An integrated framework to identify and map gullies in a Mediterranean region of Turkey

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dc.authoridBUDAK, MESUT/0000-0001-5715-1246
dc.authoridGUNAL, Hikmet/0000-0002-4648-2645
dc.authoridKILIC, Mirac/0000-0001-8026-5540
dc.contributor.authorKilic, Mirac
dc.contributor.authorGundogan, Recep
dc.contributor.authorGunal, Hikmet
dc.contributor.authorBudak, Mesut
dc.date.accessioned2024-12-24T19:28:12Z
dc.date.available2024-12-24T19:28:12Z
dc.date.issued2022
dc.departmentSiirt Üniversitesi
dc.description.abstractThis research introduces a scientific methodology to identify areas affected by gully erosion using Geographic Object Based Image Analysis (GEOBIA) and Random Forest (RF) supervised machine learning. The GEOBIA and RF were applied in Besni district, which has a Mediterranean climate, of Adiyaman province in Turkey by including many factors in the model. Estimation Scale Parameter (ESPII) algorithm was used in the segmentation phase. The novelty of this study is the implementation of RF supervised classification algorithm to classify a large number of objects determined after the segmentation process, due to the large size of the study area. Therefore, open access data has been evaluated with high classification accuracy without the need for labor. Precision, Recall and F1-Score values were calculated using true positive (TP), true negative (TN), false positive (FP) and false negative (FN) values based on field observations and Google Earth images of the study area. The TP, TN, FP and FN values were 0.90, 0.95 and 0.92, respectively. In addition, a Kappa-index was calculated as 0.88. The gully erosion map obtained using aforementioned methodology can be used to take necessary measures to prevent further degradation and plan sustainable land uses.
dc.identifier.doi10.1080/10106049.2022.2071478
dc.identifier.endpage12866
dc.identifier.issn1010-6049
dc.identifier.issn1752-0762
dc.identifier.issue26
dc.identifier.scopus2-s2.0-85129662968
dc.identifier.scopusqualityQ1
dc.identifier.startpage12846
dc.identifier.urihttps://doi.org/10.1080/10106049.2022.2071478
dc.identifier.urihttps://hdl.handle.net/20.500.12604/6961
dc.identifier.volume37
dc.identifier.wosWOS:000792709000001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherTaylor & Francis Ltd
dc.relation.ispartofGeocarto International
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241222
dc.subjectMachine learning
dc.subjectrandom forest
dc.subjectgully
dc.subjectGEOBIA
dc.subjectobject pureness
dc.subjectsegmentation
dc.titleAn integrated framework to identify and map gullies in a Mediterranean region of Turkey
dc.typeArticle

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