Extracting Value from Fused Aerial and Terrestrial LiDAR Scans

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Abstract
Forest inventory operations require accurate and scalable methods for estimating structural tree attributes such as diameter at breast height, tree height, and crown dimensions. Terrestrial laser scanning (TLS) and mobile laser scanning (MLS) provide detailed stem information but limited spatial coverage, whereas unmanned laser scanning (ULS) provides broad coverage but sparse internal stem structure. This study evaluates whether fused LiDAR point clouds and imputation-based correction methods can transfer accurate stem information from TLS, MLS, or fused calibration areas to larger ULS-only areas. ULS, TLS, MLS, and fused laser scanning (FLS) were analysed for 10 m and 20 m radiata pine, and 30 m eucalyptus stands. The TreeLS R package and regression-based methods were used for attribute extraction, and several imputation strategies were tested, including parametric cumulative distribution function (CDF) mapping, empirical and rank-based quantile mapping, conditional mean with residual spread restoration, and voxel-based imputation. The results show that TreeLS applied to TLS, MLS, and FLS produced substantially more reliable diameter at breast height (DBH) distributions than regression models applied directly to ULS data. For ULS-only areas, uncorrected regression estimates differed from field means by 2.0 cm for tall radiata pine, 10.4 cm for medium radiata pine, and 5.4 cm for mature eucalyptus. Using terrestrially trained voxel-based imputation reduced these differences to 0.1 cm, 0.3 cm, and 0.8 cm, respectively. Other distribution-aware imputation methods also substantially improved agreement with field-observed DBH distributions. These findings indicate that relatively small areas of high-density TLS or MLS or FLS data can be used to calibrate inventory estimates across larger ULS-only regions. The approach offers a practical compromise between the structural accuracy of terrestrial LiDAR and the spatial efficiency of aerial LiDAR, supporting scalable forest inventory estimation without requiring exhaustive terrestrial coverage.

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