Land cover analysis based on descriptive statistics of Sentinel-2 time series data

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dc.contributor.authorVarga, Orsolya
dc.contributor.authorGombosné Nagy, Ildikó
dc.contributor.authorBurai, Péter
dc.contributor.authorTomor, Tamás
dc.contributor.authorLénárt, Csaba
dc.contributor.authorSzabó, Szilárd
dc.date.accessioned2021-06-28T11:08:08Z
dc.date.available2021-06-28T11:08:08Z
dc.date.issued2018-12-20
dc.description.abstractIn 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. en
dc.formatapplication/pdf
dc.identifier.citationActa Geographica Debrecina Landscape & Environment series, Vol. 12 No. 2 (2018) , 1-9
dc.identifier.doihttps://doi.org/10.21120/LE/12/2/1
dc.identifier.eissn1789-7556
dc.identifier.issn1789-4921
dc.identifier.issue2
dc.identifier.jatitleLandsc. environ.
dc.identifier.jtitleActa Geographica Debrecina Landscape & Environment series
dc.identifier.urihttps://hdl.handle.net/2437/317364en
dc.identifier.volume12
dc.languageen
dc.relationhttps://ojs.lib.unideb.hu/landsenv/article/view/2217
dc.rights.accessOpen Access
dc.subjectSentinel-2en
dc.subjectCLC2018en
dc.subjectNDVIen
dc.subjectchange-based classificationen
dc.titleLand cover analysis based on descriptive statistics of Sentinel-2 time series dataen
dc.typefolyóiratcikkhu
dc.typearticleen
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Acta Geographica Debrecina Landscape & Environment (DE-journals)