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dc.creatorSakellariou S., Cabral P., Caetano M., Pla F., Painho M., Christopoulou O., Sfougaris A., Dalezios N., Vasilakos C.en
dc.date.accessioned2023-01-31T09:52:57Z
dc.date.available2023-01-31T09:52:57Z
dc.date.issued2020
dc.identifier10.3390/s20175014
dc.identifier.issn14248220
dc.identifier.urihttp://hdl.handle.net/11615/78685
dc.description.abstractForest fires are a natural phenomenon which might have severe implications on natural and anthropogenic ecosystems. Future projections predict that, under a climate change environment, the fire season would be lengthier with higher levels of droughts, leading to higher fire severity. The main aim of this paper is to perform a spatiotemporal analysis and explore the variability of fire hazard in a small Greek island, Skiathos (a prototype case of fragile environment) where the land uses mixture is very high. First, a comparative assessment of two robust modeling techniques was examined, namely, the Analytical Hierarchy Process (AHP) knowledge-based and the fuzzy logic AHP to estimate the fire hazard in a timeframe of 20 years (1996–2016). The former technique was proven more representative after the comparative assessment with the real fire perimeters recorded on the island (1984–2016). Next, we explored the spatiotemporal dynamics of fire hazard, highlighting the risk changes in space and time through the individual and collective contribution of the most significant factors (topography, vegetation features, anthropogenic influence). The fire hazard changes were not dramatic, however, some changes have been observed in the southwestern and northern part of the island. The geostatistical analysis revealed a significant clustering process of high-risk values in the southwestern and northern part of the study area, whereas some clusters of low-risk values have been located in the northern territory. The degree of spatial autocorrelation tends to be greater for 1996 rather than for 2016, indicating the potential higher transmission of fires at the most susceptible regions in the past. The knowledge of long-term fire hazard dynamics, based on multiple types of remotely sensed data, may provide the fire and land managers with valuable fire prevention and land use planning tools. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.en
dc.language.isoenen
dc.sourceSensors (Switzerland)en
dc.source.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85090172422&doi=10.3390%2fs20175014&partnerID=40&md5=c4a8c852a46efaf5929072c6b09b4357
dc.subjectClimate changeen
dc.subjectClustering algorithmsen
dc.subjectData fusionen
dc.subjectDeforestationen
dc.subjectEcosystemsen
dc.subjectFire hazardsen
dc.subjectFireproofingen
dc.subjectFuzzy logicen
dc.subjectHierarchical systemsen
dc.subjectKnowledge based systemsen
dc.subjectLand useen
dc.subjectLandformsen
dc.subjectRemote sensingen
dc.subjectRisk assessmenten
dc.subjectTopographyen
dc.subjectAnalytical Hierarchy Processen
dc.subjectAnthropogenic ecosystemsen
dc.subjectAnthropogenic influenceen
dc.subjectComparative assessmenten
dc.subjectGeostatistical analysisen
dc.subjectSpatial autocorrelationsen
dc.subjectSpatio-temporal dynamicsen
dc.subjectSpatiotemporal analysisen
dc.subjectFiresen
dc.subjectMDPI AGen
dc.titleRemotely sensed data fusion for spatiotemporal geostatistical analysis of forest fire hazarden
dc.typejournalArticleen


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