@Article{PereiraKaSoEsBeVi:2018:ReUnMa,
               author = "Pereira, Francisca Rocha de Souza and Kampel, Milton and Soares, 
                         M{\'a}rio Luiz Gomes and Estrada, Gustavo Calderucio Duque and 
                         Bentz, Cristina and Vincent, Gregoire",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Universidade Estadual 
                         do Rio de Janeiro (UERJ)} and {Golder Associates} and Petrobras 
                         and {Universite Montpellier}",
                title = "Reducing uncertainty in mapping of mangrove aboveground biomass 
                         using airborne discrete return lidar data",
              journal = "Remote Sensing",
                 year = "2018",
               volume = "10",
               number = "4",
                pages = "e637",
                month = "Apr.",
             keywords = "discrete return lidar, mangrove, aboveground biomass, 
                         uncertainty.",
             abstract = "Remote sensing techniques offer useful tools for estimating forest 
                         biomass to large extent, thereby contributing to the monitoring of 
                         land use and landcover dynamics and the effectiveness of 
                         environmental policies. The main goal of this study was to 
                         investigate the potential use of discrete return light detection 
                         and ranging (lidar) data to produce accurate aboveground biomass 
                         (AGB) maps of mangrove forests. AGB was estimated in 34 small 
                         plots scatted over a 50 km2 mangrove forest in Rio de Janeiro, 
                         Brazil. Plot AGB was computed using either species-specific or 
                         non-species-specific allometric models. A total of 26 descriptive 
                         lidar metrics were extracted from the normalized height of the 
                         lidar point cloud data, and various model forms (random forest and 
                         partial least squares regression with backward selection of 
                         predictors (Auto-PLS)) were tested to predict the recorded AGB. 
                         The models developed using species-specific allometric models were 
                         distinctly more accurate (R2 (calibration) = 0.89, R2 (validation) 
                         = 0.80, root-mean-square error (RMSE, calibration) = 11.20 
                         t·ha\−1 , and RMSE(validation) = 14.80 t·ha\−1 ). 
                         The use of non-species-specific allometric models yielded large 
                         errors on a landscape scale (+14% or \−18% bias depending 
                         on the allometry considered), indicating that using poor quality 
                         training data not only results in low precision but inaccuracy at 
                         all scales. It was concluded that under suitable sampling pattern 
                         and provided that accurate field data are used, discrete return 
                         lidar can accurately estimate and map the AGB in mangrove forests. 
                         Conversely this study underlines the potential bias affecting the 
                         estimates of AGB in other forested landscapes where only 
                         non-species-specific allometric equations are available.",
                  doi = "10.3390/rs10040637",
                  url = "http://dx.doi.org/10.3390/rs10040637",
                 issn = "2072-4292",
             language = "en",
           targetfile = "pereira_reducing.pdf",
        urlaccessdate = "27 abr. 2024"
}