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Remote Sensing for High-Mountain Hazards
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Remote Sensing for High-Mountain Hazards

A Special Issue of Remote Sensing (ISSN 2072-4292) belonging to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: 31 December 2026 | Viewed by 1685

Editors

Dr. Yao Li

E-Mail Website
Guest Editor
Institute for Disaster Management and Reconstruction, Sichuan University—The Hong Kong Polytechnic University, Chengdu 610200, China
Interests: remote sening; imaging processing; geo-hazards mapping; cryospheric hazards; ice-rock avalanches; glacial lake outburst floods (GLOFs); machine learning in geoscience; permafrost degradation; hazard risk assessment
Dr. Jiansheng Hao

E-Mail Website
Guest Editor
Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
Interests: remote sensing; snow avalanche; climate change; early Warning system; snow monitoring; snow physics; avalanche movement
Dr. Chenchen Qiu

E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, University of Strathclyde, Glasgow G1 1XQ, UK
Interests: natural hazard; debris flow; landslide; remote sensing; machine learning; ground motion

Special Issue Information

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-anonymized 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.

Keywords

  • 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

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

33 pages, 28556 KB  
Article
A Coupled Spatiotemporal Stability and Multi-Source Physical Constraint Method for Glacial Lake Extraction: A Case Study in the Central Himalayas
by Huilan Ding, Chengsheng Yang, Ziqian Wang, Zufeng Li, Zewei Liu, Yi Yu and Xiaoqiang Cheng
Remote Sens. 2026, 18(14), 2370; https://doi.org/10.3390/rs18142370 - 16 Jul 2026
Viewed by 342
Abstract
The increasing frequency and magnitude of glacial lake outburst floods pose a severe threat to the safety of downstream communities. However, Interference from glacier shadows and mountain shading reduces the accuracy of remote sensing-based glacial lake detection. We propose a two-level nested framework [...] Read more.
The increasing frequency and magnitude of glacial lake outburst floods pose a severe threat to the safety of downstream communities. However, Interference from glacier shadows and mountain shading reduces the accuracy of remote sensing-based glacial lake detection. We propose a two-level nested framework that integrates global spatiotemporal aggregation and local adaptive enhancement. At the global level, the 80th temporal percentile (P80) of multi-temporal AWEI imagery is used to construct a stable water-background composite and suppress short-term seasonal noise. Multi-source physical constraints, including the Normalized Difference Snow Index (NDSI), a DEM-derived slope constraint (slope < 10°), and red-band reflectance thresholds (0.3 < BandRed < 1.6), are applied to suppress interference from land, terrain shadows, snow, and glaciers. At the local scale, an adaptive dynamic segmentation strategy is proposed by establishing an equal-area buffer for each individual lake, where the temporal occurrence frequency of MNDWI is computed to build a stable water probability composite, and the Otsu algorithm is applied to independently derive lake-specific optimal thresholds. Using Landsat imagery and meteorological data from 1990 to 2025, we quantified the spatiotemporal dynamics of typical glacial lakes in the central Himalayas, and explored the driving mechanisms of climate factors on lake area changes. Over the past 35 years, the number and area of lakes have exhibited a pronounced expansion trend under a climatic regime characterized by rising temperatures, increasing precipitation, and decreasing relative humidity. During 1990–2020, lake area variations were primarily governed by strong interactions between temperature and wind speed. Summer variability exerted a more pronounced impact than winter variability. The proposed framework provides an effective approach for glacial lake extraction in the study area and may provide useful technical support for long-term monitoring of alpine lakes. Full article
(This article belongs to the Special Issue Remote Sensing for High-Mountain Hazards)
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38 pages, 14033 KB  
Article
Dynamic Assessment of Near-Surface Icing Risk in High-Mountain Regions Using Multi-Source Remote Sensing and an Energy–Moisture Coupling Model
by Yanrun Ren, Jie Liu, Yaonan Zhang, Jingqi Liu, Yufang Min and Minghao Ai
Remote Sens. 2026, 18(12), 2026; https://doi.org/10.3390/rs18122026 - 17 Jun 2026
Viewed by 524
Abstract
In summary, near-surface icing risk in complex alpine terrain is jointly controlled by freezing conditions, moisture supply, freeze–thaw transitions, and topographic energy processes. Traditional approaches relying on sparse station data or single temperature thresholds fail to capture spatial heterogeneity, and frequent cloud cover [...] Read more.
In summary, near-surface icing risk in complex alpine terrain is jointly controlled by freezing conditions, moisture supply, freeze–thaw transitions, and topographic energy processes. Traditional approaches relying on sparse station data or single temperature thresholds fail to capture spatial heterogeneity, and frequent cloud cover together with topographic errors severely limit the application of thermal infrared remote sensing. Taking the area along the Duku Highway in the Tianshan Mountains as the study region, a daily icing risk assessment framework at 250 m resolution was constructed using multi-source remote sensing, ERA5-Land reanalysis data, topographic correction, and an energy–moisture dual-constrained model. A diurnal temperature cycle model, the CAP index, and physics-constrained machine learning were integrated to reconstruct the daily minimum land surface temperature (Ts,min) at 250 m resolution under all weather conditions. A probabilistic two-tier risk assessment model was then established by incorporating moisture, topography, and freeze–thaw transitions. The results show that high-risk zones occur primarily in valleys and topographically constrained corridors rather than the coldest elevations. Validation against Landsat LST (r = 0.886) and the Bayanbulak station (bias −0.76 °C, RMSE 5.62 °C, r = 0.91) confirms spatial and seasonal accuracy. Sensitivity and Monte Carlo analyses indicate the RiskScore is mainly controlled by the low-temperature weight, while upstream parameters are less influential. The framework is best applied as a screening and early-warning product to identify sub-kilometer potential icing corridors, complementing point measurements and short-range forecasts. Full article
(This article belongs to the Special Issue Remote Sensing for High-Mountain Hazards)
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