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Reference TypeJournal Article
Sitemtc-m21d.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identifier8JMKD3MGP3W34T/465DSRP
Repositorysid.inpe.br/mtc-m21d/2022/01.04.12.53   (restricted access)
Last Update2022:01.04.15.55.04 (UTC) simone
Metadatasid.inpe.br/mtc-m21d/2022/01.04.12.53.46
Metadata Last Update2022:01.14.20.34.29 (UTC) administrator
DOI10.1016/j.rse.2021.112764
ISSN0034-4257
Label20220104
Citation KeyLeiteSBALAMGCHFBHMDZCMSGVMSAGLMHXHDFVSK:2022:LaScMu
TitleLarge scale multi-layer fuel load characterization in tropical savanna using GEDI spaceborne lidar data
Year2022
MonthJAN
Access Date2022, Jan. 26
Secondary TypePRE PI
Number of Files1
Size9626 KiB
Context area
Author 1 Leite, Rodrigo Vieira
 2 Silva, Carlos Alberto
 3 Broadbent, Eben North
 4 Amaral, Cibele Hummel do
 5 Liesenberg, Veraldo
 6 Almeida, Danilo Roberti Alves de
 7 Mohan, Midhun
 8 Godinho, Sergio
 9 Cardil, Adrian
10 Hamamura, Caio
11 Faria, Bruno Lopes de
12 Brancalion, Pedro H. S.
13 Hirsch, Andre
14 Marcatti, Gustavo Eduardo
15 Dalla Corte, Ana Paula
16 Zambrano, Angelica Maria Almeyda
17 Costa, Maira Beatriz Teixeira da
18 Matricardi, Eraldo Aparecido Trondoli
19 Silva, Anne Laura da
20 Goya, Lucas Ruggeri Re Y.
21 Valbuena, Ruben
22 Mendonca, Bruno Araujo Furtado de
23 Silva Júnior, Celso Henrique Leite
24 Aragão, Luiz Eduardo Oliveira e Cruz de
25 Garcia, Mariano
26 Liang, Jingjing
27 Merrick, Trina
28 Hudak, Andrew T.
29 Xiao, Jingfeng
30 Hancock, Steven
31 Duncason, Laura
32 Ferreira, Matheus Pinheiro
33 Valle, Denis
34 Saatchi, Sassan
35 Klauberg, Carine
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23 DIOTG-CGCT-INPE-MCTI-GOV-BR
24 DIOTG-CGCT-INPE-MCTI-GOV-BR
Affiliation 1 Universidade Federal de Viçosa (UFV)
 2 University of Florida
 3 University of Florida
 4 Universidade Federal de Viçosa (UFV)
 5 Universidade do Estado de Santa Catarina (UDESC)
 6 Universidade de São Paulo (USP)
 7 University of California—Berkeley
 8 University of Évora
 9 Technosylva Inc
10 Instituto Federal de Educação, Ciência e Tecnologia de São Paulo (IFSP)
11 Universidade Federal dos Vales do Jequitinhonha e Mucuri (UFVJM)
12 Universidade de São Paulo (USP)
13 Universidade Federal de São João Del Rei (UFSJ)
14 Universidade Federal de São João Del Rei (UFSJ)
15 Universidade Federal do Paraná (UFPR)
16 University of Florida
17 Universidade de Brasília (UnB)
18 Universidade de Brasília (UnB)
19 Universidade Federal de São João Del Rei (UFSJ)
20 Universidade Federal de São João Del Rei (UFSJ)
21 Bangor University
22 Universidade Federal Rural do Rio de Janeiro (UFRRJ)
23 Instituto Nacional de Pesquisas Espaciais (INPE)
24 Instituto Nacional de Pesquisas Espaciais (INPE)
25 Universidad de Alcalá
26 Purdue University
27 Vanderbilt University
28 US Department of Agriculture, Forest Service
29 University of New Hampshire
30 University of Edinburgh
31 University of Maryland
32 Instituto Militar de Engenharia (IME)
33 University of Florida
34 NASA-Jet Propulsion Laboratory
35 Universidade Federal de São João Del Rei (UFSJ)
Author e-Mail Address 1 rodrigo.leite@ufv.br
 2 c.silva@ufl.edu
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23 celsohlsj@gmail.com
24 luiz.aragao@inpe.br
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35 carine_klauberg@hotmail.com
JournalRemote Sensing of Environment
Volume268
Secondary MarkA1_INTERDISCIPLINAR A1_GEOCIÊNCIAS A1_ENGENHARIAS_I A1_CIÊNCIAS_BIOLÓGICAS_I A1_CIÊNCIAS_AMBIENTAIS A1_CIÊNCIAS_AGRÁRIAS_I A1_BIODIVERSIDADE
History (UTC)2022-01-04 12:53:46 :: administrator -> simone ::
2022-01-04 15:55:05 :: simone -> administrator :: 2022
2022-01-14 20:34:29 :: administrator -> simone :: 2022
Content and structure area
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
KeywordsActive remote sensing
Fire
Modeling
Machine learning
UAV-lidar
Cerrado
Vegetation structure
AbstractQuantifying 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.
AreaSRE
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Languageen
Target FileLeite_large_2022.pdf
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Visibilityshown
Archiving Policydenypublisher allowfinaldraft24
Read Permissiondeny from all and allow from 150.163
Update Permissionnot transferred
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DisseminationWEBSCI; PORTALCAPES; MGA; COMPENDEX; SCOPUS.
Host Collectionurlib.net/www/2021/06.04.03.40
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