Large scale multi-layer fuel load characterization in tropical savanna using GEDI spaceborne lidar data

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NSTL
Elsevier

外文摘要：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.

外文关键词：

Active remote sensingFireModelingMachine learningUAV-lidarCerradoVegetation structureFOREST ABOVEGROUND BIOMASSTANDEM-X INSARWAVE-FORMSVEGETATION STRUCTUREBRAZILIAN CERRADOFIRE MANAGEMENTMISSIONMODELSCONSERVATIONPERFORMANCE

作者：

Klauberg, Carine、Silva, Carlos Alberto、Broadbent, Eben North、do Amaral, Cibele Hummel、Liesenberg, Veraldo、Alves de Almeida, Danilo Roberti、Mohan, Midhun、Godinho, Sergio、Cardil, Adrian、Hamamura, Caio、de Faria, Bruno Lopes、Brancalion, Pedro H. S.、Hirsch, Andre、Marcatti, Gustavo Eduardo、Dalla Corte, Ana Paula、Almeyda Zambrano, Angelica Maria、Teixeira da Costa, Maira Beatriz、Trondoli Matricardi, Eraldo Aparecido、da Silva, Anne Laura、Goya, Lucas Ruggeri Re Y.、Valbuena, Ruben、Furtado de Mendonca, Bruno Araujo、Silva Junior, Celso H. L.、Aragao, Luiz E. O. C.、Garcia, Mariano、Liang, Jingjing、Leite, Rodrigo Vieira、Merrick, Trina、Hudak, Andrew T.、Xiao, Jingfeng、Hancock, Steven、Duncason, Laura、Ferreira, Matheus Pinheiro、Valle, Denis、Saatchi, Sassan

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作者单位：

Fed Univ Sao Joao Del Rei UFSJ

Univ Florida

Fed Univ Vicosa UFV

Santa Catarina State Univ UDESC

Univ Sao Paulo USP ESALQ

Univ Calif Berkeley

Univ Evora

Technosylva Inc

Fed Inst Educ Sci & Technol Sao Paulo

Fed Univ Vales Jequitinhonha & Mucuri UFVJM

Fed Univ Parana UFPR

Univ Brasilia

Bangor Univ

Univ Fed Rural Rio de Janeiro

Natl Inst Space Res

Univ Alcala De Henares

Purdue Univ

Vanderbilt Univ

US Forest Serv

Univ New Hampshire

Univ Edinburgh

Univ Maryland

Mil Inst Engn IME

CALTECH

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出版年：

2022

DOI：

10.1016/j.rse.2021.112764

Remote Sensing of Environment

EISCI

ISSN：0034-4257

年,卷(期)：2022.268

被引量10
参考文献量115