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Using the FORE-SCE model to project land-cover change in the southeastern United States

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  • Sohl, Terry
  • Sayler, Kristi

Abstract

A wide variety of ecological applications require spatially explicit current and projected land-use and land-cover data. The southeastern United States has experienced massive land-use change since European settlement and continues to experience extremely high rates of forest cutting, significant urban development, and changes in agricultural land use. Forest-cover patterns and structure are projected to change dramatically in the southeastern United States in the next 50 years due to population growth and demand for wood products [Wear, D.N., Greis, J.G. (Eds.), 2002. Southern Forest Resource Assessment. General Technical Report SRS-53. U.S. Department of Agriculture, Forest Service, Southern Research Station, Asheville, NC, 635 pp]. Along with our climate partners, we are examining the potential effects of southeastern U.S. land-cover change on regional climate. The U.S. Geological Survey (USGS) Land Cover Trends project is analyzing contemporary (1973–2000) land-cover change in the conterminous United States, providing ecoregion-by-ecoregion estimates of the rates of change, descriptive transition matrices, and changes in landscape metrics. The FORecasting SCEnarios of future land-cover (FORE-SCE) model used Land Cover Trends data and theoretical, statistical, and deterministic modeling techniques to project future land-cover change through 2050 for the southeastern United States. Prescriptions for future proportions of land cover for this application were provided by ecoregion-based extrapolations of historical change. Logistic regression was used to develop relationships between suspected drivers of land-cover change and land cover, resulting in the development of probability-of-occurrence surfaces for each unique land-cover type. Forest stand age was initially established with Forest Inventory and Analysis (FIA) data and tracked through model iterations. The spatial allocation procedure placed patches of new land cover on the landscape until the scenario prescriptions were met, using measured Land Cover Trends data to guide patch characteristics and the probability surfaces to guide placement. The approach provides an efficient method for extrapolating historical land-cover trends and is amenable to the incorporation of more detailed and focused studies for the establishment of scenario prescriptions.

Suggested Citation

  • Sohl, Terry & Sayler, Kristi, 2008. "Using the FORE-SCE model to project land-cover change in the southeastern United States," Ecological Modelling, Elsevier, vol. 219(1), pages 49-65.
    • Handle: RePEc:eee:ecomod:v:219:y:2008:i:1:p:49-65
    DOI: 10.1016/j.ecolmodel.2008.08.003
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    References listed on IDEAS

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    1. Eugenia Kalnay & Ming Cai, 2003. "Impact of urbanization and land-use change on climate," Nature, Nature, vol. 423(6939), pages 528-531, May.
    2. Gatrell, A. C. & Bailey, T. C., 1996. "Interactive spatial data analysis in medical geography," Social Science & Medicine, Elsevier, vol. 42(6), pages 843-855, March.
    3. Ian Hardie & Peter Parks & Peter Gottleib & David Wear, 2000. "Responsiveness of Rural and Urban Land Uses to Land Rent Determinants in the U.S. South," Land Economics, University of Wisconsin Press, vol. 76(4), pages 659-673.
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    Cited by:

    1. Veronique Beckers & Jeroen Beckers & Matthias Vanmaercke & Etienne Van Hecke & Anton Van Rompaey & Nicolas Dendoncker, 2018. "Modelling Farm Growth and Its Impact on Agricultural Land Use: A Country Scale Application of an Agent-Based Model," Land, MDPI, vol. 7(3), pages 1-19, September.
    2. Ryan Nedd & Aavudai Anandhi, 2022. "Land Use Changes in the Southeastern United States: Quantitative Changes, Drivers, and Expected Environmental Impacts," Land, MDPI, vol. 11(12), pages 1-25, December.
    3. Stephen M. Strader, 2018. "Spatiotemporal changes in conterminous US wildfire exposure from 1940 to 2010," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 92(1), pages 543-565, May.
    4. Le Giang Thi & Nguyen Thuan Duc & Tran Vinh Quoc, 2016. "Research and Application of Remote Sensing and GIS Technologies in Determining and Forecasting Land Use Changes by Markov Chain in Y Yen District - Nam Dinh Province," Real Estate Management and Valuation, Sciendo, vol. 24(3), pages 27-39, September.
    5. Adam J Terando & Jennifer Costanza & Curtis Belyea & Robert R Dunn & Alexa McKerrow & Jaime A Collazo, 2014. "The Southern Megalopolis: Using the Past to Predict the Future of Urban Sprawl in the Southeast U.S," PLOS ONE, Public Library of Science, vol. 9(7), pages 1-8, July.
    6. Druga, Michal & Minár, Jozef, 2023. "Cost distance and potential accessibility as alternative spatial approximators of human influence in LUCC modelling," Land Use Policy, Elsevier, vol. 132(C).
    7. Alexandra M. Thorn & Jonathan R. Thompson & Joshua S. Plisinski, 2016. "Patterns and Predictors of Recent Forest Conversion in New England," Land, MDPI, vol. 5(3), pages 1-17, September.

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