RayExtract: A fast, scalable method for tree volume reconstruction from terrestrial laser scanning

Abstract

Accurate tree volume and structure are crucial for forest biomass estimation and ecosystem investigations. While terrestrial laser scanning (TLS) offers non-destructive pathways for the detailed three-dimensional tree reconstruction, current methods overestimate small branch volumes and often require tree segmentation and leaf–wood separation as a priori. This study introduces and validates RayExtract, a novel method for reconstructing woody volume from TLS data, utilising tools from the RayCloudTools library, to automate the extraction of tree structural metrics from point clouds. Our method incorporates two key morphological rules — Self-Similarity and Leonardo’s Rule — to aid branch radius and taper calculations. Likewise, it enables rapid and automated plot-scale reconstruction by integrating tree segmentation and woody structure modelling without requiring leaf point classification. In this study, RayExtract demonstrated high accuracy across four high-quality destructive harvest reference sets with concordance correlation coefficient (CCC) values ranging from 0.82 to 0.97 (n=124). To explore algorithm behaviours under different leaf conditions and point densities, we implement a framework using TLS simulation of highly realistic synthetic trees. Results from the simulation framework show consistent high accuracy of total woody volume, with CCC ranging from 0.97 to 0.98 (n=18) across four distinct scanning configurations. Fine-scale volumetric analysis revealed that incorporating simple morphological rules can effectively inform branch taper and reduce woody volume overestimation, particularly in smaller components. Furthermore, it identifies a limitation in volumetric accuracy in trees exhibiting significant taper in the lower stem. Analysis of RayExtract’s computational efficiency demonstrates that runtime and memory usage scale predictably with input data size, primarily driven by point count and the associated structural complexity within the point cloud, positioning the algorithm as well suited for large-scale applications. RayExtract represents a significant advancement in forest reconstruction, biomass estimation, and vegetation structural analysis. The method’s efficiency, accuracy, and robustness across varied forest conditions mark a substantial improvement in forest structural assessment techniques using laser scanning and have broad implications for improving regional biomass estimations, and contributing to the calibration and validation of broad-scale remote sensing observations.