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Improved Cylinder-Based Tree Trunk Detection in LiDAR Point Clouds for Forestry Applications.

Shaobo Ma1, Yongkang Chen1, Zhefan Li1

  • 1College of Resources and Environment, South China Agricultural University, Guangzhou 510642, China.

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Summary

This study introduces Random Sample Consensus Cylinder Fitting (RANSAC-CyF), an improved algorithm for detecting individual tree trunks from LiDAR data. RANSAC-CyF enhances accuracy and robustness, especially for tilted trees in complex forests.

Keywords:
individual tree trunk detectionpoint cloudrandom sample consensus cylinder fittingterrestrial LiDAR

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Area of Science:

  • Forestry Science
  • Remote Sensing
  • Computer Vision

Background:

  • LiDAR technology is vital for forest surveys, requiring accurate individual tree trunk detection for parameter extraction.
  • Existing 2D and 3D algorithms for tree trunk detection from LiDAR data face limitations in accuracy, spatial information loss, and performance in complex forest environments.
  • Tilted trees and sensitive parameters in current cylinder fitting methods reduce detection success in challenging conditions.

Purpose of the Study:

  • To develop and validate an improved algorithm, Random Sample Consensus Cylinder Fitting (RANSAC-CyF), for accurate individual tree trunk detection from LiDAR point clouds.
  • To address the limitations of existing methods, particularly concerning spatial information loss and sensitivity to parameters in complex forest settings.
  • To enhance the robustness and efficiency of tree trunk detection for forestry surveys and ecological research.

Main Methods:

  • Proposed the Random Sample Consensus Cylinder Fitting (RANSAC-CyF) algorithm, optimized for detecting cylindrical tree trunks in 3D LiDAR point cloud data.
  • Validated the RANSAC-CyF algorithm across three forest plots with varying complexities in Tianhe District, Guangzhou.
  • Compared the performance of RANSAC-CyF against Least Squares Circle Fitting (LSCF) and Random Sample Consensus Circle Fitting (RANSAC-CF) algorithms.

Main Results:

  • RANSAC-CyF demonstrated significantly higher inlier rates for tree trunks compared to LSCF and RANSAC-CF (average differences of 0.59, 0.63, 0.52 vs. lower rates, p < 0.05).
  • Achieved 100% detection success rate with fewer clusters (2 and 8) in Plots 1 and 2 compared to other algorithms (26 and 40 clusters).
  • Exhibited a wider effective distance threshold range and maintained stable inlier rates (>0.9) across all tilt angles, outperforming comparison algorithms in challenging conditions.

Conclusions:

  • The RANSAC-CyF algorithm offers superior accuracy, robustness, and adaptability for individual tree trunk detection in complex forest environments using LiDAR data.
  • The proposed method significantly improves the efficiency and precision of forestry surveys and ecological research by overcoming limitations of existing detection techniques.
  • RANSAC-CyF demonstrates effective performance even in challenging plots where other algorithms failed, highlighting its practical applicability.