@InProceedings{PletschKortEscaSian:2017:StCaSo,
               author = "Pletsch, Mikhaela Alo{\'{\i}}sia J{\'e}ssie Santos and Korting, 
                         Thales Sehn and Escada, Maria Isabel Sobral and Siani, Sacha 
                         Maru{\~a} Ortiz",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)}",
                title = "Data mining applied to temporal dynamics of deforestation pattern: 
                         a study case in Southern Amazon forest, Brazil",
            booktitle = "Anais...",
                 year = "2017",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de",
                pages = "6959--6966",
         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 = "The Amazon forest is one of the most prominent tropical rainforest 
                         worldwide. Providing several benefits, measures to its protection 
                         have been included in governmental decision making. In this 
                         context, the Brazilian initiative known as PRODES was implemented 
                         in 1988. Coordinated by the National Institute of Space Research, 
                         INPE, this project monitors annually deforestation in Brazilian 
                         Amazon. Even though, it is not enough to avoid deforestation and 
                         more analysis are required. In such manner, landscape metrics are 
                         commonly used to support analysis of deforestation dynamics and 
                         spatial patterns. It is just possible, considering that a real 
                         landscape reflects certain spatial patterns and structures. 
                         Nonetheless, taking into account Amazon extension, classifying 
                         landscape manually is considered a very time consuming task. In 
                         this manner, the aim of this study is to automate landscape 
                         classification, based on an already visually classified area in 
                         Southern Amazon forest. After that, we applied the decision tree 
                         to 1985 and 2015 data. Although data mining techniques were used, 
                         the final classification was not satisfactory for all the 
                         applications. Thus, we propose as further researches alternatives 
                         to overcome these issues and to validate the process. Finally, a 
                         discussion about the algorithm is also held as well as local 
                         temporal dynamics of deforestation.",
  conference-location = "Santos",
      conference-year = "28-31 maio 2017",
                 isbn = "978-85-17-00088-1",
                label = "59996",
             language = "en",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3PSMDRS",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3PSMDRS",
           targetfile = "59996.pdf",
                 type = "Classifica{\c{c}}{\~a}o e minera{\c{c}}{\~a}o de dados",
        urlaccessdate = "27 abr. 2024"
}