Urban subsidence monitoring by SBAS-InSAR technique with multi-platform SAR images: a case study of Beijing Plain, China

Abstract. InSAR time series analysis is widely used for detection and monitoring of slow surface deformation. In this paper, 15 TerraSAR-X radar images acquired in stripmap mode between 2012 and 2013 are processed for land subsidence monitoring with the Small Baseline Subset (SBAS) approach in Beijing Plain in China. Mapping results produced by SBAS show that the subsidence rates in the area of Beijing Plain range from −97.5 (subsidence) and to +23.8 mm yr−1 (uplift), relative to a presumably stable benchmark. The mapping result also reveals that there are the five subsidence centers formed by surface deformation spreading north to south east of the downtown. An uneven subsidence patten was detected near the Beijing Capital International Airpor, which may be related to loading of buildings and the aircraft.

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MONITORING GROUND SUBSIDENCE IN AREAS COVERED BY DENSE VEGETATION USING TERRASAR-X IMAGES: A CASE STUDY OF HANGZHOU

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

10.5194/isprsarchives-xli-b7-55-2016 ◽

2016 ◽

Vol XLI-B7 ◽

pp. 55-59

Author(s):

H. A. Wu ◽

Y. H. Zhang ◽

G. F. Luo ◽

Y. K. Kang ◽

Y. M. Zhu

Keyword(s):

High Resolution ◽

Good Accuracy ◽

Ground Subsidence ◽

Sar Images ◽

Dense Vegetation ◽

The Past ◽

Rms Error ◽

Subsidence Monitoring

Hangzhou, the capital of Zhejiang province has suffered serious ground subsidence during the past several decades, due to long term over-exploration of groundwater. In this paper, the time series InSAR technique using high resolution SAR images is investigated for the generation of subsidence maps over Hangzhou region. 29 TerraSAR-X images acquired from May 2012 to Sep 2015 are used. The results show that serious subsidence has mainly taken place in suburban area, including Yuhang district, Xiaoshan district and Binjiang district. 4 subsidence centers are discovered, namely Tangqi town in Yuhang with an average subsiding velocity of -29.6 mm/year, Xintang (-30.7 mm/year) in Xiaoshan, Zhujiaqiao town (-25.6mm/year) in Xiaoshan, and Miaohouwang town (-30.1mm/year) in Binjiang. The urban area is stable and ground rebound even take place in some places. The results are compared with 19 levelling measurements. The RMS error between them is 2.9 mm/year, which demonstrates that the high resolution TerraSAR-X images has good accuracy for subsidence monitoring in the southeast of China, covered by dense vegetation.

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MONITORING GROUND SUBSIDENCE IN AREAS COVERED BY DENSE VEGETATION USING TERRASAR-X IMAGES: A CASE STUDY OF HANGZHOU

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

10.5194/isprs-archives-xli-b7-55-2016 ◽

2016 ◽

Vol XLI-B7 ◽

pp. 55-59

Author(s):

H. A. Wu ◽

Y. H. Zhang ◽

G. F. Luo ◽

Y. K. Kang ◽

Y. M. Zhu

Keyword(s):

High Resolution ◽

Good Accuracy ◽

Ground Subsidence ◽

Sar Images ◽

Dense Vegetation ◽

The Past ◽

Rms Error ◽

Subsidence Monitoring

Hangzhou, the capital of Zhejiang province has suffered serious ground subsidence during the past several decades, due to long term over-exploration of groundwater. In this paper, the time series InSAR technique using high resolution SAR images is investigated for the generation of subsidence maps over Hangzhou region. 29 TerraSAR-X images acquired from May 2012 to Sep 2015 are used. The results show that serious subsidence has mainly taken place in suburban area, including Yuhang district, Xiaoshan district and Binjiang district. 4 subsidence centers are discovered, namely Tangqi town in Yuhang with an average subsiding velocity of -29.6 mm/year, Xintang (-30.7 mm/year) in Xiaoshan, Zhujiaqiao town (-25.6mm/year) in Xiaoshan, and Miaohouwang town (-30.1mm/year) in Binjiang. The urban area is stable and ground rebound even take place in some places. The results are compared with 19 levelling measurements. The RMS error between them is 2.9 mm/year, which demonstrates that the high resolution TerraSAR-X images has good accuracy for subsidence monitoring in the southeast of China, covered by dense vegetation.

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Flood Mapping Using Multi-temporal Sentinel-1 SAR Images: A Case Study—Inaouene Watershed from Northeast of Morocco

Iranian Journal of Science and Technology Transactions of Civil Engineering ◽

10.1007/s40996-021-00683-y ◽

2021 ◽

Author(s):

Brahim Benzougagh ◽

Pierre-Louis Frison ◽

Sarita Gajbhiye Meshram ◽

Larbi Boudad ◽

Abdallah Dridri ◽

...

Keyword(s):

Flood Mapping ◽

Sar Images ◽

Multi Temporal

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Remote Sensing X-Band SAR Data for Land Subsidence and Pavement Monitoring

Sensors ◽

10.3390/s20174751 ◽

2020 ◽

Vol 20 (17) ◽

pp. 4751 ◽

Cited By ~ 2

Author(s):

Sadra Karimzadeh ◽

Masashi Matsuoka

Keyword(s):

Land Subsidence ◽

Sar Images ◽

Nw Iran ◽

Traffic Jams ◽

X Band ◽

Pavement Monitoring ◽

Negative Impacts ◽

Small Baseline ◽

Sbas Insar ◽

The City

In this study, we monitor pavement and land subsidence in Tabriz city in NW Iran using X-band synthetic aperture radar (SAR) sensor of Cosmo-SkyMed (CSK) satellites (2017–2018). Fifteen CSK images with a revisit interval of ~30 days have been used. Because of traffic jams, usually cars on streets do not allow pure backscattering measurements of pavements. Thus, the major paved areas (e.g., streets, etc.) of the city are extracted from a minimum-based stacking model of high resolution (HR) SAR images. The technique can be used profitably to reduce the negative impacts of the presence of traffic jams and estimate the possible quality of pavement in the HR SAR images in which the results can be compared by in-situ road roughness measurements. In addition, a time series small baseline subset (SBAS) interferometric SAR (InSAR) analysis is applied for the acquired HR CSK images. The SBAS InSAR results show land subsidence in some parts of the city. The mean rate of line-of-sight (LOS) subsidence is 20 mm/year in district two of the city, which was confirmed by field surveying and mean vertical velocity of Sentinel-1 dataset. The SBAS InSAR results also show that 1.4 km2 of buildings and 65 km of pavement are at an immediate risk of land subsidence.

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