Since its inception 30 years ago perhaps no innovation in radar technology has made such a tremendous impact on the field as that of radar interferometry. Radar interferometry uses two or more observations separated in either time or space to measure fraction of a wavelength scale range differences between the two observations. Radar interferometry is used by scientific, commercial and government institutions for numerous applications including topographic map generation, surface deformation mapping, landslide monitoring, current velocity measurement, vegetation structure determination and change detection. Radar interferometers can be flown on either spaceborne or airborne platforms or be fixed observing systems. This course is designed to provide an overview of the basic concepts of radar interferometry and an introduction to some of its applications. The course will cover basic radar imaging principles, a geometric and imaging signal perspective of the interferometric phase, interferometric correlation, basic sensitivity equations, phase unwrapping, topographic mapping and repeat pass interferometry for deformation measurements. The principles will be illustrated with examples from both spaceborne and airborne interferometric data sets. An overview of some of the major applications of radar interferometry will be presented with an emphasis on topographic and deformation mapping. The course will also briefly touch upon permanent scatter methods and polarimetric interferometry.

Radar interferometry

Terrestrial Radar Interferometry (TRI) is a measurement technique capable of measuring displacements with high temporal resolution at high accuracy. Current implementations of TRI use large and/or movable antennas for generating two-dimensional displacement maps. Multiple Input Multiple Output Synthetic Aperture Radar (MIMO-SAR) systems are an emerging alternative. As they have no moving parts, they are more easily deployable and cost-effective. These features suggest the potential usage of MIMO-SAR interferometry for structural health monitoring (SHM) supplementing classical geodetic and mechanical measurement systems. The effects impacting the performance of MIMO-SAR systems are, however, not yet sufficiently well understood for practical applications. In this paper, we present an experimental investigation of a MIMO-SAR system originally devised for automotive sensing, and assess its capabilities for deformation monitoring. The acquisitions generated for these investigations feature a 180∘ Field-of-View (FOV), distances of up to 60 m and a temporal sampling rate of up to 400 Hz. Experiments include static and dynamic setups carried out in a lab-environment and under more challenging meteorological conditions featuring sunshine, fog, and cloud-cover. The experiments highlight the capabilities and limitations of the radar, while allowing quantification of the measurement uncertainties, whose sources and impacts we discuss. We demonstrate that, under sufficiently stable meteorological conditions with humidity variations smaller than 1%, displacements as low as 25 μm can be detected reliably. Detecting displacements occurring over longer time frames is limited by the uncertainty induced by changes in the refractive index.

MIMO-SAR Interferometric Measurements for Structural Monitoring: Accuracy and Limitations

Terrestrial radar interferometry (TRI) can measure displacements at high temporal resolution, potentially with high accuracy. An application of this method is the observation of the surface flow velocity of steep, fast-flowing aglaciers. For these observations, the main factor limiting the accuracy of TRI observations is the spatial and temporal variabilities in the distribution of atmospheric water vapor content, causing a phase delay [atmospheric phase screen (APS)] whose magnitude is similar to the displacement phase. This contribution presents a geostatistical analysis of the spatial and temporal behaviors of the APS in Ku-Band TRI. The analysis is based on the assumption of a separable spatiotemporal covariance structure, which is tested empirically using variogram analysis. From this analysis, spatial and temporal APS statistics are estimated and used in a two-step procedure combining regression-Kriging with generalized least squares (GLS) inversion to estimate a velocity time-series. The performance of this method is evaluated by cross-validation using phase observations on stable scatterers. This analysis shows a considerable reduction in residual phase variance compared with the standard approach of combining the linear models of APS stratification and interferogram stacking.

Geostatistical Analysis and Mitigation of the Atmospheric Phase Screens in Ku-Band Terrestrial Radar Interferometric Observations of an Alpine Glacier

Full coverage and continuous deformation information retrieval are key aspects for dam health diagnosis Ground-based synthetic aperture radar (GB-SAR) interferometry is used for the remote monitoring of the Geheyan Dam, China Although the monitoring of a dam with ground-based interferometry is not an innovation, specific issues have been found out in the case study discussed due to the large dimension of the monitored structure More than 400 images were used for interferogram generation Radar signals reflected from the dam were carefully analyzed: a sort of tunneling effect caused by multireflection is observed, and deformations caused by water level and temperature variations were detected during a six-day monitoring campaign Radar monitoring results were compared to the data recorded by plummets installed in the dam The agreement between the displacements retrieved from interferometric data and the plummets demonstrates the capability of GB-SAR for deformation monitoring, with the advantage of large area coverage

Ground-based synthetic aperture radar interferometry for deformation monitoring: a case study at Geheyan Dam, China

In this article, an atmospheric phase screen (APS) compensation algorithm for a near real-time ground-based interferometry synthetic aperture radar (GB-InSAR) over a mountainous area is investigated. A novel APS compensation scheme is proposed to compensate the fluctuated APS caused by a spatial 3-D inhomogeneous refractivity index distribution without any a priori knowledge of moving location. The proposed method simultaneously addresses to identify moving pixels by a criterion of absolute velocity estimated by the coherent pixels technique (CPT). The proposed method consists mainly of three steps: 1) the stratified APS compensation; 2) identification of moving pixel candidate; and 3) the residual APS [remained APS after 1)] compensation by Kriging interpolation. The steps mentioned above are iteratively applied in order to increase the accuracy of the whole process. In this framework, we develop the 2-D quadratic polynomial model of the refractivity index with respect to slant range and topographic height for modeling the stratified APS. Furthermore, a prediction of the residual APS is achieved by applying the intrinsic random function of order $k$ (IRF- $k$ ) Kriging interpolation, taking into account the nonstationarity of the residual APS. We evaluate the proposed method using zero-baseline GB-differential InSAR (GB-DInSAR) data over a mountainous area located in Minami-Aso, Kumamoto, Japan, through the near real-time post-landslide measurement campaign.

/pdf/iterative-atmospheric-phase-screen-compensation-for-near-3trm1g4h6y.pdf

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

Abstract The benefits of terrestrial radar interferometry (TRI) for deformation monitoring are restricted by the influence of changing meteorological conditions contaminating the potentially highly precise measurements with spurious deformations. This is especially the case when the measurement setup includes long distances between instrument and objects of interest and the topography affecting atmospheric refraction is complex. These situations are typically encountered with geo-monitoring in mountainous regions, e.g. with glaciers, landslides or volcanoes. We propose and explain an approach for the mitigation of atmospheric influences based on the theory of intrinsic random functions of order k (IRF-k) generalizing existing approaches based on ordinary least squares estimation of trend functions. This class of random functions retains convenient computational properties allowing for rigorous statistical inference while still permitting to model stochastic spatial phenomena which are non-stationary in mean and variance. We explore the correspondence between the properties of the IRF-k and the properties of the measurement process. In an exemplary case study, we find that our method reduces the time needed to obtain reliable estimates of glacial movements from 12 h down to 0.5 h compared to simple temporal averaging procedures.

Intrinsic random functions for mitigation of atmospheric effects in terrestrial radar interferometry

Investigating surface displacements in high alpine environments is often subject to challenges due to the difficult accessibility or harsh climatic conditions. Measurement systems have improved greatly in recent years regarding accuracy, range, or energy consumption. Continuously receiving high-precision, real-time monitoring data from a remote location can still support a better understanding of slope dynamics and risk. We present the design, construction, operation, and performance of a complex surface displacement monitoring system installed in the surroundings of the Great Aletsch Glacier in the Swiss Alps, based on two robotic total stations to continuously measure 3D displacements with high accuracies. In addition, GNSS stations are also considered in order to pass from a local to a geographic reference system, as well as to improve the measurement accuracy. The monitoring network is aimed at studying several types of deformation processes, i.e., (i) gravitationally driven and irreversible rockslide movements around the tongue of the Great Aletsch Glacier, (ii) reversible rock slope deformations caused by annual cycles of groundwater recharge and depletion, and (iii) small irreversible deformations of stable rock slopes resulting from progressive rock damage driven by glacier retreat and cyclic hydraulic and thermal loading. We describe the technical details of the monitoring system, which has been in operation successfully for 6 years, and discuss the system performance in terms of its robustness and accuracy.

Robotic Total Station Monitoring in High Alpine Paraglacial Environments: Challenges and Solutions from the Great Aletsch Region (Valais, Switzerland)

The interferometric phase obtained from terrestrial radar data allows measuring the line-ofsight deformation component of surfaces discretized in azimuth-range bins. The amplitude information representing the strength of the backscattered signals is typically used for quality assessment of the phase. However, it can also be used to directly obtain information about the deformation process by tracking features in the temporal sequence of radar images as they travel through azimuth-range bins. Their trajectories provide valuable complementary information in cases where interferometry fails, e.g. due to loss of temporal coherence. Furthermore, this approach can potentially yield 2D displacement information (in range and azimuth) using images from a single radar instrument. We present two approaches for amplitude feature tracking of natural surfaces within radar image sequences, one based on least squares template matching (LSM) and one on optical flow (OF). Spatial and temporal corrections are applied to increase the initially poor signal-to-noise ratio by mitigating atmospheric and material dependent effects. The resulting algorithms are demonstrated using one month of data from the continuous monitoring of an alpine glacier (Bisgletscher/CH) with a radar interferometer from a distance of up to 6.5 km. Clusters of crevasses causing spatial variations of the amplitude were successfully tracked over the entire monitoring period. While LSM has particular potential for delivering accurate pointwise displacement information (without using artificial reflectors), OF is better suited for tracking spatially smooth deformation fields.

Exploiting amplitude of terrestrial radar data for feature tracking in areal monitoring of inhomogeneous surfaces

The potential benefits of terrestrial radar interferometry (TRI) for deformation monitoring are restricted by the influence of changing meteorological conditions contaminating the potentially highly precise measurements with spurious deformations. This is the case especially when the measurement setup includes long distances between instrument and objects of interest and the topography affecting atmospheric refraction is complex. These situations are typically encountered with geo-monitoring in mountainous regions, e.g. with glaciers, landslides or volcanoes. We propose and explain an approach for the mitigation of atmospheric influences based on the theory of intrinsic random functions of order k (IRF-k) generalizing existing approaches based on ordinary least squares estimation of trend functions. This class of random functions retains convenient computational properties allowing for rigorous statistical inference while still permitting to model stochastic spatial phenomena which are non-stationary in mean and variance. We explore the correspondence between the properties of the IRF-k and the properties of the measurement process. Drifts in variance are linked to the additive nature of atmospheric effects along the propagation path and to the irregularity of the terrain. In an exemplary case study, we find that our method reduces the time needed to obtain reliable estimates of glacial movements from 12 h down to 0.5 h. We relate the estimated error variance of the results to details of our measurement campaign like roughness of terrain, altitude differences and involved distances thus indicating how this method is expected to perform when applied under different conditions.

Intrinsic random functions for mitigation of atmospheric effects in ground based radar interferometry

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Papers

Intrinsic random functions for mitigation of atmospheric effects in terrestrial radar interferometry

Robotic Total Station Monitoring in High Alpine Paraglacial Environments: Challenges and Solutions from the Great Aletsch Region (Valais, Switzerland)

Exploiting amplitude of terrestrial radar data for feature tracking in areal monitoring of inhomogeneous surfaces

Intrinsic random functions for mitigation of atmospheric effects in ground based radar interferometry