@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 = "25 jun. 2024"
}