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<p>Landslides pose a persistent and catastrophic threat globally, necessitating advanced monitoring techniques for effective early warning and risk mitigation. Global Navigation Satellite System (GNSS) technology provides real-time, high-precision displacement data and is widely used in landslide monitoring. However, its sparse spatial sampling limits the ability to fully capture slope dynamics. To overcome this, the integration of GNSS with complementary sensing modalities has emerged as a critical research frontier. This review systematically examines the state-of-the-art in landslide displacement monitoring, focusing on the evolution from single-sensor GNSS applications to sophisticated multi-modal data integration methodologies. We first provide a comprehensive overview of various real-time GNSS positioning techniques (e.g., Real-Time Kinematic [RTK], Network Real-Time Kinematic [NRTK], and Precise Point Positioning-Real-Time Kinematic [PPP-RTK]) and critically compare their performance against other mainstream geodetic and geotechnical methods. Subsequently, we delve into four key integration paradigms: (1) The fusion of GNSS and accelerometers to capture the full spectrum of displacement from quasi-static creep to high-frequency motions; (2) The integration of GNSS and Interferometric Synthetic Aperture Radar (InSAR) to generate high-resolution, three-dimensional deformation fields with enhanced spatiotemporal coverage; (3) Data fusion of GNSS and other in-situ sensor, such as inclinometer to improve overall monitoring accurate; (4) The integration of surface and sub-surface monitoring data for more comprehensive description of landslide dynamics. Thereafter, the application of machine learning, evolving from data-driven predictive models to Physics-Informed Neural Networks (PINNs) are also reviewed, for intelligent displacement estimation and the interpretation of complex landslide trigger-response mechanisms. Finally, we discuss persistent challenges, including data quality, real-time processing, and system scalability, and propose future research directions centered on the development of landslide digital twins and intelligent, ubiquitous sensing networks. This review aims to provide a holistic reference for researchers and practitioners, charting a course toward more robust, intelligent, and predictive landslide monitoring systems.</p>

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GNSS and multi-modal data integration methods for landslide displacement monitoring: a review

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月14日

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  • Xuanyu Qu,
  • Wenkun Yu,
  • Xinrui Li,
  • Huineng Yan,
  • Wujiao Dai,
  • Xiaoli Ding

摘要

Landslides pose a persistent and catastrophic threat globally, necessitating advanced monitoring techniques for effective early warning and risk mitigation. Global Navigation Satellite System (GNSS) technology provides real-time, high-precision displacement data and is widely used in landslide monitoring. However, its sparse spatial sampling limits the ability to fully capture slope dynamics. To overcome this, the integration of GNSS with complementary sensing modalities has emerged as a critical research frontier. This review systematically examines the state-of-the-art in landslide displacement monitoring, focusing on the evolution from single-sensor GNSS applications to sophisticated multi-modal data integration methodologies. We first provide a comprehensive overview of various real-time GNSS positioning techniques (e.g., Real-Time Kinematic [RTK], Network Real-Time Kinematic [NRTK], and Precise Point Positioning-Real-Time Kinematic [PPP-RTK]) and critically compare their performance against other mainstream geodetic and geotechnical methods. Subsequently, we delve into four key integration paradigms: (1) The fusion of GNSS and accelerometers to capture the full spectrum of displacement from quasi-static creep to high-frequency motions; (2) The integration of GNSS and Interferometric Synthetic Aperture Radar (InSAR) to generate high-resolution, three-dimensional deformation fields with enhanced spatiotemporal coverage; (3) Data fusion of GNSS and other in-situ sensor, such as inclinometer to improve overall monitoring accurate; (4) The integration of surface and sub-surface monitoring data for more comprehensive description of landslide dynamics. Thereafter, the application of machine learning, evolving from data-driven predictive models to Physics-Informed Neural Networks (PINNs) are also reviewed, for intelligent displacement estimation and the interpretation of complex landslide trigger-response mechanisms. Finally, we discuss persistent challenges, including data quality, real-time processing, and system scalability, and propose future research directions centered on the development of landslide digital twins and intelligent, ubiquitous sensing networks. This review aims to provide a holistic reference for researchers and practitioners, charting a course toward more robust, intelligent, and predictive landslide monitoring systems.