Tropical deforestation modelling: comparative analysis of different predictive approaches. The case study of Peten, Guatemala

  • M Follador
  • N Villa
  • M Paegelow
  • F Renno
  • R Bruno
Part of the Environmental Science and Engineering book series (ESE)


The frequent use of predictive models for analyzing of complex, natural or artificial phenomena is changing the traditional approaches to environmental and hazard problems. The continuous improvement of computer performance allows for more detailed numerical methods, based on space-time discretisation, to be developed and run for a predictive modelling of complex real systems, reproducing the way their spatial patterns evolve and pointing out the degree of simulation accuracy. In this contribution we present an application of several methods (Geomatics, Neural Networks, Land Cover Modeler and Dinamica EGO) in the tropical training area of Peten, Guatemala. During the last few decades this region, included in the Biosphere Maya reserve, has seen a fast demographic raise and a subsequent uncontrolled pressure on its own geo-resources. The test area can be divided into several sub-regions characterized by different land use dynamics. Understanding and quantifying these differences permits a better approximation of a real system; moreover we have to consider all the physical, socio-economic parameters, which will be of use for representing the complex and sometimes random human impact. Because of the absence of detailed data from our test area, nearly all the information was derived from the image processing of 11 ETM+, TM and SPOT scenes; we studied the past environmental dynamics and we built the input layers for the predictive models. The data from 1998 and 2000 were used during the calibration to simulate the land cover changes in 2003, selected as reference date for the validation. The basic statistics permit to highlight the qualities or the weaknesses for each model on the different sub-regions.


Predictive Models Space-time discretisation Remote Sensing Neural Networks Markov Chains MCE Dinamica Risk management Deforestation Peten Guatemala 


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© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • M Follador
  • N Villa
  • M Paegelow
  • F Renno
  • R Bruno

There are no affiliations available

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