NH6.3

Accurate remote analyses of high–alpine landslides are a key requirement for future alpine safety. In critical stages of alpine landslides, UAV (unmanned aerial vehicle) data can be employed, using image registration techniques to derive ground motion with high temporal and spatial resolution. Nevertheless, the classical area–based algorithms, dynamic surface alterations, and limited velocity range restrict detection, which results in noise from decorrelation, preventing their application to fast and complex landslides.

Here for the first time to our knowledge, we apply optical flow time series to analyse one of the fastest and most critical debris flow source zones in Austria. The benchmark site Sattelkar (2’130-2’730 m asl), a steep, high–alpine cirque in Austria, is highly sensitive to rainfall and melt–water events, which led to a 70.000 m³ debris slide event in July 2014. We use a UAV data set (0.16 m) collected over three years (five acquisitions, 2018-2020). Our novel approach is to employ optical flow, which, along with phase correlation, is incorporated into the software IRIS. To test the performance, we compared the two algorithms by applying them to image stacks to calculate time–series displacement curves and ground motion maps. These maps enable us to precisely identify compartments of the complex landslide body and reveal different displacement patterns, with displacement curves reflecting an increased acceleration. Traceable boulders in the UAS orthophotos independently validate the methodology applied. We demonstrate that UAV optical flow time series analysis generates a better signal extraction and a wider observable velocity range, highlighting how it can be applied to a fast, high–alpine landslide.

How to cite: Hermle, D., Gaeta, M., Krautblatter, M., Mazzanti, P., and Keuschnig, M.: Performance testing of optical flow time series analyses based on a fast, high–alpine landslide, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-881, https://doi.org/10.5194/egusphere-egu22-881, 2022.

In New Zealand, earthflows and gullies are - next to shallow landslides - important erosion processes and sediment sources in hill country areas. They can cause damage to infrastructure, affect the productivity of farmland, and impact water quality due to fine sediment input to streams. Implementing effective erosion mitigation measures requires detailed information on the location, extent, and spatial distribution of these features over large areas. Remote sensing provides an excellent opportunity to gain such knowledge, whereby different approaches can be applied. In this study, we present two approaches for detecting earthflow and gully erosion features on the North Island of New Zealand.

Earthflows are complex mass movement features that can occur on gentle to moderate slopes in plastic, mixed, and disturbed earth with significant internal deformation, whereby vegetation cover usually remains on the earthflow bodies during movement. High-resolution aerial photography and a LiDAR digital elevation model (DEM), including a range of derived products such as slope, surface roughness, terrain wetness index, were used within a knowledge-based object-based image analysis (OBIA) workflow to semi-automatically map potential earthflows. Specific earthflow characteristics discernible from the optical imagery, such as the presence of bare ground at the toe and rushes, were identified on different hierarchical segmentation levels and subsequently aggregated. Additionally, morphological and contextual properties (e.g. connection to streams) were integrated into the mapping workflow. Gully erosion is an indicator of land degradation, which occurs due to the removal of soil along drainage channels through surface water runoff. We tested a region-based convolutional neural network (Mask-RCNN) deep learning approach for object detection to map gully features. The deep learning was performed on three LiDAR DEM terrain derivatives, namely, slope length and steepness (LS) factor, hillshade and terrain ruggedness index. Labelled chips for training data were generated with reference gully features mapped manually on historical aerial photography.

Semi-automated earthflow detection appeared to be very challenging due to their complexity and the lack of distinct characteristics to differentiate them from other features. The initial results suggest the knowledge-based OBIA workflow has potential, but a major challenge is the creation of objects that represent one earthflow. Hence, the current mapping results may better indicate terrain susceptible to potential earthflow occurrence rather than correctly detecting single earthflows. As for gully mapping, the data-driven deep learning framework shows promising results regarding gully presence and absence. Validation resulted in detected gullies overlapping 60% of the reference gully area. However, a limiting factor related to the available reference data that was mapped on historical aerial photography and does not align with the LiDAR DEM. Given the significant impact of earthflows and gullies, it is essential to develop reliable and targeted analysis methods to better understand their spatial occurrence and enable improved representation of these erosion processes in catchment sediment budget models.

How to cite: Hölbling, D., Abad, L., Spiekermann, R., Smith, H., Neverman, A., and Betts, H.: Exploring knowledge-based and data-driven approaches to map earthflow and gully erosion features in New Zealand, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1013, https://doi.org/10.5194/egusphere-egu22-1013, 2022.

We have calculated the deformation velocity field for the Makran subduction and North Tibet region with the spatial range of [25°N - 31°N; 55°E-67°E] and [30N°-41°N; 85°E-97°E], respectively. There are two significant deformation signals in the epicenter of the 2013 Mw 7.7 Balochistan earthquake and the 2001 Mw 7.8 Kokoxili earthquake. For the Balochistan earthquake, we found that the 7-year post-seismic deformation was due to the widespread aseismic slip along the megathrust and not due to the viscoelastic relaxation. For the Kokoxili earthquake, we probed whether the viscoelastic relaxation of 2001 Kokoxili earthquake is still continuing. We first simulate the deformation caused by the interseismic slip along the major active faults in Tibet. By comparing the simulated deformation and the observed deformation, we found the maximum ratio of the simulated deformation to the observation is 42%, which means that the viscoelastic relaxation of 2001 Kokoxili earthquake is still continuing. The effective viscosities of lower crust and upper mantle are inverted as 1.78*10¹⁹Pas and 1.78 * 10²⁰Pas, respectively.

How to cite: Lv, X., Amelung, F., Shao, Y., and Wu, X.: Large deformation field from InSAR during 2015 to 2021 for the Makran subduction and North Tibet, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1139, https://doi.org/10.5194/egusphere-egu22-1139, 2022.

Ground deformation at volcanic areas is mainly driven by the interaction between lithology, morphology, seismology and volcanism. In the latest decades, radar interferometry has contributed to understand the volcanic dynamics through the measurement of ground deformations. This work focuses on the displacement analysis at Ischia, an active volcanic island located at the north-western end of the Gulf of Naples and characterized by a long eruptive and seismic history. The central portion of the island is dominated by Mt. Epomeo, a volcano-tectonic horst formed by caldera resurgence, tilted southward and bordered by a system of faults and fractures which represent the preferred degassing pathway of the hydrothermal system beneath the island. Seismicity is mainly concentrated in the northern area and the most recent and severe seismic sequence started with the Mw 3.9 earthquake on August 21^st 2017 producing several damages and also victims. In this study, the investigation of surface displacement was carried out over a continuous time interval of about 17 years by using Synthetic Aperture Radar (SAR) dataset with different temporal and spatial resolutions. The Small Baseline Subset interferometric technique was applied to the dataset allowing the identification of the areas more potentially prone to trigger slope instability phenomena. The resulting ground displacement maps identified the highest deformations along the north-western, western and southern slopes of Mt. Epomeo and were validated by using GPS data acquired by local geodetic network. Mean velocity maps obtained from C-band Envisat and Sentinel-1 and X-band COSMO-SkyMed SAR data will be presented together with the ground deformation effects caused by the 2017 seismic swarm.

How to cite: Beccaro, L., Tolomei, C., Spinetti, C., Bisson, M., Colini, L., De Ritis, R., and Gianardi, R.: InSAR measurements of ground deformations at Ischia island (Naples, Italy) along two decades dataset, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1173, https://doi.org/10.5194/egusphere-egu22-1173, 2022.

In the last few decades, the effort of the scientific community to clarify the issue of short-term forecasting of earthquakes has grown fast also thanks to the increasing number of data coming from networks of ground stations and satellites. This led to the discovery of several atmospheric and ionospheric anomalies statistically related to seismic activity, such as ionospheric plasma density perturbations and/or atmospheric temperature and pressure changes. With the aim to contribute in the understanding of the physical mechanisms behind the coupling between the lithosphere, lower atmosphere, ionosphere and magnetosphere during an earthquake, this paper presents a multi-instrumental analysis of a low latitude seismic event (Mw = 7.2), occurred in the Caribbean region on 14 August 2021. The earthquake happened during both super solar quiet and fair weather conditions, representing an optimal case study to the attempt of reconstructing the seismic scenario in terms of the link between lithosphere, atmosphere, ionosphere and magnetosphere. The proposed reconstruction based on ground and satellites high quality observations, suggests that the fault break generated an atmospheric gravity wave able to perturb mechanically the ionospheric plasma density, which, in turn, drove the generation of both electromagnetic waves and magnetospheric field line resonance frequency variation. The comparison between observations and the recent analytical Magnetospheric Ionospheric Lithospheric Coupling (M.I.L.C.) model confirms the activation of the lithosphere–atmosphere–ionosphere–magnetosphere chain. In addition, the observations of the China Seismo-Electromagnetic Satellite (CSES-01), which was flying over the epicentre some hours before the earthquake, confirms both the presence of electromagnetic wave activity coming from the lower ionosphere and plasma density variation consistent with the anomaly distribution of plasma density detected at ground by a chain of Global Navigation Satellite System stations located around the epicentre.

How to cite: D'Angelo, G., Piersanti, M., Battiston, R., Bertello, I., Cicone, A., Diego, P., Follega, F., Iuppa, R., Neubuser, C., Papini, E., Parmentier, A., Recchiuti, D., and Ubertini, P.: Investigation of the Magnetospheric–Ionospheric–Lithospheric Coupling on occasion of the 14 August 2021 Haitian Earthquake, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-1329, https://doi.org/10.5194/egusphere-egu22-1329, 2022.

Deep-seated gravitational slope deformations (DSGSDs) pose serious threats to buildings and infrastructure in mountain regions. The understanding of past movement behavior are essential requirements for enhancing process knowledge and potential mitigation measures. In this context historical aerial imagery provides a unique possibility to assess and reconstruct the deformation history of DSGSDs. This study investigates the feasibility of 3D point clouds derived from historical aerial imagery using free and open-source (FOSS) photogrammetric tools for analyzing the long-term behavior of the Reissenschuh DSGSD in the Schmirn valley (Tyrol, Austria) and assessing related secondary processes as changes in creep velocity, rockfall or debris flows. For the photogrammetric analyses, scanned analogue and digital imagery of six acquisition flights, conducted in 1954, 1971/1973, 2007, 2010, and 2019, have been processed using the FOSS photogrammetric suite MicMac. Further point cloud processing was carried out in CloudCompare. An improved version of the image correlation approach (IMCORR) implemented in SAGA GIS was used for the area-wide assessment of slope deformation. For the georeferencing and scaling an airborne laser scanning (ALS) point cloud of 2008 provided by the Federal State of Tyrol (Austria) was used. In total five photogrammetric 3D point clouds covering the period from 1954 to 2019 were derived and analyzed in terms of displacement, velocity and acceleration. The accuracy assessment with computed Multiscale Model to Model Cloud Comparison (M3C2) distances between photogrammetric 3D point clouds and reference ALS 3D point cloud, showed an overall uncertainty of about ±1.2 m (95% quantile) for all 3D point clouds produced with scanned analogue aerial images (1954, 1971/1973 and 2007), whereas 3D point clouds produced with digital aerial imagery (2010, 2019) showed a distinctly lower uncertainty of about ±0.3 m (95% quantile). Also, digital elevation models (DEM) of difference (DoD) for each epoch were calculated. IMCORR and DoD results indicate significant displacements up to 40 meters in 65 years for the central part of the landslide. The historical datasets further indicate a change of spatio-temporal patterns of movement rates and a minor but overall acceleration of the landslide. The main challenges were the (i) gaps in the 3D point clouds on areas of steep, shadowed slopes and high vegetation, (ii) ground filtering on the photogrammetric point clouds for accurate calculation of digital terrain models (DTMs) and (iii) the quality of the scanned aerial imagery showing scratches, cuts, color irritations and linear artefacts. This research enabled the characterization of the spatio-temporal movement patterns of the Reissenschuh DSGSD over more than six decades. Further research will use the results as a reference for modelling the discussed multi-hazard processes.

This research was partly conducted within the project EMOD-SLAP funded by the Tyrolean Science Fund (TWF).

How to cite: Branke, J., Zieher, T., Pfeiffer, J., Bremer, M., Rutzinger, M., Gems, B., Keiler, M., and Schneider-Muntau, B.: Extending the integrated monitoring of deep-seated landslide activity into the past using free and open-source photogrammetry, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4849, https://doi.org/10.5194/egusphere-egu22-4849, 2022.

The definition of the statistical distribution of the ionospheric electromagnetic (EM) waves energy in absence of seismic activity and other anomalous inputs (such as the ones derived by solar forcing) is a necessary step in order to determine a background in the ionospheric EM emissions over seismic regions. An EM signal which differs from the background (exceeding a statistically meaningful threshold) should be considered as a potential event to be investigated. In this work, by means of the FIF (Fast Iterative Filtering) data analysis technique, we performed a multiscale analysis of the ionospheric environmental background, using almost the entire CSES01 (China Seismo-ElectroMagnetic Satellite) electric and magnetic field dataset (2019 - 2021), by creating the map of the averaged relative energy (ε_rel) over a 3° x 3° latitude-longitude cell, depending on both spatial and temporal scale of the ionospheric medium.
In order to make a robust discrimination between external (atmospheric, ionospheric, magnetospheric, solar activities) and internal (earthquakes, volcanoes) sources generating anomalous signals, we took into account geomagnetic activity conditions in terms of the Sym-H index.
Here we present the results obtained for the August 14, 2021 Haitian earthquake (7.2 M_W) and the September 27, 2021 Crete (Greece) earthquake (6.0 M_W). 

How to cite: Recchiuti, D., D'angelo, G., Papini, E., Diego, P., Cicone, A., Parmentier, A., Ubertini, P., Battiston, R., and Piersanti, M.: Electromagnetic anomalies detection over seismic regions during an earthquake, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5291, https://doi.org/10.5194/egusphere-egu22-5291, 2022.

Since 2014, the Committee on Earth Observation Satellites (CEOS) has been working on means to increase the contribution of satellite data to recovery from major disasters. The 4 year-long Recovery Observatory (RO) pilot project, led by CNIGS with technical support from CNES [www.recovery-observatory.org], was triggered to address the needs of the Haitian community in the south-west of the country involved in recovery after the impact of Hurricane Matthew in October 2016. Following that experience, the RO Concept was published in an Advocacy Paper [1] and the RO Demonstrator Team was created with the aim to activate a series of 3 to 6 ROs after major events between 2021 and late 2023 [2].

It is with regard to the RO pilot and the latest RO demonstrator activation after the 7.2 Mw earthquake and Hurricane Grace occurred in August 2021, that the following lessons learnt in Haiti are discussed:

• technical achievements and challenges in the use of SAR data from high revisit sensors (e.g. Sentinel-1) and on-demand acquisitions from high resolution missions (e.g. COSMO-SkyMed, TerraSAR-X) for terrain motion and land surface change applications;
• the role that the collaboration with users and stakeholders can play to add value to SAR-based scientific products;
• capacity building and training enabling local champions and public stakeholders to effectively uptake SAR technology for their own duties of disaster risk management.

During the pilot, a wide-area regional analysis was undertaken by processing Sentinel-1 in ESA’s Geohazards Exploitation Platform [3], to identify areas affected by ground motions not suitable for reconstruction. The exercise also allowed the understanding of the factors limiting the exploitation of this resource by users (e.g. skill gap, limited internet connectivity).

The high resolution monitoring activity with ASI’s COSMO-SkyMed data, CNES’ Pléiades images and ground-truth validation over 3 priority areas defined by the Haitian users, allowed the identification of the following categories of surface changes:

(a) environmental, along the Grand’Anse River south of Jérémie, mixed with quarrying and unregulated waste disposal [4];

(b) geological, along the rock cliffs north-west of Jérémie where toppling and lateral spreading may be worsened by future disasters, thus causing potential risks to small villages and isolated dwellings;

(c) urban, within the outskirts of Jérémie due to reconstruction and new constructions in unstable areas;

(d) rural, due to landslides to be distinguished by similar signals associated with agricultural practices along the slopes in Camp Perrin.

This knowledge was used as the most up-to-date baseline to assess the impact of the August 2021 earthquake and hurricane, and the current process of recovery on south-west Haiti peninsula in the framework of the RO demonstrator activation. The RO collaborated closely with local partners and the CNIGS performed satellite based analysis of damage after the earthquake. A long-term objective of the RO remains strong capacity development of local actors.

References:

[1] https://www.gfdrr.org/en/publication/use-of-eo-satellites-recovery

[2] https://ceos.org/document_management/Working_Groups/WGDisasters/WGMeetings/WGDisasters_Mtg16_Virtual/CEOS_WGD16_RO_Demonstrator.pdf

[3] Cigna, F. et al. (2020) Proceedings of 2020 IEEE IGARSS, pp. 6867–6870. https://doi.org/10.1109/IGARSS39084.2020.9323231

[4] De Giorgi, A. et al. (2021) Remote Sensing, 13 (17), 3509. https://doi.org/10.3390/rs13173509

How to cite: Tapete, D., Cigna, F., Collet, A., de Boissezon, H., Faivre, R., Eddy, A., Danzeglocke, J., Mondesir, P., Telcy, D., Manasse, E., Piard, B. E., and Généa, S.: SAR-based scientific products in support to recovery from hurricanes and earthquakes: lessons learnt in Haiti from the CEOS Recovery Observatory pilot to the demonstrator, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5803, https://doi.org/10.5194/egusphere-egu22-5803, 2022.

We investigate the kinematic behaviour of active landslides at several well-known locations around the Panachaikon Mountain, Achaia (Peloponnese, Greece), using space geodetic data (InSAR/GNSS). We process LiCSAR interferograms produced by Sentinel-1 (C-band) acquisitions using the open-source software LiCSBAS and we obtain average displacement maps for the period 2016-2021. The maximum displacement rate of each landslide is located at about the centre of each landslide. The average E-W velocity of the Krini landslide is 4 cm/yr (towards east) and 1 cm/yr downwards. The line-of-sight (LOS) velocity of this landslide compares well to a co-located GNSS station within (±) 3 mm/yr (25mm/yr for InSAR and 28mm/yr for GNSS for the descending orbit). Our results also suggest that there is a correlation between rainfall and landslide motion. A cross-correlation analysis of our data suggests that the mean time lag was 13.5 days between the maximum seasonal rainfall and the change of LOS displacement rate. Also, it seems that the amount of total seasonal rainfall controls the increase of displacement rate as 40-550% changes of the displacement rate of the Krini landslide were detected, following a seasonal maximum of rainfall values at the nearby meteorological station. A large part of this mountainous region of Achaia suffers from slope instability that is manifested in various degrees of ground displacement (detectable using space geodesy) affecting greatly its morphological features and inhabited areas.

We acknowledge funding by the project PROIΟΝ “Multiparametric microsensor monitoring platform of the Enceladus Hellenic Supersite” co-financed by Greece and the European Union

How to cite: Tsironi, V., Ganas, A., Karamitros, I., Efstathiou, E., Koukouvelas, I., and Sokos, E.: Mapping and kinematic history of active landslides in Panachaikon Mountain, Achaia (Peloponnese, Greece) by InSAR Time Series analysis and its relationship to rainfall patterns, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-5958, https://doi.org/10.5194/egusphere-egu22-5958, 2022.

Individually delineated landslide inventories are essential in analyzing post-earthquake-induced landslides (EIL) hazard assessments, particularly examining statistical correlations between landslides (e.g., frequency and size) and physical parameters. Despite rapid advances in remote sensing technology, previous recorded EIL inventories still have limitations in carrying out fine quality inventories, mainly due to limitations in delineating individual landslides manually over large areas by low-resolution satellite images. To be specific, fine quality inventory requires the ability to detect landslide scars and deposits separately over whole affected areas, recognizing smaller landslide sizes (<10³ m²) under canopies, as well as avoiding amalgamations, i.e., a combination of several individual landslides in a single polygon, which can lead to severe distortion of landslide statistics. The latest technology from LiDAR-Digital Terrain Model (DTM) allows geomorphologists to manually delineate landslides precisely, but most studies had only focused on deep-seated landslides. Thus, the main objective of this study was to delineate the recent EIL based on LiDAR-DTM visualization over whole landslide-affected areas and test preliminary statistics between our manual LiDAR-based inventory (MLI) with automatic aerial-based inventory (AAI) in the same areas, in addition to NASA’s global EIL database.

We manually delineated the recent landslides affected by the 2018 Eastern Iburi earthquake in the Atsuma basin in Hokkaido within an area of 266 km², accounting for about 90% of the total area affected by landslides. Shaded relief derived from LiDAR-DTM (0.5 m), and aerial photograph (0.2 m) were used to identify landslide morphometrics. AAI collected in the same study area (Kita, 2018) was used to compare with MLI. As a result, our MLI was able to detect a total of 17,160 landslides (total landslide area: 27.5 km²) while the automatic AAI was only 4241 landslides (total landslide area: 33 km²), probably because our MLI was able to recognize more small landslides and separate individual landslides from amalgams. The mean landslide density for MLI is four times greater (64 landslides/km²) compared to AAI (16 landslides/km²), also considered the densest landslide inventory recorded so far in 20 years based on NASA's global EIL inventory database. Based on the binned frequency area distribution (FAD), MLI has a power-law exponent (β) of 3.4 and a rollover point of 800 m², whereas AAI is 2.7 and 3×10³ m², respectively, probably because AAI's inventory overestimates its delineation by inserting channels and depositional regions in the delineated polygons. Compared with all global EIL inventories (mean β: 2.4), the value of the MLI was found to be larger, indicating that the Iburi EIL is the smallest size EIL so far in history (50% landslides are smaller than 10³ m²), but very dense. Our findings suggest that MLI might reveal hidden unexpected statistics of the number and size of EILs, including exposing smaller landslides under the canopy and splitting amalgams.

How to cite: Ritonga, R. P., Gomi, T., Sidle, R. C., Arata, Y., and Noviandi, R.: Small size but densely distributed: Insights from a LiDAR-based manual inventory of the recent earthquake-induced landslides case in Japan, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-8990, https://doi.org/10.5194/egusphere-egu22-8990, 2022.

Flood in the North-Eastern part of India is a chronic event occurring from the River Brahmaputra and its tributaries and causes immense loss to the human life and property. Particularly, during the monsoon period, the north bank tributaries cause havoc on the nearby regions especially in the Dhemaji District. These tributaries mainly originate from the glacier fed regions and inundate the different locations of the Dhemaji district. In this work a fuzzy multi-criteria decision analysis model is developed to prepare the flood hazard map of the Dhemaji district. Six different layers are considered in the analysis such as elevation profile, Flood occurrence period, River confluence points of the second order tributaries, historical embankment breach locations, normalized difference vegetation index and normalized difference moisture index. The outputs from the model are categorized into very low to high hazard zone. The consistency ratio calculated from the assigned weights is found as 0.092. The computed flood hazard map from the present model is compared with the observed flood occurrence events and found to be realistic and satisfactory.

Keywords: Fuzzy AHP, Multi criteria decision analysis, Flood occurrence, Embankment breach, River confluence points

How to cite: barman, D., baruah, A., bm, A., and aggarwal, S. P.: A fuzzy multi-criteria decision tree model for flood hazard assessment in the Dhemaji district of the state of Assam in India, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9066, https://doi.org/10.5194/egusphere-egu22-9066, 2022.

Gathering systematic information on the effects of extreme weather events (e.g., floods, landslides and debris flows, windthrows) is a fundamental prerequisite to establishing rapid-response strategies and putting into practice management policies. However, the collection of field data requires significant economic and human efforts by local authorities. Furthermore, events occurring in remote areas are rarely detected and mapped accurately as they have a low chance of intersecting human infrastructures. These missed detections lead to incorrect assumptions in relation to both the extreme events’ spatial distribution and, especially, the real occurrence probability. This work proposes a framework for obtaining the automatic identification of severe weather events that may have caused important erosion processes or vegetation damage, combined with a rapid preliminary change detection mapping over the identified areas. The proposed approach leverages the free availability of both high-resolution global scale radar rainfall products and Sentinel-2 multi-spectral images to identify the areas to be analyzed and to carry out change detection algorithms, respectively. Radar rainfall data are analyzed and the areas where high-intensity rainfall and/or very important cumulative precipitation has occurred, are used as a mask for restricting the subsequent analysis, which, in turn, is based on a multi-spectral change detection algorithm. The whole procedure feeds a geodatabase (storing identified events, retrieved data and computed changes) for proper data management and subsequent analyses. The testing phase of the proposed methodology has provided encouraging results: applications to selected mountain catchments hit by intense events in northeastern Italy were capable of recognizing flooded areas, debris-flow and shallow landslide activations, and windthrows. The described approach can serve as a preliminary step toward detailed post-event surveys, but also as a preliminary “quick and dirty” mapping framework for local authorities especially when resources for ad hoc field surveys are not available, or in the case of an event that triggers changes in remote areas. Such a systematic potential change identification can help for a more homogeneous and systematic detection and census of the events and their effects. The workflow herein presented is intended as a starting point on top of which more modules can be added (e.g., radar climatology, SAR change detection for near real-time, other severe sources such as lightning, earthquakes or wildfires, machine learning algorithms for image classification, land use and morphological filtering of the results). Future improvements of the described procedure could be finally devised for allowing a continuous operational activity and for maintaining an open-source software implementation.

How to cite: Crema, S., Sarretta, A., Maio, D., Marra, F., Macchi, G., Coviello, V., Borga, M., Marchi, L., and Cavalli, M.: Thunderslide - from rainfall to preliminary landslide mapping: an automated open-data workflow for regional authorities, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-9901, https://doi.org/10.5194/egusphere-egu22-9901, 2022.

Snow-mantle extent (or area), its local thickness (or height) and mass (often expressed by the snow water equivalent, SWE) are the main parameters characterizing snow deposits. Such parameters result of particular importance in meteorology, hydrology, and climate monitoring applications. The considerable geographical extension of snow layers and their typical spatial heterogeneity makes it impractical to monitor snow by means of direct or indirect in situ measurements, suggesting the exploitation of satellite technologies. Space-borne C-band synthetic aperture radar (SAR) sensors (such as those operating in Sentinel-1 A and B missions) are particularly suitable for the analysis of snow deposits, providing data with resolutions up to some meters with global coverage and 6-day revisit time. Most of the satellite remote sensing applications have been focused on major mountain systems, such as the Andes, the Alps, or the Himalayan region. Other important mountain systems, like the Italian Apennines, have not been extensively considered, probably due to their complex orography and the high variability of their snow cover. Nevertheless, the central Apennine has a central role for the meteorological dynamics in the Mediterranean area, and it hosts the southernmost European glacier – namely, the Calderone glacier whose evolution represents a relevant indicator, at least for the medium latitudes, of climatic changes.

The implementation of the objectives of the SMIVIA (Snow-mantle Modeling, Inversion and Validation using multi-frequency multi-mission InSAR in central Apennines) project is based on the development of innovative simulation techniques and snow parameter estimators from SAR and differential interferometric SAR (DInSAR) measurements, based on the synergy with spatial measurements from optical remote sensing sensors, data from ground weather radar and simulations from dynamic snow cover models and on an inverse problem approach with a robust physical-statistical rationale. Furthermore, the scientific validity of the achievable results is supported by an enormous systematic validation effort in the Apennine area with in-situ measurements, identifying 3 pilot sites manned with meteorological and snow measurements, dielectric and georadar measurements, trenches and micro-macrophysical sampling, 6 sites of semi-automatic verification, 31 remote auxiliary sites and 1 site of glaciological interest (Calderone) with ad hoc campaigns. SAR data processing can be performed in different ways to retrieve snow parameters.

In this work we exploit SAR backscattering coefficient to study the effects of backscattering at the air-snow interface, at the snow-ground interface, together with the volumetric effects of the snow layer. The distinction between wet and dry snow is obtained exploiting the copolar and cross-polar SAR returns. DInSAR is exploited to analyze the effects of air-snow refraction and the snow-ground reflection, together with the coherence and phase-shifts between two sequential images. In this work we will present the Sentinel-1 DInSAR processing chain to estimate snowpack height (SPH) combined with SAR-backscattered data for wet snow discrimination. The potential of using physically based analytical and statistical inversion algorithms, trained by forward electromagnetic and snowpack models, is introduced, and discussed. The processing chain is tested in central Apennines, using validation sites with snow-pit in-situ measurements, discussing potential developments and critical issues. 

How to cite: Palermo, G., Raparelli, E., Alvan Romero, N., Papa, M., Orlandi, M., Tuccella, P., Lombardi, A., Picciotti, E., Di Fabio, S., Pettinelli, E., Mattei, E., Lauro, S., Cosciotti, B., Cappelletti, D., Pecci, M., and Marzano, F.: Differential SAR interferometry for estimating snow water equivalent in central Apennines complex orography from Sentinel-1 satellite within SMIVIA project, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10149, https://doi.org/10.5194/egusphere-egu22-10149, 2022.

How to cite: García Cruzado, S., Ramírez Serrato, N., Herrera Zamarrón, G., Yépez Rincón, F., Hernández Hernández, M., Hernandez Espriu, J., and Velasco Herrera, V.: Correlation analysis between the subsides reported as sinkholes and the thickness of the clays of the shallow aquifer in Mexico City (CDMX)., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-10737, https://doi.org/10.5194/egusphere-egu22-10737, 2022.

The increasing diffusion of the PS (Persistent Scatterers) InSAR services across the world and the early adoption of PS-Monitoring techniques, provide to the civil protection authorities effective and objective tools for disaster risk prevention, empowering the capability to detect early-stage terrain deformations even in unpopulated areas.

More in detail, the PS Monitoring technique exploits the high temporal resolution provided by the recent satellite constellations (e.g. Sentinel 2), with revisitation times of about 14 days  detecting, at a regional scale, the so called “anomalies” (i.e.: the Persistent Scatterers which show acceleration trends compared to a given deformation trend). Considering that the deformation anomalies could be provoked by many factors not related to an incipient landslide, the so-called “false positives”, terrain investigations are usually required to assess a real landslide hazard .

Furthermore, to be effective, the terrain investigations aimed at validating a potential incipient landslide situation should be conducted within a short time, to allow an effective implementation of the safety measures by the civil protection authorities.

Many constraints such as the limited availability of human resources and terrain conditions usually hamper an extensive terrain validation of the anomalies provided by PS-InSAR monitoring services. It is thus necessary a fast and objective method to filter and prioritize the terrain deformation anomalies which have the highest probability to indicate an incipient landslide, and require an immediate terrain investigation.

To make that possible, we developed a semiautomated GIS-based information system, called ARTEMIS (Advanced Regional TErrain Motion Information System), which allows an objective and fast selection of the PS InSAR anomalies to be investigated, detected twice a month by the PS-Monitoring services.

The ARTEMIS is a multi-stage workflow operating a preliminary validation of the anomaly itself, followed by a danger assessment stage and a final risk-assessment stage. At the end of the process, a risk-rating score to prioritize the field investigation is provided. 

ARTEMIS is a flexible and scalable tool, which can be adapted to different geographical realities and PS-Monitoring services. Its workflow is openly available for non-commercial use.

How to cite: Bertolo, D., Stra, M., and Thuegaz, P.: ARTEMIS – An operational tool to manage the information provided by Persistent Scatterers Monitoring at a regional scale., EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-12393, https://doi.org/10.5194/egusphere-egu22-12393, 2022.

There’s still a poor understanding of how submarine volcanism works, although the majority of Earth’s volcanic activity happen in submarine context, forming new crust and ejection large amounts of material into the ocean.

This type of eruption has associated risks such as tsunamis and problems with shipping and air traffic, and is a source of natural pollution - gases such as sulphur and particulates are released into the atmosphere - hence the need for monitoring. Also, the study of submarine volcanic products will help understand in more detail how these volcanic processes evolve. Due to the remote location of submarine volcanoes, the use of remote sensing and earth observation techniques can be helpful in the monitoring process in order to mitigate the consequences of volcanic activity.

To answer this problem, a database of pre-registered submarine volcanic eruptions between 2000 and 2018 was created, with results stating 60 eruptions referring to 31 different volcanoes. A total of 450 satellite images were detected through observations of discoloration plumes associated with submarine events, and 82 of these images were subsequently selected for extraction of spectral signature, through what were considered to be the most representative images for the eruption in question, in order to proceed to the extraction of spectral signatures.

The spectral signature of the 263 sample points has similar characteristics within the different types of discoloration plumes (green coloration, brown coloration, and associated with pumice rafts) and can therefore be classified into several classes.

It can be concluded that the detection and differentiation of discoloration plumes associated with submarine volcanic events using remote sensing data can be accomplished effectively, confirming why remote sensing is an efficient and affordable technique for the regular detection, monitoring, and study of submarine volcanic eruptions in near-real time.

How to cite: Domingues, J. R., Mantas, V., and Pereira, A.: Characterisation of discolouration plumes resulting from submarine volcanism using remote sensing techniques between 2000 and 2018, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13029, https://doi.org/10.5194/egusphere-egu22-13029, 2022.