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

Integrating Remote Sensing, GIS and Prediction Models to Monitor the Deforestation and Erosion in Peten Reserve, Guatemala