Precise urban tree species identification and biomass estimation using UAV–Handheld LiDAR Synergy and YOLOv11 deep learning

• Developed a deep learning-driven SVP method to identify urban trees with LiDAR. • Compared multi-source LiDAR for tree species classification and structure retrieval. • Fused and HMLS LiDAR were comparable, significantly outperforming UAV-borne. • YOLOv11 achieved accurate tree species identification with fused data. • Support whole-organ & single-tree biomass estimation within urban landscape. Accurate estimation of individual tree biomass in urban landscapes is critical for carbon stock assessment and urban forest management but remains challenging because of the structural complexity and species diversity of urban trees. This study presents an integrated methodological framework that combines deep learning-based tree species identification with LiDAR-derived structural parameter estimation to enable rapid and precise biomass mapping at the individual tree level. Using multiplatform LiDAR data (UAV-borne and handheld mobile laser scanning), we developed a lightweight sample generation method derived from side-view tree projections (SVP) to efficiently construct a species-adaptive training library and proposed a novel individual tree identification approach optimized with the YOLOv11 deep learning algorithm. Our framework systematically evaluated the performance of single-source versus fused LiDAR point clouds across three key metrics: species classification accuracy (achieving 87.3 % on independent test data), structural parameter retrieval (R 2 = 0.925 for DBH, R 2 = 0.844 for height), and biomass estimation fidelity (86.3 % agreement with field measurements). The results demonstrated that compared with single-source alternatives, data fusion reduces parameter estimation errors by 4.8–56.2 %, while the SVP strategy enables computationally efficient species-specific allometric model matching. This work advances urban forest monitoring by providing a scalable solution that balances scientific rigor with operational practicality, addressing critical gaps in high-resolution biomass mapping for heterogeneous urban ecosystems.