Species-independent estimation of dipterocarp wood density using drilling resistance: implications for carbon accounting

Nature-based carbon credit markets are crucial for climate change mitigation in tropical regions. Accurate estimates of aboveground biomass (AGB) in these forests depend on wood density (WD), typically sourced from global databases that do not account for local variability, introducing substantial uncertainties in carbon stock assessments. This study evaluates the resistograph as a novel tool for improving WD predictions in dipterocarp forests of northern Borneo, addressing critical gaps in current carbon stock estimation methodologies. Using multiple linear regression, we developed a two-variable model linking WD to drilling resistance (DR) variables. The optimal model, incorporating the standard deviation of DR (DRStD) and the slope of DR (DRSlope), produced the best predictions (adjusted R² = 0.604, RMSE = 0.083 g cm-3) and showed no significant difference from field WD. Log-transformed models were also tested as a robustness check and yielded lower prediction errors, but the final model was selected for its higher explanatory power and direct interpretability. By comparison, database WD values significantly overestimated field WD and resulted in inflated AGB estimates (RMSE = 32.260%) compared to those derived from the resistograph-based model (RMSE = 25.933%). Our findings demonstrate that the resistograph-based methodology accurately predicts field WD. As a non-destructive and portable tool, it has large potential to enhance the credibility of biomass quantification for nature-based carbon credit projects in Southeast Asia. Further work should expand geographical and species coverage to support broader application across tropical forest landscapes.