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@InProceedings{CassolMoraShim:2015:DeTeSe,
               author = "Cassol, Henrique Luis Godinho and Moraes, Elisabete Caria and 
                         Shimabukuro, Yosio Edemir",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)}",
                title = "Detec{\c{c}}{\~a}o temporal semiautom{\'a}tica de desmatamento 
                         na Floresta Ombr{\'o}fila Mista via sensoriamento remoto",
            booktitle = "Anais...",
                 year = "2015",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de",
                pages = "4914--4921",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 17. (SBSR)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             abstract = "In this work, we discuss the use of principal component analysis 
                         (PCA) and Asymmetric Fragmentation Operator (AFO) for evaluating 
                         the deforestation in Araucaria Forest. Thus, we asked: is it 
                         possible to detect forest loss basing on anomalies of temporal 
                         spectrum from forest? To answer this question were utilized two 
                         changed detection analyses, PCA and AFO, on two singular pixels 
                         with deforestation behavior time-series EVI with 16 days 
                         composition derived from MODIS. The results showed that PCA 
                         separated deforestation dates of forest time series mainly on two 
                         principal components. These principal components could be used to 
                         detecting the date of forest loss in a semi-automated approach. 
                         Semi-automated because is necessary a forest class masking before 
                         analysis. The AFO do not adequately separated deforestation from 
                         temporal spectrum of forest, however, data transformation could 
                         improve its analysis and the change detection.",
  conference-location = "Jo{\~a}o Pessoa",
      conference-year = "25-29 abr. 2015",
                 isbn = "978-85-17-0076-8",
                label = "959",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3JM4DDF",
                  url = "http://urlib.net/rep/8JMKD3MGP6W34M/3JM4DDF",
           targetfile = "p0959.pdf",
                 type = "Mudan{\c{c}}a de uso e cobertura da Terra",
        urlaccessdate = "27 nov. 2020"
}


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