Continuous and slow-moving deep-seated landslides entail challenges for the effective planning of mitigation strategies aiming at the reduction of landslide movements. Given that the activity of most of these landslides is governed by pore pressure variations within the shear zone, profound knowledge about their hydrogeological control is required. In this context, the present study presents a new approach for the spatial assessment of probable recharge areas to better understand a slope's hydrogeological system. The highly automated geo-statistical approach derives recharge probability maps of groundwater based on stable isotope monitoring and a digital elevation model (DEM). By monitoring stable isotopes in both groundwater and precipitation, mean elevations of recharge areas can be determined and further constrained in space with the help of the DEM. The approach was applied to the Vögelsberg landslide, an active slab of a deep-seated gravitational slope deformation (DSGSD) in the Watten valley (Tyrol, Austria). Resulting recharge probability maps indicate that shallow groundwater emerging at springs on the landslide recharges between 1000 and 1650 a.s.l. In contrast, groundwater encountered in wells up to 49ĝ€¯m below the landslide's surface indicates a mean recharge elevation of up to 2200 a.s.l. matching the highest parts of the catchment. Further inferred proxies, including flow path length, estimated recharge area sizes, and mean transit times of groundwater, resulted in a profound understanding of the hydrogeological driver of the landslide. It is shown that the new approach can provide valuable insights into the spatial pattern of probable recharge areas where mitigation measures aiming at reducing groundwater recharge could be most effective.

The 3rd edition of the international summer school "Close-range Sensing Techniques in Alpine terrain"took place in Obergurgl, Austria, in June 2019. This article reports on results from the training and seminar activities and the outcome of student questionnaire survey. Comparison between the recent edition and the past edition in 2017 shows no significant differences on the level of satisfaction on organizational and training aspects. Gender balance was present both in candidates and in the outcome of selections. Selection was based on past research activities and on topic relevance. The majority of trainees were therefore doctoral candidates and postdoctoral researchers, but also motivated master students participated. The training took place through keynotes, lectures, seminars, in the field with hands-on surveys followed by data analysis in the lab, and teamwork for preparing a final team presentation over different assignments.

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.

The 2nd international summer school "Close-range sensing techniques in Alpine terrain" was held in July 2017 in Obergurgl, Austria. Participants were trained in selected close-range sensing methods, such as photogrammetry, laser scanning and thermography. The program included keynotes, lectures and hands-on assignments combining field project planning, data acquisition, processing, quality assessment and interpretation. Close-range sensing was applied for different research questions of environmental monitoring in high mountain environments, such as geomorphologic process quantification, natural hazard management and vegetation mapping. The participants completed an online questionnaire evaluating the summer school, its content and organisation, which helps to improve future summer schools.