Coastal subsidence detection and characterization caused by brine mining over the Yellow River Delta using time series InSAR and PCA

Wang, Guoyang; Li, Zhenhong; Liang, Cunren; Wang, Houjie

doi:10.1016/j.jag.2022.103077

Cited by 12 publications

(17 citation statements)

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“…The main stream of the Yellow River has a total length of more than 5,400 km and a drainage area of more than 750,000 km 2 (Wang et al., 2007). The upper reaches of the Yellow River are dominated by mountains, whereas the middle and lower reaches are dominated by plains and hills (Figures 1a–1c), forming the youngest delta in China (Wang et al., 2022). Nearly 90% of the sediment originates from the middle reaches, and 60% of the river runoff originates from the upper reaches (Chang et al., 2022; Wang et al., 2017; Zhu et al., 2021).…”

Section: Methodsmentioning

confidence: 99%

High Spatiotemporal Resolution River Networks Mapping on Catchment Scale Using Satellite Remote Sensing Imagery and DEM Data

Li,

Zhang,

Liang

et al. 2024

Geophysical Research Letters

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Characterizing and understanding the changes in the flow regimes of rivers have been challenging. Existing global river network data sets are not updated and can only identify rivers wider than 30 m. We propose a novel automated method to map river networks on a monthly basin scale for the first time at 10‐m resolution using Sentinel‐1 Synthetic Aperture Radar, Sentinel‐2 multispectral images, and the AW3D30 Digital Surface Model. This method achieved an overall accuracy of 95.8%. The total length of the Yellow River network produced is 40,280 km, approximately 3.2 times that of the Global River Widths from Landsat (GRWL) database, more effectively covering small and medium rivers. The monthly river geometry revealed a positive correlation between river network area and precipitation. This study is expected to provide a cost‐effective alternative to accurately mapping global river networks and advance our understanding of the changes and drivers of river systems.

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Section: Methodsmentioning

confidence: 99%

High Spatiotemporal Resolution River Networks Mapping on Catchment Scale Using Satellite Remote Sensing Imagery and DEM Data

Li,

Zhang,

Liang

et al. 2024

Geophysical Research Letters

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“…Finally, the eigenvectors and eigenvalues of the covariance matrix are computed to identified the PC 13 , 33 .…”

Section: Methodsmentioning

confidence: 99%

Reconstructing missing time-varying land subsidence data using back propagation neural network with principal component analysis

Liu,

Ku,

Hsu

2023

Sci Rep

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Land subsidence, a complex geophysical phenomenon, necessitates comprehensive time-varying data to understand regional subsidence patterns over time. This article focuses on the crucial task of reconstructing missing time-varying land subsidence data in the Choshui Delta, Taiwan. We propose a novel algorithm that leverages a multi-factorial perspective to accurately reconstruct the missing time-varying land subsidence data. By considering eight influential factors, our method seeks to capture the intricate interplay among these variables in the land subsidence process. Utilizing Principal Component Analysis (PCA), we ascertain the significance of these influencing factors and their principal components in relation to land subsidence. To reconstruct the absent time-dependent land subsidence data using PCA-derived principal components, we employ the backpropagation neural network. We illustrate the approach using data from three multi-layer compaction monitoring wells from 2008 to 2021 in a highly subsiding region within the study area. The proposed model is validated, and the resulting network is used to reconstruct the missing time-varying subsidence data. The accuracy of the reconstructed data is evaluated using metrics such as root mean square error and coefficient of determination. The results demonstrate the high accuracy of the proposed neural network model, which obviates the need for a sophisticated hydrogeological numerical model involving corresponding soil compaction parameters.

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“…PCA is a multivariate analysis that converts a collection of intercorrelated variables to generate a new set of variables (components) that are uncorrelated and are ordered in their capability to explain variability in the original set [47]. Previous authors used PCA to identify ground deformation patterns in A-DInSAR TS [48][49][50][51]. Chaussard et al (2014) [48] and Wang et al (2022) [50] employed Principal Component Analysis (PCA) to distinguish longer-term from seasonal deformation.…”

Section: A-dinsar Data Processingmentioning

confidence: 99%

“…Previous authors used PCA to identify ground deformation patterns in A-DInSAR TS [48][49][50][51]. Chaussard et al (2014) [48] and Wang et al (2022) [50] employed Principal Component Analysis (PCA) to distinguish longer-term from seasonal deformation. However, neither study conducted clustering to isolate ground deformation hotspots.…”

Section: A-dinsar Data Processingmentioning

confidence: 99%

Development of a Proof-of-Concept A-DInSAR-Based Monitoring Service for Land Subsidence

Righini,

Bonì,

Sapio

et al. 2024

Remote Sensing

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The increasing availability of SAR images and processing results over wide areas determines the need for systematic procedures to extract the information from this dataset and exploit the enhanced quality of the displacement time series. The aim of the study is to propose a new pre-operational workflow of an A-DInSAR-based land subsidence monitoring and interpretation service. The workflow is tested in Turano Lodigiano (Lombardy region, Italy) using COSMO-SkyMed data, processed using the SqueeSAR™ algorithm, and covering the time span from 2016 to 2019. The test site is a representative peri-urban area of the Po plain susceptible to land subsidence. The results give insight about new value-added products and enable non-expert users to exploit the potential of the interferometric results.

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Coastal subsidence detection and characterization caused by brine mining over the Yellow River Delta using time series InSAR and PCA

Cited by 12 publications

References 50 publications

High Spatiotemporal Resolution River Networks Mapping on Catchment Scale Using Satellite Remote Sensing Imagery and DEM Data

High Spatiotemporal Resolution River Networks Mapping on Catchment Scale Using Satellite Remote Sensing Imagery and DEM Data

Reconstructing missing time-varying land subsidence data using back propagation neural network with principal component analysis

Development of a Proof-of-Concept A-DInSAR-Based Monitoring Service for Land Subsidence

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