Permanent geoelectrical monitoring, using the GEOMON 4D 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.techniques, long-term continuous monitoring of deformation and triggering factors and by establishing early-warning systems/centres. The most commonly used early-warning parameters are pore pressure and displacement. However, recent research has shown that other parameters exist, which might give indications of impending triggering before an actual displacement is measurable.The geoelectrical method (direct current DC) has recently been established as a routine geophysical method to investigate subsurface geometry and structural pattern of landslides in Europe (Mauritsch et al.
Abstract: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\ud the feasibility of Differential SAR Interferometric (DInSAR) applications for landslideaffected\ud slopes. For this purpose, the range index RI, introduced for the determination of\ud areas in layover and foreshortening on both ascending and descending acquisition\ud geometries, is computed and applied to the most diffuse X-C-L band SAR sensors. A new\ud method is introduced to improve the accuracy of those products by fusing CORINE data with\ud sharper European JRC forest map and Imperviousness Copernicus map. The results are\ud tested with six different available PSI datasets over Austria. Then, a priori visibility map and\ud a PSI density map are also derived for seven different satellites by combining the RI index\ud and an enhanced CORINE land cover map. Finally, PSI velocity values, along the Line of\ud Sight (VLos) and projected along the steepest slope direction (VSlope), are used in order to\ud produce a landslide velocity map for the Austrian region of Vorarlberg
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.
<p>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.</p>
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