Improving Lidar-based aboveground biomass estimation of temperate hardwood forests with varying site productivity

Accurate quantification of forest aboveground biomass (AGB) is the foundation to the responses of diverse forest ecosystems to the changing climate. Lidar-based statistical models have been used to accurately estimate AGB in large spatial extents, especially in boreal and temperate softwood forest ecosystems. However, the few available models for temperate hardwood and hardwood-dominated mixed forests are low in accuracy due both to the deliquescent growth form of hardwood trees and the strong site-to-site variations in height-diameter relationship. In this study, we established multiplicative nonlinear regression models that incorporated both lidar-derived metrics and soil-based site productivity classes (high and low productivity sites) to estimate aboveground biomass in temperate hardwood forests. The final optimized model had high accuracy (R2 = 0.81; RMSE = 45.5 Mg ha− 1) with reliable performance in ABG estimation by integrating relative height metrics at 75 and 70 percentiles (RH75 and RH70), canopy coverage and site productivity class. An optimized model that included an index of site productivity explained 14% more variance than the best-fit model without the term. Moreover, the relationship between AGB and lidar-based metrics was nonlinear on low productivity sites and nearly linear on high productivity sites, further indicating the importance of including direct or indirect measures of site productivity in lidar-based biomass models, particularly for those applied to temperate hardwood forests. Our new lidar-based model provides a potential framework to integrate lidar-based structural information and soil-based site productivity to improve AGB estimation in temperate hardwood forests.