@InProceedings{GirolamoNetoFonValNevKor:2017:DeClAu,
               author = "Girolamo Neto, Cesare Di and Fonseca, Leila Maria Garcia and 
                         Valeriano, Dalton de Morisson and Neves, Alana Kasahara 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)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)} and {Instituto Nacional de Pesquisas Espaciais 
                         (INPE)}",
                title = "Desafios na classifica{\c{c}}{\~a}o autom{\'a}tica de 
                         fitofisionomias do Cerrado brasileiro com base em mapas de 
                         refer{\^e}ncia na escala 1:250.000",
            booktitle = "Anais...",
                 year = "2017",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de",
                pages = "6647--6654",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 18. (SBSR)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             abstract = "Brazilian Savanna, also known as Cerrado, is one of the most 
                         important biomes in world in terms of biodiversity. Mapping 
                         Cerrado is an important task, considering that deforestation 
                         reached almost half of its original area. Two classification 
                         systems are currently used to map Cerrado Land Cover and reference 
                         maps are available on the scale of 1:250.000. Thus, the aim of 
                         this study is to discuss the challenges regarding the automatic 
                         classification of Cerrado land cover using the reference maps of 
                         1:250.000 produced by Brazilian Institute of Geography and 
                         Statistics (IBGE). Three protected Cerrado areas were used in this 
                         study, Bras{\'{\i}}lia National Park (DF), Emas National Park 
                         (GO) and Chapada das Mesas National Park (MA). Images from the 
                         Landsat-8 satellite, acquired in the dry and wet seasons, were 
                         used in the classification. Images were segmented and classified 
                         according to Brazilian vegetation classification system. 
                         Classification was performed through the random forest algorithm. 
                         The classification results pointed out an overall accuracy of 
                         84.4%. The main source of classification error was transition 
                         areas among the vegetation formations. Due to the map scale, some 
                         areas close to the edges are not distinguished with precision and 
                         may be incorrectly classified. The use of high resolution images 
                         can improve the classification results in the vegetation 
                         boundaries. Another notable problem is that the Brazilian 
                         vegetation classification system does not separate Gallery Forests 
                         from other classes although the segmentation has distinguished 
                         these segments quite well.",
  conference-location = "Santos",
      conference-year = "28-31 maio 2017",
                 isbn = "978-85-17-00088-1",
                label = "59460",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3PSMDAS",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3PSMDAS",
           targetfile = "59460.pdf",
                 type = "Classifica{\c{c}}{\~a}o e minera{\c{c}}{\~a}o de dados",
        urlaccessdate = "27 abr. 2024"
}