@InProceedings{JorgeShimSantGasp:2017:InTrDe,
               author = "Jorge, Anderson Alex and Shimabukuro, Yosio Edemir and Santos, 
                         Erone Ghizoni and Gasparini, Kaio",
          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 = "Individual tree detection in intact forest and degraded forest 
                         areas in the north region of Mato Grosso State, Brazilian Amazon",
            booktitle = "Proceedings...",
                 year = "2017",
         organization = "AGU Fall Meeting",
             abstract = "The State of Mato Grosso - MT has the second largest area with 
                         degraded forest among the states of the Brazilian Legal Amazon. 
                         Land use and land cover change processes that occur in this region 
                         cause the loss of forest biomass, releasing greenhouse gases that 
                         contribute to the increase of temperature on earth. These degraded 
                         forest areas lose biomass according to the intensity and magnitude 
                         of the degradation type. The estimate of forest biomass, commonly 
                         performed by forest inventory through sample plots, shows high 
                         variance in degraded forest areas. Due to this variance and 
                         complexity of tropical forests, the aim of this work was to 
                         estimate forest biomass using LiDAR point clouds in three distinct 
                         forest areas: one degraded by fire, another by selective logging 
                         and one area of intact forest. The approach applied in these areas 
                         was the Individual Tree Detection (ITD). To isolate the trees, we 
                         generated Canopy Height Models (CHM) images, which are obtained by 
                         subtracting the Digital Elevation Model (MDE) and the Digital 
                         Terrain Model (MDT), created by the cloud of LiDAR points. The 
                         trees in the CHM images are isolated by an algorithm provided by 
                         the Quantitative Ecology research group at the School of Forestry 
                         at Northern Arizona University (SILVA, 2015). With these points, 
                         metrics were calculated for some areas, which were used in the 
                         model of biomass estimation. The methodology used in this work was 
                         expected to reduce the error in biomass estimate in the study 
                         area. The cloud points of the most representative trees were 
                         analyzed, and thus field data was correlated with the individual 
                         trees found by the proposed algorithm. In a pilot study, the 
                         proposed methodology was applied generating the individual tree 
                         metrics: total height and area of the crown. When correlating 339 
                         isolated trees, an unsatisfactory Rē was obtained, as heights 
                         found by the algorithm were lower than those obtained in the 
                         field, with an average difference of 2.43 m. This shows that the 
                         algorithm used to isolate trees in temperate areas did not 
                         obtained satisfactory results in the tropical forest of Mato 
                         Grosso State. Due to this, in future works two algorithms, one 
                         developed by Dalponte et al. (2015) and another by Li et al. 
                         (2012) will be used.",
  conference-location = "New Orleans",
      conference-year = "11-15 Dec.",
             language = "en",
           targetfile = "jorge_individual.pdf",
        urlaccessdate = "11 maio 2024"
}