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1. Identity statement
Reference TypeJournal Article
Sitemtc-m21d.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identifier8JMKD3MGP3W34T/49P9R55
Repositorysid.inpe.br/mtc-m21d/2023/09.04.13.17   (restricted access)
Last Update2023:09.04.13.17.39 (UTC) simone
Metadata Repositorysid.inpe.br/mtc-m21d/2023/09.04.13.17.39
Metadata Last Update2024:01.02.17.16.46 (UTC) administrator
DOI10.3390/f14081669 View more
ISSN1999-4907
Citation KeyShimabukuroASHDMDMCA:2023:MaLaUs
TitleMapping Land Use and Land Cover Classes in São Paulo State, Southeast of Brazil, Using Landsat-8 OLI Multispectral Data and the Derived Spectral Indices and Fraction Images
Year2023
MonthAug.
Access Date2024, May 18
Type of Workjournal article
Secondary TypePRE PI
Number of Files1
Size14893 KiB
2. Context
Author 1 Shimabukuro, Yosio Edemir
 2 Arai, Egidio
 3 Silva, Gabriel Máximo da
 4 Hoffmann, Tânia Beatriz
 5 Duarte, Valdete
 6 Martini, Paulo Roberto
 7 Dutra, Andeise Cerqueira
 8 Mataveli, Guilherme Augusto Verola
 9 Cassol, Henrique Luís Godinho
10 Adami, Marcos
Resume Identifier 1 8JMKD3MGP5W/3C9JJCQ
 2 8JMKD3MGP5W/3C9JGUP
 3
 4
 5 8JMKD3MGP5W/3C9JJAU
 6 8JMKD3MGP5W/3C9JJ3M
ORCID 1 0000-0002-1469-8433
 2
 3 0000-0003-2105-9055
 4 0000-0002-8246-5666
 5
 6
 7 0000-0002-4454-7732
 8 0000-0002-4645-0117
 9 0000-0001-6728-4712
10 0000-0003-4247-4477
Group 1 DIOTG-CGCT-INPE-MCTI-GOV-BR
 2 DIOTG-CGCT-INPE-MCTI-GOV-BR
 3 SER-SRE-DIPGR-INPE-MCTI-GOV-BR
 4 SER-SRE-DIPGR-INPE-MCTI-GOV-BR
 5 DIOTG-CGCT-INPE-MCTI-GOV-BR
 6 DIOTG-CGCT-INPE-MCTI-GOV-BR
 7 SER-SRE-DIPGR-INPE-MCTI-GOV-BR
 8 DIOTG-CGCT-INPE-MCTI-GOV-BR
 9 DIOTG-CGCT-INPE-MCTI-GOV-BR
10 DIOTG-CGCT-INPE-MCTI-GOV-BR
Affiliation 1 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 Instituto Nacional de Pesquisas Espaciais (INPE)
 7 Instituto Nacional de Pesquisas Espaciais (INPE)
 8 Instituto Nacional de Pesquisas Espaciais (INPE)
 9 Instituto Nacional de Pesquisas Espaciais (INPE)
10 Instituto Nacional de Pesquisas Espaciais (INPE)
Author e-Mail Address 1 yosio.shimabukuro@inpe.br
 2 egidio.arai@inpe.br
 3 gabriel.maximo@inpe.br
 4 tania.hoffmann@inpe.br
 5 valdete.duarte@inpe.br
 6 paulo.martini@inpe.br
 7 andeise.dutra@inpe.br
 8 guilherme.mataveli@inpe.br
 9 henrique.cassol@inpe.br
10 marcos.adami@inpe.br
JournalForests
Volume14
Number8
Pagese1669
Secondary MarkB2_INTERDISCIPLINAR B5_SOCIOLOGIA B5_CIÊNCIAS_AMBIENTAIS B5_CIÊNCIAS_AGRÁRIAS_I
History (UTC)2023-09-04 13:18:01 :: simone -> administrator :: 2023
2024-01-02 17:16:46 :: administrator -> simone :: 2023
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
Version Typepublisher
Keywordsagriculture
forest
forest plantation
Land Use and Land Cover (LULC)
Linear Spectral Mixing Model (LSMM)
pasture
spectral indices
urban
AbstractThis work aims to develop a new method to map Land Use and Land Cover (LULC) classes in the São Paulo State, Brazil, using Landsat-8 Operational Land Imager (OLI) data. The novelty of the proposed method consists of selecting the images based on the spectral and temporal characteristics of the LULC classes. First, we defined the six classes to be mapped in the year 2020 as forest, forest plantation, water bodies, urban areas, agriculture, and pasture. Second, we visually analyzed their variability spectral characteristics over the year. Then, we pre-processed these images to highlight each LULC class. For the classification, the Random Forest algorithm available on the Google Earth Engine (GEE) platform was utilized individually for each LULC class. Afterward, we integrated the classified maps to create the final LULC map. The results revealed that forest areas are primarily concentrated in the eastern region of São Paulo, predominantly on steeper slopes, accounting for 19% of the study area. On the other hand, pasture and agriculture dominated 73% of all São Paulos landscape, reaching 39% and 34%, respectively. The overall accuracy of the classification achieved 89.10%, while producer and user accuracies were greater than 84.20% and 76.62%, respectively. To validate the results, we compared our findings with the MapBiomas Project classification, obtaining an overall accuracy of 85.47%. Therefore, our method demonstrates its potential to minimize classification errors and offers the advantage of facilitating post-classification editing for individual mapped classes.
AreaSRE
Arrangement 1urlib.net > BDMCI > Fonds > Produção pgr ATUAIS > SER > Mapping Land Use...
Arrangement 2urlib.net > BDMCI > Fonds > Produção a partir de 2021 > CGCT > Mapping Land Use...
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4. Conditions of access and use
Languageen
Target Fileforests-14-01669.pdf
User Groupsimone
Reader Groupadministrator
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Visibilityshown
Read Permissiondeny from all and allow from 150.163
Update Permissionnot transferred
5. Allied materials
Next Higher Units8JMKD3MGPCW/3F3NU5S
8JMKD3MGPCW/46KUATE
Citing Item Listsid.inpe.br/bibdigital/2022/04.03.22.23 2
sid.inpe.br/bibdigital/2013/10.18.22.34 2
sid.inpe.br/mtc-m21/2012/07.13.15.01.20 1
DisseminationWEBSCI
Host Collectionurlib.net/www/2021/06.04.03.40
6. Notes
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