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Time Series Analysis Of Satellite Data: Deforestation In Southern Mexico


  • Geoghegan, Jacqueline
  • Hewitt, Julie A.
  • Vance, Colin


Tropical deforestation is significant to a range of themes that have relevance for the study of environmental change and economic development, including global warming, land degradation, species extinction, and sustainability issues. Recognition that both the location and pattern of forest clearance are often as important as its magnitude has motivated an increasing number of econometric studies that link satellite data and government census data with the aim of modeling the spatial dimensions of deforestation processes. Initial research focused on time series analysis, while recent work has started developing models that make use of time series data on land use. In this paper, we use satellite data from three dates over an approximate 15-year period to estimate the probability of a satellite pixel being in a forested or human-disturbed state. Our study focuses on land-use change in an agricultural frontier spanning the southern Mexican states of Campeche and Quintana Roo. This region contains one of the largest and oldest expanses of tropical forests in the Americas outside of Amazonia and has been identified as a "hot spot" of forest and biotic diversity loss. Over the past 30 years, these forests have been under sustained pressure following the construction of a highway in 1972 that opened the frontier to settlement. The road was part of a larger development effort to promote agricultural colonization and has contributed to a prolonged period of land transformation that has been captured by Thematic Mapper (TM) satellite imagery. We capture these landscape dynamics by assembling a spatial database that links the pixels from three TM images spanning the years 1986-1997 and other spatial environmental and GIS-location derived data with government census socio-economic data of data. We develop a simple utility-maximizing model of the forest clearance decision. Based on previous research, the theoretical model suggests many possible determinants of forest clearance in an economic environment characterized by missing or thin markets, as typifies frontier regions in the nascent stages of economic development. We subsequently test the significance of these determinants using discrete choice analysis These modeling questions have particular relevance for informing carbon sequestration and global warming policy initiatives. Other on-going research conducted by the ecologists associated with the project focus on the species composition, abundance, structure, and re-growth of the different forests types in the region. In addition, litter and biomass studies have been completed which included carbon estimates for the different forest types, including forest re-growth on agricultural land, as function of fallow cycle dynamics. Fallow cycle dynamics are extremely important as the region is dominated by semi-subsistence agriculture with very little chemical inputs, so farmers depend on the fallow cycle to restore soil productivity. It will be these detailed data that will be used to calculate baseline carbon sequestration amounts.

Suggested Citation

  • Geoghegan, Jacqueline & Hewitt, Julie A. & Vance, Colin, 2003. "Time Series Analysis Of Satellite Data: Deforestation In Southern Mexico," 2003 Annual meeting, July 27-30, Montreal, Canada 22123, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
  • Handle: RePEc:ags:aaea03:22123

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    References listed on IDEAS

    1. Irwin, Elena G. & Bell, Kathleen P. & Geoghegan, Jacqueline, 2003. "Modeling and Managing Urban Growth at the Rural-Urban Fringe: A Parcel-Level Model of Residential Land Use Change," Agricultural and Resource Economics Review, Cambridge University Press, vol. 32(01), pages 83-102, April.
    2. Gerald C. Nelson & Daniel Hellerstein, 1997. "Do Roads Cause Deforestation? Using Satellite Images in Econometric Analysis of Land Use," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 79(1), pages 80-88.
    3. Nelson, Gerald C. & Geoghegan, Jacqueline, 2002. "Deforestation and land use change: sparse data environments," Agricultural Economics, Blackwell, vol. 27(3), pages 201-216, November.
    4. Gary Chamberlain, 1980. "Analysis of Covariance with Qualitative Data," Review of Economic Studies, Oxford University Press, vol. 47(1), pages 225-238.
    5. Cropper, Maureen & Griffiths, Charles & Mani, Muthukumara, 1997. "Roads, population pressures, and deforestation in Thailand, 1976-89," Policy Research Working Paper Series 1726, The World Bank.
    6. Pfaff, Alexander S. P., 1999. "What Drives Deforestation in the Brazilian Amazon?: Evidence from Satellite and Socioeconomic Data," Journal of Environmental Economics and Management, Elsevier, vol. 37(1), pages 26-43, January.
    7. Munroe, Darla K. & Southworth, Jane & Tucker, Catherine M., 2002. "The dynamics of land-cover change in western Honduras: exploring spatial and temporal complexity," Agricultural Economics, Blackwell, vol. 27(3), pages 355-369, November.
    8. Butler, J S & Moffitt, Robert, 1982. "A Computationally Efficient Quadrature Procedure for the One-Factor Multinomial Probit Model," Econometrica, Econometric Society, vol. 50(3), pages 761-764, May.
    9. Munroe, Darla K. & Southworth, Jane & Tucker, Catherine M., 2002. "The dynamics of land-cover change in western Honduras: exploring spatial and temporal complexity," Agricultural Economics of Agricultural Economists, International Association of Agricultural Economists, vol. 27(3), November.
    10. Gerald C. Nelson & GVirginia Harris & Steven W. Stone, 2001. "Deforestation, Land Use, and Property Rights: Empirical Evidence from DariƩn, Panama," Land Economics, University of Wisconsin Press, vol. 77(2), pages 187-205.
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