Extracting Value from Fused Aerial and Terrestrial LiDAR Scans

Accurate estimation of forest structural attributes over operational scales remains challenging because unmanned laser scanning (ULS) provides extensive spatial coverage but limited representation of internal stem structure, whereas terrestrial and mobile laser scanning (TLS/MLS) provide detailed stem measurements over relatively small areas. This study investigates a calibration-transfer framework in which small areas of terrestrial or fused LiDAR are used to improve diameter at breast height (DBH) estimation across much larger regions surveyed only by ULS. ULS, TLS, MLS and fused laser scanning (FLS) datasets were analysed for radiata pine and eucalyptus plantations. TreeLS-derived DBH measurements from terrestrial and fused point clouds were used as reference data to evaluate several distribution-aware and voxel-based imputation approaches for correcting regression-derived ULS estimates. Across the study sites, the best-performing imputation methods reduced stand-level mean DBH differences by as much as 95% relative to the uncorrected ULS regression estimates, resulting in substantially improved agreement with field-observed stand means while simultaneously producing DBH distributions that more closely matched the corresponding TreeLS-derived reference distributions. Voxel-based imputation performed particularly well for radiata pine and remained competitive for eucalyptus, while several distribution-based approaches achieved comparable or better performance in particular stands. These findings demonstrate the potential for transferring information from relatively small terrestrial LiDAR calibration areas to larger ULS-only acquisitions, improving stand-level DBH distribution estimates without requiring complete terrestrial coverage. Because validation was performed using stand-level field summary statistics rather than matched individual trees, the reported performance should be interpreted as demonstrating the potential of the approach under the conditions evaluated rather than universal individual-tree accuracy.