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@Article{BispoRZMCFBLRGERPGRBFSABWSOTB:2020:WoAbBi,
               author = "Bispo, Polyanna da Concei{\c{c}}{\~a}o and 
                         Rodr{\'{\i}}guez-Veiga, Pedro and Zimbres, Barbara and Miranda, 
                         Sabrina do Couto de and Cezare, Cassio Henrique Giusti and 
                         Fleming, Sam and Baldacchino, Francesca and Louis, Valentin and 
                         Rains, Dominik and Garcia, Mariano and Esp{\'{\i}}rito Santo, 
                         Fernando Del Bon and Roitman, Iris and Pacheco Pascagaza, Ana 
                         Mar{\'{\i}}a and Gou, Yaqing and Roberts, John and Barrett, 
                         Kirsten and Ferreira, Laerte Guimaraes and Shimbo, Julia Zanin and 
                         Alencar, Ane and Bustamante, Mercedes and Woodhouse, Iain Hector 
                         and Sano, Edson Eyji and Ometto, Jean Pierre Henry Balbaud and 
                         Tansey, Kevin and Balzter, Heiko",
          affiliation = "{University of Manchester} and {University of Leicester} and 
                         {Amazon Environmental Research Institute (IPAM)} and {Universidade 
                         do Estado de Go{\'{\i}}as (UEG)} and {Universidade Federal de 
                         Goi{\'a}s (UFG)} and {Carbomap Ltd.} and {Carbomap Ltd.} and 
                         {University of Leicester} and {Ghent University} and {University 
                         of Alcal{\'a} de Henares} and {University of Leicester} and 
                         {Universidade de Bras{\'{\i}}lia (UnB)} and {University of 
                         Leicester} and {University of Leicester} and {University of 
                         Leiceste} and {University of Leicester} and {Universidade Federal 
                         de Goi{\'a}s (UFG)} and {Amazon Environmental Research Institute 
                         (IPAM)} and {Amazon Environmental Research Institute (IPAM)} and 
                         {Universidade de Bras{\'{\i}}lia (UnB)} and {Carbomap Ltd.} and 
                         {Empresa Brasileira de Pesquisa Agropecu{\'a}ria (EMBRAPA)} and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)} and {University 
                         of Leicester} and {University of Leicester}",
                title = "Woody aboveground biomass mapping of the brazilian savanna with a 
                         multi-sensor and machine learning approach",
              journal = "Remote Sensing",
                 year = "2020",
               volume = "12",
               number = "17",
                pages = "e3262",
                month = "Sept.",
             keywords = "aboveground biomass, Cerrado ecosystem, random forest, SAR.",
             abstract = "The tropical savanna in Brazil known as the Cerrado covers circa 
                         23% of the Brazilian territory, but only 3% of this area is 
                         protected. High rates of deforestation and degradation in the 
                         woodland and forest areas have made the Cerrado the second-largest 
                         source of carbon emissions in Brazil. However, data on these 
                         emissions are highly uncertain because of the spatial and temporal 
                         variability of the aboveground biomass (AGB) in this biome. 
                         Remote-sensing data combined with local vegetation inventories 
                         provide the means to quantify the AGB at large scales. Here, we 
                         quantify the spatial distribution of woody AGB in the Rio Vermelho 
                         watershed, located in the centre of the Cerrado, at a high spatial 
                         resolution of 30 metres, with a random forest (RF) 
                         machine-learning approach. We produced the first high-resolution 
                         map of the AGB for a region in the Brazilian Cerrado using a 
                         combination of vegetation inventory plots, airborne light 
                         detection and ranging (LiDAR) data, and multispectral and radar 
                         satellite images (Landsat 8 and ALOS-2/PALSAR-2). A combination of 
                         random forest (RF) models and jackknife analyses enabled us to 
                         select the best remote-sensing variables to quantify the AGB on a 
                         large scale. Overall, the relationship between the ground data 
                         from vegetation inventories and remote-sensing variables was 
                         strong (R2 = 0.89), with a root-mean-square error (RMSE) of 7.58 
                         Mg ha-1 and a bias of 0.43 Mg ha-1.",
                  doi = "10.3390/RS12172685",
                  url = "http://dx.doi.org/10.3390/RS12172685",
                 issn = "2072-4292",
             language = "en",
           targetfile = "remotesensing-12-02685-v3.pdf",
        urlaccessdate = "21 abr. 2021"
}


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