Spatio‐temporal variations of precipitation are presumed to influence the displacement rate of slow moving deep‐seated landslides by controlling groundwater recharge, pore‐water pressure and shear strength. Phases of landslide acceleration responding to long lasting rainfall and snowmelt events occur under site‐ and event‐specific time delays. Assessing groundwater recharge and simultaneous recording of landslide's displacement in a sufficient spatial and temporal resolution is essential to deepen the understanding of mechanisms controlling a landslide's deformation behaviour and is indispensable when it comes to identifying and developing target‐oriented mitigation strategies. Objective of this study is to asses hydrologic landslide drivers (solid and liquid precipitation, snowmelt and evapotranspiration) and to investigate their spatio‐temporal distribution in the context of movements recorded at the Vögelsberg landslide (Tyrol, Austria). Hydro‐meteorological variables were simulated using the AMUNDSEN (Alpine MUltiscale Numerical Distributed Simulation ENgine) hydro‐climatological model and landslide movements were continuously monitored using an automated tracking total station. Area‐wide simulated time series of available water were used: (i) to separate them into single landslide triggering hydro‐meteorological events, (ii) to analyse spatio‐temporal patterns of water availability per triggering event including individual response times, (iii) to delineate an effective hydrological landslide catchment, and (iv) to identify relations between assessed water input and landslide displacement rate. For the observation period from 05‐2016 until 06‐2019 we identified three distinctive hydro‐meteorological events causing time‐delayed periods of landslide acceleration. Spatio‐temporal differences in water availability per triggering event result in spatially diverse response times varying from 20 to 60 days for rainfall‐triggered events and between 0 and 8 days for events triggered by snowmelt. Pronounced spatio‐temporal differences of snowmelt within the model domain were identified to offer a unique possibility to delineate the effective hydrological landslide catchment. While considering event‐specific time‐lags, logarithmic correlations between incoming water and landslide displacement rate become apparent.

Deep-seated gravitational slope deformations (DSGSDs) endanger settlements and infrastructure in mountain areas all over the world. To prevent disastrous events, their activity needs to be continuously monitored. In this paper, the movement of the Reissenschuh DSGSD in the Schmirn valley (Tyrol, Austria) is quantified based on point clouds acquired with a Riegl VZ^®-6000 long-range laser scanner in 2016 and 2017. Geomorphological features (e.g., block edges, terrain ridges, scarps) travelling on top of the landslide are extracted from the acquired point clouds using morphometric attributes based on locally computed eigenvectors and -values. The corresponding representations of the extracted features in the multi-temporal data are exploited to derive 3D displacement vectors based on a workflow exploiting the iterative closest point (ICP) algorithm. The subsequent analysis reveals spatial patterns of landslide movement with mean displacements in the order of 0.62 ma ${}^{-1}$ , corresponding well with measurements at characteristic points using a differential global navigation satellite system (DGNSS). The results are also compared to those derived from a modified version of the well-known image correlation (IMCORR) method using shaded reliefs of the derived digital terrain models. The applied extended ICP algorithm outperforms the raster-based method particularly in areas with predominantly vertical movement.

Multi-temporal 3D point clouds acquired with a laser scanner can be efficiently used for an area-wide assessment of landslide-induced surface changes. In the present study, displacements of the Vögelsberg landslide (Tyrol, Austria) are assessed based on available data acquired with airborne laser scanning (ALS) in 2013 and data acquired with an unmanned aerial vehicle (UAV) equipped with a laser scanner (ULS) in 2018. Following the data pre-processing steps including registration and ground filtering, buildings are segmented and extracted from the datasets. The roofs, represented as multi-temporal 3D point clouds are then used to derive displacement vectors with a novel matching tool based on the iterative closest point (ICP) algorithm. The resulting mean annual displacements are compared to the results of a geodetic monitoring based on an automatic tracking total station (ATTS) measuring 53 retroreflective prisms across the study area every hour since May 2016. In general, the results are in agreement concerning the mean annual magnitude (ATTS: 6.4 cm within 2.2 years, 2.9 cm a⁻¹; laser scanning data: 13.2 cm within 5.4 years, 2.4 cm a⁻¹) and direction of the derived displacements. The analysis of the laser scanning data proved suitable for deriving long-term landslide displacements and can provide additional information about the deformation of single roofs.

Perennial ice deposits in caves are an underexplored component of the cryosphere preserving a largely untapped archive of long‐term changes in landscape and climate whose existence is threatened by climate change. This study demonstrates how terrestrial laser scanning (TLS) can be used to fully and accurately (registration accuracy < 1 cm standard deviation of point differences) assess the geometry of an ice‐bearing cave in the Eastern Alps (Tyrol, Austria). Three TLS campaigns and 255 scan positions were used to acquire point clouds with a high sampling density (2 cm average point spacing) in order to minimise shading effects and to assure a precise and highly resolved 3D documentation of the cave. A semi‐automated registration and point cloud‐processing approach adapted to the site‐specific demands ensured a complete and error‐minimised assessment of the cave's geometry serving as a solid basis for future quantifications of snow and ice content dynamics. Dominant cave surface structures were investigated by performing a multiscale principal component analysis (PCA) to identify a detailed and computationally efficient basis for future airflow modelling tasks.

Slow‐moving deep‐seated landslides are characterised by continuous deformation, constantly changing topography and sliding‐mass geometry. Deformation rates are predominantly controlled by temporal dynamics of pore pressure. Progressing movements typically cause an over‐steepening of a landslide's foot, making these areas more susceptible to secondary slope failures and piggyback slides that, once they occur, change the geometric boundary conditions of a slope. This study presents an integrated topographic monitoring and geomechanical modelling approach, which is suitable for both model‐based replication of the landslide's hydro‐meteorological drivers and assessment of the long‐term effect of topographic changes on the stability behaviour of a large deep‐seated landslide. Parametrised at the Vögelsberg landslide (Tyrol, Austria) the integrated approach quantified considerable mass relocations between 2007 and 2020 at the landslide's foot and assessed respective effects on slope stability. Additionally, scenarios of past and future topographies were reconstructed and projected. Mass relocations of the order of 25 000 m³ were assessed between multiple airborne laser scanning acquisitions covering a period of 13 years. Based on annual uncrewed aerial vehicle laser scanning campaigns, area‐wide 3D displacements were analysed, exceeding a magnitude of 200 cm a⁻¹ at small parts (2.500 m²) on the steeper foot of the active landslide. The main landslide body (0.28 km²) moves considerably slower with movements of 2–10 cm a⁻¹. Besides spatio‐temporally varying hydrological drivers, topographic changes can have a severe impact on slope stability and therefore modify the spatiotemporal activity of the landslide. It is shown that, besides the hydrometeorological drivers, the varying elevation of the landslide's toe is a key parameter determining the long‐term trend of slope stability. With the presented approach the formation and evolution of the Vögelsberg landslide can be understood and explained.