Views: 0 Author: Site Editor Publish Time: 2026-05-26 Origin: Site
New LiDAR technology enables "single-tree level" 3D modeling, capable of penetrating forest canopies and performing automatic individual tree segmentation (accuracy >95%) while extracting tree height, DBH, and volume with high precision, significantly boosting forest inventory efficiency. However, this progress is the result of synergistic evolution across multi-platforms (airborne/backpack/handheld) and AI algorithms, rather than an isolated technological breakthrough disclosed solely in May 2026.
Penetration Capability & Individual Tree Segmentation:
Current multi-echo LiDAR systems (e.g., DJI Zenmuse L3, CHCNAV RS30), combined with point cloud classification algorithms (based on echo count, intensity, and spatial clustering), effectively distinguish between canopy, trunk, and ground points. AI-driven instance segmentation models (such as PointNet++, 3D CNNs, or the latest Transformer architectures) have achieved an F1-score above 0.95 (corresponding to accuracy >95%) for individual tree recognition in closed-canopy stands in research from 2025–2026.
Automatic Parameter Extraction:
Tree height (error <5%) and Diameter at Breast Height (DBH, error ±3–5%) are derived through cylindrical fitting of trunk point clouds or least squares methods. Volume is estimated using allometric equations (e.g., DBH² × Height) integrated with species libraries, achieving overall regional errors of <3% (as verified by CHCNAV’s ForestPoint software).
Evolution of Technical Platforms:
The industry has moved beyond static terrestrial LiDAR to integrate UAV-borne LiDAR (for large-scale coverage), backpack/handheld LiDAR (to supplement understory details), and SLAM real-time positioning, enabling collaborative "air-to-ground" point cloud acquisition. In early 2026, teams from Northeast Forestry University and GreenValley International validated the engineering capability of this system at single-tree precision levels (<10 cm positioning error).
Key Breakthroughs:
The advancement does not stem from "new physical penetration," but rather from the convergence of increased point cloud density (>20 pts/m² in understory) + multi-echo processing + deep learning segmentation (e.g., Mask3D) + joint terrain-vegetation modeling. Additionally, the "canopy edge-aware clipping algorithm" proposed by the Chinese Academy of Forestry in 2025 has significantly improved the integrity of individual tree separation in dense stands.
Practical Applications:
Pilot programs in the forests of Northeast and Southwest China have demonstrated efficiency gains of over 10 times compared to traditional plot surveys, with errors meeting the Class III accuracy requirements for National Forest Inventory (LY/T 1953–2021). This technology is driving forestry from "plot-based estimation" toward "precise per-tree surveying and mapping."
