Spatiotemporal Dynamics of Tree Species-Level Aboveground Carbon Storage at the Canal Scale Under Green Engineering with a Random Forest Model

Monitoring spatiotemporal dynamics of aboveground carbon (AGC) storage at the tree species level is crucial for evaluating the ecological impacts of large-scale infrastructure projects and facilitating accurate ecological environmental management. However, existing studies heavily rely on interannual coarse-spatial-resolution forest-type products, leading to significant uncertainties in carbon estimation, particularly in fragmented linear engineering zones. This study integrated Sentinel-1/2 data with a random forest (RF) model to map tree species distribution (overall accuracy = 85.18%; Kappa = 0.8319) and AGC estimation (R2 = 0.7057; RMSE = 13.35 Mg ha−1) at a 10 m resolution in the Pinglu Canal Basin from 2019 to 2024. The results revealed a total AGC decline of 16.88% across the watershed. Spatially, the Environmental Impact Area (EIA) functioned as the primary disturbance core (experiencing a 28.91% loss), while the Ecological Buffer Area (EBA) acted as a regional carbon stabilizer. At the species level, while Eucalyptus grandis accounted for the majority of carbon depletion, Pinus massoniana exhibited a resilience-driven rebound in the mid-construction phase. Meanwhile, Litchi chinensis and other native species demonstrated steady gains. Cumulatively, these species-specific carbon gains associated with natural growth and restoration initiatives effectively offset 34.45% of the carbon loss. These findings provide quantitative evidence supporting the potential of green engineering to mitigate the ecological footprint of infrastructure development. This study offers a robust monitoring tool for low-carbon infrastructure and directly supports the United Nations Sustainable Development Goal 15 (SDG 15) related to forest conservation and ecological restoration.