879 resultados para Geociencias - Sensoriamento remoto


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A presença de algumas substâncias na água, como pigmentos fotossintetizantes, particulados, etc., afeta sua cor, provocando mudanças na radiância da água registrada por sensores orbitais. Nesse sentido, o sensoriamento remoto pode se constituir em uma fonte complementar de dados para o monitoramento da qualidade da água em grandes reservatórios (Novo et al., 1994). No contexto de um projeto de pesquisa realizado na AES Tietê S.A., com o objetivo de desenvolver técnicas para a avaliação da área com infestação de plantas aquáticas, imagens orbitais multiespectrais foram usadas tanto para mapear a dispersão espacial e estimar a área de ocorrência de macrófitas aquáticas, em duas épocas distintas, quanto para orientar a definição de pontos de amostragem in loco, visando a coleta e posterior análise da água e de sedimentos nos reservatórios. Este trabalho apresenta uma descrição do procedimento metodológico adotado na análise das imagens multiespectrais, bem como os resultados obtidos na caracterização dos reservatórios de Barra Bonita, Bariri, Ibitinga, Promissão e Nova Avanhandava, em termos de seu dimensionamento, variabilidade espectral da água e presença de macrófitas emersas, nas duas épocas do ano consideradas.

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A cidade de Ilha Solteira está propondo o projeto Conquista da Água, visando ao desenvolvimento sustentável do Município, a partir do turismo, da cultura e da ciência e tecnologia. Para atingir esse objetivo, os planejadores do meio físico-ambiental necessitam de dados que possam auxiliá-los na seleção dos melhores locais onde serão instalados as avenidas, o aeroporto e os demais espaços que comporão esse projeto. A elaboração do mapa de uso e cobertura do solo da área de interesse do projeto constitui um dos temas necessários ao banco de dados a ser utilizado pelos planejadores. Neste trabalho, é apresentado o estado de degradação dessa área, o que auxiliará na definição das estratégias de conservação ambiental.

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Remote sensing is one technology of extreme importance, allowing capture of data from the Earth's surface that are used with various purposes, including, environmental monitoring, tracking usage of natural resources, geological prospecting and monitoring of disasters. One of the main applications of remote sensing is the generation of thematic maps and subsequent survey of areas from images generated by orbital or sub-orbital sensors. Pattern classification methods are used in the implementation of computational routines to automate this activity. Artificial neural networks present themselves as viable alternatives to traditional statistical classifiers, mainly for applications whose data show high dimensionality as those from hyperspectral sensors. This work main goal is to develop a classiffier based on neural networks radial basis function and Growing Neural Gas, which presents some advantages over using individual neural networks. The main idea is to use Growing Neural Gas's incremental characteristics to determine the radial basis function network's quantity and choice of centers in order to obtain a highly effective classiffier. To demonstrate the performance of the classiffier three studies case are presented along with the results.

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This work was developed in the Potengi river estuary, northern coastal city of Natal, located in the State of Rio Grande do Norte. The objective was to study the dynamics of Multitemporal Space in that estuary, however, analyze how this environment has behaved during the years 1988, 1994 and 2006. The definition of that, there was the fact that during that period, which occurred in that area and more intensive space exploration to practice economic activities as well as a greater and more rapid expansion of urban area. This study was supported by the Remote Sensing, who have been shown today, as an efficient analysis of the environmental studies, through geoprocessing techniques. From the performed analysis it was found that occurred during the period of study (1988 to 2006), a huge change in the design of the estuary of Potengi river. The figures showed the area of vegetation was decreased 65.22%, the deforested area increased by 70.44% and the shrimp activity grow 452.07% and the urban area was increased in 52.65% along that period described. Considering the numbers shown there that the process of occupying space in that area requires attention, because it is an environment that represents a huge contribution to the ecological balance.

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Soil use for the development of activities as agriculture and livestock has been causing great alterations in the environment, mainly when these are practiced intensively, disrespecting the fragility and aptitude of the natural resources. Therefore, it is essential that the planning of the agricultural activities is done, taking into consideration the several environmental criteria involved in the decision-making process. Thus, this study aimed to analyze the agricultural aptitude classes of lands from a watershed through geographical information system (GIS). The Arroio Ajuricaba watershed is located in the Municipality of Marechal Cândido Rondon - PR among the coordinates UTM 787309m E and 793892m E; 7275026m N and 7281310m N, in the Spindle 21, presenting an area of 1681ha. Soil maps, in semi detail scale, was the digital cartographic base used. The results allowed to conclude that 42.41% of the basin presented a good aptitude for farming in handling level of high technology (handling level C), regular aptitude for B, and restricted aptitude for A [class 1(a)bC] and that 12% of the area had regular aptitude for farming in the three handling levels (class 1abc). The watershed lands (14.24%) presented regular aptitude for farming in handling level C, restricted aptitude for B, and inapt for A [class of aptitude 2(b)c]; 15.85% presented good aptitude for planted pasture (class 4P) and 12.21% were considered without aptitude for agricultural use (class 6). We can say that 71.94% of the lands has aptitude for farming, although they present different degrees of limitations that request different treatments for its conservation.

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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Pós-graduação em Ciências Cartográficas - FCT

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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Pós-graduação em Matematica Aplicada e Computacional - FCT