scholarly journals Application of Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) technique to Land Deformation Monitoring in Warri Metropolis, Delta State, Nigeria

2016 ◽  
Vol 100 ◽  
pp. 1220-1227 ◽  
Author(s):  
Muhammad Umar Mahmud ◽  
Tahir A. Yakubu ◽  
Olawale Oluwafemi ◽  
Joaquim João Sousa ◽  
Antonio Miguel Ruiz-Armenteros ◽  
...  
Keyword(s):  
Synthetic Aperture Radar ◽  
Deformation Monitoring ◽  
Synthetic Aperture ◽  
Interferometric Synthetic Aperture Radar ◽  
Multi Temporal ◽  
Land Deformation ◽  
Aperture Radar

scholarly journals A SHORT REVIEW ON PERSISTENT SCATTERER INTERFEROMETRY TECHNIQUES FOR SURFACE DEFORMATION MONITORING

2022 ◽  
Vol XLVI-4/W3-2021 ◽  
pp. 23-31
Author(s):  
A. M. H. Ansar ◽  
A. H. M. Din ◽  
A. S. A. Latip ◽  
M. N. M. Reba
Keyword(s):  
Synthetic Aperture Radar ◽  
Surface Deformation ◽  
Deformation Monitoring ◽  
Synthetic Aperture ◽  
Structural Development ◽  
Interferometric Synthetic Aperture Radar ◽  
Persistent Scatterer Interferometry ◽  
Persistent Scatterers ◽  
Persistent Scatterer ◽  
Aperture Radar

Abstract. Technology advancement has urged the development of Interferometric Synthetic Aperture Radar (InSAR) to be upgraded and transformed. The main contribution of the InSAR technique is that the surface deformation changes measurements can achieve up to millimetre level precision. Environmental problems such as landslides, volcanoes, earthquakes, excessive underground water production, and other phenomena can cause the earth's surface deformation. Deformation monitoring of a surface is vital as unexpected movement, and future behaviour can be detected and predicted. InSAR time series analysis, known as Persistent Scatterer Interferometry (PSI), has become an essential tool for measuring surface deformation. Therefore, this study provides a review of the PSI techniques used to measure surface deformation changes. An overview of surface deformation and the basic principles of the four techniques that have been developed from the improvement of Persistent Scatterer Interferometric Synthetic Aperture Radar (PSInSAR), which is Small Baseline Subset (SBAS), Stanford Method for Persistent Scatterers (StaMPS), SqueeSAR and Quasi Persistent Scatterer (QPS) were summarised to perceive the ability of these techniques in monitoring surface deformation. This study also emphasises the effectiveness and restrictions of each developed technique and how they suit Malaysia conditions and environment. The future outlook for Malaysia in realising the PSI techniques for structural monitoring also discussed in this review. Finally, this review will lead to the implementation of appropriate techniques and better preparation for the country's structural development.

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.

A deep learning approach for efficient multi-temporal interferometric synthetic aperture radar (MT-InSAR) processing

2021 ◽  
Author(s):  
Ashutosh Tiwari ◽  
Avadh BIhari Narayan ◽  
Onkar Dikshit
Keyword(s):  
Deep Learning ◽  
Synthetic Aperture Radar ◽  
Phase Unwrapping ◽  
Synthetic Aperture ◽  
Interferometric Synthetic Aperture Radar ◽  
Displacement Estimation ◽  
Interferometric Phase ◽  
Selection Step ◽  
Multi Temporal ◽  
Aperture Radar

<p>Multi-temporal interferometric synthetic aperture radar (MT-InSAR) technique has been effectively used to monitor deformation events over the last two decades. The processing steps generally involve pixel selection, phase unwrapping and displacement estimation. The pixel selection step takes most of the processing time, while a reliable method for phase unwrapping is still not available. This study demonstrates the effect of using deep learning (DL) architectures for MT-InSAR processing. The architectures are applied to reduce time computations and further to improve the quality of pixel selection. Some promising results for pixel selection have been shown earlier with the proposed architecture. In this study, we investigate the performance of the proposed architectures on newer datasets with larger temporal interval. To achieve this objective, the models are retrained with interferometric stacks covering larger temporal period and large time steps (for better estimation of interferometric phase components). Pixel selection results are compared with those obtained using open access algorithms used for MT-InSAR processing.</p>

Reclaimed-Airport Surface-Deformation Monitoring by Improved Permanent-Scatterer Interferometric Synthetic-Aperture Radar: A Case Study of Shenzhen Bao'an International Airport, China

2021 ◽  
Vol 87 (2) ◽  
pp. 105-116
Author(s):  
Lu Miao ◽  
Kailiang Deng ◽  
Guangcai Feng ◽  
Kaifeng Li ◽  
Zhiqiang Xiong ◽  
...  
Keyword(s):  
Synthetic Aperture Radar ◽  
Dynamic Load ◽  
Surface Deformation ◽  
Deformation Monitoring ◽  
Synthetic Aperture ◽  
Interferometric Synthetic Aperture Radar ◽  
Filling Material ◽  
Reclaimed Land ◽  
International Airport ◽  
Aperture Radar

Reclaimed airports usually have fragile geological structures and are susceptible to the uneven ground settlements caused by filling-material consolidation, underground construction, and dynamic loading from takeoff and landing of aircrafts. Therefore, deformation monitoring is of great significance to the safe operation of reclaimed airports. This study adopts an improved permanent-scatterer interferometric synthetic-aperture radar strategy to map the spatiotemporal deformation of Shenzhen Bao'an International Airport in China using ascending and descending Envisat/ASAR data acquired from 2007 to 2010 and Sentinel-1 data from 2015 to 2019. The results show that uneven settlements of the airport concentrate in the new reclaimed land. Then we explore the settlement characteristics of each functional area. Furthermore, we separate out the dynamic-load settlement of runway No. 2 and confirm the settlements caused by dynamic load. This study provides new ideas for studying deformation in similar fields, and technical references for the future construction of Shenzhen Airport.

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.

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