Speaker
Description
Reliable forest road condition monitoring is essential for safe and cost-efficient timber transport. Conventional visual inspections are labour-intensive and often fail to provide objective, repeatable data for maintenance planning. This study presents the development and field validation of a vehicle-mounted multi-sensor LiDAR system for automated forest road damage detection under operational driving conditions.
The system integrates 16 single-beam LiDAR sensors with a central controller that continuously measures road surface deviations and records georeferenced damage events when calibrated thresholds are exceeded. A minimum vehicle speed limit (1 m s⁻¹) prevents duplicate detections. The system is designed for installation on transport vehicles, enabling routine road condition monitoring without additional inspection costs.
Field trials were conducted on a 2.3 km forest road section at speeds from 10 to 80 km h⁻¹, reflecting typical timber transport conditions. Manual GNSS-based surveys of potholes ≥ 5 cm depth were used for validation. Spatial agreement between manual and automated detections was assessed using the Jaccard index at tolerance distances of 2, 3 and 5 m.
From 260 manually recorded potholes and 211 automated detections, agreement increased with tolerance distance: 0.53 (2 m), 0.64 (3 m), and 0.73 (5 m). Results indicate reliable detection performance when accounting for GNSS uncertainty. Higher driving speeds reduced precise localisation of individual defects but allowed identification of damaged road segments relevant for maintenance prioritisation.
The system supports scalable, data-driven forest road management, improves transport safety, and provides a practical tool for integrating digital road quality monitoring into forest logistics operations.
| Keywords | LiDAR; Automation; Infrastructure; Monitoring |
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