Remotely sensed data, such as high-resolution point clouds of Terrestrial Laser Scanning (TLS), and automated rock structure modelling help us efficiently assess cliff rockslide prone areas where direct contact measurement of fracture characteristics is very difficult. Our approach is performed in four main steps which include: 1) collection of true 3D geomorphology and fracture pattern by means of TLS; 2) visualisation of 3D relief using HSV-colour (H for hue, S for saturation, V for lightness value) and measurement of rock structure; 3) computation of the distinctly located blocks in 3D cliff morphology; 4) assessment of rockfall hazards. The work flow has been illustrated using the limestone escarpment Feldkofel in Carinthia.
There are challenges in rock engineering where the exact spatial structure of a fracture system must be established to evaluate the main risks. One of the examples is the assessment of rockfall hazard in the Alps. For a trajectory modelling and the derived design of structural countermeasures a reliable identification of the position-dependent instable blocks in the detaching area is necessary. Practical examples (e.g. Cabernard et al. 2003, Bauer & Neumann 2011, and Liu et al. 2013) show that some of the source areas are cliffs situated directly a few hundred meters above densely populated areas. How can we get the rock structure data in such a cliff? How can we identify the distinctly located removable blocks there? The answers are primarily relating to an integrated implementation of remote sensing technologies to capture surface structure data, on which the real fracture pattern embedding in 3D morphology should be processed.
Figure 1-A shows the stepped limestone escarpment Feldkofel located in Carinthia, Austria. Figure 1-B is a detailed picture of the top middle area, which is located 540 m above the village road and has a dimension of 80 m (L) × 30 m (W) × 50 m (H). At this site frequent rockfall incidents have been reported since many years. We select this site here to illustrate the application of our methods.
Such cliff rockfall prone areas are innumerable in Western Austria, Northern Italy and Switzerland. Our experiences show that to evaluate the rockfall risk the following engineering geological and geomorphological data are indispensable: the orientation, location and 3D spatial area extent of block-forming fractures; the genetic type and surface roughness of the involving fractures for a reasonable estimation of friction; and the location-dependent geometry of natural slope surface.