GNSS and multi-modal data integration methods for landslide displacement monitoring: a review
发布时间：2026-07-18
点击次数：
DOI码：10.1007/s44517-026-00006-w
发表刊物：Geohazards & Remediation
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.
合写作者：Xinrui Li, Huineng Yan, Wujiao Dai, Xiaoli Ding
第一作者：Xuanyu Qu
论文类型：期刊论文
通讯作者：Wenkun Yu
学科门类：工学
一级学科：测绘科学与技术
文献类型：J
卷号：1
是否译文：否
发表时间：2026-07-14
收录刊物：SCI
附件：