Monitoring Grassland Functional Diversity in a Semi-Arid Ecosystem using Multi-Source Close-Range Remote Sensing

Vegetation diversity has been found to influence ecosystem function and provide essential ecosystem services, intimately linked to societal wellbeing. However, the relationship between vegetation diversity and function is very complex and still not fully comprehended at different spatial-temporal scales. Indeed, in recent years, remote sensing has shown great promise to better monitor plant diversity at different scales, most commonly through the Spectral Variability Hypothesis (SVH), which links spectral diversity to plant diversity. However, there is still some debate over the generality of the SVH, especially in semi-arid grasslands, which tend to be less studied even though they dominate the trend and inter-annual variability of global water and carbon fluxes. This study focused on examining the relationship between functional diversity (FD) and optical traits of the herbaceous understory of a Mediterranean tree-grass ecosystem (TGE) using field spectroscopy and high resolution imagery from unmanned aerial vehicles (UAVs). Multiple field campaigns were performed from 2021 to 2023 in the Majadas de Tiétar experimental station located in Western Spain to collect in-situ measurements of plant traits (e.g. specific leaf area (SLA), chlorophyll content (Cab)), diversity metrics (functional dispersion (Fdis), Rao’s entropy (Qrao)), hyperspectral field spectroscopy (ASD Fieldspec® 3 portable spectroradiometer) and high-resolution visible-near-infrared (VNIR) and thermal infrared (TIR) imagery onboard UAVs. By applying partial-least-square regression (PLSR) models, high correlations were observed between field spectroscopy and plant traits (r2 > 0.7) with SWIR bands having the most weight in the predictive power of these empirical models, perhaps related to water being the principal limiting factor for herbaceous plants in these semi-arid conditions. By contrast, in-situ PLSR models showed little/no relation to plant diversity metrics (r2 < 0.1). However, preliminary results from the UAV images showed that the spatial heterogeneity of NDVI and land surface temperature (LST), quantified through Qrao using a 5 x 5 pixel window, were positively related to in-situ diversity metrics such as Fdis. Indeed, Qrao based on LST was found to have a more significant relationship to Fdis (p-value < 0.05) compared to Qrao based on NDVI (p-value > 0.05). While the remote sensing of plant functional diversity has concentrated on shortwave reflectances, the use of TIR imagery has large potential as it is more directly related to ecosystem function with its capabilities to act as a proxy for plant transpiration and inform on water use efficiency (WUE). This work is step forward to better understand the optical-diversity relationship in a semi-arid grassland using data acquired at different scales but also from different sources ranging from in-situ hyperspectral measurements to high-resolution TIR imagery.