Identifying Areas Prone to Windthrow Damage and Generating Susceptibility Maps Utilizing a Novel Vegetation Index Extracted from Sentinel-2A Imagery

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dc.authoridAYDIN, Abdurrahim/0000-0002-6572-3395en_US
dc.authoridOzdemir, Serkan/0000-0002-9425-3724en_US
dc.authorscopusid57205743969en_US
dc.authorscopusid56001186700en_US
dc.authorscopusid36460530900en_US
dc.contributor.authorCinar, Tunahan
dc.contributor.authorOzdemir, Serkan
dc.contributor.authorAydin, Abdurrahim
dc.date.accessioned2024-08-23T16:07:03Z
dc.date.available2024-08-23T16:07:03Z
dc.date.issued2023en_US
dc.departmentDüzce Üniversitesien_US
dc.description.abstractForests can be significantly affected by windthrow damage, which negatively impacts the process of forest utilization. Therefore, it is important to identify areas with potential windthrow damage and include them in the planning processes. Based on this idea, a windthrow susceptibility map was created using windthrow data obtained from the extraordinary yield reports of Turkiye-Zonguldak Forest Regional Directorate (FRD) for the years 2017-2022. Firstly, Sentinel-2A satellite images from one week before (pre-windthrow) and one week after (post-windthrow) the occurrence dates of each of the 325 windthrow events were acquired. Subsequently, a cloud mask was applied using the Python programming language in Google Earth Engine (GEE), and the Normalized Difference Fraction Index (NDFI) was calculated. Each identified damage area was saved as a polygon vector data format, and within each polygon, a point was assigned for every 100 m2, resulting in a total of 929 windthrow areas. Data related to wind speed, slope, precipitation, elevation, and distance-to-road variables were obtained for each point. Then, the component values of the axis with the highest variance explanation ratio were modeled using the Random Forest (RF) method. Ultimately, the predictive values of the model were extrapolated to the study area to generate the susceptibility windthrow map. The predictive map revealed that the southern parts of the study area had relatively higher windthrow potential. In this study, for the first time, the detection of windthrow areas was performed using NDFI, and the coefficients of environmental parameters were determined to generate a susceptibility mapping.en_US
dc.description.sponsorshipDuezce University Scientific Research Projects [2022.02.02.1352]en_US
dc.description.sponsorshipThis study has been supported by Duezce University Scientific Research Projects with Project Number 2022.02.02.1352.en_US
dc.identifier.doi10.1007/s12524-023-01772-3
dc.identifier.endpage2402en_US
dc.identifier.issn0255-660X
dc.identifier.issn0974-3006
dc.identifier.issue12en_US
dc.identifier.scopus2-s2.0-85175790837en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage2391en_US
dc.identifier.urihttps://doi.org/10.1007/s12524-023-01772-3
dc.identifier.urihttps://hdl.handle.net/20.500.12684/14458
dc.identifier.volume51en_US
dc.identifier.wosWOS:001096317800001en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofJournal of the Indian Society of Remote Sensingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectRemote Sensingen_US
dc.subjectSentinel-2Aen_US
dc.subjectPrincipal component analysisen_US
dc.subjectRandom foresten_US
dc.subjectWindthrowen_US
dc.subjectBoreal Forestsen_US
dc.subjectSoilen_US
dc.subjectProductivityen_US
dc.subjectStandsen_US
dc.subjectTemperateen_US
dc.subjectDynamicsen_US
dc.subjectTreesen_US
dc.subjectStormen_US
dc.subjectPineen_US
dc.titleIdentifying Areas Prone to Windthrow Damage and Generating Susceptibility Maps Utilizing a Novel Vegetation Index Extracted from Sentinel-2A Imageryen_US
dc.typeArticleen_US

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