@InProceedings{GerenteSothNegrKort:2017:MaMoSc,
               author = "Gerente, J{\'e}ssica and Sothe, Camile and Negr{\~a}o, Priscila 
                         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)}",
                title = "Mass movements? scars classification using data mining 
                         techniques",
            booktitle = "Anais...",
                 year = "2017",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de",
                pages = "3553--3560",
         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 = "Mass movements are destructive natural phenomena that can lead to 
                         serious problems such as economic loss, damage to natural 
                         resources and even injuries and deaths. Efforts have been made to 
                         semi automate the interpretation of remote sensing data in order 
                         to improve efficiency and support specialists in recognizing mass 
                         movements scars. However, this approach is still incipient in 
                         Brazil. This study presents results of semiautomatic 
                         classification of mass movements scars that occurred in Nova 
                         Friburgo (Rio de Janeiro state, Brazil) in 2011 by using 
                         segmentation and applying data mining techniques. Two 
                         classifications were compared, from C4.5 and CART decision tree 
                         algorithms. Data mining techniques confirmed that mass movements 
                         have different spectral characteristics from other classes, 
                         allowing its detection from remote sensing images. The overall 
                         accuracy of C4.5 algorithm was 62.6%, while CART was 66.4%. The 
                         errors occurred mainly in urban areas and in unpaved roads located 
                         at higher altitudes. Spectral digital elevation model (DEM) 
                         average, blue band and NDVI were the more appropriate attributes 
                         to distinguish mass movements patterns. This methodology offered 
                         an alternative, that still needs improvements, to produce data 
                         about statistics and spatial distribution of mass movements, 
                         providing information to be used, for instance, as parameters in 
                         susceptibility maps and models, assisting public policies focused 
                         on natural disasters.",
  conference-location = "Santos",
      conference-year = "28-31 maio 2017",
                 isbn = "978-85-17-00088-1",
                label = "60051",
             language = "en",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3PSLT6K",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3PSLT6K",
           targetfile = "60051.pdf",
                 type = "Geomorfologia",
        urlaccessdate = "28 abr. 2024"
}