Forest roads allow access for silvicultural operations, harvesting, recreational activities, wildlife management, and fire suppression. In British Columbia, Canada, roads that are no longer required must be deactivated (temporarily, semipermanently, or permanently) in order to minimize the impact on the overall forested ecosystem. However, the remoteness and size of the road network present challenges for monitoring. Our aim was to examine the utility of airborne laser scanning data to assess the status and quality of forest roads across 52,000 hectares of coastal forest in British Columbia. Within the forest estate, roads can be active or deactivated, or have an unknown status. We classified road segments based on the vegetation growth on the road surface, and edges, by classifying the height distribution of airborne laser scanning returns within each road segment into four groups: no vegetation, minor vegetation, dense understory vegetation, and dense overstory vegetation. Validation indicated that 73 percent of roads were classified correctly when compared to independent field observations. The majority were classified as active roads with no vegetation or deactivated with dense vegetation. The approach presented herein can aid forest managers in verifying the status of the roads in their management area, especially in remote areas where field assessments are costly and time-consuming.

Research Highlights: A Topographic Wetness Index calculated using LiDAR-derived elevation models can help in identifying unpaved forest roads that need maintenance. Materials and Methods: Low-pulse LiDAR data were used to calculate a Topographic Wetness Index to predict unpaved forest roads’ quality. Results: The results of this analysis and comparison of road-quality features derived from LiDAR data at resolutions of 1, 10 and 25 m for assessing road quality in the boreal forests of Finnish Lakeland show that the wetness index can predict road quality correctly in up to 70% of cases and up to 86% when combined with other auxiliary GIS-based variables. Conclusions: Road-quality assessments, using airborne LiDAR data, can greatly help forest managers to decide which sections of the ageing road network will benefit the most from maintenance, while reducing the need of field visits.

Two different pulse density airborne laser scanning datasets were used to develop a quality assessment methodology to determine how airborne laser scanning derived variables with the use of reference surface can determine forest road quality. The concept of a reference DEM (Digital Elevation Model) was used to guarantee locally invariant topographic analysis of road roughness. Structural condition, surface wear and flatness were assessed at two test sites in Eastern Finland, calculating surface indices with and without the reference DEM. The high pulse density dataset (12 pulses m) gave better classification results (77% accuracy of the correctly classified road sections) than the low pulse density dataset (1 pulse m). The use of a reference DEM increased the precision of the road quality classification with the low pulse density dataset when the classification was performed in two-steps. Four interpolation techniques (Inverse Weighted Distance, Kriging, Natural Neighbour and Spline) were compared, and spline interpolation provided the best classification. The work shows that applying a spline reference DEM it is possible to identify 66% of the poor quality road sections and 78% of the good ones. Locating these roads is essential for road maintenance.–2–2

This study creates new methods for assessing unpaved forest road quality using airborne laser scanning (ALS) data. The low and high pulse density ALS data were first processed and digital elevation models (DEMs) created at several resolutions from 0.2 m to 1 m. Different interpolation methods such as IDW, NN, Spline and Kriging were compared in the first phase, and IDW was chosen for further calculations. The work focuses on road quality properties such as surface flatness, surface wear quality, road structure, ditch quality, road drying properties and water accumulation, and also the vegetation cover on and beside the road.The roads were divided into three categories using the Metsäteho forest road quality assessment system. Active/deactivated road status was assessed on Vancouver Island, Canada. Linear discriminant analysis was used to find the best predictors of the road quality classes, the result being validated using confusion matrices, by k-fold cross-validation, and/or by calculating kappa values. A combination of surface indices, the topographic wetness index and soil information provided high precision (81.6-89.8%) information about unpaved forest road quality. Simultaneously, the indices individually showed promising results when applied to high pulse density data. The classification based on vegetation growth was up to 73% correct, while the presence of a ditch system and its status as mapped using the high resolution LiDAR data was up to 92% correct.The findings indicate that the use of LiDAR data can help forest managers gain more information about the quality and status of forest roads in remote areas without spending extra resources (time, transportation costs, personnel) on checking the road network manually. Although the use of ALS data for road quality assessment cannot yet replace field visits, it opens up possibilities for further research and offer the option of combining these novel approaches with other road assessments.