Gravel road surface evaluation using field-collected profile measurements and airborne laser-scanned data

Accurate information on the quality of forest roads provides valuable input for those responsible for managing forest road infrastructure. In our study, we investigated the possibility of directly measuring road damage by deriving road profiles from an adaptive good-reference surface to provide data on roadway disturbance. The method was tested in the field by comparing the profiles of damaged roads with those of undamaged roads. We also tested the same concept by creating a digital twin of the road, utilizing digital terrain model (DTM)-derived elevation based on the lowest return hits from 6 points/m2 airborne laser scanning (ALS) data. We found that, in 95% of cases, the sum of profile differences measured in the field was greater for the damaged road than the undamaged road. While this measure was suboptimal for virtual data, an independent samples t-test indicated statistical significance in the profile difference means between well-maintained and damaged road profiles in 68% of cases. In addition to differentiating between damaged and undamaged roads, we also found that our methodology could be used to directly estimate the volume of gravel needed for road maintenance. Relating the volumes measured in the field to our virtual measurements using linear regression indicated an adjusted R2 value of 0.71 and a root mean square error of 0.21 m3. Apart from the introduction of a novel profile measurement approach, the study also shows the suitability of medium density ALS data to evaluate the geometry of gravel road surfaces.