@InProceedings{SantosJorgShimGonį:2017:EsBiAc,
               author = "Santos, Erone Ghizoni dos and Jorge, Anderson and Shimabukuro, 
                         Yosio Edemir and Gon{\c{c}}alves, Fabio Guimar{\~a}es",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)}",
                title = "Estimativa de biomassa acima do solo para uma {\'a}rea queimada e 
                         uma {\'a}rea de corte seletivo no munic{\'{\i}}pio de Feliz 
                         Natal MT por meio de dados LiDAR",
            booktitle = "Anais...",
                 year = "2017",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de",
                pages = "4321--4328",
         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 = "Remote sensing techniques have aided measurement and estimation of 
                         forest area and the identification of deforestation and forest 
                         degradation. Light Detection and Ranging (LiDAR) allows mapping 
                         the vertical structure of forests and helps obtaining information 
                         in areas of difficult access. This study was conducted in two 
                         areas in the municipality of Feliz Natal, Mato Grosso, Brazil. The 
                         first area (area 1) was burned in 2006, 2008 and 2011, while the 
                         second area (area 2) was subjected to selective logging in 2006 
                         and 2007. Both areas were inventoried in the field: area 1 in 2013 
                         and area 2 in 2015, totalizing 27 samples. In addition to the 
                         field data, airborne LiDAR data were acquired for the two areas in 
                         August 2013. The objective of this study was to use LiDAR data to 
                         estimate aboveground biomass (AGB) in these areas and understand 
                         the differences in their carbon stocks as a result of fire and 
                         selective logging. Structure metrics extracted from the point 
                         cloud data were linearly and highly correlated with AGB. The 
                         multiple regression model created with the stepwise procedure 
                         presented an R2 of 0.96 and a root mean square error of 8.7 Mg/ha 
                         (25.3%). Using LiDAR data, it was possible to model the 
                         relationship between AGB and LiDAR metrics for areas that have 
                         been degraded by fire and selective logging. The results showed a 
                         difference in carbon stocks of 15.8% for these areas, indicating 
                         that the degradation by fire was considerably more intense in this 
                         site.",
  conference-location = "Santos",
      conference-year = "28-31 maio 2017",
                 isbn = "978-85-17-00088-1",
                label = "59392",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3PSM2SA",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3PSM2SA",
           targetfile = "59392.pdf",
                 type = "LIDAR: sensores e aplica{\c{c}}{\~o}es",
        urlaccessdate = "27 abr. 2024"
}