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Ground Deformation Analysis Using InSAR and Backpropagation Prediction with Influencing Factors in Erhai Region, China

Author

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  • Yuyi Wang

    (The State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China)

  • Yahui Guo

    (The State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China
    Beijing Key Laboratory of Urban Hydrological Cycle and Sponge City Technology, College of Water Sciences, Beijing Normal University, Beijing 100875, China
    College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China)

  • Shunqiang Hu

    (College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China)

  • Yong Li

    (Sinopec Research Institute of Petroleum Engineering, Beijing 100101, China)

  • Jingzhe Wang

    (Key Laboratory of Smart City and Environment Modelling of Higher Education Institute, College of Resources and Environment Science, Xinjiang University, Urumqi 800046, China
    Key Laboratory of Oasis Ecology, Xinjiang University, Urumqi 830046, China)

  • Xuesong Liu

    (Department of Computer Science and Technology, Tstinghua University, Beijing 100084, China)

  • Le Wang

    (Beijing New Media Technical College, Beijing 102200, China)

Abstract

The long continuity of Interferometric Synthetic Aperture Radar (InSAR) can provide high space and resolution data for ground deformation investigations. The ground deformation in this paper appeared in the city’s development, although it is close to the Erhai region, which is different from a water-deficient city. Therefore, the analysis and prediction of ground deformation using a new method is required. In this study, Sentinel-1 Synthetic Aperture Radar (SAR) images from 2015 to 2018 were used to study the characteristics of ground deformation in the Erhai region using the Small Baseline Subset Interferometric SAR (SBAS-InSAR) technique. The results were cross-validated using ascending and descending direction images to ensure the accuracy. In addition, the results showed that there was little ground deformation in the northern part of the Erhai region, while there was obvious ground deformation in the southern part. Four influencing factors—including the building area, water level, cumulative precipitation, and cumulative temperature of the southern Erhai region—were used together to predict the cumulative ground deformation using back-propagation (BP). The R of all the involved data was 0.966, and the root mean square errors (RMSEs) between the simulated values using BP and the true measured values were 3.063, 1.003, and 1.119, respectively. The results showed that BP has great potential in predicting the change tendency of ground deformation with high precision. The main reason for ground deformation is the continuous increase of building area; the water level followed. The cumulative precipitation and cumulative temperature are the reasons for the seasonal ground deformation. Some countermeasures and suggestions are given to face the challenge of serious ground deformation.

Suggested Citation

  • Yuyi Wang & Yahui Guo & Shunqiang Hu & Yong Li & Jingzhe Wang & Xuesong Liu & Le Wang, 2019. "Ground Deformation Analysis Using InSAR and Backpropagation Prediction with Influencing Factors in Erhai Region, China," Sustainability, MDPI, vol. 11(10), pages 1-23, May.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:10:p:2853-:d:232569
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    References listed on IDEAS

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    Cited by:

    1. Hyung-Sup Jung & Saro Lee & Biswajeet Pradhan, 2020. "Sustainable Applications of Remote Sensing and Geospatial Information Systems to Earth Observations," Sustainability, MDPI, vol. 12(6), pages 1-6, March.
    2. Shubham Awasthi & Divyesh Varade & Sutapa Bhattacharjee & Hemant Singh & Sana Shahab & Kamal Jain, 2022. "Assessment of Land Deformation and the Associated Causes along a Rapidly Developing Himalayan Foothill Region Using Multi-Temporal Sentinel-1 SAR Datasets," Land, MDPI, vol. 11(11), pages 1-21, November.
    3. Fengyan Wang & Qing Ding & Lei Zhang & Mingchang Wang & Qing Wang, 2019. "Analysis of Land Surface Deformation in Chagan Lake Region Using TCPInSAR," Sustainability, MDPI, vol. 11(18), pages 1-20, September.

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