ArticleSensors (Basel, Switzerland)2025
Improved Cylinder-Based Tree Trunk Detection in LiDAR Point Clouds for Forestry Applications.
Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Stand Height Increment from Two-Epoch Aerial Laser Scanning Data and Inventory Data.Sensors (Basel, Switzerland) · 2025Article
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5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
The application of LiDAR technology in extracting individual trees and stand parameters plays a crucial role in forest surveys. Accurate identification of individual tree trunks is a critical foundation for subsequent parameter extraction. For LiDAR-acquired forest point cloud data, existing two-dimensional (2D) plane-based algorithms for tree trunk detection often suffer from spatial information loss, resulting in reduced accuracy, particularly for tilted trees. While cylinder fitting algorithms provide a three-dimensional (3D) solution for trunk detection, their performance in complex forest environments remains limited due to sensitivity to parameters like distance thresholds. To address these challenges, this study proposes an improved individual tree trunk detection algorithm, Random Sample Consensus Cylinder Fitting (RANSAC-CyF), specifically optimized for detecting cylindrical tree trunks. Validated in three forest plots with varying complexities in Tianhe District, Guangzhou, the algorithm demonstrated significant advantages in the inlier rate, detection success rate, and robustness for tilted trees. The study showed the following results: (1) The average difference between the inlier rates of tree trunks and non-tree points for the three sample plots using RANSAC-CyF were 0.59, 0.63, and 0.52, respectively, which were significantly higher than those using the Least Squares Circle Fitting (LSCF) algorithm and the Random Sample Consensus Circle Fitting (RANSAC-CF) algorithm (
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