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

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.

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An Improved Time-Series Model Considering Rheological Parameters for Surface Deformation Monitoring of Soft Clay Subgrade

Sensors ◽

10.3390/s19143073 ◽

2019 ◽

Vol 19 (14) ◽

pp. 3073 ◽

Cited By ~ 4

Author(s):

Xing ◽

Chen ◽

Yuan ◽

Shi

Keyword(s):

Time Series ◽

Surface Deformation ◽

Soft Clay ◽

Deformation Monitoring ◽

Rheological Parameters ◽

Deformation Model ◽

Rheological Parameter ◽

Functional Relationships ◽

Soft Clay Subgrade ◽

Deformation Models

Building deformation models consistent with reality is a crucial step for time-series deformation monitoring. Most deformation models are empirical mathematical models, lacking consideration of the physical mechanisms of observed objects. In this study, we propose an improved time-series deformation model considering rheological parameters (viscosity and elasticity) based on the Kelvin model. The functional relationships between the rheological parameters and deformation along the Synthetic Aperture Radar ( SAR) line of sight are constructed, and a method for rheological parameter estimation is provided. To assess the feasibility and accuracy of the presented model, both simulated and real deformation data over a stretch of the Lungui highway (built on soft clay subgrade in Guangdong province, China) are investigated with TerraSAR-X satellite imagery. With the proposed deformation model, the unknown rheological parameters over all the high coherence points are obtained and the deformation time-series are generated. The high-pass (HP) deformation component and external leveling ground measurements are utilized to assess the modeling accuracy. The results show that the root mean square of the residual deformation is ±1.6 mm, whereas that of the ground leveling measurements is ±5.0 mm, indicating an improvement in the proposed model by 53%, and 34% compared to the pure linear velocity model. The results indicate the reliability of the presented model for the application of deformation monitoring of soft clay highways. The estimated rheological parameters can be provided as a reference index for the interpretation of long-term highway deformation and the stability control of subgrade construction engineering.

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

Applied Sciences ◽

10.3390/app9102038 ◽

2019 ◽

Vol 9 (10) ◽

pp. 2038 ◽

Cited By ~ 4

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.

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Semi-automated extraction of Deviation Indexes (DI) from satellite Persistent Scatterers time series: tests on sedimentary volcanism and tectonically-induced motions

Nonlinear Processes in Geophysics ◽

10.5194/npg-19-643-2012 ◽

2012 ◽

Vol 19 (6) ◽

pp. 643-655 ◽

Cited By ~ 16

Author(s):

F. Cigna ◽

D. Tapete ◽

N. Casagli

Keyword(s):

Time Series ◽

Southern Italy ◽

A Priori ◽

Deformation Model ◽

Automated Extraction ◽

Persistent Scatterers ◽

Information Level ◽

Coherence Factors ◽

The Village ◽

The Town

Abstract. We develop a methodology based on satellite Persistent Scatterers (PS) time series and aimed to calculate two indexes which are capable to depict the deviation from a deformation model defined a priori. Through a simple mathematical approach, these indexes reproduce the visual process of identification of trend deviations that is usually performed manually by the radar-interpreter, and guide the prioritization of further interpretation for those areas recording significant variations within their motion history. First tests on semi-automated extraction of the Deviation Indexes (DI) from RADARSAT-1 PS data available over Southern Italy allowed the quantification of tectonically-induced land motions which occurred in February 2005 within the town of Naro, and also the clear recognition of the precursors to mud volcano eruptions which occurred in August 2008 in the village of St. Barbara. For these areas, the information level brought by the DI increases and adds onto that of other PS parameters, such as yearly velocity, standard deviation and coherence. Factors exerting influence on the DI are critically tackled within the discussions, together with the analysis of the potentials of these indexes for monitoring and warning activities of geohazards.

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Surface Deformation Monitoring of a Section of Gongyu Expressway Based on SBAS-InSAR Technology

E3S Web of Conferences ◽

10.1051/e3sconf/202123301149 ◽

2021 ◽

Vol 233 ◽

pp. 01149

Author(s):

Ying Yang ◽

Yifang Sun ◽

Shihong Wu ◽

Xuegang Dong ◽

Hanyao Huang ◽

...

Keyword(s):

Time Series ◽

Surface Deformation ◽

Deformation Monitoring ◽

Tibet Plateau ◽

Seasonal Effect ◽

Climate Conditions ◽

Sar Data ◽

Sbas Insar

It is difficult to monitor the surface deformation along the expressway for the critical climate conditions in Tibet plateau. In this paper, based on sentinel-1A SAR data, the surface deformation along the Gongyu expressway was tried to evaluate using time-series SBAS-InSAR method. The results indicate that the surface deformation in most regions is within the safe acquirement of the expressway. Moreover, the surface deformation indicates a strong seasonal effect. Finally, two special spots with dangerous surface deformation are identified along the expressway.

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INVESTIGATION ON INSAR TIME SERIES DEFORMATION MODEL CONSIDERING RHEOLOGICAL PARAMETERS FOR SOFT CLAY SUBGRADE MONITORING

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-3-1977-2018 ◽

2018 ◽

Vol XLII-3 ◽

pp. 1977-1982 ◽

Cited By ~ 1

Author(s):

X. Xing ◽

Z. Yuan ◽

L. F. Chen ◽

X. Y. Yu ◽

L. Xiao

Keyword(s):

Time Series ◽

Soft Clay ◽

Stability Control ◽

Deformation Monitoring ◽

Rheological Parameters ◽

Deformation Model ◽

Unknown Parameters ◽

New Model ◽

Soft Clay Subgrade ◽

Deformation Models

The stability control is one of the major technical difficulties in the field of highway subgrade construction engineering. Building deformation model is a crucial step for InSAR time series deformation monitoring. Most of the InSAR deformation models for deformation monitoring are pure empirical mathematical models, without considering the physical mechanism of the monitored object. In this study, we take rheology into consideration, inducing rheological parameters into traditional InSAR deformation models. To assess the feasibility and accuracy for our new model, both simulation and real deformation data over Lungui highway (a typical highway built on soft clay subgrade in Guangdong province, China) are investigated with TerraSAR-X satellite imagery. In order to solve the unknows of the non-linear rheological model, three algorithms: Gauss-Newton (GN), Levenberg-Marquarat (LM), and Genetic Algorithm (GA), are utilized and compared to estimate the unknown parameters. Considering both the calculation efficiency and accuracy, GA is chosen as the final choice for the new model in our case study. Preliminary real data experiment is conducted with use of 17 TerraSAR-X Stripmap images (with a 3-m resolution). With the new deformation model and GA aforementioned, the unknown rheological parameters over all the high coherence points are obtained and the LOS deformation (the low-pass component) sequences are generated.

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Performance Analysis of Open Source Time Series InSAR Methods for Deformation Monitoring over a Broader Mining Region

Remote Sensing ◽

10.3390/rs12091380 ◽

2020 ◽

Vol 12 (9) ◽

pp. 1380

Author(s):

Kleanthis Karamvasis ◽

Vassilia Karathanassi

Keyword(s):

Time Series ◽

Land Cover ◽

Open Source ◽

Coal Mine ◽

Surface Deformation ◽

Ground Deformation ◽

A Priori ◽

Deformation Monitoring ◽

Comparison Analysis ◽

Temporal Sampling

Time Series Interferometric Synthetic Aperture Radar (TSInSAR) methods have been widely and successfully applied for spatiotemporal ground deformation monitoring. The main groups of methodological approaches are often referred to as Persistent Scatterer (PS), Small Baseline (SB), and hybrid approaches that incorporate PS and SB concepts. While TSInSAR techniques have long been able to provide accurate deformation rates for various applications, their corresponding performance in complex environments such as mining areas has to be investigated. This study focuses on comparing the performance of three open source TSInSAR toolboxes (Stamps, Giant, Mintpy) over an extended region that includes an active opencast coal mine. We present the deformation results of each TSInSAR method on a Sentinel-1 dataset of 125 acquisitions spanning around 2.5 years over the Ptolemaida-Florina coal mine site that is characterized by several environmental and surface deformation conditions. First, a cross-comparison analysis is presented over different land cover classes. The study shows that all TSInSAR methods are capable for generating similar ground deformation results when the area has stable ground scattering conditions and the dataset sufficient temporal sampling. The most controversial results between TSInSAR approaches were found in land cover classes that include medium to high vegetation. An external comparative analysis between the different results from TSInSAR methods and leveling measurements is also performed. Stamps approach presented the best agreement with the in-situ deformation rates. The Giant approach yielded the best cumulative deformation results due to our a priori knowledge of temporal behavior of deformation in the vicinity of the leveling locations. Finally, we discuss the main pros and cons of each TSInSAR approach and we highlight the importance of comparison analysis that can provide insights and can lead to better interpretation of the results.

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Testing deformation hypotheses by constraints on a time series of geodetic observations

Journal of Applied Geodesy ◽

10.1515/jag-2017-0028 ◽

2018 ◽

Vol 12 (1) ◽

pp. 77-93 ◽

Cited By ~ 4

Author(s):

Hiddo Velsink

Keyword(s):

Time Series ◽

Geodetic Network ◽

Deformation Analysis ◽

Statistical Criterion ◽

Deformation Model ◽

Adjustment Model ◽

Least Squares Adjustment ◽

Deformation Models ◽

Congruence Model

AbstractIn geodetic deformation analysis observations are used to identify form and size changes of a geodetic network, representing objects on the earth’s surface. The network points are monitored, often continuously, because of suspected deformations. A deformation may affect many points during many epochs. The problem is that the best description of the deformation is, in general, unknown. To find it, different hypothesised deformation models have to be tested systematically for agreement with the observations. The tests have to be capable of stating with a certain probability the size of detectable deformations, and to be datum invariant. A statistical criterion is needed to find the best deformation model. Existing methods do not fulfil these requirements. Here we propose a method that formulates the different hypotheses as sets of constraints on the parameters of a least-squares adjustment model. The constraints can relate to subsets of epochs and to subsets of points, thus combining time series analysis and congruence model analysis. The constraints are formulated as nonstochastic observations in an adjustment model of observation equations. This gives an easy way to test the constraints and to get a quality description. The proposed method aims at providing a good discriminating method to find the best description of a deformation. The method is expected to improve the quality of geodetic deformation analysis. We demonstrate the method with an elaborate example.

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Optimization of the time series surface deformation analysis using machine learning algorithms on the interferogram simulation data

10.5194/egusphere-egu21-3755 ◽

2021 ◽

Author(s):

Muhammad Fulki Fadhillah ◽

SeulKi Lee ◽

Chang-Wook Lee

Keyword(s):

Time Series ◽

Surface Deformation ◽

Machine Learning Algorithms ◽

Deformation Analysis ◽

Support Vector ◽

Detection Accuracy ◽

Deformation Model ◽

Measurement Point ◽

Persistent Scatterers ◽

Optimum Measurement

<p>Time-series InSAR techniques, such as Stanford Method for Persistent Scatterers (StaMPS) are commonly used to measure time-series surface deformation. This study presents a novel approach of optimized time series deformation analysis based on a support vector regression (SVR) algorithm and optimization Hot-Spot Analysis on persistent scatterers (PS). To examine the performances of the optimized process in time-series, we generated a synthetic interferogram using a Mogi model equation to construct a simulated surface deformation phase. Topography errors simulated orbital error and atmospheric error phases have been added to synthetic interferogram construction. All the synthetic interferogram based on Sentinel-1 SAR Image acquisition dates over Seoul, Korea. An SVR algorithm was used to find an optimum measurement point and reduce error points in time-series analysis. Then, the OHSA approach was implemented on the optimum measurement point through the analysis of Getis-Ord Gi* statistics. As the result, the optimization measurement point indicates refined results in the mean velocity deformation map and time-series graph. In addition, the detection accuracy can be improved by more than 10% with synthetic data. Then, the correlation coefficient between the optimization result and the deformation model shows a good correlation (> 0.8). Also, the standard deviation of time-series results can be reduced by more than 7% after optimizing the process. The proposed method is useful to detect a low deformation rate and can be implemented for several deformation cases. &#160;&#160;</p>

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

Remote Sensing ◽

10.3390/rs11040429 ◽

2019 ◽

Vol 11 (4) ◽

pp. 429 ◽

Cited By ~ 4

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.

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Saline-Soil Deformation Extraction Based on an Improved Time-Series InSAR Approach

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10030112 ◽

2021 ◽

Vol 10 (3) ◽

pp. 112

Author(s):

Wei Xiang ◽

Rui Zhang ◽

Guoxiang Liu ◽

Xiaowen Wang ◽

Wenfei Mao ◽

...

Keyword(s):

Time Series ◽

Surface Deformation ◽

Saline Soil ◽

Ground Deformation ◽

Salt Lake ◽

High Accuracy ◽

Deformation Monitoring ◽

Least Square ◽

Soil Deformation ◽

Qarhan Salt Lake

Significant seasonal fluctuations could occur in the regional scattering characteristics and surface deformation of saline soil, and cause decorrelation, which limits the application of the conventional time-series InSAR (TS-InSAR). For extending the saline-soil deformation monitoring capability, this paper presents an improved TS-InSAR approach, based on the interferometric coherence statistics and high-coherence interferogram refinement. By constructing a network of the refined interferograms, high-accuracy ground deformation can be extracted through the weighted least square estimation and the coherent target refinement. To extract the high-accuracy deformation of a representative saline soil area in the Qarhan Salt Lake, 119 C-band Sentinel-1A images collected between May 2015 and May 2020 are selected as the data source. Subsequently, 845 refined interferograms are selected from all possible interferograms to conduct the network inversion, based on the related thresholds (the temporal baseline <49 days, the average spatial coherences >0.5, respectively). Compared with the conventional TS-InSAR measurements, both the accuracy and reliability of the extracted deformation results of the saline soil increased dramatically. Furthermore, the testing results indicate that the improved TS-InSAR method has advantages on the deformation extraction in the saline soil region, and is adaptive to reflecting the typical seasonal variations of the saline soil.

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