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Forest fire risk mapping with Landsat 8 OLI images: Evaluation of the potential use of vegetation indices

dc.contributor.authorSivrikaya, F.
dc.contributor.authorGünlü, A.
dc.contributor.authorKüçük, Ö.
dc.contributor.authorÜrker, O.
dc.date.accessioned2024-03-22T09:56:18Z
dc.date.available2024-03-22T09:56:18Z
dc.date.issued2024.01.01
dc.description.abstractFire is one of the most important natural catastrophes threatening the forest ecosystem. The severity and frequency of forest fires are increasing daily due to the increase in population in vulnerable areas and the effects of global climate change. Creating fire risk maps and using them to take the required protective actions to prevent fires will decrease the adverse effects of forest fires. This study focused on producing and comparing fire risk maps based on four vegetation indices, the Normalized Burn Ratio (NBR) index, Normalized Burn Ratio Thermal (NBRT) index, Normalized Difference Vegetation Index (NDVI), and Normalized Difference Water Index (NDWI) and data gathered with the use of remote sensing devices. The Mugla Regional Directorate of Forestry, which is in the Mediterranean climate zone and has experienced mega-fires, was selected as the case study area. Fire risk maps were prepared for the four vegetation indices from Landsat 8 OLI satellite images. Receiver operating characteristic curves and 195 fire ignition points that occurred in 2021 from July 5 to the end of the year were used to assess the accuracy of fire risk maps. Most fire ignition locations (>90%) were in high- and extremely high-risk fire areas on the maps prepared according to the NBR, NDWI, and NDVI. The fact that almost all of the fires occurred in high-risk areas revealed that the study area was sensitive to fire and that the vegetation indices used to draw up the risk maps were highly accurate in predicting where fires might occur. The accuracy results showed that the area under the curve was 0.842 for the NBR, 0.835 for the NDWI, 0.812 for the NBRT, and 0.810 for the NDVI. The NBR approach was more precise than the other models in providing information for fire risk maps. Risk maps created with the NBR could help decision-makers to take precautions and minimize fire damage.
dc.identifier.doi10.1016/j.ecoinf.2024.102461
dc.identifier.eissn1878-0512
dc.identifier.endpage
dc.identifier.issn1574-9541
dc.identifier.issue
dc.identifier.startpage
dc.identifier.urihttps://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=dspace_ku&SrcAuth=WosAPI&KeyUT=WOS:001158725500001&DestLinkType=FullRecord&DestApp=WOS_CPL
dc.identifier.urihttps://hdl.handle.net/20.500.12597/19158
dc.identifier.volume79
dc.identifier.wos001158725500001
dc.language.isoen
dc.relation.ispartofECOLOGICAL INFORMATICS
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectRemote sensing
dc.subjectNormalized band indices
dc.subjectFire risk map
dc.subjectNBR
dc.titleForest fire risk mapping with Landsat 8 OLI images: Evaluation of the potential use of vegetation indices
dc.typeArticle
dspace.entity.typeWos

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