0100495844

open access

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Drone LiDAR Occlusion Analysis and Simulation from Retrieved Pathways to Improve Ground Mapping of Forested Environments

English (英語)

Drones

9(2)

135

MDPI

2025-02

2025-05-14

Drone-mounted LiDAR systems have revolutionized forest mapping, but data quality is often compromised by occlusions caused by vegetation and terrain features. This study presents a novel framework for analyzing and predicting LiDAR occlusion patterns in forested environments, combining the geometric reconstruction of flight paths with the statistical modeling of ground visibility. Using field data collected at Unzen Volcano, Japan, we first developed an algorithm to retrieve drone flight paths from timestamped pointclouds, enabling post-processing optimization, even when original flight data are unavailable. We then created a mathematical model to quantify the shadow effects from obstacles and implemented Monte Carlo simulations to optimize flight parameters for different forest stand characteristics. The results demonstrate that lower-altitude flights (40 m) with narrow scanning angles achieve the highest ground visibility (81%) but require more flight paths, while higher-altitude flights with wider scanning angles offer efficient coverage (47% visibility) with single flight paths. For a forest stand with 250 trees per 25 hectares (heights 5–15 m), statistical analysis showed that scanning angles above 90 degrees consistently delivered 46–47% ground visibility, regardless of the flight height. This research provides quantitative guidance for optimizing drone LiDAR surveys in forested environments, though future work is needed to incorporate canopy complexity and seasonal variations.

pointcloud

学術雑誌論文

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