NH6.2

High-resolution geodetic measurements of crustal deformation from InSAR have the potential to provide crucial constraints on a region’s tectonics, geodynamics and seismic hazard. Here, we present a high-resolution crustal velocity field for the Alpine-Himalayan Seismic Belt (AHSB) derived from Sentinel-1 InSAR and GNSS. We create time series and average velocities from ~220,000 interferograms covering an area of 15 million km², with an average of 170 acquisitions per measurement point. We tie the velocities to a Eurasian reference frame by jointly inverting the InSAR data with GNSS data to produce a low-resolution model of 3D surface velocities. We then use the referenced InSAR velocities to invert for high-resolution east-west and sub-vertical velocity fields for the entire region. The sub-vertical velocities, which also include a small component of north-south motion, are dominated by non-tectonic deformation, such as subsidence due to water extraction. The east-west velocity field, however, reveals the tectonics of the AHSB with an unprecedented level of detail.

The approach described above only provides good constraints on horizontal displacement in the east-west direction, with the north-south component provided by low-resolution GNSS measurements. Sentinel-1 does also have the potential to provide measurements that are sensitive to north-south motion, through exploitation of the burst overlap areas produced by the TOPS acquisition mode. These along-track measurements have lower precision than line-of-sight InSAR and are more effected by ionospheric noise, but have the advantage of being almost insensitive to tropospheric noise. We present a time series approach to tease out the subtle along-track signals associated with interseismic strain. Our approach includes improvements to the mitigation of ionospheric noise and we also investigate different filtering approaches to optimize the reduction of decorrelation noise. In contrast to the relative measurements of line-of-sight InSAR, these along-track measurements are automatically provided in a global reference frame. We present results from five years of data for the West-Lut Fault in eastern Iran and the Chaman Fault in Pakistan and Afghanistan. Our results agree well with independent GNSS measurements; however, the denser coverage of the technique allows us to also detect the variation in slip rate along the faults.

Finally, we demonstrate the improvement in the resolution of horizontal strain rates when including these along-track measurements, in addition to the conventional line-of-sight InSAR measurements.

How to cite: Hooper, A., Piromthong, P., Wright, T., Weiss, J., Milan Lazecky, M., Maghsoudi, Y., Rollins, C., Morishita, Y., Elliott, J., and Parsons, B.: Large-scale, high-resolution maps of interseismic strain accumulation from Sentinel-1, and incorporation of along-track measurements, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15946, https://doi.org/10.5194/egusphere-egu21-15946, 2021.

The spatial resolution and deformation-mapping capability of SAR remote sensing fit into the scope of scientific investigations of landslides that move slowly at millimeters to meters per year. The SAR technique has become an efficient tool to detect, monitor and characterize slow-moving landslides. However, north-south motions are nearly unresolvable for the present-day, spaceborne, polar-orbiting and side-looking Interferometric SAR (InSAR) line-of-sight (LOS) mapping systems, and unstable slopes may often not face favorable directions of SAR satellites. In addition, a complete 3D displacement field cannot be obtained with only two distinct InSAR LOS measurements from ascending and descending satellite orbits. Arbitrary assumptions such as simply no north-south motions or constraints imposed by topographic gradients can provide a quasi-3D displacement estimate, yet this is subject to large bias. The Uninhabited Aerial Vehicle SAR (UAVSAR) is an airborne SAR system deployed by NASA/JPL that can acquire measurements along user-specified flight paths. UAVSAR operates with an L-band wavelength (0.24 m) and the single-look pixel spacings along the azimuth and the range directions are as small as 0.6 m and 1.67 m, respectively. Here we will focus on the contributions of UAVSAR and satellite SAR systems to studying the Slumgullion landslide in Colorado, USA with persistent movements at 1-2 cm/day, and even slower-moving landslides (cm to m per year) in the San Francisco East Bay Hills and the Eel River catchment in California, USA. As a complement to InSAR LOS measurements, the high-resolution UAVSAR data and appreciable velocity at a level of m/yr (e.g., Slumgullion landslide and numerous Eel River landslides) make it possible to extract motions by tracking pixel offsets in both azimuth and LOS directions. The flexible trajectory of the aircraft and the additional information from UAVSAR’s sub-meter resolution and multiple flight trajectories allows for an optimal 3D displacement solution, which can be further used for quantitative analysis of the formation of morphological structures, landslide-fault interactions, inferring rheology, understanding slope channel modulation, and capturing the spatiotemporally dependent sensitivity to hydroclimatic variability. New knowledge gained on the precipitation thresholds, landslide volume, and the identification of potential nucleation zones of slope failures will directly assist landslide hazard mitigation and reduction.

How to cite: Hu, X., Bürgmann, R., Fielding, E., and Handwerger, A.: Explicit landslide characterization using high-resolution and multi-trajectory airborne UAVSAR data, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-290, https://doi.org/10.5194/egusphere-egu21-290, 2021.

Several major cities in central Mexico suffer from aquifer depletion and land subsidence driven by overexploitation of groundwater resources to address increasing water demands for domestic, industrial and agricultural use. Ground settlement often combines with surface faulting, fracturing and cracking, causing damage to urban infrastructure, including private properties and public buildings, as well as transport infrastructure and utility networks. These impacts are very common and induce significant economic loss, thus representing a key topic of concern for inhabitants, authorities and stakeholders. This work provides an Interferometric Synthetic Aperture Radar (InSAR) 2014-2020 survey based on parallel processing of Sentinel-1 IW big data stacks within ESA’s Geohazards Exploitation Platform (GEP), using hosted on-demand services based on multi-temporal InSAR methods including Small BAseline Subset (SBAS) and Persistent Scatterers Interferometry (PSI). Surface faulting hazard is constrained based on differential settlement observations and the estimation of angular distortions that are produced on urban structures. The assessment of the E-W deformation field and computation of horizontal strain also allows the identification of hogging (tensile strain or extension) and sagging (compression) zones, where building cracks are more likely to develop at the highest and lowest elevations, respectively. Sentinel-1 observations agree with in-situ observations, static GPS surveying and continuous GNSS monitoring data. The distribution of field surveyed faults and fissures compared with maps of angular distortions and strain also enables the identification of areas with potentially yet-unmapped and incipient ground discontinuities. A methodology to embed such information into the process of surface faulting risk assessment for urban infrastructure is proposed and demonstrated for the Metropolitan Area of Mexico City [1], one of the fastest sinking cities globally (up to 40 cm/year subsidence rates), and the state of Aguascalientes [2], where a structurally-controlled fast subsidence process (over 10 cm/year rates) affects the namesake valley and capital city. The value of this research lies in the demonstration that InSAR data and their derived parameters are not only essential to constrain the deformation processes, but can also serve as a direct input into risk assessment to quantify (at least, as a lower bound) the percentage of properties and population at risk, and monitor how this percentage may change as land subsidence evolves.

[1] Cigna F., Tapete D. 2021. Present-day land subsidence rates, surface faulting hazard and risk in Mexico City with 2014–2020 Sentinel-1 IW InSAR. Remote Sens. Environ. 253, 1-19, doi:10.1016/j.rse.2020.112161

[2] Cigna F., Tapete D. 2021. Satellite InSAR survey of structurally-controlled land subsidence due to groundwater exploitation in the Aguascalientes Valley, Mexico. Remote Sens. Environ. 254, 1-23, doi:10.1016/j.rse.2020.112254

How to cite: Cigna, F. and Tapete, D.: Sentinel-1 InSAR survey to constrain subsidence-induced surface faulting and quantify its induced risk in major cities of central Mexico, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15723, https://doi.org/10.5194/egusphere-egu21-15723, 2021.

In mountainous areas, triggering of landslides is one of the main reasons for causing casualties during large earthquakes. These landslides are also important for shaping the landscapes by transporting large volume of sediment from slopes to catchments. The dynamic landslide triggering mechanism have been well studies with seismic derived rupture processes and ground shaking simulations. However, whether the static coseismic displacements play a role is less investigated. Here, given the high-resolution 3D coseismic displacements of three large earthquakes, we study how landslides with different slope and aspect angles response to the directions and magnitudes of coseismic displacements, in order to better understand the landslides distribution along ruptured faults.

The 2008 Wenchuan earthquake in China, the 2015 Gorkha earthquake in Nepal, and the 2016 Kaikoura earthquake in New Zealand all triggered numerous landslides distributed in the epicenter regions. The locations of these landslides have been carefully mapped by remote sensing and field investigations. Their coseismic displacements have also been well captured by Synthetic Aperture Radar (SAR) imaging geodesy from different geometries. Surrounding each coseismic landslide, we can calculate the 3D coseismic displacements from SAR images. Their slope and aspect angles can be obtained from topography data. For nearby landslides with similar peak ground acceleration, we can project the 3D displacement along and normal to the sliding slops, and then quantitively evaluate which slope geometry favors triggering landslides. Our geostatistical analysis can help hazards mitigation in mountainous area with threads of seismic events, and also shed lights on understanding the role of landslides in shaping the topography.

Key Words: SAR imaging geodesy; coseismic landslide; coseismic deformation

How to cite: Liu, R. and Wang, T.: Analysis of earthquake triggered landslides with slope geometry and 3D coseismic deformation, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3777, https://doi.org/10.5194/egusphere-egu21-3777, 2021.

SAR interferometry (InSAR) is inherently a relative geodetic technique requiring one temporal and one spatial reference to obtain the datum-free estimates on millimetre-level displacements within the network of radar scatterers. To correct the systematic errors, such as the varying atmospheric delay, and solve the phase ambiguities, it relies on the first-order estimation network of coherent point scatterers (PS).

For vegetated and sparsely urbanized areas, commonly affected by landslides in Slovakia, it is often difficult to construct a reliable first-order estimation network, as they lack the PS. Purposedly deploying corner reflectors (CR) at such areas strengthens the estimation network and, if these CR are collocated with a Global Navigation Satellite Systems (GNSS), they provide an absolute geodetic reference to a well-defined terrestrial reference frame (TRF), as well as independent quality control.

For landslides, line-of-sight (LOS) InSAR displacements can be difficult to interpret. Using double CR, i.e. two reflectors for ascending/descending geometries within a single instrument, enables the assumption-less decomposition of the observed cross-track LOS displacements into the vertical and the horizontal displacement components.

In this study, we perform InSAR analysis on the one-year of Sentinel-1 time series of five areas in Slovakia, affected by landslides. 24 double back-flipped trihedral CR were carefully deployed at these sites to form a reference network, guaranteeing reliable displacement information over the critical landslide zones. To confirm the measurement quality, we show that the temporal average Signal-to-Clutter Ratio (SCR) of the CR is better than 20 dB. The observed CR motions in vertical and east-west directions vary from several millimetres up to 3 centimetres, with average standard deviation better than 0.5 mm.
Repeated GNSS measurements of the CR confirm the displacement observed by the InSAR, improve the positioning precision of the nearby PS, and attain the transformation into the national TRF.

How to cite: Czikhardt, R., Papco, J., Ondrejka, P., Ondrus, P., and Liscak, P.: Corner Reflector Network as a Geodetic Reference for Landslide Monitoring via InSAR Time Series: Case Study from Slovakia, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12373, https://doi.org/10.5194/egusphere-egu21-12373, 2021.

Located in the middle of Shanxi Province, northern China, Taiyuan basin is a dry and water-short region. This region is reaching alarming levels of aquifer depletion due to decades of groundwater overexploitation, which has caused severe land subsidence in the basin. The Wanjiazhai Water Diversion Project (WWDP) was designed to ease water scarcity by transporting water from the Yellow River to the Taiyuan basin through 452.4 km-long canals. By the end of 2018, the WWDP had supplied 2.87 billion m³ of water to Shanxi Province, which replenish the basin’s surface water body as well as the underground aquifer. The groundwater levels have continued to rise since 2003, with rising levels of more than 70 meters by 2018 in comparison with its low stand in 2000.

In this study, we use 2007-2010 ENVISAT, ALOS-1 data, and 2017-2020 Sentinel-1 data to study the response of the basin’s aquifer to the groundwater rebound against the background of the water transfer project. We addressed the issue of tropospheric delay and its impact on the seasonal deformation by combing GACOS (Generic Atmospheric Correction Online Service) and a common-point stacking method. The accuracy improvement of deformation by this correction method was validated with measurements from seven continuous GPS stations in the basin. Groundwater rebound triggers ground uplift, which was identified in five areas by InSAR with a rate up to 25 mm/yr. The uplifting displacement time series are well correlated with groundwater level recovery. The land subsidence in the south of the basin continues but the rates decreased significantly in 2017-2020 detected from Sentinel-1 as compared to that in period 2007-2010 from ENVISAT and ALOS-1. All these uplifting signals and the decreasing rates of land subsidence found in Taiyuan city provide the indication that water management practices are successful in mitigating further subsidence.

We found a significant seasonal displacement concentrated within the central region of the basin corresponding to the main irrigated areas in the Taiyuan basin. The maximum peak-to-peak amplitude is 43 mm observed from ENVISAT and decreases to 20 mm observed from Sentinel-1. The seasonal amplitudes change rapidly across faults, indicating that the fault is an effective barrier to across-fault fluid flow. To further quantify the causal relationships between water level and ground displacement, groundwater levels and ground displacement at three wells located near the area affected by significant seasonal land subsidence are analyzed by Cross Wavelet Transform (XWT) method. We found the time lags of about one month between land subsidence and the forcing groundwater level declines. Such a cross wavelet analysis with high spatial-temporal resolution therefore enables tracking the health of the aquifer system and highlights the system’s sustainability in aiding water resources allocation against the background of the water diversion project.

How to cite: Tang, W., Zhao, X., Bi, G., Li, J., Motagh, M., Chen, M., and Chen, H.: Investigation of present-day ground displacement in Taiyuan basin by InSAR in the context of interbasin water transfer project, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9340, https://doi.org/10.5194/egusphere-egu21-9340, 2021.

Groundwater is a critical resource that provides fresh drinking water to at least 50% of the global population and accounts for 43% of all of the water used for irrigation (Siebert et al., 2010; UNESCO, 2012). A main consequence of groundwater depletion in overexploited aquifers is land subsidence, which ensues other impacts, such as increasing flooding risk (specially in coastal areas), damages to infrastructures and reduction of storage capacity in aquifer systems. Aquifer deformation and groundwater flow models are essential to design sustainable management strategies. In this context, A-DInSAR techniques provide valuable surface displacement data to understand the deformational behaviour of the aquifer and to characterise its properties.

RESERVOIR project, which is part of the PRIMA programme supported by the European Union, aims to provide new products and services for a sustainable groundwater management model to be developed and tested in four water-stressed Mediterranean pilot sites. Each of them is representative of a different aquifer system flow scheme. They are located in Italy (coastal aquifer of Comacchio), Spain (Alto Guadalentín Basin), Turkey (Gediz River Basin) and Jordan (Azraq Wetland Reserve). The water usages of these aquifers are irrigation, drinking water and/or power generation. Each site is prone to different issues such as land subsidence, salt water intrusion, water pollution, over-exploitation and insufficient recharge.

One of the primary objectives of the project is the use of advanced satellite-based Earth Observation (EO) techniques for the hydrogeological characterization and their integration into numerical groundwater flow and geomechanical models. This will lead to improve the knowledge about the current capacity to store water and the future response of aquifer systems to natural and human-induced stresses. Free Sentinel-1 SAR acquisitions available at the Copernicus Open Access Hub will be used to perform A-DInSAR processing in representative areas of each pilot site. Additionally, the InSAR processing tools of the Geohazards Exploitation Platform (GEP) funded by the European Space Agency, will be used for a first assessment of ground deformation. In this work we present the preliminary results obtained with Sentinel-1 images using the P-SBAS web tool on GEP (De Luca et al., 2015) at the four pilot sites.

De Luca, C., Cuccu, R., Elefante, S., Zinno, I., Manunta, M., Casola, V., Rivolta, G., Lanari, R., and Casu, F., 2015, An on-demand web tool for the unsupervised retrieval of earth’s surface deformation from SAR data: The P-SBAS service within the ESA G-POD environment: Remote Sensing, v. 7, no. 11, p. 15630-15650.

Siebert, S., Burke, J., Faures, J.-M., Frenken, K., Hoogeveen, J., Döll, P., and Portmann, F. T., 2010, Groundwater use for irrigation—a global inventory: Hydrology and earth system sciences, v. 14, no. 10, p. 1863-1880.

UNESCO, 2012, World’s Groundwater Resources Are Suffering from Poor Governance, UNESCO Publishing: Paris, France, UNESCO Publishing.

How to cite: Bru, G., Ezquerro, P., Guardiola-Albert, C., Béjar-Pizarro, M., Herrera, G., Tomás, R., Navarro-Hernández, M. I., Meisina, C., and Boni, R.: Towards aquifer deformation models integrating SAR remote sensing: preliminary land subsidence results using GEP tools, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12806, https://doi.org/10.5194/egusphere-egu21-12806, 2021.

Deep-seated, slow moving bedrock landslides are significant natural disasters with severe socio-economic repercussions. During the past decades, an acceleration of these hazards has been reported globally due to changes in seasonal freeze-thaw cycles, permafrost thawing, infrastructure development and other anthropogenic sources, changes of precipitation and groundwater levels, and variation in seismic activity. Interferometric Synthetic Aperture Radar(InSAR) is a powerful tool to map landslides movement from space and the Sentinel 1 C-band radar mission provides a high temporal resolution data source to investigate seasonal and intra-annual changes of landslide behaviour.

To construct a 2D/3D displacement field, we decompose a combination of different look angles and InSAR ascending and descending tracks of different sensors including Sentinel and ALOS 1 PALSAR data. The ionospheric delay for InSAR observations is estimated with a split range-spectrum technique because significant ionospheric total electron content variation is common in our study area in the Central Andes. Both statistical phase-based and weather model estimation approaches are implemented to minimize the effect of tropospheric signal on InSAR observations.

Our observations identify several areas with rapid translational slide movements exceeding 5-10 cm/y. Multi-annual and inter-annual behaviour of deformation is extracted through time series analysis and a hierarchical clustering approach is used to identify geographic areas with similar characteristics and rates. We show the wide-spread spatial distribution of unstable hill slopes in the Eastern Cordillera of the south-central Andes, especially at high elevations where field observations are difficult. We identify driving forces to be a combination of pre-existing geologic structures and climatic parameters.

How to cite: M.Aref, M., Bookhagen, B., and R. Strecker, M.: Evolution of Large Bedrock Landslides in the South-Central Andes of NW Argentina, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14234, https://doi.org/10.5194/egusphere-egu21-14234, 2021.

On 30 October 2020 11:51 UTC a large Mw = 7.0 earthquake occurred offshore of the island of Samos, Greece. In this contribution we present the characteristics of the seismic fault (location, geometry, geodetic moment) as inferred from the processing of geodetic data (InSAR and GNSS). We use the InSAR displacement data from Sentinel-1 interferograms (ascending orbit 29 and descending 36) and the GNSS offsets from eleven (11) permanent stations in Greece and Turkey to invert for the fault parameters. Our inversion modeling indicates the activation of a normal fault north of Samos with a length of 32 km, width of 17 km, average slip of 2.1 m, a moderate dip-angle (37°) and with a dip-direction towards North. The inferred fault is located adjacent to Samos northern coastline, with the top of the slip ~1 km below surface, and ~2 km off-shore at its closest to the island. The earthquake caused the permanent uplift of the island up to 10 cm with the exception of a coastal strip along the NE part of the northern shore (near Kokkari) that subsided 2-6 cm. The effects of the earthquake included liquefaction, rock falls, rock slides, road cracks and deep-seated landslides, all due to the strong ground motion and associated down-slope mobilization of soil cover and loose sediments.

How to cite: Elias, P., Ganas, A., Briole, P., Valkaniotis, S., Escartin, J., Tsironi, V., Karasante, I., and Kosma, C.: Co-seismic deformation, field observations and seismic fault model of the Oct. 30, 2020 Mw=7.0 Samos earthquake, Aegean Sea, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14595, https://doi.org/10.5194/egusphere-egu21-14595, 2021.

Flood disasters cause severe damages to African communities (destroyed infrastructure, submerged fields, loss of life) and have an increasing occurrence under the changing climate. The spatial and temporal resolutions of the Sentinel-1 radar data are favourable assets towards improving the capacity to monitor flood events. Flood mapping is included among the services developed within the AfriCultuReS project, with the overall aim to improve food security in Africa (http://africultures.eu/). The widely used SAR threshold imaging technique for the automatic mapping of flood extent from Sentinel-1 images was tested in two pilot sites of the project, in Ghana and Niger. Two flood events with different characteristics were considered: the spillage of the Bagre dam in the south of Burkina Faso which caused farmlands to flood in north-east Ghana in August 2018 and the flood of the Niger river around Niamey after torrential rain in August 2017. These two case studies in west Africa allowed the assessment of the robustness of the method to provide timely flood delineation maps to end users and potential stakeholders. The results were evaluated through expert opinion and comparison to available reference data such as maps from the Copernicus Management Service (CEMS) which was activated in the case of the riverine flood in Niger (Copernicus EMSR235). The results show that the approach can be applied for a rapid and near-real time mapping of the flood extent in the pilot sites of the project AfriCultuReS. The near real time maps can lead to a faster assessment of the flood event severity and its damages on the local communities, help initial reporting to national institutions, and feed existing flood databases such as MifMASS for west Africa. Based on this approach a flood mapping service is under development as part of the AfriCultuReS Disasters Mapping and Monitoring service (AfriCRS-S4-P03).

How to cite: Cherif, I., Ovakoglou, G., Alexandridis, T. K., Mensah, F., and Garba, I.: Near real time high resolution mapping of flood extent in west African sites, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15170, https://doi.org/10.5194/egusphere-egu21-15170, 2021.

The Vaðlaheiði tunnel is a 7.4 km long tunnel located in north Iceland, linking the Eyjafjörður fjord and the Fnjóskadalur valley. It goes through the Vaðlaheiði mountain at maximum depth of about 500 m. The tunnel was built in order to shorten the main road around Iceland (road 1) by 16 km and avoid a mountain pass which was often blocked by snow during winters. The drilling started in July 2013. On the 16^th of February, after having excavated about 1.9 km, a water vein was encountered and started to leak in the tunnel at a rate of about 350 L/s. Drilling was complete in April 2017 and the tunnel opened for traffic in December 2018. As of January 2021, about 250 L/s of a mix of geothermal and cold water is still going out of the tunnel.

The Sentinel-1 SAR satellites from the Copernicus mission provide acquisitions over Iceland since summer 2015. InSAR time-series analysis were conducted for four tracks covering Vaðlaheiði: two ascending (T118, T147) and two descending (T111, T9). Results show that part of the hill subsided about 10 mm between summer 2015 and summer 2016. It also appears that the same area was subsiding about 5 mm per year between summer 2016 and summer 2020. Older datasets from the Envisat SAR mission covering 2004-2010 were analysed and show no evidence of subsidence in the same location. Therefore, it appears there could potentially be a link between the water going out of the tunnel and the subsidence. Especially since water withdrawal at depth is known to cause surface subsidence, like in the case of agriculture irrigation or geothermal exploitation. Using numerical modelling, we attempt to explain this relation between water withdrawal and subsidence in the case of the Vaðlaheiði tunnel.

How to cite: Drouin, V. and Gautason, B.: Potential link between mountain subsidence and water discharge from a tunnel in north Iceland, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15503, https://doi.org/10.5194/egusphere-egu21-15503, 2021.

SAR interferometry has stepped in the big-data era, particularly with the acquisition capability and open-data policy of ESA’s Sentinel-1 SAR mission. Large amount of Sentinel-1 SAR images has been acquired and archived, allowing for generating thousands of interferograms, covering millions of square kilometers. In such a large-scale interferometry scenario, many applications still focus on monitoring kilometer-scale local deformation, sparsely distributed in a large area. It is thus not effective to apply the time-series InSAR analysis to the whole image stack, but to focus on areas with deformation. Aiming at this target, we present our recent work built upon deep neural networks to firstly detect localized deformation and then carry on the time-series analysis on small interferogram patches with deformation signals.

Here, we first introduce our burst-based Sentinel-1 processor, which has been fully paralleled for large-scale InSAR processing. From these interferograms, we adapt and train several deep neural networks for masking decorrelation areas, detecting local deformation, and unwrapping high-gradient phases. We apply our networks for mining subsidence and landslides monitoring. Comparing with traditional time-series InSAR analysis, the presented strategy not only reduces the computation time, but also avoids the influence of large-scale tropospheric delays and the propagation of possible unwrapping errors.

The presented methods introduce artificial intelligence to the time-series InSAR processing chain and make the mission of regularly monitoring localized deformation sparsely distributed in large scale feasible and more efficient. As future work, we can further improve the temporal resolution of InSAR based local deformation monitoring by training networks combining interferograms from C-band and L-band SAR images, which will be available soon from future SAR missions such as NiSAR and LuTan-1.

How to cite: Wang, T., Luo, H., Wu, Z., Fu, L., and Zhang, Q.: Deep learning facilitated local deformation monitoring with large-scale SAR interferometry, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3722, https://doi.org/10.5194/egusphere-egu21-3722, 2021.

The PAZ SAR satellite, launched in 2018, routinely delivers X-band SAR (synthetic aperture radar) imagery in co-polarimetric HH and VV channels on a weekly basis. It has the potential to reveal surface elevation and deformations and to categorize scattering characteristics. Yet, few relevant experiments and studies have been carried out so far [1], probably due to the limited PAZ data availability to the public. Using a relatively small stack of 10 PAZ co-polarimetric SAR images, we investigate and demonstrate the applicability of PAZ co-polarimetric SAR imagery for monitoring surface deformation. Images were acquired between September 2019 and April 2020, covering the northern part of the Netherlands. This InSAR (interferometric SAR) time series of images allowed us to classify radar scatterers in terms of scattering mechanisms.

A key linchpin in time series analysis for surface deformation monitoring is to identify reliable constantly coherent scatterers (CCS) and to maximize their number separately in the VV and HH channels. Sufficient and reliable CCS can facilitate spatio-temporal phase unwrapping, and map surface deformation evolution. A real-valued IRF (impulse response function) correlation method is suggested for CCS selection as it generates the CCS with exact radar location and maximum exclusion of incoherent scatterers and scatterers at the sidelobes. In this way it serves as an alternative to classical methods such as the normalized amplitude dispersion (NAD). The results of our study show that 34% CCS in the VV channel and 47% in the HH channel have an ensemble temporal coherence > 0.9 using the real-valued IRF correlation method, while 5% CCS in both the VV and the HH channel have an ensemble temporal coherence > 0.9 using the NAD method. Therefore, using the real-valued IRF correlation method we obtain better-quality results of the selected CCS.  

By using SAR images in both the VV and HH channels, co-polarimetric phase differencing (CPD) can be applied to classify the CCS into three classes: surface scattering, dihedral scattering and volume scattering. The results of our study show that by predefining an allowable noise range, in our study equal to 0.4, and using the temporal averaged CPD, we can achieve a reliable CPD-based classification. A higher percentage of CCS in the VV channel is classified as dihedral scatterers (24%), while a higher percentage of CCS in the HH channel is classified as surface scatterers (36%) and volume scatterers (47%).

We conclude that PAZ co-polarimetric SAR imagery improves monitoring of surface deformation as compared to existing methods, and is suited to characterize radar scatterers.

[1] Ling Chang and Alfred Stein (2020). Exploring PAZ co-polarimetric SAR data for surface movement mapping and scattering characterization. International Journal of Applied Earth Observation and Geoinformation. (https://doi.org/10.1016/j.jag.2020.102280)

How to cite: Chang, L. and Stein, A.: Deformation monitoring and scattering characterization using PAZ co-polarimetric SAR imagery, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-541, https://doi.org/10.5194/egusphere-egu21-541, 2021.

Subsurface mining is one of the human activities with the highest impact in terms of induced ground motion. The excavation of the mining layers creates a geotechnically and hydrogeologically unstable context. The generation of chimney collapses and sinkholes is the most evident surface consequence of underground mining which, in general, creates the optimal conditions for the development of subsidence bowls. Considering this, the need for ground motion monitoring tools is evident. Topographic measurements have been the obvious choice for many years. Nowadays, the flourishing of Multi-Temporal Satellite Interferometry (MTInSAR) algorithms and techniques offers a new way to measure ground motion in mining areas. MTInSAR fully covers the accuracy requirements asked by mining companies and authorities, adding new potentialities in term of area coverage and number of measurement points. The technique has some intrinsic limitations in mining areas, e.g. coherence loss, but the algorithms are being pushed to their technical limits in order to provide the best coverage and quality of measures.

This work presents a detailed scale MTInSAR approach designed to characterize ground deformation in the salt solution mining area of Saline di Volterra (Tuscany Region, central Italy). In summary, salt solution mining consists in the injection at the depth of interest of a dissolving fluid and in the extraction of the resultant saturated brine. In Saline di Volterra, this mining activity created ground motion, sinkholes and groundwater depletion. The MTInSAR processing approach used is based on the direct integration of interferograms derived from Sentinel-1 images and on the phase splitting between low and high frequency components. Phase unwrapping is separately performed for the two components that are then recombined to avoid error accumulation. Before generating the final deformation map, a classical atmospheric phase filtering is applied to remove the residual low frequency signal. The results obtained reveal the presence of several subsidence bowls, sometimes corresponding to sinkholes formed in the recent past. These moving areas register velocities up to -250 mm/yr with different spatial and temporal patterns according to the distribution and age of formation of sinkholes. This is the first time an interferometric analysis is performed here. It is hoped that such information could increase the awareness of local entities on the ground effects induced by this mining activity.

How to cite: Solari, L., Montalti, R., Barra, A., Monserrat, O., Bianchini, S., and Crosetto, M.: Ground motion detection in a salt solution mining area, an application of Multi-Temporal Satellite Interferometry, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1336, https://doi.org/10.5194/egusphere-egu21-1336, 2021.

The 2018 northern Osaka earthquake with a magnitude 6.1 earthquake struck on June 18, 2018 in northern Osaka, causing enormous damage. SAR interferometry using satellite synthetic aperture radar (SAR) data can detect surface displacement distribution over a wide area and is effective for observing surface displacement during an earthquake. On the other hand, it is also important to observe the tendency of long-term surface displacement around active faults on a yearly basis in order to monitor the deformation at and around active faults. In this study, we used persistent scatter SAR interferometry (PS-InSAR) to clarify the recent surface displacement including before and after the 2018 northern Osaka earthquake near the Arima-Takatsuki Fault Zone and the Mt. Rokko active segment, near the epicenter of the earthquake. PS-InSAR analysis is a method that analyzes coherent pixels only, and can extract surface displacements with less noise than the conventional two-pass SAR interferometry. By using Sentinel-1 data, we expect to understand a long-term surface displacement and temporal changes in displacement pattern by comparing with the results using other satellites in previous studies. As a result of our analysis, we found that (i) ground subsidence occurred near the Mt. Rokko active segment, (ii) subsidence or eastward displacement occurred in the eastern part of the Takarazuka GNSS station, (iii) surface displacement in the wedge-shaped area located between the Arima-Takatsuki Fault Zone and the Mt. Rokko active segment is suggested to be caused by groundwater level changes, (iv) groundwater level changes may have caused surface displacement considered to be uplift in the wide area between the Ikoma Fault Zone and Uemachi Fault Zone, and (v) slip of the source fault may have caused surface displacement around the epicenter of the 2018 northern Osaka earthquake. Furthermore, we validated the estimated surface displacements by comparison with GNSS measurements and previous studies. These results suggest that surface displacement near the Arima-Takatsuki fault zone was caused by the 2018 northern Osaka earthquake. In order to reveal the mechanism of surface displacement in the vicinity of the fault, it is necessary to continue to monitor the surface displacement in this area using time-series SAR interferometry.

We acknowledge Sentinel-1 data provided from the European Space Agency (ESA) based on the open data policy.

How to cite: Shigemitsu, Y., Ishitsuka, K., and Lin, W.: Estimating surface displacement in the Hanshin area, Japan, by PS-InSAR analysis using satellite Sentinel-1 data, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10472, https://doi.org/10.5194/egusphere-egu21-10472, 2021.

Westward migration of M>7 earthquakes along North Anatolian fault with the latest one, Izmit 1999 event, led focus of studies to the seismic gap in the main Marmara fault. For this purpose, the coastal ranges of the Marmara Sea, mainly Istanbul megacity, are renowned for earthquake and ground motion hazards, associated with faulting, landslides and sediment compaction processes. Ground motion associated with man-made activities, however, have been barely studied. The Thrace region of Turkey, some 50 km to the North of the Marmara Sea, expresses pronounced ground motions affecting large areas. We use the Persistent InSAR technique to monitor the Marmara region using Sentinel-1 satellites’ TOPSAR data between 2014 and 2020. Results for both ascending (T131 and T58) and descending (T36) tracks reveals 10 mm/yr rate of subsidence in the Thrace region of Turkey, affecting an area ~15400km² with dimensions of ~110 km by ~140 km. There are two plausible mechanisms for this deformation; (1) excessive pumping of groundwater for agricultural purposes, or (2) natural gas extraction activities taking place in the region. To better understand the observed deformation source, as a first step, we model potential gas extraction by volume change. No piezometric data are available for this region for the time being. Thick sediments including sandstone, reefal carbonates, amongst others, are aimed for gas exploration in the Thrace basin for more than half century. Depth of gas extraction wells and sediment thickness is compiled from previous studies to compare the subsided area with sediment and well depth variations. 

We use  the Poly3D boundary element method to model the surface. Poly3D uses planar triangular elements of constant model to model displacement’s source. Using triangular elements provides models with complex and smooth 3D surfaces avoiding overlaps or gaps, and hence allowing one to construct realistic models. Poly3dinv inverse model applies a fast non-negative/non-positive least squares solver to optimize the solution. We construct a surface enveloping tips of the wells and use it to produce deformation at surface due by allowing opening on it. Small residuals between the observation and model based on opening suggests that deformation is likely caused by natural gas extraction.

How to cite: Nozadkhalil, T., Ergintav, S., Cakir, Z., Dogan, U., and R. Walter, T.: Investigation of Land Subsidence in Eastern Thrace (Turkey) using Multi Temporal InSAR, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11343, https://doi.org/10.5194/egusphere-egu21-11343, 2021.

The determination of ground deformation may be carried out by applying various measurement methods such as levelling, laser scanning, satellite navigation systems, Synthetic Aperture Radar (SAR) and many others. In this work, we focus on the comparison of the deformation effects measured by Global Navigation Satellite Systems (GNSS) and satellite Interferometric SAR (InSAR) methods in the Upper-Silesian coal mining region (SW Poland).

An unquestionable advantage of GNSS technology is the possibility of continuous monitoring of deformations in three-dimensional space. Moreover, the evolution of real-time (RT) techniques such as: near real-time (NRT), ultra-fast NRT or RT allows to obtain a high precise position determination with a relatively slight latency (ranging from a few seconds to less than one hour). The limitation of the satellite navigation technology is the spatial range of the measurements. The deformation can only be observed at the point where the GNSS antenna is located. Furthermore, the acquisition, installation and maintenance of the equipment may also involve high costs.

In contrast to the GNSS technique, the InSAR methods enable measurement of the large-scale subsidence areas with possibility to use free products and software (e.g. Sentinel-1 and SNAP). The large-scale InSAR investigations provide a better overview of local terrain changes. Unfortunately, InSAR methods also have some limitations related to data acquisition technology:  

• a few days latency in acquiring a new image,
• insensitivity to changes in the northern component,
• acquiring deformation only in the LOS direction.

The main goal of this research is to analyse the deformation results obtained using GNSS and InSAR methods with respect to the capabilities and limitations of these two techniques. We investigated the level of agreement of eight GNSS and InSAR time series in areas with no subsidence, together with results acquired on seven regions of mining deformation where the maximum subsidence velocity exceeds 50 cm/year. The mean RMS time series fitting error obtained in subsidence basin is more than 5 cm and in non-deformed areas is equal to 2 cm. The study also investigated the effect of coherence threshold levels (0.3 ÷ 0.6) with introduction of the northern GNSS component on the InSAR decomposition process. Assuming the same GNSS deformation value in each directions (north, east, and up), the impact of the northern component was estimated as 10% of the total LOS subsidence.

How to cite: Tondaś, D., Ilieva, M., Rohm, W., and Kapłon, J.: Towards synergy of GNSS and InSAR mining deformation monitoring with Sentinel-1 data, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11416, https://doi.org/10.5194/egusphere-egu21-11416, 2021.

The EPOS-PL project is the Polish realization of the European Plate Observing System (EPOS) initiative, which aims at the integration of existing and newly created research infrastructures to facilitate the use of multidisciplinary data and products in the field of Earth sciences in Europe. Within the EPOS, one of the tasks aims at SAR data utilization for deformation monitoring in the area of Rydułtowy mine. The Rydułtowy mine is the oldest active mining in the Upper Silesia Coal Basin in Poland. In the area of this mine, five Corner Reflectors (CRs) have been deployed in the framework of the EPOS- PL. Additionally, in the area of interest one high-frequency GNSS receiver working permanently has been placed. This GNSS permanent station (RES100POL) enables estimating of deformation time-series based on multi-GNSS observation in post-processing.

In this study, we use Sentinel-1A/B TOPSAR images acquired between 25 June 2018 and 14 July 2019 in one ascending and two descending geometries with revisiting time of 6-days. Additionally, we use ground truths of two leveling and GNSS measurement campaigns carried out to precisely estimate deformations on five CRs (2^nd-4^th of July 2018 and 28^th-30^th of June 2019). GNSS static measurements were carried out via three independent measurement sessions. Coordinates of the station RES100POL and static GNSS and leveling measurements ware were used for validation of SAR measurements.

SAR data has been processed by means of classical consecutive Differential Interferometry (DInSAR) as well as Persistent Scattering (PSInSAR) approach. During SAR data processing, snow coverage accumulated on the CRs caused that some Sentinel-1 images from the winter season have been removed from DInSAR as well as PSInSAR processing. Results from ascending and descending orbits allow the estimation of vertical as well as east-west deformation components. Root Mean Square Error (RMSE) between CRs measured by conventional geodetic techniques and DInSAR was estimated as 31mm and 38mm for east-west and vertical deformation components, respectively. RMSE measured between PSInSAR and GNSS was estimated as 5mm and 7mm for east-west and vertical components, respectively. RMSE of 15mm and 3mm was estimated for DInSAR with respect to GNSS from RES100POL station for east-west and vertical components, respectively. Subsequently, RMSE of 4mm and 5mm was estimated as deformation time variations between PSInSAR and GNSS from RES1 station for east-west and vertical components, respectively. These measures indicate clearly the advantage of the PSInSAR method. However,  the PSInSAR approach was able to estimate deformations only for three CRs due to the fast and non-linear deformation pattern observed on other two CRs.

How to cite: Wielgocka, N., Pawluszek-Filipiak, K., Tondaś, D., and Borkowski, A.: Satellite Radar Interferometry on corner reflectors in the area of mining region in Poland, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12150, https://doi.org/10.5194/egusphere-egu21-12150, 2021.

The near-surface geology of northern Germany is characterized by glacial deposits, deformed by rising Permian and Upper Triassic salt structures. Ground motions potentially associated with salt tectonic processes are very slow and are superimposed by signals of e.g. hydrological and anthropogenic sources. To measure them requires the detection of motion rates in the range of a few millimeters per year with sufficient spatial coverage. For large areas little is known about the rates and the characteristics of ground motions, even though they directly affect anthropogenic infrastructure and could have an impact on the future use of the underground for storage purposes or the exploitation of geothermal energy.

To measure ground motion, we use radar interferometric time series data provided by the German Aerospace Center and the Federal Institute for Geosciences and Natural Resources' Ground motion service. These data are based on Synthetic Aperture Radar images acquired by ESA's ERS and Sentinel satellites. Time-series analyses are possible for temporally stable backscattering objects (persistent scatterers) on the ground. Generally, this results in spatially dense observations over built-up areas and sparse observations over rural areas.

We use a set of geostatistical methods to analyze these time series data. We see signals of large-scale surface-deforming processes such as the subsidence of the marshes and small-scale signals like the swelling of Permian anhydrite at the Segeberger "Kalkberg". And we can observe subsidence processes over the historic town of Lübeck.

Our work extends the area of application of the PS-InSAR technique from areas with high motion rates to regions with particulary low motion rates. We discuss methods that can be used to link ERS data to the Sentinel-1 data, in particular, to separate long-term motion processes from short-term effects. We are working on techniques that shall help to decompose different signal sources. Finally, we aim to prepare a set of tools, that can be used by the community.

How to cite: Hoogestraat, D., Sudhaus, H., and Omlin, A.: Detecting ground motion in Schleswig-Holstein from radar satellite data, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13454, https://doi.org/10.5194/egusphere-egu21-13454, 2021.

We present an analysis of subsidence phenomena and mechanisms affecting urban areas developed on soft volcanic rock where sinkholes frequently occur. The study focuses on the metropolitan area of Naples (Southern Italy), an important example of an urbanized area affected by instability issues. The sub-surface of Naples is characterised by tunnels and cavities excavated in Neapolitan Yellow Tuff (NYT) through history for aqueducts and sewer systems, as places of worship or to extract building materials. The study was carried out in the UNESCO area (about 31 km²) considering ground surface measurements acquired by C-band radar sensors on board the ESA platforms ERS-1/2 and ENVISAT, as well as the X-band sensors of the COSMO-SkyMed (CSK) constellation and the TerraSAR-X/Tandem-X (TSX) satellites (processed by TRE Altamira). SAR data show different wavelengths, spatial/temporal resolution, revisit time and monitored period. ERS-1/2 and ENVISAT are both characterized by revisit time of 35 days and spatial resolution of 5x20m, while second-generation X-band sensors determine an extremely high resolution and PS (Persistent Scatterer) density distribution (TSX PS density is 26769 PS/km²). Data from CSK and TSX show spatial resolution of few km² and reduced revisit time (8 days for CSK and 11 days for TSX). SAR data are capable of detecting ground subsidence or uplift deformations on urban areas. The available cavities and sinkholes (Guarino et al., 2018) inventories were considered as well as available thematic maps (piezometric level, NYT roof depth, water supply, sewerage, waterwork and historical buildings). The cavity dataset, counting 888 polygons, was related to the PS mean velocities to detect possible correlations between them. Finite Element Analysis (FEA) for three-dimensional modelling were performed using MIDAS GTS NX code to simulate failure mechanisms of real cavities. Numerical results highlight that the cavity planimetry and its height, the overburden thickness and the mechanical properties of the tuff material are the most influencing parameters. Saturation effect and tuff degradation were evaluated computing the safety factor by means of the strength reduction method. The role played by pillars in complex cavities in terms of stress distribution and stability conditions was investigated. Numerical results and InSAR measurements of subsiding areas are in agreement, although some differences due to local effects are encountered, variation in properties and the assumptions of a constant length of the cavities. Finally, an example of a structural collapse occurred on 8th January 2021 affecting the Ospedale del Mare parking lot, in the Ponticelli district, was examined. Ground displacement pattern and time series comprised between January 2016 and December 2019 obtained with the TSX data display downward trends, clearly showing that the area experienced “subsidence” over at least the past two years. This study demonstrates the usefulness of numerical analysis combined with InSAR measurement technology to assess cavity stability conditions and the study of subsidence phenomena in urban areas.

Guarino, P. M., Santo, A., Forte, G., De Falco, M., Niceforo, D. M. A. (2018). Analysis of a database for anthropogenic sinkhole triggering and zonation in the Naples hinterland (Southern Italy). Natural Hazards, 91(1), 173-192.

How to cite: Rigamonti, S., Bellotti, F., Dattola, G., Frattini, P., Guarino, P. M., and Crosta, G. B.: Analysis of subsidence in the metropolitan area of Naples based on SAR data, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15169, https://doi.org/10.5194/egusphere-egu21-15169, 2021.

Architectural heritage is cultural and spiritual symbol of our predecessors with immeasurable historical, artistic, and technological value. However, these heritages are exposed to long-term degradation due to the combination impacts from the natural erosion and anthropogenic activities. Consequently, it is important to establish an effective deformation monitoring system to support the sustainable conservation of those properties. In order to make complementary to conventional geodetic measurements such as global navigation satellite systems (GNSS) and leveling in terms of spatial density, we propose a landscape-ontology scale multi-temporal InSAR (MTInSAR) solution for the preventive deformation monitoring of large-scale architectural heritage sites through the adaption of current MTInSAR approaches. We apply different solutions in Shanhaiguan section of the Great Wall in China and the Angkor Wat in Cambodia based on their onsite characteristics. At the cultural landscape scale, we improve the small baseline subset (SBAS) approach by the induced pseudo-baseline strategy in order to avoid the errors caused by inaccurate external DEM, resulting in a robust deformation estimation in mountainous areas where the architecture heritage of the Great Wall located; at the ontology scale, we integrate the differential SAR tomography (DTomoSAR) with the finite element method (FEM) for the structural instability detection of the Angkor Wat Temple, pinpointing the structural defects from the 3D deformation measurements and simulation. This study demonstrates the capability of adaptive MTInSAR approaches for the preventive monitoring the deformation of large-scale architectural heritage sites.

Keywords: Architectural heritage; two-scale; deformation; MTInSAR

How to cite: Xu, H. and Chen, F.: A landscape-ontology scale MTInSAR deformation monitoring solution for the sustainable conservation of architectural heritage sites, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1555, https://doi.org/10.5194/egusphere-egu21-1555, 2021.

Interferometric processing of series of data acquired over time by synthetic aperture radar (SAR) satellites makes it possible to measure millimetric ground motions (typically induced by landslides, subsidence and earthquake or volcanic phenomena), and to monitor the stability of buildings and infrastructures. In this work, we present the first application of the interferometric SAR (InSAR) technology to high-resolution monitoring of ground deformations over an entire continent, based on full-resolution processing of the whole archive of past and future Sentinel-1 (S1) satellite acquisitions over most parts of Europe. The European Ground Motion Service (EGMS) is funded by the European Commission and forms an essential element of the Copernicus Land Monitoring Service (CLMS) managed by the European Environment Agency. Upscaling from existing national precursor services to pan-European scale will be challenging. Although low-resolution datasets have been recently produced at this scale, full-resolution processing is more complex, potentially revealing errors that would be disguised or suppressed otherwise at coarser scale. The project will utilise the most advanced persistent scatterer (PS) and distributed scatterer (DS) InSAR processing techniques, and a high-quality GNSS model, required to calibrate the InSAR products. To foster acceptance and a maximum/optimum use of the service by the growing Copernicus user community and the public at large, the EGMS will provide tools for visualization, exploration, analysis and download of the ground deformation measurements, as well as elements to promote best practice and user uptake.

How to cite: Costantini, M., Minati, F., Trillo, F., Ferretti, A., Novali, F., Passera, E., Dehls, J., Larsen, Y., Marinkovic, P., Eineder, M., Brcic, R., Siegmund, R., Kotzerke, P., Kenyeres, A., Proietti, S., Solari, L., and Andersen, H.: European Ground Motion Service (EGMS), EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13748, https://doi.org/10.5194/egusphere-egu21-13748, 2021.

The increasing amount of SAR data available opens new challenges in terms of data storage management and processing load. Fully exploit those large databases requires the developement of automatic processing  chains. The InSAR Mass processing Toolbox for Multidimensional time series (MasTer) is able to combine any type of SAR data to produce automatic unsupervised 2D ground deformation time series, from data download up to updated displaying of 2D time series results on a web page, updated incrementally as soon as a new image is available. We present our last methodological improvement based on the computation of a coherence proxy to guide a pair selection optimization, balancing the use of each image as master and slave. Whereas this new tool reduces the number of DInSAR interferograms computed by up to 75%, it also increases the signal to noise ratio of the time series by reducing the influence of DEM errors and atmospheric noise.

How to cite: Smittarello, D., d'Oreye, N., Derauw, D., Samsonov, S., and Jaspard, M.: A New Optimisation Tool for Automatic InSAR Time Series Processing with MasTer., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-410, https://doi.org/10.5194/egusphere-egu21-410, 2021.

How to cite: Piter, A., Haghshenas Haghighi, M., and Motagh, M.: Towards Efficient Online Deformation Monitoring of Transport Infrastructure using Sentinel-1 Interferometry, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4115, https://doi.org/10.5194/egusphere-egu21-4115, 2021.

InSAR can measure surface deformation in all-weather conditions and has been widely used to study landslides, land subsidence, and many geophysical processes. Since the phase of radar echo is measured in 2π rad modulo (wrapped), phase unwrapping is an indispensable step for InSAR, and its reliability directly determines the feasibility of deformation monitoring. However, temporal and spatial decorrelation often leads to severe noises, localized deformation or strong atmospheric turbulence may result in dense fringes, both making traditional unwrapping methods fail in acquiring continual unwrapped phases. Here, we present a deep convolutional neural network (DENet) to identify the probability of phase discontinuities between every two adjacent pixels in the interferogram and apply the probability as cost in the widely-used minimal cost flow solver to achieve phase unwrapping. To train the network effectively, we design a simulation strategy to generate sufficient training samples: the terrain-related phases are used as the background phases, and the deformation phases, atmospheric turbulence phases, and noises are superimposed to build the training samples. Unlike classical methods such as GAMMA and SNAPHU that use the coherence map as the quality index, we use the probability of phase discontinuities estimated by the DENet as the arc-cost of the minimum cost flow problem. We apply the proposed method to unwrap simulated and real interferograms and compare the results with 8 existing methods (including traditional and deep learning-based ones).  On the simulated data set, the root-mean-square error (RMSE) of the proposed method is lower than all the 8 existing methods. We also test different methods to unwrap the real Sentinel-1 interferograms and verified the reliability using ALOS-2 data with a nearly identical acquisition period. Our results show strong robustness and stability when unwrapping very large interferograms with complicated phase patterns. The proposed method takes advantages of both deep learning and traditional minimal cost flow solver, which can effectively unwrap interferograms with low coherence and/or dense fringes, providing strong potential for large-scale SAR interferometry applications.

How to cite: Wu, Z., Wang, T., Wang, Y., and Ge, D.: Two-dimensional phase unwrapping integrating deep learning and minimal cost flow, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3878, https://doi.org/10.5194/egusphere-egu21-3878, 2021.

Satellite remote sensing is playing an increasing role in rapid mapping of damage after natural disasters. In particular, synthetic aperture radar (SAR) can image the Earth’s surface and map damage in all weather conditions, day and night. However, current SAR damage mapping methods struggle to separate damage from other changes in the Earth’s surface. In this study, we propose to map damage using the full time history of SAR observations of an impacted region from a single satellite constellation in order to detect anomalous variations in the Earth’s surface properties due to a natural disaster. We quantify Earth surface change using time series of sequential interferometric SAR coherence, then use a recurrent neural network (RNN) as a probabilistic anomaly detector on these coherence time series. The RNN is first trained on pre-event coherence time series, and then forecasts a probability distribution of the coherence between pre- and post-event SAR images. The difference between the forecast and observed co-event coherence provides a measure of the confidence in the identification of damage. The method allows the user to choose a damage detection threshold that is customized for each location, based on the local temporal behavior before the event. We apply this method to calculate estimates of damage for three earthquakes using multi-year time series of Sentinel-1 SAR acquisitions. Our approach shows good agreement with measured damage and quantitative improvement compared to using pre- to co-event coherence loss as a damage proxy.

How to cite: Stephenson, O., Köhne, T., Zhan, E., Cahill, B., Yun, S.-H., Ross, Z., and Simons, M.: Deep Learning-based Damage Mapping with InSAR Coherence Time Series, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6995, https://doi.org/10.5194/egusphere-egu21-6995, 2021.

 In InSAR analysis, the effect of microwave propagation delay in the Earth's atmosphere such as the nuetral atmospheric delay and the ionospheric delay is recognized as the primary noise for surface deformation researchs like Earthquake source modeling, tectonic fault motion, and volcanic activity monitoring. Although, for the ionospheric delay, we can now apply the range split spectrum method (SSM) to effectively mitigate it, the mitigation of the neutral atmospheric delay noise remains difficult and is the research problem to be solved. Recently, Arief and Heki (2020) developed a new method to retrieve two-dimensional Zenith Wet Delay (ZWD) distribution at sea level based on the GNSS ZWD and delay gradient derived from the Japanese GNSS network named GEONET. Here we proposed a new InSAR delay correction method based on modifying the Arief and Heki's method and applied it to the ALOS-2 ScanSAR interferograms to evaluate its effectiveness.
  In our study, we used 5-minute interval GNSS PPP data provided by the Nevada Geodetic Laboratory in Nevada University, Reno. Since InSAR atmospheric delay contains both hydrostatic and wet components, we estimated two-dimensional Zenith Total Delay (ZTD) distribution at sea level instead of ZWD, and we simaltaneously estimated height dependence of ZTD as a linear function. The model cosists of the regularly distributed grids with 5 km interval and the height dependence. The retrieval of ZTD distribution is performed by the least squares inversion with the smoothing constraint. The retrieved ZTD is then projected onto the InSAR line-of-sight direction and calculated a difference of two epochs to generate an InSAR delay model. Interferograms were generated by RINC ver.0.41r using 16 ALOS-2 ScanSAR level 1.1 full-aperture data covering Kanto Plain in Japan. We applied the SSM to all of interferograms to correct the ionospheric delay noise before applying the proposed tropospheric delay correction.
  The result of applying proposed correction method showed that the correction effectively reduced the phase variance, especially in the long-wavelength phase variation. The phase standard deviation (STD) in the whole scene decreased from 35.95 mm to 25.84 mm by applying the proposed GNSS-based correction method. For comparing effectiveness of the proposed method with existing methods, we also calculated the phase STD derived by applying the GACOS model and the numerical weather model-based correction using the Japan Meteorological Agency's Meso-scale model data. The result of comparison showed that the proposed GNSS-based method most reduced the whole-scene phase STD. The GACOS model decreased the STD to 30.96 mm, and the JMA MSM decrease to 27.71 mm, respectively. We then calculate the distance-dependence of the phase STD based on the variogram model. The variogram derived from all the interferograms showed the speriority of the GNSS-based correction, although the STD in distance shorter than 20 km seemed no differences between correction methods.

How to cite: Kinoshita, Y.: Developing InSAR atmospheric delay correction model based on GEONET ZTD and its gradient, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8146, https://doi.org/10.5194/egusphere-egu21-8146, 2021.

Geomorphic hazards such as landslides and flash floods (hereafter called GH) often result from a combination of complex interacting physical and anthropogenic processes across multiple spatial and temporal scales. In many instances, landslides and flash floods occur very quickly, sometimes in a matter of a few hours occasionally leading to catastrophic impact on human lives. Given that they are mostly related to common meteorological events, landslides and flash floods frequently co-occur and interact, leading to more severe impacts. The tropics are environments where GH are under-researched while, in the meantime, GH disproportionately impact these regions. In addition, GH frequency and/or risks in the tropics are expected to increase in the future in response to increasing demographic pressure, climate change and land use/cover changes. To understand the role of climate and landscape (topographic and land use/cover) in controlling the spatio-temporal distribution of GH in the context of environmental changes, establishing a regional-scale inventory of GH events that are localised accurately in space and time is essential. Since the tropics are frequently cloud covered, an accurate characterization of the timing of GH at a regional scale can only be achieved through the combined use of optical and Synthetic Aperture Radar (SAR) remote sensing. Here, the objective is to present the first phase of the ongoing development of a remote sensing methodology that aims to identify accurately in space and time the GH events in the western branch of the East African Rift using a multi-temporal change analysis approach combining optical and SAR amplitude and phase coherence data. Copernicus Sentinel 1 (SAR imagery) and Sentinel 2 (optical imagery) are the key satellite products used. Next to being open access, they offer a very good trade-off between frequency of acquisition and spatial resolution. The detection methodology is calibrated and validated using information from three citizen observer networks and higher spatial resolution imagery. Preliminary results show clear changes in SAR amplitude and phase coherence time-series at the time of GH event occurence. Various change detection approaches (difference, log-ratio, normalized difference, correlation) are explored and provide ideas for detection of GH timing within the time-series. We present the ongoing method development with a specific focus on recent extreme GH events in the region.

How to cite: Deijns, A., Kervyn, F., Dewitte, O., Thiery, W., Malet, J.-P., and d'Oreye, N.: A multi-sensor satellite method to spatial and temporal detection of landslides and flash floods in cloud-covered tropical environments: the western branch of the East African Rift, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8909, https://doi.org/10.5194/egusphere-egu21-8909, 2021.

The Persistent Scatterer Interferometry (PSI) technique is being more and more used at all scale’s applications. Several regional and national Ground Motions Services based on PSI are nowadays active and operational. The European Ground Motion Service project is going to generate a displacement map over the whole Europe once per year. This context makes indispensable tools and methodologies that facilitate the management and analysis of huge amount of data and information. The ADATools are a set of tools that can be considered a first step in this direction, they are simple and fast tools to firstly extract and make a preliminary interpretation of the main detected Active Deformation Areas (ADA). The ADATools includes: i. the ADAFinder, detecting the main ADA and giving information for each polygon as well as a Quality Index (representing the noise of time series of deformation); ii. the LOS2hv, that is used in case we have datasets from both ascending and descending geometries to derive the horizontal (east-west) and vertical components of the movement; and iii. the ADAClassifier, that makes a preliminary classification of the ADA between landslide, subsidence, settlement, and sinkholes, based on available external data (i.e., DEM, geology, inventories, infrastructures). In this presentation, the algorithm, and performances of the ADATools are presented and some results of their application are showed. Specifically, some results over an area of the Granada Province (S Spain), achieved in the framework of the Project RISKCOAST (funded by the IV Interreg Sudoe Programme through the European Regional Development Fund), will be used to illustrate ADATools performance.

How to cite: Monserrat, O., Barra, A., Reyes, C., Tomas, R., Navarro, J., Galve, J. P., Solari, L., Sarro, R., Azañon, J. M., Luque, J. A., and Mateos, R. M.: ADATools for Persistent Scatterer Interferometry based displacement maps analysis: an example in Granada Province (Spain), EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9063, https://doi.org/10.5194/egusphere-egu21-9063, 2021.

The region of the Argentine Central Andes located between 21° S and 25° S is characterized by multiple morphotectonic provinces that strongly control structural and geomorphologic surface deformation. This work focuses on the Puna Plateau and the Eastern Cordillera. The Puna is part of the orogenic Central Andean Plateau and is hydrologically dissected into internally drained catchments with mostly hyper-arid climatic conditions. The Puna’s eastern edge is bordered by the fold-and-thrust belt of the Eastern Cordillera with peaks up to ~6000 m. Both areas are repeatedly affected by earthquakes with surface deformation but seldom surface ruptures.

This research focuses on the first assessment of the L-band SAOCOM 1A data for estimating surface deformation rates. The SAOCOM 1A satellite, launched in 2018, integrates the SAOCOM mission managed by the Argentinean Space Agency (Comisión Nacional de Actividades Espaciales, CONAE). These interferometric analyses are combined with results from C-band Sentinel-1 data. Examples are shown from the surface deformation associated with the magnitude 6.3 earthquake on 30 November 2020, with an epicenter located around 70 km W of San Antonio de los Cobres village in the Southeastern portion of the Puna Plateau (~24.332° S, ~67.005° W; United States Geological Survey). Additional examples are shown for slow-moving landslide velocity estimation in the Calchaquíes range (Eastern Cordillera). Our research highlights the capabilities of the new SAOCOM satellite mission for estimating surface deformation and exploits the strength of L-band SAR in vegetated terrain.

How to cite: Viotto, S., Bookhagen, B., Toyos, G., and Torrusio, S.: Assessing ground deformation in the Central Andes (NW Argentina) with Interferometric Synthetic Aperture Radar analyses: First results of SAOCOM data and Sentinel-1 data, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12474, https://doi.org/10.5194/egusphere-egu21-12474, 2021.

On August 24, 2016, a magnitude-6.2 earthquake in Central Italy resulted in at least 290 deaths, significant ground failure (including landslides and liquefaction), and building damage. After the event, the NASA Advanced Rapid Imaging and Analysis team produced Damage Proxy Maps (DPM) that reflect earthquake-induced surficial changes using synthetic aperture data from the COSMO-SkyMed satellite. However, exact causes of these surface changes, e.g., ground failure, building damage, or other environmental changes, are difficult to directly differentiate from the satellite images alone. For example, changes could reflect building damage, landslides, the co-occurrence of both, or numerous other processes that are not related to the earthquake. Alternatively, existing ground failures models are useful in locating areas of higher likelihoods but suffer from high false alarm rates due to inaccurate or incomplete geospatial proxies and complex physical interdependencies between shaking and specific sites of ground failure. In this work, we present a joint Bayesian updating framework using a causal graph strategy. The Bayesian causal graph models physical interdependencies among ground shaking, ground failures, building damage, and remote sensing observations. Based on the graph, a variational inference approach is designed to jointly update the estimates of ground failure and building damage through fusing traditional geospatial models and the remotely sensed data. As a case study, the DPMs in Central Italy are input to the model for jointly calibrating and updating the probability of ground failure estimations as well as for estimating building damage probabilities. The results showed that by incorporating high-resolution imagery, our model significantly reduces the false alarm rate of ground failure estimates and improves the spatial accuracy and resolution of ground failure and building damage inferences.

How to cite: Xu, S., Dimasaka, J., Wald, D. J., and Noh, H. Y.: Fusing Damage Proxy Maps with Geospatial Models for Bayesian Updating of Seismic Ground Failure Estimations: A Case Study in Central Italy, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13619, https://doi.org/10.5194/egusphere-egu21-13619, 2021.

To improve safety in large cities, products and services exploiting Earth Observation (EO) technologies can be used to map vulnerable urban areas potentially affected by geohazards, with the aim of reducing human and economic losses caused by natural disasters. This work aims to increase the use of multi-mission EO derived products and services to assess urban vulnerability and geohazards, raising early awareness and training key users and decision makers on the use of EO derived products and services.

Currently, the InSAR processing tools from Geohazards Exploitation Platform (GEP) funded by European Space Agency, provide massive and dense surface displacement information, and availability of such data is expected to be expanded soon with the upcoming European Ground Motion Service being developed by the European Environment Agency. As the main end users are not trained to understand and analyze this type of data, the EU founded e-Shape project, in collaboration with the national Geological Surveys, is introducing a methodology for the use of InSAR products and supporting them to co-design specific products useful for the dissemination of information to the users active in key societal sectors  (local and regional administrations, and civil protection authorities). To this end, four products with different requirements have been developed, including the InSAR map, the InSAR validation report, the active geohazards report and the vulnerable urban areas report. These four products describe the displacements of the area, their accuracy, their relationship to triggers and the potential problems they could create, providing information for both technical staff and non-technical managers and decision-makers.

How to cite: López-Vinielles, J., Ezquerro, P., Herrera-García, G., Béjar-Pizarro, M., Comerci, V., Sheehy, M., Poyiadji, E., Koçiu, A., Mikulėnas, V., and Jemec-Auflič, M.: Assessing Geo-hazard Vulnerability of Cities and Critical Infrastructures: Working in the e-Shape Framework, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16135, https://doi.org/10.5194/egusphere-egu21-16135, 2021.