Collapse Hotspot Detection in Urban Area Using Sentinel-1 and TerraSAR-X Dataset with SBAS and PSI Techniques
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 reside...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-12-01
|
| Series: | Land |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-445X/13/12/2237 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850037278259281920 |
|---|---|
| author | Niloofar Alizadeh Yasser Maghsoudi Tayebe Managhebi Saeed Azadnejad |
| author_facet | Niloofar Alizadeh Yasser Maghsoudi Tayebe Managhebi Saeed Azadnejad |
| author_sort | Niloofar Alizadeh |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-6130953a2bbb446e8506cbbcfe62fcd2 |
| institution | DOAJ |
| issn | 2073-445X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Land |
| spelling | doaj-art-6130953a2bbb446e8506cbbcfe62fcd22025-08-20T02:56:55ZengMDPI AGLand2073-445X2024-12-011312223710.3390/land13122237Collapse Hotspot Detection in Urban Area Using Sentinel-1 and TerraSAR-X Dataset with SBAS and PSI TechniquesNiloofar Alizadeh0Yasser Maghsoudi1Tayebe Managhebi2Saeed Azadnejad3Department of Photogrammetry and Remote Sensing, Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology, Tehran 19967-15433, IranDepartment of Earth and Environmental Sciences, University of Exeter, Penryn Campus, Penryn TR10 9FE, UKSchool of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran 14399-57131, IranUCD School of Civil Engineering, University College Dublin, D04 C1P1 Dublin, IrelandUrban 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.https://www.mdpi.com/2073-445X/13/12/2237collapseInSARPSISBASSentinel-1TerraSAR-X |
| spellingShingle | Niloofar Alizadeh Yasser Maghsoudi Tayebe Managhebi Saeed Azadnejad Collapse Hotspot Detection in Urban Area Using Sentinel-1 and TerraSAR-X Dataset with SBAS and PSI Techniques Land collapse InSAR PSI SBAS Sentinel-1 TerraSAR-X |
| title | Collapse Hotspot Detection in Urban Area Using Sentinel-1 and TerraSAR-X Dataset with SBAS and PSI Techniques |
| title_full | Collapse Hotspot Detection in Urban Area Using Sentinel-1 and TerraSAR-X Dataset with SBAS and PSI Techniques |
| title_fullStr | Collapse Hotspot Detection in Urban Area Using Sentinel-1 and TerraSAR-X Dataset with SBAS and PSI Techniques |
| title_full_unstemmed | Collapse Hotspot Detection in Urban Area Using Sentinel-1 and TerraSAR-X Dataset with SBAS and PSI Techniques |
| title_short | Collapse Hotspot Detection in Urban Area Using Sentinel-1 and TerraSAR-X Dataset with SBAS and PSI Techniques |
| title_sort | collapse hotspot detection in urban area using sentinel 1 and terrasar x dataset with sbas and psi techniques |
| topic | collapse InSAR PSI SBAS Sentinel-1 TerraSAR-X |
| url | https://www.mdpi.com/2073-445X/13/12/2237 |
| work_keys_str_mv | AT niloofaralizadeh collapsehotspotdetectioninurbanareausingsentinel1andterrasarxdatasetwithsbasandpsitechniques AT yassermaghsoudi collapsehotspotdetectioninurbanareausingsentinel1andterrasarxdatasetwithsbasandpsitechniques AT tayebemanaghebi collapsehotspotdetectioninurbanareausingsentinel1andterrasarxdatasetwithsbasandpsitechniques AT saeedazadnejad collapsehotspotdetectioninurbanareausingsentinel1andterrasarxdatasetwithsbasandpsitechniques |