This work approaches the study of Cellular Automata to the simulation of Satellite Remote Sensing images applied to modeling environment landscape dynamics. The images were collected by SPOT and Landsat-MSS from one forest in different times. After the geometric correction and images treatment a binary map will be formed by pixels that contain information about the forest existence. The main purpose is to predict in a geographic map what will happen with the landscape forest in the future. The simulation is done through the analysis of the temporal maps in accordance with their progression, regression or stability in time and with rules that describes how CA do the
simulation. The results achieved are predict maps very useful for a environmental analysis. The experimental tests have showed promising results for studies related to forestry modeling.
Spatial evolutions of anthropized ecosystems and the progressive transformation of spaces in the course of time emerge more and more as a special interest issue in researches about the environment. This evolution constitutes one of the major concerns in the domain of environmental space management. The landscape evolution of a region area and the perspectives for a future state rises an issue particularly important. What will be the state of the region in 15, 30 or 50 years? Time can produce transformations over a region area like emergence, disappearance or 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. The proposed method is based on the analysis of terrain landscape given a sequence of n satellite images, which represent the state of a region area in different years. For these purposes, we have developed a specific spatio-temporal prediction approach, linking results of forestry evolution analysis and fuzzy logic. The method is supported by the analysis of the landscape dynamics of a test-site located in a tropical rain country: the oriental piedmont of Andes Mountain in Venezuela. This large area - at the scale of a spot satellite image - is typical of tropical deforestation in a pioneer front. The presented approach allows the geographer interested in environmental prospective problems to get type cartographical documents showing future conditions of a landscape. The experimental tests have showed promising results.
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.
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