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Article

Intra-Annual Variabilities of Rubus caesius L. Discrimination on Hyperspectral and LiDAR Data

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
2
Department of Biogeography, Paleoecology and Nature Conservation, Faculty of Biology and Environmental, University of Lodz, 90-237 Łódź, Poland
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
*
Author to whom correspondence should be addressed.
Received: 13 November 2020 / Revised: 25 December 2020 / Accepted: 28 December 2020 / Published: 31 December 2020
(This article belongs to the Special Issue Hyperspectral Remote Sensing: Current Situation and New Challenges)
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. caesius in the vegetation patches. The analysis was based on hyperspectral HySpex images and Airborne Laser Scanning (ALS) products. Data were acquired during three campaigns: early summer, summer, and autumn. Differentiation based on Linear Discriminate Analysis (LDA) and Non-Parametric Multivariate Analysis of Variance (NPMANOVA) analysis was successful for each of the analysed campaigns using optical data, but the ALS data were less useful for identification. The analysis indicated that selected spectral ranges (VIS, red-edge, and parts of the NIR and possibly SWIR ranges) can be useful for differentiating R. caesius from non-Rubus. The most useful indices were ARI1, CRI1, ARVI, GDVI, CAI, NDNI, and MRESR. The obtained results indicate that it is possible to classify R. caesius using images with lower spectral resolution than hyperspectral data. View Full-Text
Keywords: dewberry; HySpex; imaging spectroscopy; vegetation indices; airborne laser scanning; non-parametric multivariate analysis of variance; linear discriminant analysis dewberry; HySpex; imaging spectroscopy; vegetation indices; airborne laser scanning; non-parametric multivariate analysis of variance; linear discriminant analysis
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MDPI and ACS Style

Jarocińska, A.; Kopeć, D.; Tokarska-Guzik, B.; Raczko, E. Intra-Annual Variabilities of Rubus caesius L. Discrimination on Hyperspectral and LiDAR Data. Remote Sens. 2021, 13, 107. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13010107

AMA Style

Jarocińska A, Kopeć D, Tokarska-Guzik B, Raczko E. Intra-Annual Variabilities of Rubus caesius L. Discrimination on Hyperspectral and LiDAR Data. Remote Sensing. 2021; 13(1):107. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13010107

Chicago/Turabian Style

Jarocińska, Anna, Dominik Kopeć, Barbara Tokarska-Guzik, and Edwin Raczko. 2021. "Intra-Annual Variabilities of Rubus caesius L. Discrimination on Hyperspectral and LiDAR Data" Remote Sensing 13, no. 1: 107. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13010107

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