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

Abstract

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.

Monitoring Plant Diversity to Support Agri-Environmental Schemes: Evaluating Statistical Models Informed by Satellite and Local Factors in Southern European Mountain Pastoral Systems

“…Our study did not find a relationship between parcel area and species richness. Concerning the species-spectral heterogeneity pathway, our findings are consistent with very recent studies that suggested a dependency of this relationship on the grassland ecosystems studied [42,56]. In fact, Imran and colleagues [42,56] described that, in more natural conditions as those occurring in mountain grasslands, the spectral heterogeneity pathway did not perform adequately.…”

“…Concerning the species-spectral heterogeneity pathway, our findings are consistent with very recent studies that suggested a dependency of this relationship on the grassland ecosystems studied [42,56]. In fact, Imran and colleagues [42,56] described that, in more natural conditions as those occurring in mountain grasslands, the spectral heterogeneity pathway did not perform adequately. Although interesting, our limited sampling size and set of variables do not allows us to further elaborate in this regard.…”

“…Although interesting, our limited sampling size and set of variables do not allows us to further elaborate in this regard. Other approaches [56] and metrics of spectral heterogeneity [42,57] not considered in this study may alter these findings. Further investigation should be conducted to maturate the preliminary findings presented in this study.…”

“…Hence, to evaluate the support of this hypothesis, we used the standard deviation (SD) of the reflectance in the NIR and Red bands of Sentinel-2 in the spring and summer scenes in each sample parcel (Equation ( 3)) as predictor variables. SD is one commonly used diversity metric in the spectral heterogeneity research [41,42]. X ijλ is the reflectance for the spectral band λ and pixel (i, j), X is the mean reflectance in the sample parcel, n is the number of pixels in the sample parcel.…”

Flexible estimation of biodiversity with short-range multispectral imaging in a temperate grassland

“…Increases in cost-effectiveness may also be increased by automated flight paths over survey locations, for which unsupervised spectral reflectance data could be collected. Furthermore, while rapid advancements have been made on spectral diversity, previous studies have utilised high-resolution multi/hyperspectral sensors that are either immobile, high-cost, or both (Frye et al, 2021; Gholizadeh et al, 2019; Imran et al, 2021; Lopatin et al, 2017). The current complete monitoring system is available for purchase for < 10,000 USD, relative to >50,000 USD for many hyperspectral sensors.…”