The European Ground Motion Service (EGMS) is part of the Copernicus Land Monitoring Service (CLMS) managed by the EEA (European Environment Agency) [1]. EGMS is based on the full resolution InSAR processing of ESA Sentinel-1 (S1) acquisitions over Europe (Copernicus Participating states) [2]. The first release or baseline includes ground motion time-series between 2015 and 2020. This open data service has yearly updates.The EGMS employs persistent scatterer (PS) and distributed scatterer (DS) in combination with a Global Navigation Satellite System (GNSS) model to calibrate the ground motion products. This public dataset consists of three products levels (L2a/Basic, L2b/Calibrated and L3/Ortho). The Basic and Calibrated product levels are full resolution (20x5m) Line of sight (Los) velocity maps from ascending/descending orbits. The Ortho product offers horizontal (East-West) and vertical (Up-Down) anchored to the reference geodetic model resampled at 100x100m.The objective of this paper is to describe the independent validation of this continental scale ground motion time-series dataset. The goal is to ensure that the EGMS products are consistent with user requirements and product specifications and they cover the expected range of applications. To evaluate the fitness of the EGMS ground motion data service 7 reproducible validation activities (VA) have been developed gathering validation data from 50 sites across 12 European countries. Last but not least, a software environment has been developed to carry out all the validation activities and describe/store the data coming from the validation sites.

The advent of the EGMS service offers chances and opportunities to EU Member States practitioners and researchers into the field of landslide monitoring. As member of the EGMS validation team, under the lead of SIXENSE, the Geosphere Austria carried out the in situ validation activity for five test sites spread over Europe. The focus of this paper is the inter-comparison of an automatic geodetic monitoring system installed at two landslide locations in Tyrol, Austria against the main products offered by the EGMS:level 2a level 2b level 3 The comparison was performed in a Jupiter hub environment created ad hoc for the validation project by our partner Terrasigna. The workflow was developed in R language and validates error, precision and accuracy of the (in-situ) velocities and time series (TS) against the correspondent MT-InSAR values of the EGMS.The workflow is made of several highly customisable modules:reads and visualises the two datasets; performs a series of analysis such as smoothing (simplification), outliers search and trends for both time series; inter-compares all the combinations of derived TS datasets and calculates for each couple RMSE, Coefficient of Determination (R2) and index of agreement; plots the TS and bar diagrams of the best scores in terms of minimum errors, maximum accuracy and maximum precision; delivers a Quality Index (QI) between 0-1 for each EGMS product; The results of the in-situ validation activity will be presented and explained. In fact, considering the type of natural hazard (deep-seated gravitational slope deformation) and his location (vegetated and high relief alpine morphology), this validation set the perfect example to discuss strength and weakness of the EGMS if compared to state-of-the art in-situ monitoring systems installed in such extreme and remote areas.

This paper focuses on the study of the Vögelsberg landslide located in the municipality of Wattens (Tyrol, Austria), which reactivated in 2016, causing damages to nearby buildings and infrastructures. Since the date of reactivation, a modern monitoring system has been implemented with the installation of in-situ geodetic automated tracking total stations (ATTS), an inclinometer and two piezometers. Here, we describe two distinctive methods, the Breaks for Additive Seasonal and Trend (BFAST) and the Vector Inclination Method (VIM) used to characterize the landslide from the kinematic and geometrical point of view. The main input data, used for both methods, derive from processing a stack of several Sentinel-1 differential interferograms with the Multiple Small Baseline Subset (MSBAS) 2D and 3D algorithms. BFAST allowed highlighting the seasonality of the phenomenon from the analysis of the time series as well as the trend and the breakpoints that identify the landslide reactivation phases. These latter were then correlated with the main triggering factors such as rain and snow melting. The application of the VIM through the exploitation of the MSBAS displacement vectors allowed the reconstruction of the depth of the landslide slip surface along both the longitudinal and transversal direction and, in turn, the evaluation of the volumes of material mobilized by the landslide. The results obtained further prove that procedures for the in-depth analysis of Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) data can contribute to slow-moving landslide characterization, which represents a fundamental step for landslide hazard assessment within quantitative risk analyses.

In August 2005, numerous shallow landslides occurred in the region of Vorarlberg (Austria), particularly induced by unfavourable event-related weather conditions. Two scenes of TERRA-ASTER sensor were used for the identification of the vegetation change induced by the landslides. The focus of this study is the establishment of a reliable method, comparable to aerialphoto visual interpretation standards, able to identify accurately landslides by processing a series of medium-resolution remote sensing optical data, before and after a catastrophic event. A very intuitive workflow for a semi-automatic image classification for the detection of landslide-induced change on the image data is proposed. The accuracy and validation assessment was carried out by means of a landslide (aerial-photos derived) inventory. By taking into account the central area of investigation, the landslide detection method, which adopted an innovative double classification workflow (a first supervised followed by an unsupervised algorithm), delivered a very high producer accuracy (81.5%) coupled to a more-than-acceptable user accuracy (68.9%) and kappa coefficient (72.9%).

In August 2005, numerous shallow landslides occurred in the region of Vorarlberg (Austria), particularly induced by unfavourable event-related weather conditions. Two scenes of TERRA-ASTER sensor were used for the identification of the vegetation change induced by the landslides. The focus of this study is the establishment of a reliable method, comparable to aerial-photo visual interpretation standards, able to identify accurately landslides by processing a series of medium-resolution remote sensing optical data, before and after a catastrophic event. A very intuitive workflow for a semi-automatic image classification for the detection of landslide-induced change on the image data is proposed. The accuracy and validation assessment was carried out by means of a landslide (aerial-photos derived) inventory. By taking into account the central area of investigation, the landslide detection method, which adopted an innovative double classification workflow (a first supervised followed by an unsupervised algorithm), delivered a very high producer accuracy (81.5%) coupled to a more-than-acceptable user accuracy (68.9%) and kappa coefficient (72.9%).

Continuous INSAR-monitoring of slow mass movements in the surrounding of fast (m/year) or acute processes can deliver important data complementing geomorphologic information in order to understand the broader dynamic context in which a landslide is situated. In course of the Landslide-EVO project (NERC/SHEAR funded), focusing on flood and landside risk assessment and mitigation in the Karnali river basin region in Far Western Nepal by inclusion of local community, this has been evaluated within a test of integrated monitoring methods (comprising eg. ERT, UAV-photogrammetry, D-GPS/geodesy, microseismics, soil water saturation, rainfall, and other) on regional as well as local scale at two selected sites at Bajura and Sunkoda. It was possible to derive extended information about movements in a ROI covering 120 km by 120 km. The PSI/SBAS based velocity analysis exhibits density variations due to specific slope/sensor system geometry, vegetation, data gaps, atmospheric conditions, and high velocities in the most active sites, which causes decorrelation. However, in the less active surrounding of active landslides the velocity information shows generally higher density. INSAR techniques could well complement optical image analysis in the low velocity range of centimetres to several decimetres per year, generally too slow for optical satellite image analysis in this time scale. InSAR-data has the potential to be used for estimating a slow moving masses acceleration or a deep-seated gravitational slope deformations cumulative displacement leading to a partial or total reactivation before other indication appears. It has been shown that large and difficult accessible areas can be monitored with InSAR techniques, while specific sites are equipped with corner reflectors for better signal. The study represents the first of this kind in the region and proves the ability of INSAR techniques for retrieving critical information about mass movements affecting local communities in the Karnali river basin as an example of a developing region.

In the year 2019, at Kartais (Hüttschlag, Austria) parts of an approximately 100 m high and fractured rock wall mainly composed of calcareous-mica-schists became unstable and collapsed two times. The first failure event was a wedge failure and occurred on the 25th of March 2019 and released about 3.000 m3 of rock material. Blocks with a maximum volume of about 100 m3 were falling, bouncing and sliding to the valley bottom, but did not reach the Großarl River and the local infrastructure (road, bicycle track and houses). The second failure event happened on the 15th of July 2019 involving a volume of about 5.000 m3 with a maximum block size of 200 m³. This event had a longer runout but also did not reach the infrastructure. A Helicopter-based observation by the Geological Survey of Salzburg has shown that new cracks at the top of the failure area have already opened to apertures in the scale of decimetres to metres. It is assumed that the newly formed potential failure mass could reach 10.000 m³ and thus is even larger than the two previous events. In order to study the deformation behaviour of the rock face a multi-methodical observation and monitoring campaign has been initiated recently. A UAV-photogrammetry survey has shown that the foliation of the calcareous-mica-schist is dipping moderately into the slope and the rock wall is dissected by at least 4 different joint sets, whereas two of them intersect to form wedge failures. Since November 2019 a GBInSAR system (LisaLab) is continuously monitoring the slope. Additionally, multi-temporal terrestrial laserscanning (TLS) surveys and satellite based InSAR analysis were performed. In this contribution, the set-up of the investigation and monitoring campaign as well as some preliminary results will be presented.

Displacement rates of mountain slope deformations that can affect entire valley mountain flanks are often measured spatially distributed in‐situ without spatial significance. The spatially explicit measurement and recording of time series of slope deformations is a challenge, as the unstable slopes are often disintegrated into several subdomains, which move with different deformation rates. The current state‐of‐the‐art monitoring systems detect slow to very slow deformation rates between mm/a and several m/a. Using the examples of slope deformations in Saalbach‐Hinterglemm and the deep rock slide Marzellkamm in Austria this paper presents the results of terrestrial laser scans, extensometer measurements, Spaceborne InSAR data, unmanned Aerial System Photogrammetry (UAS‐P), and fixed‐point measurements. The different measurements complement each other and are optimally aligned for different application areas. InSAR data can help to identify hot spots on regional and local scale, while UAS‐P enables for spatially high level accuracy in the detection of subdomains moving at different speeds. For local warning systems TLS, extensometers and GBInSAR deliver higher accuracy.

(1) Interfaculty Department of Geoinformatics Z_GIS, University of Salzburg, Salzburg, Austria (florian.albrecht@sbg.ac.at; elisabeth.weinke@sbg.ac.at; daniel.hoelbling@sbg.ac.at; barbara.friedl@sbg.ac.at), (2) GRID-IT Gesellschaft für angewandte Geoinformatik mbH, Innsbruck, Austria (eisank@grid-it.at), (3) Geologische Bundesanstalt (GBA), Vienna, Austria (filippo.vecchiotti@geologie.ac.at; arben.kociu@geologie.ac.at)

Object-based image analysis (OBIA) has been increasingly used to map geohazards such as landslides on optical satellite images. OBIA shows various advantages over traditional image analysis methods due to its potential for considering various properties of segmentation-derived image objects (spectral, spatial, contextual, and textural) for classification. For accurately identifying and mapping landslides, however, visual image interpretation is still the most widely used method. The major question therefore is if semi-automated methods such as OBIA can achieve results of comparable quality in contrast to visual image interpretation. In this paper we apply OBIA for detecting and delineating landslides in five selected study areas in Austria and Italy using optical Earth Observation (EO) data from different sensors (Landsat 7, SPOT-5, WorldView-2/3, and Sentinel-2) and compare the OBIA mapping results to outcomes from visual image interpretation. A detailed evaluation of the mapping results per study area and sensor is performed by a number of spatial accuracy metrics, and the advantages and disadvantages of the two approaches for landslide mapping on optical EO data are discussed. The analyses show that both methods produce similar results, whereby the achieved accuracy values vary between the study areas.

This paper proposes a multi-sensor a priori PSI visibility map for Austria in order to evaluate the feasibility of Differential SAR Interferometric (DInSAR) applications for landslide-affected slopes. For this purpose, the range index RI, introduced for the determination of areas in layover and foreshortening on both ascending and descending acquisition geometries, is computed and applied to the most diffuse X-C-L band SAR sensors. A new method is introduced to improve the accuracy of those products by fusing CORINE data with sharper European JRC forest map and Imperviousness Copernicus map. The results are tested with six different available PSI datasets over Austria. Then, a priori visibility map and a PSI density map are also derived for seven different satellites by combining the RI index and an enhanced CORINE land cover map. Finally, PSI velocity values, along the Line of Sight (V-Los) and projected along the steepest slope direction (V-Slope), are used in order to produce a landslide velocity map for the Austrian region of Vorarlberg.

National and regional authorities and infrastructure maintainers in mountainous regions require accurate knowledge of the location and spatial extent of landslides for hazard and risk management. Information on landslides is often collected by a combination of ground surveying and manual image interpretation following landslide triggering events. However, the high workload and limited time for data acquisition result in a trade-off between completeness, accuracy and detail. Remote sensing data offers great potential for mapping and monitoring landslides in a fast and efficient manner. While facing an increased availability of high-quality Earth Observation (EO) data and new computational methods, there is still a lack in science-policy interaction and in providing innovative tools and methods that can easily be used by stakeholders and users to support their daily work.

National and regional authorities and infrastructure maintainers in mountainous regions require accurate knowledge of the location and spatial extent of landslides for hazard and risk management. Information on landslides is often collected by a combination of ground surveying and manual image interpretation following landslide triggering events. However, the high workload and limited time for data acquisition result in a trade-off between completeness, accuracy and detail. Remote sensing data offers great potential for mapping and monitoring landslides in a fast and efficient manner. While facing an increased availability of high-quality Earth Observation (EO) data and new computational methods, there is still a lack in science-policy interaction and in providing innovative tools and methods that can easily be used by stakeholders and users to support their daily work.

In the presented paper the technical and user-centric analysis steps for the development of a web-based landslide mapping service are discussed. The generation of the service is based on remote sensing and open-source geodata. The service will support various stakeholders to monitor landslides, to update and publish landslide information, as well as to analyze affected infrastructures. For the service development, an iterative and incremental approach of agile software development is used. With the agile approach, the users are directly involved in all software development phases. Furthermore, the software is developed in single components and its enhancement is incrementally performed.

ABSTRACTPermanent geoelectrical monitoring, using the GEOMON4D instrumentation in combination with high resolution displacement monitoring by means of the D.M.S. system, was performed at two active landslide areas: Ampflwang/Hausruck in Austria, and Bagnaschino in Italy. These sites are part of the Austrian geoelectrical monitoring network, which currently comprises six permanently monitored landslides in Europe. Within the observation intervals, several displacement events, triggered by intense precipitation, were monitored and analysed. All of these events were preceded by a decrease of electric resistivity. The application of an innovative 4D inversion algorithm made it possible to investigate the potential processes which led to the triggering of these events. We conclude that resistivity monitoring can significantly help in the investigation of the causes of landslide reactivation. Since the results also contribute to the extrapolation of local displacement monitoring data to a larger scale, resistivity monitoring can definitely support decision‐finding in emergencies.

The main focus of this paper it ́s to elaborate a cost effective strategy for SAR images processing in the aim of assess the Austrian mass movement cadastre through the use of open source software. A new method, aimed at verify the best compromise between statistical probability to detect movements on the ground, temporal and geometric decorrelation, attenuation of the electromagnetic wave by the atmosphere for DINSAR products, is proposed. A comparison of DINSAR displacement maps with GEORIOS (georisken Österreich) cadastre, geological, geomorphological and geotechnical available data were carried out. The ability to detect very slow deformation phenomena was positively assessed. However the possibility to associate precursory vertical displacements to on-going slope degradation processes requires a more detailed advanced DINSAR investigation.