The accurate estimation and inversion of stand characteristic parameters and biomass is the premise for the evaluation of forest productivity, forest ecosystem carbon storage, and ecological forest service capacity. The horizontal accuracies of structure from motion (SfM) and light detection and ranging (LiDAR) point clouds were identical, whereas the descriptions of the upper canopy indices were similar (R = 0.91 to 0.93). However, the descriptions of the lower canopy indices and vertical structure variables were different based on comparisons of the two point cloud types in the same region. Variables that described the vertical structure of the canopy in the SfM point clouds were much less important in the factor estimation models, exhibiting high correlations with basal area and volume. The factor estimation models with high correlations with tree height and diameter at breast height (DBH) exhibited similar importance for LiDAR variables. LiDAR (R2 = 0.515 to 0.895) was superior to SfM (R2 = 0.63 to 0.835) in estimating the same parameters, particularly those with high correlations with vertical structures. The combination of unmanned aerial vehicle aerial photogrammetry and LiDAR technology might enable the estimation of stand characteristic parameters and the inversion of biomass, as well as the estimation of characteristic parameters such as Lorey’s H and DBH, which are highly sensitive for large datasets. The LiDAR and SfM model outcomes were comparable. We found that the model outcomes exhibited the greatest differences for volume (ΔrRMSE % = 13.94 % ) and the lowest for average DBH (ΔrRMSE = 0.8 % )
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