Ground-based Interferometric Synthetic Aperture Radar Research Articles

Rectifying Discrepancies Between GB-SAR Images and Terrain Model Caused by Measurement Deviations in Open-Pit Mines

Ground-based Interferometric Synthetic Aperture Radar (GB-InSAR) is a valuable technique for monitoring deformation of landslides. GB-InSAR can construct high-resolution 2D images in a range-doppler plane. However, it is difficult for geo-engineers who are unfamiliar with radar monitoring geometry to interpret the results. Geometric mapping method has been applied to the co-registration of terrain models and radar images for the deformation zonation, but the mismatching correction with GB-InSAR and terrain model is not fully investigated by existing researches. To address this issue, this paper proposes a method exploiting an optimum linear transformation based on ground control points (GCPs). The proposed method was evaluated on simulated data and a field campaign in an open pit mine. The deformation mapping result of the method was verified by a collapse event. The method proposed in this paper has an RMSE value ranging from 0.502 to 0.720[Formula: see text]m (@700[Formula: see text]m average monitoring range) when the average density of the terrain point cloud is 0.5[Formula: see text]m. Although this method cannot accurately evaluate the antenna footprint vector deflection parameters, it can meet the needs of coarse matching calibration in relatively flat slope sub-target areas of open-pit mines for engineering applications.

A tool for estimating ground-based InSAR acquisition characteristics prior to monitoring installation and survey and its differences from satellite InSAR

Abstract. Synthetic Aperture Radar (SAR) acquisition can be performed from satellites or from the ground by means of a so-called GB-InSAR (Ground-Based Interferometry SAR), but the signal emission and the output image geometry slightly differ between the two acquisition modes. Those differences are rarely mentioned in the literature. This paper proposes to compare satellite and GB-InSAR in terms of (1) acquisition characteristics and parameters to consider; (2) SAR image resolution; and (3) geometric distortions that are foreshortening, layover, and shadowing. If in the case of satellite SAR, the range and azimuth resolutions are known and constant along the orbit path, in the case of GB-InSAR their values are terrain-dependent. It is worth estimating the results of a GB-InSAR acquisition that one can expect in terms of range and azimuth resolution, line of sight (LoS) distance, and geometric distortions to select the best installation location when several are possible. We developed a novel tool which estimates those parameters from a digital elevation model (DEM), knowing the GB-InSAR and the slope of interest (SoI) coordinates. This tool, written in MATLAB, was tested on a simple synthetic point cloud representing a cliff with a progressive slope angle to highlight the influence of the SoI geometry on the acquisition characteristics and on two real cases of cliffs located in Switzerland, namely one in the Ticino canton and one in the Vaud canton.

Seismic and thermal precursors of crater collapses and overflows at Stromboli volcano

Lava overflows are highly hazardous phenomena that can occur at Stromboli. They can destabilize the crater area and the “Sciara del Fuoco” unstable slope, formed by several sector collapses, which can generate potentially tsunamigenic landslides. In this study, we have identified precursors of the October-November 2022 effusive crisis through seismic and thermal camera measurements. We analyzed the lava overflow on October 9, which was preceded by a crater-rim collapse, and the overflow on November 16. In both cases, seismic precursors anticipating the overflow onset have been observed. The analysis of the seismic and thermal data led to the conclusion that the seismic precursors were caused by an escalating degassing process from the eruptive vent, which climaxed with the overflows. Volcano deformation derived from ground-based InSAR and strainmeter data showed that inflation of the crater area accompanied the escalating degassing process up to the beginning of the lava overflows. The inflation of the crater area was especially evident in the October 9 episode, which also showed a longer seismic precursor compared to the November 16 event (58 and 40 min respectively). These results are important for understanding Stromboli’s eruptive mechanisms and open a perspective for early warning of potentially dangerous phenomena.

Prediction analysis of landslide displacement trajectory based on the gradient descent method with multisource remote sensing observations

Landslides can result in extensive casualties and huge economic losses. The accurate discrimination of the main slip direction and deformation trajectory is an important prerequisite for studying landslide formation mechanisms and designing landslide control schemes. In the process of landslide evolution over time, the main slip direction also changes dynamically and provides a comprehensive reflection of the landslide displacement state. However, few studies on this topic have been published. In this paper, a new methodology for analyzing slope stability is proposed based on three techniques: interferometric synthetic aperture radar (InSAR), unmanned aerial vehicle (UAV), and ground-based interferometric synthetic aperture radar (GB-InSAR). The Small Baseline Subset Interferometric SAR (SBAS-InSAR) technique is combined with an overall analysis of the study area to identify the regions of interest (ROIs) with large deformation and the starting target points, and the fusion results of radar deformation data (RDD) and digital surface model (DSM) data are used to fit the deformation surface field of the ROIs. The gradient descent approach is executed to obtain the running trajectory points of the target masses so that the main slip direction and displacement trajectory in the study area can be predicted at small scales. The measured data for the Hongshiyan landslide in Yunnan Province are used to verify the effectiveness of the method, and the predicted results are consistent with the actual landslide direction. The experimental results show that the method can exactly identify the deformation area, especially in the case of a fast-changing deformation trend. This approach can provide more accurate monitoring area results to support the rapid control and prevention of landslide hazards by analyzing the minimum pixel grid (i.e. points), as the smallest spatial unit at a time interval of minutes. The study shows that the method can efficiently combine space–Earth multisource monitoring data to clarify the main slide direction and improve the postslide trajectory prediction of the slope, which is beneficial for assessing disaster risks and improving landslide prevention and control effects, reflecting the engineering application value of the approach.

Multiple-input multiple-output radar, ground-based MIMO SAR for ground deformation monitoring

ABSTRACT This study focuses on investigating the capabilities of a Multiple-input multiple-output RADAR. A Radar interferometer, based on an electronically scanned array in MIMO configuration (MIMO‐SAR), has been assessed for operational use in monitoring phenomena of geological interest, such as landslides unstable slopes. The system applies the very well‐known and proven Ground-Based Interferometric technique. It guarantees a very short refreshing time compared to traditional systems based on the mechanical movement of the radar transceiver on a rail or the mechanical steering of a real antenna. The system can monitor several phenomena having deformation rates too high to be correctly retrieved by traditional systems currently in use. Implementing a prototype termed MELISSA allowed the testing technique’s performances in two real case studies: a landslide and an unstable volcanic flank. The experimental results were compared with LISA, a well-known Ground-Based Interferometric Synthetic Aperture Radar (GBInSAR) interferometer. MELISSA allows for obtaining an excellent accuracy, better than 0.01 mm. The range and angular resolution are on the same order of magnitude as those obtained through LISA. However, the refreshing rate obtained from MELISSA, 0.01 s, guarantees a strong coherence even in challenging environmental scenarios as a flank of an active volcano.

A Deep Learning Application for Deformation Prediction from Ground-Based InSAR

Ground-based synthetic aperture radar interferometry (GB-InSAR) has the characteristics of high precision, high temporal resolution, and high spatial resolution, and is widely used in highwall deformation monitoring. The traditional GB-InSAR real-time processing method is to process the whole data set or group in time sequence. This type of method takes up a lot of computer memory, has low efficiency, cannot meet the timeliness of slope monitoring, and cannot perform deformation prediction and disaster warning forecasting. In response to this problem, this paper proposes a GB-InSAR time series processing method based on the LSTM (long short-term memory) model. First, according to the early monitoring data of GBSAR equipment, the time series InSAR method (PS-InSAR, SBAS, etc.) is used to obtain the initial deformation information. According to the deformation calculated in the previous stage and the atmospheric environmental parameters monitored, the LSTM model is used to predict the deformation and atmospheric delay at the next time. The phase is removed from the interference phase, and finally the residual phase is unwrapped using the spatial domain unwrapping algorithm to solve the residual deformation. The predicted deformation and the residual deformation are added to obtain the deformation amount at the current moment. This method only needs to process the difference map at the current moment, which greatly saves time series processing time and can realize the prediction of deformation variables. The reliability of the proposed method is verified by ground-based SAR monitoring data of the Guangyuan landslide in Sichuan Province.

Partition of GB-InSAR deformation map based on dynamic time warping and $k$-means

Ground-based interferometric synthetic aperture radar (GB-InSAR) can take deformation measurement with a high accuracy. Partition of the GB-InSAR deformation map benefits analyzing the deformation state of the monitoring scene better. Existing partition methods rely on labelled datasets or single deformation feature, and they cannot be effectively utilized in GB-InSAR applications. This paper proposes an improved partition method of the GB-InSAR deformation map based on dynamic time warping (DTW) and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> -means. The DTW similarities between a reference point and all the measurement points are calculated based on their time-series deformations. Then the DTW similarity and cumulative deformation are taken as two partition features. With the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> -means algorithm and the score based on multi evaluation indexes, a deformation map can be partitioned into an appropriate number of classes. Experimental datasets of West Copper Mine are processed to validate the effectiveness of the proposed method, whose measurement points are divided into seven classes with a score of 0.315 1.

Deformation monitoring and evaluation of unstable slope based on ground-based and spaceborne SAR images

Ground-based and spaceborne interferometric synthetic aperture radar (InSAR) were combined to monitor slope deformation and analyze the main influencing factors of an unstable slope on the right bank of the Dajinchuan River in Danba County, Sichuan Province, China. We applied the small baseline subset time series strategy with 656 ground-based radar scenes acquired in September 13 to 17, 2019, and 62 Sentinel-1 InSAR scenes from July, 2018, to October, 2020. The qualitative and quantitative deformation features of the unstable slope were studied, and the possible influencing factors contributing to slope instability, including the geological structure, topography, external environment, and human activities, were discussed. The area of maximum deformation detected by the ground-based radar is located in the bedrock above the target area with a maximum cumulative deformation of more than 30 mm during the observation period, whereas the maximum deformation rate detected by spaceborne InSAR exceeds 40 mm/year. The differences between these results were analyzed, and the reasons were discussed. We provide references and suggestions for investigating potential landslide risks by combining ground-based with spaceborne InSAR.

An Improved Multi-Baseline Phase Unwrapping Method for GB-InSAR

Ground-based interferometric synthetic aperture radar (GB-InSAR) technology can be applied to generate a digital elevation model (DEM) with high spatial resolution and high accuracy. Phase unwrapping is a critical procedure, and unwrapping errors cannot be effectively avoided in the interferometric measurements of terrains with discontinuous heights. In this paper, an improved multi-baseline phase unwrapping (MB PU) method for GB-InSAR is proposed. This method combines the advantages of the cluster-analysis-based MB PU algorithm and the minimum cost flow (MCF) method. A cluster-analysis-based MB PU algorithm (CA-based MB PU) is firstly utilized to unwrap the clustered pixels with high phase quality. Under the topological constraints of a triangulation network, the connectivity graph of any non-clustered pixel is established with its adjacent unwrapped cluster pixels. Then, the absolute phase of these non-clustered pixels can be identified using the MCF method. Additionally, a spatial-distribution-based denoising algorithm is utilized to denoise the data in order to further improve the accuracy of the phase unwrapping. The DEM generated by one GB-InSAR is compared with that generated by light detection and ranging (LiDAR). Both simulated and experimental datasets are utilized to verify the effectiveness and robustness of this improved method.

Changes in the Eruptive Style of Stromboli Volcano before the 2019 Paroxysmal Phase Discovered through SOM Clustering of Seismo-Acoustic Features Compared with Camera Images and GBInSAR Data

Two paroxysmal explosions occurred at Stromboli on 3 July and 28 August 2019, the first of which caused the death of a young tourist. After the first paroxysm an effusive activity began from the summit vents and affected the NW flank of the island for the entire period between the two paroxysms. We carried out an unsupervised analysis of seismic and infrasonic data of Strombolian explosions over 10 months (15 November 2018–15 September 2019) using a Self-Organizing Map (SOM) neural network to recognize changes in the eruptive patterns of Stromboli that preceded the paroxysms. We used a dataset of 14,289 events. The SOM analysis identified three main clusters that showed different occurrences with time indicating a clear change in Stromboli’s eruptive style before the paroxysm of 3 July 2019. We compared the main clusters with the recordings of the fixed monitoring cameras and with the Ground-Based Interferometric Synthetic Aperture Radar measurements, and found that the clusters are associated with different types of Strombolian explosions and different deformation patterns of the summit area. Our findings provide new insights into Strombolian eruptive mechanisms and new perspectives to improve the monitoring of Stromboli and other open conduit volcanoes.

Time-Series Clustering Methodology for Estimating Atmospheric Phase Screen in Ground-Based InSAR Data

In multitemporal interferometric synthetic aperture radar (InSAR) applications, propagation delay in the troposphere introduces a major source of disturbance known as atmospheric phase screen (APS). This study proposes a novel framework to compensate for the APS from multitemporal ground-based InSAR data. The proposed framework first performs time-series clustering in accordance with the temporal APS behavior realized by the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -means clustering approach. In the second step, joint estimation of the APS and displacement velocity is performed. For this purpose, a novel interferometric signal model, including the APS modeled by the median profiles defined in each cluster, is proposed. The proposed framework is validated with the Ku-band ground-based synthetic aperture radar data sets measured over a mountainous area in Kumamoto, Japan. Tests on these data sets reveal that compared with the conventional approach, the presented approach improves displacement estimation accuracy under severe atmospheric conditions.

Application of Slope Radar (S-SAR) in Emergency Monitoring of the “11.03” Baige Landslide

On October 11 and November 3, 2018, the disaster chain of landslide-barrier lake occurred twice in Baige Village, Xizang Province. After the second sliding of the landslide, the danger of the landslide dam was eliminated by the manual excavation of the drain grooves. During this period, a ground-based interferometric synthetic aperture radar (GB-InSAR) called “S-SAR” was utilized for real-time monitoring and analyzing 48 selected target pixels on the residual deformation bodies of landslides (divided into K1, K2, and K3 deformation zones) for 8 days. Through the real-time deformation map of pixels in the monitoring area obtained by S-SAR, the ranges of five strong deformation regions were identified and delineated. Based on the apparent cumulative deformation-time curve of each target pixel, the overall deformation law of K1, K2, and K3 deformation zones could be monitored and analyzed in real time. Based on a curve graph of the deformation rate, acceleration, and time of each target pixel, the K1, K2, and K3 deformation zones were within a uniform deformation stage. Taking the target pixel point and the corresponding time in which the deformation rate and deformation acceleration had a large, abrupt jump at the same time as the position and time of the near-slip failure, the 11 positions and moments of the near-slip failure were counted. The results presented here may represent a workable reference for emergency monitoring and early warning of similar sudden geological disasters.

A Ground-Based Interferometric Synthetic Aperture Radar Design and Experimental Study for Surface Deformation Monitoring

A new ground-based interferometric synthetic aperture radar has been developed by iRadar Sdn Bhd, in collaboration with the Centre for Remote Sensing and Surveillance Technologies, Multimedia University, Malaysia. This ground-based radar operates at Ku-band, single VV-polarization, with range resolution up to 0.175 m and azimuth resolution of 5.81 mrad at 1 km. Indoor test and outdoor field measurements have been conducted to verify the performance and specifications of the developed ground-based synthetic aperture radar (GBSAR). This system can be deployed for surface deformation monitoring especially at the high-risk slope for landslide monitoring and detection. This article presents the basic theory of the GBSAR system, the hardware description, as well as some indoor and outdoor field measurement results of the developed system.

Monitoring and analysis of the exceptional displacements affecting debris at the top of a highly disaggregated rockslide

The activity of large rockslides is dependent on a combination of interplaying factors that control strain localization within the slope. In some cases, continued deformation may lead to widespread disaggregation of the slide mass (i.e., loss of cohesion and development of debris). Layers with markedly different composition and strength may eventually form, making it difficult to assess the actual nature of hazards and related risks posed to the valley bottom. This paper provides updated insights into the highly disaggregated and rapidly evolving Ruinon rockslide (Central Italian Alps) based on more than a decade of monitoring by means of a Ground-Based Interferometric Synthetic Aperture Radar (GBInSAR). The rockslide was recently affected by a prolonged period of exceptional surface velocities—consistently exceeding 1 m/day. Monitoring data are examined in order to estimate the thickness of the rapidly moving layer of upper chaotic debris by means of the balanced cross-section method as well as determine the effects of hydrological forcing on the slope displacements. Finite-element modelling is then used to derive hypotheses concerning the deformation behavior of the slide mass at greater depth and different elevations. It is suggested that the upper debris moves at rates several orders of magnitude higher than the underlying substrate, and that in relative terms the sensitivity to sliding of the two layers is similarly governed by the increase of piezometric levels in the spring/summer. On lower slopes, the activity of the upper debris appears to be also influenced by precipitation events that are not accompanied by a notable increase of the piezometric levels measured at the rear of the slide. Our findings show the importance of implementing long-term GBInSAR monitoring at challenging sites like Ruinon, where fieldwork and installation of instruments on the slide mass are not feasible.

Comparison of different atmospheric phase screen correction models in ground-based radar interferometry for landslide and open-pit mine monitoring

ABSTRACT Although the atmospheric compensation models developed to date have been generally robust and effective for InSAR, how to choose the right atmospheric correction method for ground-based InSAR is worth studying. This paper uses different methods for atmospheric phase screen (APS) compensation based on studies of ground-based radar (GBR) to zero-baseline acquisitions over the Woda landslide in Sichuan, China. This landslide has a steep topography, thick fog, turbulent rivers and strong rains. Data were acquired at the Ku-band using a GAMMA Portable Radar Interferometer II (GPRI-II) in multiple campaigns (4 July 2019–13 July 2019). In 2019, the Miyun open-pit mine was also studied; the maximum deformation reached 4.03 mm/hour. The collected data were processed within one unit based on the small baseline set (SBAS) approach. The experimental results show the following (1) For long-distance monitoring with complex atmospheric disturbances, range- and/or height-dependent models fail. (2) The iterative decomposition (ITD) method can effectively address complex atmospheric disturbances. (3) If the threshold of wavelength is set to be large, the ITD method becomes a stratified model.

Long-Term and Emergency Monitoring of Zhongbao Landslide Using Space-Borne and Ground-Based InSAR

This work presents the ideal combination of space-borne and ground-based (GB) Interferometric Synthetic Aperture Radar (InSAR) applications. In the absence of early investigation reporting and specialized monitoring, the Zhongbao landslide unexpectedly occurred on 25 July 2020, forming a barrier lake that caused an emergency. As an emergency measure, the GB-InSAR system was installed 1.8 km opposite the landslide to assess real-time cumulative deformation with a monitoring frequency of 3 min. A zone of strong deformation was detected, with 178 mm deformation accumulated within 15 h, and then a successful emergency warning was issued to evacuate on-site personnel. Post-event InSAR analysis of 19 images acquired by the ESA Sentinel-1 from December 2019 to August 2020 revealed that the landslide started in March 2020. However, the deformation time series obtained from satellite InSAR did not show any signs that the landslide had occurred. The results suggest that satellite InSAR is effective for mapping unstable areas but is not qualified for rapid landslide monitoring and timely warning. The GB-InSAR system performs well in monitoring and providing early warning, even with dense vegetation on the landslide. The results show the shortcomings of satellite InSAR and GB-InSAR and a clearer understanding of the necessity of combining multiple monitoring methods.

Joint exploitation of space-borne and ground-based multitemporal InSAR measurements for volcano monitoring: The Stromboli volcano case study

We present a joint exploitation of space-borne and ground-based Synthetic Aperture Radar Interferometry (InSAR) and Multi Temporal (MT) InSAR measurements for investigating the Stromboli volcano (Italy) deformation phenomena. In particular, we focus our analysis on three periods: a) the time interval following the 2014 flank eruption, b) the July–August 2019 eruption and c) the following post-eruptive phase. To do this, we take advantage from an unprecedented set of space-borne and ground-based SAR data collected from April 2015 up to November 2019 along two (one ascending and one descending) Sentinel-1 (S-1) tracks, as well as, in the same period, by two ground-based systems installed along the Sciara del Fuoco northern rim. Such data availability permitted us to first characterize the volcano long-term 3D deformation behavior of the pre-eruptive period (April 2015–June 2019), by jointly inverting the space-borne and ground-based InSAR measurements. Then, the GB-SAR measurements allowed us to investigate the sin-eruptive time span (3rd July 2019 – 30th August 2019) which revealed rapid deformation episodes (e.g. more than 30 mm/h just 2 min before the 3rd July 2019 explosion) associated with the eruptive activity, that cannot be detected with the weekly S-1 temporal sampling. Finally, the S-1 measurements permitted to better constrain the post 2019 eruption deformations (31st August 2019 – 5th November 2019), which are mainly located outside the GB-SAR sensed area. The presented results demonstrate the effectiveness of the joint exploitation of the InSAR measurements obtained through satellite and terrestrial SAR systems, highlighting their strong complementarity to map and interpret the deformation phenomena affecting volcanic areas.

Corrections to “Iterative Atmospheric Phase Screen Compensation for Near-Real-Time Ground-Based InSAR Measurements Over a Mountainous Slope”

Corrections to “Iterative Atmospheric Phase Screen Compensation for Near-Real-Time Ground-Based InSAR Measurements Over a Mountainous Slope”

Updated understanding of the deformation characteristics of the Checkerboard Creek rock slope through GB-InSAR monitoring

Ground-based, interferometric, synthetic aperture radar (GB-InSAR) has been successfully implemented for the monitoring and assessment of both natural and man-made slopes. Its advantages include high-frequency data acquisition and high spatial density, providing maps of displacement. The implementation of this tool for civil projects in remote areas and under severe weather conditions present several challenges. This paper presents the implementation of GB-InSAR, particularly using discrete monitoring campaigns and temporal aggregation of data, for resolving logistical issues associated with power availability. This was implemented to monitor a very slow-moving (average displacement rate of ~10 mm/y) 2 to 3 million m3 bedrock landslide known as The Checkerboard Creek Rock Slope located within the Revelstoke dam reservoir, BC, Canada. GB-InSAR deployment has been successful as it confirmed the toppling-like deformation mode, improved definition of the most active area within the exposed slope cut, improved understanding of the seasonality observed in some instruments at the site, and identification of a potential unstable block north of the known active area. Further, the findings in this paper verify the feasibility of implementing GB-InSAR technology to monitor similar slopes associated with the safety of civil infrastructure and in challenging conditions characteristic of the Canadian environment.

GB-InSAR monitoring of vegetated and snow-covered slopes in remote mountainous environments

As a member of the International Consortium on Landslides, the University of Alberta is actively working to improve technologies for monitoring natural hazards. Though ground-based, interferometric, synthetic aperture radar (GB-InSAR) has been successfully implemented for near real-time monitoring of natural and man-made slopes, it has seen limited use in Canada outside of the mining industry. This lack of adoption in the rural areas of Canada is due to difficulties associated with providing continuous power to remote sites, other logistical challenges, and concerns with the impact of snow cover on the data collected. This paper presents the results of a project to install a GB-InSAR system to monitor a 2 to 3 million m3 bedrock landslide with a measured average rate of displacement of ~ 10 mm/year. The site is characterized by long periods of deep snow cover, cold temperatures, no connection to the electrical grid, and a location within a steep valley that limits hours of daylight for solar power. Challenges with, and solutions developed for, the deployment of GB-InSAR equipment at this site are described, and the results of the monitoring program are validated with existing geotechnical instrumentation.