Quantifying the Effects of Stand and Climate Variables on Biomass of Larch Plantations Using Random Forests and National Forest Inventory Data in North and Northeast China

Abstract

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.

Understanding the contribution of structural diversity to stand biomass for carbon management of mixed forests using machine learning algorithms

“…RF and BRT have the "%IncMSE" metric way of quantifying the importance of each feature (Grömping, 2009;He et al, 2022). Other algorithms, like ANN and SVM, are not capable of doing so.…”

“…Although the ML models had a good accuracy compared with the traditional models (He et al, 2022), it was necessary to understand the effects of stand factors and structure on biomass prediction through the interpretability of the ML model, and found the relationship between input and output variables because of black-box characteristics. So, this study used relative variable importance and partial dependence effect to identify the role of BA, H and N_size_div in stand biomass prediction to strengthen the stand biomass ML model applications.…”

Coupling of Forest Carbon Densities with Landscape Patterns and Climate Change in the Lesser Khingan Mountains, Northeast China

“…At present, many scholars focus on building various models to assess forest biomass and carbon density. Xiao He et al's results show that [26] the random forest (RF) algorithm can compensate for the shortcomings of traditional regression models in estimating stands' biological carbon density and climate factors' influences on it. Research on coupling has focused on coupled models of the local community climate and global climate.…”