Abstract. The quantification of soil bulk density (ρB) is a cumbersome and time-consuming task when traditional soil density sampling techniques are applied. However, it can be important for terrestrial cosmogenic nuclide (TCN) production rate scaling when deriving ages or surface process rates from buried samples, in particular when short-lived TCNs such as in situ 14C are applied. Here, we show that soil density determinations can be made using structure-from-motion multi-view stereo (SfM-MVS) photogrammetry-based volume reconstructions of sampling pits. Accuracy and precision tests as found in the literature and as conducted in this study clearly indicate that photographs taken from both a consumer-grade digital single-lens mirrorless (DSLM) and a smartphone camera are of sufficient quality to produce accurate and precise modelling results, i.e. to regularly reproduce the “true” volume and/or density by >95 %. This finding holds also if a freeware-based computing workflow is applied. The technique has been used to measure ρB along three small-scale (<1 km) N–S transects located in the semi-arid to arid Altos de Talinay, northern central Chile (∼30.5∘ S, ∼71.7∘ W), during a TCN sampling campaign. Here, long-term differences in microclimatic conditions between south-facing and north-facing slopes (SFSs and NFSs, respectively) explain a sharp contrast in vegetation cover, slope gradient and general soil condition patterns. These contrasts are also reflected by the soil density data, generally coinciding with lower densities on SFSs. The largest differences between NFSs and SFSs are evident in the lower portion of the respective slopes, close to the thalwegs. In general, field-state soil bulk densities were found to vary by about 0.6 g cm−3 over a few tens of metres along the same slope. As such, the dataset that was mainly generated to derive more accurate TCN-based process rates and ages can be used to characterise the present-day condition of soils in the study area, which in turn can give insight into the long-term soil formation and prevailing environmental conditions. This implies that the method tested in this study may also being applied in other fields of research and work, such as soil science, agriculture or the construction sector.

&lt;p&gt;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.&lt;/p&gt;&lt;p&gt;In this study, we use a series of dm-resolved annual (2014 to 2020) terrestrial laser scan surveys along 5km&lt;sup&gt;2&lt;/sup&gt; 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 (3&lt;sup&gt;2&lt;/sup&gt;m&lt;sup&gt;2&lt;/sup&gt;, 5&lt;sup&gt;2&lt;/sup&gt;m&lt;sup&gt;2&lt;/sup&gt;, 10&lt;sup&gt;2&lt;/sup&gt;m&lt;sup&gt;2&lt;/sup&gt;, 15&lt;sup&gt;2&lt;/sup&gt;m&lt;sup&gt;2&lt;/sup&gt;, 25&lt;sup&gt;2&lt;/sup&gt;m&lt;sup&gt;2&lt;/sup&gt;, and 40&lt;sup&gt;2&lt;/sup&gt;m&lt;sup&gt;2&lt;/sup&gt;), we trained Na&amp;#239;ve-Bayes-Classifier models. These were then used to predict rockfall susceptibility per cell, based on our observations of surface parameters, and assessed using Kullback-Leibler Divergence analysis and the misclassification cost score.&lt;/p&gt;&lt;p&gt;Results indicate the overall best model (accounting for the parameters roughness, edge, topography and overhang area) and for the lowest cell size (3&lt;sup&gt;2&lt;/sup&gt;m&lt;sup&gt;2&lt;/sup&gt;) could predict rockfall cells with a probability of 0.73 (against a mean of 0.3 for all cells). Predictions on another rockwall section with observed rockfall, located on the opposite side of the valley, verified the model&amp;#8217;s applicability by both comparable probabilities (0.6 vs 0.25) and visual surveys on overhangs. We find our approach could reliably extend this spatial rockfall susceptibility classification to all Lauterbrunnen rockwalls. The classification model generally identified overhang areas and fractured zones as high rockfall risks, matching the general insight of these zones to be of major susceptibility. Interestingly, our method is based only on orientation-independent variables that are directly calculated from the 3D point cloud. Thus, it should be principally transferable to other sites of fractured limestone walls. Specifically, there is no need to determine fracture sets from the point cloud as is generally done for susceptibility studies, since we account for topography that would anyway be used to calculate fracture planes (facets). Hence, this method provides a simple means to predict spatial rockfall susceptibility, applicable for both hazard mapping and landscape evolution studies.&lt;/p&gt;

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Abstract. The quantification of soil bulk density (ρB) is a cumbersome and time-consuming task when traditional soil density sampling techniques are applied. However, it can be important for terrestrial cosmogenic nuclide (TCN) production rate scaling when deriving ages or surface process rates from buried samples, in particular when short-lived TCN such as in situ 14C are applied. Here we show that soil density determinations can be made using structure-from-motion multi-view stereo (SfM-MVS) photogrammetry-based volume reconstructions of sampling pits. Accuracy and precision tests as found in the literature and as conducted in this study clearly indicate that photographs taken from both a consumer-grade digital single lens mirrorless (DSLM) and a smartphone camera are of sufficient quality to produce accurate and precise modelling results, i.e. to regularly reproduce the true volume and/or density by > 95 %. This finding holds also if a freeware-based computing workflow is applied. The technique has been used to measure ρB along three small-scale (