Detecting stony areas based on ground surface curvature distribution

Presence of ground surface stones is one indicator of economically important landmass deposits in the Arctic. The other indicator is a geomorphological category of the area. This work shows that ground stoniness can be automatically predicted with practical accuracy. Northern forests have less biomass and foliage, thus direct analysis of stoniness is possible from airborne laser scanning (ALS) data. A test set of 88 polygons covering 3.3 km2 was human-classified and a method was developed to perform the stoniness classification over this set. The local curvature of the surface is approximated directly from the point cloud data without generating the Digital Terrain Model (DTM). The method performs well with area under curve AUC = 0.85 from Leave-Pair-Out cross-validation, and is rather insensitive to missing data, moderate forest cover and double-scanned areas.