CAMPOS-DE SOUZA, Waldir Wagner. Analyzes and proposals of techniques procedures for development of landscape maps applied in the environmental planning of the RMSP. 2018. 281 f. Tese (Doutorado em Geografia Física) -Faculdade de Filosofia Letras e Ciências Humanas, Universidade de São Paulo (FFLCH -USP), São Paulo, 2018.In Geography there was a theoretical and methodological convergence between landscape and the Remote Sensing. Landscape is a concept that guides systemic analyzes, with real and visible results, defined by different scales of component interactions. These explicit dimensions are also present in Remote Sensing which uses techniques and equipments to detect objects and interpret natural and anthropic components in models and thematic maps. Landscape Ecology analyzes maps and data produced by the association between land cover classes or landscape structure metrics that indicate current conditions, fragility and future scenarios, highlighting the aptitude and conflicts with anthropic use. Providing an approach that integrates spatial patterns and ecological processes applied in conservation strategies, environmental planning and spatial planning. The maps are the basis for obtaining results, being indispensable make sure an accuracy that avoids errors and incorrect propositions. Technical advances, efforts to manually identify land cover classes and the less need analysts' decision become common to use automatic classifiers of satellite imagery. The lack of protocols and tests to guide or direct the choice of classifiers, considering landscape attributes, lead to incorrect or weak analyzes. Our goal was to analyze the adequacy of Remote Sensing and Landscape Ecology methodological procedures in high resolution RapidEye satellite images. We selected a landscape sample of the Metropolitan Region of São Paulo (APRMSP), and we did a visual classification and applied supervised and unsupervised five pixel-based classifiers: Mahalanobis Distance, Maximum Likelihood, Spectral Angle Mapper, Support Vector Machines and Iterative Self-Organizing Data Analysis (ISODATA). We calculated the overall accuracy of classifications using confusion matrix and Kappa statistics, and the classifiers performance by voting technique. The maps of Visual Classification and ISODATA Classification, which had the highest accuracy (78,1%), were simplified in forest and non-forest for the landscape structure metrics calculation in the categories of patch, class, shape and graph theory. In the visual classification the most difficult units to separate were the pairs Exposed Soil and Urbanized Area, Forest and Reforestation. In the reference samples of classifiers, the lowest separate index was between Reforestation and Forest, Exposed Soil and Urbanized Area, Wetland and Field and highest separate index pairs were: Wetlands and Water, Water and Field, Water and Forest. We verified that voting technique can be used to select a classifier considering a focal unit, such as ISODATA that obtained the highest index (83.9 %) for the Forest...