Interferometric Synthetic Aperture Radar Method Research Articles

Forecasting surface movements based on PSI time series using machine learning algorithms

ABSTRACT Surface movements pose a critical issue requiring prompt detection and monitoring to ensure safety. To address this concern, the detection and continuous monitoring of surface deformations have gained importance. While numerous point-based surveying methods exist, multi-temporal interferometric synthetic aperture radar (InSAR) analysis has emerged as a powerful technique capable of detecting deformations across large areas with freely available SAR images and software. However, the significance of surface movement forecasting must be addressed, as it plays a significant role in preventing accidents and implementing precautionary measures. In light of this, a comprehensive study was conducted at the new Istanbul Airport in Türkiye, employing the Persistent Scatterer InSAR (PSI) method with a collection of 211 Sentinel-1 SAR images. The results obtained were investigated, and six test case regions were determined for the forecasting analysis in terms of time series movement type (subsidence, uplift, and stable) and structure type (runway and building) using various machine learning (ML) algorithms. To accomplish this, regression-based and sequential-based ML algorithms were employed and compared, showing the potential of cutting-edge techniques. The Stacked LSTM algorithm was the most effective in generating accurate forecasts. Furthermore, by incorporating exogenous variables into the forecasting analysis, the study improved the accuracy of the results. To gain deeper insights into the impact of these exogenous variables, feature importance analysis was conducted using the permutation feature importance method. The results obtained were examined, considering both the structure type and the characteristics of the time series data.

Integration of satellite SAR and optical acquisitions for the characterization of the Lake Sarez landslides in Tajikistan

AbstractTajikistan is an area characterized by several lakes created after rockfalls and moraine deposits collapsed due to its geographic position on the collisional area of the Euro-Asian and Indian tectonic plates. In 1911, a violent earthquake generated the Usoi dam, which led to the creation of Lake Sarez. The purpose of the article is to provide an overview of the ground deformation of the two landslides that affect and threaten the area of Lake Sarez exploiting the combination of two different techniques, the interferometric synthetic aperture radar (InSAR) method with the SqueeSAR approach and optical image analysis through the use of COSI-Corr software. The combination of these two techniques turned out to be useful and provide more information because InSAR method balances lack the optical analysis and vice versa giving the possibility to obtain a complete overview of the displacement of the area. Moreover, InSAR data have been used to reconstruct the geometry and depth of the sliding surface of a potential landslide that could affect the lake, generating a wave that could overtop the dam and affect the villages downstream.

Research on Time Series Monitoring of Surface Deformation in Tongliao Urban Area Based on SBAS-PS-DS-InSAR.

As urban economies flourish and populations become increasingly concentrated, urban surface deformation has emerged as a critical factor in city planning that cannot be overlooked. Surface deformation in urban areas can lead to deformations in structural supports of infrastructure such as road bases and bridges, thereby posing a serious threat to public safety and creating significant safety hazards. Consequently, research focusing on the monitoring of urban surface deformation holds paramount importance. Interferometric synthetic aperture radar (InSAR), as an important means of earth observation, has all-day, wide-range, high-precision, etc., characteristics and is widely used in the field of surface deformation monitoring. However, traditional solitary InSAR techniques are limited in their application scenarios and computational characteristics. Additionally, the manual selection of ground control points (GCPs) is fraught with errors and uncertainties. Permanent scatterers (PS) can maintain high interferometric coherence in man-made building areas, and distributed scatterers (DS) usually show moderate coherence in areas with short vegetation; the combination of DS and PS solves the problem of manually selecting GCPs during track re-flattening and regrading, which affects the monitoring results. In this paper, 45 Sentinel-1B data from 16 February 2019 to 14 December 2021 are used as the data source in the urban area of Horqin District, Tongliao City, Inner Mongolia Autonomous Region, for example. A four-threshold (coherence coefficient threshold, FaSHPS adaptive threshold, amplitude divergence index threshold, and deformation velocity interval) GCPs point screening method for PS-DS, as well as a Small Baseline Subset-Permanent Scatterers-Distributed Scatterers-Interferometric Synthetic Aperture Radar (SBAS-PS-DS-InSAR) method for selecting PS and DS points as ground control points for orbit refinement and re-flattening, are proposed. The surface deformation results obtained using the Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) and the SBAS-PS-DS-InSAR proposed in this paper were comparatively analysed and verified. The maximum cumulative line-of-sight settlements were -90.78 mm and -83.68 mm, and the maximum cumulative uplifts are 74.94 mm and 97.56 mm, respectively; the maximum annual average line-of-sight settlements are -35.38 mm/y and -30.38 mm/y, and the maximum annual average uplifts are 25.27 mm/y and 27.92 mm/y. The results were evaluated and analysed in terms of correlation, mean absolute error (MAE), and root mean square error (RMSE). The deformation results of the two InSAR methods were evaluated and analysed in terms of correlation, MAE, and RMSE. The errors show that the Pearson correlation coefficients between the vertical settlement results obtained using the SBAS-PS-DS-InSAR method and the GPS monitoring results were closer to 1. The maximum MAE and RMSE were 13.7625 mm and 14.8004 mm, respectively, which are within the acceptable range; this confirms that the monitoring results of the SBAS-PS-DS-InSAR method were better than those of the original SBAS-InSAR method. SBAS-InSAR method, which is valid and reliable. The results show that the surface deformation results obtained using the SBAS-InSAR, SBAS-PS-DS-InSAR, and GPS methods have basically the same settlement locations, extents, distributions, and temporal and spatial settlement patterns. The deformation results obtained using these two InSAR methods correlate well with the GPS monitoring results, and the MAE and RMSE are within acceptable limits. By comparing the deformation information obtained using multiple methods, the surface deformation in urban areas can be better monitored and analysed, and it can also provide scientific references for urban municipal planning and disaster warning.

Long-Term SAR Data Analysis for Subsidence Monitoring and Correlation Study at Beijing Capital Airport

Land subsidence, resulting from natural or human activities, is a global environmental geological disaster. The Interferometric Synthetic Aperture Radar (InSAR) time-series analysis technique offers high spatial and continuous temporal resolution, providing data and a foundation for investigating regional land subsidence and its evolution mechanism. Beijing Capital International Airport (BCIA) has experienced uneven land subsidence since 1935, together with severe fissures significantly affecting its normal operations. In this study, the time-series InSAR method was successfully applied to monitor the gradual increase in uneven local subsidence and ground fissures activity at BCIA from June 2003 to March 2023. Initially, ENVISAT-ASAR, Cosmo-SkyMed, and Sentinel-1 data were processed by time-series InSAR techniques to generate deformation rate maps and time series for the airport area. Subsequently, a comparison was made between the displacement time series from InSAR and ground leveling measurements to assess the accuracy of InSAR-derived measurements. Through a comprehensive analysis of the distribution characteristics of land subsidence at the airport, a long-standing ground fault was located within the airport was identified. A preliminary discussion on the development status of this ground fissure was carried out based on the visual interpretation of optical images. Lastly, the inducing factors and evolutionary conditions of land subsidence were discussed. This case demonstrates the applicability of InSAR technology in identifying and monitoring geological processes such as land subsidence and ground fissure activities. It provides a scientific approach to exploring and studying the causes and formation mechanisms of land subsidence and ground fissures in the Beijing Capital Airport area.

A novel machine learning and deep learning semi-supervised approach for automatic detection of InSAR-based deformation hotspots

Over the past two decades, Interferometric synthetic aperture radar (InSAR) has been invaluable for studying earth surface deformation and related effects. Deformation maps generated through multi-temporal InSAR processing methods are however difficult to interpret accurately by general individual users, decision-makers, and non-domain experts owing to the volume, variety, and velocity they are produced. This paper proposes a semi-supervised machine learning based information mining approach to simplify these deformation maps and detect hotspots by extracting prominent signals from time series deformation. The approach initially combines two machine learning based clustering methods named time series k-means (TSKM) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithms to derive clusters with unique spatiotemporal deformation behavior, using time series deformation output generated from Wavelet-based InSAR (WabInSAR) method. Clustering results generated from this unsupervised machine learning approach are later used as training labels to develop two deep learning models, one using long short term memory (LSTM) networks alone and another using a combination of LSTM and single-layer perceptron for supervised training. The developed LSTM and LSTM + Perceptron models efficiently learn from the cluster labels, reaching an accuracy of 97.3 %. Further, the deep learning models significantly reduce the computational time from orders of days (∼5) to hours (∼2) while training and from hours to minutes during prediction. We evaluate the developed approach over Los Angeles, a highly challenging area affected by umpteen deformation events that are challenging to categorize. The outcome of the proposed approach produces hotspots of deforming areas in Los Angeles, providing a generalized and more precise picture of events, much appreciable to non-domain experts. The approach can augment any of the multi-temporal InSAR processing chains and is applicable to different deformation prone sites, aiding in derivation of deformation hotspots from time series deformation maps.

Evaluation of InSAR Tropospheric Delay Correction Methods in the Plateau Monsoon Climate Region Considering Spatial–Temporal Variability

The tropospheric delay caused by the temporal and spatial variation of meteorological parameters is the main error source in interferometric synthetic aperture radar (InSAR) applications for geodesy. To minimize the impact of tropospheric delay errors, it is necessary to select the appropriate tropospheric delay correction method for different regions. In this study, the interferogram results of the InSAR, corrected for tropospheric delay using the Linear, Generic Atmospheric Correction Online Service for InSAR (GACOS) and ERA-5 atmospheric reanalysis dataset (ERA5) methods, are presented for the study area of the junction of the Hengduan Mountains and the Yunnan–Kweichow Plateau, which is significantly influenced by the plateau monsoon climate. Four representative regions, Eryuan, Binchuan, Dali, and Yangbi, are selected for the study and analysis. The phase standard deviation (STD), phase–height correlation, and global navigation satellite system (GNSS) data were used to evaluate the effect of tropospheric delay correction by integrating topographic, seasonal, and meteorological factors. The results show that all three methods can attenuate the tropospheric delay, but the correction effect varies with spatial and temporal characteristics.

Geostatistical and multitemporal InSAR method and elastic deformation model for detailed characterizations of topographic change and magma reservoirs: Application to the Tangkuban Parahu Volcano, Indonesia

This study proposes the entire detection of the surface deformation of a volcanic area using interferometric synthetic aperture radar (InSAR) with geostatistics to specify the locations and shapes of the sources causing the deformation. We selected the Tangkuban Parahu area, West Java, Indonesia, as our study site and used a Sentinel-1 dataset. In this study, we combine persistent scatterer and small baseline subset InSAR with a geostatistical technique, i.e., turning bands simulation. This combination can compensate for undetected displacement pixels and decompose the line-of-sight displacement into vertical and horizontal east–west displacements using both the ascending and descending mode results and the least-squares method. This two-dimensional geostatistics and multitemporal InSAR approach is referred to as 2D GMT-InSAR. A detailed displacement pattern is revealed by 2D GMT-InSAR for undetected pixels in low-coherence areas. Then, the vertical displacement pattern in the study period from 2014 to 2019, mainly uplift with local subsidence, is reproduced via the joint use of the Mogi and Okada models for circular and rectangular parallelepiped sources of the deformation, respectively. Via a Bayesian inversion, one circular source, equivalent to a magma reservoir, and one dyke source are identified. The displacement pattern is likely caused by pressure increases in the magma reservoir, associated with the volcanic activation, and in the dyke, associated with the gas and fluid flows from the reservoir, which induced a phreatic explosion in July 2019. A connection of the dyke with the old Sunda volcanic system is found; this new finding is a result of the caldera collapse during the Sunda volcanic eruption.

Sentinel-1 InSAR-derived land subsidence assessment along the Texas Gulf Coast

Mapping large-scale coastal subsidence is significant in providing valuable support to decision-making stakeholders to recognize impacts of potential natural disasters. However, this task presents significant challenges due to its highly complex nature of the spatial–temporal variability. Recent advances in the observation capability of synthetic aperture radar (SAR) missions and the processing algorithms of interferometric SAR (InSAR) techniques have made it possible to efficiently map large-scale subsidence from space. This study utilized the persistent scatterer (PS) InSAR method to process four swathes of Sentinel-1 SAR images captured between 2016 and 2022 to map large-scale subsidence along the Texas coastline. The subsidence map, encompassing over 1.7 million PS points, was calibrated/validated with observations of 115 continuously operating global navigation satellite system (cGNSS) stations. Nineteen subsiding hotspots were identified and potential subsidence drivers were analyzed, including hydrocarbon extraction (HE), groundwater withdrawal (GW), and salt domes. The results suggest that HE activities were the primary driver of observed subsidence in the Coastal Bend and South Texas, as well as in the Southeast Texas regions. In the Houston-Galveston Area, subsidence appears to be influenced by a combination of GW, HE, and salt dome movements. Within 50 km of the Texas shorelines, subsidence rates were approximately −1.0 mm/yr within the coastal vicinity, while inland areas experience a gradually increasing trend. Specific coastal areas, such as Corpus Christi, Freeport, Seabrook, San Leon, and others, may face elevated flooding risks during high tide and storm events with the sea-level rise trend up to 24.0 mm/yr in relation to coastal subsiding land.

Adaptive High Coherence Temporal Subsets SBAS-InSAR in Tropical Peatlands Degradation Monitoring

Peatlands in Southeast Asia have been undergoing extensive and rapid degradation in recent years. Interferometric Synthetic Aperture Radar (InSAR) technology has shown excellent performance in monitoring surface deformation. However, due to the characteristics of high vegetation cover and large dynamic changes in peatlands, it is difficult for classical InSAR technology to achieve satisfactory results. Therefore, an adaptive high coherence temporal subsets (HCTSs) small baseline subset (SBAS)-InSAR method is proposed in this paper, which captures the high coherence time range of pixels to establish adaptive temporal subsets and calculates the deformation results in corresponding time intervals, combining with the time-weighted strategy. Ninety Sentinel-1 SAR images (2019–2022) in South Sumatra province were processed based on the proposed method. The results showed that the average deformation rate of peatlands ranged from approximately −567 to 347 mm/year and was affected by fires and the changes in land cover. Besides, the dynamic changes of peatlands’ deformation rate a long time after fires were revealed, and the causes of changes were analyzed. Furthermore, the deformation results of the proposed method observed 2 to 127 times as many measurement points as the SBAS-InSAR method. Pearson’s r (ranged from 0.44 to 0.75) and Root Mean Square Error (ranged from 50 to 75 mm/year) were calculated to verify the reliability of the proposed method. Adaptive HCTSs SBAS-InSAR can be considered an efficient method for peatland degradation monitoring, which provides the foundation for investigating the mechanisms of peatland degradation and monitoring it in broader regions.

Model of the Seismic Rupture Surface of the Chignik Earthquake (Alaska, USA) 07.29.2021 Based on SAR Interferometry and GNSS Data

The paper presents a new rupture model for the Mw = 8.2 “Chignik” earthquake, which occurred off the coast of the Alaska Peninsula on 29.07.2021. The model is based on the Earth’s surface displacement fields obtained by InSAR (Interferometric synthetic aperture radar) method using images of the ESA Sentinel-1 satellites from 17.07 to 10.08.2021 and data on horizontal displacements at nearest permanent GPS sites from 18.07 to 08.08.2021. Obtained displacement fields include both coseismic and part of postseismic displacements. When constructing a model of the seismic rupture surface, we used F. Pollitz’s solution of the problem of the displacement fields at the surface of a spherical radially stratified planet caused by displacements on a rectangular discontinuity located inside it. For the regularization of the inverse problem, we added the condition that the direction of slip on each element of the fault plane is close to the rake angle, determined from seismological data. In the constructed model, the seismic rupture area was approximated by a single plane with a length of 225 km along the strike, 126 km along the dip, divided into 48 identical rectangles. According to the constructed model, the type of displacements is almost pure thrust, and displacements, in general, occurred throughout all the source area. The maximum displacement was 5.7 m, with an average displacement over the entire plane of 2.2 m, which is close to the USGS and GCMT estimates derived from seismological data.

MONITORING THE SLOWLY DEVELOPING LANDSLIDE WITH THE INSAR TECHNIQUE IN SAMSUN PROVINCE, NORTHERN TURKEY

Abstract. Landslides are prominent natural events with high destructive power. Since they affect large areas, it is important to monitor the areas they cover and analyse their movement. Remote sensing data and image processing techniques have been used to monitor landslides in different areas. Synthetic aperture radar (SAR) data, particularly with the Interferometric SAR (InSAR) method, is used to determine the velocity vector of the surface motion. This study aims to detect the landslide movements in Samsun, located in the north of Turkey, using persistent scattering InSAR method. Archived Copernicus Sentinel-1 satellite images taken between 2017 and 2022 were used in both descending and ascending directions. The results revealed surface movements in the direction of the line of sight, ranging between −6 and 6 mm/year in the study area. Persistent Scatterer (PS) points were identified mainly in human structures such as roads, coasts, ports, and golf courses, especially in settlements. While some regions exhibited similar movements in both descending and ascending results, opposite movements were observed in some regions. The results produced in both descending and ascending directions were used together and decomposed into horizontal and vertical deformation components. It was observed that the western coastal part experienced approximately 4.5 cm/year vertical deformation, while the central part there is more significant horizontal deformation, reaching up to approximately 6 cm/year.

Using InSAR and GPR Techniques to Detect Subsidence: Application to the Coastal Area of “A Xunqueira” (NW Spain)

Climate change represents an important cause of subsidence, especially in coastal cities affected by changes in surface water level and water table. This paper presents a complementary study of Interferometric Synthetic Aperture Radar (InSAR) and Ground Penetrating Radar (GPR) for the early detection of subsidence and sinkhole phenomena. The methodology was applied to a coastal urban area in Galicia, northwest Spain (humid region), showing apparent signs of subsidence and building settlement during the last two years. Two different InSAR methods are compared for the period from June 2021 to March 2022: PSI (Persistent Scatterer Interferometry) and SBAS (Small Baseline Subsets), and the average deformation velocities obtained resulted in −3.0 mm/yr and −4.1 mm/yr, respectively. Additional GPR data were collected in January 2022 to validate the InSAR results, which detected subsidence in agreement with the persistent scatters obtained from the PSI method. This is crucial information to plan preventive maintenance.

PREDICTION OF LONG-TERM SENTINEL-1 INSAR TIME SERIES ANALYSIS

Abstract. This paper presents an initial analysis of predicting time series derived from long-term interferometric Synthetic Aperture Radar (InSAR) data. Time series analysis provides insights into the temporal evolution, variation, and dynamic nature of events. In this study, we focus on the Istanbul region, which is the most populous city in Turkey and spans both Europe and Asia. While the area is prone to seismic risks caused by active tectonic faults, it is also susceptible to other risks due to various phenomena. Therefore, this study investigates landslides triggered by geological structure and human-induced activities, particularly in Tepekent, a landslide-prone area in the town of Buyukcekmece located on the European side. We utilized the StaMPS persistent scatterer InSAR (PSI) method to detect slow movements over time. A total of 157 archive Copernicus Sentinel-1 data, acquired over the region between June 2017 and August 2022, were processed, primarily observing human structures with a maximum displacement amount of approximately 1 cm/year. About 500 persistent scatterer points were identified in the region, and the time series was formed by taking the average of these points. The Long Short-Term Memory (LSTM) neural network method was used to estimate motion. We trained the model with data from the first three years of the time series and used the data from the remaining two years for estimation, while the accuracy analysis was performed with the 5-year time series data. The RMSE values for the training and test data were determined to be 0.725 mm/year and 0.656 mm/year, respectively. Additionally, we estimated the time series for the period from August 2022 to August 2024. The observation and prediction results could be beneficial in developing efficient mitigation risk actions and sustainable urban management strategies.

Landslide Detection Using Time-Series InSAR Method along the Kangding-Batang Section of Shanghai-Nyalam Road

Due to various factors such as urban development, climate change, and tectonic movements, landslides are a common geological phenomenon in the Qinghai–Tibet Plateau region, especially on both sides of a road, where large landslide hazards often result in traffic disruptions and casualties. Identifying the spatial distribution of landslides and monitoring their stability are essential for predicting landslide occurrence and implementing prevention measures. In this study, taking the Kangding-Batang section of Shanghai-Nyalam Road as the study area, we adopted a semi-automated time-series interferometric synthetic aperture radar (InSAR) method to identify landslides and monitor their activity. A total of 446 Sentinel-1 ascending and descending SAR images from January 2018 to December 2021 were thus collected and processed by using open-source InSAR processing software. After a series of error corrections, we obtained surface deformation maps covering the study area, and a total of 236 potential landslides were subsequently identified and classified into three categories, namely slow-sliding rockslides, debris flows, and debris avalanches, by combining deformation maps, optical images, and a digital elevation model (DEM). For a typical landslide, we performed deformation decomposition and analyzed the relationship between its deformation and rainfall, revealing the contribution of rainfall to the landslide. In addition, we discussed the effect of SAR geometric distortion on landslide detection, highlighting the importance of joint ascending and descending observations in mountainous areas. We analyzed the controlling factors of landslide distribution and found that topographic conditions are still the dominant factor. Our results may be beneficial for road maintenance and disaster mitigation. Moreover, the entire processing is semi-automated based on open-source tools or software, which provides a paradigm for landslide-related studies in other mountainous regions of the world.

Determination of periodic deformation from InSAR results using the FFT time series analysis method in Gediz Graben.

Permanent Scatterers (PS) point velocities obtained by the interferometric synthetic aperture radar (InSAR) method are generally determined using the linear regression model, which ignores periodic and seasonal effects. In this study, software was developed that can detect periodic effects by applying fast Fourier transformation (FFT) time series analysis to InSAR results. Using the FFT time series analysis, the periodic components of the surface movements at the PS points were determined, and then the annual velocity values free from periodic effects were obtained. The study area was chosen as the Gediz Graben, a tectonically active region where aseismic surface deformations have been observed in recent years. As a result, using the developed method, seasonal effects were successfully determined with the InSAR method at the PS points in the study area with a period of 384days and an average amplitude of 19mm. In addition, groundwater level changes of a water well in the region were modeled, and 0.93 correlation coefficient values were calculated between seasonal InSAR displacement values and water level changes. Thus, using the developed methodology, the relationship between the tectonic movement in the Gediz Graben in Turkey and the seasonal movements and the change in the groundwater level was determined.

Evaluation of InSAR Tropospheric Delay Correction Methods in a Low-Latitude Alpine Canyon Region

Tropospheric delay error must be reduced during interferometric synthetic aperture radar (InSAR) measurement. Depending on different geographical environments, an appropriate correction method should be selected to improve the accuracy of InSAR deformation monitoring. In this study, surface deformation monitoring was conducted in a high mountain gorge region in Yunnan Province, China, using Sentinel-1A images of ascending and descending tracks. The tropospheric delay in the InSAR interferogram was corrected using the Linear, Generic Atmospheric Correction Online Service for InSAR (GACOS) and ERA-5 meteorological reanalysis data (ERA5) methods. The correction effect was evaluated by combining phase standard deviation, semi-variance function, elevation correlation, and global navigation satellite system (GNSS) deformation monitoring results. The mean value of the phase standard deviation (Aver) of the linear correction interferogram and the threshold value (sill) of the semi-variogram were reduced by –20.98% and –41%, respectively, while the accuracy of the InSAR deformation points near the GNSS site was increased by 58%. The results showed that the three methods reduced the tropospheric delay error of InSAR deformation monitoring by different degrees in low-latitude mountains and valleys. Linear correction was the best at alleviating the tropospheric delay, followed by GACOS, while ERA5 had poor correction stability.

Joint Analysis of InSAR, ERT, and in-Situ Geological Survey in Landslide Prone Area: A Case in Laeya Village, North Buton Regency, Indonesia

Landslide investigation in Laeya Village of North Buton Regency, Indonesia, is necessary since this area is frequently experiencing landslides. In this study, we applied a joint analysis of interferometric synthetic aperture radar (InSAR), electrical resistivity tomography (ERT), and in-situ geology observation. The objectives of this multidisciplinary study are: a) to estimate ground deformation through the InSAR method, b) estimation of subsurface characteristics based on the electrical resistivity of rocks, as well as estimation of the shape of the slip plane and the type of landslide that resulted; and c) to identify the surface geology conditions of the site from an in-situ geology survey. The InSAR data of the Sentinel-1A acquired from 2015 to 2017 were processed using the Sentinel toolbox. The resistivity data collected utilizing the electrodes array of the Wenner-Schlumberger configuration were processed employing the RES2DINV. The in-situ geology survey involving the observation of elevation, slope, and soil and rock type was also carried out in the landslide area. The InSAR method may estimate the deformation of slope material of the investigated sites with a magnitude ranging from -53 mm to +55 mm. The negative mark of the magnitude means that the ground deformation is ground subsidence and the positive means ground uplift. The ERT method has successfully estimated the existence of the saturated zones, the clay layer that acts as a slip plane, and the slip surface type. There are at least two types of slip planes estimated, namely the planar and uneven curvature slip planes that generate the translational and the complex landslides, respectively. In-situ observations of geological conditions support the results obtained from the InSAR and ERT methods.

Ground Investigations and Detection and Monitoring of Landslides Using SAR Interferometry in Gangtok, Sikkim Himalaya

The Himalayan state of Sikkim is prone to some of the world’s largest landslides, which have caused catastrophic damage to lives, properties, and infrastructures in the region. The settlements along the steep valley sides are particularly subject to frequent rainfall-triggered landslide events during the monsoon season. The region has also experienced smaller rock slope failures (RSF) after the 2011 Sikkim earthquake. The surface displacement field is a critical observable for determining landslide depth and constraining failure mechanisms to develop effective mitigation techniques that minimise landslide damage. In the present study, the persistent scatterers InSAR (PSI) method is employed to process the series of Sentinel 1-A/B synthetic aperture radar (SAR) images acquired between 2015 and 2021 along ascending and descending orbits for the selected areas in Gangtok, Sikkim, to detect potentially active, landslide-prone areas. InSAR-derived ground surface displacements and their spatio-temporal evolutions are combined with field investigations to better understand the state of activity and landslide risk assessment. Field investigations confirm the ongoing ground surface displacements revealed by the InSAR results. Some urban areas have been completely abandoned due to the structural damage to residential housing, schools, and office buildings caused by displacement. This paper relates the geotechnical investigations carried out on the ground to the data obtained through interferometric synthetic aperture radar (InSAR), focusing on the triggering mechanisms. A strong correlation between seasonal rainfall and landslide acceleration, as well as predisposing geological-structural setting, suggest a causative mechanism of the landslides.

Earthquake-induced risk assessment of cultural heritage based on InSAR and seismic intensity: A case study of Zhalang temple affected by the 2021 Mw 7.4 Maduo (China) earthquake

Earthquakes have been acknowledged as threats to heritages for a long time in the past decades. Many scientific publications and international guidelines have addressed this issue. However, the related reports are still lacking particularly for the 2021 Mw 7.4 Maduo earthquake and its surrounding heritage sites, even though this event was the largest earthquake in China after 2008 with 13 National Key Protected Cultural Relic Units (NKPCRUs) of China located in the surrounding areas within 200 km from the epicenter. On May 21, 2021, an Mw 7.4 earthquake struck Maduo County in Qinghai Province, China. The earthquake was large in magnitude and caused ∼150 km of surface rupture. The integrity and authenticity of cultural heritages near the epicenter are under severe threat, making it critically important to quantify the potential damages and vulnerability of these sites affected by the Mw 7.4 earthquake. Nevertheless, many geological and geophysical studies related to this earthquake have been conducted, but none of them consider the seismic damage to the heritage sites. To fulfill this gap, the Zhalang temple (also known as “Golok Peaceful Liberation Memorial”) which was a Tibetan Buddhist monastery located ∼140 km away from the Mw 7.4 epicenter of the 2021 Maduo earthquake was taken as an example to investigate whether and how the strong earthquake could affect a cultural heritage in such a distance. Collaborative monitoring based on InSAR and seismic intensity was conducted to measure the earthquake-induced deformation and characterize the risks and damages to cultural heritages by earthquakes. First, fast responses within 30 min based on the seismic method were performed. The PGV-vS30 intensity, that was the peak ground velocities (PGV) further site-corrected by the site conditions of the time-averaged shear-wave velocity to 30 m depth (vS30) seismic intensity map was obtained, which provided a general and fast guidance to preliminary determinations of the severe disaster area and the scope of the disaster area. Initial decisions could be made about whether or where a certain location should be taken into further on-site inspection. Then, the Interferometric Synthetic Aperture Radar (InSAR) processing of Sentinel-1 images was performed to define two-dimensional (2D) East-West (E-W) and vertical co-seismic deformation maps near the Zhalang temple. Finally, the measurements obtained by seismic and InSAR methods were validated by unmanned aerial vehicle (UAV) imagery and field surveying. The results showed that: the maximum E-W striking deformation was ∼2.54 cm toward the east and the vertical subsidence was ∼0.01 cm centered on the Zhalang temple, suggesting that the E-W striking deformation significantly affected the surrounding region. Moreover, the spatial heterogeneity indicator of the InSAR deformation maps revealed the locations of underground faults with surface expressions. Further field survey detected surface fractures at Zhalang temple due to secondary effects of the Mw 7.4 earthquake. The genetic process of faulting and fracturing is crucial in terms of hazard assessment and mitigation strategies. In countries with strong environmental and seismic risks, the presented work contributes to the disaster risk reduction (DRR) of heritages in natural-hazard-prone areas and also takes on a social value because it provided supplements for the protection of a heritage that represents the very identity nationwide.

Landslide Detection and Mapping Based on SBAS-InSAR and PS-InSAR: A Case Study in Gongjue County, Tibet, China

The rock mass along the Jinsha River is relatively broken under complex geological action. Many ancient landslides were distributed along the Jinsha River in Gongjue County, which is very dangerous under the action of gravity, tectonic stress and river erosion. Efficient and accurate identification and monitoring of landslides is important for disaster monitoring and early warning. Interferometric synthetic aperture radar (InSAR) technology has been proved to be an effective technology for landslide hazard identification and mapping. However, great uncertainty inevitably exists due to the single deformation observation method, resulting in wrong judgment during the process of landslide detection. Therefore, to address the uncertainties arising from single observations, a cross-comparison method is put forward using SBAS-InSAR (small baseline subset InSAR) and PS-InSAR (permanent scatterers InSAR) technology. Comparative analysis of the spatial complementarity of interference points and temporal deformation refined the deformation characteristics and verified the reliability of the InSAR results, aiding in the comprehensive identification and further mapping of landslides. Landslides along the Jinsha River in Gongjue County were studied in this paper. Firstly, 14 landslides with a total area of 20 km2 were identified by using two time-series InSAR methods. Then, the deformation characteristics of these landslides were validated by UAV (unmanned aerial vehicle) images, multiresource remote sensing data and field investigation. Further, the precipitation data were introduced to analyze the temporal deformation characteristics of two large landslides. Lastly, the influence of fault activity on landslide formation is further discussed. Our results demonstrate that the cross-comparison of the time-series InSAR method can effectively verify the accuracy of landslide identification.