Remote Sensing for High-Mountain Hazards

Dear Colleagues,

High-mountain regions are among the most climate-sensitive environments on Earth. Under ongoing atmospheric warming, glaciers are retreating rapidly, permafrost is degrading, snow cover is changing, and glacial lakes are expanding in many mountain ranges worldwide. These changes are increasing the frequency, intensity, and complexity of high-mountain hazards, including ice-rock avalanches, snow avalanches, glacial lake outburst floods (GLOFs), debris flows, landslides, and other cascading processes. Such hazards often develop rapidly in remote terrain, yet can propagate far downstream, causing severe damage to infrastructure, settlements, hydropower facilities, transportation corridors, and fragile mountain ecosystems.

Recent catastrophic events in the Himalaya, Tibetan Plateau, Alps, Andes, and other high-mountain regions have demonstrated that climate-driven mountain hazards are no longer isolated local phenomena, but a growing global concern. However, understanding, monitoring, and forecasting these hazards remain challenging because of the complex topography, harsh environmental conditions, limited field accessibility, and the strong coupling among cryospheric, hydrological, geomorphological, and geological processes.

Remote sensing provides an effective means to overcome these limitations by enabling large-scale, repeated, and non-invasive observations of high-mountain environments. Satellite, airborne, UAV, and ground-based remote sensing techniques offer valuable information on surface deformation, glacier dynamics, snow conditions, lake expansion, slope instability, and post-event impacts. Meanwhile, recent advances in artificial intelligence, machine learning, and multi-source Earth observation have created new opportunities for automated hazard detection, spatiotemporal pattern recognition, dynamic monitoring, and early warning in high-mountain regions.

The primary aim of this Special Issue is to showcase the latest advances in the use of remote sensing technologies for the detection, monitoring, and warning of high-mountain hazards. Topics may range from reconstructing the evolutionary history of high-mountain hazards in hotspot regions, analyzing glacier dynamics prior to hazard events—such as changes in glacier velocity, mass balance fluctuations, and glacial lake expansion. Overall, this Special Issue aims to highlight how multi-source remote sensing data can offer new insights into the formation and evolution of high-mountain hazards, their developmental conditions, and future trends. Through the integration of diverse remote sensing approaches, we seek to improve the scientific understanding and forecasting of high-mountain disasters in the context of ongoing climate change.

We welcome submissions that explore a wide range of topics, including but not limited to:

• Remote sensing detection and monitoring of high-mountain hazards;
• Glacier instability, ice-rock avalanches, and glacial collapse processes;
• Monitoring and early warning of glacial lake outburst floods (GLOFs);
• Snow avalanche detection and snow hazard assessment;
• Permafrost degradation and mountain slope instability;
• Multi-source remote sensing data fusion for hazard monitoring;
• InSAR, LiDAR, UAV, and ground-based observations of mountain hazards;
• AI for hazard identification and prediction;
• Deep learning for image classification, object detection, and semantic segmentation of mountain hazards;
• Time-series analysis and change detection using remote sensing and AI;
• Foundation models, transfer learning, and few-shot learning in hazard mapping;
• Hybrid modeling combining physical mechanisms and data-driven algorithms;
• Real-time monitoring, threshold analysis, and intelligent early warning systems;
• Climate change impacts on high-mountain hazard evolution.

Dr. Yao Li
Dr. Jiansheng Hao
Dr. Chenchen Qiu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• high-mountain hazards
• remote sensing
• artificial intelligence
• machine learning
• deep learning
• early warning
• ice-rock avalanche
• glacial lake outburst floods
• multi-source data fusion
• risk assessment