High-Resolution Mapping and Impact Assessment of Forest Aboveground Carbon Stock in the Pinglu Canal Basin: A Multi-Sensor and Multi-Model Machine Learning Approach

Abstract

Accurate estimation of forest aboveground carbon stock (AGC) is critical for climate change mitigation and ecological management. This study develops a high-resolution AGC estimation workflow for the Pinglu Canal basin, integrating Sentinel-2, Sentinel-1, ALOS PALSAR, and SRTM data with field survey measurements. Feature selection via Recursive Feature Elimination and modeling with a Random Forest algorithm—optimized through hyperparameter tuning—yielded high predictive accuracy under the ALL data combination (R2 = 0.818, RMSE = 11.126 tC/ha), enabling the generation of a 10 m-resolution AGC map. The total AGC in 2024 was estimated at 2.26 × 106 tC. To evaluate human-induced changes, we established a baseline scenario based on historical AGC trends (2002–2021) and climate data. Comparisons revealed that afforestation and vegetation restoration during canal construction led to higher AGC values than projected under natural conditions. This positive deviation highlights the effectiveness of targeted ecological interventions in mitigating carbon loss and promoting forest recovery. Our results demonstrate a cost-effective, scalable method for AGC mapping using freely accessible remote sensing data and machine learning. The findings also provide insights into balancing large-scale infrastructure development with ecosystem conservation.