Automated Estimation of Standing Dead Tree Volume Using Voxelized Terrestrial Lidar Data

Standing dead trees (SDTs) are an important forest component and impact a variety of ecosystem processes, yet the carbon pool dynamics of SDTs are poorly constrained in terrestrial carbon cycling models. The ability to model wood decay and carbon cycling in relation to detectable changes in tree structure and volume over time would greatly improve such models. The specific objectives of this paper were to: 1) develop an automated SDT volume estimation algorithm providing accurate volume estimates for trees scanned with terrestrial lidar in dense forests; 2) assess the volume estimation algorithm's accuracy with respect to large and small branches; and 3) characterize the impact of occlusion with regards to volume estimation accuracy and the ability of the algorithm to mitigate challenges posed by lower quality point clouds. A voxel-based volume estimation algorithm, “TreeVolX,” was developed and incorporates several methods designed to robustly process point clouds of varying quality levels. The algorithm operates on horizontal voxel slices by segmenting the slice into distinct branch or stem sections then applying an adaptive contour interpolation and interior filling process to create solid reconstructed tree models. The concept of vertical point cloud resampling is introduced to facilitate the modeling of lower quality point clouds with a small voxel size. TreeVolX estimated large and small branch volume with a root-mean-square error of 7.3% and 13.8%, respectively, and the adaptive contour interpolation was shown to significantly reduce volume estimation errors in the case of significantly occluded point clouds.