Automated tree segmentation for scalable terrestrial laser scanning-based biomass reference data: assessment of volumetric and allometric approaches

Terrestrial laser scanning (TLS) enables non-destructive estimation of aboveground biomass (AGB) through direct volumetric modelling and allometric model calibration. However, manual individual tree segmentation of TLS plot point clouds creates a processing bottleneck that limits scalability. We evaluated whether automated tree segmentation using RayExtract can replace manual segmentation to provide plot-level volume estimates aimed at satellite calibration/validation data. We evaluated direct volume estimates and allometric models calibrated using RayExtract tree predictions across seven TLS plots spanning various forest types, ranging from mountainous conifer to tropical rainforests. For direct volume modelling at 0.25 ha resolution, automated segmentation introduced a systematic positive bias of 20.5% (± 5.5) relative to manual segmentation. Filtering using outlier detection reduced this to 6.9% (± 4.3), while a filtering approach based on visual verification reduced bias to 4.2% (± 2.7) on average. After visual verification, which requires only 0.7% of the active processing time compared to manual segmentation, RayExtract produced direct volume estimates with uncertainties comparable to other well-established AGB error sources across all forest types. For allometric model fitting, RayExtract-derived models substantially increased uncertainty and bias. Visual verification generally improved metrics, resulting in uncertainty and bias increases of 9 and 15 percentage points (pp) on V∼D models respectively, and 5 and 19 pp on V∼HCD models relative to manual segmentation-based models. Bias and uncertainty of automated segmentation-based models were largest in tropical forests, and targeted manual segmentation or correction of a set of representative trees likely remains necessary for local allometric model calibration.