Investigating the subsidence pattern of southwest Tehran using interferometric SAR time series

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

We studied land subsidence occurrence in Surabaya, considering the rapid urban growth in the city. It is evidenced by the increasing number of buildings (e.g., houses, apartments, offices) that have been realized before 2015 and the increasing transport activities in the industrial area. Land subsidence is a vertical deformation on the land surface that can be caused by landslides, earthquakes, soil consolidation process, or even human-made activities, depending on the material characteristics of the area. In this study, Time-series InSAR (TS-InSAR), specifically the Small Baseline Subset (SBAS) method, is applied to obtain the Line-of-Sight (LOS) velocities and time-series LOS displacements in Surabaya between May 2015 and September 2017. We used 28 Sentinel-1A data (IW mode) acquired by using Terrain Observation with Progressive Scan (TOPS) operation with the spatial resolution of 5 ´ 20 m and covers the area of 250 km2. DEM SRTM with 30 m spatial resolution was used to reduce topography effect contribution in the interferograms. Based on the SBAS method, interferogram pair selection is applied with the maximum value of 150 m for the perpendicular baseline and 100 days for the temporal baseline. The DInSAR process successfully generated seventy-four (74) interferogram pairs, and then by using SBAS algorithm, the LOS velocity and time-series LOS displacement were estimated. The results showed the varying displacements respond with the significant subsidence occurred in the North, East, and Southeast part of Surabaya. The SBAS result confirmed that the LOS velocity over the Surabaya area between May 2015 and September 2017 ranges from -40 to +30 mm/year. Significant land subsidence occurred in the Asemrowo area (North Surabaya) with the value of LOS displacement velocity up to -45 mm/yr.

Glacier retreats, along with associated geomorphological and periglacial processes, can significantly impact hiking infrastructure and have consequences for the local tourism industry, which heavily depends on high-altitude mountaineering. Interferometric Synthetic Aperture Radar (InSAR) time-series techniques, such as the Small Baseline Subset (SBAS) method, have gained considerable attention for analysing surface deformation and slope instability. InSAR utilises phase information to measure time-series surface deformations with sub-centimetre accuracy.The primary objective of this study is to identify and measure surface deformation and slope instability using InSAR, and to investigate the potential impacts on selected alpine huts in high mountain regions in Austria. We use time-series Sentinel-1 data and open-source software, including the InSAR Scientific Computing Environment (ISCE) tool for SAR data processing and the Miami InSAR Time-series software in PYthon (Mintpy) for SBAS analysis. By integrating the InSAR results, slope units derived from a high-resolution digital elevation model (DEM), and alpine infrastructure locations, we identify areas showing significant deformation rates. The initial results provide insights into the slope instabilities and surface deformation that may affect alpine infrastructure. The results highlight the potential of advanced InSAR time-series analysis for monitoring surface deformation in highly dynamic alpine landscapes, where increasing natural hazards, such as landslides, necessitate improved natural hazard and risk management. Future steps include discussion and validation of the results in collaboration with experts from alpine associations.

Review of the SBAS InSAR Time-series algorithms, applications, and challenges

Earth surface displacements from interferometric synthetic aperture radar (InSAR) have long been used to study deformation from a wide range of geophysical processes. Whereas deformation rates can be robustly estimated from InSAR by averaging many individual deformation observations, noise in these observations has limited their utility for generating deformation time series. In this article, we introduce a novel combination of InSAR and Global Positioning System (GPS) data that align InSAR displacements to an absolute reference and reduces long-wavelength spatial errors prior to InSAR time series construction. We test our GInSAR (GPS-enhanced InSAR) methodology on Sentinel-1 data over the southern Central Valley, CA, USA, comparing GInSAR displacement velocities and time series with those from three other referencing techniques. We find that the GInSAR approach outperforms alternative methods, yielding mm-level displacement differences with respect to collocated cGPS. By contrast, other referencing methods can overestimate peak subsidence velocities in the Central Valley by upwards of 10%, deviate by tens of millimeters relative to cGPS validation time series, and contain spatial biases absent in the GInSAR methodology. We also present a modification to the widely used small baseline subset (SBAS) technique for time series estimation, whereby we use a temporal connectedness constraint to regularize the mathematical inversion and increase the number of InSAR pixels with valid time series estimates.

Fifty-five descending images from the ENVISAT satellite were processed using the small baseline subset (SBAS) method to derive the spatial and temporal ground deformation of the Bailong River Basin between 2003 and 2010. The basin is one of the most severely landslide- and debris flow-affected areas of China. As a result, 104 sites with high deformation areas were identified. Interferometric Synthetic Aperture Radar (InSAR) analysis was combined with landslide inventory data and field surveys, and anomalous areas were classified into three main types: landslide; debris; and subsidence. Displacement rates up to 35 mm/yr were evaluated away from the sensor along a line-of-sight (LOS) direction. The results gained should allow a more accurate prediction and monitoring of landslides, debris, and subsidence; further, they demonstrate the capability of the SBAS method to analyze any displacement effect and identify dangerous and uninhabitable areas in the basin. The small baseline subset method can thus contribute to the prediction and prevention of geohazards in the area.

In recent years, the enormous losses caused by urban surface deformation have received more and more attention. Traditional geodetic techniques are point-based measurements, which have limitations in using traditional geodetic techniques to detect and monitor in areas where geological disasters occur. Therefore, we chose Interferometric Synthetic Aperture Radar (InSAR) technology to study the surface deformation in urban areas. In this research, we discovered the land subsidence phenomenon using InSAR and Global Navigation Satellite System (GNSS) technology. Two different kinds of time-series InSAR (TS-InSAR) methods: Small BAseline Subset (SBAS) and the Permanent Scatterer InSAR (PSI) process were executed on a dataset with 31 Sentinel-1A Synthetic Aperture Radar (SAR) images. We generated the surface deformation field of Shenzhen, China and Hong Kong Special Administrative Region (HKSAR). The time series of the 3d variation of the reference station network located in the HKSAR was generated at the same time. We compare the characteristics and advantages of PSI, SBAS, and GNSS in the study area. We mainly focus on the variety along the coastline area. From the results generated by SBAS and PSI techniques, we discovered the occurrence of significant subsidence phenomenon in the land reclamation area, especially in the metro construction area and the buildings with a shallow foundation located in the land reclamation area.

Ningdong coal base area located in northwestern China is one of the largest coal-producing bases in China. The aim of this work is to investigate a regional-scale mining subsidence over the Ningdong coal base area, by using both conventional and advanced Differential Synthetic Aperture Radar Interferometry (DInSAR) methods. Fifteen L-band SAR images from ALOS-2 satellite and 102 C-band images from Sentinel-1A satellite spanning between November 2014 and July 2019 were used for the analysis. To increase the spatial extent of the displacement signal because of decorrelated effects, we modified the traditional Small Baseline Subset (SBAS) method to incorporate the coherence into the inverse problem, hereafter we call it coherence-based SBAS method. Instead of excluding decorrelated pixels present in the interferograms, we keep all the pixels in the time series analysis and down-weighted the decorrelated pixels with coherence. We performed the coherence-based SBAS method to both the two SAR datasets to obtain the subsidence rate maps and displacement time-series over the mining areas, and compared the results with that from the traditional stacking InSAR method. We evaluated the effectiveness of L-band and C-band DInSAR for monitoring mining subsidence by comparing differential interferograms and displacements derived from SBAS method between ALOS-2 and Sentinel-1A data. Compared to C-band, L-band SAR are less affected by phase aliasing due to large displacement gradients. The most significant subsidence was found at Maliantai mine with −264 mm/year detected by SBAS method from Sentinel-1 data. We validated the InSAR displacement accuracy by comparing both ALOS-2 and Sentinel-1 results with 18 GPS stations above five active mining regions. The average RMSE between InSAR and GPS measurements is 28.4 mm for Sentinel-1 data and 21 mm for ALOS-2 data. Our results demonstrate that the combined exploitation of L-band and C-band SAR data through both conventional and advanced DInSAR methods could be crucial to monitor ground subsidence in mining areas, which provides insights into subsidence dynamics and determine the characteristic surface response to longwall advance.

Atmospheric water-vapor effects represent a major limitation of interferometric synthetic aperture radar (InSAR) techniques, including InSAR time-series (TS) approaches (e.g., persistent or permanent scatterers and small-baseline subset). For the first time, this paper demonstrates the use of InSAR TS with precipitable water-vapor (InSAR TS + PWV) correction model for deformation mapping. We use MEdium Resolution Imaging Spectrometer (MERIS) near-infrafred (NIR) water-vapor data for InSAR atmospheric correction when they are available. For the dates when the NIR data are blocked by clouds, an atmospheric phase screen (APS) model has been developed to estimate atmospheric effects using partially water-vapor-corrected interferograms. Cross validation reveals that the estimated APS agreed with MERIS-derived line-of-sight path delays with a small standard deviation (0.3-0.5 cm) and a high correlation coefficient (0.84-0.98). This paper shows that a better TS of postseismic motion after the 2003 Bam (Iran) earthquake is achievable after reduction of water-vapor effects using the InSAR TS + PWV technique with coincident MERIS NIR water-vapor data.

Slow-moving landslides in high-mountain regions pose a significant natural hazard and are capable of delivering large sediment volumes to the fluvial system. Time series analysis of Interferometric Synthetic Aperture Radar (InSAR) allows us to identify unstable and potentially dangerous areas prone to landsliding, but this technique also helps quantify seasonal dynamics for predicting landslide behavior.Our study in the Eastern Cordillera of the Argentine Andes focuses on enhancing InSAR's reliability for landslide mapping. This region is characterized by moisture changes along the topographic gradient across the orogen and seasonal variability associated with the South American Summer Monsoon. We extract InSAR time series data from Sentinel-1A/B's C-band (2014-2022) and ALOS1 PALSAR's L-band (2006-2011). Tropospheric delay is caused by atmospheric turbulence and vertical stratification changes. These delays can introduce significant errors in deformation measurements, thus impacting the quality of maps portraying landslide deformation rates. To address this problem, we apply various correction techniques, ranging from spatial and temporal filtering to water-vapor estimation from an atmospheric model. Fading signal noise, another challenge caused by multi-looking and short temporal baselines in the Small Baseline Subset (SBAS) technique, additionally compromises InSAR time series accuracy. We investigate the pattern and magnitude of fading signals in landslide areas using Small Baseline Subset (SBAS) with different neighboring connections and non-linear phase inversion methods, such as the Eigenvalue Decomposition-based Maximum Likelihood (EMI), Eigenvalue Decomposition (EVD), and the Phase Triangulation Algorithm (PTA).Our research evaluates both statistical methods and Global Atmospheric Models for correcting tropospheric delays and fading signal noise. We explore statistical methods, such as double-difference filtering and corrections based on phase elevation, for different spatial windows, including individual catchments, moving windows, and adaptive window sizes. The efficiency of these methods varies with the environmental and topographic conditions in the orogen. Both stratified and turbulent components of the troposphere, along with fading signal noise, can significantly influence tropospheric delay and time series quality. In the context of the factors that influence deformation signals and the combined array of methods to obtain robust measurements, we can identify the spatial and temporal characteristics of slow-moving landslides and assess the different impacts on rate changes.

Documenting surface deformation at the first geothermal power plant in South America (Cerro Pabellón, Chile) by satellite InSAR time-series

The identification of areas that are susceptible to damage due to earthquakes is of utmost importance in tectonically active regions like Northeast India. This may provide valuable inputs for seismic hazard analysis; however, it poses significant challenges. The present study emphasized the integration of Interferometric Synthetic Aperture Radar (InSAR) deformation rates with conventional geological and geophysical data to investigate earthquake damage susceptibility in the Barapani Shear Zone (BSZ) region of Northeast India. We used MintPy v1.5.1 (Miami INsar Timeseries software in PYthon) on the OpenSARLab platform to derive time series deformation using the Small Baseline Subset (SBAS) technique. We integrated geology, geomorphology, gravity, magnetic field, lineament density, slope, and historical earthquake records with InSAR deformation rates to derive earthquake damage susceptibility using the weighted overlay analysis technique. InSAR time series analysis revealed distinct patterns of ground deformation across the Barapani Shear Zone, with higher rates in the northern part and lower rates in the southern part. The deformation values ranged from 6 mm/yr to about 18 mm/yr in BSZ. Earthquake damage susceptibility mapping identified areas that are prone to damage in the event of earthquakes. The analysis indicated that about 46.4%, 51.2%, and 2.4% of the area were low, medium, and high-susceptibility zones for earthquake damage zone. The InSAR velocity rates were validated with Global Positioning System (GPS) velocity in the region, which indicated a good correlation (R2 = 0.921; ANOVA p-value = 0.515). Additionally, a field survey in the region suggested evidence of intense deformation in the highly susceptible earthquake damage zone. This integrated approach enhances our scientific understanding of regional tectonic dynamics, mitigating earthquake risks and enhancing community resilience.

Present-day tectonic activity along the central section of the Altyn Tagh fault derived from time series InSAR

Wide-swath synthetic aperture radar (SAR) missions with short revisit times, such as Sentinel-1 and the planned NISAR and Tandem-L, provide an unprecedented wealth of interferometric SAR (InSAR) time series. However, the processing of the emerging Big Data is challenging for state-of-the-art InSAR analysis techniques. This contribution introduces a novel approach, named Sequential Estimator , for efficient estimation of the interferometric phase from long InSAR time series. The algorithm uses recursive estimation and analysis of the data covariance matrix via division of the data into small batches, followed by the compression of the data batches. From each compressed data batch artificial interferograms are formed, resulting in a strong data reduction. Such interferograms are used to link the “older” data batches with the most recent acquisitions and thus to reconstruct the phase time series. This scheme avoids the necessity of reprocessing the entire data stack at the face of each new acquisition. The proposed estimator introduces negligible degradation compared to the Cramer–Rao lower bound under realistic coherence scenarios. The estimator may therefore be adapted for high-precision near-real-time processing of InSAR and accommodate the conversion of InSAR from an offline to a monitoring geodetic tool. The performance of the Sequential Estimator is compared to state-of-the-art techniques via simulations and application to Sentinel-1 data.

ABSTRACTThis research compares two time-series interferometric synthetic aperture radar (InSAR) methods, namely persistent scatterer SAR interferometry (PS-InSAR) and small baseline subset (SBAS) to retrieve the deformation signal from pixels with different scattering characteristics. These approaches are used to estimate the surface deformation in the L’Aquila region in Central Italy where an earthquake of magnitude Mw 6.3 occurred on 6 April 2009. Fourteen Environmental Satellite (ENVISAT) C-band Advanced Synthetic Aperture Radar (ASAR) images, covering the pre-seismic, co-seismic, and post-seismic period, are used for the study. Both the approaches effectively extract measurement pixels and show a similar deformation pattern in which the north-west and south-east regions with respect to the earthquake epicentre show movement in opposite directions. The analysis has revealed that the PS-InSAR method extracted more number of measurement points (21,103 pixels) as compared to the SBAS method (4886 pixels). A comparison of velocity estimates shows that out of 833 common pixels in both the methods, about 62% (517 pixels) have the mean velocity difference below 3 mm year−1 and nearly 66% pixels have difference below 5 mm year−1. It is concluded that StaMPS-based PS-InSAR method performs better in terms of extracting more number of measurement pixels and in the estimation of mean line of sight (LOS) velocity as compared to SBAS method.