Fechar
Metadados

1. Identificação
Tipo de ReferênciaArtigo em Revista Científica (Journal Article)
Sitemtc-m21c.sid.inpe.br
Código do Detentorisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identificador8JMKD3MGP3W34R/457CSLS
Repositóriosid.inpe.br/mtc-m21c/2021/08.03.12.49   (acesso restrito)
Última Atualização2021:08.03.12.49.36 (UTC) simone
Repositório de Metadadossid.inpe.br/mtc-m21c/2021/08.03.12.49.36
Última Atualização dos Metadados2022:04.03.22.28.49 (UTC) administrator
DOI10.1016/j.rsase.2021.100577
ISSN2352-9385
Chave de CitaçãoSilvaNoLoBaCaNoRo:2021:MaLeAp
TítuloA machine learning approach for monitoring Brazilian optical water types using Sentinel-2 MSI
Ano2021
MêsAug.
Data de Acesso29 abr. 2024
Tipo de Trabalhojournal article
Tipo SecundárioPRE PI
Número de Arquivos1
Tamanho7823 KiB
2. Contextualização
Autor1 Silva, Edson Filisbino Freire da
2 Novo, Evlyn Márcia Leão de Moraes
3 Lobo, Felipe de Lucia
4 Barbosa, Cláudio Clemente Faria
5 Cairo, Carolline Tressmann
6 Noernberg, Maurício Almeida
7 Rotta, Luiz Henrique da Silva
Identificador de Curriculo1
2 8JMKD3MGP5W/3C9JH39
3
4 8JMKD3MGP5W/3C9JGSB
ORCID1 0000-0002-1097-9801
Grupo1 SER-SRE-DIPGR-INPE-MCTI-GOV-BR
2 DIOTG-CGCT-INPE-MCTI-GOV-BR
3
4 DIOTG-CGCT-INPE-MCTI-GOV-BR
5 SER-SRE-DIPGR-INPE-MCTI-GOV-BR
Afiliação1 Instituto Nacional de Pesquisas Espaciais (INPE)
2 Instituto Nacional de Pesquisas Espaciais (INPE)
3 Universidade Federal de Pelotas (UFPel)
4 Instituto Nacional de Pesquisas Espaciais (INPE)
5 Instituto Nacional de Pesquisas Espaciais (INPE)
6 Universidade Federal do Paraná (UFPR)
7 Universidade Estadual Paulista (UNESP)
Endereço de e-Mail do Autor1 edson.freirefs@gmail.com
2 evlyn.novo@inpe.br
3
4 claudio.barbosa@inpe.br
5 carolline.cairo@inpe.br
RevistaRemote Sensing Applications: Society and Environment
Volume23
Páginase100577
Histórico (UTC)2021-08-03 12:49:36 :: simone -> administrator ::
2022-04-03 22:28:49 :: administrator -> simone :: 2021
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-ChaveClassification
Machine learning
Novelty detection
Optical water type
ResumoOptical Water Type (OWT) is a useful parameter for assessing water quality changes related to different turbidity levels, trophic state and colored dissolved organic matter (CDOM) while also helpful for tuning chlorophyll-a algorithms. For this reason, interest in the satellite remote sensing of OWTs has recently increased in recent years. This study develops a machine learning method for monitoring Brazilian OWTs using the Sentinel-2 MSI, which can detect OWTs already assessed by field measurements and recognize new OWTs. The already assessed OWTs used for calibrating the machine learning algorithm are clear, moderate turbid, eutrophic turbid, eutrophic clear, hypereutrophic, CDOM richest, turbid, and very turbid waters. The classification method consists of two Support Vector Machines for classifying the known OWTs, while a novelty detection method based on sigmoid functions is used for assessing new OWTs. Results show the classification based on Sentinel-2 MSI bands simulated using field radiometric data is accurate (accuracy = 0.94). However, when radiometric errors are simulated, the accuracy significantly decreases to 0.75, 0.56, 0.45, and 0.37 as the mean absolute percent error increases to 10%, 20%, 30%, and 40%, respectively. Considering the errors retrieved when comparing the field and satellite measurements, the expected accuracy of Sentinel-2 MSI images is 0.78. In the satellite images, the novelty detection distinguishes new OWTs originated from the mixture among the known OWTs and a new OWT that was not part of the training database (clear blue waters). Two examples of time series in the Funil reservoir and the Curuai lake are used to show the applicability of monitoring OWTs. In the Funil reservoir, OWTs could indicate eutrophication and turbid changes caused by river inflow and sediment sinking. In the Curuai lake, OWTs could indicate areas susceptible to algae bloom and turbidity increases related to river inflow and particle resuspension. In the future, the proposed algorithm could be used for large-scale assessment of water quality degradation and supports rapid mitigation and recovery responses. For improving the classification accuracy, adjacency correction and more robust glint removal methods should be developed.
ÁreaSRE
Arranjo 1urlib.net > BDMCI > Fonds > Produção pgr ATUAIS > SER > A machine learning...
Arranjo 2urlib.net > Produção a partir de 2021 > CGCT > A machine learning...
Conteúdo da Pasta docacessar
Conteúdo da Pasta sourcenão têm arquivos
Conteúdo da Pasta agreement
agreement.html 03/08/2021 09:49 1.0 KiB 
4. Condições de acesso e uso
Idiomaen
Arquivo Alvosilva_machine_2021.pdf
Grupo de Usuáriossimone
Visibilidadeshown
Permissão de Leituradeny from all and allow from 150.163
Permissão de Atualizaçãonão transferida
5. Fontes relacionadas
Unidades Imediatamente Superiores8JMKD3MGPCW/3F3NU5S
8JMKD3MGPCW/439EAFB
8JMKD3MGPCW/46KUATE
DivulgaçãoPORTALCAPES; SCOPUS.
Acervo Hospedeirourlib.net/www/2017/11.22.19.04
6. Notas
Campos Vaziosalternatejournal archivingpolicy archivist callnumber copyholder copyright creatorhistory descriptionlevel e-mailaddress format isbn label lineage mark mirrorrepository nextedition notes number parameterlist parentrepositories previousedition previouslowerunit progress project readergroup rightsholder schedulinginformation secondarydate secondarykey secondarymark session shorttitle sponsor subject tertiarymark tertiarytype url
7. Controle da descrição
e-Mail (login)simone
atualizar 


Fechar