remote sensing Article Multi-Temporal Sentinel-2 Data in Classification of Mountain Vegetation Martyna Wakuli ´ nska and Adriana Marcinkowska-Ochtyra * Department of Geoinformatics, Cartography and Remote Sensing, Chair of Geomatics and Information Systems, Faculty of Geography and Regional Studies, University of Warsaw, 00-927 Warsaw, Poland; m.wakulinska@student.uw.edu.pl * Correspondence: adriana.marcinkowska@uw.edu.pl; Tel.: +48-2255-21507 Received: 30 June 2020; Accepted: 19 August 2020; Published: 20 August 2020   Abstract: The electromagnetic spectrum registered via satellite remote sensing methods became a popular data source that can enrich traditional methods of vegetation monitoring. The European Space Agency Sentinel-2 mission, thanks to its spatial (10–20 m) and spectral resolution (12 spectral bands registered in visible-, near-, and mid-infrared spectrum) and primarily its short revisit time (5 days), helps to provide reliable and accurate material for the identification of mountain vegetation. Using the support vector machines (SVM) algorithm and reference data (botanical map of non-forest vegetation, field survey data, and high spatial resolution images) it was possible to classify eight vegetation types of Giant Mountains: bogs and fens, deciduous shrub vegetation, forests, grasslands, heathlands, subalpine tall forbs, subalpine dwarf pine scrubs, and rock and scree vegetation. Additional variables such as principal component analysis (PCA) bands and selected vegetation indices were included in the best classified dataset. The results of the iterative classification, repeated 100 times, were assessed as approximately 80% median overall accuracy (OA) based on multi-temporal datasets composed of images acquired through the vegetation growing season (from late spring to early autumn 2018), better than using a single-date scene (70%–72% OA). Additional variables did not significantly improve the results, showing the importance of spectral and temporal information themselves. Our study confirms the possibility of fully available data for the identification of mountain vegetation for management purposes and protection within national parks. Keywords: alpine; mapping; subalpine; support vector machine; vegetation types 1. Introduction Mountain vegetation is particularly vulnerable to climate change, where the changes of the tree line and plant floor borders become visible [1]. The occurrence of species from various geographical regions in a relatively small area, often being glacial relics, endemics, or endangered species, makes their identification and monitoring extremely important for preserving natural wealth [2]. To achieve this, it is important to provide up-to-date vegetation maps of mountain protected areas. Despite its high precision, field mapping requires a lot of time and work. In the case of high-mountain vegetation, the limited availability and shorter vegetation period compared to lowlands significantly affect the possibilities of field research. Due to rapid technological progress, remote sensing data, characterized by both greater objectivity and spatial coverage, are increasingly used [3]. The electromagnetic spectrum registered by remote sensing instruments, which create unique spectral characteristics of the analyzed objects, can support traditional methods of vegetation mapping by the use of image classification [3]. Recently, non-parametric classifiers are increasingly employed in vegetation classification [4–8] because of their more flexible approach to training data use than in parametric classifiers, Remote Sens. 2020, 12, 2696; doi:10.3390/rs12172696 www.mdpi.com/journal/remotesensing