scholarly journals Countrywide Monitoring of Ground Deformation Using InSAR Time Series: A Case Study from Qatar

2021 ◽  
Vol 13 (4) ◽  
pp. 702
Author(s):  
Mustafa Kemal Emil ◽  
Mohamed Sultan ◽  
Khaled Alakhras ◽  
Guzalay Sataer ◽  
Sabreen Gozi ◽  
...  
Keyword(s):  
Time Series ◽  
Arabian Peninsula ◽  
Ground Deformation ◽  
Anthropogenic Activities ◽  
Urban Centers ◽  
Alos Palsar ◽  
Groundwater Levels ◽  
Temporal Correlations ◽  
Time Series Approach ◽  
Sbas Insar

Over the past few decades the country of Qatar has been one of the fastest growing economies in the Middle East; it has witnessed a rapid increase in its population, growth of its urban centers, and development of its natural resources. These anthropogenic activities compounded with natural forcings (e.g., climate change) will most likely introduce environmental effects that should be assessed. In this manuscript, we identify and assess one of these effects, namely, ground deformation over the entire country of Qatar. We use the Small Baseline Subset (SBAS) InSAR time series approach in conjunction with ALOS Palsar-1 (January 2007 to March 2011) and Sentinel-1 (March 2017 to December 2019) synthetic aperture radar (SAR) datasets to assess ground deformation and conduct spatial and temporal correlations between the observed deformation with relevant datasets to identify the controlling factors. The findings indicate: (1) the deformation products revealed areas of subsidence and uplift with high vertical velocities of up to 35 mm/yr; (2) the deformation rates were consistent with those extracted from the continuously operating reference GPS stations of Qatar; (3) many inland and coastal sabkhas (salt flats) showed evidence for uplift (up to 35 mm/yr) due to the continuous evaporation of the saline waters within the sabkhas and the deposition of the evaporites in the surficial and near-surficial sabkha sediments; (4) the increased precipitation during Sentinel-1 period compared to the ALOS Palsar-1 period led to a rise in groundwater levels and an increase in the areas occupied by surface water within the sabkhas, which in turn increased the rate of deposition of the evaporitic sediments; (5) high subsidence rates (up to 14 mm/yr) were detected over landfills and dumpsites, caused by mechanical compaction and biochemical processes; and (6) the deformation rates over areas surrounding known sinkhole locations were low (+/−2 mm/yr). We suggest that this study can pave the way to similar countrywide studies over the remaining Arabian Peninsula countries and to the development of a ground motion monitoring system for the entire Arabian Peninsula.

scholarly journals Long-range ground deformation monitoring by InSAR analysis

2015 ◽  
Vol 372 ◽  
pp. 343-346 ◽  
Author(s):  
S. Rokugawa ◽  
T. Nakamura
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Synthetic Aperture Radar ◽  
Surface Deformation ◽  
Ground Deformation ◽  
Synthetic Aperture ◽  
Alos Palsar ◽  
Envisat Asar ◽  
Series Analysis ◽  
Kanto District

Abstract. InSAR (Interferometric Synthetic Aperture Radar) analysis is an effective technique to map 3-dimensional surface deformation with high spatial resolution. The aim of this study was to evaluate the capability of InSAR analysis when applied to ground monitoring of an environmental disaster. We performed a time series InSAR analysis using ENVISAT/ASAR and ALOS/PALSAR data and commercial software to investigate subsidence around the Kanto District of Japan. We also investigated techniques for efficient early detection of landslides in Kyushu using time series analysis that incorporated synthetic aperture radar (SAR) images. ENVISAT/ASAR data acquired from 2003–2010 and ALOS/PALSAR data acquired from 2006–2011 were used to detect poorly expressed geomorphological deformation by conducting time series analyses of periodically acquired SAR data. In addition, to remove noise caused by geographical feature stripes or phase retardation, we applied median filtering, histogram extraction processing, and clarification of the displacement with a Laplacian filter. The main functions of the InSAR time series analysis are the calculation of phase differences between two images and the inversion with smoothness constraint for the estimation of deformation along the line of sight. The results enabled us to establish criteria for the selection of suitable InSAR data pairs, and provided the final error estimation of the derived surface deformation. The results of the analysis in the Kanto District suggested that localized areas of uplift and subsidence have occurred at irregular intervals in this area. Furthermore, the method offers the possibility of early warning of environmental disasters such as landslide and abrupt subsidence. Our results confirm the effectiveness of InSAR analysis for the monitoring of ground deformation over wide areas via the detection of localized subsidence and landslides.

scholarly journals Radar Interferometry Time Series to Investigate Deformation of Soft Clay Subgrade Settlement—A Case Study of Lungui Highway, China

2019 ◽  
Vol 11 (4) ◽  
pp. 429 ◽  
Author(s):  
Xuemin Xing ◽  
Hsing-Chung Chang ◽  
Lifu Chen ◽  
Junhui Zhang ◽  
Zhihui Yuan ◽  
...  
Keyword(s):  
Time Series ◽  
Ground Deformation ◽  
Soft Clay ◽  
Velocity Model ◽  
Radar Interferometry ◽  
Suitable Model ◽  
Seasonal Model ◽  
Soft Clay Subgrade ◽  
Sbas Insar ◽  
Deformation Models

Monitoring surface movement near highways over soft clay subgrades is fundamental for understanding the dynamics of the settlement process and preventing hazards. Earlier studies have demonstrated the accuracy and cost-effectiveness of using time series radar interferometry (InSAR) technique to measure the ground deformation. However, the accuracy of the advanced differential InSAR techniques, including short baseline subset (SBAS) InSAR, is limited by the temporal deformation models used. In this study, a comparison of four widely used time series deformation models in InSAR, namely Multi Velocity Model (MVM), Permanent Velocity Model (PVM), Seasonal Model (SM) and Cubic Polynomial Model (CPM), was conducted to measure the long-term ground deformation after the construction of road embankment over soft clay subgrade. SBAS-InSAR technique with TerraSAR-X satellite imagery were conducted to generate the time series deformation data over the studied highway. In the experiments, three accuracy indices were applied to show the residual phase, mean temporal coherence and the RMS of high-pass deformation, respectively. In addition, the derived time series deformation maps of the highway based on the four selected models and 17 TerraSAR-X images acquired from June 2014 to November 2015 were compared. The leveling data was also used to validate the experimental results. Our results suggested the Seasonal Model is the most suitable model for the selected study site. Consequently, we analyzed two bridges in detail and three single points distributed near the highway. Compared with the ground leveling deformation measurements and results of other models, SM showed better consistency, with the accuracy of deformation to be ±7 mm.

scholarly journals Correction to: Measuring the impact of the Afordable Care Act Medicaid expansion on access to primary care using an interrupted time series approach

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Elizabeth A. Brown ◽  
Brandi M. White ◽  
Walter J. Jones ◽  
Mulugeta Gebregziabher ◽  
Kit N. Simpson
Keyword(s):  
Primary Care ◽  
Time Series ◽  
Interrupted Time Series ◽  
Medicaid Expansion ◽  
Time Series Approach ◽  
Access To Primary Care ◽  
The Impact

An amendment to this paper has been published and can be accessed via the original article.

THE IMPACT OF THE HOTEL ROOM TAX: AN INTERRUPTED TIME SERIES APPROACH

1992 ◽  
Vol 45 (4) ◽  
pp. 433-441 ◽  
Author(s):  
CARL BONHAM ◽  
EDWIN FUJII ◽  
ERIC IM ◽  
JAMES MAK
Keyword(s):  
Time Series ◽  
Interrupted Time Series ◽  
Hotel Room ◽  
Time Series Approach ◽  
The Impact

scholarly journals Constructing Adaptive Deformation Models for Estimating DEM Error in SBAS-InSAR Based on Hypothesis Testing

2021 ◽  
Vol 13 (10) ◽  
pp. 2006
Author(s):  
Jun Hu ◽  
Qiaoqiao Ge ◽  
Jihong Liu ◽  
Wenyan Yang ◽  
Zhigui Du ◽  
...  
Keyword(s):  
Time Series ◽  
Hypothesis Testing ◽  
Surface Deformation ◽  
A Priori ◽  
Least Square ◽  
Deformation Model ◽  
Phase Time ◽  
Testing Theory ◽  
Sbas Insar ◽  
Deformation Models

The Interferometric Synthetic Aperture Radar (InSAR) technique has been widely used to obtain the ground surface deformation of geohazards (e.g., mining subsidence and landslides). As one of the inherent errors in the interferometric phase, the digital elevation model (DEM) error is usually estimated with the help of an a priori deformation model. However, it is difficult to determine an a priori deformation model that can fit the deformation time series well, leading to possible bias in the estimation of DEM error and the deformation time series. In this paper, we propose a method that can construct an adaptive deformation model, based on a set of predefined functions and the hypothesis testing theory in the framework of the small baseline subset InSAR (SBAS-InSAR) method. Since it is difficult to fit the deformation time series over a long time span by using only one function, the phase time series is first divided into several groups with overlapping regions. In each group, the hypothesis testing theory is employed to adaptively select the optimal deformation model from the predefined functions. The parameters of adaptive deformation models and the DEM error can be modeled with the phase time series and solved by a least square method. Simulations and real data experiments in the Pingchuan mining area, Gaunsu Province, China, demonstrate that, compared to the state-of-the-art deformation modeling strategy (e.g., the linear deformation model and the function group deformation model), the proposed method can significantly improve the accuracy of DEM error estimation and can benefit the estimation of deformation time series.

scholarly journals The Role of Assumptions in Ohlson Model Performance: Lessons for Improving Equity-Value Modeling

Mathematics ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 513
Author(s):  
Olga Fullana ◽  
Mariano González ◽  
David Toscano
Keyword(s):  
Time Series ◽  
Model Performance ◽  
Listed Firms ◽  
Valuation Model ◽  
Residual Income Valuation ◽  
Equity Value ◽  
Short Run ◽  
Time Series Approach ◽  
Linear Information

In this paper, we test whether the short-run econometric conditions for the basic assumptions of the Ohlson valuation model hold, and then we relate these results with the fulfillment of the short-run econometric conditions for this model to be effective. Better future modeling motivated us to analyze to what extent the assumptions involved in this seminal model are not good enough approximations to solve the firm valuation problem, causing poor model performance. The model is based on the well-known dividend discount model and the residual income valuation model, and it adds a linear information model, which is a time series model by nature. Therefore, we adopt the time series approach. In the presence of non-stationary variables, we focus our research on US-listed firms for which more than forty years of data with the required cointegration properties to use error correction models are available. The results show that the clean surplus relation assumption has no impact on model performance, while the unbiased accounting property assumption has an important effect on it. The results also emphasize the uselessness of forcing valuation models to match the value displacement property of dividends.

scholarly journals Subsidence Monitoring of Fill Area in Yan’an New District Based on Sentinel-1A Time Series Imagery

2021 ◽  
Vol 13 (15) ◽  
pp. 3044
Author(s):  
Mingjie Liao ◽  
Rui Zhang ◽  
Jichao Lv ◽  
Bin Yu ◽  
Jiatai Pang ◽  
...  
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Ground Deformation ◽  
Dynamic Change ◽  
Deformation Monitoring ◽  
Chinese Loess Plateau ◽  
Ground Subsidence ◽  
Shanxi Province ◽  
Environment Change ◽  
Loess Area

In recent years, many cities in the Chinese loess plateau (especially in Shanxi province) have encountered ground subsidence problems due to the construction of underground projects and the exploitation of underground resources. With the completion of the world’s largest geotechnical project, called “mountain excavation and city construction,” in a collapsible loess area, the Yan’an city also appeared to have uneven ground subsidence. To obtain the spatial distribution characteristics and the time-series evolution trend of the subsidence, we selected Yan’an New District (YAND) as the specific study area and presented an improved time-series InSAR (TS-InSAR) method for experimental research. Based on 89 Sentinel-1A images collected between December 2017 to December 2020, we conducted comprehensive research and analysis on the spatial and temporal evolution of surface subsidence in YAND. The monitoring results showed that the YAND is relatively stable in general, with deformation rates mainly in the range of −10 to 10 mm/yr. However, three significant subsidence funnels existed in the fill area, with a maximum subsidence rate of 100 mm/yr. From 2017 to 2020, the subsidence funnels enlarged, and their subsidence rates accelerated. Further analysis proved that the main factors induced the severe ground subsidence in the study area, including the compressibility and collapsibility of loess, rapid urban construction, geological environment change, traffic circulation load, and dynamic change of groundwater. The experimental results indicated that the improved TS-InSAR method is adaptive to monitoring uneven subsidence of deep loess area. Moreover, related data and information would provide reference to the large-scale ground deformation monitoring and in similar loess areas.

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