After a slow uptake, spaceborne radar interferometry techniques are becoming a key tool for wide area ground deformation mapping and for frequent, local, monitoring programs. Wide area processing (WAP) allows users to obtain synoptic views of displacement phenomena over thousands - or even millions - of square km, paving the way for new environmental monitoring programs providing invaluable information on a variety of natural and anthropogenic hazards. In this paper, we argue that the growing number of projects at national or even continental scale (such as the European Ground Motion Service - EGMS) is the result of three factors: (1) a proper space segment, allowing systematic SAR acquisitions at global scale suitable for InSAR analyses; (2) increased computational resources, typically via cloud computing, allowing scalable processing chains; (3) new visualization platforms for 4D data, where the temporal dimension can be easily displayed and interrogated. In WAP projects, several aspects - having limited impact at local scale - become extremely important, e.g.: careful estimation and compensation of atmospheric artifacts (exhibiting a variance increasing with the distance from the reference point); proper data calibration via GNSS measurements (important to make the data more easily interpreted and integrated with other information sources and to better estimate vertical and east-west displacement components from ascending and descending satellite passes); data mosaicking (in fact, the final result is usually the merge of the results of different data stacks of SAR images). Although significant advances have been made in recent years in all these processing steps, some challenges remain. Another topic which is becoming increasingly important is the size of InSAR databases. In fact, data screening tools are becoming a must to take advantage of the huge amount of information associated with a typical WAP project. Which points are exhibiting a change in trend over the last few months? What areas are accelerating? Which points suffered an abrupt change in location after the last seismic event? These are typical questions users want to answer in a few seconds and not after hours or even days spent on a GIS platform. After a gallery of examples of different WAP projects at regional, national, and continental scale, the paper reports some suggestions and recommendations to improve the quality, the effectiveness and the usability of future WAP InSAR projects.

How to cite: Rucci, A., Ferretti, A., Fumagalli, A., and Passera, E.: Large-Scale InSAR Monitoring: Status and Challenges, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14963, https://doi.org/10.5194/egusphere-egu23-14963, 2023.

Indonesia is located in the so-called "Ring of Fire," a region of high seismic activity surrounding the Pacific Ocean. The country is particularly vulnerable to earthquakes due to its location on the Sunda megathrust, a major boundary between the Eurasian and Indo-Australian tectonic plates. On November 21, 2022, a magnitude 5.6 earthquake struck the Indonesian island of Java, with its epicentre located in Cianjur, West Java. The earthquake caused significant damage to the region's buildings and infrastructure, and several reports of injuries and fatalities were reported. In this study, we used multisensor and multitemporal data to investigate land deformation in the study area. We used three pairs of Sentinel-1 datasets, acquired before and after the earthquake and used the InSAR algorithm to produce land displacement maps. Furthermore, we acquired aerial photogrammetry to produce very high-resolution images of the affected areas. We also classified the Planet imagery using a random forest classifier to extract the landslide events in the study area. Our results show that the earthquake caused significant land deformation in the area, with surface displacements up to 9.8 cm and 11 cm for land uplift and land subsidence, respectively. We found that the deformation was primarily concentrated in the southeastern and northwestern parts of the study area. The earthquake led to secondary disasters such as landslides and collapsed residential buildings. It’s due to the combination of geological factors and the building structures. Where the buildings structures that were not built to be earthquake-resistant stood on the old volcano products have experienced weathering. Our findings highlight the usefulness of radar dan optical remotely-sensed data in studying the effects of earthquakes and can be used to inform future disaster response and recovery efforts.

How to cite: Ramdani, F., Wibowo, A., Supriatna, S., and Setiani, P.: A multitemporal and multisensor study of land displacement due to 5.6M earthquake in Cianjur, West Java, Indonesia, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1936, https://doi.org/10.5194/egusphere-egu23-1936, 2023.

Interferometric Synthetic Aperture Radar (InSAR) measurements suffer from undesirable errors caused by tropospheric delays. Generally, two classes of methods are used to reduce InSAR tropospheric errors: Methods based on independent external information and methods using directly the InSAR data themselves (i.e., data-driven methods). External information methods use GNSS data, meteorological data, atmospheric model outputs, etc., and can be reliable but the external information is usually of significantly lower spatial resolution than needed to correct InSAR data. Data-driven methods, on the other hand, are based on the InSAR data directly and thus do not require any external data. Given that tropospheric delays are usually divided into two components, i.e., the stratified and turbulent components, and that these two components have different spatiotemporal characteristics, they are usually treated separately in data-driven correction methods. However, during such separated error reduction, the existence of one component affects the mitigation performance of the other component, which results in somewhat biased reduction of the tropospheric delays.

Therefore, in this study we propose a new method to simultaneously model and mitigate the InSAR turbulent and stratified delays by taking their spatiotemporal characteristics as a priori information. In this method, which we call DetrendInSAR, the turbulent delay is regarded as a spatially slow-changing process and can therefore be fitted by position-related polynomials within a small area (e.g., 1 km x 1 km); the stratified delay can be linearly fitted with the local terrain height; and these a priori information is combined to establish a solvable mathematical model with respect to the tropospheric delay based on a novel pixel-by-pixel window-based modeling strategy. Besides, the displacement signals in the InSAR observations are assumed to be a temporally smooth process and therefore providing additional constraints for distinguishing between the displacements and turbulent delays in the DetrendInSAR modeling process. We validate the DetrendInSAR method using simulated datasets and a 16-month-long Sentinel-1 SAR data sequence of the postseismic deformation after the 22 May 2021 Maduo earthquake, China. We compare our results with the traditional data-driven strategy that fits a ramp and a terrain-related linear function over the whole image based on far-field signals and suppresses the turbulent delay by temporally averaging adjacent SAR-image acquisitions. The results obtained from ascending and descending orbits illuminate the logarithmic decay of the postseismic deformation after this earthquake. We also calculated the one-year postseismic east-west and vertical displacements of this earthquake, indicating that poroelastic rebound contributed to the postseismic deformation, rather than only the afterslip considered in previous studies.

How to cite: Liu, J., Jónsson, S., Hu, J., and Burgmann, R.: Modeling InSAR tropospheric delay based on their spatiotemporal characteristics: Application to postseismic displacements of the 2021 Maduo earthquake, China, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4643, https://doi.org/10.5194/egusphere-egu23-4643, 2023.

Modern satellites produce massive datasets. So, handling and processing bulk Synthetic Aperture Radar (SAR) imagery often represents a high entry level for researchers to tackle scientific challenges at regional or global scale. SAR imaging has been used as a remote sensing tool for studying Earth’s surface, such as measuring topography, terrain discrimination, forestry, and differential interferometry (InSAR) for monitoring the Earth’s surface deformation at millimetre scale. Due to the growing application of SAR imaging and the advancement of InSAR technique, more SAR satellites have been launched over the years. Moreover, those satellites have increased its temporal and spatial sampling rate, which contributes to the current rapid and massive data volume available for InSAR processing. Storage (on-line and off-line hardisks) requirements for InSAR processing is therefore constantly growing - over the past 7 years, Sentinel-1 SAR data downloaded by users has been increased by 620% (Serco, 2022). In the future, more SAR satellites with higher resolutions will be launched, not only increasing the carbon footprint by storing massive data in energy-intensive data centre, but also putting higher pressure on the computing resources of both the platforms and individual users for its scientific exploitation.

In this study, we explored compression algorithms to downsize Sentinel-1 single look complex (SLC) images by 2 to 4 times. 162 ascending SLC images covering an area of around 19,335 km² over the Pearl River Delta Region in the southern China were used in the test. In order to evaluate the performances of these compressed images for ground deformation monitoring, we compressed SLCs  generated by ISCE, calculate interferograms from compressed SLCs, and then compute time series of surface displacements using StaMPS InSAR processing software. Bulk SLC images can be compressed using the Julia package developed in this study and only decompress during calculation of interferograms, therefore images will not be saved in their expanded format.

Our error analysis for signal reconstruction and the processed time-series results suggests that original 32-bit complex images can be can be optimally compressed using different quantization methods, reducing the storage required to handle large processing InSAR tasks. We confirmed that complex radar images retrieved from SAR satellites to be compressed up to a factor of 4 times, and achieving data reduction without sacrificing significant ground displacement precision.

How to cite: Yip, M. W., Webb, A. A. G., and González, P. J.: Making InSAR processing hardisks small again: Estimating the impact of Quantized Compressed SAR images on the precision of PS InSAR time series, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3519, https://doi.org/10.5194/egusphere-egu23-3519, 2023.

Usually, ground deformations are the result of complex interactions of multiple triggering factors due to natural processes and anthropogenic causes. For this reason, areas affected by ground deformations require appropriate monitoring systems, analyses, and methodologies to implement the necessary risk mitigation strategies. In this context, Synthetic Aperture Radar (SAR) sensors enable the monitoring of displacements of the Earth's surface, providing time series with high spatial resolution and wide temporal coverage.

In this contribution, we present a new multi-method approach for analysing main trends and seasonal signals in the time series of ground displacements in order to correlate ground deformation phenomena with triggering factors (e.g. rainfall, snow, temperature, piezometric level, pumping/injection) and to recognize specific footprints and patterns of the different phenomena.

We analysed large datasets of ground displacement data in different areas of Italy, spanning in total the period from 1992 to 2021, acquired from C-band radar sensors on board ERS-1/2 and ENVISAT platforms of the European Space Agency (ESA), as well as from X-band sensors of COSMO-SkyMed (CSK) constellation, TerraSAR-X (TSX) and Sentinel-1 satellites processed with the PSInSAR (Permanent Scatterer Interferometric Synthetic Aperture Radar) technique by TRE Altamira.

In the first step, we applied and optimized T-mode PCA, ICA and MSSA to perform a spatial-temporal separation of the data into a set of components/functions. Then, hierarchical clustering (HC) approach was implemented to group the PSInSAR time series of characteristic deformation patterns and, finally, wavelet transforms were applied to analyse the time series in the time-frequency domain, detecting localised non-stationary periodicities and identifying possible causal relationships in time-frequency space.

The approach has been validated on different surface phenomena at local and regional scale, including subsidence, uplift and sinkholes in urban areas, landslides, rock glaciers and slope creep movements, which differ in dynamics, exposure, land cover, triggers, and evolutionary behaviour. As result, we were able to recognize and separate a limited number of main components/functions that occur systematically in the time series, describing, in particular, the long-term displacement, the seasonal periodicity, and changes in the displacement rate. The weight and ranking of these components may provide a footprint for the different phenomena (e.g., seasonal periodicity for rock glaciers, change of displacements for active landslides, etc.), potentially allowing to recognize the phenomena based on the time series analysis. Finally, the application of the wavelet transforms to the components/functions separated from the times series seems to optimize the analysis of the correlation between the displacements and the natural/anthropogenic triggers.

In conclusion, interpreting the results obtained from the multi-method approach, considering geological geotechnical, hydrogeological and environmental factors, allows a deeper understanding and characterisation of the phenomena and their triggers, overcoming the limitations due to the application of single techniques.

How to cite: Rigamonti, S., Colombo, F., Crosta, G. B., Dattola, G., Frattini, P., Presta Asciutto, A., and Previati, A.: A multivariate time series analysis of ground deformation using Persistent Scatterer Interferometry, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15347, https://doi.org/10.5194/egusphere-egu23-15347, 2023.

In the last decade, with the launch of high-resolution and short revisit time Synthetic Aperture Radar (SAR) satellite missions new possibilities arose in the structure and infrastructure monitoring field. In particular, with the use of multi-temporal interferometric techniques applied to high-resolution SAR data, the displacement time series of stable ground targets (the Persistent Scatterers or PS) can be reconstructed with an accuracy of 1-2 mm/yr and a spatial resolution of few meters. The information extracted from SAR products could be relevant both for the preventive conservation and maintenance and for the health assessment of the existing built heritage, in particular when exposed to ground deformation phenomena. Some issues related to the use of multi-temporal satellite interferometric techniques in structural applications need to be carefully investigated; in particular, the reliability of SAR-derived data in these applications needs to be assessed due to the small displacements affecting a damaged structure.

The proposed work investigates the potentialities of multi-temporal satellite interferometric techniques in the structural monitoring field. In particular, the 3D rigid motion of isolated buildings is reconstructed computing the motion parameters from a dual-orbit set of Persistent Scatterers. The uncertainties affecting the estimated parameters are also assessed on the basis of an error model taking into account the uncertainties related to the displacement measurements from the interferometric technique and the expected errors in the positioning of the scatterers.

The method has been tested on COSMO-SkyMed SAR data processed with a SNAP-StaMPS open-source procedure complemented by in-house procedures for the calibration of SAR products with velocities from GNSS observations. Moreover, the topographic error affecting the elevation of the Persistent Scatterers was estimated and the planimetric coordinates of the scatterers were corrected accordingly, since an accurate 3D positioning of the scatters is fundamental when dealing with structural investigations.

The obtained results show that 3D rigid motions can be estimated in the order of few mm/yr for the displacements and mrad/yr for the rotations with corresponding precision at one order of magnitude smaller than the associated parameters.

Funding

The methodology adopted in the present research was developed in the frame of the FAR Mission Oriented 2021 Project (Satellite Methods for Structural Monitoring, SM4SM, contract E95F21002900007) with the financial support of the University of Modena and Reggio Emilia and Fondazione di Modena.

How to cite: Grassi, F., Mancini, F., Bassoli, E., and Vincenzi, L.: Multi-temporal satellite interferometric technique for structures 3D rigid motion assessment with uncertainty estimation, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5336, https://doi.org/10.5194/egusphere-egu23-5336, 2023.

The authors present an innovative approach for structural assessment of bridges undergoing slow deformation phenomena induced by hazard sources such as landslides, ground consolidation, subsidence or foundation scouring. The methodology is multidisciplinary in nature and is based on the combination of displacement measurements derived from Synthetic Aperture Radar Interferometry (InSAR), applied to satellite images, with structural and collapse analyses performed through advanced numerical modelling with Applied Element Method (AEM). InSAR allows to follow the temporal evolution of slow deformations affecting the structure and, exploiting observations from two different viewing geometries of the satellite radar antenna, it is possible reconstructing the two-dimensional movements of a bridge over time, with proper defined error bounds on the estimated displacements. AEM instead is capable of reproducing with a high degree of accuracy the structural behavior from the elastic stage to crack initiation and propagation, steel yielding, up to element separation and collision, therefore is particularly suitable for collapse simulations, allowing to improve the comprehension of the structural behavior and identify the most critical structural elements. The combination of these two powerful tools is aimed at detecting anomalies in the deformation trends, identifying potential critical conditions and evaluating the time to failure of the bridge in the event that the slow movements progress with a trend consistent with the measurements in the monitored period. The application of the methodology to the case study of the Albiano-Magra Bridge, in Italy, which collapsed on April 8th, 2020, is discussed. The integration of InSAR displacement measurement and collapse simulations with AEM has allowed identifying the most probable triggering cause of the collapse and estimating the residual service life of the bridge, whose reliability increases with the extent of the available satellite monitoring period.

How to cite: Farneti, E., Cavalagli, N., Venanzi, I., Salvatore, W., and Ubertini, F.: Interferometric synthetic aperture radar and numerical collapse simulation for residual service life prediction of bridges affected by slow deformation phenomena, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5966, https://doi.org/10.5194/egusphere-egu23-5966, 2023.

Differential Synthetic Aperture Radar Interferometry (DInSAR) is a well-known technique that allows the investigation of surface displacements affecting large areas on the Earth, in both natural and anthropogenic hazard scenarios, with limited costs and with a centimeter to millimiter accuracy. In the last decades the effectiveness of the satellite DInSAR technology for ground deformation analyses, and its crucial role in emergency scenarios, have been largely demonstrated, thus pushing the space agencies to develop new space-borne SAR systems. In particular, important investments on the development of L-band SAR systems are ongoing, with the forthcoming missions of ESA (ROSE-L), JAXA (PALSAR-3) and NASA-ISRO (NISAR), as well as the already operating SAOCOM-1 and PALSAR-2 systems clearly showing the relevance of these sensors, particularly for what attains their DInSAR applications. Indeed, it is worth to remark that the L-band DInSAR interferograms are effective in maintaining coherence for a long period over rather vegetated areas and in various cases of snow/ice covered zones, thus allowing to overcome the typical limitations of higher frequency systems, operating at C- and/or X-band, which, in the above scenarios, typically guarantee sufficient coherence only for a few weeks.

In this work, we present the first results achieved by processing stripmap L-band SAR images acquired by the Argentinian SAOCOM-1 constellation. In particular, we show some algorithmic developments made for an efficient DInSAR exploitation of stripmap SAOCOM-1 images. These improvements can play a significant role in different scenarios as for creating a national scale L-band ground motion service and for civil protection purposes, thanks to the potential capability to provide, in several portions of Earth, systematic space-borne L-band products with a revisit time, typically, of 24 days that can be reduced down to 8 days. Finally, we present the surface displacement maps and time-series retrieved through the Parallel Small BAseline Subset (P-SBAS) processing chain, properly adapted to process the SAOCOM-1 images, over some selected areas of interest, which involve both volcanic hazard contexts (Campi Flegrei caldera and Etna and Stromboli volcano in Italy), and fast-/slow-moving hydrogeological phenomena (Zeri, Tuscany region and Garessio, Piemonte region, in Italy).

The activities of this work were carried out within the project referred to as DInSAR-3M, funded by the Italian Space Agency (ASI), which is aimed at generating, through advanced DInSAR methodologies, surface deformation time series and mean velocity maps, spatially and temporally dense, for the multi-scale analysis of natural and anthropogenic phenomena.

How to cite: De Luca, C., Roa, Y. L. B., Bonano, M., Casu, F., Franzese, M., Manunta, M., Muhammad, Y., Onorato, G., Striano, P., Zinno, I., and Lanari, R.: On the exploitation of L-band DInSAR products retrieved through the SAOCOM-1 constellation for the investigation of natural and anthropogenic hazards., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7005, https://doi.org/10.5194/egusphere-egu23-7005, 2023.

How to cite: Maghsoudi, Y., Wright, T., Lazecký, M., Ou, Q., Elliott, J., Watson, A., Rollins, C., Hooper, A., Fang, J., Shen, L., Watson, S., and Parsons, B.: Recent Updates on Alpine-Himalayan Belt High Resolution Surface Velocities with Sentinel-1 InSAR and GNSS Observations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15348, https://doi.org/10.5194/egusphere-egu23-15348, 2023.

Rock glaciers are characterised by a mix of ice and rock, which is related to the presence of permafrost in mountainous areas.  The external temperature is considered one of the most important factors controlling rock glacier flow variation at both inter-annual and seasonal time scales, showing mean velocities ranging from centimetres to meters per year. Hence, the temperature rising due to climate change leads to changes in kinematics of rock glaciers that increase hazards for mountainous settlements and infrastructures.

Despite differential SAR interferometry (DInSAR) is a very effective tool for measuring ground stability, its application to rock glacier monitoring poses critical issues relate to signal decorrelation due to changeable snow cover conditions, as well as to displacement kinematics characterised by both linear and non-linear components and high displacement rates leading to measurements corrupted by aliasing.

This work investigates the rock glacier stability in Val Senales (Italian Alps) by processing a dataset of 345 Sentinel-1 SAR images acquired between 2015 and 2022. Multi-temporal DInSAR processing has been performed by exploiting both persistent and distributed scatterers through SPINUA algorithm. Ad hoc processing strategies have been adopted in order to overcome both signal decorrelation due to changeable snow cover conditions, and aliasing due to very high displacement rates. The algorithm has been run by selecting spring-summer acquisitions, and forced to search for solutions corresponding to phase changes behind the aliasing limit.

The resulting mean velocity map shows several areas affected by ground displacements, that have been further analysed for investigating the rock glacier activity in the area of interest. To this aim, the DInSAR results (both mean velocity and displacement time series) have been ingested into a GIS environment together with other informative layers such as rock glacier classes (according to [1]) optical orthoimages, multi-temporal mean SAR amplitude, DInSAR coherence maps, permafrost index map, and  Difference Vegetation Index (NDVI).

Then, the rock glacier activity has reclassified by adopting the more recent procedure proposed in [2], which is based on the DInSAR products too. This new classification has been compared to that derived according to [1] showing several differences.

An further interesting issue is related to the lacking of DInSAR coherent targes just within the rock glacier borders that could be related to the presence of very high displacement rates. This has been investigated by exploring changes in orthoimages from different years as well as maps of DInSAR phase and coherence.

References

[1] Bollmann, L. Rieg, L., M. Spross, R. Sailer, k. Bucher, M. Maukisch, M. Monreal, A. Zischg, V. Mair, K. Lang, and J. Stötter, “Blockgletscherkataster in Südtirol-Erstellung und Analyse,” Permafrost in Südtirol, Innsbrucker Geographische Studien. J. Stötter & R. Sailer Eds., pp. 147–171, 2012.

[2] IPA Action Group - Rock glacier inventories and kinematics. Towards standard guidelines for inventorying rock glaciers: practical concepts (version 2.0), pp. 1–10, 2022. 

Acknowledgments

This work was carried out in the framework of the project “CRIOSAR: Applicazioni SAR multifrequenza alla criosfera”, funded by ASI under grant agreement n. ASI N. 2021-12-U.0. 

How to cite: Bovenga, F., Argentiero, I., Belmonte, A., Refice, A., Nitti, D., and Nutricato, R.: Exploiting SAR interferometry for assessing rock glacier activity, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7731, https://doi.org/10.5194/egusphere-egu23-7731, 2023.

The use of SAR interferometry is globally regarded as a powerful tool able to evaluate spatial and temporal patterns of slope motion in alpine areas. Accordingly, the availability of large multi-temporal interferometric datasets compels the scientific community to find efficient value-adding tools to boost the interpretation and management of radar-based information via automated routines in the framework of multi-hazard mapping and analysis. Here it is presented an unsupervised and automated approach based on Principal Component Analysis (PCA) and K-means clustering to detect patterns of natural or human-induced ground deformation from InSAR Time Series. For our proof-of-concept, the focus is placed on Valle d’Aosta region (Northwest Italy) where different landslide types, deep-seated gravitational slope deformations and permafrost creep interact with human activities and infrastructures. The large volumes of Sentinel-1 data produced allows for retrieving horizontal and vertical Time Series from multi-geometry data fusion of LOS InSAR measurements. Therefore, the added benefit of combining ascending/descending InSAR data and interpolating displacements in time at different time steps is here explored prior to data dimensionality reduction and feature extraction through PCA. The retrieved principal components serve as a continuous solution for cluster membership indicators in the K-means clustering method, allowing to define spatially and temporally coherent displacement phenomena. The signal of the ground deformation clusters is deconstructed into the underlying trend and seasonality components to enhance the interpretability of the classified satellite InSAR features. Using InSAR Time series data spanning 2014-2020, the proposed framework detects several mass wasting processes and anthropogenic deformations with both linear and seasonal displacement behaviours. The results demonstrate the potential applicability of the proposed transferable approach to the development of automated ground motion analysis systems.

How to cite: Festa, D., Novellino, A., Hussain, E., Bateson, L., Casagli, N., Confuorto, P., Del Soldato, M., and Raspini, F.: Spatial and temporal screening of slope motion patterns in alpine environment via unsupervised analysis of large InSAR datasets, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14022, https://doi.org/10.5194/egusphere-egu23-14022, 2023.

A combination of historical levelling surveys, recent Global Navigation Satellite System (GNSS) campaign, and Persistent Scatterers Interferometry (PSI) measurements reveal that the harbour of Antwerp in Belgium has been sinking for the last 77 years. By integrating recently acquired data using PSI and historic databases, this study aims to provide the longest possible time series of data coverage for ground deformation in Antwerp. All data on subsidence in the area is assessed using multiple techniques and has been coherently included in a Geographic Information System (GIS). The long-term impact of ground subsidence on the harbour potentially has both natural and human-caused sources.

The oldest dataset is a map of altitude changes in Belgium, based on a comparison of two first-order levelling surveys conducted in 1946-1948 and between 1976-1980 (Pissart and Lambot, 1989). The iso-displacement map for the entire country was calculated by subtracting the elevation map of the second levelling network from the first. The harbour of Antwerp was crossed by two iso-displacement lines of -20 and -10 mm, representing the overall displacement values over 31 years. This historical data demonstrates that there was a minor sinking in the region likely linked to natural consolidation when the anthropogenic changes in the harbour had not been made.

As the second dataset, three PSI datasets including ERS1/2, Envisat, and Sentinel-1A spanning the area in the periods 1991-2005, 2003-2010, and 2016-2019 respectively were collected and post-processed. The rate of subsidence in the Antwerp harbor and its city centre differs noticeably from one another, based on this data set. The average velocity of PS data in the city centre is 0.002, -0.06, and -0.6 mm/year and in the harbour is -0.83, -2.71, and -1.62 mm/year during the three time spans (Declercq et al., 2021). This study extends Sentinel-1A processing until 2022.

Among the 33 permanent Real-Time Kinematic (RTK) GNSS stations, there are three available stations to monitor the deformation of the region. ANTW (ANTWerp) and ATWR (AnTWeRp) are 70 meters away from each other and both are located within the city centre, and BEZA (BErendrecht-ZAndvliet-Lillo) is in the northeast of harbour. The vertical velocities at the locations of ANTW, ATWR, and BEZA during the periods 2003-2018, 2018-present, and 2010-present, are measured as -0.5, -1.9, and –2.2 mm/year respectively.

First, occurring at a rate of a sub-millimetre per year between 1946 and 1980 as measured in the levelling survey, land subsidence has recently increased substantially, reaching a maximum rate of -7 mm/year observed by the PSI technique. The previous low rate of subsidence as measured by the levelling shows that the natural consolidation of Holocene sediments probably occurred from the beginning. However, this sinking has increased recently, as shown by the most recent PSI and GNSS data. This is probably mostly a man-induced process linked to the consolidation of the constructed backfill and its underlying layer due to its overpressure, together with the consolidation of the most compressible and less permeable layers (aquitards) due to pore pressure decrease induced by groundwater pumping in the aquifers.

How to cite: Choopani, A., Declercq, P.-Y., Verbeurgt, J., De Doncker, F., Orban, P., Devleeschouwer, X., and Dassargues, A.: Subsidence Evolution of Antwerp Region, Belgium over 77 Years, Using Historical Levelling and GNSS Data and Recent Persistent Scatterers Interferometry Observations , EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9863, https://doi.org/10.5194/egusphere-egu23-9863, 2023.

To extract the most information from a national InSAR dataset it is imperative to understand the mechanisms leading to motion and how these manifest in an InSAR dataset such as the EGMS. The British Geological Survey (BGS) have been at the forefront of UK InSAR ground motion interpretation for the past 22 years; in projects such as ESA’s Terrafirma, thematic FP7 projects e.g. PanGeo and SubCoast, and provide expert advice to the UK government surrounding potential fracking sites and CCS sites along with a sustained research programme. BGS also produce national hazard susceptibility mapping, known as GeoSure, which is routinely used by insurance companies to assess ground motion hazards.

The study of many epochs of InSAR data for many study sites provide the opportunity to examine how patterns of subsidence have evolved with time and how this relates to the processes taking place. This presentation will illustrate the evolution of ground motions within the UK over the last 30 years, case studies will highlight the lessons learnt especially with respect to how the change in geological process manifests in the InSAR signal. For example, a striking change in ground motion patterns over a time period occurred in the Newcastle and Durham Coalfield (Gee et al., 2017) where a dramatic change in the pattern of motion was observed between subsidence in the 1990’s ERS data and uplift in the 2000’s ENVISAT data. This was found to relate to change in minewater pumping. Therefore, to create a national interpreted ground motion product it is important to not only understand the mechanism of motion but also understand how that process changes over time. 

Recent research has focused on the prediction of which UK hazards will be visible and measurable by InSAR (Novellino et al., 2023), and on the prediction of likely natural motion rates for the natural geological subsidence processes (Jones et al., 2013). BGS have developed automatic AI and ML tools which examine not only the InSAR average velocity but also the time series to group areas of similar motion characteristics and to then detect when changes occur (Festa et al., 2023, Hussain et al., 2021.). The application of such tools to the EGMS time series and integration of results with BGS GeoSure national hazard susceptibility datasets provide a pathway to the ongoing interpretation of national GB wide InSAR datasets.

The above experience puts the BGS in a unique position for the exploitation of the new Copernicus European Ground Motion Service which represents the first freely available national UK InSAR dataset. Over the coming years it is BGS’ ambition is to apply our experience of UK hazard susceptibility, how these hazards manifest in InSAR data, our fledgling automated interpretation tools and visibility mapping to produce the first dynamic country-wide interpreted ground motion information layer; a value added product which not only tells the user what the motion is but also identifies the likely reason for the motion along with forecasts of how such motion might evolve in the coming years. 

How to cite: Bateson, L., Novellino, A., Hussain, E., Festa, D., and Medici, C.: 30 years of ground motions in the UK: lessons learnt to produce a national interpreted ground deformation map., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8032, https://doi.org/10.5194/egusphere-egu23-8032, 2023.

Large-scale subsidence and uplift pose a significant risk to buildings and infrastructure. While subsidence due to groundwater removal or construction activities can easily be constrained on a local scale, regional changes caused by climate change are more difficult to detect. These phenomena are investigated within the „Umwelt 4.0, Cluster I - Use of digital terrain models and Copernicus data" project, which is carried out by the Hessian Agency for Nature Conservation, Environment and Geology in cooperation with the TU Darmstadt and funded by the Hessian Minister for Digital Strategy and Development. Within the framework of this project, we are creating a systematic workflow to detect ground motion over a period of several years. We focus on the state of Hessen, Germany, where several regions are known for landslide activity, e.g., Hoher Meissner, or for widespread subsidence, e.g., in the industrial areas surrounding Frankfurt a.M.. In this way, occurring ground movements and even mass movements could be detected at an early stage and, if necessary, measures can be initiated. Based on these results, future decisions on regulations or even information for the general public on risk areas can be created.

We utilize two major datasets based on remote sensing. High-resolution digital elevation and surface models (DGM 1 and DSM 1) from airborne LiDAR surveys by the Hessian Administration for Land Management and Geoinformation. For the most parts of Hessen, it was possible to calculate differences in elevation between the years 2014, 2019 and 2021. The second dataset are persistent scatterer interferometry points (PSIs) from the BodenBewegungsdienst Deutschland with a temporal resolution of 6 days since 2015. Both datasets are integrated and linked with other data sources, such as geological maps, known subsidence-sensitive layers, hydrogeological and climatic data. For the InSAR data a toolbox has been developed that automatically detects regions with strong movement (Ground Motion Analyzer). A major challenge for integrating both datasets is the large difference in spatial coverage and temporal resolution. Advantages of LiDAR data are the high spatial resolution and the possibility to detect even small-scale movements (<5 x 5 m) below vegetation cover, for example the re-tracing of forest roads or the creation of logging trails. A disadvantage is the low temporal resolution of several years between flights in comparison with the 6 days of the PSI data. From the latter, even seasonal variations can be detected and measured. However, the spatial distribution of the points is highly heterogeneous, so in cities the point density is very high, whereas in rural areas hardly any measurements exist. Other problems are the strong fluctuations both within a time series of a single PSI point and between neighbouring points.

With our contribution we want to highlight a typical use case of both data sets and their implementation into regulatory decision-making processes. Furthermore, we want to show a possible integrative method combining remote sensing data with ground based geoinformation and future use of advanced classification schemes to automatically detect affected regions in big datasets.

How to cite: Rudolf, M., Krzepek, K., Treffeisen, T., Homuth, B., Iwaszczuk, D., and Henk, A.: Detection and classification of large-scale ground motion from remote sensing data: A case study in Hesse, Germany, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13046, https://doi.org/10.5194/egusphere-egu23-13046, 2023.

Major geological hazards can devastate essential infrastructure and result in widespread injury and death. Understanding the underlying processes that can lead to these hazards and providing analysis-ready datasets in a timely fashion is crucial for hazard monitoring and disaster response and recovery efforts. In support of NASA's vision, we are committed to an open-source science initiative enabling the transparency, inclusivity and accessibility, and reproducibility of  Earth observation data – all fundamental to the pace and quality of scientific progress. Under a NASA ACCESS effort, we have: 1) significantly lowered the latency of delivering displacement products, i.e. the Sentinel-1 Geocoded Unwrapped (S1-GUNW) products, and 2) enabled the expansion of the displacement data archive to over one million S1-GUNW products, currently making ARIA one of the largest open InSAR archives spanning continental scales across most major active tectonic and volcanic regions (Sangha et al., 2022). The scientific analysis of these products is streamlined via the open-source ARIA-tools, which simplifies the download and preparation of S1-GUNWs for time-series analysis through the open-source MintPy software (Yunjun et al., 2019). The derived datasets can support science applications as well as timely science-driven decision-making efforts, particularly, after or during disaster and recovery periods.

Here we demonstrate how our updated infrastructure, driven by an open-source Hybrid Pluggable Processing Pipeline (HyP3) cloud architecture, can be leveraged to support open science and disaster response applications ranging from analysis of volcanic unrest and earthquakes, to characterizing broader-scale tectonic processes.

How to cite: Sangha, S., Bato, M. G., Arenas, N., Bekaert, D., Buzzanga, B., Gens, R., Govorcin, M., Kennedy, J., Johnston, A., Havazli, E., Hogenson, K., Lu, Z., Marshak, C., Meyer, F., Short, G., Tiampo, K., Wang, J., and Zinke, R.: Over one million displacement products from ARIA and counting: Enabling open-science and disaster response for everyone, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10185, https://doi.org/10.5194/egusphere-egu23-10185, 2023.

A-DInSAR techniques allow to identify and map new deformation processes and to update inventories of existing landslide phenomena over large time periods and at different spatial scales. Multi-temporal data derived from both PSInSAR and ISBAS techniques were combined to map the state of activity of slow-moving landslides affecting several urban areas of the Benevento Province (Campania region, south Italy). The proposed method was performed at two different scale, provincial and local, and it was structured into three phases: (i) grid-based approach; (ii) multi-platform approach; (iii) state of activity matrix approach. Two vector grids with uniform reference cell size were generated at two different resolutions, 20x20 m and 100x100 m. These grids allowed the homogenization of ERS 1/2 (1992-2000), ENVISAT (2002-2010) and RADARSAT (2003-2007) PS and Sentinel-1 (2017-2020) ISBAS data in the same vector format. A statistic value calculation was executed from the velocity along LOS of each dataset first on the 20 m cell grid and then from this to the 100 m cell grid. In order not to overestimate the deformation velocity, for the 100 m cell grid a Weighted Average Velocity (V_WA) was computed, which takes into account both the statistically calculated mean value and the area actually covered by the cells. With the multi-platform approach the resulting V_WA maps were analysed individually and also compared to each other. This step leads to a single scale velocity representation, which allows a better multi-temporal observation of the movements affecting the Benevento Province. From the results obtained an activity threshold of ± 3 mm/y was also established and a preliminary stability code was executed for each cell to discriminate stable, unstable and no data areas. These classes were used to construct four two-factor matrices by combining pairs of temporally consecutive satellite data (e.g. ERS-ENVISAT); four activity maps were then obtained. Although this step already provided results for the identification of potential hotspots, in order to achieve a complete deformation overview of the province, two three-factor matrices were processed. Two “historical” state of activity maps for the entire time-span considered (1992-2020) were thus generated. The examination of both V_WA and state of activity maps at 100 m cell grid and their comparison with pre-existing landslide maps available for the Benevento Province allowed to identify specific hotspots interested by currently active deformation processes, corresponding to built-up areas and infrastructures. For representative case studies a detailed analysis of the PS distribution and deformation trends was carried out, also including their correlation with rainfall events. The V_WA and state of activity maps were produced at 20x20 m resolution and made it possible to reconstruct the deformation history of each case.

The methodology applied demonstrates how the availability of multi-temporal satellite data allows interpretation at different spatial scale. The results achieved can be conceived as proper management tool for the assessment of slow-moving landslides, enabling the study of deformation processes, in terms of state and distribution of activity.

How to cite: Spagnolo, C., Focareta, M., Guadagno, F. M., and Revellino, P.: Correlation of multi-platform SAR data for multi-temporal slope instability analysis of the Benevento Province, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13790, https://doi.org/10.5194/egusphere-egu23-13790, 2023.