Local identification of groundwater dependent vegetation using high-resolution Sentinel-2 data : a Mediterranean case study

Abstract

Groundwater dependent ecosystems (GDEs) are biodiversity hotspots and provide important ecosystem services. This study presents a novel multi-instrument concept for the local identification of groundwater dependent vegetation (GDV) in the Mediterranean. The concept integrates high-resolution Sentinel-2 remote sensing data with available geodata and requires in situ vegetation data for validation and calibration. The approach combines five criteria to identify GDV: 1) high vitality, and wetness during dry period, 2) low seasonal changes in vitality and leaf area, 3) low interannual changes in vitality, 4) high topographic potential of water accumulation and low water table depth, 5) high potential inflow dependency. Iso Cluster Unsupervised Classification (ICUC) was applied to identify GDV in the study area (Campania, Italy). Botanical field mapping was utilized for validating the remote sensing approach, as it exhibited significant differences between GDV and Non-GDV in terms of ecohydrological indicator values, leaf anatomy and phreatophyte coverage. According to a new simple ecohydrological rule set that considers phreatophyte cover and mean moisture value of non-phreatophyte species, 9% of vegetation plots are considered GDV and 33% likely GDV. 80% of all GDV derived from classification occur in hydrostratigraphic units (HSU) that are characterized by surficial groundwater circulation and low permeability. The overall accuracy of classifying likelihoods is 62.7%. For 14.6% of the plots, non-GDVs were classified as GDVs (false positives), and only one GDV plot has been classified falsely as non-GDV (false negative). Local results on GDV locations can be overlayed with aquifer use or aquifer reaction to climate change in order to identify GDV under threat and implement sustainable managements of groundwater resources.