Land Use and Land Cover (LULC) Mapping Accuracy Using Single-Date Sentinel-2 MSI Imagery with Random Forest and Classification and Regression Tree Classifiers

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dc.contributor.authorGulci, Sercan
dc.contributor.authorWing, Michael
dc.contributor.authorAkay, Abdullah Emin
dc.date.accessioned2026-02-08T15:15:57Z
dc.date.available2026-02-08T15:15:57Z
dc.date.issued2025
dc.departmentBursa Teknik Üniversitesi
dc.description.abstractThe use of Google Earth Engine (GEE), a cloud-based computing platform, in spatio-temporal evaluation studies has increased rapidly in natural sciences such as forestry. In this study, Sentinel-2 satellite imagery and Shuttle Radar Topography Mission (SRTM) elevation data and image classification algorithms based on two machine learning techniques were examined. Random Forest (RF) and Classification and Regression Trees (CART) were used to classify land use and land cover (LULC) in western Oregon (USA). To classify the LULC from the spectral bands of satellite images, a composition consisting of vegetation difference indices NDVI, NDWI, EVI, and BSI, and a digital elevation model (DEM) were used. The study area was selected due to a diversity of land cover types including research forest, botanical gardens, recreation area, and agricultural lands covered with diverse plant species. Five land classes (forest, agriculture, soil, water, and settlement) were delineated for LULC classification testing. Different spatial points (totaling 75, 150, 300, and 2500) were used as training and test data. The most successful model performance was RF, with an accuracy of 98% and a kappa value of 0.97, while the accuracy and kappa values for CART were 95% and 0.94, respectively. The accuracy of the generated LULC maps was evaluated using 500 independent reference points, in addition to the training and testing datasets. Based on this assessment, the RF classifier that included elevation data achieved an overall accuracy of 92% and a kappa coefficient of 0.90. The combination of vegetation difference indices with elevation data was successful in determining the areas where clear-cutting occurred in the forest. Our results present a promising technique for the detection of forests and forest openings, which was helpful in identifying clear-cut sites. In addition, the GEE and RF classifier can help identify and map storm damage, wind damage, insect defoliation, fire, and management activities in forest areas.
dc.description.sponsorshipTUBITAK [1059B192301729]
dc.description.sponsorshipThis study was conducted with support from TUBITAK BIDEB 2219 as part of the visiting scholar program, under grant number 1059B192301729. No financial assistance was provided for research materials, equipment, or publication. Authors thank the European Space Agency (ESA)for providing satellite images, and the Google Inc. providers of the cloud-based environment fornoncommercial use. The authors have reviewed and edited the output and take full responsibility forthe content of this publication.
dc.identifier.doi10.3390/geomatics5030029
dc.identifier.issn2673-7418
dc.identifier.issue3
dc.identifier.scopus2-s2.0-105017427912
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.3390/geomatics5030029
dc.identifier.urihttps://hdl.handle.net/20.500.12885/6056
dc.identifier.volume5
dc.identifier.wosWOS:001579938400001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherMdpi
dc.relation.ispartofGeomatics
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzWOS_KA_20260207
dc.subjectlandscape management
dc.subjectforestry
dc.subjectmachine learning
dc.subjectforest soil
dc.subjectforest openings
dc.subjectcloud IT
dc.titleLand Use and Land Cover (LULC) Mapping Accuracy Using Single-Date Sentinel-2 MSI Imagery with Random Forest and Classification and Regression Tree Classifiers
dc.typeArticle

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