Landscape Ecology

, Volume 15, Issue 6, pp 495–504 | Cite as

A geography of ecosystem vulnerability

  • James D. Wickham
  • Robert V. O'Neill
  • K. Bruce Jones


Land-cover change and the subsequent potential loss of natural resources due to conversion to anthropogenic use is regarded as one of the more pervasive environmental threats. Population and road data were used to generate interpolated surfaces of land demand across a large region, the mid-Atlantic states of Pennsylvania, Delaware, Maryland, Virginia, and West Virginia. The land demand surfaces were evaluated against land-cover change, as estimated using temporal decline in Normalized Difference Vegetation Index (NDVI). In general, the interpolated surfaces exhibited a plateau along the eastern seaboard that sank to a valley in the center of the study area, and then rose again to a plateau in the west that was of overall lower height than the plateau on the eastern seaboard. The spatial pattern of land-cover change showed the same general pattern as the interpolated surfaces of land demand. Correlations were significant regardless of variations used to generate the interpolated surfaces. The results suggest that human activity is the principal agent of land-cover change at regional scales in this region, and that natural resources that change as land cover changes (e.g., water, habitat) are exposed to a gradient of vulnerability that increases from west to east.

GIS land-cover change population modeling roads 


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  1. Barkley, D.L., Henry, M.S. and Shuming, B. 1996. Identifying 'spread’ versus ‘backwash’ effects in regional economic areas: a density function approach. Land Economics 72 (3): 336–357.Google Scholar
  2. Beaulac, M.N. and Reckhow, K.H. 1982. An examination of land use-nutrient export relationships. Water Res. Bull. 18 (2): 1013–1024.Google Scholar
  3. Berry, B.J.L., Conkling, E.C. and Ray, D.M. 1990. The global economy: resource use, locational choice, and international trade. Prentice Hall, Englewood Cliffs, NJ, USA.Google Scholar
  4. Bockstael, N.E. 1996. Modeling economics and ecology: the importance of a spatial perspective. Am. J. Agric. Econ. 78: 1168–1180.Google Scholar
  5. Carrothers, G.A.P. 1956. A historical review of the gravity and potential concepts of human interaction. J. Am. Inst. Planners 22: 94–102.Google Scholar
  6. Clark, C. 1951. Urban population densities. J. Roy. Stat. Soc. Series A, 114: 490–496.Google Scholar
  7. Environmental Systems Research Institute (ESRI). 1992. Grid Command Reference, Functions G-Z. Environmental Systems Research Institute, Redlands, CA, USA.Google Scholar
  8. Franke, R. 1982. Smooth Interpolation of scattered data by local thin plate splines. Comput. Math. Appl., 8(4): 273–281.Google Scholar
  9. Frink, C.R. 1991. Estimating nutrient exports to estuaries. J. Environ. Qual. 20: 717–724.Google Scholar
  10. Fung, T. and LeDrew, E. 1988. The determination of optimal threshold levels for change detection using various accuracy indices. Photogr. Eng. Remote Sensing 54 (10): 1449–1454.Google Scholar
  11. Jensen, J.E. 1981. Urban change detection mapping using Landsat digital data. Am. Cartographer 8 (2): 127–147.Google Scholar
  12. Jernigan, R.W. 1986. A primer on kriging. U.S. Environmental Protection Agency, Washington, DC.Google Scholar
  13. Jones, K. B., Riitters, K. H., Wickham, J. D., Tankersley, R. D., O'Neill, R. V., Chaloud, D., Smith, E. R. and Neale, A. C. 1997. An Ecological Assessment of the United States Mid-Atlantic Region: A Landscape Atlas. USEPA 600/R-97/130, U.S. Environmental Protection Agency, Office of Research and Development, Washington, DC, USA.Google Scholar
  14. Khorram, S., Biging, G., Colby, D., Congalton, R., Dobson, J., Ferguson, R., Goodchild, M., Jensen, J. and Mace, T. 1999. Accuracy assessment of remote sensing derived change products. Monograph, American Society for Photogrammetry and Remote Sensing, Bethesda, MD, USA, 64 pp.Google Scholar
  15. Lancaster, P. and Sakauskas, K. 1986. Curve and surface fitting: an introduction. Academic Press, NY, USA.Google Scholar
  16. Markham, B.L. and Barker, J.L. 1987. Radiometric properties of U.S. processed Landsat MSS data. Remote Sensing Environ. 22: 39–71.Google Scholar
  17. Mitas, L. and Mitasova, H. 1988. General Variational approach to the interpolation problem. Comput. Math. Appl. 16 (12): 983–992.Google Scholar
  18. O'Neill, R.V. Hunsaker, C.T., Jones, K.B., Riitters, K.H., Wickham, J.D., Schwartz, P.M., Goodman, I.A., Jackson, B.L. and Baillargeon, W.S. 1997. Monitoring environmental quality at the landscape scale. Bioscience 47 (8): 513–519.Google Scholar
  19. Riebsame, W.E., Parton, W.J., Galvin, K.A., Burke, I.C., Bohren, L., Young, R., and Knop, E. 1994. Integrated modeling of land use and cover change. Bioscience 44 (5): 350–356.Google Scholar
  20. Riitters, K.H., O'Neill, R.V. and Jones, K.B. 1997. Assessing habitat suitability at multiple scales: a landscape-level approach. Biol. Cons. 81: 191–202.Google Scholar
  21. Schott, J.R., Salvaggio, C. and Volchok, W.J. 1988. Radiometric scene normalization using pseudoinvariant features. Remote Sensing Environ. 26: 1–16.Google Scholar
  22. Sheppard, E. and Barnes, T.J. 1990. The capitalist space economy: analysis after ricardo, Marx, and Sraffa. Unwin Hyman, Inc., Cambridge, MA, USA.Google Scholar
  23. Shi, Y.J., Phipps, T.T. and Colyer, D. 1997. Agricultural land values under urbanizing influences. Land Economics 73(1): 90–100.Google Scholar
  24. Singh, A. 1989. Digital change detection techniques using remotely sensed data. Int. J. Remote Sensing 10 (6): 989–1003.Google Scholar
  25. Tucker, C.J. 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing Environ. 8: 127–150.Google Scholar
  26. Turner, M.G. 1989. Landscape ecology: the effect of pattern on process. Ann. Rev. Ecol. Syst. 20: 171–197.Google Scholar
  27. U.S. Environmental Protection Agency. 1993. North American Landscape Characterization (NALC) Research Plan. USEPA/600/R-93/135, Office of Research and Development, U.S. Environmental Protection Agency, Washington, DC, USA.Google Scholar
  28. Watson, D.F. 1992. Contouring: a guide to display and analysis of spatial data. Pergamon Press, NY.Google Scholar
  29. Watson, D.F. and Phillips, G.M. 1985. A refinement of inverse distance weighted interpolation. Geo-Processing 2: 315–327.Google Scholar
  30. Wear, D.N., Turner, M.G. and Naiman, R.J. 1998. Land-cover change along urban-rural gradients. Ecol. Appl. 8 (3): 619–630.Google Scholar
  31. Wickham, J.D., Jones, K.B., Riitters, K.H., O'Neill, R.V., Tankersley, R.D., Smith, E.R., Neale, A.C. and Chaloud, D. 1999. An integrated environmental assessment of the U.S. mid-Atlantic region. Environ. Manag. 24(4): 553–560Google Scholar
  32. Wickham, J.D., O'Neill, R.V. and Jones, K.B. in press. Forest fragmentation as an economic indicator. Landscape Ecol.Google Scholar
  33. Wilcove, D.S., McLellan, C.H. and Dobson, A.P. 1986. Habitat fragmentation in the temperate zone. In Soule, M.D. (Ed.) The science of scarcity and diversity. pp. 237–256. Edited by M.D. Soule. Sinauer Associates, Sunderland, MA, USA.Google Scholar

Copyright information

© Kluwer Academic Publishers 2000

Authors and Affiliations

  • James D. Wickham
    • 1
  • Robert V. O'Neill
    • 2
  • K. Bruce Jones
    • 3
  1. 1.U.S. Environmental Protection Agency (MD-56)U.S.A.
  2. 2.Oak Ridge National LaboratoryOak RidgeU.S.A.
  3. 3.U.S. Environmental Protection AgencyLas VegasU.S.A.

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