一种考虑地形影响的GNSS-IR冻土冻融形变监测方法

A New GNSS-IR Method to Monitor Permafrost Freeze-Thaw Deformation Considering the Terrain Effect

  • 摘要: 多年冻土区地表高程因活动层每年的融化和冻结而发生季节性下沉和隆起,对工程建筑的安全、生态环境的平衡以及全球气候变化等方面都具有重要的影响。利用全球导航卫星系统干涉反射(global navigation satellite system-interferometric reflectometry,GNSS-IR)遥感对冻土冻融形变进行监测是一种新型技术手段。针对格洛纳斯(GLONASS)、伽利略(Galileo)系统等长重访周期卫星因每日轨迹不重复造成的地形影响,提出了一种考虑地形的冻土冻融形变监测方法,通过引入反射面倾斜角消除地形变化影响,反演得到更接近实际情况的季节性冻融形变。利用位于美国阿拉斯加北部的SG27站点2018年、2019年无雪日GNSS信噪比数据进行了实验,并与现有研究中的反演方法所得结果进行了对比分析,验证了该方法在冻土冻融形变监测中的有效性。实验结果表明,相比于不考虑地形影响的方法,GLONASS和Galileo所得地表高程变化具有更小的离散性以及更小的不确定性,与复合模型拟合的一致性和决定系数R2都有了一定提升,标准差总平均分别减小约28.9%和36.9%,R2总平均分别提高约0.23和0.24,且不可重复轨道每日数据利用率总平均增幅分别约为19.6%和22.8%。研究结果为监测冻土冻融形变提供了有价值的参考,同时拓展了多GNSS系统的GNSS-IR冻土冻融监测应用。

     

    Abstract:
    Objectives The seasonal subsidence and uplift of the surface elevation in permafrost area occur due to the annual melting and freezing of active layer, which has an important impact on the safety of engineering construction, the balance of ecological environment, and global climate change. Using global navigation satellite system-interferometric reflectometry (GNSS-IR) to monitor frozen soil deformation is a new technique. Aiming at the terrain influence caused by the non-daily-repeatable orbits of GLONASS and Galileo satellites, this paper proposes a new method to calculate the freeze-thaw deformation of permafrost using these GNSS data.
    Methods By introducing the inclination angle of reflector surface, the effects of terrain changes are eliminated, and the seasonal freeze-thaw deformation closer to the actual situation is obtained. The GNSS signal-to-noise ratio data of 2018 and 2019 snow-free days at SG27 site in northern Alaska are used for experiments and compared with the results obtained by existing retrieval methods to verify the effectiveness of this method in monitoring permafrost deformation.
    Results The results show that compared with the methods without considering the terrain effect, the surface elevation changes obtained by GLONASS and Galileo have smaller discreteness and smaller uncertainty, and the fitting consistency and determination coefficient R2 of the composite model have been improved. The total average standard deviations are reduced by about 28.9 % and 36.9 %, and the total average R2 are increased by about 0.23 and 0.24, respectively. The total average increases of daily data utilization rate of non-repetitive orbits are about 19.6 % and 22.8 %.
    Conclusions This study provides a valuable reference for monitoring permafrost freeze-thaw deformation, and expands the application of GNSS-IR in GNSS system for permafrost freeze-thaw monitoring.

     

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