Potential and Limitations of Grasslands α-Diversity Prediction Using Fine-Scale Hyperspectral Imagery

• Published in: Remote sensing (Basel, Switzerland) Vol. 13; no. 14; p. 2649
• Main Authors: Imran, Hafiz Ali, Gianelle, Damiano, Scotton, Michele, Rocchini, Duccio, Dalponte, Michele, Macolino, Stefano, Sakowska, Karolina, Pornaro, Cristina, Vescovo, Loris
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.07.2021
• Subjects: Accuracy; Agricultural production; Biodiversity; biodiversity indices; Canopies; Coefficient of variation; coefficient of variation (CV); Data acquisition; Ecosystem services; Ecosystems; Experimental farms; Flowers & plants; Grasslands; Heterogeneity; Hyperspectral imaging; Image resolution; man-made grasslands; Mathematical analysis; natural grasslands; optical diversity; Optical properties; Pixels; Plant communities; Remote monitoring; Remote sensing; Resampling; Sensors; Spatial heterogeneity; Species composition; Species richness; Spectra; standard deviation (SD); Vegetation
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs13142649

Plant biodiversity is an important feature of grassland ecosystems, as it is related to the provision of many ecosystem services crucial for the human economy and well-being. Given the importance of grasslands, research has been carried out in recent years on the potential to monitor them with novel remote sensing techniques. In this study, the optical diversity (also called spectral diversity) approach was adopted to check the potential of using high-resolution hyperspectral images to estimate α-diversity in grassland ecosystems. In 2018 and 2019, grassland species composition was surveyed and canopy hyperspectral data were acquired at two grassland sites: Monte Bondone (IT-MBo; species-rich semi-natural grasslands) and an experimental farm of the University of Padova, Legnaro, Padua, Italy (IT-PD; artificially established grassland plots with a species-poor mixture). The relationship between biodiversity (species richness, Shannon’s, species evenness, and Simpson’s indices) and optical diversity metrics (coefficient of variation-CV and standard deviation-SD) was not consistent across the investigated grassland plant communities. Species richness could be estimated by optical diversity metrics with an R = 0.87 at the IT-PD species-poor site. In the more complex and species-rich grasslands at IT-MBo, the estimation of biodiversity indices was more difficult and the optical diversity metrics failed to estimate biodiversity as accurately as in IT-PD probably due to the higher number of species and the strong canopy spatial heterogeneity. Therefore, the results of the study confirmed the ability of spectral proxies to detect grassland α-diversity in man-made grassland ecosystems but highlighted the limitations of the spectral diversity approach to estimate biodiversity when natural grasslands are observed. Nevertheless, at IT-MBo, the optical diversity metric SD calculated from post-processed hyperspectral images and transformed spectra showed, in the red part of the spectrum, a significant correlation (up to R = 0.56, p = 0.004) with biodiversity indices. Spatial resampling highlighted that for the IT-PD sward the optimal optical pixel size was 1 cm, while for the IT-MBo natural grassland it was 1 mm. The random pixel extraction did not improve the performance of the optical diversity metrics at both study sites. Further research is needed to fully understand the links between α-diversity and spectral and biochemical heterogeneity in complex heterogeneous ecosystems, and to assess whether the optical diversity approach can be adopted at the spatial scale to detect β-diversity. Such insights will provide more robust information on the mechanisms linking grassland diversity and optical heterogeneity.