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

Remote Sensing

Duccio Rocchini¹  Stefano Macolino²  Cristina Pornaro²  Michele Scotton²  Karolina Sakowska³  Hafiz Ali Imran⁴  Loris Vescovo⁴  Damiano Gianelle⁴  Michele Dalponte⁴ 

[1] BIOME Lab, Department of Biological, Geological and Environmental Sciences, Alma Mater Studiorum, University of Bologna, Via Irnerio 42, 40126 Bologna, BO, Italy;Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), Viale dell’Università 16, 35020 Legnaro, PD, Italy;Institute of BioEconomy, National Research Council (IBE-CNR), Via Biasi 75, 38098 San Michele all’Adige, TN, Italy;Research and Innovation Centre, Sustainable Ecosystems and Bioresources Department, Fondazione Edmund Mach, Via E. Mach 1, 38098 San Michele all’Adige, TN, Italy;

关键词: biodiversity indices;    coefficient of variation (CV);    man-made grasslands;    natural grasslands;    optical diversity;    standard deviation (SD);   

DOI  :  10.3390/rs13142649

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown