• Energy Research

The accurate estimation of forest biomass is crucial for supporting climate change mitigation efforts such as sustainable forest management. Although traditional regression models have been widely used to link stand biomass with biotic and abiotic predictors, this approach has several disadvantages, including the difficulty in dealing with data autocorrelation, model selection, and convergence. While machine learning can overcome these challenges, the application remains limited, particularly at a large scale with consideration of climate variables. This study used the random forests (RF) algorithm to estimate stand aboveground biomass (AGB) and total biomass (TB) of larch (Larix spp.) plantations in north and northeast China and quantified the contributions of different predictors. The data for modelling biomass were collected from 445 sample plots of the National Forest Inventory (NFI). A total of 22 independent variables (6 stand and 16 climate variables) were used to develop and train climate-sensitive stand biomass models. Optimization of hyper parameters was implemented using grid search and 10-fold cross-validation. The coefficient of determination (R2) and root mean square error (RMSE) of the RF models were 0.9845 and 3.8008 t ha−1 for AGB, and 0.9836 and 5.1963 t ha−1 for TB. The cumulative contributions of stand and climate factors to stand biomass were >98% and <2%, respectively. The most crucial stand and climate variables were stand volume and annual heat-moisture index (AHM), with relative importance values of >60% and ~0.25%, respectively. The partial dependence plots illustrated the complicated relationships between climate factors and stand biomass. This study illustrated the power of RF for estimating stand biomass and understanding the effects of stand and climate factors on forest biomass. The application of RF can be useful for mapping of large-scale carbon stock.

Tree height–diameter relationship is very important in forest investigation, describing forest structure and estimating carbon storage. Climate change may modify the relationship. However, our understanding of the effects of climate change on the height–diameter allometric relationship is still limited at large scales. In this study, we explored how climate change effects on the relationship varied with tree species and size for larch plantations in northern and northeastern China. Based on the repeated measurement data of 535 plots from the 6th to 8th national forest inventory of China, climate-sensitive tree height–diameter models of larch plantations in north and northeast China were developed using two-level nonlinear mixed effect (NLME) method. The final model was used to analyze the height–diameter relationship of different larch species under RCP2.6, RCP 4.5, and RCP8.5 climate change scenarios from 2010 to 2100. The adjusted coefficient of determination Radj2, mean absolute error (MAE) and root mean squared error (RMSE) of the NLME models for calibration data were 0.92, 0.76 m and 1.06 m, respectively. The inclusion of climate variables mean annual temperature (MAT) and Hargreaves climatic moisture deficit (CMD) with random effects was able to increase Radj2 by 19.5% and reduce the AIC (Akaike’s information criterion), MAE and RMSE by 22.2%, 44.5% and 41.8%, respectively. The climate sensitivity of larch species was ranked as L. gmelinii > the unidentified species group > L. principis > L. kaempferi > L. olgensis under RCP4.5, but L. gmelinii > L. principis > the unidentified species group > L. olgensis > L. kaempferi under RCP2.6 and RCP8.5. Large trees were more sensitive to climate change than small trees.