Evaluation of the ability of SLSTR (Sentinel-3B) and MODIS (Terra) images to detect burned areas using spatial-temporal attributes and SVM classification
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | https://hdl.handle.net/1822/84869 |
Resumo: | Forest fires are considered one of the major dangers and environmental issues across the world. In the Cerrado biome (Brazilian savannas), forest fires have several consequences, including increased temperature, decreased rainfall, genetic depletion of natural species, and increased risk of respiratory diseases. This study presents a methodology that uses data from the Sea and Land Surface Temperature Radiometer (SLSTR) sensor of the Sentinel-3B satellite and the Moderate Resolution Imaging Spectroradiometer (MODIS) of the Terra satellite to analyze the thematic accuracy of burned area maps and their sensitivity under different spectral resolutions in a large area of 32,000 km<sup>2</sup> in the Cerrado biome from 2019 to 2021. The methodology used training and the Support Vector Machine (SVM) classifier. To analyze the spectral peculiarities of each orbital platform, the Transformed Divergence (TD) index separability statistic was used. The results showed that for both sensors, the near-infrared (NIR) band has an essential role in the detection of the burned areas, presenting high separability. Overall, it was possible to observe that the spectral mixing problems, registration date, and the spatial resolution of 500 m were the main factors that led to commission errors ranging between 15% and 72% and omission errors between 51% and 86% for both sensors. This study showed the importance of multispectral sensors for monitoring forest fires. It was found, however, that the spectral resolution and burning date may gradually interfere with the detection process. |
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Evaluation of the ability of SLSTR (Sentinel-3B) and MODIS (Terra) images to detect burned areas using spatial-temporal attributes and SVM classificationForest firesRemote sensingSpace-Time Equivalence Coefficient (STEC)Machine learningScience & TechnologyForest fires are considered one of the major dangers and environmental issues across the world. In the Cerrado biome (Brazilian savannas), forest fires have several consequences, including increased temperature, decreased rainfall, genetic depletion of natural species, and increased risk of respiratory diseases. This study presents a methodology that uses data from the Sea and Land Surface Temperature Radiometer (SLSTR) sensor of the Sentinel-3B satellite and the Moderate Resolution Imaging Spectroradiometer (MODIS) of the Terra satellite to analyze the thematic accuracy of burned area maps and their sensitivity under different spectral resolutions in a large area of 32,000 km<sup>2</sup> in the Cerrado biome from 2019 to 2021. The methodology used training and the Support Vector Machine (SVM) classifier. To analyze the spectral peculiarities of each orbital platform, the Transformed Divergence (TD) index separability statistic was used. The results showed that for both sensors, the near-infrared (NIR) band has an essential role in the detection of the burned areas, presenting high separability. Overall, it was possible to observe that the spectral mixing problems, registration date, and the spatial resolution of 500 m were the main factors that led to commission errors ranging between 15% and 72% and omission errors between 51% and 86% for both sensors. This study showed the importance of multispectral sensors for monitoring forest fires. It was found, however, that the spectral resolution and burning date may gradually interfere with the detection process.This research was supported by POAIUJA 2021-22 and CEACTEMA from the University of Jaén (Spain), and RNM-282 research group from the Junta de Andalucía (Spain). This work was also supported by Portuguese national funding awarded by FCT—Foundation for Science and Technology, I.P., projects UIDB/04683/2020 and UIDP/04683/2020.Multidisciplinary Digital Publishing Institute (MDPI)Universidade do Minhoda Silva Junior, Juarez AntonioPacheco, Admilson da PenhaRuiz-Armenteros, Antonio MiguelHenriques, Renato F.20232023-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/1822/84869engda Silva Junior, J.A.; Pacheco, A.d.P.; Ruiz-Armenteros, A.M.; Henriques, R.F.F. Evaluation of the Ability of SLSTR (Sentinel-3B) and MODIS (Terra) Images to Detect Burned Areas Using Spatial-Temporal Attributes and SVM Classification. Forests 2023, 14, 32. https://doi.org/10.3390/f140100321999-490710.3390/f1401003232https://www.mdpi.com/1999-4907/14/1/32info: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:RCAAP2023-12-23T01:27:43Zoai:repositorium.sdum.uminho.pt:1822/84869Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T18:49:30.431466Repositório 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 |
Evaluation of the ability of SLSTR (Sentinel-3B) and MODIS (Terra) images to detect burned areas using spatial-temporal attributes and SVM classification |
title |
Evaluation of the ability of SLSTR (Sentinel-3B) and MODIS (Terra) images to detect burned areas using spatial-temporal attributes and SVM classification |
spellingShingle |
Evaluation of the ability of SLSTR (Sentinel-3B) and MODIS (Terra) images to detect burned areas using spatial-temporal attributes and SVM classification da Silva Junior, Juarez Antonio Forest fires Remote sensing Space-Time Equivalence Coefficient (STEC) Machine learning Science & Technology |
title_short |
Evaluation of the ability of SLSTR (Sentinel-3B) and MODIS (Terra) images to detect burned areas using spatial-temporal attributes and SVM classification |
title_full |
Evaluation of the ability of SLSTR (Sentinel-3B) and MODIS (Terra) images to detect burned areas using spatial-temporal attributes and SVM classification |
title_fullStr |
Evaluation of the ability of SLSTR (Sentinel-3B) and MODIS (Terra) images to detect burned areas using spatial-temporal attributes and SVM classification |
title_full_unstemmed |
Evaluation of the ability of SLSTR (Sentinel-3B) and MODIS (Terra) images to detect burned areas using spatial-temporal attributes and SVM classification |
title_sort |
Evaluation of the ability of SLSTR (Sentinel-3B) and MODIS (Terra) images to detect burned areas using spatial-temporal attributes and SVM classification |
author |
da Silva Junior, Juarez Antonio |
author_facet |
da Silva Junior, Juarez Antonio Pacheco, Admilson da Penha Ruiz-Armenteros, Antonio Miguel Henriques, Renato F. |
author_role |
author |
author2 |
Pacheco, Admilson da Penha Ruiz-Armenteros, Antonio Miguel Henriques, Renato F. |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
Universidade do Minho |
dc.contributor.author.fl_str_mv |
da Silva Junior, Juarez Antonio Pacheco, Admilson da Penha Ruiz-Armenteros, Antonio Miguel Henriques, Renato F. |
dc.subject.por.fl_str_mv |
Forest fires Remote sensing Space-Time Equivalence Coefficient (STEC) Machine learning Science & Technology |
topic |
Forest fires Remote sensing Space-Time Equivalence Coefficient (STEC) Machine learning Science & Technology |
description |
Forest fires are considered one of the major dangers and environmental issues across the world. In the Cerrado biome (Brazilian savannas), forest fires have several consequences, including increased temperature, decreased rainfall, genetic depletion of natural species, and increased risk of respiratory diseases. This study presents a methodology that uses data from the Sea and Land Surface Temperature Radiometer (SLSTR) sensor of the Sentinel-3B satellite and the Moderate Resolution Imaging Spectroradiometer (MODIS) of the Terra satellite to analyze the thematic accuracy of burned area maps and their sensitivity under different spectral resolutions in a large area of 32,000 km<sup>2</sup> in the Cerrado biome from 2019 to 2021. The methodology used training and the Support Vector Machine (SVM) classifier. To analyze the spectral peculiarities of each orbital platform, the Transformed Divergence (TD) index separability statistic was used. The results showed that for both sensors, the near-infrared (NIR) band has an essential role in the detection of the burned areas, presenting high separability. Overall, it was possible to observe that the spectral mixing problems, registration date, and the spatial resolution of 500 m were the main factors that led to commission errors ranging between 15% and 72% and omission errors between 51% and 86% for both sensors. This study showed the importance of multispectral sensors for monitoring forest fires. It was found, however, that the spectral resolution and burning date may gradually interfere with the detection process. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023 2023-01-01T00:00:00Z |
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 |
https://hdl.handle.net/1822/84869 |
url |
https://hdl.handle.net/1822/84869 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
da Silva Junior, J.A.; Pacheco, A.d.P.; Ruiz-Armenteros, A.M.; Henriques, R.F.F. Evaluation of the Ability of SLSTR (Sentinel-3B) and MODIS (Terra) Images to Detect Burned Areas Using Spatial-Temporal Attributes and SVM Classification. Forests 2023, 14, 32. https://doi.org/10.3390/f14010032 1999-4907 10.3390/f14010032 32 https://www.mdpi.com/1999-4907/14/1/32 |
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.publisher.none.fl_str_mv |
Multidisciplinary Digital Publishing Institute (MDPI) |
publisher.none.fl_str_mv |
Multidisciplinary Digital Publishing Institute (MDPI) |
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 |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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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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1799132259755753472 |