Spatially Stratified and Multi-Stage Approach for National Land Cover Mapping Based on Sentinel-2 Data and Expert Knowledge
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , , |
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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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 |
dc.format.none.fl_str_mv |
21 application/pdf |
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 |
repository.mail.fl_str_mv |
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1799138087680344064 |