduction The left column refers to 2003 NAIP imagery while the rightuses 2007 NAIP imagery Black shaded areas indicate current year forest extent and the red hashedareas indicate loss

Forests 2022 13 x FOR PEER REVIEW 12 of 23

Figure 5 Visual depictions of two forms of loss one from hurricanes on Hackberry Beach and the

other documenting whittling reduction The left column refers to 2003 NAIP imagery while the right

uses 2007 NAIP imagery Black shaded areas indicate current year forest extent and the red hashed

areas indicate loss

33 Interaction with Water Level

Water plays a critical role in the natural function of this ecosystem As such we con‐

sidered the interplay of temperature and MSL over a decadal time scale and the local

rates of temperature and gage height from both USGS stations (Figure 6) In Figure 6

forest extent is superimposed for each yearrsquos classification to show visual correspondence

between the two drivers Temperature and rise in sea‐level were negatively correlated

until an inflection point around 2010 after which there was a relatively stronger correla‐

tion increasing to a maximum of 025 The correlative increase lends evidence to the cor‐

respondence between climate and water pressure in coastal areas An increase in the av‐

erage MSL has been observed for the site The two stream gages showed an annualized

MSL rise of 136 mmyear for Vermilion Bay and 59 mmyear for the Calcasieu River

There was a weak but positive trend for temperature to increase slightly from the onset of

the study period Time proved to be a good indicator for increase in water level and for

decrease in forest cover (Figure 6) The lack of tree‐cover samples prohibited many statis‐

tical comparisons but visually the correspondence seems evident

Figure 6 Trends in mean water levels and water temperature over 19 years in Vermillion Parish

Data obtained from the USGS Station 07387050 Vermilion Bay (minus921355 296744) with gage datum

165 m above NAVD88 and from the USGS Station 08017118 Calcasieu River at Cameron LA

(minus933488 298155) with gage datum 107m above NAVD88

Rsup2 = 09453

Rsup2 = 01283

Rsup2 = 06529

08

085

09

095

1

105

11

115

12

125

1995 2000 2005 2010 2015 2020 2025

15

20

25

30

35

40

45

50

Average

water level (m)

Year

Forest cover loss Average

water temp

Loss of tree cover (sqkm) Average water temp (deg C) Average waterlevel (m)

Figure 6 Trends in mean water levels and water temperature over 19 years in Vermillion Parish Dataobtained from the USGS Station 07387050 Vermilion Bay (minus921355 296744) with gage datum 165 mabove NAVD88 and from the USGS Station 08017118 Calcasieu River at Cameron LA (minus933488298155) with gage datum 107m above NAVD88

Forests 2022 13 1132 12 of 22

4 Discussion41 Classification Reliability and Constraints

NAIP imagery proved efficacious for fine-scale analysis in this region shown by thestrong average producerrsquos accuracy and Kappa coefficient This is a positive indicatorthat variability between years or areas did not influence classification accuracies to alarge extent and that the final classification is a reliable interpretation [42] Many otherstudies conducting land-cover classifications using NAIP imagery have also shown robustaccuracies [43ndash45] However there were noted issues seen in certain parts of the workflowA problem preeminent in the initial classification was a limitation of spectral resolution Thisproblem became pronounced in areas with similar shades of green or between dark bluesand black hues Without the use of ancillary datasets the classification accuracy was poorbetween dark water and shadows of trees with considerable overlap (misclassificationsof approximately 50) Mixed land use can also prove problematic in NAIP land-coverclassifications [46] This became especially pronounced in transitional forestmarsh areasin which there was no dominant spectral signature for a particular land use Local variationbetween adjoining image sets highlighted another shortcoming of NAIP imagery Theviewing geometry illumination and time between the capture of neighboring imagetiles created edge effects including artificially high or low pixel values and refractionproblems on water [47] Each yearrsquos dataset was collected by an external contractor causingdifferences in sensor instrumentation and parametrization between years By 2019 many ofthese problems had been mitigated and images were of much higher quality

Compared to a large swath sensor such as Landsat-8 or Sentinel-2 an innate non-uniformity poses problems for pixel-wise classifications of VHR imagery Typically datafrom satellite imagery have been the primary choice for analyzing coastal forests marshesand forests at large [48ndash50] The spaceborne sensors obtain information over eight bandsallowing much finer spectral discrimination of certain classes of objects [51] Even amongstdiverse forest types space-borne Sentinel-2 and ASTER imagery has been used to mapdistinct species clusters of broadleaf and deciduous plant species The large spatial scaleenables the use of these created geospatial products to analyze the efficacy of governmentalreforestation programs in remote areas of the world [52] The usage of normalized differ-ence indices including the Normalized Difference Vegetation Index (NDVI) EnhancedVegetation Index (EVI) and Enhanced Water Index (EWI) can also provide greater confi-dence in the separation of water and marsh features from deciduous broadleaf forests [53]However a spatial resolution of 10m is still too coarse for the detection of remnant andlandscape trees especially those alongside homogenous flat features such as maintainedgrass or water bodies The most practical solution would be to assess this land with ahigher resolution dataset such as unmanned aerial systems (UAS) imagery which cancapture a much finer GSD in a plethora of bands if multispectral or hyperspectral sensorsare used [54] In fact scientists are now pushing the boundaries of the scale of analysisconducted through remote sensing Broad groupings (eg clades or forest types) have beenthe historic norm in the classification of imagery for use in land management A greatermixture of classes allows more accurate measurements for those interested in landscape-scale ecological processes However VHR can be used to map stands at the species levelwhich allows forest managers an unparalleled insight into the dynamics of recruitmentdisease or resource availability [55]

Google Earth Engine allowed us to utilize NAIP imagery at scale which had for sometime been a limiting factor in processing this data source locally Even so some limitationshindered the functional utility of GEE for detailed land-cover classification In our relativelyuniform environment we discriminated between many shades of green When viewinggeometries cause variations in hue different classes begin to overlap within the samespectral regions Solutions using Geographic Object-Based Image Analysis (GEOBIA) cancircumvent this issue yet this segmentation algorithm is currently not supported by GEEMany consider GEOBIA the optimal tool for segmenting landscapes and for the productionof land-cover classifications from this source of imagery [3856] Its successful integration

Forests 2022 13 1132 13 of 22

could substantially elevate the use and utility of mapping within GEE Additionally theallocation of increased processing bandwidth on GEE servers may help overcome somelimits in classification Greater bandwidth allows larger and more diverse training setsin addition to more complex parameterizations of machine learning classifiers If oneis proceeding on local workstations the analysis of NAIP imagery requires an intimateknowledge of data structures and process optimization Custom architectures processingpipelines and simpler binary classifications can allow an expansion of the study area tocover coterminous areas on a larger scale [16]

42 Forest Loss Contextually

We mapped forest cover change dynamics between 2003ndash2019 on the southwest coastof Louisiana 1580 hectares of tree cover 13 of the terrestrial area was converted todifferent land use The total loss of forest cover in the study period was 3573 over sixteenyears which is indicative of a larger rate of forest loss than at sites in a similar physio-geographic region yet is certainly not a global outlier [18] These findings validate thesentiments of people in the region and underscore a major irreversible trend in the area Ourestimate should be considered conservative due to the way we approached the definition ofldquoforest coverrdquo within the landscape Abandoned pastoral land exhibiting dense scrubbinessand shrub cover was demarcated as forest cover even though the target species (live oak)was probably not present Spoil banks which are the byproduct of historic oil explorationin the region indicating possible mortality were also often considered a component offorest cover if the imagery could not indicate the absence of high vegetation The primarymethod of identifying whether or not forest cover was present was the observation of ashadow adjoining the vegetation indicating a vertical structure

The Big Bend region of Florida one of the longest undisturbed coastlines in thecontinental US offers some insight into the vegetation dynamics this region has beenexperiencing It has been the focus of numerous ecological studies has a similar climateand is exposed to storms with similar regularity Raabe and Stumpf [57] found that fromthe early to late 1900s over 82 km2 of forest land was converted to marsh and 66 km2 wasin a transitional state to marsh McCarthy et al [58] document an annual forest cover lossof 744 from 2010ndash2017 an acceleration in the trend within that area A new study bythe same team found rates of loss totaling approximately 10 km2 a year 800 larger thanpreviously stated in the literature [59] Rates of annual mortality ranged from 4ndash16 inSoutheastern Louisiana from 1987 to 1997 [60] In comparison the annualized rate of lossfound in our study never exceeded 5 Plots located in South Carolina that have beenless significantly impacted by MSL rise show rates of loss closer to 1ndash2 in line withour observed rate of loss from 2007ndash2019 However we cannot assume that percentagesof forest loss across the country have been constant within the last century therefore it iscritical to carry out intensive geospatial studies at regular intervals

In the adjoining southeastern part of the state of Louisiana in the Atchafalaya andTerrebonne basins forested wetlands occupy 121871 hectares or 41 of all vegetatedland use [12] In comparison the terrestrial forested land use in our study area was 21in 2019 The wide variation can be interpreted in several ways each with importantimplications Historic rates of deforestation differ in the two regions with a greateremphasis on the preservation of wilderness areas in the Atchafalaya basin [61] TheAtchafalaya and Terrebonne basins also have productive land used in the growth of timberwhich promotes long-term forest cover The regeneration of forests in our study region alsodiffers substantially Chenier ecosystems and traditional alluvial bottomland hardwoodforests are compositionally different in vegetation and soil even when accounting for theirunique hydrologic regimes Communities of live oak may be less resilient to the types offorcings that cause changes in community composition within these ecosystems furtheredby proximity to the sea If stocking density rates are similar to the adjoining basin as theywere in the past we would see an areal extent well over 50000 hectares

Forests 2022 13 1132 14 of 22

The analysis vis-agrave-vis elevation shows weak trends in the manners of loss on eleva-tional gradients Logically in the paradigm of coastal retreat there should be an aggregationof forest resources towards the highest bands of topography in a region Our analysis doesnot follow this chain of thought with the largest losses observed in the 1ndash2 m categoryrather than the lowest 0ndash1 m (Table 2 2007 C 2019 D) While we do see a large loss bypercentage in the highest elevation band it is important to note that a small sample causesinherent variance Forest cover on the highest reaches of the chenier ridges is typically lessthan 1 of total forest cover for that region The reduction can be attributed to the loss of23 features in section A accounting for 33 hectares bordering the Sabine River (Figure 1minus9386286 2973355) The loss of forest cover was driven by intense weather Vegetationin the area was completely destroyed and substantial amounts of sedimentation could beobserved as evident in the 2007 NAIP imagery The only gain in forest cover was seen inthe 2ndash3 m elevation band in the period 2007 in an area that had a high density of pastoralland that was abandoned Forest retreat is often discussed as one method of bufferingforest loss along coasts [62] Unfortunately the geomorphology of this area does not allowgreat spatial movement as the current extent is already located within the highest bandsof elevation

43 Land Use Conversion and Driving Factors

The leading cause of land-use conversion from tree cover was to a saltmarsh ecotype(606) often following mortality and replacement of vegetative biomass The loss offorest cover can largely be typified into two categories The ingression of saltwater causingsalinification within the soil matrix paired with increased flooding caused mortality ofoverstorey vegetation over a multi-year timescale [6364] Hurricanes or other storm eventscaused rapid mortality through wind-throws severe defoliation saltwater inundationduring a storm surge or canopy damage Following intense weather events between2003 and 2007 the vast majority of cheniers located within a 1km buffer of the coast wereunable to support tree cover as seen in Figure 7AB Clearing for industry or developmentalso caused immediate shifts in forest composition as can be seen in Figure 8AB Thischange was more pronounced on reaches A and B especially on Hackberry Beach Thisarea was the site of landfall for Hurricane Rita which caused massive devastation to othercomponents of the ecosystem [6566] Singular storm events have a transformative impacton vegetation communities and the weight of this driver on annual rates of forest lossis a topic that should be explored further Wayward shifts in species composition are atrend that has been observed globally especially in forests that border some source of waterTrees like Juniperus virginiana L that have distinctive water-use patterns in comparison toother coastal tree species are susceptible to salinification at much faster rates as evidencedthrough isotope analyses [67] As with many ecological systems the entangled effectsof climate and vegetation require diverse means of looking at change Isotope analysesdendrochronology field sensors and remote sensing data are needed to characterize land-use change at the stand and community levels

Large forest remnants such as Cow Island (minus9283581 2973842) Tiger Island (minus92784392972832) and Grand Chenier Ridge (minus9281175 2972986) among others have diminishedmore slowly By 2019 these stands had lost an average of 15 of their maximum extentThe land use changed to both marsh and managed pasture largely dependent on thesurrounding land use The conversion to managed pasture (282) accounted for a largerthan expected amount of land-cover change Many of these changes were in areas wheretree cover may have been more ldquoornamentalrdquo in nature Trees beside houses or alongsideroads are some of the largest specimens of live-oak to be found However these trees areoften solitary and perhaps more vulnerable to wind-borne disturbance The concern is thattheir loss due to abiotic drivers may not be easily mitigated leading to a permanent declineOur data support evidence on the whittling of forest remnants and the eventual fate ofthese trees becoming lsquorelict forestsrsquo unable to recruit new generations in the short term andin the long-term total disappearance of these cheniers along the Louisiana Gulf Coast [68]

Forests 2022 13 1132 15 of 22Forests 2022 13 x FOR PEER REVIEW 16 of 23

Figure 7 Visual depictions of two forms of loss one from hurricanes on Hackberry Beach (AB) and

the other documenting whittling reduction (CD) The left column displays 2003 NAIP imagery and

the right is 2007 NAIP imagery Black shaded areas indicate current year forest extent red hashed

areas indicate a loss

Figure 7 Visual depictions of two forms of loss one from hurricanes on Hackberry Beach (AB) andthe other documenting whittling reduction (CD) The left column displays 2003 NAIP imagery andthe right is 2007 NAIP imagery Black shaded areas indicate current year forest extent red hashedareas indicate a loss

Beyond loss in forest cover there is a noticeable trend towards an altered hydrologywhere pastoral areas and intact forest remnants are associated with pockets of standingwater An increase in the correlative factor between temperature and water levels could bea worrying trend a direct conversion of tree cover to water at 34 could be an unforeseenoutcome Chenier ridges in their geomorphic context should not support non-ephemeralponding It is not documented how increased tidal influence which is one factor of risingMSL influences the hydrology of this ecosystem and its associated communities Albeitin a more inland context Magolan and Halls [69] documented a transition of over 40 ofland to emergent marsh from closed-canopy cover from 1949 to 2018 due to hydrologicinfluence Recent rates of relative sea rise have been documented as high as 119 mmyearcompared to background Holocene rates of 17 mmyear [70] This has resulted in a rate oflateral coastal forest retreat between 2ndash14 times greater than pre-industrial levels [26] Therate of shoreline retreat in this region increased from ~61 to ~94 myear based on the 100-and 30-year moving averages amounting to over 10000 hectares of land loss every yearfrom 1800 to 2005 [27]

Forests 2022 13 1132 16 of 22Forests 2022 13 x FOR PEER REVIEW 17 of 23

Figure 8 A depiction of clearing for industry in (AB) and a depiction of forest whittling from (CD)

The black extent represents the current forest area red hashed lines indicate a loss The left panels

show 2007 NAIP imagery while the right are 2019 NAIP imagery

Large forest remnants such as Cow Island (minus9283581 2973842) Tiger Island

(minus9278439 2972832) and Grand Chenier Ridge (minus9281175 2972986) among others have

diminished more slowly By 2019 these stands had lost an average of 15 of their maxi‐

mum extent The land use changed to both marsh and managed pasture largely depend‐

ent on the surrounding land use The conversion to managed pasture (282) accounted

for a larger than expected amount of land‐cover change Many of these changes were in

areas where tree cover may have been more ldquoornamentalrdquo in nature Trees beside houses

or alongside roads are some of the largest specimens of live‐oak to be found However

these trees are often solitary and perhaps more vulnerable to wind‐borne disturbance The

concern is that their loss due to abiotic drivers may not be easily mitigated leading to a

permanent decline Our data support evidence on the whittling of forest remnants and

the eventual fate of these trees becoming lsquorelict forestsrsquo unable to recruit new generations

in the short term and in the long‐term total disappearance of these cheniers along the

Louisiana Gulf Coast [68]

Beyond loss in forest cover there is a noticeable trend towards an altered hydrology

where pastoral areas and intact forest remnants are associated with pockets of standing

water An increase in the correlative factor between temperature and water levels could

be a worrying trend a direct conversion of tree cover to water at 34 could be an unfore‐

seen outcome Chenier ridges in their geomorphic context should not support non‐ephem‐

eral ponding It is not documented how increased tidal influence which is one factor of

Figure 8 A depiction of clearing for industry in (AB) and a depiction of forest whittling from (CD)The black extent represents the current forest area red hashed lines indicate a loss The left panelsshow 2007 NAIP imagery while the right are 2019 NAIP imagery

44 Future of Chenier Forests

The chenier complexes of Louisiana do not have the natural benefaction of maintaininglarge original tracts untouched by axe or dozer The extent map through the years (Figure 9)exemplifies the scattered nature of forest remnants The largest single continuous chenierforest (Grand Chenier Ridgemdash185 ha) encompasses less than 1 of the total forest areaExposed forest edges pose a problem for the continued integrity of the stand as adaptedspecies fail to compete in these new microclimates [71] Slow-growing saplings of desirabletree species (live oak hackberry) are subject to unfavorable growing conditions fromweather and light availability Vegetation surveys following aspect and inundation alongthe Florida coast have all but confirmed the eventual extirpation of overstory vegetationthrough poor recruitment of native species [31] Chinese tallow (Triadica sebifera) a non-native tree is likely to fill empty niche space This aggressive invader is problematicthroughout the Gulf Coastal Plain from Texas to Florida [72] The tree has a wide meansof dispersal being primarily distributed through frugivory via avian populations A highrate of germination compounded with quick growth and a sexual maturity of less thanthree years make it a difficult species to control Its invasive capability is seen in nearlyall forest types in the region although it is more pervasive in coastal forests than inlandModeling studies show a high likelihood of Chinese tallow becoming the dominant specieswithin this ecosystem and in surrounding areas [73] Future publications from this lab will

Forests 2022 13 1132 17 of 22

provide more in-depth data and analysis on the state of existing stands and recruitmenttrends within remnants of the Chenier Plains via field sampling

Louisiana has enacted a historic response to the problem of land-loss following a publicoutcry after devastating storms in 2005 The Coastal Protection and Restoration Authority(CPRA) have been tasked with enacting a management plan to combat coastal erosion TheComprehensive Master Plan details proposals to create marshes build sediment diversionsform new hydrologic structures and promote barrier island restoration through builtand natural means As of 2017 over 135 projects had been completed impacting over14568 ha [74] An update to this document is due to be released in 2023 The projecthas an overall operating budget of over 50 billion US dollars with equal shares dedicatedto restoration and risk reduction The primary funding source is a settlement associatedwith the Deepwater Horizon Oil Spill in addition to various initiatives at the state andfederal levels However there are no current plans to supplement vegetation or performany geoengineering work on the cheniers for afforestation

Coastal wetlands rank among the most important ecosystems in terms of the valuationof ecosystem services [75] Provisioning services such as water filtration or storm-surgeprotection are commonly considered when discussing coastal areas Beyond these com-monalities chenier forests provide vital wildlife habitats being the first encountered sourceof vertical structure for migratory neo-tropical birds and migrating monarch butterflies inthe Mississippi alluvial valley flyway [76] This en-route habitat is of critical importancedue to its closeness to the Gulf of Mexico During favorable weather conditions 10 ofbirds migrating through the Gulf use these forests as a stopover spot rising dramatically to80 during adverse weather conditions [29] With losses mounting many species may notbe able to seek refuge during storms The area also has a unique suite of amphibians andreptiles being home to 13 obligate chenier forest species of herpetofauna [77]

Lu et al [78] used the eddy covariance method (ECM) to calculate strong carbon sinkeffects for a chenier ecosystem driven by forest vegetation Net energy production valueswere significantly higher than surrounding marshland signifying the importance of densevegetative cover on a per-unit basis Counter to this there is also strong evidence thatdegraded forest ecosystems act as net carbon sources A study in North Carolina also usingECM found that a seasonally inundated tide-influenced forest was a net carbon sourceannually and only functioned as a sink for 3 months of the growing year [79] Similarly astudy in the northeastern part of the state showed net negative rates of carbon sequestrationfor an ephemerally flooded forest [80] Unique environments demand individual attentiona characterization of this ecosystem for nitrogen or carbon flux with respect to a similarsystem may not be functionally sound [81]

Forests 2022 13 1132 18 of 22Forests 2022 13 x FOR PEER REVIEW 19 of 23

Figure 9 The total land area classified in GEE for the years 2003 2007 and 2019 Light green repre‐

sents the marsh area tan represents pasture The water layer has been removed for purposes of

clarity The dark black outline is the outline of the forest cover for the respective year

Louisiana has enacted a historic response to the problem of land‐loss following a

public outcry after devastating storms in 2005 The Coastal Protection and Restoration

Authority (CPRA) have been tasked with enacting a management plan to combat coastal

erosion The Comprehensive Master Plan details proposals to create marshes build sedi‐

ment diversions form new hydrologic structures and promote barrier island restoration

through built and natural means As of 2017 over 135 projects had been completed im‐

pacting over 14568 ha [74] An update to this document is due to be released in 2023 The

project has an overall operating budget of over 50 billion US dollars with equal shares

dedicated to restoration and risk reduction The primary funding source is a settlement

associated with the Deepwater Horizon Oil Spill in addition to various initiatives at the

state and federal levels However there are no current plans to supplement vegetation or

perform any geoengineering work on the cheniers for afforestation

Coastal wetlands rank among the most important ecosystems in terms of the valua‐

tion of ecosystem services [75] Provisioning services such as water filtration or storm‐

surge protection are commonly considered when discussing coastal areas Beyond these

commonalities chenier forests provide vital wildlife habitats being the first encountered

source of vertical structure for migratory neo‐tropical birds and migrating monarch but‐

terflies in the Mississippi alluvial valley flyway [76] This en‐route habitat is of critical

importance due to its closeness to the Gulf of Mexico During favorable weather condi‐

tions 10 of birds migrating through the Gulf use these forests as a stopover spot rising

Figure 9 The total land area classified in GEE for the years 2003 2007 and 2019 Light greenrepresents the marsh area tan represents pasture The water layer has been removed for purposes ofclarity The dark black outline is the outline of the forest cover for the respective year

5 Conclusions

The cheniers of southwestern Louisiana were found to be declining in size at a concern-ing rate Many of the driving factors are ecosystem-wide trends that have been increasingin intensity within the last century The results of this study suggest that coastal forestsare an ecosystem in peril and the ecology of such systems should be a closely monitoredsubject The 365 loss of forest cover is an alarming but not wholly unexpected result ofmany decades of intense perturbation and human exploitation A dynamic of continuedforest loss is evident Unless significant investment is made in engineering solutions tomitigate storm effects and land subsidence we will continue to see the decline of chenierforests in Louisiana Through targeted mitigation governmental programs such as theWetland or Conservation Reserve Program may be able to supplement existing forestsas well as the incomes of local people who have lost their livelihoods In this study wehave begun the process of accurate areal estimates of forest extent and have shed light onecosystem processes in the region As this community continues to diminish we see theforest not by the trees but rather the lack of them

Forests 2022 13 1132 19 of 22

Author Contributions Conceptualization PT methodology PT software PT and PR validationPT and PR formal analysis PT PR and PT writingmdashoriginal draft preparation PT and JBwritingmdashreview and editing PT and JB visualization PT and JB supervision JB All authorshave read and agreed to the published version of the manuscript

Funding This research received no external funding

Data Availability Statement The data will be published in and stored on the Harvard Dataverse athttpsdataverseharvardedudatasetxhtmlpersistentId=doi107910DVN3J7SNF (accessed on1 May 2022)

Acknowledgments The authors would like to recognize the hard work of undergraduate studentsParusha Raut and Subash Sapkota in manually validating thousands of points and creating numeroustraining sets We also extend thanks to our institution and numerous people within the lab who leantboth mental and emotional fortitude

Conflicts of Interest The authors declare no conflict of interest

References1 Michener WK Blood ER Bildstein KL Brinson MM Gardner LR Climate change hurricanes and tropical storms and

rising sea level in coastal wetlands Ecol Appl 1997 7 770ndash801 [CrossRef]2 Donoghue JF Sea level history of the northern Gulf of Mexico coast and sea level rise scenarios for the near future Clim Chang

2011 107 17ndash33 [CrossRef]3 Stanturf JA Goodrick SL Outcalt KW Disturbance and coastal forests A strategic approach to forest management in

hurricane impact zones For Ecol Manag 2007 250 119ndash135 [CrossRef]4 Augustinus P Cheniers and chenier plains A general introduction Mar Geol 1989 90 219ndash229 [CrossRef]5 Fu Z Wang P Sun J Lu Z Yang H Liu J Li T Composition seasonal variation and salinization characteristics of soil

salinity in the Chenier Island of the Yellow River Delta Glob Ecol Conserv 2020 24 e01318 [CrossRef]6 Marques IG Campelo F Rivaes R Albuquerque A Ferreira MT Rodriacuteguez-Gonzaacutelez PM Tree rings reveal long-term

changes in growth resilience in Southern European riparian forests Dendrochronologia 2018 55 167ndash176 [CrossRef]7 Daigle JJ Griffith GE Omernik JM Faulkner PL McCulloh RP Handley LR Smith LM Chapman SS Ecoregions of

Louisiana (Color Poster with Map Descriptive Text Summary Tables and Photographs) US Geological Survey Reston VI USA 20068 McBride RA Taylor MJ Byrnes MR Coastal morphodynamics and Chenier-Plain evolution in southwestern Louisiana USA

A geomorphic model Geomorphology 2007 88 367ndash422 [CrossRef]9 Gosselink JG An Ecological Characterization Study of the Chenier Plain Coastal Ecosystem of Louisiana and Texas Narrative Report

National Coastal Ecosystems Team Office of Biological Services Fish and Wildlife Service US Department of the InteriorWashington DC USA 1979 Volume 1

10 Blum MD Roberts HH The Mississippi delta region Past present and future Annu Rev Earth Planet Sci 2012 40 655ndash683[CrossRef]

11 Hansen MC Potapov PV Moore R Hancher M Turubanova SA Tyukavina A Townshend J High-resolution globalmaps of 21st-century forest cover change Science 2013 342 850ndash853 [CrossRef]

12 Doyle TW Krauss KW Conner WH From AS Predicting the retreat and migration of tidal forests along the northern Gulfof Mexico under sea-level rise For Ecol Manag 2010 259 770ndash777 [CrossRef]

13 Thomas N Simard M Castantildeeda-Moya E Byrd K Windham-Myers L Bevington A Twilley R High-resolution mappingof biomass and distribution of marsh and forested wetlands in southeastern coastal Louisiana Int J Appl Earth Obs Geoinf 201980 257ndash267 [CrossRef]

14 Desantis LR Bhotika S Williams K Putz FE Sea-level rise and drought interactions accelerate forest decline on the GulfCoast of Florida USA Glob Chang Biol 2007 13 2349ndash2360 [CrossRef]

15 Boyd DS Foody G Ripple W Evaluation of approaches for forest cover estimation in the Pacific Northwest USA usingremote sensing Appl Geogr 2002 22 375ndash392 [CrossRef]

16 Valjarevic A Djekic T Stevanovic V Ivanovic R Jandzikovic B GIS numerical and remote sensing analyses of forest changesin the Toplica region for the period of 1953ndash2013 Appl Geogr 2018 92 131ndash139 [CrossRef]

17 He T Sun YJ Xu JD Wang X Hu C Enhanced land usecover classification using support vector machines and fuzzyk-means clustering algorithms J Appl Remote Sens 2014 8 083636 [CrossRef]

18 Michez A Pieacutegay H Lisein J Claessens H Lejeune P Classification of riparian forest species and health condition usingmulti-temporal and hyperspatial imagery from unmanned aerial systems Environ Monit Assess 2016 188 146 [CrossRef][PubMed]

19 Hampton SE Strasser C Tewksbury J Gram W Budden A Batcheller A Duke C Porter J Big data and the future ofecology Front Ecol Environ 2013 11 156ndash162 [CrossRef]

20 Curtis PG Slay CM Harris NL Tyukavina A Hansen MC Classifying drivers of global forest loss Science 2018 3611108ndash1111 [CrossRef]

Forests 2022 13 1132 11 of 22

Forests 2022 13 x FOR PEER REVIEW 11 of 23

Table 2 Differences in the amount of forest area within each elevation band and the associated

percentage loss of forest cover between years Sections A B C and D represent areas delineated for

this study within the two parishes (for details see Figure 1)

Sites and Forest Cover (Ha)

Percentage Loss Yearmdash2003

Elevation (m) A B C D Total

0ndash1 13083 25525 66334 187236 292178

1ndash2 27168 36374 25583 39048 128172

2ndash3 1934 2188 6481 4640 15242

gt3 361 170 005 343 879

Yearmdash2007

A B C D Total 2003ndash2007

0ndash1 18371 16502 56349 166776 257997 minus1170

1ndash2 17834 20210 21289 4205 63538 minus5043

2ndash3 1228 1783 4118 5446 12575 minus1750

gt3 048 208 002 404 662 minus2474

Yearmdash2019

A B C D Total 2003ndash2019

0ndash1 7161 8227 37762 142102 195253 minus3317

1ndash2 10283 12913 24656 4401 52254 minus5923

2ndash3 1506 932 5789 4253 12480 minus1812

gt3 045 141 002 402 590 minus3288

0

1

2

3

4

5

6

7

8

9

10

0

500

1000

1500

2000

2500

3000

3500

Land area 3mdash4m ‐(ha)

Land area 0mdash3m ‐(ha)

Year

Forest cover in elevational bands over time

0‐1m

1‐2m

2‐3m

3‐4m

Figure 5 Visual depictions of two forms of loss one from hurricanes on Hackberry Beach and theother documenting whittling reduction The left column refers to 2003 NAIP imagery while the rightuses 2007 NAIP imagery Black shaded areas indicate current year forest extent and the red hashedareas indicate loss

Forests 2022 13 x FOR PEER REVIEW 12 of 23

Figure 5 Visual depictions of two forms of loss one from hurricanes on Hackberry Beach and the

other documenting whittling reduction The left column refers to 2003 NAIP imagery while the right

uses 2007 NAIP imagery Black shaded areas indicate current year forest extent and the red hashed

areas indicate loss

33 Interaction with Water Level

Water plays a critical role in the natural function of this ecosystem As such we con‐

sidered the interplay of temperature and MSL over a decadal time scale and the local

rates of temperature and gage height from both USGS stations (Figure 6) In Figure 6

forest extent is superimposed for each yearrsquos classification to show visual correspondence

between the two drivers Temperature and rise in sea‐level were negatively correlated

until an inflection point around 2010 after which there was a relatively stronger correla‐

tion increasing to a maximum of 025 The correlative increase lends evidence to the cor‐

respondence between climate and water pressure in coastal areas An increase in the av‐

erage MSL has been observed for the site The two stream gages showed an annualized

MSL rise of 136 mmyear for Vermilion Bay and 59 mmyear for the Calcasieu River

There was a weak but positive trend for temperature to increase slightly from the onset of

the study period Time proved to be a good indicator for increase in water level and for

decrease in forest cover (Figure 6) The lack of tree‐cover samples prohibited many statis‐

tical comparisons but visually the correspondence seems evident

Figure 6 Trends in mean water levels and water temperature over 19 years in Vermillion Parish

Data obtained from the USGS Station 07387050 Vermilion Bay (minus921355 296744) with gage datum

165 m above NAVD88 and from the USGS Station 08017118 Calcasieu River at Cameron LA

(minus933488 298155) with gage datum 107m above NAVD88

Rsup2 = 09453

Rsup2 = 01283

Rsup2 = 06529

08

085

09

095

1

105

11

115

12

125

1995 2000 2005 2010 2015 2020 2025

15

20

25

30

35

40

45

50

Average

water level (m)

Year

Forest cover loss Average

water temp

Loss of tree cover (sqkm) Average water temp (deg C) Average waterlevel (m)

Figure 6 Trends in mean water levels and water temperature over 19 years in Vermillion Parish Dataobtained from the USGS Station 07387050 Vermilion Bay (minus921355 296744) with gage datum 165 mabove NAVD88 and from the USGS Station 08017118 Calcasieu River at Cameron LA (minus933488298155) with gage datum 107m above NAVD88

Forests 2022 13 1132 12 of 22

4 Discussion41 Classification Reliability and Constraints

NAIP imagery proved efficacious for fine-scale analysis in this region shown by thestrong average producerrsquos accuracy and Kappa coefficient This is a positive indicatorthat variability between years or areas did not influence classification accuracies to alarge extent and that the final classification is a reliable interpretation [42] Many otherstudies conducting land-cover classifications using NAIP imagery have also shown robustaccuracies [43ndash45] However there were noted issues seen in certain parts of the workflowA problem preeminent in the initial classification was a limitation of spectral resolution Thisproblem became pronounced in areas with similar shades of green or between dark bluesand black hues Without the use of ancillary datasets the classification accuracy was poorbetween dark water and shadows of trees with considerable overlap (misclassificationsof approximately 50) Mixed land use can also prove problematic in NAIP land-coverclassifications [46] This became especially pronounced in transitional forestmarsh areasin which there was no dominant spectral signature for a particular land use Local variationbetween adjoining image sets highlighted another shortcoming of NAIP imagery Theviewing geometry illumination and time between the capture of neighboring imagetiles created edge effects including artificially high or low pixel values and refractionproblems on water [47] Each yearrsquos dataset was collected by an external contractor causingdifferences in sensor instrumentation and parametrization between years By 2019 many ofthese problems had been mitigated and images were of much higher quality

Compared to a large swath sensor such as Landsat-8 or Sentinel-2 an innate non-uniformity poses problems for pixel-wise classifications of VHR imagery Typically datafrom satellite imagery have been the primary choice for analyzing coastal forests marshesand forests at large [48ndash50] The spaceborne sensors obtain information over eight bandsallowing much finer spectral discrimination of certain classes of objects [51] Even amongstdiverse forest types space-borne Sentinel-2 and ASTER imagery has been used to mapdistinct species clusters of broadleaf and deciduous plant species The large spatial scaleenables the use of these created geospatial products to analyze the efficacy of governmentalreforestation programs in remote areas of the world [52] The usage of normalized differ-ence indices including the Normalized Difference Vegetation Index (NDVI) EnhancedVegetation Index (EVI) and Enhanced Water Index (EWI) can also provide greater confi-dence in the separation of water and marsh features from deciduous broadleaf forests [53]However a spatial resolution of 10m is still too coarse for the detection of remnant andlandscape trees especially those alongside homogenous flat features such as maintainedgrass or water bodies The most practical solution would be to assess this land with ahigher resolution dataset such as unmanned aerial systems (UAS) imagery which cancapture a much finer GSD in a plethora of bands if multispectral or hyperspectral sensorsare used [54] In fact scientists are now pushing the boundaries of the scale of analysisconducted through remote sensing Broad groupings (eg clades or forest types) have beenthe historic norm in the classification of imagery for use in land management A greatermixture of classes allows more accurate measurements for those interested in landscape-scale ecological processes However VHR can be used to map stands at the species levelwhich allows forest managers an unparalleled insight into the dynamics of recruitmentdisease or resource availability [55]

Google Earth Engine allowed us to utilize NAIP imagery at scale which had for sometime been a limiting factor in processing this data source locally Even so some limitationshindered the functional utility of GEE for detailed land-cover classification In our relativelyuniform environment we discriminated between many shades of green When viewinggeometries cause variations in hue different classes begin to overlap within the samespectral regions Solutions using Geographic Object-Based Image Analysis (GEOBIA) cancircumvent this issue yet this segmentation algorithm is currently not supported by GEEMany consider GEOBIA the optimal tool for segmenting landscapes and for the productionof land-cover classifications from this source of imagery [3856] Its successful integration

Forests 2022 13 1132 13 of 22

could substantially elevate the use and utility of mapping within GEE Additionally theallocation of increased processing bandwidth on GEE servers may help overcome somelimits in classification Greater bandwidth allows larger and more diverse training setsin addition to more complex parameterizations of machine learning classifiers If oneis proceeding on local workstations the analysis of NAIP imagery requires an intimateknowledge of data structures and process optimization Custom architectures processingpipelines and simpler binary classifications can allow an expansion of the study area tocover coterminous areas on a larger scale [16]

42 Forest Loss Contextually

We mapped forest cover change dynamics between 2003ndash2019 on the southwest coastof Louisiana 1580 hectares of tree cover 13 of the terrestrial area was converted todifferent land use The total loss of forest cover in the study period was 3573 over sixteenyears which is indicative of a larger rate of forest loss than at sites in a similar physio-geographic region yet is certainly not a global outlier [18] These findings validate thesentiments of people in the region and underscore a major irreversible trend in the area Ourestimate should be considered conservative due to the way we approached the definition ofldquoforest coverrdquo within the landscape Abandoned pastoral land exhibiting dense scrubbinessand shrub cover was demarcated as forest cover even though the target species (live oak)was probably not present Spoil banks which are the byproduct of historic oil explorationin the region indicating possible mortality were also often considered a component offorest cover if the imagery could not indicate the absence of high vegetation The primarymethod of identifying whether or not forest cover was present was the observation of ashadow adjoining the vegetation indicating a vertical structure

The Big Bend region of Florida one of the longest undisturbed coastlines in thecontinental US offers some insight into the vegetation dynamics this region has beenexperiencing It has been the focus of numerous ecological studies has a similar climateand is exposed to storms with similar regularity Raabe and Stumpf [57] found that fromthe early to late 1900s over 82 km2 of forest land was converted to marsh and 66 km2 wasin a transitional state to marsh McCarthy et al [58] document an annual forest cover lossof 744 from 2010ndash2017 an acceleration in the trend within that area A new study bythe same team found rates of loss totaling approximately 10 km2 a year 800 larger thanpreviously stated in the literature [59] Rates of annual mortality ranged from 4ndash16 inSoutheastern Louisiana from 1987 to 1997 [60] In comparison the annualized rate of lossfound in our study never exceeded 5 Plots located in South Carolina that have beenless significantly impacted by MSL rise show rates of loss closer to 1ndash2 in line withour observed rate of loss from 2007ndash2019 However we cannot assume that percentagesof forest loss across the country have been constant within the last century therefore it iscritical to carry out intensive geospatial studies at regular intervals

In the adjoining southeastern part of the state of Louisiana in the Atchafalaya andTerrebonne basins forested wetlands occupy 121871 hectares or 41 of all vegetatedland use [12] In comparison the terrestrial forested land use in our study area was 21in 2019 The wide variation can be interpreted in several ways each with importantimplications Historic rates of deforestation differ in the two regions with a greateremphasis on the preservation of wilderness areas in the Atchafalaya basin [61] TheAtchafalaya and Terrebonne basins also have productive land used in the growth of timberwhich promotes long-term forest cover The regeneration of forests in our study region alsodiffers substantially Chenier ecosystems and traditional alluvial bottomland hardwoodforests are compositionally different in vegetation and soil even when accounting for theirunique hydrologic regimes Communities of live oak may be less resilient to the types offorcings that cause changes in community composition within these ecosystems furtheredby proximity to the sea If stocking density rates are similar to the adjoining basin as theywere in the past we would see an areal extent well over 50000 hectares

Forests 2022 13 1132 14 of 22

The analysis vis-agrave-vis elevation shows weak trends in the manners of loss on eleva-tional gradients Logically in the paradigm of coastal retreat there should be an aggregationof forest resources towards the highest bands of topography in a region Our analysis doesnot follow this chain of thought with the largest losses observed in the 1ndash2 m categoryrather than the lowest 0ndash1 m (Table 2 2007 C 2019 D) While we do see a large loss bypercentage in the highest elevation band it is important to note that a small sample causesinherent variance Forest cover on the highest reaches of the chenier ridges is typically lessthan 1 of total forest cover for that region The reduction can be attributed to the loss of23 features in section A accounting for 33 hectares bordering the Sabine River (Figure 1minus9386286 2973355) The loss of forest cover was driven by intense weather Vegetationin the area was completely destroyed and substantial amounts of sedimentation could beobserved as evident in the 2007 NAIP imagery The only gain in forest cover was seen inthe 2ndash3 m elevation band in the period 2007 in an area that had a high density of pastoralland that was abandoned Forest retreat is often discussed as one method of bufferingforest loss along coasts [62] Unfortunately the geomorphology of this area does not allowgreat spatial movement as the current extent is already located within the highest bandsof elevation

43 Land Use Conversion and Driving Factors

The leading cause of land-use conversion from tree cover was to a saltmarsh ecotype(606) often following mortality and replacement of vegetative biomass The loss offorest cover can largely be typified into two categories The ingression of saltwater causingsalinification within the soil matrix paired with increased flooding caused mortality ofoverstorey vegetation over a multi-year timescale [6364] Hurricanes or other storm eventscaused rapid mortality through wind-throws severe defoliation saltwater inundationduring a storm surge or canopy damage Following intense weather events between2003 and 2007 the vast majority of cheniers located within a 1km buffer of the coast wereunable to support tree cover as seen in Figure 7AB Clearing for industry or developmentalso caused immediate shifts in forest composition as can be seen in Figure 8AB Thischange was more pronounced on reaches A and B especially on Hackberry Beach Thisarea was the site of landfall for Hurricane Rita which caused massive devastation to othercomponents of the ecosystem [6566] Singular storm events have a transformative impacton vegetation communities and the weight of this driver on annual rates of forest lossis a topic that should be explored further Wayward shifts in species composition are atrend that has been observed globally especially in forests that border some source of waterTrees like Juniperus virginiana L that have distinctive water-use patterns in comparison toother coastal tree species are susceptible to salinification at much faster rates as evidencedthrough isotope analyses [67] As with many ecological systems the entangled effectsof climate and vegetation require diverse means of looking at change Isotope analysesdendrochronology field sensors and remote sensing data are needed to characterize land-use change at the stand and community levels

Large forest remnants such as Cow Island (minus9283581 2973842) Tiger Island (minus92784392972832) and Grand Chenier Ridge (minus9281175 2972986) among others have diminishedmore slowly By 2019 these stands had lost an average of 15 of their maximum extentThe land use changed to both marsh and managed pasture largely dependent on thesurrounding land use The conversion to managed pasture (282) accounted for a largerthan expected amount of land-cover change Many of these changes were in areas wheretree cover may have been more ldquoornamentalrdquo in nature Trees beside houses or alongsideroads are some of the largest specimens of live-oak to be found However these trees areoften solitary and perhaps more vulnerable to wind-borne disturbance The concern is thattheir loss due to abiotic drivers may not be easily mitigated leading to a permanent declineOur data support evidence on the whittling of forest remnants and the eventual fate ofthese trees becoming lsquorelict forestsrsquo unable to recruit new generations in the short term andin the long-term total disappearance of these cheniers along the Louisiana Gulf Coast [68]

Forests 2022 13 1132 15 of 22Forests 2022 13 x FOR PEER REVIEW 16 of 23

Figure 7 Visual depictions of two forms of loss one from hurricanes on Hackberry Beach (AB) and

the other documenting whittling reduction (CD) The left column displays 2003 NAIP imagery and

the right is 2007 NAIP imagery Black shaded areas indicate current year forest extent red hashed

areas indicate a loss

Figure 7 Visual depictions of two forms of loss one from hurricanes on Hackberry Beach (AB) andthe other documenting whittling reduction (CD) The left column displays 2003 NAIP imagery andthe right is 2007 NAIP imagery Black shaded areas indicate current year forest extent red hashedareas indicate a loss

Beyond loss in forest cover there is a noticeable trend towards an altered hydrologywhere pastoral areas and intact forest remnants are associated with pockets of standingwater An increase in the correlative factor between temperature and water levels could bea worrying trend a direct conversion of tree cover to water at 34 could be an unforeseenoutcome Chenier ridges in their geomorphic context should not support non-ephemeralponding It is not documented how increased tidal influence which is one factor of risingMSL influences the hydrology of this ecosystem and its associated communities Albeitin a more inland context Magolan and Halls [69] documented a transition of over 40 ofland to emergent marsh from closed-canopy cover from 1949 to 2018 due to hydrologicinfluence Recent rates of relative sea rise have been documented as high as 119 mmyearcompared to background Holocene rates of 17 mmyear [70] This has resulted in a rate oflateral coastal forest retreat between 2ndash14 times greater than pre-industrial levels [26] Therate of shoreline retreat in this region increased from ~61 to ~94 myear based on the 100-and 30-year moving averages amounting to over 10000 hectares of land loss every yearfrom 1800 to 2005 [27]

Forests 2022 13 1132 16 of 22Forests 2022 13 x FOR PEER REVIEW 17 of 23

Figure 8 A depiction of clearing for industry in (AB) and a depiction of forest whittling from (CD)

The black extent represents the current forest area red hashed lines indicate a loss The left panels

show 2007 NAIP imagery while the right are 2019 NAIP imagery

Large forest remnants such as Cow Island (minus9283581 2973842) Tiger Island

(minus9278439 2972832) and Grand Chenier Ridge (minus9281175 2972986) among others have

diminished more slowly By 2019 these stands had lost an average of 15 of their maxi‐

mum extent The land use changed to both marsh and managed pasture largely depend‐

ent on the surrounding land use The conversion to managed pasture (282) accounted

for a larger than expected amount of land‐cover change Many of these changes were in

areas where tree cover may have been more ldquoornamentalrdquo in nature Trees beside houses

or alongside roads are some of the largest specimens of live‐oak to be found However

these trees are often solitary and perhaps more vulnerable to wind‐borne disturbance The

concern is that their loss due to abiotic drivers may not be easily mitigated leading to a

permanent decline Our data support evidence on the whittling of forest remnants and

the eventual fate of these trees becoming lsquorelict forestsrsquo unable to recruit new generations

in the short term and in the long‐term total disappearance of these cheniers along the

Louisiana Gulf Coast [68]

Beyond loss in forest cover there is a noticeable trend towards an altered hydrology

where pastoral areas and intact forest remnants are associated with pockets of standing

water An increase in the correlative factor between temperature and water levels could

be a worrying trend a direct conversion of tree cover to water at 34 could be an unfore‐

seen outcome Chenier ridges in their geomorphic context should not support non‐ephem‐

eral ponding It is not documented how increased tidal influence which is one factor of

Figure 8 A depiction of clearing for industry in (AB) and a depiction of forest whittling from (CD)The black extent represents the current forest area red hashed lines indicate a loss The left panelsshow 2007 NAIP imagery while the right are 2019 NAIP imagery

44 Future of Chenier Forests

The chenier complexes of Louisiana do not have the natural benefaction of maintaininglarge original tracts untouched by axe or dozer The extent map through the years (Figure 9)exemplifies the scattered nature of forest remnants The largest single continuous chenierforest (Grand Chenier Ridgemdash185 ha) encompasses less than 1 of the total forest areaExposed forest edges pose a problem for the continued integrity of the stand as adaptedspecies fail to compete in these new microclimates [71] Slow-growing saplings of desirabletree species (live oak hackberry) are subject to unfavorable growing conditions fromweather and light availability Vegetation surveys following aspect and inundation alongthe Florida coast have all but confirmed the eventual extirpation of overstory vegetationthrough poor recruitment of native species [31] Chinese tallow (Triadica sebifera) a non-native tree is likely to fill empty niche space This aggressive invader is problematicthroughout the Gulf Coastal Plain from Texas to Florida [72] The tree has a wide meansof dispersal being primarily distributed through frugivory via avian populations A highrate of germination compounded with quick growth and a sexual maturity of less thanthree years make it a difficult species to control Its invasive capability is seen in nearlyall forest types in the region although it is more pervasive in coastal forests than inlandModeling studies show a high likelihood of Chinese tallow becoming the dominant specieswithin this ecosystem and in surrounding areas [73] Future publications from this lab will

Forests 2022 13 1132 17 of 22

provide more in-depth data and analysis on the state of existing stands and recruitmenttrends within remnants of the Chenier Plains via field sampling

Louisiana has enacted a historic response to the problem of land-loss following a publicoutcry after devastating storms in 2005 The Coastal Protection and Restoration Authority(CPRA) have been tasked with enacting a management plan to combat coastal erosion TheComprehensive Master Plan details proposals to create marshes build sediment diversionsform new hydrologic structures and promote barrier island restoration through builtand natural means As of 2017 over 135 projects had been completed impacting over14568 ha [74] An update to this document is due to be released in 2023 The projecthas an overall operating budget of over 50 billion US dollars with equal shares dedicatedto restoration and risk reduction The primary funding source is a settlement associatedwith the Deepwater Horizon Oil Spill in addition to various initiatives at the state andfederal levels However there are no current plans to supplement vegetation or performany geoengineering work on the cheniers for afforestation

Coastal wetlands rank among the most important ecosystems in terms of the valuationof ecosystem services [75] Provisioning services such as water filtration or storm-surgeprotection are commonly considered when discussing coastal areas Beyond these com-monalities chenier forests provide vital wildlife habitats being the first encountered sourceof vertical structure for migratory neo-tropical birds and migrating monarch butterflies inthe Mississippi alluvial valley flyway [76] This en-route habitat is of critical importancedue to its closeness to the Gulf of Mexico During favorable weather conditions 10 ofbirds migrating through the Gulf use these forests as a stopover spot rising dramatically to80 during adverse weather conditions [29] With losses mounting many species may notbe able to seek refuge during storms The area also has a unique suite of amphibians andreptiles being home to 13 obligate chenier forest species of herpetofauna [77]

Lu et al [78] used the eddy covariance method (ECM) to calculate strong carbon sinkeffects for a chenier ecosystem driven by forest vegetation Net energy production valueswere significantly higher than surrounding marshland signifying the importance of densevegetative cover on a per-unit basis Counter to this there is also strong evidence thatdegraded forest ecosystems act as net carbon sources A study in North Carolina also usingECM found that a seasonally inundated tide-influenced forest was a net carbon sourceannually and only functioned as a sink for 3 months of the growing year [79] Similarly astudy in the northeastern part of the state showed net negative rates of carbon sequestrationfor an ephemerally flooded forest [80] Unique environments demand individual attentiona characterization of this ecosystem for nitrogen or carbon flux with respect to a similarsystem may not be functionally sound [81]

Forests 2022 13 1132 18 of 22Forests 2022 13 x FOR PEER REVIEW 19 of 23

Figure 9 The total land area classified in GEE for the years 2003 2007 and 2019 Light green repre‐

sents the marsh area tan represents pasture The water layer has been removed for purposes of

clarity The dark black outline is the outline of the forest cover for the respective year

Louisiana has enacted a historic response to the problem of land‐loss following a

public outcry after devastating storms in 2005 The Coastal Protection and Restoration

Authority (CPRA) have been tasked with enacting a management plan to combat coastal

erosion The Comprehensive Master Plan details proposals to create marshes build sedi‐

ment diversions form new hydrologic structures and promote barrier island restoration

through built and natural means As of 2017 over 135 projects had been completed im‐

pacting over 14568 ha [74] An update to this document is due to be released in 2023 The

project has an overall operating budget of over 50 billion US dollars with equal shares

dedicated to restoration and risk reduction The primary funding source is a settlement

associated with the Deepwater Horizon Oil Spill in addition to various initiatives at the

state and federal levels However there are no current plans to supplement vegetation or

perform any geoengineering work on the cheniers for afforestation

Coastal wetlands rank among the most important ecosystems in terms of the valua‐

tion of ecosystem services [75] Provisioning services such as water filtration or storm‐

surge protection are commonly considered when discussing coastal areas Beyond these

commonalities chenier forests provide vital wildlife habitats being the first encountered

source of vertical structure for migratory neo‐tropical birds and migrating monarch but‐

terflies in the Mississippi alluvial valley flyway [76] This en‐route habitat is of critical

importance due to its closeness to the Gulf of Mexico During favorable weather condi‐

tions 10 of birds migrating through the Gulf use these forests as a stopover spot rising

Figure 9 The total land area classified in GEE for the years 2003 2007 and 2019 Light greenrepresents the marsh area tan represents pasture The water layer has been removed for purposes ofclarity The dark black outline is the outline of the forest cover for the respective year

5 Conclusions

The cheniers of southwestern Louisiana were found to be declining in size at a concern-ing rate Many of the driving factors are ecosystem-wide trends that have been increasingin intensity within the last century The results of this study suggest that coastal forestsare an ecosystem in peril and the ecology of such systems should be a closely monitoredsubject The 365 loss of forest cover is an alarming but not wholly unexpected result ofmany decades of intense perturbation and human exploitation A dynamic of continuedforest loss is evident Unless significant investment is made in engineering solutions tomitigate storm effects and land subsidence we will continue to see the decline of chenierforests in Louisiana Through targeted mitigation governmental programs such as theWetland or Conservation Reserve Program may be able to supplement existing forestsas well as the incomes of local people who have lost their livelihoods In this study wehave begun the process of accurate areal estimates of forest extent and have shed light onecosystem processes in the region As this community continues to diminish we see theforest not by the trees but rather the lack of them

Forests 2022 13 1132 19 of 22

Author Contributions Conceptualization PT methodology PT software PT and PR validationPT and PR formal analysis PT PR and PT writingmdashoriginal draft preparation PT and JBwritingmdashreview and editing PT and JB visualization PT and JB supervision JB All authorshave read and agreed to the published version of the manuscript

Funding This research received no external funding

Data Availability Statement The data will be published in and stored on the Harvard Dataverse athttpsdataverseharvardedudatasetxhtmlpersistentId=doi107910DVN3J7SNF (accessed on1 May 2022)

Acknowledgments The authors would like to recognize the hard work of undergraduate studentsParusha Raut and Subash Sapkota in manually validating thousands of points and creating numeroustraining sets We also extend thanks to our institution and numerous people within the lab who leantboth mental and emotional fortitude

Conflicts of Interest The authors declare no conflict of interest

References1 Michener WK Blood ER Bildstein KL Brinson MM Gardner LR Climate change hurricanes and tropical storms and

rising sea level in coastal wetlands Ecol Appl 1997 7 770ndash801 [CrossRef]2 Donoghue JF Sea level history of the northern Gulf of Mexico coast and sea level rise scenarios for the near future Clim Chang

2011 107 17ndash33 [CrossRef]3 Stanturf JA Goodrick SL Outcalt KW Disturbance and coastal forests A strategic approach to forest management in

hurricane impact zones For Ecol Manag 2007 250 119ndash135 [CrossRef]4 Augustinus P Cheniers and chenier plains A general introduction Mar Geol 1989 90 219ndash229 [CrossRef]5 Fu Z Wang P Sun J Lu Z Yang H Liu J Li T Composition seasonal variation and salinization characteristics of soil

salinity in the Chenier Island of the Yellow River Delta Glob Ecol Conserv 2020 24 e01318 [CrossRef]6 Marques IG Campelo F Rivaes R Albuquerque A Ferreira MT Rodriacuteguez-Gonzaacutelez PM Tree rings reveal long-term

changes in growth resilience in Southern European riparian forests Dendrochronologia 2018 55 167ndash176 [CrossRef]7 Daigle JJ Griffith GE Omernik JM Faulkner PL McCulloh RP Handley LR Smith LM Chapman SS Ecoregions of

Louisiana (Color Poster with Map Descriptive Text Summary Tables and Photographs) US Geological Survey Reston VI USA 20068 McBride RA Taylor MJ Byrnes MR Coastal morphodynamics and Chenier-Plain evolution in southwestern Louisiana USA

A geomorphic model Geomorphology 2007 88 367ndash422 [CrossRef]9 Gosselink JG An Ecological Characterization Study of the Chenier Plain Coastal Ecosystem of Louisiana and Texas Narrative Report

National Coastal Ecosystems Team Office of Biological Services Fish and Wildlife Service US Department of the InteriorWashington DC USA 1979 Volume 1

10 Blum MD Roberts HH The Mississippi delta region Past present and future Annu Rev Earth Planet Sci 2012 40 655ndash683[CrossRef]

11 Hansen MC Potapov PV Moore R Hancher M Turubanova SA Tyukavina A Townshend J High-resolution globalmaps of 21st-century forest cover change Science 2013 342 850ndash853 [CrossRef]

12 Doyle TW Krauss KW Conner WH From AS Predicting the retreat and migration of tidal forests along the northern Gulfof Mexico under sea-level rise For Ecol Manag 2010 259 770ndash777 [CrossRef]

13 Thomas N Simard M Castantildeeda-Moya E Byrd K Windham-Myers L Bevington A Twilley R High-resolution mappingof biomass and distribution of marsh and forested wetlands in southeastern coastal Louisiana Int J Appl Earth Obs Geoinf 201980 257ndash267 [CrossRef]

14 Desantis LR Bhotika S Williams K Putz FE Sea-level rise and drought interactions accelerate forest decline on the GulfCoast of Florida USA Glob Chang Biol 2007 13 2349ndash2360 [CrossRef]

15 Boyd DS Foody G Ripple W Evaluation of approaches for forest cover estimation in the Pacific Northwest USA usingremote sensing Appl Geogr 2002 22 375ndash392 [CrossRef]

16 Valjarevic A Djekic T Stevanovic V Ivanovic R Jandzikovic B GIS numerical and remote sensing analyses of forest changesin the Toplica region for the period of 1953ndash2013 Appl Geogr 2018 92 131ndash139 [CrossRef]

17 He T Sun YJ Xu JD Wang X Hu C Enhanced land usecover classification using support vector machines and fuzzyk-means clustering algorithms J Appl Remote Sens 2014 8 083636 [CrossRef]

18 Michez A Pieacutegay H Lisein J Claessens H Lejeune P Classification of riparian forest species and health condition usingmulti-temporal and hyperspatial imagery from unmanned aerial systems Environ Monit Assess 2016 188 146 [CrossRef][PubMed]

19 Hampton SE Strasser C Tewksbury J Gram W Budden A Batcheller A Duke C Porter J Big data and the future ofecology Front Ecol Environ 2013 11 156ndash162 [CrossRef]

20 Curtis PG Slay CM Harris NL Tyukavina A Hansen MC Classifying drivers of global forest loss Science 2018 3611108ndash1111 [CrossRef]

Forests 2022 13 1132 12 of 22

4 Discussion41 Classification Reliability and Constraints

NAIP imagery proved efficacious for fine-scale analysis in this region shown by thestrong average producerrsquos accuracy and Kappa coefficient This is a positive indicatorthat variability between years or areas did not influence classification accuracies to alarge extent and that the final classification is a reliable interpretation [42] Many otherstudies conducting land-cover classifications using NAIP imagery have also shown robustaccuracies [43ndash45] However there were noted issues seen in certain parts of the workflowA problem preeminent in the initial classification was a limitation of spectral resolution Thisproblem became pronounced in areas with similar shades of green or between dark bluesand black hues Without the use of ancillary datasets the classification accuracy was poorbetween dark water and shadows of trees with considerable overlap (misclassificationsof approximately 50) Mixed land use can also prove problematic in NAIP land-coverclassifications [46] This became especially pronounced in transitional forestmarsh areasin which there was no dominant spectral signature for a particular land use Local variationbetween adjoining image sets highlighted another shortcoming of NAIP imagery Theviewing geometry illumination and time between the capture of neighboring imagetiles created edge effects including artificially high or low pixel values and refractionproblems on water [47] Each yearrsquos dataset was collected by an external contractor causingdifferences in sensor instrumentation and parametrization between years By 2019 many ofthese problems had been mitigated and images were of much higher quality

Compared to a large swath sensor such as Landsat-8 or Sentinel-2 an innate non-uniformity poses problems for pixel-wise classifications of VHR imagery Typically datafrom satellite imagery have been the primary choice for analyzing coastal forests marshesand forests at large [48ndash50] The spaceborne sensors obtain information over eight bandsallowing much finer spectral discrimination of certain classes of objects [51] Even amongstdiverse forest types space-borne Sentinel-2 and ASTER imagery has been used to mapdistinct species clusters of broadleaf and deciduous plant species The large spatial scaleenables the use of these created geospatial products to analyze the efficacy of governmentalreforestation programs in remote areas of the world [52] The usage of normalized differ-ence indices including the Normalized Difference Vegetation Index (NDVI) EnhancedVegetation Index (EVI) and Enhanced Water Index (EWI) can also provide greater confi-dence in the separation of water and marsh features from deciduous broadleaf forests [53]However a spatial resolution of 10m is still too coarse for the detection of remnant andlandscape trees especially those alongside homogenous flat features such as maintainedgrass or water bodies The most practical solution would be to assess this land with ahigher resolution dataset such as unmanned aerial systems (UAS) imagery which cancapture a much finer GSD in a plethora of bands if multispectral or hyperspectral sensorsare used [54] In fact scientists are now pushing the boundaries of the scale of analysisconducted through remote sensing Broad groupings (eg clades or forest types) have beenthe historic norm in the classification of imagery for use in land management A greatermixture of classes allows more accurate measurements for those interested in landscape-scale ecological processes However VHR can be used to map stands at the species levelwhich allows forest managers an unparalleled insight into the dynamics of recruitmentdisease or resource availability [55]

Google Earth Engine allowed us to utilize NAIP imagery at scale which had for sometime been a limiting factor in processing this data source locally Even so some limitationshindered the functional utility of GEE for detailed land-cover classification In our relativelyuniform environment we discriminated between many shades of green When viewinggeometries cause variations in hue different classes begin to overlap within the samespectral regions Solutions using Geographic Object-Based Image Analysis (GEOBIA) cancircumvent this issue yet this segmentation algorithm is currently not supported by GEEMany consider GEOBIA the optimal tool for segmenting landscapes and for the productionof land-cover classifications from this source of imagery [3856] Its successful integration

Forests 2022 13 1132 13 of 22

could substantially elevate the use and utility of mapping within GEE Additionally theallocation of increased processing bandwidth on GEE servers may help overcome somelimits in classification Greater bandwidth allows larger and more diverse training setsin addition to more complex parameterizations of machine learning classifiers If oneis proceeding on local workstations the analysis of NAIP imagery requires an intimateknowledge of data structures and process optimization Custom architectures processingpipelines and simpler binary classifications can allow an expansion of the study area tocover coterminous areas on a larger scale [16]

42 Forest Loss Contextually

We mapped forest cover change dynamics between 2003ndash2019 on the southwest coastof Louisiana 1580 hectares of tree cover 13 of the terrestrial area was converted todifferent land use The total loss of forest cover in the study period was 3573 over sixteenyears which is indicative of a larger rate of forest loss than at sites in a similar physio-geographic region yet is certainly not a global outlier [18] These findings validate thesentiments of people in the region and underscore a major irreversible trend in the area Ourestimate should be considered conservative due to the way we approached the definition ofldquoforest coverrdquo within the landscape Abandoned pastoral land exhibiting dense scrubbinessand shrub cover was demarcated as forest cover even though the target species (live oak)was probably not present Spoil banks which are the byproduct of historic oil explorationin the region indicating possible mortality were also often considered a component offorest cover if the imagery could not indicate the absence of high vegetation The primarymethod of identifying whether or not forest cover was present was the observation of ashadow adjoining the vegetation indicating a vertical structure

The Big Bend region of Florida one of the longest undisturbed coastlines in thecontinental US offers some insight into the vegetation dynamics this region has beenexperiencing It has been the focus of numerous ecological studies has a similar climateand is exposed to storms with similar regularity Raabe and Stumpf [57] found that fromthe early to late 1900s over 82 km2 of forest land was converted to marsh and 66 km2 wasin a transitional state to marsh McCarthy et al [58] document an annual forest cover lossof 744 from 2010ndash2017 an acceleration in the trend within that area A new study bythe same team found rates of loss totaling approximately 10 km2 a year 800 larger thanpreviously stated in the literature [59] Rates of annual mortality ranged from 4ndash16 inSoutheastern Louisiana from 1987 to 1997 [60] In comparison the annualized rate of lossfound in our study never exceeded 5 Plots located in South Carolina that have beenless significantly impacted by MSL rise show rates of loss closer to 1ndash2 in line withour observed rate of loss from 2007ndash2019 However we cannot assume that percentagesof forest loss across the country have been constant within the last century therefore it iscritical to carry out intensive geospatial studies at regular intervals

In the adjoining southeastern part of the state of Louisiana in the Atchafalaya andTerrebonne basins forested wetlands occupy 121871 hectares or 41 of all vegetatedland use [12] In comparison the terrestrial forested land use in our study area was 21in 2019 The wide variation can be interpreted in several ways each with importantimplications Historic rates of deforestation differ in the two regions with a greateremphasis on the preservation of wilderness areas in the Atchafalaya basin [61] TheAtchafalaya and Terrebonne basins also have productive land used in the growth of timberwhich promotes long-term forest cover The regeneration of forests in our study region alsodiffers substantially Chenier ecosystems and traditional alluvial bottomland hardwoodforests are compositionally different in vegetation and soil even when accounting for theirunique hydrologic regimes Communities of live oak may be less resilient to the types offorcings that cause changes in community composition within these ecosystems furtheredby proximity to the sea If stocking density rates are similar to the adjoining basin as theywere in the past we would see an areal extent well over 50000 hectares

Forests 2022 13 1132 14 of 22

The analysis vis-agrave-vis elevation shows weak trends in the manners of loss on eleva-tional gradients Logically in the paradigm of coastal retreat there should be an aggregationof forest resources towards the highest bands of topography in a region Our analysis doesnot follow this chain of thought with the largest losses observed in the 1ndash2 m categoryrather than the lowest 0ndash1 m (Table 2 2007 C 2019 D) While we do see a large loss bypercentage in the highest elevation band it is important to note that a small sample causesinherent variance Forest cover on the highest reaches of the chenier ridges is typically lessthan 1 of total forest cover for that region The reduction can be attributed to the loss of23 features in section A accounting for 33 hectares bordering the Sabine River (Figure 1minus9386286 2973355) The loss of forest cover was driven by intense weather Vegetationin the area was completely destroyed and substantial amounts of sedimentation could beobserved as evident in the 2007 NAIP imagery The only gain in forest cover was seen inthe 2ndash3 m elevation band in the period 2007 in an area that had a high density of pastoralland that was abandoned Forest retreat is often discussed as one method of bufferingforest loss along coasts [62] Unfortunately the geomorphology of this area does not allowgreat spatial movement as the current extent is already located within the highest bandsof elevation

43 Land Use Conversion and Driving Factors

The leading cause of land-use conversion from tree cover was to a saltmarsh ecotype(606) often following mortality and replacement of vegetative biomass The loss offorest cover can largely be typified into two categories The ingression of saltwater causingsalinification within the soil matrix paired with increased flooding caused mortality ofoverstorey vegetation over a multi-year timescale [6364] Hurricanes or other storm eventscaused rapid mortality through wind-throws severe defoliation saltwater inundationduring a storm surge or canopy damage Following intense weather events between2003 and 2007 the vast majority of cheniers located within a 1km buffer of the coast wereunable to support tree cover as seen in Figure 7AB Clearing for industry or developmentalso caused immediate shifts in forest composition as can be seen in Figure 8AB Thischange was more pronounced on reaches A and B especially on Hackberry Beach Thisarea was the site of landfall for Hurricane Rita which caused massive devastation to othercomponents of the ecosystem [6566] Singular storm events have a transformative impacton vegetation communities and the weight of this driver on annual rates of forest lossis a topic that should be explored further Wayward shifts in species composition are atrend that has been observed globally especially in forests that border some source of waterTrees like Juniperus virginiana L that have distinctive water-use patterns in comparison toother coastal tree species are susceptible to salinification at much faster rates as evidencedthrough isotope analyses [67] As with many ecological systems the entangled effectsof climate and vegetation require diverse means of looking at change Isotope analysesdendrochronology field sensors and remote sensing data are needed to characterize land-use change at the stand and community levels

Large forest remnants such as Cow Island (minus9283581 2973842) Tiger Island (minus92784392972832) and Grand Chenier Ridge (minus9281175 2972986) among others have diminishedmore slowly By 2019 these stands had lost an average of 15 of their maximum extentThe land use changed to both marsh and managed pasture largely dependent on thesurrounding land use The conversion to managed pasture (282) accounted for a largerthan expected amount of land-cover change Many of these changes were in areas wheretree cover may have been more ldquoornamentalrdquo in nature Trees beside houses or alongsideroads are some of the largest specimens of live-oak to be found However these trees areoften solitary and perhaps more vulnerable to wind-borne disturbance The concern is thattheir loss due to abiotic drivers may not be easily mitigated leading to a permanent declineOur data support evidence on the whittling of forest remnants and the eventual fate ofthese trees becoming lsquorelict forestsrsquo unable to recruit new generations in the short term andin the long-term total disappearance of these cheniers along the Louisiana Gulf Coast [68]

Forests 2022 13 1132 15 of 22Forests 2022 13 x FOR PEER REVIEW 16 of 23

Figure 7 Visual depictions of two forms of loss one from hurricanes on Hackberry Beach (AB) and

the other documenting whittling reduction (CD) The left column displays 2003 NAIP imagery and

the right is 2007 NAIP imagery Black shaded areas indicate current year forest extent red hashed

areas indicate a loss

Figure 7 Visual depictions of two forms of loss one from hurricanes on Hackberry Beach (AB) andthe other documenting whittling reduction (CD) The left column displays 2003 NAIP imagery andthe right is 2007 NAIP imagery Black shaded areas indicate current year forest extent red hashedareas indicate a loss

Beyond loss in forest cover there is a noticeable trend towards an altered hydrologywhere pastoral areas and intact forest remnants are associated with pockets of standingwater An increase in the correlative factor between temperature and water levels could bea worrying trend a direct conversion of tree cover to water at 34 could be an unforeseenoutcome Chenier ridges in their geomorphic context should not support non-ephemeralponding It is not documented how increased tidal influence which is one factor of risingMSL influences the hydrology of this ecosystem and its associated communities Albeitin a more inland context Magolan and Halls [69] documented a transition of over 40 ofland to emergent marsh from closed-canopy cover from 1949 to 2018 due to hydrologicinfluence Recent rates of relative sea rise have been documented as high as 119 mmyearcompared to background Holocene rates of 17 mmyear [70] This has resulted in a rate oflateral coastal forest retreat between 2ndash14 times greater than pre-industrial levels [26] Therate of shoreline retreat in this region increased from ~61 to ~94 myear based on the 100-and 30-year moving averages amounting to over 10000 hectares of land loss every yearfrom 1800 to 2005 [27]

Forests 2022 13 1132 16 of 22Forests 2022 13 x FOR PEER REVIEW 17 of 23

Figure 8 A depiction of clearing for industry in (AB) and a depiction of forest whittling from (CD)

The black extent represents the current forest area red hashed lines indicate a loss The left panels

show 2007 NAIP imagery while the right are 2019 NAIP imagery

Large forest remnants such as Cow Island (minus9283581 2973842) Tiger Island

(minus9278439 2972832) and Grand Chenier Ridge (minus9281175 2972986) among others have

diminished more slowly By 2019 these stands had lost an average of 15 of their maxi‐

mum extent The land use changed to both marsh and managed pasture largely depend‐

ent on the surrounding land use The conversion to managed pasture (282) accounted

for a larger than expected amount of land‐cover change Many of these changes were in

areas where tree cover may have been more ldquoornamentalrdquo in nature Trees beside houses

or alongside roads are some of the largest specimens of live‐oak to be found However

these trees are often solitary and perhaps more vulnerable to wind‐borne disturbance The

concern is that their loss due to abiotic drivers may not be easily mitigated leading to a

permanent decline Our data support evidence on the whittling of forest remnants and

the eventual fate of these trees becoming lsquorelict forestsrsquo unable to recruit new generations

in the short term and in the long‐term total disappearance of these cheniers along the

Louisiana Gulf Coast [68]

Beyond loss in forest cover there is a noticeable trend towards an altered hydrology

where pastoral areas and intact forest remnants are associated with pockets of standing

water An increase in the correlative factor between temperature and water levels could

be a worrying trend a direct conversion of tree cover to water at 34 could be an unfore‐

seen outcome Chenier ridges in their geomorphic context should not support non‐ephem‐

eral ponding It is not documented how increased tidal influence which is one factor of

Figure 8 A depiction of clearing for industry in (AB) and a depiction of forest whittling from (CD)The black extent represents the current forest area red hashed lines indicate a loss The left panelsshow 2007 NAIP imagery while the right are 2019 NAIP imagery

44 Future of Chenier Forests

The chenier complexes of Louisiana do not have the natural benefaction of maintaininglarge original tracts untouched by axe or dozer The extent map through the years (Figure 9)exemplifies the scattered nature of forest remnants The largest single continuous chenierforest (Grand Chenier Ridgemdash185 ha) encompasses less than 1 of the total forest areaExposed forest edges pose a problem for the continued integrity of the stand as adaptedspecies fail to compete in these new microclimates [71] Slow-growing saplings of desirabletree species (live oak hackberry) are subject to unfavorable growing conditions fromweather and light availability Vegetation surveys following aspect and inundation alongthe Florida coast have all but confirmed the eventual extirpation of overstory vegetationthrough poor recruitment of native species [31] Chinese tallow (Triadica sebifera) a non-native tree is likely to fill empty niche space This aggressive invader is problematicthroughout the Gulf Coastal Plain from Texas to Florida [72] The tree has a wide meansof dispersal being primarily distributed through frugivory via avian populations A highrate of germination compounded with quick growth and a sexual maturity of less thanthree years make it a difficult species to control Its invasive capability is seen in nearlyall forest types in the region although it is more pervasive in coastal forests than inlandModeling studies show a high likelihood of Chinese tallow becoming the dominant specieswithin this ecosystem and in surrounding areas [73] Future publications from this lab will

Forests 2022 13 1132 17 of 22

provide more in-depth data and analysis on the state of existing stands and recruitmenttrends within remnants of the Chenier Plains via field sampling

Louisiana has enacted a historic response to the problem of land-loss following a publicoutcry after devastating storms in 2005 The Coastal Protection and Restoration Authority(CPRA) have been tasked with enacting a management plan to combat coastal erosion TheComprehensive Master Plan details proposals to create marshes build sediment diversionsform new hydrologic structures and promote barrier island restoration through builtand natural means As of 2017 over 135 projects had been completed impacting over14568 ha [74] An update to this document is due to be released in 2023 The projecthas an overall operating budget of over 50 billion US dollars with equal shares dedicatedto restoration and risk reduction The primary funding source is a settlement associatedwith the Deepwater Horizon Oil Spill in addition to various initiatives at the state andfederal levels However there are no current plans to supplement vegetation or performany geoengineering work on the cheniers for afforestation

Coastal wetlands rank among the most important ecosystems in terms of the valuationof ecosystem services [75] Provisioning services such as water filtration or storm-surgeprotection are commonly considered when discussing coastal areas Beyond these com-monalities chenier forests provide vital wildlife habitats being the first encountered sourceof vertical structure for migratory neo-tropical birds and migrating monarch butterflies inthe Mississippi alluvial valley flyway [76] This en-route habitat is of critical importancedue to its closeness to the Gulf of Mexico During favorable weather conditions 10 ofbirds migrating through the Gulf use these forests as a stopover spot rising dramatically to80 during adverse weather conditions [29] With losses mounting many species may notbe able to seek refuge during storms The area also has a unique suite of amphibians andreptiles being home to 13 obligate chenier forest species of herpetofauna [77]

Lu et al [78] used the eddy covariance method (ECM) to calculate strong carbon sinkeffects for a chenier ecosystem driven by forest vegetation Net energy production valueswere significantly higher than surrounding marshland signifying the importance of densevegetative cover on a per-unit basis Counter to this there is also strong evidence thatdegraded forest ecosystems act as net carbon sources A study in North Carolina also usingECM found that a seasonally inundated tide-influenced forest was a net carbon sourceannually and only functioned as a sink for 3 months of the growing year [79] Similarly astudy in the northeastern part of the state showed net negative rates of carbon sequestrationfor an ephemerally flooded forest [80] Unique environments demand individual attentiona characterization of this ecosystem for nitrogen or carbon flux with respect to a similarsystem may not be functionally sound [81]

Forests 2022 13 1132 18 of 22Forests 2022 13 x FOR PEER REVIEW 19 of 23

Figure 9 The total land area classified in GEE for the years 2003 2007 and 2019 Light green repre‐

sents the marsh area tan represents pasture The water layer has been removed for purposes of

clarity The dark black outline is the outline of the forest cover for the respective year

Louisiana has enacted a historic response to the problem of land‐loss following a

public outcry after devastating storms in 2005 The Coastal Protection and Restoration

Authority (CPRA) have been tasked with enacting a management plan to combat coastal

erosion The Comprehensive Master Plan details proposals to create marshes build sedi‐

ment diversions form new hydrologic structures and promote barrier island restoration

through built and natural means As of 2017 over 135 projects had been completed im‐

pacting over 14568 ha [74] An update to this document is due to be released in 2023 The

project has an overall operating budget of over 50 billion US dollars with equal shares

dedicated to restoration and risk reduction The primary funding source is a settlement

associated with the Deepwater Horizon Oil Spill in addition to various initiatives at the

state and federal levels However there are no current plans to supplement vegetation or

perform any geoengineering work on the cheniers for afforestation

Coastal wetlands rank among the most important ecosystems in terms of the valua‐

tion of ecosystem services [75] Provisioning services such as water filtration or storm‐

surge protection are commonly considered when discussing coastal areas Beyond these

commonalities chenier forests provide vital wildlife habitats being the first encountered

source of vertical structure for migratory neo‐tropical birds and migrating monarch but‐

terflies in the Mississippi alluvial valley flyway [76] This en‐route habitat is of critical

importance due to its closeness to the Gulf of Mexico During favorable weather condi‐

tions 10 of birds migrating through the Gulf use these forests as a stopover spot rising

Figure 9 The total land area classified in GEE for the years 2003 2007 and 2019 Light greenrepresents the marsh area tan represents pasture The water layer has been removed for purposes ofclarity The dark black outline is the outline of the forest cover for the respective year

5 Conclusions

The cheniers of southwestern Louisiana were found to be declining in size at a concern-ing rate Many of the driving factors are ecosystem-wide trends that have been increasingin intensity within the last century The results of this study suggest that coastal forestsare an ecosystem in peril and the ecology of such systems should be a closely monitoredsubject The 365 loss of forest cover is an alarming but not wholly unexpected result ofmany decades of intense perturbation and human exploitation A dynamic of continuedforest loss is evident Unless significant investment is made in engineering solutions tomitigate storm effects and land subsidence we will continue to see the decline of chenierforests in Louisiana Through targeted mitigation governmental programs such as theWetland or Conservation Reserve Program may be able to supplement existing forestsas well as the incomes of local people who have lost their livelihoods In this study wehave begun the process of accurate areal estimates of forest extent and have shed light onecosystem processes in the region As this community continues to diminish we see theforest not by the trees but rather the lack of them

Forests 2022 13 1132 19 of 22

Author Contributions Conceptualization PT methodology PT software PT and PR validationPT and PR formal analysis PT PR and PT writingmdashoriginal draft preparation PT and JBwritingmdashreview and editing PT and JB visualization PT and JB supervision JB All authorshave read and agreed to the published version of the manuscript

Funding This research received no external funding

Data Availability Statement The data will be published in and stored on the Harvard Dataverse athttpsdataverseharvardedudatasetxhtmlpersistentId=doi107910DVN3J7SNF (accessed on1 May 2022)

Acknowledgments The authors would like to recognize the hard work of undergraduate studentsParusha Raut and Subash Sapkota in manually validating thousands of points and creating numeroustraining sets We also extend thanks to our institution and numerous people within the lab who leantboth mental and emotional fortitude

Conflicts of Interest The authors declare no conflict of interest

References1 Michener WK Blood ER Bildstein KL Brinson MM Gardner LR Climate change hurricanes and tropical storms and

rising sea level in coastal wetlands Ecol Appl 1997 7 770ndash801 [CrossRef]2 Donoghue JF Sea level history of the northern Gulf of Mexico coast and sea level rise scenarios for the near future Clim Chang

2011 107 17ndash33 [CrossRef]3 Stanturf JA Goodrick SL Outcalt KW Disturbance and coastal forests A strategic approach to forest management in

hurricane impact zones For Ecol Manag 2007 250 119ndash135 [CrossRef]4 Augustinus P Cheniers and chenier plains A general introduction Mar Geol 1989 90 219ndash229 [CrossRef]5 Fu Z Wang P Sun J Lu Z Yang H Liu J Li T Composition seasonal variation and salinization characteristics of soil

salinity in the Chenier Island of the Yellow River Delta Glob Ecol Conserv 2020 24 e01318 [CrossRef]6 Marques IG Campelo F Rivaes R Albuquerque A Ferreira MT Rodriacuteguez-Gonzaacutelez PM Tree rings reveal long-term

changes in growth resilience in Southern European riparian forests Dendrochronologia 2018 55 167ndash176 [CrossRef]7 Daigle JJ Griffith GE Omernik JM Faulkner PL McCulloh RP Handley LR Smith LM Chapman SS Ecoregions of

Louisiana (Color Poster with Map Descriptive Text Summary Tables and Photographs) US Geological Survey Reston VI USA 20068 McBride RA Taylor MJ Byrnes MR Coastal morphodynamics and Chenier-Plain evolution in southwestern Louisiana USA

A geomorphic model Geomorphology 2007 88 367ndash422 [CrossRef]9 Gosselink JG An Ecological Characterization Study of the Chenier Plain Coastal Ecosystem of Louisiana and Texas Narrative Report

National Coastal Ecosystems Team Office of Biological Services Fish and Wildlife Service US Department of the InteriorWashington DC USA 1979 Volume 1

10 Blum MD Roberts HH The Mississippi delta region Past present and future Annu Rev Earth Planet Sci 2012 40 655ndash683[CrossRef]

11 Hansen MC Potapov PV Moore R Hancher M Turubanova SA Tyukavina A Townshend J High-resolution globalmaps of 21st-century forest cover change Science 2013 342 850ndash853 [CrossRef]

12 Doyle TW Krauss KW Conner WH From AS Predicting the retreat and migration of tidal forests along the northern Gulfof Mexico under sea-level rise For Ecol Manag 2010 259 770ndash777 [CrossRef]

13 Thomas N Simard M Castantildeeda-Moya E Byrd K Windham-Myers L Bevington A Twilley R High-resolution mappingof biomass and distribution of marsh and forested wetlands in southeastern coastal Louisiana Int J Appl Earth Obs Geoinf 201980 257ndash267 [CrossRef]

14 Desantis LR Bhotika S Williams K Putz FE Sea-level rise and drought interactions accelerate forest decline on the GulfCoast of Florida USA Glob Chang Biol 2007 13 2349ndash2360 [CrossRef]

15 Boyd DS Foody G Ripple W Evaluation of approaches for forest cover estimation in the Pacific Northwest USA usingremote sensing Appl Geogr 2002 22 375ndash392 [CrossRef]

16 Valjarevic A Djekic T Stevanovic V Ivanovic R Jandzikovic B GIS numerical and remote sensing analyses of forest changesin the Toplica region for the period of 1953ndash2013 Appl Geogr 2018 92 131ndash139 [CrossRef]

17 He T Sun YJ Xu JD Wang X Hu C Enhanced land usecover classification using support vector machines and fuzzyk-means clustering algorithms J Appl Remote Sens 2014 8 083636 [CrossRef]

18 Michez A Pieacutegay H Lisein J Claessens H Lejeune P Classification of riparian forest species and health condition usingmulti-temporal and hyperspatial imagery from unmanned aerial systems Environ Monit Assess 2016 188 146 [CrossRef][PubMed]

19 Hampton SE Strasser C Tewksbury J Gram W Budden A Batcheller A Duke C Porter J Big data and the future ofecology Front Ecol Environ 2013 11 156ndash162 [CrossRef]

20 Curtis PG Slay CM Harris NL Tyukavina A Hansen MC Classifying drivers of global forest loss Science 2018 3611108ndash1111 [CrossRef]

Forests 2022 13 1132 13 of 22

could substantially elevate the use and utility of mapping within GEE Additionally theallocation of increased processing bandwidth on GEE servers may help overcome somelimits in classification Greater bandwidth allows larger and more diverse training setsin addition to more complex parameterizations of machine learning classifiers If oneis proceeding on local workstations the analysis of NAIP imagery requires an intimateknowledge of data structures and process optimization Custom architectures processingpipelines and simpler binary classifications can allow an expansion of the study area tocover coterminous areas on a larger scale [16]

42 Forest Loss Contextually

We mapped forest cover change dynamics between 2003ndash2019 on the southwest coastof Louisiana 1580 hectares of tree cover 13 of the terrestrial area was converted todifferent land use The total loss of forest cover in the study period was 3573 over sixteenyears which is indicative of a larger rate of forest loss than at sites in a similar physio-geographic region yet is certainly not a global outlier [18] These findings validate thesentiments of people in the region and underscore a major irreversible trend in the area Ourestimate should be considered conservative due to the way we approached the definition ofldquoforest coverrdquo within the landscape Abandoned pastoral land exhibiting dense scrubbinessand shrub cover was demarcated as forest cover even though the target species (live oak)was probably not present Spoil banks which are the byproduct of historic oil explorationin the region indicating possible mortality were also often considered a component offorest cover if the imagery could not indicate the absence of high vegetation The primarymethod of identifying whether or not forest cover was present was the observation of ashadow adjoining the vegetation indicating a vertical structure

The Big Bend region of Florida one of the longest undisturbed coastlines in thecontinental US offers some insight into the vegetation dynamics this region has beenexperiencing It has been the focus of numerous ecological studies has a similar climateand is exposed to storms with similar regularity Raabe and Stumpf [57] found that fromthe early to late 1900s over 82 km2 of forest land was converted to marsh and 66 km2 wasin a transitional state to marsh McCarthy et al [58] document an annual forest cover lossof 744 from 2010ndash2017 an acceleration in the trend within that area A new study bythe same team found rates of loss totaling approximately 10 km2 a year 800 larger thanpreviously stated in the literature [59] Rates of annual mortality ranged from 4ndash16 inSoutheastern Louisiana from 1987 to 1997 [60] In comparison the annualized rate of lossfound in our study never exceeded 5 Plots located in South Carolina that have beenless significantly impacted by MSL rise show rates of loss closer to 1ndash2 in line withour observed rate of loss from 2007ndash2019 However we cannot assume that percentagesof forest loss across the country have been constant within the last century therefore it iscritical to carry out intensive geospatial studies at regular intervals

In the adjoining southeastern part of the state of Louisiana in the Atchafalaya andTerrebonne basins forested wetlands occupy 121871 hectares or 41 of all vegetatedland use [12] In comparison the terrestrial forested land use in our study area was 21in 2019 The wide variation can be interpreted in several ways each with importantimplications Historic rates of deforestation differ in the two regions with a greateremphasis on the preservation of wilderness areas in the Atchafalaya basin [61] TheAtchafalaya and Terrebonne basins also have productive land used in the growth of timberwhich promotes long-term forest cover The regeneration of forests in our study region alsodiffers substantially Chenier ecosystems and traditional alluvial bottomland hardwoodforests are compositionally different in vegetation and soil even when accounting for theirunique hydrologic regimes Communities of live oak may be less resilient to the types offorcings that cause changes in community composition within these ecosystems furtheredby proximity to the sea If stocking density rates are similar to the adjoining basin as theywere in the past we would see an areal extent well over 50000 hectares

Forests 2022 13 1132 14 of 22

The analysis vis-agrave-vis elevation shows weak trends in the manners of loss on eleva-tional gradients Logically in the paradigm of coastal retreat there should be an aggregationof forest resources towards the highest bands of topography in a region Our analysis doesnot follow this chain of thought with the largest losses observed in the 1ndash2 m categoryrather than the lowest 0ndash1 m (Table 2 2007 C 2019 D) While we do see a large loss bypercentage in the highest elevation band it is important to note that a small sample causesinherent variance Forest cover on the highest reaches of the chenier ridges is typically lessthan 1 of total forest cover for that region The reduction can be attributed to the loss of23 features in section A accounting for 33 hectares bordering the Sabine River (Figure 1minus9386286 2973355) The loss of forest cover was driven by intense weather Vegetationin the area was completely destroyed and substantial amounts of sedimentation could beobserved as evident in the 2007 NAIP imagery The only gain in forest cover was seen inthe 2ndash3 m elevation band in the period 2007 in an area that had a high density of pastoralland that was abandoned Forest retreat is often discussed as one method of bufferingforest loss along coasts [62] Unfortunately the geomorphology of this area does not allowgreat spatial movement as the current extent is already located within the highest bandsof elevation

43 Land Use Conversion and Driving Factors

The leading cause of land-use conversion from tree cover was to a saltmarsh ecotype(606) often following mortality and replacement of vegetative biomass The loss offorest cover can largely be typified into two categories The ingression of saltwater causingsalinification within the soil matrix paired with increased flooding caused mortality ofoverstorey vegetation over a multi-year timescale [6364] Hurricanes or other storm eventscaused rapid mortality through wind-throws severe defoliation saltwater inundationduring a storm surge or canopy damage Following intense weather events between2003 and 2007 the vast majority of cheniers located within a 1km buffer of the coast wereunable to support tree cover as seen in Figure 7AB Clearing for industry or developmentalso caused immediate shifts in forest composition as can be seen in Figure 8AB Thischange was more pronounced on reaches A and B especially on Hackberry Beach Thisarea was the site of landfall for Hurricane Rita which caused massive devastation to othercomponents of the ecosystem [6566] Singular storm events have a transformative impacton vegetation communities and the weight of this driver on annual rates of forest lossis a topic that should be explored further Wayward shifts in species composition are atrend that has been observed globally especially in forests that border some source of waterTrees like Juniperus virginiana L that have distinctive water-use patterns in comparison toother coastal tree species are susceptible to salinification at much faster rates as evidencedthrough isotope analyses [67] As with many ecological systems the entangled effectsof climate and vegetation require diverse means of looking at change Isotope analysesdendrochronology field sensors and remote sensing data are needed to characterize land-use change at the stand and community levels

Large forest remnants such as Cow Island (minus9283581 2973842) Tiger Island (minus92784392972832) and Grand Chenier Ridge (minus9281175 2972986) among others have diminishedmore slowly By 2019 these stands had lost an average of 15 of their maximum extentThe land use changed to both marsh and managed pasture largely dependent on thesurrounding land use The conversion to managed pasture (282) accounted for a largerthan expected amount of land-cover change Many of these changes were in areas wheretree cover may have been more ldquoornamentalrdquo in nature Trees beside houses or alongsideroads are some of the largest specimens of live-oak to be found However these trees areoften solitary and perhaps more vulnerable to wind-borne disturbance The concern is thattheir loss due to abiotic drivers may not be easily mitigated leading to a permanent declineOur data support evidence on the whittling of forest remnants and the eventual fate ofthese trees becoming lsquorelict forestsrsquo unable to recruit new generations in the short term andin the long-term total disappearance of these cheniers along the Louisiana Gulf Coast [68]

Forests 2022 13 1132 15 of 22Forests 2022 13 x FOR PEER REVIEW 16 of 23

Figure 7 Visual depictions of two forms of loss one from hurricanes on Hackberry Beach (AB) and

the other documenting whittling reduction (CD) The left column displays 2003 NAIP imagery and

the right is 2007 NAIP imagery Black shaded areas indicate current year forest extent red hashed

areas indicate a loss

Figure 7 Visual depictions of two forms of loss one from hurricanes on Hackberry Beach (AB) andthe other documenting whittling reduction (CD) The left column displays 2003 NAIP imagery andthe right is 2007 NAIP imagery Black shaded areas indicate current year forest extent red hashedareas indicate a loss

Beyond loss in forest cover there is a noticeable trend towards an altered hydrologywhere pastoral areas and intact forest remnants are associated with pockets of standingwater An increase in the correlative factor between temperature and water levels could bea worrying trend a direct conversion of tree cover to water at 34 could be an unforeseenoutcome Chenier ridges in their geomorphic context should not support non-ephemeralponding It is not documented how increased tidal influence which is one factor of risingMSL influences the hydrology of this ecosystem and its associated communities Albeitin a more inland context Magolan and Halls [69] documented a transition of over 40 ofland to emergent marsh from closed-canopy cover from 1949 to 2018 due to hydrologicinfluence Recent rates of relative sea rise have been documented as high as 119 mmyearcompared to background Holocene rates of 17 mmyear [70] This has resulted in a rate oflateral coastal forest retreat between 2ndash14 times greater than pre-industrial levels [26] Therate of shoreline retreat in this region increased from ~61 to ~94 myear based on the 100-and 30-year moving averages amounting to over 10000 hectares of land loss every yearfrom 1800 to 2005 [27]

Forests 2022 13 1132 16 of 22Forests 2022 13 x FOR PEER REVIEW 17 of 23

Figure 8 A depiction of clearing for industry in (AB) and a depiction of forest whittling from (CD)

The black extent represents the current forest area red hashed lines indicate a loss The left panels

show 2007 NAIP imagery while the right are 2019 NAIP imagery

Large forest remnants such as Cow Island (minus9283581 2973842) Tiger Island

(minus9278439 2972832) and Grand Chenier Ridge (minus9281175 2972986) among others have

diminished more slowly By 2019 these stands had lost an average of 15 of their maxi‐

mum extent The land use changed to both marsh and managed pasture largely depend‐

ent on the surrounding land use The conversion to managed pasture (282) accounted

for a larger than expected amount of land‐cover change Many of these changes were in

areas where tree cover may have been more ldquoornamentalrdquo in nature Trees beside houses

or alongside roads are some of the largest specimens of live‐oak to be found However

these trees are often solitary and perhaps more vulnerable to wind‐borne disturbance The

concern is that their loss due to abiotic drivers may not be easily mitigated leading to a

permanent decline Our data support evidence on the whittling of forest remnants and

the eventual fate of these trees becoming lsquorelict forestsrsquo unable to recruit new generations

in the short term and in the long‐term total disappearance of these cheniers along the

Louisiana Gulf Coast [68]

Beyond loss in forest cover there is a noticeable trend towards an altered hydrology

where pastoral areas and intact forest remnants are associated with pockets of standing

water An increase in the correlative factor between temperature and water levels could

be a worrying trend a direct conversion of tree cover to water at 34 could be an unfore‐

seen outcome Chenier ridges in their geomorphic context should not support non‐ephem‐

eral ponding It is not documented how increased tidal influence which is one factor of

Figure 8 A depiction of clearing for industry in (AB) and a depiction of forest whittling from (CD)The black extent represents the current forest area red hashed lines indicate a loss The left panelsshow 2007 NAIP imagery while the right are 2019 NAIP imagery

44 Future of Chenier Forests

The chenier complexes of Louisiana do not have the natural benefaction of maintaininglarge original tracts untouched by axe or dozer The extent map through the years (Figure 9)exemplifies the scattered nature of forest remnants The largest single continuous chenierforest (Grand Chenier Ridgemdash185 ha) encompasses less than 1 of the total forest areaExposed forest edges pose a problem for the continued integrity of the stand as adaptedspecies fail to compete in these new microclimates [71] Slow-growing saplings of desirabletree species (live oak hackberry) are subject to unfavorable growing conditions fromweather and light availability Vegetation surveys following aspect and inundation alongthe Florida coast have all but confirmed the eventual extirpation of overstory vegetationthrough poor recruitment of native species [31] Chinese tallow (Triadica sebifera) a non-native tree is likely to fill empty niche space This aggressive invader is problematicthroughout the Gulf Coastal Plain from Texas to Florida [72] The tree has a wide meansof dispersal being primarily distributed through frugivory via avian populations A highrate of germination compounded with quick growth and a sexual maturity of less thanthree years make it a difficult species to control Its invasive capability is seen in nearlyall forest types in the region although it is more pervasive in coastal forests than inlandModeling studies show a high likelihood of Chinese tallow becoming the dominant specieswithin this ecosystem and in surrounding areas [73] Future publications from this lab will

Forests 2022 13 1132 17 of 22

provide more in-depth data and analysis on the state of existing stands and recruitmenttrends within remnants of the Chenier Plains via field sampling

Louisiana has enacted a historic response to the problem of land-loss following a publicoutcry after devastating storms in 2005 The Coastal Protection and Restoration Authority(CPRA) have been tasked with enacting a management plan to combat coastal erosion TheComprehensive Master Plan details proposals to create marshes build sediment diversionsform new hydrologic structures and promote barrier island restoration through builtand natural means As of 2017 over 135 projects had been completed impacting over14568 ha [74] An update to this document is due to be released in 2023 The projecthas an overall operating budget of over 50 billion US dollars with equal shares dedicatedto restoration and risk reduction The primary funding source is a settlement associatedwith the Deepwater Horizon Oil Spill in addition to various initiatives at the state andfederal levels However there are no current plans to supplement vegetation or performany geoengineering work on the cheniers for afforestation

Coastal wetlands rank among the most important ecosystems in terms of the valuationof ecosystem services [75] Provisioning services such as water filtration or storm-surgeprotection are commonly considered when discussing coastal areas Beyond these com-monalities chenier forests provide vital wildlife habitats being the first encountered sourceof vertical structure for migratory neo-tropical birds and migrating monarch butterflies inthe Mississippi alluvial valley flyway [76] This en-route habitat is of critical importancedue to its closeness to the Gulf of Mexico During favorable weather conditions 10 ofbirds migrating through the Gulf use these forests as a stopover spot rising dramatically to80 during adverse weather conditions [29] With losses mounting many species may notbe able to seek refuge during storms The area also has a unique suite of amphibians andreptiles being home to 13 obligate chenier forest species of herpetofauna [77]

Lu et al [78] used the eddy covariance method (ECM) to calculate strong carbon sinkeffects for a chenier ecosystem driven by forest vegetation Net energy production valueswere significantly higher than surrounding marshland signifying the importance of densevegetative cover on a per-unit basis Counter to this there is also strong evidence thatdegraded forest ecosystems act as net carbon sources A study in North Carolina also usingECM found that a seasonally inundated tide-influenced forest was a net carbon sourceannually and only functioned as a sink for 3 months of the growing year [79] Similarly astudy in the northeastern part of the state showed net negative rates of carbon sequestrationfor an ephemerally flooded forest [80] Unique environments demand individual attentiona characterization of this ecosystem for nitrogen or carbon flux with respect to a similarsystem may not be functionally sound [81]

Forests 2022 13 1132 18 of 22Forests 2022 13 x FOR PEER REVIEW 19 of 23

Figure 9 The total land area classified in GEE for the years 2003 2007 and 2019 Light green repre‐

sents the marsh area tan represents pasture The water layer has been removed for purposes of

clarity The dark black outline is the outline of the forest cover for the respective year

Louisiana has enacted a historic response to the problem of land‐loss following a

public outcry after devastating storms in 2005 The Coastal Protection and Restoration

Authority (CPRA) have been tasked with enacting a management plan to combat coastal

erosion The Comprehensive Master Plan details proposals to create marshes build sedi‐

ment diversions form new hydrologic structures and promote barrier island restoration

through built and natural means As of 2017 over 135 projects had been completed im‐

pacting over 14568 ha [74] An update to this document is due to be released in 2023 The

project has an overall operating budget of over 50 billion US dollars with equal shares

dedicated to restoration and risk reduction The primary funding source is a settlement

associated with the Deepwater Horizon Oil Spill in addition to various initiatives at the

state and federal levels However there are no current plans to supplement vegetation or

perform any geoengineering work on the cheniers for afforestation

Coastal wetlands rank among the most important ecosystems in terms of the valua‐

tion of ecosystem services [75] Provisioning services such as water filtration or storm‐

surge protection are commonly considered when discussing coastal areas Beyond these

commonalities chenier forests provide vital wildlife habitats being the first encountered

source of vertical structure for migratory neo‐tropical birds and migrating monarch but‐

terflies in the Mississippi alluvial valley flyway [76] This en‐route habitat is of critical

importance due to its closeness to the Gulf of Mexico During favorable weather condi‐

tions 10 of birds migrating through the Gulf use these forests as a stopover spot rising

Figure 9 The total land area classified in GEE for the years 2003 2007 and 2019 Light greenrepresents the marsh area tan represents pasture The water layer has been removed for purposes ofclarity The dark black outline is the outline of the forest cover for the respective year

5 Conclusions

The cheniers of southwestern Louisiana were found to be declining in size at a concern-ing rate Many of the driving factors are ecosystem-wide trends that have been increasingin intensity within the last century The results of this study suggest that coastal forestsare an ecosystem in peril and the ecology of such systems should be a closely monitoredsubject The 365 loss of forest cover is an alarming but not wholly unexpected result ofmany decades of intense perturbation and human exploitation A dynamic of continuedforest loss is evident Unless significant investment is made in engineering solutions tomitigate storm effects and land subsidence we will continue to see the decline of chenierforests in Louisiana Through targeted mitigation governmental programs such as theWetland or Conservation Reserve Program may be able to supplement existing forestsas well as the incomes of local people who have lost their livelihoods In this study wehave begun the process of accurate areal estimates of forest extent and have shed light onecosystem processes in the region As this community continues to diminish we see theforest not by the trees but rather the lack of them

Forests 2022 13 1132 19 of 22

Author Contributions Conceptualization PT methodology PT software PT and PR validationPT and PR formal analysis PT PR and PT writingmdashoriginal draft preparation PT and JBwritingmdashreview and editing PT and JB visualization PT and JB supervision JB All authorshave read and agreed to the published version of the manuscript

Funding This research received no external funding

Data Availability Statement The data will be published in and stored on the Harvard Dataverse athttpsdataverseharvardedudatasetxhtmlpersistentId=doi107910DVN3J7SNF (accessed on1 May 2022)

Acknowledgments The authors would like to recognize the hard work of undergraduate studentsParusha Raut and Subash Sapkota in manually validating thousands of points and creating numeroustraining sets We also extend thanks to our institution and numerous people within the lab who leantboth mental and emotional fortitude

Conflicts of Interest The authors declare no conflict of interest

References1 Michener WK Blood ER Bildstein KL Brinson MM Gardner LR Climate change hurricanes and tropical storms and

rising sea level in coastal wetlands Ecol Appl 1997 7 770ndash801 [CrossRef]2 Donoghue JF Sea level history of the northern Gulf of Mexico coast and sea level rise scenarios for the near future Clim Chang

2011 107 17ndash33 [CrossRef]3 Stanturf JA Goodrick SL Outcalt KW Disturbance and coastal forests A strategic approach to forest management in

hurricane impact zones For Ecol Manag 2007 250 119ndash135 [CrossRef]4 Augustinus P Cheniers and chenier plains A general introduction Mar Geol 1989 90 219ndash229 [CrossRef]5 Fu Z Wang P Sun J Lu Z Yang H Liu J Li T Composition seasonal variation and salinization characteristics of soil

salinity in the Chenier Island of the Yellow River Delta Glob Ecol Conserv 2020 24 e01318 [CrossRef]6 Marques IG Campelo F Rivaes R Albuquerque A Ferreira MT Rodriacuteguez-Gonzaacutelez PM Tree rings reveal long-term

changes in growth resilience in Southern European riparian forests Dendrochronologia 2018 55 167ndash176 [CrossRef]7 Daigle JJ Griffith GE Omernik JM Faulkner PL McCulloh RP Handley LR Smith LM Chapman SS Ecoregions of

Louisiana (Color Poster with Map Descriptive Text Summary Tables and Photographs) US Geological Survey Reston VI USA 20068 McBride RA Taylor MJ Byrnes MR Coastal morphodynamics and Chenier-Plain evolution in southwestern Louisiana USA

A geomorphic model Geomorphology 2007 88 367ndash422 [CrossRef]9 Gosselink JG An Ecological Characterization Study of the Chenier Plain Coastal Ecosystem of Louisiana and Texas Narrative Report

National Coastal Ecosystems Team Office of Biological Services Fish and Wildlife Service US Department of the InteriorWashington DC USA 1979 Volume 1

10 Blum MD Roberts HH The Mississippi delta region Past present and future Annu Rev Earth Planet Sci 2012 40 655ndash683[CrossRef]

11 Hansen MC Potapov PV Moore R Hancher M Turubanova SA Tyukavina A Townshend J High-resolution globalmaps of 21st-century forest cover change Science 2013 342 850ndash853 [CrossRef]

12 Doyle TW Krauss KW Conner WH From AS Predicting the retreat and migration of tidal forests along the northern Gulfof Mexico under sea-level rise For Ecol Manag 2010 259 770ndash777 [CrossRef]

13 Thomas N Simard M Castantildeeda-Moya E Byrd K Windham-Myers L Bevington A Twilley R High-resolution mappingof biomass and distribution of marsh and forested wetlands in southeastern coastal Louisiana Int J Appl Earth Obs Geoinf 201980 257ndash267 [CrossRef]

14 Desantis LR Bhotika S Williams K Putz FE Sea-level rise and drought interactions accelerate forest decline on the GulfCoast of Florida USA Glob Chang Biol 2007 13 2349ndash2360 [CrossRef]

15 Boyd DS Foody G Ripple W Evaluation of approaches for forest cover estimation in the Pacific Northwest USA usingremote sensing Appl Geogr 2002 22 375ndash392 [CrossRef]

16 Valjarevic A Djekic T Stevanovic V Ivanovic R Jandzikovic B GIS numerical and remote sensing analyses of forest changesin the Toplica region for the period of 1953ndash2013 Appl Geogr 2018 92 131ndash139 [CrossRef]

17 He T Sun YJ Xu JD Wang X Hu C Enhanced land usecover classification using support vector machines and fuzzyk-means clustering algorithms J Appl Remote Sens 2014 8 083636 [CrossRef]

18 Michez A Pieacutegay H Lisein J Claessens H Lejeune P Classification of riparian forest species and health condition usingmulti-temporal and hyperspatial imagery from unmanned aerial systems Environ Monit Assess 2016 188 146 [CrossRef][PubMed]

19 Hampton SE Strasser C Tewksbury J Gram W Budden A Batcheller A Duke C Porter J Big data and the future ofecology Front Ecol Environ 2013 11 156ndash162 [CrossRef]

20 Curtis PG Slay CM Harris NL Tyukavina A Hansen MC Classifying drivers of global forest loss Science 2018 3611108ndash1111 [CrossRef]

Forests 2022 13 1132 14 of 22

The analysis vis-agrave-vis elevation shows weak trends in the manners of loss on eleva-tional gradients Logically in the paradigm of coastal retreat there should be an aggregationof forest resources towards the highest bands of topography in a region Our analysis doesnot follow this chain of thought with the largest losses observed in the 1ndash2 m categoryrather than the lowest 0ndash1 m (Table 2 2007 C 2019 D) While we do see a large loss bypercentage in the highest elevation band it is important to note that a small sample causesinherent variance Forest cover on the highest reaches of the chenier ridges is typically lessthan 1 of total forest cover for that region The reduction can be attributed to the loss of23 features in section A accounting for 33 hectares bordering the Sabine River (Figure 1minus9386286 2973355) The loss of forest cover was driven by intense weather Vegetationin the area was completely destroyed and substantial amounts of sedimentation could beobserved as evident in the 2007 NAIP imagery The only gain in forest cover was seen inthe 2ndash3 m elevation band in the period 2007 in an area that had a high density of pastoralland that was abandoned Forest retreat is often discussed as one method of bufferingforest loss along coasts [62] Unfortunately the geomorphology of this area does not allowgreat spatial movement as the current extent is already located within the highest bandsof elevation

43 Land Use Conversion and Driving Factors

The leading cause of land-use conversion from tree cover was to a saltmarsh ecotype(606) often following mortality and replacement of vegetative biomass The loss offorest cover can largely be typified into two categories The ingression of saltwater causingsalinification within the soil matrix paired with increased flooding caused mortality ofoverstorey vegetation over a multi-year timescale [6364] Hurricanes or other storm eventscaused rapid mortality through wind-throws severe defoliation saltwater inundationduring a storm surge or canopy damage Following intense weather events between2003 and 2007 the vast majority of cheniers located within a 1km buffer of the coast wereunable to support tree cover as seen in Figure 7AB Clearing for industry or developmentalso caused immediate shifts in forest composition as can be seen in Figure 8AB Thischange was more pronounced on reaches A and B especially on Hackberry Beach Thisarea was the site of landfall for Hurricane Rita which caused massive devastation to othercomponents of the ecosystem [6566] Singular storm events have a transformative impacton vegetation communities and the weight of this driver on annual rates of forest lossis a topic that should be explored further Wayward shifts in species composition are atrend that has been observed globally especially in forests that border some source of waterTrees like Juniperus virginiana L that have distinctive water-use patterns in comparison toother coastal tree species are susceptible to salinification at much faster rates as evidencedthrough isotope analyses [67] As with many ecological systems the entangled effectsof climate and vegetation require diverse means of looking at change Isotope analysesdendrochronology field sensors and remote sensing data are needed to characterize land-use change at the stand and community levels

Large forest remnants such as Cow Island (minus9283581 2973842) Tiger Island (minus92784392972832) and Grand Chenier Ridge (minus9281175 2972986) among others have diminishedmore slowly By 2019 these stands had lost an average of 15 of their maximum extentThe land use changed to both marsh and managed pasture largely dependent on thesurrounding land use The conversion to managed pasture (282) accounted for a largerthan expected amount of land-cover change Many of these changes were in areas wheretree cover may have been more ldquoornamentalrdquo in nature Trees beside houses or alongsideroads are some of the largest specimens of live-oak to be found However these trees areoften solitary and perhaps more vulnerable to wind-borne disturbance The concern is thattheir loss due to abiotic drivers may not be easily mitigated leading to a permanent declineOur data support evidence on the whittling of forest remnants and the eventual fate ofthese trees becoming lsquorelict forestsrsquo unable to recruit new generations in the short term andin the long-term total disappearance of these cheniers along the Louisiana Gulf Coast [68]

Forests 2022 13 1132 15 of 22Forests 2022 13 x FOR PEER REVIEW 16 of 23

Figure 7 Visual depictions of two forms of loss one from hurricanes on Hackberry Beach (AB) and

the other documenting whittling reduction (CD) The left column displays 2003 NAIP imagery and

the right is 2007 NAIP imagery Black shaded areas indicate current year forest extent red hashed

areas indicate a loss

Figure 7 Visual depictions of two forms of loss one from hurricanes on Hackberry Beach (AB) andthe other documenting whittling reduction (CD) The left column displays 2003 NAIP imagery andthe right is 2007 NAIP imagery Black shaded areas indicate current year forest extent red hashedareas indicate a loss

Beyond loss in forest cover there is a noticeable trend towards an altered hydrologywhere pastoral areas and intact forest remnants are associated with pockets of standingwater An increase in the correlative factor between temperature and water levels could bea worrying trend a direct conversion of tree cover to water at 34 could be an unforeseenoutcome Chenier ridges in their geomorphic context should not support non-ephemeralponding It is not documented how increased tidal influence which is one factor of risingMSL influences the hydrology of this ecosystem and its associated communities Albeitin a more inland context Magolan and Halls [69] documented a transition of over 40 ofland to emergent marsh from closed-canopy cover from 1949 to 2018 due to hydrologicinfluence Recent rates of relative sea rise have been documented as high as 119 mmyearcompared to background Holocene rates of 17 mmyear [70] This has resulted in a rate oflateral coastal forest retreat between 2ndash14 times greater than pre-industrial levels [26] Therate of shoreline retreat in this region increased from ~61 to ~94 myear based on the 100-and 30-year moving averages amounting to over 10000 hectares of land loss every yearfrom 1800 to 2005 [27]

Forests 2022 13 1132 16 of 22Forests 2022 13 x FOR PEER REVIEW 17 of 23

Figure 8 A depiction of clearing for industry in (AB) and a depiction of forest whittling from (CD)

The black extent represents the current forest area red hashed lines indicate a loss The left panels

show 2007 NAIP imagery while the right are 2019 NAIP imagery

Large forest remnants such as Cow Island (minus9283581 2973842) Tiger Island

(minus9278439 2972832) and Grand Chenier Ridge (minus9281175 2972986) among others have

diminished more slowly By 2019 these stands had lost an average of 15 of their maxi‐

mum extent The land use changed to both marsh and managed pasture largely depend‐

ent on the surrounding land use The conversion to managed pasture (282) accounted

for a larger than expected amount of land‐cover change Many of these changes were in

areas where tree cover may have been more ldquoornamentalrdquo in nature Trees beside houses

or alongside roads are some of the largest specimens of live‐oak to be found However

these trees are often solitary and perhaps more vulnerable to wind‐borne disturbance The

concern is that their loss due to abiotic drivers may not be easily mitigated leading to a

permanent decline Our data support evidence on the whittling of forest remnants and

the eventual fate of these trees becoming lsquorelict forestsrsquo unable to recruit new generations

in the short term and in the long‐term total disappearance of these cheniers along the

Louisiana Gulf Coast [68]

Beyond loss in forest cover there is a noticeable trend towards an altered hydrology

where pastoral areas and intact forest remnants are associated with pockets of standing

water An increase in the correlative factor between temperature and water levels could

be a worrying trend a direct conversion of tree cover to water at 34 could be an unfore‐

seen outcome Chenier ridges in their geomorphic context should not support non‐ephem‐

eral ponding It is not documented how increased tidal influence which is one factor of

Figure 8 A depiction of clearing for industry in (AB) and a depiction of forest whittling from (CD)The black extent represents the current forest area red hashed lines indicate a loss The left panelsshow 2007 NAIP imagery while the right are 2019 NAIP imagery

44 Future of Chenier Forests

The chenier complexes of Louisiana do not have the natural benefaction of maintaininglarge original tracts untouched by axe or dozer The extent map through the years (Figure 9)exemplifies the scattered nature of forest remnants The largest single continuous chenierforest (Grand Chenier Ridgemdash185 ha) encompasses less than 1 of the total forest areaExposed forest edges pose a problem for the continued integrity of the stand as adaptedspecies fail to compete in these new microclimates [71] Slow-growing saplings of desirabletree species (live oak hackberry) are subject to unfavorable growing conditions fromweather and light availability Vegetation surveys following aspect and inundation alongthe Florida coast have all but confirmed the eventual extirpation of overstory vegetationthrough poor recruitment of native species [31] Chinese tallow (Triadica sebifera) a non-native tree is likely to fill empty niche space This aggressive invader is problematicthroughout the Gulf Coastal Plain from Texas to Florida [72] The tree has a wide meansof dispersal being primarily distributed through frugivory via avian populations A highrate of germination compounded with quick growth and a sexual maturity of less thanthree years make it a difficult species to control Its invasive capability is seen in nearlyall forest types in the region although it is more pervasive in coastal forests than inlandModeling studies show a high likelihood of Chinese tallow becoming the dominant specieswithin this ecosystem and in surrounding areas [73] Future publications from this lab will

Forests 2022 13 1132 17 of 22

provide more in-depth data and analysis on the state of existing stands and recruitmenttrends within remnants of the Chenier Plains via field sampling

Louisiana has enacted a historic response to the problem of land-loss following a publicoutcry after devastating storms in 2005 The Coastal Protection and Restoration Authority(CPRA) have been tasked with enacting a management plan to combat coastal erosion TheComprehensive Master Plan details proposals to create marshes build sediment diversionsform new hydrologic structures and promote barrier island restoration through builtand natural means As of 2017 over 135 projects had been completed impacting over14568 ha [74] An update to this document is due to be released in 2023 The projecthas an overall operating budget of over 50 billion US dollars with equal shares dedicatedto restoration and risk reduction The primary funding source is a settlement associatedwith the Deepwater Horizon Oil Spill in addition to various initiatives at the state andfederal levels However there are no current plans to supplement vegetation or performany geoengineering work on the cheniers for afforestation

Coastal wetlands rank among the most important ecosystems in terms of the valuationof ecosystem services [75] Provisioning services such as water filtration or storm-surgeprotection are commonly considered when discussing coastal areas Beyond these com-monalities chenier forests provide vital wildlife habitats being the first encountered sourceof vertical structure for migratory neo-tropical birds and migrating monarch butterflies inthe Mississippi alluvial valley flyway [76] This en-route habitat is of critical importancedue to its closeness to the Gulf of Mexico During favorable weather conditions 10 ofbirds migrating through the Gulf use these forests as a stopover spot rising dramatically to80 during adverse weather conditions [29] With losses mounting many species may notbe able to seek refuge during storms The area also has a unique suite of amphibians andreptiles being home to 13 obligate chenier forest species of herpetofauna [77]

Lu et al [78] used the eddy covariance method (ECM) to calculate strong carbon sinkeffects for a chenier ecosystem driven by forest vegetation Net energy production valueswere significantly higher than surrounding marshland signifying the importance of densevegetative cover on a per-unit basis Counter to this there is also strong evidence thatdegraded forest ecosystems act as net carbon sources A study in North Carolina also usingECM found that a seasonally inundated tide-influenced forest was a net carbon sourceannually and only functioned as a sink for 3 months of the growing year [79] Similarly astudy in the northeastern part of the state showed net negative rates of carbon sequestrationfor an ephemerally flooded forest [80] Unique environments demand individual attentiona characterization of this ecosystem for nitrogen or carbon flux with respect to a similarsystem may not be functionally sound [81]

Forests 2022 13 1132 18 of 22Forests 2022 13 x FOR PEER REVIEW 19 of 23

Figure 9 The total land area classified in GEE for the years 2003 2007 and 2019 Light green repre‐

sents the marsh area tan represents pasture The water layer has been removed for purposes of

clarity The dark black outline is the outline of the forest cover for the respective year

Louisiana has enacted a historic response to the problem of land‐loss following a

public outcry after devastating storms in 2005 The Coastal Protection and Restoration

Authority (CPRA) have been tasked with enacting a management plan to combat coastal

erosion The Comprehensive Master Plan details proposals to create marshes build sedi‐

ment diversions form new hydrologic structures and promote barrier island restoration

through built and natural means As of 2017 over 135 projects had been completed im‐

pacting over 14568 ha [74] An update to this document is due to be released in 2023 The

project has an overall operating budget of over 50 billion US dollars with equal shares

dedicated to restoration and risk reduction The primary funding source is a settlement

associated with the Deepwater Horizon Oil Spill in addition to various initiatives at the

state and federal levels However there are no current plans to supplement vegetation or

perform any geoengineering work on the cheniers for afforestation

Coastal wetlands rank among the most important ecosystems in terms of the valua‐

tion of ecosystem services [75] Provisioning services such as water filtration or storm‐

surge protection are commonly considered when discussing coastal areas Beyond these

commonalities chenier forests provide vital wildlife habitats being the first encountered

source of vertical structure for migratory neo‐tropical birds and migrating monarch but‐

terflies in the Mississippi alluvial valley flyway [76] This en‐route habitat is of critical

importance due to its closeness to the Gulf of Mexico During favorable weather condi‐

tions 10 of birds migrating through the Gulf use these forests as a stopover spot rising

Figure 9 The total land area classified in GEE for the years 2003 2007 and 2019 Light greenrepresents the marsh area tan represents pasture The water layer has been removed for purposes ofclarity The dark black outline is the outline of the forest cover for the respective year

5 Conclusions

The cheniers of southwestern Louisiana were found to be declining in size at a concern-ing rate Many of the driving factors are ecosystem-wide trends that have been increasingin intensity within the last century The results of this study suggest that coastal forestsare an ecosystem in peril and the ecology of such systems should be a closely monitoredsubject The 365 loss of forest cover is an alarming but not wholly unexpected result ofmany decades of intense perturbation and human exploitation A dynamic of continuedforest loss is evident Unless significant investment is made in engineering solutions tomitigate storm effects and land subsidence we will continue to see the decline of chenierforests in Louisiana Through targeted mitigation governmental programs such as theWetland or Conservation Reserve Program may be able to supplement existing forestsas well as the incomes of local people who have lost their livelihoods In this study wehave begun the process of accurate areal estimates of forest extent and have shed light onecosystem processes in the region As this community continues to diminish we see theforest not by the trees but rather the lack of them

Forests 2022 13 1132 19 of 22

Author Contributions Conceptualization PT methodology PT software PT and PR validationPT and PR formal analysis PT PR and PT writingmdashoriginal draft preparation PT and JBwritingmdashreview and editing PT and JB visualization PT and JB supervision JB All authorshave read and agreed to the published version of the manuscript

Funding This research received no external funding

Data Availability Statement The data will be published in and stored on the Harvard Dataverse athttpsdataverseharvardedudatasetxhtmlpersistentId=doi107910DVN3J7SNF (accessed on1 May 2022)

Acknowledgments The authors would like to recognize the hard work of undergraduate studentsParusha Raut and Subash Sapkota in manually validating thousands of points and creating numeroustraining sets We also extend thanks to our institution and numerous people within the lab who leantboth mental and emotional fortitude

Conflicts of Interest The authors declare no conflict of interest

References1 Michener WK Blood ER Bildstein KL Brinson MM Gardner LR Climate change hurricanes and tropical storms and

rising sea level in coastal wetlands Ecol Appl 1997 7 770ndash801 [CrossRef]2 Donoghue JF Sea level history of the northern Gulf of Mexico coast and sea level rise scenarios for the near future Clim Chang

2011 107 17ndash33 [CrossRef]3 Stanturf JA Goodrick SL Outcalt KW Disturbance and coastal forests A strategic approach to forest management in

hurricane impact zones For Ecol Manag 2007 250 119ndash135 [CrossRef]4 Augustinus P Cheniers and chenier plains A general introduction Mar Geol 1989 90 219ndash229 [CrossRef]5 Fu Z Wang P Sun J Lu Z Yang H Liu J Li T Composition seasonal variation and salinization characteristics of soil

salinity in the Chenier Island of the Yellow River Delta Glob Ecol Conserv 2020 24 e01318 [CrossRef]6 Marques IG Campelo F Rivaes R Albuquerque A Ferreira MT Rodriacuteguez-Gonzaacutelez PM Tree rings reveal long-term

changes in growth resilience in Southern European riparian forests Dendrochronologia 2018 55 167ndash176 [CrossRef]7 Daigle JJ Griffith GE Omernik JM Faulkner PL McCulloh RP Handley LR Smith LM Chapman SS Ecoregions of

Louisiana (Color Poster with Map Descriptive Text Summary Tables and Photographs) US Geological Survey Reston VI USA 20068 McBride RA Taylor MJ Byrnes MR Coastal morphodynamics and Chenier-Plain evolution in southwestern Louisiana USA

A geomorphic model Geomorphology 2007 88 367ndash422 [CrossRef]9 Gosselink JG An Ecological Characterization Study of the Chenier Plain Coastal Ecosystem of Louisiana and Texas Narrative Report

National Coastal Ecosystems Team Office of Biological Services Fish and Wildlife Service US Department of the InteriorWashington DC USA 1979 Volume 1

10 Blum MD Roberts HH The Mississippi delta region Past present and future Annu Rev Earth Planet Sci 2012 40 655ndash683[CrossRef]

11 Hansen MC Potapov PV Moore R Hancher M Turubanova SA Tyukavina A Townshend J High-resolution globalmaps of 21st-century forest cover change Science 2013 342 850ndash853 [CrossRef]

12 Doyle TW Krauss KW Conner WH From AS Predicting the retreat and migration of tidal forests along the northern Gulfof Mexico under sea-level rise For Ecol Manag 2010 259 770ndash777 [CrossRef]

13 Thomas N Simard M Castantildeeda-Moya E Byrd K Windham-Myers L Bevington A Twilley R High-resolution mappingof biomass and distribution of marsh and forested wetlands in southeastern coastal Louisiana Int J Appl Earth Obs Geoinf 201980 257ndash267 [CrossRef]

14 Desantis LR Bhotika S Williams K Putz FE Sea-level rise and drought interactions accelerate forest decline on the GulfCoast of Florida USA Glob Chang Biol 2007 13 2349ndash2360 [CrossRef]

15 Boyd DS Foody G Ripple W Evaluation of approaches for forest cover estimation in the Pacific Northwest USA usingremote sensing Appl Geogr 2002 22 375ndash392 [CrossRef]

16 Valjarevic A Djekic T Stevanovic V Ivanovic R Jandzikovic B GIS numerical and remote sensing analyses of forest changesin the Toplica region for the period of 1953ndash2013 Appl Geogr 2018 92 131ndash139 [CrossRef]

17 He T Sun YJ Xu JD Wang X Hu C Enhanced land usecover classification using support vector machines and fuzzyk-means clustering algorithms J Appl Remote Sens 2014 8 083636 [CrossRef]

18 Michez A Pieacutegay H Lisein J Claessens H Lejeune P Classification of riparian forest species and health condition usingmulti-temporal and hyperspatial imagery from unmanned aerial systems Environ Monit Assess 2016 188 146 [CrossRef][PubMed]

19 Hampton SE Strasser C Tewksbury J Gram W Budden A Batcheller A Duke C Porter J Big data and the future ofecology Front Ecol Environ 2013 11 156ndash162 [CrossRef]

20 Curtis PG Slay CM Harris NL Tyukavina A Hansen MC Classifying drivers of global forest loss Science 2018 3611108ndash1111 [CrossRef]

Forests 2022 13 1132 15 of 22Forests 2022 13 x FOR PEER REVIEW 16 of 23

Figure 7 Visual depictions of two forms of loss one from hurricanes on Hackberry Beach (AB) and

the other documenting whittling reduction (CD) The left column displays 2003 NAIP imagery and

the right is 2007 NAIP imagery Black shaded areas indicate current year forest extent red hashed

areas indicate a loss

Figure 7 Visual depictions of two forms of loss one from hurricanes on Hackberry Beach (AB) andthe other documenting whittling reduction (CD) The left column displays 2003 NAIP imagery andthe right is 2007 NAIP imagery Black shaded areas indicate current year forest extent red hashedareas indicate a loss

Beyond loss in forest cover there is a noticeable trend towards an altered hydrologywhere pastoral areas and intact forest remnants are associated with pockets of standingwater An increase in the correlative factor between temperature and water levels could bea worrying trend a direct conversion of tree cover to water at 34 could be an unforeseenoutcome Chenier ridges in their geomorphic context should not support non-ephemeralponding It is not documented how increased tidal influence which is one factor of risingMSL influences the hydrology of this ecosystem and its associated communities Albeitin a more inland context Magolan and Halls [69] documented a transition of over 40 ofland to emergent marsh from closed-canopy cover from 1949 to 2018 due to hydrologicinfluence Recent rates of relative sea rise have been documented as high as 119 mmyearcompared to background Holocene rates of 17 mmyear [70] This has resulted in a rate oflateral coastal forest retreat between 2ndash14 times greater than pre-industrial levels [26] Therate of shoreline retreat in this region increased from ~61 to ~94 myear based on the 100-and 30-year moving averages amounting to over 10000 hectares of land loss every yearfrom 1800 to 2005 [27]

Forests 2022 13 1132 16 of 22Forests 2022 13 x FOR PEER REVIEW 17 of 23

Figure 8 A depiction of clearing for industry in (AB) and a depiction of forest whittling from (CD)

The black extent represents the current forest area red hashed lines indicate a loss The left panels

show 2007 NAIP imagery while the right are 2019 NAIP imagery

Large forest remnants such as Cow Island (minus9283581 2973842) Tiger Island

(minus9278439 2972832) and Grand Chenier Ridge (minus9281175 2972986) among others have

diminished more slowly By 2019 these stands had lost an average of 15 of their maxi‐

mum extent The land use changed to both marsh and managed pasture largely depend‐

ent on the surrounding land use The conversion to managed pasture (282) accounted

for a larger than expected amount of land‐cover change Many of these changes were in

areas where tree cover may have been more ldquoornamentalrdquo in nature Trees beside houses

or alongside roads are some of the largest specimens of live‐oak to be found However

these trees are often solitary and perhaps more vulnerable to wind‐borne disturbance The

concern is that their loss due to abiotic drivers may not be easily mitigated leading to a

permanent decline Our data support evidence on the whittling of forest remnants and

the eventual fate of these trees becoming lsquorelict forestsrsquo unable to recruit new generations

in the short term and in the long‐term total disappearance of these cheniers along the

Louisiana Gulf Coast [68]

Beyond loss in forest cover there is a noticeable trend towards an altered hydrology

where pastoral areas and intact forest remnants are associated with pockets of standing

water An increase in the correlative factor between temperature and water levels could

be a worrying trend a direct conversion of tree cover to water at 34 could be an unfore‐

seen outcome Chenier ridges in their geomorphic context should not support non‐ephem‐

eral ponding It is not documented how increased tidal influence which is one factor of

Figure 8 A depiction of clearing for industry in (AB) and a depiction of forest whittling from (CD)The black extent represents the current forest area red hashed lines indicate a loss The left panelsshow 2007 NAIP imagery while the right are 2019 NAIP imagery

44 Future of Chenier Forests

The chenier complexes of Louisiana do not have the natural benefaction of maintaininglarge original tracts untouched by axe or dozer The extent map through the years (Figure 9)exemplifies the scattered nature of forest remnants The largest single continuous chenierforest (Grand Chenier Ridgemdash185 ha) encompasses less than 1 of the total forest areaExposed forest edges pose a problem for the continued integrity of the stand as adaptedspecies fail to compete in these new microclimates [71] Slow-growing saplings of desirabletree species (live oak hackberry) are subject to unfavorable growing conditions fromweather and light availability Vegetation surveys following aspect and inundation alongthe Florida coast have all but confirmed the eventual extirpation of overstory vegetationthrough poor recruitment of native species [31] Chinese tallow (Triadica sebifera) a non-native tree is likely to fill empty niche space This aggressive invader is problematicthroughout the Gulf Coastal Plain from Texas to Florida [72] The tree has a wide meansof dispersal being primarily distributed through frugivory via avian populations A highrate of germination compounded with quick growth and a sexual maturity of less thanthree years make it a difficult species to control Its invasive capability is seen in nearlyall forest types in the region although it is more pervasive in coastal forests than inlandModeling studies show a high likelihood of Chinese tallow becoming the dominant specieswithin this ecosystem and in surrounding areas [73] Future publications from this lab will

Forests 2022 13 1132 17 of 22

provide more in-depth data and analysis on the state of existing stands and recruitmenttrends within remnants of the Chenier Plains via field sampling

Louisiana has enacted a historic response to the problem of land-loss following a publicoutcry after devastating storms in 2005 The Coastal Protection and Restoration Authority(CPRA) have been tasked with enacting a management plan to combat coastal erosion TheComprehensive Master Plan details proposals to create marshes build sediment diversionsform new hydrologic structures and promote barrier island restoration through builtand natural means As of 2017 over 135 projects had been completed impacting over14568 ha [74] An update to this document is due to be released in 2023 The projecthas an overall operating budget of over 50 billion US dollars with equal shares dedicatedto restoration and risk reduction The primary funding source is a settlement associatedwith the Deepwater Horizon Oil Spill in addition to various initiatives at the state andfederal levels However there are no current plans to supplement vegetation or performany geoengineering work on the cheniers for afforestation

Coastal wetlands rank among the most important ecosystems in terms of the valuationof ecosystem services [75] Provisioning services such as water filtration or storm-surgeprotection are commonly considered when discussing coastal areas Beyond these com-monalities chenier forests provide vital wildlife habitats being the first encountered sourceof vertical structure for migratory neo-tropical birds and migrating monarch butterflies inthe Mississippi alluvial valley flyway [76] This en-route habitat is of critical importancedue to its closeness to the Gulf of Mexico During favorable weather conditions 10 ofbirds migrating through the Gulf use these forests as a stopover spot rising dramatically to80 during adverse weather conditions [29] With losses mounting many species may notbe able to seek refuge during storms The area also has a unique suite of amphibians andreptiles being home to 13 obligate chenier forest species of herpetofauna [77]

Lu et al [78] used the eddy covariance method (ECM) to calculate strong carbon sinkeffects for a chenier ecosystem driven by forest vegetation Net energy production valueswere significantly higher than surrounding marshland signifying the importance of densevegetative cover on a per-unit basis Counter to this there is also strong evidence thatdegraded forest ecosystems act as net carbon sources A study in North Carolina also usingECM found that a seasonally inundated tide-influenced forest was a net carbon sourceannually and only functioned as a sink for 3 months of the growing year [79] Similarly astudy in the northeastern part of the state showed net negative rates of carbon sequestrationfor an ephemerally flooded forest [80] Unique environments demand individual attentiona characterization of this ecosystem for nitrogen or carbon flux with respect to a similarsystem may not be functionally sound [81]

Forests 2022 13 1132 18 of 22Forests 2022 13 x FOR PEER REVIEW 19 of 23

Figure 9 The total land area classified in GEE for the years 2003 2007 and 2019 Light green repre‐

sents the marsh area tan represents pasture The water layer has been removed for purposes of

clarity The dark black outline is the outline of the forest cover for the respective year

Louisiana has enacted a historic response to the problem of land‐loss following a

public outcry after devastating storms in 2005 The Coastal Protection and Restoration

Authority (CPRA) have been tasked with enacting a management plan to combat coastal

erosion The Comprehensive Master Plan details proposals to create marshes build sedi‐

ment diversions form new hydrologic structures and promote barrier island restoration

through built and natural means As of 2017 over 135 projects had been completed im‐

pacting over 14568 ha [74] An update to this document is due to be released in 2023 The

project has an overall operating budget of over 50 billion US dollars with equal shares

dedicated to restoration and risk reduction The primary funding source is a settlement

associated with the Deepwater Horizon Oil Spill in addition to various initiatives at the

state and federal levels However there are no current plans to supplement vegetation or

perform any geoengineering work on the cheniers for afforestation

Coastal wetlands rank among the most important ecosystems in terms of the valua‐

tion of ecosystem services [75] Provisioning services such as water filtration or storm‐

surge protection are commonly considered when discussing coastal areas Beyond these

commonalities chenier forests provide vital wildlife habitats being the first encountered

source of vertical structure for migratory neo‐tropical birds and migrating monarch but‐

terflies in the Mississippi alluvial valley flyway [76] This en‐route habitat is of critical

importance due to its closeness to the Gulf of Mexico During favorable weather condi‐

tions 10 of birds migrating through the Gulf use these forests as a stopover spot rising

Figure 9 The total land area classified in GEE for the years 2003 2007 and 2019 Light greenrepresents the marsh area tan represents pasture The water layer has been removed for purposes ofclarity The dark black outline is the outline of the forest cover for the respective year

5 Conclusions

The cheniers of southwestern Louisiana were found to be declining in size at a concern-ing rate Many of the driving factors are ecosystem-wide trends that have been increasingin intensity within the last century The results of this study suggest that coastal forestsare an ecosystem in peril and the ecology of such systems should be a closely monitoredsubject The 365 loss of forest cover is an alarming but not wholly unexpected result ofmany decades of intense perturbation and human exploitation A dynamic of continuedforest loss is evident Unless significant investment is made in engineering solutions tomitigate storm effects and land subsidence we will continue to see the decline of chenierforests in Louisiana Through targeted mitigation governmental programs such as theWetland or Conservation Reserve Program may be able to supplement existing forestsas well as the incomes of local people who have lost their livelihoods In this study wehave begun the process of accurate areal estimates of forest extent and have shed light onecosystem processes in the region As this community continues to diminish we see theforest not by the trees but rather the lack of them

Forests 2022 13 1132 19 of 22

Author Contributions Conceptualization PT methodology PT software PT and PR validationPT and PR formal analysis PT PR and PT writingmdashoriginal draft preparation PT and JBwritingmdashreview and editing PT and JB visualization PT and JB supervision JB All authorshave read and agreed to the published version of the manuscript

Funding This research received no external funding

Data Availability Statement The data will be published in and stored on the Harvard Dataverse athttpsdataverseharvardedudatasetxhtmlpersistentId=doi107910DVN3J7SNF (accessed on1 May 2022)

Acknowledgments The authors would like to recognize the hard work of undergraduate studentsParusha Raut and Subash Sapkota in manually validating thousands of points and creating numeroustraining sets We also extend thanks to our institution and numerous people within the lab who leantboth mental and emotional fortitude

Conflicts of Interest The authors declare no conflict of interest

References1 Michener WK Blood ER Bildstein KL Brinson MM Gardner LR Climate change hurricanes and tropical storms and

rising sea level in coastal wetlands Ecol Appl 1997 7 770ndash801 [CrossRef]2 Donoghue JF Sea level history of the northern Gulf of Mexico coast and sea level rise scenarios for the near future Clim Chang

2011 107 17ndash33 [CrossRef]3 Stanturf JA Goodrick SL Outcalt KW Disturbance and coastal forests A strategic approach to forest management in

hurricane impact zones For Ecol Manag 2007 250 119ndash135 [CrossRef]4 Augustinus P Cheniers and chenier plains A general introduction Mar Geol 1989 90 219ndash229 [CrossRef]5 Fu Z Wang P Sun J Lu Z Yang H Liu J Li T Composition seasonal variation and salinization characteristics of soil

salinity in the Chenier Island of the Yellow River Delta Glob Ecol Conserv 2020 24 e01318 [CrossRef]6 Marques IG Campelo F Rivaes R Albuquerque A Ferreira MT Rodriacuteguez-Gonzaacutelez PM Tree rings reveal long-term

changes in growth resilience in Southern European riparian forests Dendrochronologia 2018 55 167ndash176 [CrossRef]7 Daigle JJ Griffith GE Omernik JM Faulkner PL McCulloh RP Handley LR Smith LM Chapman SS Ecoregions of

Louisiana (Color Poster with Map Descriptive Text Summary Tables and Photographs) US Geological Survey Reston VI USA 20068 McBride RA Taylor MJ Byrnes MR Coastal morphodynamics and Chenier-Plain evolution in southwestern Louisiana USA

A geomorphic model Geomorphology 2007 88 367ndash422 [CrossRef]9 Gosselink JG An Ecological Characterization Study of the Chenier Plain Coastal Ecosystem of Louisiana and Texas Narrative Report

National Coastal Ecosystems Team Office of Biological Services Fish and Wildlife Service US Department of the InteriorWashington DC USA 1979 Volume 1

10 Blum MD Roberts HH The Mississippi delta region Past present and future Annu Rev Earth Planet Sci 2012 40 655ndash683[CrossRef]

11 Hansen MC Potapov PV Moore R Hancher M Turubanova SA Tyukavina A Townshend J High-resolution globalmaps of 21st-century forest cover change Science 2013 342 850ndash853 [CrossRef]

12 Doyle TW Krauss KW Conner WH From AS Predicting the retreat and migration of tidal forests along the northern Gulfof Mexico under sea-level rise For Ecol Manag 2010 259 770ndash777 [CrossRef]

13 Thomas N Simard M Castantildeeda-Moya E Byrd K Windham-Myers L Bevington A Twilley R High-resolution mappingof biomass and distribution of marsh and forested wetlands in southeastern coastal Louisiana Int J Appl Earth Obs Geoinf 201980 257ndash267 [CrossRef]

14 Desantis LR Bhotika S Williams K Putz FE Sea-level rise and drought interactions accelerate forest decline on the GulfCoast of Florida USA Glob Chang Biol 2007 13 2349ndash2360 [CrossRef]

15 Boyd DS Foody G Ripple W Evaluation of approaches for forest cover estimation in the Pacific Northwest USA usingremote sensing Appl Geogr 2002 22 375ndash392 [CrossRef]

16 Valjarevic A Djekic T Stevanovic V Ivanovic R Jandzikovic B GIS numerical and remote sensing analyses of forest changesin the Toplica region for the period of 1953ndash2013 Appl Geogr 2018 92 131ndash139 [CrossRef]

17 He T Sun YJ Xu JD Wang X Hu C Enhanced land usecover classification using support vector machines and fuzzyk-means clustering algorithms J Appl Remote Sens 2014 8 083636 [CrossRef]

18 Michez A Pieacutegay H Lisein J Claessens H Lejeune P Classification of riparian forest species and health condition usingmulti-temporal and hyperspatial imagery from unmanned aerial systems Environ Monit Assess 2016 188 146 [CrossRef][PubMed]

19 Hampton SE Strasser C Tewksbury J Gram W Budden A Batcheller A Duke C Porter J Big data and the future ofecology Front Ecol Environ 2013 11 156ndash162 [CrossRef]

20 Curtis PG Slay CM Harris NL Tyukavina A Hansen MC Classifying drivers of global forest loss Science 2018 3611108ndash1111 [CrossRef]

Forests 2022 13 1132 16 of 22Forests 2022 13 x FOR PEER REVIEW 17 of 23

Figure 8 A depiction of clearing for industry in (AB) and a depiction of forest whittling from (CD)

The black extent represents the current forest area red hashed lines indicate a loss The left panels

show 2007 NAIP imagery while the right are 2019 NAIP imagery

Large forest remnants such as Cow Island (minus9283581 2973842) Tiger Island

(minus9278439 2972832) and Grand Chenier Ridge (minus9281175 2972986) among others have

diminished more slowly By 2019 these stands had lost an average of 15 of their maxi‐

mum extent The land use changed to both marsh and managed pasture largely depend‐

ent on the surrounding land use The conversion to managed pasture (282) accounted

for a larger than expected amount of land‐cover change Many of these changes were in

areas where tree cover may have been more ldquoornamentalrdquo in nature Trees beside houses

or alongside roads are some of the largest specimens of live‐oak to be found However

these trees are often solitary and perhaps more vulnerable to wind‐borne disturbance The

concern is that their loss due to abiotic drivers may not be easily mitigated leading to a

permanent decline Our data support evidence on the whittling of forest remnants and

the eventual fate of these trees becoming lsquorelict forestsrsquo unable to recruit new generations

in the short term and in the long‐term total disappearance of these cheniers along the

Louisiana Gulf Coast [68]

Beyond loss in forest cover there is a noticeable trend towards an altered hydrology

where pastoral areas and intact forest remnants are associated with pockets of standing

water An increase in the correlative factor between temperature and water levels could

be a worrying trend a direct conversion of tree cover to water at 34 could be an unfore‐

seen outcome Chenier ridges in their geomorphic context should not support non‐ephem‐

eral ponding It is not documented how increased tidal influence which is one factor of

Figure 8 A depiction of clearing for industry in (AB) and a depiction of forest whittling from (CD)The black extent represents the current forest area red hashed lines indicate a loss The left panelsshow 2007 NAIP imagery while the right are 2019 NAIP imagery

44 Future of Chenier Forests

The chenier complexes of Louisiana do not have the natural benefaction of maintaininglarge original tracts untouched by axe or dozer The extent map through the years (Figure 9)exemplifies the scattered nature of forest remnants The largest single continuous chenierforest (Grand Chenier Ridgemdash185 ha) encompasses less than 1 of the total forest areaExposed forest edges pose a problem for the continued integrity of the stand as adaptedspecies fail to compete in these new microclimates [71] Slow-growing saplings of desirabletree species (live oak hackberry) are subject to unfavorable growing conditions fromweather and light availability Vegetation surveys following aspect and inundation alongthe Florida coast have all but confirmed the eventual extirpation of overstory vegetationthrough poor recruitment of native species [31] Chinese tallow (Triadica sebifera) a non-native tree is likely to fill empty niche space This aggressive invader is problematicthroughout the Gulf Coastal Plain from Texas to Florida [72] The tree has a wide meansof dispersal being primarily distributed through frugivory via avian populations A highrate of germination compounded with quick growth and a sexual maturity of less thanthree years make it a difficult species to control Its invasive capability is seen in nearlyall forest types in the region although it is more pervasive in coastal forests than inlandModeling studies show a high likelihood of Chinese tallow becoming the dominant specieswithin this ecosystem and in surrounding areas [73] Future publications from this lab will

Forests 2022 13 1132 17 of 22

provide more in-depth data and analysis on the state of existing stands and recruitmenttrends within remnants of the Chenier Plains via field sampling

Louisiana has enacted a historic response to the problem of land-loss following a publicoutcry after devastating storms in 2005 The Coastal Protection and Restoration Authority(CPRA) have been tasked with enacting a management plan to combat coastal erosion TheComprehensive Master Plan details proposals to create marshes build sediment diversionsform new hydrologic structures and promote barrier island restoration through builtand natural means As of 2017 over 135 projects had been completed impacting over14568 ha [74] An update to this document is due to be released in 2023 The projecthas an overall operating budget of over 50 billion US dollars with equal shares dedicatedto restoration and risk reduction The primary funding source is a settlement associatedwith the Deepwater Horizon Oil Spill in addition to various initiatives at the state andfederal levels However there are no current plans to supplement vegetation or performany geoengineering work on the cheniers for afforestation

Coastal wetlands rank among the most important ecosystems in terms of the valuationof ecosystem services [75] Provisioning services such as water filtration or storm-surgeprotection are commonly considered when discussing coastal areas Beyond these com-monalities chenier forests provide vital wildlife habitats being the first encountered sourceof vertical structure for migratory neo-tropical birds and migrating monarch butterflies inthe Mississippi alluvial valley flyway [76] This en-route habitat is of critical importancedue to its closeness to the Gulf of Mexico During favorable weather conditions 10 ofbirds migrating through the Gulf use these forests as a stopover spot rising dramatically to80 during adverse weather conditions [29] With losses mounting many species may notbe able to seek refuge during storms The area also has a unique suite of amphibians andreptiles being home to 13 obligate chenier forest species of herpetofauna [77]

Lu et al [78] used the eddy covariance method (ECM) to calculate strong carbon sinkeffects for a chenier ecosystem driven by forest vegetation Net energy production valueswere significantly higher than surrounding marshland signifying the importance of densevegetative cover on a per-unit basis Counter to this there is also strong evidence thatdegraded forest ecosystems act as net carbon sources A study in North Carolina also usingECM found that a seasonally inundated tide-influenced forest was a net carbon sourceannually and only functioned as a sink for 3 months of the growing year [79] Similarly astudy in the northeastern part of the state showed net negative rates of carbon sequestrationfor an ephemerally flooded forest [80] Unique environments demand individual attentiona characterization of this ecosystem for nitrogen or carbon flux with respect to a similarsystem may not be functionally sound [81]

Forests 2022 13 1132 18 of 22Forests 2022 13 x FOR PEER REVIEW 19 of 23

Figure 9 The total land area classified in GEE for the years 2003 2007 and 2019 Light green repre‐

sents the marsh area tan represents pasture The water layer has been removed for purposes of

clarity The dark black outline is the outline of the forest cover for the respective year

Louisiana has enacted a historic response to the problem of land‐loss following a

public outcry after devastating storms in 2005 The Coastal Protection and Restoration

Authority (CPRA) have been tasked with enacting a management plan to combat coastal

erosion The Comprehensive Master Plan details proposals to create marshes build sedi‐

ment diversions form new hydrologic structures and promote barrier island restoration

through built and natural means As of 2017 over 135 projects had been completed im‐

pacting over 14568 ha [74] An update to this document is due to be released in 2023 The

project has an overall operating budget of over 50 billion US dollars with equal shares

dedicated to restoration and risk reduction The primary funding source is a settlement

associated with the Deepwater Horizon Oil Spill in addition to various initiatives at the

state and federal levels However there are no current plans to supplement vegetation or

perform any geoengineering work on the cheniers for afforestation

Coastal wetlands rank among the most important ecosystems in terms of the valua‐

tion of ecosystem services [75] Provisioning services such as water filtration or storm‐

surge protection are commonly considered when discussing coastal areas Beyond these

commonalities chenier forests provide vital wildlife habitats being the first encountered

source of vertical structure for migratory neo‐tropical birds and migrating monarch but‐

terflies in the Mississippi alluvial valley flyway [76] This en‐route habitat is of critical

importance due to its closeness to the Gulf of Mexico During favorable weather condi‐

tions 10 of birds migrating through the Gulf use these forests as a stopover spot rising

Figure 9 The total land area classified in GEE for the years 2003 2007 and 2019 Light greenrepresents the marsh area tan represents pasture The water layer has been removed for purposes ofclarity The dark black outline is the outline of the forest cover for the respective year

5 Conclusions

The cheniers of southwestern Louisiana were found to be declining in size at a concern-ing rate Many of the driving factors are ecosystem-wide trends that have been increasingin intensity within the last century The results of this study suggest that coastal forestsare an ecosystem in peril and the ecology of such systems should be a closely monitoredsubject The 365 loss of forest cover is an alarming but not wholly unexpected result ofmany decades of intense perturbation and human exploitation A dynamic of continuedforest loss is evident Unless significant investment is made in engineering solutions tomitigate storm effects and land subsidence we will continue to see the decline of chenierforests in Louisiana Through targeted mitigation governmental programs such as theWetland or Conservation Reserve Program may be able to supplement existing forestsas well as the incomes of local people who have lost their livelihoods In this study wehave begun the process of accurate areal estimates of forest extent and have shed light onecosystem processes in the region As this community continues to diminish we see theforest not by the trees but rather the lack of them

Forests 2022 13 1132 19 of 22

Author Contributions Conceptualization PT methodology PT software PT and PR validationPT and PR formal analysis PT PR and PT writingmdashoriginal draft preparation PT and JBwritingmdashreview and editing PT and JB visualization PT and JB supervision JB All authorshave read and agreed to the published version of the manuscript

Funding This research received no external funding

Data Availability Statement The data will be published in and stored on the Harvard Dataverse athttpsdataverseharvardedudatasetxhtmlpersistentId=doi107910DVN3J7SNF (accessed on1 May 2022)

Acknowledgments The authors would like to recognize the hard work of undergraduate studentsParusha Raut and Subash Sapkota in manually validating thousands of points and creating numeroustraining sets We also extend thanks to our institution and numerous people within the lab who leantboth mental and emotional fortitude

Conflicts of Interest The authors declare no conflict of interest

References1 Michener WK Blood ER Bildstein KL Brinson MM Gardner LR Climate change hurricanes and tropical storms and

rising sea level in coastal wetlands Ecol Appl 1997 7 770ndash801 [CrossRef]2 Donoghue JF Sea level history of the northern Gulf of Mexico coast and sea level rise scenarios for the near future Clim Chang

2011 107 17ndash33 [CrossRef]3 Stanturf JA Goodrick SL Outcalt KW Disturbance and coastal forests A strategic approach to forest management in

hurricane impact zones For Ecol Manag 2007 250 119ndash135 [CrossRef]4 Augustinus P Cheniers and chenier plains A general introduction Mar Geol 1989 90 219ndash229 [CrossRef]5 Fu Z Wang P Sun J Lu Z Yang H Liu J Li T Composition seasonal variation and salinization characteristics of soil

salinity in the Chenier Island of the Yellow River Delta Glob Ecol Conserv 2020 24 e01318 [CrossRef]6 Marques IG Campelo F Rivaes R Albuquerque A Ferreira MT Rodriacuteguez-Gonzaacutelez PM Tree rings reveal long-term

changes in growth resilience in Southern European riparian forests Dendrochronologia 2018 55 167ndash176 [CrossRef]7 Daigle JJ Griffith GE Omernik JM Faulkner PL McCulloh RP Handley LR Smith LM Chapman SS Ecoregions of

Louisiana (Color Poster with Map Descriptive Text Summary Tables and Photographs) US Geological Survey Reston VI USA 20068 McBride RA Taylor MJ Byrnes MR Coastal morphodynamics and Chenier-Plain evolution in southwestern Louisiana USA

A geomorphic model Geomorphology 2007 88 367ndash422 [CrossRef]9 Gosselink JG An Ecological Characterization Study of the Chenier Plain Coastal Ecosystem of Louisiana and Texas Narrative Report

National Coastal Ecosystems Team Office of Biological Services Fish and Wildlife Service US Department of the InteriorWashington DC USA 1979 Volume 1

10 Blum MD Roberts HH The Mississippi delta region Past present and future Annu Rev Earth Planet Sci 2012 40 655ndash683[CrossRef]

11 Hansen MC Potapov PV Moore R Hancher M Turubanova SA Tyukavina A Townshend J High-resolution globalmaps of 21st-century forest cover change Science 2013 342 850ndash853 [CrossRef]

12 Doyle TW Krauss KW Conner WH From AS Predicting the retreat and migration of tidal forests along the northern Gulfof Mexico under sea-level rise For Ecol Manag 2010 259 770ndash777 [CrossRef]

13 Thomas N Simard M Castantildeeda-Moya E Byrd K Windham-Myers L Bevington A Twilley R High-resolution mappingof biomass and distribution of marsh and forested wetlands in southeastern coastal Louisiana Int J Appl Earth Obs Geoinf 201980 257ndash267 [CrossRef]

14 Desantis LR Bhotika S Williams K Putz FE Sea-level rise and drought interactions accelerate forest decline on the GulfCoast of Florida USA Glob Chang Biol 2007 13 2349ndash2360 [CrossRef]

15 Boyd DS Foody G Ripple W Evaluation of approaches for forest cover estimation in the Pacific Northwest USA usingremote sensing Appl Geogr 2002 22 375ndash392 [CrossRef]

16 Valjarevic A Djekic T Stevanovic V Ivanovic R Jandzikovic B GIS numerical and remote sensing analyses of forest changesin the Toplica region for the period of 1953ndash2013 Appl Geogr 2018 92 131ndash139 [CrossRef]

17 He T Sun YJ Xu JD Wang X Hu C Enhanced land usecover classification using support vector machines and fuzzyk-means clustering algorithms J Appl Remote Sens 2014 8 083636 [CrossRef]

18 Michez A Pieacutegay H Lisein J Claessens H Lejeune P Classification of riparian forest species and health condition usingmulti-temporal and hyperspatial imagery from unmanned aerial systems Environ Monit Assess 2016 188 146 [CrossRef][PubMed]

19 Hampton SE Strasser C Tewksbury J Gram W Budden A Batcheller A Duke C Porter J Big data and the future ofecology Front Ecol Environ 2013 11 156ndash162 [CrossRef]

20 Curtis PG Slay CM Harris NL Tyukavina A Hansen MC Classifying drivers of global forest loss Science 2018 3611108ndash1111 [CrossRef]

Forests 2022 13 1132 17 of 22

provide more in-depth data and analysis on the state of existing stands and recruitmenttrends within remnants of the Chenier Plains via field sampling

Louisiana has enacted a historic response to the problem of land-loss following a publicoutcry after devastating storms in 2005 The Coastal Protection and Restoration Authority(CPRA) have been tasked with enacting a management plan to combat coastal erosion TheComprehensive Master Plan details proposals to create marshes build sediment diversionsform new hydrologic structures and promote barrier island restoration through builtand natural means As of 2017 over 135 projects had been completed impacting over14568 ha [74] An update to this document is due to be released in 2023 The projecthas an overall operating budget of over 50 billion US dollars with equal shares dedicatedto restoration and risk reduction The primary funding source is a settlement associatedwith the Deepwater Horizon Oil Spill in addition to various initiatives at the state andfederal levels However there are no current plans to supplement vegetation or performany geoengineering work on the cheniers for afforestation

Coastal wetlands rank among the most important ecosystems in terms of the valuationof ecosystem services [75] Provisioning services such as water filtration or storm-surgeprotection are commonly considered when discussing coastal areas Beyond these com-monalities chenier forests provide vital wildlife habitats being the first encountered sourceof vertical structure for migratory neo-tropical birds and migrating monarch butterflies inthe Mississippi alluvial valley flyway [76] This en-route habitat is of critical importancedue to its closeness to the Gulf of Mexico During favorable weather conditions 10 ofbirds migrating through the Gulf use these forests as a stopover spot rising dramatically to80 during adverse weather conditions [29] With losses mounting many species may notbe able to seek refuge during storms The area also has a unique suite of amphibians andreptiles being home to 13 obligate chenier forest species of herpetofauna [77]

Lu et al [78] used the eddy covariance method (ECM) to calculate strong carbon sinkeffects for a chenier ecosystem driven by forest vegetation Net energy production valueswere significantly higher than surrounding marshland signifying the importance of densevegetative cover on a per-unit basis Counter to this there is also strong evidence thatdegraded forest ecosystems act as net carbon sources A study in North Carolina also usingECM found that a seasonally inundated tide-influenced forest was a net carbon sourceannually and only functioned as a sink for 3 months of the growing year [79] Similarly astudy in the northeastern part of the state showed net negative rates of carbon sequestrationfor an ephemerally flooded forest [80] Unique environments demand individual attentiona characterization of this ecosystem for nitrogen or carbon flux with respect to a similarsystem may not be functionally sound [81]

Forests 2022 13 1132 18 of 22Forests 2022 13 x FOR PEER REVIEW 19 of 23

Figure 9 The total land area classified in GEE for the years 2003 2007 and 2019 Light green repre‐

sents the marsh area tan represents pasture The water layer has been removed for purposes of

clarity The dark black outline is the outline of the forest cover for the respective year

Louisiana has enacted a historic response to the problem of land‐loss following a

public outcry after devastating storms in 2005 The Coastal Protection and Restoration

Authority (CPRA) have been tasked with enacting a management plan to combat coastal

erosion The Comprehensive Master Plan details proposals to create marshes build sedi‐

ment diversions form new hydrologic structures and promote barrier island restoration

through built and natural means As of 2017 over 135 projects had been completed im‐

pacting over 14568 ha [74] An update to this document is due to be released in 2023 The

project has an overall operating budget of over 50 billion US dollars with equal shares

dedicated to restoration and risk reduction The primary funding source is a settlement

associated with the Deepwater Horizon Oil Spill in addition to various initiatives at the

state and federal levels However there are no current plans to supplement vegetation or

perform any geoengineering work on the cheniers for afforestation

Coastal wetlands rank among the most important ecosystems in terms of the valua‐

tion of ecosystem services [75] Provisioning services such as water filtration or storm‐

surge protection are commonly considered when discussing coastal areas Beyond these

commonalities chenier forests provide vital wildlife habitats being the first encountered

source of vertical structure for migratory neo‐tropical birds and migrating monarch but‐

terflies in the Mississippi alluvial valley flyway [76] This en‐route habitat is of critical

importance due to its closeness to the Gulf of Mexico During favorable weather condi‐

tions 10 of birds migrating through the Gulf use these forests as a stopover spot rising

Figure 9 The total land area classified in GEE for the years 2003 2007 and 2019 Light greenrepresents the marsh area tan represents pasture The water layer has been removed for purposes ofclarity The dark black outline is the outline of the forest cover for the respective year

5 Conclusions

The cheniers of southwestern Louisiana were found to be declining in size at a concern-ing rate Many of the driving factors are ecosystem-wide trends that have been increasingin intensity within the last century The results of this study suggest that coastal forestsare an ecosystem in peril and the ecology of such systems should be a closely monitoredsubject The 365 loss of forest cover is an alarming but not wholly unexpected result ofmany decades of intense perturbation and human exploitation A dynamic of continuedforest loss is evident Unless significant investment is made in engineering solutions tomitigate storm effects and land subsidence we will continue to see the decline of chenierforests in Louisiana Through targeted mitigation governmental programs such as theWetland or Conservation Reserve Program may be able to supplement existing forestsas well as the incomes of local people who have lost their livelihoods In this study wehave begun the process of accurate areal estimates of forest extent and have shed light onecosystem processes in the region As this community continues to diminish we see theforest not by the trees but rather the lack of them

Forests 2022 13 1132 19 of 22

Author Contributions Conceptualization PT methodology PT software PT and PR validationPT and PR formal analysis PT PR and PT writingmdashoriginal draft preparation PT and JBwritingmdashreview and editing PT and JB visualization PT and JB supervision JB All authorshave read and agreed to the published version of the manuscript

Funding This research received no external funding

Data Availability Statement The data will be published in and stored on the Harvard Dataverse athttpsdataverseharvardedudatasetxhtmlpersistentId=doi107910DVN3J7SNF (accessed on1 May 2022)

Acknowledgments The authors would like to recognize the hard work of undergraduate studentsParusha Raut and Subash Sapkota in manually validating thousands of points and creating numeroustraining sets We also extend thanks to our institution and numerous people within the lab who leantboth mental and emotional fortitude

Conflicts of Interest The authors declare no conflict of interest

References1 Michener WK Blood ER Bildstein KL Brinson MM Gardner LR Climate change hurricanes and tropical storms and

rising sea level in coastal wetlands Ecol Appl 1997 7 770ndash801 [CrossRef]2 Donoghue JF Sea level history of the northern Gulf of Mexico coast and sea level rise scenarios for the near future Clim Chang

2011 107 17ndash33 [CrossRef]3 Stanturf JA Goodrick SL Outcalt KW Disturbance and coastal forests A strategic approach to forest management in

hurricane impact zones For Ecol Manag 2007 250 119ndash135 [CrossRef]4 Augustinus P Cheniers and chenier plains A general introduction Mar Geol 1989 90 219ndash229 [CrossRef]5 Fu Z Wang P Sun J Lu Z Yang H Liu J Li T Composition seasonal variation and salinization characteristics of soil

salinity in the Chenier Island of the Yellow River Delta Glob Ecol Conserv 2020 24 e01318 [CrossRef]6 Marques IG Campelo F Rivaes R Albuquerque A Ferreira MT Rodriacuteguez-Gonzaacutelez PM Tree rings reveal long-term

changes in growth resilience in Southern European riparian forests Dendrochronologia 2018 55 167ndash176 [CrossRef]7 Daigle JJ Griffith GE Omernik JM Faulkner PL McCulloh RP Handley LR Smith LM Chapman SS Ecoregions of

Louisiana (Color Poster with Map Descriptive Text Summary Tables and Photographs) US Geological Survey Reston VI USA 20068 McBride RA Taylor MJ Byrnes MR Coastal morphodynamics and Chenier-Plain evolution in southwestern Louisiana USA

A geomorphic model Geomorphology 2007 88 367ndash422 [CrossRef]9 Gosselink JG An Ecological Characterization Study of the Chenier Plain Coastal Ecosystem of Louisiana and Texas Narrative Report

National Coastal Ecosystems Team Office of Biological Services Fish and Wildlife Service US Department of the InteriorWashington DC USA 1979 Volume 1

10 Blum MD Roberts HH The Mississippi delta region Past present and future Annu Rev Earth Planet Sci 2012 40 655ndash683[CrossRef]

11 Hansen MC Potapov PV Moore R Hancher M Turubanova SA Tyukavina A Townshend J High-resolution globalmaps of 21st-century forest cover change Science 2013 342 850ndash853 [CrossRef]

12 Doyle TW Krauss KW Conner WH From AS Predicting the retreat and migration of tidal forests along the northern Gulfof Mexico under sea-level rise For Ecol Manag 2010 259 770ndash777 [CrossRef]

13 Thomas N Simard M Castantildeeda-Moya E Byrd K Windham-Myers L Bevington A Twilley R High-resolution mappingof biomass and distribution of marsh and forested wetlands in southeastern coastal Louisiana Int J Appl Earth Obs Geoinf 201980 257ndash267 [CrossRef]

14 Desantis LR Bhotika S Williams K Putz FE Sea-level rise and drought interactions accelerate forest decline on the GulfCoast of Florida USA Glob Chang Biol 2007 13 2349ndash2360 [CrossRef]

15 Boyd DS Foody G Ripple W Evaluation of approaches for forest cover estimation in the Pacific Northwest USA usingremote sensing Appl Geogr 2002 22 375ndash392 [CrossRef]

16 Valjarevic A Djekic T Stevanovic V Ivanovic R Jandzikovic B GIS numerical and remote sensing analyses of forest changesin the Toplica region for the period of 1953ndash2013 Appl Geogr 2018 92 131ndash139 [CrossRef]

17 He T Sun YJ Xu JD Wang X Hu C Enhanced land usecover classification using support vector machines and fuzzyk-means clustering algorithms J Appl Remote Sens 2014 8 083636 [CrossRef]

18 Michez A Pieacutegay H Lisein J Claessens H Lejeune P Classification of riparian forest species and health condition usingmulti-temporal and hyperspatial imagery from unmanned aerial systems Environ Monit Assess 2016 188 146 [CrossRef][PubMed]

19 Hampton SE Strasser C Tewksbury J Gram W Budden A Batcheller A Duke C Porter J Big data and the future ofecology Front Ecol Environ 2013 11 156ndash162 [CrossRef]

20 Curtis PG Slay CM Harris NL Tyukavina A Hansen MC Classifying drivers of global forest loss Science 2018 3611108ndash1111 [CrossRef]

Forests 2022 13 1132 18 of 22Forests 2022 13 x FOR PEER REVIEW 19 of 23

Figure 9 The total land area classified in GEE for the years 2003 2007 and 2019 Light green repre‐

sents the marsh area tan represents pasture The water layer has been removed for purposes of

clarity The dark black outline is the outline of the forest cover for the respective year

Louisiana has enacted a historic response to the problem of land‐loss following a

public outcry after devastating storms in 2005 The Coastal Protection and Restoration

Authority (CPRA) have been tasked with enacting a management plan to combat coastal

erosion The Comprehensive Master Plan details proposals to create marshes build sedi‐

ment diversions form new hydrologic structures and promote barrier island restoration

through built and natural means As of 2017 over 135 projects had been completed im‐

pacting over 14568 ha [74] An update to this document is due to be released in 2023 The

project has an overall operating budget of over 50 billion US dollars with equal shares

dedicated to restoration and risk reduction The primary funding source is a settlement

associated with the Deepwater Horizon Oil Spill in addition to various initiatives at the

state and federal levels However there are no current plans to supplement vegetation or

perform any geoengineering work on the cheniers for afforestation

Coastal wetlands rank among the most important ecosystems in terms of the valua‐

tion of ecosystem services [75] Provisioning services such as water filtration or storm‐

surge protection are commonly considered when discussing coastal areas Beyond these

commonalities chenier forests provide vital wildlife habitats being the first encountered

source of vertical structure for migratory neo‐tropical birds and migrating monarch but‐

terflies in the Mississippi alluvial valley flyway [76] This en‐route habitat is of critical

importance due to its closeness to the Gulf of Mexico During favorable weather condi‐

tions 10 of birds migrating through the Gulf use these forests as a stopover spot rising

Figure 9 The total land area classified in GEE for the years 2003 2007 and 2019 Light greenrepresents the marsh area tan represents pasture The water layer has been removed for purposes ofclarity The dark black outline is the outline of the forest cover for the respective year

5 Conclusions

The cheniers of southwestern Louisiana were found to be declining in size at a concern-ing rate Many of the driving factors are ecosystem-wide trends that have been increasingin intensity within the last century The results of this study suggest that coastal forestsare an ecosystem in peril and the ecology of such systems should be a closely monitoredsubject The 365 loss of forest cover is an alarming but not wholly unexpected result ofmany decades of intense perturbation and human exploitation A dynamic of continuedforest loss is evident Unless significant investment is made in engineering solutions tomitigate storm effects and land subsidence we will continue to see the decline of chenierforests in Louisiana Through targeted mitigation governmental programs such as theWetland or Conservation Reserve Program may be able to supplement existing forestsas well as the incomes of local people who have lost their livelihoods In this study wehave begun the process of accurate areal estimates of forest extent and have shed light onecosystem processes in the region As this community continues to diminish we see theforest not by the trees but rather the lack of them

Forests 2022 13 1132 19 of 22

Author Contributions Conceptualization PT methodology PT software PT and PR validationPT and PR formal analysis PT PR and PT writingmdashoriginal draft preparation PT and JBwritingmdashreview and editing PT and JB visualization PT and JB supervision JB All authorshave read and agreed to the published version of the manuscript

Funding This research received no external funding

Data Availability Statement The data will be published in and stored on the Harvard Dataverse athttpsdataverseharvardedudatasetxhtmlpersistentId=doi107910DVN3J7SNF (accessed on1 May 2022)

Acknowledgments The authors would like to recognize the hard work of undergraduate studentsParusha Raut and Subash Sapkota in manually validating thousands of points and creating numeroustraining sets We also extend thanks to our institution and numerous people within the lab who leantboth mental and emotional fortitude

Conflicts of Interest The authors declare no conflict of interest

References1 Michener WK Blood ER Bildstein KL Brinson MM Gardner LR Climate change hurricanes and tropical storms and

rising sea level in coastal wetlands Ecol Appl 1997 7 770ndash801 [CrossRef]2 Donoghue JF Sea level history of the northern Gulf of Mexico coast and sea level rise scenarios for the near future Clim Chang

2011 107 17ndash33 [CrossRef]3 Stanturf JA Goodrick SL Outcalt KW Disturbance and coastal forests A strategic approach to forest management in

hurricane impact zones For Ecol Manag 2007 250 119ndash135 [CrossRef]4 Augustinus P Cheniers and chenier plains A general introduction Mar Geol 1989 90 219ndash229 [CrossRef]5 Fu Z Wang P Sun J Lu Z Yang H Liu J Li T Composition seasonal variation and salinization characteristics of soil

salinity in the Chenier Island of the Yellow River Delta Glob Ecol Conserv 2020 24 e01318 [CrossRef]6 Marques IG Campelo F Rivaes R Albuquerque A Ferreira MT Rodriacuteguez-Gonzaacutelez PM Tree rings reveal long-term

changes in growth resilience in Southern European riparian forests Dendrochronologia 2018 55 167ndash176 [CrossRef]7 Daigle JJ Griffith GE Omernik JM Faulkner PL McCulloh RP Handley LR Smith LM Chapman SS Ecoregions of

Louisiana (Color Poster with Map Descriptive Text Summary Tables and Photographs) US Geological Survey Reston VI USA 20068 McBride RA Taylor MJ Byrnes MR Coastal morphodynamics and Chenier-Plain evolution in southwestern Louisiana USA

A geomorphic model Geomorphology 2007 88 367ndash422 [CrossRef]9 Gosselink JG An Ecological Characterization Study of the Chenier Plain Coastal Ecosystem of Louisiana and Texas Narrative Report

National Coastal Ecosystems Team Office of Biological Services Fish and Wildlife Service US Department of the InteriorWashington DC USA 1979 Volume 1

10 Blum MD Roberts HH The Mississippi delta region Past present and future Annu Rev Earth Planet Sci 2012 40 655ndash683[CrossRef]

11 Hansen MC Potapov PV Moore R Hancher M Turubanova SA Tyukavina A Townshend J High-resolution globalmaps of 21st-century forest cover change Science 2013 342 850ndash853 [CrossRef]

12 Doyle TW Krauss KW Conner WH From AS Predicting the retreat and migration of tidal forests along the northern Gulfof Mexico under sea-level rise For Ecol Manag 2010 259 770ndash777 [CrossRef]

13 Thomas N Simard M Castantildeeda-Moya E Byrd K Windham-Myers L Bevington A Twilley R High-resolution mappingof biomass and distribution of marsh and forested wetlands in southeastern coastal Louisiana Int J Appl Earth Obs Geoinf 201980 257ndash267 [CrossRef]

14 Desantis LR Bhotika S Williams K Putz FE Sea-level rise and drought interactions accelerate forest decline on the GulfCoast of Florida USA Glob Chang Biol 2007 13 2349ndash2360 [CrossRef]

15 Boyd DS Foody G Ripple W Evaluation of approaches for forest cover estimation in the Pacific Northwest USA usingremote sensing Appl Geogr 2002 22 375ndash392 [CrossRef]

16 Valjarevic A Djekic T Stevanovic V Ivanovic R Jandzikovic B GIS numerical and remote sensing analyses of forest changesin the Toplica region for the period of 1953ndash2013 Appl Geogr 2018 92 131ndash139 [CrossRef]

17 He T Sun YJ Xu JD Wang X Hu C Enhanced land usecover classification using support vector machines and fuzzyk-means clustering algorithms J Appl Remote Sens 2014 8 083636 [CrossRef]

18 Michez A Pieacutegay H Lisein J Claessens H Lejeune P Classification of riparian forest species and health condition usingmulti-temporal and hyperspatial imagery from unmanned aerial systems Environ Monit Assess 2016 188 146 [CrossRef][PubMed]

19 Hampton SE Strasser C Tewksbury J Gram W Budden A Batcheller A Duke C Porter J Big data and the future ofecology Front Ecol Environ 2013 11 156ndash162 [CrossRef]

20 Curtis PG Slay CM Harris NL Tyukavina A Hansen MC Classifying drivers of global forest loss Science 2018 3611108ndash1111 [CrossRef]

Forests 2022 13 1132 19 of 22

Author Contributions Conceptualization PT methodology PT software PT and PR validationPT and PR formal analysis PT PR and PT writingmdashoriginal draft preparation PT and JBwritingmdashreview and editing PT and JB visualization PT and JB supervision JB All authorshave read and agreed to the published version of the manuscript

Funding This research received no external funding

Data Availability Statement The data will be published in and stored on the Harvard Dataverse athttpsdataverseharvardedudatasetxhtmlpersistentId=doi107910DVN3J7SNF (accessed on1 May 2022)

Acknowledgments The authors would like to recognize the hard work of undergraduate studentsParusha Raut and Subash Sapkota in manually validating thousands of points and creating numeroustraining sets We also extend thanks to our institution and numerous people within the lab who leantboth mental and emotional fortitude

Conflicts of Interest The authors declare no conflict of interest

References1 Michener WK Blood ER Bildstein KL Brinson MM Gardner LR Climate change hurricanes and tropical storms and

rising sea level in coastal wetlands Ecol Appl 1997 7 770ndash801 [CrossRef]2 Donoghue JF Sea level history of the northern Gulf of Mexico coast and sea level rise scenarios for the near future Clim Chang

2011 107 17ndash33 [CrossRef]3 Stanturf JA Goodrick SL Outcalt KW Disturbance and coastal forests A strategic approach to forest management in

hurricane impact zones For Ecol Manag 2007 250 119ndash135 [CrossRef]4 Augustinus P Cheniers and chenier plains A general introduction Mar Geol 1989 90 219ndash229 [CrossRef]5 Fu Z Wang P Sun J Lu Z Yang H Liu J Li T Composition seasonal variation and salinization characteristics of soil

salinity in the Chenier Island of the Yellow River Delta Glob Ecol Conserv 2020 24 e01318 [CrossRef]6 Marques IG Campelo F Rivaes R Albuquerque A Ferreira MT Rodriacuteguez-Gonzaacutelez PM Tree rings reveal long-term

changes in growth resilience in Southern European riparian forests Dendrochronologia 2018 55 167ndash176 [CrossRef]7 Daigle JJ Griffith GE Omernik JM Faulkner PL McCulloh RP Handley LR Smith LM Chapman SS Ecoregions of

Louisiana (Color Poster with Map Descriptive Text Summary Tables and Photographs) US Geological Survey Reston VI USA 20068 McBride RA Taylor MJ Byrnes MR Coastal morphodynamics and Chenier-Plain evolution in southwestern Louisiana USA

A geomorphic model Geomorphology 2007 88 367ndash422 [CrossRef]9 Gosselink JG An Ecological Characterization Study of the Chenier Plain Coastal Ecosystem of Louisiana and Texas Narrative Report

National Coastal Ecosystems Team Office of Biological Services Fish and Wildlife Service US Department of the InteriorWashington DC USA 1979 Volume 1

10 Blum MD Roberts HH The Mississippi delta region Past present and future Annu Rev Earth Planet Sci 2012 40 655ndash683[CrossRef]

11 Hansen MC Potapov PV Moore R Hancher M Turubanova SA Tyukavina A Townshend J High-resolution globalmaps of 21st-century forest cover change Science 2013 342 850ndash853 [CrossRef]

12 Doyle TW Krauss KW Conner WH From AS Predicting the retreat and migration of tidal forests along the northern Gulfof Mexico under sea-level rise For Ecol Manag 2010 259 770ndash777 [CrossRef]

13 Thomas N Simard M Castantildeeda-Moya E Byrd K Windham-Myers L Bevington A Twilley R High-resolution mappingof biomass and distribution of marsh and forested wetlands in southeastern coastal Louisiana Int J Appl Earth Obs Geoinf 201980 257ndash267 [CrossRef]

14 Desantis LR Bhotika S Williams K Putz FE Sea-level rise and drought interactions accelerate forest decline on the GulfCoast of Florida USA Glob Chang Biol 2007 13 2349ndash2360 [CrossRef]

15 Boyd DS Foody G Ripple W Evaluation of approaches for forest cover estimation in the Pacific Northwest USA usingremote sensing Appl Geogr 2002 22 375ndash392 [CrossRef]

16 Valjarevic A Djekic T Stevanovic V Ivanovic R Jandzikovic B GIS numerical and remote sensing analyses of forest changesin the Toplica region for the period of 1953ndash2013 Appl Geogr 2018 92 131ndash139 [CrossRef]

17 He T Sun YJ Xu JD Wang X Hu C Enhanced land usecover classification using support vector machines and fuzzyk-means clustering algorithms J Appl Remote Sens 2014 8 083636 [CrossRef]

18 Michez A Pieacutegay H Lisein J Claessens H Lejeune P Classification of riparian forest species and health condition usingmulti-temporal and hyperspatial imagery from unmanned aerial systems Environ Monit Assess 2016 188 146 [CrossRef][PubMed]

19 Hampton SE Strasser C Tewksbury J Gram W Budden A Batcheller A Duke C Porter J Big data and the future ofecology Front Ecol Environ 2013 11 156ndash162 [CrossRef]

20 Curtis PG Slay CM Harris NL Tyukavina A Hansen MC Classifying drivers of global forest loss Science 2018 3611108ndash1111 [CrossRef]

Forests 2022 13 1132 20 of 22

21 Maxwell AE Warner TA Vanderbilt BC Ramezan CA Land cover classification and feature extraction from NationalAgriculture Imagery Program (NAIP) Orthoimagery A review Photogramm Eng Remote Sens 2017 83 737ndash747 [CrossRef]

22 Basu S Ganguly S Nemani RR Mukhopadhyay S Zhang G Milesi C Li S A semiautomated probabilistic framework fortree-cover delineation from 1-m NAIP imagery using a high-performance computing architecture IEEE Trans Geosci RemoteSens 2015 53 5690ndash5708 [CrossRef]

23 Kumar L Mutanga O Google Earth Engine applications since inception Usage trends and potential Remote Sens 201810 1509 [CrossRef]

24 Tamiminia H Salehi B Mahdianpari M Quackenbush L Adeli S Brisco B Google Earth Engine for geo-big dataapplications A meta-analysis and systematic review ISPRS J Photogramm Remote Sens 2020 164 152ndash170 [CrossRef]

25 Kindinger JL Buster NA Flocks JG Bernier JC Kulp MA Louisiana Barrier Island Comprehensive Monitoring (BICM)Program Summary Report Data and Analyses 2006 through 2010 US Department of the Interior US Geological Survey WashingtonDC USA 2013

26 Martinez L OrsquoBrien S Bethel M Penland S Kulp M Louisiana Barrier Island Comprehensive Monitoring Program (BICM)Volume 2 Shoreline Changes and Barrier Island Land Loss 1800rsquosndash2005 USGS Reston VI USA 2009

27 Reed D Wang Y Meselhe E White E Modeling wetland transitions and loss in coastal Louisiana under scenarios of futurerelative sea-level rise Geomorphology 2020 352 106991 [CrossRef]

28 Neyland R Meyer HA Species diversity of Louisiana chenier woody vegetation remnants J Torrey Bot Soc 1997 124 254ndash261[CrossRef]

29 Barrow WC Chao-Chieh C Hamilton RB Ouchley K Spengler TJ Disruption and restoration of en route habitat a casestudy The Chenier Plain Stud Avian Biol 2002 20 71ndash87

30 Schieder NW Kirwan ML Sea-level driven acceleration in coastal forest retreat Geology 2019 47 1151ndash1155 [CrossRef]31 Williams K Ewel KC Stumpf RP Putz FE Workman TW Sea-level rise and coastal forest retreat on the west coast of

Florida USA Ecology 1999 80 2045ndash2063 [CrossRef]32 Hurricanes in History Available online httpswwwnhcnoaagovoutreachhistory (accessed on 13 June 2022)33 Field CB Barros V Stocker TF Dahe Q Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation

Special Report of the Intergovernmental Panel on Climate Change Cambridge University Press Cambridge UK 201234 Turner RE Tide gauge records water level rise and subsidence in the northern Gulf of Mexico Estuaries 1991 14 139ndash147

[CrossRef]35 Soil Survey Staff Natural Resources Conservation Service United States Department of Agriculture Official Soil Series

Descriptions Available online httpswwwnrcsusdagovwpsportalnrcsdetailsoilssurveygeocid=nrcs142p2_053587(accessed on 23 November 2021)

36 Kniffen FB Gregory HF Stokes GA The Historic Indian Tribes of Louisiana From 1542 to the Present Louisiana LSU Press BatonRouge LA USA 1994

37 Karalius T Alpert P High abundance of introduced plants on ancient Native American middens Biol Invasions 2010 121125ndash1132 [CrossRef]

38 Leo B Jerome HF Richard AO Charles JS Classification and Regression Trees Routledge London UK 198439 Fricker GA Ventura JD Wolf JA North MP Davis FW Franklin J A convolutional neural network classifier identifies

tree species in mixed-conifer forest from hyperspectral imagery Remote Sens 2019 11 2326 [CrossRef]40 Congalton RG Exploring and evaluating the consequences of vector-to-raster and raster-to-vector conversion Photogrammetr

Eng Remote Sens 1997 63 425ndash43441 Simley J Applying the national hydrography dataset Water Resour Impact 2008 10 5ndash842 McHugh ML Interrater reliability The kappa statistic Biochem Med 2012 22 276ndash282 [CrossRef]43 Hogland J Anderson N St Peter J Drake J Medley P Mapping forest characteristics at fine resolution across large

landscapes of the southeastern United States using NAIP imagery and FIA field plot data ISPRS Int J Geo-Inf 2018 7 140[CrossRef]

44 Hartfield KA Landau KI Van Leeuwen WJ Fusion of high resolution aerial multispectral and LiDAR data Land cover inthe context of urban mosquito habitat Remote Sens 2011 3 2364ndash2383 [CrossRef]

45 Hulet A Roundy BA Petersen SL Bunting SC Jensen RR Roundy DB Utilizing national agriculture imagery programdata to estimate tree cover and biomass of pinon and juniper woodlands Rangel Ecol Manag 2014 67 563ndash572 [CrossRef]

46 Maxwell AE Strager MP Warner TA Ramezan CA Morgan AN Pauley CE Large-area high spatial resolution landcover mapping using random forests GEOBIA and NAIP orthophotography Findings and recommendations Remote Sens 201911 1409 [CrossRef]

47 Wagner FH Sanchez A Tarabalka Y Lotte R Ferreira M Aidar M Gloor E Phillips O Aragao LE Using the U-netconvolutional network to map forest types and disturbance in the Atlantic rainforest with very high resolution images RemoteSens Ecol Conserv 2019 5 360ndash375 [CrossRef]

48 Baker BA Warner TA Conley JF McNeil BE Does spatial resolution matter A multi-scale comparison of object-based andpixel-based methods for detecting change associated with gas well drilling operations Int J Remote Sens 2013 34 1633ndash1651[CrossRef]

Forests 2022 13 1132 21 of 22

49 Heumann BW Satellite remote sensing of mangrove forests Recent advances and future opportunities Prog Phys Geogr 201135 87ndash108 [CrossRef]

50 Xie Y Sha Z Yu M Remote sensing imagery in vegetation mapping A review J Plant Ecol 2008 1 9ndash23 [CrossRef]51 Barakat A Khellouk R El Jazouli A Touhami F Nadem S Monitoring of forest cover dynamics in eastern area of Beacuteni-Mellal

Province using ASTER and Sentinel-2A multispectral data Geol Ecol Landsc 2018 2 203ndash215 [CrossRef]52 Smart LS Vukomanovic J Taillie PJ Singh KK Smith JW Quantifying Drivers of Coastal Forest Carbon Decline Highlights

Opportunities for Targeted Human Interventions Land 2021 10 752 [CrossRef]53 Phiri D Morgenroth J Developments in Landsat land cover classification methods A review Remote Sens 2017 9 967

[CrossRef]54 Sothe C La Rosa LEC De Almeida CM Gonsamo A Schimalski MB Castro JDB Tommaselli AMG Evaluating a

Convolutional Neural Network for Feature Extraction and Tree-species classification using UAV-hyperspectral images ISPRSAnn Photogramm Remote Sens Spat Inf Sci 2020 5 193ndash199 [CrossRef]

55 Zhang X Liu L Chen X Gao Y Jiang M Automatically Monitoring Impervious Surfaces Using Spectral Generalization andTime Series Landsat Imagery from 1985 to 2020 in the Yangtze River Delta J Remote Sens 2021 2021 9873816 [CrossRef]

56 Wulder MA Coops NC Roy DP White JC Hermosilla T Land cover 20 Int J Remote Sens 2018 39 4254ndash4284 [CrossRef]57 Raabe EA Stumpf RP Expansion of tidal marsh in response to sea-level rise Gulf Coast of Florida USA Estuaries Coasts 2016

39 145ndash157 [CrossRef]58 McCarthy MJ Dimmitt B Muller-Karger FE Rapid coastal forest decline in Floridarsquos big bend Remote Sens 2018 10 1721

[CrossRef]59 McCarthy MJ Dimmitt B DiGeronimo S Muller-Karger FE Forest Loss is Accelerating along the US Gulf Coast Estuaries

Coasts 2022 45 913ndash919 [CrossRef]60 Conner WH Mihalia I Wolfe J Tree community structure and changes from 1987 to 1999 in three Louisiana and three South

Carolina forested wetlands Wetlands 2002 22 58ndash70 [CrossRef]61 Ford M Nyman JA Preface An overview of the Atchafalaya River Hydrobiologia 2011 658 1ndash5 [CrossRef]62 Enwright NM Griffith KT Osland MJ Barriers to and opportunities for landward migration of coastal wetlands with

sea-level rise Front Ecol Environ 2016 14 307ndash316 [CrossRef]63 Tully K Gedan K Epanchin-Niell R Strong A Bernhardt ES BenDor T Weston NB The invisible flood The chemistry

ecology and social implications of coastal saltwater intrusion BioScience 2019 69 368ndash378 [CrossRef]64 Kozlowski TT Responses of woody plants to flooding and salinity Tree Physiol 1997 17 490 [CrossRef]65 Neyland R The effects of Hurricane Rita on the aquatic vascular flora in a large fresh-water marsh in Cameron Parish Louisiana

Castanea 2007 72 1ndash7 [CrossRef]66 Williams HF Magnitude of Hurricane Ike storm surge sedimentation Implications for coastal marsh aggradation Earth Surf

Processes Landf 2012 37 901ndash906 [CrossRef]67 Williams K MacDonald M Sternberg LD Interactions of storm drought and sea-level rise on coastal forest A case study

J Coast Res 2003 19 1116ndash112168 Kirwan ML Gedan KB Sea-level driven land conversion and the formation of ghost forests Nat Clim Chang 2019 9 450ndash457

[CrossRef]69 Magolan JL Halls JN A multi-decadal investigation of tidal creek wetland changes water level rise and ghost forests Remote

Sens 2020 12 1141 [CrossRef]70 Penland S Connor PF Jr Beall A Fearnley S Williams SJ Changes in Louisianarsquos shoreline 1855ndash2002 J Coast Res 2005

44 7ndash3971 Davies-Colley RJ Payne GW Van Elswijk M Microclimate gradients across a forest edge N Z J Ecol 2000 24 111ndash12172 Fan Z Yang S Cheng N Liu X Song A Dong L Invasibility of fire-managed ecosystems to the Chinese tallow tree

(Triadica sebifera) in the lower Gulf Coastal Plain USA Mechanisms and key factors at the landscape level For Ecol Manag 202124 479 [CrossRef]

73 Zhaofei F Yuan T Michael KC Factors associated with the spread of Chinese tallow in East Texas forestlands Open J Ecol2012 2 10

74 Coastal Protection and Restoration Authority of Louisiana Louisianarsquos Comprehensive Master Plan for a Sustainable Coast CoastalProtection and Restoration Authority of Louisiana Baton Rouge LA USA 2017

75 Costanza R drsquoArge R De Groot R Farber S Grasso M Hannon B Van Den Belt M The value of the worldrsquos ecosystemservices and natural capital Nature 1997 387 253ndash260 [CrossRef]

76 Ribbeck K King S Twedt D Restoration Management and Monitoring of Forest Resources in the Mississippi Alluvial ValleyRecommendations for Enhancing Wildlife Habitat LMVJV Vicksburg MS USA 2007

77 Selman W Herpetofaunal diversity and seasonality from a remnant coastal chenier forest in southwestern Louisiana SoutheastNat 2015 14 491ndash505 [CrossRef]

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Forests 2022 13 1132 22 of 22

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81 Mcleod E Chmura GL Bouillon S Salm R Bjoumlrk M Duarte CM Silliman BR A blueprint for blue carbon Towardan improved understanding of the role of vegetated coastal habitats in sequestering CO2 Front Ecol Environ 2011 9 552ndash560[CrossRef]