Drivers of Land-Use/Land-Cover Changes and Dynamic ... · Drivers of Land-Use/Land-Cover Changes...

Drivers of Land-Use/Land-Cover Changes and Dynamic Modeling for the

Atlanta, Georgia Metropolitan Area C.P. Lo and Xiaojun Yang

Abstract Landsat images and census data were integrated in a zone-based cellular approach to analyze the drivers of land-uselland-cover changes in Atlanta, Georgia, a postmodern metropolis. Land- uselland-cover statistics, which were extracted from Landsat MSS, TM, and ETM+ images for 1973,1979,1987,1993, and 1999 for the 2 3 metro counties of the Atlanta metropolitan area, revealed rapid increases in high-density and low-density urban use at the expense of cropland and forests during this period of rapid population growth. To understand the underlying causes of all these changes, demographic and socio-economic data from the censuses were integrated with the land-uselland-cover change data and location data. A total of 17 themes comprising 78 variables were used in the analysis at three different spatial levels: the whole metropolis, county, and census tract, all unified at the 60-meter grid-cell level. It was found that proximity to highways, nodes, and shopping malls tended to promote urban development in Atlanta, and the increasing affluence of the population has induced rapid suburbanization, with con- sequent adverse impact on the greenness and fragmentation of the environment in recent years. The results of the driving force analysis were incorporated into a dynamic model, namely, cellular automaton, at the census tract level, which simulated the land-uselland-cover change of Atlanta from 1999 to 2050. It predicted the continued growth of edge cities and the loss of forest, if unchecked, within a time span of 10 to 20 years. The limitations of the cellular automaton model as applied to Atlanta were also discussed.

Introduction For the past three decades, the city of Atlanta, Georgia has expe- rienced very rapid growth both in terms of population and spatial extent as it emerged to become the premier commercial, industrial, and transportation center of the southeast (Research Atlanta, 1993). Population has increased 27 percent from 1970 to 1980,33 percent from 1980 to 1990, and 40 percent from 1990 to 2000. The city expanded outward at the expense of crop and forest land. This has given rise to urban sprawl along highways radiating from the city center. Research on Atlanta's internal structure led to the formulation of the urban realms model to depict the multi-nuclei nature of the city in contrast to the usual single-core urban form of many Ameri- can cities (Hartshorn and Muller, 1989; Fujii and Hartshorn, 1995). Edge cities are formed at the intersection of an urban beltway and a hub-and-spoke lateral road (Garreau, 1991). All these are characteristics of the postmodern city (Dear, 2000). A

study of the driving force of land-uselland-cover changes of Atlanta will contribute to a better understanding of the processes of urban sprawl and their spatial consequences under postmodern urbanism.

The study of drivers of land-uselland-cover change in the past has focused primarily on biophysical variables, such as elevation, slope, or soil type. However, it has been increasingly realized that data on socio-economic drivers of change have to be incorporated (Veldkamp and Lambin, 2000). The integration of biophysical and socio-economic data has been hampered by the spatial unit problem because the relevant spatial units for biophysical processes are different from the spatial units of decision making, on which most socio-economic data are based (Martin, 1996).

In this paper, the drivers of land-uselland-cover changes in Atlanta are studied using Landsat Multispectral Scanner (MSS), Thematic Mapper (TM), and Enhanced Thematic Mapper Plus (ETM+) data for 1973,1979,1987,1993, and 1999 supple- mented by socio-economic data obtained from the United States Census Bureau for 1970,1980, and 1990 at three spatial levels: the whole Atlanta metropolitan area as delimited by the 13 urban counties, the urban counties, and census tracts, in view of the fact that drivers of land-use change are scale dependent (Veldkamp and Lambin, 2001). The findings are then employed in a process-based cellular automata (CA) modeling to simulate the urban growth of Atlanta up to the year 2050. CA modeling allows a spatially explicit display of land-use deci- sions made by planners. In applying to an urban environment, this research aims to demonstrate a new approach to drivers of land-uselland-cover change analysis, and the use of dynamic modeling in solving the spatial unit problem commonly encountered in data integration.

Land-Use/Land-Cover Change Detection from Landsat Data Three generations of Landsat data were used to extract land- uselland-cover information of Atlanta for 1973 (MSS), 1979 (MSS), 1987 (TM), 1993 (TM), and 1999 (ETM+) at roughly six- year intervals. The Landsat data have been preprocessed by geometric rectification and radiometric normalization (Yang and Lo, 2000). The MSS and TM data were resampled to a spatial resolution of 57 meters and 25 meters, respectively. A six-class land-uselland-cover classification scheme was adopted: (1) high-density urban use, (2) low-density urban use, (3) cultivatedlexposed land, (4) croplandlgrassland, (5) forest,

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C.P. Lo is with the Department of Geography, University of Georgia, Athens, GA 30602 ([email protected]).

X. Yang is with Environmental Studies, The University of West Florida, Pensacola, FL 32514 (xyang8uwf.edu).

PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING

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Photogrammetric Engineering & Remote Sensing Vol. 68, No. 10, October 2002, pp. 1073-1082.

0099-1112/02/681&1073$3.00/0 O 2002 American Society for Photogrammetry

and Remote Sensing

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TABLE 1. LANDUSE/LANDCOVER CLASSES AND DEFINITIONS -

No. Classes Definitions

High-Density Urban Use Approximately 80 to 100 percent construction materials, e.g., asphalt, concrete, etc.; typically commercial and industrial buildings with large open roofs as well as large open transportation facilities, e.g., large airports, parking lots, and multilane interstatelstate highways; with low percentage of residential development residing in the city cores.

Low-Density Urban Use Approximately 50 to 80 percent construction materials; often residential development including mostly singlelmultiple family houses and public rental housing estate as well as local roads and small open (transi- tional) space as can be always found in a residential area; with certain amount of vegetation cover (up to 20 percent).

Cultivated/Exposed Land Areas of sparse vegetation cover (less than 20 percent) that are likely to change or be converted to other uses in the near future; including clearcuts, all quarry areas, cultivated land without crops, and barren rock or sand along riverlstream beaches.

CroplandIGrassland Characterized by high percentages of grasses, other herbaceous vegetation, and crops; including lands that are regularly mowed for hay andlor grazed by livestock, golf courses and city parks, and regularly tilled and planted cropland.

Forest Including coniferous, deciduous, and mixed forests (90 to 100 percent). Water All areas of open water, generally with greater than 95 percent cover of water, including streams, rivers,

lakes, and reservoirs.

and (6) water. Table 1 provides definitions of these six classes of land-uselland-cover based on their image characteristics. An unsupervised image classification approach, known as ISO- DATA (Iterative Self-organizing Data Analysis) was adopted, which produced natural clusters of homogeneous pixels. The overall accuracies of classification for the five land-uselland- cover maps produced varied between 87 percent and 90 percent , while producer's and user's accuracies were well above 80 percent (Yang and Lo, 2002). These are acceptable accuracies. It is clearly revealed from the land-uselland-cover statistics extracted from these five maps that between 1973 and 1999 the areas for both the high-density urban use and low-density urban use have increased while those for cropland and forest land have declined (Table 2). The increase has been particu- larly impressive for the low-density urban use (hom 76,910 ha in 1973 to 282,959 ha in 1999, or an increase of about 268 percent in 26 years!) The low-density urban use consists mainly of suburban residential housing. As a result of the urban sprawl, almost 37 percent of cropland and 27 percent of forest land have been lost.

Method of Driving Force Analysis Data Preparation The land-uselland-cover changes in Atlanta as noted above occurred as a result of the interactions of a number of environmental as well as demographic, social, and economic forces. Therefore, integrating biophysical variables as extracted from the Landsat images for individual pixels in raster format with area unit based socio-economic data from the decennial censuses in vector format is needed. For this study, a zonal approach was adopted. A zonal file with the appropriate areal unit, in either vector or raster format, is produced. This file is

used to extract characteristics of each data layer zone by zone by means of cross-tabulation. Thus, a zone-based table was created in which each row is a zone identification number and each column is a theme or a data layer associated with each zone. Such a table can be easily analyzed using GIS built-in spatial analysis functionality or imported to a stand-alone statistical software package for more advanced analysis. In this study, two basic areal units were employed: counties and census tracts, so that the drivers of land-uselland-cover changes at two different spatial scales can be compared. One notable limitation of this approach is the modifiable areal unit problem (MAUP) (Openshaw, 1984; Green and Flowerdew, 1996).

A total of 17 data layers or themes were used in this analysis. They can be grouped into six major categories: (1) administrativelstatistical boundaries, (2) land-uselland-cover data, (3) landscape ecological measures, (4) topographic measures, (5) population and income, and (6) location measures. The extraction of each specific data layer under each category is briefly explained below.

AdministrativelStatistical Boundaries The Atlanta metropolitan area under study is covered by 13 counties whose boundaries can be extracted from the 1992 TIGER line files. The boundaries of 339 census tracts for 1980 were extracted from the 1980 Census database published by GeoLytics, Inc. The boundaries of 444 census tracts for 1990 were extracted from the 1995 TIGER street centerline files. All these boundary data have a nominal scale of 1:100,000. These are vector data and have been converted to raster data with a resolution of 60 meters, which is compatible with the spatial resolution of Landsat MSS data used in land-uselland-cover mapping.

TABLE 2. ~ANDUSE/LANDCOVER STATISTICS FOR THIRTEEN METRO COUNTIES IN ATLANTA: 1973-1999

1973 1979 1987 1993 1999

No Land-UseILand-Cover Area (ha) % Area (ha) % Area (ha) % Area (ha) % Area (ha) %

1 High-density urban 29722 2.85 38015 3.64 54280 5.2 67633 6.48 87477 8.3 2 Low-density urban 76910 7.36 129174 12.37 177825 17.03 214484 20.54 282959 27.1 3 Cultivated/exposed land 14534 1.39 20595 1.97 15511 1.49 21132 2.02 5358 0.51 4 Croplandlgrassland 159345 15.26 117365 11.24 117686 11.27 96700 9.26 101122 9.68 5 Forest land 750366 71.85 724967 69.42 663673 63.55 625984 59.94 545148 52.2 6 Water 13404 1.28 14166 1.36 15306 1.47 18348 1.76 22217 2.13 In Total 1044281 100 1044281 100 1044281 100 1044281 100 1044281 100


Land-UselLand-Cover Data Only five classes of land-uselland-cover, namely, high-density urban use, low-density urban use, cropland/grassland, forest, and water for 1973,1987, and 1999 were used for this analysis. Each land-uselland-cover class was converted to a binary mask of 0 (for the background) and 1 (for the specific land-uselland- cover type). This has resulted in 15 layers of data. The land- uselland-cover data layers have also been resampled to a spatial resolution of 60 meters.

Landscape Ecological Measures TWO indices were computed: (a) greenness and (b) fragmentation. Greenness was extracted from the visible and near-infrared bands of the Landsat images, using the Tasseled Cap Transformation (TCT). The second component of TCT is greenness. The formulas used for this computation were (Crist and Kauth, 1986; Jensen, 1995):

For Landsat Mss data:

Greenness = -0.2830"bl - 0.6600kb2 + 0.5770*b3

For Landsat TM and E m + data:

Greenness = -0.2848*bl - 0.2435*b2 - 0.5436*b3

where bl, b2, ..., b7 are the band numbers. It should be noted that, for the Landsat TM and ETM+ data, the thermal infrared band (b6) was not used in the computation of greenness.

Fragmentation measures the tendency of the land cover to break up into many small patches, which can affect species diversity and density of animal species. It is therefore a measure of ecological quality of a habitat. The following formula was used to compute fragmentation using a kernel-based approach (Monmonier, 1974):

Fragmentation = (c - l)l(k - 1) (3)

where cis the number of different land-uselland-cover classes present in the kernel, and kis the number of cells considered. A 7 by 7 kernel was used for this computation so that k equaled 49.

Greenness and fragmentation were calculated for all the three years of study at the spatial resolution of 60 meters.

Topographic Measures Two topographic measures, namely, terrain elevation and slope, were obtained from the USGS 7.5-minute Digital Elevation Model (DEM). The 159 DEMs that cover the study area were mosaicked to form a single image with a 30-meter pixel size. The resulting DEM mosaic exhibited some discontinuities. To produce a seamless mosaic, a 5 by 5 median filter was applied and a mask image was created in which the discontinuities were filled with the median values of the surrounding pixels. This mask image was overlaid back to the areas of the original DEM mosaic where the discontinuities occurred. The improved DEM mosaic was then resampled to a spatial resolution of 60 meters. Each pixel of the DEM gives the elevation of the terrain in feet. A terrain slope (in percent) was also calculated using the built-in function of ERDAS Imagine (ERDAS, 1997) . Population and Income Population and income at county and census tract levels were extracted using different approaches. At the county level, census data for 1970,1980, and 1990 and population estimates for 2000 were obtained from the U.S. Bureau of Census. The population and income for the three study years for each of the 13

counties, 1973,1987, and 1999, were interpolated assuming that population and per capita income increased at the same rate during a decade in a specific county. Thus, for the estimated 1987 population in a county, the following sequence of equations was used:

where pop80 and pop90 are the known total population for 1980 and 1990, respectively, in the county, and xis the annual rate of population increase, which can be computed using the following formula:

where In is natural logarithm and e is the mathematical con- stant, namely, 2.718281828459. Once xis known, the total population in 1987 (pop87) for the county was estimated as

The per capita income for the 13 counties (pci) was computed using the following formula:

13

2 (pcii * popj) pci = '=I

POP

where i denotes each of the 13 counties. The computation of population and per capita income for

the three different years at census tract level is much more complicated than that at the county level because of data unavailability and the changes in census tract number and boundaries through time. The population and income at the census tract level are available for the census years of 1970, 1980, and 1990. However, the 1970 data were not used because of the large number of census tract boundary changes caused by highway construction and the poor compatibility to the 1980 and 1990 data. Using the 1980 and 1990 data to estimate population and income for 1973,1987, and 1999 is not easy because of differences in the number of census tracts and their boundaries. It is important that the same number and shape of census tracts should be used to ensure comparability of the results of analysis. While those tracts in 1980 that have been split in 1990 can be re-combined, newly created census tracts cannot be restored to be compatible with those in 1980.

A dasymetric mapping approach based on the work of Langford et al. (1991) and Langford and Unwin (1994) was adopted to harmonize the data with the spatial units used. The 1980 census tract vector layer was converted to two raster layers at a 60-meter spatial resolution, one showing population and the other per capita income per pixel. The population and per capita income for each tract were re-distributed according to the spatial pattern of low-density urban use (which repre- sents residential use) of the land-uselland-cover map for 1979, the closest year to 1980. Pixels in other types of land uselland cover were assigned a value of zero. A population density layer was obtained by excluding the zero pixels for each tract in the calculation. By overlaying the 1990 census tracts layer over the 1980 adjusted population and income layers, the 1980 data were made to conform to the boundaries of the 1990 census tracts.

To estimate the population and per capita income for 1973, 1987, and 1999 at the census tract level, the increase rates for population (x,) and per capita income (xi) for each tract between 1980 and 1990 were calculatedusing an equation similar in form to that of Equation 5. Based on the statistics for the

PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING October 2002 1075

entire study area, it was found that the annual increase rate of population tended to rise during 1970-1990, but to slow down after 1990, while the increase rate of per capita income tended to be lower during the past three decades. Adjustments to the yearly increase rates for the 1970s and 1990s were needed, which involved multiplying scaling factors to the population and income increase rates, as shown in the following equations:

where tpop87, tpop73, and tpop99 are estimated population for 1987,1973, and 1999 by census tracts; tpop80 and tpop90 are census population for 1980 and 1990 by census tracts; tpci73, tpci87, and tpci99 are estimated per capita income for 1987, 1973,1999 by census tracts; and tpci8O and tpci90 are per capita income from the 1980 and 1990 census by tracts.

Location Measures Four location measures, namely, urban centerproximity, highway proximity node point proximity, and shopping mall proximity, were generated. They were used only in the analysis at the census tract level. Inside the Atlanta metropolitan area, there are urban centers with commercial, retailing, and service activities. There were a total of 133 such urban centers of varying sizes within the 13 counties of Atlanta in 1990. Because they are not equal in importance, buffers of varying radii were created based on the product of their population and areal extent. These buffer rings of urban centers were converted into a binary image with 1 for the buffers and 0 as the background. Highways and roads have played a very important role in the formation of edge cities, the spatial expression of urban sprawl. A hierarchy of highways and roads can be identified in Atlanta: limited-access divided highways, duty highways, and local neighborhood roads. The AND Global Highway Database was acquired to extract these highways and roads for the analysis, which were then updated with satellite images to form highway layers for the three years of study (1973,1987, and 1999). The relative importance of highways was determined by the traffic volume and highway classification. Variable width buffers were then created around the highways to reflect their relative importance. A binary image was created with 1 for the area close to highways and 0 for the background. Node point proximity is represented by highway exits, junctions, or towns where major highways run across, which are favored sites for commercial and industrial activities to locate. These node point data were extracted from the 1998 AND Global Highway Database. Similar to the other two proximity measures described above, a weighting system was assigned to create radius-variable buffer rings around these node points, from which a binary mask image of 1 as the area close to node points and 0 as the background was formed. Finally, shopping mall proximity suggests accessibility to a large population with substantial buying power. The sizes of the shopping malls were used to determine the radii of the buffer rings around them.

Three layers of shopping mall proximity were created by dig- itizing the large shopping mall polygons on-screen from the 1973,1987, and 1999 Landsat images. They were also converted into binary images with 1 as the area close to the malls and 0 as the background.

Statistical Analysis of RelatIonshIps The data created basically consist of landscape metrics as well as demographic, economic, and topographic measures at three different spatial levels of aggregation: (1) the overall Atlanta metropolitan area, (2) 13 counties, and (3) 444 census tracts. They were all subjected to statistical analysis with the objective of discovering the interrelationships among these variables, and hence explanations for the driving forces and characteristics of land-uselland-cover changes.

For the entire Atlanta metropolitan area, simple visual analysis is adequate to reveal trends with three years of data with 18 variables for one single observation (Table 3). At the county level, there are 13 observations for 66 variables. At the census tract level, there are 444 observations for 60 variables. These variables include population densities, population density changes, per capita income, and per capita income changes; mean elevation, mean slope, percentages of county in urban center, road, node, and shopping mall buffers; proportions in high-density urban use, low-density urban use, cropland, forest, and water as well as their changes over time; mean greenness values, mean landscape fragmentation and their changes over time, all for the three years of 1973,1987, and 1999. Simple correlation analysis was used, in which Pearson correlation coefficients were calculated for pairs of the variables in order to determine if an association exists as well as the magnitude and direction of the significant association. The correlation coefficients are deemed statistically significant at the 0.01 level (2-tailed) or 0.05 level (2-tailed), based on the p value, the probability that a statistical result as extreme as the one observed would occur if the null hypotheses were true (Norusis, 1998). If the observed significance level is less than 0.05 or 0.01, the null hypothesis of no association is rejected. After the analysis, it was found that there was a larger number of correlation coefficients that were more statistically significant at the census tract level than at the county level, clearly supporting the impact of MAUP mentioned previously.

For the data at the census tract level, a multivariate regression was also conducted to examine the relationship between landscape metric proportions (and their changes) as dependent variables with a group of independent variables. The stepwise variable selection was employed during the regression in order to determine which variable to include in the multivariate regression. All variables to be entered must pass the tolerance criterion specified as the probability of F (the square oft value) less than 0.050. A variable was also not entered if it would cause the tolerance of another variable already in the regression model to drop below the tolerance criterion, specified as the probability of F larger than 0.100. All entered variables with tolerance larger than the specified level were removed from the model. A total of 35 multiple regression models was computed, which relate land-uselland-cover types, landscape indices, as well as demographic and socio-economic variables together. The R2 (coefficient of determination) calculated for each model indicates the proportion of variability of the dependent variable accounted for by the regression model. It was adjusted to account for the complexity of the regression model relative to the complexity of the data. Another parame- ter, tolerance, was also obtained to measure the strength of the linear relationships among the independent variables. The multiple regression models revealed large tolerance values, indicating that there is little evidence of multicollinearity among independent variables.

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TABLE 3. LANDSCAPE METR~CS AND DEMOGRAPHIC, ECONOMIC, AND TOPOGRAPHIC MEASURES

1973-1987 1987-1999

Items 1973 1987 1999 change in percent change in percent

land-uselland-cover high-density urban 2.846 5.198 8.377 2.352 82.626 3.179 61.159 proportion (%) low-density urban 7.365 17.028 27.096 9.664 131.212 10.068 59.122

cultivated/exposed land 1.392 1.485 0.513 0.094 6.722 -0.972 -65.457 croplandlgrassland 15.259 11.270 9.683 -3.989 -26.144 -1.586 -14.075 forest 71.855 63.553 52.203 -8.302 -11.553 -11.350 -17.859 water 1.284 1.466 2.127 -0.182 14.190 0.662 45.152

mean greenness 27.733 24.867 16.983 -2.866 -10.334 -7.884 -31.705 mean fragmentation index (X20000) 8.731 10.250 10.339 1.519 17.401 0.089 0.869 per capita income (X1000) 5.509 18.368 29.244 12.859 233.413 10.877 59.216 population density (per hectare) 1.614 2.331 3.159 0.717 44.404 0.828 35.543 mean elevation high-density urban 9.578 9.626 9.663 0.048 0.504 0.037 0.381

(100 feet) low-density urban 9.516 9.484 9.432 -0.032 -0.340 -0.052 -0.549 croplandlgrassland 9.653 9.563 9.463 -0.090 -0.931 -0.010 -1.041 forest 9.469 9.494 9.525 0.025 0.263 0.031 0.331

mean slope high-density urban 4.862 4.986 5.557 0.124 2.550 0.571 11.452 (percent) low-density urban 5.509 5.812 6.220 0.303 5.500 0.408 7.020

croplandlgrassland 5.081 5.327 5.277 0.246 4.842 -0.050 -0.939 forest 8.095 8.303 8.542 0.208 2.569 0.239 2.878

Results of Analysis Drivlng Forces of Land-Use/LanbCover Changes For the Atlanta metropolitan area as a whole, Table 3 reveals that the mean landscape fragmentation index and the proportions of high-density urban, low-density urban, and water uses have increased since 1973, while the mean greenness and the proportions of croplandlgrassland and forest have decreased. At the same time, population density and per capita income have rapidly increased, thus suggesting their impact on the land-uselland-cover changes and the landscape characteristics. It is also revealed from Table 3 that the mean elevation for high- density urban use and forest has increased from 1973 to 1999, while that for low-density urban use and cropland/grassland has decreased during the same period. The implication is that high-density urban use tended to develop on land with higher elevations, while low-density urban use occupied land with lower elevations. Much of the urban development occurred in forested areas at lower elevations, and as a result, only forest land at higher elevations has not been touched. For all these urban developments, land with increasingly steeper slopes was used.

At finer spatial levels, the results of analysis are much more complicated as the land-uselland-cover changes and demographic, economic, and terrain characteristics have become more differentiated. The results of analysis for four types of land-uselland-cover, namely, high-density urban use, low-density urban use, cropland/grassland, and forest, are discussed below at the county and census tract levels. All indicated coefficients of correlation in the discussion are statistically significant at either the 0.01 or 0.05 level, as explained above.

High-Density Urban Use At the county level, the proportions of high-density urban use for 1973,1987, and 1999 were positively correlated with population density (0.92,0.93, and 0.92), urban center proximity (0.74,0.73, and 0.75), node proximity (0.77,0,72, and 0.74), mall proximity (0.68,0.73, and 0.73), and highway proximity (0.66,0.58, and 0.60). The change in the high-density urban use proportion from 1973 and 1987 was positively correlated with population density change (0.66). Income and terrain factors did not exhibit significant statistical relationship with either the high-density urban use proportions or their changes through time.


At the census tract level, more subtle relationships were revealed. The proportions of high-density urban use for 1973, 1987, and 1999 correlatedpositively with road proximity (0.53, 0.59, and 0.56), node proximity (0.5 1,0.5 7, and 0.54), urban center proximity (0.26, 0.26, and 0.411, population density (0.24,0.26, and 0.41), and mean elevation (0.13,0.15, and 0.16), but negatively with mean slope gradient (-0.39, -0.42, and (-0.42). The change in high-density urban use proportion fsom 1973 to 1999 correlated positively with road proximity (0.38), node proximity (0.36), urban center proximity (0.26), mean elevation (0.14), and mall proximity (0.10), but negatively with mean slope (-0.30). It appeared that, at the county level, population was more significant than were location factors in explaining high-density urban use change. However, at the census tract level, location and tenrain conditions were clearly more significant than was population in explaining the high-density urban use change over time. This is because commercial and industrial developments (the two major compo- nents of high-density urban use) were attracted to highways, node points, and urban centers as revealed at the much finer census tract level than that of county.

Low-Density Urban Use At the county level, the proportion of low-density urban use (which basically is residential use) for 1973,1987, and 1999 correlated positively with population density (0.90,0.86, and 0.72). Other variables that correlated positively with low-density urban use proportion for at least one year included urban center proximity (1973 and 1987), node proximity (1973 and 1987), mall proximity (1973 and 1987), highway proximity (1973), and per capita income (1973). The mean slope gradient was found to correlate negatively with the proportion of low- density urban use in 1987 and 1999. The change in low-density urban use proportion from 1973 to 1999 correlated negatively with urban center proximity (-0.69), mall proximity (-0.68), node proximity (-0.66), and highway proximity (-0.60).

At the finer census tract level, the proportion of low-density urban use for 1973,1987, and 1999 correlated positively with population density (0.79,0.83, and 0.54), node proximity (0.64,0.51, and 0.281, and highway proximity (0.60,0.41, and 0.15), but negatively with mean slope gradient (-0.43, -0.33, and -0.24). Other variables that correlated positively with the low-density urban use proportion for at least one year included per capita income (1973 and 1987) and mall proximity (1987 and 1999). The change in the proportion of low-density urban

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use from 1973 to 1999 correlated positively with population density change (0.53), but negatively with road proximity (-0.41) and node proximity (-0.37). Thus, population has been the most important factor for explaining changes in the proportion of low-density urban use at both the county and census tract levels. At the census tract level, it is very clear that highway proximity and node proximity were conducive to the rapid growth of low-density urban use, manifested by suburban housing. However, as more suburban housing has been built, its correlation with the location measures became less important, thus explaining the decreasing value of the correlation coefficient with time as well as the negative correlation between the proportion of 1973 to 1999 low-density urban use change and these location measures. It is also interesting to note that the proportion of low-density urban use correlated positively with per capita income for the years 1973 (0.62) and 1987 (0.16) only. The weakening correlation in more recent years indicated that in the beginning suburban housing was affordable only to people with higher income, but in recent years, as the general affluence of the population increased, more and more people could afford suburban housing.

CroplandlGrassland This category of land-uselland-cover includes grassland, which may be used for agricultural or recreational purposes, such as golf courses and public parks. At the county level, the proportions of croplandlgrassland for 1987 and 1999 correlated negatively with population density (-0.82 and -0.66), urban center proximity (-0.70 and -0.62), node proximity (-0.68 and -0.63), and mall proximity (-0.68 and -0.62). Changes in the proportion of this class of land-uselland-cover from 1973 to 1999 were negatively correlated with per capita income change (-0.64).

At the census tract level, the proportion of croplandlgrassland use for 1973,1987, and 1999 correlated negatively with population density (-0.32, -0.55, and -0.42), node proximity (-0.39, -0.59, and -0.46), highway proximity (-0.34, -0.53, and -0.41), and urban center proximity (-0.20, -0.38, and -0.35). The correlation with per capita income was negative in 1973 and 1987 (-0.36 and -0.341, but positive in 1999 (0.15). Other variables that correlated negatively with croplandlgrassland for at least one year's data included mean elevation (1973 and 1987) and mall proximity (1973 and 1987). The change in the proportion of croplandlgrassland from 1973 to 1999 correlated positively with road proximity (0.12) and node proximity (0.15), and was negatively correlated with mean elevation (-0.20). Thus, population and location appeared to be two significant variables associated negatively with the proportion of croplandlgrassland. Much of the loss of cropland/grassland in recent years occurred in places far away from major highways and node points as more land was acquired for suburban development. Another interesting observation is that per capita income correlated negatively with the proportion of cropland1 grassland for 1973 and 1987 (-0.36 and -0.34), but became positively correlated in 1999 (0.15). The implication is that, with increased affluence, people have more leisure time, thus explaining the increase in the areas of golf courses and public parks in Atlanta in recent years, while the agricultural use of the cropland/grassland has declined.

Forest Much of the suburban development took up forest land. Analy- ses conducted at the county level revealed that the proportion of forest land in 1973,1987, and 1999 correlated positively with mean slope gradient (0.60,0.69, and 0.71, respectively), thus indicating that forests are found at increasingly steeper gradi- ents as a result of the loss of forest land at the more accessible gentle slopes for housing development, Population density exhibited negative correlation with the proportion of forest

land for 1987 and 1999 (-0.76 and -0.69), another indication of urban encroachment in recent years. The change in forest- land area from 1973 to 1999 correlated positively with mean slope gradient (0.70).

At the census tract level, the proportion of forest land in 1973,1987, and 1999 correlated positively with mean slope gradient (0.54,0.57, and 0.59), but negatively with road proximity (-0.61, -0.64, and -0.57), node proximity (-0.59, -0.67, and -0.61), population density (-0.55, -0.59, and -0.60), and urban center proximity (-0.28, -0.38, and -0.36). In other words, forests are found at unfavorable sites for development. The change in forest-land area from 1973 to 1999 correlated positively with road proximity (0.35), node proximity (0.29), and mean elevation (0.19), but negatively with population density change (- 0.48), mall proximity (-0.251, and mean slope gradient (-0.21). The independent variables that explained about 44 percent of the variability in the forest class proportion change during the same period were population density change, mean elevation, and urban center proximity.

At the census tract level, population density and its change have been the most consistent variables associated negatively with the proportion of forest and its change. The correlation between forest class proportion and per capita income was negative in 1973 and 1987 (-0.66 and -0.23), but was positive in 1999 (0.29). This reflects the process of suburbanization. In earlier years, most of the affluent population concentrated not too far away from large urban centers with a sparse forest cover. Later, this group of people moved into the urban fringes for better environment with dense forest cover. Therefore, affluence tended to be associated with the density of forest cover.

Ecological Impacts of Land-Use/Land-Cover Changes Two landscape measures, namely, greenness and fragmentation, have been included in the analysis at the three spatial levels. As has been observed for the Atlanta metropolitan area as a whole, mean greenness has decreased while mean fragmentation index has increased since 1973. They are further examined at the county and census tract levels below.

Landscape Greenness At the county level, the mean greenness for 1987 only correlated negatively with population density (-0.81), urban center proximity (-0.68), node proximity (-0.67), mall proximity (-0.64), and per capita income (-0.63). At the census tract level, the mean greenness for 1973,1987, and 1999 correlated positively with mean slope gradient (0.21,0.42, and 0.50), but negatively with road proximity (-0.49, -0.62, and -0.57), node proximity -0.44, -0.62, and -0.57), population density (-0.26, -0.40, and -0.51), urban center proximity (-0.18, -0.34, and -0.31), and mean elevation (-0.11, -0.16, and -0.10). Those variables that explained 51 to 53 percent of the variance in the mean greenness of 1987 and 1999 were road proximity, mean slope gradient, mean elevation, population density, and per capita income. The change in the mean greenness from 1973 to 1999 correlated positively with mean slope gradient (0.13), but negatively with population density change (-0.16), highway proximity (-0.15), and node proximity (-0.14). Those variables that explained about 59 percent of the variance in the mean greenness change were mean slope gradient, population density change, mall proximity, urban center proximity, and road proximity. Essentially, population, highway proximity, node proximity, and urban center proximity have been negatively associated with mean greenness and its change. The decrease in greenness has worsened in recent years as a result of the increase in population. Much of the decrease in greenness occurred in gentle terrains close to major highways and nodes. It is interesting to note that the relationship between greenness and per capita income was negative in 1973

October 2002 PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING

and 1987, but positive in 1999. As a result, it is similar in inter- pretation to the relationship between the proportion of forest land cover and per capita income discussed above as the consequence of suburbanization.

Landscape Fragmentation At the county level, the mean fragmentation index for 1973, 1987, and 1999 correlated negatively with mean slope gradient for all three years and mean elevation for 1973 and 1987 only. The change in the mean fragmentation index from 1973 to 1999 correlated positively with mean elevation (0.72) and mean slope gradient (0.70). In other words, landscape fragmentation occurred mostly at sites with lower elevations and gentler slopes, which urban development prefers. At the census tract level, the mean fragmentation index correlated positively with population density in 1973 and 1987 (0.52 and 0.16). The change in mean fragmentation index from 1973 to 1999 correlated positively with mean slope gradient (0.39) and population density change (0.32), and negatively with node proximity (-0.64), highway proximity (-0.58), urban center proximity (-0.33), and mall proximity (-0.17). Variables that explained 34 percent of the variance of the mean fragementation index change were node proximity, mean elevation, urban center proximity, mall proximity, and population density change. The implication is clearly that, as population increased, the environment was changed by the construction of highways and malls, and, as a result, the landscape became more fragmented. The increased fragmentation of the landscape occurred in sites with steeper slopes, again an indication of the intensifying suburbanization.

The above analyses of land-uselland-cover changes and their ecological impacts in the Atlanta metropolitan area indicated the importance of spatial scale in affecting the results. Because a zone-based approach has been used to integrate satellite image data with socio-economic data from the census at a unified spatial resolution of 60 meters, analysis at the much finer census tract level makes more sense, and is statistically more significant. This has been proven by the fact that population density, per capita income, and location measures are more significant variables at the census tract level than at the county level in explaining the land-uselland-cover changes engen- dered by the suburbanization process, which expresses itself spatially in the formation of edge cities. The interactions of the socio-economic and location factors are clearly very important.

Modeling the Future Urban Growth and Landscape Changes Baslcs of Cellular Automaton Moddlng The driving force analysis suggested that gentle slopes, highways, nodes, and shopping malls generally promoted urban development in the Atlanta metropolitan area as a result of the great demand for suburban housing stimulated by economic development and population growth. It is therefore possible to simulate the future growth of Atlanta by using land-uselland- cover, highways, nodes, and terrain data. The cellular automaton (CA) model as developed by Clarke and Gaydos (1998) was selected for this simulation. The model is dynamic, scale independent, and future oriented. The behavior rules used to guide urban growth in the model consider not only the spatial proper- ties of neighboring cells but also existing urban spatial extent, transportation, and terrain slopes. These behavior rules have, therefore, realistically accounted for the driving forces in the formation of edge cities in a postmodern metropolis. The model can also modify itself if extensive growth or stagnation leads to aberrations from the linear normal growth development. The model can be verified through rigorous past-to-present calibration using historical land-uselland-cover data.

The five growth control ~arameters to be initiated in the model, whicgdetermine the ;umber of Monte Carlo iterations, are diffusion coefficient, breed coefficient, spread coefficient, slope resistance, and road gravity. These pa&meters must be determined with intensive model calibration in which each coefficient combination needs to be tested individually and the modeled result compared to historical urban and land-use1 land-cover data by suitable statistical methods. The urban growth computation is based on a set of transition rules as defined by the four types of growth: spontaneous, diffusive, organic, and road-influenced (Project Gigalopolis, 1999). Dur- ing the urban growth computation, a second level of growth rules, termed self modification, is invoked when the model's growth rate is larger or smaller than a critical number. In that case, the model will modify certain parameters to ensure that the growth rate is in the normal range.

Version 2.1 of the model permits uSGS Level I land-use/ land-cover transitions to be incorporated. Six themes of data were employed to run the model. These include (1) the urban extent, (2) the land-uselland-cover data, (3) highways with nodes and large shopping malls, (4) slopes computed from the U. S. Geological Survey 7.5-minute DEM data, (5) hillshaded images also computed from the USGS 7.5-minute DEM, and (6) excluded areas for urban development consisting of public lands, water bodies, and streams. Both the urban extent and land-uselland-cover were extracted from Landsat images for five years as previously explained. All the input data layers were then converted into a single raster format, namely, the ERDAS WIAGINE format. Under IMAGINE, all images were further resampled into a spatial resolution of 240 m, a choice determined by the limitation of computing resources at the time of conducting this research. The next step was to convert these images into an 8-bit GIF format required for input to the model. The model then went through three forms of calibration: coarse, fine, and final to determine the best values for the five growth control parameters mentioned above. Only four control years were used: 1979,1987,1993, and 1999.

Using the best values for the growth parameters, computer simulation of land-uselland-cover changes began, initially from 1974 to 1999. To minimize the error level in the simulation, 100 Monte Carlo computations were used. The simulated results were compared with the actual land-uselland-cover maps extracted from Landsat images, thus providing visual verification of the accuracy of the model calibration. Animated movies were also generated that allowed the general trend of urban development in Atlanta to be verified. By comparing the number of pixels of the modeled land-uselland-cover type that spatially matched the corresponding land-uselland-cover derived from the Landsat images, the simulation accuracy was poor for cultivatedlexposed land but best for forest and water. Urban use was about 40 percent accurate (Table 4). However, the overall accuracies varied from 61 percent to 74 percent. Simulation of future changes was continued for a time span from 2000 to 2050.

Results of Computer Slmulatlon: Atlanta's Gmwth to the Year 2050 To save space, only the results of the land-uselland-cover change simulation for 2010,2020,2030,2040, and 2050 are dis- played in Plate 1. The simulation assumed that suburbanization will continue unchanged as the factors for the growth of postmodern metropolis remain unchanged. The simulated urban area for 2050 will be 844,656 ha within the limit of the 13 metro counties of Atlanta (Table 5). The net increase in urban land between 1999 and 2050 will be 474,220 ha, or 25 ha per day, representing an increase of 128 percent for the entire period. As a result of such a dramatic growth, urban land will occupy about 81 percent of the total modeled land by 2050. The number of urban clusters will decrease from 19,815 in 1999 to 2,234 in 2050, while the average size of each cluster will

PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING October 2002 1079

TABLE 4. SPATIAL FIT OFTHE MODELED RESULTS WITH THE KNOWN IANDUSE/LANDCOVER IN DIFFERENT CONTROL YEARS*.

Land-UselLand-Cover

Control Years urban use croplandlgrassland cultivatedlexposed land forest water total

1979 modeled 8486 44468 5356 198101 4916 261327 spatial fit 3484 13057 958 159403 2804 179706 % correct 41.01 29.36 17.89 80.47 57.04 68.77




*All the values given in this table are pixel numbers, with the exception of percentage values. The pixel size is 240 meters.

increase from about 25 ha in 1999 to 586 ha in 2050. This means that smaller urban clusters will grow outward and join together to form much larger clusters. Ecologically, such a mas- sive growth of urban land will cause substantial change to the landscape. The average slope steepness for urban land will increase from 5.87 percent in 1999 to 8.29 percent in 2050, indicating that many forested areas will be converted into urban use. This is the nightmarish scenario of unchecked urban sprawl.

There are certain limitations of the CA models that should be noted. First, the urban use extent is underpredicted. A major cause is that the model has not considered other factors control- ling new development, such as urban or regional development

policies, human behaviors, tax, income, or zoning. Another rea- son is that the model emphasizes linear growth although, in the real world, non-linear urban growth is also quite common. Second, for application to a rapidly suburbanizing city such as Atlanta, the model's transition rules for new urban development may need to be adjusted. The urban growth model over- whelmingly favors the so-called "organic growth," or expansion from established urban cells to their surroundings. This growth pattern is generally true for the development of high-density urban uses such as commercial, industrial, and large transportation facilities. But for low-density urban uses dominated by residential, new developments tend to move away from existing urban facilities in search of a better living

LEGEND

Plrdlctrd u h l r s d land

C u w land

CroplandlOrauland

Fonrt land

m- The boundaty of 13 counties

is shown

Plate 1. Simulation of the spatial consequences of future urban growth and landscape change in the Atlanta metropolitan area for 2010, 2020, 2030, 2040, and 2050.

1 1080 Octobe r 2002 PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING

Urban Cropland/Grassland Cultivated/Exposed Forest Water Total

environment. In this sense, the other three types of urban growth, namely, spontaneous, diffusive, and road-influenced growth, should be more strongly represented, which will be the objective of the next stage of modeling research.

Because of the limitations of the model and the fluctua- tions in the world economy, it is unrealistic to believe that Atlanta will remain unchanged for a period of 50 years. Near- term simulation to 2010 and 2020, for a time span of 10 to 20 years, should be more realistic. Within the limit of the 13 metro counties of Atlanta, urban land will increase by about 44 percent from 1999 to 2010; and 26 percent from 2010 to 2020. For- est areas will decline by 26 percent from 1999 to 2010 and 29 percent from 2010 to 2020. All this will happen if urban sprawl continues to proceed in the Atlanta metropolitan area for the next 10 to 20 years.

Conclusions In this paper, a zone-based cellular approach to integrating satellite remote sensing data with demographic and socio-economic data from the census was adopted to discover the driving forces of urban development in Atlanta, Georgia, a postmodern metropolis that has seen rapid population growth in the past 30 years. Simple and multiple correlation analyses conducted revealed that population and site location characteristics were important in determining the land-uselland-cover changes for the three years of the study: 1973,1987, and 1999. Per capita income has shown a strong relationship with cropland and forest. Suburbanization, originally driven by the desire of the affluent group of population to live in a more spacious environment, has caused the loss of cropland and forests in the more accessible terrain, with the consequence that existing cropland and forests are found in steeper terrain in more remote areas. Both the high-density urban use and low-density urban use exhibited the importance of location and site characteristics in their development since 1973. The relationship between greenness and affluence, which changed from negative to positive correlation in more recent times was also revealed. All these relationships were much more significant at the census tract level, the appropriate spatial scale of discernable people- environment and people-people interactions for the city.

The driving force analysis indicates that for the past 30 years an increasingly affluent population has generated the demand for high quality housing based on such site characteristics as proximity to highways, nodes, and shopping malls, which become important factors promoting the growth of edge cites in Atlanta. Dynamic modeling by cellular automaton (a) has been found to be suitable in taking into account of these factors. The model has succeeded in simulating the land-use1 land-cover changes from 1999 to 2050, projecting the coalesc- ing of small urban clusters to form bigger ones, and within the limit of the 13 counties, there will no longer be any more suburban areas to develop. Such a scenario may not be realistic in the long range because the economy of the United States and the world will change. On the other hand, the near-term projec- tion of land-uselland-cover change to the years 2010 and 2020 may be realistic, which predicts the continuation of highway- driven suburbanization as population and per capita income continues to grow. We expect the continued decline of forest

land and cropland, and the landscape will become further fragmented at least up to 2010. The main limitation of the CA model is that not all forms of urban growth have been represented. Overall, this study has demonstrated the interplay among biophysical, location site, and socio-economic characteristics of the population in shaping the growth of Atlanta as a postmodern metropolis.

Acknowledgments The research reported in this paper has been supported by a NASA EOS Interdisciplinary Science (IDS) research grants NAS8- 97081 and NASA H-33023D awarded to C.P. Lo as well as an NSF Doctoral Dissertation Improvement Grant for Xiaojun Yang.

Clarke, K.C., and J. Gaydos, 1998. Loose-coupling a cellular automaton model and GIS: long-term urban growth prediction for San Fran- cisco and WashingtonIBaltimore, International Journal of Geo- graphic Information Science, 12(7):699-714.

Grist, E.P., and R.T. Kauth, 1986. The Tasseled Cap de-mystified, Photo- grammetric Engineering 6. Remote Sensing, 52(1):81-86.

Dear, M.J., 2000. The Postmodern Urban Condition, Blackwell Publish- ers, Oxford, United Kingdom, 352 p.

ERDAS, 1997. ERDASField Guide, ERDAS Inc., Atlanta, Georgia, 656 p. Fujii, T., and T. Hartshorn, 1995. The changing metropolitan structure

of Atlanta, Georgia: locations of functions and regional structure in a multinucleated urban area, Urban Geography, 16(8):680-707.

Garreau, J., 1991. Edge City: Life on the New Frontier, New York: Dou- bleday, New York, N.Y., 546 p.

Green, M., and R. Flowerdew, 1996. New evidence on the modifiable areal unit problem, Spatial Analysis: Modelling in a GIs Environ- ment, (P. Longley and M. Batty, editors), GeoInformation Interna- tional, Cambridge, United Kingdom, pp. 41-54.

Jensen, J.R., 1995. Introductory Digital Image Processing: A Remote Sensing Perspective, Prentice-Hall, Upper Saddle River, New Jer- sey, 316 p.

Langford, M., D.J. Maguire, and D.J. Unwin, 1991. The areal interpola- tion problem: Estimating population usin remote sensing in a GIS framework, Handling Geographical Information: Methodology and Potential Applications (Ian Masser and Michael Blakemore, editors), Longman Scientific and Technical, Burnt Mill, Harlow, United Kingdom, pp. 55-77.

Langford, M., and D.J. Unwin, 1994. Generating and mapping population density surfaces within a geographical information system, The Cartographic Journal, 31:21-26.

Martin, D., 1996. Geographic Information Systems: Socioeconomic Applications, Second Edition, Routledge, London, United King- dom, 210 p.

Monmonier, M.S., 1974. Measures of pattern complexity for choropleth maps, The American Cartogmpher, 1(2):159-169.

Norusis, M.J., 1998. SPSS 8.0 Guide to Data Analysis, Prentice Hall, Upper Saddle River, New Jersey, 563 p.

Openshaw, S., 1984. The Modifiable Areal Unit Problem, CATMOG 38, Geo Abstracts, Norwich, United Kingdom.

Project Gigdopolis, 1999. The Clarke Urban Growth Model (Version 2.11, National Center for Geographic Information and Analysis (NCGIA),

I PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING October 2002 1081

University of California, Santa Barbara, URL: http:llwww. Yang, X., and C.P. Lo, 2000. Relative radiometric normalization ncgia.ucsb.edulprojects/gig (date last accessed 10 June 2002). performance for change detection from multi-date satellite

Research Atlanta, Inc., 1993. The Dynamics of Change: An Analysis of images, Photogrammetric Engineering &+ Remote Sensing, Growth in Metropolitan Atlanta Over the Past lhro Decades, Policy 66(8):967-980.

Research Center, Georgia State University, Atlanta, Georgia, 82 p. - , 2002. Using a time series of satellite imagery to detect land Veldkamp, A,, and E.E Lambin, 2001. Predicting land-use change, use and land cover changes in the Atlanta, Georgia metropolitan

Agriculture, Ecosystems and Environment, 85:l-6. area, International Journal of Remote Sensing, 23(9):1775-1798.

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