@InProceedings{AlmeidaGoTrSaGrVi:2017:MoEsBi,
               author = "Almeida, Andr{\'e} Quint{\~a}o and Gon{\c{c}}alves, Fabio 
                         Guimar{\~a}es and Treuhaft, Robert Neil and Santos, Jo{\~a}o 
                         Roberto dos and Gra{\c{c}}a, Paulo Maur{\'{\i}}cio Lima de 
                         Alencastro and Vi{\'e}gas, Rafael Rossi",
                title = "Modelos de estimativa de biomassa a{\'e}rea utilizando dados 
                         RapidEye para a Floresta Nacional do Tapaj{\'o}s-PA",
            booktitle = "Anais...",
                 year = "2017",
               editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz 
                         Eduardo Oliveira e Cruz de",
                pages = "6445--6452",
         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 objective of this study was to develop statistical models for 
                         estimating aboveground biomass (AGB) at Tapajos National Forest, 
                         Par{\'a}, Brazil, using spectral metrics derived from RapidEye. 
                         Measurements of diameter at breast height (DBH) and tree height 
                         were collected for 88 forest inventory plots (50 m x 50 m). All 
                         trees were identified to the genus and/or species level and their 
                         biomasses were estimated using allometric equations. The 
                         explanatory variables were extracted from the five spectral bands 
                         of the RapidEye satellite (5 m spatial resolution) and included 
                         individual bands, band ratios, and vegetation indices. Biomass 
                         estimation models were fitted using multiple linear regression and 
                         the non-parametric algorithm Random Forest. The predictive 
                         performance of the models was assessed based on the coefficient of 
                         determination (r2) and the root mean square error (RMSE) 
                         calculated using a cross-validation procedure. The best regression 
                         model selected included three variables and presented a 
                         cross-validation r2 of 0.67 and a RMSE of 95,4 Mg ha-1 (50%). The 
                         Random Forest algorithm presented a better performance, with an r2 
                         of 0.75 and a RMSE of 84,1 Mg ha-1 (45%). We conclude that metrics 
                         derived from the RapidEye sensor have the potential to explain a 
                         large portion of the variability in biomass at Tapajos, when 
                         combined with a more powerful statistical framework such as Random 
                         Forest.",
  conference-location = "Santos",
      conference-year = "28-31 maio 2017",
                 isbn = "978-85-17-00088-1",
                label = "59775",
             language = "pt",
         organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
                  ibi = "8JMKD3MGP6W34M/3PSMCTD",
                  url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3PSMCTD",
           targetfile = "59775.pdf",
                 type = "Floresta e outros tipos de vegeta{\c{c}}{\~a}o",
        urlaccessdate = "27 abr. 2024"
}