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1. Identity statement
Reference TypeJournal Article
Sitemtc-m21d.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identifier8JMKD3MGP3W34T/47UCFCS
Repositorysid.inpe.br/mtc-m21d/2022/11.07.18.13   (restricted access)
Last Update2022:11.07.18.13.18 (UTC) simone
Metadata Repositorysid.inpe.br/mtc-m21d/2022/11.07.18.13.18
Metadata Last Update2023:01.03.16.46.23 (UTC) administrator
DOI10.3390/f13101716
ISSN1999-4907
Citation KeyShimabukuroASDMDMCFJ:2022:MaMoFo
TitleMapping and Monitoring Forest Plantations in Sao Paulo State, Southeast Brazil, Using Fraction Images Derived from Multiannual Landsat Sensor Images
Year2022
MonthOct.
Access Date2024, May 18
Type of Workjournal article
Secondary TypePRE PI
Number of Files1
Size14599 KiB
2. Context
Author 1 Shimabukuro, Yosio Edemir
 2 Arai, Egidio
 3 Silva, Gabriel Máximo da
 4 Dutra, Andeise Cerqueira
 5 Mataveli, Guilherme Augusto Verola
 6 Duarte, Valdete
 7 Martini, Paulo Roberto
 8 Cassol, Henrique Luís Godinho
 9 Ferreira, Danilo S.
10 Junqueira, Luis R.
Resume Identifier 1 8JMKD3MGP5W/3C9JJCQ
 2 8JMKD3MGP5W/3C9JGUP
 3
 4
 5
 6 8JMKD3MGP5W/3C9JJAU
 7 8JMKD3MGP5W/3C9JJ3M
ORCID 1 0000-0002-1469-8433
 2
 3 0000-0003-2105-9055
 4 0000-0002-4454-7732
 5 0000-0002-4645-0117
 6
 7
 8 0000-0001-6728-4712
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 SEREL-COGAB-INPE-MCTI-GOV-BR
 8 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 Sylvamo
10 Sylvamo
Author e-Mail Address 1 yosio.shimabukuro@inpe.br
 2 egidio.arai@inpe.br
 3 gabrielmaximo04@gmail.com
 4 andeise.dutra@inpe.br
 5 guilherme.mataveli@inpe.br
 6 valdete.duarte@inpe.br
 7 paulo.martini@inpe.br
 8 hlcassol@hotmail.com
JournalForests
Volume13
Number10
Pagese1716
Secondary MarkB2_INTERDISCIPLINAR B5_SOCIOLOGIA B5_CIÊNCIAS_AMBIENTAIS B5_CIÊNCIAS_AGRÁRIAS_I
History (UTC)2022-11-07 18:14:00 :: simone -> administrator :: 2022
2023-01-03 16:46:23 :: administrator -> simone :: 2022
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
Version Typepublisher
Keywordslinear spectral mixing model
fraction images
eucalypt
pine
forest plantation
image processing
AbstractThis article presents a method, based on orbital remote sensing, to map the extent of forest plantations in Sao Paulo State (Southeast Brazil). The proposed method uses the random forest machine learning algorithm available on the Google Earth Engine (GEE) cloud computing platform. We used 30 m annual mosaics derived from Landsat-5 Thematic Mapper (TM) images and from Landsat-8 Operational Land Imager (OLI) images for the 1985 to 1995 and 2013 to 2021 time periods, respectively. These time periods were selected based on the planted areas' rotation, especially the eucalypt's short rotation. To classify the forest plantations, green, red, NIR, and MIR spectral bands, NDVI, GNDVI, NDWI, and NBR spectral indices, and vegetation, shade, and soil fractions were used for both sensors. These indices and the fraction images have the advantage of reducing the volume of data to be analyzed and highlighting the forest plantations' characteristics. In addition, we also generated one mosaic for each fraction image for the TM and OLI datasets by computing the maximum value through the period analyzed, facilitating the classification of areas occupied by forest plantations in the study area. The proposed method allowed us to classify the areas occupied by two forest plantation classes: eucalypt and pine. The results of the proposed method compared with the forest plantation areas extracted from the land use and land cover maps, provided by the MapBiomas product, presented the Kappa values of 0.54 and 0.69 for 1995 and 2020, respectively. In addition, two pilot areas were used to evaluate the classification maps and to monitor the phenological stages of eucalypt and pine plantations, showing the rotation cycle of these plantations. The results are very useful for planning and managing planted forests by commercial companies and can contribute to developing an automatic method to map forest plantations on regional and global scales.
AreaSRE
Arrangement 1urlib.net > BDMCI > Fonds > Produção pgr ATUAIS > SER > Mapping and Monitoring...
Arrangement 2urlib.net > BDMCI > Fonds > Produção a partir de 2021 > CGCT > Mapping and Monitoring...
Arrangement 3urlib.net > BDMCI > Fonds > Produção a partir de 2021 > COGAB > Mapping and Monitoring...
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4. Conditions of access and use
Languageen
Target Fileforests-13-01716.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
8JMKD3MGPCW/46L2F3E
Citing Item Listsid.inpe.br/bibdigital/2013/10.18.22.34 6
sid.inpe.br/bibdigital/2022/04.04.04.41 3
sid.inpe.br/mtc-m21/2012/07.13.14.45.03 2
DisseminationWEBSCI
Host Collectionurlib.net/www/2021/06.04.03.40
6. Notes
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