Land subsidence monitoring in city area by time series interferometric SAR data

In this paper, DInSAR technique was used to monitor land subsidence in mining area. The study area was selected in the coal mine area located in Yuanbaoshan District, Chifeng City, and Sentinel-1 data were used to carry out DInSAR techniqu. We analyzed the interferometric results by Sentinel-1 data from December 2015 to May 2016. Through the comparison of the results of DInSAR technique and the location of the mine on the optical images, it is shown that DInSAR technique can be used to effectively monitor the land subsidence caused by underground mining, and it is an effective tool for law enforcement of over-mining.

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LAND SUBSIDENCE MONITORING USING PS-InSAR TECHNIQUE FOR L-BAND SAR DATA

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

10.5194/isprs-archives-xli-b7-995-2016 ◽

2016 ◽

Vol XLI-B7 ◽

pp. 995-997 ◽

Cited By ~ 2

Author(s):

S. Thapa ◽

R. S. Chatterjee ◽

K. B. Singh ◽

D. Kumar

Keyword(s):

Time Series ◽

Land Subsidence ◽

Land Surface ◽

Surface Deformation ◽

Underground Mining ◽

Ground Surface ◽

Sar Interferometry ◽

Persistent Scatterer Interferometry ◽

Long Time ◽

Subsidence Monitoring

Differential SAR-Interferometry (D-InSAR) is one of the potential source to measure land surface motion induced due to underground coal mining. However, this technique has many limitation such as atmospheric in homogeneities, spatial de-correlation, and temporal decorrelation. Persistent Scatterer Interferometry synthetic aperture radar (PS-InSAR) belongs to a family of time series InSAR technique, which utilizes the properties of some of the stable natural and anthropogenic targets which remain coherent over long time period. In this study PS-InSAR technique has been used to monitor land subsidence over selected location of Jharia Coal field which has been correlated with the ground levelling measurement. This time series deformation observed using PS InSAR helped us to understand the nature of the ground surface deformation due to underground mining activity.

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Adjacent-Track InSAR Processing for Large-Scale Land Subsidence Monitoring in the Hebei Plain

Remote Sensing ◽

10.3390/rs13040795 ◽

2021 ◽

Vol 13 (4) ◽

pp. 795

Author(s):

Xi Li ◽

Li Yan ◽

Lijun Lu ◽

Guoman Huang ◽

Zheng Zhao ◽

...

Keyword(s):

Time Series ◽

Synthetic Aperture Radar ◽

Land Subsidence ◽

Large Scale ◽

Synthetic Aperture ◽

Hebei Plain ◽

Deformation Velocity ◽

Large Scale Land ◽

Subsidence Monitoring ◽

Aperture Radar

Large-scale land subsidence has threatened the safety of the Hebei Plain in China. For tens of thousands of square kilometers of the Hebei Plain, large-scale subsidence monitoring is still one of the most difficult problems to be solved. In this paper, we employed the small baseline subset (SBAS) and NSBAS technique to monitor the land subsidence in the Hebei Plain (45,000 km2). The 166 Sentinel-1A data of adjacent-track 40 and 142 collected from May 2017 to May 2019 were used to generate the average deformation velocity and deformation time-series. A novel data fusion flow for the generation of land subsidence velocity of adjacent-track is presented and tested, named as the fusion of time-series interferometric synthetic aperture radar (TS-InSAR) results of adjacent-track using synthetic aperture radar amplitude images (FTASA). A cross-comparison analysis between the two tracks results and two TS-InSAR results was carried out. In addition, the deformation results were validated by leveling measurements and benchmarks on bedrock results, reaching a precision 9 mm/year. Twenty-six typical subsidence bowls were identified in Handan, Xingtai, Shijiazhuang, Hengshui, Cangzhou, and Baoding. An average annual subsidence velocity over −79 mm/year was observed in Gaoyang County of Baoding City. Through the cause analysis of the typical subsidence bowls, the results showed that the shallow and deep groundwater funnels, three different land use types over the building construction, industrial area, and dense residential area, and faults had high spatial correlation related to land subsidence bowls.

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Estimation of Equivalent Number of Looks in Time-Series Pol(In)SAR Data

Remote Sensing ◽

10.3390/rs12172715 ◽

2020 ◽

Vol 12 (17) ◽

pp. 2715

Author(s):

Peng Shen ◽

Changcheng Wang ◽

Haiqiang Fu ◽

Jianjun Zhu ◽

Jun Hu

Keyword(s):

Time Series ◽

Speckle Noise ◽

Statistical Characteristics ◽

Data Types ◽

Sar Images ◽

Practical Applications ◽

Equivalent Number ◽

Essential Parameter ◽

Sar Data ◽

Interferometric Sar

As an essential parameter in synthetic aperture radar (SAR) images, the equivalent number of looks (ENL) not only indicates the speckle noise level in multi-look SAR data but also can be used for evaluating the region homogeneity level. Currently, time-series polarimetric (interferometric) SAR (TSPol(In)SAR) data are increasingly abundant, but traditional equivalent number of looks (ENL) estimators only use polarimetric information from a mono-temporal observation and do not consider the temporal characteristics or interferometric coherence of ground targets. Therefore, this paper puts forward four novel ENL estimators to overcome the restrictions of inadequate observation information. Firstly, based on the traditional trace moment estimator for polarimetric SAR data (TM-PolSAR), we extend it to both PolInSAR and TSPolInSAR data and then propose both TM-PolInSAR and TM-TSPolInSAR estimators, respectively. Secondly, for both TSPolSAR and single-reference TSPolInSAR data, we estimate the ENL by stacking the trace moments (STM) of multitemporal coherency matrices, called STM-TSPolSAR and STM-TSPolInSAR estimators, respectively. Therefore, these proposed ENL estimators can effectively deal with most of the requirements of TSPol(In)SAR data types in practical applications, mainly including statistical distribution modeling and region homogeneity evaluation. The simulation and real experiments detailedly compare the proposed four ENL estimators to the classical TM-PolSAR estimator and quantitatively analyze the estimation performance. The proposed estimators have obtained the ENL with less bias and standard deviation than the traditional estimator, especially in case of small spatial samples coherency matrices. Additionally, these STM-TSPolSAR, STM-TSPolInSAR, and TM-TSPolInSAR estimators have provided more effective statistical characteristics with the increase of the time-series size. It has been demonstrated that the proposed STM-TSPolSAR estimator considers the time-varying polarimetric characteristics of the crop and detects many edges that the traditional estimator cannot discover, which means a superior capability of region homogeneity evaluation.

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MONITORING THE SURFACE SUBSIDENCE OF HANDAN CITY USING SENTINEL-1A IMAGES AND SBAS-INSAR TECHNOLOGY

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

10.5194/isprs-archives-xlii-2-w13-1933-2019 ◽

2019 ◽

Vol XLII-2/W13 ◽

pp. 1933-1937 ◽

Cited By ~ 1

Author(s):

G. Huang ◽

H. Chen ◽

X. Li ◽

G. Cheng ◽

Z. Yu ◽

...

Keyword(s):

Time Series ◽

Urban Area ◽

Land Subsidence ◽

Maximum Settlement ◽

Research Areas ◽

Cumulative Deformation ◽

Sbas Insar ◽

Surrounding Areas ◽

Subsidence Monitoring ◽

Handan City

<p><strong>Abstract.</strong> The Sentinel-1 data is currently the latest free SAR data and is well suited for land subsidence monitoring based on InSAR technology due to its 6-day revisit cycle. In this paper, the urban area and surrounding areas of Handan City are used as research areas, and 29 scences of S1A data (December 2017 &ndash; December 2018) was used for time series SBAS processing. The results shows that the surface cumulative deformation ranged from -42 to 30&thinsp;mm in most regions of Handan City. The maximum settlement is 69&thinsp;mm, which is near the Hankuang Group Julong Company. The areas where the settlement is obvious include Wu'an City, Daishan Village, Dashe Town, Zhangxibao Town, Baijia Street, Dongxingtai Village, Gaonan Village and Hankuang Group Julong Company. Slightly elevated in the southeast of Handan City.</p>

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