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Approach of the spatiotemporal prediction using vectorial geographic data

Spatial evolutions of the anthropized ecosystems and the progressive transformation of spaces in the course of time emerge more and more as a special interest issue in research about the environment. This evolution can present a large preoccupation in space accommodation and environmental domains, and it gives rise to a considerable problem in terms of prospective. How will be the conditions of a region area, between now and 15, 30, or 50 years? In fact, the time consists of hierarchical events and can produce transformations upon a terrain landscape as emergence, disappearing, union of spatial entities. These transformations are called temporal phenomena. We propose to predict the forestry evolution in the forthcoming years on an experimental area which reveals these spatial transformations. For these purposes, we have developed a specific spatio-temporal prediction approach. The idea we present here takes a first step in attacking this problematic, it turns out very interesting results in this domain. We describe in this paper a method for analysis and prediction of terrain landscape for an established date. This method is founded on n geographic maps representing the terrain conditions for distinct years. The basic idea is to employ the observation of the temporal phenomena evolution. In fact, results of this observation represent the evolution of each region area on maps in the course of time. The evolution modeling of the regions is obtained with the help of a sequence of aerial photographies compared through different dates. It allows the geographer interested in environmental prospective problems to get type cartographical documents showing the future conditions of a landscape. This method makes use of vectorial geographic data and it achieves a prediction by means of different comparisons between attributes of regions such as the surface, center and distance between regions. The final shapes and positions of the regions are determined by combining the results stemming from applications of a linear regression method and from mathematic morphology in vectorial domain. The implemented approach model the evolution of the forest in a region of the south of France by using maps for the years 1942, 1962, and 1993. We used this method to study a region located in the Ariege mountains called 'Soulave' to describe the evolution of its landscape for the years 2000, 2005, 2010, 2015, and 2020. The experimental tests have shown promising results.