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Modelling deforestation using GIS and artificial neural networks

✍ Scribed by J.F. Mas; H. Puig; J.L. Palacio; A. Sosa-López

Publisher
Elsevier Science
Year
2004
Tongue
English
Weight
631 KB
Volume
19
Category
Article
ISSN
1364-8152

No coin nor oath required. For personal study only.

✦ Synopsis

This study aims to predict the spatial distribution of tropical deforestation. Landsat images dated 1974Landsat images dated , 1986Landsat images dated and 1991 were classified in order to generate digital deforestation maps which locate deforestation and forest persistence areas. The deforestation maps were overlaid with various spatial variables such as the proximity to roads and to settlements, forest fragmentation, elevation, slope and soil type to determine the relationship between deforestation and these explanatory variables. A multi-layer perceptron was trained in order to estimate the propensity to deforestation as a function of the explanatory variables and was used to develop deforestation risk assessment maps. The comparison of risk assessment map and actual deforestation indicates that the model was able to classify correctly 69% of the grid cells, for two categories: forest persistence versus deforestation. Artificial neural networks approach was found to have a great potential to predict land cover changes because it permits to develop complex, non-linear models.

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