The experimentation takes place in the <i>Maya Biosphere Reserve</i>, in the heart of the Peten region in Guatemala. In this natural area intermingled rivers and lakes, the forest which was in balance with environmental conditions dominated all the space. However, the landscape has just suffered a real transformation for the last 15 years. Since 1987, populating has settle up regularly by succesive waves. They have appropriated, cleared and changed the native forest in pasture and <i>milpa</i> (field of corn). This process of systematic deforestation by large fires, permits the creation of new rural societies, a new area of distinctly diverse uses. But the sudden and non control setting up of these populations threaten environment conditions. A conflict for the land has been appeared around and inside of the <i>Maya Biosphere Reserve</i>, whitch is itself threatened. The State of Guatemala, as the NGO need a local and regional perception. And yet, faced with this speedy phenomenon non finished, the lack of updated cartographic data in a area little known and badly statistical informed, high resolution remote sensing becomes an irreplaceable tool to understand such radical transformations. To understand spatio temporal process of this new rural pioneer front, to make a dynamic diagnosis, to date, to follow, to map, to update environmental and statistical data, the method of image processing proposed is based on satellite data fusions--Landsat-TM and Spot--by multidated approaches (11 images over 15 years), multi-scale (from local to regional) and multispectral (only one image resultant of 41 georeferenced channels) ; the results have been ratified by field work.
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.