Unsupervised burned areas detection using multitemporal synthetic aperture radar data

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorSimões, José Victor Orlandi-
Autor(es): dc.creatorNegri, Rogerio Galante-
Autor(es): dc.creatorSouza, Felipe Nascimento-
Autor(es): dc.creatorMendes, Tatiana Sussel Gonçalves-
Autor(es): dc.creatorBressane, Adriano-
Data de aceite: dc.date.accessioned2025-08-21T18:49:05Z-
Data de disponibilização: dc.date.available2025-08-21T18:49:05Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2023-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1117/1.JRS.18.014513-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/308117-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/308117-
Descrição: dc.descriptionClimate change is a critical concern that has been greatly affected by human activities, resulting in a rise in greenhouse gas emissions. Its effects have far-reaching impacts on both living and non-living components of ecosystems, leading to alarming outcomes such as a surge in the frequency and severity of fires. This paper presents a data-driven framework that unifies time series of remote sensing images, statistical modeling, and unsupervised classification for mapping fire-damaged areas. To validate the proposed methodology, multiple remote sensing images acquired by the Sentinel-1 satellite between August and October 2021 were collected and analyzed in two case studies comprising Brazilian biomes affected by burns. Our results demonstrate that the proposed approach outperforms another method evaluated in terms of precision metrics and visual adherence. Our methodology achieves the highest overall accuracy of 58.15% and the highest F1 score of 0.72, both of which are higher than the other method. These findings suggest that our approach is more effective in detecting burned areas and may have practical applications in other environmental issues such as landslides, flooding, and deforestation.-
Descrição: dc.descriptionSão Paulo State University Science and Technology Institute-
Descrição: dc.descriptionSão Paulo State University Brazilian Center for Early Warning and Monitoring for Natural Disasters Graduate Program in Natural Disasters-
Descrição: dc.descriptionSão Paulo State University Civil and Environmental Engineering Department Faculty of Engineering-
Descrição: dc.descriptionSão Paulo State University Science and Technology Institute-
Descrição: dc.descriptionSão Paulo State University Brazilian Center for Early Warning and Monitoring for Natural Disasters Graduate Program in Natural Disasters-
Descrição: dc.descriptionSão Paulo State University Civil and Environmental Engineering Department Faculty of Engineering-
Idioma: dc.languageen-
Relação: dc.relationJournal of Applied Remote Sensing-
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Palavras-chave: dc.subjectburned areas-
Palavras-chave: dc.subjectremote sensing-
Palavras-chave: dc.subjectstatistical modeling-
Palavras-chave: dc.subjectsynthetic aperture radar-
Palavras-chave: dc.subjectunsupervised approach-
Título: dc.titleUnsupervised burned areas detection using multitemporal synthetic aperture radar data-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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