remote sensing Article Intra-Annual Variabilities of Rubus caesius L. Discrimination on Hyperspectral and LiDAR Data Anna Jaroci ´nska 1,* , Dominik Kope´c 2,3 , Barbara Tokarska-Guzik 4 and Edwin Raczko 1 1 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;
[email protected] 2 Department of Biogeography, Paleoecology and Nature Conservation, Faculty of Biology and Environmental, University of Lodz, 90-237 Łód´z,Poland;
[email protected] 3 MGGP Aero sp. z o.o., 33-100 Tarnów, Poland 4 Research Team of Botany and Nature Protection, Institute of Biology, Biotechnology and Environmental Protection, Faculty of Natural Sciences, University of Silesia in Katowice, 40-032 Katowice, Poland;
[email protected] * Correspondence:
[email protected]; Tel.: +48-606491444 Abstract: The study was focused on a plant native to Poland, the European dewberry Rubus caesius L., which is a species with the ability to become excessively abundant within its original range, potentially causing significant changes in ecosystems, including biodiversity loss. Monitoring plant distributions over large areas requires mapping that is fast, reliable, and repeatable. For Rubus, different types of data were successfully used for classification, but most of the studies used data with a very high spectral resolution. The aim of this study was to indicate, using hyperspectral and Light Detection and Ranging (LiDAR) data, the main functional trait crucial for R. caesius differentiation from non-Rubus. This analysis was carried out with consideration of the seasonal variability and different percentages of R.