Full Terms & Conditions of access and use can be found at http://www.tandfonline.com/action/journalInformation?journalCode=tres20 Download by: [Claremont Colleges Library] Date: 24 January 2017, At: 07:11 International Journal of Remote Sensing ISSN: 0143-1161 (Print) 1366-5901 (Online) Journal homepage: http://www.tandfonline.com/loi/tres20 UAV data for multi-temporal Landsat analysis of historic reforestation: a case study in Costa Rica Andrew Marx, Donald McFarlane & Ahmed Alzahrani To cite this article: Andrew Marx, Donald McFarlane & Ahmed Alzahrani (2017): UAV data for multi-temporal Landsat analysis of historic reforestation: a case study in Costa Rica, International Journal of Remote Sensing, DOI: 10.1080/01431161.2017.1280637 To link to this article: http://dx.doi.org/10.1080/01431161.2017.1280637 Published online: 23 Jan 2017. Submit your article to this journal View related articles View Crossmark data
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Full Terms & Conditions of access and use can be found athttp://www.tandfonline.com/action/journalInformation?journalCode=tres20
UAV data for multi-temporal Landsat analysis ofhistoric reforestation: a case study in Costa Rica
Andrew Marx, Donald McFarlane & Ahmed Alzahrani
To cite this article: Andrew Marx, Donald McFarlane & Ahmed Alzahrani (2017): UAV datafor multi-temporal Landsat analysis of historic reforestation: a case study in Costa Rica,International Journal of Remote Sensing, DOI: 10.1080/01431161.2017.1280637
To link to this article: http://dx.doi.org/10.1080/01431161.2017.1280637
UAV data for multi-temporal Landsat analysis of historicreforestation: a case study in Costa RicaAndrew Marx a, Donald McFarlaneb and Ahmed Alzahrania
aCenter for Information Systems and Technology, Claremont Graduate University, Claremont, CA, USA;bW. M. Keck Science Department, The Claremont Colleges, Claremont, CA, USA
ABSTRACTThe use of the Landsat constellation to quantify historic deforesta-tion and reforestation over time is well established. This analysis,however, requires ground-referenced data that is often inaccessiblein remote areas or expensive if no existing high-resolution satelliteimagery exists. In response, we evaluate the capability of unmannedaerial vehicle (UAV) imagery to serve as ground-reference data foridentifying land-cover classes in Landsat imagery. We then applythese classes to quantify 30 years of historical deforestation andreforestation of an ecological reserve in Costa Rica. While spatialand spectral disparities between the sensors limit the generalizationof the approach, our results demonstrate the ability of UAV andLandsat data to inexpensively classify a reserve’s historic land coverover time and suggest an 11 year period for land cover to transitionfrom pasture to secondary forest in lowland tropical environments.
ARTICLE HISTORYReceived 2 August 2016Accepted 26 December 2016
1. Introduction
Tropical deforestation has long been recognized as one of the greatest environmentalchallenges of our time. Although they constitute only 7% of the Earth’s land surface,tropical forests are home to more than half of the Earth’s species (Wilson and Peter1988). Moreover, with the loss of tropical forests comes the loss of ecosystem servicesthat are of regional importance – for example the maintenance of watersheds – andglobal importance for their role in carbon dynamics (Laurance 1999).
Direct preservation of remaining tracts of primary forest is an obvious and widelyused conservation strategy. However, natural and directed forest recovery in formerlydeforested areas is much less studied. In some cases, this recovery can be very sig-nificant, as in the case of Puerto Rico (Aide and Grau 2004). In the southwestern coastalarea of Costa Rica, dramatic increases in land values, resulting from tourism andexpatriate relocation, have led to a transition from low-value cattle ranching to pro-tected lands with recovering forests (Redondo-Brenes, Chiu, and Snow 2010). However,historic records in this area are inadequate to document the phenomenon.
Owing to its multispectral sensors and 30 m pixel resolution, the Landsat constellationhas been well established at successfully detecting significant land-cover changes, such as
CONTACT Andrew Marx [email protected] Center for Information Systems and Technology, ClaremontGraduate University, Claremont, CA, USA
INTERNATIONAL JOURNAL OF REMOTE SENSING, 2017http://dx.doi.org/10.1080/01431161.2017.1280637
urbanization (Masek, Lindsay, and Goward 2000), deforestation, and reforestation (Skoleand Tucker 1993). First launched in 1972, the Landsat constellation has provided globalimagery of the Earth’s land surface approximately every 16 days. In recent years, research-ers have capitalized on advancements in the constellation’s inter-calibrated surface reflec-tance products (USGS 2015; Wolfe et al. 2004), free access to the data (Woodcock et al.2008), and improvements in computing to understand the nature and trajectory of theseland-cover changes over time (Olofsson et al. 2016). These inter-calibrated surface reflec-tance products are currently limited to Landsat images as far back as 1984 (Landsatsatellites 4, 5, 7, and 8) and do not extend to 1972 (Landsat 1, 2, and 3).
Beyond research, environmental managers and practitioners are using this valuablesource of information to both manage large environmental reserves (Zhang et al. 2016),and to understand the nature and history of their reserves over time (Koh and Wich2012). While these global products have proven revolutionary (Hansen et al. 2013), usersinterested in localized ecosystems with specific monitoring goals receive best resultswhen they can connect ground-reference data in their area of interest with Landsat’smultispectral imagery (Foody 2015; Aziz et al. 2015). Such ground-reference data iseither collected through ground survey work, manually recording the GPS locations orareas of different land cover, or by digitizing land covers seen in high-resolution satelliteor aerial imagery.
In extremely dense primary forest, on-the-ground survey is often not possible due toGPS signals not permeating the thick canopy. Such problems are compounded in foreststhat are not near existing roads or airfields. Commercial, high-resolution satellite imageryprovides the ability for organizations to purchase ground-reference imagery, but this canprove costly for local ecological organizations. Additionally, in very remote locations,such as this study area, high-resolution imagery still does not exist through free servicessuch as Google Maps or Bing Imagery.
Unmanned aerial vehicles (UAVs) are emerging as a remote sensing platform that canfill the space between aircraft and high-resolution satellites and moderate resolutionearth observing satellites in ecological applications (Koh and Wich 2012). In very inac-cessible terrain, where ground-survey is not feasible, their utility, as a ground-referencefor Landsat, is increased. Capable quadcopters, such as DJI’s Phantom 3, are available forunder $1000 (www.dji.com), making them affordable for local organizations. With theintegrated red–green–blue cameras, these units can cover a grid of 30 ha in 10 min at100 m above ground level (AGL), providing imagery better than 0.5 cm per pixel. Unitscan fly more than a mile from an accessible launch location to the study area. UAV-compatible multispectral cameras, such as the $3500 MicaSense Sequoia (www.micasense.com/sequoia), promise improved vegetation classification by adding red edge andnear-infrared bands to visible bands.
While a number of studies have demonstrated the ability of UAVs for land-coverclassification including urban vegetation (Feng, Liu, and Gong 2015) and crops (Peñaet al. 2013), the use of UAV in classifying Landsat is only recently being explored in time-coincident Landsat and UAV imagery on crop vigour (Lukas et al. 2016) and glacialextent (Fugazza et al. 2015). This study extends this connection by evaluating the use ofUAV imagery as ground-reference data in a Landsat-based time-series analysis of refor-estation. Such an approach promises an affordable approach for local environmentalorganizations to quantify the change in their reserve’s land cover over time.
Costa Rica is well established as the principal ecotourism destination in Central America,with travel and tourism contributing a quarter of a million jobs and 12.5% of the nation’sGDP in 2014 (Turner 2015). Strong ecotourism is driven in part by the very high levels ofbiodiversity in the country – for example, Costa Rica is home to 903 species of birds,more than in all of the United States and Canada combined (Woltmann 2015). Thisbiodiversity in turn results from a varied topography in the form of a central spine ofmountains creating distinct eastern ‘Caribbean’, and western ‘Pacific’ provinces, with astrong clinal precipitation pattern in the Pacific zone (Figure 1). Furthermore, 27% of thecountry is afforded some level of protection (Bank 2016) through 27 national parks, 58wildlife refuges, and a variety of other protected categories and private reserves.Nevertheless, outside of these reserves, Costa Rica has suffered very high levels ofdeforestation, from ~ 85% forest cover in 1940 to ~ 35% in 1990 (Mongabay 2016).Partial forest recovery in the twenty-first century has resulted from land use changes aslow-value agriculture has been replaced in some areas by eco-tourism and ecological-science-based initiatives.
The Firestone Center for Restoration Ecology (FCRE) is a small (60 ha) private reserve onthe south western coast of Costa Rica that forms one component of the regionallyimportant 82,000 ha Path of the Tapir project (Redondo-Brenes, Chiu, and Snow 2010).The property was largely cleared of Pacific lowland moist forest in 1950–1970 and operatedas a cattle farm until 1993. Thereafter, the FCRE experienced primarily natural regrowthwith some mixed native hardwood replanting returning it to secondary forest by 2016.
The FCRE provides an excellent case study for measuring reforestation with Landsatbecause it incorporates a range of well-documented vegetation types and recoverystages in a topographically varied terrane, all within an area readily manageable bysmall UAVs. Further, the FCRE began its land cover transition from open pasture at theend of 1992 and reached near-complete secondary forest cover by 2016, making itpossible to measure the reforestation rate with high confidence that we are observing anatural phenomenon. Because much of the FCRE is now reforested, the study area wasexpanded from the FCRE boundary to include adjacent land that has remained as cattlepasture for training samples (Figure 2).
3. Data
The input data for the study includes annual Landsat surface reflectance products for1986–2016, visible-band, UAV imagery of the FCRE and surrounding area, and ground-survey data of the FCRE.
3.1 Satellite-derived data
Analysis of change in land-cover classes was performed using data derived from theLandsat Thematic Mapper (TM), Landsat Enhanced Thematic Mapper Plus (ETM+), andthe Operational Land Imager (OLI) on Landsat 4, 5, 7, and 8. Climate Data Record imageswere requested and downloaded from US Geological Survey (USGS) (http://earthexplorer.usgs.gov). Landsat 4, 5, and 7 surface reflectances were calculated using the
Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) (Wolfe et al.2004). Landsat 8 surface reflectance is generated from the Landsat 8 SurfaceReflectance (L8SR) algorithm specific to the sensor (USGS 2015).
A total of 27 Landsat images were used in the analysis (Table 1). Because bothLandsat path/row 15/53 and 15/54 cover the study area, annual were images chosenfrom either path/row from 1986 to 2016. The study location receives monsoonal rainsfrom May through November with significant cloud-cover during those months. Whileimages were selected to occur before the monsoonal rains, three images (1987, 1993,and 1996) were collected during June as the earliest date available due to cloudcontamination and image availability. The late season of these three images introduceserror in the classification procedure as the phenology of pasture likely changes after thearrival of the monsoonal rains. Additionally, no usable images were available for 1991,1994, 1995, or 2000 due to cloud contamination or image availability. All images werevisually inspected for cloud-cover over the study area with some manual masking ofcloud and cloud shadows. Surface reflectance products were pre-processed into a
Figure 1. The Firestone Center for Restoration Ecology (FCRE) receives strong clinal precipitationsitting to the west of Costa Rica’s central spine of mountains.
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Figure 2. The FCRE boundary (red) consists of 60 ha and is nearly fully reforested. A larger studyarea (black) was delineated to allow the collection of the pasture land cover. Training areas wereselected manually for either forest (green) or pasture (beige).
Table 1. List of Landsat images used in analysis.Year Julian Date Sensor Path/row
1986 69 10 March L4-5 TM 15/541987 168 17 June L4-5 TM 15/541988 115 24 April L4-5 TM 15/541989 133 13 May L4-5 TM 15/541990 64 5 March L4-5 TM 15/541992 94 3 April L4-5 TM 15/531993 176 25 June L4-5 TM 15/541996 177 25 June L4-5 TM 15/541997 339 5 December L4-5 TM 15/541998 54 23 February L4-5 TM 15/541999 73 14 March L4-5 TM 15/542001 30 30 January L4-5 TM 15/542002 73 14 March L7 ETM+ 15/532003 12 12 January L7 ETM+ 15/532004 31 31 January L7 ETM+ 15/532005 113 23 April L7 ETM+ 15/532006 4 4 January L7 ETM+ 15/532007 55 24 February L7 ETM+ 15/532008 26 26 January L7 ETM+ 15/532009 60 1 March L7 ETM+ 15/542010 31 31 January L7 ETM+ 15/532011 66 7 March L7 ETM+ 15/542012 21 21 January L7 ETM+ 15/542013 23 23 January L7 ETM+ 15/542014 18 18 January L8 OLI 15/542015 149 29 May L8 OLI 15/542016 136 15 May L8 OLI 15/54
Twenty-seven Landsat constellation images were used in the analysis. The land-cover classes for study area. No usableimages were used for 1991, 1994, 1995, or 2000 due to cloud contamination or image availability.
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clipped, multiband raster dataset in Esri ArcMap through custom ModelBuilder code.Only bands that correspond to TM and ETM+ bands 1, 2, 3, 4, 5, and 7 were used in theanalysis.
3.2 UAV-derived data
Four missions from a DJI Phantom 3 Advanced were flown between 31 May and 3 June2016 at 300 m AGL to collect imagery for the study (Figure 3(b)). While the quadcopterhas a 300 kg load capacity, the authors used the integrated 12 megapixel, f 2.8, 94° fieldof view integrated, red–green–blue camera from GoPro. The camera’s spectral limits,only capturing light in the visible bands, could be significantly improved by capturinglonger wavelengths such as near-infrared or shortwave infrared (see Section 6). On onebattery charge, the DJI Phantom 3 Advanced had an endurance of approximately 15 minwhich was reduced to approximately 10 min when winds exceed 20 km h–1.
An unexpected complication in processing the UAV imagery was the inability of theDrone2Map software to create an orthomosaic when initial flights were conducted at120 m AGL. It was discovered that relief displacement (tree lean) of tall trees in the studyarea was preventing the software from correctly stitching the images together (seeWhitehead and Hugenholtz (2014) for a discussion on tree lean in UAV imagery).Subsequent flights were flown at 300 m creating a coarser orthomosaic, but resolvingthe relief displacement effect. The resulting spatial resolution was a maximum of 13 cmpixel–1.
The UAV imagery was collected autonomously through pre-programmed routescreated in the MapsMadeEasy software (www.mapsmadeeasy.com). This free softwareallows the user to designate the study area, image overlap and AGL and the softwarecreates the route. The four flights flew with a 90% image overlap with each covering
Figure 3. Using the Trimble Geo7 GPS, three groups of field researchers each took 6 h to collect 75ground-reference points in the dense jungle (left). The authors imaged the entire study area in four15 min flights with autonomous imagery collection by a UAV (right). Photographs by KeithChristenson.
approximately 20 ha, collecting a total of 260 UAV images. The image’s metadata areeach tagged with the UAV’s altitude, GPS coordinates and camera orientation andspecifications. The images were uploaded to an Azure virtual machine where theywere processed into a georeferenced orthomosaic in Esri’s Drone2Map software(Figure 2). The software creates the orthomosaic by reading the geolocation of eachimage and searching for tiepoints between the images, or points that connect theimages on the X, Y, and Z coordinates. Because the missions were flows with a highper cent of overlap, each image had at least 95 computed tiepoints. The software usesthe combination of each images geolocation and these tiepoints to create a georefer-enced orthomosaic with geolocational error of less than 1 cm (Table 2).
3.3 Ground-survey data
Using ArcGIS, the research team randomly generated 75 ground-reference points acrossthe study area. Undergraduate students from the Claremont College’s Keck ScienceDepartment navigated to these points guided by a Trimble Geo7 GPS (Figure 3(a)).Locations were buffered to 1 m, and allocated to the dominant land-cover class. Denseunderstory growth typical of secondary tropical forests required the addition of experi-enced guides with machetes to access many points. The reference points were coded aseither ‘pasture’ (first, second, or third stage regrowth) or ‘forest’ (fourth stage regrowth orprimary forest) (Figure 4). While the approach provided a feasible way to collect ground-reference points in the dense jungle, the spatial mismatch between a 1 m buffered GPSpoint and Landsat’s 30 m pixel introduced additional error in the study (see Section 6).
4. Analytical approach
The analytical approach consisted first of a trained graduate student, familiar with theFCRE, manually delineating training areas for ‘pasture’ or ‘forest’ on the UAV orthomo-saic (Figure 2). These training areas were then overlaid on a 15 May 2016 Landsat 8image, which allowed the Esri ArcMap software to create a signature file from the green,blue, red, near-infrared (NIR), shortwave infrared 1 (SWIR-1), and shortwave infrared 2(SWIR-2) bands (Table 3). In this case, SWIR-1 and SWIR-2 have the largest spectralseparation, or distance between the two classes in the identified training areas(Figure 4). This is because SWIR wavelengths are highly reflective from soil comparedwith vegetation (Clark et al. 1993). Because pasture has sparse grass to penetrate and insome cases, exposed soil, this class is highly reflective in the SWIR-1 and SWIR-2 bands.
This signature file was then used in a Maximum Likelihood Classifier, which computeswhich class a pixel belongs to by its proximity to that training classes band’s average. Acustom Esri ModelBuilder code was used to classify 1986 to 2015 annual Landsat images.A bilinear classification scheme of ‘pasture’ and ‘forest’ was selected because while
Table 2. Geolocational error for UAV orthomosaic.X Y Z
fieldwork could identify first, second, third, fourth stage regrowth or primary for ground-reference points, the UAV red–green–blue orthomosaic only allowed for the identifica-tion of the general classes ‘pasture’ (first, second, third stage regrowth) or ‘forest’ (fourthstage regrowth or primary) (Figure 5).
5. Validation and results
The use of error and accuracy assessments is critical in the evaluation of remotely senseddata in environmental applications (Congalton 2001).Validation and evaluation of theapproach occurred in two steps. First, the bilinear classification of the 2016 Landsatimage was validated with randomly-generated ground-reference points in Esri software.Second, the only available high-resolution image from the study period, a 1997 aerialimage, was compared with the 1997 bilinear classification model output for that year.
5.1 Validation of visual analysis of UAV imagery
First we evaluated our ability to use UAV imagery, and the manual training areas basedon that imagery, to classify a time-coincident Landsat image. Our 75 ground-collectedpoints showed a reasonably high correlation with the Landsat modelled classifications of‘pasture’ or ‘forest’ (Figure 6) (Table 4), but the user’s accuracy of pasture (67%) is poor.In other words, the classifier consistently identified a 30 m section of the FCRE as‘pasture’, when the ground-reference collected point identified a 1 m buffer in thatlocation as ‘forest’.
Figure 4. Classes delineated with UAV training areas on Landsat imagery show the greatest spectraldifferentiation in the SWIR-1 (1.57–1.65 μm) and SWIR-2 (2.11–2.29 μm) bands.
Table 3. Average surface reflectance for the two classes by Landsat band.Class B G R NIR SWIR-1 SWIR-2
Separation is defined as the distance between the two classes’ average surface reflectance.
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This highlights a shortcoming of the analytical approach. In many cases the ground-reference collected point was under a tree (‘forest’), but some of that 30 m pixel’s areawas pasture. If a pixel contains any pasture, the SWIR-1 and SWIR-2 reflectance signifi-cantly increases (Figure 4). Because the classifier relies heavily on these bands due totheir spectral separation, the classifier frequently classified that pixel as ‘pasture’ even ifthere is some forest in the pixel. The opposite however is not true and is shown in thehigh users’ accuracy for ‘forest’ (95%). If a 1 m location was ground-referenced as‘pasture’ but there were several trees within the pixel, it remains ‘pasture’ because thefew trees don’t significantly change the SWIR-1 or SWIR-2 surface reflectance across thepixel (Figure 7).
5.2 Validation of Landsat imagery classification
We compared a 1997 aerial image of the study area with the 1997 model’s assessed land-cover classes to validate our ability to classify other years. Fifty random sample points werecreated and analysed producing a 70% overall accuracy rate (Figure 8) (Table 5).
Figure 5. The forest recovery process divided into four stages. The first three stages of regrowthwere classified as ‘pasture’ and the fourth as ‘forest’ in the bilinear classification scheme. Illustrationby Donald McFarlane.
INTERNATIONAL JOURNAL OF REMOTE SENSING 9
Table 5 highlights the shortcomings of using a single point from high-resolution, red–green-aerial image with a 30 m Landsat classified pixel. As with the validation of the2016 Landsat classified image, often the accuracy assessment points would land on atree, but because there was pasture in the pixel, it was classified as pasture. Thisproduced a very low user’s accuracy of ‘pasture’ (41%) (Figure 7).
Figure 6. Seventy-five ground reference points indicating pasture (yellow dot) or forest (green dot)validated an 88% overall accuracy rate (top) when compared with the 2016 Landsat classifications of‘pasture’ (beige) or ‘forest (green) (bottom).
Table 4. Accuracy assessment of the 2016 Landsat classes with 75 ground reference points.Actual category: ground truth
Classified category (1) Pasture (2) Forest Total User’s accuracy
Our results demonstrate that UAV imagery can be used as training samples for Landsatimagery and inexpensively produce a time-series analysis of major land covers in areaswhere no ground-reference data exists. Specifically, we evaluated that UAV-derived datacan be used to delineate the very basic land-cover classes of ‘forest’ and ‘pasture’ intropical areas, and how those areas can be used to accurately classify time-coincidentLandsat imagery (88% accuracy). This classification can in turn be converted into aLandsat spectral signature file that can be applied to historic Landsat imagery (70%accuracy), although challenges remain in this approach due to the spatial and spectraldisparities between Landsat and a visible-band, UAV camera.
When applying to this methodology to 30 years of Landsat imagery, we can detectthe nature and trajectory of land-cover transition in the FCRE from pasture to forest(Figure 9). With this analysis, FCRE managers could validate historical accounts of thereserve’s land-cover transition with relatively fine spatial (30 m) and temporal (annual)fidelity over a 30 year period.
By the beginning of 1993, the land that was to become the FCRE had been purchasedby a new owner who ended cattle grazing and allowed large areas to regrow as forest.This single transition point for the start of reforestation is corroborated with theclassification model (Figure 10) and allows for an estimation of the time frame for the
Figure 7. The spatial disparity between accuracy assessment points (green and yellow points) andLandsat’s 30 m pixel (black grid) became evident in the confusion matrices, such as this subset ofthe 2016 accuracy assessment (Figure 6). Points ground-referenced as ‘forest’ were often misclassi-fied as ‘pasture’ (beige) because some of the pixel’s area was pasture (sparse vegetation) andtherefore highly reflective to the shortwave infrared bands. ‘Forest’ pixels (green pixels) were rarelymisclassified.
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Figure 8. Fifty random points were classified as ‘pasture’ (yellow) or ‘forest’ (green) based on a 1997aerial image (top). These points were compared with the 1997 modelled classes for ‘pasture’ (beige)or forest (green) (bottom). Several clouds and cloud shadows were masked (white) from the 1997classification model (bottom).
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land-cover transition between our pasture and forest designations. In this case, it took11 years for the land to transition from 92% pasture and 8% forest in 1992 to 7% pastureand 93% forest by 2003; a remarkably rapid phenomenon.
6.1 Sources of error
While this transition corroborates historical accounts of the property’s management, andaccuracy assessments validate the land-cover models for 1997 and 2016, several sig-nificant sources of error make this analysis best suited for general historic or forensictrends of a region, with less confidence about individual pixels or years.
6.1.1 Spectral disparityA significant shortfall in this approach is the disparity in spectral resolution between theUAV’s visible-band camera and Landsat’s multispectral sensor. In the training data fromthe UAV camera, the analyst uses the red, green, and blue wavelengths on the imageryto identify the colours, textures and shapes associated with forest. The Landsat classifierhowever relies most heavily on the SWIR-1 and SWIR-2 bands due to their spectralseparation of ‘pasture’ and ‘forest’ (Figure 4). This is because these Landsat bands aresensitive to the thickness of the vegetation as measured by how much soil is available toreflect the longer SWIR wavelengths (Clark et al. 1993).
The use of a multispectral UAV camera such as the MicaSense Sequoia (www.micasense.com/sequoia) would improve the results with a near-infrared sensor. The spectralseparation between the classes for near-infrared (8.5%) was higher than the best visibleband red (6.3%), but still not nearly as effective as SWIR-1 (15.7%) (Table 3). Shortwaveinfrared cameras are being developed for UAVs by companies such as Sensors Unlimited(www.sensorsinc.com), but their cost, weight and required customization of the UAVslimit their current usage.
6.1.2 Spatial disparityThis analytical approach also presents challenges resulting from the high spatial resolu-tion of the UAV imagery and ground-reference points and Landsat’s 30 m pixel. In manycases the ground-reference points used in the 2016 accuracy assessment fell into a 30 mpixel, which contained a combination of forest and pasture. As discussed in Section6.1.1, when a pixel contains a small amount of ‘pasture’, the pixel tends to be classifiedas ‘pasture’ due to the strong SWIR-1 and SWIR-2 reflectance of the pasture areas. Whilethe 75 1 m buffered GPS points was a feasible way to collect ground-reference in the
Table 5. Accuracy assessment of the 1997 Landsat classes with 50 ground-reference points.Actual category: ground truth
Classified category (1) Pasture (2) Forest Total User’s accuracy
Figure 9. The assessed land cover in 1986 (left) corroborates reports that the entire reserve was usedfor grazing with only two ravine areas still forested. By 2016 (right) the reserve was classified asalmost all forest.
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dense jungle, the spatial mismatch between a buffered GPS point and a 30 m classifiedpixel introduced additional error in the study.
In future studies, ground-reference data should be collected as per cent ‘forest’ or‘pasture’ in a 30 m grid which corresponds to Landsat’s pixels. This will likely provechallenging in the dense jungle, but would yield significantly better accuracy assess-ments. Additionally, training samples should be derived by overlaying a Landsat 30 mgrid on the UAV imagery. This should aid the assessor in selecting pixels that are all‘forest’ or ‘pasture’.
6.1.3 Julian datesA temporal source of error is the various days of the year (Julian dates) that Landsatimagery was used in the analysis (Table 1). While efforts were made to acquire images inthe first few months of each year before the monsoonal rains, three images (1987, 1993,and 1996) were collected during June as the earliest date available due to cloudcontamination and image availability. The late season of these three images introduceserror in the classification procedure as the phenology of pasture likely changes after thearrival of the monsoonal rains.
6.1.4 Classification schemeThe bilinear classification scheme of ‘pasture’ and ‘forest’ used in the analysis alsocontributed to error in the study. Starting the 1993 the FCRE began its transition frompasture to forest, but the point at which a class tips from one class to the other in theLandsat models is likely influenced by inter-annual greenup differences caused byvariations in Julian dates and rainfall. Several pixels between 1997 and 2000 movedback and forth between classes and years as they likely transitioned between third andfourth stage regrowth (Figure 5). While the overall trend was consistent, this reduces theability of managers to look back in time to a specific year and pixel with confidence.Flexibility should be included in the classifier, allowing for ‘unclassified’ or ‘mixed’ pixels.
Figure 10. With the FCRE operating as a reserve by 1993 a dramatic change in land cover occurredresulting in the ‘forest’ class (solid line) transitioning from 8% to 93% area in 11 years with the‘pasture’ class (dashed line) decreasing accordingly.
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This will help prevent pixels from flipping classes over transition years and give man-agers greater confidence on those pixels which are classified as ‘pasture’ or ‘forest’.
The bilinear classification scheme was selected because the 2016 study area only hadthese two distinct classes, either the FCRE reserve (forest) or neighbouring fields (pas-ture), and because the UAV camera provided only red, green, and blue bands complicat-ing the ability to train the various regrowth classes (Figure 5). Future planned researchincludes documenting these regrowth classes with additional fieldwork in nearby areas,using 30 m grids which correspond to Landsat pixels.
7. Conclusion
Managers of environmental reserves are increasingly interested in leveraging the wealthof historical satellite imagery to better understand the legacy of their reserves and tobetter inform their decisions today. Such land-cover analysis, however, requires ground-referencing, which is often not possible because high-resolution satellite imagery isunavailable and/or expensive, or because collecting ground sample points is extremelydifficult and time-consuming in these rugged ecological reserves.
This study evaluates the use of UAV imagery to provide an inexpensive approachby which local environmental reserve managers analyse the land-cover changes oftheir reserves over time. UAV imagery collected for the study was easily collected infour 15 min flights using autonomous flight software and a relatively inexpensivequadcopter. The imagery was processed overnight producing a high-resolution ortho-mosaic with excellent geolocational accuracy. In contrast, three field teams tookapproximately 6 h each to collect a total of 75 ground reference over the samestudy area in dense jungle.
While the imagery was very easy to collect, the spectral limitations of the visible bandUAV camera reduce its ability to train Landsat imagery. Landsat classifiers relied mostheavily on shortwave infrared wavelengths to delineate ‘pasture’ and ‘forest’ throughsoil reflectivity. In contrast, analysts delineating training areas on UAV imagery manuallyidentified the classes by the colour, texture, and shape of the vegetation. This contrastintroduces error because while the analyst may incorrectly identify second or third stageregrowth as ‘forest’, the Landsat classifier will identify this as ‘pasture’ because thesparse canopy permits shortwave infrared reflectance from the soil. UAVs equippedwith near-infrared cameras promise to improve these classifications, though until inex-pensive shortwave infrared cameras are available, this will remain a limitation.
While this study is only a basic first step in UAV-trained Landsat land-cover transi-tion analysis, it has provided the FCRE managers with a new approach with which tounderstand the nature and trajectory of land covers in their reserve at an appropriatespatial (30 m) and temporal (annual) scale. Future research will refine this approachand extend the analysis of historical land-cover transition to adjacent reserves andlandholdings. Specifically, it can be applied to the management of the regionallyimportant 82,000 ha Path of the Tapir project (Redondo-Brenes, Chiu, and Snow2010) by informing and directing additional reserve purchases and managementagreements in the knowledge that significant forest regrowth can be achieved withina decadal timescale.
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Disclosure statement
No potential conflict of interest was reported by the authors.
ORCID
Andrew Marx http://orcid.org/0000-0002-4663-6384
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