Assessing the Impacts of Groundwater-Dependent Invasive Plants in Heuningnes Catchment, Using Big Data and Machine Learning Algorithms (MLA).

Groundwater-dependent invasive alien plants (GDIAPs) pose significant threats to biodiversity, ecosystems function and water resources by exploiting groundwater during dry periods. This study aims to detect and monitor GDIAPs, and analyse their effects on groundwater levels and groundwater-dependent ecosystems. By leveraging advanced technologies like Remote Sensing, GIS, MLA, and cloud computing, the study demonstrates the potential of Sentinel-2 and Landsat-8 data combined with algorithms such as Random Forest (RF) to achieve high accuracy in delineating GDIAPs, contributing to more effective environmental management and conservation strategies.