Nonlinear climate–ecosystem functional interactions shape aboveground forest biomass dynamics in the Qilian Mountains: Insights from remote sensing and explainable machine learning

作者：Xuan Yang, Jin Ma, Lina Wang, Ding Wang, Wenzhe Lang, Xuelu Liu · 发表于：Ecological Informatics · 年份：2025 · DOI：10.1016/j.ecoinf.2025.103546 · 被引用次数：6 · 研究领域：Remote Sensing in Agriculture、Remote Sensing and LiDAR Applications、Land Use and Ecosystem Services

Forest aboveground biomass (AGB) is an essential indicator of carbon storage and productivity in forest ecosystems. Its temporal dynamics reflect how ecosystems respond and adapt to climate change. Using Tianzhu County in the eastern section of the Qilian Mountains as the study area, this study integrated multi-source remote sensing data and a suite of multi-dimensional ecological variables to estimate forest AGB through multiple machine learning algorithms. The Shapley Additive Explanations (SHAP) framework was employed to quantify the nonlinear and lagged effects of climatic drivers. The main findings are as follows: (1) A total of 42 variables were analyzed. After feature selection with the Least Absolute Shrinkage and Selection Operator (Lasso) and modeling with the Extreme Gradient Boosting (XGBoost) algorithm, the optimal model achieved a test-set R 2 =0.68 and RMSE ≈15.01 Mg/ ha, demonstrating robust predictive accuracy and good interpretability. (2) From 2009 to 2023, forest biomass exhibited an overall increasing trend, with a mean annual growth rate of 0.0246 Mg/ ha. Regions with significant increases accounted for 27.21%, indicating continuous ecological recovery and improvement of stand structure. (3) Linear correlations were generally weak: precipitation showed a slight positive relationship with AGB (r = 0.045), whereas the effect of temperature was near zero and directionally unstable. Fewer than 10% of pixels were significant, and all turned insignificant afte...