Individual Tree Structural Parameter Extraction and Volume Table Creation Based on Near-Field LiDAR Data: A Case...

Area of Science:

• Forestry Science
• Remote Sensing Technology
• Geospatial Analysis

Background:

• Precision silviculture relies on accurate individual tree structural parameters.
• Subtropical planted forests require efficient methods for data acquisition and analysis.

Purpose of the Study:

• To extract individual tree structural parameters using near-field LiDAR (ULS and BLS).
• To fit and select optimal tree taper models.
• To create standing timber volume tables for key species.

Main Methods:

• Individual tree segmentation algorithms applied to ULS (tree height) and BLS (diameter at breast height) data.
• Fusion of ULS and BLS point clouds for complete forest canopy data.
• Fitting five tree taper models and selecting the optimal one.
• Utilizing standard volume models to calculate stand volume.

Main Results:

• High accuracy in extracting individual tree structural parameters.
• Effective fitting and selection of tree taper models.
• Successful creation of standing timber volume tables for dawn redwood and poplar.
• Demonstrated good performance of the volume model in calculating standing volume.

Conclusions:

• Near-field LiDAR (ULS and BLS) is highly capable of extracting tree structural parameters in subtropical planted forests.
• LiDAR-derived data facilitates the creation of accurate volume tables, supporting precision silviculture.
• The integrated approach provides a robust methodology for forest inventory and management.