Development of Estimation Models for Individual Tree Aboveground Biomass Based on TLS-Derived Parameters

Abstract

Forest biomass is a foundation for evaluating the contribution to the carbon cycle of forests, and improving biomass estimation accuracy is an urgent problem to be addressed. Terrestrial laser scanning (TLS) enables the accurate restoration of the real 3D structure of forests and provides valuable information about individual trees; therefore, using TLS to accurately estimate aboveground biomass (AGB) has become a vital technical approach. In this study, we developed individual tree AGB estimation models based on TLS-derived parameters, which are not available using traditional methods. The height parameters and crown parameters were extracted from the point cloud data of 1104 trees. Then, a stepwise regression method was used to select variables for developing the models. The results showed that the inclusion of height parameters and crown parameters in the model provided an additional 3.76% improvement in model estimation accuracy compared to a DBH-only model. The optimal linear model included the following variables: diameter at breast height (DBH), minimum contact height (Hcmin), standard deviation of height (Hstd), 1% height percentile (Hp1), crown volume above the minimum contact height (CVhcmin), and crown radius at the minimum contact height (CRhcmin). Comparing the performance of the models on the test set, the ranking is as follows: artificial neural network (ANN) model > random forest (RF) model > linear mixed-effects (LME) model > linear (LN) model. Our results suggest that TLS has substantial potential for enhancing the accuracy of individual-tree AGB estimation and can reduce the workload in the field and greatly improve the efficiency of estimation. In addition, the model developed in this paper is applicable to airborne laser scanning data and provides a novel approach for estimating forest biomass at large scales.