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Article

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

1
Institute of Water Studies, Department of Earth Sciences, University of the Western Cape, Private Bag X17, Bellville 7535, South Africa
2
Department of Geography and Environmental Sciences, University of Venda, Thohoyandou 0950, South Africa
3
Aquatic Systems Research Group, School of Biology and Environmental Sciences, University of Mpumalanga, Nelspruit 1200, South Africa
4
South African Institute for Aquatic Biodiversity, Makhanda 6140, South Africa
5
Wissenshaftskolleg zu Berlin Institute for Advanced Study, 14193 Berlin, Germany
*
Author to whom correspondence should be addressed.
Academic Editors: Duccio Rocchini, Giovanni Bacaro, Enrico Tordoni, Francesco Petruzzellis, Daniele Da Re and Mário Gabriel Santiago dos Santos
Remote Sens. 2022, 14(13), 2995; https://doi.org/10.3390/rs14132995
Received: 2 February 2022 / Revised: 8 April 2022 / Accepted: 11 April 2022 / Published: 23 June 2022
(This article belongs to the Special Issue Remote Sensing of Ecosystem Diversity)
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. View Full-Text
Keywords: biodiversity targets; Khakea-Bray Transboundary Aquifer; Rao’s Q; vegetation diversity; GDE biodiversity targets; Khakea-Bray Transboundary Aquifer; Rao’s Q; vegetation diversity; GDE
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MDPI and ACS Style

Mpakairi, K.S.; Dube, T.; Dondofema, F.; Dalu, T. Spatial Characterisation of Vegetation Diversity in Groundwater-Dependent Ecosystems Using In-Situ and Sentinel-2 MSI Satellite Data. Remote Sens. 2022, 14, 2995. https://doi.org/10.3390/rs14132995

AMA Style

Mpakairi KS, Dube T, Dondofema F, Dalu T. Spatial Characterisation of Vegetation Diversity in Groundwater-Dependent Ecosystems Using In-Situ and Sentinel-2 MSI Satellite Data. Remote Sensing. 2022; 14(13):2995. https://doi.org/10.3390/rs14132995

Chicago/Turabian Style

Mpakairi, Kudzai S., Timothy Dube, Farai Dondofema, and Tatenda Dalu. 2022. "Spatial Characterisation of Vegetation Diversity in Groundwater-Dependent Ecosystems Using In-Situ and Sentinel-2 MSI Satellite Data" Remote Sensing 14, no. 13: 2995. https://doi.org/10.3390/rs14132995

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