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@InProceedings{MohanSKCACJH:2019:FoPlFi,
               author = "Mohan, Midhun and Silva, Carlos Alberto and Klauberg, Carine and 
                         Cardil, Adri{\'a}n and Almeida, Danilo and Carvalho, Samuel de 
                         P{\'a}dua Chaves and Jaafar, Wan Shafrina Wan Mohd and Hudak, 
                         Andrew T.",
          affiliation = "{North Carolina State University} and {NASA Goddard Space Flight 
                         Center} and {Universidade Federal de S{\~a}o Jo{\~a}o Del-Rei 
                         (UFSJ)} and {Tecnosylva. Parque Tecnol{\'o}gico de Le{\'o}n} and 
                         {Universidade de S{\~a}o Paulo (USP)} and {Universidade Federal 
                         de Mato Grosso (UFMT)} and {University of Edinburgh} and {USDA 
                         Forest Service}",
                title = "Applying mixed-effects model for estimating individual tree 
                         attributes in Eucalyptus spp. forest plantations from field and 
                         airborne LiDAR data",
            booktitle = "Anais...",
                 year = "2019",
               editor = "Gherardi, Douglas Francisco Marcolino and Sanches, Ieda DelArco 
                         and Arag{\~a}o, Luiz Eduardo Oliveira e Cruz de",
                pages = "1055--1059",
         organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 19. (SBSR)",
            publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
              address = "S{\~a}o Jos{\'e} dos Campos",
             keywords = "plantation forestry, forest productivity, above ground carbon 
                         estimation, tree level attributes.",
             abstract = "Forest plantations cover a large part of tropical countries such 
                         as Brazil and eucalyptus plantations in particular account for 57% 
                         of Brazils reforested area. As plantations offer multiple benefits 
                         such as an option to offset natural forests, simplify otherwise 
                         complex forest ecosystems, meet energy, pulp and paper demands, 
                         restore ecological services and combat climate change by 
                         sequestering carbon to the society, monitoring and tracking the 
                         growth and productivity of forest plantations should be given high 
                         priority. In this regard, remote sensing techniques have been 
                         found highly efficient. The core objective of this study is to 
                         estimate individual tree attributes, such as tree height, diameter 
                         at breast height (dbh) and above ground carbon (AGC) stocks of 
                         eucalyptus plantations from lidar data using linear mixed effects 
                         (LME) models; Ordinary Least Square (OLS) regression models are 
                         also built for comparison purposes. From our results, it can be 
                         inferred that hierarchy existing within the plantation datasets 
                         can be well handled by LME models and predictive models, for 
                         tracking tree level AGC and forest productivity, with satisfactory 
                         accuracies possible by combining lidar and LME modeling 
                         techniques.",
  conference-location = "Santos",
      conference-year = "14-17 abril 2019",
                 isbn = "978-85-17-00097-3",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3U3RP7H",
                  url = "http://urlib.net/rep/8JMKD3MGP6W34M/3U3RP7H",
           targetfile = "97585.pdf",
                 type = "LIDAR: sensores e aplica{\c{c}}{\~o}es",
        urlaccessdate = "15 jan. 2021"
}


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