Mapping Fire Susceptibility in the Brazilian Amazon Forests Using Multitemporal Remote Sensing and Time-Varying Unsupervised Anomaly Detection

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorUniversidade de São Paulo (USP)-
Autor(es): dc.creatorLuz, Andréa Eliza O.-
Autor(es): dc.creatorNegri, Rogério G.-
Autor(es): dc.creatorMassi, Klécia G.-
Autor(es): dc.creatorColnago, Marilaine-
Autor(es): dc.creatorSilva, Erivaldo A.-
Autor(es): dc.creatorCasaca, Wallace-
Data de aceite: dc.date.accessioned2025-08-21T22:29:47Z-
Data de disponibilização: dc.date.available2025-08-21T22:29:47Z-
Data de envio: dc.date.issued2023-03-01-
Data de envio: dc.date.issued2023-03-01-
Data de envio: dc.date.issued2022-05-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.3390/rs14102429-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/241078-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/241078-
Descrição: dc.descriptionThe economic and environmental impacts of wildfires have leveraged the development of new technologies to prevent and reduce the occurrence of these devastating events. Indeed, identifying and mapping fire-susceptible areas arise as critical tasks, not only to pave the way for rapid responses to attenuate the fire spreading, but also to support emergency evacuation plans for the families affected by fire-related tragedies. Aiming at simultaneously mapping and measuring the risk of fires in the forest areas of Brazil’s Amazon, in this paper we combine multitemporal remote sensing, derivative spectral indices, and anomaly detection into a fully unsupervised methodology. We focus our analysis on recent forest fire events that occurred in the Brazilian Amazon by exploring multitemporal images acquired by both Landsat-8 Operational Land Imager and Modis sensors. We experimentally confirm that the current methodology is capable of predicting fire outbreaks immediately at posterior instants, which attests to the operational performance and applicability of our approach to preventing and mitigating the impact of fires in Brazilian forest regions.-
Descrição: dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)-
Descrição: dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)-
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.descriptionScience and Technology Institute (ICT) São Paulo State University (UNESP)-
Descrição: dc.descriptionInstitute of Mathematics and Computer Science (ICMC) São Paulo University (USP)-
Descrição: dc.descriptionFaculty of Science and Technology (FCT) São Paulo State University (UNESP)-
Descrição: dc.descriptionInstitute of Biosciences Letters and Exact Sciences (IBILCE) 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.descriptionScience and Technology Institute (ICT) São Paulo State University (UNESP)-
Descrição: dc.descriptionFaculty of Science and Technology (FCT) São Paulo State University (UNESP)-
Descrição: dc.descriptionInstitute of Biosciences Letters and Exact Sciences (IBILCE) São Paulo State University (UNESP)-
Descrição: dc.descriptionCNPq: 164326/2020-0-
Descrição: dc.descriptionFAPESP: 2021/01305-6-
Descrição: dc.descriptionFAPESP: 2021/03328-3-
Descrição: dc.descriptionCNPq: 304402/2019-2-
Descrição: dc.descriptionCNPq: 316228/2021-4-
Descrição: dc.descriptionCNPq: 427915/2018-0-
Idioma: dc.languageen-
Relação: dc.relationRemote Sensing-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectanomaly detection-
Palavras-chave: dc.subjectforest fires-
Palavras-chave: dc.subjectmultitemporal data-
Palavras-chave: dc.subjectremote sensing-
Palavras-chave: dc.subjectspectral indices-
Título: dc.titleMapping Fire Susceptibility in the Brazilian Amazon Forests Using Multitemporal Remote Sensing and Time-Varying Unsupervised Anomaly Detection-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

Não existem arquivos associados a este item.