Fechar
Metadados

@MastersThesis{Yi:1997:MaMoVe,
               author = "Yi, Jos{\'e} Luiz Rodriguez",
                title = "Mapeamento e monitoramento da vegeta{\c{c}}{\~a}o do estado do 
                         Mato Grosso atrav{\'e}s de imagens AVHRR-NOAA",
               school = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                 year = "1997",
              address = "Sao Jose dos Campos",
                month = "1997-08-26",
             keywords = "vegeta{\c{c}}{\~a}o, Mato Grosso, {\'{\i}}ndice de 
                         vegeta{\c{c}}{\~a}o, {\'{\i}}ndice de vegeta{\c{c}}{\~a}o da 
                         diferen{\c{c}}a normalizada, NDVI, monitoramento, radi{\^o}metro 
                         avan{\c{c}}ado de resolu{\c{c}}{\~a}o muito alta, AVHRR, 
                         vegetation, vegetation index, index of normalized difference 
                         vegetation, monitoring radiometer advanced very high resolution.",
             abstract = "A vegeta{\c{c}}{\~a}o reflete em primeira ordem a complexa 
                         intera{\c{c}}{\~a}o do homem com o meio ambiente. Portanto de 
                         seu conhecimento depender{\'a} a compreens{\~a}o dos 
                         fen{\^o}menos que ocorrem em qualquer n{\'{\i}}vel. O objetivo 
                         deste trabalho {\'e} apresentar a classifica{\c{c}}{\~a}o e 
                         monitoramento da cobertura vegetal em escala regional atrav{\'e}s 
                         de imagens AVHRR/NOAA-11. A {\'a}rea de estudo (estado de Mato 
                         Grosso) foi selecionada devido {\`a} diversidade de tipos de sua 
                         cobertura vegetal. Neste estudo foram utilizadas as imagens AVHRR 
                         formato HRPT do per{\'{\i}}odo de agosto de 1992 a junho de 1994 
                         e a partir das mesmas foram elaborados 6 mosaicos utilizando a 
                         t{\'e}cnica do m{\'a}ximo valor do NDVI. Para a 
                         classifica{\c{c}}{\~a}o da vegeta{\c{c}}{\~a}o foi escolhido o 
                         mosaico de junho de 1993 por ser o melhor da s{\'e}rie em termos 
                         de qualidade radiom{\'e}trica e menor cobertura de nuvens. Foram 
                         mapeadas 8 classes de vegeta{\c{c}}{\~a}o atrav{\'e}s de 
                         classifica{\c{c}}{\~o}es supervisionadas utilizando o algoritmo 
                         MAXVER (para as bandas 1 e 2, e para as imagens fra{\c{c}}{\~a}o 
                         {"}vegeta{\c{c}}{\~a}o{"}, {"}solo{"} e {"}sombra{"})e o 
                         algoritmo {"}Bhattacharrya distance{"} por {"}regi{\~o}es{"} para 
                         as bandas 1 e 2 segmentadas. Essas classifica{\c{c}}{\~o}es 
                         foram avaliadas utilizando-se a estat{\'{\i}}stica Kappa, e como 
                         refer{\^e}ncia o mapa de vegeta{\c{c}}{\~a}o do Brasil na 
                         escala de 1:5.000.000 elaborado pelo IBGE-IBAMA. O maior valor de 
                         Kappa (0,39) correspondeu {\`a} classifica{\c{c}}{\~a}o 
                         supervisionada por {"}regi{\~o}es{"} das bandas segmentadas. Os 
                         mosaicos das outras datas foram utilizados para a 
                         avalia{\c{c}}{\~a}o das mudan{\c{c}}as sazonais das 8 classes 
                         de vegeta{\c{c}}{\~a}o mapeadas. As varia{\c{c}}{\~o}es do 
                         NDVI foram similares {\`a}s encontradas em outros trabalhos sobre 
                         a rela{\c{c}}{\~a}o existente entre a precipita{\c{c}}{\~a}o e 
                         o NDVI. Os resultados obtidos demonstraram a potencialidade das 
                         imagens AVHRR/NOAA para o estudo e mapeamento da 
                         vegeta{\c{c}}{\~a}o em escala regional. ABSTRACT: Vegetation 
                         reflects in first order the complex interaction of human and the 
                         environment. Therefore from its knowledge will depend the 
                         understanding of the phenomenon in any leveI. The objective of 
                         this work is to present the classification and monitoring of 
                         vegetation cover, in a regional scale, using AVHRR/NOAA-11 images. 
                         The study area, Mato Grosso state, was selected due to its high 
                         diversity of vegetation cover types. The AVHRR/HRPT images 
                         acquired in the period from August 1992 to June 1994 were used in 
                         this study. Six mosaics using the maximum value NDVI composition 
                         techniques were built. For vegetation classification, the mosaic 
                         from June 1993 was selected because it was the best mosaic in that 
                         period considering the radiometric quality and cloud cover. Eight 
                         vegetation classes were mapped using supervised classification 
                         techniques using: Maxver algoritm (for 1 and 2 AVHRR bands, and 
                         for fraction images {"}vegetation{"}, {"}soil{"}, and {"}shade{"}) 
                         and Bhattacharrya distance {"}regions{"} algorithm for 1 and 2 
                         segmented AVHRR bands. These classifications were evaluated using 
                         Kappa statistics based on the vegetation map of Brazil in 1: 
                         5.000.000 scale elaborated by IBGE-IBAMA as reference ({"}ground 
                         truth{"}). The highest Kappa value (0,39) corresponded to 
                         supervised classification using {"}regions{"} algorithm from 
                         segmented bands. Other mosaics were used for the assessment of 
                         seasonal changes for the eight vegetation classes mapped. NDVI 
                         variations were in agreement with other works concerned to the 
                         relation between NDVI and precipitation. The obtained results 
                         demonstrated the potentiality of AVHRR-NOAA images for vegetation 
                         mapping in a regional scale.",
            committee = "Shimabukuro, Yosio Edemir (orientador/presidente) and Batista, 
                         Get{\'u}lio Teixeira and Carvalho, Vitor Celso de and Miranda, 
                         Evaristo Eduardo de",
           copyholder = "SID/SCD",
         englishtitle = "Classification and monitoring of vegetation using AVHRR-NOAA 
                         images",
                label = "7995",
             language = "pt",
                pages = "157",
                  ibi = "6qtX3pFwXQZ3r59YD6/GPi5D",
                  url = "http://urlib.net/rep/6qtX3pFwXQZ3r59YD6/GPi5D",
           targetfile = "publicacao.pdf",
        urlaccessdate = "30 nov. 2020"
}


Fechar