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@MastersThesis{Freitas:2007:TéAnSé,
               author = "Freitas, Ramon Morais de",
                title = "T{\'e}cnicas de an{\'a}lise de s{\'e}ries temporais aplicadas 
                         {\`a} detec{\c{c}}{\~a}o de desflorestamento em tempo real",
               school = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                 year = "2007",
              address = "S{\~a}o Jos{\'e} dos Campos",
                month = "2007-02-28",
             keywords = "Amaz{\^o}nia, detec{\c{c}}{\~a}o de mudan{\c{c}}as, 
                         desflorestamento, MODIS, sensoriamento remoto, an{\'a}lise de 
                         s{\'e}ries temporais, Amazon region, change detection, 
                         deforestation, MODIS, remote sensing, time series analysis.",
             abstract = "A detec{\c{c}}{\~a}o de desflorestamento em tempo real ou 
                         pr{\'o}ximo do real {\'e} de fundamental import{\^a}ncia para 
                         a{\c{c}}{\~o}es conjuntas de car{\'a}ter preventivo e punitivo 
                         dos {\'o}rg{\~a}os governamentais no que tange a 
                         pol{\'{\i}}tica de controle e preven{\c{c}}{\~a}o do 
                         desflorestamento na Amaz{\^o}nia. Neste contexto, este trabalho 
                         tem o objetivo de propor uma metodologia para detec{\c{c}}{\~a}o 
                         de desflorestamento em tempo real a partir de imagens MODIS. A 
                         metodologia consiste em caracterizar e detectar {\'a}reas 
                         desflorestadas atrav{\'e}s de s{\'e}ries espa{\c{c}}o temporais 
                         de imagens MODIS. A {\'a}rea de estudo proposta para 
                         realiza{\c{c}}{\~a}o da pesquisa compreende tr{\^e}s 
                         micro-regi{\~o}es do estado de Mato Grosso que tem sido 
                         caracterizada pela alta taxa de desflorestamento nos {\'u}ltimos 
                         anos. A detec{\c{c}}{\~a}o do desflorestamento em tempo real 
                         utilizou-se das imagens MOD02 di{\'a}rias adquiridas no 
                         per{\'{\i}}odo de 2005 a 2006. Seguindo a metodologia PRODES e 
                         DETER a detec{\c{c}}{\~a}o das {\'a}reas desflorestadas 
                         basearam-se nas fra{\c{c}}{\~o}es vegeta{\c{c}}{\~a}o e solo 
                         derivadas do modelo linear de mistura espectral. Para 
                         constru{\c{c}}{\~a}o das s{\'e}ries espa{\c{c}}o-temporais 
                         foram utilizados os produtos de reflect{\^a}ncia e temperatura de 
                         superf{\'{\i}}cie. A caracteriza{\c{c}}{\~a}o das s{\'e}ries 
                         temporais foi baseada em 4 t{\'e}cnicas: wavelets, an{\'a}lise 
                         de padr{\~o}es de gradientes, diverg{\^e}ncia de 
                         KullBack-Leibler e expoente de Hurst. Os dados de campanhas de 
                         campo, Projeto DETER, PRODES, imagens CBERS e TM, foram utilizados 
                         como verdade terrestre para valida{\c{c}}{\~a}o da metodologia. 
                         A utiliza{\c{c}}{\~a}o de imagens multitemporal do produto MOD02 
                         apresentou uma exatid{\~a}o global de detec{\c{c}}{\~a}o dos 
                         focos de desflorestamento (92,72%) quando comparados com os dados 
                         de verdade terrestre. Com a utiliza{\c{c}}{\~a}o das 
                         transformadas de wavelets foi poss{\'{\i}}vel filtrar e 
                         caracterizar a data e o uso do solo ap{\'o}s o desflorestamento, 
                         i.e., mudan{\c{c}}a din{\^a}mica da cobertura do solo. Com a 
                         an{\'a}lise de padr{\~o}es de gradiente {\'e} proposta uma 
                         metodologia para redu{\c{c}}{\~a}o da dimensionalidade de dados 
                         que permite identificar {\'a}reas desflorestadas. Atrav{\'e}s da 
                         Diverg{\^e}ncia de Kullback-Leibler e Expoente de Hurst foi 
                         poss{\'{\i}}vel analisar a complexidade estat{\'{\i}}stica e 
                         textura das imagens fra{\c{c}}{\~a}o vegeta{\c{c}}{\~a}o para 
                         {\'a}reas desflorestadas e {\'a}reas de floresta. ABSTRACT: The 
                         detection of deforestation in a near real time is of fundamental 
                         importance for Government policy and surveillance of forest areas. 
                         A near real-time detection would allow control of the increase of 
                         new clearings and monitoring of the deforestation pattern and 
                         dynamics in Amazonia. In this context, this work has the objective 
                         to propose a methodology to detect deforestation in near real time 
                         using MODIS images. The methodology consists on to characterize 
                         and detect deforested areas using temporal spatial time series of 
                         MODIS images. The study area is located in the Mato Grosso State, 
                         Brazilian Amazonia, encompassing three micro regions that has been 
                         characterized by high deforestation rates in the last years. The 
                         detection of deforestation in a near real time used daily MODIS 
                         images (MOD02) acquired in 2005 to 2006 time period. Following the 
                         PRODES and DETER methodology the detection of deforested areas was 
                         based on multitemporal soil and vegetation fraction images derived 
                         from linear spectral mixing model. The time-series analysis was 
                         based on the surface reflectance and surface temperature products 
                         aquired from 2000 to 2006. For the characterization of 
                         spatiotemporal time series was used 4 technics: wavelets 
                         transforms, gradiente patterns analysis, Hurst exponent and 
                         Kullback-Leibler divergency. The field campaign data, PRODES and 
                         DETER information, and Landsat TM and CBERS CCD images were 
                         utilized as ground truth for validation of the methodology. The 
                         use of multitemporal images of MOD02 product presented a global 
                         accuracy of 92.72% to detect the deforestation when compared with 
                         ground truth. With the use of wavelets transform it was possible 
                         to characterize the deforestation date and pos-deforestation land 
                         use type. (croplands, pasture or regrowth), i.e., the landcover 
                         change dynamics. The Gradient Pattern Analysis showed a new 
                         aproach to reduce the dimensionality of data volume for in 
                         deforestation detection. The Kullback-Leibler divergency and Hurst 
                         exponent were used to analyze the statistical complexity and 
                         texture of vegetation fraction images for forest and deforested 
                         areas.",
            committee = "Novo, Evlyn Marcia Le{\~a}o de Moraes (presidente) and 
                         Shimabukuro, Yosio Edemir (orientador) and Rosa, Reinaldo Roberto 
                         (orientador) and Valeriano, Dalton de Morisson and Haertel, Vitor 
                         Francisco de Ara{\'u}jo",
           copyholder = "SID/SCD",
         englishtitle = "Times-series analysis applied to deforestation detection and 
                         characterization in real time",
             language = "pt",
                pages = "197",
                  ibi = "6qtX3pFwXQZGivnK2Y/Q5cs9",
                  url = "http://urlib.net/ibi/6qtX3pFwXQZGivnK2Y/Q5cs9",
           targetfile = "publicacao.pdf",
        urlaccessdate = "23 jun. 2024"
}


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