Fechar
Metadados

1. Identificação
Tipo de ReferênciaArtigo em Revista Científica (Journal Article)
Sitemtc-m21d.sid.inpe.br
Código do Detentorisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identificador8JMKD3MGP3W34T/47CHPUP
Repositóriosid.inpe.br/mtc-m21d/2022/08.02.13.20   (acesso restrito)
Última Atualização2022:08.02.13.20.09 (UTC) simone
Repositório de Metadadossid.inpe.br/mtc-m21d/2022/08.02.13.20.09
Última Atualização dos Metadados2023:01.03.16.46.11 (UTC) administrator
DOI10.5194/isprs-archives-XLIII-B3-2022-841-2022
ISSN0256-1840
Chave de CitaçãoBendiniFoMaMaHaVa:2022:EvSeBe
TítuloEvaluating the separability beteween dry tropical forests and Savanna woodlands in the brazilian Savanna using Landsat dense image time series and artificial intelligence
Ano2022
MêsJune
Data de Acesso25 abr. 2024
Tipo de Trabalhojournal article
Tipo SecundárioPRE PI
Número de Arquivos1
Tamanho1200 KiB
2. Contextualização
Autor1 Bendini, Hugo do Nascimento
2 Fonseca, Leila Maria Garcia
3 Matosak, Bruno Menini
4 Mariano, Ravi Fernandes
5 Haidar, R. F.
6 Valeriano, Dalton de Morisson
Identificador de Curriculo1
2 8JMKD3MGP5W/3C9JHLD
3
4
5
6 8JMKD3MGP5W/3C9JGT4
Grupo1 DIOTG-CGCT-INPE-MCTI-GOV-BR
2 DIOTG-CGCT-INPE-MCTI-GOV-BR
3 SER-SRE-DIPGR-INPE-MCTI-GOV-BR
4 DIOTG-CGCT-INPE-MCTI-GOV-BR
5
6 DIOTG-CGCT-INPE-MCTI-GOV-BR
Afiliação1 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 Universidade Federal do Tocantins (UFTO)
6 Instituto Nacional de Pesquisas Espaciais (INPE)
Endereço de e-Mail do Autor1 hugo.bendini@inpe.br
2 leila.fonseca@inpe.br
3 bruno.matosak@inpe.br
4 ravimariano@hotmail.com
5 ricardohaidar@yahoo.com.br
6 dalton.valeriano@inpe.br
RevistaInternational Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
Volume1,
Número2
Páginas841-847
Histórico (UTC)2022-08-02 13:20:43 :: simone -> administrator :: 2022
2023-01-03 16:46:11 :: 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-ChaveCerrado
Dry Forests
Machine Learning
Random Forest
Recurrent Neural Networks
ResumoThe Brazilian Savanna is the second largest biogeographical region in Brazil and present different vegetation types, consisting mostly of tropical savannas, grasslands, and forests. The forest types have different tree cover and floristic composition, which is associated to leaf deciduousness. Considering the importance of Cerrado to biodiversity conservation and the maintaining of environmental services, the development of methods to map the different forest types in Cerrado is important for conservation programmes, subsidize restauration plains, and to allow estimations of carbon sink and stock. Mapping heterogeneous tropical areas, such as the Brazilian Savanna, is very complex due to the natural factors and peculiarities of the vegetation types, and it's still particularly challenging to separate between different forest formations. In this study we tested machine learning approaches based on the use of dense image time series, in order to evaluate the separability Dry Tropical Forests and Savanna woodlands. We considered the Brazilian State of Tocantins as the study area, which is located in the Northern region of the country. RF classification of Landsat dense time series showed an overall accuracy of 0.85005, while the LSTM approach presented an overall accuracy of 0.88601, with the highest f1-score for the savanna woodlands class, suggesting the capability of the recurrent neural networks on handling complex long-term dependencies such as the EVI dense time series data. This study showed the potential for the development of a semi-automatic method for discriminating the different types of forest formations in the Brazilian Savanna, based on remote sensing.
ÁreaSRE
Arranjo 1urlib.net > BDMCI > Fonds > Produção pgr ATUAIS > SER > Evaluating the separability...
Arranjo 2urlib.net > BDMCI > Fonds > Produção a partir de 2021 > CGCT > Evaluating the separability...
Conteúdo da Pasta docacessar
Conteúdo da Pasta sourcenão têm arquivos
Conteúdo da Pasta agreement
agreement.html 02/08/2022 10:20 1.0 KiB 
4. Condições de acesso e uso
Idiomaen
Arquivo Alvoisprs-archives-XLIII-B3-2022-841-2022.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
Lista de Itens Citandosid.inpe.br/bibdigital/2013/10.18.22.34 4
sid.inpe.br/bibdigital/2022/04.03.22.23 2
sid.inpe.br/mtc-m21/2012/07.13.14.44.17 2
DivulgaçãoPORTALCAPES; SCOPUS.
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 orcid parameterlist parentrepositories previousedition previouslowerunit progress project rightsholder schedulinginformation secondarydate secondarykey secondarymark session shorttitle sponsor subject tertiarymark tertiarytype url
7. Controle da descrição
e-Mail (login)simone
atualizar 


Fechar