InSAR Research Articles

A Bayesian Source Model for the 2022 Mw6.6 Luding Earthquake, Sichuan Province, China, Constrained by GPS and InSAR Observations

Until the Mw 6.6 Luding earthquake ruptured the Moxi section of the Xianshuihe fault (XSHF) on 5 September 2022, the region had not experienced an Mw >6 earthquake since instrumental records began. We used Global Positioning System (GPS) and Sentinel-1 interferometric synthetic aperture radar (InSAR) observations to image the coseismic deformation and constrain the location and geometry of the seismogenic fault using a Bayesian method We then present a distributed slip model of the 2022 Mw6.6 Luding earthquake, a left-lateral strike-slip earthquake that occurred on the Moxi section of the Xianshuihe fault in the southwest Sichuan basin, China. Two tracks (T26 and T135) of the InSAR data captured a part of the coseismic surface deformation with the line-of-sight displacements range from ∼−0.16 m to ~0.14 m in the ascending track and from ~−0.12 m to ~0.10 m in the descending track. The inverted best-fitting fault model shows a pure sinistral strike-slip motion on a west-dipping fault plane with a strike of 164.3°. We adopt a variational Bayesian approach and account for the uncertainties in the fault geometry to retrieve the distributed slip model. The inverted result shows that the maximum slip of ~1.82 m occurred at a depth of 5.3 km, with the major slip concentrated within depths ranging from 0.9–11 km. The InSAR-determined moment is 1.3 × 1019 Nm, with a shear modulus of 30 GPa, equivalent to Mw 6.7. The published coseismic slip models of the 2022 Luding earthquake show apparent differences despite the use of similar geodetic or seismic observations. These variations underscore the uncertainty associated with routinely performed source inversions and their interpretations for the underlying fault model.

Landslide Hazard Assessment Combined with InSAR Deformation: A Case Study in the Zagunao River Basin, Sichuan Province, Southwestern China

Landslides are common natural disasters that cause serious damage to ecosystems and human societies. To effectively prevent and mitigate these disasters, an accurate assessment of landslide hazards is necessary. However, most traditional landslide hazard assessment methods rely on static assessment factors while ignoring the dynamic changes in landslides, which may lead to false-positive errors in the assessment results. This paper presents a novel landslide hazard assessment method for the Zagunao River basin, China. In this study, an updated landslide inventory was obtained for the Zagunao River basin using data from interferometric synthetic aperture radar (InSAR) and optical images. Based on this inventory, a landslide susceptibility map was developed using a random forest algorithm. Finally, an evaluation matrix was created by combining the results of deformation rates from both ascending and descending data to establish a hazard level that considers surface deformation. The method presented in this study can reflect recent landslide hazards in the region and produce dynamic assessments of regional landslide hazards. It provides a basis for the government to identify and manage high-risk areas.

Analysis of the Spatial Distribution and Deformation Types of Active Landslides in the Upper Jinsha River, China, Using Integrated Remote Sensing Technologies

The Upper Jinsha River (UJSR) has great water resource potential, but large-scale active landslides hinder water resource development and utilization. It is necessary to understand the spatial distribution and deformation trend of active landslides in the UJSR. In areas of high elevations, steep terrain or otherwise inaccessible to humans, extensive landslide studies remain challenging using traditional geological surveys and monitoring equipment. Stacking interferometry synthetic aperture radar (stacking-InSAR) technology, optical satellite images and unmanned aerial vehicle (UAV) photography are applied to landslide identification. Small baseline subset interferometry synthetic aperture radar (SBAS-InSAR) was used to obtain time-series deformation curves of samples to reveal the deformation types of active landslides. A total of 246 active landslides were identified within the study area, of which 207 were concentrated in three zones (zones I, II and III). Among the 31 landslides chosen as research samples, six were linear-type landslides, three were upward concave-type landslides, 10 were downward concave-type landslides, and 12 were step-type landslides based on the curve morphology. The results can aid in monitoring and early-warning systems for active landslides within the UJSR and provide insights for future studies on active landslides within the basin.

A Fourier–Legendre Polynomial Forest Height Inversion Model Based on a Single-Baseline Configuration

In this article, we propose a Fourier–Legendre (FL) polynomial forest height estimation algorithm based on low-frequency single-baseline polarimetric interferometric synthetic aperture radar (PolInSAR) data. The algorithm can obtain forest height with a single-baseline PolInSAR configuration while capturing a high-resolution vertical profile for the forest volume. This is based on the consideration that the forest height remains constant within neighboring pixels. Meanwhile, we also assume that the coefficients of the FL polynomials remain unchanged within neighboring pixels, except for the last polynomial coefficient. The idea of using neighboring pixels to increase the observations provides us with the possibility to obtain high-order FL polynomials. With this approach, it is possible to obtain a high-resolution vertical profile that is suitable for forest height estimation without losing too much spatial resolution. P-band PolInSAR data acquired in Mabounie in Gabon and Krycklan in Sweden were selected for testing the proposed algorithm. The results show that the algorithm outperforms the random volume over ground (RVoG) model by 18% and 16.7% in forest height estimation for the Mabounie and Krycklan study sites, respectively.

Multi‐Temporal InSAR, GNSS and Seismic Measurements Reveal the Origin of the 2021 Vulcano Island (Italy) Unrest

AbstractLa Fossa Caldera at Vulcano (Italy) has been showing signs of unrest since September 2021. To investigate this phenomenon, we conducted an analysis of geodetic and seismological data from July to December 2021. In particular, we analyzed Multi Temporal Interferometric Synthetic Aperture Radar and Global Navigation Satellite System data, showing a pronounced elliptical uplift signal, which we elaborated using analytical source modeling. Additionally, seismic data were used to identify seismicity associated with hydrothermal system activity and assess its temporal evolution. The results indicate that the observed deformation is consistent with the expansion of the hydrothermal system within the La Fossa Caldera. These findings align with the analysis of seismic data, revealing signals indicative of hydrothermal activity, such as Very Long Period events. The results suggest that the ongoing phenomenon since 2021 represents a hydrothermal unrest, similar to the one observed during the late 1970s to early 1990s.

Identification and hazard analysis of landslides triggered by earthquakes and rainfall

This study aims to utilize the Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technique and Google Earth optical remote sensing images to analyze the area within 20 ​km around the epicenter of a M 3.9, earthquake that occurred in Tanchang County, Gansu Province, on December 28, 2020. The objective is to identify potential earthquake-induced landslides, assess their scale, and determine their impact range. The study results reveal the successful identification of two potential landslides in the 20 ​km radius around the epicenter. Through time-series deformation analysis, it was observed that these potential landslides were significantly influenced by both the earthquake and rainfall. Further estimation of these potential landslides indicates maximum depths of 7.4 ​m and 14.1 ​m for the failure surfaces, with volumes of 9.02 ​× ​104 ​m3 and 25.5 ​× ​104 ​m3, respectively. Finally, based on the simulation analysis of Massflow software, the maximum thickness of soil accumulation in the final accumulation area after sliding of the potential landslide in Shangyaai is 12 ​m, the area of the final accumulation area is 1.75 ​× ​104 ​m2, and the farthest movement distance is 1124 ​m. The maximum thickness of soil accumulation in the final accumulation area after sliding of the potential landslide in Wangshancun is 8 ​m, the area of the final accumulation area is 7.89 ​× ​104 ​m2, and the farthest movement distance is 742 ​m.

Spatial and Temporal Patterns of Land Subsidence and Sinkhole Occurrence in the Konya Endorheic Basin, Turkey

The endorheic Konya Basin is a vast aggradational plain in Central Anatolia, Türkiye. It occupies a significant portion of Konya Province, covering approximately 50,000 km2. The basin is subjected to intense groundwater withdrawal and extensive agricultural activities with excessive irrigation. These activities have led to human-induced hazards, such as sinkholes and regional land subsidence. Although sinkhole occurrence mainly occurs in the Karapınar area, land subsidence is primarily observed in the central sector of Konya city, with 2 million inhabitants, as well as in various parts of the basin. This study focuses on determining the extent and rate of land subsidence throughout the basin, understanding sinkhole formation, and unraveling their relationship with anthropogenic activities. For this purpose, Interferometric Synthetic Aperture Radar (InSAR) analysis of Sentinel-1 data from 2014 to 2022 was conducted to identify and assess land subsidence. We also used the land cover data and groundwater-level information to better understand the spatial and temporal patterns of land subsidence and sinkhole occurrence. Additionally, the land cover data were used to resolve spatial–temporal variations in the cultivated area and urbanization, which are the main factors governing groundwater exploitation in the region. Our study identified widespread subsidence zones with rates as high as 90 mm/y. Groundwater overexploitation to sustain extensive agricultural operations is the main cause of the high rate of land subsidence. Additionally, it was discovered that the number of sinkholes has substantially increased due to anthropogenic influences, currently amounting to as many as 660.

Trend Classification of InSAR Displacement Time Series Using SAE–CNN

Multi-temporal Interferometric Synthetic Aperture Radar technique (MTInSAR) has emerged as a valuable tool for measuring ground motion in a wide area. However, interpreting displacement time series and identifying dangerous signals from millions of InSAR coherent targets is challenging. In this study, we propose a method combining stacked autoencoder (SAE) and convolutional neural network (CNN) to classify InSAR time series and ease the interpretation of movements. The InSAR time series are classified into five categories, including stable, linear, accelerating, deceleration, and phase unwrapping error (PUE). The accuracy of labeled samples reaches 95.1%, reflecting the performance of the proposed method. This method was applied to the InSAR results for Kunming extracted from 171 ascending Sentinel-1 images from January 2017 to September 2022. The classification map of the InSAR time series shows that stable coherent points dominate around 79.28% of the area, with linear patterns at 10.70%, decelerating at 5.30%, accelerating at 4.72%, and PUE patterns at 3.60%. The results demonstrate that this method can distinguish different ground motion features and detect nonlinear deformation signals on a large scale without human intervention.

Evolution of the magma system at Makushin volcano, Alaska, from 2004 to 2021

Located in the northeast of Unalaska Island in the central part of the Aleutian, Alaska, Makushin volcano is one of the more active volcanoes in the United States, with the most recent eruption occurred in 1995. Pre-eruptive inflation and post-eruptive deflation were identified from 1993 to 1995 and 1996 to 2000, respectively, and were interpreted as volume changes associated with magma movement within the plumbing system. In this study, we investigate the surface deformation of Makushin from 2004 to 2021 to evaluate its present-day activity, using Synthetic Aperture Radar (SAR) interferometry technique. SAR images acquired from C-band Envisat and Sentinel-1, X-band TerraSAR-X, and L-band ALOS-2 satellites are analyzed using an advanced multiple temporal Interferometric SAR (InSAR) algorithm. The produced deformation time series are then used to track the temporal evolution of the volcanic system with Ensemble Kalman Filter (EnKF) assuming Mogi sources. The results show that Makushin is characterized by time-varying deformation episodes including quiescence, and inflation/deflation cycles at varied rates since 2004. The derived deformation histories can be well reproduced by a spatially stationary pressure source located to the east-northeast of the summit, at a depth about 6 km beneath the sea level (BSL). The inflation/deflation cycles with distinct temporal patterns are interpreted as results from volatile-dominated volume accumulation/loss in the magmatic reservoir. A secondary reservoir connected to the main magma chamber is revealed from the deformation time series. No surface displacement associated with the large earthquake swarm in 2020 is identified from the InSAR-derived deformation. The cumulative volume change in the magma reservoir is about −6×106m3 from 2004 to 2021, indicating a volume/pressure deficit in the magma plumbing system.

Implementing the European Space Agency’s SentiNel Application Platform’s Open-Source Python Module for Differential Synthetic Aperture Radar Interferometry Coseismic Ground Deformation from Sentinel-1 Data

Differential SAR Interferometry is a largely exploited technique to study ground deformations. A key application is the detection of the effects promoted by earthquakes, including detailed variations in ground deformations at different scales. In this work, an implemented Python script (Snap2DQuake) based on the “snappy” module by SNAP software 9.0.8 (ESA) for the processing of satellite imagery is proposed. Snap2DQuake is aimed at producing detailed coseismic deformation maps using Sentinel-1 C-band data by the DInSAR technique. With this alternative approach, the processing is simplified, and several issues that may occur using the software are solved. The proposed tool has been tested on two case studies: the Mw 6.4 Petrinja earthquake (Croatia, December 2020) and the Mw 5.7 to Mw 6.3 earthquakes, which occurred near Tyrnavós (Greece, March 2021). The earthquakes, which occurred in two different tectonic contexts, are used to test and verify the validity of Snap2DQuake. Snap2DQuake allows us to provide detailed deformation maps along the vertical and E-W directions in perfect agreement with observations reported in previous works. These maps offer new insights into the deformation pattern linked to earthquakes, demonstrating the reliability of Snap2DQuake as an alternative tool for users working on different applications, even with basic coding skills.

A Statistical Approach for the Integration of Multi-Temporal InSAR and GNSS-PPP Ground Deformation Measurements.

Determining and monitoring ground deformations is critical for hazard management studies, especially in megacities, and these studies might help prevent future disaster conditions and save many lives. In recent years, the Golden Horn, located in the southeast of the European part of Istanbul within a UNESCO-protected region, has experienced significant changes and regional deformations linked to rapid population growth, infrastructure work, and tramway construction. In this study, we used Interferometric Synthetic Aperture Radar (InSAR) and Global Navigation Satellite System (GNSS) techniques to investigate the ground deformations along the Golden Horn coastlines. The investigated periods are between 2015 and 2020 and 2017 and 2020 for InSAR and GNSS, respectively. For the InSAR analyses, we used sequences of multi-temporal synthetic aperture radar (SAR) images collected by the Sentinel-1 and ALOS-2 satellites. The ground displacement products (i.e., time series and velocity maps) were then cross-compared with those achievable using the Precise Point Positioning (PPP) technique for the GNSS solutions, which can provide precise positions with a single receiver. In the proposed analysis, we compared the ground displacement velocities obtained by both methods by computing the standard deviations of the difference between the relevant observations considering a weighted least square estimation procedure. Additionally, we identified five circle buffers with different radii ranging between 50 m and 250 m for selecting the most appropriate coherent points to conduct the cross-comparison analysis. Moreover, a vertical displacement rate map was produced. The comparison of the vertical ground velocities derived from PPP and InSAR demonstrates that the PPP technique is valuable. For the coherent stations, the vertical displacement rates vary between -4.86 mm/yr and -23.58 mm/yr and -9.50 and -27.77 mm/yr for InSAR and GNSS, respectively.

An integrated neural network method for landslide susceptibility assessment based on time-series InSAR deformation dynamic features

ABSTRACT We develop an integrated neural network landslide susceptibility assessment (LSA) method that integrates temporal dynamic features of interferometry synthetic aperture radar (InSAR) deformation data and the spatial features of landslide influencing factors. We construct a time-distributed convolutional neural network (TD-CNN) and bidirectional gated recurrent unit (Bi-GRU) to better understand the temporal dynamic features of InSAR cumulative deformation, and construct a multi-scale convolutional neural network (MSCNN) to determine the spatial features of landslide influencing factors, and construct a parallel unified deep learning network model to fuse these temporal and spatial features for LSA. Compared with the traditional MSCNN method, the accuracy of the proposed model is improved by 1.20%. The performance of the proposed model is preferable to MSCNN. The area under the receiver operating characteristic curve (AUC) of the testing set reaches 0.91. Our LSA results show that the proposed model clearly depicts areas with very high susceptibility landslides. Further, only 10.18% of the study area accurately covers 84.79% of historical landslide areas. Subjective consequences and objective indicators show that the proposed model that is integrated time-series InSAR deformation dynamic features can make full use of landslide characteristics and effectively improve the reliability of LSA.

Research on the Surface Deformation, Fault Rupture, and Coseismic Geohazard of the 2022 Luding Mw 6.8 Earthquake.

An Mw 6.8 earthquake occurred in Luding County, Ganzi Tibetan Autonomous Prefecture, Sichuan Province, on 5 September 2022. This seismic event triggered numerous coseismic geohazards in the seismic zone. In this study, the ascending- and descending-track synthetic aperture radar (SAR) images observed by the Sentinel-1A satellite are utilized to extract the coseismic surface deformation of the Luding earthquake. Subsequently, a faulting model is estimated based on the elastic dislocation theory, under the constraint of the InSAR observation. Additionally, the POT technique was employed to detect coseismic geohazards. High-spatial-resolution optical remote sensing images served to validate the reliability of the detection results. The coseismic interferometric synthetic aperture radar (InSAR) deformation field indicated a maximum deformation of ~190 mm and ~140 mm along the ascending and descending tracks, respectively. The estimated best-fitting faulting model suggests that the optimal seismogenic fault strike and dip angles are 169.3° and 70°, respectively. The fault slip predominantly exhibits left-lateral strike-slip characteristics and is concentrated at depths of 3-12 km. The estimated maximum fault slip was 2.67 m, occurring at a depth of 7 km. The pixel offset tracking (POT) result derived from the pre- and post-earthquake SAR images found a total of 245 medium- to large-scale coseismic geohazards, with a verification rate from optical images exceeding 64%. The distribution of these geohazards is notably dense within the significant fault rupture segment. Geohazards on the fault hanging wall are densely packed, whereas landslides along the Dadu River's fault footwall are also notably frequent.

A High-Precision Baseline Calibration Method Based on Estimation of Azimuth Fringe Frequency with THz Interferometry SAR

In this study, repeat-pass synthetic aperture radar interferometry (repeat-pass THz InSAR) is first extended to the terahertz band, and it has tremendous potential in the application of high-resolution three-dimensional (3D) imaging due to its shorter wavelength, larger bandwidth, and greater sensitivity to elevation variation. The super-resolution and high sensitivity of THz InSAR pose greater demands on the baseline calibration for high-precision digital elevation model (DEM) generation. To meet the elevation accuracy requirement of THz InSAR, we propose a baseline calibration method relying on the estimation of the azimuth fringe frequency (EAFF) of the interferometric phase. Initially, a model for non-parallel sampling path errors within the squint SAR repeat-pass interferometry was established, and then, we conducted the theoretical analysis of the phase errors induced by the non-parallel errors. Following this, using a reference DEM, the relationship between the fringe frequency of the error phase and the bias in the repeat-path positioning was established. This allowed the estimation of the position errors to be transformed into the frequency spectrum estimation based on the FFT, which would mitigate the impact of unknown SAR sampling positions. Ultimately, we investigated the accuracy of the proposed EAFF calibration method, and the simulation showed that it can achieve the theoretical accuracy when the correlation coefficient exceeds 0.3. Furthermore, we configured the repeat-pass THz InSAR system with the 0.3 THz stepped-frequency radar. Compared to the conventional calibration based on ground control points (GCPs), the 3D reconstruction of both a knife and a terrain model, calibrated using the proposed EAFF algorithm, demonstrated that the elevation accuracy can achieve millimeter-level precision across the entire image swath. The above results also proved the great potential of THz InSAR in high-precision 3D imaging and remote sensing.

A machine learning-based method for multi-satellite SAR data integration

Large- and small-scale subsidence coexist in the world's coastal cities due to extensive land reclamation and fast urbanization. Synthetic aperture radar (SAR) images are typically limited by either low resolution or small coverage, making them ineffective for fully monitoring displacement in coastal areas. In this research, a machine learning-based method is developed to investigate the reclaimed land subsidence based on multi-satellite SAR data integration. The proposed method requires at least a pair of SAR images from complementary tracks. First, the line-of-sight (LOS) displacements are recovered in connection to a series of extremely coherent points based on the differential interferometry synthetic aperture radar (DInSAR). These LOS displacements are then converted into their vertical component, geocoded to a common grid, and simultaneously integrated (i.e., pixel-by-pixel) based on Support Vector Regression (SVR). The proposed methodology does not necessitate the simultaneous processing of huge DInSAR interferogram sequences. The experiments include high-resolution COSMO-SkyMed (CSK) and TerraSAR-X (TSX) images, as well as a small monitoring cycle Sentinel-1 (S1) images of reclaimed territories near Hong Kong Kowloon City. The overall average annual displacement (AAD) ranges from -12.86 to 11.63 mm/year derived from 2008 to 2019. The evaluation metrics including RMSE, MAE, correlation coefficient, and R-squared are used to investigate the impact of SVR in the integration of SAR datasets. Based on these evaluation metrics, SVR is superior in terms of integration performance, accuracy, and generalization ability. Thus, the proposed method has potentially performed multi-satellite SAR data integration.

INVESTIGATIONS ON INSAR DATA PROCESSING STANDARD FOR VOLCANO ISLAND MONITORING IN INDONESIA

Abstract. In critical situations such as disaster and humanitarian relief, the reliable geospatial data will present an appropriate decision on a timely manner. As fundamental datasets, large-scale topographic maps are are mandatory in order to perform geospatial analysis for many societal challenges. Indeed, Large Scale Topographic Mapping in Indonesia shall be accelerated in an innovative and productive way to consider the production efficiency, especially for the geospatial data acquisition as its primary source. Interferometric Synthetic Aperture Radar (InSAR) data acquisition presents some advantages, especially in conjunction with the security clearance and global weather as some main constraints for the geospatial data acquisition. In terms of disaster preparedness and emergency response, radar interferometry techniques can play an important role by generating a Digital Elevation Model (DEM) as an input to Decision Support Systems (DSS). In addition, the techniques of differential Interferometric Synthetic Aperture Radar (D-InSAR) can provide the earth surface deformation from time series Radar Datasets. A set of standards related to the InSAR data acquisition are essential, especially with regards to the improved sensors, and processing methods implemented by the worldwide space agencies nowadays. Hence, the precise definition of a standard is fundamental in order to prepare the InSAR data as one potential source of geospatial data and information, especially for LSTM data production. Generated time series of Digital Elevation Model (DEM) can identify the potential cause of 2018’s Sunda Strait tsunami (Tampubolon, 2019). It was related with the increasing eruption activity of Anak Krakatau (Child of Krakatoa). From DEM analysis, the Volcanic mudflow material avalanche has been detected before the tsunami hit the coastal surrounding area. In this paper, more advanced techniques was investigated by the combination between Time Series DEM Analysis and Ground Displacement detection. The focus on the integration between reliable TanDEM-X and up-to-date Sentinel 1A/B was proposed on another volcano island of Gamalama in Ternate. As TanDEM-X uses X-band as its medium wave, it can produce more reliable DEM as a reference data. On the other hand, Sentinel 1 uses C-band that can contribute to detect the ground deformation over the aforementioned volcano island. TanDEM-X uses a more robust and consistent approach to scan the earth surface by single data acquisition scheme, which is generally more accurate and reliable for the DEM generation. However, many factors influence the selection of a platform that can be best suited for radar interferometry. Initially, the German TanDEM-X Coregistered Singlelook Slant-range Complex (CoSSC) using bi-static InSAR approach has been implemented in order to generate DEMs comply with the LSTM specification in Indonesia. The basic principle is performing a simultaneous measurement of the same scene and identical doppler spectrum by using 2 sensors, thereby avoiding temporal decorrelation (DLR, 2012). Subsequently, the differential InSAR techniques by using Sentinel 1A/B has been performed in order to detect the ground deformation. Finally, tt can be shown that the reliable TanDEM-X data has increased the ground deformation resolution and accuracy in order to provide potential solution for disaster preparedness.

LAND SUBSIDENCE IN WUHAN REVEALED USING A MULTI-SENSOR INSAR TIME SERIES FUSION APPROACH

Abstract. Satellite Interferometric Synthetic Aperture Radar (InSAR) is widely utilized for topographic, geological, and natural resource investigations. However, many existing InSAR studies on ground deformation are limited to relatively short observation periods and single sensors. This paper introduces a novel method for fusing multi-sensor InSAR time series data, specifically designed to address scenarios involving partial overlaps and temporal gaps. The method employs a new Power Exponential Knothe Model (PEKM) to fit and fuse overlaps in the deformation curves, while a Long Short-Term Memory (LSTM) neural network predicts and fuses data during temporal gaps in the series. In this study, the city of Wuhan in China was selected as the experimental area. SAR datasets from COSMO-SkyMed (2011–2015), TerraSAR-X (2015–2019), and Sentinel-1 (2019–2021) were fused to map long-term surface deformation over the past decade. An independent InSAR time series analysis from 2011 to 2020, based on 230 COSMO-SkyMed scenes, was used as a reference for comparison. The correlation coefficient between the fusion algorithm’s results and the reference data is 0.87 in the time overlapping region and 0.97 in the time-interval dataset. The overall correlation coefficient of 0.78 demonstrates that the proposed algorithm achieves a similar trend as the reference deformation curve. Based on the long time series settlement results obtained through fusion, a detailed analysis of the causes of settlement was conducted for several subsidence zones. The subsidence in the Houhu area is primarily attributed to the consolidation and compression of soft soil. Soil mechanics were employed to estimate the expected completion time of subsidence and calculate the degree of consolidation for each year. The COSMO-SkyMed PSInSAR results indicate that the area has entered the late stage of consolidation and compression, gradually stabilizing over time. Furthermore, the subsidence curve observed in the area around Xinrong reveals that the construction of an underground section of subway Line 21 has caused significant settlement in that particular region. The high temporal granularity of the PSInSAR time series also enables precise detection of a rebound phase following a major flooding event in 2016. The experimental results demonstrate the accuracy of the proposed fusion method in providing robust time series for analyzing long-term land subsidence mechanisms. Additionally, these findings unveil previously unknown characteristics of land subsidence in Wuhan, thus clarifying the relationship with urban causative factors.

TIME SERIES INSAR ANALYSIS FOR SLOPE STABILITY MONITORING USING SENTINEL-1 IN OPEN PIT MINING

Abstract. Surface mining is an important economic activity in many regions where mineral resources are located close to the surface. The process of ore extraction is characterised by high pit walls with steep slopes. The mining process introduces significant stress and strain changes that could lead to movement on the slope face. Slope failure on the pit walls can occur without warning and can lead to loss of life and disruptions to the mining process. The monitoring of slopes remains the most reliable way to detect movement on unstable slopes. Therefore, slope monitoring programmes have been established to monitor and measure slope movement to help mine management with the stability status of open pit slopes. Conventional monitoring techniques include visual inspections and in situ instrumentation that are point-based, time-consuming, and costly. Open-source remote sensing techniques such as Synthetic Aperture Radar interferometry have been used to monitor slope movements on open-pit mine walls. Time series interferometric Synthetic Aperture Radar (InSAR) can be used to overcome limitations of in-situ monitoring for monitoring slope movements in open-pit mines.

DEFORMATION MONITORING AND TIME-SERIES & DISASTER POTENTIALITY ANALYSIS OF GAS PIPELINE USING SENTINEL DATA

Abstract. Synthetic aperture radar interferometry (InSAR) measurement technology is a new remote sensing technology that can effectively monitor slight land deformation. Compared with traditional monitoring technology, InSAR technology has the advantages of wide coverage, all-weather and low cost, providing a technical means of high-resolution, high-precision and low-cost for hidden geological hazard identification and deformation monitoring along pipelines. For purpose of this paper, we performed deformation time-series comprehensive processing and analysis on gas pipeline based on Sentinel-1 image data through small baseline data processing, and extracted pipeline deformation quantities from 2020 to 2022. The land deformation rate of the ascending track data during this period ranges from −43 mm/year to 25 mm/year, and that of the descending track data from −66 mm/year to 33 mm/year. The results show that the area along the gas pipeline is in stable condition on the whole, deformation mainly occurred along a section in the northwest of Haidian District, and a large quantity of deformation occurred since January of 2020 until December of 2021, with the maximum deformation quantity of −70mm, This result provided a reliable reference for safety monitoring and repair & maintenance of the gas pipeline.

SURFACE DISPLACEMENT MONITORING OF SUBURBAN EXPRESSWAY UNDER CONSTRUCTION BASED ON SENTINEL-1 SBAS-INSAR ANALYSIS

Abstract. The Synthetic Aperture Radar Interferometry (InSAR) technique can quickly obtain millimeter-level surface deformation in urban areas with high coherence. However, expanding the application of time series InSAR in non-urban areas is an important research focus. An improved SBAS-InSAR analysis approach is applied in this study to present the surface displacement along two expressways under construction. Taking the Kejiao and Yicheng Highway in the Shenzhen Shanwei Special Cooperation Zone as examples, the deformation along the highways under construction and the surrounding ground objects is revealed. Moreover, the time series displacements and construction conditions are combined to explain the possible mechanisms behind the different displacements of various ground objects along the expressways. The results show that the climate, environment, and construction conditions of the cooperation zone had an impact on the deformation of various ground objects along the route. Compared to traditional PS-InSAR methods, the density and accuracy of coherence points are improved, which better overcomes the spatiotemporal incoherence effects in non-urban areas.