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1. Identity statement
Reference TypeJournal Article
Sitemtc-m21c.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identifier8JMKD3MGP3W34R/43LSLHL
Repositorysid.inpe.br/mtc-m21c/2020/11.27.11.36
Last Update2020:11.27.11.36.37 (UTC) simone
Metadata Repositorysid.inpe.br/mtc-m21c/2020/11.27.11.36.37
Metadata Last Update2022:01.04.01.35.39 (UTC) administrator
DOI10.3390/rs12223827
ISSN2072-4292
Citation KeyShimabukuroDuArDuCaPeCa:2020:MaBuAr
TitleMapping burned areas of mato grosso state brazilian amazon using multisensor datasets
Year2020
MonthNov.
Access Date2024, May 24
Type of Workjournal article
Secondary TypePRE PI
Number of Files1
Size8854 KiB
2. Context
Author1 Shimabukuro, Yosio Edemir
2 Dutra, Andeise Cerqueira
3 Arai, Egídio
4 Duarte, Valdete
5 Cassol, Henrique Luis Godinho
6 Pereira, Gabriel
7 Cardozo, Francielle da Silva
Resume Identifier1 8JMKD3MGP5W/3C9JJCQ
2
3 8JMKD3MGP5W/3C9JGUP
4 8JMKD3MGP5W/3C9JJAU
ORCID1 0000-0002-1469-8433
2 0000-0002-4454-7732
3
4
5 0000-0001-6728-4712
6 0000-0002-2093-9942
7 0000-0002-4775-4649
Group1 DIDSR-CGOBT-INPE-MCTIC-GOV-BR
2 SER-SRE-SESPG-INPE-MCTIC-GOV-BR
3 DIDSR-CGOBT-INPE-MCTIC-GOV-BR
4 DIDSR-CGOBT-INPE-MCTIC-GOV-BR
5 SER-SRE-SESPG-INPE-MCTIC-GOV-BR
Affiliation1 Instituto Nacional de Pesquisas Espaciais (INPE)
2 Instituto Nacional de Pesquisas Espaciais (INPE)
3 Instituto Nacional de Pesquisas Espaciais (INPE)
4 Instituto Nacional de Pesquisas Espaciais (INPE)
5 Instituto Nacional de Pesquisas Espaciais (INPE)
6 Universidade Federal de São João del-Rei (UFSJ)
7 Universidade Federal de São João del-Rei (UFSJ)
Author e-Mail Address1 yosio@dsr.inpe.br
2 andeise.dutra@inpe.br
3 egidio@dsr.inpe.br
4 valdete.duarte@inpe.br
5 henrique@dsr.inpe.br
6 pereira@ufsj.edu.br
7 franciellecardozo@ufsj.edu.br
JournalRemote Sensing
Volume12
Number22
Pages1-23
Secondary MarkB3_GEOGRAFIA B3_ENGENHARIAS_I B4_GEOCIÊNCIAS B4_CIÊNCIAS_AMBIENTAIS B5_CIÊNCIAS_AGRÁRIAS_I
History (UTC)2020-11-27 11:36:37 :: simone -> administrator ::
2020-11-29 14:30:11 :: administrator -> simone :: 2020
2020-12-14 14:12:46 :: simone -> administrator :: 2020
2022-01-04 01:35:39 :: administrator -> simone :: 2020
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
Version Typepublisher
Keywordsburned areas detection
shade fraction image
linear spectral mixing model
VIIRS
PROBA-V
Landsat-8 OLI
AbstractQuantifying forest fires remain a challenging task for the implementation of public policies aimed to mitigate climate change. In this paper, we propose a new method to provide an annual burned area map of Mato Grosso State located in the Brazilian Amazon region, taking advantage of the high spatial and temporal resolution sensors. The method consists of generating the vegetation, soil, and shade fraction images by applying the Linear Spectral Mixing Model (LSMM) to the Landsat-8 OLI (Operational Land Imager), PROBA-V (Project for On-Board AutonomyVegetation), and Suomi NPP-VIIRS (National Polar-Orbiting Partnership-Visible Infrared Imaging Radiometer Suite) datasets. The shade fraction images highlight the burned areas, in which values are represented by low reflectance of ground targets, and the mapping was performed using an unsupervised classifier. Burned areas were evaluated in terms of land use and land cover classes over the Amazon, Cerrado and Pantanal biomes in the Mato Grosso State. Our results showed that most of the burned areas occurred in non-forested areas (66.57%) and old deforestation (21.54%). However, burned areas over forestlands (11.03%), causing forest degradation, reached more than double compared with burned areas identified in consolidated croplands (5.32%). The results obtained were validated using the Sentinel-2 data and compared with active fire data and existing global burned areas products, such as the MODIS (Moderate Resolution Imaging Spectroradiometer product) MCD64A1 and MCD45A1, and Fire CCI (ESA Climate Change Initiative) products. Although there is a good visual agreement among the analyzed products, the areas estimated were quite different. Our results presented correlation of 51% with Sentinel-2 and agreement of r2 = 0.31, r2 = 0.29, and r2 = 0.43 with MCD64A1, MCD45A1, and Fire CCI products, respectively. However, considering the active fire data, it was achieved the better performance between active fire presence and burn mapping (92%). The proposed method provided a general perspective about the patterns of fire in various biomes of Mato Grosso State, Brazil, that are important for the environmental studies, specially related to fire severity, regeneration, and greenhouse gas emissions.
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGP3W34R/43LSLHL
zipped data URLhttp://urlib.net/zip/8JMKD3MGP3W34R/43LSLHL
Languageen
Target Fileremotesensing-12-03827-v2.pdf
User Groupsimone
Reader Groupadministrator
simone
Visibilityshown
Archiving Policyallowpublisher allowfinaldraft
Read Permissionallow from all
Update Permissionnot transferred
5. Allied materials
Next Higher Units8JMKD3MGPCW/3ER446E
8JMKD3MGPCW/3F3NU5S
Citing Item Listsid.inpe.br/bibdigital/2013/10.18.22.34 4
sid.inpe.br/bibdigital/2013/09.13.21.11 3
sid.inpe.br/mtc-m21/2012/07.13.14.45.03 2
DisseminationWEBSCI; PORTALCAPES; MGA; COMPENDEX; SCOPUS.
Host Collectionurlib.net/www/2017/11.22.19.04
6. Notes
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