  This contribution provides a strategy for studying and modelling the
deforestation and soil deterioration in the natural forest reserve of Peten,
Guatemala, using a poor spatial database. A Multispectral Image Processing of
Spot and TM Landsat data permits to understand the behaviour of the past land
cover dynamics; a multi-temporal analysis of Normalized Difference Vegetation
and Hydric Stress index, most informative RGB (according to statistical
criteria) and Principal Components, points out the importance and the direction
of environmental impacts. We gain from the Remote Sensing images new
environmental criteria (distance from roads, oil pipe-line, DEM, etc.) which
influence the spatial allocation of predicted land cover probabilities. We are
comparing the results of different prospective approaches (Markov Chains, Multi
Criteria Evaluation and Cellular Automata; Neural Networks) analysing the
residues for improving the final model of future deforestation risk.
