Mapping Burned Areas with Multitemporal–Multispectral Data and Probabilistic Unsupervised Learning

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorVictoria University of Wellington (VUW)-
Autor(es): dc.creatorNegri, Rogério G.-
Autor(es): dc.creatorLuz, Andréa E. O.-
Autor(es): dc.creatorFrery, Alejandro C.-
Autor(es): dc.creatorCasaca, Wallace-
Data de aceite: dc.date.accessioned2025-08-21T22:38:04Z-
Data de disponibilização: dc.date.available2025-08-21T22:38:04Z-
Data de envio: dc.date.issued2023-07-29-
Data de envio: dc.date.issued2023-07-29-
Data de envio: dc.date.issued2022-10-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.3390/rs14215413-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/246293-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/246293-
Descrição: dc.descriptionThe occurrence of forest fires has increased significantly in recent years across the planet. Events of this nature have resulted in the leveraging of new automated methodologies to identify and map burned areas. In this paper, we introduce a unified data-driven framework capable of mapping areas damaged by fire by integrating time series of remotely sensed multispectral images, statistical modeling, and unsupervised classification. We collect and analyze multiple remote-sensing images acquired by the Landsat-8, Sentinel-2, and Terra satellites between August–October 2020, validating our proposal with three case studies in Brazil and Bolivia whose affected regions have suffered from recurrent forest fires. Besides providing less noisy mappings, our methodology outperforms other evaluated methods in terms of average scores of 90%, 0.71, and 0.65 for overall accuracy, F1-score, and kappa coefficient, respectively. The proposed method provides spatial-adherence mappings of the burned areas whose segments match the estimates reported by the MODIS Burn Area product.-
Descrição: dc.descriptionScience and Technology Institute (ICT) São Paulo State University (UNESP)-
Descrição: dc.descriptionGraduate Program in Natural Disasters São Paulo State University (UNESP) National Center for Monitoring and Early Warning of Natural Disasters (CEMADEN)-
Descrição: dc.descriptionSchool of Mathematics and Statistics Victoria University of Wellington (VUW)-
Descrição: dc.descriptionInstitute of Biosciences Letters and Exact Sciences (IBILCE) São Paulo State University (UNESP)-
Descrição: dc.descriptionScience and Technology Institute (ICT) São Paulo State University (UNESP)-
Descrição: dc.descriptionGraduate Program in Natural Disasters São Paulo State University (UNESP) National Center for Monitoring and Early Warning of Natural Disasters (CEMADEN)-
Descrição: dc.descriptionInstitute of Biosciences Letters and Exact Sciences (IBILCE) São Paulo State University (UNESP)-
Idioma: dc.languageen-
Relação: dc.relationRemote Sensing-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectforest fires-
Palavras-chave: dc.subjectmultitemporal-
Palavras-chave: dc.subjectremote sensing-
Palavras-chave: dc.subjectspectral index-
Palavras-chave: dc.subjectunsupervised mapping-
Título: dc.titleMapping Burned Areas with Multitemporal–Multispectral Data and Probabilistic Unsupervised Learning-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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