Fire Detection with Multitemporal Multispectral Data and a Probabilistic Unsupervised Technique
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , , |
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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Repositório Institucional da UNESP |
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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:29462024-08-06T00:13:22.458336Repositório 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 |
|
_version_ |
1808129597265412096 |