scholarly journals Ground subsidence monitoring over Jiaxing region, Zhejiang province from 2006 to 2010 using time series InSAR technique

2015 ◽  
Vol XL-7/W4 ◽  
pp. 181-184
Author(s):  
H. A. Wu ◽  
Y. H. Zhang ◽  
G. F. Luo ◽  
W. H. Mao ◽  
Y. H. Kang ◽  
...  
Keyword(s):  
Time Series ◽  
Standard Deviation ◽  
Good Accuracy ◽  
Zhejiang Province ◽  
Ground Subsidence ◽  
Envisat Asar ◽  
The Past ◽  
Long Time ◽  
Subsidence Monitoring

Because of over-exploration of groundwater for a long time, Jiaxing region, northeast of Zhejiang province has suffering serious ground subsidence during the past several decades. In this paper, we investigate the time series InSAR technique for the generation of subsidence maps over Jiaxing region. 25 ENVISAT ASAR images acquired from Jan 2006 to Aug 2010 are used. The results show that serious subsidence has taken place in Xiuzhou district, Jiashan county, Pinghu county and Haiyan county. 4 subsidence centers, namely Huanggu town in Pinghu, Wangjiangting town in Xiuzhou, Tianning townin Jiashan, and Wuyuan town in Haiyan. 3 obvious subsidence belts are distributed in Jiaxing. Meanwhile in Tongxiang county, Haining county and Nanhu district, there is relatively seldom subsidence. The results are compared with 35 levelling measurements. The standard deviation of the error between the two data is 4.25 mm/year, which demonstrate that time series InSAR technique has good accuracy for subsidence monitoring in Zhejiang province, southeast of China, covered with lots of vegetation and waters.

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

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.

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

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.

scholarly journals Persistent Scatterer Interferometry of Sentinel-1 time series to detect ground subsidence in the city of Recife, Brazil

2020 ◽  
Vol 10 (1) ◽  
pp. 1
Author(s):  
Enton Bedini
Keyword(s):  
Time Series ◽  
Ground Subsidence ◽  
Persistent Scatterer Interferometry ◽  
Persistent Scatterer ◽  
Radar Imagery ◽  
Long Time ◽  
Satellite Radar ◽  
Surrounding Areas ◽  
Satellite Radar Imagery ◽  
The City

Persistent Scatterer Interferometry (PSI) analysis of Sentinel-1 time series was carried out to detect ground subsidence in the city of Recife, Brazil. The dataset consisted of sixty-eight Sentinel-1A Interferometric Wide (IW) Single Look Complex (SLC) images of the time period April 2017 – September 2019. The images were acquired in descending orbit in VV (vertical transmitting, vertical receiving) polarization. The results of the PSI analysis show that in the city of Recife occur several ground subsidence areas. The largest ground subsidence area occurs between the neighborhoods of Afogados, Torrŏes and Cordeiro. The subsidence rates in this area range from few mm/year up to -15 mm/year. This ground subsidence could be a result of groundwater extraction or of subsidence processes in urbanized reclaimed lands. Similar but smaller ground subsidence areas occur in several localities in Recife. In some cases, subsidence with rates of up to -25 mm/year is noted in small zones where new buildings have been constructed in the last decade. This should be due to ground settlement processes, taking a long time due to the particular soils and geology of the locality. This study can serve as a first contribution for further research on the ground subsidence hazard in the city of Recife and the surrounding areas by means of satellite radar imagery.

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

2016 ◽  
Vol XLI-B7 ◽  
pp. 995-997 ◽  
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.

SBAS-InSAR time series ground subsidence monitoring along Metro Line 13 in Qingdao, China

2021 ◽  
Vol 14 (23) ◽  
Author(s):  
Qiuxiang Tao ◽  
Zaijie Guo ◽  
Fengyun Wang ◽  
Qingguo An ◽  
Yu Han
Keyword(s):  
Time Series ◽  
Ground Subsidence ◽  
Sbas Insar ◽  
Subsidence Monitoring

scholarly journals THREE-DIMENSIONAL SUBSIDENCE MONITORING OF RECLAMATION AREA BASED ON TS-INSAR TECHNOLOGY

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 297-302
Author(s):  
C. Ren ◽  
X. J. Shi ◽  
L. Zhou ◽  
Y. L. Huang ◽  
Y. J. Liang ◽  
...  
Keyword(s):  
Time Series ◽  
Land Subsidence ◽  
Three Dimensional ◽  
Interferometric Synthetic Aperture Radar ◽  
Envisat Asar ◽  
Subsidence Rate ◽  
Aster Gdem ◽  
Reclamation Area ◽  
Maximum Subsidence ◽  
Subsidence Monitoring

Abstract. This study used the Time Series Interferometric Synthetic Aperture Radar (TS-InSAR) technology and the ENVISAT ASAR dataset obtained between 2007 and 2010 and the Sentinel-1A dataset from 2017 to 2019 to determine the subsidence in the Shenzhen reclamation area, and used ASTER GDEM V2 to remove the phase jump caused by spectral decorrelation between bursts. Based on the time series of land subsidence of the reclamation area, a three-dimensional subsidence model of the reclamation area was established. By comparing and analyzing the subsidence results of two sets of satellite time series in the reclamation area, the temporal and spatial evolution of land subsidence and the cause of land subsidence in the reclamation area were analyzed. The results show that the test results of time series ENVISAT ASAR and Sentinel-1A have good continuity in subsidence spatialtemporal variation: From 2007 to 2010, the land subsidence in most areas of Shenzhen reclamation area is relatively stable, and the area where subsidence occurs is mainly distributed in Baoan District, the maximum subsidence rate in Baoan District is −23.12 mm/year. From 2017 to 2019, land subsidence in the reclamation area showed a tendency to spread and spread from west to east. The subsidence characteristics of Nanshan District are the most obvious, and the maximum subsidence rate is −17.52 mm/year.

‘The past is the future of the present’: Learning from long-time series of marine monitoring

2016 ◽  
Vol 566-567 ◽  
pp. 698-711 ◽  
Author(s):  
Ángel Borja ◽  
Guillem Chust ◽  
José G. Rodríguez ◽  
Juan Bald ◽  
Mª. Jesús Belzunce-Segarra ◽  
...  
Keyword(s):  
Time Series ◽  
Marine Monitoring ◽  
The Past ◽  
Long Time Series ◽  
The Future ◽  
Long Time

scholarly journals Predicting Fraud Victimization Using Classical Machine Learning

Entropy ◽  
2021 ◽  
Vol 23 (3) ◽  
pp. 300
Author(s):  
Mark Lokanan ◽  
Susan Liu
Keyword(s):  
Machine Learning ◽  
Financial Literacy ◽  
Learning Algorithm ◽  
Demographic Characteristics ◽  
Financial Knowledge ◽  
The Past ◽  
Machine Learning Model ◽  
Long Time ◽  
Regulatory Organization ◽  
Investment Fraud

Protecting financial consumers from investment fraud has been a recurring problem in Canada. The purpose of this paper is to predict the demographic characteristics of investors who are likely to be victims of investment fraud. Data for this paper came from the Investment Industry Regulatory Organization of Canada’s (IIROC) database between January of 2009 and December of 2019. In total, 4575 investors were coded as victims of investment fraud. The study employed a machine-learning algorithm to predict the probability of fraud victimization. The machine learning model deployed in this paper predicted the typical demographic profile of fraud victims as investors who classify as female, have poor financial knowledge, know the advisor from the past, and are retired. Investors who are characterized as having limited financial literacy but a long-time relationship with their advisor have reduced probabilities of being victimized. However, male investors with low or moderate-level investment knowledge were more likely to be preyed upon by their investment advisors. While not statistically significant, older adults, in general, are at greater risk of being victimized. The findings from this paper can be used by Canadian self-regulatory organizations and securities commissions to inform their investors’ protection mandates.

scholarly journals Sea Ice Concentration Products over Polar Regions with Chinese FY3C/MWRI Data

2021 ◽  
Vol 13 (11) ◽  
pp. 2174
Author(s):  
Lijian Shi ◽  
Sen Liu ◽  
Yingni Shi ◽  
Xue Ao ◽  
Bin Zou ◽  
...  
Keyword(s):  
Time Series ◽  
Sea Ice ◽  
Ocean Circulation ◽  
The Arctic ◽  
Retrieval Algorithm ◽  
Atmospheric Effects ◽  
Observation Data ◽  
Long Time Series ◽  
Ice Concentration ◽  
Long Time

Polar sea ice affects atmospheric and ocean circulation and plays an important role in global climate change. Long time series sea ice concentrations (SIC) are an important parameter for climate research. This study presents an SIC retrieval algorithm based on brightness temperature (Tb) data from the FY3C Microwave Radiation Imager (MWRI) over the polar region. With the Tb data of Special Sensor Microwave Imager/Sounder (SSMIS) as a reference, monthly calibration models were established based on time–space matching and linear regression. After calibration, the correlation between the Tb of F17/SSMIS and FY3C/MWRI at different channels was improved. Then, SIC products over the Arctic and Antarctic in 2016–2019 were retrieved with the NASA team (NT) method. Atmospheric effects were reduced using two weather filters and a sea ice mask. A minimum ice concentration array used in the procedure reduced the land-to-ocean spillover effect. Compared with the SIC product of National Snow and Ice Data Center (NSIDC), the average relative difference of sea ice extent of the Arctic and Antarctic was found to be acceptable, with values of −0.27 ± 1.85 and 0.53 ± 1.50, respectively. To decrease the SIC error with fixed tie points (FTPs), the SIC was retrieved by the NT method with dynamic tie points (DTPs) based on the original Tb of FY3C/MWRI. The different SIC products were evaluated with ship observation data, synthetic aperture radar (SAR) sea ice cover products, and the Round Robin Data Package (RRDP). In comparison with the ship observation data, the SIC bias of FY3C with DTP is 4% and is much better than that of FY3C with FTP (9%). Evaluation results with SAR SIC data and closed ice data from RRDP show a similar trend between FY3C SIC with FTPs and FY3C SIC with DTPs. Using DTPs to present the Tb seasonal change of different types of sea ice improved the SIC accuracy, especially for the sea ice melting season. This study lays a foundation for the release of long time series operational SIC products with Chinese FY3 series satellites.

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