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Groundwater dependent vegetation (GDV) is increasingly threatened by the transformation of the natural environment. An understanding of the nature of GDV at the appropriate scale helps environmental managers make suitable decisions. This study assesses the potential for mapping the distribution of GDV within the Heuningnes Catchment using multispectral remotely sensed data (i.e., Landsat 8 (L8) and Sentinel 2 (S2)), the derived vegetation indices (Normalised Difference Vegetation Index (NDVI) and the Soil-Adjusted Vegetation Index (SAVI)) and  in-situ  data. The GDV distribution maps were produced by integrating vegetation productivity, landcover, and topographic layers as GDV indicators.  The findings of the study revealed that the spectral indices had a significant influence on the sensor’s GDV classification performance. Specifically, the S2-derived SAVI mapped the GDV areas with the highest overall accuracy (97%), followed by the S2-derived NDVI, with an accuracy of 95%. Comparatively, the L8(NDVI) GDV map was achieved with an overall accuracy of 92% and the L8(SAVI) map had an overall accuracy of 96%. The estimated coverage of potential GDV within the catchment ranges between 2.34 and 2.60%.  This work demonstrated the capabilities of a combined remote sensing and GIS methodological framework, which can improve our knowledge on GDV.

Multispectral remote sensing of potential groundwater dependent vegetation in the greater Floristic region of the Western Cape, South Africa