Surface Deformation Monitoring in Dongying City Based on SBAS-InSAR

It is difficult to monitor the surface deformation along the expressway for the critical climate conditions in Tibet plateau. In this paper, based on sentinel-1A SAR data, the surface deformation along the Gongyu expressway was tried to evaluate using time-series SBAS-InSAR method. The results indicate that the surface deformation in most regions is within the safe acquirement of the expressway. Moreover, the surface deformation indicates a strong seasonal effect. Finally, two special spots with dangerous surface deformation are identified along the expressway.

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Mapping and Characterizing Displacements of Landslides with InSAR and Airborne LiDAR Technologies: A Case Study of Danba County, Southwest China

Remote Sensing ◽

10.3390/rs13214234 ◽

2021 ◽

Vol 13 (21) ◽

pp. 4234

Author(s):

Qiang Xu ◽

Chen Guo ◽

Xiujun Dong ◽

Weile Li ◽

Huiyan Lu ◽

...

Keyword(s):

Surface Deformation ◽

Deformation Monitoring ◽

Airborne Lidar ◽

Mountainous Area ◽

Deformation Characteristics ◽

Detection And Identification ◽

Geomorphic Features ◽

Sbas Insar ◽

Surrounding Areas ◽

And Control

Interferometric synthetic aperture radar (InSAR) technology is known as one of the most effective methods for active landslide identification and deformation monitoring in large areas, and thus it is conducive to preventing and mitigating the losses caused by landslides. However, great uncertainty inevitably exists due to influences of complex terrains, dense vegetations, and atmospheric interferences in the southwestern mountainous area of China, and this is associated with false or erroneous judgment during the process of landslide identification. In this study, a landslide identification method is put forward by integrating InSAR technology and airborne light detection and ranging (LiDAR) technology. Via this method, surface deformation characteristics detected by InSAR technology and micro-geomorphic features reflected by LiDAR technology were used to identify and map landslides of large areas. Herein, the method was applied to process 224 Sentinel-1 images covering Danba County and its surrounding areas (540 km2) from October 2014 to September 2020. Firstly, 44 active landslides with total areas of 59 km2 were detected by stacking InSAR technology. Then, major regions up to 135 km2 were validated by data gained from the airborne LiDAR technology. Particularly, several large landslides with lengths and/or widths of more than 2 km were found. Further, the precipitation data were integrated with the above results to analyze the temporal deformation characteristics of three typical landslides from major regions via SBAS InSAR technology. The key findings were as follows: (1) The combination of InSAR and LiDAR technologies could improve the accuracy of landslide detection and identification; (2) there was a significant correlation between temporal deformation characteristics of some landslides and monthly rainfall, with an obvious hysteretic effect existing between the initiation timing of rainfall and that of deformation; (3) the results of this study will be important guidance for the prevention and control of geological hazards in Danba County and areas with similar complex geomorphological conditions by helping effectively identify and map landslides.

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Automatic Interferogram Selection for SBAS-InSAR Based on Deep Convolutional Neural Networks

Remote Sensing ◽

10.3390/rs13214468 ◽

2021 ◽

Vol 13 (21) ◽

pp. 4468

Author(s):

Yufang He ◽

Guangzong Zhang ◽

Hermann Kaufmann ◽

Guochang Xu

Keyword(s):

Surface Deformation ◽

Classical Method ◽

Local Deformation ◽

Classification Performance ◽

Selection Method ◽

Deformation Monitoring ◽

Image Feature ◽

Deep Convolutional Neural Networks ◽

High Quality ◽

Sbas Insar

The small baseline subset of spaceborne interferometric synthetic aperture radar (SBAS-InSAR) technology has become a classical method for monitoring slow deformations through time series analysis with an accuracy in the centimeter or even millimeter range. Thereby, the selection of high-quality interferograms calculated is one of the key operations for the method, since it mainly determines the credibility of the deformation information. Especially in the era of big data, the demand for an automatic and effective selection method of high-quality interferograms in SBAS-InSAR technology is growing. In this paper, a deep convolutional neural network (DCNN) for automatichigh-quality interferogram selection is proposed that provides more efficient image feature extraction capabilities and a better classification performance. Therefore， the ResNet50 (a kind of DCNN) is used to identify and delete interferograms that are severely contaminated. According to simulation experiments and calculated Sentinel-1A data of Shenzhen, China, the proposed approach can significantly separate interferograms affected by turbulences in the atmosphere and by the decorrelation phase. The remarkable performance of the DCNN method is validated by the analysis of the standard deviation of interferograms and the local deformation information compared with the traditional selection method. It is concluded that DCNN algorithms can automatically select high quality interferogram for the SBAS-InSAR method and thus have a significant impact on the precision of surface deformation monitoring.

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Application of Sentinel-1 and-2 Images in Measuring the Deformation of Kuh-e-Namak (Dashti) Namakier, Iran

Remote Sensing ◽

10.3390/rs13040785 ◽

2021 ◽

Vol 13 (4) ◽

pp. 785

Author(s):

Sen Zhang ◽

Qigang Jiang ◽

Chao Shi ◽

Xitong Xu ◽

Yundi Gong ◽

...

Keyword(s):

Synthetic Aperture Radar ◽

Rock Salt ◽

Rainy Season ◽

Surface Deformation ◽

Meteorological Data ◽

Dry Season ◽

Synthetic Aperture ◽

Advantages And Disadvantages ◽

Sbas Insar ◽

Aperture Radar

Kuh-e-Namak (Dashti) namakier is one of the most active salt diapirs along the Zagros fold–thrust belt in Iran. Its surface deformation should be measured to estimate its long-term kinematics. Ten Sentinel-2 optical images acquired between October 2016 and December 2019 were processed by using Co-Registration of Optically Sensed Images and Correlation (COSI-Corr) method. Forty-seven Sentinel-1 ascending Synthetic Aperture Radar (SAR) images acquired between April 2017 and December 2019 were processed by using Small Baseline Subset Synthetic Aperture Radar Interferometry (SBAS-InSAR) method. The deformation of Kuh-e-Namak (Dashti) namakier was measured using both methods. Then, meteorological data were utilized to explore the relationship between the kinematics of the namakier and weather conditions and differences in macrodeformation behavior of various rock salt types. The advantages and disadvantages of COSI-Corr and SBAS-InSAR methods in measuring the deformation of the namakier were compared. The results show that: (1) The flank subsides in the dry season and uplifts in the rainy season, whereas the dome subsides in the rainy season and uplifts in the dry season. Under extreme rainfall conditions, the namakier experiences permanent plastic deformation. (2) The “dirty” rock salt of the namakier is more prone to flow than the “clean” rock salt in terms of macrodeformation behavior. (3) In the exploration of the kinematics of the namakier via the two methods, COSI-Corr is superior to SBAS-InSAR on a spatial scale, but the latter is superior to the former on a time scale.

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Constructing Adaptive Deformation Models for Estimating DEM Error in SBAS-InSAR Based on Hypothesis Testing

Remote Sensing ◽

10.3390/rs13102006 ◽

2021 ◽

Vol 13 (10) ◽

pp. 2006

Author(s):

Jun Hu ◽

Qiaoqiao Ge ◽

Jihong Liu ◽

Wenyan Yang ◽

Zhigui Du ◽

...

Keyword(s):

Time Series ◽

Hypothesis Testing ◽

Surface Deformation ◽

A Priori ◽

Least Square ◽

Deformation Model ◽

Phase Time ◽

Testing Theory ◽

Sbas Insar ◽

Deformation Models

The Interferometric Synthetic Aperture Radar (InSAR) technique has been widely used to obtain the ground surface deformation of geohazards (e.g., mining subsidence and landslides). As one of the inherent errors in the interferometric phase, the digital elevation model (DEM) error is usually estimated with the help of an a priori deformation model. However, it is difficult to determine an a priori deformation model that can fit the deformation time series well, leading to possible bias in the estimation of DEM error and the deformation time series. In this paper, we propose a method that can construct an adaptive deformation model, based on a set of predefined functions and the hypothesis testing theory in the framework of the small baseline subset InSAR (SBAS-InSAR) method. Since it is difficult to fit the deformation time series over a long time span by using only one function, the phase time series is first divided into several groups with overlapping regions. In each group, the hypothesis testing theory is employed to adaptively select the optimal deformation model from the predefined functions. The parameters of adaptive deformation models and the DEM error can be modeled with the phase time series and solved by a least square method. Simulations and real data experiments in the Pingchuan mining area, Gaunsu Province, China, demonstrate that, compared to the state-of-the-art deformation modeling strategy (e.g., the linear deformation model and the function group deformation model), the proposed method can significantly improve the accuracy of DEM error estimation and can benefit the estimation of deformation time series.

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Preliminary Identification of Geological Hazards from Songpinggou to Feihong in Mao County along the Minjiang River Using SBAS-InSAR Technique Integrated Multiple Spatial Analysis Methods

Sustainability ◽

10.3390/su13031017 ◽

2021 ◽

Vol 13 (3) ◽

pp. 1017

Author(s):

Kuanxing Zhu ◽

Peihua Xu ◽

Chen Cao ◽

Lianjing Zheng ◽

Yue Liu ◽

...

Keyword(s):

Spatial Analysis ◽

Surface Deformation ◽

Hot Spot ◽

Field Investigation ◽

Geometric Distortion ◽

Disaster Prevention ◽

Geological Hazards ◽

Visibility Analysis ◽

Preliminary Identification ◽

Sbas Insar

Landslides and collapses are common geological hazards in mountainous areas, posing significant threats to the lives and property of residents. Therefore, early identification of disasters is of great significance for disaster prevention. In this study, we used Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technology to process C-band Sentinel-1A images to monitor the surface deformation from Songpinggou to Feihong in Maoxian County, Sichuan Province. Visibility analysis was used to remove the influence of geometric distortion on the SAR images and retain deformation information in the visible area. Hot spot and kernel density analyses were performed on the deformation data, and 18 deformation clusters were obtained. Velocity and slope data were integrated, and 26 disaster areas were interpreted from the 18 deformation clusters, including 20 potential landslides and 6 potential collapses. A detailed field investigation indicated that potential landslides No. 6 and No. 8 had developed cracks and were severely damaged, with a high probability of occurrence. Potential collapse No. 22 had developed fissures, exposing a dangerous rock mass and posing significant threats to the lives and property of residents. This study shows that the proposed method that combines visibility analysis, InSAR deformation rates, and spatial analysis can quickly and accurately identify potential geological disasters and provide guidance for local disaster prevention and mitigation.

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Copernicus Sentinel-1 MT-InSAR, GNSS and Seismic Monitoring of Deformation Patterns and Trends at the Methana Volcano, Greece

Applied Sciences ◽

10.3390/app10186445 ◽

2020 ◽

Vol 10 (18) ◽

pp. 6445 ◽

Cited By ~ 1

Author(s):

Theodoros Gatsios ◽

Francesca Cigna ◽

Deodato Tapete ◽

Vassilis Sakkas ◽

Kyriaki Pavlou ◽

...

Keyword(s):

Land Surface ◽

Surface Deformation ◽

Ground Deformation ◽

Local Population ◽

Seasonal Fluctuation ◽

Satellite System ◽

Deformation Monitoring ◽

Persistent Scatterers ◽

Geothermal Activity ◽

Global Navigation Satellite

The Methana volcano in Greece belongs to the western part of the Hellenic Volcanic Arc, where the African and Eurasian tectonic plates converge at a rate of approximately 3 cm/year. While volcanic hazard in Methana is considered low, the neotectonic basin constituting the Saronic Gulf area is seismically active and there is evidence of local geothermal activity. Monitoring is therefore crucial to characterize any activity at the volcano that could impact the local population. This study aims to detect surface deformation in the whole Methana peninsula based on a long stack of 99 Sentinel-1 C-band Synthetic Aperture Radar (SAR) images in interferometric wide swath mode acquired in March 2015–August 2019. A Multi-Temporal Interferometric SAR (MT-InSAR) processing approach is exploited using the Interferometric Point Target Analysis (IPTA) method, involving the extraction of a network of targets including both Persistent Scatterers (PS) and Distributed Scatterers (DS) to augment the monitoring capability across the varied land cover of the peninsula. Satellite geodetic data from 2006–2019 Global Positioning System (GPS) benchmark surveying are used to calibrate and validate the MT-InSAR results. Deformation monitoring records from permanent Global Navigation Satellite System (GNSS) stations, two of which were installed within the peninsula in 2004 (METH) and 2019 (MTNA), are also exploited for interpretation of the regional deformation scenario. Geological, topographic, and 2006–2019 seismological data enable better understanding of the ground deformation observed. Line-of-sight displacement velocities of the over 4700 PS and 6200 DS within the peninsula are from −18.1 to +7.5 mm/year. The MT-InSAR data suggest a complex displacement pattern across the volcano edifice, including local-scale land surface processes. In Methana town, ground stability is found on volcanoclasts and limestone for the majority of the urban area footprint while some deformation is observed in the suburban zones. At the Mavri Petra andesitic dome, time series of the exceptionally dense PS/DS network across blocks of agglomerate and cinder reveal seasonal fluctuation (5 mm amplitude) overlapping the long-term stable trend. Given the steepness of the slopes along the eastern flank of the volcano, displacement patterns may indicate mass movements. The GNSS, seismological and MT-InSAR analyses lead to a first account of deformation processes and their temporal evolution over the last years for Methana, thus providing initial information to feed into the volcano baseline hazard assessment and monitoring system.

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CO2 Injection Deformation Monitoring Based on UAV and InSAR Technology: A Case Study of Shizhuang Town, Shanxi Province, China

Remote Sensing ◽

10.3390/rs14010237 ◽

2022 ◽

Vol 14 (1) ◽

pp. 237

Author(s):

Tian Zhang ◽

Wanchang Zhang ◽

Ruizhao Yang ◽

Dan Cao ◽

Longfei Chen ◽

...

Keyword(s):

Carbon Capture ◽

Surface Deformation ◽

Deformation Monitoring ◽

Shanxi Province ◽

Co2 Injection ◽

Climate Mitigation ◽

Measurement Technology ◽

Surface Model ◽

Integrated Monitoring ◽

Migration Pathway

Carbon Capture, Utilization and Storage, also referred to as Carbon Capture, Utilization and Sequestration (CCUS), is one of the novel climate mitigation technologies by which CO2 emissions are captured from sources, such as fossil power generation and industrial processes, and further either reused or stored with more attention being paid on the utilization of captured CO2. In the whole CCUS process, the dominant migration pathway of CO2 after being injected underground becomes very important information to judge the possible storage status as well as one of the essential references for evaluating possible environmental affects. Interferometric Synthetic Aperture Radar (InSAR) technology, with its advantages of extensive coverage in surface deformation monitoring and all-weather traceability of the injection processes, has become one of the promising technologies frequently adopted in worldwide CCUS projects. In this study, taking the CCUS sequestration area in Shizhuang Town, Shanxi Province, China, as an example, unmanned aerial vehicle (UAV) photography measurement technology with a 3D surface model at a resolution of 5.3 cm was applied to extract the high-resolution digital elevation model (DEM) of the study site in coordination with InSAR technology to more clearly display the results of surface deformation monitoring of the CO2 injection area. A 2 km surface heaving dynamic processes before and after injection from June 2020 to July 2021 was obtained, and a CO2 migration pathway northeastward was observed, which was rather consistent with the monitoring results by logging and micro-seismic studies. Additionally, an integrated monitoring scheme, which will be the trend of monitoring in the future, is proposed in the discussion.

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