NH6.3 | SAR remote sensing for natural and human-induced hazard applications
EDI
SAR remote sensing for natural and human-induced hazard applications
Convener: Ling Chang | Co-conveners: Xie Hu, Mahdi Motagh, Nicușor NeculaECSECS
Orals
| Tue, 16 Apr, 10:45–12:30 (CEST), 14:00–15:40 (CEST), 16:15–18:00 (CEST)
 
Room 0.15
Posters on site
| Attendance Wed, 17 Apr, 10:45–12:30 (CEST) | Display Wed, 17 Apr, 08:30–12:30
 
Hall X4
Posters virtual
| Attendance Wed, 17 Apr, 14:00–15:45 (CEST) | Display Wed, 17 Apr, 08:30–18:00
 
vHall X4
Orals |
Tue, 10:45
Wed, 10:45
Wed, 14:00
SAR remote sensing is an invaluable tool for monitoring and responding to natural and human-induced hazards. Especially with the unprecedented spatio-temporal resolution and the rapid increase of SAR data collections from legacy SAR missions, we are allowed 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 surface displacements and disturbance from SAR imagery, synergizing cross-disciplinary big data, aggregating useful information by multimodal remote sensing fusion, and bridging the linking knowledge between observations and mechanisms of different hazardous events are still challenging. Therefore, in this session, we welcome contributions that focus on (1) new algorithms, including machine and deep learning approaches and multi-modal/platform integration, to retrieve critical products from SAR remote sensing big data in an accurate, automated, and efficient framework; (2) SAR applications for natural and human-induced hazards including such as flooding, landslides, earthquakes, volcanic eruptions, glacial movement, permafrost destroying, mining, oil/gas production, fluid injection/extraction, peatland damage, urban subsidence, sinkholes, oil spill, and land degradation; (3) multimodal remote sensing fusion to enhance information extraction related to hazards, agriculture, forestry, land management, and environmental monitoring; and (4) 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.

Orals: Tue, 16 Apr | Room 0.15

Chairpersons: Ling Chang, Mahdi Motagh
10:45–10:50
10:50–11:10
|
EGU24-21827
|
solicited
|
Highlight
|
On-site presentation
Ramon Hanssen and Paolo Bazzocchi

Precise and accurate geolocation of (point) scatterers is crucial for the correct interpretation of InSAR time series in the built environment, since this allows the scatterers to be linked to physical objects. The precision and accuracy of the geolocation is dependent on orbit precision, sub-pixel scatterer localization within the SAR images, as well as a range of geophysical, SAR processor, and instrument-related corrections.

 

Focusing on the abundantly available Sentinel-1 SAR acquisitions, previous studies on 40 corner reflectors in  Australia, with 30 acquisitions aligned towards ascending orbits (Garthwaite et al., 2015), and georeferenced using annual GNSS campaigns, yielded a positioning dispersion (1sigma) of 6~cm in range and 26~cm in azimuth, and residual offsets of 3~cm (range) and 18~cm (azimuth) (Gisinger et al., 2021).  

 

Here we report on new results applied on the network of Integrated Geodetic Reference Stations (IGRS) in the Netherlands, which currently consists of 80 corner reflectors on 40 stations (i.e., ascending and descending) on the same physical construction, equipped with permanent GNSS stations.   The already developed end-to-end methodology for SAR geolocation is revised, and applied to Sentinel-1 interferometric wide swath (IW) data from 257 ascending and 263 descending acquisitions.  Our results confirm the validity of the applied corrections.

How to cite: Hanssen, R. and Bazzocchi, P.: Geolocation accuracy and precision for InSAR point positioning; validation using the Dutch IGRS network, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21827, https://doi.org/10.5194/egusphere-egu24-21827, 2024.

11:10–11:20
|
EGU24-12149
|
Highlight
|
On-site presentation
Ou Ku, Fakhereh Alidoost, Pranav Chandramouli, Thijs van Lankveld, Francesco Nattino, Meiert Grootes, Freek van Leijen, and Ramon Hanssen

Modern satellite missions continuously generate extensive observation datasets for Interferometry Synthetic Aperture Radar (InSAR), which is a crucial technology for monitoring ground surface deformation. The efficient processing and analysis of these extensive InSAR datasets poses two computational challenges: 1) the growing volume of the datasets that needs to be incorporated into the data processing workflow, and 2) the integration of contextual information associated with the InSAR data to reveal the mechanisms driving deformation.   

To address these challenges, we present two open-source Python libraries: SARXarray [1] and STMTools [2]. They facilitate common InSAR data processing tasks and are developed as extensions of the open-source Python library, Xarray, which handles labelled multi-dimensional arrays and is well-suited to the space-time nature of InSAR data. SARXarray is designed to work with coregistered raster stacks, such as SLC or interferogram stacks, offering functionalities like multi-looking, coherence computation, and coherence scatterers selection. STMTools, on the other hand, focuses on large spatio-temporal datasets in the form of a Space-Time Matrix (STM) [3], for instance, coherent scatterers. It can query background contextual data, such as geospatial polygons, and add the attributes-of-interest to the corresponding STM. Furthermore, both SARXarray and STMTools support data chunking and lazy evaluation, enabling the scaling up of the data processing pipeline and parallel processing of larger-than-memory data across various computational infrastructures. 

[1] Ku, O., et al., sarxarray [Computer software]. github.com/MotionbyLearning/sarxarray 

[2] Ku, O., et al., stmtools [Computer software]. github.com/MotionbyLearning/stmtools 

[3] Bruna, M. F. D., van Leijen, F. J., & Hanssen, R. F. (2021). A Generic Storage Method for Coherent Scatterers and Their Contextual Attributes. 

How to cite: Ku, O., Alidoost, F., Chandramouli, P., van Lankveld, T., Nattino, F., Grootes, M., van Leijen, F., and Hanssen, R.: New Open-Source Python libraries for Radar Interferometry Data Processing and Analysis, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12149, https://doi.org/10.5194/egusphere-egu24-12149, 2024.

11:20–11:30
|
EGU24-21683
|
Highlight
|
On-site presentation
Wietske Brouwer, Ling Chang, and Ramon Hanssen

In InSAR time series analysis for displacement studies, the essential parameter estimation part is performed on arcs between a reference point and an evaluation point. Usually, both points are scatterers that satisfy certain optimality conditions. The set of parameters to be estimated in the functional model can be different for each arc. Especially in the built environment, individual points may behave rather differently. Conventional approaches to estimate average displacement velocities for the entire time series length are therefore often sub-optimal. However, deviation from a single uniform parameterization for all arcs implies that for each arc the optimal model needs to be selected.

Chang and Hanssen (2016) proposed a method to select the optimal functional model, i.e. parameterization, for each arc using multiple hypothesis testing (MHT). The selection was based on rejecting the conventional null hypothesis of linear steady-state displacement, satisfying Newton’s first law, in favor of an alternative hypothesis that is chosen from a library of canonical models. This procedure required the a priori selection of a significance level (related to the impact of the erroneous rejection of the null hypothesis), the discriminatory power (related to the impact of erroneously sustaining the null hypothesis), and the stochastic model of the arc observations. For the latter, a conservative uniform approximation was chosen.

Recently, Brouwer and Hanssen (2023) developed a methodology to approximate the stochastic model for each scatterer in an InSAR time series analysis, based on amplitude behavior. By combining both approaches, i.e., applying the MHT approach for functional model selection using a point- and epoch-specific stochastic model, we significantly reduce both Type-1 and Type-2 errors, leading to the improved identification of dynamic mechanisms in a complex environment. We report on the mathematical background, the level of improvement in practical case studies as well as the numerical consequences of the approach.

References:

W.S. Brouwer, Y. Wang, F.J. van Leijen, and R.F. Hanssen. ”On the stochastic model for InSAR single arc point scatterer time series.” In IGARSS 2023-2023 IEEE International Geoscience and Remote Sensing Symposium, pp. 7902-7905. IEEE, 2023.

L. Chang and R.F. Hanssen, ”A Probabilistic Approach for InSAR Time-Series Postprocessing,” in IEEE Transactions on Geoscience and Remote Sensing, vol. 54, no. 1, pp. 421-430, Jan. 2016, doi: 10.1109/TGRS.2015.2459037.

How to cite: Brouwer, W., Chang, L., and Hanssen, R.: Selecting optimal displacement models using an improved stochastic model in InSAR arc-based time series analysis, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21683, https://doi.org/10.5194/egusphere-egu24-21683, 2024.

11:30–11:40
|
EGU24-2017
|
ECS
|
On-site presentation
Xu Zhang, Ling Chang, and Alfred Stein

Satellite missions have delivered a wealth of SAR images for Earth monitoring applications since the 1990s. Due to the complex nature of SAR images and a limited amount of accessible SAR labeling data, these images remain underutilized in providing reference information for machine learning. In response to this gap, we designed a SAR feature creation workflow in an operational framework by releasing Jupyter tools to the public.  The workflow is developed upon Doris-5 and consists of two streams. The first stream utilizes SAR images to generate basic SAR and SAR interferometric and polarimetric features. The second stream capitalizes on other available geospatial datasets, such as optical images, cadastral and geological maps, to generate additional features for SAR data that can be treated as reference data. They are first radar-coded to align with the extracted SAR features and then geo-coded in geographic coordinates. All SAR features are concatenated as separate layers in the NetCDF data format, which contains STAC (spatio-temporal asset catalogs) for the data querying.

For the demonstration, an area in the province of Groningen, the Netherlands, was selected as the test site. Seven ascending Sentinel-1A images in VV and VH modes on track 15 between January and March 2022 were used, along with the topographic base map – TOP10NL dataset as a reference. The extracted features encompass VV amplitude, VH amplitude, VV interferometric phase, VV coherence, intensity summation, intensity difference, intensity ratio, cross-pol correlation coefficient, cross-pol cross product, entropy, buildings, roads, water and railways. The first ten features were created via the first stream, while the last four features via the second stream. By applying a random forest classifier to these fourteen SAR features, the model resulted into four types of classified SAR images: building, road, water and railway. The overall accuracy was 0.8558, 0.9939, 0.9065, and 0.8191, with corresponding F1-scores of 0.9191, 0.9669, 0.9490, and 0.9006, respectively.

We conclude that the created SAR features well facilitate machine learning, and that even a simple random forest classification can yield relatively high-accuracy results. In addition, our workflow to create SAR features is well suited to prepare labeled features for machine learning analyses that are even friendly to a user with limited knowledge of SAR.  

How to cite: Zhang, X., Chang, L., and Stein, A.: An operational way of SAR feature creation to facilitate machine learning analyses, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2017, https://doi.org/10.5194/egusphere-egu24-2017, 2024.

11:40–11:50
|
EGU24-22189
|
On-site presentation
Nicole Richter, Malte Schade, Oliver Cartus, and Tom Hart

Synthetic Aperture Radar (SAR) remote sensing has become indispensable for monitoring natural and anthropogenic hazards. Here we present a novel application of SAR technology in the ecological domain. Focusing on Zavodovski Island, home to the world's largest penguin colony, we address the challenges posed by limited optical satellite observations due to persistent cloud cover and rare ground surveys due to the extreme remoteness of the study site.

Our proposed approach involves the application of an AI-driven classification algorithm to high spatiotemporal resolution X-band radar interferometric coherence data. Despite the inherent limitations of optical observations, we showcase the potential of dual-polarimetric, high-resolution SpotLight SAR in measuring phenology and its effectiveness in detecting, mapping, and monitoring penguin colonies on Zavodovski Island. The study leverages temporally dense SAR data, utilizing the TerraSAR-X and PAZ satellite systems, and spatially high-resolution data gathered during two field campaigns in February and December 2023.

This research not only highlights the innovative use of SAR in ecological monitoring but also underscores the broader applicability of SAR technology in diverse domains. By contributing to the understanding of penguin colony dynamics, our study exemplifies the transformative impact of SAR remote sensing on ecological health indices. This contribution demonstrates the capabilities of SAR technology in addressing unique challenges and expanding its utility beyond traditional hazard applications.

How to cite: Richter, N., Schade, M., Cartus, O., and Hart, T.: AI-Driven Classification of X-band Radar Dual-Polarimetric Coherence Data for Mapping and Monitoring Penguin Colonies on Zavodovski Island, South Sandwich Islands, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-22189, https://doi.org/10.5194/egusphere-egu24-22189, 2024.

11:50–12:00
|
EGU24-11830
|
ECS
|
On-site presentation
Mahmud Haghshenas Haghighi and Andreas Piter

Since the launch of the Sentinel-1 mission and with the upcoming NISAR mission, SAR data is continuously and freely available, which can be used for geodetic monitoring and assessing hazards related to infrastructure. Continous deformation monitoring requires a regular update of the displacement time series based on newly acquired images to assess the current structural health status of the observed infrastructure. Existing InSAR time series methods, in particular PSI methods, however, are designed for a fixed SAR stack size of a particular study period and unwrap the phase time series for pixels which exhibit a coherent signal over the whole study period. Therefore, before phase unwrapping, it is essential to assess if the pixels preserve phase coherence in the interferograms related to the new image.

Previous studies proposed an amplitude change detection which divides the study period into two subsets and tests whether the amplitude distributions of the subsets stem from different Rayleigh distributions and, hence, from different scatterers. All possible splits of the stack into two subsets are tested and the split with the highest test score is selected as the change point. This approach has the drawback that the changes can hardly be identified if one of the subsets contains only very few images, which would be the case for a sequential update for continuous monitoring purposes. Moreover, other studies demonstrated that the time of change in the amplitude might not coincide with the time of change in the coherence of the phase. Therefore, they suggested an unwrapping-based change point refinement to the amplitude-based method to identify the change point in the temporal coherence of the phase.

Here, we propose a new method for assessing the decorrelation in a sequential updating framework using a Kalman filter.
Our approach estimates the temporal coherence for the newly acquired image. We extend the Temporal Phase Coherence (TPC) approach from Zhao and Mallorqui (2019) which approximates the phase noise of a scatterer by subtracting the spatial low-pass filtered phase of the immediate neighbourhood of the scatterer in each interferogram. To assess the coherence of the new image, we connect the new image to all previous images within a fixed time of e.g. one year. We use the Kalman filter to predict and update the TPC for each new image and apply a threshold on the TPC to distinguish coherent from incoherent pixels. This approach comes with the advantage that neither amplitude analysis nor unwrapping of the phases is required to assess the coherence of a scatterer in the new image.

We perform a case study in Nordrhein-Westfalen, Germany, along the Sauerland-Autobahn to demonstrate the effectiveness of the proposed Kalman filter for sequential coherence estimation. Along the Sauerland-Autobahn, several highway bridges need to be rebuilt due to structural health problems arising from their ageing process. We coregister a stack of Sentinel-1 images using the InSAR Scientific Computing Environment (ISCE).
The dataset covers eight years of data from ascending and descending orbits. We compare our proposed phase-based coherence estimation with the results from amplitude-based change detection.

How to cite: Haghshenas Haghighi, M. and Piter, A.: Kalman filter for sequential temporal coherence estimation for multi-temporal InSAR, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11830, https://doi.org/10.5194/egusphere-egu24-11830, 2024.

12:00–12:10
|
EGU24-16359
|
Highlight
|
On-site presentation
Federica Casamento, Riccardo Lanari, and Ivana Zinno

Differential Synthetic Aperture Radar Interferometry (DInSAR) products are often contaminated by atmospheric contributions, frequently referred to as atmospheric artifacts or atmospheric phase screen (APS) signals [1]. Specifically, the velocity and, consequently, the path length of the radar signal propagating through the inhomogeneous atmosphere layers can vary among repeat-pass SAR acquisitions. Therefore, these variations can be erroneously interpreted as due to deformation signals. Accordingly, filtering out the APS component from DInSAR products, for correctly retrieving deformation measurements, represents a challenging issue.

In this work we present a statistical analysis to investigate the performance of the Sentinel-1 (S1) Extended Timing Annotation Dataset (ETAD) data for mitigating the APS component in both DInSAR interferograms and deformation time-series. The ETAD product consists of different correction layers that provide the azimuth and range timing shifts for each S1 burst to achieve a precise geolocation with centimetre accuracy [2]. It is worth noting that, although the ETAD dataset is not primarily designed for the interferometric phase filtering, some of its correction layers, specifically, the tropospheric, the ionospheric, and the solid Earth tidal displacement ones, may be effectively used to filter the APS signal component affecting the DInSAR measurements [2].

For the presented experimental analisys we used 104 S1 images acquired along ascending orbits over Central/Southern Italy, during the 2018-2020 time-span. The exploited DInSAR products have been generated at medium spatial resolution (about 40 m) and are represented by 278 interferograms and by the corresponding displacement time series retrieved through the P-SBAS processing chain [3].

To quantitively evaluate the ETAD APS correction performance, we analyzed first their impact on DInSAR interferograms. In particular, we considered the following statistical metrics for the entire dataset of unwrapped interferograms, before and after the application of the ETAD APS correction: i) standard deviation of the interferograms at different spatial scales and ii) empirical variograms of the unwrapped phase fitted with a parametric exponential model. Subsequently, to investigate the impact of the ETAD APS correction on the deformation time-series, we analyzed the behavior of their variance before and after the application of the ETAD correction.

The measured metrics results, although still preliminary, demonstrate the effectiveness of the ETAD APS corrections for both the DInSAR interferograms and the deformation time-series in more than 90% of the cases.

[1]  I. Zinno et al., "On The Exploitation of ETAD Data for the Atmospheric Phase Screen Filtering of Medium/High Resolution DInSAR Products," IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Pasadena, CA, USA, 2023, pp. 7882-7885.

[2]  C. Gisinger et al., "The Extended Timing Annotation Dataset for Sentinel-1Product Description and First Evaluation Results,"  IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-22, 2022, Art no. 5232622

[3]  M. Manunta et al., "The parallel SBAS approach for Sentinel-1 interferometric wide swath deformation time-series generation: Algorithm description and products quality assessment," IEEE Transactions  on Geoscience and Remote Sensing, vol. 57, no. 9, pp. 6259-6281, 2019

How to cite: Casamento, F., Lanari, R., and Zinno, I.: On the exploitation of ETAD data to filter out atmospheric artifacts from Sentinel-1 medium-resolution DInSAR products: a performance evaluation analysis, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16359, https://doi.org/10.5194/egusphere-egu24-16359, 2024.

12:10–12:30
Lunch break
Chairpersons: Xie Hu, Mahdi Motagh, Nicușor Necula
14:00–14:20
|
EGU24-13336
|
solicited
|
Highlight
|
On-site presentation
Mario Costantini, Federico Minati, Francesco Vecchioli, and Massimo Zavagli

Synthetic aperture radar (SAR) interferometry (InSAR) is a well-established technology for precise monitoring of ground motions (due to subsidence, landslides, volcanic and seismic phenomena) with millimeter accuracy from time series of satellite SAR images. A crucial aspect of this technology involves identifying points exhibiting interferometric phase coherence across acquisitions in an image stack, typically corresponding to man-made structures, rocks, or bare soil, irrespective of the scattering mechanism (point-like or distributed). Coherent point identification faces challenges, particularly due to atmospheric and other systematic disturbances affecting the phase. Various techniques have been presented in the scientific literature, relying on statistics of stack image amplitudes (such as amplitude dispersion and signal-to-clutter ratio) and/or phases in spatial and temporal domains.

This work presents a new algorithm we have recently developed, named Point Coherence Estimation (PCE), for identification of coherent points. The temporal coherence (related to phase noise) of each point is derived from the coherences between pairs of points, directly calculable, through an effective and clean procedure, without the need for spatial averages, amplitude/phase calibrations, or critical assumptions.

The algorithm begins by examining phase differences between neighboring points, within tens or hundreds of meters. As known, temporal coherences of these point pairs can be estimated exploiting the cancellation of spatially correlated components in phase differences (such as atmospheric and orbital artifacts, large scale motions) and determining temporally correlated components related to ground motion and elevation. The temporal coherence of each point pair primarily depends on the phase noises (temporal, spectral, geometric decorrelations, thermal noises) of the two points.

Assuming statistically independent phase noises in neighboring points (possibly excluding the nearest neighboring pixels if the images are oversampled), the expected value of temporal coherence for each pair is shown to be the product of the temporal coherence expected values for the two paired points. By taking the logarithm of these equations, an overdetermined system of linear equations is derived, which can be solved by minimizing the equation residuals according to the L1 or L2 norm, using existing efficient solvers such as linear or quadratic (LP or QP) programming solvers. The solution provides a reliable estimate of the temporal coherence for each point.

Importantly, the PCE method operates without assuming any probability distribution of phase noise. Moreover, the PCE algorithm can be applied to full-resolution data as well as to data with degraded resolution for a previous multi-look or distributed scattering processing. In addition, the algorithm provides consistent results if applied to preselected candidate coherent points to reduce computational time (however absolutely affordable even without point preselection).

Extensive testing, including stacks of Sentinel-1 interferometric SAR images over different scenarios, showcased the method effectiveness. PCE provided reliable measurements of temporal coherence and phase noise variance for each point, enabling detection of coherent points with minimal missing or false detections. The tested areas, affected by diverse displacement phenomena, showcased the algorithm applicability across different land cover types and geological features.

The PCE method will be made available to the geoscience community as software as a service (SaaS).

How to cite: Costantini, M., Minati, F., Vecchioli, F., and Zavagli, M.: Point Coherence Estimation (PCE) in SAR interferometry, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13336, https://doi.org/10.5194/egusphere-egu24-13336, 2024.

14:20–14:30
|
EGU24-8929
|
ECS
|
On-site presentation
Amira Zaki, Irene Manzella, Milan Lazecky, Andy Hooper, Ling Chang, Mark van der Meijde, and Islam Fadel

The Nile Delta represents the most critical part of Egypt, hosting more than 50% of the population and approximately two-thirds of the nation’s agricultural lands. During the last decades, the Nile Delta has suffered from significant surface deformation that has led to damage to transport networks and infrastructures, thus becoming a significant risk in the area. This deformation is mainly due to environmental changes and anthropogenic activities such as the over-extraction of groundwater for different purposes and the building of dams inside and outside Egypt along the river Nile. These activities have led to shortage of water inflow, changing the discharge rates, reduced sedimentation in the delta and changes in the water recharge rates of the Nile, the primary source of water for the Nile Delta aquifers. Regarding the environmental changes, the shifts in climate patterns, including variations in precipitation, rising temperatures, and rising sea levels, have all altered the hydrological balance of the Nile Delta, consequently leading to variations in surface deformation patterns. Many studies have studied the rate and patterns of land deformations in the Nile Delta based on geodetic tools such as Global Positioning System (GPS) and Interferometric Synthetic Aperture Radar (InSAR). But still, there is a gap in understanding the relationship between surface deformation rates and the causative factors. Such understanding could potentially enable the estimation of system response for future scenarios.

In this research, we present the results of a system that uses Sentinel-1 SAR data characterized by VV polarization, with ascending and descending orbital directions, acquired between 2015 and 2023 along the Nile Delta. We utilized open-source LiCSBAS tools to analyze the surface deformation rates from InSAR Sentinel-1 data. Then, we calculated the vertical deformation velocity over time by decomposing the ascending and descending LOS data. Then, we analyzed the surface deformation results obtained with the present methodology against the freely available geospatial data, which represents the possible causative factors (such as rainfall, water body change, total terrestrial water storage, land use-landcover, temperature, etc.), to understand their relations and their impact. By linking the surface deformation to its causative factors through machine learning techniques such as Random Forest, our research aims to provide a better understanding of the system dynamics and an appropriate model for prediction. This model can be utilized by decision-makers to consider and manage risks associated with severe surface deformation for possible future scenarios. The results should enable the design of future mitigation actions to protect Egyptian society and make it more resilient to the consequences of land deformation over the Nile Delta.

How to cite: Zaki, A., Manzella, I., Lazecky, M., Hooper, A., Chang, L., Meijde, M. V. D., and Fadel, I.: Surface deformation along the Nile Delta, Egypt: From monitoring towards prediction, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8929, https://doi.org/10.5194/egusphere-egu24-8929, 2024.

14:30–14:40
|
EGU24-20069
|
On-site presentation
Nils Dörr, Andreas Schenk, and Stefan Hinz

The Vietnamese Mekong Delta (VMD) is facing several environmental challenges, including coastal erosion and land subsidence. Subsidence rates of up to several centimeters per year have been reported, which are an order larger than the regional sea level rise of about 3.3 mm/yr. The associated risks are an increased vulnerability to flooding, salinization of water resources and permanent inundation. Precise monitoring of land subsidence with high spatial and temporal resolution is essential to support the study of the associated causes and hazards as well as appropriate countermeasures.

Here, we present results of land subsidence monitoring between 2017 and 2022 in the VMD based on Sentinel-1 Persistent Scatterer Interferometry (PSI). We used Sentinel-1 scenes from ascending and descending orbits and applied an advanced PSI approach. The advancements of the algorithm include the integration of Temporary Persistent Scatterers to derive the best possible Persistent Scatterer network for long time series. Furthermore, we developed a method to optimally integrate reference pixels in order to suppress spatially correlated noise in the subsidence time series. Due to a lack of well distributed geodetic references, we made use of an infrastructural reference network consisting of large bridges with deep foundation depths, which are nearly unaffected by subsidence.

We present the derived subsidence rates and exemplary subsidence time series across the study area. Additionally, we highlight specific spatial and temporal features in the subsidence, which can be associated with land use characteristics and environmental influences.

How to cite: Dörr, N., Schenk, A., and Hinz, S.: Recent Land Subsidence in the Vietnamese Mekong Delta derived from Advanced Sentinel-1 SAR Interferometry, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20069, https://doi.org/10.5194/egusphere-egu24-20069, 2024.

14:40–14:50
|
EGU24-20708
|
ECS
|
On-site presentation
Anna Barra, María Cuevas-González, José Navarro, Marta Béjar-Pizarro, Pablo Ezquerro, Silvia Bianchini, Jose Luis Zezere, Camilla Medici, Matteo Del Soldato, Riccardo Palamà, Saeedeh Shahbazi, Rosa María Mateos, Eleftheria Poyiadji, David Alfonso Jorde, Michele Crosetto, and Oriol Monserrat

The availability of displacement maps across extensive regions, based on Multi-Temporal SAR satellite interferometry techniques (MT-InSAR), has notably increased in recent years. The launch of the Copernicus Sentinel-1 satellites in 2014, ensuring global and regular acquisitions under an open and free data distribution policy, marked a turning point in both exploitation and application. A significant outcome of this progress is the development of regional, national, and continental ground motion services, providing comprehensive displacement maps that offer highly detailed information regarding both human activities and natural phenomena. Since 2022, the European Ground Motion Service (EGMS) freely provides billions of displacement Measurements Points (MP) updated annually, covering nearly the entire European territory, characterized by high reliability and millimetric precision. Despite the full potential usefulness of these maps for territorial management and risk assessment, these data remain still underexploited. With the aim of improving the operational use of this extensive catalogue, there is a critical need for automated tools that can simplify and accelerate the extraction, analysis, and interpretation processes. The production of derived and simplified maps is crucial for the uptake of the EGMS products by both expert and non-expert InSAR users. In response to this need, projects such as the European RASTOOL (DG-HECHO, UCPM-PJG-101048474) and the Spanish SARAI (MCIN/AEI) have made concerted efforts to explore both artificial intelligence and deterministic approaches. In this context, we present the deterministic methodologies and the developed tools, called ADATools (i.e., Active Deformation Areas Tools). Designed to be flexible, adaptable, and user-friendly, these tools aim to support territorial and risk management, with specific attention given to compatibility with EGMS data formats. The first tool, ADAFinder, is an improvement of a previously consolidated tool developed in the frame of previous European projects. ADAFinder allows the automatic extraction and selection of most significant ADAs, a crucial initial step in transitioning from a multitude of individual MPs to a manageable number of polygons to be further analysed or used. Following the identification of moving areas or ADAs, the ADAClassifier permits a preliminary evaluation of the processes likely causing the displacement. Incorporating auxiliary data, each ADA receives a preliminary characterization, allowing to visualize associations with landslides, subsidence, sinkhole, construction settlements, or uplift. Furthermore, a temporal characterization based on the automatic analysis of the time series from all MPs within each ADA is provided. Both temporal and phenomena characterizations are then used for an initial ranking of ADAs, considering their potential impact on structures and infrastructures, using the ADAImpact. In this presentation, examples of results obtained by applying the ADATools to the EGMS data will be presented, emphasizing strengths, and outlining perspectives for future improvements. This work is part of the Spanish Grant SARAI, PID2020-116540RB-C21, funded by MCIN/AEI/ 10.13039/501100011033.

How to cite: Barra, A., Cuevas-González, M., Navarro, J., Béjar-Pizarro, M., Ezquerro, P., Bianchini, S., Zezere, J. L., Medici, C., Del Soldato, M., Palamà, R., Shahbazi, S., Mateos, R. M., Poyiadji, E., Alfonso Jorde, D., Crosetto, M., and Monserrat, O.: ADATools: free and user-friendly tools to semiautomatically extract and analyse wide PSI displacement maps. Applications to the European Ground Motion Service (EGMS). , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20708, https://doi.org/10.5194/egusphere-egu24-20708, 2024.

14:50–15:00
|
EGU24-20003
|
Virtual presentation
Bing Yu and Deying Ma

Severe subsidence poses a significant threat to coastal river deltas, bringing substantial hazards. The Yellow River Delta, being the youngest of such deltas, is particularly vulnerable due to its unstable sedimentary structure. It has experienced significant subsidence as a result of frequent human activities. The InSAR technology facilitates large-area, long-term deformation monitoring of the Yellow River Delta region. Current research has identified that subsidence in the Yellow River Delta is primarily influenced by groundwater extraction and oil exploitation. However, there is a lack of comprehensive study on the spatiotemporal patterns of subsidence induced by different human activities. To acquire extensive and long-term surface subsidence information in the Yellow River Delta region and analyze its causes, this study utilizes 92 ascending Sentinel-1 satellite images and 79 descending Sentinel-1 satellite images. Applying the SBAS-DInSAR technique, the research investigates ground subsidence in the area from January 2019 to April 2022. The study includes an integration of interferometric fringes and DInSAR deformation monitoring results, along with ascending and descending orbit deformation rates for internal accuracy cross-checking. Additionally, it examines the spatiotemporal evolution characteristics and causes in different human-impacted areas. The results indicate that the deformation rates from ascending and descending orbits show high consistency, with a correlation coefficient exceeding 0.8. Significant subsidence areas in the Yellow River Delta are concentrated in the eastern coastal regions, with the maximum subsidence rate reaching approximately -255 mm/year. The primary human-induced factors contributing to subsidence include the extraction of underground brine, oil, and groundwater. Additionally, natural factors like temperature, precipitation, and evaporation also impact subsidence. Notably, the deformation rate changes induced by precipitation exhibit a delayed response. Human activities, with their varying types and intensities, impart distinct temporal characteristics to subsidence. In areas like wetlands, urban regions, and farmlands, deformation is influenced by changes in groundwater levels, resulting in smaller deformation magnitudes and fluctuating deformation rates affected by variations in rainfall, temperature, and evaporation. Oil extraction deformation exhibits long-term change characteristics, influenced by the volume of oil extracted and water injection; from January 2019 to July 2020, a subsidence trend was observed, followed by a slow rebound after July 2020. Subsidence induced by groundwater extraction is characterized by rapid and stable deformation rates. Temporally, significant linear rate subsidence occurred from January to August 2019, with varying degrees of rebound from June 2019 to March 2020, followed by a return to subsidence. Influenced by heavy rainfall, there is a minor rebound each year from July to August.

How to cite: Yu, B. and Ma, D.:  Spatiotemporal Patterns of Human-Induced Subsidence in the Yellow River Delta Region revealed by Ascending and Descending Orbit Time-Series InSAR , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20003, https://doi.org/10.5194/egusphere-egu24-20003, 2024.

15:00–15:10
|
EGU24-13793
|
Highlight
|
Virtual presentation
Cathleen Jones, Francis Monaldo, Benjamin DeChamps, Lisa Di Pinto, Oscar Garcia-Pineda, George Graettinger, Sean Helfrich, Benjamin Holt, Malin Johansson, Cornelius Quigley, Ellen Ramirez, Gordon Staples, and Dana Tulis

In 2018, NASA funded a project to develop and mature automated oil spill detection and thickness estimates from synthetic aperture radar (SAR) and optical imagery, based on focused field testing combined with in situ oil sampling and airborne imaging with the UAVSAR L-band SAR.  The goal was to implement these new algorithms and databases in a semi-automatic system that NOAA uses operationally to detect and assess oil spills and post-storm offshore damage and debris.  The study's field validation site was the Coal Oil Point seep field, an area of natural seep activity located in the Santa Barbara channel, California, which leaks approximately 100 barrels of crude oil per day.  There were three campaigns to collect calibration/validation data, in May 2021, October 2021, and June 2022, during which UAVSAR overflew boat crews collecting optical images from a drone and some water samples for thickness and viscosity analysis.  The data was combined with available satellite SAR and optical imagery collected around the same time and used to develop an algorithm for classifying oil by relative thickness based upon contrast with clean ocean.  One algorithm is tailored for Sentinel-1 data and uses Machine Learning (ML) methods, and the other is an analytical analysis that can be applied to any radar frequency's data.  The data acquired in June 2022 has been used additionally to evaluate differentiation of mineral oil slicks from low wind radar-dark areas using a series of rapid repeat SAR images. In this talk, the study, data collections, and developed algorithms and methods will be presented.

How to cite: Jones, C., Monaldo, F., DeChamps, B., Di Pinto, L., Garcia-Pineda, O., Graettinger, G., Helfrich, S., Holt, B., Johansson, M., Quigley, C., Ramirez, E., Staples, G., and Tulis, D.: Overview and Outcome of the NASA Marine Oil Spill Thickness (MOST) Project, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13793, https://doi.org/10.5194/egusphere-egu24-13793, 2024.

15:10–15:20
|
EGU24-8212
|
ECS
|
On-site presentation
xing zhang and jun hu

The long-term over-extraction of groundwater in the North China Plain(NCP) has led to disasters such as ground subsidence, ground fissures, and seawater intrusion. These have posed serious threats to infrastructure, aquifer systems, and the ecological environment. By establishing a functional model of surface deformation and groundwater changes, we can enhance our understanding of the mechanisms behind ground subsidence and quantitatively assess how groundwater storage evolves under the dual influences of human activities and natural processes. InSAR (Interferometric Synthetic Aperture Radar) has proven to be the most effective tool for long-term、wide-area ground deformation monitoring and underground hydrological parameter inversion. Considering the characteristics of ground subsidence such as long-term, progressive, and wide distribution, in this study, eight stacks of 1496 Sentinel-1A/1B SAR scenes spanning from 2017 to 2023 were acquired in ascending mode along tracks T40 and T142. Through methods like (InSAR) time series analysis method、phase unwrapping correction and spatio-temporal smoothing fitting, we obtained a long-time-series high-precision LOS deformation field for the NCP. Secondly, by introducing the spatial domain network adjustment method, we can implement joint adjustment and correction of wide-area multi-map results to obtain a unified spatio-temporal reference for the NCP's wide-area InSAR vertical ground deformation field. Finally, we use the InSAR-VSM model based on elastic half-space and the one-dimensional poroelastic model considering elastic unloading to obtain independent quantitative estimates of groundwater loss in NCP. For the first time, we have reduced the spatial resolution of groundwater reserves inversion in the NCP from hundreds of kilometers to several kilometers, breaking through the bottleneck of insufficient spatial resolution in groundwater hydrological research. By integrating the South-to-North Water Diversion Project, groundwater extraction policies in the NCP, meteorological datasets, and groundwater level data, we have found that there are significant differences in the response mechanisms of groundwater and land subsidence between piedmont plains and flood plains in the NCP. Our data and results have enhanced our understanding of changes in groundwater storage in the North China Plain and provide scientific support for scientifically managing groundwater resources and carrying out related work on preventing and mitigating ground subsidence disasters.

How to cite: zhang, X. and hu, J.: Wide-area deformation surveying and Groundwater Volume Loss assessment in the North China Plain by Multi-track InSAR Observations and mechanical models, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8212, https://doi.org/10.5194/egusphere-egu24-8212, 2024.

15:20–15:40
Coffee break
Chairpersons: Xie Hu, Ling Chang
16:15–16:35
|
EGU24-17107
|
solicited
|
Highlight
|
On-site presentation
Othmar Frey, Charles Werner, and Rafael Caduff

Geohazards related to ground motion are widespread in mountainous regions. Time series of spaceborne SAR data are commonly used to retrieve maps of ground motion with extensive spatial coverage. However, there are situations where terrestrial radar systems are more suitable or even necessary for measuring ground motion. Such situations include slopes facing north or south, where the line of sight of current space-based SAR systems is nearly perpendicular to the prevalent direction of ground motion; slopes that lie in radar shadow or layover for current spaceborne SAR geometries; fast-moving landslides requiring shorter interferometric measurement intervals; and cases demanding higher spatial resolution or higher frequencies (e.g., Ku-band) with better sensitivity to line-of-sight motion.

Terrestrial stationary radar/SAR systems, typically operating at Ku- or X-band, have been employed for many years to address these challenges. However, their limited synthetic aperture (or antenna size in the case of real-aperture radars) result in a constant angular resolution in the azimuth direction, leading to a  reduced spatial azimuth resolution with increasing distance.

Monitoring a landslide from a moving car or a UAV with a longer synthetic aperture allows using lower frequencies such as L-band, offering good spatial resolution at the meter level. Aperture synthesis from a car or UAV at higher frequencies (e.g., Ku-band) with smaller radar antennas can significantly improve the azimuth resolution to sub-meter or even decimeter level compared to stationary terrestrial radar systems which typically have azimuth resolutions in the order of 10m and more at range distances of several kilometers.

In our previous work, we had demonstrated mobile mapping of ground motion using a compact repeat-pass L-band interferometric SAR system on a car and a UAV. Recently, a Ku-band SAR system (a modified version of the Gamma Portable Radar Interferometer (GPRI)) was added to the car-borne InSAR measurement setup. The new configuration allows simultaneous data acquisitions at both frequencies during repeat-pass SAR measurements while driving along a road.

Frequency diversity proves to be advantageous in mountainous areas with varying land cover and motion processes with different velocities and scales. In this contribution, we present recent results from car-borne mobile mapping campaigns in the Swiss Alps showcasing the dual-frequency car-borne SAR setup (Gamma L-band SAR and modified GPRI at Ku-band). In particular, we present ground motion measurements of the Brinzauls landslide in Switzerland taken in fall 2023 at both frequencies, Ku- and L-band, and at different time intervals. The case study strikingly shows the complementary properties of the two frequencies in terms of sensitivity to motion and temporal decorrelation. The unprecedented high-resolution SAR imagery and interferograms obtained with the car-borne Ku-band SAR (decimeter-level azimuth resolution) allows discriminating different bodies of the landslide moving at different velocities in detail.

How to cite: Frey, O., Werner, C., and Caduff, R.: Dual-frequency high-resolution mobile mapping of ground motion of the Brinzauls landslide in Switzerland with a car-based interferometric SAR system at L- and Ku-band, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17107, https://doi.org/10.5194/egusphere-egu24-17107, 2024.

16:35–16:45
|
EGU24-2957
|
ECS
|
On-site presentation
Xiaoqiong Qin, Yuanjun Huang, Linfu Xie, Xuguo Shi, and Chisheng Wang

The planned soil dumping in Shenzhen has been insufficient for a long time, and improper disposal of excess soil would pose significant safety hazards such as soil settlement and landslides. Therefore, analyzing the causes and potential impacts of soil dumping disasters is crucial for effective risk prevention and control. Interferometry Synthetic Aperture Radar (InSAR) is an effective land surface deformation monitoring technology with unique advantages, including low costs, large-scale implementation potential, and a high coherence level in the settlement analysis of soil dumps without vegetation.

This study investigated a soil dump with the highest risk potential in the Shenzhen-Shanwei Special Cooperation Zone, processing 91 Sentinel-1 images from 2019 to 2022 for deformation monitoring. An improved Small Baseline Subset-InSAR (SBAS-InSAR) method was utilized to analyze the soil dump’s time-series deformation, and multi-source remote sensing data were used for auxiliary interpretation. The experimental results indicate that rainfall, high temperature, and construction vibrations may cause instability of the soil dump. When the monthly rainfall is 200 mm/month and the temperature is 30℃, the meteorological conditions significantly impact the soil dump’s stability. In addition, vegetation and drainage procedures can help resist the impact of high temperatures and rainfall. Activities such as slope excavation, earthwork filling, and gravel production are also the main causes of settlement fluctuation in the soil yard. However, with artificial excavation, unloading, and comprehensive management, the soil dump’s LOS velocity decreases by 10% to 45%.

The Chishi soil dump remains stable during the original period and does not show a subsidence trend until the soil dump is formed. The overall settlement rate is around -33.3mm/yr, and most severe settlement occurs near the north slope with a maximum deformation rate of about -51mm/yr. The closer to the landfill’s center, the greater the soil thickness and the more severe the settlement. Moreover, with similar soil thicknesses, the settlement rate on the slope is higher than that at the top of the soil dump.

The limit equilibrium analysis of the soil dump indicates a risk of instability under continuous heavy rainfall. Therefore, a depth-integrated continuous medium model was introduced to simulate the surface process and analyze the potential landslide risk. The landslide simulation results demonstrate that a landslide will likely occur, harm personnel, and damage the buildings on the north side of the soil dump when it is saturated (≥ 0.50). This research can provide case references for the analysis, interpretation, disaster prevention, and control evaluation of similar soil dumps.

 

How to cite: Qin, X., Huang, Y., Xie, L., Shi, X., and Wang, C.: Settlement and Landslide Risk Analysis of Temporary Soil Dump Based on InSAR and Continuous Medium Model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2957, https://doi.org/10.5194/egusphere-egu24-2957, 2024.

16:45–16:55
|
EGU24-4011
|
ECS
|
On-site presentation
Gökhan Aslan, Marcello de Michele, Tim Redfield, Mikis van-Boeckel, Reginald Hermanns, François Noël, and John Dehls

In the dynamic landscape of the northwest Indian Himalayas, the Leo Pargil Landslide stands as a monumental example of slope instability. This study marks the first detection of this giant landslide through Interferometric Synthetic Aperture Radar (InSAR) techniques. Spanning an impressive 55 km² and mobilizing approximately 25 km³ of rock material towards the Spiti River at a rate of 80 mm/year, it poses significant risks to several villages, towns, and the NH505 highway located atop and along its path.

Deep-Seated Gravitational Slope Deformations (DSGSDs), such as the Leo Pargil Landslide, are pivotal in shaping mountainous landscapes. These giant landslides significantly influence topographic evolution, particularly in regions marked by rapid rock uplift in steep terrain. Understanding these processes is crucial, given their geological significance and the natural hazards they pose to communities in tectonically active regions. However, their inherent unpredictability, influenced by factors like geology, geomorphology, climate, and seismic activities, makes evaluating landslide dynamics a challenging task.

The Leo Pargil Landslide, bounded by the northeast-trending Leo Pargil Shear Zone (LPSZ) and incorporating several brittle normal faults, is conditioned by at least three geological factors: steep slope terrain, the bedding structure of the rock formation, and deep river incision at the base of the landslide. Our study investigates the factors conditioning the landslide, the driving forces behind it, and its evolution, offering new insights into the underlying mechanisms of failure.

In this study, we utilized Sentinel 1A/B satellites, applying Persistent Scatterer (PS) InSAR processing techniques to analyze the active dynamics of the Leo Pargil landslide. By combining InSAR derived velocity field data with the local geology, geomorphological features of the slope and previously published geochronological data we tried to elucidate the possible mechanisms involved in the initiation and development this landslide.

The findings underscore the role of dome exhumation as a geomechanical driver of slope dynamics. The transition in stress fields, tectonic and structural influences, and the interplay between erosion, river incision, and monsoon precipitation anomalies are highlighted as significant factors in the landslide's development. This comprehensive understanding is vital for slope stability assessments and risk mitigation strategies in the Himalayan region.

In conclusion, the study links geomechanical, geochronological, and geomorphological analyses to unravel the complexities of the Leo Pargil Landslide. It emphasizes the significance of the Leo Pargil Dome, not merely as a backdrop but as an active contributor to landslide dynamics, highlighting the critical need to consider both local and broader tectonic contexts in understanding slope instability

How to cite: Aslan, G., de Michele, M., Redfield, T., van-Boeckel, M., Hermanns, R., Noël, F., and Dehls, J.: InSAR Insights into the Massive Himalayan Leo Pargil Landslide, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4011, https://doi.org/10.5194/egusphere-egu24-4011, 2024.

16:55–17:05
|
EGU24-11881
|
ECS
|
Highlight
|
On-site presentation
Katy Burrows, Aadityan Sridharan, and Maria Francesca Ferrario

When shallow landslides are triggered by sequences of earthquakes or storms, we need to know when in the sequence they occurred and whether they were later reactivated in order to better understand and model the hazard. In tropical environments, cloud cover often obscures all or part of the optical imagery acquired during the sequence, so that it is necessary to use images acquired after the sequence of events to map the landslides. Because of this, we often cannot tell which earthquake or storm triggered a particular landslide. This limits our understanding of how landslide hazard and mass wasting evolve in space and time during such sequences.

Synthetic Aperture Radar (SAR) images can be acquired through cloud cover and since 2015, Sentinel-1 has acquired data globally every 6-12 days. Landslides alter the scattering properties of the Earth’s surface and so can be detected in SAR images. SAR amplitude time series can be used to constrain the timings of individual landslides. We apply these methods to three sequences of triggers in order to better understand how landsliding evolved during them: the 2018 Lombok, Indonesia earthquake sequence; the 2019 Cotabato – Davao del Sur earthquake and the earthquake-hurricane sequence that occurred in Haiti in 2021.

The 2018 Lombok earthquake sequence also offers an ideal opportunity to test new methods of using InSAR coherence (the level of noise in an interferogram) to detect multi-stage failure. High resolution cloud-free images were acquired halfway through this sequence of four Mw > 6.0 earthquakes and many landslides mapped after the first two earthquakes were then observed to have grown in size or changed shape by the end of the sequence. We demonstrate that for large, favourably oriented landslides, InSAR coherence can be sensitive to this reactivation and so could potentially be used in the future for cases where no cloud-free images are available.

How to cite: Burrows, K., Sridharan, A., and Ferrario, M. F.: Timing landslides and identifying reactivations during sequences of earthquakes and storms with Sentinel-1 amplitude and coherence, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11881, https://doi.org/10.5194/egusphere-egu24-11881, 2024.

17:05–17:15
|
EGU24-9502
|
On-site presentation
Matteo Mantovani, Angelo Ballaera, Giulia Bossi, Federica Ceccotto, Gianluca Marcato, and Alessandro Pasuto

This study presents a new application of the Sentinel-1 dataset for the detection and measurement of earthflow displacements. The proposed methodology utilizes a multi-baseline interferometric hybrid approach, leveraging the backscattered radiation from both point-like and distributed radar targets. The analysis takes into consideration eight datasets acquired between 2017 and 2020, both on ascending and descending orbits, but it is restricted to seven months of the yearly acquisitions, spanning from late March to the beginning of November, to mitigate temporal decorrelation and minimize the impact of snow cover. The investigated area is part of the Dolomites Unesco World heritage site (Eastern Italian Alps). The results so far obtained are then compared with those derived from the European Ground Motion Service (EGMS) and in-situ Global Navigation Satellite System (GNSS) monitoring networks. Preliminary findings reveal the promising reliability of this approach, demonstrating its efficacy and accuracy. Furthermore, this methodology offers a notable advantage in terms of spatial sampling, resulting in the enhancement of the capability to identify and characterize earthflows movement. Overall, this study underscores the potential of utilizing Sentinel-1 data for monitoring landslides distinguished by significantly high rates of displacement, by loosening some of the well-known constraints of the interferometric analyses. The findings highlight the importance of space-borne SAR missions in providing valuable insights in landslide risk mitigation and management.

How to cite: Mantovani, M., Ballaera, A., Bossi, G., Ceccotto, F., Marcato, G., and Pasuto, A.: An effective method to detect and measure earthflow displacements using a hybrid interferometric approach, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9502, https://doi.org/10.5194/egusphere-egu24-9502, 2024.

17:15–17:25
|
EGU24-2842
|
ECS
|
Virtual presentation
Wei Tang

Land subsidence has a great impact on coastal plains near sea level, leading to permanent inundation. The Shuguang oilfield, located in Liaohe River Delta (LRD), northeastern China, is one of the most significant subsidence areas as a direct consequence of oil production. We studied the production-induced deformation in the LRD region by Sentinel-1 radar images. Images from two ascending and two descending tracks are processed by an Interferometric Synthetic Aperture Radar (InSAR) time series analysis over the 2017 to 2021 period, providing deformation rate maps and time series in the radar line-of-sight (LOS) direction.

Previous researches carried out in this area assumed the oil production-induced deformation corresponds only to vertical deformation. Here, we proposed a method to retrieve the three-dimensional (3D) displacement field over the oilfield. We retrieved the vertical and east-west deformation components by combining the multiple InSAR geometries LOS observations and retrieved the north-south component based on the assumption of a physical relationship between the horizontal and vertical displacement.

The derived 3D displacement fields over Shuguang oilfield exhibit a circular subsidence bowl with a maximum subsiding rate reaching 212 mm/year, accompanied by a centripetal pattern of horizontal displacements with maximum rates up to 50-60 mm/year moving towards the subsidence center. The retrieved-3D displacements are in good agreement with predictions from the geomechanical modeling by assuming a disk-shaped reservoir subject to a uniform reduction in pore fluid pressure. Finally, we show the importance of knowing both the vertical and horizontal displacement in characterizing the lateral boundary of the subsurface reservoir.

The Liaohe River Delta region is often affected by heavy rainfall and storm surge in flood season, and flood disasters occur frequently in this low-lying coastal area. This deltaic region is vulnerable to floods not only from the extreme heavy rainfall, but also the land subsidence related to oil production. By spatial overlay analysis of the land subsidence distribution and the inundation extent of a flood event in August 2022, we reveal the impacts of land subsidence on flood inundation in this region. Our findings provide scientific support for oil production-related subsidence control and flood planning and designing in this deltaic region.

How to cite: Tang, W.: Three-dimensional deformation over Shuguang oilfield in Liaohe River Delta, China, from multi-track InSAR and its impacts on flood inundation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2842, https://doi.org/10.5194/egusphere-egu24-2842, 2024.

17:25–17:35
|
EGU24-17912
|
Virtual presentation
Mohammad Omidalizarandi, Kourosh Shahryarinia, Bahareh Mohammadivojdan, and Ingo Neumann

Large-scale, cost-effective, and reliable deformation monitoring of natural objects or man-made infrastructures is still challenging. Numerous past studies have employed the Persistent Scatterer Interferometry (PSI) technique, utilising open-source synthetic aperture radar (SAR) data from C-band of satellite Sentinel-1, for this purpose. However, a limited number of investigations have been performed to evaluate the quality of the Persistent Scatterer (PS) data points.

In this research, a comprehensive and sophisticated multi-step procedure is developed and implemented to perform quality assessment of the PS data points using vector-autoregressive-based spatio-temporal (VAR-ST-PS) modelling. Firstly, the PS points are classified into buildings and ground types using LoD2 building models. Multivariate PSI time series analysis is then carried out to understand the temporal behaviours of groups of PS points in local geometric patches. This involves modelling and analysing PSI time series to estimate deterministic and stochastic parameters such as offset, velocity, standard deviation, and corresponding distributional parameters. A spatio-temporal modelling is employed within the local geometric patches of PS points using a mathematical surface approximation model. A 95% confidence interval is estimated for the approximated surfaces using a bootstrapping approach. Subsequently, an appropriate quality model for the PS points is derived from the above-mentioned temporal and spatial modelling.

The quality assessment and subsequent deformation analysis are carried out for areas of interest in the state of Lower Saxony, Germany. The PS data points for this study are extracted from the freely available online platform of the BodenBewegungsdienst Deutschland (Ground Motion Service Germany) provided by the Federal Institute for Geosciences and Natural Resources (BGR), Germany. For validation purposes, a time series of leveling and Global Navigation Satellite System (GNSS) measurements in the Hengstlage area, Germany, are considered, which provided by Landesamt für Geoinformation und Landesvermessung Niedersachsen (LGLN). In addition, cross-validation is performed for different local geometric patches. In the end, the results of the deformation analysis are compared with those obtained from the BGR. The outcomes of this study can be used to track earth surface displacements over time. This information could be valuable in understanding natural hazard processes such as landslides, earthquakes, and floods, and in improving the safety and resilience of communities and infrastructure.

How to cite: Omidalizarandi, M., Shahryarinia, K., Mohammadivojdan, B., and Neumann, I.: Quality assessment of Persistent Scatterer Interferometry time series using vector-autoregressive-based spatio-temporal (VAR-ST-PS) modelling, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17912, https://doi.org/10.5194/egusphere-egu24-17912, 2024.

17:35–18:00

Posters on site: Wed, 17 Apr, 10:45–12:30 | Hall X4

Display time: Wed, 17 Apr, 08:30–Wed, 17 Apr, 12:30
Chairpersons: Mahdi Motagh, Xie Hu, Ling Chang
X4.51
|
EGU24-889
Merve Ercan, Tülay Kaya Eken, Çağkan Serhun Zoroğlu, Emre Havazlı, and Haluk Özener

Abstract

Tectonic features of Türkiye are mainly controlled by the relative northward movements of the Arabian and subducting African plates with respect to the Anatolian and Eurasian plates. Resultant extensional and collisional tectonics lead to a westward material extrusion accommodated along the right- and left-lateral strike-slip North Anatolian and East Anatolian Fault Zones (NAFZ and EAFZ), respectively. This lateral motion continues southward along the Dead Sea Fault Zone (DSFZ) at the southeastern of Türkiye. February 20, 2023, Mw 6.3 Hatay Earthquake occurred two weeks after the seismic energy release of the February 6, 2023, Kahramanmaraş earthquakes at the intersection of EAFZ, DSFZ, and the onshore extension of the Cyprus Arc. The N-S trending DSFZ starts from the south of the EAFZ and continues through Syria, Lebanon, and Israel. Although the broken segment in Hatay is not as active as the northern segments of the EAFZ, it has accumulated strain leading to significant seismic activity in the past in this region, i.e., the 1872 M7.2 earthquake occurred on the Karasu Fault. 2023 Kahramanmaraş and Hatay earthquakes caused severe damage in Hatay and the surrounding area. To determine the co-seismic deformation during the February 20, 2023, Hatay Earthquake, we applied the Interferometric Synthetic Aperture Radar (InSAR) technique on the Sentinel-1 data. Ascending and descending track SAR images before and after the Kahramanmaraş and Hatay earthquakes were analyzed using the TopsApp module of the InSAR Scientific Computing Environment (ISCE) software to obtain Interferograms of co-seismic deformation in and around Hatay region. Finally, we investigated source parameters by performing an inversion on geodetic constraints considering the Okada elastic dislocation model.

How to cite: Ercan, M., Kaya Eken, T., Zoroğlu, Ç. S., Havazlı, E., and Özener, H.: Surface deformation and Source Parameters of 2023 Hatay Earthquake inferred from the InSAR Data Analysis, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-889, https://doi.org/10.5194/egusphere-egu24-889, 2024.

X4.52
|
EGU24-1484
Jiacheng Xiong and Ling Chang

Time Series Interferometric Synthetic Aperture Radar (TS-InSAR) technique can monitor displacement processes intarget areas with millimeter precision. However, limited by decimeter- or meter- level InSAR geolocation accuracy, directly associating InSAR points with actual ground targets merely based on InSAR-derived geolocation estimates is not always reliable. Especially for linear infrastructure like dams, poor-quality geolocation estimations can lead to deviations in the three- dimensional (3D) position of InSAR points, thereby failing to accurately link InSAR points with the specific structures of the dam. Here, we propose a method for 3D geolocation improvement of InSAR points based on the 3D error ellipsoid of InSAR positioning estimation and aided by Light Detection and Ranging (LiDAR) data (0.5 x 0.5 m). By establishing an error ellipsoid of every InSAR point and utilizing rotation and projection matrices for LiDAR datum transformation, we extract all LiDAR points located within the error ellipsoid and update InSAR point geolocation based on the extracted LiDAR values and their statistics. This process recalculates the 3-D geolocation of InSAR points and improves its accuracy. We applied this method to the Houtribdijk dam in the Netherlands, andimproved the InSAR points obtained with 152 and 148 Sentinel-1A IW SAR data (10 x 5m) in ascending and descending orbits acquired between 2018 and 2022, using the Actueel Hoogtebestand Nederland 3 (AHN3) LiDAR point cloud with centimeter-level accuracy. The results show that the InSAR points with 3D error ellipsoid properly link with structures over the entire dam compared with the points without concerning positioning uncertainty. For the SAR data in ascending and descending orbit, the Root Mean Square Errors (RMSE) of the heights between the LiDAR-based improved InSAR points and AHN3 LiDAR points are 0.4 m and 0.5 m, respectively. In contrast, the RMSE values for the InSAR points without LiDAR-based improvement are 1.4 m and 1.6 m, respectively. Furthermore, we compared the correlation of heights between all InSAR points on the dam and the AHN3-derived digital terrain model (DTM). The correlation of heights between the InSAR points without and with geolocation improvement and the AHN3 DTM is 0.14 and 0.95 with the RMSE values of 1.9 m and 0.5 m for ascending, 0.11 and 0.95 with the RMSE values of 1.2 m and 0.5 m for descending, respectively. All this demonstrates the efficacy of our method, and allows us to further precisely identify InSAR points from the slopes and top of the dam for the dam structures’ displacement assessment.

 

[1] Dheenathayalan P, Small D, Schubert A, et al. High-precision positioning of radar scatterers. Journal of Geodesy, 2016, 90(5): 403-422.

[2] Chang L, Sakpal N P, Elberink S O, et al. Railway infrastructure classification and instability identification using Sentinel-1 SAR and laser scanning data. Sensors, 2020, 20(24): 7108.

[3] Zhang, B., Chang, L., Stein, A., 2021. Spatio-temporal linking of multiple SAR satellite data from medium and high resolution Radarsat-2 images. ISPRS Journal of Photogrammetry and Remote Sensing, 176.

How to cite: Xiong, J. and Chang, L.: InSAR points’ geolocation uncertainty estimation and geolocation improvement using LiDAR, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1484, https://doi.org/10.5194/egusphere-egu24-1484, 2024.

X4.53
|
EGU24-1814
|
ECS
Spatiotemporal evolution characteristics of Hanyuan landslide in Sichuan Province, China on August 21, 2020
(withdrawn)
shuaishuai Xu
X4.54
|
EGU24-7906
Monitoring Building Instability by Sentinel-1 PS-InSAR:A Case Study in Taipei, Taiwan
(withdrawn after no-show)
Yun-Chiao Chang and Kuo-Hsin Tseng
X4.55
|
EGU24-12522
|
ECS
Mohammad M.Aref, Bodo Bookhagen, and Manfred R. Strecker

Slow-moving landslides in high-mountain regions pose a significant natural hazard and are capable of delivering large sediment volumes to the fluvial system. Time series analysis of Interferometric Synthetic Aperture Radar (InSAR) allows us to identify unstable and potentially dangerous areas prone to landsliding, but this technique also helps quantify seasonal dynamics for predicting landslide behavior.

Our study in the Eastern Cordillera of the Argentine Andes focuses on enhancing InSAR's reliability for landslide mapping. This region is characterized by moisture changes along the topographic gradient across the orogen and seasonal variability associated with the South American Summer Monsoon. We extract InSAR time series data from Sentinel-1A/B's C-band (2014-2022) and ALOS1 PALSAR's L-band (2006-2011). Tropospheric delay is caused by atmospheric turbulence and vertical stratification changes. These delays can introduce significant errors in deformation measurements, thus impacting the quality of maps portraying landslide deformation rates. To address this problem, we apply various correction techniques, ranging from spatial and temporal filtering to water-vapor estimation from an atmospheric model. Fading signal noise, another challenge caused by multi-looking and short temporal baselines in the Small Baseline Subset (SBAS) technique, additionally compromises InSAR time series accuracy. We investigate the pattern and magnitude of fading signals in landslide areas using Small Baseline Subset (SBAS) with different neighboring connections and non-linear phase inversion methods, such as the Eigenvalue Decomposition-based Maximum Likelihood (EMI), Eigenvalue Decomposition (EVD), and the Phase Triangulation Algorithm (PTA).

Our research evaluates both statistical methods and Global Atmospheric Models for correcting tropospheric delays and fading signal noise. We explore statistical methods, such as double-difference filtering and corrections based on phase elevation, for different spatial windows, including individual catchments, moving windows, and adaptive window sizes. The efficiency of these methods varies with the environmental and topographic conditions in the orogen. Both stratified and turbulent components of the troposphere, along with fading signal noise, can significantly influence tropospheric delay and time series quality. In the context of the factors that influence deformation signals and the combined array of methods to obtain robust measurements, we can identify the spatial and temporal characteristics of slow-moving landslides and assess the different impacts on rate changes.

How to cite: M.Aref, M., Bookhagen, B., and R. Strecker, M.: Impact of Tropospheric Delay Correction on the Quality of Landslide Mapping in the Southern Central Andes, Northwestern Argentina, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12522, https://doi.org/10.5194/egusphere-egu24-12522, 2024.

X4.56
|
EGU24-12802
|
ECS
Zahra Dabiri and Faramarz Nilfouroushan

Landslides cause significant socioeconomic impacts on people and national infrastructures like railways and roads and are considered one of the common geohazards that demand more attention. In Sweden, many areas are prone to landslides due to the presence of underlying quick-clay sediments, which may lead to minor to large slides. Ground deformation monitoring in such hazardous areas is important for a better understanding of the landslide processes and mitigation of hazards. Over the last decade, Interferometric Synthetic Aperture Radar (InSAR) time-series techniques, such as Persistent Scatterer Interferometry (PSI) and the Small Baseline Subset (SBAS), have become a crucial tool for ground surface deformation analysis. SBAS and PSI use SAR data to retrieve the time-series cumulative phase of the Persistent Scatters (PS). The main objective of this study was to demonstrate the advantage of using advanced InSAR time series analysis for a better understanding of surface deformation before a landslide event. We focused on the recent landslide on the E6 Sweden-Norway highway near Stenungsund in Southern Sweden, which occurred on 23 September 2023. Sentinel-1 SAR data was collected between 2018 and September 2023, with ascending flight direction to measure the pre-event deformation in the landslide zone. We used Alaska Facility (ASF) on-demand product processes based on Hybrid Pluggable Processing Pipeline (Hyp3) to search, process, and download time series Sentinel-1 data. We also used Miami INsar Time-series software in PYthon (Mintpy) to perform cloud-based SBAS processing using unwrapped interferograms stack derived from Sentinel-1 time series data. In addition, we employed Basic PSI products (ground motion in the Line-of-Sight (LOS) direction) provided by the European Ground Motion Service (EGMS). The initial SBAS results and EGMS Basic products for the same ascending orbit showed continuous deformation on the highway segment in the landslide zone over the last EGMS update period, 2018 to 2022 for the PSI results and 2018 to 2023  for the SBAS results. The first five-year period of the EGMS Basic and Ortho products, i.e., 2015-2021, was also checked and showed the same results over the longer period between 2015 and 2021. Both sets of PSI and SBAS results agree on the annual cm-level (10-15 mm/year) subsidence rate of the highway before the landslide, with SBAS analysis yielding more measurement points, especially in the vegetated and unbuilt areas. The initial results showed that the SBAS technique could provide more information within the hazardous zone; nevertheless, due to Sentinel-1 C-band data, the quality of the results can be degraded by coherence variations in the vegetated areas.  The comparison of preliminary results of InSAR data processing and available EGMS products provides insights into ground movements, facilitating a comprehensive understanding of evolving conditions before the landslide. Nevertheless, the results emphasize the importance of incorporating advanced time series InSAR techniques for continuously monitoring infrastructures such as railroads and highways to support sustainable development and natural hazard assessments.

How to cite: Dabiri, Z. and Nilfouroushan, F.: Enhancing Landslide Preparedness: Leveraging EGMS Products and SBAS-InSAR for Pre-Event Ground Deformation Monitoring along the E6 Highway near Stenungsund in Southern Sweden, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12802, https://doi.org/10.5194/egusphere-egu24-12802, 2024.

X4.57
|
EGU24-16066
Yan Yang, Mahdi Motagh, Mahmud Haghshenas Haghighi, and Andreas Piter

Landslide is a geological disaster with extremely destructive effects, resulting in huge casualties and economic losses in China. The Loess Plateau is widely covered by several to tens of meters of loess, and the underlying bedrock with good water barrier properties. Due to the frequent rains, the soil body is easy to flow or slide along weak structural surfaces. There are lots of typical loess landslides in the Loess Plateau and they seriously affect the lives of residents, so studying landslide deformation in Loess Plateau is of great significance for both society and geological expert. Interferometric Synthetic aperture radar (InSAR), with the advantages of wide monitoring range, high density, high accuracy, and not affected by weather conditions, has become the most effective technical means for regional surface deformation monitoring and landslide identification. In this paper we perform landslide deformation survey based on the small baseline Subset InSAR (SBAS-InSAR) method in Tianshui, which is located on the Loess Plateau, using 594 interferograms from the Sentinel-1 satellite ranging from January, 2017 to December, 2022. SBAS-InSAR time series analysis connects independent SAR images based on certain spatial baseline and time baseline thresholds, and finally gets time series and velocity of the Loess Plateau. The locations of landslides from National Geological Disaster Survey Database provided by China Geological Survey are compared with our results to verify the applicability of SBAS-InSAR technology in the Loess Plateau. The comparison results show that SBAS-InSAR technique using sentinel-1 dataset can effectively identify landslides in most areas except for the areas covered by forest. The results of velocity map and landslide maps can be used for landslide identify and assessment in the Loess Plateau.

How to cite: Yang, Y., Motagh, M., Haghshenas Haghighi, M., and Piter, A.: SBAS-InSAR Analysis and Assessment of Landslide deformation in the Loess Plateau, China, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16066, https://doi.org/10.5194/egusphere-egu24-16066, 2024.

X4.58
|
EGU24-19890
|
ECS
Imeime Uyo, Mahdi Motagh, and Mahmud Haghshenas Haghighi

The Niger Delta basin located at the apex of the Gulf of Guinea on the west African coast has a vast deposit of hydrocarbon from which Nigeria’s oil and gas is derived. Although the export of oil and gas resources from this region has significantly improved the nation’s economy over the years, activities associated with hydrocarbon exploration have a significant impact on the land surface among other environmental detriments. If the benefits of hydrocarbon exploration and production must be realized in tandem with the environment, understanding the magnitude and nature of surface deformation within the production zones is crucial.

In this study, we present first results of InSAR-derived spatial and temporal variations in surface deformations over the oil production fields within the Niger Delta Basin. The mean deformation velocity maps and time-series of displacements for measurement points are used to assess rates of ground deformation.

Sentinel-1 C-band SAR data acquired between 2014 and 2023 are analyzed using both Persistent Scatterer Interferometry (PSI) and Small Baseline Subset (SBAS) InSAR techniques to assess deformation over oil and gas fields. We utilized the Stanford Method for Persistent Scatterer (StaMPS) technique for PSI processing and the MintPy package for SBAS analysis. Interferometric Synthetic Aperture Radar Scientific Environment (ISCE) developed by NASA's Jet Propulsion Laboratory is used to generate the interferograms. The findings of the study will reveal the rate of subsidence and uplift in the line-of-sight (LOS) direction over the active production oil/gas wells within the Niger Delta basin.

The findings of this study would be an invaluable input in decision-making for the benefit of affected communities and other stakeholders in the oil and gas industry. Monitoring subsidence helps to prevent hazards, ensures operational safety, and supports sustainable resource management in the affected areas.

How to cite: Uyo, I., Motagh, M., and Haghshenas Haghighi, M.: Ground Surface Deformation in the Niger-Delta Basin Caused by Hydrocarbon Exploration: First Results from Satellite InSAR Surveys, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19890, https://doi.org/10.5194/egusphere-egu24-19890, 2024.

X4.59
|
EGU24-19329
|
Highlight
Xie Hu, Yuqi Song, and Yiling Lin

Landslides can move at divergent rates on the terrestrial planets. The occurrence and evolution of landslides are strongly affected by the stochastic nature of environment. Landslide activities are complicated by climate change and the attendant escalating number of extreme precipitation events. Here we use multi-source geodetic and remote sensing data (e.g., SAR and optical scenes, as well as climate reanalysis products) and skillsets (e.g., InSAR, pixel offset tracking, and AI) to disentangle the role of environmental players (e.g., water, wind, temperature, and tectonics) in the lifespan of landslides. The perpetual slow-moving landslide in Colorado Plateau will be exemplified to highlight the importance of pore fluid water from rainwater and snowmelt in regulating landslide speeds. An analog landslide to those on Mars will be exemplified to demonstrate an appropriate orientation and layout of topography may help promote eolian abrasion and landslide reactivation. The growth in the area and number of retrogressive thaw slumps in Qinghai-Tibet Plateau will be exemplified to unveil the tragedy of permafrost degradation due to warming temperature and ice-rich permafrost thaw. The spatial proximity of landslides to tectonic faults in California will be exemplified to show exacerbated landslide hazards by occasional dynamic shaking and prolonged weakening of materials.

How to cite: Hu, X., Song, Y., and Lin, Y.: Environmental Players in the Lifespan of Landslides, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19329, https://doi.org/10.5194/egusphere-egu24-19329, 2024.

X4.60
|
EGU24-10697
|
ECS
Florian Leder, Simon Daout, Jérôme Lavé, Nicolas d'Oreye de Lantremange, and Pascal Lacroix

The steep Himalayan slopes are highly exposed to landslides, primarily triggered by earthquakes and monsoon intense precipitation. Along the Himalayan southern slopes, a specific landslide type involves old slided hillslope, characterized by intense internal fracturing, and prone to rapid retrogressive erosion through deep gullies incision, ultimately leading to catastrophic collapse of secondary landslides. Anticipating such events requires understanding if subsiding slices at the edge of the deeply incised talweg exhibit signs of acceleration preceding their rapid collapse and establishing a potential relationship between triggering factors (such as rain) and displacement amplitude. While optical images are commonly used for rapid landslides (with displacements superior to 20 cm/yr), their effectiveness is hindered by cloud cover during monsoon period, limiting sampling frequency and impeding the identification of transient deformation signals.

In this study, we integrated satellite-based optical and radar remote sensing data with high spatial and temporal resolution to characterize the dynamics of a slow-moving landslide located in the Marsyandi valley (84.418° E ; 28.411° N ; 1900m a.s.l.) in Nepal, and to understand how it responds to monsoon rainfall. We developed a processing chain to apply sub-pixel image correlation to a data set comprising spotlight TerraSAR-X and PAZ radar images (1m spatial resolution), as well as medium resolution Sentinel-2 (10m), and high-resolution Pleiades (1m) amplitude optical images. We derived time series of ground displacements in range, azimuth, east-west, and north-south directions. Vertical displacements were additionally produced by comparing high-resolution Digital Surface Models (DSM) obtained from tri-stereo Pleiades images. 

The displacement time series revealed metric transient ground displacements in the upper part of the landslide at the end of the monsoon, along with linear displacements in downstream gullies. Field observations validated our satellite measurements, indicating that during the monsoon, the south-eastern part of the landslide remained relatively stable and revegetated, while the north-western part experienced downward sliding. By comparing these displacements with precipitation data, we characterized the response of the slow-moving landslide to seasonal forcing and gained insights into the mechanisms of collapsed hillslopes.

How to cite: Leder, F., Daout, S., Lavé, J., d'Oreye de Lantremange, N., and Lacroix, P.: Recent satellite-based radar and optical monitoring of the activity of a slow-moving landslide in Nepal during monsoon , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10697, https://doi.org/10.5194/egusphere-egu24-10697, 2024.

X4.61
|
EGU24-20139
|
ECS
Parva Shoaeifar and Katsuichiro Goda

The Ridgecrest earthquake sequence occurred in July 2019 in the Eastern California Mojave Desert. The sequence included two large events with moment magnitude (Mw) 6.4 and 7.1 and thousands of aftershocks above lower magnitude cutoff Mw 3.2. There was severe damage to critical infrastructures, such as major cracks and pavement failures along roads and highways, and disruption to utility service due to surface displacements. These infrastructures were important for post-disaster response and recovery operations. Fault displacement hazard analysis serves as an essential tool for characterizing the fault rupture hazard at sites of interest. Advancements in remote sensing approaches provide opportunities to study earthquake ground deformation hazard by modeling fault rupture process and surface displacements more reliably.   

In the present study, a stochastic source-based fault displacement hazard analysis is conducted. The methodology of the present study is based on statistical scaling relationships of source parameters (e.g., fault length, fault width, mean slip, and maximum slip), and heterogeneous earthquake slip distributions are synthesized to generate various stochastic source models. The method uses ground-truth and remotely sensed data, such as Global Positioning System (GPS) and Interferometric Synthetic Aperture Radar (InSAR) data to search for the source model with a satisfactory match with the available data. The present study differs from conventional fault displacement assessment practices in utilizing stochastic source modeling, instead of empirical predictive relationships. The methodology can be applied to all faulting mechanisms and consider multi-segment fault rupture. The use of Okada’s equations facilitates the calculation of three translational displacements and provides physically consistent fault displacement modeling at two locations for a given earthquake scenario, thereby allowing the estimation of the differential fault displacement at two sites.

This study evaluates the effect of applying InSAR data to the stochastic source modeling approach for the 2019 Ridgecrest earthquakes which involved the complex interaction of multiple faults having different mechanisms. InSAR data provide useful information on the geometry and the extent of the rupture system and contributes to the hazard assessment efficiently. The capability of the method is evaluated in the framework of retrospective analyses by comparing the results with available data as well as existing studies and their associated model weighted errors. The performance of the models in earthquake source characterization is also analyzed considering InSAR data by the changes in model weighted errors for the cases of the surface displacement results with and without InSAR data. Based on the obtained results, InSAR data play an integral part in mainshock displacement analyses. Considering all the merits of applying ground truth and remote sensing data to the practice of stochastic source-based fault displacement hazard analysis, the obtained results characterize fault displacements more realistically and contribute to emergency management and disaster risk mitigation of critical facilities and infrastructure.

How to cite: Shoaeifar, P. and Goda, K.: Evaluation of stochastic source models for the fault displacement hazard analyses using InSAR data: 2019 Ridgecrest earthquakes , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20139, https://doi.org/10.5194/egusphere-egu24-20139, 2024.

X4.62
|
EGU24-19138
Characterization of the Groundwater Induced Land Subsidence and Aquifer Parameters Using InSAR Measurements in the Xingtai, North China Plain
(withdrawn after no-show)
Sha Song and Lin Bai
X4.63
|
EGU24-912
|
ECS
Divyeshkumar Rana, Prof. Paolo Mazzanti, and Prof. Francesca Bozzano

Soil moisture is an important parameter in many fields, including agriculture, climatology, hydrology, and geohazards. Accurate and high spatial resolution soil moisture estimation can improve our understanding of hydrological processes, and climatic interaction, and a more complete view of the domain. Soil moisture estimation can enhance our understanding of preparedness for natural hazards such as landslides, sinkholes, and subsidence. Single-dual polarimetric data is widely used for assessing and monitoring soil moisture due to the availability of datasets. This research proposes a synergized approach using the change detection method based on backscatter information using SAOCOM L-Band Synthetic Aperture Radar (SAR) datasets from 2021 to 2023 to estimate soil moisture and ground deformation using Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) using CosmoSkyMED X-Band datasets from 2011 to 2022. We present a case study of the Petacciato landslide, Molise Region, Italy. The Petacciato landslide is a coastal area in Europe highly prone to mass movements. It is in the northwestern sector of the Molise region (central Italy) in the outermost portion of the central-southern Apennine chain. Timeseries soil moisture results were further compared with the historical open-source meteorological datasets. Precipitation events lead to the most soil moisture that is observed between November to February months. The average ground deformation (LOS velocity) observed on unstable slopes ranged from -1 mm/year to -20 mm/year in the study area.

How to cite: Rana, D., Mazzanti, P. P., and Bozzano, P. F.: Assessing the correlation of Time-Series Soil Moisture and Ground Deformation At Petacciato Landslide, Italy, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-912, https://doi.org/10.5194/egusphere-egu24-912, 2024.

X4.64
|
EGU24-2419
Atif Ahmad, Alessandro Mercurio, Benedikt Bayer, Silvia Franceschini, and Alessandro Simoni

Landslides in mountainous regions are a major concern due to their potential impact on infrastructure and human lives. Many deep-seated slope movements alternate between phases of sustained movement to phases of dormancy. Detecting and monitoring active movements over large territories can effectively support risk mitigation efforts. Satellite radar interferometry is widely used for such purposes, despite its poor coverage in uninhabited rural areas. Our study aims to overcome such limitations by using conventional two-pass interferometry. We use interferometric stacking to improve the signal-to-noise ratio and carefully select interferogram duration and coherence to enhance the ability to detect active slow-moving landslides. The current study focuses on five large catchments of the Northern Apennines, Italy. In the first phase of the study, yearly stacks from 2016 to 2023 were used to identify InSAR Deformation Signals likely related to slope deformation processes. More than 80 signals were detected in the study area, showing sustained deformation in multiple interferometric stacks either in ascending and/or descending geometry. This provides strong evidence for the effectiveness of our approach. We compare our results with geomorphological and geological information, as well as the landslide inventory, illustrating how active landslides are favored by weak lithologies and pre-existing slope instability. The analysis of the evolution of selected signals over time, representative of the pattern of landsliding in our study area, shows distinct trends for landslides involving fine-grained materials and arenaceous bedrock. Earthslides and earthflows show sustained downslope motion with seasonal velocity changes, while rockslides are subject to short-duration acceleration episodes. Time series analysis show that surface displacements can be observed throughout most part of the year, with exceptions during periods of snow cover and the summer peak of vegetation. These findings highlight the potential of standard InSAR for mapping and monitoring active landslides. 

How to cite: Ahmad, A., Mercurio, A., Bayer, B., Franceschini, S., and Simoni, A.: Mapping and monitoring different types of landslides through InSAR in the Northern Apennines of Italy, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2419, https://doi.org/10.5194/egusphere-egu24-2419, 2024.

X4.65
|
EGU24-2972
|
ECS
Zhangfeng Ma, Yu Jiang, Chenglong Li, and Shengji Wei

“Long or short baseline interferogram” is a well-known concept in InSAR time series analysis. In different cases, scientists often choose interferograms with different baselines based on some specific criteria, such as the level of coherence, the degree of atmospheric delay, and the length of the spatiotemporal baseline to get the best results. However, a question behind the selection of interferograms still keeps intact, that is, why do different interferograms get different results? The recent case of deriving the postseismic deformation of 2021 Maduo Mw7.4 Earthquake illustrates just how important this question is, in which almost every team achieved different results. We highlight the roles of unwrapping error and fading signal in this case, which can explain why different interferograms can get different results. We also proposed a new method to correct these two error sources. After the correction, we unify the time series results from different interferograms. In addition, we also explored the relationship between fading signal and soil moisture, and successfully mapped the liquefaction related to earthquake using InSAR, which to our knowledge is the first time in the world.

How to cite: Ma, Z., Jiang, Y., Li, C., and Wei, S.: Unify Different Interferograms to Get the Unique InSAR Time Series, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2972, https://doi.org/10.5194/egusphere-egu24-2972, 2024.

X4.66
|
EGU24-4831
Guangli Su and Chunbao Xiong

Over the past six decades, the groundwater became the main source of water following a serious shortage in surface water in the North China Plain (NCP). This resulted in a large area of groundwater level (GWL) depression and land subsidence cones. To address this crisis, the Chinese government implemented the largest water transfer project in human history—the South-to-North Water Transfer Project (SNWTP), the middle route of which was completed and put into operation in 2014. In this context, Tianjin, one of the main beneficiaries of this project, has been relieved from water shortages and begun to implement Groundwater Management Plans (GMP) such as water source conversion and ecological water replenishment for rivers and lakes since 2018, which undoubtedly have a significant effect on the groundwater recovery. Meanwhile, this provides a good case for studying the coupled process of ground settlement and groundwater dynamics, especially the soil deformation pattern driven by groundwater level (GWL) rebound. To analyze these issues in detail, field well data was collected to depict groundwater flow field. Moreover, geodetic data was also collated, including leveling, GPS, and InSAR, so that a vertical deformation field with high spatiotemporal resolution could be generated. The results reveal that the GWL of the third confined aquifer which is the main exploitation layer in Tianjin recovered significantly since 2018 with a rate of 2.1 m/yr. The area of GWL depression cones with a depth greater than 70 m has decreased by 85%. The dynamic deformation patterns indicate that the area of land subsidence cones in Tianjin has reduced significantly, accompanied by a sharply declining subsidence rate (decreased from -32.2 mm/yr to -4.5 mm/yr). Particularly, a significant poroelastic rebound has occurred in the Wuqing and Beichen districts since 2020, with the uplift rates in some areas exceeding 10 mm/yr. Furthermore, due to the delayed pore pressure dissipation in the aquitard, we find a time delay of 0.3–5.5 years between land subsidence and GWL time series, which is far less than that estimated by hydrogeological parameters, as the latter ignored the recharge and recovery capacity of the aquifer system. Finally, a evolution model in Tianjin was presented to illustrate interactive process among the deformation, pore pressure, and hydraulic head. In general, the SNWDP and the GMP has restored the pore pressure of aquifer, reduced the land subsidence, and alleviated the groundwater storage depletion of Tianjin, China.

How to cite: Su, G. and Xiong, C.: Coupled processes of groundwater dynamics and land subsidence in Tianjin, China after the South-to-North Water Transfer Project, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4831, https://doi.org/10.5194/egusphere-egu24-4831, 2024.

X4.67
|
EGU24-11483
Robert Siegmund, Paul Kotzerke, Jürgen Langenwalter, and Arnd Berns

Alpine terrain – in high and low altitudes - faces changes in stability due to climate change (permafrost and mass movements or landslides), effecting natural conditions and therefore human infrastructure. Those changes are mainly driven by changing atmospheric patterns leading to shifts in the temperature and precipitation regime. In high alpine terrain dwindling permafrost potentially results in an increasing instability. Recently several incidents of rockfalls and massive landslides were reported, e.g. the village of Brienz only narrowly escaped a fatal disaster. Especially low-lying areas or valleys are of special socioeconomic interest forming settlement areas with transport, tourism and human infrastructure. For example, an increasing number of land- and mudslide events along the “Brenner Highway” were reported by the Austrian motorway authority (ASFINAG). The highway and railway line act as a main and highly frequented transport route connecting Innsbruck (Austria) with Bolzano (Italy). Under a changing climate regime more of these incidents are expected in the near future. With the increased dynamics and probability of landslides the risk for human infrastructure and inhabitants of alpine regions could increase.

Consequently, the use of continuous and accurate ground deformation information becomes evident. EGMS, as a Pan-European service, provides validated and accurate data since 2021, therefore key information for ground deformation monitoring on a wide area basis. However, the utilisation of EGMS products in alpine terrain is not yet fully addressed due to technical plus natural constraints regarding alpine topography, climate, etc. versus the applied interferometric measurement approach.

Our analysis provides an assessment of ground deformation data on a wide area plus detail level in its temporal and spatial context and its applicability to changing permafrost and ground stability conditions. Based on EGMS products the distribution of deformation features is evaluated in correspondence to alpine permafrost index maps. An indication of potential hot spots is modelled by respective active areas and their relation to infrastructure elements and settlements. This includes the integration of auxiliary and reference data.

With this approach considerations of the distribution and temporal characteristics of deformation areas, in terms of mean velocities and deformation time series, are deduced together with spatial relations of measurement points to the objects of interest, e.g. settlements, road, railway or touristic infrastructure.

Finally, conclusions for a wide area utilisation of EGMS deformation information are drawn including the provision of an assessment of required reference and auxiliary information plus conceptualisation of an adapted and optimised monitoring approach addressing alpine geohazards.

How to cite: Siegmund, R., Kotzerke, P., Langenwalter, J., and Berns, A.: Examination of Ground Deformation Information in Alpine Terrain - The Potential of EGMS, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11483, https://doi.org/10.5194/egusphere-egu24-11483, 2024.

X4.68
|
EGU24-16520
|
ECS
Yingbo Dong, Mario Floris, and Filippo Catani

In alpine regions, Deep-seated Gravitational Slope Deformations (DsGSDs) pose significant risks due to their continuous deformation rates, potentially leading to sudden and accelerated transformations that can cause unpredictable damage to local communities and infrastructure. Monitoring DsGSDs is crucial for effective risk assessment and land-use planning. Advances in remote sensing technologies, particularly InSAR (Interferometric Synthetic Aperture Radar), offer substantial advantages in monitoring and studying these widespread and slow processes. The European Ground Motion Service (EGMS), which provides Europe-wide ground motion data, emerges as a viable tool for detecting, monitoring, and characterizing DsGSDs. This study aimed to develop and evaluate an automated workflow for identifying and analyzing trends in DsGSDs in alpine areas using deformation time series datasets. The approach involves utilizing advanced statistical methods to characterize DsGSD phenomena in alpine regions. Focusing on the Carnic Alps area in the northern part of the Veneto Region and Friuli-Venezia Giulia Region (NE, Italy), our objective is to explore supervised machine learning (ML) and deep learning (DL) algorithms to automatically identify DsGSD areas and analyze the spatiotemporal behavior of long time series of ground deformations. The findings will be compared with data from the Italian Landslide Inventory (IFFI), serving to not only validate the newly extracted information but also assess the potential of integrating multi-source datasets. This work sets the foundation for further analyses on how transient climatic factors could influence DsGSDs regimes.

How to cite: Dong, Y., Floris, M., and Catani, F.: Deep-seated Gravitational Slope Deformations identification and trend analysis from ground deformation time series: A case study in the Italian Alps, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16520, https://doi.org/10.5194/egusphere-egu24-16520, 2024.

Posters virtual: Wed, 17 Apr, 14:00–15:45 | vHall X4

Display time: Wed, 17 Apr, 08:30–Wed, 17 Apr, 18:00
Chairpersons: Ling Chang, Xie Hu, Mahdi Motagh
vX4.3
|
EGU24-9466
|
ECS
|
Jinghui Xiao and Xie Hu

Urban sprawl results in an increasing area of land being transferred into impervious layers. The city of Yan'an in Shaanxi Province in China, located in the hilly gully area of the Loess Plateau, has implemented the Mountain Excavation and City Construction project, aiming to transform the loess and gully area into an urbanized environment with an area of 78.5 km2. The increased flat land will be used to accommodate 400,000 people. The Mountain Excavation and City Construction project on the Loess Plateau is by far the largest geotechnical project in the loess area in the world. It is also an engineering trail to seek the balance between urbanization and sustainable development.

It is challenging to carry out such a large-scale construction in loess gullies where the hydrogeological and engineering conditions are complicated. The loess are naturally prone to deform. Our study applies time-series Interferometric Synthetic Aperture Radar (InSAR) analysis to measure the ground deformation in Yan'an New District. The reported deformation rates reach as large as 70 mm/yr, primarily in the filling areas. The main contributing factor to the deformation is sediment compaction. A stabilization of the new landform is anticipated in several years. Continuous monitoring plays an integral role in mitigating hazards in such loess environment.

How to cite: Xiao, J. and Hu, X.: Ground Deformation Monitoring in Yan'an New District Using Time Series InSAR Method, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9466, https://doi.org/10.5194/egusphere-egu24-9466, 2024.

vX4.4
|
EGU24-4398
|
ECS
Wei Zhai, Jianqing Du, and Gangyu Yang

The collapse of buildings caused by destructive earthquakes often caused severe casualties and economic losses. After an earthquake, the assessment of building damage is one of the most important tasks in earthquake emergency response. Accurate assessment of building damage will be essential in making plans of emergency responses. Four-Polarimetric Synthetic Aperture Radar (PolSAR) data has the advantages of Synthetic Aperture Radar (SAR) imaging that is not occluded by sunlight and clouds, it also contains the most abundant information of four polarimetric channels. Due to the large amount of information in PolSAR data, only a single post-earthquake PolSAR image can be used to identify building damage of post-earthquake. It is easy to overestimate the number of collapsed buildings and the damage degree of earthquakes only using a traditional polarimetric decomposition method for PolSAR data. The layout of urban buildings can be diverse. Buildings can stand in parallel in typical SAR imaging with strong scattering features, there are also some oriented standing buildings with lower scattering intensity and with similar scattering characteristics of collapsed buildings, thus these oriented buildings are often misconstrued as collapsed buildings. The spatial frequency of SAR images can be clearly rendered in the frequency domain. In this study, we propose a new texture feature based on Fourier transform, namely the sector texture feature of the Fourier amplitude spectrum (STFFAS), to solve the overestimate of damage of buildings, which are caused by earthquakes. STFFAS can well describe the difference in texture between oriented buildings and collapsed buildings and accurately recognize the two types of buildings. The STFFAS index can be defined as follows:

             (1)

where ‘FFT’, ‘std’, ‘mean’ and ‘lg’ represent the function of 2D fast Fourier transform, standard deviation, mean values and logarithm to the base 10, respectively; ‘real’ and ‘imag’ represent the real parts and imaginary parts of complex numbers, respectively. Meanwhile, based on the Yamaguchi four-component decomposition method and the STFFAS texture feature parameter, we develop a solution to identify the damage of buildings only using a single post-earthquake PolSAR image. The Ms7.1 Yushu earthquake, which occurred in Yushu County of China on 14th April, 2010, is used as a study case to carry out the experiment with 75000 undamaged and damaged building samples. With the proposed method, the overall accuracy of correct building damage recognition with STFFAS is 81.30%. The Producer‘s Accuracy (PA) of damaged buildings, which is the correct recognition rate of collapsed buildings, is 81.06%; and the PA of undamaged buildings, which is the correct recognition rate of undamaged buildings, reaches 81.42%. Compared with the traditional polarimetric decomposition method, 70.18% standing buildings are successfully isolated from the mixture of collapsed buildings. Therefore this new method has greatly improved the accuracy and reliability of extracting damage information of buildings. This result well confirmed that the texture feature in frequency domain is effective for building damage recognition.

How to cite: Zhai, W., Du, J., and Yang, G.: Building Earthquake Damage Recognition Based on Frequency Domain Texture Features from PolSAR Data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4398, https://doi.org/10.5194/egusphere-egu24-4398, 2024.

vX4.5
|
EGU24-9076
|
ECS
|
Feng Lin, Yuqi Song, and Xie Hu

Rapid post-earthquake response remains a significant challenge for humanity. Emergency response to earthquakes requires accurate and timely information about the geographic locations of secondary hazards and the likely compound effects, such as landslides, liquefaction, and building damage. Current methods rely on data-driven approaches, and also start to consider the complex causal dependencies associated with earthquake-induced disasters. However, the accuracy of existing pipeline is limited due to factors like atmospheric noise contaminating satellite imagery.

To improve the accuracy of predicting multiple hazards and impacts, we introduce the principles of time-series Interferometric Synthetic Aperture Radar (InSAR) analysis to generate high-quality Damage Proxy Maps (DPM). Subsequently, we adopt a rapid seismic multi-hazard and impact estimation system leveraging advanced statistical causal inference and remote sensing techniques. This approach, by modeling causal dependencies from satellite images, infers multiple hazard scenarios on a regional scale at high accuracy and resolution.

Data we using include landslides, liquefaction, and building damage. We also created DPMs using SAR images from the Sentinel-1 satellite. Beides the accuracy, our approach’s results also reveal quantitative causal mechanisms among earthquake-triggered multi-hazard and impact events. Our system provides a new approach to InSAR data processing and offers a novel avenue for understanding the complex interactions of multiple hazards and impacts in seismic geological processes.

How to cite: Lin, F., Song, Y., and Hu, X.: Characterization of compound earthquake damage empowered by AI remote sensing, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9076, https://doi.org/10.5194/egusphere-egu24-9076, 2024.

vX4.6
|
EGU24-20126
Deying Ma and Bing Yu

High-intensity mining leads to severe ground deformation and secondary geological disasters in coal mines. Persistent Scatters Synthetic Aperture Radar Interferometry (PSInSAR) has strong deformation monitoring capability, but cannot detect enough target points in the mining core and surrounding low-coherence areas. This paper attempts to combine Distributed Target (DT) and Slowly-decorrelating Filtered Phase Target (SDFPT) to improve the density and coverage of deformation monitoring points in mining areas. The Fast Statistically Homogenous Pixel Selection (FaSHPS) and the amplitude dispersion index method were used to select DT and SDFPT candidate points, respectively. Then phase optimization and stability analysis were carried out for the two types of points, and the qualified DT and SDFPT were screened out.  Both kinds of points were then fused, and three-dimensional phase unwrapping was performed. The phase time series were recovered. The spatiotemporal filtering was performed, and the deformation time series and the annual average deformation rate of the fused point set were finally obtained. The 60-scenario Sentinel-1 images covering the Buertai Coal Mine acquired from April 2018 to April 2020 were selected for deformation monitoring. The results show that the density and coverage of deformation points are significantly improved after the fusion of DT and SDFPT, and the maximum deformation level that can be monitored is also increased. There are 5 deformation funnels in the experimental area, and the maximum cumulative deformation reaches -309.76 mm. The influence range of deformation and the difference between time series deformation in different years are highly correlated to mining activities in mining areas.

How to cite: Ma, D. and Yu, B.: Monitoring the mining deformations by Time Series InSAR Integrating DT and SDFPT, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20126, https://doi.org/10.5194/egusphere-egu24-20126, 2024.