Comparison of Different Supervised Classification Algorithms for Mapping Paddy Rice Areas Using Landsat 9 Imageries

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dc.contributor.authorİnalpulat, Melis
dc.date.accessioned2025-01-27T19:36:41Z
dc.date.available2025-01-27T19:36:41Z
dc.date.issued2023
dc.departmentÇanakkale Onsekiz Mart Üniversitesi
dc.description.abstractRice is known to be one of the most essential crops in Turkey, as well as many other countries especially in Asia, whereas paddy rice cropping systems have a key role in many processes ranging from human nutrition to environment-related perspectives. Therefore, determination of cultivation area is still a hot topic among researchers from various disciplines, planners, and decision makers. In present study, it was aimed to evaluate performances of three classifications algorithms among most widely used ones, namely, maximum likelihood (ML), random forest (RF), and k-nearest neighborhood (KNN), for paddy rice mapping in a mixed cultivation area located in Biga District of Çanakkale Province, Turkey. Visual, near-infrared and shortwave infrared bands of Landsat 9 acquired in dry season of 2022 year was utilized. The classification scheme included six classes as dense vegetation (D), sparse vegetation (S), agricultural field (A), water surface (W), residential area – base soil (RB), and paddy rice (PR). The performances were tested using the same training samples and accuracy control points. The reliability of each classification was evaluated through accuracy assessments considering 150 equalized randomized control points. Accordingly, RF algorithym could identify PR areas with over 96.0% accuracy, and it was followed by KNN with 92.0%.
dc.identifier.doi10.46810/tdfd.1266393
dc.identifier.endpage59
dc.identifier.issn2149-6366
dc.identifier.issue3
dc.identifier.startpage52
dc.identifier.trdizinid1198555
dc.identifier.urihttps://doi.org/10.46810/tdfd.1266393
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay/1198555
dc.identifier.urihttps://hdl.handle.net/20.500.12428/16926
dc.identifier.volume12
dc.indekslendigikaynakTR-Dizin
dc.language.isoen
dc.relation.ispartofTürk Doğa ve Fen Dergisi
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_TRD_20250125
dc.subjectBilgisayar Bilimleri
dc.subjectYazılım Mühendisliği
dc.subjectBahçe Bitkileri
dc.subjectÇevre Çalışmaları
dc.subjectZiraat Mühendisliği
dc.subjectMeteoroloji ve Atmosferik Bilimler
dc.titleComparison of Different Supervised Classification Algorithms for Mapping Paddy Rice Areas Using Landsat 9 Imageries
dc.typeArticle

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