Monitoring and predicting railway subsidence using InSAR and time series prediction techniques

Improvements in railway capabilities have resulted in heavier axle loads and higher speed operations, which increase the dynamic loads on the track. As a result, railway subsidence has become a threat to good railway performance and safe railway operation. The author of this thesis provides an approach for railway performance assessment through the monitoring and prediction of railway subsidence. The InSAR technique, which is able to monitor railway subsidence over a large area and long time period, was selected for railway subsidence monitoring. Future trends of railway subsidence should also be predicted using subsidence prediction models based on the time series deformation records obtained by InSAR. Three time series prediction models, which are the ARMA model, a neural network model and the grey model, are adopted in this thesis. Two case studies which monitor and predict the subsidence of the HS1 route were carried out to assess the performance of HS1. The case studies demonstrate that except for some areas with potential subsidence, no large scale subsidence has occurred on HS1 and the line is still stable after its 10 years' operation. In addition, the neural network model has the best performance in predicting the subsidence of HS1

Point target interferometry as applied to the characterization of localized deformation features

Title from PDF of title page (University of Missouri--Columbia, viewed on Feb. 23, 2010).The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file.Dr. Brent Rosenblad, Dissertation Supervisor.Vita.Ph. D. University of Missouri--Columbia 2008.Monitoring of ground deformation is a critical component of geotechnical engineering practice. This study investigated the application of synthetic aperture radar interferometry (InSAR), using point target analysis (IPTA) for characterizing localized deformation features that are often associated with geotechnical engineering activities. In contrast to discrete point in-situ deformation measurement techniques, InSAR can be used to obtain a broader view of deformation processes at a site. Satellite data available for the time period of construction of the Los Angeles Metro Rail Red Line was utilized to characterize the technique in terms of dependence of the feasibility in its application on SAR image acquisition parameters. Additionally, a statistical assessment of the sensitivity of deformation rates and the associated standard errors to the size of the dataset analyzed was performed by analyzing randomly generated subsets of data. While the spatial and temporal signatures corresponding to tunneling during the construction of the Red Line were successfully detected, it was found that a minimum of twenty SAR acquisitions were required in order to constrain the deformation history of the study area. From the sensitivity analysis, it was found that the variability of the derived estimates of deformation parameters varied inversely as a function of the size of the dataset used for analysis.Includes bibliographical references

Geodetic monitoring of complex shaped infrastructures using Ground-Based InSAR

In the context of climate change, alternatives to fossil energies need to be used as much as possible to produce electricity. Hydroelectric power generation through the utilisation of dams stands out as an exemplar of highly effective methodologies in this endeavour. Various monitoring sensors can be installed with different characteristics w.r.t. spatial resolution, temporal resolution and accuracy to assess their safe usage. Among the array of techniques available, it is noteworthy that ground-based synthetic aperture radar (GB-SAR) has not yet been widely adopted for this purpose. Despite its remarkable equilibrium between the aforementioned attributes, its sensitivity to atmospheric disruptions, specific acquisition geometry, and the requisite for phase unwrapping collectively contribute to constraining its usage. Several processing strategies are developed in this thesis to capitalise on all the opportunities of GB-SAR systems, such as continuous, flexible and autonomous observation combined with high resolutions and accuracy. The first challenge that needs to be solved is to accurately localise and estimate the azimuth of the GB-SAR to improve the geocoding of the image in the subsequent step. A ray tracing algorithm and tomographic techniques are used to recover these external parameters of the sensors. The introduction of corner reflectors for validation purposes confirms a significant error reduction. However, for the subsequent geocoding, challenges persist in scenarios involving vertical structures due to foreshortening and layover, which notably compromise the geocoding quality of the observed points. These issues arise when multiple points at varying elevations are encapsulated within a singular resolution cell, posing difficulties in pinpointing the precise location of the scattering point responsible for signal return. To surmount these hurdles, a Bayesian approach grounded in intensity models is formulated, offering a tool to enhance the accuracy of the geocoding process. The validation is assessed on a dam in the black forest in Germany, characterised by a very specific structure. The second part of this thesis is focused on the feasibility of using GB-SAR systems for long-term geodetic monitoring of large structures. A first assessment is made by testing large temporal baselines between acquisitions for epoch-wise monitoring. Due to large displacements, the phase unwrapping can not recover all the information. An improvement is made by adapting the geometry of the signal processing with the principal component analysis. The main case study consists of several campaigns from different stations at Enguri Dam in Georgia. The consistency of the estimated displacement map is assessed by comparing it to a numerical model calibrated on the plumblines data. It exhibits a strong agreement between the two results and comforts the usage of GB-SAR for epoch-wise monitoring, as it can measure several thousand points on the dam. It also exhibits the possibility of detecting local anomalies in the numerical model. Finally, the instrument has been installed for continuous monitoring for over two years at Enguri Dam. An adequate flowchart is developed to eliminate the drift happening with classical interferometric algorithms to achieve the accuracy required for geodetic monitoring. The analysis of the obtained time series confirms a very plausible result with classical parametric models of dam deformations. Moreover, the results of this processing strategy are also confronted with the numerical model and demonstrate a high consistency. The final comforting result is the comparison of the GB-SAR time series with the output from four GNSS stations installed on the dam crest. The developed algorithms and methods increase the capabilities of the GB-SAR for dam monitoring in different configurations. It can be a valuable and precious supplement to other classical sensors for long-term geodetic observation purposes as well as short-term monitoring in cases of particular dam operations

Method for landslides detection with semi-automatic procedures: The case in the zone center-east of Cauca department, Colombia

Landslides are a common natural hazard that causes human casualties, but also infrastructure damage and land-use degradation. Therefore, a quantitative assessment of their presence is required by means of detecting and recognizing the potentially unstable areas. This research aims to develop a method supported on semiautomatic methods to detect potential mass movements at a regional scale. Five techniques were studied: Morphometry, SAR interferometry (InSAR), Persistent Scatterer InSAR (PS-InSAR), SAR polarimetry (PolSAR) and NDVI composites of Landsat 5, Landsat 7, and Landsat 8. The case study was chosen within the mid-eastern area of the Cauca state, which is characterised by its mountainous terrain and the presence of slope instabilities, officially registered in the CGS-SIMMA landslide inventory. This inventory revealed that the type `slide' occurred with 77.4% from the entire registries, `fall' with 16.5%, followed by `creeps' with 3%, flows with 2.6%, and `lateral spread' with 0.43%. As a result, we obtained the morphometric variables: slope, CONVI, TWI, landform, which were highly associated with landslides. The effect of a DEM in the processing flow of the InSAR method was similar for the InSAR coherence variable using the DEMs ASTER, PALSAR RTC, Topo-map, and SRTM. Then, a multiInSAR analysis gave displacement velocities in the LOS direction between -10 and 10 mm/year. With the dual-PolSAR analysis (Sentinel-1), VH and VV C-band polarised radar energy emitted median values of backscatters, for landslides, about of -14.5 dB for VH polarisation and -8.5 dB for VV polarisation. Also, L-band fully polarimetric NASA-UAVSAR data allowed to nd the mechanism of dispersion of CGS landslide inventory: 39% for surface scattering, 46.4% for volume dispersion, and 14.6% for double-bounce scattering. The optical remote sensing provided NDVI composites derived from Landsat series between 2012 and 2016, showing that NDVI values between 0.40 and 0.70 had a high correlation to landslides. In summary, we found the highest categories related to landslides by Weight of Evidence method (WofE) for each spaceborne technique applied. Finally, these results were merged to generate the landslide detection model by using the supervised machine learning method of Random Forest. By taking training and test samples, the precision of the detection model was of about 70% for the rotational and translational types.Los deslizamientos son una amenaza natural que causa pérdidas humanas, daños a la infraestructura y degradación del suelo. Una evaluación cuantitativa de su presencia se requiere mediante la detección y el reconocimiento de potenciales áreas inestables. Esta investigación tuvo como alcance desarrollar un método soportado en métodos semi-automáticos para detectar potenciales movimientos en masa a escala regional. Cinco técnicas fueron estudiadas: Morfometría, Interferometría radar, Interferometría con Persistent Scatterers, Polarimetría radar y composiciones del NDVI con los satélites Landsat 5, Landsat 7 y Landsat 8. El caso de estudio se seleccionó dentro de la región intermedia al este del departamento del Cauca, la cual se caracteriza por terreno montañoso y la presencia de inestabilidades de la pendiente oficialmente registrados en el servicio SIMMA del Servicio Geológico Colombiano. Este inventario reveló que el tipo de movimiento deslizamiento ocurrió con una frecuencia relativa de 77.4%, caidos con el 16.5% de los casos y reptaciones con 3%, flujos con 2.6% y propagación lateral con 0.43%. Como resultado, se obtuvo las variables morfométricas: pendiente, convergencia, índice topográfico de humedad y forma del terreno altamente asociados con los deslizamientos. El efecto de un DEM en el procesamiento del método InSAR fue similar para la variable coherencia usando los DEMs: ASTER, PAlSAR RTC, Topo-map y SRTM. Un análisis Multi-InSAR estimó velocidades de desplazamiento en dirección de vista del radar entre -10 y 10 mm/año. El análisis de polarimetría dual del Sentinel-1 arrojó valores de retrodispersión promedio de -14.5 dB en la banda VH y -8.5dB en la banda VV. Las cuatro polarimetrías del sensor aéreo UAVSAR permitió caracterizar el mecanismo de dispersión del Inventario de Deslizamiento así: 39% en el mecanismo de superficie, 46.4% en el mecanismo de volumen y 14.6% en el mecanismo de doble rebote. La información generada en el rango óptico permitió obtener composiciones de NDVI derivados de la plataforma Landsat entre los años 2012 y 2016, mostrando que el rango entre 0.4 y 0.7 tuvieron una alta asociación con los deslizamientos. En esta investigación se determinaron las categorías de las variables de Teledetección más altamente relacionadas con los movimientos en masa mediante el método de Pesos de Evidencias (WofE). Finalmente, estos resultados se fusionaron para generar el modelo de detección de deslizamientos usando el método supervisado de aprendizaje de máquina Random Forest. Tomando muestras aleatorias para entrenar y validar el modelo en una proporción 70:30, el modelo de detección, especialmente los movimientos de tipo rotacional y traslacional fueron clasificados con una tasa general de éxito del 70%.Ministerio de CienciasConvocatoria 647 de 2014Research line: Geotechnics and Geoenvironmental HazardDoctorad

Interferometric Synthetic Aperture Sonar Signal Processing for Autonomous Underwater Vehicles Operating Shallow Water

The goal of the research was to develop best practices for image signal processing method for InSAS systems for bathymetric height determination. Improvements over existing techniques comes from the fusion of Chirp-Scaling a phase preserving beamforming techniques to form a SAS image, an interferometric Vernier method to unwrap the phase; and confirming the direction of arrival with the MUltiple SIgnal Channel (MUSIC) estimation technique. The fusion of Chirp-Scaling, Vernier, and MUSIC lead to the stability in the bathymetric height measurement, and improvements in resolution. This method is computationally faster, and used less memory then existing techniques

Elevation and Deformation Extraction from TomoSAR

3D SAR tomography (TomoSAR) and 4D SAR differential tomography (Diff-TomoSAR) exploit multi-baseline SAR data stacks to provide an essential innovation of SAR Interferometry for many applications, sensing complex scenes with multiple scatterers mapped into the same SAR pixel cell. However, these are still influenced by DEM uncertainty, temporal decorrelation, orbital, tropospheric and ionospheric phase distortion and height blurring. In this thesis, these techniques are explored. As part of this exploration, the systematic procedures for DEM generation, DEM quality assessment, DEM quality improvement and DEM applications are first studied. Besides, this thesis focuses on the whole cycle of systematic methods for 3D & 4D TomoSAR imaging for height and deformation retrieval, from the problem formation phase, through the development of methods to testing on real SAR data. After DEM generation introduction from spaceborne bistatic InSAR (TanDEM-X) and airborne photogrammetry (Bluesky), a new DEM co-registration method with line feature validation (river network line, ridgeline, valley line, crater boundary feature and so on) is developed and demonstrated to assist the study of a wide area DEM data quality. This DEM co-registration method aligns two DEMs irrespective of the linear distortion model, which improves the quality of DEM vertical comparison accuracy significantly and is suitable and helpful for DEM quality assessment. A systematic TomoSAR algorithm and method have been established, tested, analysed and demonstrated for various applications (urban buildings, bridges, dams) to achieve better 3D & 4D tomographic SAR imaging results. These include applying Cosmo-Skymed X band single-polarisation data over the Zipingpu dam, Dujiangyan, Sichuan, China, to map topography; and using ALOS L band data in the San Francisco Bay region to map urban building and bridge. A new ionospheric correction method based on the tile method employing IGS TEC data, a split-spectrum and an ionospheric model via least squares are developed to correct ionospheric distortion to improve the accuracy of 3D & 4D tomographic SAR imaging. Meanwhile, a pixel by pixel orbit baseline estimation method is developed to address the research gaps of baseline estimation for 3D & 4D spaceborne SAR tomography imaging. Moreover, a SAR tomography imaging algorithm and a differential tomography four-dimensional SAR imaging algorithm based on compressive sensing, SAR interferometry phase (InSAR) calibration reference to DEM with DEM error correction, a new phase error calibration and compensation algorithm, based on PS, SVD, PGA, weighted least squares and minimum entropy, are developed to obtain accurate 3D & 4D tomographic SAR imaging results. The new baseline estimation method and consequent TomoSAR processing results showed that an accurate baseline estimation is essential to build up the TomoSAR model. After baseline estimation, phase calibration experiments (via FFT and Capon method) indicate that a phase calibration step is indispensable for TomoSAR imaging, which eventually influences the inversion results. A super-resolution reconstruction CS based study demonstrates X band data with the CS method does not fit for forest reconstruction but works for reconstruction of large civil engineering structures such as dams and urban buildings. Meanwhile, the L band data with FFT, Capon and the CS method are shown to work for the reconstruction of large manmade structures (such as bridges) and urban buildings

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

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

The SAR Handbook: Comprehensive Methodologies for Forest Monitoring and Biomass Estimation

This Synthetic Aperture Radar (SAR) handbook of applied methods for forest monitoring and biomass estimation has been developed by SERVIR in collaboration with SilvaCarbon to address pressing needs in the development of operational forest monitoring services. Despite the existence of SAR technology with all-weather capability for over 30 years, the applied use of this technology for operational purposes has proven difficult. This handbook seeks to provide understandable, easy-to-assimilate technical material to remote sensing specialists that may not have expertise on SAR but are interested in leveraging SAR technology in the forestry sector

GPS and PSI integration for monitoring urban land motion

Urban ground motion due to natural or man-made geological processes is an issue of major importance for local authorities, property developers, planners and buyers. Increased knowledge of this phenomena would benefit all involved but the measurement techniques in common use have either spatial or temporal inadequacies. A technique known as Persistent Scatterer Interferometry (PSI) has been developed which can map ground motion to high precision over large areas with a temporal scale measured in years. PSI takes advantage of the high number of Synthetic Aperture Radar (SAR) images available to mitigate the atmospheric effects that inhibit standard Interferometric SAR (InSAR) techniques. This however involves assumptions about the nature of atmospheric variability, such as its randomness over time, or its spatial extent. In addition, little is known about the Persistent Scatterers (PS) themselves and PSI is only able to provide results relative to a reference PS. The reference PS point is often arbitrarily chosen and may itself be in an area undergoing ground motion, thus adding a degree of ambiguity to any relatively derived motion. The purpose of this work is to investigate possible solutions to these shortfalls and quantify any improvements made. A corner reflector network is established in the Nottingham area of the UK. A data archive is collated over three years containing Global Positioning System (GPS) data at the corner reflector sites, data from surrounding Continuous GPS (CGPS) sites and levelling data. Due to conflicts with the European Space Agency (ESA) Environmental Satellite (ENVISAT), there were insufficient SAR images to com- pute a fully integrated corner reflector PSI study. Instead, the project focussed on atmospheric correction of PSI results using absolute ZWD estimates. Zenith Wet Delay (ZWD) estimates are derived from a Precise Point Positioning (PPP) GPS processing method which does not rely on a network of ground stations and therefore produces absolute ZWD estimates which are less prone to biases and noise. These are interpolated across a PSI study area and used to mitigate the long wavelength effects of atmopheric water vapour in the PSI differential interferograms. The corrected PSI results are then compared to uncorrected results, GPS derived motion and levelling data. Results between the ZWD corrected PSI study and the uncorrected study show statistical improvements in some areas and reductions in others. Correlation factors between double-differenced levelling observations and double-differenced PSI results improve from 0.67 to 0.81. PSI deformation rates also show improvement when compared to GPS deformation rates, although some results do not satisfy statistical tests