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@InProceedings{CapanemaEsca:2017:GeInEs,
               author = "Capanema, Vin{\'{\i}}cius do Prado and Escada, Maria Isabel 
                         Sobral",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)}",
                title = "Degrada{\c{c}}{\~a}o Florestal na Amaz{\^o}nia: 
                         Gera{\c{c}}{\~a}o de um Indicador Espacial de Suscetibilidade 
                         {\`a} Degrada{\c{c}}{\~a}o Florestal",
            booktitle = "Anais...",
                 year = "2017",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de",
                pages = "6994--7001",
         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 = "Forest degradation and deforestation are responsible for the 
                         decrease in the area of pristine forests in Amazon. The remote 
                         sensing and GIS tools use facilitate the study and the 
                         understanding of these striking phenomena in the forest. Thus, 
                         this study aimed to generate a susceptibility spatial index of 
                         forest degradation using a grid of 1km2, considering variables 
                         generated from data of deforestation, fire, road network, 
                         sustainable forestry management plans, indigenous lands and 
                         density edges. Two different methods were tested for variable 
                         integration: simple and weighted average. The influence of 
                         landscape metrics in the susceptibility index generation was 
                         tested. The method that obtained the best result was the simple 
                         average. Through this method, a new susceptibility spatial index 
                         of forest degradation was generated using 2014 year data. The 
                         results showed that the most susceptible areas are close to the 
                         largest density hotspots and road network. The results also showed 
                         the great influence of edge landscape metric in the forest 
                         degradation susceptibility index when comparing with the index 
                         generated without this variable.",
  conference-location = "Santos",
      conference-year = "28-31 maio 2017",
                 isbn = "978-85-17-00088-1",
                label = "59753",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3PSMETF",
                  url = "http://urlib.net/rep/8JMKD3MGP6W34M/3PSMETF",
           targetfile = "59753.pdf",
                 type = "Degrada{\c{c}}{\~a}o de florestas",
        urlaccessdate = "27 nov. 2020"
}


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