  Using former maps, geographers intend to study the evolution of the land
cover in order to have a prospective approach on the future landscape;
predictions of the future land cover, by the use of older maps and
environmental variables, are usually done through the GIS (Geographic
Information System). We propose here to confront this classical geographical
approach with statistical approaches: a linear parametric model (polychotomous
regression modeling) and a nonparametric one (multilayer perceptron). These
methodologies have been tested on two real areas on which the land cover is
known at various dates; this allows us to emphasize the benefit of these two
statistical approaches compared to GIS and to discuss the way GIS could be
improved by the use of statistical models.
