Estimating the aboveground carbon stock growth of individual trees in poplar plantation forests using TLS data

Forest ecosystems are the largest terrestrial carbon sink, playing a crucial role in the global carbon cycle and climate change mitigation. Accurate estimation of forest carbon stocks is essential for building reliable carbon cycle models and supporting carbon neutrality policies. Traditional methods for dynamic carbon stock assessment are generally conducted at the stand scale and lack the resolution to capture variations at the individual tree level. This study systematically estimated the aboveground carbon stock growth of individual trees in poplar plantations. We investigated 12 poplar (Populus L.) plantation plots with varying planting spacings and clones, using high-resolution terrestrial laser scanning (TLS) data acquired in 2019 and 2021. Several basic tree measuring factors were extracted from branch skeletons based on a novel algorithm of the incomplete simulation of tree transmitting water and nutrients (ISTTWN). After correlation analysis and multicollinearity diagnostics, the Boruta algorithm was applied to select significant predictors for modeling. Both linear and nonlinear approaches were compared, along with four machine learning models: random forest, k-nearest neighbors, support vector machine, and CatBoost. Hyperparameters were optimized using the Optuna framework with mean squared error (MSE) as the objective function in 5-fold cross-validation. The best-performing model was selected to estimate individual tree aboveground carbon stock and carbon stock growth. Two estimation approaches were evaluated: a direct method that modeled carbon stock growth as the dependent variable, and an indirect method that derived growth from the difference between carbon stocks estimated at two time points. The more accurate approach was identified for estimating aboveground carbon stock growth at the individual tree level, and the optimal planting configuration was determined for poplars in the study area. Correlation analysis indicated that both the individual tree aboveground carbon stock and carbon stock growth showed strong correlations with diameter at breast height (D_BH) and tree volume (V). Among crown structural variables, crown surface area (C_SA) exhibited the strongest correlation. Due to the significant multicollinearity among the basic tree measuring factors, variables with high variance inflation factor (VIF) values were excluded. Consequently, tree height (H), branch number (N), average branch length (BLa), average branch diameter (B_Da), and average depth into crown (D_INCa) were ultimately selected for constructing linear and nonlinear models to estimate carbon stock. For modeling carbon stock growth, the selected predictors were tree height (H), crown width (C_W), branch number (N), average branch diameter (B_Da), and average depth into crown (D_INCa). Results showed that the random forest model with Boruta-selected variables outperformed other models, achieving an R² of 0.944 for aboveground carbon stock and 0.798 for carbon stock growth. Specifically, the direct estimation approach using the random forest model yielded the most accurate predictions for growth, with an R² of 0.821, a root mean square error (RMSE) of 0.920 kg, and a mean absolute error (MAE) of 0.733 kg. These results demonstrate the high precision and robustness of machine learning in capturing complex nonlinear relationships between basic tree measuring factors and carbon dynamics. Furthermore, the NL-797 poplar clone planted at a spacing of 6 m × 6 m exhibited the highest absolute carbon stock growth per tree, along with a consistently high growth rate, indicating that this configuration is favorable for carbon accumulation in poplars. This highlights how specific genotypes combined with optimal planting density can significantly enhance carbon sequestration in plantation forests. In conclusion, this study presents a non destructive and accurate method for estimating aboveground carbon stock growth of individual trees by integrating TLS data and advanced machine learning modeling. The findings offer valuable insights for forest management strategies aimed at maximizing carbon storage and provide a scientific basis for designing carbon oriented plantation forests.