Dear Colleagues,

This Special Issue of Geosciences aims to gather high-quality original research articles, reviews, and technical notes on the use of remote sensing for geomorphological hazard monitoring.

Every year, geohazards severely affect humans, in terms of deaths and economic losses. The World Bank estimates that disasters cost the global economy USD 520 billion annually. Additionally, the Sendai Framework for Disaster Risk Reduction 2015-2030, the global plan to reduce disaster losses, focuses primarily on prevention, aiming to recognize disaster risks before they trigger events that lead to the loss of life, homes, and livelihoods. In fact, their impact can be reduced with a clearer understanding of both where and when potential geohazards are likely to occur.

In this context, there is no doubt about the value of remote sensing monitoring which has a crucial role in quantifying geohazards and their associated risks. Such an approach shows great potential for providing valuable information at various spatial and temporal scales for both knowledge and control purposes. The ongoing advances in remote sensing technologies and analysis, in terms of sensors, platforms, and processing algorithms, are strengthening the role of monitoring in prevention activities.

With this Special Issue, we propose state-of-the-art research that specifically addresses multiple aspects on the use of remote sensing for geomorphological hazard monitoring. The aim is to collect innovative methodologies, expertise, and capabilities to detect, assess, map, monitor, and model geomorphological hazards such as mass movements, floods, coastal processes, etc. We are interested in studies that showcase the broad range of applications of optical and multi/hyper-spectral data, SAR, thermal imaging, aerial photogrammetry, LiDAR, UAV surveys, structure-from-motion, etc.

Review contributions are welcomed, as well as papers describing novel sensors and promising applications for hazard assessments and monitoring, and for the implementation of early warning systems based on remote sensing data (either from terrestrial, airborne, or satellite sensors).

Dr. Saverio Romeo
Dr. Francesco Mugnai
Dr. Mauro Bonasera
Dr. Roberta Bonì
Dr. Ciro Cerrone
Guest Editors

Manuscript Submission Information

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• engineering geology
• remote sensing
• geomorphology
• geomatics
• natural hazards
• monitoring
• InSAR
• LiDAR
• imaging
• photogrammetry
• digital image iorrelation
• hazard assessment and monitoring
• early warning system