Harrison 1. Identifying Wetlands by GIS Software Submitted July 30, ,470 words By Catherine Harrison University of Virginia

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1 Harrison 1 Identifying Wetlands by GIS Software Submitted July 30, ,470 words By Catherine Harrison University of Virginia cch2fy@virginia.edu

2 Harrison 2 ABSTRACT The Virginia Department of Transportation identifies wetlands in Virginia in order to avoid building roads and other transportation systems on wetlands because of their already endangered state. Using ArcGIS software, wetlands were identified more efficiently than current methods by taking into account hydrography flow lines, soil characteristics, land use, Landsat data, digital elevation models, and flood plain maps. A tool was created and tested on 12-digit HUCs between Suffolk and Petersburg along Route 460 using these quantities and found to be more efficient and accurate than the original manual identification methods used. BACKGROUND Wetlands are a home for vegetation and animals, a protection from flooding, and a natural water filtering system (1). Due to digging up of the wetlands, pollutant run off, eutrophication, and construction, wetlands have undergone destruction. The Virginia Department of Transportation (VDOT) uses satellite remote sensing to identify wetlands by incorporating the data collected into a geographic information system (GIS). However, there are clear limitations to using this method. The limited resolution of the imagery decreases the ability of smaller areas of wetland to be identified (2). The accuracy of satellite remote sensing is not sufficient enough to precisely identify all wetlands. OBJECTIVE By using hydrography, multispectral imagery, flood maps, roadways (460), soil characteristics, and National Wetlands Identification classified by the U.S. Fish and Wildlife Service, wetlands are able to be identified more efficiently and accurately in Virginia with a tool created on a program called ArcGIS. AREA The area chosen to be studied extends from Suffolk to Petersburg and is made up of 12-digit hydrologic unit codes (HUCs). It follows along US Route 460. This area is considered a part of the coastal plain region of Virginia which contains tidal swamps and marshes (3). The coastal plain also has vernal pools. Vernal pools are dried out during the drier seasons which makes it more difficult to identify them as wetlands. There are also pocosins located in the coastal plain. The Virginia Department of Environmental Quality defines them, pocosins typically sit on hillside plateaus and accumulate acidic peat like northern bogs. Pocosins experience occasional fires and therefore exhibit a diversity of shrubby evergreens (4). The area is also a part of the country s Middle Atlantic Coastal Plain which is made up of parts of New Jersey, Pennsylvania, Delaware, Maryland, Virginia, North Carolina, South Carolina, Georgia, and Florida. Its land is mainly agriculture, forest, and wetlands and it is known to have hot, humid summers and mild winters (5). DATA Soil Survey Geographic Database

3 Harrison 3 The Soil Survey Geographic Database (SSURGO) provides data characterizing the soils in each county of Virginia. It includes the hydric content of the soil which provides the soil moisture content. The data was collected through the National Resources Conservation Service s Web Soil Survey (6). The Microsoft Access Data file was opened for each county and linked to the associated tabular folder in order to load all of the data. Then, the soilmu_a_va### shapefile was uploaded for each county. The polygon was, then, joined with data from the component table from the.mdb file. Then, the symbology of each was changed to show the hydricating feature of the component table. Soils are labeled as yes, no, or unranked, depending if they are hydric or non-hydric. The new data layers were exported. Then they were merged, projected to NAD83 Virginia South State Plane FIPS, and clipped to the HUCs. FIGURE 2 SSURGO data of the area of interest. Digital Elevation Model The Digital Elevation Models (DEMs) were retrieved from the U.S. Geological Survey s National Map Viewer (7). The data allows the ability to identify areas of pooled water by comparing the elevation of one area with the elevation of the areas around it. The resolution of the data was 1/9 arc-second National Elevation Dataset (NED), the highest resolution available from National Map Viewer. It correlates to approximately 3 meter resolution. However, parts of the area of interest had missing DEMs. Therefore, two areas of data were downloaded with a 1/3 arc-second NED resolution which was a lower resolution than desired. The data with 3 meter resolution and the data with 10 m resolution were merged separately. Then, the data with a 1/3 arc-second NED resolution was resampled in order to match the resolution of the 1/9 arc-second NED resolution data. It was all merged together, projected to NAD83 Virginia South State Plane FIPS, and clipped to the HUCs. Unfortunately, there was still an area without DEM data available. Therefore, this space will have to be neglected while identifying wetlands. It is seen has black in the figure below.

Harrison 4 FIGURE 3 DEM data of the area of interest. National Hydrography Dataset The National Hydrography Dataset (NHD) for the area of interest was collected from USGS s National Map Viewer (7).

4 Harrison 4 FIGURE 3 DEM data of the area of interest. National Hydrography Dataset The National Hydrography Dataset (NHD) for the area of interest was collected from USGS s National Map Viewer (7). The flow lines were extracted from the NHDflowline shapefile. They were retrieved by county and the shapefiles of the area of interest were merged, projected onto NAD83 Virginia South State Plane FIPS, and clipped to the 12 digit HUCs. FIGURE 4 NHD data of the area of interest. Federal Emergency Management Agency s Flood Plain Maps Floodplain maps are used to identify areas of flooding for heavy storms. One hundred year floodplain maps were collected from Federal Emergency Management Agency s Flood Map Service Center (8). They are considered one hundred year floodplain zones. The S_FLD_HAZ_AR shapefile for each floodplain area in the area of interest was extracted. Then,

5 Harrison 5 the shapefiles were merged, projected onto NAD83 Virginia South State Plane FIPS, and clipped to the 12 digit HUCs. FIGURE 4 Flood plain map for the area of interest. National Land Cover Data National Land Cover Data (NLCD) portrays the different types of land, varying from developed land to wetlands. The data was retrieved from Multi-Resolution Land Characteristics Consortium and is at a spatial resolution of 30 meters (9). It was based upon a classification from Landsat images taken in The data was projected onto NAD83 Virginia State South Plane FIPS and clipped to the 12 digit HUCs. FIGURE 5 NLCD data for the area of interest.

6 Harrison 6 Landsat 8 OLI The surface reflectance and Normalized Difference Vegetation Index (NDVI) of two Landsat 8 OLI scenes of interest were retrieved from USGS s LandsatLook Viewer and downloaded with USGS s Earth Resources Observation and Science (EROS) Science Processing Architecture (ESPA) (10). This gives conversion to top of the atmosphere (TOA) from digital numbers and surface reflectance values using atmospheric corrections using MODIS correction routines and the Second Simulation of a Satellite Signal in the Solar System (6S) radiative transfer models. Using LandsatLook Viewer, two appropriate scenes were chosen in order get data for the entire area of interest, images from July 6, 2014 and August 14, These dates were chosen because they occur during the wet season, were from the past year and are therefore more accurate than those from later dates, and they 1% cloud coverage. Although 0% cloud coverage is desired, a recent image during the wet season with no clouds was not available. However, no clouds are over the area of interest in these images. The scene IDs for the downloaded images are LC LGN00 and LC LGN00, on path 14, row 34 and path 15, row 24, respectively. Bands 2, 3, 4, 5, 6, and 7 for each of the scenes were extracted. Each band was merged from the images, band 2 of one image with band 2 of the other, and so on. They were projected to NAD83 Virginia South State Plane FIPS, resampled and snapped to DEM, and clipped to the 12 digit HUCs. FIGURE 5 Landsat 8 OLI Bands.

7 Harrison 7 FIGURE 6 Landsat Data of the area of interest.

Harrison 8 CONCLUSION With this data of the area of HUC12, along with multispectral imagery collected with a drone, Ben s tool is able to identify where wetlands are located.

8 Harrison 8 CONCLUSION With this data of the area of HUC12, along with multispectral imagery collected with a drone, Ben s tool is able to identify where wetlands are located. It takes into account multiple aspects of the area, including soil quality, elevation, flood maps, hydrology flow lines, Landsat images, and land cover. Therefore, it identifies these wetlands much more efficiently than the process done at VDOT with a great amount of accuracy. However, because the resolution is limited and is lower than desired, some existing wetlands may have been unaccounted for because of their small size. They would have been looked over when the area was averaged out to be decided if it was or was not a wetland. This may be fixed easily with a higher resolution camera. The time the tool took to process was initially over 24 hours. However, after improvements were made, it took only six to eight hours. Most of the time was observed to be for processing the DEM, which had missing data. Therefore, it is predicted that if the full extent of DEM data was available, the tool would take less time for processing. Because this tool identified wetlands aligning with the NWI wetlands and took less time than other methods used, it is determined that this tool is very practical and useful. FIGURE 10 Model output of wetlands.

9 Harrison 9 FIGURE 11 NWI, Model output, and VDOT wetlands. ACKNOWLEDGMENTS Special thanks to Benjamin Felton and Professor Jon Goodall for heading this research, thanks to Aaron Capelouto who partnered in this research with Benjamin Felton and Professor Goodall, and thanks to Emily Parkany who headed this internship opportunity. REFERENCES 1. Ecological Services: Overview. US Fish & Wildlife Service: National Wetlands Inventory. (2015). Accessed July 27, Ozesmi, S., & Bauer, M. Satellite Remote Sensing of Wetlands. Wetlands Ecology and Management. (2002). Accessed July 27, Virginia s Coastal Plain (Tidewater) Region. The Geography of Virginia. (2012). Accessed July 28, Wetlands. Virginia DEQ. (n.d.). Accessed July 28, Auch, R. Middle Atlantic Coastal Plain. USGS. (2014). Accessed July 28, Web Soil Survey. United States Department of Agriculture. (2013). Accessed June 10, Dollison, R.M. The National Map: New viewer, services, and data download: U.S. Geological Survey Fact Sheet , 2 p. (2002). Accessed July 10, FEMA s National Flood Hazard Layer (Official). FEMA Geoplatform. (2015). Accessed July 1, 2015.

10 Harrison National Land Cover Data. Multi-Resolution Land Characteristics Consortium. (2011). Accessed July 18, LandsatLook Viewer. USGS. (2015). Accessed June 28, 2015.

Delineation of high landslide risk areas as a result of land cover, slope, and geology in San Mateo County, California

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