Time-Series Remote Sensing for Geohazard Monitoring and Early Warning

Dear Colleagues,

The increasing frequency and intensity of geohazards—such as landslides, earthquakes, volcanic activity, land subsidence, sinkholes, and glacier movements—demand robust monitoring and early warning systems. Advances in remote sensing now make it possible to observe Earth’s surface dynamics at unprecedented spatial and temporal scales. Time-series remote sensing, integrating continuous observations from SAR, optical, LiDAR, UAV, and GNSS data, enables the detection of subtle surface changes and precursory signals essential for understanding and forecasting geohazard processes.

This Special Issue aims to showcase innovative research and practical applications that advance the use of time-series remote sensing for geohazard monitoring and early warning. We encourage studies that develop or apply new algorithms, combine multi-sensor datasets, explore data fusion and machine learning, or demonstrate operational workflows for hazard detection and decision support. Both methodological developments and case studies from diverse geographic and hazard contexts are welcome.

Topics of interest include (but are not limited to):

• Time-series analysis and deformation modeling using SAR, optical, and LiDAR data;
• Multi-sensor and multi-temporal data fusion for geohazard assessment;
• Real-time and near-real-time geohazard monitoring and alerting systems;
• Detection of precursory signals and change-point analysis;
• Machine learning and AI for geohazard pattern recognition and forecasting;
• Integration of remote sensing data with in situ measurements and physical models;
• Uncertainty assessment and validation of time-series deformation results;
• Case studies on any specific type of ground failure, such as landslides, sinkholes, and liquefaction.

This Special Issue seeks to build a bridge between scientific innovation and operational implementation, advancing the role of remote sensing in reducing geohazard risks and enhancing early warning capabilities worldwide.

Dr. Weiwei Zhan
Dr. Yunus Ali Pulpadan
Prof. Dr. Weile Li
Dr. Adel Asadi
Guest Editors

Manuscript Submission Information

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