Non-destructive aboveground biomass estimation of coniferous trees using terrestrial LiDAR

•A novel algorithm is presented to estimate individual tree biomass from TLS data.•The algorithm uses the shape of the tree rather than forcing a cylinder to fit.•TLS biomass estimates were validated with destructive sampling data.•Estimates of whole-tree and trunk biomass were accurate.•The method advances efficient single-tree biomass estimation, improving allometry.

Global estimates of forest aboveground biomass and carbon storage have major discrepancies linked to limitations in tree-level biomass estimates. Robust allometric equations can improve biomass estimates; however, destructive sampling to measure single-tree biomass is expensive, challenging, and prone to measurement error. We present a method to efficiently and non-destructively estimate single-tree biomass from terrestrial LiDAR scan data and test the approach on 21 destructively-sampled lodgepole pine (Pinus contorta) trees. The approach estimates branch and foliage volume using voxelization and estimates trunk volume using a method developed in this study called the Outer Hull Model (OHM). The OHM iteratively fits convex hulls, accurately handles noisy scan data, and fits the true shape of the trunk rather than forcing a cylindrical fit. Volume from the LiDAR scans is converted to biomass using density values from the literature and from field sampling to assess model sensitivity to density values. Whole-tree aboveground biomass estimates derived from the LiDAR scans were nearly unbiased and agreed strongly with destructive sampling data (R2 = 0.98, RMSE = 20.4 kg). Estimation of the trunk component biomass (R2 = 0.99, RMSE = 12.3 kg) was stronger than foliage and needle component estimates (R2 = 0.54, RMSE = 21.4 kg). The approach presented in this study accurately and non-destructively estimated the aboveground biomass of needleleaf trees with minimal user input. The promising performance on coniferous trees advances efficient sampling of single-tree biomass.