MapWAPS Invasive Alien Plant map for the Olifants-Doring Catchments

This dataset was generated as part of the MapWAPS project (Mapping Woody invasive Alien Plant Species), funded by the Water Research Commission (WRC) and launched on 1 April 2022. The project aimed to map woody invasive alien plants and their impacts in key strategic water source areas across South Africa using freely available satellite imagery and cloud computing.

This archive contained the following data for the Olifants-Doring Catchments (Western Cape, South Africa): (1) a map of the occurrence of key invasive alien plants, derived using a Random Forest classification of Sentinel-2 satellite imagery at 10 m spatial resolution (.tif file); (2) a metadata file for the map; (3) field training data (.shp); and (4) a metadata file for the training data.

Sentinel-2 imagery was sourced from the European Space Agency (ESA) Copernicus programme. The satellite constellation captured multispectral data in 13 spectral bands at a 5-day revisit frequency. Classification was performed using the Google Earth Engine cloud computing platform. Targeted taxa typically included woody invasive species such as pines (Pinus spp.), wattles (Acacia spp.), gums (Eucalyptus spp.), poplars (Populus spp.), and shrubby taxa such as Bugweed (Solanum mauritianum), with the exact taxa mapped per catchment guided by key stakeholders.

The Olifants-Doring river system is located in the northwestern Western Cape Province and encompasses a combined catchment of approximately 46,220 km2. The system flows north-westward from the Winterhoek and Cederberg mountains before draining into the Atlantic Ocean near Papendorp. The catchment was recognised as a strategic water source area, supplying a disproportionately large volume of water relative to its geographic extent. Invasive alien trees were known to consume excess water compared to indigenous vegetation, representing a significant threat to already scarce water resources in this semi-arid region.

Field data collection was carried out through extensive stakeholder engagement and community collaboration. Training data were made publicly available via iNaturalist.org. Maps were archived on the SUNScholar Data Repository and the associated classification code was made available via GitHub. All academics using this dataset in any scientific publication were required to offer the authors first right of refusal for co-authorship on any manuscripts. Practitioners were free to use the dataset without restriction.