author = "Jorge, Anderson and Santos, Erone Ghizoni dos and Shimabukuro, 
                         Yosio Edemir and Moreira, Maur{\'{\i}}cio Alves",
          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 = "Potencial das imagens Landsat ? OLI e RapidEye para identificar 
                         {\'a}reas de degrada{\c{c}}{\~a}o florestal em Quer{\^e}ncia e 
                         Canarana ? MT comparadas com imagens LiDAR",
            booktitle = "Anais...",
                 year = "2017",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de",
                pages = "2107--2114",
         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 degradation process happens when a reduction in forests 
                         quality occurs. In this context, the state of Mato Grosso - MT, 
                         Brazil, is known to have the largest forest degraded areas that 
                         increased during the last years. A recent technique to study the 
                         forest degradation is the Light Detection and Ranging LiDAR, which 
                         allows the assessment of forests in a 3D form. The area studied in 
                         this work is located at northern part of Mato Grosso state, 
                         comprising 1006 ha. We used two approaches to identify the 
                         degradation areas in the OLI and RapdEye images. These results 
                         obtained by these approaches were compared with a LiDAR image 
                         result which have a better spatial resolution. The techniques used 
                         to estimate degradation areas were: the Linear Spectral Mixture 
                         Model (LSMM) and the Maximum Likelihood Classification. The 
                         RapidEye image was better identify forest degradation in isolated 
                         and small areas; and on the other hand, the OLI image was better 
                         to depict the sum of degraded areas. Overall, the LSMM showed a 
                         more accurate classification than the Maximum Likelihood 
                         Classification. Forest degradation is better identified with LiDAR 
                         image, but optical images are a possibility when there isnt the 
                         option to use the cloud points 3D.",
  conference-location = "Santos",
      conference-year = "28-31 maio 2017",
                 isbn = "978-85-17-00088-1",
                label = "59808",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3PSLQ22",
                  url = "http://urlib.net/rep/8JMKD3MGP6W34M/3PSLQ22",
           targetfile = "59808.pdf",
                 type = "LIDAR: sensores e aplica{\c{c}}{\~o}es",
        urlaccessdate = "17 jan. 2021"