Semi-Automatic Methodology for Fire Break Maintenance Operations Detection with Sentinel-2 Imagery and Artificial Neural Network

Detalhes bibliográficos
Autor(a) principal: Pereira-Pires, João
Data de Publicação: 2020
Outros Autores: Aubard, Valentine, Ribeiro, Rita A., Fonseca, José M., Silva, João M. N., Mora, André
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/116901
Resumo: PTDC/CCI-COM/30344/2017 PCIF/SSI/0102/2017 UID/EEA/00066/2019 UIDB/00239/2020
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spelling Semi-Automatic Methodology for Fire Break Maintenance Operations Detection with Sentinel-2 Imagery and Artificial Neural NetworkArtificial neural networksChange detectionFire breakObject-based classificationRemote sensingSentinel-2WildfiresEarth and Planetary Sciences(all)PTDC/CCI-COM/30344/2017 PCIF/SSI/0102/2017 UID/EEA/00066/2019 UIDB/00239/2020The difficult job of fighting fires and the nearly impossible task to stop a wildfire without great casualties requires an imperative implementation of proactive strategies. These strategies must decrease the number of fires, the burnt area and create better conditions for the firefighting. In this line of action, the Portuguese Institute of Nature and Forest Conservation defined a fire break network (FBN), which helps controlling wildfires. However, these fire breaks are efficient only if they are correctly maintained, which should be ensured by the local authorities and requires verification from the national authorities. This is a fastidious task since they have a large network of thousands of hectares to monitor over a full year. With the increasing quality and frequency of the Earth Observation Satellite imagery with Sentinel-2 and the definition of the FBN, a semi-automatic remote sensing methodology is proposed in this article for the detection of maintenance operations in a fire break. The proposed methodology is based on a time-series analysis, an object-based classification and a change detection process. The change detection is ensured by an artificial neural network, with reflectance bands and spectral indices as features. Additionally, an analysis of several bands and spectral indices is presented to show the behaviour of the data during a full year and in the presence of a maintenance operation. The proposed methodology achieved a relative error lower than 4% and a recall higher than 75% on the detection of maintenance operations.CTS - Centro de Tecnologia e SistemasUNINOVA-Instituto de Desenvolvimento de Novas TecnologiasRUNPereira-Pires, JoãoAubard, ValentineRibeiro, Rita A.Fonseca, José M.Silva, João M. N.Mora, André2021-05-03T22:55:14Z2020-03-122020-03-12T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10362/116901engPURE: 28851036https://doi.org/10.3390/rs12060909info: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:RCAAP2024-03-11T04:59:47Zoai:run.unl.pt:10362/116901Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:43:21.746501Repositório 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 Semi-Automatic Methodology for Fire Break Maintenance Operations Detection with Sentinel-2 Imagery and Artificial Neural Network
title Semi-Automatic Methodology for Fire Break Maintenance Operations Detection with Sentinel-2 Imagery and Artificial Neural Network
spellingShingle Semi-Automatic Methodology for Fire Break Maintenance Operations Detection with Sentinel-2 Imagery and Artificial Neural Network
Pereira-Pires, João
Artificial neural networks
Change detection
Fire break
Object-based classification
Remote sensing
Sentinel-2
Wildfires
Earth and Planetary Sciences(all)
title_short Semi-Automatic Methodology for Fire Break Maintenance Operations Detection with Sentinel-2 Imagery and Artificial Neural Network
title_full Semi-Automatic Methodology for Fire Break Maintenance Operations Detection with Sentinel-2 Imagery and Artificial Neural Network
title_fullStr Semi-Automatic Methodology for Fire Break Maintenance Operations Detection with Sentinel-2 Imagery and Artificial Neural Network
title_full_unstemmed Semi-Automatic Methodology for Fire Break Maintenance Operations Detection with Sentinel-2 Imagery and Artificial Neural Network
title_sort Semi-Automatic Methodology for Fire Break Maintenance Operations Detection with Sentinel-2 Imagery and Artificial Neural Network
author Pereira-Pires, João
author_facet Pereira-Pires, João
Aubard, Valentine
Ribeiro, Rita A.
Fonseca, José M.
Silva, João M. N.
Mora, André
author_role author
author2 Aubard, Valentine
Ribeiro, Rita A.
Fonseca, José M.
Silva, João M. N.
Mora, André
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv CTS - Centro de Tecnologia e Sistemas
UNINOVA-Instituto de Desenvolvimento de Novas Tecnologias
RUN
dc.contributor.author.fl_str_mv Pereira-Pires, João
Aubard, Valentine
Ribeiro, Rita A.
Fonseca, José M.
Silva, João M. N.
Mora, André
dc.subject.por.fl_str_mv Artificial neural networks
Change detection
Fire break
Object-based classification
Remote sensing
Sentinel-2
Wildfires
Earth and Planetary Sciences(all)
topic Artificial neural networks
Change detection
Fire break
Object-based classification
Remote sensing
Sentinel-2
Wildfires
Earth and Planetary Sciences(all)
description PTDC/CCI-COM/30344/2017 PCIF/SSI/0102/2017 UID/EEA/00066/2019 UIDB/00239/2020
publishDate 2020
dc.date.none.fl_str_mv 2020-03-12
2020-03-12T00:00:00Z
2021-05-03T22:55:14Z
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/10362/116901
url http://hdl.handle.net/10362/116901
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv PURE: 28851036
https://doi.org/10.3390/rs12060909
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 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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