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@InProceedings{CostaFonsKort:2015:ClPaCu,
               author = "Costa, Wanderson Santos and Fonseca, Leila Maria Garcia and 
                         Korting, Thales Sehn",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)}",
                title = "Classifica{\c{c}}{\~a}o de pastagens cultivadas e 
                         forma{\c{c}}{\~o}es campestres nativas no Cerrado brasileiro a 
                         partir da an{\'a}lise de s{\'e}ries temporais extra{\'{\i}}das 
                         de {\'{\i}}ndices EVI do sensor MODIS",
            booktitle = "Anais...",
                 year = "2015",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de",
                pages = "1516--1523",
         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 = "One of the most biodiverse regions on the planet, the Brazilian 
                         Cerrado has an area of approximately 2 million km2 and it is the 
                         second largest biome in Brazil. Among the land cover modifications 
                         in the biome, over one fourth of its area has been changed into 
                         cultivated pastures in the last few years. Categorizing types of 
                         land use and cover in the Cerrado and its native formations is 
                         important for protection policy and monitoring of the Brazilian 
                         Cerrado. Based on remote sensing techniques, this work aims at 
                         developing a methodology to map pasture and native grassland areas 
                         in the biome. Data related to EVI vegetation indices obtained by 
                         MODIS images were used to perform image classification. This study 
                         encompasses a Cerrado area that comprises a region of Serra da 
                         Canastra National Park and neighboring regions, that contains all 
                         targets of interest. Support Vector Machines, Decision Trees and 
                         Random Forests algorithms were compared, and the results showed 
                         that the analysis of different attributes extracted from EVI 
                         indices can aid in the classification process. As a means to 
                         distinguish grassland and cultivated pasture zones, we obtained 
                         accuracies up to 85,96% in the study area, identifying data and 
                         attributes required to identify these areas by remote sensing 
                         images.",
  conference-location = "Jo{\~a}o Pessoa",
      conference-year = "25-29 abr. 2015",
                 isbn = "978-85-17-0076-8",
                label = "284",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3JM487R",
                  url = "http://urlib.net/rep/8JMKD3MGP6W34M/3JM487R",
           targetfile = "p0284.pdf",
                 type = "An{\'a}lise de s{\'e}ries de tempo de imagens de sat{\'e}lite",
        urlaccessdate = "27 nov. 2020"
}


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