Conditional Benefits of Point-Level UAV LiDAR--Multispectral Fusion for 3D Vegetation Classification

Multisensor remote-sensing fusion combines complementary measurements of landscape structure and surface reflectance, but adding a second modality does not necessarily produce uniform classification gains. This study evaluates point-level data fusion of unmanned aerial vehicle (UAV) LiDAR and multispectral imagery within a three-dimensional (3D) point-cloud representation. Tree--shrub classification in a heterogeneous tropical landscape in Benguet, Philippines, was used as a diagnostic test case because tall shrubs and lower tree canopies can occupy overlapping geometric spaces. Green, Red, RedEdge and Near-Infrared bands, together with the Normalized Difference Vegetation Index, were projected from separately acquired orthomosaics onto individual LiDAR points to create a spectrally augmented point cloud. Classification was performed using an extended implementation of the open-source OpenPointClass framework. On the independent test set, fusion increased overall accuracy from 83.8\% to 84.7\%, tree F1-score from 57.6\% to 61.8\%, and tree Intersection over Union from 40.5\% to 44.7\%, while shrub F1-score changed from 90.0\% to 90.4\%. A spatial analysis across five tree--shrub composition groups showed that the contribution of the spectral modality was conditional: the clearest tree gains occurred in shrub-dominated areas where geometric ambiguity was greatest, whereas fusion produced a small decline in the balanced group. Residual displacement between the separately acquired LiDAR and multispectral products was observed but could not be quantitatively isolated. The results show that point-level multimodal fusion can improve 3D vegetation classification, particularly for structurally ambiguous classes, but that its benefit depends on landscape composition and spatial alignment between the sensor products rather than being uniformly positive.