GIS for Water Resources and Watershed Management - Chapter 10 docx

109 CHAPTER 10 Evaluation of the Albemarle-Pamlico Estuarine Study Area Utilizing Population and Land Use Information Robert E. Holman BACKGROUND The Albemarle-Pamlico Estuarine Study (APES) has been funding many information acquisi- tion projects over the last five years in the areas of resource critical areas, water quality, fisheries, and human environment (Steel and Scully, 1991). Most of these projects have transferred their data over to the APES’s Geographic Information System (GIS) which was created through a sub- contract with the North Carolina Center for Geographic Information System (CGIA). GIS has the ability to bring together (enter, display, edit, and manipulate) data based information with digital mapping (locational attributes). At the time of this study, the Center (CGIA) had or was creating all of the needed databases. CGIA was able to combine the data layers in various ways to analyze the relationship among different layers in a visual as well as a statistical manner. The study area encompasses approximately 23,250 square miles and includes all or portions of 37 counties in eastern North Carolina and 19 counties in Southeastern Virginia. There are six counties along the coastline, 9 counties along the sounds, and 41 cities/counties that lie in the upper drainage basin (Figure 10.1). This study also incorporates all or portions of 6 major river basins including the Chowan, Pasquotank, Lower Roanoke, Tar-Pamlico, Neuse, and White Oak (Figure 10.2). Each basin is divided into subbasins as follows: Chowan, 13; Pasquotank, 8; Lower Roanoke, 3; Tar-Pamlico, 8; Neuse, 14; and White Oak, 5. METHOD The analytical method was broken into three phases. Phase One was the creation of county land use maps from the existing Landsat classification scheme. These map products were sent to U.S. Fish and Wildlife and county officials to determine the accuracy of the defined land use classes. The land use maps were also used by the author during flights over the coastal and metropolitan areas to further clarify classification errors. Phase Two was to correct some of the errors in the ex- isting classification. This was carried out by digitizing the corrections to the map products that were returned from Fish and Wildlife and county officials. The map information was also supple- mented with other sources of information such as U.S. Fish and Wildlife Service—National Wet- land Inventory, U.S. Forest Service—Forest Inventory and Analysis, U.S. Bureau of Census—Census of Agriculture, and U.S. Soil Conservation Service—National Resources Inven- tory and Hydric Soils in North Carolina counties. Phase Three was identifying correlation between © 2003 Taylor & Francis Chapters 1, 3, 5 & 6 © American Water Resources Association; Chapter 13 © Elsevier Science; Chapter 14 © American Society for Photogrammetry and Remote Sensing 110 GIS FOR WATER RESOURCES AND WATERSHED MANAGEMENT different data sets such as county census population and county acreage of developed land. If a strong correlation was found, then a simple linear regression model was applied in order to predict the relationship between the two parameters. These models were used to correct some of the errors in specific land use categories. Map Development There were three tasks in the development of land use and population estimates for the entire APES area: (1) defining the actual drainage area; (2) having all the basin and subbasin boundaries digitized in order to determine the land use and population; and (3) correcting for errors associated with the different land uses. First, the study area was defined as the entire drainage area of the Albemarle and Pamlico Sound system including Core and Bogue Sounds. The upper Roanoke Basin and a portion of the White Oak Basin were not included because: (1) the upper Roanoke River Basin covers approxi- mately 8,370 square miles in Virginia/North Carolina and stretches over two-thirds the length of North Carolina, and would add one-third more area to the study area; and (2) a decision was made early in APES to have Carteret County as the furthest area south. However, due to the watershed approach in defining the study area in this project, all the subbasins in the White Oak Basin were included except the furthest one southwest that starts at Camp Lejeune. There was no compatible land use data available for this subbasin. Figure 10.1. Map of study area. © 2003 Taylor & Francis Chapters 1, 3, 5 & 6 © American Water Resources Association; Chapter 13 © Elsevier Science; Chapter 14 © American Society for Photogrammetry and Remote Sensing EVALUATION OF THE ALBEMARLE-PAMLICO ESTUARINE STUDY AREA 111 Second, all North Carolina basins and subbasins were digitized by the Research Triangle Insti- tute and compared closely with the U.S. Geological Survey subbasins for North Carolina. Virginia subbasin information was supplied by Information Support Systems Laboratory within Virginia Polytechnic Institute and State University and was based on Soil Conservation Service (SCS) in- formation. Due to the large number of subbasins identified by SCS in the Virginia portion of the Chowan and Pasquotank Basins, subbasins were combined to create areas of the same size range as subbasins identified in North Carolina. All subbasins were digitized from U.S. Geological Sur- vey’s 1:24,000 scale topographic maps. Specific subbasins were identified by a six number code that was broken into two-digit sets. The first two digits identified the regional basin; the second two digits identified the basin; and the third two digits identified the subbasin (Figure 10.2). Codes used in this report were the same ones adopted by the North Carolina Division of Environmental Management. The third task was to identify the accuracy of the land use data and to develop methods to cor- rect for the errors. Khorram and others (1992) found that with the Landsat data, the urban or built- up land use category was only 46% accurate, and the accuracy of forested wetlands was unknown. In addition, the classification of mixed pixels in the existing land use data set had to be resolved. Mixed pixels are defined as areas that could not be classified because the resolution or pixels were a mixture of many categories. The land use classification from 1987–1988 developed by Khorram will be referred to as the “Landsat” classification in this study. Figure 10.2. The six APES basins and their subbasins. © 2003 Taylor & Francis Chapters 1, 3, 5 & 6 © American Water Resources Association; Chapter 13 © Elsevier Science; Chapter 14 © American Society for Photogrammetry and Remote Sensing Accuracy and Errors The first task was to define which level of land use to utilize. Landsat land use classification defined 18 separate classes that can be generally broken into similar U.S. Geological Survey level I and level II groupings (Anderson and others, 1976). The land use classification was based on Landsat data which Khorram interpreted mostly as land cover (the actual extent of vegetative and other cover) and some land use (interpretation of activities taking place on the land). Interpretation of land use is much more subjective than land cover and is dependent on the knowledge of the in- dividual interpreter. A level II map with 18 individual classes was provided to officials of two Fish and Wildlife Refuges within the APES area for their evaluation as to land cover accuracy. The Great Dismal Swamp National Wildlife Refuge staff reviewed the Landsat land use map of the refuge. This refuge, located on the border between North Carolina and Virginia just south of Portsmouth, Vir- ginia covers approximately 110,000 acres and is predominantly forested wetland. The staff felt there was good separation among development, agriculture, water, and forest; however, the differ- ent forest cover types had serious reliability problems. A major problem was the misclassification of wetter deciduous stands like cypress/gum and maple/gum as pine/hardwood forest. A second land cover map was sent to Mattamuskeet and Swan Quarter National Wildlife Refuges personnel for their review. These two refuges are located entirely in Hyde County, North Carolina, and Swan Quarter is adjacent to the Pamlico Sound. These refuges together cover approximately 65,700 acres and are predominantly water, wetland, and forest. The staff found quite a few areas that were referred to as mixed pixels that were actually open water and irregularly flooded brackish marshes. White Cedar stands were actually marsh impoundment areas around Lake Mattamuskeet, and pine forest was actually mixed pine/hardwood or hardwood/cypress/pine forest. In general, both refuges indicated accuracy problems with the different forest and the mixed pixel classifications. After indicating they could not evaluate all the classifications in level II land cover maps, offi- cials of counties in the Currituck Sound Basin south of Virginia Beach and adjacent to the Atlantic Ocean were sent, for comment, land cover maps which included the following attributes: USGS level I with 6 categories shown in color; U.S. Census TIGER files that displayed the road network, map scale of 1:100,000; and modified LUDA land use data for the urban or built-up category. Land Use Data Analysis (LUDA) was an early GIS effort started by U.S. Geological Survey in 1975 to define the land use for the entire United States. All the photographs were manually pho- tointerpreted. The map series consisted of 1:250,000 scale maps of North Carolina defining 37 uses based on the level II classification system. Source imagery was 1:56,000 color infrared pho- tography and 1:80,000 black and white photography dating back to 1970. Resolution was 10 acres for the urban or built-up categories and 40 acres for the remaining classifications (Kleckner, 1981). For comparison, the Khorram classification was based on 1987–1988 Landsat satellite im- agery that was semiautomatically interpreted. The county map series was at a scale of 1:100,000 with a final resolution of 1 acre. In 1991 and 1992, the author flew along the Outer Banks and inland around the estuarine por- tion of the study area and over portions of Wake, Durham, and Orange Counties to verify the prob- lems with the categories of urban or built-up, wetland, and mixed pixels. The urban or built-up class was underestimated on the 1987–1988 land use maps mainly due to forest crown cover that obscured the true land use on the ground. High spectral reflectance of bare agricultural fields was also a problem because these fields were being classified as developed areas. The problem with fields being identified as developed areas was especially evident in the Landsat scene furthest west that included the Raleigh metropolitan area. This category was a very small percentage (3.3 to 112 GIS FOR WATER RESOURCES AND WATERSHED MANAGEMENT © 2003 Taylor & Francis Chapters 1, 3, 5 & 6 © American Water Resources Association; Chapter 13 © Elsevier Science; Chapter 14 © American Society for Photogrammetry and Remote Sensing 7.9%) of the overall land use of the study area but is critical in that urban or built-up land use can cause the greatest impact on natural resources of the APES area. Problems associated with the wetland class were found to be interference from forest crown cover. Open marsh and pocosin wetlands were usually accurately defined by the 1987–1988 land use maps but closed forest canopy prevented standing water below the forest to be seen. Therefore these true wetland types were usually defined as forest. The category of mixed pixels is a grouping the classification scheme could not identify. Flights over the coastal and metropolitan areas verified that in most cases they were a mixture of standing water and wetland vegetation. The only exception to this observation was in Pasquotank County where poorly drained agricultural land was defined as mixed pixels or wetland on the county land use map. These land use classification problems and others were identified in a workshop the author at- tended to verify remotely sensed land cover data for the Coastwatch Change Analysis Program of the National Oceanic and Atmospheric Administration (Burgess and others, 1992). The problems fell into four categories: classification error, cover versus land use, categorical resolution, and change de- tection. Classification errors included “salt and pepper” effect of individual pixels, shadows and bare ground as urban areas, and problems with the degree of wetness during image acquisition. Cover ver- sus land use had the inherent problem with distinguishing land uses and required ancillary data. Cat- egorical resolution was related to the spatial resolution and improper classification. The change detection problem involved the ability to detect a change but not always the nature of the change. From the author’s own observations and the results of this workshop, methods were developed to overcome some of the problems associated with remotely sensed land cover data. Land use information used in this study was analyzed according to the Khorram and others (1992) classification system but condensed from 18 to 7 categories. Certain corrections were in- corporated into some classes depending on the observed and documented error associated with each class. The LUDA data set was used to determine “developed land” because the information appeared to be closer to the actual extent and location than the original 1987–1988 Landsat data set. Corrected Landsat built-up areas on the maps returned by county officials were found to have a high degree of correlation (R 2 of 0.9) with the LUDA “developed” category. A linear regression model was used to predict built-up land from the LUDA data. U.S. Fish and Wildlife’s National Wetland Inventory (NWI) data were used as a reliable source of wetland acreage for the coastal plains of North Carolina (Wilen, 1990 and Burgess and others, 1992). Wetland acres for twelve of the coastal counties was provided by Kevin Morehead of the Savannah River Ecology Laboratory. The same procedure used to correct built-up areas was used to reconcile the Landsat wetland cat- egory with the NWI acreage. A high correlation resulted with a R-squared of 0.9 and a simple lin- ear regression model was used to predict wetland acres from the NWI values. The mixed pixel unidentified category was determined by the U.S. Fish and Wildlife personnel and two overflights of the APES area to be predominantly wetland in nature. The mixed pixel figures were incorpo- rated into the wetland classification. RESULTS Land Use The entire APES area land use classification is based on a modified U.S. Geological Survey’s level I classification scheme. One fact to keep in mind is that the water class is not a true land use but is a very important classification. There were seven classes with the following percentages: “urban” 4.8%, “agriculture” 28.2%, “forest” 28.4%, “water” 14.6%, “wetland” 20.5%, “shrub EVALUATION OF THE ALBEMARLE-PAMLICO ESTUARINE STUDY AREA 113 © 2003 Taylor & Francis Chapters 1, 3, 5 & 6 © American Water Resources Association; Chapter 13 © Elsevier Science; Chapter 14 © American Society for Photogrammetry and Remote Sensing 114 GIS FOR WATER RESOURCES AND WATERSHED MANAGEMENT land” 3.3%, and “barren land” 0.2% (Figure 10.3). In general, the study area is rural in nature with less than 5% of the total area developed. More than 55% of the total APES acreage came from the categories of agriculture and forest. Population The population of the study area was almost 2 million people (Figure 10.4). About 51.2% re- side in the Neuse Basin, which occupies only 26.8% of the land area. Population density for the basins ranged from 163.1 persons/square mile in the Neuse to 39.9 persons/square mile in the Chowan. Since the population density of all but two basins fell below the U.S average of 69 per- sons/square mile, most of the study area can be characterized as nonmetropolitan (U.S. Bureau of Census 1960, 1970, 1980, 1990). Population Versus Developed Land When the subbasins with the highest number of persons/square mile are compared to the sub- basins that have the greatest amount of developed land there appears to be a great deal of agree- ment. If a strong correlation exists between these two parameters then a powerful planning tool can be created to predict the amount of developed land from the existing or projected population for a certain area. The APES area has only two comprehensive land use databases and one does not correlate well with the category of urban or built-up land. Therefore, another source of long-term land use Figure 10.3. APES 1990 land use/land cover. © 2003 Taylor & Francis Chapters 1, 3, 5 & 6 © American Water Resources Association; Chapter 13 © Elsevier Science; Chapter 14 © American Society for Photogrammetry and Remote Sensing EVALUATION OF THE ALBEMARLE-PAMLICO ESTUARINE STUDY AREA 115 data is needed to determine if the relation between population and developed land is statistically sound. Land use data from the State of Maryland has been gathered since 1973 and has been taken as frequently as every five years during the past 15 year period (Maryland State Planning Office, 1991). The acres of total developed land were compared to the closest population census data for each county in Maryland. Three periods (1973, 1981, and 1990) had correlation with a R 2 value of greater than or equal to 0.9. A simple regression model was developed for each cor- relation and the results were very similar for all three periods. A population of 200,000 people was equated to between 39,000 and 43,000 acres of developed land. Since this relationship held for the Maryland data set, could the same relationship be established with the limited land use data sets in North Carolina? The earlier LUDA data set appeared to correlate well with devel- oped land, but how could the existing Landsat accuracy for developed land be improved? Land- sat land use maps of 21 counties in the APES area were sent out to county planners or other county officials for their review. Each county official was to shade in the extent of development that took place in his county during 1990 and change any land use that was not properly classi- fied. The returned maps were digitized and new acreage for developed land was obtained for each county. Both the LUDA and the corrected Landsat land use maps were compared to the population census data in the same manner as the Maryland information. Both correlation had a R 2 value of greater than or equal to 0.8. Again, a simple linear regression model was developed for each correlation and the results were very similar for both 1970 and 1990 (Figures 10.5 and 10.6). A population of 200,000 people was equated to between 45,000 and 60,000 acres of de- veloped land. A statistical relationship between population census and developed land for the same time frame has been established for land area in Maryland and the APES area. The relationship is not the same for both areas and probably will vary from region to region. Based on this relationship Figure 10.4. APES basin populations in 1990. © 2003 Taylor & Francis Chapters 1, 3, 5 & 6 © American Water Resources Association; Chapter 13 © Elsevier Science; Chapter 14 © American Society for Photogrammetry and Remote Sensing 116 GIS FOR WATER RESOURCES AND WATERSHED MANAGEMENT the number of acres of developed land for the years 1970, 1980, and 1990 has been estimated for each of the six basins in the APES area. The resulting pattern is similar to that of population, with the Neuse Basin having the largest amount of developed land over the last 30 years. The 1990 figures show the Neuse Basin with approximately 306,000 acres of developed land and the Figure 10.5. 1970 population vs 1972 land use. Figure 10.6. 1990 population vs 1990 land use. © 2003 Taylor & Francis Chapters 1, 3, 5 & 6 © American Water Resources Association; Chapter 13 © Elsevier Science; Chapter 14 © American Society for Photogrammetry and Remote Sensing EVALUATION OF THE ALBEMARLE-PAMLICO ESTUARINE STUDY AREA 117 White Oak Basin with the least at 65,000 acres of developed land. Developed land for the entire study area for 1990 was approximately 597,000 acres based on this predictive method. Projec- tions for the year 2000 and 2010 (NCDC, 1991; VEC, 1991) have also been estimated and com- pared to the year 1990. Developed land for the entire study area for the year 2010 is approximately 752,000 acres based on this predictive method. The Neuse River Basin continues to have the most developed land with 407,000 acres and the White-Oak Basin has the least with 26,000 acres (Figure 10.7). The Pasquotank Basin appears to have outpaced the Chowan Basin in the amount of developed land and contains the third largest acreage behind the Neuse and Tar-Pamlico. CONCLUSIONS This study found that land use data from Landsat was not adequate by itself to properly identify seven land use classifications. The greatest errors appeared to be associated with the classes of built-up and wetlands. This was due to the forest crown cover obscuring the true land use of rural residential development on the ground and data sources combined with existing data sets in the form of a linear regression appeared to compensate for these two major errors. A high correlation between population and developed land was found on a county level from Maryland, Virginia, and North Carolina data. Based on this correlation a linear regression model was developed to predict the number of acres of developed land based on the projected population for a particular county. The relation between population and developed land will not be the same for each region but this method can be a powerful tool in predicting where and how development will take place in a particular county. Figure 10.7. Developed land in basins. © 2003 Taylor & Francis Chapters 1, 3, 5 & 6 © American Water Resources Association; Chapter 13 © Elsevier Science; Chapter 14 © American Society for Photogrammetry and Remote Sensing 118 GIS FOR WATER RESOURCES AND WATERSHED MANAGEMENT REFERENCES Anderson, J.R., E.E. Hardy, J.T. Roach, and R.E. Witmer, 1976. A Land Use and Land Cover Clas- sification System for Use with Remote Sensor Data. U.S. Department of the Interior, U.S. Ge- ological Survey Professional Paper 964. U.S. Geological Survey. Washington, D.C. Burgess, W., E. Christoffers, J. Dobson, R. Ferguson, A. Frisch, P. Lade, and J. Thomas, 1992. Re- sults of a Field Reconnaissance of Remotely Sensed Land Cover Data. Maryland Department of Natural Resources. Annapolis, MD. Khorram, S., H. Cheshire, K. Sideralas, and Z. Nagy, 1992. Mapping and GIS Development of Land Use and Land Cover Categories for the Albemarle-Pamilco Drainage Basin. Albemarle- Pamlico Estuarine Study. Project No. 91-08. North Carolina Department of Environment, Health, and Natural Resources. Raleigh, NC. Kleckner, R., 1981. A National Program of Land Use and Land Cover Mapping and Data Compi- lation. In Planning Future Land Use, American Society of Agronomy, Special Publication No. 42. Maryland State Planning Office, 1991. Maryland’s Land 1973–1990: A Changing Resource. Balti- more, MD. North Carolina Data Center (NCDC), 1991. Population Projections for the Years 2000 and 2010. Raleigh, NC. Steel, J., and M. Scully, 1991. Projects Funded by the Albemarle-Pamlico Estuarine Study. Albe- marle-Pamlico Estuarine Study. Project No. 91-00. North Carolina Department of Environ- ment, Health, and Natural Resources. Raleigh, NC. Virginia Employment Commission (VEC), 1991. County Population Projections for the Years 2000 and 2010. Richmond, VA. Wilen, B.O., 1990. The U.S. Fish and Wildlife Service’s National Wetland Inventory. In Federal Coastal Wetland Mapping Programs: A Report by the National Ocean Pollution Policy Board’s Habitat Loss and Modification Working Group. Kiraly, S.A. and F.A. Cross, Eds., U.S. Depart- ment of the Interior. Washington, D.C. © 2003 Taylor & Francis Chapters 1, 3, 5 & 6 © American Water Resources Association; Chapter 13 © Elsevier Science; Chapter 14 © American Society for Photogrammetry and Remote Sensing . © American Water Resources Association; Chapter 13 © Elsevier Science; Chapter 14 © American Society for Photogrammetry and Remote Sensing 110 GIS FOR WATER RESOURCES AND WATERSHED MANAGEMENT different. © Elsevier Science; Chapter 14 © American Society for Photogrammetry and Remote Sensing 114 GIS FOR WATER RESOURCES AND WATERSHED MANAGEMENT land” 3.3%, and “barren land” 0.2% (Figure 10. 3). In general,. Society for Photogrammetry and Remote Sensing 116 GIS FOR WATER RESOURCES AND WATERSHED MANAGEMENT the number of acres of developed land for the years 1970, 1980, and 1990 has been estimated for