ESTIMATING LAND-COVER CHANGE IN RSIM: PROBLEMS AND CONSTRAINTS

Transcription

1 ESTIMATING LAND-COVER CHANGE IN RSIM: PROBLEMS AND CONSTRAINTS Latha Baskaran Virginia Dale Chuck Garten David Vogt Colleen Rizy Rebecca Efroymson Oak Ridge National Laboratory Oak Ridge, TN Matthew Aldridge Michael Berry Murray Browne Eric Lingerfelt University of Tennessee Knoxville, TN Farhan Ahktar Michael Chang Georgia Institute of Technology Atlanta, GA Catherine Stewart U.S. Army for Health Promotion and Preventive Medicine Aberdeen Proving Ground, MD ABSTRACT The changes that accompany growth and development over a region are reflected in many ways. Changes to the land can be observed using data derived from remote sensing to create land-cover maps. Land-cover data provide a snapshot of the land status at a specific period of time. Hence any major growth or development in a region should reflect associated changes in the land cover. To explore causes and implications of land-cover changes, land-cover change rules have been developed as part of a regional simulation model, RSim. This study analyzes the problems of developing and calibrating the land-cover change rules in RSim. Land-cover change rules are usually derived from past trends in the landscape and also from trends in the use of land. However, some of the major issues while calibrating and developing change rules are the inconsistencies in data availability and description. Fundamental differences in the data type such as information describing land use or land cover also affect interpretations from such data. The results of this study show that human demographic variables such as urban and rural population could not be compared with high and low intensity land-cover classes directly. However urban land cover as a whole could

2 be compared to population. Also, land-cover trends observed from similarly developed land-cover datasets could be used to derive land-cover change rules. INTRODUCTION Land-cover changes that result from expansion of urban and suburban areas is an inherent aspect of society. However, many such changes affect the environment and natural resources (Meyer and Turner, 1992). With growing awareness of the implications of growth on the environment, more environmental protection regulations and ordinances are now in place. The need to plan any development is important and often mandated. A good understanding of the implications of changes in land use and land cover can assist in foreseeing potential effects on the environment and society. Yet, this understanding is often complicated by nuances of technical terms and by difficult interpretations of land-cover classes over time. The terminology challenges and changes in land-cover classes may make it very different to interpret how land cover changes in any one area. This paper illustrates these problems and offers solutions by using a regional model, RSim, to explore recent land-cover changes in a fivecounty region of west central Georgia. The Regional Simulation Model (RSim) explores resource use and constraints as influenced by growth and development issues in and around Fort Benning, Georgia (Dale et al., 2006). RSim simulates the effects of changes in land cover on the quality of air, water, noise, habitat of rare species under different scenarios. The scenarios in RSim include urban growth, road improvement, military expansion, and a hurricane. In addition, RSim allows users to change parameters in each submodel. RSim includes a submodel that simulates changes in the land cover. The land-cover submodel is divided into urban and non-urban components. For both these components, rules of land-cover change are derived and applied for a base land cover. To calibrate and derive the growth rules, past trends in land-cover change are observed and analyzed along with changes in demography. This paper describes the methods used to calibrate urban land-cover growth rules and to derive and calibrate non-urban land cover change rules in RSim. DATA AND STUDY REGION Since a major aspect of this study was to combine and compare different data types, terminology and definition of the data used is important. Some of the data sets used in this study include data from the U.S. Census Bureau and land-cover data sets from different organizations. Demographic Data Demographic data for the study was derived from the U.S. Census Bureau. The census data classifies population into a variety of categories. Among those, the categories relevant to this study are population within urbanized area, urban clusters, and rural area. Urbanized areas consists of a central place(s) and adjacent territory with a general population density of at least 1,000 people per square mile of land area that together have a minimum residential population of at least 50,000 people (U.S. Census Bureau, Glossary). Urban clusters are densely settled territory that has at least 2,500 people but fewer than 50,000 and rural areas are territories, population and housing units not classified as urban. "Rural" classification cuts across other hierarchies and can be in metropolitan or nonmetropolitan areas. Land-Cover Data Land-cover data was derived from four time periods. The 1998 land-cover data for the study region was obtained from the Natural Resource Spatial Analysis Laboratory, University of Georgia. This 18 class land-cover map was originally generated from Landsat TM images and has a resolution of 30 m. Of the 18 categories in the 1998 land cover, the low intensity and high intensity urban are important with respect to urban land-cover change. The low intensity urban class includes single-family residential areas, urban recreational areas, cemeteries, playing fields, campus-like institutions, parks, and schools. High intensity urban class includes central business districts, multi-family dwellings, commercial facilities, industrial facilities, and high impervious surface areas of institutional facilities. The 1990 land-cover map was created by the Georgia Department of Natural Resources. This map is based on Landsat TM imageries dated 1988 to 1990 and has a resolution of 60 m. There are 15 land-cover categories for the

3 data. The low density and high density urban are the two major urban land-cover categories. The low density urban class represents urban areas with moderate vegetative cover. However any area with high reflectivity, such as isolated industrial sites, may fall into this or the high density urban class. The edges of some bodies of water are spectrally similar to this class. It is typical for residential areas to be shown as a matrix of this class and forest class pixels. Low density urban may be interspersed with high density urban. The high density urban class is distinguished from low density urban by an even higher reflectivity of the land cover. Paved areas with buildings and little vegetation are typical of this land-cover class. Roads are often shown as linear features composed of high and low density urban pixels. High density urban pixels found outside of urban areas are indicative of any type of highly reflective structure/ feature such as power substations, grain storage buildings. The 1992 and 2001 land cover data were obtained from the National Land Cover Data (NLCD) project (Vogelmann et al., 2001). These data sets are of 30 m resolution and have 21 land-cover classes with water, developed, barren, forested upland, shrubland, non-natural woody land, herbaceous and wetland classes. These datasets were not used for urban land-cover change analysis since their time frames were not coincident with the U.S. Census Bureau data time periods (1990 and 2000). Study Region The study region encompasses five counties around Fort Benning, Georgia (Figure 1). The five counties are Chattahoochee, Harris, Marion, Muscogee and Talbot. The city of Columbus is in the western extent of the study region, and lies mostly within Muscogee. Most of the population and urban growth is concentrated in and around Columbus. The other regions are predominantly forested with a few agricultural areas. Figure 1. Five-county study region. URBAN GROWTH RSim incorporates land-cover change in two stages urban and non-urban land-cover change. Urban landcover change is explained in this section. Non-urban land-cover change is described in the next section. Urban growth has been occurring at a fast pace in these five counties in recent decades. Columbus, which is a major

4 metropolis, has been driving much of this growth. Further, the RSim study region is south of Atlanta, one of the fastest growing metropolises in the world (Yang and Lo, 2003). Changes in Atlanta affect the economy and growth in the five-county study region. In RSim, urban land-cover growth rules have been incorporated based on those designed by the SLEUTH model (Clarke et al., 1997; Clarke and Gaydos, 1998; Candau, 2002). This method for simulating urban growth spontaneously generates new urban pixels and also allows patch growth (growth of preexisting urban patches). Urban growth rules are applied at each iteration of RSim to create new urban land cover and also to change preexisting cover. Calibrating Urban Growth RSim has been modeled such that it runs iterations of rules from a base year. However while implementing the urban land-cover growth submodel, the time step for each rule was not initially known. To determine the rate of urban land-cover change in the RSim study area and to calibrate the urban growth submodel, historical changes to the urban pixels of the land cover were analyzed along with changes in the population of the region. This analysis is based on the premise that urban land-cover classes derived from remote sensing data are directly related to the population of a region. Population trends in the study region. Analyses involving human demographic and economic characteristics such as population, employment in various sectors, market value of commodities, income, and commuting patterns were done for each of the five counties in the study region for different years. The major population growth trends within the five counties are presented in Figure 2. As can be seen from the graph, the growth trends are vastly different among the counties. Harris has the fastest growth rate; whereas Chattahoochee has experienced a decline in growth. Figure 2. Population trends in the RSim region (Source U. S. Census Bureau). In addition to the general population trends in the region, certain demographic variables such as population in urban and rural areas were analyzed for the years 1990 and 2000 (Table 1). We hypothesize that the classification of population as occurring within urbanized, urban, and rural areas may be related to high intensity and low intensity urban land-cover classes.

5 Table and 2000 Urban and Rural Population by (Source U. S. Census Bureau) Urban: Inside urbanized areas Inside urban clusters Rural: Farm Nonfarm Chattahoochee Harris Marion Muscogee Talbot % 79% 4% 3% 0% 0% 97% 97% 2% 0% 86% 79% 0% 0% 0% 0% 97% 97% 0% 0% 0% 0% 4% 3% 0% 0% 0% 0% 2% 0% 14% 21% 96% 97% 100% 100% 3% 3% 98% 100% 0% 0% 3% 1% 7% 4% 0% 0% 5% 6% 14% 21% 93% 96% 93% 96% 3% 3% 93% 94% Historical urban land-cover change. To understand past trends of urban land-cover change in the study region, land-cover maps of 1990 and 1998 were compared to identify changes between the two time periods. These two years of data were used since their time frames are similar to the 10-year census time period for which human demographic data us available. A comparison of the urban land cover from both the data sets is presented in table 2. Table 2. Urban land cover in 1990 and 1998 land-cover data (Source Land-cover maps from Georgia Department of Natural Resources and Natural Resources Spatial Analysis Laboratory, University of Georgia). Region Low density urban area in 1990 (Hectares) High density urban area in 1990 (Hectares) Low intensity urban area in 1998 (Hectares) High intensity urban area in 1998 (Hectares) Five-county region Chattahoochee county Harris county Marion county Muscogee county Talbot county Comparison and correlation of land-cover change and population trends. Based on analysis of population growth and urban land-cover change in the study region, several methods of deriving the calibration for rules for changes in urban land cover in RSim were attempted. For example, demographic variables such as population within urban and rural areas were compared with the high intensity and low intensity urban land covers, respectively. However, because of the differences in the definition of each of these categories and because of their inherent contrast of one category being a land use and the other a land cover, such a comparison could not be made. Land cover is a description of the status of the vegetation at a site (e.g., forest or bare ground). Land use refers to the management regimes humans impose (e.g., clearcut or pasture). In the land-cover data, the term urban is not the same as that referred by the census data. The census definition of urban includes population density and number. However the land-cover data refers to the reflectance of buildings as a function of urban nature. This difference makes it difficult to directly relate land cover and census information. For example in Marion, the 1990 and 2000 census data documents no urban population and only rural (predominantly non-farm) population (Table 1).

6 However the land-cover data suggests the presence of low and high density urban areas (Table 2). Hence in this case, non-farm rural could not be equated to only low density urban areas. Our hypothesis of equating low intensity urban land cover to rural population and high intensity urban land cover to urban population does not apply here. As an alternative approach, a relationship between the population and urban land cover as a whole was established. Area of urban land-cover area was assumed to be proportional to the population in the region. The RSim urban growth model was run from 1990 for 10 iterations. Each step in the iteration showed an increase in the number or urban pixels. Using the ratio between the population in 1990 (census data) and the urban area in 1990 as a base, the population was estimated for each time step. This analysis is shown in table 3. Comparing the growth in population from 1990 to 2000, with the growth in population from 1990 to the first time step gives an estimate of the number of years for one step (Table 4). In this case, one time step corresponded to approximately 11.7 years. However such a rate of growth was found to be very high for the region. Hence the growth rules were calibrated such that one model run corresponds to approximately one year time step. Table 3. Population estimation based on 1990 urban area and population and as projected by RSim over ten iterations Area of Urban land cover (Hectares) (original from 1990 land cover and the others are projected by RSim) Human Population (original population from U.S. Census Bureau and the others are projected by RSim) Source of land cover information Original 1990 land cover RSim Time step RSim Time step RSim Time step RSim Time step RSim Time step RSim Time step RSim Time step RSim Time step RSim Time step RSim Time step Table 4. Information used for initial time step calculation Change in population Number of years 1990 to 2000 (Source - U.S. Census Bureau) Years 1990 to time step 1 (as projected by RSim) NON URBAN LAND-COVER CHANGE In addition to considering urban growth, land-cover change includes non-urban change, i.e., change in forests, cropland, barren area, and so on. In order to incorporate the growth and changes that may happen in non-urban landcover types, an analysis of past growth trends helped to set specific growth patterns and trends for the future. This approach is based on the assumption that growth trends remain constant over the years of analysis and over the spatial area being considered. Since forest management activities are different within Fort Benning and the surrounding private lands, the transition rules were calculated only for regions outside Fort Benning. The land inside the Fort Benning military reservation is maintained for training exercises (Fort Benning, 2001; Kress, 2001)

7 Land-cover trend was determined by using change detection procedures in ArcGIS 9.0 that helped in identifying changes from one land-cover type to another. Land-cover maps for 1992 and 2001 created by National Land Cover Data (NLCD) project (Vogelmann et al., 2001) were used for the analysis. The 2001 data set covers only the northern part of the RSim study region. The data for the remaining regions is yet to be released. Hence currently, the changes observed in the northern portion are assumed to be representative of changes in all areas outside Fort Benning in the five-county study region. In spite of the availability of a number of other land-cover maps, these data sets were used since they were created by the same organization and had identical land-cover class definitions. Different classes make it difficult to perform any change detection. Changes to and from urban classes were not considered in the results since they are considered using the previously described growth rules. In addition, it was assumed there are no changes in land covers in categories of open water, beaches, utility swaths, quarries/strip mines, and golf courses. The change detection results from 1992 to 2001 were used to develop a matrix of probabilities of change for one land-cover class to change to another class in one year (annual change). Based on the land-cover changes happening over the nine-year period, the annual rate of change was derived. These changes were incorporated into a transition matrix from which the annual probabilities of change in land cover for the regions outside Fort Benning were derived (Table 5). Table 5. Annual probabilities of change for the five-county region outside Fort Benning calculated from changes that occurred in the region from 1992 to 2001 Deciduous Evergreen Mixed Clearcut Pasture Row crops Forested wetland Deciduous Evergreen Mixed Clearcut Pastures Row crops Forested wetland RSim is designed to allow user interaction during its runs. In the land-cover change module, the user is able to change the probabilities of change listed in table 5. However under a normal scenario the changes from one land cover is restricted to a maximum of 10% of the original land cover. However to allow for extreme events, there is an option to allow for changes of up to 75%. For example, a widespread and long drought may force most of the row crops into clear cut regions or pastures. RSim allows for the modeling of such hypothetical events. DISCUSSION AND RESULTS An example of the application of the land-cover growth rules is presented in Figures 3 and 4. Figure 3, which is a map of the Columbus region, represents the original 1998 land cover. Figure 4 represents the land cover as observed after four RSim iterations. RSim projects a 4% increase in low-intensity urban land cover and a small increase of 0.7% for high-intensity urban land cover after four iterations. Deciduous forests show a 4.7% increase and evergreen forests increase by 6.7%. From figure 4, it can be seen that the changes in land cover are random and create a salt and pepper look to the landscape of the region. However, this spatial projection may not be the case on the ground. Changes will likely occur along edges and along regions of transition. Hence to avoid changes in homogeneous patches (that maybe unlikely in the real world), the changes will be restricted to pixels that are in contact with at least one different land-cover pixel. Applying this constrain restricts changes to the edge of clusters. This constaint has not yet been tested in RSim.

8 Figure landcover. Reno, Nevada May 1-5, 2006

9 Figure 4. Projected land cover after four time steps. The land-cover rules developed in this study improve the ability to understand the implications of growth and development on a landscape level. However, these rules are not interpretable at a fine level of analysis. For example, the non-urban growth rules are applied in a semi constrained random way. But on the ground, land-cover change is not random. Factors such as climate, adjacency to other land-cover classes, land use, and socio-economic factors play an important role. Such effects have not been modeled in RSim. The land-cover rules in this study have been calibrated or derived from only 10 years of data. In many cases, 10 years may not be an ideal representative of the long-term growth trends of a region, but in the current study region changes on trends are rapid and ongoing. Furthermore, most of the available data for the period before 1990 had different land-cover classification schemes, which made it impossible to compare to current land cover. Also, as seen with the urban land-cover class definitions, each classification scheme is unique in describing what they classify. Comparison of such inconsistent land-cover classes gives erroneous results. Availability of consistent and well documented early time period datasets may have enabled a longer time period analysis and a different understanding of the growth trends in the study region. In spite of the above-mentioned shortcomings, the land-cover growth rules developed in this study helps project potential changes to land at a regional level. Such a scale of analysis is useful for understanding changes that may occur in several other ecological systems (Dale et al., 2006). These systems can be interpreted at broad scales (such as county level or watershed level). These results can be used for planning purposes and also to understand implications of changes in the landscape. Reno, Nevada May 1-5, 2006

10 ACKNOWLEDGEMENTS The project was funded by a contract from the Strategic Environmental Research and Development Program (SERDP) projects CS-1259 to Oak Ridge National Laboratory. Oak Ridge National Laboratory is managed by the UT-Battelle, LLC, for the U.S. Department of Energy under contract DE-AC05-00OR REFERENCES Baskaran, L.M., V.H. Dale, R. A. Efroymson, and W. Birkhead. (In press). Habitat modeling within a regional context: An example using Gopher Tortoise. American Midland Naturalist. Candau. J. C. Temporal calibration sensitivity of the SLEUTH urban growth model. (2002). M.A. Thesis. University of California, Santa Barbara. Clarke, K. C., L. Gaydos,and S. Hoppen. (1997). A self-modifying cellular automaton model of historical urbanization in the San Francisco Bay area. Environment and Planning, 24: Clarke, K. C. and L. J. Gaydos. (1998). Loose-coupling a cellular automation model and GIS: long-term urban growth prediction for San Francisco and Washington/Baltimore. Geographical Information Science, 12(7): Dale, V. H., M. Aldridge, T. Arthur, L. Baskaran, M. Berry, M. Chang, R. Efroymson, C. Garten, C. Stewart and R. Washington-Allen. (2006). Bioregional Planning in Central Georgia, A special issue on The Future of Bioregions and Bioregional Planning in the journal Futures. Efroymson, R. A., V. H. Dale, L. M. Baskaran, M. Chang, M. Aldridge, and M. Berry. (2005). Planning transboundary ecological risk assessments at military installations. Human and Ecological Risk Assessment. 11: Fort Benning Army Installation (2001). Integrated Natural Resources Management Plan (INRMP) Kress, M. R. (2001). Long-term monitoring program, Fort Benning, GA; Ecosystem Characterization and Monitoring Initiative, Version 2.1. Report ERDC/EL TR-01-15, Strategic Environmental Research and Development Program. Meyer, W. B., and B. L. Turbner II. (1992). Human population growth and global land-use/cover change. U.S. Census Bureau; "Glossary of Decennial Census Terms and Acronyms;" < (accessed: 17 June 2005). Vogelmann, J.E., S.M. Howard, L. Yang, C.R. Larson, B.K. Wylie, N. Van Driel. (2001). Completion of the 1990s National Land Cover Data Set for the Conterminous United States from Landsat Thematic Mapper Data and Ancillary Data Sources. Photogrammetric Engineering and Remote Sensing, 67: Yang, X., and C. P. Lo. (2003). Modelling urban growth and landscape changes in the Atlanta metropolitan area. International Journal of Geographical Information Science, 17: