Integrating SAR and Geographic Information Data Revealing Land Subsidence and Geological Risks of Shanghai City
Abstract
:1. Introduction
2. Methods
2.1. SBAS-InSAR
2.2. SSA
2.3. Entropy Method
3. Study Areas and Data Process
3.1. Overview of Study Areas
3.2. Data Introduction
3.3. Data Process
3.3.1. Land Subsidence Experiment
3.3.2. Risk Assessment Experiment
4. Validation and Discussion of Land Subsidence Results
4.1. Land Subsidence Results
4.2. Deformation Result Validation
- (1)
- Standard deviation verification
- (2)
- TerraSAR displacement result comparison
- (3)
- Results comparison with different SAR techniques
4.3. Typical Land Subsidence Areas Discussion
4.3.1. Feature Point Deformation Analysis
4.3.2. Land Subsidence along the Subway Analysis
4.4. Correlation Analysis
4.4.1. Correlation Analysis between Land Subsidence and Rainfall
4.4.2. Correlation Analysis of Land Subsidence and Human Activities
5. Risk Assessment
5.1. Risk Assessment Results
5.2. Risk Assessment Result Validation
5.3. Risk Assessment Result Discussion
6. Conclusions
- (1)
- We obtain the land surface deformation time series for Shanghai city during the period of 2016–2022 based on Sentinel-1A data. The maximum annual average ground subsidence rate is −37.8 mm/a, and the maximum cumulative deformation is −188.6 mm. The reliability of the experiment is verified by different technologies and data. Six typical settlement area points are mainly distributed in the BS area, MH region, and JS region of PD New Zone and present a linear distribution. Most subway lines occur at different degrees to settlements, among which the Lines 14, 15, 17, and 18 show the most severe settlement with the maximum velocity of −18.2 mm/a.
- (2)
- The related factors of land subsidence are discussed. The subsidence mainly occurs in engineering construction areas and there is a certain correlation with the subway development. With the construction area of Shanghai gradually developing from an urban area to a suburban area, the settlement also transforms from urban to suburban. Meanwhile, the correlation between land settlement and rainfall is relatively week.
- (3)
- According to the risk assessment results, the low-risk areas are mainly QP, SJ, FX, PD central, and other areas, while higher-risk areas are HP, YP, HK, and BS, covering areas of 65.47 square kilometers and accounting for 1.27% of the experimental area. The influencing factors of the risk assessment results in the Shanghai area are complex. The results of the risk assessment can provide basic data and a model basis for prevention, monitoring, and early warning of geological disasters in Shanghai and provide an optimization scheme for government decision making. However, we initially discuss risk assessment using ten influencing factors, and there may be more related factors to consider in the future.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Parameters | Sentinel-1A | TerraSAR-X |
---|---|---|
Wavelength | C | X |
Polarization mode | VV | VV |
Orbit direction | Ascending | Descending |
Incidence | 39.28° | 42.8° |
Image number | 94 | 9 |
Hazard Indicators | Data Sources | Vulnerability Indicators | Data Sources |
---|---|---|---|
Type of land use | Global land cover data official website (http://www.globallandcover.com/, accessed on 30 June 2023) | Population density | Shanghai Statistical Yearbook 2022 |
Water system density | GDP per unit area | ||
Vertical velocity | SBAS-InSAR results | Green space density | |
Road density | OpenStreet’s official website (https://www.openstreetmap.org/, accessed on 30 June 2023) | ||
Subsidence | Building density |
Type | 0–0.2 | 0.21–0.4 | 0.41–0.6 | 0.61–0.8 | 0.81–1 |
---|---|---|---|---|---|
Risk level | Lower | Low | Medium | High | Higher |
First-Grade Index | Second-Grade Index | Secondary Index Weight | Comprehensive Weight |
---|---|---|---|
Hazard | Land use type | 0.2381 | 0.1071 |
Drainage density | 0.1919 | 0.0854 | |
Subsidence | 0.3426 | 0.2188 | |
Vertical velocity | 0.2274 | 0.1424 | |
Vulnerability | Population density | 0.2203 | 0.0651 |
GDP per unit | 0.2059 | 0.0781 | |
Road density | 0.1273 | 0.092 | |
Building density | 0.1582 | 0.1302 | |
Green space density | 0.1301 | 0.0379 | |
Ground elevation | 0.1582 | 0.043 |
Land Type | 2000 | 2010 | 2020 | Change |
---|---|---|---|---|
Cultivated land | 3118.52 | 2456.50 | 2032.91 | ↓ |
Grassland | 1.67 | 6.17 | 69.24 | ↑ |
Forest land | 13.68 | 13.96 | 72.23 | ↑ |
Water body | 192.49 | 189.96 | 222.54 | ↑ |
Construction land | 1320.90 | 1981.31 | 2245.17 | ↑ |
Unused land | 0.81 | 0.18 | 5.99 | ↑ |
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Wang, X.; Yang, Y.; Xia, Y.; Chen, S.; She, Y. Integrating SAR and Geographic Information Data Revealing Land Subsidence and Geological Risks of Shanghai City. Appl. Sci. 2023, 13, 12091. https://doi.org/10.3390/app132112091
Wang X, Yang Y, Xia Y, Chen S, She Y. Integrating SAR and Geographic Information Data Revealing Land Subsidence and Geological Risks of Shanghai City. Applied Sciences. 2023; 13(21):12091. https://doi.org/10.3390/app132112091
Chicago/Turabian StyleWang, Xiaying, Yumei Yang, Yuanping Xia, Shuaiqiang Chen, and Yulin She. 2023. "Integrating SAR and Geographic Information Data Revealing Land Subsidence and Geological Risks of Shanghai City" Applied Sciences 13, no. 21: 12091. https://doi.org/10.3390/app132112091