Compatible Biomass Model with Measurement Error Using Airborne LiDAR Data

Research on the inversion of forest aboveground biomass based on airborne light detection and ranging (LiDAR) data focuses on finding the relationship between the two, such as established linear or nonlinear models. However, these models may have poorer estimation accuracy for tree-components biomass and cannot guarantee the additivity of each component. Therefore, we aimed to develop an error-in-variable biomass model system that ensures both the compatibility of the individual tree component biomass with the diameter at breast height and the additivity of component biomass. The system we developed used the airborne LiDAR data and field-measured data of principis-rupprechtii (Larix gmelinii var.) trees, collected from north China. Our model system not only ensured the additivity of nonlinear biomass models, it also accounted for the impact of measurement errors. We first selected the airborne LiDAR-derived variable with the highest contribution to the biomass of each component and then developed an inversion model system with that variable as an independent variable and with the biomass of each component as the dependent variable using allometric functions. Moreover, two model estimation methods, two-stage error (TSEM) and nonlinear seemingly unrelated regression (NSUR) with one-step, two-step, and summation methods, were also applied, and their performances were compared. The results showed that both NSUR-one-step and TSEM-one-step led to similar parameter estimates and performance for a system, and the fitting accuracy of a model system was not very attractive. The variance function included in a model system reduced the heteroscedasticity effectively and improved the model accuracy. Overall, this study successfully combined the error-in-variable modeling with the airborne LiDAR data, proposed methods that can be used for the extension of component biomass from an individual tree to a stand and that might improve the estimation accuracy of carbon storage. A compatible model system can be further improved if various sources of error in the variables are identified, and their impacts on the system are effectively accounted for.