Abstract
Global change research includes an extensive body of literature covering population-environment interactions focusing on the central issues of migration and demography, environmental site and situation, and socioeconomic structures addressed within a spatially-explicit but temporally dependent form. Quite often this is simply because neither the data nor the modeling methodology combine well to effectively address uncertainty and spatially defined time-series data within a nonlinear context. In this research, a cellular automaton model is proposed as an effective framework for the predictive modeling of landuse/landcover change (LULCC) associated with the spatial pattern and rates of deforestation and agricultural extensification in the Ecuadorian Amazon. The model employs user-defined rules based upon spatially explicit probabilities of LULCC derived from remotely sensed time-series data and both biophysical and socioeconomic regional characteristics to produce an output image of the "anthropomorphized" landscape over time and space.
Author: Joseph P. Messina, Stephen J. Walsh
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