Abstract. Trees play a key role in our planet. They regulate climate, preserve biodiversity, and contribute to human well-being. Each species has different contributions to our globe and a specific carbon storage potential. Identifying tree species enables better measurement of global carbon and helps authorities better manage forests and green spaces. Unmanned Aerial System (UAS) LiDAR has become a powerful source of 3D point cloud for vegetation analysis, given its ability to capture a large area in a short time and its capacity to penetrate canopy layers. Deep learning methods extract discriminative features directly from raw point clouds and generalize well to unseen datasets. This study optimises PointNet++ deep learning architecture for tree species classification by analysing the influence of sampling configurations on the performance of model detection, by using an open-source dataset “FOR-species20K”. Three-point cloud sampling configurations (4 096, 8 192, and 16 384 points per tree) were tested with three random seeds (0,42 and 123) to assess their impact on classification accuracy and ensure stability of prediction. Results on a separate test set of 508 trees show a consistent improvement in performance of PointNet++ with a sampling configuration of 8 192 points per tree, reaching a macro-average F1-score of 89.65%, surpassing the 74.9 % reported by (Puliti et al., 2025a) for evaluating the same architecture. Dominant species such as Fagus sylvatica, Picea abies, and Pinus sylvestris achieve F1-scores exceeding 90%, indicating high model robustness. This study approves that the performance of PointNet++ could be improved by raising the number of points from 4 096 to 8 192, but further increasing to 16 384 points introduces interspecific confusion and requires extensive computational time for model training. This research aligns and complements the global initiative led by Federal Institute of Technology Zurich in Switzerland (ETH Zurich), which is interested in identifying tree species using deep learning (“ETH Zurich”, 2025).
Hamdani et al. (Fri,) studied this question.