Terrain subsidence and landslides II

Monitoring of flood defense systems is an aspect of crucial importance in achieving safety standards, and avoiding catastrophic failure events. The current monitoring methods rely on expert observers, which lead to subjective and often unreliable inspections. Hence, there is a need for innovative techniques to monitor levee stability in order to help the authorities making right decisions in the future. Previous studies shown that Persistent Scatterer Synthetic Aperture Radar Interferometry (PS-InSAR) provides high precision measurements of levee deformation. In this contribution, we explore the feasibility of using the PS-InSAR technique in order to enable continuous levee deformation monitoring, such as detecting, tracking and analyzing changes that are indicative of potential problematic locations. These locations may be very localized, only detected by a single PS, or cover a longer levee segment, showing a range of anomalously behaving PS. Aiming towards an automatic detection algorithm for such locations using ‘fingerprinting’, an expert-supported approach is developed, which considers properties and conditions of the levee sections. From a monitoring perspective, the objective is to minimize the number of false alarms and missed detections. The applicability of the proposed approach is demonstrated via case studies located in different parts of the Netherlands. Levees on these locations have been monitored from both ascending and descending orbits, using Envisat, TerraSAR-X and Radarsat-2 acquisitions in order to estimate the line-of-sight deformation time series of each Persistent Scatterer (PS) between 2002 and 2016. The proposed approach intends to improve the applicability of the technique by using all available prior information on the structural behavior of levees. It is concluded that a systematic application of satellite radar interferometry may complement existing approaches for assessing levee stability and failure investigations in an innovative, frequent and cost-effective way.

We present a detailed survey of the Mosul dam ongoing destabilization process. The dam  is located on the Tigris river and is the biggest hydraulic structure in Iraq. From a geological point of view the dam foundation is very poor because of the the site geology formed by alternate and variable strates of highly soluble materials as gypsum, anhydrite, marl and limestone. Here we present the first comprehensive multi sensor cumulative deformation map for the dam generated from space-based synthetic-aperture radar (SAR) measurements from the Italian constellation COSMO-SkyMed and the European sensor Sentinel-1a. We compared  2014-2016  data to historic dataset spanning 2004-2010 acquired with the Envisat ASAR sensor. We found that Deformation was rapid during 2004-2010, slowed down in 2012-2014 and restarted since August 2014 when grouting operation stopped due to the temporary capture of the dam by the self-proclaimed islamic state (ISIS). We took advantage of the availability of data from multiple SAR constellations to infer the deformation at the dam in great spatial and temporal detail and shed new lights on the dynamics of ongoing destabilization. We calibrated SAR-amplitude-based water level measurements using OSTM altimeter data to infer water level changes at the dam wall and disclose possible causes of the subsidence slow-down in 2012-2014. We modelled the inferred deformation using a Markov chain Monte Carlo approach to solve for change in volume for a simple tensile dislocation. The model identifies the reservoir-induced pressure as the main cause of an increase in the dissolution rates. This study highlights how new constellations of SAR sensors together with the availability of historical datasets are leading to important advances in deformation monitoring of small scale geologic and manmade structures. 

The increasing aging civil infrastructures, such as buildings, roads, subways, high-speed railways and bridges have given rise to problems in national economic development and public safety. The Synthetic Aperture Radar Interferometry (InSAR) technique has been considered as an effective earth observation tool that empowers periodic or continuous monitoring to master the routine operation status and identify possible structural problems in civil engineering.

However, the practical applicability of InSAR in civil infrastructure monitoringstill suffers from limitations of decorrelation and presence of seasonal thermal dilation. Regarding the decorrelation, a multi-temporal strategy effectively combines improved Persistent Scatterer Interferometry (PSI) and Small Baseline (SBAS) methods is implemented to enhance the density and precision of Point Targets (PTs) for long-term deformation inversion. On the one hand, the amplitude dispersion, coherence and priori information of structures are effectively evaluated to identify those dominant scatterers. On the other hand, the spectrally-filtered interferograms with small baselines are used to detect those semi-stable scatterers.

Time-series InSAR phase observations of steel structures can be decomposed into the trend, periodical thermal dilation and stochastic noise components. Specially, the periodical thermal dilation effects always cause significant difficulties in civil infrastructure deformation monitoring. In order to quantitatively explore the seasonal thermal dilations due to temperature variations, a regression-based thermal response model was established by linear fitting between the residual interferometric phase signal obtained by iterative spatial-temporal filtering and temperature differences. From a remote sensing perspective, this component is extremely useful because it is related to specific properties of the observed targets. After the separation of thermal component, the trend component of time-series displacements was investigated. It represents the long-term slow changes and can be approximated by a linear function.

As a typical city with the earliest urbanization and rapid population growth in China, Shanghai is chosen as our study area. The quantification of deformation components requires a copious number of SAR acquisitions to support a reliable statistics analysis. In our case, we processed long time-series TerraSAR-X StripMap datasets from 2009 to 2016 with sufficient temporal sampling and deformation sensitivity. The long-term influences of deformation components on the civil infrastructures were investigated. More importantly, different characteristics of statistically significant periodical thermal dilations on the horizontal linear features and vertical buildings were analyzed.

1) For the horizontal linear features such as roads, subways, high-speed railways and bridges, the PTs distribution and deformation patterns are different depending on the construction periods and building modes. From a structural viewpoint, they are hyper-static structure where the presence of constraints may hinder the accumulation of thermal dilation along the structures. Therefore, the maximum accumulation thermal dilations usually occurred in their central part. With the assumption that temperature is homogeneous along the structure, seasonal thermal dilation effects and thermal coefficient parameter of the structure can be accurately estimated. The actual physical property of material can be used to verify the estimated thermal coefficient parameter. After removal of the thermal displacements, the trend subsidence rate and gradient magnitude along the route are weighted for hazardous grading evaluation.

2) For the vertical buildings, interferograms with different temperature gap indicate a temperature-dependent thermal dilation. The vertical and horizontal thermal displacements play a different role in buildings with different geometry structure. The higher buildings mainly show strong vertical distribution of thermal dilation pattern which is related to elevation while lower buildings with much more width mainly show periodical horizontal distribution of thermal dilation pattern like linear features. The vertical thermal dilation of high-rise buildings can be fitted by an elevation-dependent linear model and the horizontal thermal dilation of lower buildings can be fitted by a geometry structure related piecewise linear model. 

Dissolution of the carbonate beds by acidic-groundwater flowing through fractures and joints in the bedrock alters land surface and enhances the development of sinkholes, especially in water-soluble and carbonate bedrocks such as limestone, dolomite or gypsum. Susceptible regions to sinkhole formation can often be detected based on geology and groundwater conditions. Sinkhole detection is critical for understanding hydrological processes and mitigating geological hazards in karst areas.

Sinkhole formation causes the surface to subside or even collapse suddenly without any obvious prior warning, leading to extensive damage and sometimes loss of life, especially in urban areas. So there are clear benefits in detecting early warning signs of sinkhole collapse.

InSAR measurements have been successfully utilized to detect subsidence in areas prone to sinkhole formation. The extent of subsidence features on the surface often expands beyond the edge of later on visible sinkhole collapse and then can continue for years or even decades after the initial sinkhole formation.

The recent availability of high resolution TanDEM-X (TDX) derived digital elevation models (DEM) enables us to delineate and analyze geomorphologic features and landscape structures with a high degree of spatial detail. In this study, we develop an adaptive sinkhole-delineating method based on image processing technique to detect karst sinkholes in Hamedan, west Iran, using TDX-derived DEMs. We show that using high-resolution TDX data from different geometries and time periods enable us to effectively distinguish sinkholes from other depression features of the basin. We also use interferometric synthetic aperture radar (InSAR) technique with SAR data acquired from Envisat, ALOS, TerraSAR-X and Sentinel-1 to quantify long-term subsidence in areas prone to sinkhole formation. Our results indicate that the formation of a lot of sinkholes is influenced by land subsidence, affecting the region over large areas with the maximum rate of 4-5 cm/yr during the observed period (2003-2016).