PD Dr. Andrea Manconi

Landslides are the downward and outward movement of slope-forming (geological) materials under the influence of gravity. These mass movements are observed on Earth and other planetary bodies like Mercury, Venus, and Mars, and small bodies like the Moon, dwarf planet Ceres, and asteroid Vesta. A map showing the spatial distribution of landslides (also called a landslide inventory) is typically created to understand the landform evolution, preconditioning factors, and associated hazards. However organizing a field mapping campaign for generating an inventory is a slow process and might even be difficult or impossible due to the associated logistical challenges. This has encouraged the use of aerial photographs and satellite images (mid to high resolution) as the preferred data source for landslide mapping. This approach was further supported by an increasing number of remote sensing satellite launches, which has exponentially increased the availability of suitable optical and synthetic aperture radar (SAR) images. Recent satellites like the European Space Agency Copernicus Sentinel-1 and -2 provide access to systematically acquired Earth observation free of cost to the users. Large commercial constellations, like that of PlanetScope’s satellites, can image the entire land surface of the Earth every day at approximately 3 m spatial resolution. Similarly, ICEYE and Capella satellite constellation have plans to provide X-band SAR images acquired at a much higher temporal resolution allowing for multiple acquisitions in one day. Data acquired by the increasing number of lunar and Martian orbiters like NASA’s Lunar and Martian Reconnaissance Orbiters (LRO and MRO) have enabled detailed landslide mapping efforts focused on our planetary neighbors.

Rapid detection of landslides after an exceptional event is critical for planning effective disaster management. Previous works have typically used machine learning-based methods, including the recently popular deep-learning approaches, to identify characteristics surface features from satellite remote sensing data, especially from optical images. However, data acquisition from optical images is not possible in cloudy conditions, leading to unpredictable delays in any mapping task from future events. These methods also rely on large manually labelled inventories for training, which is often not available before the event. In this work, we propose an active training strategy to generate a landslide map after an event using the first available synthetic-aperture radar (SAR) image and improve it once subsequent cloud-free optical images are acquired. The proposed active learning workflow can start with a small (∼100m²) and incomplete inventory,- and can grow the extent and completeness in iterative steps with manual updates after each step. This significantly reduces the slow manual mapping typically required for generating a large training inventory. We designed our experiments to map the landslides triggered by the M_w 6.6 Hokkaido Eastern Iburi earthquake of 2018 in Japan using sequentially ALOS-2 (SAR) and PlanetScope (Optical) scenes in the order they are acquired. The choice of active learning prioritizes speed over accuracy. However, we note only a modest reduction in performance (∼10% drop in F1 and MCC scores), with our method allowing a preliminary landslide inventory to be completed within a single day. This is of major importance in disaster response, improving performance and reducing the potential subjectivity associated with manual mapping.

We demonstrate how high spatial and temporal resolution spaceborne synthetic aperture radar (SAR) imagery can improve slope deformation monitoring. We process ICEYE data over the Brienz/Brinzauls slope instability in the Swiss Alps, where a catastrophic failure occurred on 15 June 2023. Image correlation applied to retrieve surface velocities shows results comparable to in situ measurements. Pre- and post-failure acquisitions used to map areas invaded by debris and to compute associated volumetric changes are in good agreement with photogrammetric data. This study sets the baseline for new-generation satellite SAR data aimed at providing timely information in landslide early warning scenarios.

We performed an extensive analysis of C-band SAR datasets provided by the European Space Agency (ESA) satellites ERS-1/2, Envisat ASAR, and Sentinel-1 in the period 1992–2020 aiming at reconstructing the multi-decadal spatial and temporal evolution of the surface displacements at the Brienz/Brinzauls landslide complex, located in canton Graubünden (Switzerland). To this end, we analyzed about 1000 SAR images by applying differential interferometry (InSAR), multitemporal stacking, and persistent scatterer interferometry (PSI) approaches. Moreover, we jointly considered digital image correlation (DIC) on high-resolution multi-temporal digital terrain models (DTM) generated from airborne surveys and InSAR results to compute 3-D surface deformation fields. The extensive network of GNSS stations across the Brienz landslide complex allowed us to extensively validate the deformation results obtained in our remote sensing analyses. Here, we illustrate the limitations occurring when relying on InSAR and/or PSI measurements for the analysis and interpretation of complex landslide scenarios, especially in cases of relevant spatial and temporal heterogeneities of the deformation field. The joint use of InSAR and DIC can deliver a better picture of the evolution of the deformation field, however, not for all displacement components. Since InSAR, PSI and DIC measurements are nowadays routinely used in the framework of local investigations, as well as in regional, national, and/or continental monitoring programs, our results are of major importance for users aiming at a comprehensive understanding of these datasets in landslide scenarios.

Flow transitions from rock avalanches to multi-material flows and water saturated debris flows are frequently observed. In 2017, a rock mass of Piz Cengalo (CH) collapsed on a glacier triggering a rock/ice avalanche that resulted into a series of debris flows. At Flüela Wisshorn (CH), in 2019, a rock face collapsed in winter, resulting in a snow avalanche. These transitions depend on the composition of the release and entrainment zones, and on the terrain as ice and snow can melt via frictional heating, increasing the flow water content.
Here, we compute the water content and localisation for the Piz Cengalo event depending on the input parameters. We show that water localizes at the front of the flow, favoring a transition to a debris flow. At Flüela Wisshorn, we show the transition from a rock/snow release to a snow avalanche. We highlight the importance of field and remote sensing data for models, as entrainment is critical for the simulations' outcome and hazard assessment.

In alpinen Gebieten sind Rutschungen weit verbreitet und setzen Menschen und Infrastrukturen einer potenziellen Gefährdung durch katastrophale Ereignisse aus (z. B. Brienz/Brinzauls). Die Überwachung von Oberflächenbewegungen mit an Hängen installierten Sensoren oder mit Fernerkundungsdaten ist eine der wirksamsten Methoden, um deren Entwicklung zu verstehen. Allerdings sind diese Informationen nur für ein begrenztes Zeitfenster verfügbar. Die Struktur der Jahrringe wurde untersucht, um Anzeichen von Hanginstabilität und Rutschungsbewegungen zu erkennen. Wir haben uns dabei auf das Seehorn in Davos Dorf konzentriert, um zu verstehen, ob dem Felssturz im Jahr 2020 Veränderungen im Baumwachstum vorausgegangen sind und ob solche Veränderungen mit tiefgreifenden Verformungen am gesamten Hang in Verbindung gebracht werden können.

Extreme rainfall intensities, degrading permafrost and glacier melt in combination with ongoing construction of hydropower plants and settlement growth are leading to an everincreasing amount of mass movements in alpine terrain. Geologically young mountain ranges such as the Indian Himalaya are experiencing severe consequences due to the combination of climatic changes and anthropogenic expansion, leading to higher risk for human lives and infrastructures. We present an approach to generate hazard indication maps of mass movements in high alpine areas using a combination of remote sensing and numerical simulations. We use satellite radar interferometry from Sentinel-1 and geomorphological feature classification based on optical satellite data. We show two exemplary case studies and model one of these to assess its hazard potential using the newly developed RAMMS::ROCKICE module.

Die neuesten Fortschritte in der Satellitengeodäsie haben unsere Möglichkeiten zur Kartierung und Überwachung von Massenbewegungen verbessert. Weltraumgestützte Radaraufnahmen mit synthetischer Apertur (SAR) spielen eine wichtige Rolle bei der Beobachtung von Oberflächenveränderungen und der Überwachung ihrer räumlichen und zeitlichen Entwicklung. Die Ergebnisse dieser Methode werden der Öffentlichkeit zunehmend zugänglich gemacht, und ihre Interpretation erfordert daher ein Verständnis der wichtigsten Grundsätze, Einschränkungen, Vorteile und Grenzen.

Recent advances in satellite geodesy have improved our ability to map and monitor mass movements. Spaceborne synthetic aperture radar (SAR) imagery plays an important role in determining surface displacements and monitoring their spatial and temporal evolution. The results of this method are increasingly being made available to the public, and their interpretation therefore requires a correct understanding of the key principles, constraints, advantages, and limitations. In this contribution, I will show a relevant example related to the large mass movement located in the vicinity of the Aletsch glacier, i.e., the Moosfluh slope instability. Persistent Scatterer Interferometry (PSI) has been applied to detect and follow the spatial and temporal evolution during the period 2015-2016, when a strong acceleration was observed. The results show that the PSI techniques might not be suitable in such cases, and that surface displacements might thus be underestimated. Detailed analyses of the SAR interferograms, combined with in-situ observations, are currently the most suitable solution to investigate and forecast the evolution of mass movements during critical phases.

The identification of morphological changes occurring along river channels is essential to support river process understanding, assess sediment budgets and evaluate the effectiveness of river management. Among available remote sensing techniques, space-borne synthetic aperture radar (SAR) could potentially provide a powerful complement to optical imagery for this task. However, very few studies have been carried out on the use of SAR datasets to study erosion and deposition processes in river channels. In this work, we investigate the potential of change detection analysis based on Sentinel-1 data, by comparing variations of radar backscattering to river morphology changes identified through highresolution drone acquisitions. We considered a time series of two years of Sentinel-1 data relative to a period where, despite a moderate fluvial event occurred, morphological changes have been significantly detected in multitemporal drone point clouds. Satellite optical imagery (planet.com) and hydro-meteorological data were used to support the analysis and interpret results. The results show that the spatial and temporal resolution of Sentinel-1 is currently not suitable for accurate discrimination of morphological changes related to river dynamics at local scale. Other spaceborne sensors with sub-metric ground sampling distance and/or daily revisit time would be probably suitable; however, so far, this option would need the use of commercial solutions with a consistent increase of the costs of the investigation.

The increase of social media use in recent years has shown potential also for the identification of specific trends in the data that could be used to locate earthquakes. In this work, we implemented a pipeline that uses Twitter data to identify locations of earthquakes and use the information to trigger EO data analysis. We tested the pipeline for almost a year over Japan, an area where earthquake events are frequent, as well as the use of social media in the population. Here we show the results and discuss the potential development of such procedures. In the future, considering the rapid development and the increase of satellite constellations aimed at global coverage with revisit short revisit times, algorithms of this kind could be used to prioritize satellite acquisitions for the detection of the areas mostly affected by earthquake damages.

Multi-temporal, high-resolution, and homogeneous geospatial datasets acquired by space- and/or airborne sensors provide unprecedented opportunities for the characterization and monitoring of surface changes on very large spatial scales. Here, we demonstrate how an off-the-shelf, open-source image correlation algorithm can be combined with SwissALTI3D LiDAR-derived elevation data from different tracking periods to create country-scale surface displacement and vertical change maps of Switzerland, including Liechtenstein, with minimal computational effort. The results show that glacier displacement and ablation make up the most significant fraction of the detected surface changes in the last two decades. In addition, we identify numerous landslides and other geomorphic features, as well as manmade changes such as construction sites and landfills. All produced maps and data products are available online, free of charge.

Die Bewegungen und die kinematische Interpretation des Rutschungskomplexes von Brienz/Brinzauls (GR) sind Gegenstand detaillierter Untersuchungen. Sie liefern Grundlagen, um die Gefährdung des Dorfes zu beurteilen und entsprechende Massnahmen zu planen. Neben den klassischen Methoden Total Station (TPS), Global Navigation Satellite Systems (GNSS) und Inklinometer kommen auch terrestrische Radarinterferometrie und digitale Bildanalyse zum Einsatz. Dieser Artikel beschreibt neue Methoden der Integration Satelliten-basierter Radar-Interferometrie (DInSAR) und der digitalen Bildkorrelationsanalyse, die ein flächendeckendes Abbild des Verschiebungsverhaltens in drei Dimensionen generieren. In der Folge beschreiben wir die Stapelung differenzieller Interferogramme von C-, L- und X-Band-Radar-Satellitendaten sowie die Integration von Ergebnissen der Interferometrie mit digitaler Korrelation von hochaufgelösten optischen und Radarbildern der Jahre 2015 bis 2020. Auf der Basis dieser Resultate werden anschliessend verschiedene Verformungskomponenten des Rutschungskomplexes ermittelt, die für die Ermittlung von Rutschungskompartimenten und ihrer Kinematik wesentlich sind. Die Ergebnisse werden mit existierenden GNSS-Messdaten validiert.

We present a procedure to detect landslide events by analyzing in-sequence data acquired from regional broadband seismic networks and spaceborne radar imagery. The combined used of these techniques is meant to exploit their complementary elements and mitigate their limitations when used singularly. To test the method, we consider a series of six slope failures associated to the Piz Cengalo rock avalanche that recently occurred in the Swiss Alps, a region where we can benefit from high spatial density and quality of seismic data, as well as from the high spatial and temporal resolution of the European Space Agency (ESA) Copernicus Sentinel-1 radar satellites. The operational implementation of the proposed approach, in combination with the future increase in availability of seismic and satellite data, can offer a new and efficient solution to build and/or expand landslide catalogues based on quantitative measurements and, thus, help in hazard assessments and the definition of early warning systems at regional scales.

The Albula Region in the canton Grisons in Switzerland is prone to a large number of landslides. In order to spatially estimate the surface deformation of these phenomena, we performed a multi-temporal interferometric analysis on point (i.e., persistent scatterers) phases by jointly exploiting ALOS-2 PALSAR-2 ScanSAR and StripMap data acquired along the same orbit. Our results indicate a widespread presence of large-scale rock slope instabilities with linear LOS displacement rates in the range of a few cm/year. In comparison with a PSI Sentinel-1 analysis, valid information is retrieved with L-Band also over forests, where only very limited information is available at C-Band. Over built-up regions and for alpine areas above the tree line, the density of points is higher from Sentinel-1 than from ALOS-2 PALSAR-2 because of the moderate resolution of the ALOS-2 PALSAR-2 ScanSAR data.

Investigating surface displacements in high alpine environments is often subject to challenges due to the difficult accessibility or harsh climatic conditions. Measurement systems have improved greatly in recent years regarding accuracy, range, or energy consumption. Continuously receiving high-precision, real-time monitoring data from a remote location can still support a better understanding of slope dynamics and risk. We present the design, construction, operation, and performance of a complex surface displacement monitoring system installed in the surroundings of the Great Aletsch Glacier in the Swiss Alps, based on two robotic total stations to continuously measure 3D displacements with high accuracies. In addition, GNSS stations are also considered in order to pass from a local to a geographic reference system, as well as to improve the measurement accuracy. The monitoring network is aimed at studying several types of deformation processes, i.e., (i) gravitationally driven and irreversible rockslide movements around the tongue of the Great Aletsch Glacier, (ii) reversible rock slope deformations caused by annual cycles of groundwater recharge and depletion, and (iii) small irreversible deformations of stable rock slopes resulting from progressive rock damage driven by glacier retreat and cyclic hydraulic and thermal loading. We describe the technical details of the monitoring system, which has been in operation successfully for 6 years, and discuss the system performance in terms of its robustness and accuracy.