Varga, OrsolyaGombosné Nagy, IldikóBurai, PéterTomor, TamásLénárt, CsabaSzabó, Szilárd2021-06-282021-06-282018-12-20Acta Geographica Debrecina Landscape & Environment series, Vol. 12 No. 2 (2018) , 1-91789-4921https://hdl.handle.net/2437/317364In our paper we examined the opportunities of a classification based on descriptive statistics of NDVI throughout a year’s time series dataset. We used NDVI layers derived from cloud-free Sentinel-2 images in 2018. The NDVI layers were processed by object-based image analysis and classified into 5 classes, in accordance with Corine Land Cover (CLC) nomenclature. The result of classification had a 76.2% overall accuracy. We described the reasons for the disagreement in case of the most remarkable errors. application/pdfSentinel-2CLC2018NDVIchange-based classificationfolyóiratcikkOpen Accesshttps://doi.org/10.21120/LE/12/2/1Acta Geographica Debrecina Landscape & Environment series212Landsc. environ.1789-7556