Quantifying the Human Impact on Vegetation Health in Rawdat Al Khafs, Saudi Arabia, Using Multi-Source Geospatial Data and Machine Learning

Abstract

Arid and hyper-arid environments are vulnerable to land degradation and have become focal targets for large-scale afforestation initiatives combating desertification. The One Million Tree Initiative at Rawdat Al Khafs, a hyper-arid rawdah ~100 km northeast of Riyadh receiving < 50 mm annual rainfall, operates under the Saudi Green Initiative and Vision 2030. Evaluating afforestation effectiveness in such settings remains methodologically challenging, as existing frameworks rarely separate climatedriven from human-induced vegetation change in hyper-arid conditions. This study addresses these gaps through three inter-connected objectives: (i) establishing a climate-driven vegetation baseline using a Random Forest (RF) regression model trained on pre-plantation Sentinel-2 imagery and multi-source climate data (20182021); (ii) detecting and quantifying residual vegetation trends (2018–2025) that exceed this baseline and may represent a non-climatic signal conditionally attributable to afforestation; and (iii) characterising the spatial fingerprint of the detected anomaly through phase-based proximity analysis and distance-decay modelling. The RF–RESTREND framework combines 10 m Sentinel-2 imagery, CHIRPS precipitation, and ERA5-Land temperature data with Mann–Kendall trend testing, Sen's slope, Difference-in-Differences (DiD) correction, and SHAP analysis, applied to both NDVI and EVI. A persistent positive vegetation anomaly was observed within the plantation boundary from mid-2022 onwards, consistent across both indices and not fully explained by the climate counterfactual. Within the study area, 77.6% of NDVI pixels and 81.7% of EVI pixels exhibited statistically significant greening (Mann–Kendall Z > +1.96), with mean Z-scores of +3.97 and +4.45, respectively. The EVI distance-decay model (R² = 0.53; decay length = 65 m) indicated a localised boundary effect, while SHAP analysis identified spatial position and long-term climatic trends as dominant predictors, consistent with micro-topographic moistureconcentration mechanisms in rawdah ecosystems. Attribution is framed as conditional, reflecting the short pre-plantation training window and the widening climate variability of the post-plantation period. These findings provide an early satellitebased, climate-corrected assessment of an ecological response associated with the One Million Tree Initiative. The RF–RESTREND framework is reproducible and scalable to comparable arid restoration contexts, supporting Vision 2030 monitoring.