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.

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%).

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.

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.