@InProceedings{RexCoKlHuKäSiSi:2019:CoRaFo,
               author = "Rex, Franciel Eduardo and Corte, Ana Paula Dalla and Klauberg, 
                         Carine and Hudak, Andrew Thomas and K{\"a}fer, P{\^a}mela 
                         Su{\'e}len and Silva, Vanessa Sousa da and Silva, Carlos 
                         Alberto",
          affiliation = "{Universidade Federal do Paran{\'a} (UFPR)} and {Universidade 
                         Federal do Paran{\'a} (UFPR)} and {Universidade Federal de 
                         S{\~a}o Jo{\~a}o Del-Rei (UFSJ)} and USDA Forest Service, Rocky 
                         Mountain Research Station and {Universidade Federal do Rio Grande 
                         do Sul (UFRGS)} and {Universidade Federal de Pernambuco (UFPE)} 
                         and {University of Maryland}",
                title = "Comparison between random forest and linear regression for 
                         tropical forest aboveground biomass estimation",
            booktitle = "Anais...",
                 year = "2019",
               editor = "Gherardi, Douglas Francisco Marcolino and Sanches, Ieda DelArco 
                         and Arag{\~a}o, Luiz Eduardo Oliveira e Cruz de",
                pages = "827--830",
         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 = "LiDAR, Machine Learning, Remote Sensing.",
             abstract = "The objective was to compare two methods for estimating 
                         aboveground biomass (AGB) in tropical rainforest using airborne 
                         LiDAR data. The study was conducted at Fazenda Cauxi in northern 
                         Brazil. Data from LiDAR and field inventory collected in 2014 were 
                         used. A total of 85 plots were used for the modeling. In the R 
                         environment, Random Forest (RF) and Linear Regression (lm) were 
                         compared in terms of RMSE, Bias and adj.R² through a LOOCV process 
                         with 500 replicates. The best performance was verified for the LM 
                         algorithm.",
  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/3U3N93L",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3U3N93L",
           targetfile = "97582.pdf",
                 type = "LIDAR: sensores e aplica{\c{c}}{\~o}es",
        urlaccessdate = "02 maio 2024"
}