Abstract
Conventional ground survey data are very accurate, but expensive. Airborne lidar data can reduce the costs and effort required to conduct large-scale forest surveys. It is critical to improve biomass estimation and evaluate carbon stock when we use lidar data. Bayesian methods integrate prior information about unknown parameters, reduce the parameter estimation uncertainty, and improve model performance. This study focused on predicting the independent tree aboveground biomass (AGB) with a hierarchical Bayesian model using airborne LIDAR data and comparing the hierarchical Bayesian model with classical methods (nonlinear mixed effect model, NLME). Firstly, we chose the best diameter at breast height (DBH) model from several widely used models through a hierarchical Bayesian method. Secondly, we used the DBH predictions together with the tree height (LH) and canopy projection area (CPA) derived by airborne lidar as independent variables to develop the AGB model through a hierarchical Bayesian method with parameter priors from the NLME method. We then compared the hierarchical Bayesian method with the NLME method. The results showed that the two methods performed similarly when pooling the data, while for small sample sizes, the Bayesian method was much better than the classical method. The results of this study imply that the Bayesian method has the potential to improve the estimations of both DBH and AGB using LIDAR data, which reduces costs compared with conventional measurements.
Highlights
On the world’s land surface, forests cover about 4200 million hectares, and the carbon stock of forests accounts for about 45% of the world’s terrestrial carbon reserves [1]
We used the hierarchical Bayesian method to choose the best diameter at breast height (DBH) and AGB model to predict DBH and aboveground biomass on the basis of LIDAR point cloud data, integrating the advantages of the Bayesian approach and LIDAR data to reduce costs compared with conventional measurements [13,14]
A total of 402 individual P. crassifolia tree crowns in the 16 subplots nested in the permanent sample plot (PSP) were delineated; the individual tree LIDAR crown projection area and field measurement data were matched by individual tree locations measured by total station—Fu et al [3] has drawn a figure to explain this
Summary
On the world’s land surface, forests cover about 4200 million hectares, and the carbon stock of forests accounts for about 45% of the world’s terrestrial carbon reserves [1]. These variables, including tree height, stem volume, crown projection area, diameter at breast height, and other variables, are significantly connected to the forest biomass [3,11,12] Using these LIDAR variables, individual tree DBH and aboveground biomass can be estimated by developing DBH and AGB models, respectively [3,12,13]. We used the hierarchical Bayesian method to choose the best DBH and AGB model to predict DBH and aboveground biomass on the basis of LIDAR point cloud data, integrating the advantages of the Bayesian approach and LIDAR data to reduce costs compared with conventional measurements [13,14]. Compared with the classical prediction method, using the Bayesian method can improve the accuracy to a certain degree
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