| Summary: | Rockfall both is a major process in shaping steep topography and a hazard in mountainous regions. Besides increasing thread due to thawing permafrost-stabilization in high-elevation areas, there are abundant permafrost-free over-steepened rockwalls releasing rockfall due to other triggers. General rockfall event susceptibility is addressed to frost cracking, earthquake shacking and hydrologic pressure in the walls, and to geotechnical rock properties. Spatial rockwall surface surveys or scans (delivering 3D point clouds) have been used to both deduce rock fracture patterns and to measure individual rockfall events from comparing subsequent scans. Though, the actually measured rockwall topography data has rarely been used as a general predictor of rockfall susceptibility against the background of observed events. In this study, we use a series of dm-resolved annual (2014 to 2020) terrestrial laser scan surveys along 5km2 of limestone cliffs in the Lauterbrunnen Valley, Switzerland. The annual scan data were hand-cut to remove vegetation and fringes, and then referenced to detect subsequent topographic change in the direction of the wall. From the change-detection point clouds individual rockfall event volumes were detected from cluster and filtering analyses. One surveyed rockwall section of 2014 was used as training data for our Bayesian classification model of rockfall susceptibility, while the adjacent remaining section served for model validation. We rasterized their 3D data points and calculated several surface parameters per cell, including roughness, topography, mean distances for the three main fracture systems, fracture density, local dip, percent of overhang area, normal vector change rate (called edge) and percentage of overhang area. For various parameter sets and different cell sizes (32m2, 52m2, 102m2, 152m2, 252m2, and 402m2), we trained Naïve-Bayes-Classifier models. These were then used to predict rockfall susceptibility per cell, based on our observations of surface parameters, and assessed ...
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