SAR remote sensing is a valuable tool for monitoring and responding to natural and anthropogenic hazards. Especially with the unprecedented spatio-temporal resolution and the rapid accumulation of SAR data collections from various dedicated SAR missions, we have more opportunities to exploit hazard-related signals from the SAR phase and amplitude imagery, characterize the associated spatio-temporal ground deformations and land alterations, and decipher the operating mechanism of the geosystems in geodetic timescales. Yet, optimally extracting relevant information from SAR imagery, designing a proper strategy for each individual hazard, optimally managing and archiving SAR data, and integrating all possible data, are still considerable challenges. Therefore, in this session, we welcome contributions that focus on (1) new algorithms to retrieve critical products from SAR remote sensing (big data) in an accurate, automated, and efficient framework; (2) SAR applications for anthropogenic and natural hazards including mining, oil/gas production, fluid injection/extraction, critical infrastructure, sinkholes, land degradation, peatlands, glaciers, permafrost, flooding, landslides, earthquakes, and volcanoes; and (3) mathematical and physical modeling of the SAR products such as estimating displacement velocities and time series for a better understanding on the surface and subsurface processes. In addition, we welcome applications on ground deformation in coastal areas in the context of sea level rise.
vPICO presentations: Wed, 28 Apr
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, 2020.
The objective of the research was to investigate the process of rock mass recompaction related to groundwater rebound induced by underground mining. Research has been conducted in the area of the closed copper ore mine (Konrad) as well as the anhydrite and gypsum mine (Lubichów) in south-eastern Poland.
The mining operation was carried out in the years 1944-2001 in the area of the Konrad mine and 1944-2015 in the area of the Lubichów mine. It resulted in substantial land subsidence of up to 1.4 m and drainage of the aquifer system. However, it is estimated that the subsidence caused by groundwater pumping during these periods was 0.3 m in total. Furthermore, the spatial extent of the depression cone in the aquifer system immediately after the cessation of exploitation significantly exceeded the limits of the mining areas. Following the closure of the mine, a continuous increase in the groundwater head and land uplift is observed.
Classical survey results and the Persistent Scatter Satellite Radar Interferometry (PSInSAR) method were used to determine land surface movements in the period from November 2015 to November 2020. The results of the research show in the area of the Lubichów mine closed in June 2015, vertical land uplift reached a maximum of approx. 92 mm in that period. At the same time, in the Konrad mine area, closed in March 2001, no significant land uplift was observed. However, the main part of the investigation concerned the development of a novel method of land uplifting prediction. As a result, an attempt was made to comparatively analyze the dynamics of land uplift associated with the life cycle of the mine and the increase in the groundwater head.
These analyzes allowed the time factor for the modelling of the land uplift to be determined. This time factor is approx. 5 months in the area of the Lubichów mine and indicates that there is a time lag between the start of the groundwater head increase and the land uplift occurrence. Also, the investigation revealed that land uplift will occur in the analyzed area for the next five years. However, the dynamics of such movements will gradually decline in the years to come.
The methodology developed could be applied to any post-mining area where groundwater rebound-related uplifts are observed. It may be an appropriate tool for estimating both the time during which the land uplift is expected to begin after the mine drainage has been stopped, as well as the total duration of the land uplift phenomena.
How to cite: Guzy, A., Witkowski, W., Hejmanowski, R., and Malinowska, A.: The Model of Land Surface Movements Induced by Groundwater Rebound in the Area of Former Mining Exploitation, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5675, https://doi.org/10.5194/egusphere-egu21-5675, 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 , one of the fastest sinking cities globally (up to 40 cm/year subsidence rates), and the state of Aguascalientes , 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.
 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
 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.
The cavern field at Epe has been brined out of a salt deposit belonging to the lower Rhine salt flat, which extends under the surface of the North German lowlands and part of the Netherlands, and is used to store e.g. natural gas, brine and petroleum. Cavern convergence and operational pressure changes cause surface displacements that have been studied for this work with the help of SAR interferometry (InSAR) using distributed and persistent scatterers. Vertical and East-West movements have been determined based on Sentinel-1 data from ascending and descending orbit. Simple geophysical modeling is used to support InSAR processing and helps to interpret the observations. In particular, an approach is presented that allows to relate the deposit pressures with the observed surface displacements. Seasonal movements occurring over a fen situated over the western part of the storage site further complicate the analysis. Findings are validated with ground truth from levelling and groundwater level measurements.
For porous storage sites the geomechanic response can be described as elastic: displacement is almost proportional to reservoir pressure and displays the same pronounced seasonal behavior. At Epe the visco-elastic response of the salt layer has to be considered. The general appearance of the surface displacement is that of a strongly smoothed and shifted version of the cavern pressure curve. To deal with this situation a temporal model for displacement with pressure changes (pressure response) is derived that relates cavern pressure with observed displacement based on the theory for visco-elastic behavior of a Kelvin-Voigt body.
In order to deal successfully with the challenging displacement field at Epe several algorithmic improvements were implemented. To obtain a more complete picture of the displacement field DS pre-processing has been combined with StaMPS. Furthermore, StaMPS was modified in order to support unwrapping with a phase model composed of linear trend, pressure response and a seasonal component (caused by ground water level changes). Finally, refining the iterative estimation scheme of StaMPS helped avoiding leakage of the displacement signal to the spatially correlated noise term.
Determining vertical and east-west displacements from InSAR line-of-sight displacements is fundamental for interpretation and integration with levelling data. In this study, a basic method of orbit combination and another one supported by a simplistic geophysical model were applied in order to obtain 2D-displacements. For the basic method the north-south component was handled as if it were zero, while the geophysical model predicts the LOS effect of NS displacements. It assumes that caverns act as spherical pressure/volume sources embedded in an elastic half space (“Mogi” sources). To incorporate the visco-elastic component, each cavern is encompassed by a spherical salt shell that obeys the Kelvin-Voigt differential equations. The model is used here to describe either the parameters of the linear component of the displacement model or of the pressure response. A novelty of the orbit combinations implemented for this study is that the different components of the phase model are combined separately. This allows for a better understanding of the phenomena that contribute to the displacement field.
How to cite: Even, M., Westerhaus, M., and Simon, V.: Analysis of Complex Surface Displacements above a Storage Cavern Field in NW-Germany, Observed by InSAR, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6345, https://doi.org/10.5194/egusphere-egu21-6345, 2021.
Unrestrained urbanisation and rapid land use land cover changes can impact underlying aquifer systems, resulting in the instances of land subsidence. Thus, monitoring of groundwater induced land movement is an important part of environmental information systems and helps maintain the safety and economics of a city. Interferometric Synthetic Aperture Radar (InSAR) can facilitate monitoring of land movement and observed boreholes can facilitate groundwater monitoring. In this study, we used Sentinel-1 radar images to obtain land movement using Persistent Scatterer InSAR (PSInSAR) technique in the ENVI SARscape software package. The land movement has been studied between October 2016 and October 2020, using 98 SAR images for Delhi and 100 SAR images for London. This is the first time that such a comparison has been made between these two great cities.
The land movement InSAR velocity maps for both these cities showed long-term, decreasing, complex, non-linear patterns in the spatial and temporal domain, with few areas of heave and a fair amount of subsidence. The land movement varied between -18 mm/year to +20 mm/year for Delhi and -10 mm/year to +9 mm/year for London. The underground metro construction played an important role in controlling the land movement pattern of Delhi. Its Phase III metro line was mostly built between the years 2015 and 2020 with 28 underground stations, 11 route extension and 3 new lines, namely Pink, Magenta and Grey lines. Similarly, construction of the northern line extension, the Channel Tunnel Rail Link and the Lee tunnel directly affected the land movement pattern of London. In addition, the ground movement was compared to observed groundwater values obtained from various boreholes across both these cities. The extraction and recharge of groundwater to meet the demands of an ever-increasing population directly affected the land movement patterns in both cities. It was observed that when large volumes of groundwater are extracted, then it leads to land subsidence, and when groundwater is recharged, then surface uplift is witnessed. The reasons for this subsidence pattern are consistent for both these cities in a few places, while they are completely different at some other locations.
Delhi has been declared as groundwater critical zone by the government of India, while London is not under critical zone. Delhi is one of the most exploited city with regards to groundwater, owing to its urban fabric and ever-increasing population, and these results reflect that. A similar pressure is exerted on London’s groundwater by its ever-increasing population, which is not recognised by a critical status but is borne out by these results. Along with the groundwater extraction, sub-surface geology, underground construction, and metro extensions all contribute to form a complex land movement pattern. This study can serve as a guideline to government agencies in identifying the areas and extent of groundwater induced land subsidence, so that they can take proper steps to mitigate it.
How to cite: Agarwal, V., Kumar, A., Gomes, R. L., and Marsh, S.: Comparison of groundwater induced Land subsidence in London and Delhi using PSInSAR, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10707, https://doi.org/10.5194/egusphere-egu21-10707, 2021.
On 17 June 2020, a large debris flow triggered by continuous heavy precipitation hit the Danba County in southwest China, blocked the river and a barrier lake was formed. Meanwhile, on the other side of the river, a large-scale landslide was triggered due to the reactivation of the ancient landslide body. Then an evacuation of more than 20000 people leaving their home town was urgently conducted.
This study exploits multi-sensor remote sensing techniques to assess landslide deformation, precursory deformation and post-failure motion of Danba landslide. We start with optical remote sensing images using the cross correlation method to investigate the overall information about this collapse, such as magnitude and moving direction of the sliding. Two high-resolution remote sensing optical images from Planet are processed right before and after the failure.
Moreover, we apply the advanced Multi-temporal InSAR (MTI) techniques such as Persistent Scatterer Interferometry (PSI) and Small Baseline Subsets (SBAS) to analyze the precursors of the landslide over the long term. Based on the results of optical remote sensing, the descending Sentinel-1 data in 2014-2020 are extensively exploited with a better geometry of satellite observation. The long-term and transient of the deformation are analyzed against variations of precipitation, and then the related early warning systems are further explored.
The last stage of the work is the monitoring of current movements in the collapse region after the failure. It is explored by using multiple SAR datasets including C-band Sentinel-1 and X-band TerraSAR-X (TSX) high-resolution SAR images. With the help of the field works by our collaborators, stable artificial corner reflectors (CR) are deployed on selected sites to evaluate their performance in deriving landslide kinematics. Different from the traditional Triangle CR (TCR), the new design of dihedral CR (DCR) are introduced and exploited on the scene. The performance of this new design towards MTI processing and sub-pixel offset-tracking processing is examed and tested in this study. Results are presented and further discussed for a better assessment of Danba landslide.
The results of this paper can provide new strategies for developing an early warning system in this landslide using remote sensing technologies. Besides, the post-failure results are compared with the pre-event analysis, which could give an associated and comprehensive understanding of the whole landslide kinematics.
How to cite: Xia, Z., Motagh, M., and Li, T.: Precursory and post-failure analysis of landslide deformation in Danba County, China using optical remote sensing and Multi-temporal InSAR (MTI) methods with corner reflectors, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6664, https://doi.org/10.5194/egusphere-egu21-6664, 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.
Small-to-moderate size earthquakes occur much more frequently than large ones but are general less studied by InSAR, despite that they also provide critical information about the physics of faulting and earthquake mechanisms. The weak coseismic deformations contaminated by severe atmosphere turbulences make them difficult to be studied by single interferogram. Since the launchings on April, 2014 and April, 2016, Sentinel-1 A/B satellites began to provide large-coverage SAR images in short revisited period (6 or 12 days) with 250 km frame width. The high-temporal sampling rate of Sentinel-1 data produce sufficient images for the stacking process to greatly reduce the local atmospheric turbulence that is difficult to be handled by numerical weather models. This procedure allows the extraction of very weak coseismic deformation (i.e. sub-centimeter) for small-to-moderate size earthquakes and systematical static slip inversions of the earthquakes in a tectonically active region by InSAR.
Here we report this stacking method and a new downsampling strategy based on quadtree mesh obtained from preliminary slip model to efficiently reduce the number of unwanted data points. Applying the proposed methods, we successfully retrieve coseismic deformations for 33 earthquakes (Mw4.1-Mw6.6) occurred in west China from Nov, 2014 to Jul 2020. Among these earthquakes, the smallest peak Line-of-Sight coseismic deformation is only ~6 mm. These InSAR-based earthquake catalogs show robust and precise absolute location (latitude, longitude and depth), therefore can be used as benchmark events to calibrate seismic based catalogues. However, strong trade-offs between earthquake source parameters (e.g. fault size vs slip) exist when the earthquake magnitude is small (in general smaller than Mw5.5). Such trade-offs are rooted due to the smaller deformation gradient in comparison with larger earthquakes. For the moderate size earthquakes (Mw6.0-6.6), the comparison between equivalent moment tensor from InSAR slip models and GCMT/W-phase solutions show that large CLVD components, as shown in the seismic-based moment tensor solutions, are mostly not necessary to explain the InSAR data. We suggest to combine geodetic and seismic datasets for more comprehensive and accurate earthquake source parameter inversions.
How to cite: Luo, H., Wang, T., Wei, S., and Liao, M.: Deriving centimeter-level coseismic deformations and source parameters of small-to-moderate earthquakes from time-series Sentinel-1 SAR images, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5361, https://doi.org/10.5194/egusphere-egu21-5361, 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.
Landslide is one of the major geohazards that endangers the human society and threatens the safety of life and properties. In recent years, attentions have been paid to the Synthetic Aperture Radar interferometry (InSAR) for landslide monitoring with many successful applications. However, it is still difficult to effectively and automatically identify slow-moving landslides distributed in a large area because of phase unwrapping errors, troposphere turbulence and vegetation cover. Here we propose a method combining phase-gradient stacking and the widely-used neural network for tiny object detection: You Only Look Once (YOLOv3) to detect slow-moving landslides from large-scale interferograms. Using the time-series Sentinel-1 SAR images acquired since 2016, we develop a burst-based, phase-gradient stacking algorithm to sum up phase gradients along the azimuth and range directions of short-temporal-baseline interferograms. The stacked phase gradients clearly present the characteristics of localized surface deformation, mainly caused by slow-moving landslides, avoiding the errors result of multiple phase unwrapping in time-series analysis and atmospheric effects. We then train the YOLOv3 network with the stacked phase-gradient maps of known landslides to achieve the quick and automatic landslide detection. We apply our method in the middle section of the Yalong River in mountainous area of western China, with an area of 180,000 km2. In addition to the slides that have been published in the inventory, we identify many more slow-moving landslides that cannot be detected by traditional time-series InSAR analysis methods. Our results demonstrate the potential usage of the proposed methods for slow-moving landslide detection in large area, which can be applied before the time-consuming time-series InSAR analysis.
How to cite: Fu, L. and Wang, T.: Detection of slow-moving landslides in large area using InSAR phase-gradients stacking and YOLOv3, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3794, https://doi.org/10.5194/egusphere-egu21-3794, 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 16th 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.
Land deformation due to natural and anthropogenic impacts considered to be one of the challenging environmental problems in the Aswan area located in the southern part of Egypt. Specifically, we applied multi-sensor analysis in order to record the slow rate of subsidence with a high spatial resolution of COSMO-SkyMed (X-band) and Sentinel-1 TOPSAR (C-band) scenes. We proposed multi-temporal DInSAR data analysis by means of ascending and descending orbit tracks during the recent time period of 2012-2017. The stacked DInSAR results reported the occurrence of land subsidence of active urban areas. A strong correlation between the ground truth data, ground leveling, and the estimated Line of Sight (LOS) displacement time series values are reached, assuming the ground deformation controlled by seasonal surface water loading, lithological units, and subsurface water activity. The detection of short-term displacement highlights the priority of groundwater management plans in the affected urban areas.
How to cite: Medhat, N., Yamamoto, M., and El-Qady, G.: Multi-Frequency and Multi-Temporal InSAR Analysis for Monitoring Land Subsidence, Aswan City, Egypt, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-292, https://doi.org/10.5194/egusphere-egu21-292, 2020.
Tehran, as a megacity in Iran, is exposed to a high rate of land deformation. Recent research shows average land deformation speed is up to 39.9 mm/year in southeast plain (from 2014 to 2017) and groundwater extraction in Tehran plain for agricultural and industrial demands is the most probable driving mechanism. It is undisputed that infrastructure and structure in Tehran are continuously under threat by such rapid land subsidence, and this subsidence may also lead to significant economic losses such as structural damage and high maintenance costs for roads, railways, dikes, pipelines, and buildings. Therefore, when, where and why the subsidence did/does occur has to be closely monitored and analysed considering future planning and the importance of infrastructure and structure damage, which has a profound effect on human activities. This study attempts to use Sentinel 1 SAR data to map land subsidence in Tehran and validate the results by using GPS data.
We implemented the standard persistent scatterer interferometry (PSI) approach with the customized parameter configuration, for Tehran with an area of about 1600 km2. 52 Sentinel 1A (C-band) dataset acquired between 2018 and 2019 were collected. There were 1,746,317 PS measurement points generated. The PSI results illustrate that the maximum loss of elevation over the time period did amount to 11.7 cm/year.
We used the GPS observations between 1/1/2018 and 27/10/2019, from the two GPS stations GPS-m318 (35.64 N, 51.29 E), GPS-m020 (35.58 N, 51.42 E) to evaluate the PSI deformation results. We found that the maximum and minimum double difference between GPSs and PSs were 0.0536 m, 0.0015 m respectively; moreover, the corresponding histogram shows that most of the double-difference values are in the interval of [-0.01 0.01] m, and the RMSE is 0.011 m. Besides, we also applied the velocity comparison of double-differenced GPS and PS, which shows that the PS measurements matched well with the GPS observations.
By comparing the water table variations and PSI-derived land deformation, we found that the groundwater withdraw could be a major driving mechanism but the variation in soil type also plays an important role. For instance, although the groundwater levels (Xutm = 503498, Yutm = 3948916) has decreased by approximately 13m from 2012 to 2017 at the place of Andisheh-Jadid, no subsidence was detected possibly due to the presence of well grade layers at that location.
How to cite: Rajabi Baniani, S., Chang, L., and Maghsoudi, Y.: Mapping and analyzing land subsidence for Tehran using Sentinel-1 SAR and GPS and geological data, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-295, https://doi.org/10.5194/egusphere-egu21-295, 2020.
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 , 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.
 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.
Satellite images have been interpreted to establish the basic patterns of land surface deformations and predict the development of hazardous geological processes within the Solotvyno salt dome structure and the adjacent territories (Transcarpathia, Ukraine). Solotvyno rock salt deposit is one of the largest in Ukraine. Geotechnical and hydro-geological problems at the deposit have started to accumulate since the mid-90s and have led to a dangerous environmental technogenic situation that was given the national emergency status in 2010. This multi-hazard geo-ecological situation is a result of overlapping both anthropogenic “post-mining” (karst, subsidence, sinkhole formation, ground surface collapses, mine flooding, slope mass movements) and natural (flooding, landslides, etc.) hazardous geological processes, in particular, the disturbance of land surface, which is the sign of the uncontrolled development of salt karst, flooding and could result in transboundary pollution of the Tysa River, etc.
To forecast the development of hazardous geological processes, monitoring was implemented by using innovative techniques for processing satellite radar data, such as “Persistent Scatterers (PS)”, “Small Baseline Subset (SBAS)”. Due to the use of long-time series of images obtained with a synthetic aperture radar (SAR), the errors of orbital data and the effects of atmospheric phenomena have been effectively suppressed. The results of processing are digital maps, with the accuracy of evaluating the average vertical displacement rate of the objects being 2–4 mm/year when using the “PS” technique and 6–15 mm/year if using the “SBAS”.
A highly accurate evaluation of the vertical displacements of objects and land surface has been carried out using interferometric processing of satellite radar monitoring data by means of new satellite constellations including Sentinel-1A and 1B (DInSAR analysis data for 2016–2020, SBAS approach, Copernicus EMSN-030, EMSN-064; PS+SBAS approach, Center of the Special Information Receiving and Processing and the Navigating Field Control, Ukraine). The research area was 33 sq. km. Information end products (raster and vector) have been created, which permitted the changes in spatial and temporal dimensions to be analyzed. The values and areas of concentrated land surface deformations have been determined within the zone of anthropogenic and natural karst development. The areas of land surface subsidence with the average rate of vertical displacements from -6 to -94 mm/year have been digitized using GIS tools.
The assessment of anthropogenic hazards for the Solotvyno salt dome structure and adjacent territories has been provided. It has been determined that mines No7, 8, and 9 pose an anthropogenic threat to the safety of Solotvyno community inhabitants.
The reconstruction of land surface vertical displacements in time has been carried out within the studies performed. In order to ensure life safety in Solotvyno, the results will be used in territory development and in setting up the system of monitoring. In view of the complicated geo-ecological situation, the development and functioning of a permanent geo-ecological monitoring system for the Solotvyno mining area and the adjacent territories is the top-priority objective.
The research has been carried out with the EU financial support: projects REVITAL 1 (HURSKOVA/1702/6.1/0072) and ImProDiReT (No. 783232).
How to cite: Pakshin, M., Shekhunova, S., Stadnichenko, S., and Liaska, I.: The satellite radar monitoring for anthropogenic and natural geological hazards mapping within the Solotvyno mining area (Transcarpathia, Ukraine), EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8417, https://doi.org/10.5194/egusphere-egu21-8417, 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.
Ground settlement and associated deformation of existing infrastructure is a major risk in urban development projects. Project owners have a responsibility to document and manage settlement records before, during and after construction works. Traditionally, land surveying (e.g. leveling and total station) techniques have been the state-of-practice to provide settlement monitoring data. However, in big infrastructure projects, conventional geodetic data acquisition is a major cost driver. Modern space-borne radar interferometry (InSAR) provides the opportunity to drastically increase the number of monitored locations, while at the same time reducing expenses for traditional geodetic survey work. Furthermore, the method allows for highly effective monitoring during all phases of a project.
The application of InSAR technology is demonstrated for three large development projects near Oslo, the capital of Norway. Showcase examples include a new highway development project and two railway line upgrade projects. In two of the cases, InSAR monitoring was performed by exploiting very high resolution TerraSAR-X data (ca. 1.5 x 1.5 m spatial ground resolution), and in one case, using high resolution Radarsat-2 data (ca. 7 x 7 m spatial ground resolution). A combined area of 127 km2 was monitored for all three projects, yielding a total of roughly 800,000 measurement points on the ground. Achieved measurement point density based on the TerraSAR-X data was around 37,000 points per km2, while density based on the Radarsat-2 data resulted in approximately 6,000 points per km2 in built-up areas. Both data resolutions offer millimetric deformation precision, with surfaces of buildings and infrastructure providing the best signal reflection and phase coherence, resulting in high-quality results. In all cases, the interferometric time series analyses were communicated to the end users through a web-based map portal, enabling simple visual interpretation of the results, as well as integration with the settlement records of the project.
How to cite: Frauenfelder, R., Vöge, M., Salazar, S. E., and Hauser, C.: Space-borne terrain deformation monitoring for large infrastructure projects, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11945, https://doi.org/10.5194/egusphere-egu21-11945, 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 (2nd-4th of July 2018 and 28th-30th 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 occurrence of land subsidence phenomenon has been investigated for the wider area of the city of Tirana, Albania. A set of Ninety-four SAR images acquired between January 2015 and 23 of November 2019 by the European Space Agency (ESA) Sentinel1, have been processed by applying the Persistent Scatterer Interferometry (PSI).
Interpretation of PSI analysis output results, revealed subsidence deformations at the northwest and near the center of Tirana, mainly due to natural land compaction. The deformation rates reach up to 9.6 mm/yr.
The most intensive phenomena have been identified at the Laknas and Breg Shkoze- Rinas regions. In particular at the ``Mother Tereza`` National Airport of Tirana, located at the Rinas area, land subsidence ranges between 2.3mm/yr and 4.5mm/yr. Whereas in Tirana e Re, close to the city center, less intensive subsiding movements have been identified, ranging from 1.5 to a maximum of 5.2 mm/yr.
By evaluating geological, geotechnical, and hydrological data it was determined that except for Laknas, in all other areas, land subsidence is caused by natural compression of alluvial deposits of the Ishmi River. At the Laknas zone, besides natural compression, water withdrawal due to over pumping of ground water can be identified as well. This was proved by the piezometric surface monitoring data referring to the period 2015-2019.
Besides the interesting findings about the deformation pattern at the wider area of Tirana, the current study highlights the potential of PSI as a suitable, accurate, and cost-efficient technique for the study of land subsidence phenomena.
How to cite: Skenderas, D., Loupasakis, C., Papoutsis, I., Alatza, S., and Kontoes, C. (.: Investigation of Land Subsidence Phenomena in the wider Tirana (Albania) Region by applying Persistent Scatterer Interferometry Techniques, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12199, https://doi.org/10.5194/egusphere-egu21-12199, 2021.
Land subsidence hazard affects many highly populated urban areas of the world as a consequence of natural and/or anthropogenic derived geomechanical rock alterations. Here we exploit the full archive of Synthetic Aperture Radar (SAR data) and present a 16-years history (2004-2020) of surface displacement affecting the federal capital of Maceió (Alagoas, Brazil), where sinkhole formation and fractures on infrastructures have been intensified since early 2018, forcing authorities to relocate the affected residence and pose the building under demolition. The geodetic result shows that precursory deformations were already visible in early 2000’s, reaching in November 2020 a maximum cumulative subsidence of approximately 2 m near the Mundaú lagoon coast. The maximum rate of subsidence is estimated at 27 cm/year. Numerical elastic source modelling proves that the subsidence is associated with localized, deep seated material removal at the location and depth where salt mining is performed. More sophisticated 2D distinct element method highlights the formation of cracks in sedimentary layers that eventually enables strong water percolation from rather superficial aquifers into the deeper underground, with potential increase of material dissolution and erosion. We discuss the accelerating subsidence rates, the influence of severe precipitation events to the aforementioned geological instability and the related dynamic evolution of the subsidence hazard by generating dynamic geohazard maps valuable for further infrastructure risk assessment.
How to cite: Vassileva, M., Al-Halbouni, D., Motagh, M., R. Walter, T., Dahm, T., and Wetzel, H.-U.: Man-made disaster on urban area: subsidence and underground salt dissolution in Maceio (Brazil) revealed by remote sensing and numerical modelling, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12371, https://doi.org/10.5194/egusphere-egu21-12371, 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.
In April 2019, large parts of Khuzestan province in Iran were affected by intense record rainfall in the Zagros mountains. Persian Gulf catchment received approximately 30% of its long-term average rainfall over the course of a few days. Karkheh and Dez, two of the major rivers in this catchment, overflowed their banks. As several dams, including Karkheh, with the country's largest capacity, reached their limits, the water had to be released from the reservoirs, which resulted in flooding downstream of the dams. Several cities and more than 200 villages were flooded, and many people had to be evacuated. Many of the dams affected by the 2019 flood were embankment dams, previously reported to exhibit post-construction settlements, at places reaching 13 cm/yr. Therefore, during and after the flood, significant concerns were raised about their health and stability.
In this study, we use Sentinel-1 InSAR to monitor embankment dams' response in Khuzestan to the 2019 flood event. We process the full archive of Sentinel-1 using the Small Baseline Subset approach and estimate the time series of displacement for three different embankment dams in Khuzestan province. The first two studied dams are Karkheh and Gotvand, which have the country's largest capacities and became operational in 2001 and 2012, respectively. The third studied dam is the Masjed-Soleyman dam, previously reported to sustain a high displacement rate since its operation in 2002.
The Sentinel-1 InSAR displacement results indicate that all observed dams exhibit long-term post-construction settlement before the flood, with rates varies from approximately 1 cm/yr for the Karkheh dam to 5 cm/yr for Gotvand dam and 8 cm/yr for Masjed-Soleyman dam. The time series of displacement for Karkheh and Gotvand dams show gentle changes of displacement in response to the increase in water level following the flood. However, for the Masjed-Soleyman dam, the movement accelerates sharply after the flood with more than 2 cm of displacement on the crest in only two months. For the Masjed-Soleyman dam experiencing the most severe effect of the flood, we also analyzed high-resolution data from TerraSAR-X and COSMO-SkyMed. The results provide a detailed picture of the displacement pattern over the crest and the dam's body before and after the flood.
How to cite: Haghshenas Haghighi, M. and Motagh, M.: Monitoring stability of embankment dams in response to 2019 Iran Flood event, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14308, https://doi.org/10.5194/egusphere-egu21-14308, 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.
Land surface is in constant motion due to both natural causes and human activity. Over time, many measurement techniques have been developed to study the deformation of the earth's surface. Some of them, despite having different levels of accuracy, are slow and time consuming (e.g., classical geodetic techniques). The introduction of space geodesy techniques such as GNSS systems and SAR remote sensing have offered new opportunities for precision deformation control in the field of space geodesy. In particular, using satellite radar interferometry (InSAR) as an Earth Observation routine technique, the deformation of large areas of the terrain can be monitored providing displacements at a relatively low cost compared with other ground-based techniques. Nowadays, we are living in the golden age of InSAR as there has never been as much SAR data from different missions as there is today. Of particular importance is the Copernicus program of the European Commission and ESA, which provides us with an inexhaustible source of free SAR data with extraordinary potential for monitoring the earth's surface thanks to the constellation of Sentinel-1 SAR satellites. Thanks to the great capability of SAR remote sensing, many civil infrastructures can be monitored and inspected from space without the need for physical intervention on the ground, greatly reducing costs and execution time. The advanced InSAR time series algorithms allow us to investigate the displacements of these infrastructures with uncertainties of the order of 1 mm/year, interpreting time series of interferometric phases at coherent point reflectors (PS). The use of C-band SAR data from ERS-1/2, Envisat, and Sentinel-1 has allowed us to monitor the southeast of the province of Málaga in southern Spain during the last thirty years, obtaining a deformation pattern of some critical infrastructures in the area. We can highlight, among them: the Limonero dam inaugurated in 1983, whose reservoir regulates the avenues of the Guadalmedina river and serves as a water supplying source for the city of Malaga; the Málaga-Costa del Sol international airport, an important airport for Spanish tourism as it is the main airport serving the Costa del Sol; the Málaga harbor, an industrial area, or some roads and railways. Of special importance is an urban sector with an intensive overexploitation of aquifers. Due to the increase in population because of the expansion of the tourism industry in the Benalmádena coast and Torremolinos area, the aquifers are being affected after the intensive overexploitation of groundwater with the consequent subsidence of the terrain, continuous and increased over time. In this contribution, we show our results of the SAR remote sensing application in this area of the southern Spanish coast.
How to cite: Ruiz-Armenteros, A. M., Ruiz-Constán, A., Lazecky, M., Bakoň, M., Delgado-Blasco, J. M., Sousa, J. J., Galindo-Zaldívar, J., Sanz de Galdeano, C., Martos-Rosillo, S., Lamas-Fernández, F., Marchamalo-Sacristán, M., and Perissin, D.: Monitoring critical infrastructure and anthropogenic hazards in Malaga province (southern Spain) using SAR remote sensing, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15567, https://doi.org/10.5194/egusphere-egu21-15567, 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.
TecVolSA (Tectonics and Volcanoes in South America) is a project dedicated to the development of an intelligent Earth Observation (EO) data exploitation system for monitoring various geophysical activities in South America. Three partners from the German Aerospace Center (DLR) and the German Research Centre for Geosciences (GFZ) are involved to combine their expertise in signal processing, geophysics and Artificial Intelligence (AI).
The first milestone of the project is to perform interferometric processing on tens of terabytes of SAR data to generate deformation products. Efficient algorithms have been designed to accommodate big data processing. Employing these algorithms, five-year data archives of Sentinel-1 have been processed thus far. The data archives span an area of over 770,000 km² surrounding the central volcanic zone of the Andes. Products in the form of surface deformation velocity and displacement time series are generated as point-wise measurements. To ensure highly accurate deformation estimates, two novel techniques have been utilized: large-scale atmospheric correction and covariance-based phase estimation for distributed scatterers.
The second milestone is automatic mining of the wealth of the deformation products to gain insights about anthropogenic and geophysical signals in the region. Here two challenges are faced: the variety of crustal deformation processes as well as the sheer volume of the data. A closer analysis of the estimated deformation velocity verifies the presence of various signals including tectonic movements, volcanic unrest and slope-induced deformations. Such variety requires the classification of the observed signals. Furthermore, the dataset includes displacement time series and velocity estimates of over 750 million data points. This data volume necessitates the incorporation of AI for efficient mining of the products. The aforementioned challenges are met by combining geophysical and signal processing expertise of the project partners, and translating them to the AI algorithms.
The use of AI in EO is a growing topic with numerous successful applications. However, compared to the well-established AI applications of cartography and ground cover classification, there is not enough training data available for the analysis of tectonic and volcanic signals. Therefore, there is a need for synthetic data generation. GFZ produces geophysical models for the simulation of a diverse database that is used for the training of neural networks to autonomously discover significant events in deformation products.
DLR employs supervised machine learning techniques based on simulated data to automatically detect volcanic deformation from InSAR products. Apart from this application, signals which are not attributed to volcanic deformation are automatically clustered for further studies by expert geologists. For this approach, we depend on InSAR and geometrical feature engineering as well as advanced unsupervised learning algorithms. In the presentation, examples of clustering similar points in terms of temporal progression and a prototype system for the automatic detection of volcanic deformations will be illustrated.
Our system is being developed with scalability and transferability in mind. South America serves as a generic and challenging case for this development, as it reveals manifold geophysical and anthropogenic signals. Our ultimate goal is to apply the developed AI-assisted system for global processing.
How to cite: Montazeri, S., Ansari, H., De Zan, F., Mania, R., Shau, R., Beker, T., Parizzi, A., Haghshenas Haghighi, M., Niemz, P., Cesca, S., Motagh, M., Walter, T., Eineder, M., and Zhu, X. X.: TecVolSA: InSAR and Machine Learning for Surface Displacement Monitoring in South America, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6086, https://doi.org/10.5194/egusphere-egu21-6086, 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.
Detecting and measuring transient episodes of crustal deformation is important for a wide range of solid earth and natural hazard applications, e.g. for improving understanding of seismic and volcanological hazards and for monitoring anthropogenic deformation. InSAR is one of the most suitable techniques for this purpose, due to the frequent, regular and global coverage of current-generation satellite missions. However, both the size of the global InSAR dataset, and the large magnitude of atmospheric and other nuisance signals relative to deformation signals of interest, makes this task difficult and precludes systematic manual analysis.
In order to address this issue, here we have developed a new, state-of-the-art deep-learning based approach for the automatic identification of transient deformation events in noisy time-series of unwrapped InSAR images, without requiring supervision or labelling of known example events. To achieve this, we have adopted an anomaly detection framework where anomalies correspond to any transient phenomena that deviates from the ‘normal’ spatio-temporal pattern of phase-change (predominantly due to changes in atmospheric conditions). Our novel workflow learns such patterns in the InSAR dataset, leveraging the unique three-dimensional structure of the interferogram stack and its relationship to the unknown 2D fields of nuisance non-tectonic signals that correspond to individual SAR acquisition dates (epochs). This approach offers major benefits over previously published work using machine-learning to detect signals in InSAR data; those attempts have either largely focused on learning spatial or temporal patterns alone and/or have required an extensive ‘labelled’ dataset of known signals of interest, precluding detection of any signals with different or unexpected spatio-temporal characteristics.
In detail, our framework includes fully convolutional autoencoders that embed and share the feature encodings of a sequence of interferograms, and then decode them to an estimation of their corresponding epochs. The autoencoders consist of convolutional LSTM (Long Short-Term Memory) cells that are trained on an InSAR dataset of a fixed size. First, in order to learn the general spatio-temporal structure of the dataset, a prior model is trained independently on overlapping sequences of 26 interferograms only (each made up of 9 epochs, covering 14 km by 12 km area on ground). We then successfully learn the temporal dependency when the weights of this model are used to initialize the succeeding model, which is trained iteratively by also considering features predicted in previous sequences. During testing, normal atmospheric signals are accurately reconstructed, while anomalies result in large residuals. The residuals are then passed to a detection algorithm that flags and estimates anomalous deformation.
To initially train and test our method, we use InSAR data from several Sentinel-1 tracks in Turkey, obtained from COMET’s LiCSAR processing system. Here we present our initial results, showing that our unsupervised and event-agnostic pipeline accurately detects both real and synthesized anomalous signals and recovers both the spatio-temporal structure of flagged deformation events and the time-series of non-deformation ‘nuisance’ signals. This new approach presents great promise for future automated analysis of large, global InSAR datasets, and for automated and robust separation of deformation from nuisance signals in InSAR data.
How to cite: Shakeel, A., Walters, R., Al Moubayed, N., and Allen, M.: Unsupervised feature learning and automatic detection of transient phenomena in InSAR time-series, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1352, https://doi.org/10.5194/egusphere-egu21-1352, 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.
Atmospheric heterogeneity mainly exposes itself as tropospheric phase delay in satellite interferometric synthetic aperture radar (InSAR) observations, which smears or even overshadows the deformation component of InSAR measurements. In this study, we estimated the performance of four global atmospheric models (GAMs), i.e. ERA5, ERA-Interim (ERA-I), MERRA2 and GACOS, for tropospheric phase delay reduction in InSAR applications in the Tibetan plateau, of which ERA5 is the latest global atmospheric model released by ECMWF. We demonstrated the effectiveness of atmospheric phase screen (APS) correction using the four GAMs for more than 700 Sentinel-1 TOPS interferograms covering two study areas in the southern (R1) and northwest margins (R2) of the Tibetan plateau. Topography-correlated signals have been widely observed in these interferograms, which are most likely due to the APS effects. We calculated the standard deviations (STD) and correlation coefficients between InSAR Line of Sight (LOS) measurements and topography before and after applying APS correction. The results show that the STDs of non-deformation areas from the GAMs decrease to ~4 mm from ~10 mm and ~12 mm originally on average for R1 and R2, respectively, and the correlation coefficients after the APS correction are reduced below 0.4 from ~0.8 for the selected interferometric pairs. In addition, as the newly released GAM, ERA5 has similar performance with GACOS products and outperforms other models generally. This suggests that GAMs, particularly ERA5, have great potentials in the APS correction for InSAR applications in the Tibetan plateau.
How to cite: Wang, Y., Chang, L., Feng, W., Samsonov, S., and Zheng, W.: Topography-correlated atmospheric signal mitigation for InSAR applications in the Tibetan plateau based on global atmospheric models, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8124, https://doi.org/10.5194/egusphere-egu21-8124, 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.
Synthetic Aperture Radar (SAR), with its capability of imaging day or night, ability to penetrate dense cloud cover, and suitability for interferometry, is a robust dataset for event/change monitoring. SAR data can be used to inform decision makers dealing with natural and anthropogenic hazards such as floods, earthquakes, deforestation and glacier movement. However, SAR data has only recently become freely available with global coverage, and requires complex processing with specialized software to generate analysis-ready datasets. Furthermore, processing SAR is often resource-intensive, in terms of computing power and memory, and the sheer volume of data available for processing can be overwhelming. For example, ESA's Sentinel-1 has produced ~10PB of data since launch in 2014. Even subsetting the data to a small scientific area of interest can result in many thousands of scenes, which must be processed into an analysis-ready format.
The Alaska Satellite Facility (ASF) Hybrid Pluggable Processing Pipeline (HyP3), which is now out of beta and available to the public, provides custom, on-demand processing of Sentinel-1 SAR data at no cost to users. HyP3 is integrated directly into Vertex, ASF's primary data discovery tool, so users can easily select an area of interest on the Earth, find available SAR products, and click a button to send them (individually, or as a batch) to HyP3 for Radiometric Terrain Correction (RTC), Interferometric SAR (InSAR), or Change Detection processing. Processing leverages AWS cloud computing and is done in parallel for rapid product generation. Each process provides options to customize the processing and final output products, and provides metadata-rich, analysis-ready final products to users.
In addition to the Vertex user interface, HyP3 provides a RESTful API and a python software developers kit (SDK) to allow programmatic access and the ability to build HyP3 into user workflows. HyP3 is open source and designed to allow users to develop new processing plugins or stand up their own custom processing pipeline.
We will present an overview of using HyP3, both inside Vertex and programmatically, and the available output products. We will demonstrate using HyP3 to investigate the consequences of natural hazards and very briefly discuss the technologies and software design principles used in the development of HyP3 and how users could contribute new plugins, or stand up their own custom processing pipeline.
How to cite: Kennedy, J. H., Hogenson, K., Johnston, A., Kristenson, H., Lewandowski, A., Logan, T. A., Meyer, F. J., and Rine, J.: Get HyP3! SAR processing for everyone, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8973, https://doi.org/10.5194/egusphere-egu21-8973, 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.
Tropospheric delays are the main source of error when measuring ground displacements using InSAR. Increasingly, global atmospheric models (GAMs), e.g., ERA5 and MERRA2 reanalysis data, are used to reduce tropospheric signals in InSAR deformation observations. However, due to the coarse spatial resolution of current GAMs (~10s of kilometers), it is still challenging to obtain tropospheric corrections for high-resolution InSAR data (~10s of meters). Here we present an advanced GAM-based correction method, aimed at improving InSAR geodesy, that incorporates spatial stochastic models of the troposphere in the corrections. We first estimate stochastic models of the tropospheric parameters (temperature, pressure, and partial pressure of water vapor) at different GAM altitude layers and we then interpolate the parameters according to the correlation between pixels of interest and the GAM grid locations (3D). The interpolation accounts for spatial variabilities of the tropospheric random field, instead of subjectively using an inverse distance method or using a local spline function, which are commonly used in current GAM-correction methods. We also estimate the integral of the tropospheric delays along the satellite line-of-sight (LOS) direction directly, instead of calculating the projected zenith-delays, because the troposphere is not purely stratified. Our new method can easily be applied using any of the present GAMs; here we implemented it with the latest ECMWF ERA5 reanalysis outputs. We validate the new method for both interferograms and time-series analysis products (deformation velocities and time-series solutions), using hundreds of the Sentinel-1 images over the island of Hawaii from 2015 to 2020. The results show that the average standard deviation of non-deforming interferograms reduces from 2.55 cm to 1.91 cm when applying the new method, compared with standard deviations of 2.47 cm (PyAPS), 2.44 cm (d-LOS), and 2.10 cm (GACOS), after using three common GAM correction methods. In addition, the new method improves most (87%, i.e., 243 out of 280) of the interferograms, while only about half (52%, 53%, and 66%) are improved by the earlier correction methods. The results demonstrate the importance of considering (1) tropospheric stochastic models in GAM-corrections, (2) horizontal heterogeneities when estimating the LOS delays, and (3) tropospheric delays when mapping long-wavelength or small-magnitude deformation using InSAR.
How to cite: Cao, Y., Jónsson, S., and Li, Z.: Robust InSAR Tropospheric Delay Correction Using Global Atmospheric Models, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11636, https://doi.org/10.5194/egusphere-egu21-11636, 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.
Delta areas are more likely to suffer from land subsidence due to tectonic and geological processes. Po Delta evolution shows a succession and superposition of complex processes caused by both natural and anthropogenic factors. The factors include sediment loading and compaction, post-glacial rebound, coastal flooding and erosion, sea level rise, land use changes, underground resources exploitation, population growth and urbanization. The natural subsidence has been estimated in the order of millimeters per year and the anthropogenic subsidence is greater than 10 mm/year. Several areas are located under the mean sea level and are exposed to flooding. These areas have been protected by embankments which represent a crucial element for flood risk mitigation. Multi-temporal interferometric synthetic aperture radar (MT-InSAR) and global navigation satellite system (GNSS) allow the continuous monitoring of land subsidence and structures and infrastructures deformations. The Po Delta landscape is characterized by large mudflats, farmland, and wetlands, and a low level of urbanization. Interferometry survey is difficult in this area, due to the temporal decorrelation caused by variations of the scattering properties associated with soil moisture and volume scattering, especially in the case of summer acquisitions. Then, MT-InSAR has to be integrated with ground-based measurements techniques which are costly and time consuming. In this study, MT-InSAR and GNSS techniques are combined to monitor the land subsidence and the deformations of the elements at risk, in particular the flood protection infrastructures. C-band Sentinel-1 and X-band COSMO-SkyMed SAR data acquired in 2014-2020 and 2012-2020, respectively, are considered. An MT-InSAR technique is exploited using the interferometric point target analysis (IPTA) method, making a network of targets including both distributed scatterers (DS) and persistent scatterers (PS). GNSS data have been collected by 3 permanent stations and 46 non-permanent stations (NPS) distributed in the Po Delta. The NPS were measured during three survey campaigns in 2016, 2018, and 2020. Results from MT-InSAR applied to Sentinel-1 data and GNSS techniques are compared and integrated to estimate the subsidence rates for most of the area. The monitoring of the embankments is possible using COSMO-SkyMed data due to their high resolution and high backscatter on structures and infrastructures. For future studies, the regression analysis between the natural/anthropogenic processing and the land subsidence of the Po Delta area can be performed to identify the major driving factors of the deformations in the different periods, which can improve the risk mitigation strategies.
How to cite: Chen, X., Achilli, V., Cenni, N., Fabris, M., Menin, A., Monego, M., and Floris, M.: Monitoring land subsidence and element at risk in the Po Delta area (Northern Italy) through MT-InSAR and GNSS surveys, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15859, https://doi.org/10.5194/egusphere-egu21-15859, 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.
Synthetic Aperture Radar (SAR) Interferometry (InSAR) is a powerful tool in radar remote sensing. However, due to the unavoidable inherent limits of the SAR mechanism, there are different challenges to be tackled based on the user’s aim of use. Common issues that have been discussed in the application of topography mapping are temporal decorrelation, surface deformation, atmospheric disturbance, and phase unwrapping problems. These difficulties expose the quality of the final DEM products under high risks, depending on the selection of InSAR image pairs and the environment of area of interest. In this research, we are aiming at investigating the relationship between the SAR-based digital elevation model (DEM) and the related factors which contribute to the error budget. This research will allow InSAR technique users to obtain a better understanding of the severity of errors that were induced by the factors. Furthermore, by knowing which factor degrades the InSAR-generated DEMs the most, one could accordingly apply appropriate methods to reduce the error.
In this research, eight pairs of Sentinel-1A images are used. They are characterized by a 12-day temporal baseline and over 90 meters of perpendicular baseline. The conventional InSAR processing workflow is conducted in each pair. In the post-processing stage, phase gradient removal is applied in order to mitigate unwrapping problems. Surface deformation and water vapor variation are chosen as two factors that introduce errors in InSAR DEM. GPS data are collected to obtain deformation information, and atmospheric water vapor data are collected by weather prediction models. Finally, the multiple linear regression analysis is applied in order to find out the relationship between SAR-based DEM and the selected factors.
How to cite: Wu, Y.-Y. and Ren, H.: Study on the relationship between SAR-based digital elevation models and water vapor contents and surface deformation, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16161, https://doi.org/10.5194/egusphere-egu21-16161, 2021.
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