Application of InSAR and GIS Techniques to Ground Subsidence Assessment in the Nobi Plain, Central Japan

Zheng, Minxue; Fukuyama, Kunio; Kazadi, Sanga-Ngoie

doi:10.3390/s140100492

Cited by 11 publications

(9 citation statements)

References 25 publications

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“…Remote sensing (RS) and Geographical information system (GIS) data are useful tools to integrate the development of the land subsidence studies [ 11 , 12 ]. On the other hand, accurate land subsidence inventories may still be challenging to acquire, although modern technologies such as Global Positioning Systems (GPS), RS and GIS may assist spatial prediction and localization of visible land subsidence features [ 13 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

Land Subsidence Susceptibility Mapping in South Korea Using Machine Learning Algorithms

Bui

Shahabi

Shirzadi

et al. 2018

Sensors

131

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In this study, land subsidence susceptibility was assessed for a study area in South Korea by using four machine learning models including Bayesian Logistic Regression (BLR), Support Vector Machine (SVM), Logistic Model Tree (LMT) and Alternate Decision Tree (ADTree). Eight conditioning factors were distinguished as the most important affecting factors on land subsidence of Jeong-am area, including slope angle, distance to drift, drift density, geology, distance to lineament, lineament density, land use and rock-mass rating (RMR) were applied to modelling. About 24 previously occurred land subsidence were surveyed and used as training dataset (70% of data) and validation dataset (30% of data) in the modelling process. Each studied model generated a land subsidence susceptibility map (LSSM). The maps were verified using several appropriate tools including statistical indices, the area under the receiver operating characteristic (AUROC) and success rate (SR) and prediction rate (PR) curves. The results of this study indicated that the BLR model produced LSSM with higher acceptable accuracy and reliability compared to the other applied models, even though the other models also had reasonable results.

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Section: Introductionmentioning

confidence: 99%

Land Subsidence Susceptibility Mapping in South Korea Using Machine Learning Algorithms

Bui

Shahabi

Shirzadi

et al. 2018

Sensors

131

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“…InSAR technique has been applied to monitor the land subsidence in coal mining area [17][18][19][20][21][22][23][24][25][26][27][28][29][30]. Ji et al [18] demonstrated InSAR's ability to cost-effectively monitor illegal mining activities.…”

Section: Introductionmentioning

confidence: 99%

“…A DInSAR-based illegal-mining detection system (DIMDS) was proposed to exploit the geometric, spatial, and temporal characteristics of those subsidence patterns [19]. Zheng et al [20] analyzed land subsidence induced by coal mining in a 200 km 2 area in the Ordos Basin for the time period 2006-2015 using SBAS InSAR and D-InSAR. Hayman et al [21] investigated the performance of the three satellite missions (Radarsat-2, Sentinel-1, and ALOS-2) with different imaging modes for mapping longwall mine subsidence.…”

Section: Introductionmentioning

confidence: 99%

Monitoring, Analyzing, and Modeling for Single Subsidence Basin in Coal Mining Areas Based on SAR Interferometry with L-Band Data

Wang

Zhang

et al. 2021

Scientific Programming

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Excessive exploitation of underground mine resources has caused serious land subsidence in China. This paper focused on monitoring and modeling the single subsidence basin in coal mining area based on SAR interferometry (InSAR). The optimum InSAR processing strategy to monitor the mining subsidence was built to obtain the land subsidence with large deformation. And a method of three-dimensional mathematical modeling of single subsidence basin based on InSAR measurements was presented. Using Jining Coalfield (China) as the study area, we acquired 7 L-band PALSAR images from January 2008 to February 2010 to monitor the land subsidence in Jining Coalfield. The deformation maps in Jining Coalfield in different periods were obtained. Taking the Geting Coal Mine within the Jining coalfield as an example, we finely analyzed and interpreted the deformation maps. Compared with the simultaneous filed measurements, the precision of deformation measurement using D-InSAR in mining area was analyzed. The root mean square error was 1.37 cm. The method of fine interpretation and analysis for a single subsidence basin was established. The experiments have proved that InSAR technique with L-band InSAR data is suitable for monitoring mining subsidence with large deformation. And the 3D mathematical modeling method could be used for the single subsidence basin in coal mining area.

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“…Due to the factors such as clouds, atmospheric and ionosphere caused radar echo signal phase delay phenomenon called atmospheric delay [21]. In the three-pass method, the three imaging time interval for several cycles.…”

Section: Atmospheric Effectmentioning

confidence: 99%

Study on Characteristics of SAR Data in Ravine Reservoir Area

Xie¹,

Huang²,

Huang³

2016

IJEECS

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Application of InSAR and GIS Techniques to Ground Subsidence Assessment in the Nobi Plain, Central Japan

Cited by 11 publications

References 25 publications

Land Subsidence Susceptibility Mapping in South Korea Using Machine Learning Algorithms

Land Subsidence Susceptibility Mapping in South Korea Using Machine Learning Algorithms

Monitoring, Analyzing, and Modeling for Single Subsidence Basin in Coal Mining Areas Based on SAR Interferometry with L-Band Data

Study on Characteristics of SAR Data in Ravine Reservoir Area

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