The oil spills in Brazil are more and frequents, causing several impacts on environments and biological communities. Envronmental sensitivy index maps of oil spils are indispensbles components of contingency and emergency answer plans for this tpe of accident. These maps present a system of classification based on geomorphologic characteristics of the áreas, which are defined by the following factors: wave and tidal energy exposure relative degree, shoreline slope and substrate type, and also, the easiness to clean and remove the oil impacted áreas. In this contect, the “Golfão Maranhense” region, located on Northern “Maranhão” State, was chosen in order to map and analyze the environmental sensitivity indexes (ESis) of oil spills on coastal environments, for in this área, we find the second largest port in draught of the world, the Porto f “Itaqui”. Moreover, this region is the route of six hundred oil tankers per year, which are potential agents that cause the oil spill. The methodological approach for creating the maps of environmental sensitivity index included the integrated analysis of coastal environments based on digital image processing from remote optical sensores, in this case, Landsat-4TM, CBERG-2 CCD and SPOT-2 HRV, SAR (Synthetic Aperture Radar) images from RADARSAT-1 Wide 1, SRTM (Shuttle Radar Topography Mission) elevation data, geographic information system and Field surveys related to geomorphology, topography and sedimentology. Using the aforementioned methods, the coastal environments recognized in “Golfão Maranhense” were grouped according to their envronmental sensitivity index: 1 – Solid men-made structures (ESI 1B); 2- Cliffs (ESI 1C); 3 – Fine grained sand beaches and móbile dunes (ESI 3ª); 4- Tidal sandflats (ESI 7); 5- Mixed intertidal Banks, tidal mudflats and ebb-tidal delta (ESI 9ª); 6- Supratidal sandflat (ESI 9C); 7- Saltmarshes (ESI 10ª); 8- Fresh marhes and intermittent lakes (ESI 10B) and 9- Mangrove (ESI 10C). This approach is efficient to recognize and analyze coastal environments and, therefore, it pernitted the sensivity index attribution for the oil spill on yhese environments, in a georefenced data base, which allows making faster e more efficiently decisions in case oil spills come to happen.
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