Precise aboveground biomass estimation of plantation forest trees using the novel allometric model and UAV-borne LiDAR

Abstract

Introduction Plantation forest is an important component of global forest resources. The accurate estimation of tree aboveground biomass (AGB) in plantation forest is of great significance for evaluating the carbon sequestration capacity. In recent years, UAV-borne LiDAR has been increasingly applied to forest survey, but the traditional allometric model for AGB estimation cannot be directly used without the diameter at breast height (DBH) of individual trees. Therefore, it is practicable to construct a novel allometric model incorporating the crown structure parameters, which can be precisely extracted from UAV LiDAR data. Additionally, the reduction effect of adjacent trees on crown area (A c ) should be taken into account. Methods In this study, we proposed an allometric model depending on the predictor variables of A c and trunk height (H). The UAV-borne LiDAR was utilized to scan the sample plot of dawn redwood (DR) trees in the test site. The raw point cloud was first normalized and segmented into individual trees, whose A c s and Hs were sequentially extracted. To mitigate the effects of adjacent trees, the initial A c s were corrected to refer to the potential maximum A c s under undisturbed growth conditions. Finally, the corrected A c s (A cc ) and Hs were input into the constructed allometric model to achieve the AGBs of DR trees. Results and discussion According to accuracy assessment, coefficient of determination ( R 2 ) and root mean square error (RMSE) of extracted Hs were 0.9688 and 0.51 m; R 2 and RMSE of calculated AGBs were 0.9432 and 10.91 kg. The unrestricted growth parts of the tree crowns at the edge of a plantation forest could be used to derive the potential maximum A c . Compared with the allometric models for AGB estimation relying only on trunk H or on initial A c and H, the novel allometric model demonstrated superior performance in estimating the AGBs of trees in a plantation forest.