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Spatio-temporal linking of multiple SAR satellite data from medium and high resolution Radarsat-2 images
Abstract
A recent development in Interferometric Synthetic Aperture Radar (InSAR) technology is integrating multiple SAR satellite data to dynamically extract ground features. This paper addresses two relevant challenges: identification of common ground targets from different SAR datasets in space, and concatenation of time series when dealing with temporal dynamics. To address the first challenge, we describe the geolocation uncertainty of InSAR measurements as a three-dimensional error ellipsoid. The points, among InSAR measurements, which have error ellipsoids with a positive cross volume are identified as tie-point pairs representing common ground objects from multiple SAR datasets. The cross volumes are calculated using Monte Carlo methods and serve as weights to achieve the equivalent deformation time series. To address the second challenge, the deformation time series model for each tie-point pair is estimated using probabilistic methods, where potential deformation models are efficiently tested and evaluated. As an application, we integrated two Radarsat-2 datasets in Standard and Extra-Fine modes to map the subsidence of the west of the Netherlands between 2010 and 2017. We identified 18128 tie-point pairs, 5 intersection types of error ellipsoids, 5 deformation models, and constructed their long-term deformation time series. The detected maximum mean subsidence velocity in Line-Of-Sight direction is up to 15 mmyr-1. We conclude that our method removes limitations that exist in single-viewing-geometry SAR when integrating multiple SAR data. In particular, the proposed time-series modeling method is useful to achieve a long-term deformation time series of multiple datasets.
The Groningen gas field, the largest natural gas field in Europe, was discovered in 1959 and started its production in 1963. The Earth surface above it experienced subsidence over the past six decades because of gas extraction activities. To accurately reveal this surface movement with satellite SAR data, our study first proposes and demonstrates a model-backfeed (MBF) InSAR deformation estimation method to improve InSAR deformation time series modeling. This method allowed us to include a priori knowledge and to iteratively optimize functional and stochastic models. Using this method and employing a spatio-temporal SAR data integration method based upon Monte Carlo and Multiple Hypothesis Testing methods, we retrieved the 20-year subsidence history of the Groningen gas field by integrating 32 ERS-1/2, 68 Envisat and 82 Radarsat-2 SAR images. The results show that the maximum cumulative surface subsidence in this gas field has been as much as 15.5cm between 1995 and 2015. In terms of precision and accuracy, our MBF method offered a better result than the standard Multi-Temporal InSAR analysis method: the Ensemble Coherence increased by 10%–19% and Spatio-Temporal Consistency decreased by 2%–20%. In terms of accuracy, our results better concur with the external GNSS reference observations. We further show that the spatio-temporal SAR data integration method has better links with multi-platform SAR data if the uncertainties of the InSAR geolocation and temporal deformation are included. The study demonstrates that the MBF method optimized the estimation of deformation parameters and mitigated unwrapping errors.
To correctly interpret the estimated displacements in InSAR point clouds, especially in the built environment, these need to be linked to real-world structures. This requires the accurate and precise 3D positioning of each point. Artificial ground control points (GCPs), such as corner reflectors, serve this purpose, but since they require efforts and resources, there is a need for criteria to assess their usefulness. Here we evaluate the value and necessity of using GCPs for different scenarios, concerning the required efforts, and compare this to alternatives such as digital surface models (DSM) and advanced (geo) physical corrections. We consider single-epoch as well as multi-epoch GCP deployment, reflect on the number of GCPs required in relation to the number of SAR data acquisitions, and compare this with digital surface models of different quality levels. Analyzing the geolocation performance using TerraSAR-X and Sentinel-1 data, we evaluate the pros and cons of various deployment options and show that the multi-epoch deployment of a GCP yields optimal geolocalization results in terms of precision, accuracy, and reliability.
Associating a radar scatterer to a physical object is crucial for the correct interpretation of interferometric synthetic aperture radar measurements. Yet, especially for medium-resolution imagery, this is notoriously difficult and dependent on the accurate 3-D positioning of the scatterers. Here, we investigate the 3-D positioning capabilities of ENVISAT medium-resolution data. We find that the data are perturbed by range-and-epoch-dependent timing errors and calibration offsets. Calibration offsets are estimated to be about 1.58 m in azimuth and 2.84 m in range and should be added to ASAR products to improve geometric calibration. The timing errors involve a bistatic offset, atmospheric path delay, solid earth tides, and local oscillator drift. This way, we achieve an unbiased positioning capability in 2-D, while in 3-D, a scatterer was located at a distance of 28 cm from the true location. 3-D precision is now expressed as an error ellipsoid in local coordinates. Using the Bhattacharyya metric, we associate radar scatterers to real-world objects. Interpreting deformation of individual infrastructure is shown to be feasible for this type of medium-resolution data.
Persistent Scatterer Interferometry (PSI) is an advanced multitemporal InSAR technique that is capable of retrieving the 3D coordinates and the underlying deformation of time-coherent scatterers. Various factors degrade the localization accuracy of PSI point clouds in the geocoding process, which causes problems for interpretation of deformation results and also making it difficult for the point clouds to be compared with or integrated into data from other sensors. In this study, we employ the SAR imaging geodesy method to perform geodetic corrections on SAR timing observations and thus improve the positioning accuracy in the horizontal components. We further utilize geodetic stereo SAR to extract large number of highly precise ground control points (GCP) from SAR images, in order to compensate for the unknown height offset of the PSI point cloud. We demonstrate the applicability of the approach using TerraSAR-X high resolution spotlight images over the city of Berlin, Germany. The corrected results are compared with a reference LiDAR point cloud of Berlin, which confirms the improvement in the geocoding accuracy.
The identification and measurement of ground deformations in urban areas is of great importance for determining the vulnerable parts of the cities that are prone to geohazards, which is a crucial element of both sustainable urban planning and hazard mitigation. Interferometric synthetic aperture radar (InSAR) time series analysis is a very powerful tool for the operational mapping of ground deformation related to urban subsidence and landslide phenomena. With an analysis spanning almost 25 years of satellite radar observations, we compute an InSAR time series of data from multiple satellites (European Remote Sensing satellites ERS-1 and ERS-2, Envisat, Sentinel-1A, and its twin sensor Sentinel-1B) in order to investigate the spatial extent and rate of ground deformation in the megacity of Istanbul. By combining the various multi-track InSAR datasets (291 images in total) and analysing persistent scatterers (PS-InSAR), we present mean velocity maps of ground surface displacement in selected areas of Istanbul. We identify several sites along the terrestrial and coastal regions of Istanbul that underwent vertical ground subsidence at varying rates, from 5 ± 1.2 mm/yr to 15 ± 2.1 mm/yr. The results reveal that the most distinctive subsidence patterns are associated with both anthropogenic factors and relatively weak lithologies along the Haramirede valley in particular, where the observed subsidence is up to 10 ± 2 mm/yr. We show that subsidence has been occurring along the Ayamama river stream at a rate of up to 10 ± 1.8 mm/yr since 1992, and has also been slowing down over time following the restoration of the river and stream system. We also identify subsidence at a rate of 8 ± 1.2 mm/yr along the coastal region of Istanbul, which we associate with land reclamation, as well as a very localised subsidence at a rate of 15 ± 2.3 mm/yr starting in 2016 around one of the highest skyscrapers of Istanbul, which was built in 2010.
Satellite radar interferometry (InSAR) is an emerging technique to monitor the stability and health of line-infrastructure assets, such as railways, dams, and pipelines. However, InSAR is an opportunistic approach as the location and occurrence of its measurements (coherent scatterers) cannot be guaranteed, and the quality of the InSAR products is not uniform. This is a problem for operational asset managers, who are used to surveying techniques that provide results with uniform quality at predefined locations. Therefore, advanced integrated products and generic performance assessment metrics are necessary. Here, we propose several new monitoring products and quality metrics for a-priori and a-posteriori performance assessment using multisensor InSAR. These products and metrics are demonstrated on a 125 km railway line-infrastructure asset in the Netherlands.
In this work we exploit X- and C-band InSAR data for detecting local deformation phenomena induced by the 2016–2017 Central Italy seismic sequence. Our goal is to highlight the usefulness of multi-band InSAR analysis for Hazard assessing and Rapid Mapping purposes when in-situ investigations are difficult or dangerous to be performed. Indeed, local seismic-induced effects (such as landslides, avalanches, subsidence, etc.) could severely impact the environment and the population in the surrounding of areas hit by earthquakes. We focused on four areas, named Monte Vettore, Podalla, Bolognola and Cicconi, where InSAR outcomes revealed how the main seismic events of the sequence activated several landslides and secondary faults interested by deformation of ~ 2–3 cm along the satellite Line-of-Sight (LoS). The use of multi-band InSAR data allows the observation of multi-scale deformation phenomena with both different spatial resolution and coverage, highlighting the limits and constraints of different SAR sensors. Moreover, it ensures the crosschecking of displacement patterns retrieved through different InSAR products, especially when no ground truth or in situ ancillary data are available for validation purposes. As a result, the retrieved InSAR information can support the Scientific Community and the Institutions in the management of crisis emergencies.
Satellite synthetic aperture radar interferometry (InSAR) has the capability to monitor railway tracks and embankments with millimeter-level precision over wide areas. The potential of detecting differential deformation along the tracks makes it one of the most powerful and economical means for monitoring the safety and stability of the infrastructure on a weekly basis. Yet, the mere capability to detect such small deformations is not sufficient for an operational application of the technique. Handling huge data volumes, homogenizing independent datasets, and the connection with expert knowledge to identify risk areas are challenges to overcome. Here, we use a probabilistic method for InSAR time series postprocessing to efficiently scrutinize the data and detect railway instability. Moreover, to detect high-strain segments of the railway, we propose a short-arc-based method to focus on localized differential deformation between nearby InSAR measurement points. Our approach is demonstrated over the entire railway network of the Netherlands, more than 3000 km long, using hundreds of Radarsat-2 acquisitions between 2010 and 2015, leading to the first satellite-based nationwide railway monitoring system.
Remote sensing radar satellites cover wide areas and provide spatially dense measurements, with millions of scatterers. Knowledge of the precise position of each radar scatterer is essential to identify the corresponding object and interpret the estimated deformation. The absolute position accuracy of synthetic aperture radar (SAR) scatterers in a 2D radar coordinate system, after compensating for atmosphere and tidal effects, is in the order of centimeters for TerraSAR-X (TSX) spotlight images. However, the absolute positioning in 3D and its quality description are not well known. Here, we exploit time-series interferometric SAR to enhance the positioning capability in three dimensions. The 3D positioning precision is parameterized by a variance–covariance matrix and visualized as an error ellipsoid centered at the estimated position. The intersection of the error ellipsoid with objects in the field is exploited to link radar scatterers to real-world objects. We demonstrate the estimation of scatterer position and its quality using 20 months of TSX stripmap acquisitions over Delft, the Netherlands. Using trihedral corner reflectors (CR) for validation, the accuracy of absolute positioning in 2D is about 7 cm. In 3D, an absolute accuracy of up to ∼66 cm is realized, with a cigar-shaped error ellipsoid having centimeter precision in azimuth and range dimensions, and elongated in cross-range dimension with a precision in the order of meters (the ratio of the ellipsoid axis lengths is 1/3/213, respectively). The CR absolute 3D position, along with the associated error ellipsoid, is found to be accurate and agree with the ground truth position at a 99% confidence level. For other non-CR coherent scatterers, the error ellipsoid concept is validated using 3D building models. In both cases, the error ellipsoid not only serves as a quality descriptor, but can also help to associate radar scatterers to real-world objects.
Monitoring the kinematic behavior of enormous amounts of points and objects anywhere on Earth is now feasible on a weekly basis using radar interferometry from Earth-orbiting satellites. An increasing number of satellitemissions are capable of delivering data that can be used to monitor geophysical processes, mining and construction activities, public infrastructure, or even individual buildings. The parameters estimated from these data are used to better understand various natural hazards, improve public safety, or enhance asset management activities. Yet, the mathematical estimation of kinematic parameters from interferometric data is an ill-posed problem as there is no unique solution, and small changes in the data may lead to significantly different parameter estimates. This problem results in multiple possible outcomes given the same data, hampering public acceptance, particularly in critical conditions. Here, we propose a method to address this problem in a probabilistic way, which is based on multiple hypotheses testing. We demonstrate that it is possible to systematically evaluate competing kinematic models in order to find an optimal model and to assign likelihoods to the results. Using the B-method of testing, a numerically efficient implementation is achieved, which is able to evaluate hundreds of competing models per point. Our approach will not solve the nonuniqueness problem of interferometric synthetic aperture radar (InSAR), but it will allow users to critically evaluate (conflicting) results, avoid overinterpretation, and thereby consolidate InSAR as a geodetic technique.
As part of the Terrafirma Validation Experiment a com-parison of Persistent Scatterer Interferometry (PSI) with in-situ measurements is performed. The experiment pur-ports to validate the processing chains of partners par-ticipating in the Terrafirma project, in order to obtain a better understanding of the performance of the tech-niques. Two test sites are chosen in the Netherlands: (i) the trajectory of a subway line in the centre of Amster-dam, still to be constructed, and (ii) the area around the city of Alkmaar, affected by subsidence due to gas ex-traction. The Amsterdam site is monitored using Envisat-ASAR data, whereas for the Alkmaar area both ERS1/2-AMI and Envisat-ASAR data sets are used. Due to the difference in deformation phenomena, available ground truth and surface characteristics, different validation pro-cedures are applied to the two test sites. Here we describe these validation methodologies and the validation results.
Synthetic Aperture Radar Interferometry (InSAR) is an alternative technique to obtain measurements of surface displacement providing better spatial resolution and comparable accuracy at an extremely lower cost per area than conventional surveying methods. InSAR is becoming more and more popular in monitoring urban deformations, however, the technique requires advanced tools and high level competence to be successfully applied. In this paper we report important results obtained by analyzing new high resolution SAR data in the Shanghai urban area. The data used in this work have been acquired by the Italian X-band sensor Cosmo-SkyMed. About 1.2 million of individual and independent targets have been detected in 600 sq km, revealing impressive details of the ground surface deformation. Using the SARPROZ InSAR tool and integrating the results with Google Earth, we were able to track subway tunnels recently excavated and several highways. Tunnels are visible due to very localized subsidence of the above surface along their path. On the other hand, highways, standing over the ground, in most cases show higher stability than the surrounding areas. The density of targets is so high to allow studying the profile of the tunnel subsidence, which is very useful to predict building damage. Finally, the identification of targets on high buildings helps checking the stability of high constructions along the subway lines, highlighting possible risky situations.
The movements produced by the 1992 earthquake in Landers, California are mapped using SAR interferometry. An interferogram is constructed by combining topographic information with SAR images obtained by the ERS-1 satellite before and after the earthquake. It is shown that the observed changes in the range from the ground surface to the satellite agree well with the slip measured in the field, with the displacements measured by surveying, and with the results of Okada's (1985) elastic dislocation model. This interferogram provides a denser spatial sampling than surveying methods and a better precision than earlier space imaging techniques.
We present here a new InSAR persistent scatterer (PS) method for analyzing episodic crustal deformation in non-urban environments, with application to volcanic settings. Our method for identifying PS pixels in a series of interferograms is based primarily on phase characteristics and finds low-amplitude pixels with phase stability that are not identified by the existing amplitude-based algorithm. Our method also uses the spatial correlation of the phases rather than a well-defined phase history so that we can observe temporally-variable processes, e.g., volcanic deformation. The algorithm involves removing the residual topographic component of flattened interferogram phase for each PS, then unwrapping the PS phases both spatially and temporally. Our method finds scatterers with stable phase characteristics independent of amplitudes associated with man-made objects, and is applicable to areas where conventional InSAR fails due to complete decorrelation of the majority of scatterers, yet a few stable scatterers are present.
The Shuttle Radar Topography Mission produced the most complete, highest-resolution digital elevation model of the Earth. The project was a joint endeavor of NASA, the National Geospatial-Intelligence Agency, and the German and Italian Space Agencies and flew in February 2000. It used dual radar antennas to acquire interferometric radar data, processed to digital topographic data at 1 arc sec resolution. Details of the development, flight operations, data processing, and products are provided for users of this revolutionary data set.
Real-time estimation of parameter sin dynamic systems, which becomes increasingly important in the field of high-precision navigation, requires real-time testing of the models underlying the navigation system. A real-time recursive testing procedure than can be used in conjunction with the Kalman filter algorithm is presented, along with diagnostic tools for inferring the detectability of particular model errors. The testing procedure consists of detection, identification, and adaptation. It can accommodate model errors in the measurement model and dynamic model of the integrated navigation system and is optimal in the uniformly-most-powerful-invariant sense.< >
We present a new differential synthetic aperture radar (SAR) interferometry algorithm for monitoring the temporal evolution of surface deformations. The presented technique is based on an appropriate combination of differential interferograms produced by data pairs characterized by a small orbital separation (baseline) in order to limit the spatial decorrelation phenomena. The application of the singular value decomposition method allows us to easily "link" independent SAR acquisition datasets, separated by large baselines, thus increasing the observation temporal sampling rate. The availability of both spatial and temporal information in the processed data is used to identify and filter out atmospheric phase artifacts. We present results obtained on the data acquired from 1992 to 2000 by the European Remote Sensing satellites and relative to the Campi Flegrei caldera and to the city of Naples, Italy, that demonstrate the capability of the proposed approach to follow the dynamics of the detected deformations.
Temporal and geometrical decorrelation often prevents SAR
interferometry from being an operational tool for surface deformation
monitoring and topographic profile reconstruction. Moreover, atmospheric
disturbances can strongly compromise the accuracy of the results. The
authors present a complete procedure for the identification and
exploitation of stable natural reflectors or permanent scatterers (PSs)
starting from long temporal series of interferometric SAR images. When,
as it often happens, the dimension of the PS is smaller than the
resolution cell, the coherence is good even for interferograms with
baselines larger than the decorrelation one, and all the available
images of the ESA ERS data set can be successfully exploited. On these
pixels, submeter DEM accuracy and millimetric terrain motion detection
can be achieved, since atmospheric phase screen (APS) contributions can
be estimated and removed. Examples are then shown of small motion
measurements, DEM refinement, and APS estimation and removal in the case
of a sliding area in Ancona, Italy. ERS data have been used
More GPS stations, faster data delivery, and better data processing provide an abundance of information for all kinds of Earth scientists.
Deformation monitoring is one of the most mature applications of space-borne InSAR technique. Firstly, we introduce the basic principle of InSAR in the monitoring of deformation and the current SAR satellites. The deformation monitoring methods of InSAR are then classified into the groups of D-InSAR, PS-InSAR, SBAS-InSAR, DS-InSAR and MAI, which are analyzed in the aspects of technical features and application scopes. Subsequently, we analyze the research progress and deficiencies of InSAR in the investigation of urban, mining area, earthquake, volcano, infrastructure, glacier, permafrost and landslide. Finally, some advanced academic problems such as deformation monitoring in multi-demension and low coherence area, atmospheric and orbital errors mitigation, and accuracy assessment are concluded.
Time series of ground deformation are used to describe motion produced by various natural and anthropocentric processes, such as earthquakes, volcanic eruptions, landslides, subsidence due to resource exploitation, and uplift due to fluid injection. Presented here, the multidimensional small baseline subset (MSBAS) technique simultaneously processes multiple ascending and descending DInSAR datasets and produces 2-D, horizontal east-west and vertical, deformation time series with combined temporal resolution over overlapped area. The set of linear equations that comprises MSBAS is usually rank deficient and is solved in the least-square sense by applying the singular value decomposition (SVD) and the zero-, first-, or second-order Tikhonov regularization. The MSBAS source code is written in C++ and is linked to the linear algebra package (LAPACK) library that provides SVD support. For demonstration of capabilities, MSBAS is used to compute 2-D deformation time series of Mexico City by simultaneously processing ascending and descending RADARSAT-2 data acquired during October 2008–December 2012. This area is known to subside due to excessive groundwater extraction that produces pore water pressure drop and compaction of highly compressible clays. During the studied period we observed subsidence with rates over 35 cm/year and horizontal motion of up to 5 cm/year. The MSBAS software can be downloaded from http://insar.ca/. 2017
In recent years, new algorithms have been proposed to retrieve maximum available information in synthetic aperture radar (SAR) interferometric stacks with focus on distributed scatterers. The key step in these algorithms is to optimally estimate single-master (SM) wrapped phases for each pixel from all possible interferometric combinations, preserving useful information and filtering noise. In this paper, we propose a new method for SM-phase estimation based on the integer least squares principle. We model the SM-phase estimation problem in a linear form by introducing additional integer ambiguities and use a bootstrap estimator for joint estimation of SM-phases and the integer unknowns. In addition, a full error propagation scheme is introduced in order to evaluate the precision of the final SM-phase estimates. The main advantages of the proposed method are the flexibility to be applied on any (connected) subset of interferograms and the quality description via the provision of a full covariance matrix of the estimates. Results from both synthetic experiments and a case study over the Torfajökull volcano in Iceland demonstrate that the proposed method can efficiently filter noise from wrapped multibaseline interferometric stacks, resulting in doubling the number of detected coherent pixels with respect to conventional persistent scatterer interferometry.
Differential synthetic aperture radar tomography (D-TomoSAR), similar to its conventional counterparts such as differential interferometric SAR and persistent scatterer interferometry, is only capable of capturing 1-D deformation along the satellite's line of sight. In this paper, we propose a method based on L1-norm minimization within local spatial cubes to reconstruct 3-D displacement vectors from TomoSAR point clouds available from at least three different viewing geometries. The methodology is applied on two pairs of cross-heading—combination of ascending and descending—TerraSAR-X (TS-X) spotlight image stacks over the city of Berlin. The linear deformation rate and the amplitude of seasonal deformation are decomposed, and the results from two test sites with remarkable deformation pattern are discussed in detail. The results, to our knowledge, demonstrate the first attempt for motion decomposition using TomoSAR data from multiple viewing geometries.
The zones of overlap between adjacent Synthetic Aperture Radar (SAR) satellite tracks are illuminated twice more frequently than elsewhere in the SAR scene. Here, an alternative approach is presented to combine the overlapping segments of SAR images acquired at adjacent tracks and generate accurate and high spatiotemporal resolution map of the surface deformation field. To this end, a new approach is developed to unify the datums. Effects due to the difference in look angle between two overlapping tracks and atmospheric delay are estimated and removed using Kalman and wavelet-based filters. This approach is first tested at Hawaii Island, where tracks 200 and 429 of Envisat C-band satellite overlap over the Kilauea south flank. The obtained time series improves the temporal sampling rate by a factor of two and comparison with GPS time series demonstrates that the presented method accurately measures the nonlinear deformation field. The advantages of this method are further demonstrated by combining SAR data sets acquired by Envisat C-band and ALOS L-band satellites over the San Francisco Bay Area, California. The validation test shows that the seamless combination of C-band and L-band time series accurately measures the surface deformation at higher resolution.
The groundwater in Tucson, Arizona, is a major source for water supply to human residential areas and agricultural use, and the groundwater extraction causes ground deformation responding to aquifer-system compaction. Using multi-temporal C-band and L-band SAR images from ERS/ENVISAT and ALOS PALSAR satellites, we mapped the ground displacement in Tucson. The InSAR-derived ground displacement based on small baseline subset technique (SBAS) is compared to compaction from extensometers, land subsidence from GPS survey, and groundwater elevation at wells. Tucson is characterized by slow and relatively small subsidence over areas with upper stratigraphic till early 2000s when the groundwater had been extremely depleted and by uplifts in areas where the water level has been recently recovered into the slight increase of water table because of recharge into the aquifer. Our multi-sensor SBAS InSAR-derived vertical displacements enabled to monitor the temporal variability in the spatial extent and magnitude of the ground deformation, suggesting that recent ground subsidence is slowing down and the ground motion in places is stable. Our results can provide scientific basis for sound management of ground water pumping and recharge over the study area.
Coseismic displacements play a significant role in characterizing earthquake causative faults and understanding earthquake dynamics. They are typically measured from InSAR using pre- and post-earthquake images. The displacement map produced by InSAR may contain missing coseismic values due to the decorrelation of ASAR images. This study focused on interpolating missing values in the coseismic displacement map of the 2003 Bam earthquake using geostatistics with the aim of running a slip distribution model. The gaps were grouped into 23 patches. Variograms of the patches showed that the displacement data were spatially correlated. The variogram prepared for ordinary kriging (OK) indicated the presence of a trend and thus justified the use of universal kriging (UK). Accuracy assessment was performed in 3 ways. First, 11 patches of equal size and with an equal number of missing values generated artificially, were kriged and validated. Second, the four selected patches results were validated after shifting them to new locations without missing values and comparing them with the observed values. Finally, cross validation was performed for both types of patch at the original and shifted locations. UK results were better than OK in terms of kriging variance, mean error (ME) and root mean square error (RMSE). For both OK and UK, only 4 out of 23 patches (1, 5, 11 and 21) showed ME and RMSE values that were substantially larger than for the other patches. The accuracy assessment results were found to be satisfactory with ME and RMSE values close to zero. InSAR data inversion demonstrated the usefulness of interpolation of the missing coseismic values by improving a slip distribution model. It is therefore concluded that kriging serves as an effective tool for interpolating the missing values on a coseismic displacement map.
We investigate the surface displacements in the area affected by the April 2009 L'Aquila earthquake (Central Italy) through an advanced DInSAR analysis. In particular, we apply the SBAS approach to retrieve deformation maps and displacement time series from ENVISAT data acquired between February 2003 and October 2009 and from COSMO-SkyMed data relevant to the six-month interval following the earthquake. Our analysis shows no evidence of pre-seismic surface deformation at the 35-day temporal sampling of the ENVISAT sensor. On the other hand, by benefiting of the high spatial resolution and temporal sampling of the COSMO-SkyMed satellites, we measure post-seismic displacements at an unprecedented level of detail for a DInSAR analysis. This allows us to identify three main areas continuing to deform after the earthquake. In addition, our modeling shows that post-seismic displacements are very likely related to the fault afterslip, since their decay times are on the order of 101–102 days.
We shall present here the motivation and a general description of a method dealing with a class of problems in mathematical physics. The method is, essentially, a statistical approach to the study of differential equations, or more generally, of integro-differential equations that occur in various branches of the natural sciences.
We investigate the surface deformation of the eastern California area that includes Long Valley caldera and Mono Basin. We apply the SAR Interferometry (InSAR) algorithm referred to as Small BAseline Subset (SBAS) approach that allows us to generate mean deformation velocity maps and displacement time series for the investigated area. The results presented in this work represent an advancement of previous InSAR studies of the area that are mostly focused on the deformation affecting the caldera. In particular, the proposed analysis is based on 21 SAR data acquired by the ERS-1/2 sensors during the 1992–2000 time interval, and demonstrates the capability of the SBAS procedure to identify and analyze displacement patterns at different spatial scales for the overall area spanning approximately 5000 km2. Two previously unreported localized deformation effects have been detected at Paoha Island, located within the Mono Lake, and in the McGee Creek area within the Sierra Nevada mountains, a zone to the south of the Long Valley caldera. In addition a spatially extended uplift effect, which strongly affects the caldera, has been identified and analyzed in detail. The InSAR results clearly show that the displacement phenomena affecting the Long Valley caldera have a maximum in correspondence of the resurgent dome and are characterized by the sequence of three different effects: a 1992–1997 uplift background, a 1997–1998 unrest phenomenon and a 1998–2000 subsidence phase. Moreover, the analysis of the retrieved displacement time series allows us to map the extent of the zone with a temporal deformation behavior highly correlated with the detected three-phases deformation pattern: background uplift-unrest-subsidence. We show that the mapped area clearly extends outside the northern part of the caldera slopes; accordingly, we suggest that future inversion models take this new evidence into account. The final discussion is dedicated to a comparison between the retrieved InSAR measurements and a set of GPS and leveling data, confirming the validity of the results achieved through the SBAS-InSAR analysis.
Six different interferograms of the “La Clapière” landslide were derived from ERS-1 SAR images during the period Aug. 20–Sept. 4, 1991. The coherence of the associated images is shown to remain significant over most of the surface of the landslide during the two weeks of the survey. The interferograms are remarkably similar, and indicate steady-state displacements over at least 12 days. The displacement field derived from the interferograms is shown to be characterized by a non-uniform displacement gradient from top-to-bottom and reveals a significantly faster motion of the western part of the landslide. The amplitude of the motion deduced from interferometry is shown to be in good agreement with ground measurements. Finally, SAR interferometry is shown to be able to evidence small-scale instabilities which may not be observed with discrete ground measurements.
An InSAR analysis approach for identifying and extracting the temporarily coherent points (TCP) that exist between two SAR acquisitions and for determining motions of the TCP is presented for applications such as ground settlement monitoring. TCP are identified based on the spatial characteristics of the range and azimuth offsets of coherent radar scatterers. A method for coregistering TCP based on the offsets of TCP is given to reduce the coregistration errors at TCP. An improved phase unwrapping method based on the minimum cost flow (MCF) algorithm and local Delaunay triangulation is also proposed for sparse TCP data. The proposed algorithms are validated using a test site in Hong Kong. The test results show that the algorithms work satisfactorily for various ground features.
Synthetic aperture radar (SAR) interferometry is a technique that provides high-resolution measurements of the ground displacement associated with many geophysical processes. Advanced techniques involving the simultaneous processing of multiple SAR acquisitions in time increase the number of locations where a deformation signal can be extracted and reduce associated error. Currently there are two broad categories of algorithms for processing multiple acquisitions, persistent scatterer and small baseline methods, which are optimized for different models of scattering. However, the scattering characteristics of real terrains usually lay between these two end-member models. I present here a new method that combines both approaches, to extract the deformation signal at more points and with higher overall signal-to-noise ratio than can either approach alone. I apply the combined method to data acquired over Eyjafjallajökull volcano in Iceland, and detect time-varying ground displacements associated with two intrusion events.
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