Cross-scale UAV and satellite observations of geomorphological change

Geomatics and Earth observation are increasingly central to monitoring rapid geomorphological change and active geohazards. UAV platforms provide highly detailed observations of surface structure, deformation, temperature, and terrain morphology, while satellite missions offer repeated regional coverage over extended periods. Recent advances in optical imaging, thermal sensing, LiDAR, Structure-from-Motion photogrammetry, and machine learning have improved the detection and interpretation of environmental change. However, differences in spatial and temporal resolution, sensor characteristics, acquisition conditions, and processing workflows continue to hinder the integration of localized UAV observations with broader satellite datasets. Inconsistent georeferencing, sensor-specific noise, and poorly constrained change-detection thresholds can introduce uncertainty into landscape models and hazard assessments. Although multi-sensor approaches are increasingly being adopted, important gaps remain in cross-scale calibration, uncertainty quantification, data assimilation, and the development of transferable methods for heterogeneous environments. Further investigation is therefore needed to establish reliable frameworks for combining complementary observations across platforms and scales.This Research Topic aims to advance methods for integrating UAV-based optical, thermal, and LiDAR observations with multi-temporal satellite imagery for the monitoring and analysis of dynamic Earth systems. It seeks to identify how multi-sensor fusion, precise georeferencing, noise characterization, and cross-scale calibration can improve the accuracy, consistency, and reproducibility of high-resolution spatial datasets. Contributions may address the development and validation of change-detection thresholds, automated feature extraction, data assimilation, and predictive models for geomorphological processes and geohazards. The Research Topic will also examine whether combining complementary sensor measurements can improve the detection of subtle deformation, support near-real-time monitoring, and strengthen regional hazard forecasting.To gather further insights into cross-scale observation, data integration, and predictive monitoring of geomorphological change and active geohazards, we welcome articles addressing, but not limited to, the following themes:• Fusion of UAV-based optical, thermal, and LiDAR data with multi-temporal satellite imagery• Structure-from-Motion photogrammetry, geometric accuracy assessment, and precise georeferencing• Cross-scale calibration, co-registration, uncertainty propagation, and validation strategies• Noise reduction and robust change-detection thresholds for heterogeneous sensor datasets• Data assimilation, downscaling, and multi-source integration for improved spatial and temporal predictions• Machine learning and deep learning for feature extraction, classification, and pattern recognition• Predictive modelling of landslides, erosion, deformation, and other active geohazards• Monitoring post-wildfire recovery, coastal erosion, deltaic and dune dynamics, and related disturbances• Edge computing and UAV-based sensor networks for real-time monitoring and rapid hazard response• Landscape evolution modelling using high-resolution, multi-temporal spatial datasetsAppendix — Article types: Original Research, Review, Methods, and Technology and Code articles. Topic editors: Semih Sami Akay, Orkan Ozcan, Sheng Nie Source: https://www.frontiersin.org/research-topics/84147/cross-scale-uav-and-satellite-observations-of-geomorphological-change