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Assessment of interventions in fuel management zones using remote sensing

2020-09-07Artigo científico Acesso aberto
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dc.contributor.authorAfonso, Ricardo
dc.contributor.authorNeves, André
dc.contributor.authorDamásio, Carlos Viegas
dc.contributor.authorPires, João Moura
dc.contributor.authorBirra, Fernando
dc.contributor.authorSantos, Maribel Yasmina
dc.contributor.institutionNOVALincs
dc.contributor.institutionDI - Departamento de Informática
dc.contributor.pblMDPI - Multidisciplinary Digital Publishing Institute
dc.date.accessioned2021-03-19T23:28:31Z
dc.date.available2021-03-19T23:28:31Z
dc.date.issued2020-09-07
dc.descriptionUIDB/04516/2020 UIDB/00319/2020 PCIF/MOG/0161/2019
dc.description.abstractEvery year, wildfires strike the Portuguese territory and are a concern for public entities and the population. To prevent a wildfire progression and minimize its impact, Fuel Management Zones (FMZs) have been stipulated, by law, around buildings, settlements, along national roads, and other infrastructures. FMZs require monitoring of the vegetation condition to promptly proceed with the maintenance and cleaning of these zones. To improve FMZ monitoring, this paper proposes the use of satellite images, such as the Sentinel-1 and Sentinel-2, along with vegetation indices and extracted temporal characteristics (max, min, mean and standard deviation) associated with the vegetation within and outside the FMZs and to determine if they were treated. These characteristics feed machine-learning algorithms, such as XGBoost, Support Vector Machines, K-nearest neighbors and Random Forest. The results show that it is possible to detect an intervention in an FMZ with high accuracy, namely with an F1-score ranging from 90% up to 94% and a Kappa ranging from 0.80 up to 0.89.en
dc.description.versionpublishersversion
dc.description.versionpublished
dc.format.extent7120991
dc.identifier.doi10.3390/ijgi9090533
dc.identifier.issn2220-9964
dc.identifier.otherPURE: 27914732
dc.identifier.otherPURE UUID: a964af92-09cf-4058-ae73-a463f0b0eb33
dc.identifier.otherScopus: 85090920250
dc.identifier.otherWOS: 000581191700001
dc.identifier.otherORCID: /0000-0001-9933-936X/work/90911825
dc.identifier.urihttp://hdl.handle.net/10362/114145
dc.identifier.urlhttps://www.scopus.com/pages/publications/85090920250
dc.language.isoeng
dc.peerreviewedyes
dc.subjectFuel Management Zones
dc.subjectMachine learning
dc.subjectRemote sensing
dc.subjectSentinel-1
dc.subjectSentinel-2
dc.subjectTime series
dc.subjectGeography, Planning and Development
dc.subjectComputers in Earth Sciences
dc.subjectEarth and Planetary Sciences (miscellaneous)
dc.titleAssessment of interventions in fuel management zones using remote sensingen
dc.typejournal article
degois.publication.issue9
degois.publication.titleISPRS International Journal of Geo-Information
degois.publication.volume9
dspace.entity.typePublication
rcaap.rightsopenAccess

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