Assessing ground deformation monitoring techniques in Midvaal, South Africa
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Thobani Maluleka
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Siphiwe Matthews Mphuthi
Abstract
Ground subsidence, the gradual sinking of the earth’s surface, poses a significant challenge globally, affecting regions like Midvaal, South Africa. This study employed Persistent Scatterer (PS) and Small Baseline Subset (SBAS) InSAR time-series analysis, continuous Global Navigation Satellite Systems (cGNSS) validation, and Random Forest machine learning to monitor and model ground subsidence using Sentinel-1 data (2019–2021). SBAS InSAR showed higher ground subsidence rates than PS InSAR, with cGNSS validation revealing InSAR limitations. Factor analysis identified NDVI, NDWI, lithology, and elevation as key drivers. The Random Forest model generated a susceptibility map, classifying areas into five vulnerability levels. Results indicate that Midvaal experiences significant ground subsidence, likely due to groundwater extraction for agricultural activities and mining, impacting approximately 3.4 % of the study area (118.85 sq. km). This study provides critical insights for sustainable land management and infrastructure planning in ground subsidence-prone regions.
Acknowledgment
The SAR data used in this study were obtained through the ASF DAAC facility provided by ESA. The cGNSS data were obtained from the Nevada Geodetic Laboratory, Sentinel-2 data were acquired from the Copernicus Data Space Ecosystem, and the AW3D30 DEM was obtained from OpenTopography provided by JAXA. The used notebooks were downloaded from OpenScienceLab, preprocessing was done using ASF Vertex HyP3, and MintPy by Yunjun et al. (2019), ISCE and StaMPS by Rosen et al. (2012) and Hooper et al. (2012) were used for InSAR time-series analysis. Computations were performed using facilities provided by the University of Cape Town’s ICTS High Performance Computing team: hpc.uct.ac.za. The authors thank the UCT high Performance Computing team, the anonymous reviewers and the editor for their constructive comments.
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Research ethics: University of Cape Town Research Ethics Committee Project Approval Letter (EBE/00757/2024).
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Informed consent: Not applicable.
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Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Use of Large Language Models, AI and Machine Learning Tools: AI and LLM tools were used to assist with correcting grammar and improving clarity during the manuscript drafting process. All content was reviewed and approved by the authors.
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Conflict of interest: The authors state no conflict of interest.
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Research funding: None declared.
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Data availability: Not applicable.
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