Submitted by Leticia Schettini (leticia@ufrrj.br) on 2016-10-10T13:01:19Z No. of bitstreams: 1 2011 - Valdemir Lucio Durigon.pdf: 4431691 bytes, checksum: bc255769946a67387959db9cc02dbb28 (MD5) / Made available in DSpace on 2016-10-10T13:01:19Z (GMT). No. of bitstreams: 1 2011 - Valdemir Lucio Durigon.pdf: 4431691 bytes, checksum: bc255769946a67387959db9cc02dbb28 (MD5) Previous issue date: 2016-02-25 / Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior-CAPES / Soil erosion is a growing problem in today's world, virtually in every continent, mainly due to the rising demand for food, fibers and biofuels. On the other hand, the correct planning of land use, avoiding farming lands with high erosion risk, could minimize the erosion process without compromising food security. The aim of this study was to evaluate erosion risk and soil losses in a watershed using remote sensing and GIS, coupled with temporal evolution of vegetation cover and occupation of land, in areas of the municipalities of Paty do Alferes and Miguel Pereira, Rio de Janeiro State. For this, the RUSLE (Revised Universal Soil Loss Equation) was used as model to predict soil loss, combined with remote sensing data and the derived NDVI (Normalized Difference Vegetation Index), to determine the soil coverage in a GIS (Geographic Information System). Satellite images used in this research included: TM sensor of Landsat-5, ETM+ of Landsat-7, CCD of CBERS-2, and CCD and HRC from CBERS 2B, obtained for the study period. The processed remotely sensed data and the geographic information system, integrated with models for predicting soil loss, showed good performance for environmental analysis. The use of NDVI was a suitable tool for determining the vegetation coverage, to be applied in soil loss prediction models. The soil losses and erosion risks were strongly associated with characteristics of relief, and they can be used for planning land usage and occupation, in urban and rural areas. More studies should be conducted linking GIS, erosion prediction models, and remote sensing data in order to better predict the occurrence of environmental disasters, also to create databases about soil?s response in relation to different causative erosional agents. / A eros?o do solo ? um problema crescente no mundo atual, em praticamente todos os continentes, que se deve ao crescimento da demanda por alimentos, ao mau uso dos solos e a necessidade de produ??o de fibras e combust?veis. Por outro lado, o planejamento correto do uso e cobertura dos solos evitando utilizar ?reas para a agricultura que tenham alto risco de eros?o e alto potencial natural de eros?o pode minimizar o processo, sem comprometer a seguran?a alimentar. O objetivo deste trabalho foi avaliar o risco de eros?o e a perda de solo em uma bacia hidrogr?fica utilizando t?cnicas de sensoriamento remoto e SIG, associados ? evolu??o temporal dos ?ndices de cobertura e ocupa??o do solo, em ?reas nos munic?pios de Paty do Alferes e Miguel Pereira, Estado do Rio de Janeiro. Para isso, utilizou-se o modelo de predi??o de perdas de solos, no caso a RUSLE (?Revised Universal Soil Loss Equation?), associado a t?cnica de sensoriamento remoto integrada ao ?ndice de vegeta??o NDVI (?Normalized Difference Vegetation Index?) para determinar a cobertura dos solos em um SIG (Sistema de Informa??o Geogr?fica). Para tal, foram utilizadas imagens de sat?lite dos sensores TM Landsat-5, ETM+ Landsat-7 CCD CBERS-2 e CCD e HRC do CBERS 2B coletadas no per?odo considerado. Os dados de sensoriamento remoto e do sistema de informa??es geogr?ficas integrados a modelos de predi??o de perdas de solos caracterizaramse como importantes instrumentos na an?lise do meio ambiente. A utiliza??o de NDVI constitui-se em ferramenta adequada para a determina??o da cobertura, para aplica??o em modelos de predi??o de perda de solo. As perdas de solo, o potencial natural de eros?o e o risco mostraram forte rela??o com as caracter?sticas do relevo, podendo ser utilizados no planejamento do uso e ocupa??o do solo em ?reas urbana e rural. Mais estudos devem ser realizados associando SIG, modelos de predi??o de eros?o e t?cnicas de sensoriamento remoto no sentido de melhor prever a ocorr?ncia de desastres ambientais, tamb?m para criar bancos de informa??es sobre o comportamento dos solos em rela??o aos diversos agentes causadores do processo erosivo.