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Abstract. Landslides endanger settlements and infrastructure in mountain areas across the world. Monitoring of landslides is therefore essential in order to understand and possibly predict their behavior and potential danger. Terrestrial laser scanning has proven to be a successful tool in the assessment of changes on landslide surfaces due to its high resolution and accuracy. However, it is necessary to classify the 3D point clouds into vegetation and bare-earth points using filtering algorithms so that changes caused by landslide activity can be quantified. For this study, three classification algorithms are compared on an exemplary landslide study site in the Oetz valley in Tyrol, Austria. An optimal set of parameters is derived for each algorithm and their performances are evaluated using different metrics. The volume changes on the study site between the years 2017 and 2019 are compared after the application of each algorithm. The results show that (i) the tested filter techniques perform differently, (ii) their performance depends on their parameterization and (iii) the best-performing parameterization found over the vegetated test area will yield misclassifications on non-vegetated rough terrain. In particular, if only small changes have occurred the choice of the filtering technique and its parameterization play an important role in estimating volume changes.

Understanding the distribution and characterization of natural and non-natural materials on the surface and near-subsurface is important for the development of infrastructure projects. This may be a challenge in highly urbanized areas, where outcrops are scarce, and anthropogenic activities have altered the morphological expression of the landscape. This study tests the integration of ground-penetrating radar (GPR), borehole drilling, aerial imagery, geological mapping, and aerial laser scanning as complementary mapping tools for determining the stratigraphy of glacial and post-glacial Quaternary sediments, the depth to the bedrock, and the distribution of anthropogenic material in Mosvatnet, a lake in Stavanger, Norway. The integration proved to be efficient and enabled the generation of a 3D holistic model, which provided a broad understanding of the subsurface geology and the induced anthropogenic changes in the area through time. Bedrock, till, fluvioglacial, and lacustrine geological units were modeled. Accumulations of post-glacial organic matter were mapped, and the distribution of non-natural infill material was determined. The interpreted dataset suggests that the eastern shoreline of Mosvatnet has artificially prograded about one hundred meters westward since the 1930s and the elevation of the corresponding area has increased by about ten meters relative to the lake level.

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