Intra-Annual Variabilities of <i>Rubus caesius</i> L. Discrimination on Hyperspectral and LiDAR Data

• Main Authors: Anna Jarocińska, Dominik Kopeć, Barbara Tokarska-Guzik, Edwin Raczko
• Format: Article
• Language: English
• Published: MDPI AG 2021-12-01
• Series: Remote Sensing
• Subjects: dewberry; HySpex; imaging spectroscopy; vegetation indices; airborne laser scanning; non-parametric multivariate analysis of variance
• Online Access: https://www.mdpi.com/2072-4292/13/1/107

The study was focused on a plant native to Poland, the European dewberry <i>Rubus caesius</i> 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 <i>Rubus,</i> 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 <i>R. caesius</i> differentiation from non-<i>Rubus.</i> This analysis was carried out with consideration of the seasonal variability and different percentages of <i>R. caesius</i> 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 <i>R. caesius</i> from non-<i>Rubus</i>. The most useful indices were ARI1, CRI1, ARVI, GDVI, CAI, NDNI, and MRESR. The obtained results indicate that it is possible to classify <i>R. caesius</i> using images with lower spectral resolution than hyperspectral data.