Various Approaches for Predicting Land Cover in Mountain Areas

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.Comment: 14 pages; Classifications: Information Theory; Probability Theory & Applications; Statistical Computing; Statistical Theory & Method

Modélisation géomatique rétrospective des paysages par évaluation multicritères et multiobjectifs

International audienceParmi les fonctions SIG pour la modélisation spatio-temporelle et l'aide à la décision, l'évaluation multicritères et multiobjectifs s'avèrent être particulièrement utile en ce qui concerne la reproductibilité des résultats et le paramétrage de scénarii en termes de prise de risque et de compensation. Ce travail illustre la méthodologie et les résultats obtenus pour la reconstitution probabiliste du paysage historique de la Alta Alpujarra Granadina (Sierra Nevada, Espagne) du 16ème, 18ème et 19ème siècle. L'évaluation multicritères génère des cartes de potentialité - ou d'aptitude - pour chaque usage (objectif), en l'occurrence les différentes catégories d'occupation du sol. Elle se base sur l'hypothèse qu'il existe, pour une date donnée, une série de critères spatialisés pouvant expliquer la variabilité des états de la variable (catégories d'occupation du sol), autrement dit l'aptitude pour un usage. L'évaluation multiobjectifs consiste, en tenant compte des superficies réellement occupées, à intégrer les objectifs concurrents (cartes d'aptitude monothématiques) afin de construire les cartes probabilistes d'occupation du sol historiques pour 1572, 1752 et 1855/61. Les résultats expriment la probabilité de présence d'une occupation du sol à un endroit donné selon les critères inclus dans l'analyse. Leur interprétation permet de se prononcer sur les apports et limites de la méthodologie mise en oeuvre sans pour autant autoriser une validation faute de documents historiques de comparaiso

Post-2013 EU Common Agricultural Policy: predictive models of land use change

This article presents a multi-temporal uncertainty-based method that incorporates a statistical regression model with a view to establishing the risk (probability) of land cover changes as a function of a set of environmental and socio-economic driving factors. The morphologic, climatic and socio-economic variables were examined using an Artificial Neural Network (ANN) model and the Multi-Layer Perceptron (MLP). Following the analysis, maps indicating the suitability to future changes were generated on the basis of observed transitions. From these maps two possible land use scenarios were built, applying the Markov chain principle. The region of Basilicata, in southern Italy, was selected for the analysis. The results highlight: a) a good inclination to change towards specialised crop systems, provided there is sufficient water supply; b) that some cropping patterns are not suitable for changes, partly because they are found in a context with severe limitations for alternative uses

Inference Various Approaches for Predicting Land Cover in Mountain Areas

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