Prioritizing Forest Road Interventions through the Integration of Surface Condition Indexing, Multisensor LiDAR Mapping, Timber Transportation, and Context-Aware Augmented Reality Visualization

Introduction

Forest roads are essential infrastructure for forest management, forest operations, and timber transportation. Accurate real-time road condition assessment is necessary to prioritize maintenance interventions, yet the implementation potential of Augmented Reality’s (AR) in this field remains limited. This study proposes a decision-support framework that combines LiDAR-based road assessment, surface condition indexing, and machine learning classification, visualized through an Augmented Reality (AR) interface for maintenance decisions.

Methods

The proposed framework combines airborne LiDAR, SLAM-based handheld LiDAR, and iPhone LiDAR data to acquire high-resolution point clouds of forest road surfaces. These point clouds are integrated to improve forest road mapping accuracy under complex terrain and canopy conditions. UAV-RTK photogrammetry is used to generate orthomosaics, Digital Surface Models, Digital Elevation Models, and road cross-sections to extract deformation related metrics that are used to calculate a Surface Deformation Index (SDI). A machine learning model is applied to support automatic road condition classification, whereas timber stack volume and spatial location serve as contextual data.

Results

The framework produces high-resolution deformation maps along forest road segments and supports the automatic classification of road surface condition. The UAV-RTK approach enabled the detailed classification of forest road and trail surface deformation, supporting the estimation of cut-and-fill volumes and the identification of road sections requiring intervention. Multisensor LiDAR analysis demonstrated that point-cloud fusion can enhance road surface representation, with the ALS and iPhone combination showing the lowest deviation from ALS reference data.

Conclusions

By linking surface deformation, timber logistics, and an AR interface, the framework enables a concise prioritization of forest road maintenance. Future research should validate the SDI across different environmental and operational conditions and develop a context-aware AR interface for field implementation.