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Spatially explicit land use / land cover (LUCC) models aim at simulating the patterns of change on the landscape. In order to simulatelandscape structure, the simulation procedures of most computational LUCC models use a cellular automata to replicate the land use/ cover patches. Generally, model evaluation is based on assessing the location of the simulated changes in comparison to the truelocations but landscapes metrics can also be used to assess landscape structure. As model complexity increases, the need to improvecalibration and assessment techniques also increases. In this study, we applied a genetic algorithm tool to optimize cellular automata’sparameters to simulate deforestation in a region of the Brazilian Amazon. We found that the genetic algorithm was able to calibratethe model to simulate more realistic landscape in term of connectivity. Results show also that more realistic simulated landscapes areoften obtained at the expense of the location coincidence. However, when considering processes such as the fragmentation impacts onbiodiversity, the simulation of more realistic landscape structure should be preferred to spatial coincidence performance

the international archives of the photogrammetry, remote sensing and spatial information sciences/international archives of the photogrammetry, remote sensing and spatial information sciences

CALIBRATING CELLULAR AUTOMATA OF LAND USE/COVER CHANGE MODELS USING A GENETIC ALGORITHM