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@Article{LeiteSBALAMGCHFBHMDZCMSGVMSAGLMHXHDFVSK:2022:LaScMu,
               author = "Leite, Rodrigo Vieira and Silva, Carlos Alberto and Broadbent, 
                         Eben North and Amaral, Cibele Hummel do and Liesenberg, Veraldo 
                         and Almeida, Danilo Roberti Alves de and Mohan, Midhun and 
                         Godinho, Sergio and Cardil, Adrian and Hamamura, Caio and Faria, 
                         Bruno Lopes de and Brancalion, Pedro H. S. and Hirsch, Andre and 
                         Marcatti, Gustavo Eduardo and Dalla Corte, Ana Paula and Zambrano, 
                         Angelica Maria Almeyda and Costa, Maira Beatriz Teixeira da and 
                         Matricardi, Eraldo Aparecido Trondoli and Silva, Anne Laura da and 
                         Goya, Lucas Ruggeri Re Y. and Valbuena, Ruben and Mendonca, Bruno 
                         Araujo Furtado de and Silva J{\'u}nior, Celso Henrique Leite and 
                         Arag{\~a}o, Luiz Eduardo Oliveira e Cruz de and Garcia, Mariano 
                         and Liang, Jingjing and Merrick, Trina and Hudak, Andrew T. and 
                         Xiao, Jingfeng and Hancock, Steven and Duncason, Laura and 
                         Ferreira, Matheus Pinheiro and Valle, Denis and Saatchi, Sassan 
                         and Klauberg, Carine",
          affiliation = "{Universidade Federal de Vi{\c{c}}osa (UFV)} and {University of 
                         Florida} and {University of Florida} and {Universidade Federal de 
                         Vi{\c{c}}osa (UFV)} and {Universidade do Estado de Santa Catarina 
                         (UDESC)} and {Universidade de S{\~a}o Paulo (USP)} and 
                         {University of California—Berkeley} and {University of {\'E}vora} 
                         and {Technosylva Inc} and Instituto Federal de 
                         Educa{\c{c}}{\~a}o, Ci{\^e}ncia e Tecnologia de S{\~a}o Paulo 
                         (IFSP) and {Universidade Federal dos Vales do Jequitinhonha e 
                         Mucuri (UFVJM)} and {Universidade de S{\~a}o Paulo (USP)} and 
                         {Universidade Federal de S{\~a}o Jo{\~a}o Del Rei (UFSJ)} and 
                         {Universidade Federal de S{\~a}o Jo{\~a}o Del Rei (UFSJ)} and 
                         {Universidade Federal do Paran{\'a} (UFPR)} and {University of 
                         Florida} and {Universidade de Bras{\'{\i}}lia (UnB)} and 
                         {Universidade de Bras{\'{\i}}lia (UnB)} and {Universidade 
                         Federal de S{\~a}o Jo{\~a}o Del Rei (UFSJ)} and {Universidade 
                         Federal de S{\~a}o Jo{\~a}o Del Rei (UFSJ)} and {Bangor 
                         University} and {Universidade Federal Rural do Rio de Janeiro 
                         (UFRRJ)} and {Instituto Nacional de Pesquisas Espaciais (INPE)} 
                         and {Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {Universidad de Alcal{\'a}} and {Purdue University} and 
                         {Vanderbilt University} and US Department of Agriculture, Forest 
                         Service and {University of New Hampshire} and {University of 
                         Edinburgh} and {University of Maryland} and {Instituto Militar de 
                         Engenharia (IME)} and {University of Florida} and {NASA-Jet 
                         Propulsion Laboratory} and {Universidade Federal de S{\~a}o 
                         Jo{\~a}o Del Rei (UFSJ)}",
                title = "Large scale multi-layer fuel load characterization in tropical 
                         savanna using GEDI spaceborne lidar data",
              journal = "Remote Sensing of Environment",
                 year = "2022",
               volume = "268",
                month = "JAN",
             keywords = "Active remote sensing, Fire, Modeling, Machine learning, 
                         UAV-lidar, Cerrado, Vegetation structure.",
             abstract = "Quantifying fuel load over large areas is essential to support 
                         integrated fire management initiatives in fire-prone regions to 
                         preserve carbon stock, biodiversity and ecosystem functioning. It 
                         also allows a better understanding of global climate regulation as 
                         a potential carbon sink or source. Large area assessments usually 
                         require data from spaceborne remote sensors, but most of them 
                         cannot measure the vertical variability of vegetation structure, 
                         which is required for accurately measuring fuel loads and defining 
                         management interventions. The recently launched NASA's Global 
                         Ecosystem Dynamics Investigation (GEDI) full-waveform lidar sensor 
                         holds potential to meet this demand. However, its capability for 
                         estimating fuel load has yet not been evaluated. In this study, we 
                         developed a novel framework and tested machine learning models for 
                         predicting multi-layer fuel load in the Brazilian tropical savanna 
                         (i.e., Cerrado biome) using GEDI data. First, lidar data were 
                         collected using an unnamed aerial vehicle (UAV). The flights were 
                         conducted over selected sample plots in distinct Cerrado 
                         vegetation formations (i.e., grassland, savanna, forest) where 
                         field measurements were conducted to determine the load of 
                         surface, herbaceous, shrubs and small trees, woody fuels and the 
                         total fuel load. Subsequently, GEDI-like full-waveforms were 
                         simulated from the high-density UAV-lidar 3-D point clouds from 
                         which vegetation structure metrics were calculated and correlated 
                         to field-derived fuel load components using Random Forest models. 
                         From these models, we generate fuel load maps for the entire 
                         Cerrado using all on-orbit available GEDI data. Overall, the 
                         models had better performance for woody fuels and total fuel loads 
                         (R-2 = 0.88 and 0.71, respectively). For components at the lower 
                         stratum, models had moderate to low performance (R-2 between 0.15 
                         and 0.46) but still showed reliable results. The presented 
                         framework can be extended to other fire-prone regions where 
                         accurate measurements of fuel components are needed. We hope this 
                         study will contribute to the expansion of spaceborne lidar 
                         applications for integrated fire management activities and 
                         supporting carbon monitoring initiatives in tropical savannas 
                         worldwide.",
                  doi = "10.1016/j.rse.2021.112764",
                  url = "http://dx.doi.org/10.1016/j.rse.2021.112764",
                 issn = "0034-4257",
                label = "20220104",
             language = "en",
           targetfile = "Leite_large_2022.pdf",
        urlaccessdate = "2022, Jan. 26"
}


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