Spatially Stratified and Multi-Stage Approach for National Land Cover Mapping Based on Sentinel-2 Data and Expert Knowledge

Detalhes bibliográficos
Autor(a) principal: Costa, Hugo
Data de Publicação: 2022
Outros Autores: Benevides, Pedro, Moreira, Francisco D., Moraes, Daniel, Caetano, Mário
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/136628
Resumo: Costa, H., Benevides, P., Moreira, F. D., Moraes, D., & Caetano, M. (2022). Spatially Stratified and Multi-Stage Approach for National Land Cover Mapping Based on Sentinel-2 Data and Expert Knowledge. Remote Sensing, 14(8), 1-21. [1865]. https://doi.org/10.3390/rs14081865 -----This research was funded by Fundação para a Ciência e a Tecnologia (FCT) through projects IPSTERS (DSAIPA/AI/0100/2018), foRESTER (PCIF/SSI/0102/2017), and SCAPEFIRE (PCIF/MOS/0046/2017), and by Compete2020 (POCI-05-5762-FSE-000368), supported by the European Social Fund. The APC was funded by project foRESTER (PCIF/SSI/0102/2017).
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spelling Spatially Stratified and Multi-Stage Approach for National Land Cover Mapping Based on Sentinel-2 Data and Expert Knowledgesatellite imagemulti-temporalLand cover land usemachine learningrandom forestCOSsimEarth and Planetary Sciences(all)SDG 15 - Life on LandCosta, H., Benevides, P., Moreira, F. D., Moraes, D., & Caetano, M. (2022). Spatially Stratified and Multi-Stage Approach for National Land Cover Mapping Based on Sentinel-2 Data and Expert Knowledge. Remote Sensing, 14(8), 1-21. [1865]. https://doi.org/10.3390/rs14081865 -----This research was funded by Fundação para a Ciência e a Tecnologia (FCT) through projects IPSTERS (DSAIPA/AI/0100/2018), foRESTER (PCIF/SSI/0102/2017), and SCAPEFIRE (PCIF/MOS/0046/2017), and by Compete2020 (POCI-05-5762-FSE-000368), supported by the European Social Fund. The APC was funded by project foRESTER (PCIF/SSI/0102/2017).Portugal is building a land cover monitoring system to deliver land cover products annually for its mainland territory. This paper presents the methodology developed to produce a prototype relative to 2018 as the first land cover map of the future annual map series (COSsim). A total of thirteen land cover classes are represented, including the most important tree species in Portugal. The mapping approach developed includes two levels of spatial stratification based on landscape dynamics. Strata are analysed independently at the higher level, while nested sublevels can share data and procedures. Multiple stages of analysis are implemented in which subsequent stages improve the outputs of precedent stages. The goal is to adjust mapping to the local landscape and tackle specific problems or divide complex mapping tasks in several parts. Supervised classification of Sentinel-2 time series and post-classification analysis with expert knowledge were performed throughout four stages. The overall accuracy of the map is estimated at 81.3% (±2.1) at the 95% confidence level. Higher thematic accuracy was achieved in southern Portugal, and expert knowledge significantly improved the quality of the map.NOVA Information Management School (NOVA IMS)Information Management Research Center (MagIC) - NOVA Information Management SchoolRUNCosta, HugoBenevides, PedroMoreira, Francisco D.Moraes, DanielCaetano, Mário2022-04-18T22:34:32Z2022-04-132022-04-13T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article21application/pdfhttp://hdl.handle.net/10362/136628eng2072-4292PURE: 43307790https://doi.org/10.3390/rs14081865info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T05:14:36Zoai:run.unl.pt:10362/136628Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:48:44.136641Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Spatially Stratified and Multi-Stage Approach for National Land Cover Mapping Based on Sentinel-2 Data and Expert Knowledge
title Spatially Stratified and Multi-Stage Approach for National Land Cover Mapping Based on Sentinel-2 Data and Expert Knowledge
spellingShingle Spatially Stratified and Multi-Stage Approach for National Land Cover Mapping Based on Sentinel-2 Data and Expert Knowledge
Costa, Hugo
satellite image
multi-temporal
Land cover land use
machine learning
random forest
COSsim
Earth and Planetary Sciences(all)
SDG 15 - Life on Land
title_short Spatially Stratified and Multi-Stage Approach for National Land Cover Mapping Based on Sentinel-2 Data and Expert Knowledge
title_full Spatially Stratified and Multi-Stage Approach for National Land Cover Mapping Based on Sentinel-2 Data and Expert Knowledge
title_fullStr Spatially Stratified and Multi-Stage Approach for National Land Cover Mapping Based on Sentinel-2 Data and Expert Knowledge
title_full_unstemmed Spatially Stratified and Multi-Stage Approach for National Land Cover Mapping Based on Sentinel-2 Data and Expert Knowledge
title_sort Spatially Stratified and Multi-Stage Approach for National Land Cover Mapping Based on Sentinel-2 Data and Expert Knowledge
author Costa, Hugo
author_facet Costa, Hugo
Benevides, Pedro
Moreira, Francisco D.
Moraes, Daniel
Caetano, Mário
author_role author
author2 Benevides, Pedro
Moreira, Francisco D.
Moraes, Daniel
Caetano, Mário
author2_role author
author
author
author
dc.contributor.none.fl_str_mv NOVA Information Management School (NOVA IMS)
Information Management Research Center (MagIC) - NOVA Information Management School
RUN
dc.contributor.author.fl_str_mv Costa, Hugo
Benevides, Pedro
Moreira, Francisco D.
Moraes, Daniel
Caetano, Mário
dc.subject.por.fl_str_mv satellite image
multi-temporal
Land cover land use
machine learning
random forest
COSsim
Earth and Planetary Sciences(all)
SDG 15 - Life on Land
topic satellite image
multi-temporal
Land cover land use
machine learning
random forest
COSsim
Earth and Planetary Sciences(all)
SDG 15 - Life on Land
description Costa, H., Benevides, P., Moreira, F. D., Moraes, D., & Caetano, M. (2022). Spatially Stratified and Multi-Stage Approach for National Land Cover Mapping Based on Sentinel-2 Data and Expert Knowledge. Remote Sensing, 14(8), 1-21. [1865]. https://doi.org/10.3390/rs14081865 -----This research was funded by Fundação para a Ciência e a Tecnologia (FCT) through projects IPSTERS (DSAIPA/AI/0100/2018), foRESTER (PCIF/SSI/0102/2017), and SCAPEFIRE (PCIF/MOS/0046/2017), and by Compete2020 (POCI-05-5762-FSE-000368), supported by the European Social Fund. The APC was funded by project foRESTER (PCIF/SSI/0102/2017).
publishDate 2022
dc.date.none.fl_str_mv 2022-04-18T22:34:32Z
2022-04-13
2022-04-13T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/136628
url http://hdl.handle.net/10362/136628
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 2072-4292
PURE: 43307790
https://doi.org/10.3390/rs14081865
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
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dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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