479 research outputs found

    Mexico City Subsidence Measured by InSAR Time Series: Joint Analysis Using PS and SBAS Approaches

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    International audienceIn multi-temporal InSAR processing, both the Permanent Scatterer (PS) and Small BAseline Subset (SBAS) approaches are optimized to obtain ground displacement rates with a nominal accuracy of millimeters per year. In this paper, we investigate how applying both approaches to Mexico City subsidence validates the InSAR time series results and brings complementary information to the subsidence pattern. We apply the PS approach (Gamma-IPTA chain) and an ad-hoc SBAS approach on 38 ENVISAT images from November 2002 to March 2007 to map the Mexico City subsidence. The subsidence rate maps obtained by both approaches are compared quantitatively and analyzed at different steps of the PS processing. The inter-comparison is done separately for low-pass (LP) and high-pass (HP) filtered difference maps to take the complementarity of both approaches at different scales into account. The inter-comparison shows that the differential subsidence map obtained by the SBAS approach describes the local features associated with urban constructions and infrastructures, while the PS approach quantitatively characterizes the motion of individual targets. The latter information, once related to the type of building foundations, should be essential to quantify the relative importance of surface loads, surface drying and drying due to aquifer over-exploitation, in subsoil compaction

    LiCSBAS: An Open-Source InSAR Time Series Analysis Package Integrated with the LiCSAR Automated Sentinel-1 InSAR Processor

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    For the past five years, the 2-satellite Sentinel-1 constellation has provided abundant and useful Synthetic Aperture Radar (SAR) data, which have the potential to reveal global ground surface deformation at high spatial and temporal resolutions. However, for most users, fully exploiting the large amount of associated data is challenging, especially over wide areas. To help address this challenge, we have developed LiCSBAS, an open-source SAR interferometry (InSAR) time series analysis package that integrates with the automated Sentinel-1 InSAR processor (LiCSAR). LiCSBAS utilizes freely available LiCSAR products, and users can save processing time and disk space while obtaining the results of InSAR time series analysis. In the LiCSBAS processing scheme, interferograms with many unwrapping errors are automatically identified by loop closure and removed. Reliable time series and velocities are derived with the aid of masking using several noise indices. The easy implementation of atmospheric corrections to reduce noise is achieved with the Generic Atmospheric Correction Online Service for InSAR (GACOS). Using case studies in southern Tohoku and the Echigo Plain, Japan, we demonstrate that LiCSBAS applied to LiCSAR products can detect both large-scale (>100 km) and localized (~km) relative displacements with an accuracy of <1 cm/epoch and ~2 mm/yr. We detect displacements with different temporal characteristics, including linear, periodic, and episodic, in Niigata, Ojiya, and Sanjo City, respectively. LiCSBAS and LiCSAR products facilitate greater exploitation of globally available and abundant SAR datasets and enhance their applications for scientific research and societal benefit

    Integration of LIDAR and IFSAR for mapping

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    LiDAR and IfSAR data is now widely used for a number of applications, particularly those needing a digital elevation model. The data is often complementary to other data such as aerial imagery and high resolution satellite data. This paper will review the current data sources and the products and then look at the ways in which the data can be integrated for particular applications. The main platforms for LiDAR are either helicopter or fixed wing aircraft, often operating at low altitudes, a digital camera is frequently included on the platform, there is an interest in using other sensors such as 3 line cameras of hyperspectral scanners. IfSAR is used from satellite platforms, or from aircraft, the latter are more compatible with LiDAR for integration. The paper will examine the advantages and disadvantages of LiDAR and IfSAR for DEM generation and discuss the issues which still need to be dealt with. Examples of applications will be given and particularly those involving the integration of different types of data. Examples will be given from various sources and future trends examined

    Potential and limits of InSAR to characterize interseismic deformation independently of GPS data: Application to the southern San Andreas Fault system

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    The evaluation of long-wavelength deformation associated with interseismic strain accumulation traditionally relies on spatially sparse GPS measurements, or on high spatial-resolution InSAR velocity fields aligned to a GPS-based model. In this approach the InSAR contributes only short-wavelength deformation and the two data sets are dependent, thereby challenging the evaluation of the InSAR uncertainties and the justification of atmospheric corrections. Here we present an analysis using 7 years of Envisat InSAR data to characterize interseismic deformation along the southern San Andreas Fault (SAF) and the San Jacinto Fault (SJF) in southern California, where the SAF bifurcates onto the Mission Creek (MCF) and the Banning (BF) fault strands. We outline the processing steps for using InSAR alone to characterize both the short- and long-wavelength deformation, and evaluate the velocity field uncertainties with independent continuous GPS data. InSAR line-of-sight (LOS) and continuous GPS velocities agree within ∼1–2 mm/yr in the study area, suggesting that multiyear InSAR time series can be used to characterize interseismic deformation with a higher spatial resolution than GPS. We investigate with dislocation models the ability of this mean LOS velocity field to constrain fault slip rates and show that a single viewing geometry can help distinguish between different slip-rate scenarios on the SAF and SJF (∼35 km apart) but multiple viewing geometries are needed to differentiate slip on the MCF and BF (<12 km apart). Our results demonstrate that interseismic models of strain accumulation used for seismic hazards assessment would benefit from the consideration of InSAR mean velocity maps

    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

    Towards an Integrated Assessment of Sea-Level Observations Along the U.S. Atlantic Coast

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    Sea levels are rising globally due to anthropogenic climate change. However, local sea levels that impact coastal ecosystems often differ from the global trend, sometimes by a factor of two or more. Improved understanding of this regional variability provides insights into geophysical processes and has implications for coastal communities developing resilience to ongoing sea-level rise. This dissertation conducts an investigation of sea level and its contributing processes at multiple spatial scales. Focusing on primarily interannual time-scales and data-driven approaches, new data sources and technologies are utilized to reduce current uncertainties. First, sea-level trends are assessed over the global ocean and at coastlines using data from the recently launched ICESat-2 satellite. These trends agree well with independent measurements, while also filling observational gaps along undersampled coastlines and at high-latitudes. Next, the spatial focus is narrowed to the U.S. East Coast, which is experiencing exceptionally high rates of relative sea-level rise, largely due to land subsidence. By incorporating new state-of-the-art estimates of land-ice melt, an existing Bayesian hierarchical space-time model is expanded to assess the relative contributions of sea surface height and vertical land motion to 20th century relative-sea level change. Model results confirm previous findings that identified regional-scale geological processes as the primary driver of spatial variability in East Coast relative sea level. By rigorously quantifying uncertainties, constraints are placed on the current state of knowledge with clear directions for future research. Finally, small-scale vertical land motion in Hampton Roads, VA is investigated using the remote-sensing technology of Interferometric Synthetic Aperture Radar (InSAR). Two different data sources and processing strategies are implemented which independently reveal substantial rates of vertical land motion that vary over short spatial scales. The results highlight the importance of vertical land motion in exacerbating negative impacts of relative sea-level rise such as flooding and inundation. Overall, this study leverages new spaceborne sensors, an innovative statistical model, and state-of-the-art processing strategies to enhance our understanding of ongoing sea-level change

    InSAR bias and uncertainty due to the systematic and stochastic tropospheric delay

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    We quantify the bias and uncertainty of interferometric synthetic aperture radar (InSAR) displacement time series and their derivatives, the displacement velocities, by analyzing the systematic and stochastic components of the temporal variation of the tropospheric delay. The biases due to the systematic seasonal delay depend on the SAR acquisition times, whereas the uncertainties depend on the standard deviation of the random delay, the number of acquisitions, the total time span covered, and the covariance of the time series of the stochastic delay between a pixel and the reference. We study the contribution of the wet delay to the InSAR observations along the western India plate boundary using (i) Moderate Resolution Imaging Spectroradiometer precipitable water vapor, (ii) stratified tropospheric delay estimated from the ERA-I global atmospheric model, and (iii) seven Envisat InSAR swaths. Our analysis indicates that the amplitudes of the annual delay vary by up to ~10 cm in this region equivalent to a maximum displacement bias of ~24 cm in InSAR line of sight direction between two epochs (assuming Envisat IS6 beam mode). The stratified tropospheric delay correction mitigates this bias and reduces the scatter due to the stochastic delay. For ~7 years of Envisat acquisitions along the western India plate boundary, the uncertainty of the InSAR velocity field due to the residual stochastic wet delay after stratified tropospheric delay correction using the ERA-I model is in the order of ~2 mm/yr over 100 km and ~4 mm/yr over 400 km. We discuss the implication of the derived uncertainties on the full variance-covariance matrix of the InSAR data
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