Landslide Deformation Monitoring and Prediction with Multi-Source Remote Sensing

Dear Colleagues,

Advances in remote sensing observation and data analysis capabilities have significantly improved our ability to observe and characterise landslide deformation across a range of spatial and temporal scales. Individual remote sensing technologies have proven highly effective for specific aspects of landslide monitoring, providing valuable insights into deformation patterns, rates, and kinematics. However, differences in spatial coverage, temporal resolution, accuracy, and operational constraints mean that no single technique can consistently capture the full complexity of slope deformation processes required for robust process-based understanding, hazard assessment, monitoring, and prediction.

This Special Issue highlights multi-source remote sensing and state-of-the-art data fusion for landslide deformation monitoring and prediction. Close-range remote sensing techniques increasingly enable high-spatial- and high-temporal-resolution four-dimensional (3D + time) monitoring of deformation but are typically limited to relatively small areas and specific viewing geometries. UAV-based sensing can extend spatial coverage, particularly in inaccessible or hazardous terrain, while satellite-based techniques such as InSAR provide precise and systematic measurements of surface deformation over broader areas and longer time periods. These complementary capabilities highlight the potential of integrated, multi-source approaches. A key opportunity lies in developing and testing strategies that meaningfully integrate observations with differing spatial and temporal characteristics, uncertainties, and viewing geometries, enabling more robust interpretation of deformation processes.

This Special Issue welcomes contributions spanning methodological developments, multi-sensor monitoring strategies, and applied case studies of landslides and rock slopes. At a broad scale, submissions may present integrated, multi-scale monitoring strategies that combine satellite, airborne, and close-range remote sensing to characterise deformation across different spatial extents and timeframes. Contributions may address specific methodological topics such as co-registration and validation across platforms, uncertainty-aware data fusion, and the assimilation of satellite or UAV observations with in situ data. Applied case studies demonstrating how integrated, multi-source observations improve deformation interpretation, monitoring design, hazard assessment, or prediction under real-world conditions are particularly encouraged, alongside contributions with a strong methodological focus.

Dr. Ryan Kromer
Guest Editor

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