The Potential of Mapping Grassland Plant Diversity with the Links among Spectral Diversity, Functional Trait Diversity, and Species Diversity

Abstract

Mapping biodiversity is essential for assessing conservation and ecosystem services in global terrestrial ecosystems. Compared with remotely sensed mapping of forest biodiversity, that of grassland plant diversity has been less studied, because of the small size of individual grass species and the inherent difficulty in identifying these species. The technological advances in unmanned aerial vehicle (UAV)-based or proximal imaging spectroscopy with high spatial resolution provide new approaches for mapping and assessing grassland plant diversity based on spectral diversity and functional trait diversity. However, relatively few studies have explored the relationships among spectral diversity, remote-sensing-estimated functional trait diversity, and species diversity in grassland ecosystems. In this study, we examined the links among spectral diversity, functional trait diversity, and species diversity in a semi-arid grassland monoculture experimental site. The results showed that (1) different grassland plant species harbored different functional traits or trait combinations (functional trait diversity), leading to different spectral patterns (spectral diversity). (2) The spectral diversity of grassland plant species increased gradually from the visible (VIR, 400–700 nm) to the near-infrared (NIR, 700–1100 nm) region, and to the short-wave infrared (SWIR, 1100–2400 nm) region. (3) As the species richness increased, the functional traits and spectral diversity increased in a nonlinear manner, finally tending to saturate. (4) Grassland plant species diversity could be accurately predicted using hyperspectral data (R2 = 0.73, p < 0.001) and remotely sensed functional traits (R2 = 0.66, p < 0.001) using cluster algorithms. This will enhance our understanding of the effect of biodiversity on ecosystem functions and support regional grassland biodiversity conservation.

Assessing the Impact of Soil on Species Diversity Estimation Based on UAV Imaging Spectroscopy in a Natural Alpine Steppe

“…However, the spectral diversity could tend to reach saturation, due to the increasing species richness with upscaling the window size, which might be similar to the empirical power-law relationship between the species richness and area [87]. For example, Zhao et al [35] found a tendency of gradual saturation based on the spectral characteristics when the species richness of grassland was above 17 within the estimation scale of 1.2 m × 1.2 m. Another study showed a contrary result that there was no saturated trend even when the species richness reached 50 within the windows of 60 m × 60 m, based on airborne data [32]. In addition, the window scale of diversity mapping is not only caused by the number of species, but also related to the environmental heterogeneity.…”

“…The ability of spectral diversity calculated based on the bands which might be sensitive to specific biochemical characteristics even exceeds that of NDVI for grassland diversity monitoring (Figure 6). With the relationships among spectral diversity, functional diversity and species diversity of grassland being further explored [35], the optimal functional components could also be used for indicating the species diversity.…”

“…Nevertheless, when the fundamental biochemical, structural and spectral characteristics related to the species diversity of grassland can be retrieved, a method of clustering by machine learning would be an alternative approach to monitor grassland diversity by remote sensing. For example, Zhao et al [35] proposed a grassland species diversity estimation model that integrated the optimal biochemical components as functional diversity and spectral diversity based on the self-adaptive fuzzy c-means clustering algorithm.…”

“…Remote sensing can provide spatiotemporal continuous data and assess species diversity loss of all scales in a repeatable and rapid manner [28][29][30], which has been recognized as an efficient tool in the global monitoring of biodiversity [31][32][33][34]. The technological advances in airborne and UAV-based imaging spectrometers, which measure continuous spectral coverage with high spatial resolution, provide great potential in monitoring grassland biodiversity efficiently across large areas [32,35,36].…”

Plant spectral diversity from high‐resolution multispectral imagery detects functional diversity patterns in coastal dune communities

“…Because differences in vegetation phenotypic characteristics correspond to variations in spectral band values (Spectral Variation Hypothesis; Palmer et al., 2002; Rocchini et al., 2004), spatio‐temporal variations in spectral bands (i.e., spectral diversity, SD) can be considered an indicator of spatio‐temporal variability of vegetation. This makes SD a cost‐ and time‐efficient proxy for different diversity facets (Surrogacy hypothesis; Gamon et al., 2020; Wang & Gamon, 2019), such as taxonomic diversity (TD, e.g., Conti et al., 2021; Marzialetti et al., 2021) and functional diversity (FD, e.g., Frye et al., 2021; Zhao et al., 2021).…”