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 offers the option of combining these novel approaches with other road assessments.