InSAR has been nowadays accepted as a standard tool for measuring the earth surface deformation in different natural and human-induced hazard applications. Despite this acceptance, the quality, and in particular the reliability, of InSAR deformation estimates under unfavorable conditions, e.g., large displacements with strong spatio-temporal variations or in highly vegetated and decorrelating terrains is still questionable and sometimes controversial. In particular, under these conditions, the InSAR algorithms are highly prone to unwrapping errors, which can result in incorrect (or biased) deformation estimates. As there is no standard analytical criterion to assess the probability of unwrapping error occurrence, a question is always raised in these scenarios: How much can we rely on InSAR to measure deformation correctly?
Although an experienced InSAR specialist may qualitatively assess the reliability of the results based on his own knowledge and analytical skills, such an assessment is not straightforward for end users of InSAR-derived products. This may end in a misinterpretation of, or a misinformation about InSAR results. In this regard, there is a need for a reliability-description approach capable of digesting the different processing factors, settings, and assumptions to quantify the probability of correct phase unwrapping, and in this way, to provide an analytical measure to assess the reliability of the results.
In this contribution, we argue that InSAR measurements are inherently ambiguous with respect to deformation, in contrast to other geodetic techniques. Therefore InSAR requires a distinctive approach for quality description. As unwrapping errors may occur due to different causes, we argue that we need different quality description approach for each cause. Here we introduce three quality measures: i) measure of unwrapping correctness to quantify the probability of correct unwrapping error for each point, ii) measure of reliability to quantify the sensitivity of the used algorithms to detect unwrapping errors, and iii) measure of falsifiability to quantify how much sensitive the results are to the used a-priori assumptions of phase unwrapping. We argue that with exploitation of these three quality measures, we can offer a comprehensive quality description framework to assess the reliability of InSAR-derived products. The idea of such quality description is demonstrated via different subsidence case studies in Iran.
How to cite: Samie Esfahany, S. and Badamfirooz, S.: How Much Can We Rely on InSAR to Measure Deformation Correctly?, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6915, https://doi.org/10.5194/egusphere-egu25-6915, 2025.
In recent years, the prolonged exploitation of natural resources has led to the depletion of reserves in some mining areas, resulting in the closure of mines worldwide. After mine closures, the fractured rock masses in abandoned mine cavities undergo weathering and degradation due to factors such as stress and groundwater, leading to reduced strength. This change alters the stress distribution and load-bearing capacity of the fractured rock within the abandoned voids, resulting in secondary or multiple deformations on the surface, which pose significant potential threats to surface infrastructure and public safety. Research into the mechanisms, patterns, and predictive methods of secondary surface subsidence in closed mines is thus of great theoretical and practical significance. Based on literature review and practical monitoring experiences in closed mine sites, this study systematically examines and analyzes the current state of surface secondary subsidence monitoring methods, formation mechanisms, spatiotemporal distribution patterns, and prediction methods in closed mines, as well as existing challenges. Initially, we compare the advantages and limitations of conventional surface deformation monitoring techniques with remote sensing techniques, emphasizing the benefits and issues of using InSAR technology. Next, by reviewing extensive data, we analyze the formation mechanisms and spatiotemporal evolution of overburden and surface secondary subsidence in closed mines. Building on this analysis, we discuss numerical and analytical methods for predicting secondary surface subsidence mechanisms in closed mines, evaluating the strengths and weaknesses of each approach. Predictive models for surface subsidence and uplift phases in the longwall collapse method are presented based on the constitutive relationships of fractured rock masses. Finally, the study highlights that the mechanisms and patterns of overburden and surface subsidence in closed mines represent a highly complex physical-mechanical process involving geological mining environments, fractured rock structures, constitutive relations, deformation characteristics, hydro-mechanical interactions, and groundwater dynamics, underscoring the need for further in-depth research. The conclusions are proved by some coal mining cases in China.This research has been supported by grants G2HOTSPOTS (PID2021-122142OB-I00), STONE (CPP2021-009072) and Defsour-PLUS (PDC2022-133304-I00) from the MCIN/AEI/10.13039/501100011033/FEDER, UE with funds from NextGenerationEU/PRTR.
How to cite: Du, S., Fernández, J., Wang, T., Hu, Z., Rodríguez, S., and Camacho, A. G.: Advances and Future Directions in Monitoring and Predicting Secondary Surface Subsidence in Abandoned Mines, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9357, https://doi.org/10.5194/egusphere-egu25-9357, 2025.
Coastal regions face a cascading sustainability crisis due to rising sea levels, stronger storms, land loss, salinization, and ecosystem collapse. These risks are particularly severe in densely populated lowland river deltas, which are highly sensitive to effective sea-level rise that combines eustatic ocean levels, subsidence, and tidal amplification. The Ganges-Brahmaputra Delta (GBD) in Bangladesh is such a region, characterized by geomorphic dynamism and rapid land-use changes associated with agriculture and urbanization, highlighting the critical need for accurate surface elevation change measurements.
In this study, we process Sentinel-1 datasets spanning 2014-2024 and derive a 30-meter resolution InSAR velocity field over coastal Bangladesh, sufficient to resolve differences between villages and fields. We incorporate a high-resolution (5-meter) Worldview DEM referenced to ICESat-2 altimeter data and implement a suite of innovative InSAR algorithms to enhance pixel recovery in coastal areas, improve atmospheric noise mitigation, and refine time series retrieval.
By integrating InSAR-derived deformation measurements with ground observations, including RSET-MH, continuous GNSS, and campaign-based GNSS resurveys of geodetic monuments, we identify higher subsidence rates in areas of active sedimentation, such as rice fields and mangrove forests, compared to urban areas containing buildings with deep foundations, revealing the influence of surface landscape on the observed deformation. We demonstrate that seasonal deformation, driven by elastic loading and poroelastic effects, can be distinguished and separated through a combination of the retrieved InSAR time series and continuous GNSS time series.
Additionally, we validate InSAR observations using a poroelastic model for coastal subsidence that incorporates shallow (<10 m depth) geomorphic and land-use processes often excluded from modern models, finding strong agreement between model predictions and observed data. This study not only advances the assessment of sea-level rise risks for the densely populated GBD but also establishes a transferable framework for addressing challenges across vulnerable coastal communities worldwide.
How to cite: Shen, L., Chadwick, A. J., Steckler, M. S., Tiampo, K. F., Wilson, C., Goodbred, S. L., and Oryan, B.: High-resolution mapping of coastal subsidence in the Ganges-Brahmaputra Delta using advanced InSAR and ground observations, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7642, https://doi.org/10.5194/egusphere-egu25-7642, 2025.
Ongoing depletion of Iran's groundwater, driven by human extraction, has contributed to 108 incidences of basin-scale land-surface subsidence covering 29,600 km2 (>10 mm/yr, 1.8 %) of the country, 75 % of which correlates with agriculture. We find Karaj city, neighbouring Iran's capital Tehran, is exposed to the steepest surface velocity gradients (angular distortion, β) caused by differential subsidence rates, with 23,000 people exposed to ‘high' subsidence induced hazard. We further use these velocity gradients to aid identification of structural and geological controls on surface velocities of seven of Iran’s most populated cities, identifying potentially unmapped tectonic faults. We demonstrate that most of Iran’s subsidence is permanent (inelastic), with the spatial pattern of the proportion of inelastic deformation potentially depending on geology. During a recent, severe regional drought (2020-2023) we demonstrate the control of precipitation on the elastic, recoverable subsidence deformation magnitude with the elastic to inelastic deformation ratio falling from 41-44 % pre-drought to 31-36 % post-drought. We use automatically processed short baseline networks of Sentinel-1 Interferometric Synthetic Aperture Radar (InSAR) data, 2014-2022, to generate and estimate these ground displacements through time. We correct for atmospheric noise using weather model data and perform time series analysis in the satellite line-of-sight direction, serving this data through an open-access online portal. For each subsidence region, we decompose line-of-sight velocities into 100 m resolution vertical and horizontal (east-west) surface velocity fields. We use temporal Independent Component Analysis to constrain automatically and manually the inelastic and elastic components of subsidence, respectively.
How to cite: Payne, J., Watson, A., Maghsoudi, Y., Ebmeier, S. K., Rigby, R., Lazecký, M., Thomas, M., and Elliott, J.: Widespread extent of irrecoverable aquifer depletion revealed by country-wide analysis of land surface subsidence hazard in Iran, 2014-2022, using two component Sentinel-1 InSAR time series, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-102, https://doi.org/10.5194/egusphere-egu25-102, 2025.
Flooding, as a common natural disaster, poses severe threats to human life, property, and economic activities. To address these challenges, rapid, reliable, and robust flood extent detection plays a critical role in disaster prevention and mitigation. Recent advancements in computer vision, such as the Segment Anything Model (SAM), have introduced innovative approaches to flood detection by leveraging their strong feature extraction capabilities. However, their reliability in Synthetic Aperture Radar (SAR)-based flood detection tasks is limited due to the lack of relevant training samples. To address this limitation, this study fine-tunes SAM on SAR-based flood datasets using multiple Parameter-Efficient Fine-Tuning (PEFT) techniques to explore the feasibility of applying SAM for flood detection with SAR imagery. Five mainstream PEFT techniques—BitFit, Adapter Tuning, Prompt Tuning, Prefix Tuning, and LoRA—were employed. The experimental results demonstrate that all fine-tuned models significantly improved their performance in terms of Intersection over Union (IoU) and accuracy. Among them, the model fine-tuned with the LoRA technique achieved the best performance, with improvements of 34.88% and 44.33% in IoU and accuracy, respectively. This study highlights the potential of fine-tuning SAM for flood detection in SAR imagery and provides a novel approach to improving the accuracy and reliability of flood mapping.
Keywords: Flood Detection, SAR imagery, Segment Anything Model, PEFT
How to cite: Wang, Z. and Zhang, C.: Adapting the Segment Anything Model for SAR-Based Flood Detection Using Parameter-Efficient Fine-Tuning Techniques, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3524, https://doi.org/10.5194/egusphere-egu25-3524, 2025.
In the frame of the project "FloodRisk: Earthquakes, uplift, and long-term liabilities – risks minimisation during mine flooding" funded by the Federal Ministry of Education and Research (BMBF), a consortium of partners from applied research and industry worked on an integrated view on the post-mining process of German hard coal mines in the eastern Ruhrarea, Ibbenbüren and Saarland. Aspects of geodesy, geophysics, soil gas technology and geology in the context of mine flooding were investigated by experts in these fields. The Geodetic Institute Karlsruhe contributed to FloodRisk by monitoring ground displacements with help of InSAR and GNSS.
The focus of our presentation will be a clustering approach that allowed to obtain an understanding of the variable spatio-temporal displacement field during mine flooding measured with help of InSAR. For mine Heinrich-Robert in the eastern Ruhrarea and for mine Ibbenbüren, both in the German state North-Rhine Westfalia, Sentinel-1-data for the observation period January 2018 to December 2022 from an ascending and a descending orbit were combined in order to obtain vertical and East-West displacements. Because of the large number of measurement points, time series with almost 300 values and variable spatio-temporal displacements, the analysis of the displacement patterns is challenging. A broken stick model (piecewise linear and continuous) with six break points proved to be able to approximate the time series quite well. Clustering feature vectors based on parameters of the broken stick model (location, break point, change of displacement rate, displacement rate after the break point) for points with considerable change of displacement rate allowed to describe the main changes of the displacement fields. In case of mine Ibbenbüren, the transition from massive subsidence during the last months of active mining to uplift caused by the rising mine water is characterized by phases that are clearly separated by certain break times that do not vary spatially. For mine Heinrich-Robert, the evolution of the displacement pattern is more complicated and the break times vary spatially.
How to cite: Even, M. and Kutterer, H.: Post-mining displacement monitoring with InSAR in the eastern Ruhrarea and Ibbenbüren, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3780, https://doi.org/10.5194/egusphere-egu25-3780, 2025.
Slope stability monitoring is a very important aspect in open pit mining processes, where landslides without warning may cause huge loss of life, injuries and infrastructure damage, interfering with mine planning and causing significant increased costs and economic losses. Slope monitoring, modeling and stability analysis help to improve the safety of mining activities and to minimize these economic effects. For slope monitoring, many techniques are available, including the use of prisms, GNSS, total stations, extensometers, inclinometers, infrasound sensors, and ground-based radar. All those techniques only give observation data from the epoch over which the sensors have been installed and cover only the specific areas where they are installed. Both aspects can be important, conditioning the results and their applicability. To complement these observation techniques and overcome their limitations remote satellite interferometric synthetic aperture radar (InSAR) analysis can be applied to detect and characterize unstable areas, although it normally is not used in an operative way. Even if the deformation data are obtained in a continuous (or nearly continuous) way, normally they are not inverted using methodologies which allow determination of the initial stages of ground fracturing, the 3D characteristics of the sources acting to produce the observed deformation, their location- and time-evolution. A study of this type could facilitate early detection, in some cases a long time before a potential landslide, helping to support decision making about preventive and/or corrective measures, and to avoid disasters, minimizing impacts. We present here a new methodology that would complement the current operational ones. This methodology implies the use of two complementary aspects in the open pits monitoring: operational monitoring of the pit and its surroundings using InSAR observation looking for precursory small line of sight (LOS) displacements; and the use of an interpretation methodology to estimate the source’s location and characteristics and their time evolution. This interpretation methodology is able to invert simultaneously ascending and descending time-series of InSAR LOS displacement data, assuming the existence of possible offset values in these data sets which will be estimated during the inversion process. 3-D sources for pressure and dislocations (strike-slip, dip-slip, and tensile, representing fractures and faults) are adjusted without having any a priori hypotheses on the source characteristics (number, nature, shape or location). This approach automatically assigns the number of sources, their type, magnitude values (MPa for pressure and cm for dislocations), as well as their position and orientation (angles of dislocation planes). The inversion methodology is nonlinear, based on an exploratory approach of the model space. To evaluate the applicability of this new approach we consider a very well-known test-case, the Manefay landslide at Bingham Canyon open pit mine, happened on April 10th, 2013, in southwest of Salt Lake City, Utah, USA. This research has been supported by grants G2HOTSPOTS (PID2021-122142OB-I00), STONE (CPP2021-009072) and Defsour-PLUS (PDC2022-133304-I00) from the MCIN/AEI/10.13039/501100011033/FEDER, UE with funds from NextGenerationEU/PRTR.
How to cite: Fernandez, J., Du, S., Samsonov, S. V., Hu, Z., Rodríguez, S., Tiampo, K. F., and Camacho, A. G.: Satellite radar observation and advanced interpretation for stability monitoring of open pits: the Manefay failure (Kennecott Copper Mine), Utah, USA, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9200, https://doi.org/10.5194/egusphere-egu25-9200, 2025.
Wildfires are becoming larger and more frequent as a result of global warming, since hotter temperatures help create the conditions for increased fire activity. Wildfires thus constitute a serious threat for human life, and can cause catastrophic damages and property losses. When such disasters occur, it is critical to quickly assess their impact, so that authorities can make informed decisions about the safety of the population, and insurance practices can be initiated for the damaged properties.
Typically, such assessment was done by manually inspecting aerial optical imagery, once available, to determine damages to the individual properties. However, safely flying over affected areas requires sufficient visibility and favorable wind conditions. Moreover, both the data acquisition and the successive manual inspection are costly and time consuming processes.
Satellite remote sensing can avoid the risk and costs associated with on-site surveys. In particular, Synthetic Aperture Radar (SAR) satellite sensors do not rely on daylight and are insensitive to smoke and clouds, therefore they can be used at all times of day and night during and after the event, in all weather conditions. The limiting factor for satellite imagery is normally the timeliness, given that the frequency with which a certain area is overpassed by most satellites is in the range of several days or even longer. This is why having access to a constellation of SAR satellites that deliver near-real time imagery, on a global scale, is a game changer in disaster assessment.
ICEYE developed a specific solution for building damage evaluation, relying on prompt tasking SAR images from their large constellation of NewSpace satellites, and using machine learning models to quickly provide situational awareness on the whole fire perimeter, at a building level. The method is based on a post-event image only, without requiring any pre-event imagery. In this presentation, we present several case studies of wildfire events that occurred in different regions of the US in 2023 and 2024, showing the damage maps obtained, and the performance achieved in classifying each building as damaged or undamaged. The results were compared with the ground truth information such as provided by official governmental entities or retrieved from aerial photographs. The automatic assessment performance metrics were then derived.
How to cite: Corucci, L., Sergeev, R., Mainvis, A., Kourkouli, P., Kn, S., Baghirli, O., and Dorn, P.: Near real-time wildfire building damage assessment with ICEYE SAR data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15970, https://doi.org/10.5194/egusphere-egu25-15970, 2025.
Landslides are common geological phenomena that occur worldwide. When triggered by external factors such as earthquakes or rainfall, internal changes like increased shear stress or reduced shear strength can lead to accelerated landslide motion. In recent years, the intensification of human activities and the increasing frequency of extreme weather events have further raised the likelihood of catastrophic landslide events. Monitoring landslides is a critical approach to mitigating the associated risks. Particularly, advancements in spaceborne Interferometric Synthetic Aperture Radar (InSAR) technology have provided higher spatial resolution data, significantly advancing the study of landslide dynamics. However, due to geometric limitations of spaceborne InSAR, the technology typically retrieves only one-dimensional line-of-sight (LOS) displacement, restricting its broader applicability. In this study, we employed advanced techniques such as SPFS and KFI-4D to extract multi-dimensional deformation fields by integrating multi-source SAR observations. We successfully derived 3-D and 4-D movement fields for the Xinpu landslide in the Three Goreges Reservoir (TGR) region of China and the Hooskanaden landslide on the west coast of the United States. Based on these results, we further applied the laws of mass conservation, a one-dimensional pore-water diffusion model, and geodynamic methods to estimate landslide kinematic parameters, including landslide thickness, effective hydraulic diffusivity, and strain invariants. These findings offer deeper insights into landslide movement behaviors. Additionally, we explored the potential of utilizing next-generation SAR satellites, such as NISAR, to obtain multi-dimensional landslide movement fields. The results indicate that integrating left-looking SAR observations from platforms like NISAR can significantly improve the accuracy of InSAR-derived multi-dimensional deformation fields and expand their application scenarios in landslide studies.
How to cite: Zheng, W., Hu, J., and Huang, B.: From 1-D to 4-D: Enhancing Landslide Monitoring through InSAR-Derived Multi-Dimensional Movement Fields, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7836, https://doi.org/10.5194/egusphere-egu25-7836, 2025.
Offset tracking using synthetic aperture radar (SAR) amplitude imagery is a valuable technique for detecting large ground displacements. However, the traditional offset tracking methods with the SAR datasets are computationally intensive and require significant time for processing. We have developed a novel cross-connection Siamese ResNet (CC-ResSiamNet). The model leverages multi-kernel offset tracking for preprocessing, followed by deep learning architectures that incorporate U-Net, cross-connections, and residual and attention blocks to predict pixel offsets between two SAR amplitude images. It is trained and tested on 200K pairs of reference and secondary SAR amplitude images, alongside corresponding target offset data from Alaska’s glaciers. The comparative analysis with multiple deep learning models confirmed that our designed model is highly generalizable, achieving rapid convergence, minimal overfitting, and high prediction accuracy. Through multi-scenario inference with glacier movements, earthquakes, and volcanic eruptions worldwide, the model demonstrates strong performance, closely matching the accuracy of traditional methods while offering significantly faster processing times through parallel computing. The model’s rapid displacement mapping capability shows particular promise for improving disaster response and near real-time surface monitoring. While the approach encounters challenges in accurately capturing small-scale displacements, it opens new possibilities for SAR-based surface displacement prediction using machine learning. This research highlights the advantages of combining deep learning with SAR imagery for advancing geophysical analysis, with future applications anticipated as more commercial and scientific SAR missions launch globally.
How to cite: Lu, Z., Kim, J., and Jung, H.-S.: Ground Surface Displacement Measurement from SAR Imagery Using Deep Learning, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16014, https://doi.org/10.5194/egusphere-egu25-16014, 2025.
Globally, there are about 1400 active volcanoes, and each year, 20 to 50 volcanic eruptions occur, many of which lack on-site monitoring. Open-source InSAR technology, like Sentinel-1, allows tracking volcanic deformations globally, even in remote or hard-to-access locations. By utilizing persistent and distributed scatterer interferometry (PSI/DSI), InSAR data can reveal subtle, millimeter-scale deformations, enabling granular tracking of volcanic activity. Furthermore, deep learning (DL) models can automatically identify and flag these changes as an alert or for further analysis.
This experiment utilizes a classification deep learning architecture, InceptionResNet v2, to detect volcanic deformations in InSAR data. The used dataset consists of 5-year-long deformation maps covering the Central Volcanic Zone in the South American Andes and reserves the known volcanic regions for testing. The remaining data and synthetic volcanic deformations are used to train the model.
GradCAM, the explainability tool, shows that accurate identification and differentiation of deformation signals are difficult on the model due to the subtle volcanic deformations observed in InSAR data. To address this, we apply wavelet transformations and filtering techniques to enhance the data, thereby improving the performance of the deep learning model.
Applying Daubechies 2 wavelet transform emphasizes subtle large-area, mostly volcanic, signals while removing the milder high-frequency patterns. The DL models are trained, and each is tested on the data with up to four wavelet transforms. The model trained and tested on original data achieves a 64.02% AUC ROC average, while when tested on data two times transformed by wavelet transform, it improves to 84.14% AUC ROC average.
We show that Daubechies 2 wavelet transform cleans data while amplifying the volcanic deformation. A side effect is that it enlarges the small area deformations, significant in intensity. This issue can be solved by filtering the data in preprocessing. Utilizing this method, models can detect even the smallest deformations of 5 mm/year.
How to cite: Beker, T. and Zhu, X. X.: Deep Learning and Wavelet Transform for InSAR Volcanic Deformation Detection, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11366, https://doi.org/10.5194/egusphere-egu25-11366, 2025.
The Permian Basin, a major oil and natural gas source in the United States, is experiencing significant surface deformation due to hydrocarbon production and wastewater injection, impacting infrastructure, seismicity, and the environment. The region’s complex geology and presence of thousands of active hydrocarbon wells make deformation prediction challenging. Simple elastic models fail to capture these complexities, necessitating a more detailed approach. This study uses poroelastic modeling and InSAR to investigate the role of geology and subsurface pressure changes in surface deformation within the Delaware Basin, the most productive sub-basin of the Permian Basin.
First, Sentinel-1 SAR data were processed using persistent scatterer interferometry (PSI) techniques to obtain surface deformation time series. The results indicate that a large portion of the Delaware Basin is subsiding, with two prominent deformation hotspots to the north of the Grisham Fault Zone (GFZ), subsiding at a rate of 3-4 cm/yr.
Then, focusing on the Northern Delaware Basin, where seismicity is minimal and subsidence primarily exhibits radial patterns, we developed a fully coupled poroelastic model in COMSOL® Multiphysics that integrates the conservation of momentum and mass to simulate subsurface fluid behavior. The model incorporates well data, fluid injection/extraction volumes, fault layers, and geological stratigraphy to simulate stress and pore pressure changes from hydrocarbon extraction and wastewater injection. Faults are modeled as discrete elements that either block or facilitate fluid movement, depending on their orientation and permeability. The results highlight the complex relationship between hydrocarbon production, wastewater injection, subsurface geology, fluid pressure propagation, and surface deformation.
The model’s predictions are then validated using InSAR-derived surface deformation data, offering a detailed understanding of stress and strain dynamics in the region. This study provides valuable insights into subsurface deformation in hydrocarbon-producing regions, with potential applications for assessing risks to infrastructure, seismicity, and environmental health.
How to cite: Karanam, V. and Lu, Z.: Poroelastic Modeling and InSAR Analysis of Hydrocarbon Production-Induced Surface Deformation in the Permian Basin, USA, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14878, https://doi.org/10.5194/egusphere-egu25-14878, 2025.
The aim of the study is to analyse the ground displacement behaviour observed above an Underground Gas Storage (UGS) site located at Hatfield Moors (United Kingdom), with a focus on understanding its implications for decarbonisation efforts. Hatfield is a larger wetland system in England, located in South Yorkshire. The UGS reservoir is located below an extensive peatland and serves as an active onshore analogue for a Carbon Capture and Storage (CCS) site used by the British Geological Survey (BGS) as part of the SENSE (assuring integrity of CO2 storage sites through ground surface monitoring) project. The investigation uses satellite Interferometric Synthetic Aperture Radar (InSAR) data from the European Ground Motion Service (EGMS) to assess the environmental impact of UGS operations, leveraging the need for continuous and real-time monitoring of ground movements induced by gas storage activities. The utilisation of freely available, open-source and user-friendly Sentinel-1 data facilitates the analysis of ground motion patterns over Hatfield Moors, thereby highlighting displacements ranging from -5.0 to -10.0 mm/year within the peat bog. Furthermore, the Time Series (TS) of vertical ground displacement from January 2018 to December 2022 reveals a seasonality in ground motion, with uplift observed in late winter and subsidence in late summer, with a periodicity of approximately 1 year and a magnitude of ±10.0 mm. The study emphasises the need to investigate the underlying causes of ground fluctuations at gas storage sites through in-depth analysis. The results highlight the versatility of InSAR in integrating with a range of monitoring tools and methodologies, thereby facilitating multidisciplinary and holistic analyses. Cross-correlation analyses further elucidate temporal relationships between different datasets, evaluating InSAR TS, UGS injection/withdrawal data and piezometric data. This involves decomposing the TS into distinct components, including trend, seasonality and residuals. The case of Hatfield Moore shows a significant discrepancy between the UGS data and the InSAR TS, while it demonstrates a clear correlation between the groundwater data and the InSAR TS. By integrating insights from geology, hydrology and remote sensing technologies, the study navigates the complexities inherent in areas of overlapping phenomena. The work demonstrates the huge important of free available data and how much they that accurate interpretation is fundamental for informed decision-making, particularly at sites such as Hatfield Moors, where the convergence of peat activities and storage operations highlights the need for interdisciplinary analysis to understand the underlying causes of ground fluctuation.
How to cite: Fibbi, G., Novellino, A., Bateson, L., Fanti, R., and Del Soldato, M.: Benefits of Cross-Correlation Analysis for Monitoring Underground Gas Storage Operations Using EGMS data: A Case Study of Hatfield Moors (UK), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16422, https://doi.org/10.5194/egusphere-egu25-16422, 2025.
Differential SAR interferometry (DInSAR) and Multi-Temporal DInSAR (MTInSAR) are largely exploited for measuring slope stabilities. However, both suffer from the typical critical environmental setting of areas affected by slope instability. First, the steep topography may lead to unfavourable illuminating conditions in terms of either unfeasible detection over layover and shadow areas or low sensitivity to the ground displacement. Second, the presence of dense vegetation and changeable cover conditions causes DInSAR signal decorrelation and a low density of MTinSAR coherent targets (CTs). Third, displacement kinematics are characterised by non-linear components and high displacement rates, leading to measurements corrupted by aliasing. All these critical issues negatively impact the applicability and interpretation of this well-established technology.
We developed a QGIS plugin based on the PyQGIS library, which, starting from standard DInSAR/MTInSAR products and a few ancillary layers, derives additional products useful for supporting the interpretation of the DInSAR results and the assessment of the slope stability over the area under investigation.
First, the tool estimates the visibility of the area of interest (AOI) with respect to the satellite line of sight (LOS). It combines the satellite acquisition geometry and the ground geomorphic information to derive an index of visibility, which allows end users to check the applicability of DInSAR analysis over the AOI just based on geometrical factors and before performing DInSAR processing.
If MTInSAR displacement products are available, the IPA tool derives further outputs. First, it computes the percentage of the AOI surface covered by CTs. This allows end users to estimate how significant the information derivable from MTInSAR within the AOI is.
Moreover, the reliability of DInSAR products also depends on the orientation of the slope within the AOI. For instance, for slopes facing north or south, the downslope movement is basically perpendicular to the LOS direction, thus leading to unfeasible DInSAR-based estimation of displacements. Hence, the IPA tool estimates the percentage of downslope movement captured from the DInSAR geometry along the LOS and, for each CT, computes the downslope mean displacement rate corresponding to the LOS component measured by MTInSAR.
These IPA products are combined with other layers such as NDVI, DInSAR coherence, and landslide inventory for performing a feasibility analysis before DInSAR/MTInSAR processing, for checking the reliability of DInSAR/MTInSAR products to assess the slope instability, and for supporting the interpretation of the DInSAR displacement in analysing slope instabilities.
Finally, the IPA tool performs a displacement time series analysis based on automated procedures recently developed for identifying CTs with nonlinear signals and based on fuzzy entropy and Fisher statistics. This allows a focus on a smaller set of CTs affected by nonlinear displacements (including warning signals) and potentially deserving further geophysical or geotechnical analysis.
The work introduces the methodologies and provides some examples based on DInSAR displacement products derived by processing Sentinel-1 data.
Acknowledgment
This work was supported by the European Union - Next Generation EU, Mission 4, Component 2, CUP H53D23001660006 (PRIN22 Project "MIRAGE:
Mass movement Investigation and prediction through geomorphology, Remote sensing and Artificial intelligence").
How to cite: Bovenga, F. and Piccolino, F.: InSAR Product Analysis (IPA): a QGIS tool for slope instability assessment based on SAR interferometry, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17224, https://doi.org/10.5194/egusphere-egu25-17224, 2025.
Differential Synthetic Aperture Radar (SAR) Interferometry (DInSAR) is a powerful remote sensing technique that allows us to measure surface displacements with centimetric to millimetric accuracy. This approach exploits the phase difference between pairs of complex SAR images, relevant to radar acquisitions collected at different times, to retrieve the surface displacements measured along the radar line-of-sight (LOS). Initially developed to analyze single deformation events, the DInSAR methodology has advanced to study the evolution of the detected displacements over time through multi-temporal (or advanced) DInSAR techniques. These advanced methods enable the generation of displacement time series, revealing spatial and temporal deformation patterns. Among these approaches, the Small Baseline Subset (SBAS) technique permits the generation of LOS displacement time series and the corresponding velocity maps by utilizing SAR data pairs with small spatial and temporal baselines, which help to reduce noise decorrelation effects and increase the number of coherent points. Traditionally, the satellite SAR systems used for DInSAR applications are positioned in a sun-synchronous orbit (SSO), meaning they repeat (nearly) the same orbit at the same local time. This orbital configuration is particularly suitable for remote sensing applications because it allows global coverage, the same illumination geometry, and relatively stable environmental conditions among successive temporal passes.
However, SSO-DInSAR has limitations when measuring the North-South component. Indeed, the LOS direction of a sun-synchronous SAR satellite is primarily oriented towards the East-West direction (the orbits have a quasi-polar direction), making it mainly sensitive to Vertical and East-West displacements. As a result, retrieving accurate North-South displacement information from SSO-DInSAR data can be challenging, especially for slow-moving deformation processes. Conversely, when focusing on mid- to low-latitude regions, Mid-Inclination Orbits (MIOs) may offer an effective solution for retrieving the three-dimensional (3D) field of the occurring displacements. Indeed, MIOs provide advantageous geometries for measuring the North-South displacements.
However, using MIOs is not straightforward due to challenges such as the lack of access to polar ground stations and variations in local time across the Area of Interest (AoI), which increases the temporal variability of the atmospheric DInSAR phase component.
In this work, we present the first results achieved by processing, through the Parallel SBAS processing chain, three different DInSAR datasets generated from the 45° MIO SAR data experimentally collected by Capella Space over the Campi Flegrei caldera (Italy), where the bradyseism phenomenon, restarted in 2005, is still ongoing. To our knowledge, the results described in this work represent the first application to fully retrieve the 3D deformation field with multi-angle/multi-temporal DInSAR data, thus demonstrating the feasibility of the MIO satellite configurations for such DInSAR purposes.
How to cite: Manunta, M., Berardino, P., Bonano, M., Casu, F., Cazcarra-Bes, V., Cotugno, F., Farquharson, G., Lanari, R., Renga, A., Stringham, C., and Yague-Martinez, N.: On the retrieval of the Campi Flegrei caldera (Italy) 3D displacements by exploiting Capella Space Mid-Inclination Orbits DInSAR measurements: first results, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17734, https://doi.org/10.5194/egusphere-egu25-17734, 2025.
Monitoring surface displacements that occur above storage caverns in salt bodies is important to estimate risks for infrastructure in the immediate vicinity. As the pressure inside a storage cavern is usually lower than in the surrounding rock, the cavern converges continuously. This volume loss leads to a prominent subsidence bowl at the surface. Often this subsidence is approximated by linear velocities, However, in particular for gas storage caverns, fields of multiple caverns in close proximity can cause displacement fields which are spatio-temporally complex. The amount of convergence and subsequent surface subsidence depends on the pressure inside the caverns, which for gas caverns changes with the cavern filling levels. This causes seasonal displacement patterns, but also different total subsidence in subsequent years. Moreover, it can result in different superposing displacement patterns from neighboring caverns. Monitoring such a displacement field therefore requires geodetic measurements with dense spatial coverage and high temporal resolution.
Multi-temporal SAR interferometry (MT-InSAR) analysis can fulfill these requirements in optimal conditions, with the Sentinel-1 mission providing free C-band SAR-data with a revisit time of 6 to 12 days. However, as MT-InSAR depends on temporally stable backscattering characteristics of the ground targets, there are often many areas in practice without sufficient data available. Also, current SAR-satellite missions have low sensitivity to north-south directed displacements, which complicates the analysis of the 3D-displacement field with InSAR alone. A geophysical source model derived from surface displacements, that describes the relation between cavern filling levels and surface response, can help with both of these issues.
We derive such a geophysical source model for the storage cavern field Epe in NRW Germany, which consists of 114 caverns, more than 50 of them storing natural gas, from time series of eight years and four tracks of InSAR Sentinel-1 data. We use Persistent and Distributed Scatterers to process the data and validate our results with data of three permanent GNSS stations and annual leveling measurements. As parts of the cavern field in Epe experience other strong displacement effects such as the surface response to groundwater level changes that superpose the cavern signals, we use Independent Component Analysis to separate displacements from different sources. We combine a Kelvin-Voigt body with the Norton power law to relate the pressure differences in each cavern to volume change in the viscoelastic salt body. Then, we use a Sroka-Schober-model to translate this volume change through elastic layers to the surface. There, the effects of all caverns are superposed. We use the cavern related InSAR displacement data to optimize for local parameters and to obtain spatio-temporally high-resolution model predictions for 3D surface displacements.
Not only does this model provide displacement estimates for areas with no measurement data, with a causal relation to the cavern usage, but it also can give more insights to the dynamic convergence of the caverns, as cavern volumes are usually only measured every few years.
How to cite: Seidel, A., Even, M., and Kutterer, H.: Developing a geophysical source model for 3D surface displacements above storage cavern fields with InSAR time series at the example of Epe (NRW, Germany), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16645, https://doi.org/10.5194/egusphere-egu25-16645, 2025.
Corner reflectors (CRs) are artificial installations at specific locations that reflect radar or other electromagnetic waves toward their emission source. There are two types of corner reflector: passive CRs lack electronics, while active CRs have electronics to amplify the reflected signals. CRs have a high and stable radar cross section, a well-defined scattering centre, and are easily detectable in the image, making them suitable for SAR radiometric, geometric, and polarimetric calibration. CRs are also used for SAR interferometry (InSAR) applications over areas with few natural coherent scatterers, and for InSAR datum connection and geodetic integration. Passive CRs are often made of metal plates, such as aluminium. Drawbacks of using metal CRs include (i) their high cost, especially when many reflectors are required for monitoring purposes; (ii) creation of localized ground motion in soft or unstable soils and (iii) attractiveness for thieves. The main objective of this study is to investigate the use of low-cost and lightweight materials for making CRs. A cubic trihedral CR, made of 2mm thick aluminium plates, served as a baseline for our analysis. It was compared to CRs built either from (a) 10mm thick multiwall polycarbonate sheets covered by 1mm thick aluminium foil tape, or from (b) multiwall polycarbonate sheets coated with metallic paint. In addition, the microstructure of these materials was analysed by using scanning electron microscope (SEM) technique in a laboratory. To assess the SAR reflectivity of the different CRs they were temporarily installed at a test site and their visibility and backscattering properties were assessed in Sentinel-1 images. Furthermore, two CRs were installed in a landslide to investigate their performance in a real InSAR application. The study revealed that low-cost materials can deliver performance levels comparable to metal materials, in terms of visibility and backscattering properties, while reducing the weight and cost.
How to cite: Azadnejad, S., Trafford, A., O'Loughlin, F., P Holohan, E., and Donohue, S.: Development of cost-effective passive corner reflectors using low-cost materials for SAR and InSAR applications , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12851, https://doi.org/10.5194/egusphere-egu25-12851, 2025.
Over the past three decades, SAR remote sensing has evolved into an instrumental tool for quantifying surface deformation and has significantly facilitated the advancement of landslide science, especially for studying slow-moving landslides. Here, we present multiple exemplary studies to showcase SAR’s essential values in large-area landslide mapping, continuous and near-real-time deformation monitoring, unveiling of spatiotemporal landslide dynamics, and commensurate hazard assessment and runout inundation forecast. These case studies entail exploration of P/L/C/X-band SAR data acquired from variable spaceborne and airborne platforms with distinct temporal and spatial resolutions, integration of multi-sensor remote sensing and field measurements, and SAR-observation-informed mechanistic modeling of landslide physics and hazards. In addition, we discuss the current challenges of landslide studies using SAR and the potential solutions and pathways forward, in the context of increasingly available and diverse SAR datasets globally. Importantly, the capabilities and challenges of SAR remote sensing highlighted here extend beyond landslide research, offering valuable insights for addressing other human-induced and natural hazards, including glacier movement, tectonic faulting, volcanic unrest, and urban subsidence.
How to cite: Xu, Y., Bürgmann, R., Lu, Z., and Fielding, E.: SAR Remote Sensing for Landslide Dynamics and Hazards, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10286, https://doi.org/10.5194/egusphere-egu25-10286, 2025.
Postseismic deformation following large earthquakes provides critical insights to the stress state and rheology of seismogenic zones. Here, we use high-resolution geodetic observations to analyze the postseismic response to the 2024 moment magnitude (Mw) 7.5 Noto earthquake, highlighting complex interactions between coseismic slip and afterslip on subparallel faults. By examining approximately six months of postseismic deformation using InSAR and GNSS data, we observe dramatic subsidence exceeding 8 cm across the Noto Peninsula, alongside horizontal deformation extending over 400 km west-northwest into central Japan. Numerical models indicate that both viscoelastic relaxation and afterslip are responsible for the observed deformation, with viscoelastic relaxation playing a more significant role in the pronounced subsidence in the peninsula. A weak zone, characterized by viscoelastic behavior, is required to explain localized deformations westward of the volcanic arc. Static stress analysis suggests that shallow afterslip overlaps with coseismic slip but may occur on unknown parallel faults beneath the primary seismogenic fault, and that the afterslip is primarily driven by normal stress change rather than the commonly assumed shear stress. These findings highlight the complexity of afterslip and suggest that postseismic observations reflects both rheological heterogeneity and fault system complexity in the region.
How to cite: Ma, Z., Luo, H., Li, C., Liu, J., and Wei, S.: Sub-parallel Fault Afterslip and Weak Zone Relaxation after the 2024 Noto Earthquake, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3487, https://doi.org/10.5194/egusphere-egu25-3487, 2025.
As an important part of geomorphic unit in high mountain area, Steep Canyon is one of the areas where slow-moving Landslide occur most frequently and with the largest scale due to its complex geological setting, steep terrain and intense river erosion, which pose serious risks to infrastructure and people downstream. We focus here on the slow-moving landslides along Jinshajiang steep canyon in the Southeast Tibet of China to revel the scientific issues what factor control the evolution process of these landslide. We estimate the ground displacement from time series analysis of Landsat series images and Sentinel-1 SAR images, spanning a more than 10 year period. Then field surveys on typical landslides were carried out, including reconstructing their three-dimensional structure, obtaining their material composition and rock mass structure and crevices information. The results show that there are significant differences in the deformation velocity of slow-moving landslides in the steep canyon. Specifically, the fastest landslide deformation velocity reaches 67 meters per year, so that this change can only be reversed by the correlation analysis on optical image. On the contrary , the slowest landslide deformation velocity is less than 1 meter per year, and this deformation can usually only be retrieved by time-series SAR technology. Combined with the field investigation and data analysis of meteorological stations and hydrological stations, we found an interesting phenomenon that the factors affecting the accelerated deformation of landslides are determined by the material and structure of the landslide. Accelerated deformation of high-level bedrock landslide have an obvious response to rainfall infiltration damage, but accelerated deformation response of loose accumulation landslide and ancient landslide is resulted from to river peak discharge. These observations provide a basis for us to build a regional landslide dynamic prediction model in steep canyon that pave the way of dynamic risk management of slow-moving landslide.
How to cite: Li, Y., Cui, Y., and Guo, J.: Reconstructing the Evolution of Slow-Moving Landslides in Steep Canyons using multi-platform satellite images, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4670, https://doi.org/10.5194/egusphere-egu25-4670, 2025.
Over the past 50 years, the Choushui River Fluvial Plan (CRFP) has been plagued by land subsidence caused by excessive groundwater extraction in the central Taiwan. While many geodetic techniques have successfully monitored surface deformation in this area, high spatiotemporal resolution data on vertical surface deformation remains insufficient. Thanks to the high observation frequency and moderate spatial resolution of Sentinel-1 satellite series, combined with well-developed multi-temporal InSAR (MTI) analysis techniques, this limitation has gradually been addressed. This study applied Persistent Scatterer InSAR (PSI) to analyze Sentinel-1 satellite data from 2016 to 2021, obtaining LOS displacement information from two orbits in the CRFP. Temporally, the study reduced disturbances in the LOS time series and constructed synchronized LOS observation data for both tracks at the same time intervals. Spatially, a 200-meter averaging grid was constructed to resolve PSI points mismatch issues from both orbits. The results were ultimately resolved into two-dimensional (E-W and U-D) time-series displacement components. By performing k-means clustering on the time-series vertical displacement data, the land subsidence characteristics of the study area were categorized into four groups: severe subsidence, moderate subsidence, mild subsidence, and normal condition. These clustering results can aid governmental agencies in drafting groundwater usage regulations. In the future, this study will integrate borehole data and groundwater level information to infer hydrogeological parameters and explore the spatial variability of groundwater volumes and geological materials.
How to cite: Lu, C.-H., Yen, J.-Y., Wang, C.-C., Ren, H., Chen, Y.-G., Yeh, T.-K., Ni, C.-F., and Chang, C.-P.: Establishing Time-Series 2D Surface Deformation to Investigate the Characteristics and Mechanisms of Land Subsidence in the Central Taiwan, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6090, https://doi.org/10.5194/egusphere-egu25-6090, 2025.
Monitoring and identifying surface deformation in hydrocarbon fields are fundamental for assessing and managing risks associated with hydrocarbon exploration. Gaining a clear understanding of the scale and characteristics of surface deformation within production areas is critical for managing potential environmental impacts. This knowledge enables the development of effective strategies to mitigate risks, ensuring that exploration and production activities are carried out in a way that minimizes environmental harm and supports long-term sustainability.
In this study, we identify risk-prone areas within the hydrocarbon-rich Niger Delta Nigeria region using wide-area PSI displacement maps. The most prominent Active Deformation Areas (ADAs) are analyzed to derive key outputs: the Gradient Intensity Map, Gradient Vectors and Time Series, and the Potential Damage Map. These outputs facilitate the identification of infrastructure within the study area that may be at risk of damage. This preliminary identification can be further refined through detailed, infrastructure-specific vulnerability and risk assessments.
The findings offer essential insights into the connection between surface deformation and hydrocarbon production activities in the Niger Delta. These insights are crucial for promoting sustainable resource management, guiding infrastructure development, and mitigating environmental impacts in regions rich in hydrocarbons.
How to cite: Uyo, I., Motagh, M., and Haghighi, M.: Surface Deformation Risk Assessment in Hydrocarbon Fields: Insights from the Niger Delta Nigeria, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9931, https://doi.org/10.5194/egusphere-egu25-9931, 2025.
Large landslides near water reservoirs represent a critical hazard, with the potential to cause dam breaches or overtopping, posing severe risks to infrastructure and downstream areas. To evaluate landslide activity on the slopes above the Mingachevir and Shamkir Water Reservoirs, we applied InSAR time series analysis using the Small Baseline Subset (SBAS) method. Our study focused on ground movement along the northeast limb of the Boz Dag anticline near the Mingachevir Reservoir and cliffs above the Shamkir Reservoir. Results revealed ground displacements of up to 5 cm per year on the northern slopes of the Boz Dag anticline, with the most active areas located over 2 km from the dam. The observed movement generally exhibited a linear trend, with no evidence of noticeable acceleration. Overall, the largest movements appear to be at the base of the slope, where the dip slopes of the anticline are steepest. Although landslide activity has been evident in recent years, the extent of the landslides is relatively small compared to the volume of the reservoir, and even a potential collapse is unlikely to cause a significant displacement wave or damage to the dam. In contrast to Mingachevir area, the landslide on the cliff above the Shamkir Reservoir exhibited no significant ground displacement.
How to cite: Brezny, M.: Landslide Dynamics in the Vicinity of the Mingachevir and Shamkir Reservoirs (Azerbaijan): Insights from InSAR Analysis, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10534, https://doi.org/10.5194/egusphere-egu25-10534, 2025.
The U.S. Geological Survey (USGS) provides rapid (within 30 min) estimates of earthquake-induced impacts and ground failure following significant events. These products are based solely on pre-event data and event-specific shaking estimates and do not include direct observations of building damage, casualties, or ground failure following an earthquake. To this end, the USGS is developing an intermediate-timeframe (within days to a week) pipeline for post-earthquake products that combines the current rapid estimation products with post-event observations to identify the most affected areas more accurately. As a vital component of this pipeline, the USGS is developing in-house capabilities to identify post-earthquake building damage and ground failure using Interferometric synthetic aperture radar (InSAR) coherence-based change detection maps (CDMs). We have previously shown that high-quality CDMs—in conjunction with accurate building footprints, prior building damage, and ground failure model estimates—improve upon a priori models of building damage and help differentiate building damage from ground failure effects. However, there is no standardized method for CDM generation, and approaches can vary substantially in computational cost and storage requirements. In this study, we evaluate the trade-offs between different CDM generation methods by assessing: (1) the number of pre-event images and coherence pairs, (2) the specific change detection method, and (3) earthquake-specific factors such as regional climate and timing relative to seasonal cycles. To quantify the accuracy of the different CDM generation methods, we compare our results with direct observations of building damage and ground failure data from three large events: the 2021 Haiti earthquake, the 2023 Morocco earthquake, and the 2023 Türkiye/Syria earthquake sequence. This work is an important step towards incorporating valuable post-event observations into near-real-time USGS earthquake products.
How to cite: Burgi, P., Wald, D., Xu, S., Li, X., and Noh, H.: Assessing fast and accurate InSAR coherence change detection methods for near real-time earthquake response applications, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12045, https://doi.org/10.5194/egusphere-egu25-12045, 2025.
Excessive groundwater extraction in the Taipei Basin, Taiwan, has resulted in significant land subsidence in the past.
In the 1950s, the Taipei basin experienced strong subsidence rates due to excessive groundwater pumping and regulations were necessary to control them. The geodetic monitoring of ground deformation in the basin started in 1948 when the government established levelling routes to monitor the land subsidence impact. From 1975 to 1989 subsidence rates decreased and the aquifer exhibited signs of recovery turning into uplift due to elastic rebound from 1990s until early 2000s. Since then, the basin has experienced interchangeable periods of subsidence and uplift, showing the high variability and complexity with its geological setting.
In this study, we use the remote sensing technique of Differential Interferometric Synthetic Aperture Radar (DInSAR) to quantify contemporary deformation in the Taipei basin from October 2014 until October 2024 using the open access satellite images from Sentinel-1. Additionally, we have investigated the basin along the same time period from different sources of data as groundwater level, levelling data and a rainfall station in the center of Taipei.
We applied the Small BAseline Subset (SBAS) approach to retrieve the deformation time series by using multi-look and single-look interferograms. For the multi-look processing, we formed a network of interferograms with temporal baselines between 30 and 90 days with the open source software Miami InSAR time series in Python (MintPy), in order to minimize the impact of the phase bias. The single-look processing was performed by using a stack of coregistered SLC images to form a network of interferograms with a maximum temporal baseline of 120 days using the recently released open source software SARvey (Survey with SAR) for InSAR time series. The results show various subsidence deformation clusters in the basin with subsidence rates of 2-3 cm/yr, most of which also exhibit high seasonal deformations with an amplitude of 2 cm. Additionally, an uplifting signal was identified from late 2021, characterised by a well-defined spatial boundary with a cumulative displacement of 3-4 cm. Comparison against groundwater level data suggests that this uplift signal in the center of the basin is associated with a rapid recovery going from -17 m in mid 2021 until -2 m by 2024 with a net increase of approximately -15 m. This might indicate an recharge event, however, no significant changes were identified in the rainfall data during this period, suggesting that there is reduction in the groundwater extraction activities.
How to cite: Rivas, E., Haghighi, M., Motagh, M., Hu, J.-C., and Lin, S.-H.: Land subsidence analysis in Taipei Basin, Taiwan, integrating Sentinel-1 InSAR, groundwater and rainfall data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12121, https://doi.org/10.5194/egusphere-egu25-12121, 2025.
Golestan Province in Iran is famous for its extensive agricultural production, where groundwater serves as the main source of irrigation. Continuous groundwater extraction in the region has led to declining water levels, resulting in widespread land subsidence, reducing groundwater storage capabilities and posing risks to infrastructure. To assess the impact of land subsidence in the area on the environment and infrastructure, we employ Interferometric Synthetic Aperture Radar (InSAR) technology. We use the Small Baseline Subset (SBAS) InSAR technique integrated within MintPy software to analyze the overall land subsidence in Golestan Province, utilizing data from various SAR sensors, including Sentinel-1, ALOS, Envisat, and ERS. Additionally, we apply the Persistent Scatterer Interferometry (PSI) method integrated into SARvey software to estimate localized subsidence affecting infrastructure. We analyze Sentinel-1 data from 2014 to 2025 in both ascending and descending tracks to obtain the current rates of subsidence. Furthermore, we use ALOS, Envisat, and ERS data to estimate the historical rates of subsidence in the region. The results show that long-term subsidence is predominant in the Gorgan Plain, characterized by an east-west orientation and a maximum subsidence rate > 10 cm/year from 2014 to 2025. Results are analyzed to separate the effect of elastic from inelastic deformation and assess changes in the storativity of the aquifers over the last 3 decades.
How to cite: Ma, M., Motagh, M., and Haghshenas Haghighi, M.: Decadal-scale analysis of Land Subsidence in Golestan province, Iran, Using SBAS-InSAR , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13214, https://doi.org/10.5194/egusphere-egu25-13214, 2025.
Land subsidence represents a critical geohazard, significantly impacting regions such as Louisiana's Capital Area, where a combination of natural processes and anthropogenic activities exacerbates land deformation. This study develops a high-resolution susceptibility mapping framework by integrating Interferometric Synthetic Aperture Radar (InSAR) data, geostatistical methods, and advanced machine learning algorithms. The research explicitly addresses deformation across East Baton Rouge, West Baton Rouge, East Feliciana, West Feliciana, and Pointe Coupee parishes.
The framework utilizes multi-source datasets, incorporating Landsat images, SRTM-DEM, Synthetic Aperture Radar (SAR) from Sentinel-1 imagery (2017–2020) and Global Navigation Satellite System (GNSS). The SAR data were processed via the PyGMTSAR package to generate precise displacement velocity fields and corrected for atmospheric effects and phase unwrapping errors. On the other hand, the QGIS open-source software was used to analyze and classify the Landsat images into several land cover categories. These outputs form the foundation for subsequent geostatistical analyses integrating geophysical, geological, and anthropogenic variables to model subsidence susceptibility.
Key drivers of subsidence are ranked and weighted through Frequency Ratio (FR) and Analytical Hierarchy Process (AHP) methodologies to quantify the influence of causative factors. Temporal deformation trends are modeled using Long Short-Term Memory (LSTM) neural networks, capturing non-linear relationships and dynamic interactions in the temporal domain. A Weighted Linear Combination (WLC) approach synthesizes weighted spatial layers, with the Optimum Index Factor (OIF) applied to reduce multicollinearity and enhance model robustness. Validation incorporates observed deformation data and Receiver Operating Characteristic (ROC) curve analysis, providing quantitative metrics such as the Area Under the Curve (AUC) for assessing predictive accuracy. The model outputs were classified into five sustainable categories representing areas at risk from this phenomenon using the Natural Break method.
This integrated approach advances the precision and reliability of subsidence susceptibility mapping, enabling detailed spatial resolution and enhanced predictive capability. The findings facilitate targeted risk assessments, support disaster mitigation strategies, and optimize resource allocation for land use planning and critical infrastructure protection. By addressing Louisiana's Capital Area's unique geophysical and socio-environmental characteristics, the framework provides a scalable solution applicable to subsidence-prone regions worldwide, contributing to the broader discourse on geohazard resilience and sustainable development.
How to cite: Abdalla, A., Salih, A., and Kangah, D.: Effective susceptibility mapping of land subsidence in Louisiana’s Capital Area using Data-Driven GIS and InSAR technologies, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18344, https://doi.org/10.5194/egusphere-egu25-18344, 2025.
Globally, the land subsidence (LS) calamity crisis has intensified, adversely affecting the infrastructure, ecology and population. The Instability in climate intensifies the extreme weather conditions and impacts the natural cycles leading to various natural/human made calamities. LS process is instigated due to aquifer compaction, peat decomposition, permafrost degradation, earthquake etc, which results due to extreme climate interplay. Inversely, some LS events also release the greenhouse gases accelerating the climate change. This study seeks to explore the intricate dynamics of the Eastern Ganga Plain (EGP) and the complex relation between climate change and LS. The EGP is a geomorphologically dynamic region featuring deltas, estuaries, wetlands, and floodplains shaped by fluvial and depositional processes. In the current study remote sensing based radar interferometry is applied to understand the evolution of LS scenario in Kolkata Urban Area (KUA) of EGP, Interferometric SAR can assess the ground deformation with high precision by analyzing phase differences using multi-temporal images, suggesting potential subsidence region and infrastructure stability. The hydro-climatic parameters of the EGP are analyzed for long-term climatic behavioral patterns for understanding the feedback of climate driven LS in conjunction with interconnected anthropization and significant climate variation. The interpretation of groundwater level data (2013 to 2023) and groundwater storage data (2004 to 2023) for the KUA reveals distinctive results that diverge significantly from previous studies, suggesting additional factors contributing to subsidence beyond partial aquifer compaction. The presence of wetlands and swamps in the Eastern Kolkata Region presents a high potential for earthquake-induced liquefaction, given the area's association with various tectonic features. Organic deposition in the Bengal Basin, associated with Holocene sediments as confirmed by borehole lithology, contributes to land subsidence through peat decomposition, as evidenced by methane emissions detected using Sentinel-5P TROPOMI data. Climatic variables significantly contribute to subsidence in the Kolkata Urban Area (KUA), beyond the effects of partial aquifer compaction, particularly through liquefaction, peat decomposition, and seismicity. Multi-temporal analysis of 192 Sentinel-1A SAR scenes (2017–2023) and GRACE data (2004–2023) identifies 13 potential subsidence hotspots, with rates ranging from -2.9 to 5.1 mm/year. Time-series GPM and GRACE data reveal increasing groundwater storage in the EGP alongside abrupt precipitation changes. Geotechnical and borehole analyses reveal that peat decomposition and liquefaction significantly impact the eastern Kolkata Urban Area (KUA). Geotechnical and borehole lithology analyses indicate a significant interplay between peat decomposition and liquefaction potential, predominantly affecting the Eastern Kolkata Region of the KUA. Climate change and extreme weather accelerate subsidence, requiring proactive, interdisciplinary strategies to mitigate and reverse human-induced impacts. Comprehensive surveys and expanded in-situ data are crucial to assess contributing factors and subsidence severity.
How to cite: Kannojiya, P. K. and Raju, A.: Effect of Climate change on subsidence in Eastern Gangetic Plain, India: A Correlation of untrodden factors, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21166, https://doi.org/10.5194/egusphere-egu25-21166, 2025.
Interferometric Synthetic Aperture Radar (InSAR) is widely used for detecting slow-moving landslides due to its high spatial resolution and millimeter-level accuracy over large areas. However, the computational demands of processing SAR data have hindered the development of national-wide slow-moving landslide inventories for many mountainous regions worldwide. This study examines a probabilistic approach to identify hillslope deformation anomalies as proxies for slow-moving landslide locations. We generated surface deformation data for the southeastern region of Türkiye, leveraging the high coherence of Sentinel-1 SAR imagery in areas with sparse vegetation cover. On the basis of the InSAR-derived hillslope deformation spatiotemporal pattern, a modeling framework inspired by extreme value theory will be developed. This will feature a suite of topographic, seismic, anthropogenic, and climatic variables. The model aims at predicting surface deformation and calculating the exceedance probability above a threshold suitable for classifying slow-moving hillslopes. After training, the objective is to transfer the model to the entirety of Türkiye to identify hillslopes exhibiting significant surface deformation and locate potential slow-moving landslides. This protocol will lay the foundation for advancing landslide hazard assessments and guiding further risk investigations.
How to cite: Lombardo, L., Wang, Y., Sadhasivam, N., Dahal, A., van Westen, C., Tiwari, A., Werth, S., Shirzaei, M., and Tanyas, H.: Towards Nationwide Probabilistic Mapping of Slow-Moving Landslides in Turkey Using InSAR, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16050, https://doi.org/10.5194/egusphere-egu25-16050, 2025.
Landslides pose a significant hazard to lives and property worldwide. Understanding the triggering factors of landslides provides essential information for hazard mitigation. While much research has focused on the effects of precipitation, underground mining, water level changes, and earthquakes on landslides, there remains a gap in understanding the impact of long-term and subtle tectonic interseismic motion, particularly over large-scale areas. Interferometric Synthetic Aperture Radar (InSAR) is widely used in landslide research, effectively detecting wide-area landslides and monitoring high-risk individual landslides. Additionally, it provides insights into the triggering factors and failure mechanisms of landslides. This study focuses on the Chuandian block area in southeastern Tibet, China, an area characterized by active tectonic motion.
First, we proposed an automated method for detecting landslides from wide-area InSAR deformation rates, utilizing density clustering and minimum boundary extraction. Using this method, potential landslides were successfully detected in the Chuandian block. The relationship between landslide distribution and the shallow coupling and creep of faults in the Chuandian block was then comprehensively analyzed based on the results of wide-area landslide distribution and interseismic deformation. Specifically, three-dimensional deformation along the Ganzi-Yushu and Xianshuihe faults was monitored using multi-orbit Sentinel-1 SAR and GNSS observations. An elastic dislocation model was also applied to invert shallow creep along these faults. Finally, the development patterns of landslides under the combined influence of internal and external dynamics were summarized. In high-creep areas along the faults, long-term and subtle interseismic motion of the shallow surface led to significant fissure development and structural deterioration in rock and soil, creating internal conditions conducive to landslide formation. External dynamics, including river erosion, precipitation, freezing, and thawing, further accelerated landslide development. Our findings underscore the importance of understanding the relationship between interseismic motion and landslides to enhance knowledge of how tectonic processes influence landslide formation and to support improved hazard mitigation strategies.
How to cite: Chen, L., Lu, Z., Zhao, C., and Zhao, J.: Fault coupling and creep control landslide distribution in southeastern Tibet, China, from SAR interferometry, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9853, https://doi.org/10.5194/egusphere-egu25-9853, 2025.
In the management and maintenance of port area operations, the ability to swiftly and effectively monitor and predict the settlement deformation of breakwaters remains a critical challenge. Addressing the difficulties in monitoring and predicting breakwaters under harsh offshore and deep-water conditions, we propose an innovative method combining PS-InSAR and CNN-LSTM-SE for predictive analysis. This study utilized 88 Sentinel-1A ascending radar satellite images acquired between January 2019 and December 2021, employing PS-InSAR technology to invert and derive deformation values along the radar line of sight in the port area. Focusing on the eastern breakwater, we extracted time-series settlement data from nine monitoring points through data transformation and applied the CNN-LSTM-SE neural network algorithm for predictive analysis, coupled with a risk assessment of breakwater settlement. The results indicate that from 2019 to 2021, the settlement rate of the eastern breakwater ranged from -140 to -20 mm/a, exhibiting a wave-like trend that progressively intensified from the shoreward side to the deep-water side, with a maximum cumulative settlement reaching 356.1 mm. The predictions from the CNN-LSTM-SE model aligned closely with monitoring results, with a correlation coefficient exceeding 0.95. Compared to other methods, CNN-LSTM-SE demonstrated superior predictive accuracy, making it well-suited for settlement forecasting of offshore deep-water breakwaters. High-risk settlement areas in the port are likely to face structural instability due to settlement rates and differential settlement. Specifically, Zone I of the western breakwater reclamation and the eastern breakwater slope are vulnerable to ground settlement and structural damage caused by heavy loads and uneven load distribution, respectively. To mitigate these risks, it is imperative to establish a multi-tiered monitoring and early warning system to capture real-time changes in the foundation. These research findings provide essential technical support and data reference for the safe operation and maintenance of port areas.
How to cite: Han, X., Guan, Y., and Li, G.: Settlement monitoring and prediction of offshore deepwater breakwater based on PS-InSAR and CNN-LSTM-SE, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4651, https://doi.org/10.5194/egusphere-egu25-4651, 2025.
Underground mining of natural resources disrupts the original stress balance of the surrounding rock masses, causing deformation in overlying rock layers and the ground surface. These disturbances may cause various geohazards, such as collapses, landslides, structural damage to buildings and infrastructure, and ecological degradation. Therefore, it is crucial to accurately extract and predict mining-induced ground deformation to assess and prevent mining-related geohazards. Interferometric synthetic aperture radar (InSAR) has been widely applied to monitor mining-induced deformation. However, due to the rapid rates and high spatial gradients of the mining-induced deformation, as well as rapid changes in ground topography, it is difficult to extract accurate and continuous deformation measurements using InSAR. To this end, this study proposed a novel method for extracting mining-induced deformation based on the InSAR and Weibull model.
The core concept behind the proposed method is to link time-interval InSAR-derived deformation using a time-series model, enabling the extraction and prediction of mining-induced deformation. Specifically, the method for connecting the deformation of line-of-sight (LOS) is first established based on the Weibull model. The initial model parameters are then derived using the genetic algorithm-particle swarm optimization (GA-PSO) approach. These parameters are subsequently optimized according to their spatial distribution characteristics. Finally, the trust-region reflective least squares (TRRLS) algorithm is applied to determine the final model parameters, enabling the extraction of mining-induced deformation during the monitoring period. The results indicate that the extracted deformation is accurate and consistent overall, with root mean square errors (RMSE) of approximately 9.8mm and 14.1mm observed for the simulation and field experiments, respectively. Furthermore, leveling data are also used to validate the accuracy of the proposed method, yielding an RMSE of 32.6mm. Additionally, the relationships between the Weibull model parameters, ground subsidence values, and initial subsidence time are analyzed. The effects of various factors—estimation algorithms, number of observations, time intervals, and monitoring errors—on the proposed method are examined. These results suggest that the proposed algorithm can be a practical and cost-effective tool for extracting mining-induced displacements and assessing and mitigating mining-related geohazards.
This work has been supported in part by the National Natural Science Foundation of China under Grant 52474184 and Grant 42474018, in part by China Postdoctoral Science Foundation under Grant 2023T160685 and Grant 2020M671646, in part by Young Elite Scientists Sponsorship Program by CAST under Grant 2023QNRC001-YESS20230599, in part by the National Key R&D Program of China under Grant 2022YFE0102600, in part by supported by the Construction Program of Space-Air-Ground-Well Cooperative Awareness Spatial Information Project under Grant B20046, in part by the China Scholarship Council under Grant 202406420081, in part by the Spanish Agencia Estatal de Investigacion under Grant G2HOTSPOTS (PID2021-122142OB-I00), and in part by the AEI, Ministerio de Ciencia, Innovación y Universidades. Convocatoria Proyectos en Colaboración Público Privada, 2021, under Grant CPP2021-009072 (STONE), and Defsour-PLUS (PDC2022-133304-I00) from the MCIN/AEI/10.13039/501100011033/FEDER, UE with funds from NextGenerationEU/PRTR.
How to cite: wang, T., Wang, Y., Zhao, F., Du, S., and Fernández, J.: A Novel Method for Extracting Mining-Induced Ground Deformation Using InSAR and the Weibull Model, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6765, https://doi.org/10.5194/egusphere-egu25-6765, 2025.
The Vietnamese Mekong Delta has been affected by environmental challenges for several decades, including land subsidence and an increased frequency of droughts. While the former leads to a growing vulnerability to coastal erosion, floodings and permanent inundation, the latter has led to considerable crop failures in the past. In this work, we use InSAR-derived subsidence time series and remote sensing based meteorological information to study seasonal vertical displacements in the Mekong Delta, which align with the distinct dry and wet seasons in most locations and overlay a background subsidence trend. We show that a drought in 2020 lead to a significant increase in the seasonal subsidence in parts of the delta. The magnitude of this drought-induced subsidence, which was up to several centimeters in a few months, was related to the surface water management and land use. It was compensated by uplift in the following rainy season in several but not all regions. We argue that the observed surface drop in some regions was caused by inelastic deformation in the aquifer-aquitard system and/or the shallow soil. The findings of this work highlight the importance of further research on drought-induced subsidence in the Mekong Delta, especially under the assumption that the frequency of droughts might further increase in the future due to climate change and an increasing water demand.
How to cite: Dörr, N., Vu Huu, L., Schenk, A., and Hinz, S.: Remote sensing insights into drought induced land subsidence in the Vietnamese Mekong Delta, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18167, https://doi.org/10.5194/egusphere-egu25-18167, 2025.
Posters virtual: Wed, 30 Apr, 14:00–15:45 | vPoster spot 3
EGU25-19686 | ECS | Posters virtual | VPS13
InSAR Deformation Datum Connection with A Fixed Line-of-Sight Direction: A Bayesian inference and the Markov Random Field (MRF) model integrationWed, 30 Apr, 14:00–15:45 (CEST) | vP3.16
To address the challenge of inconsistent line-of-sight (LOS) deformation datum derived from interferometric measurements of different Synthetic Aperture Radar (SAR) images—and the significant variation in LOS direction between near-range and far-range within the same image—this contribution proposes an InSAR deformation datum connection method with a fixed LOS direction. The method combines Bayesian inference and the Markov Random Field (MRF) model, integrating InSAR and GNSS deformation data to achieve unified deformation datum for adjacent and even different-orbit SAR interferometric results.
A simulation experiments, using Sentinel-1 imaging parameters and GNSS velocity field data, and a real-world validation with InSAR data of the 2023 Southern Turkey earthquake are conducted. In the simulation, the root-mean-square error of LOS displacement rate difference in the overlapping regions of adjacent-track SAR images decreased 99%. In the real-world experiment, the root-mean-square error of displacement difference reduced from 20 mm to 8 mm, demonstrating the effectiveness of the proposed method.
We have three key contributions：(1) Unified Deformation datum: Successfully realize an InSAR deformation datum connection with fixed LOS direction in SAR images; (2) Adjacent-Track Stitching: Achieve seamless stitching of adjacent-track SAR deformation results from a single data source; (3) Real-Data Validation: Reduce the mean displacement difference in overlapping regions of adjacent-track SAR images of the 2023 Southern Turkey earthquake from 20 mm to 8 mm.
How to cite: Bian, W., Motagh, M., and Wu, J.: InSAR Deformation Datum Connection with A Fixed Line-of-Sight Direction: A Bayesian inference and the Markov Random Field (MRF) model integration, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19686, https://doi.org/10.5194/egusphere-egu25-19686, 2025.
