Monitoring the rehabilitation process of the windthrow area using UAS images and performance comparison of Sentinel-2A based different vegetation indexes

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dc.contributor.authorCinar, Tunahan
dc.contributor.authorUslu, Aysegul
dc.contributor.authorAydin, Abdurrahim
dc.date.accessioned2025-10-11T20:48:40Z
dc.date.available2025-10-11T20:48:40Z
dc.date.issued2025
dc.departmentDüzce Üniversitesien_US
dc.description.abstractWindthrows significantly disrupt forest ecosystems, impacting biotic community life cycles. To ensure the reformation of the ecosystem chain, it is essential to rehabilitate the windthrow area as soon as possible. Therefore, it is mandotory to determine the success of the rehabilitation processes. In this study, the rehabilitation process of windthrow that occurred in the D & uuml;zce Tatl & imath;dere Forest District (DTFD) was identified using vegetation indices calculated from Unmanned Aircraft System (UAS) images and Sentinel-2A satellite images between 2017 and 2022. The Normalized Difference Red Edge Index (NDRE), Plant Senescence Reflectance Index (PSRI), and Normalized Difference Vegetation Index (NDVI) were calculated from Sentinel-2A satellite images, and the most successful index for detecting reforested areas was identified. UAS images were used to create training data, and this data was used to classify Sentinel-2A images with the Random Forest (RF) algorithm. The classification's accuracy was assessed using the Kappa Coefficient and Overall Accuracy (%). Results showed that NDVI had the lowest accuracy in both years, whereas NDRE succesfully detected windthrow area borders. PSRI was most successful in monitoring rehabilitation progress and detecting reforested areas between 2017 and 2022. This study, he effectiveness and limitations of the NDRE, PSRI and NDVI indices in the rehabilitation process of the windthrow area have been detected, and the most important Sentinel-2A bands were determined based on the results of the RF classification. This study is pioneering in the use of NDRE and PSRI to detect reforested areas post-windthrow.en_US
dc.description.sponsorshipDuzce University [1919B012214608]en_US
dc.description.sponsorshipTUBITAK 2209-A- University Students Research Projects Support Programen_US
dc.description.sponsorshipThis study has been supported by TUBITAK 2209-A- University Students Research Projects Support Program with Project Number 1919B012214608.en_US
dc.identifier.doi10.1007/s12145-025-01701-7
dc.identifier.issn1865-0473
dc.identifier.issn1865-0481
dc.identifier.issue2en_US
dc.identifier.scopus2-s2.0-85217694224en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org/10.1007/s12145-025-01701-7
dc.identifier.urihttps://hdl.handle.net/20.500.12684/22045
dc.identifier.volume18en_US
dc.identifier.wosWOS:001402597900003en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer Heidelbergen_US
dc.relation.ispartofEarth Science Informaticsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmzKA_WOS_20250911
dc.subjectWindthrowen_US
dc.subjectRehabilitationen_US
dc.subjectRemote sensingen_US
dc.subjectVegetation indexesen_US
dc.titleMonitoring the rehabilitation process of the windthrow area using UAS images and performance comparison of Sentinel-2A based different vegetation indexesen_US
dc.typeArticleen_US

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