Measuring Surface Subsidence in Wuhan, China With Sentinel-1 Data Using Psinsar

Benattou, M. M.; Balz, Timo; Liao, Mingsheng

doi:10.5194/isprs-archives-xlii-3-73-2018

Cited by 12 publications

(11 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Benattou et al [53] measured the rate of subsidence using 36 sentinel-1A images (June 2015 and April 2017) with 5 m × 20 m (range × azimuth) spatial resolution. The average deformation ranged from −127 mm/yr to 23 mm/yr and a new center of subsidence areas (Jiufengxiang) was found.…”

Section: Discussionmentioning

confidence: 99%

Monitoring Land Subsidence in Wuhan City (China) using the SBAS-InSAR Method with Radarsat-2 Imagery Data

Zhang

Liu

Jin

et al. 2019

Sensors

View full text Add to dashboard Cite

Wuhan city is the biggest city in central China and has suffered subsidence problems in recent years because of its rapid urban construction. However, longtime and wide range monitoring of land subsidence is lacking. The causes of subsidence also require further study, such as natural conditions and human activities. We use small baseline subset (SBAS) interferometric synthetic aperture radar (InSAR) method and high-resolution RADARSAT-2 images acquired between 2015 and 2018 to derive subsidence. The SBAS-InSAR results are validated by 56 leveling benchmarks where two readings of elevation were recorded. Two natural factors (carbonate rock and soft soils) and three human factors (groundwater exploitation, subway excavation and urban construction) are investigated for their relationships with land subsidence. Results show that four major areas of subsidence are detected and the subsidence rate varies from −51.56 to 27.80 millimeters per year (mm/yr) with an average of −0.03 mm/yr. More than 83.81% of persistent scattered (PS) points obtain a standard deviation of less than −6 mm/yr, and the difference between SBAS-InSAR method and leveling data is less than 5 mm/yr. Thus, we conclude that SBAS-InSAR method with Radarsat-2 data is reliable for longtime monitoring of land subsidence covering a large area in Wuhan city. In addition, land subsidence is caused by a combination of natural conditions and human activities. Natural conditions provide a basis for subsidence and make subsidence possible. Human activities are driving factors and make subsidence happen. Moreover, subsidence information could be used in disaster prevention, urban planning, and hydrological modeling.

show abstract

Section: Discussionmentioning

confidence: 99%

Monitoring Land Subsidence in Wuhan City (China) using the SBAS-InSAR Method with Radarsat-2 Imagery Data

Zhang

Liu

Jin

et al. 2019

Sensors

View full text Add to dashboard Cite

show abstract

“…In terms of the geological environment, carbonate rock bands and silty soft soil are widely distributed at the surface and in the subsurface of the city [2][3][4][5]. Human activities, such as underground development [6], urban construction [7], and groundwater pumping [8], combined with the local geological setting, affect the stability of the surface and cause ground deformation [9,10]. Ground deformation can cause serious damage (e.g., tilting, fracturing) to public and private buildings, bridges, and roads, thus also impacting on their serviceability (e.g., [11][12][13]).…”

Section: Introductionmentioning

confidence: 99%

“…Many deformation monitoring studies using PSInSAR and SBAS have been conducted in the Wuhan area, focusing on large-scale urban surface settlement and building stability [6][7][8][9][10]. These studies were carried out from two aspects of natural and human factors, combined with urban development, to qualitatively analyze the causes of settlement in Wuhan.…”

Section: Introductionmentioning

confidence: 99%

“…These were all short-term studies, which could only detect the subsidence that occurred during the corresponding time periods, but could not analyze the overall trend or better unveil the causes, triggers, and non-linear dynamics of subsidence in Wuhan. On this basis, more recently, Benattou et al [7] and Zhang et al [8] attempted an analysis of a three-year-long period using 54 Sentinel-1 and 20 RADARSAT-2 C-band SAR images acquired in 2015-2018, respectively. The latest study was by Han et al [21], who used the multi-temporal dataset method to monitor the Wuhan area for the first time, processing 19 ALOS-1 L-band scenes from January 2007 to October 2010, three ENVISAT C-band scenes from December 2008 to May 2010, and 104 Sentinel-1 scenes from April 2015 to June 2019.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Land Subsidence in Wuhan Revealed Using a Non-Linear PSInSAR Approach with Long Time Series of COSMO-SkyMed SAR Data

et al. 2021

View full text Add to dashboard Cite

Wuhan is an important city in central China, with a rapid development that has led to increasingly serious land subsidence over the last decades. Most of the existing Interferometric Synthetic Aperture Radar (InSAR) subsidence monitoring studies in Wuhan are either short-term investigations—and thus can only detect this process within limited time periods—or combinationsof different Synthetic Aperture Radar (SAR) datasets with temporal gaps in between. To overcome these constraints, we exploited nearly 300 high-resolution COSMO-SkyMed StripMap HIMAGE scenes acquired between 2012 and 2019 to monitor the long-term subsidence process affecting Wuhan and to reveal its spatiotemporal variations. The results from the Persistent Scatterer Interferometric SAR (PSInSAR) processing highlight several clearly observable subsidence zones. Three of them (i.e., Houhu, Xinrong, and Guanggu) are affected by serious subsidence rates and non-linear temporal behavior, and are investigated in this paper in more detail. The subsidence in Houhu is caused by soft soil consolidation and compression. Soil mechanics are therefore used to estimate when the subsidence is expected to finish and to calculate the degree of consolidation for each year. The COSMO-SkyMed PSInSAR results indicate that the area has entered the late stage of consolidation and compression and is gradually stabilizing. The subsidence curve found for the area around Xinrong shows that the construction of an underground tract of the subway Line 21 caused largescale settlement in this area. The temporal granularity of the PSInSAR time series also allows precise detection of a rebound phase following a major flooding event in 2016. In the southern industrial park of Guanggu, newly detected subsidence was found. The combination of the subsidence curve with an optical time-series image analysis indicates that urban construction is the main trigger of deformation in this area. While this study unveils previously unknown characters of land subsidence in Wuhan and clarifies the relationship with the urban causative factors, it also proves the benefits of non-linear PSInSAR in the analysis of the temporal evolution of such processes in dynamic and expanding cities.

show abstract

“…Thanks to the increasing availability of a large amount of SAR data from such missions like ALOS-2, COSMO-SkyMed, PAZ, RADARSAT-2, Sentinel-1 or TerraSAR-X [3,11] and due to high-quality images covering a wide area [14,15], in the last decades, Earth observation techniques have become a valuable and indispensable remote sensing tool in geophysical monitoring of natural hazards such as earthquakes [16], volcanic activity [17] or landslides [18], mine subsidence monitoring [19] and structural engineering, especially monitoring of subsidence [20] and structural stability of buildings [15] or bridges [10].…”

Section: Introductionmentioning

confidence: 99%

RFI Artefacts Detection in Sentinel-1 Level-1 SLC Data Based On Image Processing Techniques

Chojka

Artiemjew

Rapiński

2020

Sensors

View full text Add to dashboard Cite

Interferometric Synthetic Aperture Radar (InSAR) data are often contaminated by Radio-Frequency Interference (RFI) artefacts that make processing them more challenging. Therefore, easy to implement techniques for artefacts recognition have the potential to support the automatic Permanent Scatterers InSAR (PSInSAR) processing workflow during which faulty input data can lead to misinterpretation of the final outcomes. To address this issue, an efficient methodology was developed to mark images with RFI artefacts and as a consequence remove them from the stack of Synthetic Aperture Radar (SAR) images required in the PSInSAR processing workflow to calculate the ground displacements. Techniques presented in this paper for the purpose of RFI detection are based on image processing methods with the use of feature extraction involving pixel convolution, thresholding and nearest neighbor structure filtering. As the reference classifier, a convolutional neural network was used.

show abstract

Measuring Surface Subsidence in Wuhan, China With Sentinel-1 Data Using Psinsar

Cited by 12 publications

References 16 publications

Monitoring Land Subsidence in Wuhan City (China) using the SBAS-InSAR Method with Radarsat-2 Imagery Data

Monitoring Land Subsidence in Wuhan City (China) using the SBAS-InSAR Method with Radarsat-2 Imagery Data

Land Subsidence in Wuhan Revealed Using a Non-Linear PSInSAR Approach with Long Time Series of COSMO-SkyMed SAR Data

RFI Artefacts Detection in Sentinel-1 Level-1 SLC Data Based On Image Processing Techniques

Contact Info

Product

Resources

About