Scale Effects on Dominant Drivers of Commercial Plantation Productivity: Novel Insights from High-Resolution Multi-Sensor UAV Remote Sensing and Interpretable AI

Subtropical mountain economic tree plantations are constrained by pronounced spatial heterogeneity in resource availability, yet the spatial scales at which soil properties, topography, and canopy structure regulate vegetation vigor remain poorly resolved. To address this gap, a spatially consistent multi-scale dataset combining 10 m high-density soil sampling, UAV-LiDAR, and multispectral remote sensing was used to quantify the scale-dependent drivers of the Leaf Chlorophyll Index (LCI) across 3–50 m within a Chinese hickory (Carya cathayensis Sarg.) plantation. The relative contributions of canopy, soil, and topography to LCI were decomposed across scales using an interpretable machine-learning framework (XGBoost–SHAP). At fine scales (3–10 m), vegetation vigor was primarily controlled by tree-level canopy structure, particularly tree height, reflecting localized resource acquisition. At intermediate scales (10–20 m), a distinct coupling window emerged, characterized by increased interaction complexity: LCI was predominantly driven by interactions between canopy structure and soil nutrient availability, whereas single-factor effects weakened. Notably, at 20 m this interaction pattern largely weakened and reverted to single-metric dominance. At broader scales (>30 m), complex interactions re-emerged, and dominant SHAP contributions shifted from nutrients and canopy structure toward topography and soil texture. These findings reconcile strong fine-scale drivers with weaker predictability at intermediate extents and demonstrate that soil–canopy relationships reorganize across spatial scales rather than remaining static. On the basis of these findings, a scale-hierarchical framework for precision forestry is proposed that aligns management interventions with the ecological scales at which dominant correlates operate across spatial supports.