Point cloud-based crown volume improves tree biomass estimation: Evaluating different crown volume extraction algorithms

Abstract

Accurately estimating aboveground biomass (AGB) is increasingly crucial for a better understanding of terrestrial ecosystem carbon balance and its relationship with global climate change. Terrestrial laser scanning (TLS) enables detailed reconstruction of tree crown three-dimensional structures, such as tree crown volume (CV), which has been proven to have a strong correlation with AGB. In this study, a coupled algorithm framework for extracting “optimal biomass” CV to improve AGB estimation using allometric models is proposed. The coupled algorithm framework was compared with commonly used tree CV extraction algorithms (model prediction, convex hull, alpha-shape, and voxel) based on point cloud data from 19 artificial Korean pine plots. The results reveal that the alpha-shape coupled with the voxel algorithm provides the closest approximation to the “optimal biomass” CV, exhibiting the highest correlation with AGB (r = 0.7905). α values and voxel size significantly influence the accuracy of CV extraction, while small variations in point cloud density have minimal impact on the extracted CV. Furthermore, the explanatory power of the “optimal biomass” CV for tree AGB was tested using 30 destructively harvested sampled trees. It was observed that the allometric model incorporating both “optimal biomass” volume and DBH (R2 = 0.966, RMSE = 15.321 kg) outperformed the allometric model incorporating both H and DBH (R2 = 0.953, RMSE = 16.131 kg) and the DBH-only model (R2 = 0.948, RMSE = 16.615 kg), particularly in reducing underestimation of AGB for larger trees. These findings demonstrate that the proposed alpha-shape coupled with the voxel algorithm provides an effective solution for extracting “optimal biomass” CV, significantly improving the accuracy of tree-level AGB estimation.