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%0 Journal Article
%4 sid.inpe.br/mtc-m21d/2022/01.04.12.53
%2 sid.inpe.br/mtc-m21d/2022/01.04.12.53.46
%@issn 0034-4257
%F 20220104
%T Large scale multi-layer fuel load characterization in tropical savanna using GEDI spaceborne lidar data
%D 2022
%8 JAN
%A Leite, Rodrigo Vieira,
%A Silva, Carlos Alberto,
%A Broadbent, Eben North,
%A Amaral, Cibele Hummel do,
%A Liesenberg, Veraldo,
%A Almeida, Danilo Roberti Alves de,
%A Mohan, Midhun,
%A Godinho, Sergio,
%A Cardil, Adrian,
%A Hamamura, Caio,
%A Faria, Bruno Lopes de,
%A Brancalion, Pedro H. S.,
%A Hirsch, Andre,
%A Marcatti, Gustavo Eduardo,
%A Dalla Corte, Ana Paula,
%A Zambrano, Angelica Maria Almeyda,
%A Costa, Maira Beatriz Teixeira da,
%A Matricardi, Eraldo Aparecido Trondoli,
%A Silva, Anne Laura da,
%A Goya, Lucas Ruggeri Re Y.,
%A Valbuena, Ruben,
%A Mendonca, Bruno Araujo Furtado de,
%A Silva Júnior, Celso Henrique Leite,
%A Aragão, Luiz Eduardo Oliveira e Cruz de,
%A Garcia, Mariano,
%A Liang, Jingjing,
%A Merrick, Trina,
%A Hudak, Andrew T.,
%A Xiao, Jingfeng,
%A Hancock, Steven,
%A Duncason, Laura,
%A Ferreira, Matheus Pinheiro,
%A Valle, Denis,
%A Saatchi, Sassan,
%A Klauberg, Carine,
%@affiliation Universidade Federal de Viçosa (UFV)
%@affiliation University of Florida
%@affiliation University of Florida
%@affiliation Universidade Federal de Viçosa (UFV)
%@affiliation Universidade do Estado de Santa Catarina (UDESC)
%@affiliation Universidade de São Paulo (USP)
%@affiliation University of California—Berkeley
%@affiliation University of Évora
%@affiliation Technosylva Inc
%@affiliation Instituto Federal de Educação, Ciência e Tecnologia de São Paulo (IFSP)
%@affiliation Universidade Federal dos Vales do Jequitinhonha e Mucuri (UFVJM)
%@affiliation Universidade de São Paulo (USP)
%@affiliation Universidade Federal de São João Del Rei (UFSJ)
%@affiliation Universidade Federal de São João Del Rei (UFSJ)
%@affiliation Universidade Federal do Paraná (UFPR)
%@affiliation University of Florida
%@affiliation Universidade de Brasília (UnB)
%@affiliation Universidade de Brasília (UnB)
%@affiliation Universidade Federal de São João Del Rei (UFSJ)
%@affiliation Universidade Federal de São João Del Rei (UFSJ)
%@affiliation Bangor University
%@affiliation Universidade Federal Rural do Rio de Janeiro (UFRRJ)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Universidad de Alcalá
%@affiliation Purdue University
%@affiliation Vanderbilt University
%@affiliation US Department of Agriculture, Forest Service
%@affiliation University of New Hampshire
%@affiliation University of Edinburgh
%@affiliation University of Maryland
%@affiliation Instituto Militar de Engenharia (IME)
%@affiliation University of Florida
%@affiliation NASA-Jet Propulsion Laboratory
%@affiliation Universidade Federal de São João Del Rei (UFSJ)
%@electronicmailaddress rodrigo.leite@ufv.br
%@electronicmailaddress c.silva@ufl.edu
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%@electronicmailaddress celsohlsj@gmail.com
%@electronicmailaddress luiz.aragao@inpe.br
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%@electronicmailaddress carine_klauberg@hotmail.com
%B Remote Sensing of Environment
%V 268
%K Active remote sensing, Fire, Modeling, Machine learning, UAV-lidar, Cerrado, Vegetation structure.
%X 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.
%@language en
%3 Leite_large_2022.pdf


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