Mapping and characterization of vegetation units by means of Landsat imagery and management recommendations for the Pantanal of Mato Grosso (Brazil), north of Poconé

In the present study, remote sensing in the northern region of Poconé-MT was used to identify vegetation categories, which were then mapped and characterized. The goal in generating the map was to provide information needed to support sustainable use and to formulate conservation policies. Vegetation units were identified and classified using digital images that were taken in 1990 by the Landsat Thematic Mapperc Satellite and then processed using ERDAS software. First, the vegetation classes were systematically defined. In a preliminary interpretation of the image data, Landsat-TM bands that allowed the best visual differentiation of these classes were selected and the image was georeferenced. Routes for trips to the study area to collect truth data (training samples) for further supervised classification were then determined. These data were subsequently classified according to The System of Classification of Brazilian Vegetation (VELLOSO et al. 1991), which has been used in other physiognomic maps of the Pantanal, in order to make our results comparable to those from other mappings. In addition, some modifications of this system were made due to the particular characteristics of the Pantanal and the scale used for this map. Six classes and 16 subclasses were defined for part I of the vegetation map of Pantanal, Mato Grosso, Brazil, specifically, the area north of Poconé. A distinction was made between the vegetation units of the Paraguayan Depression and those of the Pantanal due to the different characteristics of the vegetation from these two regions, and particularly the role played by inundation. The phytoecological region savanna (cerrado) covers a large part of the total area (53.05%) and consists of five sub-classes. Two forest classeswere identified: seasonal semideciduous forest and seasonal deciduous forest. These two phytoecological classes occupied 16.21 % of the total mapped area; 14.45% of the area has been strongly modified by humans (agriculture, pasture, gold mine, and construction); 0.80% is covered during the dry season by perennial water bodies. Based upon ground truth data and regional field experience, ten eco-zones are proposed and suggestions for sustainable management and conservation measures are discussed