Good road conditions are necessary for the smooth transportation of forest machines and products. High-density airborne laser scanning data were used here to determine the quality of road surfaces and ditching systems. Forest roads in Kiihtelysvaara, Finland, were assessed in August 2013. Eight categories (structural condition, seasonal damage, drying, bridges, surface wear, visibility, coppicing, and flatness) have been inventoried and divided into three quality classes: poor, satisfactory, and good. The topographic position index, standardize elevation index, and hydrology tools were used on digital elevation models with different resolutions to test which categories could be derived. The road surface quality was most clearly related to surface wearing and flatness, and the topographic position index described the road surface best at resolutions of 0.20 m and 0.25 m; however, the standardized elevation index was superior at a 0.50 m resolution. The ditching system plays an important role in the drying of roads, and the hydrological tools and land facet analysis were most suitable for identifying the location of ditches and assessing their quality at 0.20 m and 0.25 m resolutions, respectively. The road surface was classified in all resolutions at least 66% correctly, whereas the ditches were classified in all resolutions at least 60% correctly. The results confirm that airborne laser scanning data can be used for obtaining quality information on forest roads.
ABSTRACT:Good quality forest roads are important for forest management. Airborne laser scanning data can help create automatized road quality detection, thus avoiding field visits. Two different pulse density datasets have been used to assess road quality: high-density airborne laser scanning data from Kiihtelysvaara and low-density data from Tuusniemi, Finland. The field inventory mainly focused on the surface wear condition, structural condition, flatness, road side vegetation and drying of the road. Observations were divided into poor, satisfactory and good categories based on the current Finnish quality standards used for forest roads. Digital Elevation Models were derived from the laser point cloud, and indices were calculated to determine road quality. The calculated indices assessed the topographic differences on the road surface and road sides. The topographic position index works well in flat terrain only, while the standardized elevation index described the road surface better if the differences are bigger. Both indices require at least a 1 metre resolution. High-density data is necessary for analysis of the road surface, and the indices relate mostly to the surface wear and flatness. The classification was more precise (31-92%) than on low-density data (25-40%). However, ditch detection and classification can be carried out using the sparse dataset as well (with a success rate of 69%). The use of airborne laser scanning data can provide quality information on forest roads.
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