@InProceedings{CostaSanoBrit:2013:IdFlEs,
               author = "Costa, Samuel C{\'e}sar Rodarte and Sano, Edson Eyji and Brites, 
                         Ricardo Seixas",
                title = "Identifica{\c{c}}{\~a}o da floresta estacional decidual na bacia 
                         do rio S{\~a}o Miguel, regi{\~a}o do Alto S{\~a}o Francisco - 
                         MG",
            booktitle = "Anais...",
                 year = "2013",
               editor = "Epiphanio, Jos{\'e} Carlos Neves and Galv{\~a}o, L{\^e}nio 
                         Soares",
                pages = "8316--8321",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 16. (SBSR)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             abstract = "The deciduous forests (dry forests) are an important type of 
                         forestlands present in the tropical savanna (Cerrado). They are 
                         composed of deciduous species that lose up to 90% of the leaves in 
                         the dry season. The identification of representative types of 
                         tropical savannas vegetation through monotemporal satellite images 
                         is difficult, but it may be facilitated when time series of images 
                         along the hydrological cycle are analyzed. In this study, we 
                         analyzed the time series of Landsat images from the Sao Miguel 
                         basin, Minas Gerais State, to identify dry forests occurrences. 
                         Five Landsat images from May to September of 2007 were converted 
                         into the EVI (enhanced vegetation index) and summed one-by-one. 
                         After, was executed an classification of the resultant image 
                         through decision tree method. The numeric range for the dry forest 
                         was obtained by statistical analysis of pixel values contained in 
                         the area of dry forest that was bounded visiting the basin region. 
                         Approximately 125 km2 (23,5% of the basin) of dry forest were 
                         identified by this method. The results showed an increased 
                         improvement in the dry forest mapping in the study area. The dry 
                         forest has suffered intense deforestation, mainly by the increase 
                         of the agricultural frontier and the advance of mining. Palavras 
                         chave: dry forest, image processing, EVI, bacia do rio S{\~a}o 
                         Miguel, mata seca, processamento de imagens.",
  conference-location = "Foz do Igua{\c{c}}u",
      conference-year = "13-18 abr. 2013",
                 isbn = "{978-85-17-00066-9 (Internet)} and {978-85-17-00065-2 (DVD)}",
                label = "1036",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "3ERPFQRTRW34M/3E7GHKR",
                  url = "http://urlib.net/ibi/3ERPFQRTRW34M/3E7GHKR",
           targetfile = "p1036.pdf",
                 type = "Processamento de Imagens",
        urlaccessdate = "20 maio 2024"
}