http://www.ijrsa.org/paperInfo.aspx?ID=13042 Land Use and Land Cover (LULC) and wetland classification maps are an important prerequisite for many environmental studies. In order to produce accurate LULC and wetland maps at high spatial-resolution, a new approach was developed to integrate image classifications, spatial data layers, and analysis methods using Python scripting. Both Maximum Likelihood and Object-based Feature Extraction were adopted into the LULC classification. A spatial analysis approach was applied to wetland mapping based on available wetland inventories and soil data. Python scripts were created and used to automate these processes for each of the 30 reference sites across Minnesota and Wisconsin of the United States, which encompassed the entire study site. Results demonstrated that the proposed
Land Use and Land Cover (LULC) and wetland classification maps are an important prerequisite for many environmental studies. In order to produce accurate LULC and wetland maps at high spatial-resolution, a new approach was developed to integrate image classifications, spatial data layers, and analysis methods using Python scripting. Both Maximum Likelihood and Object-based Feature Extraction were adopted into the LULC classification. A spatial analysis approach was applied to wetland mapping based on available wetland inventories and soil data. Python scripts were created and used to automate these processes for each of the 30 reference sites across Minnesota and Wisconsin of the United States, which encompassed the entire study site. Results demonstrated that the proposed method allowed for the integration of geospatial data of varying sources and qualities to produce accurate LULC and wetland maps effectively. The results of accuracy assessment indicated that the classification maps for Minnesota and Wisconsin were of comparable quality. The object-based classifier extracted LULC effectively from the Wisconsin imagery with acceptable accuracy despite lacking of the NIR spectral band. These maps were used as inputs to create a hydro geomorphicap-proach (HGM) guidebook (Hauer and Smith 1998) for both states (Cook et al. unpublished). The Python-based technique was found to be especially beneficial when dealing with big datasets over large study areas, as it allowed batch processing.
Keywords
High Spatial-resolution; Land Cover Classification; Wetland Mapping; Python-based Technique; Big Datasets
Introduction
Monitoring of Land Use and Land Cover (LULC) and wetlands is fundamental to environmental studies and conservation efforts. Past land use practices and current development pressures threaten the ecological integrity of many streams and their associated riparian wetlands (Mensing et al. 1998). Due to expanding human settlement and intensifying development, more than 80% of the riparian corridor areas of North America and Europe have disappeared in the last 200 years (Naiman et al. 1993). Many earlier studies on LULC and wetland mapping have been conducted mainly with 30 m Land sat or coarser spatial resolution imagery (Ozesmi and Bauer 2002; Yuan et al. 2005a; Yuan et al. 2005b; Wright and Gallant 2007). The United States Geological Survey (USGS) has a history of providing national and global land cover products at 30 m ground sample distance, but larger scale classification maps are required for many environmental studies in order to assess the influences of environmental changes on our ecosystem more accurately. Although high spatial-resolution satellite remote sensing images have become increasingly available in the last decade, their use is limited by cost and the larger resources required for processing, which is particularly true for studies at regional or national scales.
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The recent availability of state-wide high spatial-resolution digital aerial imagery in the United States (U.S.) and advancement of geospatial technologies, such as increasing computing power and object-based classification or spatial modeling techniques, offer a great opportunity to remotely monitor wetlands and LULC at more detailed scales. However, due to the large data volume of high spatial-resolution remote sensing images to be processed, it becomes necessary to automate this process as much as possible.
The main objective of this study is to develop a new approach for high spatial-resolution LULC classification and wetland mapping over large study areas. The study is part of the development of a new regional U.S. Army Corps of Engineers (USACE) Hydro geomorphic Approach (HGM) Guidebook for assessing wetland functions focusing on organic-soil flat and slope wetlands in Minnesota and Wisconsin (Cook et al. unpublished.). The HGM approach was originally developed as a classification scheme for wetlands using “geomorphic setting, water source, and hydrodynamics” (Brinson 1993). The guidebook was later adapted by the USACE to become an assessment instrument for wetland functions (Smith et al. 1995). This study is aimed at producing accurate wetland and LULC maps to serve as model parameters for the new regional guidebook for applying the HGM, which will assess the functions of organic-soil flat and slope wetlands in Minnesota and Wisconsin. These sub-boreal peatlands are not currently as well researched as the boreal peatlands of North America. They are also generally situated in areas with higher rates of urbanization than peatlands in other regions, which makes them particularly interesting (Clement 2011).
In this study, LULC and wetland maps were produced for a set of over 30 reference sites within Minnesota and Wisconsin. This task was challenging because the study site was relatively large, but fragmented, and the available geospatial data had varying spectral qualities. In order to produce an accurate product, integrated geospatial techniques were used (including Python scripting, digital image classification, spatial analysis, and decision tree modeling). On one hand, the 1 m digital ortho-imagery for the state of Wisconsin lacked a near-infrared band, while the Minnesota imagery had this additional spectral layer. It was likely that the traditional per-pixel Maximum Likelihood Classification would deliver less accurate results for Wisconsin than that for Minnesota. Hence, alternative classification methods were examined
based on published literature. An object-based classifier was chosen for the LULC classification of Wisconsin sites. Among the advanced classification techniques, object-based image analysis has proved to be an effective classification method for high spatial-resolution remote sensing data (Benz et al. 2004; Al-Khudhairy et al. 2005; Hay et al. 2005; Budreski et al. 2007; Antonarakis et al. 2008; Yuan, 2008; Blaschke 2010; Laliberte et al. 2010; Whiteside et al. 2011). Differing from the pixel-based classifiers, object-based classification methods initially segment the original remote sensing image into objects, and then assign classes to these objects instead of single pixels in the subsequent analysis and classification process.
On the other hand, due to the fragmentation of the study site into many smaller reference sites, it was desired to automate as many of the steps involved in the new approach as possible. The Python scripting language was chosen for this due to its flexibility and its tight integration with ESRI ArcGIS software. Many studies exhibited the promise of using Python to automate GIS processes and aid in integration of various GIS systems. For example, Ghadiry et al. (2012) recently demonstrated the integration of GIS tools in ERDAS Imagine and ESRI ArcGIS through Python scripting to allow for a more efficient workflow when processing large sets of imagery. Bryan (2013) showed that Python could be used to perform environmental modeling tasks with significant performance gains, as compared with the same model processed in dedicated GIS software. Zhao et al. (2011) also showed a method of using Python to batch-preprocess satellite imagery for use in GIS or Remote Sensing software. These studies indicated that Python was versatile enough to allow for the automation of many otherwise labor-intensive GIS tasks. Therefore, Python scripts were developed to automatically process the data present for each of the 30 reference sites, and finally merge maps using rules established by a decision-tree model. This significantly decreased processing time, and manual labor, as batch processing of the many reference site maps, was done automatically by the scripts.
Methods
Study Area and Reference Wetland Sites
The study area lies within Minnesota and Wisconsin, two adjacent states that are located in the north-central part of the U.S. In particular, located in the Midwestern United States, Minnesota is known as the
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Land of 10,000 Lakes. It is part of North America's Great Lakes Region. The Mississippi River originates from Lake Itasca in the north and is joined by the Minnesota River at Fort Snelling in the Minneapolis-St. Paul (Twin Cities) area. More than half of its total 5.4 million residents live in the Twin Cities metropolitan area. Minnesota’s landscape encompasses 225,181 km2. Fifty-six percent of the state’s land is used for agriculture, mainly located in western and southern Minnesota. Northeastern Minnesota is mainly covered by forest, mining, and recreational sites. Forests cover approximately one-third of the land area while one-fifth (approximately 42,900 km²) is occupied by wetlands, making Minnesota the second only to Alaska in the amount of wetlands within its borders (Seeley, 2006). Wisconsin is also located in the Midwest and Great Lakes regions. It has a total population of 5.7 million and a total area of 169,639 km2. It shares a land and Lake Superior water border
with Minnesota to the west. Wisconsin can be divided into five geographic regions: the Lake Superior Lowland in the north and just to the south, the Northern Highland; the Central Plain; Western Upland; and Eastern Ridges and Lowlands.
Thirty reference wetlands were identified within this study area. Soil core and water samples were taken at each reference site to provide additional parameters for the HGM model. There were a total of 87 field site samples, as some wetlands were sampled in several locations. The majority of the reference sites we relocated in east-central Minnesota, along the Wisconsin border, and near the Twin Cities metropolitan area. However, the sites also spreaded to northern and south-west Minnesota and central Wisconsin. A complete list of reference wetlands with their location listed by state and county is presented in Table 1.
Fig. 1 shows the location of the study area in North America, and presents an overview of the entire study area and the individual reference sites. To create a product that enabled the assessment of the impact of the surrounding LULC on the wetland function, as the HGM approach prescribed, the data was processed as separate subsets for each reference site, with the sampling location at the center of a circular area with a 2 km radius. Therefore, the entire study area (reference domain) was over 100,000 hectares in size.
FIG. 1 OVERVIEW OF THE REFERENCE SITES ACROSS MN AND
WI, AND LOCATION OF STUDY AREA IN NORTH AMERICA.
Data Acquisition and Pre-processing
The preferred remote sensing imagery for this study was to be at least 1 m spatial resolution, with at least 8
TABLE1– WETLAND REFERENCE SITE NAMES
Site Names State County Field Validated?
Beckman Lake (CCNHA) MN Isanti Yes Cannon River Wilderness
Area MN Rice
Carlos Avery WMA MN Anoka Yes Cedar Creek NHA MN Isanti Yes
Erlandson Hunting Farm MN Chisago Yes Fens Research Facility MN St Louis
Holthe Prairie SNA MN Jackson Janet Johnson MN Chisago Yes
Jim Nelson Wetland Bank MN Kanabec Yes Joseph O'Brien WMA MN Kanabec Yes
Kettle River SNA MN Pine Ottawa WMA MN Le Sueur Yes
Peat Bog WMA MN Rice Rocket Truf MN Anoka
Rum River State Forest MN Mille Lacs Yes Savage Fen SNA MN Scott
Schuneman Marsh MN Washington St. Croix State Forest MN Pine Yes St. Croix State Park MN Pine Tamarack Swamp MN Washington Yes
Wild River State Park MN Chisago Bear Bluff Cranberry WI Jackson
Black River Forest WI Jackson East of County Road F WI Burnett Yes
Fish Lake Crex WI Burnett Yes Hayward WI Sawyer
North Flowage Impoundment
WI Monroe/Jackson
Norway Point WI Burnett Yes Union White Cedar WI Burnett Yes
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bit radiometric resolution, natural color (RGB) and near infrared (NIR) bands, and captured as close as possible to the ground sampling date. The acquired images were rectified to an accuracy of at least +/- 10 m. Digital imagery recorded by the National Agriculture Imagery Program (NAIP) of the U.S. Farm Service Agency (FSA) matched these requirements relatively well, and therefore were chosen as the data source of this study. The benefits of this dataset were its high spatial-resolution, which was useful due to the relatively small total area of each study site, its availability at no cost, yearly data collection intervals during the agricultural growing season, and the inclusion of a near-infrared band for some of the data.
Due to the way the NAIP data was funded and distributed, it had to be obtained from two separate sources. For Minnesota, the Minnesota Geospatial Information Office’s (MNGEO) geospatial data clearinghouse website (www.mngeo.state.mn.us) provided 1 m spatial resolution imagery in JPEG2000 format. The imagery for Wisconsin was obtained from Wisconsin View (www.wisconsinview.org), a data portal specializing in satellite and aerial imagery of the state. The digital imagery collected for Wisconsin generally matches the parameters of the Minnesota imagery, with the major difference being the lack of the NIR band (RGB only), and the data being delivered in uncompressed TIFF format. Both sets of images were captured during the months of July and August; however, exact capture dates and times of the day were unknown. All images were ortho-rectified to an accuracy of +/- 5 m in relation to ground control points, and mosaics were built using a last-in-last-on-top strategy, and radiometrically balanced using the Inpho Orthovista software by the data supplier (http://www.inpho.de/index.php?seite=index_orthovista).
For Minnesota, the imagery was available as full county mosaics, which were downloaded for all counties that contained reference points, as well as counties that contained parts of the 2-kilometer subset zone around each reference point. For Wisconsin, the imagery was available as quarters of U.S. Geological Survey (USGS) quadrangle tiles (3.75 by 3.75 minutes). Therefore, only those tiles that were necessary to cover all of the reference sites and their associated areas of interest were downloaded. These images were then mosaicked for each reference site using the same strategy as was used for the images in Minnesota. Further details regarding the sensor used for both image sources were unknown.
In addition to digital aerial imagery for the LULC classification, further data were obtained to map specific wetland types. In order to develop and implement the wetland classification scheme, the 1991 National Wetlands Inventory (NWI) and the 1984 Wisconsin Wetland Inventory (WWI) maps were obtained from the U.S. Fish and Wildlife Service (2009) and the Wisconsin Department of Natural Resources (1994), respectively. In addition to wetland types contained in the two wetland databases, we also acquired the Soil Survey Geographic Database (SSURGO) datasets for Minnesota and Wisconsin that contained various soil classifications from the Natural Resources Conservation Service (NRCS), and U.S. Department of Agriculture (USDA). Roads data for verifying locations of impervious surface were collected from Minnesota Department of Transportation (DOT) Basemap Roads and ESRI StreetMap for Minnesota and Wisconsin, respectively.
In summary, a wide variety of datasets were used in the study. Table 2 illustrates the type and sources of these data.
TABLE2– DATA SOURCES USED
Type of data Minnesota sites Wisconsin sites Aerial
Imagery 2008 NAIP imagery 2008 NAIP imagery
3 visible bands + 1 Near Infrared Band
3 color bands, no NIR band available
1 meter pixels, 8 bit pixel depth
1 meter pixels, 8 bit pixel depth
Wetland Classification
1991 National Wetlands Inventory polygons
1984 Wisconsin Wetlands Inventory
Soil Data Soil Survey Geographic Database for Minnesota
from USDA
Soil Survey Geographic Database for Wisconsin
from USDA
Roads
2001 Minnesota Department of
Transportation (DOT) Basemap Roads (all
types)
2000 ESRI StreetMap
Classification Scheme and Aoverview
The initial land cover map was derived using various classification techniques. The classes extracted included water (rivers or lakes), impervious surface (urban infrastructures and compacted soil), cropland (cultivated fields, fallow fields, and pasture), forested areas (deciduous, evergreen, and mixed forest land), and other vegetated areas (shrub, herbaceous plants, and non-forested wetlands). Extracting these classes accurately was challenging due to variations in vegetation types across the study site and data discrepancy between the two states. Fig. 2 provides comparison between sites in Minnesota and Wisconsin.
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FIG. 2 EXAMPLE OF SAMPLES SITES IN NAIP IMAGERY (4, 3, 2-
BAND COLOR COMPOSITE FOR MINNESOTA; 3, 2, 1-BAND COMPOSITE FOR WISCONSIN).
It shows differences in land cover and vegetation types, as well as the differences in spectral quality due to the missing near-infrared band in the Wisconsin NAIP imagery. Further variations were introduced by the time and spatial differences of the images: the data was captured over a span of two months, and contained a relatively large geographic area with some variation in phenology. These variations could lead to some difficulties in LULC classification as well. To resolve these issues without sacrificing classification accuracy, different techniques were employed. In particular, we created the classification maps based on the smallest available piece of imagery with specific training samples, and the resulting maps were merged after classification. Specifically, for Minnesota, classifications were conducted using whole county mosaics, with the exception of St. Louis County, which was split into three parts (only one of these was required). In Wisconsin, classifications were created
using the original USGS quarter quad-sized imagery.
Maximum Likelihood Classification and Guided Clustering Approach
For the Minnesota imagery, county mosaics were classified first into the five aforementioned LULC classes, which was achieved using a Maximum Likelihood Classifier (MLC). Training samples for each county mosaic were created through visual evaluation and on-screen digitizing, and samples were created as polygons. The training samples for the sites were distributed across each county mosaic, and for large mosaics, special care was taken to include samples of land cover types that had great spectral variations across the area of the mosaic. For each land cover class, a minimum of 15 training samples were selected. The cropland class presented a special case as it included both vegetated areas and non-vegetated, fallow fields. In order to accurately classify these spectrally different ground cover types, a manual guided clustering approach was employed (Yuan et al. 2005b). Specifically, to ensure a normal distribution of the cropland spectral signature, the vegetated and non-vegetated cropland areas were first treated as separate subclasses, with a separate set of training samples for each class. After classifying the data, these two subclasses were merged.
After completing the classification, an initial visual inspection of the accuracy was conducted by comparing the classified map with the original aerial image. If major misclassifications or other issues were found, either the spectral training samples were adjusted accordingly or a new set of training samples was created. The classification was then repeated for that county with the new training sample. In cases where misclassifications were minor or only found in a few areas of the images, they were manually corrected. After this step, the resulting maps were filtered using a 3 by 3 pixel majority filter to help eliminate spurious pixels that were introduced due to the high spatial-resolution of the imagery.
Object-based Classification
For the Wisconsin sites, a different approach had to be taken due to the lack of the near-infrared band. Because the near-infrared band is essential in accurate classification of vegetation types when using a spectral-based classification, it would be hard to accurately classify the three-band Wisconsin NAIP imagery using the traditional pixel-based MLC method. Instead, a feature recognition and extraction
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tool, Feature Analyst, was used and installed as an extension for ESRI ArcGIS. This tool is an object-based classifier that makes use of not only spectral, but also spatial information found in the imagery.
Currently, eCognition by Trimble and Feature Analyst by Overwatch are two well-adopted, commercial, object-based classification software packages for various applications. We chose Feature Analyst in this study for the following reasons. First, Feature Analyst is able to use ancillary data such as elevation models and texture layers to improve the classification accuracy for certain cover types. Second, its contextual classifier allows more flexibility in the types of objects that can be identified, and can increase classification accuracy in general. Lastly, the software allows for manual editing and corrections because it outputs the results in vector format.
The object-based classifier not only takes into account spectral (pixel values) and spatial (size) factors when segmenting the image, but also includes the object’s shape, texture, pattern, and spatial context (relationship with neighboring objects). Therefore, the process of training samples creation for the Feature Analyst differs from which was used to create samples for the MLC classifier. Samples represented not only the spectral properties of the LULC classes, but also their characteristics when viewed as objects by the classifier. Specifically, samples were created to reflect the typical size of the objects in relation to other objects of the same or different classes, and represented the shape of the indicated object. For example, since most buildings are rectangular, this was represented by the shape of the samples used to classify them. In contrast, rivers and lakes often have a curved, somewhat irregular shape, which was also indicated by the shape of their training samples. The training samples were also an indicator of the edges of the feature they represented.
In order to conduct the classification, Feature Analyst first stretched the input image using a histogram equalization function. It was also set up to automatically generate texture layers for each band of the image. An automated process was then used to divide the data into discrete, homogeneous objects (Overwatch 2012). The segmentation process is based on spectral homogeneity of the objects, as well as their texture, size, shape, edge type, and spatial context as defined by the training samples. After image segmentation, the classifier assigns appropriate class values to each identifier objects based on the training
samples and outputs the objects as a shape file. Besides, Feature Analyst automatically includes several other methods including artificial neural networks, decision trees, Bayesian learning, and K-nearest neighbor in its classification models. It also makes use of ensembles which are sets of classification models that are trained using the same data and whose results are combined to produce the final results (Opitz and Blundell, 2008).
In a final step, land cover maps were mosaiked to provide full coverage within the two kilometer area of interest around the reference sites for those sites where more than one county or quarter quad tile would be required to cover that entire area.
Wetland Classification
Mapping wetlands directly by remote sensing digital image classification is limited by the factors of fluctuating water levels, floating masses around wetland vegetation, and the spectral similarity between forested upland and forested wetland (Ozesmi and Bauer 2002; Wright and Gallant 2007). Therefore, instead of image classification, we developed a method that was based on spatial analysis of NWI, WWI, SSURGO soil data, as well as image interpretation to map the wetlands according to the requirements for the HGM regional guidebook model. Both the NWI and WWI were created based on manual interpretation from large-scale aerial photography. Wetland locations in the NWI were described using the hierarchically defined Cowardin system (Cowardin et al., 1979). Fresh water wetlands were divided into palustrine, riverine, and lacustrine systems at the highest level (Cowardin et al., 1979). On the other hand, the highest level classes for the WWI include aquatic bed, moss, emergent/wet meadow, scrub/shrub, forested, flats/unvegetated wet soil, and open water. More detailed information can be found on the website of Wisconsin Department of Natural Resources (http://dnr.wi.gov/topic/wetlands/).
In our analysis, the classification systems of NWI and WWI were examined first. Classification schemes were then developed for each dataset, which allowed both systems to be reclassified into one unified and simplified classification scheme. The SSURGO soil data was used to provide additional classification possibilities. The wetland classes were determined based on vegetation type and soil or parent material type. In total, nine wetland classes were created: (1) Lake, (2) River, (3) Organic Parent Material (PM)
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Forested, (4) Organic PM Shrub, (5) Organic PM Herbaceous, (6) Organic PM Altered, (7) Mineral PM Forested, (8) Mineral PM Shrub, and (9) Mineral PM Herbaceous.
To classify the wetland data based on this classification scheme, the NWI and WWI were first combined with the SSURGO soil database using a “spatial join” command in ArcGIS. The resulting database identified both the wetland type and the parent material for each wetland. To assign wetlands from the wetland/soil database, two classification sub-schemes were developed: one for the combined NWI and soil classes and the other for the combined WWI and soil classes. Both NWI and WWI data were classified at a much more detailed level than required for this study. Therefore, NWI/soil and WWI/soil classes were merged into the nine wetland classes defined in this study, which was accomplished by applying the “select by attribute” function onto the attribute tables of the wetland/soil datasets.
Before proceeding further, we evaluated the wetland inventory files carefully by overlaying them with the aerial imagery. This step was necessary because the NWI and WWI data were created over 20 years ago and therefore, actual wetland cover may have changed since creation of the data. When discrepancies were found either in the extent of a wetland feature or in its vegetation type, the necessary corrections were made: outlines of the wetland were adjusted or vegetation types were changed. The most common adjustment made here was to change wetlands to the altered wetlands class, or to assign a non-wetland class, where appropriate. After this step, sites that were of interest for further study of amphibian and bird species in relation to wetland environments were visited for field validation of the classification. The sites visited here are indicated in Table 1. The rest of sites that are not field validated were visually checked using the high resolution digital aerial imagery as the reference.
Python Scripting and Decision Tree Modeling
All the corrected vector files of wetland classification maps for all our 30 reference sites were batch-converted to raster files at 1 m spatial-resolution using a custom Python script. The wetland class field was used as the value field for the raster file, thus creating a thematic wetland raster file.
After creating the previously described maps, the information required for this study was still in two separate datasets: general land cover classification and
specific wetland classes. Therefore, a decision tree model (Fig. 3) was developed in a custom Python script to combine both classification maps and a road layer based on specific rules. In particular, the land cover map included exhaustive data coverage, while the wetland map only included data where a wetland cover was determined. This means that when the maps are merged, in almost any circumstance, some data from one of the maps has to be replaced with data from the other map.
FIG. 3 DECISION TREE MODEL FOR PRODUCTION OF FINAL
MAP. PARAMETERS CONSIDERED ARE WHETHER THERE IS A ROAD PRESENT IN THE ROADS RASTER MAP (IF YES, URBAN
CLASS VALUE IS ASSIGNED), WHETHER THERE IS A WETLAND PRESENT IN THE WETLAND MAP (IF SO,
WETLAND CLASS VALUE IS ASSIGNED), AND IF NONE ARE TRUE, THE LULC CLASS VALUE IS ASSIGNED.
The decision tree model first converts the raster files created with Python script to thematic raster files. It then evaluates each pixel from the three input files-LULC map, wetland map, and road. Specifically, roads are one of the major urban impervious types. Accurate and automatic road extraction from remote sensing imagery is difficult. Therefore, a DOT base map was used in this study. Urban class value was assigned when a road pixel presented. Otherwise, wetland class value was assigned. If neither of the other layers had available data, the LULC class value was assigned. The decision tree gives the data contained in the wetland map priority over that in the general land cover map, thus keeping wetland class values wherever there is data present in the wetland map. A merged map was generated using this approach.
A systematic approach to produce LULC maps from high spatial-resolution aerial imagery and wetland maps from existing wetland inventory and soil classification maps was developed, and involved digital image classification and validation, spatial analysis, and automatic processing using Python for intermediate steps. As a summary, Fig. 4 provides an overview of the entire classification process, indicating manual processing steps in green and Python-scripted steps in red.
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FIG. 4 OVERVIEW OF THE LULC AND WETLAND CLASSIFICATION PROCESS (GREEN COLOR: MANUAL STEPS; RED COLOR:
PYTHON-SCRIPTED STEPS).
Accuracy Assessment
An accuracy assessment of the LULC classification results was conducted. To determine the classification accuracy, a stratified random sampling scheme with a minimum of 5 points per map class was employed. The points were visually evaluated on the original aerial images. The road layers were used in addition to aerial images to visually verify locations of roads. For each point, classifier and actual class values were noted and then used to calculate per-class and overall
classification accuracy and Kappa statistics. The classification accuracy was determined in this manner for each of the reference sites. Error matrices were also produced for all the sites. To examine whether the initial assumption that different classification methods would be required to yield similar accuracy for Minnesota and Wisconsin sites, the results of the accuracy assessment were divided by state and analyzed. Additionally, the accuracy of wetland classifications was verified by eithor field visits or visual image interpretation.
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Results
Land Cover Patterns
Fig. 5 depicts example land cover classification maps of both Minnesota and Wisconsin sites. Evaluation of the land cover maps generated for each of the sample sites showed some general patterns that were found across almost all of the sites. The most prevalent land cover type among most of the sites was forest. On average, among all sites, 40% of the area was forested. The St. Croix State Park site in Minnesota was surrounded by the most forest of all sites (87%), while Holthe Prairie had the smallest amount of forest cover (8.7%). Cropland and other vegetation were also relatively common. Each of these classes made up at least 25% of the land cover that surrounded most of the sample sites.
Notable exceptions were the Hayward and Union White Cedar sites in Wisconsin, where less than 1% of the land cover was cropland. The Jim Nelson/Joseph O’Brien, Kettle River, Norway Point, St. Croix State Forest, St. Croix State Park, Wild River State Park, and Fish Lake Crex sites each had less than 10% of other vegetation cover. Water made up 6.3% of the land cover on average among all sites, with the most amount of water found in the Hayward site (Wisconsin) at 54.3%. Several sites (Fens Research Facility, Jim Nelson / Joseph O’Brien, Rum River State Forest, and St. Croix State Park) had nearly no open water. Impervious surface made up between 19.8% (Tamarack Swamp site) and 0.03% (St. Croix State Forest) of the total surface cover. The average percentage cover of impervious surface was 4.4%, only three sites had more than 10% cover (Holthe Prairie, Savage Fen, and Tamarack Swamp). A complete overview of the land cover areas for each of the sites is provided in Table 3.
Accuracies of land cover maps
The accuracy assessment of the land cover maps indicated that the maps were generally of acceptable quality. Specifically, the Minnesota sites had overall classification accuracies ranging from 70% to 94%, while Wisconsin sites ranged from 72% to 84%, with the mean standard deviations of 7.8% and 4% for Minnesota and Wisconsin sites, respectively, as shown in the last column of Table 3. Error matrices were used to assess classification accuracy. The average statistics are summarized for both states in Table 4.
In our study, imagery with 4 spectral bands (RGB and NIR) was classified using a traditional Maximum
likelihood classifier combined with a manual guided clustering approach; while 3 band imagery (RGB only) was classified using an object-based classifier. However, the overall classification accuracies of the resulting maps were comparable. High accuracies were achieved for water, forest, and impervious classes while the accuracies for cropland and other vegetation class were relatively low due to the spectral confusion between these two classes. In addition, the average accuracies of cropland and other vegetation classes of the Minnesota sites were higher than those of the Wisconsin sites, indicating the importance of NIR spectral band in vegetation classification and analysis.
FIG. 5 EXAMPLES OF LAND COVERMAPS. LEFT PANEL:
CARLOS AVERY WMA SITE IN MINNESOTA; RIGHT PANEL: BEARBLUFF CRANBERRY SITE IN WISCONSIN.
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Wetland Types and Distributions
The wetland boundaries of all 30 reference sites were cross validated by either field visits or visual image interpretation in 2010. Analysis of the wetland maps (e.g. Fig. 6) showed some patterns in the distribution of wetland types.
FIG. 6 EXAMPLES OFWETLANDMAPS. LEFT PANEL: CARLOS
AVERY WMA SITE IN MINNESOTA; RIGHT PANEL: BEARBLUFF CRANBERRY SITE IN WISCONSIN.
On average, forested wetlands with organic and mineral parent material made up more than 40% of the total wetland area. In fact, most sample sites had at least 25% of either mineral or organic soil forested
wetlands. There were four sites that contained more than 50% of forested wetland with mineral parent material (St. Croix State Forest, St. Croix State Park, Hayward, North Flowage Impoundment). Only the Fens Research Facility and Union White Cedar sites contained more than 50% cover of forested wetland with organic parent material. Where forested wetlands were not the major wetland class, altered wetlands with organic soil tended to be the major type which made up 24% of total wetlands in this study. In some sites, this type of wetland made up more than 70% of the total wetlands (Carlos Avery WMA, Ottawa WMA, and Peat Bog WMA). The third most common class was wetlands with herbaceous vegetation and mineral parent material, which made up an average of 13% of all wetlands in this study; only seven sites had less than 5% of this wetland type.
Wetlands with shrub vegetation and mineral parent material were relatively scarce and were not present in 18 of the sample sites. The only two sites with over 10% of this type of wetland were Black River Forest (36%), and Hayward (12%), which averaged 3% of total wetlands. Wetlands with shrub and organic soils were slightly more common. Four sites had more than 10% of this wetland type (Norway Point, 15%; Bear Bluff Cranberry, 37%; Fish Lake Crex, 67%; Hayward, 30%). Fig. 7 gives an example of the final combined LULC and wetland classification map.
FIG. 7 EXAMPLE OF FINAL COMBINED CLASSIFICATION MAP
WITH BOTH GENERAL LAND COVER AND WETLAND CLASSES.
Discussion
Automation of Big Data Processing Using Python
Data processing was challenging in this study because the reference wetlands were distributed across two
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U.S. states, which resulted in large datasets that were similar though not identical. In total, 16 NAIP county mosaic images for Minnesota and 20 USGS Quarter Quad Tiles for Wisconsin had to be processed. Each of the county mosaic images was approximately one gigabyte in compressed MrSID format (.sid), and each of the Quarter Quad Tile was about 1-2 hundred megabytes. To process such large data and automate the mapping process, a set of several Python scripts was developed for post-processing steps (filter, reclassification, clip, vector to raster conversion) and merging of LULC and wetland classes for all samples sites. The reason we chose Python to automate the procedure was because Python is integrated with ArcGIS software. Python communicates with ArcGIS geoprocessing through ArcPy, which is a site package that provides productive ways to perform geographic data analysis, conversion, management, and map automation with Python. In this study, the custom Python scripts were able to significantly improve the efficiency with which the large amount of data could be processed. At the same time, some intermittent steps such as land cover classification and accuracy assessment for each of the 30 sites could only be performed manually by the analyst.
Reasons for using two Classification Methods in the Maping Process
The differences between the available geospatial data from two states called for the development of new approaches to allow for a unified use of these datasets. In a pilot study, we applied both MLC and object-based techniques to an image from each of the two states. We found the object-based image analysis was a more effective classification method for the Wisconsin imagery, in which the NIR band was missing. This result conformed to a previous study by Yuan (2008), in which object-based Feature Analyst demonstrated to be effective in land feature extraction from historical black and white aerial photography that has low spectral variation. For the Minnesota imagery, similar results were obtained by both methods; however, the MLC method coupled with guided clustering process was more time-efficient. Therefore, a combination of MLC and object-based feature recognition techniques was adopted to produce accurate land cover maps for this study.
Uncertainties in high Spatial-resolution Imagery
Blaschke (2010) identified a paradigm shift in remote sensing, moving from the dominance of per-pixel
classifiers to object-based techniques, particularly when applied to high spatial-resolution data. Nevertheless, there are still uncertainties associated with this technique. For example, object-based classification aims to assign absolute class values to objects that are made of a cluster of related pixels; therefore, similar to per-pixel classification methods, object-based classification cannot compensate for the mixed-pixel problem in remote sensing imagery (Lucieer et al. 2005). While usually less mixed pixels were present in high-resolution satellite imagery, the problem still exist (Wu and Yuan, 2009). For example, Wu (2009) found IKONOS imagery contained 40–50% of mixed urban pixels for Grafton, WI. Our study sites were located in rural areas, so the mixed pixel problem was not that severe as in urban environment.
Another issue associated with high-resolution remote sensing data is the shadow problem. Again, this problem is more severe in an urban environment with buildings. The majority of the shadows presented in our study were tree shadows, which were easier to address than building shadows, since the shadowed areas of trees were not completely blacked out.
Other Challenges in Vegation Classification and Wetland Mapping
The variations in vegetation types across the large study area also introduced certain issues. If attempts were made to classify vegetation across the entire study site on one large map, the differences in vegetation types and growing seasons would likely cause difficulties in differentiating vegetation classes such as forests and cultivated crops. This was dealt with by first classifying smaller parts of the study area and then mosaic king these subsets to create a final map covering the entire study area. In addition, our classification maps showed that several wetland sites contained nearly no open bodies of water. However, this should be considered carefully as the images used here for classifications were acquired during the summer months when fluctuations in water levels may be high. Further, during visual inspection of the imagery, it was found that many water bodies had aquatic vegetation growing on their surface, which possibly gave the spectral response of vegetation rather than water during classification.
The accuracies for cropland and other vegetation class were relatively low due to the spectral confusion between these two classes. The accuracies could be improved if the two classes were combined, as
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demonstrated by Yang and Zhou (2011) who achieved an 87% Kappa accuracy for the combined cropland and grassland class. However, in our study, we had to separate these two classes in order to map the wetland class in the next step. Moreover, theoretically, multi-seasonal or multi-temporal data has the potential to produce higher classification accuracy than that can be obtained from a single date image (Coppin and Bauer, 1994; Wolter et al., 1995; Reese et al 2002; Yuan et al. 2005a; Yuan et al. 2005b). Chu et al. (2012) also demonstrated the advantages of using multi-source data over single source data. However, in practice, multi-seasonal and multi-source high spatial-resolution remote sensing data for large study area are still very limited.
Wright and Gallant (2007) found that ancillary GIS information could improve palustrine wetland detection. However, they also admitted that some ancillary data such as vegetation and landform classifications may not be widely available for large area wetland mapping, and the unavailability of thematic GIS layers would negatively impact separation of different wetland types more than wetland detection. In our study, we developed a method that was based on spatial analysis of NWI, WWI, SSURGO soil data as well as image interpretation for wetland mapping rather than image classification because classifying wetlands directly from remote sensing images was limited by several factors mentioned in the Methods section.
Uses of Land Cover Maps in the Assessment of Wetland Functions
Our results showed that forest was the most prevalent land cover type among most of the sites, covering 40% of the total area. Cropland and other vegetation each made up at least 25% of the land cover that surrounded most of the sample sites. Water accounted for around 6% of the land cover on average among all sites, while impervious surfaces covered the rest 4% of the total areas. On average, forested wetlands with organic and mineral parent material made up more than 40% of the total wetland area. Altered wetland with organic soil (24%) was also a major type, followed by wetlands with herbaceous vegetation and mineral parent material (13%). Another 22% comprised a series of miscellaneous wetland types.
Relative proportions of land cover types surrounding a wetland play a critical role in the assessment of wetland functions (HGM guidebooks). Land cover is
important to wetland hydrologic connectivity and the number of individuals and diversity of species within a wetland. A change in land cover will change surface water runoff and therefore the relative proportions of water sources entering a wetland. Changes in land cover also likely change the quantity and quality of water entering a wetland. Changes in any one of these hydrologic characteristics will likely change functional and structural characteristics of the wetland. Similarly, changes in land cover surrounding a wetland can affect habitat preference by some animal species and also affect the movement of organisms among and between wetlands. For example, some impervious surface land cover types (i.e., roads) are barriers to dispersal of small mammals and reptiles to adjacent wetlands.
Conclusions
Both the general land cover and the wetland area information extracted in this study were used as parameters in building the model for the HGM guidebook in the next step research. The data collected here is similar in nature to what would be collected by users of the HGM guidebook. However, the guidebook user is not expected to collect the data at the level of accuracy generated here. The high level of accuracy displayed here is required to successfully build the HGM model, while the data used to execute the model can be of lesser quality, collected, for example, from topological maps.
However, rather than identify land cover patterns and their compositions, this specific study focused on how to map high spatial-resolution land cover and wetland information over large areas from big datasets. We found in the case where the imagery used was of different quality and spectral resolution, acceptable and comparable results could still be achieved by using classification methods appropriate for the type of imagery. Specifically, when lacking the NIR spectral band, the object-based classifier could extract land covers effectively with reasonable accuracy. In our study, high accuracies were achieved for water, forest, and impervious classes while the accuracies for cropland and other vegetation classes were relatively low due to the spectral confusion between these two classes. In addition, the average accuracies of cropland and other vegetation classes of the Minnesota sites were slightly higher than those of the Wisconsin Sites, indicating the importance of NIR spectral band in vegetation classification and analysis. While there was some variation in accuracy, even where the same
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method was employed, there was no significant differences in the accuracy produced by the two different classification methods employed on the imagery with varying quality.
When processing big data sets for multiple sites, Python scripting allows for the automation of many otherwise labor-intensive GIS tasks. While large parts of the process can be automated by Python scripting coupling with digital image classification and spatial modeling, there are still several manual steps involved, from the initial pre-processing to the final merging steps. Despite the issues caused by variations within the study area due to different ecologic regions and geospatial data politics, the method developed here delivered desirable results and could be used in other regions for similar studies. The Python-based approach shown here allows for systematic procedure that can be applied to various data sources and large data sets that have differing standards.
ACKNOWLEDGMENT
Funding for this study was provided by the U.S. Army Corps of Engineers, Engineer Research and Development Center (ERDC). We wish to thank the Wisconsin Department of Natural Resources for providing the most current Wisconsin Wetland Inventory data. We also thank Kevin Clement for field data collection and wetland image interpretation. Thanks also go to Lucas Wandrie and Jeana Albers for field visits.
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