Spatial Characterisation of Vegetation Diversity in Groundwater-Dependent Ecosystems Using In-Situ and Sentinel-2 MSI Satellite Data

Abstract

Groundwater-Dependent Ecosystems (GDEs) are under threat from groundwater over-abstraction, which significantly impacts their conservation and sustainable management. Although the socio-economic significance of GDEs is understood, their ecosystem services and ecological significance (e.g., biodiversity hotspots) in arid environments remains understudied. Therefore, under the United Nations Sustainable Development Goal (SDG) 15, characterizing or identifying biodiversity hotspots in GDEs improves their management and conservation. In this study, we present the first attempt towards the spatial characterization of vegetation diversity in GDEs within the Khakea-Bray Transboundary Aquifer. Following the Spectral Variation Hypothesis (SVH), we used multispectral remotely sensed data (i.e., Sentinel-2 MSI) to characterize the vegetation diversity. This involved the use of the Rao’s Q to measure spectral diversity from several measures of spectral variation and validating the Rao’s Q using field-measured data on vegetation diversity (i.e., effective number of species). We observed that the Rao’s Q has the potential of spatially characterizing vegetation diversity of GDEs in the Khakea-Bray Transboundary Aquifer. Specifically, we discovered that the Rao’s Q was related to field-measured vegetation diversity (R2 = 0.61 and p = 0.00), and the coefficient of variation (CV) was the best measure to derive the Rao’s Q. Vegetation diversity was also used as a proxy for identifying priority conservation areas and biodiversity hotspots. Vegetation diversity was more concentrated around natural pans and along roads, fence lines, and rivers. In addition, vegetation diversity was observed to decrease with an increasing distance (>35 m) from natural pans and simulated an inverse piosphere (i.e., minimal utilization around the natural water pans). We provide baseline information necessary for identifying priority conservation areas within the Khakea-Bray Transboundary Aquifer. Furthermore, this work provides a pathway for resource managers to achieve SDG 15 as well as national and regional Aichi biodiversity targets.

Chlorophyll-a unveiled: unlocking reservoir insights through remote sensing in a subtropical reservoir

“…These images were retrieved from Google Earth Engine (GEE) and their acquisition dates (3–5 December 2020) coincided with when the point data for chl- a was collected. The images were retrieved as surface reflectance from GEE platform and were already processed for atmospheric attenuations and topographic effects (Mpakairi et al, 2022a , 2022b ). To ensure high data quality, cloud-quality filters and a cloud mask were also applied, eliminating potentially cloud-covered or low-quality data and ensuring that only high-quality and cloud-free pixels were used for analysis (Sharifi et al, 2022 ).…”