@InProceedings{MauranoEscaRenn:2017:DEPoSe,
               author = "Maurano, Luis Eduardo and Escada, Maria Isabel Sobral and 
                         Renn{\'o}, Camilo Daleles",
          affiliation = "{} and {Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "Desmatamento da Amazo\̂nia: O DETER pode ser utilizado como 
                         preditor das taxas anuais de desmatamento geradas pelo PRODES?",
            booktitle = "Anais...",
                 year = "2017",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de",
                pages = "4306--4313",
         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 National Institute for Space Research- INPE, developed two 
                         operational systems to monitor deforestation in the Legal Amazon: 
                         PRODES and DETER. PRODES is an annual inventory of primary forest 
                         loss and is based on Landsat image analysis, its main objective is 
                         to estimate the annual rate of deforestation. DETER provides daily 
                         Alert of deforestation and forest degradation for law enforcement 
                         based on MODIS sensor images. Although these systems have been 
                         developed to meet different goals, a frequent question arises 
                         about the possibility of predicting the PRODES rate based on DETER 
                         data. Considering this question, a regression analysis was 
                         developed combining DETER data, aggregated for a period of one 
                         year, and the annual rate produced by PRODES, for the period of 
                         2005 to 2016. The regression analysis resulted in a high 
                         coefficient of determination of 0.87, and in an average error 
                         estimated of 18.5%. However, the error can be larger. In 2015, the 
                         PRODES rate was overestimated in 40.7%. This result shows that the 
                         use of the regression to estimate deforestation rate has to be 
                         done carefully. Despite of it, DETER data can be used as a 
                         predictor of PRODES trends for the Legal Amazon extent, with the 
                         data aggregated on an annual basis. In the analyzes of the states, 
                         the results varied and DETER showed to be good at predicting rates 
                         for Mato Grosso state, with a coefficient of determination of 
                         0.95, but it wasn´t good at predicting deforestation rates for 
                         Par{\'a} state, with coefficient of determination of 0.55.",
  conference-location = "Santos",
      conference-year = "28-31 maio 2017",
                 isbn = "978-85-17-00088-1",
                label = "59490",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3PSM2RK",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3PSM2RK",
           targetfile = "59490.pdf",
                 type = "Desflorestamento",
        urlaccessdate = "27 abr. 2024"
}