Cloud-based big data analytics for monitoring invasive plants in groundwater-dependent ecosystems of Nuwejaars catchment, South Africa

• Invasive plants evolved adaptive root systems to access alternative groundwater. • They pose a growing threat to GDEs and groundwater security in drylands. • Invasive alien plants replaced more than 23 % of native fynbos vegetation in GDEs. • Invasive plants exploit hydrological favourable microsites. • Big data analytics proved efficient for monitoring invasive plant’ impacts. Groundwater-dependent ecosystems (GDEs) provide crucial ecological and hydrological stability but are increasingly threatened by groundwater-dependent invasive plants (GDIPs), particularly in regions with limited water resources. Although GDEs have been widely studied, long-term quantitative assessments of how invasive plants alter these ecosystems remain limited. Hence, this study evaluated the impacts of invasive plants within the GDEs of the Nuwejaars Catchment, South Africa, by monitoring their spatial and temporal dynamics and quantifying the extent to which they displace native plants. Landsat-8 imagery, a Random Forest classifier, and Explainable Artificial Intelligence (XAI) techniques were integrated to map and quantify the annual distribution of GDIPs over a 12-year period. XAI interpretability techniques including SHapley Additive exPlanations (SHAP), partial dependence plots (PDPs), and recursive feature elimination (RFECV) were applied to identify key environmental conditions influencing GDIP occurrence. Spatial-temporal analysis revealed that GDIPs expanded from 40.9 % (1060 ha) in 2013 to 63.9 % (1660 ha) in 2024, displacing large areas of native fynbos vegetation. Inter-annual change analysis showed accelerated GDIP growth following the extreme 2015–2018 drought, which reduced groundwater availability for native species with shallow roots. Elevation, slope, and moisture vegetation indices emerged as the most influential predictors for classification, with PDPs revealing that GDIPs favoured lower elevations and steep slopes. Classification accuracy improved over time, with F1-Scores and overall accuracies ranging between 68.4 % to 82.5 % from 2013 to 2024. Overall, these findings highlight the persistent spread of GDIPs and their potential to transform GDEs in semi-arid areas. This study demonstrates the value of integrating remote sensing and interpretable machine learning to support ecological monitoring and targeted invasive species management.