Forest fragmentation as an economic indicator

Transcription

1 Landscape Ecology 15: , Kluwer Academic Publishers. Printed in the Netherlands. 171 Forest fragmentation as an economic indicator James D. Wickham 1, Robert V. O Neill 2 & K. Bruce Jones 3 1 U.S. Environmental Protection Agency, MD-56, Research Triangle Park, NC 27711, USA 2 Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA 3 U.S. Environmental Protection Agency, Las Vegas, NV 89119, USA ( author for correspondence, wickham.james@epamail.epa.gov) Received 16 June 1998; Revised 18 February 1999; Accepted 29 May 1999 Key words: economic geography, geographic information systems (GIS), land-cover change, land use modeling Abstract Despite concern over the ecological consequences of conversion of land from natural cover to anthropogenic uses, there are few studies that show a quantitative relationship between fragmentation and economic factors. For the southside economic region of Virginia, we generated a surface (map) of urbanization pressure by interpolation of population from a ring of cities surrounding the region. The interpolated map showed a geographic gradient of urbanization pressure or demand for land that increased from northwest to southeast. Estimates of forest fragmentation were moderately correlated with the geographic gradient of urbanization pressure. The fragmentation-urbanizationrelationship was corroborated by examining land-cover change against the urbanization map. The geographic gradient in land-cover change was strongly correlated with the urbanization pressure gradient. The correspondence between geographic gradients in land-cover change and urbanization pressure suggests that forest fragmentation will occur at a greater rate in the eastern portion of the southside economic region in the future. Introduction Loss of temperate forests is a growing concern because of the pace and magnitude of its conversion to other uses (Wilcove et al. 1986). Most ecological studies of forest fragmentation have been undertaken from the perspective of the impact of habitat loss on specific taxa (see Opdam 1991, Wickham et al. 1997). There have been fewer studies from the perspective of human factors that contribute to forest fragmentation (Wickham et al. 1999). Over the last decade there have been several studies focused on land use dynamics (Turner 1990; LaGro and DeGloria 1992; Bockstael 1996; Turner et al. 1996; Wear et al. 1998). For the most part, these studies have quantified temporal changes in land cover (e.g., forest to urban) from temporal remotely sensed data for areas about a single county in size. Change has been modeled as a function of physiographic (e.g., slope) and economic variables (population). These studies have identified the important variables that determine land-cover conversion, but have maintained a predominantly single county focus (except Bockstael et al. 1996). A consistent finding among all these studies is that land-cover change is inversely related to distance to urban centers. There tends to be more change closer to an urban center and less at areas distal to cities. These findings support models and theories from economic geography. One is Clark s (1951) exponential decay model of population density, which predicts that population (and economic activity) declines at a decreasing rate with distance from a city center. Another is retail gravity theory (see Carrothers 1956). Borrowed from Newtonian physics, retail gravity theory postulates that the magnitude of retail trade at any location between two urban centers is proportional to the size of the terminal economic centers and inversely proportional to distance (see also Shi et al. 1997). Both theories postulate that demand for land for human

2 172 use is proportional to mass (population) and inversely proportional to distance from centers of mass. As demand for land declines, so should the magnitude of land-cover change. Another consistent finding of these studies is that variables such as slope and distance to a major road are important to land-cover change studies. Economic geography models such as rent theory provide an explanation. Originally proposed by David Ricardo in the 1800s (see Berry et al. 1990; Sheppard and Barnes 1990), rent theory provides a cost-benefit framework for determining marginal profit returns as a function of land productivity, production and transportation costs, and distance from market. Presumably, land that is on steep slopes or distal from major roads would have higher production and transportation costs and/or lower productivity, and therefore less likely to be converted from a natural to anthropogenic land cover. The purpose of this paper is to determine if regional patterns of forest fragmentation can be quantitatively related to regional patterns of demand for land, and to corroborate these relationships using independent data. Over the last one hundred (or more) years, urbanization has created a demand for land that has a specific spatial pattern. This demand has determined land use conversion and should explain the existing forest pattern. The urbanization process still continues and the resulting demand should also explain recent land-use change (e.g., ). By moving to a regional scale, it should be possible to create a surface of demand for land. That surface can then be correlated with the extant spatial pattern of forest and land-cover changes. Both distance-decay (Clark 1951) and retail gravity (Carrothers 1956) theory provide the conceptual basis for generating surfaces over large regions. Both are similar in their inclusion of distance from an economic center and its size as principal determinants of land demand (Barkley et al. 1996; Shi et al. 1997). Methods The southside economic region of Virginia (Fonseca 1990) was selected as the study area (Figure 1). It includes the counties of Amelia, Buckingham, Brunswick, Charlotte, Cumberland, Dinwiddie, Halifax, Lunenburg, Mecklenburg, Nottaway, and Prince Edward in southern Virginia. Population in the study area is small, with no town exceeding 7000 people, but it is surrounded by large urban centers on the east and south (Richmond-Petersburg and Norfolk-Portsmouth, VA, and Raleigh-Durham, and Greensboro/Winston-Salem, NC) and smaller urban centers to the west and north (Danville, Lynchburg, and Charlottesville, VA). We chose this geographic region for three reasons. First, the area is largely forested and the potential influence of urbanization pressures on forest fragmentation patterns cannot be determined if forest is already nearly eliminated. Second, the area is surrounded by a ring of urban centers (see Figure 1) that are likely to exert urbanization pressures within the study area. Third, since the area is mostly contained within the piedmont, there are no significant topographic factors (e.g., mountain ranges, areally extensive wetlands) which would confound geographic modeling of demand for land. Land demand surface maps were generated by splining. Splining is one of several interpolation methods for generating surfaces from discrete data (Lancaster and Saukauskas 1986). It has been used in other spatial economic studies (e.g., Barkley et al. 1996). Other interpolation methods include inverse distance weighted interpolation (Watson and Philips 1985) and kriging (Cressie 1991). The ratio of population over distance was used as input data to generate the surface. Population estimates were taken for each urban center surrounding the study area (see Figure 1) from the 1990 Census of Population for each Census Designated Place (CDP). Distance was calculated as the linear road distance from each of these cities to the nearest town within the study area (Farmville, South Boston, South Hill). Only main thoroughfares were used for determining the linear distance between the urban centers (e.g., Raleigh-Durham) and the closest town in the study area (e.g., South Hill). For example, the distance between Charlottesville and Farmville was the length of road along U.S. 29 from Charlottesville to Lynchburg plus the length of road along U.S. 460 from Lynchburg to Farmville. The more direct and shorter route (VA 20) between Charlottesville and Farmville was not used because it is not a major thoroughfare. The data used to create the surface maps of land demand are listed in Table 1. Surface curvature (i.e., rate of change) can be controlled by a term specifying the model functional form and a weight term (ESRI 1992). Regularized and tension are the two available model functional forms, with recommended ranges in weight parameters of 0

3 Figure 1. Location map. Forested land cover is in gray, water is black, and all other land-cover classes are in white. 173

4 174 Table 1. Population and distance data used to create splined interpolated surfaces. City 1990 Population Study area town Distance (km) Ratio (pop/km) Charlottesville Farmville Lynchburg Farmville Danville South Boston Norfolk South Hill Portsmouth Newport News Hampton Roads Chesapeake Virgina Beach Raleigh, Durham, South Hill Chapel Hill Greensboro South Boston Winston-Salem High Point Richmond Farmville Petersburg to 0.5 and 0 to 10, respectively (ESRI 1992). Surfaces created using the regularized functional are smoother (rates of change are less abrupt). Increasing the value of the weight term for the regularized functional form increases the smoothness of the surface. Surfaces created using the tension functional form tend to have more abrupt rates of change (i.e., the surface more closely conforms to the input values). Increasing the value of the weight term for the tension function form increases the influence of the input values. The software is based on the work Mitas and Mitasova (1988). To account for variation in the surface due to model form, four surfaces were generated representing the possible range in curvature (regularized, weight = 0; regularized, weight = 0.5; tension, weight = 0, tension, weight = 10). A cell size of 1 km 2 was used. Each surface was then split into 10 zones of approximately equal area. The median value of each zone was used as the variable to relate to estimates of forest fragmentation. Fragmentation was estimated using a ca Landsat TM-based map of land cover (Voglemann 1998). The resolution of the land-cover data was 30 m (0.09 ha). Four estimates of forest fragmentation were used. One was simply the percent of forest (Pf). The other three were the proportions of pixels with less than 40 (P4), 50 (P5), and 60 (P6) percent forest in one-kilometer neighborhoods around each pixel. These measures were chosen because 40 and 60% represent percolation thresholds for the 4- and 8-neighbor cases, respectively (Plotnick and Gardner 1993), with 50% being the middle value between the two extremes. When a landscape has less habitat than the percolating threshold, the extant habitat is usually fragmented into patches (Gardner et al. 1987). moving window analysis (Riitters et al. 1997; Jones et al. 1997) was used to estimate P4, P5, and P6. Pixels were assigned a value of 1 if the proportion of forest in the surrounding 1-kilometer neighborhood was less than the percolating threshold (P4, P5, P6) and zero (0) otherwise. Land-cover change was estimated as decreases in the Normalized Difference Vegetation Index (NDVI) from ca and 1990 Landsat Mutlispectral Scanner (MSS) data acquired from the North American Landscape Characterization (NALC) program (USEPA 1993). NDVI is an index of the amount of biomass or green vegetation, and is measured as the ratio of infrared minus red over infrared plus red reflectance (Tucker 1979). Each date of Landsat MSS data was converted to effective reflectance (Markam and Barker 1987), followed by scene-to-scene normalization using bright (e.g., parking lots) and dark (clear, deep water bodies) targets and regression (Schott et al. 1988). NDVI and its change over time were calculated following these radiometric adjustments. Change in

5 175 NDVI was measured as the difference between earlyminus late-date NDVI measurements. Differencing spectral data typically results in a distribution of values whose mean is close to zero (0) (no change), with values at some distance from the mean representing actual change in land cover (Jensen 1981). It is common practice to select a threshold at some distance from the mean where values greater than the threshold represent actual land-cover change and non-zero values less than the threshold are differences that can be attributed to atmospheric effects and other factors (Singh 1989, Table 1). In practice, selecting a threshold is a trade-off between errors of omission and commission. Thresholds close to zero are more certain of capturing all land-cover change but also tend to incorporate changes that are the result of atmospheric and other effects (commission error). At thresholds distant from the mean, there is greater certainty that all changes are changes in land-cover, but greater uncertainty that some actual land-cover changes have been left out (omitted). We chose a value of one standard deviation from the mean as our no change/change threshold. We chose this threshold because visibly identifiable patches of change (on a CRT) began to break up at higher standard deviations. A one standard deviation threshold is consistent with that found in other studies (e.g., Fung and LeDrew 1988). In measuring change, we only used decreases in NDVI (early- minus late-date >0). Increases in NDVI (early- minus late-date <0) were not included for two reasons. First, although increases in NDVI could be attributable to many factors, they intuitively suggest afforestation, and we did not feel that geographic patterns of afforestation could be attributed to urbanization pressures. Second, four different scenes (path/row 15/34, 15/35, 16/34 and 16/35) are required for complete coverage of the study area, and it was not possible to calibrate the radiometry across the four scenes because of the differences in acquisition dates. NDVI increases appeared to be sensitive to withinscene radiometric differences (i.e., change tended to concentrate in one scene regardless of the threshold chosen). This did not appear to be the case with temporal decreases in NDVI. We used simple pearson correlations to quantify the relationships between the land demand surfaces and measures of forest fragmentation, and land-cover change (from NDVI decreases). Proportions of Pf, P4, P5, P6, and land-cover change were summed by land demand zone and correlated against the zone s median Figure 2. Graph of proportion of pixels with less than 40% forest in a 1-km neighborhood (P4) versus zonal median from land demand surface. Land demand surface is from tension model and no weighting of input data points. value. The measures were correlated against each of the four land demand surfaces. We also correlated the proportion of forest versus land demand using a random draw of 1-kilometer pixels across the study region. The random draw was used to see if correlations were stable across different units of analysis (1 km 2 versus equal area zones). Results The gradient in land demand, across all splining methods, decreased from southeast to northwest, and forest fragmentation measures generally showed the same gradient (Figure 2). Absolute values of correlations ranged from a low of 0.52 to a high of 0.71, and had the expected sign (Table 2). Percent forest was negatively correlated with land demand and the other fragmentation measures (P4, P5, and P6) were positively correlated. In general, correlations were stronger using the tension functional form, which creates surfaces that more closely conform to the data points. The geographic trend in land-cover change (declines in NDVI) showed a strong relationship to the geographic trend in land demand (Figure 3). Regardless of the methods used to generate the land demand surface, correlations between land-cover change and land demand were greater than 0.9 (Table 3). These results suggest that land-cover dynamics are strongly related to geographic patterns of urban influence.

6 176 Table 2. Correlations between median land demand scores and estimates of forest fragmentation (Pf, P4, P5, and P6). Pf is percent forest, and P4, P5, and P6 are the proportion of pixels with less than 40, 50, and 60% forest in a 1-km neighborhood. Numbers in parentheses are significance scores (Prob > R under assumption of no correlation). Method Weight Class (zone) assignment Number of zones Pf P4 P5 P6 Tension 0.0 Eq. area (0.032) (0.021) (0.026) (0.028) Tension 10.0 Eq. area (0.055) (0.105) (0.072) (0.047) Regularized 0.0 Eq. area (0.073) (0.102) (0.088) (0.076) Regularized 0.5 Eq. area (0.062) (0.125) (0.104) (0.080) Table 3. Correlations between median land demand scores and proportion of land demand zone showing NDVI loss. Numbers in parentheses are significance scores. Method Weight Class (zone) assignment Number of zones NDVI Loss Tension 0.0 Eq. area (0.0001) Tension 10.0 Eq. area (0.0001) Regularized 0.0 Eq. area (0.0001) Regularized 0.5 Eq. area (0.0001) These results confirm other studies (LaGro and De- Gloria 1992; Turner et al. 1996; Wear et al. 1998) that the magnitude of land-cover change declines with distance from urban centers. However, many of these studies (Turner et al. 1996; Wear et al. 1997) were restricted to areas about a single county in size and considered only a single urban center. Our results suggests that as the size of the study area increases, the influence of multiple urban centers will need to be considered. Surface interpolation methods are perhaps a more efficient way to incorporate the influence of multiple urban centers than multivariate modeling, which would require a separate independent variable for each urban center. Compared to the correlations with change (>0.9), the correlations with present pattern are relatively weak ( ). There are two possible reasons for the difference. One explanation is that the demand surface has changed over the last century. Changes in the urban population size, economic basis (relative importance of agriculture, manufacturing, and service sectors) and road network have changed the demand for land over time. The high correlations between the present demand surface and recent changes suggest that as the demand surface has changed over time, the rates and patterns of land conversion have also changed. As a result, the present demand surface may not adequately reflect the entire course of historic demands that generated the present pattern of forest fragmentation. Another explanation is that the lack of stronger correlations may be due to local variability that is not represented in regional surfaces of land demand. The scatter in the graph of P4 versus land demand (Figure 2) suggests that local factors are responsible for maintaining a high degree of forest connectivity in the southeastern portion of the study area, which is closest to the urban centers of Richmond-Petersburg, Norfolk-Portsmouth, and Raleigh-Durham. The correlation between percent forest and land demand using a random sample of 1 km 2 blocks was not significant (r = 0.15, Prob > R =0.15). This appears to support the importance of local factors. At any given spot on the landscape, forest patterns will be influenced by factors such as soil, topography, ownership, and history, in addition to regionalized patterns of urban influence. In the geographic literature, changes in correlations that result from changes in analysis units (either size or shape) have been referred to as the modifiable

7 177 Figure 3. Splined interpolated surface showing zonal medians and NDVI loss (in black), and graph of proportion of NDVI loss versus zonal median from land demand surface. Land demand surface is from tension model and no weighting of input data points. area unit problem (MAUP) (Robinson 1950). Most research on MAUP has focused on finding solutions to the dependencies of correlations on analysis units (e.g., Openshaw 1977). More recently, others have recognized that this phenomenon can be used as a tool for inference (Jelinski and Wu 1996). To the extent that urbanization can be represented as a regional factor, a single measure of urbanization (e.g., land demand) should be related to forest fragmentation patterns using large spatial analysis units, but when small spatial analysis units are used, additional factors representing more local processes will need to be included (Meentemeyer and Box 1987).

8 178 Summary and conclusion In the southside economic region of Virginia, we found a strong relationship between rates of forest conversion and urbanization pressure as reflected in interpolated land demand surfaces. The relationship suggests there is a geographic gradient of vulnerability to future habitat loss that closely matches the geographic gradient in the land demand surfaces. We found only moderate correlations between geographic trends in present forest patterns and geographic trends in land demand. The extant forest pattern appears to be influenced by local and historical factors that are not accounted for in interpolated surfaces of land demand using only current population estimates. Interpolated demand surfaces seem to be a better indicator of recent changes than present land use patterns. 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