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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/48EG228
Repositóriosid.inpe.br/mtc-m21d/2023/01.27.12.28
Última Atualização2023:01.27.12.28.32 (UTC) simone
Repositório de Metadadossid.inpe.br/mtc-m21d/2023/01.27.12.28.32
Última Atualização dos Metadados2024:01.02.17.16.39 (UTC) administrator
DOI10.3390/rs15020521
ISSN2072-4292
Chave de CitaçãoWagnerSSCRHOS:2023:MaTrFo
TítuloMapping Tropical Forest Cover and Deforestation with Planet NICFI Satellite Images and Deep Learning in Mato Grosso State (Brazil) from 2015 to 2021
Ano2023
MêsJan.
Data de Acesso19 maio 2024
Tipo de Trabalhojournal article
Tipo SecundárioPRE PI
Número de Arquivos1
Tamanho19366 KiB
2. Contextualização
Autor1 Wagner, Fabien Hubert
2 Silva, Ricardo Dalagnol da
3 Silva-Junior, Celso Henrique Leite
4 Carter, Griffina
5 Ritz, Alison L.
6 Hirye, Mayumi C. M.
7 Ometto, Jean Pierre Henry Balbaud
8 Saatchi, Sassan
ORCID1
2
3 0000-0002-1052-5551
4
5
6
7 0000-0002-4221-1039
Grupo1
2
3
4
5
6
7 DIPE3-COGPI-INPE-MCTI-GOV-BR
Afiliação1 University of California
2 University of California
3 University of California
4 University of California
5 CTREES, Pasadena
6 Universidade de São Paulo (USP)
7 Instituto Nacional de Pesquisas Espaciais (INPE)
8 University of California
Endereço de e-Mail do Autor1 fhwagner@ucla.edu
2 ricds@hotmail.com
3
4
5
6
7 jean.ometto@inpe.br
RevistaRemote Sensing
Volume15
Número2
Páginase521
Nota SecundáriaB3_GEOGRAFIA B3_ENGENHARIAS_I B4_GEOCIÊNCIAS B4_CIÊNCIAS_AMBIENTAIS B5_CIÊNCIAS_AGRÁRIAS_I
Histórico (UTC)2023-01-27 12:28:32 :: simone -> administrator ::
2023-01-27 12:28:38 :: administrator -> simone :: 2023
2023-01-27 12:29:20 :: simone -> administrator :: 2023
2024-01-02 17:16:39 :: administrator -> simone :: 2023
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-Chaveland-cover and land-use
semantic segmentation
TensorFlow 2
tropical forests
U-net
ResumoMonitoring changes in tree cover for assessment of deforestation is a premise for policies to reduce carbon emission in the tropics. Here, a U-net deep learning model was used to map monthly tropical tree cover in the Brazilian state of Mato Grosso between 2015 and 2021 using 5 m spatial resolution Planet NICFI satellite images. The accuracy of the tree cover model was extremely high, with an F1-score >0.98, further confirmed by an independent LiDAR validation showing that 95% of tree cover pixels had a height >5 m while 98% of non-tree cover pixels had a height <5 m. The biannual map of deforestation was then built from the monthly tree cover map. The deforestation map showed relatively consistent agreement with the official deforestation map from Brazil (67.2%) but deviated significantly from Global Forest Change (GFC)s year of forest loss, showing that our product is closest to the product made by visual interpretation. Finally, we estimated that 14.8% of Mato Grossos total area had undergone clear-cut logging between 2015 and 2021, and that deforestation was increasing, with December 2021, the last date, being the highest. High-resolution imagery from Planet NICFI in conjunction with deep learning techniques can significantly improve the mapping of deforestation extent in tropical regions.
ÁreaCST
Arranjourlib.net > BDMCI > Fonds > Produção a partir de 2021 > COGPI > Mapping Tropical Forest...
Conteúdo da Pasta docacessar
Conteúdo da Pasta sourcenão têm arquivos
Conteúdo da Pasta agreement
agreement.html 27/01/2023 09:28 1.0 KiB 
4. Condições de acesso e uso
URL dos dadoshttp://mtc-m21d.sid.inpe.br/ibi/8JMKD3MGP3W34T/48EG228
URL dos dados zipadoshttp://mtc-m21d.sid.inpe.br/zip/8JMKD3MGP3W34T/48EG228
Idiomaen
Arquivo Alvoremotesensing-15-00521.pdf
Grupo de Usuáriossimone
Grupo de Leitoresadministrator
simone
Visibilidadeshown
Política de Arquivamentoallowpublisher allowfinaldraft
Permissão de Atualizaçãonão transferida
5. Fontes relacionadas
Unidades Imediatamente Superiores8JMKD3MGPCW/46L2FGP
Lista de Itens Citandosid.inpe.br/bibdigital/2022/04.04.04.47 2
DivulgaçãoWEBSCI; PORTALCAPES; MGA; COMPENDEX; SCOPUS.
Acervo Hospedeirourlib.net/www/2021/06.04.03.40
6. Notas
Campos Vaziosalternatejournal archivist callnumber copyholder copyright creatorhistory descriptionlevel e-mailaddress format isbn label lineage mark mirrorrepository nextedition notes parameterlist parentrepositories previousedition previouslowerunit progress project readpermission resumeid rightsholder schedulinginformation secondarydate secondarykey session shorttitle sponsor subject tertiarymark tertiarytype url
7. Controle da descrição
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