scholarly journals Removal of systematic seasonal atmospheric signal from interferometric synthetic aperture radar ground deformation time series

2014 ◽  
Vol 41 (17) ◽  
pp. 6123-6130 ◽  
Author(s):  
Sergey V. Samsonov ◽  
Alexander P. Trishchenko ◽  
Kristy Tiampo ◽  
Pablo J. González ◽  
Yu Zhang ◽  
...  
Keyword(s):  
Time Series ◽  
Synthetic Aperture Radar ◽  
Ground Deformation ◽  
Synthetic Aperture ◽  
Interferometric Synthetic Aperture Radar ◽  
Aperture Radar

scholarly journals Landslide Deformation Monitoring by Adaptive Distributed Scatterer Interferometric Synthetic Aperture Radar

2019 ◽  
Vol 11 (19) ◽  
pp. 2273 ◽  
Author(s):  
Hongguo Jia ◽  
Hao Zhang ◽  
Luyao Liu ◽  
Guoxiang Liu
Keyword(s):  
Time Series ◽  
Synthetic Aperture Radar ◽  
Deformation Monitoring ◽  
Synthetic Aperture ◽  
Economic Losses ◽  
Interferometric Synthetic Aperture Radar ◽  
Mountainous Areas ◽  
Landslide Area ◽  
Landslide Deformation ◽  
Aperture Radar

Landslide is the second most frequent geological disaster after earthquake, which causes a large number of casualties and economic losses every year. China frequently experiences devastating landslides in mountainous areas. Interferometric Synthetic Aperture Radar (InSAR) technology has great potential for detecting potentially unstable landslides across wide areas and can monitor surface displacement of a single landslide. However traditional time series InSAR technology such as persistent scatterer interferometry (PSI) and small-baseline subset (SBAS) cannot identify enough points in mountainous areas because of dense vegetation and steep terrain. In order to improve the accuracy of landslide hazard detection and the reliability of landslide deformation monitoring in areas lacking high coherence stability point targets, this study proposes an adaptive distributed scatterer interferometric synthetic aperture radar (ADS-InSAR) method based on the spatiotemporal coherence of the distributed scatterer (DS), which automatically adjusts its detection threshold to improve the spatial distribution density and reliability of DS detection in the landslide area. After time series network modeling and deformation calculation of the ADS target, the displacement deformation of the landslide area can be accurately extracted. Shuibuya Town in Enshi Prefecture, Hubei Province, China, was used as a case study, along with 18 Sentinal-1A images acquired from March 2016 to April 2017. The ADS-InSAR method was used to obtain regional deformation data. The deformation time series was combined with hydrometeorological and related data to analyze landslide deformation. The results show that the ADS-InSAR method can effectively improve the density of DS distribution, successfully detect existing ancient landslide groups and determine multiple potential landslide areas, enabling early warning for landslide hazards. This study verifies the reliability and accuracy of ADS-InSAR for landslide disaster prevention and mitigation.

scholarly journals Ground deformation monitoring using RADARSAT-2 DInSAR-MSBAS at the Aquistore CO2 storage site in Saskatchewan (Canada)

2014 ◽  
Vol XL-1 ◽  
pp. 81-87
Author(s):  
M. Czarnogorska ◽  
S. Samsonov ◽  
D. White
Keyword(s):  
Synthetic Aperture Radar ◽  
Surface Deformation ◽  
Ground Deformation ◽  
Deformation Monitoring ◽  
Airborne Lidar ◽  
Synthetic Aperture ◽  
Interferometric Synthetic Aperture Radar ◽  
Storage Site ◽  
Monitoring Techniques ◽  
Aperture Radar

The research objectives of the Aquistore CO<sub>2</sub> storage project are to design, adapt, and test non-seismic monitoring methods for measurement, and verification of CO<sub>2</sub> storage, and to integrate data to determine subsurface fluid distributions, pressure changes and associated surface deformation. Aquistore site is located near Estevan in Southern Saskatchewan on the South flank of the Souris River and west of the Boundary Dam Power Station and the historical part of Estevan coal mine in southeastern Saskatchewan, Canada. Several monitoring techniques were employed in the study area including advanced satellite Differential Interferometric Synthetic Aperture Radar (DInSAR) technique, GPS, tiltmeters and piezometers. The targeted CO<sub>2</sub> injection zones are within the Winnipeg and Deadwood formations located at > 3000 m depth. An array of monitoring techniques was employed in the study area including advanced satellite Differential Interferometric Synthetic Aperture Radar (DInSAR) with established corner reflectors, GPS, tiltmeters and piezometers stations. We used airborne LIDAR data for topographic phase estimation, and DInSAR product geocoding. Ground deformation maps have been calculated using Multidimensional Small Baseline Subset (MSBAS) methodology from 134 RADARSAT-2 images, from five different beams, acquired during 20120612&ndash;20140706. We computed and interpreted nine time series for selected places. MSBAS results indicate slow ground deformation up to 1 cm/year not related to CO<sub>2</sub> injection but caused by various natural and anthropogenic causes.

scholarly journals A Novel Method of Generating Deformation Time-Series Using Interferometric Synthetic Aperture Radar and Its Application in Mexico City

2018 ◽  
Vol 10 (11) ◽  
pp. 1741 ◽  
Author(s):  
Xiaying Wang ◽  
Qin Zhang ◽  
Chaoying Zhao ◽  
Feifei Qu ◽  
Juqing Zhang
Keyword(s):  
Time Series ◽  
Synthetic Aperture Radar ◽  
Mexico City ◽  
Land Subsidence ◽  
Functional Model ◽  
Synthetic Aperture ◽  
Economic Losses ◽  
Deformation Pattern ◽  
Interferometric Synthetic Aperture Radar ◽  
Aperture Radar

As a result of rapid societal development and urbanization, the pumping of groundwater has gradually increased. Land subsidence has thus become a common geological disaster, which can result in huge economic losses. Interferometric synthetic aperture radar (InSAR), with its large-scale and high-accuracy monitoring characteristics, can attain information on Earth surface deformation using the interferometric phase between couples of SAR images acquired at different times. Time-series results for the ground surface are the key information required to understand the deformation pattern and further study the reason for the subsidence. However, in recent research, most methods for resolving time-series deformation—like the Berardino method—that use residuals in functional model solving and distinguish high-pass displacement and the atmospheric component by filtering do not generally work well and functional models focusing on prior information in the time-series solution process are not always available. In this paper, to solve the above problems, 34 Sentinel-1A descending mode scenes of Mexico City captured between 2015/04/13 and 2016/09/10 are used as experimental data. Firstly, a new functional model is provided to obtain the deformation time-series. The nonlinear deformation and atmospheric phase are combined as an unknown parameter and the method of singular value decomposition (SVD) is used to solve this variable. The nonlinear displacement and atmospheric phase are then separated by the singular spectrum analysis (SSA) method. Finally, the total land subsidence time-series is obtained by adding together the linear displacement and nonlinear displacement. Two typical methods and the proposed method were compared using both unit weights and adaptive weights. The experimental results show that the proposed method can obtain a more accurate time-series deformation result. Moreover, the different weights do not result in significant differences and the solved atmospheric and nonlinear phases have good consistency with the interferogram phase.

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