Original scientific paper

Use of UAV-borne multispectral data and vegetation indices for discriminating and mapping three indigenous vine varieties of the Greek Vineyard

2021, 22 (4)   p. 762-770

Georgia Galidaki, Liza Panagiotopoulou, Theodosia Vardoulaki


In line with precision viticulture, in recent years new methods of vineyard management have been introduced, so as to optimize vine cultivation and production of wine of the highest quality. Following on the methodologies developed for mapping other crop parameters, there is currently a growing research effort for the discrimination and mapping of vine varieties, as this information is useful for vineyard-scale management, local and regional inventory and planning purposes, application of EU Directives, and support of certification and production of high quality wines. This research focuses on developing a methodology, based on UAV-borne multispectral data, for discriminating and mapping three vine varieties in Attica, Greece, employing three non-parametric classifiers, namely Random Forest (RF), Support Vector Machines (SVM) and Spectral Angle Mapper (SAM), and selected vegetation indices (VIs). The suggested methodology uses easy to obtain and process, cost-effective images and relies mostly on free open-source software. Study conclusions suggest that although the multispectral images used did not result in the accurate discrimination of the vine varieties at pixel level, expressed by highest overall accuracy (OA) 61.6%, they nevertheless proved useful in mapping varieties at the plot level. Therefore, it is considered effective for applications that require such level mapping.


classification, multispectral imagery, precision viticulture, UAV-borne imagery, vegetation indices, vine variety mapping

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