New Hybrid Algorithms for Estimating Tree Stem Diameters at Breast Height Using a Two Dimensional Terrestrial Laser...

Area of Science:

• Forestry
• Remote Sensing
• Computer Vision

Background:

• Accurate tree diameter estimation is crucial for forest management and inventory.
• Traditional methods can be time-consuming and less precise, especially in complex forest environments.
• Terrestrial laser scanning (2DTLS) offers a promising approach for non-intrusive forest data collection.

Purpose of the Study:

• To develop and validate a novel algorithm for improving the accuracy of diameter at breast height (DBH) estimation.
• To optimize the processing of 2DTLS point cloud data for precise tree trunk measurements.
• To enhance the efficiency and safety of forest operations through improved tree data acquisition.

Main Methods:

• Utilized a two-dimensional terrestrial laser scanner (2DTLS) to generate point cloud data of tree trunks.
• Applied data extraction, filtration, and arithmetic means optimization to isolate and refine trunk point clusters.
• Implemented a hybrid algorithm combining algebraic circle fitting in polar form with Levenberg-Marquardt non-linear optimization for diameter and position determination.

Main Results:

• The proposed algorithm demonstrated significant improvements in the accuracy of diameter at breast height (DBH) estimation compared to existing methods.
• A substantial reduction in calculation time was achieved, enhancing processing efficiency.
• Experimental results confirmed the method's stability and suitability for challenging forest conditions.

Conclusions:

• The novel hybrid algorithm provides a highly accurate and efficient solution for estimating tree diameters from 2DTLS data.
• This method has practical implications for improving the operational efficiency of forest harvesters and mitigating accident risks.
• The developed technique offers a robust and reliable tool for advanced forest inventory and management.