1,743 research outputs found

    A temporal phase coherence estimation algorithm and its application on DInSAR pixel selection

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    © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Pixel selection is a crucial step of all advanced Differential Interferometric Synthetic Aperture Radar (DInSAR) techniques that have a direct impact on the quality of the final DInSAR products. In this paper, a full-resolution phase quality estimator, i.e., the temporal phase coherence (TPC), is proposed for DInSAR pixel selection. The method is able to work with both distributed scatterers (DSs) and permanent scatterers (PSs). The influence of different neighboring window sizes and types of interferograms combinations [both the single-master (SM) and the multi-master (MM)] on TPC has been studied. The relationship between TPC and phase standard deviation (STD) of the selected pixels has also been derived. Together with the classical coherence and amplitude dispersion methods, the TPC pixel selection algorithm has been tested on 37 VV polarization Radarsat-2 images of Barcelona Airport. Results show the feasibility and effectiveness of TPC pixel selection algorithm. Besides obvious improvements in the number of selected pixels, the new method shows some other advantages comparing with the other classical two. The proposed pixel selection algorithm, which presents an affordable computational cost, is easy to be implemented and incorporated into any advanced DInSAR processing chain for high-quality pixels' identification.Peer ReviewedPostprint (author's final draft

    Long-term monitoring of geodynamic surface deformation using SAR interferometry

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    Thesis (Ph.D.) University of Alaska Fairbanks, 2014Synthetic Aperture Radar Interferometry (InSAR) is a powerful tool to measure surface deformation and is well suited for surveying active volcanoes using historical and existing satellites. However, the value and applicability of InSAR for geodynamic monitoring problems is limited by the influence of temporal decorrelation and electromagnetic path delay variations in the atmosphere, both of which reduce the sensitivity and accuracy of the technique. The aim of this PhD thesis research is: how to optimize the quantity and quality of deformation signals extracted from InSAR stacks that contain only a low number of images in order to facilitate volcano monitoring and the study of their geophysical signatures. In particular, the focus is on methods of mitigating atmospheric artifacts in interferograms by combining time-series InSAR techniques and external atmospheric delay maps derived by Numerical Weather Prediction (NWP) models. In the first chapter of the thesis, the potential of the NWP Weather Research & Forecasting (WRF) model for InSAR data correction has been studied extensively. Forecasted atmospheric delays derived from operational High Resolution Rapid Refresh for the Alaska region (HRRRAK) products have been compared to radiosonding measurements in the first chapter. The result suggests that the HRRR-AK operational products are a good data source for correcting atmospheric delays in spaceborne geodetic radar observations, if the geophysical signal to be observed is larger than 20 mm. In the second chapter, an advanced method for integrating NWP products into the time series InSAR workflow is developed. The efficiency of the algorithm is tested via simulated data experiments, which demonstrate the method outperforms other more conventional methods. In Chapter 3, a geophysical case study is performed by applying the developed algorithm to the active volcanoes of Unimak Island Alaska (Westdahl, Fisher and Shishaldin) for long term volcano deformation monitoring. The volcano source location at Westdahl is determined to be approx. 7 km below sea level and approx. 3.5 km north of the Westdahl peak. This study demonstrates that Fisher caldera has had continuous subsidence over more than 10 years and there is no evident deformation signal around Shishaldin peak.Chapter 1. Performance of the High Resolution Atmospheric Model HRRR-AK for Correcting Geodetic Observations from Spaceborne Radars -- Chapter 2. Robust atmospheric filtering of InSAR data based on numerical weather prediction models -- Chapter 3. Subtle motion long term monitoring of Unimak Island from 2003 to 2010 by advanced time series SAR interferometry -- Chapter 4. Conclusion and future work

    Tropospheric phase delay in interferometric synthetic aperture radar estimated from meteorological model and multispectral imagery

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    ENVISAT Medium Resolution Imaging Spectrometer Instrument (MERIS) multispectral data and the mesoscale meteorological model MM5 are used to estimate the tropospheric phase delay in synthetic aperture radar (SAR) interferograms. MERIS images acquired simultaneously with ENVISAT Advanced Synthetic Aperture Radar data provide an estimate of the total water vapor content W limited to cloud-free areas based on spectral bands ratio (accuracy 0.17 g cm^(−2) and ground resolution 300 m). Maps of atmospheric delay, 2 km in ground resolution, are simulated from MM5. A priori pertinent cumulus parameterization and planetary boundary layer options of MM5 yield near-equal phase correction efficiency. Atmospheric delay derived from MM5 is merged with available MERIS W product. Estimates of W measured from MERIS and modeled from MM5 are shown to be consistent and unbiased and differ by ~0.2 g cm^(−2) (RMS). We test the approach on data over the Lebanese ranges where active tectonics might contribute to a measurable SAR signal that is obscured by atmospheric effects. Local low-amplitude (1 rad) atmospheric oscillations with a 2.25 km wavelength on the interferograms are recovered from MERIS with an accuracy of 0.44 rad or 0.03 g cm^(−2). MERIS water product overestimates W in the clouds shadow due to mismodeling of multiple scattering and underestimates W on pixels with undetected semitransparent clouds. The proposed atmospheric filter models dynamic atmospheric signal which cannot be recovered by previous filtering techniques which are based on a static atmospheric correction. Analysis of filter efficiency with spatial wavelength shows that ~43% of the atmospheric signal is removed at all wavelengths

    Post-failure evolution analysis of a rainfall-triggered landslide by multi-temporal interferometry SAR approaches integrated with geotechnical analysis

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    Persistent Scatterers Interferometry (PSI) represents one of the most powerful techniques for Earth's surface deformation processes' monitoring, especially for long-term evolution phenomena. In this work, a dataset of 34 TerraSAR-X StripMap images (October 2013–October 2014) has been processed by two PSI techniques - Coherent Pixel Technique-Temporal Sublook Coherence (CPT-TSC) and Small Baseline Subset (SBAS) - in order to study the evolution of a slow-moving landslide which occurred on February 23, 2012 in the Papanice hamlet (Crotone municipality, southern Italy) and induced by a significant rainfall event (185 mm in three days). The mass movement caused structural damage (buildings' collapse), and destruction of utility lines (gas, water and electricity) and roads. The results showed analogous displacement rates (30–40 mm/yr along the Line of Sight – LOS-of the satellite) with respect to the pre-failure phase (2008–2010) analyzed in previous works. Both approaches allowed detect the landslide-affected area, however the higher density of targets identified by means of CPT-TSC enabled to analyze in detail the slope behavior in order to design possible mitigation interventions. For this aim, a slope stability analysis has been carried out, considering the comparison between groundwater oscillations and time-series of displacement. Hence, the crucial role of the interaction between rainfall and groundwater level has been inferred for the landslide triggering. In conclusion, we showed that the integration of geotechnical and remote sensing approaches can be seen as the best practice to support stakeholders to design remedial works.Peer ReviewedPostprint (author's final draft

    Exploring Problems and Prospective of Satellite Interferometric Data for the Seismic Structural Health Monitoring of Existing Buildings and Architectural Heritage

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    Satellite interferometric data represent a promising source of information for the Structural Health Monitoring (SHM) of the existing built environment. This is especially true because they show differential temporal-spatial displacements of remotely monitored points, which can be easily interpreted with a visual inspection of their time-histories for different locations defined a priori. However, the interferometric information is commonly referred to extended territories (at the scale of city or region), thus several problems arise in the implementation of automatic SHM techniques for the damage detection, localization, and assessment of the built environment at a point level (scale of the building or lower). Despite a long list of challenges, interferometric data have also the potential to become a useful source to assess the health of a structure, especially for helping in define structural early warning methodologies. For this reason, in the paper, the authors summarize the main challenges in the use of satellite interferometric data for civil SHM, and rather than proposing remedial actions, try to critically analyze the challenges and perspectives for future applications

    Minimizing the residual topography effect on interferograms to improve DInSAR results: estimating land subsidence in Port-Said City, Egypt

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    The accurate detection of land subsidence rates in urban areas is important to identify damage-prone areas and provide decision-makers with useful information. Meanwhile, no precise measurements of land subsidence have been undertaken within the coastal Port-Said City in Egypt to evaluate its hazard in relationship to sea-level rise. In order to address this shortcoming, this work introduces and evaluates a methodology that substantially improves small subsidence rate estimations in an urban setting. Eight ALOS/PALSAR-1 scenes were used to estimate the land subsidence rates in Port-Said City, using the Small BAse line Subset (SBAS) DInSAR technique. A stereo pair of ALOS/PRISM was used to generate an accurate DEM to minimize the residual topography effect on the generated interferograms. A total of 347 well distributed ground control points (GCP) were collected in Port-Said City using the leveling instrument to calibrate the generated DEM. Moreover, the eight PALSAR scenes were co-registered using 50 well-distributed GCPs and used to generate 22 interferogram pairs. These PALSAR interferograms were subsequently filtered and used together with the coherence data to calculate the phase unwrapping. The phase-unwrapped interferogram-pairs were then evaluated to discard four interferograms that were affected by phase jumps and phase ramps. Results confirmed that using an accurate DEM (ALOS/PRISM) was essential for accurately detecting small deformations. The vertical displacement rate during the investigated period (2007–2010) was estimated to be −28 mm. The results further indicate that the northern area of Port-Said City has been subjected to higher land subsidence rates compared to the southern area. Such land subsidence rates might induce significant environmental changes with respect to sea-level rise

    Error estimation in multitemporal InSAR deformation time series, with application to Lanzarote, Canary Islands

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    Interferometric Synthetic Aperture Radar (InSAR) is a reliable technique for measuring crustal deformation. However, despite its long application in geophysical problems, its error estimation has been largely overlooked. Currently, the largest problem with InSAR is still the atmospheric propagation errors, which is why multitemporal interferometric techniques have been successfully developed using a series of interferograms. However, none of the standard multitemporal interferometric techniques, namely PS or SB (Persistent Scatterers and Small Baselines, respectively) provide an estimate of their precision. Here, we present a method to compute reliable estimates of the precision of the deformation time series. We implement it for the SB multitemporal interferometric technique (a favorable technique for natural terrains, the most usual target of geophysical applications). We describe the method that uses a properly weighted scheme that allows us to compute estimates for all interferogram pixels, enhanced by a Montecarlo resampling technique that properly propagates the interferogram errors (variance-covariances) into the unknown parameters (estimated errors for the displacements). We apply the multitemporal error estimation method to Lanzarote Island (Canary Islands), where no active magmatic activity has been reported in the last decades. We detect deformation around Timanfaya volcano (lengthening of line-of-sight ∼ subsidence), where the last eruption in 1730–1736 occurred. Deformation closely follows the surface temperature anomalies indicating that magma crystallization (cooling and contraction) of the 300-year shallow magmatic body under Timanfaya volcano is still ongoing.Peer reviewe

    Innovative Techniques for the Retrieval of Earth’s Surface and Atmosphere Geophysical Parameters: Spaceborne Infrared/Microwave Combined Analyses

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    With the advent of the first satellites for Earth Observation: Landsat-1 in July 1972 and ERS-1 in May 1991, the discipline of environmental remote sensing has become, over time, increasingly fundamental for the study of phenomena characterizing the planet Earth. The goal of environmental remote sensing is to perform detailed analyses and to monitor the temporal evolution of different physical phenomena, exploiting the mechanisms of interaction between the objects that are present in an observed scene and the electromagnetic radiation detected by sensors, placed at a distance from the scene, operating at different frequencies. The analyzed physical phenomena are those related to climate change, weather forecasts, global ocean circulation, greenhouse gas profiling, earthquakes, volcanic eruptions, soil subsidence, and the effects of rapid urbanization processes. Generally, remote sensing sensors are of two primary types: active and passive. Active sensors use their own source of electromagnetic radiation to illuminate and analyze an area of interest. An active sensor emits radiation in the direction of the area to be investigated and then detects and measures the radiation that is backscattered from the objects contained in that area. Passive sensors, on the other hand, detect natural electromagnetic radiation (e.g., from the Sun in the visible band and the Earth in the infrared and microwave bands) emitted or reflected by the object contained in the observed scene. The scientific community has dedicated many resources to developing techniques to estimate, study and analyze Earth’s geophysical parameters. These techniques differ for active and passive sensors because they depend strictly on the type of the measured physical quantity. In my P.h.D. work, inversion techniques for estimating Earth’s surface and atmosphere geophysical parameters will be addressed, emphasizing methods based on machine learning (ML). In particular, the study of cloud microphysics and the characterization of Earth’s surface changes phenomenon are the critical points of this work

    Iterative atmospheric phase screen compensation for near-real-time ground-based InSAR measurements over a mountainous slope

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    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

    Application of DInSAR-GPS optimization for derivation of fine-scale surface motion maps of Southern California

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    A method based on random field theory and Gibbs-Markov random fields equivalency within Bayesian statistical framework is used to derive 3-D surface motion maps from sparse global positioning system (GPS) measurements and differential interferometric synthetic aperture radar (DInSAR) interferogram in the southern California region. The minimization of the Gibbs energy function is performed analytically, which is possible in the case when neighboring pixels are considered independent. The problem is well posed and the solution is unique and stable and not biased by the continuity condition. The technique produces a 3-D field containing estimates of surface motion on the spatial scale of the DInSAR image, over a given time period, complete with error estimates. Significant improvement in the accuracy of the vertical component and moderate improvement in the accuracy of the horizontal components of velocity are achieved in comparison with the GPS data alone. The method can be expanded to account for other available data sets, such as additional interferograms, lidar, or leveling data, in order to achieve even higher accuracy
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