Multi-Temporal InSAR Structural Damage Assessment: The London Crossrail Case Study

Yan'an new district (YND) is one of the largest civil engineering projects for land creation in Loess Plateau, of which the amount of earthwork exceeds 600 million m 3 , to create 78.5 km 2 of flat land. Such mega-scale engineering activities and complex geological characteristics have induced wide land deformation in the region. Small baseline subset synthetic aperture radar interferometry (SBAS-InSAR) method and 55 Sentinel-1A (S-1A) images were utilized in the present work to investigate the urban surface deformation in the Yan'an urban area and Yan'an new airport (YNA) from 2015 to 2019. The results were validated by the ground leveling measurements in the YNA. It is found that significant uneven surface deformation existed in both YND and YNA areas with maximum accumulative subsidence of 300 and 217 mm, respectively. Moreover, the average subsidence rate of the YND and YNA areas ranged from −70 to 30 mm/year and −50 to 25 mm/year, respectively. The present work shows that the land deformation suffered two periods (from 2015 to 2017 and from 2017 to 2019) and expanded from urban center to surrounding resettlement area, which are highly relevant with urban earthwork process. It is found that more than 60% of land subsidence occurs at filled areas, while more than 65% of surface uplifting occurs at excavation areas. The present work shows that the subsidence originates from the earth filling and the load of urban buildings, while the release of stress is the major factor for the land uplift. Moreover, it is found that the collapsibility of loess and concentrated precipitation deteriorates the degree of local land subsidence. The deformation discovered by this paper shows that the city may suffer a long period of subsidence, and huge challenges may exist in the period of urban maintaining buildings and infrastructure facilities.The widely applied methods for monitoring urban surface deformation are leveling measurement and global navigation satellite system (GNSS) measurement [17]. These methods can achieve high precision at single points in the monitoring of local land subsidence with low spatial resolution and high cost [18]. On the other hand, synthetic aperture radar interferometry (InSAR) has significant advantages over other methods, including fast surface deformation with large monitoring range, operability in all climatic conditions, low operational cost and high spatial resolution [19]. The first application of the InSAR was the monitoring of an earthquake in Landers, California [20], but the temporal resolution was relatively low because of the absence of analyzing on time series images. To overcome this limitation, multi-temporal InSAR was introduced for long-term and large-scale monitoring of surface deformation [21,22]. More specifically, a permanent scatterers InSAR (PS-InSAR) method proposed by Ferretti et al. and a small baseline subset InSAR (SBAS-InSAR) method introduced by Berardino et al. have been widely used in related fields with millimetric accuracy [23][24][25]. Zhang and Zhou analyzed W...

mentioning

confidence: 99%

SBAS-InSAR Based Deformation Detection of Urban Land, Created from Mega-Scale Mountain Excavating and Valley Filling in the Loess Plateau: The Case Study of Yan’an City

Jia

Chen

et al. 2019

“…Multitemporal InSAR processing has been employed with high accuracy in other studies. For example, the use of persistent scatterers to track surface deformation and associated damage in urban environments, e.g., [34]. While a multitemporal InSAR approach using methods such as persistent scatterers provides insightful and accurate results, it requires that data collection and processing be performed after the event has occurred.…”

Section: Advancements and Improvements Using Time Series Of Coherencementioning

confidence: 99%

Mapping Damage-Affected Areas after Natural Hazard Events Using Sentinel-1 Coherence Time Series

Olen

Bookhagen

2018

The emergence of the Sentinel-1A and 1B satellites now offers freely available and widely accessible Synthetic Aperture Radar (SAR) data. Near-global coverage and rapid repeat time (6-12 days) gives Sentinel-1 data the potential to be widely used for monitoring the Earth's surface. Subtle land-cover and land surface changes can affect the phase and amplitude of the C-band SAR signal, and thus the coherence between two images collected before and after such changes. Analysis of SAR coherence therefore serves as a rapidly deployable and powerful tool to track both seasonal changes and rapid surface disturbances following natural disasters. An advantage of using Sentinel-1 C-band radar data is the ability to easily construct time series of coherence for a region of interest at low cost. In this paper, we propose a new method for Potentially Affected Area (PAA) detection following a natural hazard event. Based on the coherence time series, the proposed method (1) determines the natural variability of coherence within each pixel in the region of interest, accounting for factors such as seasonality and the inherent noise of variable surfaces; and (2) compares pixel-by-pixel syn-event coherence to temporal coherence distributions to determine where statistically significant coherence loss has occurred. The user can determine to what degree the syn-event coherence value (e.g., 1st, 5th percentile of pre-event distribution) constitutes a PAA, and integrate pertinent regional data, such as population density, to rank and prioritise PAAs. We apply the method to two case studies, Sarpol-e, Iran following the 2017 Iran-Iraq earthquake, and a landslide-prone region of NW Argentina, to demonstrate how rapid identification and interpretation of potentially affected areas can be performed shortly following a natural hazard event.

“…When it is determined that an urban bridge has damage or potential damage, it is always important to seek the cause or causes of the damage, so that monitoring can be used for future preventative maintenance of the bridge. Relevant studies have shown that the causes of urban bridge damage are often closely related to the surrounding environment [26,27]. Differential Interferometry Synthetic Aperture Radar (D-InSAR) is a technique that obtains large-scale surface micro-deformation information from space [28].…”

Section: Introductionmentioning

confidence: 99%

Damage Detection and Analysis of Urban Bridges Using Terrestrial Laser Scanning (TLS), Ground-Based Microwave Interferometry, and Permanent Scatterer Interferometry Synthetic Aperture Radar (PS-InSAR)

Liu

Wang

et al. 2019

This paper presents a practical framework for urban bridge damage detection and analysis by using three key techniques: terrestrial laser scanning (TLS), ground-based microwave interferometry, and permanent scatterer interferometry synthetic aperture radar (PS-InSAR). The proposed framework was tested on the Beishatan Bridge in Beijing, China. Firstly, a Digital Surface Model (DSM) of the lower surface of the bridge was constructed based on the point cloud generated by using TLS to obtain the potential damage area. Secondly, the dynamic time-series displacement of the potential damage area was acquired by ground-based microwave interferometry, and the Extreme-Point Symmetric Mode Decomposition (ESMD) method was applied to detect damages by the use of signal decomposition and instantaneous frequency calculation. Lastly, the PS-InSAR technique was applied to obtain the surface deformation around Beishatan Bridge by using COSMO-SkyMed images with a ground resolution of 3 m × 3 m, and finally, we analyzed the causes of bridge damage. The experimental results showed that the proposed framework can effectively obtain the potential damage area of the bridge by the DSM from the point cloud by TLS and further judge whether the bridge was damaged by the ESMD method, based on the time-series displacement data. The results also showed that the subway shield construction may be the reason for damage to Beishatan Bridge.