Fire Detection with Multitemporal Multispectral Data and a Probabilistic Unsupervised Technique

Detalhes bibliográficos
Autor(a) principal: Negri, Rogerio G. [UNESP]
Data de Publicação: 2023
Outros Autores: Andrea Luz, E. O. [UNESP], Frery, Alejandro C., Casaca, Wallace [UNESP]
Tipo de documento: Artigo de conferência
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1109/MIGARS57353.2023.10064623
http://hdl.handle.net/11449/247074
Resumo: The frequency of forest fires has increased signifi- cantly in recent years across the planet. Events of this nature motivate the development of automated methodologies aimed at mapping areas affected by fire. In this context, we propose a method capable of accurately mapping areas affected by fire using time series of remotely sensed multispectral images by statistical modeling and classification. In order to evaluate the introduced proposal, we carry out a case study on a region in Brazil with recurrent history of forest fires. Furthermore, images obtained by the Landsat-8 satellite are used in this case study. Comparisons with an alternative method are included in this analysis.
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spelling Fire Detection with Multitemporal Multispectral Data and a Probabilistic Unsupervised TechniqueForest firesmultitemporalspectral indexunsupervised mappingThe frequency of forest fires has increased signifi- cantly in recent years across the planet. Events of this nature motivate the development of automated methodologies aimed at mapping areas affected by fire. In this context, we propose a method capable of accurately mapping areas affected by fire using time series of remotely sensed multispectral images by statistical modeling and classification. In order to evaluate the introduced proposal, we carry out a case study on a region in Brazil with recurrent history of forest fires. Furthermore, images obtained by the Landsat-8 satellite are used in this case study. Comparisons with an alternative method are included in this analysis.Science and Technology Institute São Paulo State UniversityVictoria University of Wellington School of Mathematics and StatisticsInstitute of Biosciences Letters and Exact Sciences São Paulo State UniversityScience and Technology Institute São Paulo State UniversityInstitute of Biosciences Letters and Exact Sciences São Paulo State UniversityUniversidade Estadual Paulista (UNESP)School of Mathematics and StatisticsNegri, Rogerio G. [UNESP]Andrea Luz, E. O. [UNESP]Frery, Alejandro C.Casaca, Wallace [UNESP]2023-07-29T13:05:37Z2023-07-29T13:05:37Z2023-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObjecthttp://dx.doi.org/10.1109/MIGARS57353.2023.100646232023 International Conference on Machine Intelligence for GeoAnalytics and Remote Sensing, MIGARS 2023.http://hdl.handle.net/11449/24707410.1109/MIGARS57353.2023.100646232-s2.0-85151234823Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPeng2023 International Conference on Machine Intelligence for GeoAnalytics and Remote Sensing, MIGARS 2023info:eu-repo/semantics/openAccess2023-07-29T13:05:37Zoai:repositorio.unesp.br:11449/247074Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462023-07-29T13:05:37Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Fire Detection with Multitemporal Multispectral Data and a Probabilistic Unsupervised Technique
title Fire Detection with Multitemporal Multispectral Data and a Probabilistic Unsupervised Technique
spellingShingle Fire Detection with Multitemporal Multispectral Data and a Probabilistic Unsupervised Technique
Negri, Rogerio G. [UNESP]
Forest fires
multitemporal
spectral index
unsupervised mapping
title_short Fire Detection with Multitemporal Multispectral Data and a Probabilistic Unsupervised Technique
title_full Fire Detection with Multitemporal Multispectral Data and a Probabilistic Unsupervised Technique
title_fullStr Fire Detection with Multitemporal Multispectral Data and a Probabilistic Unsupervised Technique
title_full_unstemmed Fire Detection with Multitemporal Multispectral Data and a Probabilistic Unsupervised Technique
title_sort Fire Detection with Multitemporal Multispectral Data and a Probabilistic Unsupervised Technique
author Negri, Rogerio G. [UNESP]
author_facet Negri, Rogerio G. [UNESP]
Andrea Luz, E. O. [UNESP]
Frery, Alejandro C.
Casaca, Wallace [UNESP]
author_role author
author2 Andrea Luz, E. O. [UNESP]
Frery, Alejandro C.
Casaca, Wallace [UNESP]
author2_role author
author
author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (UNESP)
School of Mathematics and Statistics
dc.contributor.author.fl_str_mv Negri, Rogerio G. [UNESP]
Andrea Luz, E. O. [UNESP]
Frery, Alejandro C.
Casaca, Wallace [UNESP]
dc.subject.por.fl_str_mv Forest fires
multitemporal
spectral index
unsupervised mapping
topic Forest fires
multitemporal
spectral index
unsupervised mapping
description The frequency of forest fires has increased signifi- cantly in recent years across the planet. Events of this nature motivate the development of automated methodologies aimed at mapping areas affected by fire. In this context, we propose a method capable of accurately mapping areas affected by fire using time series of remotely sensed multispectral images by statistical modeling and classification. In order to evaluate the introduced proposal, we carry out a case study on a region in Brazil with recurrent history of forest fires. Furthermore, images obtained by the Landsat-8 satellite are used in this case study. Comparisons with an alternative method are included in this analysis.
publishDate 2023
dc.date.none.fl_str_mv 2023-07-29T13:05:37Z
2023-07-29T13:05:37Z
2023-01-01
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/conferenceObject
format conferenceObject
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://dx.doi.org/10.1109/MIGARS57353.2023.10064623
2023 International Conference on Machine Intelligence for GeoAnalytics and Remote Sensing, MIGARS 2023.
http://hdl.handle.net/11449/247074
10.1109/MIGARS57353.2023.10064623
2-s2.0-85151234823
url http://dx.doi.org/10.1109/MIGARS57353.2023.10064623
http://hdl.handle.net/11449/247074
identifier_str_mv 2023 International Conference on Machine Intelligence for GeoAnalytics and Remote Sensing, MIGARS 2023.
10.1109/MIGARS57353.2023.10064623
2-s2.0-85151234823
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 2023 International Conference on Machine Intelligence for GeoAnalytics and Remote Sensing, MIGARS 2023
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.source.none.fl_str_mv Scopus
reponame:Repositório Institucional da UNESP
instname:Universidade Estadual Paulista (UNESP)
instacron:UNESP
instname_str Universidade Estadual Paulista (UNESP)
instacron_str UNESP
institution UNESP
reponame_str Repositório Institucional da UNESP
collection Repositório Institucional da UNESP
repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
repository.mail.fl_str_mv
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