Collapse Hotspot Detection in Urban Area Using Sentinel-1 and TerraSAR-X Dataset with SBAS and PSI Techniques

Niloofar Alizadeh, Yasser Maghsoudi (), Tayebe Managhebi and Saeed Azadnejad
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Niloofar Alizadeh: Department of Photogrammetry and Remote Sensing, Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology, Tehran 19967-15433, Iran
Yasser Maghsoudi: Department of Earth and Environmental Sciences, University of Exeter, Penryn Campus, Penryn TR10 9FE, UK
Tayebe Managhebi: School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran 14399-57131, Iran
Saeed Azadnejad: UCD School of Civil Engineering, University College Dublin, D04 C1P1 Dublin, Ireland

Land, 2024, vol. 13, issue 12, 1-21

Abstract: Urban areas face an imminent risk of collapse due to structural deficiencies and gradual ground subsidence. Therefore, monitoring surface movements is crucial for detecting abnormal behavior, implementing timely preventive measures, and minimizing the detrimental effects of this phenomenon in residential regions. In this context, interferometric synthetic aperture radar (InSAR) has emerged as a highly effective technique for monitoring slow and long-term ground hazards and surface motions. The first goal of this study is to explore the potential applications of persistent scatterer interferometry (PSI) and small baseline subset (SBAS) algorithms in collapse hotspot detection, utilizing a dataset consisting of 144 Sentinel-1 images. The experimental results from three areas with a history of collapses demonstrate that the SBAS algorithm outperforms PSI in uncovering behavior patterns indicative of collapse and accurately pinpointing collapse points near real collapse sites. In the second phase, this research incorporated an additional dataset of 36 TerraSAR-X images alongside the Sentinel-1 data to compare results based on radar images with different spatial resolutions in the C and X bands. The findings reveal a strong correlation between the TerraSAR-X and Sentinel-1 time series. Notably, the analysis of the TerraSAR-X time series for one study area identified additional collapse-prone points near the accident site, attributed to the higher spatial resolution of these data. By leveraging the capabilities of InSAR and advanced algorithms, like SBAS, this study highlights the potential to identify areas at risk of collapse, enabling the implementation of preventive measures and reducing potential harm to residential communities.

Keywords: collapse; InSAR; PSI; SBAS; Sentinel-1; TerraSAR-X (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
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