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

Anna Jarocińska,Barbara Tokarska-Guzik,Edwin Raczko,Dominik Kopeć

doi:10.3390/rs13010107

Anna Jarocińska, Barbara Tokarska-Guzik + Show 2 more

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https://doi.org/10.3390/rs13010107

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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. 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.

Highlights

Growing global phenomena such as land-use changes or habitat fragmentation, and the accompanying climate change yielding changes in the ecological and geographical ranges of species, lead to biodiversity loss [1,2,3,4,5]
Based on the correctness rate from Linear Discriminate Analysis (LDA) and the F-value from NPMANOVA, the values were noticeably lower for the Airborne Laser Scanning (ALS) data (Table 3 and Figure 4)
The analysis showed that Normalised Difference Nitrogen Index (NDNI) has the highest potential to differentiate R

Summary

Introduction

Growing global phenomena such as land-use changes or habitat fragmentation, and the accompanying climate change yielding changes in the ecological and geographical ranges of species, lead to biodiversity loss [1,2,3,4,5]. Plant species that spread on a massive scale beyond their original geographical ranges and native species becoming excessively abundant within their original ranges often cause significant changes in ecosystems, including biodiversity loss [6,7,8]. The present study is focused on a Rubus caesius L. species from the brambles genus (Rubus), which has traits that favour spreading, such as low trophic requirements and quick adaptation to changing habitat conditions. 2021, 13, 107 strongly modulate species interactions and community composition, leading to a decline in biodiversity in the affected habitats [9,10,11,12], includingleading protected. Europe late species interactions and community composition, to ain decline in Natura biodiversity habitats. Toto the large and diverse genus nus of (bramble) within the Rosaceae family, covering about and species of Rubus (bramble) within the Rosaceae family, covering about 430 and 750 species [13]. [13]

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