InSAR Time Series Research Articles

Monitoring and analysis of Woda landslide (China) using InSAR and Sentinel-1 data

Detecting the slow motions of highly elevated landslides in remote mountain areas has always been a problem. This paper takes the Woda landslide along the Jinsha River as an example to monitor landslide movement. Although some parts of the landslide body have been found to have moved in recent years, the timing and magnitude of motion have not been systematically monitored or interpreted. Here, we apply the SBAS time series strategy using 65-scene Sentinel-1A/B satellite InSAR images and study the spatial distribution and temporal behaviour of landslide movements between July 4, 2018, and August 29, 2020. Our research results show that the cumulative deformation on the left side of the landslide body with concentrated deformation was approximately 200 mm during the 2-year observation period. By calculating the relationship between the InSAR time series and the precipitation around the landslide, it is found that the landslide deformation is closely related to precipitation. GNSS technology is also deployed on the landslide mass and effectively complements InSAR technology. Simultaneously, based on the results of field surveys and hydrological data analysing the landslide's spatial deformation characteristics and deformation factors, the landslide deformation can also be inferred to be related to precipitation.

Landslide detection and inventory updating using the time-series InSAR approach along the Karakoram Highway, Northern Pakistan

Karakoram Highway (KKH) is frequently disrupted by geological hazards mainly landslides which pose a serious threat to its normal operation. Using documented inventory, optical imagery interpretation, and frequency-area statistics, the features of slope failure, the spatial distribution, and their link to numerous contributing factors have all been effectively explored along the KKH. An updated inventory for the area was recreated using the interferometric synthetic aperture radar (InSAR) persistent scatterer (PS) technology to further investigate millimetre-accurate measurements of slope deformation (Vslope). Utilizing the PS approach, Sentinel-1 data from Jan 2018 to Jan 2022 were processed by which we obtained a deformation rate (VSlope) that varies between 0 and 364 mm/year. A total number of 234 landslides were cited from the literature and classified while 29 new potential landslides were detected and several pre-existing landslides were redefined by the InSAR approach, which was incorporated to generate an updated landslide susceptibility model with 86.6% of prediction precision in the area under curve method. As previous studies done by applying the InSAR technique incorporated a short span temporally and they missed some highly deforming zones like Budalas and Khanabad landslides, contain mean velocities > 50 mm/yr, which we studied individually in this work. In this study, a comprehensive application of the InSAR technique to assessing its performance in detecting and analysing landslides has been applied. The deformation velocity (Vslope) model shows high displacement in some regions, which needed to be further investigated by geoscientists, and the updated developed landslide inventory and susceptibility map can be used for land use planning and landslide mitigation strategies.

Permafrost Stability Mapping on the Tibetan Plateau by Integrating Time-Series InSAR and the Random Forest Method

The ground deformation rate is an important index for evaluating the stability and degradation of permafrost. Due to limited accessibility, in-situ measurement of the ground deformation of permafrost areas on the Tibetan Plateau is a challenge. Thus, the technique of time-series interferometric synthetic aperture radar (InSAR) is often adopted for measuring the ground deformation rate of the permafrost area, the effectiveness of which is, however, degraded in areas with geometric distortions in synthetic aperture radar (SAR) images. In this study, a method that integrates InSAR and the random forest method is proposed for an improved permafrost stability mapping on the Tibetan Plateau; to demonstrate the application of the proposed method, the permafrost stability mapping in a small area located in the central region of the Tibetan Plateau is studied. First, the ground deformation rate in the concerned area is studied with InSAR, in which 67 Sentinel-1 scenes taken in the period from 2014 to 2020 are collected and analyzed. Second, the relationship between the environmental factors (i.e., topography, land cover, land surface temperature, and distance to road) and the permafrost stability is mapped with the random forest method based on the high-quality data extracted from the initial InSAR analysis. Third, the permafrost stability in the whole study area is mapped with the trained random forest model, and the issue of data scarcity in areas where the terrain visibility of SAR images is poor or InSAR results are not available in permafrost stability mapping can be overcome. Comparative analyses demonstrate that the integration of the InSAR and the random forest method yields a more effective permafrost stability mapping compared with the sole application of InSAR analysis.

A Deep-Learning-Facilitated, Detection-First Strategy for Operationally Monitoring Localized Deformation with Large-Scale InSAR

SAR interferometry (InSAR) has emerged in the big-data era, particularly benefitting from the acquisition capability and open-data policy of ESA’s Sentinel-1 SAR mission. A large number of Sentinel-1 SAR images have been acquired and archived, allowing for the generation of thousands of interferograms, covering millions of square kilometers. In such a large-scale interferometry scenario, many applications actually aim at monitoring localized deformation sparsely distributed in the interferogram. Thus, it is not effective to apply the time-series InSAR analysis to the whole image and identify the deformed targets from the derived velocity map. Here, we present a strategy facilitated by the deep learning networks to firstly detect the localized deformation and then carry out the time-series analysis on small interferogram patches with deformation signals. Specifically, we report following-up studies of our proposed deep learning networks for masking decorrelation areas, detecting local deformation, and unwrapping high-gradient phases. In the applications of mining-induced subsidence monitoring and slow-moving landslide detection, the presented strategy not only reduces the computation time, but also avoids the influence of large-scale tropospheric delays and unwrapping errors. The presented detection-first strategy introduces deep learning to the time-series InSAR processing chain and makes the mission of operationally monitoring localized deformation feasible and efficient for the large-scale InSAR.

MATHEMATICAL AND PHYSICAL APPROACHES TO INFER ABSOLUTE ZENITH WET DELAYS FROM DOUBLE DIFFERENTIAL INTERFEROMETRIC OBSERVATIONS USING ERA5 ATMOSPHERIC REANALYSIS

Abstract. Atmospheric water vapor (WV) is one of the driving constituents of the atmosphere. The modelling and forecasting of WV and derived quantities like precipitable water is reliable on regional scales but challenging on small scales because of its high spatial and temporal variation. Interferometric synthetic aperture radar (InSAR) can be exploited to retrieve integrated atmospheric water vapor (IWV) from path delay observations along the radar line of sight. InSAR-derived IWV maps feature a very high spatial resolution but the double-differential interferometric observations only provide changes of IWV between acquisition times and with respect to a certain spatial reference. In this study we present a method to derive the absolute IWV by combining ERA5 numerical weather model data with differential path delay observations from InSAR time series. We propose different functional approaches to merge the regional trend of WV from ERA5 with the high resolution IWV signal from InSAR. We apply this to a Sentinel-1 Persistent Scatterer InSAR time series in the Upper Rhine Graben and validate against IWV observations at GNSS stations of the Upper Rhine Graben Network.

Response of Guobu Slope Displacement to Rainfall and Reservoir Water Level with Time-Series InSAR and Wavelet Analysis

Reservoir bank landslides are a frequent phenomenon, and the stability of these landslides is affected by two essential factors: rainfall and reservoir level changes. Studying the response patterns of reservoir bank landslide movements to these variables is crucial in preventing their occurrence and mitigating their effects. To this end, this study employed 103 European Space Agency (ESA) Copernicus Sentinel-1 images and the SBAS-InSAR (small baseline subset interferometric synthetic aperture radar) technique to obtain a time series of the Guobu slope deformation from September 2015 to December 2019. The Guobu slope showed significant toppling damage. The satellite line of sight (LOS) detected a maximum subsidence rate of −447 mm/y (the negative sign indicates movement away from the satellite, i.e., subsidence) in the upper section of the slope. Subsequently, three wavelet tools were used to quantitatively analyze the effect of rainfall and reservoir water level on the deformation of the Guobu slope. The results demonstrate a positive correlation between rainfall and the deformation of the Guobu slope. Moreover, the deformation lags behind the rainfall by approximately 70 days. In contrast, the reservoir water level and the deformation of the Guobu slope exhibit an inverse relationship. The deformation of the leading edge of the slope body lags behind the reservoir level by approximately 19 days, while the middle and upper sections of the slope body, which have the most significant rate of variability, lag by about 80 days. Among these factors, rainfall plays a dominant role in the deformation of the Guobu slope, while reservoir levels play a synergistic role. The findings of this study highlight the importance of monitoring and understanding the impact of changes in rainfall and reservoir water levels on the stability of reservoir bank landslides. This understanding is crucial in preventing the occurrence of such landslides and minimizing their impact. The use of remote sensing techniques, together with wavelet analysis, enables the accurate and timely monitoring of the deformation of the Guobu slope, providing valuable insights for disaster warnings and disaster prevention and reduction efforts.

Retrospective monitoring of slope failure event of tailings dam using InSAR time-series observations

Retrospective monitoring of slope failure event of tailings dam using InSAR time-series observations

Parallel Optimization for Large Scale Interferometric Synthetic Aperture Radar Data Processing

Interferometric synthetic aperture radar (InSAR) has developed rapidly over the past years and is considered as an important method for surface deformation monitoring, benefiting from growing data quantities and improving data quality. However, the handing of SAR big data poses significant challenges for related algorithms and pipeline, particularly in large-scale SAR data processing. In addition, InSAR algorithms are highly complex, and their task dependencies are intricate. There is a lack of efficient optimization models and task scheduling for InSAR pipeline. In this paper, we design parallel time-series InSAR processing models based on multi-thread technology for high efficiency in processing InSAR big data. These models concentrate on parallelizing critical algorithms that have high complexity, with a focus on deconstructing two computationally intensive algorithms through loop unrolling. Our parallel models have shown a significant improvement of 10–20 times in performance. We have also developed a parallel optimization tool, Simultaneous Task Automatic Runtime (STAR), which utilizes a data flow optimization strategy with thread pool technology to address the problem of low CPU utilization resulting from multiple modules and task dependencies in the InSAR processing pipeline. STAR provides a data-driven pipeline and enables concurrent execution of multiple tasks, with greater flexibility to keep the CPU busy and further improve CPU utilization through predetermined task flow. Additionally, a supercomputing-based system has been constructed for processing massive InSAR scientific big data and providing technical support for nationwide surface deformation measurement, in accordance with the framework of time series InSAR data processing. Using this system, we processed InSAR data with the volumes of 500 TB and 700 TB in 5 and 7 days, respectively. Finally we generated two maps of land surface deformation all over China.

Unsupervised detection of InSAR time series patterns based on PCA and K-means clustering

The need for implementing efficient value-adding tools able to optimise Earth Observation data usage, compels the scientific community to find innovative solutions for the downstream of Earth Observation information. In this paper we present an unsupervised and automated approach based on Principal Component Analysis (PCA) and K-means clustering to detect patterns of natural or anthropogenic ground deformation from Interferometric Synthetic Aperture Radar (InSAR) Time Series. For our proof-of-concept, we focus on the Valle d’Aosta region (Northwest Italy) where mass wasting processes frequently occurs, interacting with human activities and infrastructures. The large volumes of Sentinel-1 data produced allows for retrieving horizontal and vertical Time Series from multi-geometry data fusion of Line-of-Sight (LOS) InSAR measurements. The added benefit of combining ascending/descending InSAR data and interpolating displacements in time at different time steps is here explored prior to data dimensionality reduction and feature extraction through PCA. The retrieved principal components serve as a continuous solution for cluster membership indicators in the K-means clustering method, allowing to define spatially and temporally coherent displacement phenomena. The signal of the ground deformation clusters is then deconstructed into the underlying trend and seasonality components to enhance the interpretability of the classified satellite InSAR features. Using InSAR Time series data spanning 2014–2020, the proposed approach detects several slope movements and anthropogenic deformations with both linear and seasonal displacement behaviours. The results demonstrate the potential applicability of our transferable approach to the development of automated ground motion analysis systems.

EZ-InSAR: An easy-to-use open-source toolbox for mapping ground surface deformation using satellite interferometric synthetic aperture radar

Satellite Interferometric Synthetic Aperture Radar (InSAR) is a space-borne geodetic technique that can map ground displacement at millimetre accuracy. Via the new era for InSAR applications provided by the Copernicus Sentinel-1 SAR satellites, several open-source software packages exist for processing SAR data. These packages enable one to obtain high-quality ground deformation maps, but still require a deep understanding of InSAR theory and the related computational tools, especially when dealing with a large stack of images. Here we present an open-source toolbox, EZ-InSAR (easy-to-use InSAR), for a user-friendly implementation of InSAR displacement time series analysis with multi-temporal SAR images. EZ-InSAR integrates the three most popular and renowned open-source tools (i.e., ISCE, StaMPS, and MintPy), to generate interferograms and displacement time series by using these state-of-art algorithms within a seamless Graphical User Interface. EZ-InSAR reduces the user’s workload by automatically downloading the Sentinel-1 SAR imagery and the digital elevation model data for the user’s area of interest, and by streamlining preparation of input data stacks for the time series InSAR analysis. We illustrate the EZ-InSAR processing capabilities by mapping recent ground deformation at Campi Flegrei (> 100 mm·yr−1) and Long Valley (~ 10 mm·yr−1) calderas with both Persistent Scatterer InSAR and Small-Baseline Subset approaches. We also validate the test results by comparing the InSAR displacements with Global Navigation Satellite System measurements at those volcanoes. Our tests indicate that the EZ-InSAR toolbox provided here can serve as a valuable contribution to the community for ground deformation monitoring and geohazard evaluation, as well as for disseminating bespoke InSAR observations for all.

An identification method of potential landslide zones using InSAR data and landslide susceptibility

Landslides are destructive to property, infrastructure and people in potential landslide zones. Identifying potential landslides is an important step in landslide preparedness and will help develop sustainable landslide risk management. Interferometric synthetic aperture radar (InSAR) and landslide susceptibility assessment (LSA) have poor reliability in individually identifying potential landslide zones. This study proposes a threshold model for identifying potential landslide zones that fuses the InSAR two-dimensional (vertical direction and east-west direction) deformation rates and LSA results. The deformation rate threshold for this threshold model is |DU| or |DE|>10 mm/year (DU and DE are the vertical and the east-west deformation rates, respectively), with threshold levels of LSA set to high and very high susceptibility. The criterion of potential landslide zones is ((|DU| or |DE|>10 mm/year) ∩ (high or very high susceptibility of LSA)), and points with similar deformation and susceptibility are clustered by the K-means algorithm, and the potential landslide zones are obtained by elimination, smooth and speckle removal operations. The results showed that the InSAR two-dimensional deformation rates DU and DE were −32.71 − 12.72 mm/year and −14.88 − 24.81 mm/year, respectively, during 2015–2020 in Lanzhou city. The LSA showed that very low, low, medium, high, and very high susceptibility accounted for 55.36%, 10.54%, 21.37%, 9.63%, 3.1% of the total area, respectively. Using the proposed threshold model, 117 potential landslide zones were identified in Lanzhou. The overlap rate between potential landslide zones and the landslide inventory was 40.17%, indicating that about 40% of the potential landslide zones overlapped with the landslide inventory and that about 60% were new potential landslide zones in Lanzhou. The feasibility of the threshold model in identifying potential landslides was confirmed by field research and time-series InSAR analysis on typical areas (L1, L2, L3, and L4), which had large deformation variables and landslide features. The proposed method can quickly determine the spatial location of potential landslides, providing targeting data for landslide field investigations, technical support for rapid early landslide identification, and data support for landslide management and prevention in Lanzhou.

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.

Strain Accommodation and Seismic Hazards of the Kalpin Fold‐And‐Thrust Belt, Southwestern Tian Shan Foreland, China: Insights From the 2020 Mw 6.0 Kalpin Earthquake

AbstractStrain accumulation in foreland zones is important for understanding mountain‐building processes and seismic hazards. Material heterogeneity in this border zone, particularly the contrast between sedimentary cover and basement, affects strain accommodation and fault behavior. One manifestation of this effect is depth separation of Interferometric Synthetic Aperture Radar (InSAR)‐derived slip models and corresponding aftershock clusters; hypotheses for this vertical separation remain controversial. In this study, we investigated strain accumulation in the Kalpin fold‐and‐thrust belt (KFTB) of southwestern Tian Shan, China, using the integration of InSAR measurements, teleseismic body‐waves, near‐field strong motion data (SM), and relocated aftershocks of the 2020 Mw 6.0 Kalpin earthquake. The SM modeling and analysis of post‐seismic InSAR time series are performed for the first time for this earthquake. Our results confirm a vertical separation of the mainshock and aftershock cluster, with the former on a weak décollement and the latter on faults within the basement. InSAR time series analysis shows that post‐seismic deformation was dominated by afterslip on a splay fault directly above the ruptured décollement. Static stress transfer of co‐seismic rupture cannot explain these observations. We speculate that fluid flow and high pore pressure along pre‐existing fault planes may have reduced the fault strength and been involved in the evolution of this aftershock cluster. We conclude that compressive strain in the KFTB is accommodated by a mixture of thin‐ and thick‐skin faulting and seismic deformation across the entire crustal thickness. Specifically, we suggest that the observed shortening is partly accommodated by infrequent large earthquakes on the weak décollement.

Iceland Kinematics From InSAR

AbstractLarge‐scale ground deformation in Iceland is dominated by extensional plate‐boundary deformation, where the Mid‐Atlantic Ridge crosses the island, and by uplift due to glacial isostatic adjustment (GIA) from thinning and retreat of glaciers. While this deformation is mostly steady over multiple years, it is modulated by smaller‐scale transient deformation associated with, for example, earthquakes, volcanic unrest, and geothermal exploitation. Here, we combine countrywide Sentinel‐1 interferometric synthetic aperture radar (InSAR) data (from six tracks) from 2015 to 2021 with continuous Global Navigation Satellite System observations to produce time series of displacements across Iceland. The InSAR results were improved in a two‐step tropospheric mitigation procedure, using (a) global atmospheric models to reduce long‐wavelength and topography‐correlated tropospheric signals, and (b) modeling of the stochastic properties of the residual troposphere. Our results significantly improve upon earlier countrywide InSAR results, which were based on InSAR stacking, as we use more data, better data weighting, and advanced InSAR corrections to produce time series of ground displacements instead of just velocities. We fuse the three ascending and three descending track results to estimate maps of East and Up velocities, which clearly show the large‐scale extension and GIA deformation. Using revised plate‐spreading and GIA models, based on these new ground velocity maps, we remove the large‐scale and steady deformation from the InSAR time series and analyze the remaining transient deformations. Our results demonstrate the importance of (a) mitigating InSAR tropospheric signals over Iceland and of (b) solving for time series of deformation, not just velocities, as multiple transient deformation processes are present.

Ground infrastructure monitoring in coastal areas using time-series inSAR technology: the case study of Pudong International Airport, Shanghai

ABSTRACT Shanghai Pudong International Airport (PDIA), with its east side built along the coast with weak geological conditions, is prone to uneven foundation settlement due to the consolidation and compression of soil and erosion of coastal tides, affecting the safe operation of the airport. Therefore, it is crucial to conduct dynamic subsidence monitoring within the airport, especially in the runway area. 29 scenes of ascending track Sentinel-1A radar images from August 2016 to June 2018 are selected to perform surface deformation inversion based on PS-InSAR and improved SBAS-InSAR for PDIA and its around coastal area. Through cross-validation, the reliability of the time-series InSAR technique for dynamic monitoring of surface deformation of coastal zone infrastructures is confirmed. The results show severely uneven settlement. By combining the monitoring results with the local geological and hydrological dataset, the driving factors of differential deformation of the infrastructures are analyzed, including stratigraphic geological conditions, ground loadings, foundation treatment methods, water erosion, and groundwater level changes. Finally, the time-series deformation characteristics and the causes of PDIA's runway are emphasized based on the PS deformation results. This case provides a reference for the safety management of critical infrastructure in coastal areas using advanced InSAR technique.

The ground deformation of the south-eastern flank of Mount Etna monitored by GNSS and SAR interferometry from 2016 to 2019

SUMMARY The south-eastern sector of the Mount Etna, Italy, is characterized by numerous active faults, in particular the Belpasso–Ognina lineament, the Tremestieri–San Gregorio–Acitrezza fault, the Trecastagni fault and the Fiandaca–Nizzeti fault including the Timpe Fault System. Their activity is the result of both volcanism and tectonics. Here, we analyse the ground deformation occurred from 2016 to 2019 across those active faults by using the GNSS data acquired at 22 permanent stations and 35 campaign points observed by the Etna Observatory (INGV) and by the University of Catania. We also use the time-series of line of sight displacement of permanent scatterers SENTINEL-1 A-DInSAR obtained by using the P-SBAS tool of the ESA GEP-TEP (Geohazards Thematic Exploitation Platform) service. We discriminate the contributions of the regional tectonic strain, the inflations, the deflations of the volcano and the gravitational sliding in order to analyse the deformation along the faults of the south-eastern flank of Etna. The shallow and destructive Mw = 4.9 earthquake of 2018 December 26 occurred within the studied area two days after a dyke intrusion, that propagated beneath the centre of the volcano accompanied by a short eruption. Both GNSS and InSAR time-series document well those events and allow to investigate the post-seismic sliding across the faults of south-eastern flank. We analyse the slow slip events (SSE) that are observed in the GNSS and InSAR time-series in the vicinity of the Acitrezza fault. We quantify and discuss the tectonic origin of the Belpasso–Ognina lineament that we interpreted as a tear fault.

Automatic identification of mining-induced subsidence using deep convolutional networks based on time-series InSAR data: a case study of Huodong mining area in Shanxi Province, China

Automatic identification of mining-induced subsidence using deep convolutional networks based on time-series InSAR data: a case study of Huodong mining area in Shanxi Province, China

Land subsidence modeling and assessment in the West Pearl River Delta from combined InSAR time series, land use and geological data

West Pearl River Delta (WPRD) is sinking as a result of the jointed effect of natural and anthropogenic factors. Land subsidence has increasingly become a concern because of tremendous population growth and rapid urbanization over this region in the last few decades. In this study, sixty-seven Sentinel-1 images, acquired between 2016 and 2021, were analyzed with the persistent scatterer interferometry technique (PSI), to monitor and reveal the ground subsidence characteristics in the WPRD. It is found that the overall vertical deformation velocities observed in the WPRD ranged between −70 mm/year and 10 mm/year. Three subsidence bowls were found in the study area (Gaolan island of Zhuhai, the junction area of Zhuhai and Zhongshan, and the junction area of Zhongshan and Jiangmen). The spatial–temporal subsidence characteristics have been analyzed. It is discovered that the ground subsidence is mostly dispersed in Quaternary deposits and is highly relevant to the thickness of sediments, indicating that soft soil consolidation is one of the primary causes contributing to land subsidence. Furthermore, land use maps for 2016 and 2021 were generated using Landsat-8 images for the investigation on the relationship between land subsidence and land use. The results obtained from analysis demonstrated that the rapid subsiding areas mainly occurred in the land-use classes as follows: aquaculture, urban land, and agricultural land. The land use conversion pattern with more significant anthropogenic influence usually causes a higher subsidence rate. In addition, based on soft soil thickness, groundwater exploitation, land use, elevation, and strata lithology, a Random Forest Regression (RFR) model was used to predict subsidence rates (R2 = 0.631, RMSE = 2.7 mm/year). The importance of these influencing factors of land subsidence was calculated based on the RFR algorithm. The results indicated that soft soil thickness, elevation, groundwater exploitation, strata lithology, and landcover type are the most significant factors affecting subsidence. The applicability of geological data and land-use history for land subsidence prediction has been demonstrated with the use of the RFR algorithm.

Monitoring Roadbed Stability in Permafrost Area of Qinghai–Tibet Railway by MT-InSAR Technology

Permafrost areas pose a threat to the safe operation of linear projects such as the Qinghai–Tibet railway due to the repeated alternating effects of frost heaving and thawing settlement of frozen soil in permafrost area. Time series InSAR technology can effectively obtain ground deformation information with an accuracy of up to millimeters. Therefore, it is of great significance to use time series InSAR technology to monitor the deformation of the permafrost section of the Qinghai–Tibet railway. This study uses multi-time InSAR (MT-InSAR) technology to monitor the deformation of the whole section of the Qinghai–Tibet railway, detect the uneven settlement of the railway roadbed in space, and detect the seasonal changes in the roadbed in the time domain. At the same time, the local deformation sections over the years are compared and discussed. The time series deformation monitoring results of the permafrost section Sentinel-1 data in 2020 show that the length of the railway roadbed from Tanggula station to Za’gya Zangbo station (TZ) section is approximately 620 m, the deformation of the east and west sides is uneven, and the average annual deformation difference is 60.68 mm/a. The impact of frozen soil in WangKun station to Budongquan station (WB) section on railway roadbed shows the distribution characteristics of high in the middle and low at both ends, and the maximum annual average settlement can reach −158.46 mm/a. This study shows that the deformation of permafrost varies with different ground layers. The impact of human activities on frozen soil deformation is less than that of topography and hydrothermal conditions. At the same time, the study determined that compared with other sections, the roadbed deformation of TZ and WB sections is more obvious.

Automatic detection and update of landslide inventory before and after impoundments at the Lianghekou reservoir using Sentinel-1 InSAR

There is an urgent demand for continuous detection and monitoring of active slopes in wide reservoir areas, as reservoir impoundments may activate unstable slopes. Although satellite time-series InSAR has been widely used in mapping active landslides, the tedious artificial interpretation of InSAR results limits the efficiency and reliability of landslides detection. We propose a set of procedures for deformation monitoring and continuous automatic landslide identification in wide reservoir areas. The local time window estimation can extract the nonlinear deformation in the time series InSAR signal, thus enhancing the deformation field. Spatial adaptive clustering enables the effective extraction of unstable slopes. The abnormal deformation trends of landslides can be updated through the continuous identification of new results and comparison with historical results. The procedure is used to continuously detect unstable slopes in the Lianghekou reservoir area before and after the impoundment. Combining the ascending and descending SAR data of Sentinel-1, 109 historically active landslides were found and 18 new landslides were activated within one year after the first water storage. The distribution and types of unstable slopes were analyzed, followed by two case studies. The first case shows the different effects of the two-stage water storage on slope deformations, indicating rising water levels' critical role in landslide activation. The second case shows the consolidation settlement of the embankment dam and the influence of water storage on slope deformation monitoring. The results demonstrate the effectiveness of the InSAR automatic landslide identification and this study provides a technical reference for similar reservoir area.