Interferometric Synthetic Aperture Radar Applications Research Articles

The landform Deformation at the Iraq-Iran Border due to Earthquakes in the Period (2017-2018)

The study targets to assess the potential of Interferometric Synthetic Aperture Radar (InSAR) in detecting ground distortion caused by earthquakes. The inSAR technique integrates Synthetic Aperture Radar (SAR) and interferometry to reveal the slightest changes on the earth's surface through a specified period, impregnable to climate or time of day. The research analyses some scholarly articles to demonstrate the interest of InSAR in examining earthquakes and their outcome. These articles demonstrate the practical application of InSAR in enhancing fault slip analysis, ground deformation assessment, and early post-seismic changes in earthquakes. InSAR is beneficial with measurements from other tools, such as GPS and time series analysis, but it still offers valuable insights into earthquake characteristics and associated effects. The insights from these methodologies can help devise solutions to mitigate the impacts of earthquakes. In addition, the paper presents information about three earthquake swarm events and their corresponding fault types. The first event occurred on November 12th, 2017, while the second happened on November 25th, 2018. Despite their differences in magnitude and location, the second event was associated with a strike-slip fault. InSAR provides extensive practical applications, from natural hazard monitoring to infrastructure projects and topographic mapping.

Application of Sentinel-1 InSAR to monitor tailings dams and predict geotechnical instability: practical considerations based on case study insights

Tailings storage facilities (TSFs) impound mining waste behind dams to ensure public safety, but failure incidents have prompted calls for more robust monitoring programs. Satellite-based interferometric synthetic aperture radar (InSAR) has grown in popularity due to its ability to remotely detect millimeter-scale displacements in most urban and some natural terrains. However, there remains a limited understanding of whether InSAR can be as accurate or representative as on-the-ground instruments, whether failures can be predicted in advance using InSAR, and what variables govern the quality and reliability of InSAR results. To address these gaps, we analyze open-source, medium-resolution Sentinel-1 data to undertake a ground-truth assessment at a test site and a forensic analysis of five failure cases. We use a commercial software with an automated Persistent Scatterer (PS) workflow (SARScape Analytics) for all case study sites except one and a proprietary algorithm (SqueeSAR) with a dual PS and Distributed Scatterer (DS) algorithm for the ground-truth site and one forensic case. The main goal is to deliver practical insights regarding the influence of algorithm/satellite selection, environmental conditions, site activity, coherence thresholds, satellite-dam geometry, and failure modes. We conclude that Sentinel-1 InSAR can serve as a hazard-screening tool to help guide where to undertake targeted investigations; however, most potential failure modes may not exhibit InSAR-detectable accelerations that could assist with time-of-failure prediction in real time. As such, long-term monitoring programs should ideally be integrated with a combination of remote sensing and field instrumentation to best support engineering practice and judgment.

Geomorphic understanding of loess landslides activity on the loess tableland:A case study in the Bailu Tableland, China

Loess landslides, predominantly triggered by human activity and precipitation, dominate the geomorphological evolution of extensive regions of the Chinese Loess Plateau. Tablelands constitute a large proportion of this Loess Plateau. Although the area has a long history of human occupation, the Bailu Tableland is relatively poorly studied. Recent landslide events along the edge of the tableland significantly affected lives and livelihoods. To develop a better understanding of geomorphological evolution and develop appropriate hazard management and mitigation strategies, we characterized recent landslide activity and determined whether there are discernable trends in activity. We used Unmanned Aerial Vehicle (UAV) observations, Interferometric Synthetic Aperture Radar (InSAR) monitoring, and Digital Terrain Analysis (DTA) to observe significant differences in the position, shape, and magnitude between old and new landslides. The geomorphological evolution of the tableland was initially driven by large landslides, while current and future morphological development is more likely affected by small landslide events. We demonstrate the successful application of InSAR to investigate potential landslides in well-vegetated loess landscapes and show that loess landslides tend to develop on slopes with high relative relief and high topographic wetness associated with gully erosion. Unlike landslides induced by groundwater variations in the Heifangtai and South Jingyang tablelands, precipitation and infrastructure construction form the main controls on landslide activity in the Bailu Tableland. We propose two geomorphological evolution models that describe loess tableland recession caused by landslides. The results from this research contribute to a better understanding of the reactivation behavior of landslides along loess tablelands, provide a scientific foundation for landslide hazard and risk management, and supplement understanding of the geomorphological evolution of loess tableland landscapes.

Dam Surface Deformation Monitoring and Analysis Based on PS-InSAR Technology: A Case Study of Xiaolangdi Reservoir Dam in China

The Xiaolangdi Dam is a key project for the control and development of the Yellow River. It bears the functions of flood control, controlling water and sediment in the lower reaches, ice prevention, industrial and agricultural water supply, power generation, and so on. Its safety is related to people’s life and property safety and local economic and social development. It is of great significance to carry out comprehensive and regular deformation monitoring for dams since the deformation is an important evaluation index for dam safety. Interferometric Synthetic Aperture Radar (InSAR) technology has been a rapidly evolving technology in the field of space geodesy in recent years. It offers advantages such as high monitoring precision, extensive coverage, and high monitoring point density, making it a powerful tool for monitoring deformations in hydraulic engineering projects. Based on Sentinel-1 data covering the Xiaolangdi Dam from September 2020 to November 2022, the PS-InSAR technique was used to obtain the surface deformation of the Xiaolangdi Dam, and reservoir water level data on image acquisition dates were obtained for joint analysis. The results show that there is a large deformation in the center of the dam crest of the Xiaolangdi Dam, while both sides of the slope and downstream dam foot are relatively stable. The time series deformation of the dam body is closely related to the reservoir water level change. When the water level increases, the dam body tends to deform downstream; when the water level decreases, the dam body tends to deform upstream. The deformation and water level of the Xiaolangdi Dam exhibit a clear negative correlation. There is no significant cumulative deformation on the dam slopes or at the base of the dam. However, cumulative deformation occurs over time in the central area of the dam’s crest. The deformation process at the central area of the dam’s crest follows a continuous and non-disruptive pattern, which is consistent with the typical deformation behavior of the Xiaolangdi earth–rock dam structure. Therefore, it is judged that the current deformation of the Xiaolangdi Dam does not impact the safe operation of the dam. InSAR technology enables the rapid acquisition of high-precision, high-density deformation information on the surfaces of reservoir dams. With an increasing number of radar satellites in various frequency bands, such as Sentinel-1 and TerraSAR-X, there is now an ample supply of available data sources for InSAR applications. Consequently, InSAR technology can be extended to routine monitoring applications for reservoir dam deformations, especially for small and medium-sized reservoirs that may not be equipped with ground measurement tools like GNSS. This holds significant importance and potential for enhancing the safety monitoring of such reservoirs.

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.

Relationship between Crustal Deformation and Thermal Anomalies in the 2022 Ninglang Ms 5.5 Earthquake in China: Clues from InSAR and RST

On 2 January 2022, an earthquake of Ms 5.5 occurred in Ninglang County, Lijiang City, the earthquake-prone area of northwestern Yunnan. Whether this earthquake caused significant deformation and thermal anomalies and whether there is a relationship between them needs further investigation. Currently, multi-source remote sensing technology has become a powerful tool for long-time-series monitoring of earthquakes and active ruptures which mainly focuses on single crustal deformation and thermal anomaly. This study aims to reveal the crustal deformation and thermal anomaly characteristics of the Ninglang earthquake by using both Interferometric Synthetic Aperture Radar (InSAR) and Robust Satellite Techniques (RST). First, Sentinel-1A satellite SAR data were selected to obtain the coseismic deformation field based on Differential InSAR (D-InSAR), and the Small Baseline Set InSAR (SBAS-InSAR) technique was exploited to invert the pre- and post-earthquake displacement sequences. Then, RST was used to extract the thermal anomalies before and after the earthquake by using Moderate Resolution Imaging Spectroradiometer Land Surface Temperature (MODIS LST). The results indicate that the seismic crustal deformation is dominated by subsidence, with 23 thermal anomalies before and after the earthquake. It is speculated that the Yongning Fault in the deformation area is the main seismogenic fault of the Ninglang earthquake, which is dominated by positive fault dip-slip motion. Meanwhile, the seismic fault system composed of NE- and NW-oriented faults is an important factor in the formation of thermal anomalies, which are accompanied by changes in stress at different stages before and after the earthquake. Moreover, the crustal deformation and seismic thermal anomalies are correlated in time and space, and the active rupture activities in the region produce deformation accompanied by changes in thermal radiation. This study provides clues from remote sensing observations for analyzing the Ninglang earthquake and provides a reference for the joint application of InSAR and RST for earthquake monitoring.

Bibliometric Analysis of Interferometric Synthetic Aperture Radar (InSAR) Application in Land Subsidence from 2000 to 2021

Land subsidence is one of the serious natural disasters which can cause heavy casualties and economic losses. As a vital method, Interferometric Synthetic Aperture Radar (InSAR) can provide quick and efficient solutions for analysis. Currently, most reviews on InSAR application in land subsidence only focused on single types of land areas, such as the land around groundwater and land of the mining area. There is a lack of discussion on all types of land areas. This study thus aims at conducting a bibliometric literature analysis of the existing literature from 2000 to 2021 to fill this gap. The authors used scientific mapping methods to analyze the InSAR applications in land subsidence so that researchers and practitioners can comprehend the procedure. Then, the authors identified the major research areas, development milestones, evolutionary stages, and the transaction dynamics of evolutionary stages. Knowledge maps of five aspects were applied and analyzed in this research, including temporal development analysis, countries and institutions, major research disciplines, high-frequency terms, and cocitation of high-citation papers. The results reveal that the research of land subsidence monitoring with InSAR is in the stage of diffusion from developing many tools and techniques to integrating with other research areas. Overall, the bibliometric results combined with evolutionary stages provide a holistic picture of the status quo and future trends in InSAR application in land subsidence.

Landslide Identification and Gradation Method Based on Statistical Analysis and Spatial Cluster Analysis

As a type of earth observation technology, interferometric synthetic aperture radar (InSAR) is increasingly widely used in the field of geological disaster detection. However, the application of InSAR in low-coherence areas, such as alpine canyon areas and vegetation coverage areas, is subject to considerable limitations. How to accurately identify landslides from InSAR measurement data in these areas remains the subject of several challenges and shortcomings. Based on statistical analysis and spatial cluster analysis, in this paper, we propose an automatic landslide identification and gradation method suitable for low-coherence areas. The proposed method combines the small baseline subset InSAR (SBAS-InSAR) method and the interferogram stacking (stacking-InSAR) method to obtain a deformation map in the study area, using statistical analysis and spatial cluster analysis to extract deformation regions and landslide polygons to propose a landslide screening model (LSM) based on multivariate features to screen landslides and reduce the interference of noise in landslide identification, in addition to proposing a landslide gradation model (LGM) based on signum function to grade the identified landslides and provide support to distinguish landslides with different deformation degrees. The method was applied to landslide identification in the upper section of the Jinsha River basin, and 47 potential landslides were identified, including 15 high-risk landslides and 13 landslides endangering villages. The experimental results show that the proposed method can identify landslides accurately and hierarchically in low-coherence areas, providing support for geological hazard investigation agencies and local departments.

An InSAR Interferogram Filtering Method Based on Multi-Level Feature Fusion CNN.

Interferogram filtering is an essential step in processing data from interferometric synthetic aperture radar (InSAR), which greatly improves the accuracy of terrain reconstruction and deformation monitoring. Most traditional interferogram filtering methods achieve noise suppression and detail preservation through morphological estimation based on the statistical properties of the interferogram in the spatial or frequency domain. However, as the interferogram’s spatial distribution is diverse and complex, traditional filtering methods struggle to adapt to different distribution and noise conditions and cannot handle detail preservation and noise suppression simultaneously. The study proposes a convolutional neural network (CNN)-based multi-level feature fusion model for interferogram filtering that differs from the traditional feedforward neural network (FNN). Adopting a multi-depth multi-path convolution strategy, the method preserves phase details and suppresses noise during interferogram filtering. In filtering experiments based on simulated data, qualitative and quantitative evaluations were used to validate the performance and generalization capabilities of the proposed method. The method’s applicability was evaluated by visual observation during filtering and unwrapping experiments on real data, and the time-series deformation acquired by time series (TS)-InSAR technique is used to evaluate the effect of interferogram filters on deformation monitoring accuracy. Compared to commonly used interferogram filtering methods, the proposed method has significant advantages in terms of performance and efficiency. The study findings suggest new directions for research on high-precision InSAR data processing and provide technical support for practical applications of InSAR.

World-wide InSAR sensitivity index for landslide deformation tracking

• Sensitivity index: a priori assessment of InSAR geometry at any landslide. • 91% of the world’s slopes allow for displacement monitoring by InSAR. • Detection potential: assess the ability to extract InSAR deformation time series. • A Google Earth Engine app for world-wide semi-automated assessment of InSAR potential. Landslides are a major geohazard in hilly and mountainous environments. In-situ inspection of downslope motion is costly, sometimes dangerous and, requires prior knowledge of the existence of a landslide. Remote sensing from space is a way to detect and characterize landslides systematically at large scale. Interferometric Synthetic Aperture Radar (InSAR) has shown to be a valuable resource of deformation information, but it requires expert knowledge and considerable computational efforts. Moreover, the successful application of InSAR for landslides requires a favorable acquisition geometry relative to the landslide deformation pattern. Consequently, there is a need for a widely applicable tool to assess the potential of InSAR at a particular location a priori. Here we present a novel, generic approach to assess the potential of InSAR-based deformation tracking, providing a standardised and automated method applicable on any slope. We define the detection potential as the sensitivity of InSAR to detect downslope displacement combined with the presence of coherently scattering surfaces. We show that deformation can be detected on at least 91% of the global landslide-prone slopes, and provide an open source Google Earth Engine tool for the quick assessment of the availability of potential coherent scatterers. This tool enables any person interested in applying InSAR to routinely assess the potential for monitoring landslide deformation in their region of interest.

A Deep Convolutional Neural Network With Multiscale Feature Dynamic Fusion for InSAR Phase Filtering

Interferometric phase filtering is a crucial step in the interferometric synthetic aperture radar (InSAR) data processing, which is also important for improving the accuracy of topography mapping and deformation monitoring. Most of the commonly used phase filtering methods perform windowing computations based on the statistical characteristics of a single interferogram in the spatial or frequency domain. However, the difficulty in taking into account the diversity and complexity of the phase image results in filtering methods with weak denoising, limited detail preservation, and poor generalization ability. At the same time, regardless of the spatial or frequency domain, improved phase filtering performance inevitably leads to the problem of declining effectiveness. This paper proposes a phase filtering method (MSFF-DCNN) based on the deep convolution neural network (DCNN) with Multi-scale feature dynamic fusion. Unlike the traditional feedforward neural networks (FNN), the proposed method adopts a strategy of multi-scale feature dynamic fusion that accounts for the deep and shallow features of the interferometric phase while also taking into account image detail preservation and noise suppression during phase filtering. Based on both subjective and objective evaluations, the experimental results using the simulated data prove that the proposed method has better noise suppression and detail preservation than the commonly used methods and that the filtering performance is less dependent on noise level. Experiments using the real data confirm that the proposed method has better generalization ability and can meet the precision requirements of practical applications. The method presented in this paper can provide a new approach for research in high-precision InSAR data processing technology while also offering technical support for practical InSAR applications.

Unveiling unknowns: Practical application of InSAR for slope performance monitoring and risk management across multiple surface mines

The ability to monitor and predict slope failures in a surface mining setting is paramount for safely executing economically feasible open pit slope designs. Ground-based monitoring systems have proven to be effective for decades; however, with the geographical extensive nature of some mineral deposits, multiple open pit mining operations typically incur additional challenges of deploying and maintaining ground-based systems (compared with a single, large open pit). Monitoring strategies in the 2020s have significant opportunity to integrate satellite based InSAR (interferometric synthetic aperture radar) as part of the control measures depending on the expected failure mechanisms and risk profile. This paper uses case studies to explore the practical application of InSAR to ‘unveil unknowns’ for multiple open pit mining operations where ground monitoring capacity was previously constrained. Case studies include the retrospective analysis of historic slope failures, improving understanding of existing geotechnical hazards and improving engineering geological and slope stability models based on real slope performance data.

Topography-correlated atmospheric signal mitigation for InSAR applications in the Tibetan plateau based on global atmospheric models

ABSTRACT Atmospheric heterogeneity mainly exposes itself as tropospheric-phase delay in Satellite Interferometric Synthetic Aperture radar (InSAR) observations, which smears or even overshadows the deformation component of InSAR measurements. In this study, we estimated the performance of four Global Atmospheric Models (GAMs), i.e. European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5), ERA-Interim (ERA-I), Modern-Era Retrospective analysis for Research and Applications v2 (MERRA2) and Generic Atmospheric Correction Online Service for InSAR (GACOS), for tropospheric-phase delay reduction in InSAR applications in the Tibetan plateau, of which ERA5 is the latest GAM released by ECMWF. We demonstrated the effectiveness of Atmospheric Phase Screen (APS) correction using the four GAMs for more than 700 Sentinel-1 Terrain Observation by Progressive Scans interferograms covering two study areas in the southern (R1) and northwest margins (R2) of the Tibetan plateau. Topography-correlated signals have been widely observed in these interferograms, which are most likely due to the APS effects. We calculated the Standard Deviations (SD) and Pearson’s Correlation Coefficients (r) between InSAR Line of Sight measurements and topography before and after applying APS correction. The results show that the SDs of non-deformation areas from the GAMs decrease to approximately 4 mm from around 10 mm and 12 mm originally on average for R1 and R2, respectively, and the r after the APS correction are reduced below 0.4 from around 0.8 for the selected interferometric pairs. In addition, as the newly released GAM, ERA5 has similar performance with GACOS products and outperforms other models generally. This suggests that GAMs, particularly ERA5, have great potential in the APS correction for InSAR applications in the Tibetan plateau.

Complex-Valued Reservoir Computing for Interferometric SAR Applications With Low Computational Cost and High Resolution

Synthetic aperture radar (SAR) is widely used for ground surface classification since it utilizes information on vegetation and soil unavailable in optical observation. Image classification often employs convolutional neural networks. However, they have serious problems such as long learning time and resolution degradation in their convolution and pooling processes. In this paper, we propose complex-valued reservoir computing (CVRC) to deal with complex-valued images in interferometric SAR (InSAR). We classify InSAR image data by using CVRC successfully with a higher resolution and a lower computational cost, i.e., one-hundredth learning time and one-fifth classification time, than convolutional neural networks. We also conduct experiments on slope angle estimation. CVRC is found applicable to quantitative tasks dealing with continuous values as well as discrete classification tasks with a higher accuracy.

Statistical assessment metrics for InSAR atmospheric correction: Applications to generic atmospheric correction online service for InSAR (GACOS) in Eastern China

The atmospheric effect represents one of the major error sources in Interferometric Synthetic Aperture Radar (InSAR), and its mitigation is found crucial for high-precision InSAR applications. Numerous studies on InSAR atmospheric correction methods and applications covering a wide range of regions worldwide have been reported with varying degrees of success. However, more efforts on the performance assessment are needed, and the conclusions may lack statistical significance due to the limited interferograms involved in most of these analyses. To optimally utilise different InSAR atmospheric correction methods and avoid potential uncertainty caused, appropriate statistical metrics to assess the correction performance must be set up. In this work, we provide a general guideline for statistical assessment of InSAR atmospheric correction. Based on the physical properties of the atmosphere, three metrics are applied: (i) the phase standard deviation which assesses the overall performance of the correction; (ii) the spatial structure function which evaluates the reduction of the long-wavelength atmospheric effect; and (iii) the phase-elevation correlation coefficient which measures the reduction of the stratified component of the atmospheric delay. The performance of the Generic Atmospheric Correction Online Service for InSAR (GACOS) products for two typical terrains in Eastern China is evaluated. Statistical results of the 1250 Sentinel-1 interferograms covering the Yellow River Delta and Shandong hilly region show that (i) GACOS reduces the interferometric phase standard deviation in 84.6% of the interferograms by an average of 36.4%; (ii) the phase decorrelation distance decreases from 321 km to 225 km on average after correction; and (iii) the mean phase-elevation correlation declines by 33.3% for the areas with considerable topographic variations. The results verify the effectiveness of GACOS products in Eastern China for the first time, and the three proposed metrics further characterise the sources of improvement after correction.

Using InSAR to identify hydrological connectivity and barriers in a highly fragmented wetland

AbstractHydrological connectivity is a critical determinant of wetland functions and health, especially in wetlands that have been heavily fragmented and regulated by human activities. However, investigating hydrological connectivity in these wetlands is challenging due to the costs of high‐resolution and large‐scale monitoring required in order to identify hydrological barriers within the wetlands. To overcome this challenge, we here propose an interferometric synthetic aperture radar (InSAR)‐based methodology to map hydrologic connectivity and identify hydrological barriers in fragmented wetlands. This methodology was applied along 70 transects across the Baiyangdian, the largest freshwater wetland in northern China, using Sentinel 1A and 1B data, covering the period 2016–2019. We generated 58 interferograms providing information on relative water level changes across the transects that showed the high coherence needed for the assessment of hydrological connectivity. We mapped the permanent and conditional (temporary) barriers affecting connectivity. In total, 11% of all transects are permanently disconnected by hydrological barriers across all interferograms and 58% of the transects are conditionally disconnected. Areas covered by reed grasslands show the most undisturbed hydrological connectivity while some of these barriers are the result of ditches and channels within the wetland and low water levels during different periods of the year. This study highlights the potential of the application of Wetland InSAR to determine hydrological connectivity and location of hydrological barriers in highly fragmented wetlands, and facilitates the study of hydrological processes from large spatial scales and long‐time scales using remote sensing technique.

InSAR monitoring surface deformation induced by underground mining using Sentinel-1 images

Abstract. Land subsidence can be caused by underground mining activities. Interferometric Synthetic Aperture Radar (InSAR) has became an economic, effective and accurate technique for land deformation survey and monitoring. In mining areas, there may be several factors to overcome for the succsessful application of InSAR, such as temporal decorrelation and detectable deformation gradient, that limit the ability of InSAR to monitoring rapid land subsidence. In this paper, images obtained by the Sentinel-1 satellite with 6 or 12 d revisiting time are used to improve the ability to detect a deformation gradient, and reduce the influence of temporal decorrelation. By combining Small Baseline Subsets (SBAS) and Interferometric Point Target Analysis (IPTA) methods, using the Nanhu mining area in Tangshan as an example, the spatial continuous results of land subsidence in this mining area are obtained with a 70 cm per year maximum rate, which clearly characterizes the deformation field and its deformation process. The results show that InSAR is a useful way to monitor land subsidence in a mining area and provides further data for environment mine restoration.

Interferometric SAR for Wetland Hydrology: An Overview of Methods, Challenges, and Trends

Since 2000, interferometric synthetic-aperture radar (InSAR) has been an effective tool to map 2D water-level changes beneath vegetated wetlands with a high spatial resolution and centimetric accuracy. In the last two decades, SAR images obtained from different wavelengths and polarization modes have been tested across various wetland systems, and InSAR-derived water-level-change maps have improved our understanding of wetland hydrology and hydraulics. In this article, we discuss the feasibility and challenges of applying InSAR to wetland areas, present a comprehensive review of InSAR applications for hydraulic and hydrologic analysis, and discuss future trends.

Bare Earth DEM Generation for Large Floodplains Using Image Classification in High-Resolution Single-Pass InSAR

The increasing frequency and severity of flood events demand improved accuracy of hydrodynamic models to better mitigate the societal and economic consequences of these disasters. Ideally, these models derive elevation data from high-accuracy LiDAR, which often captures the subtle variations in elevation and microtopography—elements that are critical to high-resolution hydrodynamic modeling. Due largely to the cost of acquisitions, these data, however, are not widely available at high spatial resolutions for large-scale areas, i.e. floodplains and coastal regions, especially outside of the U.S. and Western Europe, where flood-prone communities already lack the infrastructure and resources to manage these disasters. High-resolution interferometric synthetic aperture radar (InSAR) systems may offer a viable and cost-effective alternative to LiDAR, with comparable spatial resolutions and vertical accuracies. Like any swath mapping sensor, systematic errors in calibration knowledge increase as the lever-arm increases when viewing further off vertical. InSAR-derived digital elevation models (DEMs) are often corrected using LiDAR or ground control points, although this limits the application of InSAR to those areas where these data exist. In this paper, we present a novel approach for using near-range InSAR data to correct inherent systematic bias that propagates across adjacent, overlapping swaths for the same airborne acquisition. This eliminates the need for LiDAR in generating InSAR DEMs, while maintaining a vertical error of approximately 0.26 m relative to LiDAR, at a spatial resolution of 30 m. Data was acquired using the NASA/JPL airborne, single-pass, Ka-band GLISTIN-A (Glacier and Ice Surface Topography Interferometer) InSAR over the Red River Basin (RRB), ND. The accuracy of the final DEM is evaluated using National Geodetic Survey (NGS) GPS and a high-resolution LiDAR DEM.

Environmental baseline monitoring for shale-gas development: Insights for monitoring ground motion using InSAR analysis

Shale gas operations can be contentious, with a degree of uncertainty regarding the effects that they may, or may not, have on the environment. Several countries have moratoria on hydraulic fracturing until its potential effects can be understood better. One area of debate is whether operations could cause ground motion at the surface. This research monitored ground motion prior to operations and compared that baseline to the situation during and after shale gas operations. The test sites are the Vale of Pickering (North Yorkshire) and the Fylde (Lancashire) in the UK. Planning permission was granted in May 2016 to undertake hydraulic fracturing near Kirby Misperton (Vale of Pickering) and in August 2018 at Preston New Road in Lancashire. Hydraulic fracturing has only taken place at Lancashire as it was the only site to also get the hydraulic fracturing plan approved. Complementary Interferometric Synthetic Aperture Radar (InSAR) techniques were used to process archive and current satellite images to detect relative ground motion with millimetric accuracy in rural and semi-urban landcover. The SBAS, ISBAS and RapidSAR processing for the period from 1992 to 2019 (extending 24 years prior to hydraulic fracturing) identified broad regions with little or no surface motion, along with discrete zones of uplift or subsidence. Analysis of the average velocities and time-series data revealed that the motion, where it occurred, related to factors including compressible ground, groundwater abstraction and underground coal mining. This research concluded that the shale gas operations in Lancashire did not alter the baseline ground motion dynamics to date, as detected by InSAR. The successful application of InSAR for detecting and monitoring ground motion at shale gas sites in rural landcover in the UK, where radar coherence has traditionally been a major challenge, serves as a precedent for other regions where baseline monitoring is required.