Investigation of Riverbank Peat Erosion in a small Alpine catchment by Multi-Temporal Point Cloud Change Analysis and Time Series Clustering

We performed an analysis on semi-automatically mapped shallow landslide scarps and forest cover in the Sellrain valley, Tyrol (Austria), to investigate how the morphology and topographic profiles of landslides are affected by the forest. The results show that landslides located in dense forest cover occurred on steeper slopes and were deeper than others. The results also show that the use of forest stand density parameters, such as tree spacing, enhanced the found differences in the study area.

This research introduces methods and tools for obtaining soil input data in European case studies for environmental models like SWAT+. With various available soil datasets and prediction methods, determining the most suitable is challenging. The study aims to (i) catalogue open-access datasets and prediction methods for Europe, (ii) demonstrate and quantify differences between prediction approaches, and (iii) offer a comprehensive workflow with open-source R codes for deriving missing soil data.

The activity of slow-moving deep-seated landslides is commonly governed by pore pressure variations within the shear zone. Groundwater recharge as a consequence of precipitation therefore is a process regulating the activity of landslides. In this context, we present a highly automated geo-statistical approach to spatially assess groundwater recharge controlling the velocity of a deep-seated landslide in Tyrol, Austria.