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1. Identificação
Tipo de ReferênciaArtigo em Revista Científica (Journal Article)
Sitemtc-m21d.sid.inpe.br
Código do Detentorisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identificador8JMKD3MGP3W34T/47UCFCS
Repositóriosid.inpe.br/mtc-m21d/2022/11.07.18.13   (acesso restrito)
Última Atualização2022:11.07.18.13.18 (UTC) simone
Repositório de Metadadossid.inpe.br/mtc-m21d/2022/11.07.18.13.18
Última Atualização dos Metadados2023:01.03.16.46.23 (UTC) administrator
DOI10.3390/f13101716
ISSN1999-4907
Chave de CitaçãoShimabukuroASDMDMCFJ:2022:MaMoFo
TítuloMapping and Monitoring Forest Plantations in Sao Paulo State, Southeast Brazil, Using Fraction Images Derived from Multiannual Landsat Sensor Images
Ano2022
MêsOct.
Data de Acesso18 maio 2024
Tipo de Trabalhojournal article
Tipo SecundárioPRE PI
Número de Arquivos1
Tamanho14599 KiB
2. Contextualização
Autor 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.
Identificador de Curriculo 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
Grupo 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
Afiliação 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
Endereço de e-Mail do Autor 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
RevistaForests
Volume13
Número10
Páginase1716
Nota SecundáriaB2_INTERDISCIPLINAR B5_SOCIOLOGIA B5_CIÊNCIAS_AMBIENTAIS B5_CIÊNCIAS_AGRÁRIAS_I
Histórico (UTC)2022-11-07 18:14:00 :: simone -> administrator :: 2022
2023-01-03 16:46:23 :: administrator -> simone :: 2022
3. Conteúdo e estrutura
É a matriz ou uma cópia?é a matriz
Estágio do Conteúdoconcluido
Transferível1
Tipo do ConteúdoExternal Contribution
Tipo de Versãopublisher
Palavras-Chavelinear spectral mixing model
fraction images
eucalypt
pine
forest plantation
image processing
ResumoThis 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.
ÁreaSRE
Arranjo 1urlib.net > BDMCI > Fonds > Produção pgr ATUAIS > SER > Mapping and Monitoring...
Arranjo 2urlib.net > BDMCI > Fonds > Produção a partir de 2021 > CGCT > Mapping and Monitoring...
Arranjo 3urlib.net > BDMCI > Fonds > Produção a partir de 2021 > COGAB > Mapping and Monitoring...
Conteúdo da Pasta docacessar
Conteúdo da Pasta sourcenão têm arquivos
Conteúdo da Pasta agreement
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4. Condições de acesso e uso
Idiomaen
Arquivo Alvoforests-13-01716.pdf
Grupo de Usuáriossimone
Grupo de Leitoresadministrator
simone
Visibilidadeshown
Permissão de Leituradeny from all and allow from 150.163
Permissão de Atualizaçãonão transferida
5. Fontes relacionadas
Unidades Imediatamente Superiores8JMKD3MGPCW/3F3NU5S
8JMKD3MGPCW/46KUATE
8JMKD3MGPCW/46L2F3E
Lista de Itens Citandosid.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
DivulgaçãoWEBSCI
Acervo Hospedeirourlib.net/www/2021/06.04.03.40
6. Notas
Campos Vaziosalternatejournal archivingpolicy archivist callnumber copyholder copyright creatorhistory descriptionlevel e-mailaddress format isbn label lineage mark mirrorrepository nextedition notes parameterlist parentrepositories previousedition previouslowerunit progress project rightsholder schedulinginformation secondarydate secondarykey session shorttitle sponsor subject tertiarymark tertiarytype url
7. Controle da descrição
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