A new methodology to detect changes in displacement rates of slow-moving landslides using InSAR time series

2021 ◽  
Author(s):  
Alexandra Urgilez Vinueza ◽  
Alexander Handwerger ◽  
Mark Bakker ◽  
Thom Bogaard
Keyword(s):  
Time Series ◽  
Surface Deformation ◽  
Piecewise Linear ◽  
Regional Scale ◽  
Ground Surface ◽  
Surface Displacement ◽  
Information Criterion ◽  
Piecewise Linear Function ◽  
Linear Segment ◽  
Slow Moving

<p>Regional-scale landslide deformation can be measured using satellite-based synthetic aperture radar interferometry (InSAR). Our study focuses on the quantification of displacements of slow-moving landslides that impact a hydropower dam and reservoir in the tropical Ecuadorian Andes. We constructed ground surface deformation time series using data from the Copernicus Sentinel-1 A/B satellites between 2016 and 2020. We developed a new approach to automatically detect the onset of accelerations and/or decelerations within each active landslide. Our approach approximates the movement of a pixel as a piecewise linear function. Multiple linear segments are fitted to the cumulative deformation time series of each pixel. Each linear segment represents a constant movement. The point where one linear segment is connected to another linear segment represents the time when the pixel’s rate of movement has changed from one value to another value and is referred to as a breakpoint. As such, the breakpoints represent moments of acceleration or deceleration. Three criteria are used to determine the number of breakpoints: the timing and uncertainty of the breakpoints, the confidence intervals of the fitted segments’ slopes, and the Akaike Information Criterion (AIC). The suitable number of breakpoints for each pixel (i.e., the number of accelerations or decelerations) is determined by finding the largest number of breakpoints that complies with the three listed criteria. The application of this approach to landslides results in a wealth of information on the surface displacement of a slope and an objective way to identify changes in displacement rates. The displacement rates, their spatial variation, and the timing of acceleration and deceleration can further be used to study the physical behavior of a slow-moving slope or for (regional) hazard assessment linking the onset of change in displacement rate to causal and triggering factors.</p>

scholarly journals The Multiple Aperture SAR Interferometry (MAI) Technique for the Detection of Large Ground Displacement Dynamics: An Overview

2020 ◽  
Vol 12 (7) ◽  
pp. 1189 ◽  
Author(s):  
Pietro Mastro ◽  
Carmine Serio ◽  
Guido Masiello ◽  
Antonio Pepe
Keyword(s):  
Time Series ◽  
Synthetic Aperture Radar ◽  
Surface Deformation ◽  
Ground Deformation ◽  
Ground Surface ◽  
Surface Displacement ◽  
Sar Interferometry ◽  
Synthetic Aperture ◽  
Signal Spectrum ◽  
Aperture Radar

This work presents an overview of the multiple aperture synthetic aperture radar interferometric (MAI) technique, which is primarily used to measure the along-track components of the Earth’s surface deformation, by investigating its capabilities and potential applications. Such a method is widely used to monitor the time evolution of ground surface changes in areas with large deformations (e.g., due to glaciers movements or seismic episodes), permitting one to discriminate the three-dimensional (up–down, east–west, north–south) components of the Earth’s surface displacements. The MAI technique relies on the spectral diversity (SD) method, which consists of splitting the azimuth (range) Synthetic Aperture RADAR (SAR) signal spectrum into separate sub-bands to get an estimate of the surface displacement along the azimuth (sensor line-of-sight (LOS)) direction. Moreover, the SD techniques are also used to correct the atmospheric phase screen (APS) artefacts (e.g., the ionospheric and water vapor phase distortion effects) that corrupt surface displacement time-series obtained by currently available multi-temporal InSAR (MT-InSAR) tools. More recently, the SD methods have also been exploited for the fine co-registration of SAR data acquired with the Terrain Observation with Progressive Scans (TOPS) mode. This work is primarily devoted to illustrating the underlying rationale and effectiveness of the MAI and SD techniques as well as their applications. In addition, we present an innovative method to combine complementary information of the ground deformation collected from multi-orbit/multi-track satellite observations. In particular, the presented technique complements the recently developed Minimum Acceleration combination (MinA) method with MAI-driven azimuthal ground deformation measurements to obtain the time-series of the 3-D components of the deformation in areas affected by large deformation episodes. Experimental results encompass several case studies. The validity and relevance of the presented approaches are clearly demonstrated in the context of geospatial analyses.

scholarly journals LAND SUBSIDENCE MONITORING USING PS-InSAR TECHNIQUE FOR L-BAND SAR DATA

2016 ◽  
Vol XLI-B7 ◽  
pp. 995-997 ◽  
Author(s):  
S. Thapa ◽  
R. S. Chatterjee ◽  
K. B. Singh ◽  
D. Kumar
Keyword(s):  
Time Series ◽  
Land Subsidence ◽  
Land Surface ◽  
Surface Deformation ◽  
Underground Mining ◽  
Ground Surface ◽  
Sar Interferometry ◽  
Persistent Scatterer Interferometry ◽  
Long Time ◽  
Subsidence Monitoring

Differential SAR-Interferometry (D-InSAR) is one of the potential source to measure land surface motion induced due to underground coal mining. However, this technique has many limitation such as atmospheric in homogeneities, spatial de-correlation, and temporal decorrelation. Persistent Scatterer Interferometry synthetic aperture radar (PS-InSAR) belongs to a family of time series InSAR technique, which utilizes the properties of some of the stable natural and anthropogenic targets which remain coherent over long time period. In this study PS-InSAR technique has been used to monitor land subsidence over selected location of Jharia Coal field which has been correlated with the ground levelling measurement. This time series deformation observed using PS InSAR helped us to understand the nature of the ground surface deformation due to underground mining activity.

scholarly journals Hazard Implications of the 2016 Mw 5.0 Cushing, OK Earthquake from a Joint Analysis of Damage and InSAR Data

2018 ◽  
Vol 10 (11) ◽  
pp. 1715 ◽  
Author(s):  
Magali Barba-Sevilla ◽  
Bridger Baird ◽  
Abbie Liel ◽  
Kristy Tiampo
Keyword(s):  
Time Series ◽  
Surface Deformation ◽  
Critical Infrastructure ◽  
European Space Agency ◽  
Significant Risk ◽  
Surface Displacement ◽  
Earthquake Sequence ◽  
Joint Analysis ◽  
Oil Storage ◽  
The Impact

The Cushing Hub in Oklahoma, one of the largest oil storage facilities in the world, is federally designated as critical national infrastructure. In 2014, the formerly aseismic city of Cushing experienced a Mw 4.0 and 4.3 induced earthquake sequence due to wastewater injection. Since then, an M4+ earthquake sequence has occurred annually (October 2014, September 2015, November 2016). Thus far, damage to critical infrastructure has been minimal; however, a larger earthquake could pose significant risk to the Cushing Hub. In addition to inducing earthquakes, wastewater injection also threatens the Cushing Hub through gradual surface uplift. To characterize the impact of wastewater injection on critical infrastructure, we use Differential Interferometric Synthetic Aperture Radar (DInSAR), a satellite radar technique, to observe ground surface displacement in Cushing before and during the induced Mw 5.0 event. Here, we process interferograms of Single Look Complex (SLC) radar data from the European Space Agency (ESA) Sentinel-1A satellite. The preearthquake interferograms are used to create a time series of cumulative surface displacement, while the coseismic interferograms are used to invert for earthquake source characteristics. The time series of surface displacement reveals 4–5.5 cm of uplift across Cushing over 17 months. The coseismic interferogram inversion suggests that the 2016 Mw 5.0 earthquake is shallower than estimated from seismic inversions alone. This shallower source depth should be taken into account in future hazard assessments for regional infrastructure. In addition, monitoring of surface deformation near wastewater injection wells can be used to characterize the subsurface dynamics and implement measures to mitigate damage to critical installations.

An extended GeoNode-Based Platform for Detailed Analysis of the Spatial/Temporal DInSAR Information Contents

2020 ◽  
Author(s):  
Adele Fusco ◽  
Sabatino Buonanno ◽  
Giovanni Zeni ◽  
Michele Manunta ◽  
Maria Marsella ◽  
...  
Keyword(s):  
Time Series ◽  
Synthetic Aperture Radar ◽  
Spatial Data ◽  
High Performance ◽  
Surface Deformation ◽  
Surface Displacement ◽  
Synthetic Aperture ◽  
Sar Data ◽  
Informative Content ◽  
Aperture Radar

<p>We present an efficient tool for managing, visualizing, analysing, and integrating with other data sources, Earth Observation (EO) data for the analysis of surface deformation phenomena. In particular, we focused on specific <span>E</span><span>O</span> data that are those obtained by an <span>a</span><span>dvanced</span>-processing of Synthetic Aperture Radar (SAR) data for monitoring wide areas of the Earth's surface. More specifically, <span>we refer to the </span><span>SAR technique called </span><span>advanced differential interferometric synthetic aperture radar </span><span>(</span><span>DInSAR</span><span>)</span> <span>that </span><span>have demonstrated </span><span>its</span><span> capabilit</span><span>ies</span><span> to detect, </span><span>to </span><span>map and </span><span>to </span><span>analyse the on-going surface displacement phenomena, </span><span>both spatially and temporally, </span><span>with centimetre to millimetre accuracy t</span><span>hanks to the</span><span> generat</span><span>ion of</span><span> deformation maps and time-series</span>. Currently, the DInSAR scenario is characterized by a huge availability of SAR data acquired during the last 25 years, now with a massive and ever-increasing data flow supplied by the C-band Sentinel-1 (S1) constellation of the European Copernicus program.</p><p align="justify"><span>Considering this big picture, the Spatial Data Infrastructures (SDI) becomes a fundamental tool to implement a framework to handle the informative content of geographic data. Indeed, an SDI represents a collection of technologies, policies, standards, human resources, and related activities permitting the acquisition, processing, distribution, use, maintenance, and preservation of spatial data. </span></p><p align="justify"><span>We implemented an SDI, extending the functionalities of GeoNode, which is a web-based platform, providing an open-source framework based on the Open Geospatial Consortium (OGC) standards. </span><span>OGC</span> <span>makes easier</span><span> interoperability functionalities, </span><span>that represent an extremely important </span><span>aspect because allow the data producers to share geospatial information for all types of cooperative processes, avoiding duplication of efforts and costs. Our </span><span>implemented</span><span> GeoNode-Based Platform </span><span>extends a Geographic Information System (GIS) to a web-accessible resource and </span><span>adapt</span><span>s the SDI tools </span><span>to DInSAR-related requirements. </span></p><p align="justify"><span>O</span><span>ur efforts have been dedicated to enabling the GeoNode platform to effectively analyze and visualize the spatial/temporal characteristics of the DInSAR deformation time-series and their related products. Moreover, the implemented multi-thread based new functionalities allow us to efficiently upload and update large data volumes of the available DInSAR results into a dedicated geodatabase. </span><span>W</span><span>e </span><span>demonstrate the high performance of implemented</span><span> GeoNode-Based Platform, </span><span>showing </span><span>DInSAR results relevant to the acquisitions of the Sentinel-1 constellation, collected during 2015-2018 </span><span>over Italy</span><span>.</span></p><p align="justify">This work is supported by the 2019-2021 IREA CNR and Italian Civil Protection Department agreement; the H2020 EPOS-SP project (GA 871121); the I-AMICA (PONa3_00363) project; and the IREA-CNR/DGSUNMIG agreement.</p><p> </p><p> </p>

scholarly journals Ground Subsidence Investigation in Fuoshan, China, Based on SBAS-InSAR Technology with TerraSAR-X Images

2019 ◽  
Vol 9 (10) ◽  
pp. 2038 ◽  
Author(s):  
Yikai Zhu ◽  
Xuemin Xing ◽  
Lifu Chen ◽  
Zhihui Yuan ◽  
Pingying Tang
Keyword(s):  
Time Series ◽  
Surface Deformation ◽  
Soft Clay ◽  
Ground Surface ◽  
Ground Subsidence ◽  
Maintenance Cost ◽  
Deformation Model ◽  
Geotechnical Characteristics ◽  
Soft Clay Subgrade ◽  
Sbas Insar

Highways built on soft clay subgrade are more prone to subsidence due to the geotechnical characteristics of soft clay. Monitoring ground movements in this area is significant for understanding the deformation dynamics and reducing maintenance cost as well. In this paper, small baseline subset synthetic aperture radar interferometry (SBAS-InSAR) technique is exploited to obtain and investigate the time series ground surface deformation after the construction of a road embankment over soft clay settlement. Considering the important effect of temporal deformation models on the final accuracy of estimated deformation, both the linear velocity model and seasonal deformation model are utilized to conduct the comparative investigation of deformation time series. Two highways in Fuoshan, China—G1501 Guangzhou Belt Highway and Lungui Highway—were selected as the test area. Thirteen TerraSAR-X images acquired from October 2014 to November 2015 were analyzed. Comparative study based on two groups of analyses generated from the two models for both highways were conducted. Consequently, several feature points distributed near the two highways were analyzed in detail to understand the temporal evolution of the settlement. In order to evaluate the reliability of our measurements, the residual phase was analyzed to assess the modelling accuracy of the two models. In addition, leveling data were also used to validate the experimental results. Our measurements suggest that the seasonal model is more suitable for the test highways, with an accuracy of ±3 mm with respect to the leveling results.

Time Series Forecasting Model of Ground Surface Deformation for Coal Mine Shaft

2012 ◽  
Vol 505 ◽  
pp. 116-120
Author(s):  
Jia Long Sun ◽  
Jin Yun Guo
Keyword(s):  
Time Series ◽  
Coal Mine ◽  
Surface Deformation ◽  
Ground Surface ◽  
Forecast Model ◽  
Paper Analysis ◽  
Forecasting Model ◽  
Mine Shaft ◽  
Forecast Precision ◽  
Curtain Grouting

Some displacement and deformation for the mine shaft over past many years’ working may occur, which will cause heavy harm to both shaft itself and workers in well. Curtain grouting is one of efficient methods to protect the shaft and the earth’s surface around the shaft from the harm. The deformation caused by grouting around mine shaft is monitored with a reasonable scheme and the monitoring data are processed with the time-series theory to analyze and forecast the dangerous deformation. The paper analysis different time series of different monitoring points, finds the degree of forecast precision and the nicety ratio of forecast model are best.

scholarly journals Investigating Ground Subsidence and the Causes over the Whole Jiangsu Province, China Using Sentinel-1 SAR Data

2021 ◽  
Vol 13 (2) ◽  
pp. 179
Author(s):  
Yonghong Zhang ◽  
Hongan Wu ◽  
Mingju Li ◽  
Yonghui Kang ◽  
Zhong Lu
Keyword(s):  
Time Series ◽  
Land Surface ◽  
Surface Deformation ◽  
Underground Mining ◽  
Ground Deformation ◽  
Ground Surface ◽  
Data Retrieval ◽  
Field Work ◽  
Jiangsu Province ◽  
Ground Subsidence

Interferometric synthetic aperture radar (InSAR) mapping of localized ground surface deformation has become an important tool to manage subsidence-related geohazards. However, monitoring land surface deformation using InSAR at high spatial resolution over a large region is still a formidable task. In this paper, we report a research on investigating ground subsidence and the causes over the entire 107, 200 km2 province of Jiangsu, China, using time-series InSAR. The Sentinel-1 Interferometric Wide-swath (IW) images of 6 frames are used to map ground subsidence over the whole province for the period 2016–2018. We present processing methodology in detail, with emphasis on the three-level co-registration scheme of S-1 data, retrieval of mean subsidence velocity (MSV) and subsidence time series, and mosaicking of multiple frames of results. The MSV and subsidence time series are generated for 9,276,214 selected coherent pixels (CPs) over the Jiangsu territory. Using 688 leveling measurements in evaluation, the derived MSV map of Jiangsu province shows an accuracy of 3.9 mm/year. Moreover, subsidence causes of the province are analyzed based on InSAR-derived subsidence characteristics, historical optical images, and field-work findings. Main factors accounting for the observed subsidence include: underground mining, groundwater withdrawal, soil consolidations of marine reclamation, and land-use transition from agricultural (paddy) to industrial land. This research demonstrates not only the capability of S-1 data in mapping ground deformation over wide areas in coastal and heavily vegetated region of China, but also the potential of inferring valuable knowledge from InSAR-derived results.

scholarly journals Satellite interferometry for mapping surface deformation time series in one, two and three dimensions: A new method illustrated on a slow-moving landslide

2020 ◽  
Vol 266 ◽  
pp. 105471 ◽  
Author(s):  
Sergey Samsonov ◽  
Antoine Dille ◽  
Olivier Dewitte ◽  
François Kervyn ◽  
Nicolas d'Oreye
Keyword(s):  
Time Series ◽  
Surface Deformation ◽  
Three Dimensions ◽  
New Method ◽  
Slow Moving

scholarly journals Bayesian Detection of Piecewise Linear Trends in Replicated Time-Series with Application to Growth Data Modelling

2019 ◽  
Vol 16 (1) ◽  
Author(s):  
Panagiotis Papastamoulis ◽  
Takanori Furukawa ◽  
Norman van Rhijn ◽  
Michael Bromley ◽  
Elaine Bignell ◽  
...  
Keyword(s):  
Time Series ◽  
Posterior Distribution ◽  
Time Course ◽  
Piecewise Linear ◽  
A Priori ◽  
Fungal Growth ◽  
Simulated Data ◽  
Piecewise Linear Function ◽  
Change Points ◽  
Growth Data

AbstractWe consider the situation where a temporal process is composed of contiguous segments with differing slopes and replicated noise-corrupted time series measurements are observed. The unknown mean of the data generating process is modelled as a piecewise linear function of time with an unknown number of change-points. We develop a Bayesian approach to infer the joint posterior distribution of the number and position of change-points as well as the unknown mean parameters. A-priori, the proposed model uses an overfitting number of mean parameters but, conditionally on a set of change-points, only a subset of them influences the likelihood. An exponentially decreasing prior distribution on the number of change-points gives rise to a posterior distribution concentrating on sparse representations of the underlying sequence. A Metropolis-Hastings Markov chain Monte Carlo (MCMC) sampler is constructed for approximating the posterior distribution. Our method is benchmarked using simulated data and is applied to uncover differences in the dynamics of fungal growth from imaging time course data collected from different strains. The source code is available on CRAN.

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