Prospective modelling of environmental dynamics: A methodological comparison applied to mountain land cover changes

Abstract

During the last 10 years, scientists have made significant advances in modelling environmental dynamics. A wide range of new methodological approaches in geomatics –such as neural networks, multi-agent systems or fuzzy logics– have been developed. Despite this progress, the modelling softwares available have to be considered as experimental tools rather than improved procedures that are able to work for environmental management or decision support. In particular, the authors think that a large number of publications suffer from discrepancies, when trying to validate their model’s results. This contribution describes three different modelling approaches applied to prospective land cover prediction. The first one, a combined geomatic method, uses Markov chains for temporal transition prediction, while their spatial assignment is supervised manually by the construction of suitability maps. Compared to this directed method, the two others may be considered as semi-automatic because both the polychotomous regression and the multilayer perceptron only need to be optimized during a training step – the algorithms themselves detect the spatial-temporal changes in land cover. The authors describe the three methodological approaches and their practical applications to two mountainous studied areas: one in the French Pyrenees, the second including a large part of Sierra Nevada, Spain. The article focuses on the comparison of results. The major result is that prediction scores are higher than the actual land cover is persistent. They also underline that the geomatic model is complementary to the statistical ones which perform higher overall prediction rates but produce weaker simulations when there are numerous land cover changes.