Use este identificador para citar ou linkar para este item: http://repositorio.ufla.br/jspui/handle/1/9467
Título: Análise orientada a objetos de imagens de satélite para mapeamento de áreas de preservação em reservatório hidrelétrico
Autores: Carvalho, Mirléia Aparecida
Ramirez, Gláucia Miranda
Volpato, Margarete Marin Lordelo
Palavras-chave: Sensoriamento remoto
Quickbird
Matas ciliares
Algoritmo watersheds by immersion
Índice kappa
Exatidão global
Remote sensing
Riparian woods
Watershed by immersion algorithm
Kappa index
Global accuracy
Data do documento: 12-Mai-2015
Editor: UNIVERSIDADE FEDERAL DE LAVRAS
Citação: SOARES, J. F. Análise orientada a objetos de imagens de satélite para mapeamento de áreas de preservação em reservatório hidrelétrico. 2015. 67 p. Dissertação (Mestrado em Engenharia Agrícola)-Universidade Federal de Lavras, Lavras, 2015.
Resumo: Considered one of the vegetative mitigation practices for water resource degradation, the maintenance of riparian woods is recommended and demanded by law. However, in Brazil, these areas are still uncharacterized. In light of this reality, it becomes necessary to widen researches that allow us to characterize these areas in an integrated manner, generating efficient and quick results with low cost. Remote sensing is the option that demonstrates great application potential. Thus, in this work, we aimed at mapping and characterizing soil use and occupation in permanent preservation areas at the Funil Hydroelectric Power Plant (Funil HEP) reservoir, using high spatial resolution satellite imaging – Quickbird – in true composition (RGB-321) allied to object-oriented analysis techniques. For image segmentation, based on the watersheds by immersion algorithm, we used the Envi EX® 4.8 software. In order to classify the image, we used the algorithms K-nearest neighbor, Support vector machine and Maximum Likelihood. We analyzed the accuracy of the mappings comparing the results obtained to the map generated with the visual classification of the image of the study area (reference map). With the results, we concluded that the K-nearest neighbor algorithm was the best for mapping soil use and occupation in the study area, with kappa index of 0.88 and global accuracy of 91.40%.
URI: http://repositorio.ufla.br/jspui/handle/1/9467
Aparece nas coleções:Engenharia Agrícola - Mestrado (Dissertações)



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