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Land use/cover change (LUCC) is a cross-disciplinary research field in which remote sensing and geographic information systems (GIS) techniques have played an important role. Sugarcane expansion - a major LUCC process in Brazil - has been monitored by INPE's CANASAT project since 2003 (http://www.dsr.inpe.br/canasat/). In this study, an assessment of land use/cover conversion is presented in a region of important sugarcane expansion, the município of Barretos, in São Paulo State, in the 2003-2009 period. Detection of land use/cover changes is performed by classification of 2003 land use/cover and comparing 2003 classes with 2009 CANASAT maps. Land use/cover classification uses Landsat TM, and MODIS EVI2 time series, and two different techniques, segmentation followed by supervised classification, and object-based image analysis (OBIA) combining remotely sensed imagery and ancillary data. Results show that at least 40% of the increases in sugarcane area were due to conversion of pasture land; comparison of the two classification techniques shows better results by the OBIA technique, but both approaches agreed that most sugarcane expansion occurred on pasture land.

The objective of this study is to measure the socio-spatial inequalities in residential areas of São José dos Campos, based on an intra-urban analysis, using QuickBird-2 satellite data. The residential space was considered to connect economic, social and political dimensions, in order to verify the corresponding stratification in space. The stratification can be observed at the configuration of different residential areas, showing the social division of space. Initially the following elements were considered in the concrete space: Size of lots, Vegetation cover, Population density, Materials of roofs, Swimming pools, Shadow, Bare Soil, Organization of blocks and lots. In this work, the establishment of a correlation among spatial differentiation elements and the living space allows to understand both the social contents and the variation of social phenomena in space. The measurement of differentiation elements was possible with QuickBird-2 image data. The Object-based Image Analysis (OBIA) concept was used for the classification of differentiation elements, while the visual analysis and data crossing was considered for others. The results obtained present the distribution of these elements and the identification of patterns in residential areas, corresponding to different spatial occupation forms by social classes. This study shows that high resolution satellite images contribute with new and relevant information about the concrete space, at intra-urban scale and it opens new possibilities for the analysis of the inequality dimension: the socio-spatial differentiation.

Este estudo tem como objetivo o desenvolvimento de uma metodologia de classificação automática de áreas urbanizadas contínuas e dispersas que seja replicável em diferentes regiões do Brasil. Com essa metodologia busca-se o aumento da exatidão do mapeamento bem como reduzir a subjetividade e o tempo empregado no procedimento. Para este fim, aplicou-se, usando o software Definiens, a classificação baseada em Objeto em imagem LANDSAT da região de Piracicaba, Limeira e Rio Claro, do estado de São Paulo, obtida em 2007. Este procedimento consiste na segmentação multiresolução das imagens e na classificação baseada na lógica fuzzy. Na avaliação dos resultados foram utilizadas imagens de alta resolução, disponíveis no Google Earth. O bom desempenho obtido na classificação automática da área de estudo (índice global de 0,94 e Kappa de 0,72) indica a viabilidade do método aplicado para outras áreas urbanizadas.