Document: flood kg
Section: 5 RELATED WORK
Type: text

We summarize the related work into two aspects: KG-based systems and flood risk assessment-based systems.

Knowledge graph-based systems. Traditional KG-based systems encompass Freebase [1], DBpedia [20], WordNet [28], Wikidata [44], YAGO [41], and others. These systems primarily concentrate on general or encyclopedic knowledge, which is constructed from vast amounts of unstructured text data and structured semantic network data, such as Wikipedia. For instance, the first three systems gather structured knowledge from user contributions on Wikipedia, WordNet provides formal linguistic knowledge about words, and YAGO integrates factual information extracted from Wikipedia using rule-based and heuristic methods, along with WordNet.

Flood risk assessment-based systems. With the development of flood risk assessment in recent years [9, 33], the latest research has designed systems for terrain data management [5, 18, 40]. Wang [46] leverages multiple information sources to determine the parameters of the flood model. They also investigate the influence of different approaches to handling terrain datasets on the outcomes of flood modeling. Notably, their study emphasizes the significance of capturing the micro-features of cities to improve the accuracy of modeling results. Lyu [25] employs the FAHP-FCA method to incorporate various factors, including regional and subway longitudinal subsidence, in order to assess the flood risk of the Shanghai subway system within a subsidence environment. Rahadianto [35] uses analytical hierarchy process to help the system to assess how much impact and damage that will be hit the risky area and give the recommendation to government and people how to increase the preparedness so it can reduce the damage from flood. Li [22] proposes a hydraulic model and flood calculation program, demonstrating the feasibility of the proposed method and identifying areas with inadequate flood control capacity in the Xiushui River.


Document: flood kg
Section: 6 CONCLUSION
Type: text

In this paper, we propose the UrbanFloodKG system, a KG-based urban flood system. We propose a solution to construct KG from urban flood data with different structures and patterns and implements multi-modal data fusion. Based on the constructed UrbanFloodKG, the system seamlessly combines knowledge representation models and graph neural network models to offer comprehensive relevant operations. Finally, we classify the practical application problems of flood risk assessment into link prediction and node classification tasks and evaluate the effectiveness and feasibility of the models using multiple models, providing a new perspective for flood risk assessment


Document: flood kg
Section: 7 ACKNOWLEDGEMENTS
Type: text

The paper is supported by the Research on Key Technologies for Improving Flood Control Safety System of Nansha District, Guangzhou (823005916); the Jiangsu Province Water Conservancy Science and Technology Project (2022003); the Major Science and Technology Project of the Ministry of Water Resources (SKS-2022139).


Document: geosciences-15-00211-v2
Type: text

Abstract: Glacial Lake Outburst Floods (GLOFs) have emerged as a critical threat to highmountain communities and ecosystems, driven by accelerated glacier retreat and lake expansion under climate change. This review synthesizes advancements in remote sensing technologies and methodologies for GLOF monitoring, risk assessment, and mitigation. Through a Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA)guided systematic literature review and bibliometric analysis of studies from 2010 to 2025, we evaluate the transformative role of remote sensing in overcoming traditional fieldbased limitations. Central to this review is the exploration of multi-sensor data fusion for high-resolution lake dynamics mapping, machine learning algorithms for predictive risk modelling, and hydrodynamic simulations for flood propagation analysis. This review underscores the importance of these technologies in improving GLOF risk assessments and supporting early warning systems, which are crucial for safeguarding vulnerable high-mountain communities. It addresses existing challenges, such as data integration and model calibration, and advocates for collaborative efforts between scientists, policymakers, and local stakeholders to translate technological advancements into effective mitigation strategies, ensuring the sustainability of these at-risk regions.

**Keywords:** GLOFs; monitoring; risk assessment; remote sensing; climate change


Document: geosciences-15-00211-v2
Type: figure
Figure

Image /page/0/Picture/10 description: An icon with a white checkmark inside a yellow circle is positioned to the left of the text 'check for updates'. The words 'check for' are in a standard black font, while 'updates' is in a bold black font, all set against a white background.


Document: geosciences-15-00211-v2
Type: text

Academic Editor: Sabina Porfido

Received: 1 April 2025 Revised: 27 May 2025 Accepted: 1 June 2025 Published: 5 June 2025

Citation: Nurakynov, S.; Sydyk, N.; Baygurin, Z.; Balakay, L. Advancements in Remote Sensing for Monitoring and Risk Assessment of Glacial Lake Outburst Floods. Geosciences 2025, 15, 211. https:// doi.org/10.3390/geosciences15060211

Copyright: © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).


Document: 1. Introduction
Section: 1. Introduction
Type: text

GLOFs have emerged as one of the most critical natural hazards in high mountain regions worldwide. These catastrophic events, characterized by the sudden release of water from glacial lakes, pose a severe threat to downstream communities, infrastructure, and ecosystems [1]. Driven by accelerated glacier retreat and the rapid expansion of glacial lakes under changing climatic conditions, GLOFs have become increasingly frequent and destructive [2,3]. The growing number, size, and volume of glacial lakes, coupled with the instability of their natural dams, have heightened the risk of dam failure, leading to devastating floods with far-reaching impacts [4]. GLOFs have been documented for centuries, yet their frequency and magnitude have increased in recent decades as a direct consequence of accelerating glacier retreat driven by climate change [5].

The significance of GLOFs lies in their potential to cause widespread devastation. Downstream ecosystems are particularly vulnerable to the sudden influx of water and sediment, which can alter aquatic habitats, disrupt riparian vegetation, and change water chemistry [6]. For human communities, the consequences of GLOFs can be dire. Floodwaters can destroy roads, bridges, agricultural fields, and hydroelectric infrastructure, leading to substantial economic losses. In some cases, entire villages have been swept

away, resulting in significant loss of life [7]. Historical records and recent assessments indicate that GLOFs have caused substantial damage in regions such as High Mountain Asia (HMA), the Andes, and the European Alps [8–10].

The socio-economic impacts of GLOFs are particularly pronounced in densely populated regions like HMA, where millions of people live in close proximity to glacial lakes. Recent events, such as the 2023 South Lhonak GLOF in Sikkim, India, and the 2016 Gongbatongsha GLOF in the Tibetan Himalayas, underscore these risks [11,12]. The South Lhonak outburst destroyed critical infrastructure, including roads and hydropower projects, displacing a large number of people and causing great economic losses, while the Gongbatongsha flood inundated villages and agricultural land, disrupting livelihoods for thousands. Even modest flood events can have dramatic consequences in these areas, where infrastructure is often inadequate to withstand the force of a GLOF [13]. Moreover, the long-term impacts of GLOFs on water resources, agriculture, and energy production can exacerbate existing vulnerabilities, particularly in developing countries where adaptive capacity is limited [14]. As the number and size of glacial lakes continue to grow under the influence of climate change, the potential hazard posed by GLOFs is expected to increase, making them a critical subject of study for disaster risk reduction and management [15].

Climate change is the primary driver behind the dramatic changes observed in glacial environments over recent decades [16]. Global warming has led to increased glacier melting, resulting in the retreat of glacier fronts and the consequent formation and expansion of glacial lakes [17]. The sensitivity of glaciers to temperature variations means that even small increases in average temperatures can lead to substantial ice loss [18]. As glaciers retreat, the over-deepened bedrock is exposed, creating ideal conditions for the accumulation of meltwater in the form of glacial lakes [2].


Document: 1. Introduction
Section: 1. Introduction
Type: text

a critical subject of study for disaster risk reduction and management [ 15 ] . Climate change is the primary driver behind the dramatic changes observed in glacial environments over recent decades [ 16 ] . Global warming has led to increased glacier melting , resulting in the retreat of glacier fronts and the consequent formation and expansion of glacial lakes [ 17 ] . The sensitivity of glaciers to temperature variations means that even small increases in average temperatures can lead to substantial ice loss [ 18 ] . As glaciers retreat , the over - deepened bedrock is exposed , creating ideal conditions for the accumulation of meltwater in the form of glacial lakes [ 2 ] .

This dynamic has profound implications for GLOF hazards. With more glacial lakes forming and existing lakes expanding in size, the potential for dam failure increases significantly. Moreover, the feedback mechanisms associated with lake expansion—such as reduced albedo and enhanced meltwater production—can further accelerate glacier retreat [18,19]. These changes are not uniform across regions; some areas, such as High Mountain Asia (HMA) and the Karakoram, exhibit unique behaviors. In some parts of the Karakoram, for instance, certain glaciers have even shown a temporary mass gain due to complex climatic and topographic interactions [1,18]. However, the overall trend is one of increasing instability and heightened flood risk.

The interplay between climate change and glacial dynamics is a critical component of GLOF research. Not only does it influence the physical parameters of glacial lakes—such as their surface area and volume—but it also affects the frequency and intensity of extreme weather events that can trigger dam failure [14,20]. Heavy rainfall, rapid snowmelt, and changes in precipitation patterns all contribute to the stress on glacial dams, making the study of climate change impacts an essential part of understanding GLOF hazards [21]. Furthermore, the cascading effects of climate change—ranging from altered hydrological regimes to shifts in ecosystem composition—add additional layers of complexity to the risk posed by GLOFs [22,23].

Historically, the identification and monitoring of glacial lakes and GLOFs relied heavily on field-based investigations. Early studies utilized GPS, laser rangefinders, theodolites, and total stations for direct measurements of glacial lake characteristics [20,24]. Field surveys provide detailed insights into the geomorphology, lake bathymetry, and the hydrodynamic behavior of these lakes [25,26]. These in situ methods are essential for understanding the processes leading to lake formation and dam failure, as well as for calibrating empirical models that estimate water volumes and potential flood discharges [14,27]. However, field-based assessments are inherently challenging in high mountain environments. The

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remote and often inaccessible terrain, coupled with severe weather conditions, limits the frequency and spatial coverage of ground surveys [6,18]. Moreover, the dynamic nature of glacial environments means that conditions can change rapidly, rendering periodic field campaigns insufficient to capture the continuous evolution of glacial lakes [28]. These limitations underscore the need for methodologies that can offer extensive temporal and spatial monitoring, particularly in regions where the risk of GLOFs is high.


Document: 1. Introduction
Section: 1. Introduction
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and potential flood discharges [ 14 , 27 ] . However , field - based assessments are inherently challenging in high mountain environments . The Geosciences 2025 , 15 , 211 3 of 36 remote and often inaccessible terrain , coupled with severe weather conditions , limits the frequency and spatial coverage of ground surveys [ 6 , 18 ] . Moreover , the dynamic nature of glacial environments means that conditions can change rapidly , rendering periodic field campaigns insufficient to capture the continuous evolution of glacial lakes [ 28 ] . These limitations underscore the need for methodologies that can offer extensive temporal and spatial monitoring , particularly in regions where the risk of GLOFs is high .

In recent decades, remote sensing has emerged as a transformative tool for the detection, monitoring, and assessment of glacial lakes and GLOFs [14,28]. Unlike traditional field-based methods, remote sensing offers a synoptic and continuous view of vast and remote areas [29,30]. Satellite platforms such as Landsat, Sentinel-1, Sentinel-2, and MODIS, along with high-resolution commercial satellites, have revolutionized our ability to monitor glacial lakes on a global scale [31,32]. These technologies provide multi-temporal and multi-spectral data, enabling researchers to track changes in lake size, volume, and surrounding glacier dynamics with unprecedented accuracy. Various studies have demonstrated accuracy in satellite-derived lake boundary delineation and volume estimation when cross-verified with field measurements and previously available data, underscoring the reliability of remote sensing for GLOF risk assessment [9,33]. Remote sensing allows for continuous monitoring, even in regions that are difficult or dangerous to access [32]. This capability is particularly critical for GLOF risk assessment, as it enables the detection of early warning signs, such as sudden changes in lake area or dam stability, which can precede an outburst event [34,35].

Moreover, the integration of optical and radar data, along with advancements in machine learning and deep learning, has further enhanced the precision and reliability of remote sensing applications [19]. These innovations have not only improved the accuracy of glacial lake mapping but have also paved the way for automated detection systems and real-time monitoring. By combining remote sensing data with field observations and hydrological models, researchers can now estimate key parameters such as lake volume, potential peak discharge, and dam failure dynamics, which are essential for developing effective early warning systems [34]. The growing availability of remote sensing data and the rapid evolution of analytical techniques have made it possible to assess GLOF risks at both regional and global scales. This capability is particularly important in the context of climate change, which is driving the rapid expansion of glacial lakes and increasing the likelihood of GLOFs. As such, remote sensing has become an indispensable tool for disaster risk reduction, providing critical insights that inform mitigation strategies and safeguard vulnerable communities [35].


Document: 1. Introduction
Section: 1. Introduction
Type: text

researchers can now estimate key parameters such as lake volume , potential peak discharge , and dam failure dynamics , which are essential for developing effective early warning systems [ 34 ] . The growing availability of remote sensing data and the rapid evolution of analytical techniques have made it possible to assess GLOF risks at both regional and global scales . This capability is particularly important in the context of climate change , which is driving the rapid expansion of glacial lakes and increasing the likelihood of GLOFs . As such , remote sensing has become an indispensable tool for disaster risk reduction , providing critical insights that inform mitigation strategies and safeguard vulnerable communities [ 35 ] .

Considering the increasing threat posed by GLOFs, there is an urgent need to consolidate current knowledge on glacial lake monitoring, risk assessment, and mitigation strategies. Traditional methods of field observation, while invaluable, are no longer sufficient to capture the rapid changes occurring in glacial environments [29,36]. The integration of advanced remote sensing techniques has significantly enhanced our monitoring capabilities, enabling more precise, timely, and comprehensive assessments of glacial lakes and their associated hazards [37]. This review paper aims to provide a comprehensive synthesis of the state-of-the-art remote sensing techniques for monitoring and managing glacial lakes, with a specific focus on GLOF hazards. As the scientific community continues to unravel the complex interplay between glacier dynamics, climate change, and flood hazards, this review will serve as a comprehensive reference that outlines current achievements, identifies existing challenges, and suggests future directions for research in the monitoring and management of glacial lake outburst floods.

The following sections of this review will build on this introduction by exploring in detail the technological advancements in remote sensing, the evolution of monitoring

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methodologies, and the integrated approaches used in risk assessment. Through an interdisciplinary lens that encompasses physical processes, technological innovations, and socio-economic considerations, the review aims to offer a robust framework for understanding and mitigating the risks associated with GLOFs in a rapidly changing world.


Document: 1. Introduction
Section: 2. Methodology: PRISMA-Based Literature Search and Bibliometric Analysis
Type: text

This study aims to provide a comprehensive review of the current state of knowledge regarding remote sensing applications for GLOF monitoring and risk assessment. We adopted a rigorous and transparent approach to systematically identify, screen, and analyze the literature for this review. A systematic approach was adopted to ensure the objectivity and reproducibility of the research, encompassing both a PRISMA-based literature search (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) and a subsequent bibliometric analysis. This dual approach allowed for a robust synthesis of current knowledge while also providing quantitative insights into the trends and patterns within the field.


Document: 1. Introduction
Section: 2. Methodology: PRISMA-Based Literature Search and Bibliometric Analysis > 2.1. Database Search and Identification
Type: text

The literature search was conducted using the Scopus database, one of the most comprehensive and widely used sources for peer-reviewed scientific literature. The search was limited to the period from 2010 to 2025 to capture the most recent advancements in remote sensing technologies and their applications in GLOF monitoring and risk assessment. The search strategy was designed to identify studies that explicitly address the use of remote sensing for GLOF-related research. The search string employed was (GLOF\* OR glaci\* lake\* outburst flood\*) AND (monitor\* OR risk\* OR assess\* OR detect\* OR "hazard assessment" OR "risk management") AND (remote sensing OR "satellite imagery" OR "aerial photography" OR "SAR" OR "InSAR"). This search string ensured that the retrieved studies were relevant to the core themes of the review, including GLOF monitoring, risk assessment, and the application of remote sensing technologies such as satellite imagery, aerial photography, Synthetic Aperture Radar (SAR), and Interferometric SAR (InSAR). The initial search yielded a total of 246 records (X = 246).


Document: 1. Introduction
Section: 2. Methodology: PRISMA-Based Literature Search and Bibliometric Analysis > 2.2. Screening and Selection Process
Type: text

The selection of articles for inclusion in this review followed PRISMA guidelines, ensuring a transparent and rigorous selection process. Figure 1 illustrates the PRISMA flow diagram, which outlines the step-by-step process of identifying, screening, and finalizing the studies. A total of 246 records were initially identified through a comprehensive search of the Scopus database, covering journal articles, conference papers, and book chapters published between 2010 and 2025. After removing duplicates and records that were irrelevant based on language (non-English studies) and scope, 68 records were excluded, leaving 178 records for initial screening. The titles, abstracts, and keywords of these records were then screened to assess their relevance to the review's focus on remote sensing for GLOF monitoring and risk assessment. During this stage, 75 records were excluded due to irrelevance, such as studies focusing on non-glacial floods or unrelated remote sensing applications, resulting in 103 records for full-text review. The full texts of these records were carefully reviewed to determine their suitability for inclusion, with 18 records excluded for not explicitly addressing remote sensing applications for GLOFs or lacking sufficient methodological detail. Consequently, 85 records were retained for inclusion in the review. To ensure comprehensiveness, the reference lists of the included studies were examined, leading to the identification of seven additional records that met the inclusion

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criteria. After completing the screening process, a total of 92 studies were included in the review, forming the basis for the qualitative synthesis and bibliometric analysis presented in subsequent sections.


Document: 1. Introduction
Section: 2. Methodology: PRISMA-Based Literature Search and Bibliometric Analysis > 2.2. Screening and Selection Process
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Figure

Image /page/4/Figure/2 description: A flowchart illustrating a literature search and screening process. The process starts with 'Database Search and Identification' from Scopus for the period 2010-2025, identifying a total of 246 records (X = 246). The next step is 'Initial Screening', where 68 articles were removed due to irrelevance by language and duplicates (Z = 68). This leaves 178 records to be screened (Y = X - Z = 178). A 'Second Screening' follows, where 75 unavailable or irrelevant articles were removed based on title, abstract, and keyword (W = 75), resulting in 103 records screened (Z = Y - W = 103). From this point, the flow splits. On one side, 'Final Screening' excludes 18 articles after a full-text reading (M = 18). On the other side, 7 additional articles were reviewed after checking references (P = 7). The process concludes with the 'Total number of included studies', which is 92 (N = Z - M + P = 92).


Document: 1. Introduction
Section: 2. Methodology: PRISMA-Based Literature Search and Bibliometric Analysis > 2.2. Screening and Selection Process
Type: figure
Figure: Figure 1. PRISMA Workflow for Literature Search Strategy.

Figure 1. PRISMA Workflow for Literature Search Strategy.


Document: 1. Introduction
Section: 2. Methodology: PRISMA-Based Literature Search and Bibliometric Analysis > 2.3. Bibliometric Analysis
Type: text

To complement the systematic literature review, a bibliometric analysis of 92 studies was conducted to identify key themes, map the intellectual structure of the field, and visualize the geographical distribution of research on GLOFs and remote sensing. The bibliometric analysis was carried out using a combination of tools and software to ensure a comprehensive and multi-dimensional evaluation. An open-source Geographic Information System (GIS) software, QGIS (version Desktop 3.28.2), was employed to prepare maps highlighting the geographical distribution of study areas, giving a clear view of where GLOF-related research has been focused. This spatial analysis helps identify regions that have received significant attention and those that remain understudied, offering insights into potential gaps in the literature. For the analysis of literature metrics, Python Version 3.10 was utilized, leveraging libraries such as nltk, wordcloud, bibtexparser, pandas, tabulate, and matplotlib. These libraries enabled the extraction, processing, and visualization of key bibliometric data, including publication trends, citation patterns, keyword co-occurrence, and collaboration networks. The nltk library was used for natural language processing tasks, such as keyword extraction and text analysis, while wordcloud facilitated the visualization of frequently occurring terms and themes. The bibtexparser library was employed to parse bibliographic data, and pandas was used for data manipulation and analysis. Finally, matplotlib and tabulate were utilized to generate visualizations and tables summarizing the findings. By combining spatial analysis with advanced bibliometric techniques, this approach offers a holistic understanding of the field's evolution, intelGeosciences **2025**, *15*, 211 6 of 36

lectual structure, and geographical focus, while also identifying areas for future research and collaboration.

The geographical distribution of study areas (Figure 2) provides critical insights into the regions where research on GLOFs and remote sensing has been concentrated. We employed natural Jenks symbology and classified it into five groups: 0, 0-2, 2-13, 13-26, 26-32. Based on the analysis of the studies included in this review, the research landscape is heavily skewed toward specific high-mountain regions, particularly in Asia, which has emerged as the focal point for GLOF-related studies. The highest concentration of research is observed in the Hindu Kush, Karakoram, and Himalayan regions, with significant contributions from countries such as India, China, Nepal, and Pakistan. These regions are particularly vulnerable to GLOFs due to their extensive glacial coverage, rapid glacier retreat under climate change, and high population density in downstream areas. Central Asian countries, including Kazakhstan, Kyrgyzstan, and Tajikistan, have also been the focus of research, though to a lesser extent compared to the Himalayas. Bhutan, despite its small size, has been a notable focus due to its high vulnerability to GLOFs. In Europe, research has primarily centered on the Alpine region, with studies conducted in Switzerland, Austria, France, and Norway. In the Americas, research has been limited, with a few studies focusing on Canada, the United States, and Peru, the latter highlighting the risks posed by glacial lakes in the Andes. This geographical distribution highlights a clear concentration of research in High Mountain Asia (HMA), particularly in the Himalayas and surrounding regions, which are among the most vulnerable to GLOFs.


Document: 1. Introduction
Section: 2. Methodology: PRISMA-Based Literature Search and Bibliometric Analysis > 2.3. Bibliometric Analysis
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Figure

Image /page/5/Figure/3 description: A world map displaying 'The number of study areas' per country using a color-coded system. The map is projected with longitude values from -180.00 to 180.00 on the x-axis and latitude values from -100.00 to 140.00 on the y-axis. A legend in the bottom-left corner indicates the color scale for the number of study areas: 0 is light pink, 1-2 is light blue, 3-13 is a medium blue, 14-26 is teal, and 27-32 is dark green. The map shows the highest concentration of study areas in South and Central Asia. India is colored dark green, indicating the highest number of study areas (27-32). China, Pakistan, Nepal, and Bhutan are colored teal (14-26). Russia and some Central Asian countries are medium blue (3-13). The United States, Canada, Greenland, and Peru are light blue (1-2). Most of Africa, South America, Australia, and the Middle East are colored light pink, indicating zero study areas. The map includes a north arrow in the top-right corner and a scale bar in the bottom-right.


Document: 1. Introduction
Section: 2. Methodology: PRISMA-Based Literature Search and Bibliometric Analysis > 2.3. Bibliometric Analysis
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Figure: Figure 2. Geographic distribution of study areas.

Figure 2. Geographic distribution of study areas.


Document: 1. Introduction
Section: 2. Methodology: PRISMA-Based Literature Search and Bibliometric Analysis > 2.3. Bibliometric Analysis
Type: text

For this review, publications from 2010 to 2025 were selected, a period characterized by significant advancements in remote sensing technology and an intensified emphasis on climate-related phenomena. This period coincides with the rapid evolution of remote sensing applications for GLOF monitoring and mapping, driven by improvements in sensor accuracy, data processing capabilities, and the availability of high-resolution satellite imagery. A bar graph (Figure 3) depicting the annual number of publications reveals a clear upward trend, reflecting the increasing research activity in this field. This growth highlights the expanding reliance on remote sensing technologies for effective risk assessment and a deeper understanding of glacial lake dynamics under changing climatic conditions. To further analyze the evolution of the research focus, the frequency of key terms used in publications over the years was examined using Python for data extraction, processing, and visualization. The results, summarized in Figure 4, demonstrate notable trends and shifts in research priorities. Terms such as "lake", "glacier", and "flood" consistently dominate the literature, underscoring the central focus on glacial lakes and their outburst mechanisms. Over time, the increasing prominence of terms like "remote", "sensing", and "data" reflects the growing reliance on technological advancements and data-driven methodologies in GLOF research. These trends not only illustrate the evolution of research priorities but also emphasize the critical role of remote sensing in advancing our understanding of GLOFs and mitigating their risks.


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Section: 2. Methodology: PRISMA-Based Literature Search and Bibliometric Analysis > 2.3. Bibliometric Analysis
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Figure

Image /page/6/Figure/2 description: A vertical bar chart showing the number of publications per year from 2010 to 2024. The x-axis is labeled "Year" and the y-axis is labeled "Number of Publications," with a scale from 0.0 to 20.0. The data shows a fluctuating but generally increasing trend over the years. The approximate number of publications for each year is as follows: 2010: 1, 2011: 2, 2012: 5, 2013: 2, 2014: 3, 2015: 3, 2016: 1, 2017: 5, 2018: 8, 2019: 5, 2020: 11, 2021: 7, 2022: 5, 2023: 7. The year 2024 shows a significant spike, with the bar extending beyond the 20.0 mark to approximately 21 publications.


Document: 1. Introduction
Section: 2. Methodology: PRISMA-Based Literature Search and Bibliometric Analysis > 2.3. Bibliometric Analysis
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Figure: Figure 3. Number of publications per year.

Figure 3. Number of publications per year.


Document: 1. Introduction
Section: 2. Methodology: PRISMA-Based Literature Search and Bibliometric Analysis > 2.3. Bibliometric Analysis
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Figure

Image /page/6/Figure/4 description: A stacked bar chart showing the frequency of various terms over time. The x-axis represents the year, with labels for 2010, 2012, 2014, 2016, 2018, 2020, 2022, and 2024. The y-axis represents frequency, ranging from 0 to over 600. The legend on the right, titled 'Term', lists the terms represented by different colors in the bars: lake (blue), glacier (orange), flood (green), outburst (red), area (purple), glof (brown), study (pink), change (gray), region (olive), and hazard (turquoise). The chart shows a general upward trend in the frequency of these terms over the years, with a very large spike in 2024, where the total frequency exceeds 600. In most years, 'lake' and 'glacier' are the most frequent terms. The approximate total frequencies for the years are: 2010 (~25), 2012 (~160), 2014 (~85), 2016 (~40), 2018 (~260), 2020 (~275), 2022 (~215), and 2024 (~620).


Document: 1. Introduction
Section: 2. Methodology: PRISMA-Based Literature Search and Bibliometric Analysis > 2.3. Bibliometric Analysis
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Figure: Figure 4. Annual frequency distribution of top 10 terms (2010–2024).

Figure 4. Annual frequency distribution of top 10 terms (2010–2024).


Document: 1. Introduction
Section: 2. Methodology: PRISMA-Based Literature Search and Bibliometric Analysis > 2.3. Bibliometric Analysis
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Alongside highlighting the evolution of the literature, the co-authorship network (Figure 5) provides valuable insight into the collaborative landscape of GLOF research.

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The co-authorship network, visualized in the largest connected component, highlights the collaborative relationships among researchers in the field of GLOFs and remote sensing. The network reveals a dense web of connections, indicating strong collaboration patterns among key authors. Each node in the network represents an individual author, and edges between nodes indicate co-authored publications—where thicker edges signify stronger collaboration. This visualization underscores the interdisciplinary nature of GLOF research, where expertise in remote sensing, glaciology, and risk assessment converges. The presence of well-connected authors and collaborative clusters highlights the importance of teamwork in addressing the complex challenges posed by GLOFs, particularly in the context of climate change.


Document: 1. Introduction
Section: 2. Methodology: PRISMA-Based Literature Search and Bibliometric Analysis > 2.3. Bibliometric Analysis
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Figure

Image /page/7/Figure/2 description: A network graph on a white background, showing connections between individuals. The nodes are represented by light blue circles, each labeled with a name. The edges are thin gray lines connecting the nodes. The graph has a central hub, 'Weicai Wang', who is connected to a large number of other individuals, including 'Guoqing Zhang', 'Jinyuan Yu', 'Fenglin Xu', 'Menger Peng', 'Sonam Bindal', 'Ashutosh Ballab Kattel', 'Binod Dawadi', 'Zheenbek Kulenbekov', 'Mirlan Daryrov', 'Manoranjan Mishra', 'Daniel Joswiak', 'Meilin Zhu', 'Tandong Yao', 'Wei Yang', 'Xiaoxin Yang', 'Shichang Kang', 'Abhishek Banerjee', 'Michael EDMadhavanengupta', and 'Taigang Zhang'. From this central cluster, the network extends downwards and to the right, with 'Wanqin Guo' acting as a bridge to another cluster. This second cluster includes 'Yongjian Ding', 'Jun Liu', 'Da Li', and others. Further to the right, another cluster is centered around 'Xiaojun Yao', who is connected to individuals like 'Lei Han', 'Limin Zhao', 'Yu Wang', and 'Jiayu Hu'.


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Section: 2. Methodology: PRISMA-Based Literature Search and Bibliometric Analysis > 2.3. Bibliometric Analysis
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Figure: Figure 5. Co-authorship network.

Figure 5. Co-authorship network.


Document: 1. Introduction
Section: 2. Methodology: PRISMA-Based Literature Search and Bibliometric Analysis > 2.3. Bibliometric Analysis
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The keyword co-occurrence network (Figure 6) provides a visual representation of the most frequently occurring and interconnected terms in the literature on GLOFs and remote sensing. Central to the network are terms such as "glacial lakes", "climate change", and "glacial retreat", which highlight the primary focus of research on the formation and dynamics of glacial lakes in the context of global warming. The strong connections between these terms and "hazard assessment" and "risk assessment" underscore the emphasis on evaluating the risks posed by GLOFs to downstream communities and infrastructure. The network also reveals the integration of advanced methodologies, as evidenced by terms like "multisource remote sensing" and "hydrodynamic model", which reflect the growing reliance on diverse remote sensing technologies and modeling approaches to study GLOFs. Additionally, terms such as "geomorphology" and "glacial lake outburst flood" indicate a focus on understanding the physical processes and geomorphic impacts of these events. This visualization not only maps the key themes in GLOF research but also highlights the interdisciplinary nature of the field, where climate science, remote sensing, hydrology, and risk management converge. The interconnectedness of these terms illustrates the comprehensive approach required to address the complex challenges posed by GLOFs in a rapidly changing climate.

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Document: 1. Introduction
Section: 2. Methodology: PRISMA-Based Literature Search and Bibliometric Analysis > 2.3. Bibliometric Analysis
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Figure

Image /page/8/Figure/1 description: A network graph or concept map on a white background, illustrating the relationships between various terms related to glaciology and environmental science. The graph consists of light blue circular nodes with black text labels, connected by gray lines (edges) of varying thickness. Thicker lines suggest stronger connections between concepts. The nodes are organized into several interconnected clusters. A central hub includes terms like 'remote sensing', 'glacial lake', 'glof', and 'glacier'. A dense cluster in the upper right includes 'risk assessment', 'western himalaya', 'gangabal lake', 'jhelum basin', and 'hecras'. Other connected terms include 'climate change', 'glacier retreat', 'hazard assessment', 'himalaya', and 'karakoram'. Smaller clusters at the bottom include terms like 'glacial lake outburst flood', 'geomorphology', 'disastrous', 'anthropogenic', 'hydrodynamic model', and 'multisource remote sensing'.


Document: 1. Introduction
Section: 2. Methodology: PRISMA-Based Literature Search and Bibliometric Analysis > 2.3. Bibliometric Analysis
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Figure: Figure 6. Keyword co-occurrence network.

Figure 6. Keyword co-occurrence network.


Document: 1. Introduction
Section: 2. Methodology: PRISMA-Based Literature Search and Bibliometric Analysis > 2.3. Bibliometric Analysis
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In addition to co-authorship analysis, we further analyzed the textual data from the "Title, Abstract, and Keyword" from the literature using N-grams (Figure 7) and word cloud (Figure 8) visualizations to get additional insights into the most frequently occurring phrases and terms in GLOF research. These tools are particularly valuable in bibliometric studies because they distill large textual datasets into interpretable visual or quantitative outputs, allowing us to gauge prevalent research topics and shifts in thematic focus. Ngrams, generated using the Natural Language Toolkit (NLTK) in Python (Version 3.13.0), reveal the most common multi-word sequences, such as "glacial lake outburst flood", "remote sensing data", and "digital elevation model." These phrases highlight the central themes of the research, emphasizing the focus on GLOF dynamics, the use of remote sensing technologies, and the application of elevation models for hazard assessment. The word cloud, created using the WordCloud library in Python, visually represents the frequency of individual terms, with larger words indicating higher occurrence. Prominent terms like "lake", "glacial", "outburst", and "flood" dominate the visualization, reinforcing the primary focus of the literature. The presence of terms like "remote sensing", "climate change", "hazard", and "risk assessment" further underscores the integration of advanced technologies and the emphasis on understanding GLOFs in the context of global warming. These analyses are crucial in bibliometric studies as they provide a quantitative and visual summary of the key themes and trends in the literature. The N-grams and word cloud not only validate the findings from the keyword co-occurrence network but also offer a more granular understanding of the research priorities. While the word cloud identifies highly frequent individual concepts, N-grams reveal the critical contextual relationships between these concepts, illustrating the evolving methodological and thematic frameworks. Together, they illustrate the interdisciplinary nature of GLOF studies, where remote sensing, climate science, and risk management converge to address the challenges posed by these hazardous events. Thus, future glacial-lake research is likely to delve deeper into the interplay of these advanced technologies for proactive hazard mitigation and adaptation strategies.


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Figure

Image /page/9/Figure/1 description: A horizontal bar chart displaying the frequency of various N-grams. The vertical axis is labeled "N-grams" and lists ten phrases. The horizontal axis is labeled "Frequency" with a scale from 0 to 120. The bars are a solid light blue color. The N-grams and their approximate frequencies are as follows: "lake outburst flood" at 116, "glacial lake outburst" at 112, "outburst flood glofs" at 46, "outburst flood glof" at 26, "potentially dangerous glacial" at 18, "dangerous glacial lake" at 18, "remote sensing data" at 16, "glacier lake outburst" at 14, "using remote sensing" at 12, and "digital elevation model" at 12.


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Figure: Figure 7. N-grams showing the frequency of a "sequence of words" utilized in literature.

Figure 7. N-grams showing the frequency of a "sequence of words" utilized in literature.


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Figure

Image /page/9/Figure/3 description: A word cloud on a white background with words in various sizes and colors, including shades of purple, maroon, orange, and blue. The most prominent words, indicating their high frequency in the source text, are "lake", "outburst", "glacial", "glacier", and "flood". Other significant, slightly smaller words include "risk", "area", "change", "region", "himalaya", "glof", and "hazard". Numerous smaller words are scattered throughout, such as "monitoring", "assessment", "climate change", "retreat", "sensing", "water", "Karakoram", and "event". The collection of words suggests the topic is related to glacial lake outburst floods (GLOFs), their risks, and the impact of climate change, particularly in regions like the Himalaya.


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Figure: Figure 8. Word cloud illustrating the frequency of terms in reviewed articles.

Figure 8. Word cloud illustrating the frequency of terms in reviewed articles.


Document: 1. Introduction
Section: 2. Methodology: PRISMA-Based Literature Search and Bibliometric Analysis > 2.3. Bibliometric Analysis
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This review employs a narrative synthesis methodology, systematically organizing and integrating findings according to the specific remote sensing technologies utilized and their application in understanding GLOF dynamics and monitoring. This approach facilitates a detailed discussion on the efficacy of various remote sensing tools and the technological advancements within GLOF research. By adopting this structured framework, this review seeks to provide a comprehensive overview of how remote sensing has been implemented to enhance our understanding and monitoring of GLOFs. It focuses on summarizing technological advancements, evaluating the effectiveness of diverse remote sensing techniques, and identifying key areas for future research within this critical domain of hazard assessment.


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Section: 3. Role of Remote Sensing in GLOF Studies
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GLOFs represent a critical natural hazard that has drawn increasing scholarly attention over recent decades. The integration of remote sensing into glaciological research has evolved from early aerial photography to sophisticated multi-sensor data fusion techniques, fundamentally transforming the way researchers monitor glacier dynamics and assess flood hazards [38]. This section provides a comprehensive review of the role of remote sensing in

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GLOF studies by discussing its historical evolution, current technological advancements, and data analysis methodologies used to quantify and predict glacial lake dynamics.


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Section: 3. Role of Remote Sensing in GLOF Studies > 3.1. Historical Overview: The Evolution of Remote Sensing in Glaciology
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Remote sensing techniques have long been recognized as indispensable tools in the field of glaciology. The evolution of these techniques (Figure 9) over the decades has significantly enhanced our ability to monitor glaciers and glacial lakes, providing critical insights into their dynamics and the associated hazards, such as GLOFs [39,40].


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Figure

Image /page/10/Figure/4 description: A timeline infographic illustrating the evolution of glacier monitoring technologies from the 1950s to the 2020s. The timeline is represented by a horizontal orange line with an arrow at the right end. There are five key periods marked along the timeline with colored location pin icons.


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- 1950s-60s (Orange pin): Technology is Aerial Photography, used for baseline glacier mapping and initial surveys of glacier extent and terminus positions.

- 1970s-80s (Yellow pin): Technology is Landsat 1-3 (Multispectral Scanner System), used for multispectral monitoring of ice cover, initial detection of glacier changes, and mapping of glacier facies.

- 1990s (Light green pin): Technology is ERS-1/2 SAR, used for all-weather ice velocity and lake monitoring, detection of glacier flow dynamics, and monitoring of glacial lake expansion.

- 2000s (Dark green pin): Technologies are Terra/Aqua MODIS & ASTER, UAVs. MODIS was for large-scale ice sheet monitoring, ASTER for high-resolution thermal and elevation data, and UAVs for high-resolution 3D modeling of glacial lakes and detailed glacier surface mapping.

- 2010s-20s (Dark green pin): Technologies are Sentinel-1/2, ICESat-2, Planet Labs, and AI integration. These allow for daily revisits and high-resolution monitoring, precise elevation change measurements, automated analysis and hazard prediction, and the integration of artificial intelligence.


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Figure: Figure 9. Historical milestones in remote sensing for glaciology.

Figure 9. Historical milestones in remote sensing for glaciology.


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In the early stages of glacial research, aerial photography was the primary means of capturing images of remote glacierized areas [41]. These early studies, conducted in the 1950s and 1960s, provided the first systematic documentation of glacier extents and the formation of glacial lakes behind terminal moraines. Aerial surveys were particularly useful for creating baseline maps of glacier coverage and identifying the formation of glacial lakes behind terminal moraines [42,43]. Despite the labor-intensive nature of manual image interpretation and field validation, these pioneering efforts laid the groundwork for the systematic study of glacial processes and GLOF hazards.

The advent of satellite remote sensing in the 1970s marked a significant milestone. The launch of the Landsat program, particularly Landsat 1–3 with their Multispectral Scanner System (MSS), introduced the capability to acquire multispectral imagery at moderate spatial resolutions. This enabled researchers to monitor extensive glaciated terrains over time, detect changes in glacier facies, and map glacier extents more efficiently [37,44]. Landsat Thematic Mapper (TM) sensor, with a spatial resolution of 30 m, was particularly instrumental in identifying and mapping glacial lakes and assessing changes in glacier extents over large areas [45]. Early applications predominantly relied on optical data; however, these methods were hampered by issues such as cloud cover and seasonal snow, which limited temporal consistency in the data [46].

Despite these challenges, early satellite-based studies established the potential of remote sensing for detecting changes in glacier morphology and glacial lake dynamics. The initial inventories derived from Landsat imagery not only provided spatially extensive data but also introduced quantitative methods for assessing glacial retreat and lake expansion [46,47]. These pioneering studies highlighted that even modest changes in glacier mass balance could have significant implications for downstream flood hazards, thereby emphasizing the need for continuous monitoring and advanced analytical techniques [48].

Throughout the 1990s, advancements in Synthetic Aperture Radar (SAR) technology, particularly through satellites like ERS-1/2, complemented optical remote sensing. SAR allowed for all-weather, day-and-night capabilities, thus revolutionizing glacial monitoring under frequently cloudy or snowy conditions [49]. This technology enabled the detection of glacier flow dynamics, measurement of ice velocities, and effective monitoring of the expansion of glacial lakes, providing new insights into glacier stability and lake-induced hazards under any weather conditions [50,51]. These advancements in technology helped

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in highlighting the importance of monitoring lake expansion rates and the potential for cascading GLOF events, which could have devastating impacts on downstream communities and infrastructure [52]. Moreover, the ability to monitor these lakes year-round, regardless of weather conditions, was a significant advancement in GLOF hazard assessment [53]. However, early SAR data had coarser resolution compared to contemporary optical sensors and could be complex to process.


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expansion of glacial lakes , providing new insights into glacier stability and lake - induced hazards under any weather conditions [ 50 , 51 ] . These advancements in technology helped Geosciences 2025 , 15 , 211 12 of 36 in highlighting the importance of monitoring lake expansion rates and the potential for cascading GLOF events , which could have devastating impacts on downstream communities and infrastructure [ 52 ] . Moreover , the ability to monitor these lakes year - round , regardless of weather conditions , was a significant advancement in GLOF hazard assessment [ 53 ] . However , early SAR data had coarser resolution compared to contemporary optical sensors and could be complex to process .

The 2000s brought further advancements with the launch of Terra/Aqua MODIS and ASTER sensors. MODIS provided large-scale ice sheet monitoring, while ASTER offered high-resolution thermal and elevation data, enabling more precise mapping of glacier surfaces and glacial lakes [47,54]. Additionally, the use of unmanned aerial vehicles (UAVs) became more prevalent, allowing for high-resolution 3D modeling of glacial lakes and detailed glacier surface mapping [55]. However, their utilization could be limited by battery life, payload capacity, flight regulations, and reliance on clear weather.

In the 2010s and 2020s, the integration of advanced sensing technologies and artificial intelligence marked a new era in glaciology. Sentinel-1/2, ICESat-2, and Planet Labs, equipped with capabilities for daily revisits and high-resolution monitoring, significantly improved the frequency and detail of observations [56,57]. Precise elevation change measurements from ICESat-2 and automated analysis through AI integration transformed data processing, enhancing the predictive capabilities for identifying potential GLOF events [58,59]. These advancements not only facilitated continuous and precise monitoring but also enabled timely warnings and effective mitigation strategies.

The historical evolution of remote sensing in glaciology has been marked by significant advancements in technology and methodology. From the early days of aerial photography to the current use of high-resolution satellite imagery and AI, remote sensing has provided invaluable insights into glacier retreat, glacial lake formation, and GLOF hazards. The integration of multiple data sources and methodologies has enabled researchers to develop more accurate and comprehensive assessments of GLOF susceptibility, particularly in understudied regions like the Canadian Cordillera and the Himalayas. As climate change continues to drive glacier retreat and lake expansion, the role of remote sensing in monitoring and mitigating GLOF risks will remain critical.


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In recent years, remote sensing technologies have undergone rapid advancements, providing a suite of tools that greatly enhance the monitoring and analysis of glacial lake dynamics and GLOF hazards. These technologies, ranging from optical and radar satellites to unmanned aerial vehicles (UAVs), have revolutionized our ability to assess glacier retreat, lake expansion, and the stability of glacial lake dams.


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To understand the complex dynamics of glacial lakes and assessing the risks of outburst floods, satellites carrying both optical and radar sensors (Table 1) are essential. Their ability to provide detailed, consistent data regardless of weather conditions allows for comprehensive monitoring of lake changes, glacier movement, and surrounding terrain, all crucial for effective GLOF hazard assessment.


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Table: Table 1. Overview of satellite datasets for GLOF studies.

| Satellite        | Sensor                                                                    | Resolution (Spatial/Temporal)                                   | Type            | Key Features                                                                                | Application                                                                                |
|------------------|---------------------------------------------------------------------------|--------------------------------------------------------------------|-----------------|-----------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------|
| Sentinel-1       | Synthetic Aperture Radar (SAR) (C-band)                             | 5–20 m/6 days                                                      | Radar           | All-weather, day and night imaging; interferometric capabilities                                 | Monitoring glacier movement, detecting ground deformation, assessing dam stability, and tracking lake expansion.          |
| TerraSAR-X       | SAR (X-band)                                                              | 1–40 m/11 days                                                     | Radar           | High-resolution, all-weather imaging                                                                | Detailed mapping of glacier surfaces, monitoring ice flow, and detecting small-scale changes in glacial lakes.               |
| RADARSAT-2       | SAR (C-band)                                                              | 3–100 m/24 days                                                    | Radar           | Flexible imaging options, fine resolution capabilities; all-weather, day and night imaging | Monitoring glacier dynamics, detecting surface deformation, and assessing GLOF risks.                                        |
| ALOS-PALSAR      | SAR (L-band)                                                              | 10–100 m/46 days                                                   | Radar           | Penetrates vegetation, wide-area mapping                                                         | Monitoring glacier movement, detecting subsurface changes, and assessing glacial lake expansion in forested regions.      |
| RISAT            | SAR (C-band)                                                              | 1–50 m/25 days                                                     | Radar           | All-weather, day and night imaging; high-resolution capabilities                                 | Monitoring glacier movement, detecting surface deformation, and assessing GLOF risks in cloud-prone regions.              |
| COSMO- SkyMed | SAR (X-band)                                                              | 1–40 m/1–4 days                                                    | Radar           | High-resolution, all-weather imaging                                                                | Monitoring glacier dynamics, detecting surface deformation, and assessing GLOF risks in high-mountain regions.            |
| Envisat          | SAR                                                                       | 30–50 m/35 days                                                    | Radar           | Wide-swath imaging, all-weather capabilities                                                     | Historical monitoring of glacier retreat and glacial lake expansion, especially in remote regions.                           |
| ALOS World 3D    | SAR (L-band) derived DEM                                               | 5 m/static DEM                                                     | Radar           | 3D terrain model                                                                                | Creating high-resolution 3D models of glacial lakes and surrounding terrain for GLOF risk assessment.                        |
| Sentinel-2       | Multispectral Imager (MSI)                                             | 10–60 m/5 days                                                     | Optical         | Multispectral, frequent revisits, wide area coverage                                                | Mapping glacial lake boundaries, monitoring lake area changes, and assessing water quality and turbidity.                    |
| Satellite        | Sensor                                                                    | Resolution (Spatial/Temporal)                                   | Type            | Key Features                                                                                | Application                                                                                |
| Landsat-8        | Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) | 30 m/16 days                                                       | Optical/Thermal | Long-term record of Earth's surface, thermal infrared data                                       | Historical analysis of glacial lake expansion, monitoring glacier retreat, and assessing thermal changes in glacial lakes.               |
| SPOT-6           | High-Resolution Visible (HRV)                                          | 1.5 m/1–4 days                                                     | Optical         | High-resolution, fast revisit                                                                          | Detailed mapping of glacial lake boundaries and monitoring small-scale changes in lake morphology.                                       |
| WorldView        | HRV                                                                       | 0.31 m panchromatic, 1.24 m multispec- tral/Daily Revisit | Optical         | Very high spatial resolution, high Accuracy                                                         | High-resolution mapping of glacial lakes, monitoring dam stability, and assessing small-scale changes in glacier termini. |
| Pleides          | HRV                                                                       | 0.5 m panchromatic, 2 m multispec- tral/Daily Revisit     | Optical         | High-resolution imagery, fast revisit                                                                     | Detailed monitoring of glacial lake boundaries and dam structures, especially in remote and inaccessible regions.                        |
| Planet           | HRV                                                                       | 3–5 m/Daily Revisit                                             | Optical         | Daily revisit, global coverage                                                                         | Frequent monitoring of glacial lake changes, tracking seasonal variations, and assessing GLOF risks.                                     |
| ASTER GDEM       | VNIR, TIR                                                                 | 30 m/Static Dem                                                    | Optical         | Digital elevation model (DEM)                                                                          | Generating topographic maps of glacial lakes, assessing lake volume changes, and modelling GLOF scenarios.                   |
| SuperView-1      | HRV                                                                       | 0.5 m panchromatic, 2 m multispectral/ 2 days             | Optical         | High-resolution, short revisit time                                                                       | Detailed mapping of glacial lake boundaries and monitoring rapid changes in lake morphology.                                 |
| Rapid Eye        | HRV                                                                       | 5 m panchromatic, 15 m multispec- tral/Daily Revisit         | Optical         | Large-area monitoring, daily revisit                                                                | Monitoring large glacial lakes, tracking seasonal changes, and assessing GLOF risks over wide areas.                                     |
| LISS             | Linear Imaging Self-Scanning Sensor                                 | 5.8–70 m/5 days                                                    | Optical         | Multi-spectral imaging, wide coverage                                                               | Monitoring glacial lake boundaries, assessing lake area changes, and tracking glacier retreat over time.                     |
| Gaofen-1         | HRV                                                                       | 2 m panchromatic, 8 m multispectral/ 4 days                  | Optical         | High-resolution, wide coverage                                                                         | Monitoring glacial lake expansion, assessing water quality, and mapping glacier retreat.                                        |
| Satellite        | Sensor                                                                    | Resolution (Spatial/Temporal)                                   | Type            | Key Features                                                                                | Application                                                                                |
| CARTOSAT         | Panchromatic and Multispectral Sensors                              | 1–2.5 m/4–5 days                                                   | Optical         | High-resolution stereo mapping                                                                         | Detailed topographic mapping of glacial lakes, monitoring dam stability, and assessing GLOF risks in high-mountain areas. |
| Super Dove       | HRV                                                                       | 3 m panchromatic, 12 m multispec- tral/Daily Revisit         | Optical         | Daily global coverage, high revisit                                                                 | Frequent monitoring of glacial lake dynamics, tracking seasonal variations, and assessing GLOF risks.                        |
| GRACE            | Gravity Recovery and Climate Experiment                             | NA                                                                 | Gravity         | Measures changes in Earth's gravity field                                                           | Monitoring changes in glacial mass balance and lake water storage, which are critical for GLOF risk assessment.              |
| ICESat-2         | Advanced Topographic Laser Altimeter System (ATLAS)              | 0.7 m (along-track)/ 91 days                                 | LiDAR           | High-precision elevation measurements                                                               | Measuring glacier thickness changes, monitoring lake volume, and assessing GLOF risks.                                       |

Satellite
Sensor
Resolution (Spatial/Temporal)
Type
Key Features
Application


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Table 1. Cont.

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Table 1. Cont.

Contemporary optical sensors, such as those on Landsat 8 and Sentinel-2, offer moderate to high spatial and spectral resolution data that are critical for mapping glacial features [60,61]. Landsat 8, with its 30-m multispectral bands and 15-m panchromatic band, allows for detailed delineation of glacier and lake boundaries. Similarly, Sentinel-2, with a resolution of 10 m in several spectral bands, facilitates the detection of subtle changes in water bodies and land cover. These platforms enable time series analysis of glacial lakes, making it possible to quantify annual changes in lake area and volume. For instance, multi-temporal analyses have been used to document the rapid expansion of glacial lakes in High Mountain Asia, where increasing lake size often correlates with heightened GLOF risk [62]. Furthermore, high-resolution optical satellites like WorldView (0.3 m panchromatic) and Pleiades (0.5 m panchromatic) deliver detailed imagery to monitor small-scale morphological changes in lake boundaries and dam structures, particularly in rugged terrains [14,28]. Gaofen-1 (2 m panchromatic) and SPOT-6 (1.5 m resolution) further enhance this capability by combining wide coverage with rapid revisit times, making them ideal for large-scale glacial lake inventories [3,63]. Sensors such as LISS (IRS series) and RapidEye (5 m panchromatic) support historical trend analysis, while SuperDove (3 m panchromatic) and Planet (3-5 m resolution) enable daily global monitoring to detect abrupt changes during monsoon seasons [46,61,64]. Thermal bands on Landsat-8 also support identifying ice-melt patterns and thermal anomalies near glacial lakes [65].

However, optical systems face significant challenges that limit their effectiveness in glacial environments. Cloud cover frequently obstructs optical imagery in mountainous regions, leading to data gaps during critical monitoring periods [53]. Additionally, optical sensors are dependent on daylight, rendering them ineffective at night. They also cannot penetrate ice, snow, or vegetation to detect subsurface changes or subtle ground deformation, which are critical for assessing GLOF risks [66]. Furthermore, spectral misclassification in shadowed or turbid water areas may lead to errors in lake boundary extraction [67]. These limitations highlight the need for complementary technologies, such as radar and

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InSAR systems, which overcome environmental constraints and provide critical subsurface and deformation data.


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in mountainous regions , leading to data gaps during critical monitoring periods [ 53 ] . Additionally , optical sensors are dependent on daylight , rendering them ineffective at night . They also cannot penetrate ice , snow , or vegetation to detect subsurface changes or subtle ground deformation , which are critical for assessing GLOF risks [ 66 ] . Furthermore , spectral misclassification in shadowed or turbid water areas may lead to errors in lake boundary extraction [ 67 ] . These limitations highlight the need for complementary technologies , such as radar and Geosciences 2025 , 15 , 211 16 of 36 InSAR systems , which overcome environmental constraints and provide critical subsurface and deformation data .

Radar systems address the shortcomings of optical sensors by offering all-weather, day-and-night imaging capabilities, making them indispensable in cloud-prone glacial environments. Synthetic Aperture Radar (SAR) satellites, such as Sentinel-1 (C-band, 5–20 m resolution) and RISAT (C-band, 1–50 m resolution), utilize interferometric techniques (InSAR) to measure millimeter-scale ground deformation, glacier velocities, and dam stability [49,51,68]. For example, Sentinel-1's repeat-pass InSAR detects subsidence or bulging in moraine dams, providing early warnings of potential failure [69]. High-resolution radar platforms like TerraSAR-X (X-band, 1–40 m resolution) and COSMO-SkyMed (X-band, 1–40 m resolution) map ice flow dynamics and crevasses with unparalleled detail, even in darkness or storm conditions [70,71]. L-band SAR sensors, such as ALOS-PALSAR (10–100 m resolution), penetrate vegetation and snow cover to monitor subsurface glacier movement and buried ice in moraines, which is critical for assessing hidden GLOF triggers [72]. RADARSAT-2 (C-band, 3–100 m resolution) supports flexible imaging modes for regional-scale glacier dynamics, while historical missions like Envisat (30–150 m resolution) and ERS-1/2 provide long-term datasets to analyze glacier retreat trends [73,74].

The integration of radar and optical data further enhances GLOF monitoring. For instance, combining Sentinel-1 (radar) with Sentinel-2 (optical) improves the detection of lake area changes and dam deformation, while multi-sensor fusion with LiDAR and gravity data (e.g., GRACE) enables comprehensive assessments of glacial mass balance and lake volume changes [57,75,76]. Together, these technologies provide a robust framework for real-time monitoring, historical trend analysis, and predictive modelling of GLOF risks, empowering stakeholders to implement timely mitigation strategies in vulnerable regions.


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In addition to satellite-based remote sensing, UAVs and high-resolution aerial photography have emerged as critical tools for localized glaciological studies. UAVs are capable of capturing imagery with resolutions of less than one meter, which is invaluable for validating satellite-derived measurements and for detailed studies of dam morphology and topographic changes [77,78]. UAV-based surveys are particularly useful in areas where high-resolution satellite imagery is unavailable or when rapid, localized assessments are required following a GLOF event [55,79]. For instance, UAVs can quickly deploy to capture post-event imagery, enabling rapid damage assessment and identifying newly formed ice-dammed lakes [80].

Aerial photography has played a fundamental role in glaciology since its inception. Historically, it provided the first means to systematically document glacier extents and the formation of glacial lakes, setting the stage for modern remote sensing techniques [41]. Today, integrating historical aerial photographs with contemporary UAV imagery offers a time-extended view of glacial changes, aiding in the study of long-term glacier retreat and lake evolution [43].

The integration of UAV imagery with satellite data further refines the accuracy of mapping and modelling efforts. UAV-derived DEMs capture fine-scale topographic variations around glacial lakes, such as cracks in moraine dams or subtle changes in glacier termini, which are often missed by coarser satellite data [80]. These high-resolution DEMs enhance the precision of lake volume estimations and flood modelling efforts, providing critical inputs for GLOF risk assessments [81]. Furthermore, UAVs can be deployed quickly and provide data in real-time, which is vital for emergency response and disaster readiness in the event of a GLOF. The agility of UAVs makes them an invaluable asset in dynamic and rapidly changing environments typical of glacial landscapes.

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In summary, the application of UAVs and aerial photography in glacial studies not only supports traditional remote sensing data but also extends the capabilities of glacial monitoring systems. These tools enable more detailed and frequent observations that are crucial for the effective management and mitigation of risks associated with glacial lake outbursts.


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In the realm of glacial monitoring, the integration of data from various remote sensing technologies marks a significant evolution, enhancing the depth and accuracy of environmental analysis. Multi-sensor data fusion stands out as one of the most notable advancements in recent remote sensing research, particularly for its application in glaciology [82]. This approach synergizes the strengths of diverse sensing technologies, including optical, radar (SAR), and digital elevation models (DEMs), to forge a comprehensive understanding of glacial dynamics and GLOF risks [57].

The core of multi-sensor data fusion involves blending the high spatial resolution and spectral capabilities of optical sensors with the all-weather, day-and-night imaging capabilities of SAR systems, supplemented by the topographic and volumetric data provided by DEMs [75,83]. The fusion of these data types enables the development of sophisticated models that can accurately estimate glacial lake parameters and assess dam stability. For example, combining Sentinel-2 (optical) with Sentinel-1 (radar) allows for the simultaneous detection of surface water changes and subtle dam movements, providing early warnings of potential GLOF triggers [84]. Integrating ICESat-2 (LiDAR) with ALOS World 3D (SAR) refines volume estimations and flood modelling by capturing both surface and subsurface changes, while UAV-derived DEMs enhance the precision of satellite-based topographic models, enabling detailed assessments of moraine dam stability and glacier retreat patterns [85]. A typical workflow for multi-sensor data fusion involves data acquisition from optical, radar, LiDAR, and UAV platforms, followed by pre-processing to correct atmospheric effects, geometric distortions, and radiometric calibration [64,86]. Advanced algorithms are then used to extract key parameters such as lake boundaries, glacier velocities, and ground deformation, which are integrated using machine learning or statistical models to create a unified representation of glacial lake dynamics. This integrated approach supports the development of predictive models for GLOF risk assessment, improving accuracy and reliability by incorporating inputs from multiple sensors.

The practical implementation of multi-sensor data fusion involves several steps, beginning with the collection and preprocessing of data from various sources. Figure 10 outlines the steps in multi-sensor data fusion—from data acquisition through pre-processing to parameter extraction and model integration. The data are then aligned and integrated using algorithms that handle the different resolutions and data types to produce a cohesive dataset. Advanced analytical techniques, such as interferometric synthetic aperture radar (InSAR), are then applied to this integrated dataset. InSAR is particularly useful for detecting subtle deformations in the glacier surface or the moraine dams that might not be visible with optical images alone.

In conclusion, multi-sensor data fusion represents a transformative approach to glacial lake monitoring, enabling comprehensive assessments of GLOF hazards by leveraging the complementary strengths of optical, radar, LiDAR, and UAV technologies. This integrated framework not only improves the accuracy of risk assessments but also supports the development of early warning systems and mitigation strategies for vulnerable communities.


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Figure

Image /page/17/Figure/1 description: A flowchart titled 'Figure 10. Multi-sensor data fusion process workflow'. The chart illustrates a top-down process with color-coded stages. The flow starts with 'Data Acquisition' (dark blue), leading to 'Pre-processing' (teal), which includes 'Time Sync', 'Cleaning', and 'Calibration'. This is followed by 'Integration' (green), with sub-steps 'Alignment' and 'Association'. The next stage is 'Feature Engineering' (yellow), which breaks down into 'Parameter Extraction' and 'Dimensionality Reduction'. This leads to 'Fusion' (orange), with sub-steps 'Method Selection' and 'Execution'. Following this is 'Modeling' (red), which includes 'Development', 'Integration', and 'Refinement'. The final stage is 'Output' (purple), which results in 'Decisions' and 'Visualization'.


Document: 1. Introduction
Section: 3. Role of Remote Sensing in GLOF Studies > 3.2. Current Technologies in GLOF Monitoring > 3.2.3. Multi-Sensor Data Fusion
Type: figure
Figure: Figure 10. Multi-sensor data fusion process workflow.

Figure 10. Multi-sensor data fusion process workflow.


Document: 1. Introduction
Section: 3. Role of Remote Sensing in GLOF Studies > 3.3. Data Analysis Methods
Type: text

The effective application of remote sensing in GLOF studies depends on the availability of advanced sensors and the development of robust data analysis methodologies. These methods span change detection, physical parameter estimation, and glacier dynamics monitoring, each contributing uniquely to understanding glacial lake evolution and GLOF hazards. The following subsections detail key techniques employed in analyzing remote sensing data for monitoring glacial lakes and assessing GLOF hazards.


Document: 1. Introduction
Section: 3. Role of Remote Sensing in GLOF Studies > 3.3. Data Analysis Methods > 3.3.1. Change Detection
Type: text

Change detection is a foundational analytical technique for monitoring temporal variations in glacial lakes and their associated glaciers, playing a vital role in GLOF risk assessment [87,88]. It is a cornerstone of glacial lake monitoring, enabling researchers to quantify temporal variations in lake area, ice/snowmelt, and glacier dynamics. The ability to identify and quantify changes in lake area, ice extent, and terrain stability is critical for assessing GLOF risks, particularly in remote and inaccessible alpine regions [46]. These analyses are critical for identifying emerging GLOF risks, such as rapid lake expansion or moraine dam instability. Over time, methodologies have evolved from the manual interpretation of multi-temporal images to advanced automated workflows that can integrate multi-sensor data and advanced techniques [2]. Several methods are employed in GLOF studies, each with its strengths and limitations. Table 2 provides a comprehensive overview of key change detection techniques, detailing their specific applications in GLOF research and analyzing their strengths and weaknesses.


Document: 1. Introduction
Section: 3. Role of Remote Sensing in GLOF Studies > 3.3. Data Analysis Methods > 3.3.1. Change Detection
Type: table
Table: Table 2. Change detection techniques for GLOF studies.

| Technique                                                     | Applicability in GLOF Studies                                                                                | Strengths                                                                                | Limitations                                                                                | Data Requirements                                                                     |
|---------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------|
| Spectral Index Differencing (NDWI, MNDWI, etc.)         | Detecting changes in water surface area and ice/snow melt, lake boundary delineation                                                     | Simple, computationally efficient, ideal for large-scale monitoring.                            | Sensitive to atmospheric/cloud conditions, struggles with turbid/shadowed water, may not detect subtle changes.  | Multi-temporal optical satellite imagery (e.g., Landsat, Sentinel-2).           |
| Image Differencing (Band Ratioing, Simple Differencing) | Identifying changes in pixel values between images (e.g., ice collapse, lake expansion)                                               | Easy to implement, highlights areas of change.                                                     | Sensitive to radiometric differences, requires precise image co-registration, can generate many false positives. | Multi-temporal optical or radar satellite imagery.                              |
| Change Vector Analysis (CVA)                               | Analyzing both magnitude and direction of change in spectral space. (e.g., ice-to-water transitions)                                  | Provides more detailed change information, robust to some radiometric differences.              | Requires accurate atmospheric correction and co-registration, more complex than simple differencing.                | Multi-temporal optical or radar satellite imagery.                              |
| Principal Component Analysis (PCA) Change Detection     | Compressing multi-band data and highlighting significant changes. (e.g., glacier thinning)                                            | Reduces data dimensionality, emphasizes major changes.                                          | The interpretation of principal components can be challenging and sensitive to noise.                                  | Multi-temporal multi-spectral satellite imagery.                                |
| Post-Classification Comparison                             | Comparing classified images from different dates.                                                                                | Provides clear land-cover transition maps                                                          | Accuracy depends on classification accuracy and can propagate classification errors.                                   | Multi-temporal classified satellite imagery.                                    |
| Object-Based Image Analysis (OBIA) Change Detection     | Analyzing changes in image objects (segments) rather than individual pixels. (e.g., dam morphology, debris-covered ice mapping) | More robust to noise and radiometric variations, can incorporate contextual information.     |                                                                                |                                                                                |
| SAR Coherence Change Detection                             | Detecting changes in surface roughness and dielectric properties. (e.g., all-weather lake surface monitoring, dam stability)       | Sensitive to water surface changes, can penetrate clouds and provide all-weather monitoring. | Affected by temporal decorrelation, requires precise co-registration, interpretation can be complex.                | Multi-temporal SAR imagery (e.g., Sentinel-1).                                  |
| Digital Elevation Model (DEM) Differencing              | Monitoring changes in glacier elevation and volume.                                                                                | Provides direct measurement of elevation changes and can detect subtle changes.              | Requires accurate DEMs, affected by DEM errors and co-registration issues.                                             | Multi-temporal DEMs (e.g., from LiDAR, stereo- photogrammetry, or InSAR). |
| Thermal Infrared (TIR) Change Detection                 | Monitoring changes in surface temperature, which can indicate melting or water presence.                                              | Sensitive to temperature variations, can detect changes in thermal properties.                  | Affected by atmospheric conditions, requires accurate atmospheric correction, spatial resolution may be limited. | Multi-temporal TIR satellite imagery (e.g., Landsat thermal bands).          |

Technique
Applicability in GLOF Studies
Strengths
Limitations
Data Requirements


Document: 1. Introduction
Section: 3. Role of Remote Sensing in GLOF Studies > 3.3. Data Analysis Methods > 3.3.1. Change Detection
Type: text

Spectral index differencing, such as the Normalized Difference Water Index (NDWI) and Modified NDWI (MNDWI), remains a widely adopted method for delineating lake boundaries and tracking surface area changes [72]. NDWI, calculated using green and

Geosciences 2025, 15, 211 20 of 36

near-infrared bands, is effective for mapping clear-water lakes but struggles in turbid or shadowed conditions. MNDWI, which substitutes shortwave infrared (SWIR) for near-infrared, improves performance in debris-rich environments. Despite their simplicity, these indices are sensitive to atmospheric interference and cloud cover, limiting their utility in monsoon-prone regions [37]. Building on spectral indices, image differencing, another optical technique, identifies abrupt changes such as ice collapse or lake expansion by subtracting pixel values between two dates [89]. While straightforward, this method requires precise radiometric calibration and co-registration to avoid false positives caused by seasonal illumination variations.

Building upon the concept of differencing but offering greater dimensionality, Change Vector Analysis (CVA) extends traditional differencing by analyzing both the magnitude and direction of spectral changes in multi-dimensional space [2]. This approach is particularly useful for mapping gradual transitions, such as ice-to-water conversion or progressive glacier thinning. However, CVA demands rigorous atmospheric correction and is sensitive to misregistration errors. Similarly, Principal Component Analysis (PCA) reduces the dimensionality of multi-temporal datasets, emphasizing dominant change patterns like glacier retreat or lake growth. While effective for noise reduction, interpreting principal components requires domain expertise, as they often represent composite environmental changes [90]. Post-classification comparison, which involves classifying images from different epochs and quantifying land-cover transitions, provides intuitive results but risks propagating classification errors into change maps [91,92].

Addressing the inherent limitations of optical methods, particularly concerning weather and daylight dependency, SAR coherence change detection addresses the limitations of optical methods by operating independently of weather and daylight. By analyzing phase differences in SAR data, this technique detects surface changes such as water encroachment or dam subsidence [39]. However, temporal decorrelation caused by snowmelt or vegetation growth can degrade coherence, complicating interpretation [93]. Moving from pixel-based to object-oriented analysis, Object-Based Image Analysis (OBIA) segments images into homogeneous objects (e.g., lake patches, moraine dams) using spectral, spatial, and contextual features [94]. This approach reduces salt-and-pepper noise, which is common in pixel-based methods, and enhances dam morphology mapping. Nevertheless, OBIA requires careful parameter tuning and high-resolution data, making it computationally intensive.


Document: 1. Introduction
Section: 3. Role of Remote Sensing in GLOF Studies > 3.3. Data Analysis Methods > 3.3.1. Change Detection
Type: text

water encroachment or dam subsidence [ 39 ] . However , temporal decorrelation caused by snowmelt or vegetation growth can degrade coherence , complicating interpretation [ 93 ] . Moving from pixel - based to object - oriented analysis , Object - Based Image Analysis ( OBIA ) segments images into homogeneous objects ( e . g . , lake patches , moraine dams ) using spectral , spatial , and contextual features [ 94 ] . This approach reduces salt - and - pepper noise , which is common in pixel - based methods , and enhances dam morphology mapping . Nevertheless , OBIA requires careful parameter tuning and high - resolution data , making it computationally intensive .

Shifting focus to elevation metrics, Digital Elevation Model (DEM) differencing quantifies glacier thinning and lake volume changes by comparing multi-temporal elevation data [95]. LiDAR and UAV-derived DEMs offer sub-meter accuracy, while satellite-based DEMs (e.g., ASTER, TanDEM-X) provide broader coverage. This technique is indispensable for estimating potential flood volumes but requires precise co-registration to minimize errors. Thermal infrared (TIR) change detection identifies temperature anomalies linked to ice melt or subglacial water activity. For instance, Landsat-8's thermal bands have been used to detect warming trends near glacier termini, signaling increased meltwater input to lakes [96]. However, TIR's coarse spatial resolution (~100 m) limits its application in small-scale glacial basins.

To handle the complexity and volume of these diverse datasets, advances in machine learning have improved the accuracy of change detection. These methods are proficient in handling the complex spectral variability inherent in alpine environments, where factors such as cloud cover, shadows, and mixed pixels can complicate analysis. Automated change detection techniques not only expedite the mapping process but also enable the identification of abrupt changes in lake area that may signal a potential outburst event.


Document: 1. Introduction
Section: 3. Role of Remote Sensing in GLOF Studies > 3.3. Data Analysis Methods > 3.3.2. Estimation of Physical Parameters
Type: text

In the field of remote sensing applied to GLOF risk assessment, the estimation of physical parameters of glacial lakes and their surrounding environments is a critical component [61,97]. These parameters, which include glacier extent, lake volume, ice thickness, and topographic characteristics, provide the foundational data needed to assess hazard potential, model flood scenarios, and design mitigation strategies [64,98,99]. Remote sensing technologies have revolutionized the ability to measure these parameters over large and often inaccessible regions, offering high-resolution, multi-temporal datasets that were previously unattainable through ground-based methods alone [87,100,101]. However, the accuracy of these measurements varies depending on sensor capabilities, environmental conditions, and data processing techniques (Table 3). For instance, glacier extent mapping using optical imagery achieves high accuracy (>90%) under clear-sky conditions but suffers from errors (~20–30%) in debris-covered or shadowed areas [102,103]. Similarly, lake volume estimates derived from DEM differencing exhibit great uncertainties due to co-registration errors and terrain complexity [104], while LiDAR and UAV photogrammetry reduce these errors in localized studies [78,81]. Despite these variability-driven challenges, advancements in multi-sensor integration and error-correction algorithms enable researchers to derive robust estimates of critical parameters. This accurate knowledge of these parameters not only deepens our understanding of the physical processes leading to GLOFs but also enhances our ability to predict and mitigate these potentially catastrophic events, enabling more reliable risk assessments and early warning systems.


Document: 1. Introduction
Section: 3. Role of Remote Sensing in GLOF Studies > 3.3. Data Analysis Methods > 3.3.2. Estimation of Physical Parameters
Type: table
Table: Table 3. Comprehensive physical parameters estimated using remote sensing.

| Parameter                           | Measurement Technique                                                                            | Remote Sensing Tools                                                       | Application in GLOF Studies                                                                     | Accuracy/Reliability                                                                                |
|-------------------------------------|-----------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------|
| Glacier Extent/Area              | Optical imagery classification, feature extraction                                            | Landsat, Sentinel-2, high-resolution satellites (e.g., WorldView) | Monitoring glacier retreat, lake expansion, and overall changes in glacial environments.           | High accuracy for clear imagery; accuracy affected by cloud cover and debris.                                                                   |
| Glacier Surface Elevation/Volume | DEM differencing, InSAR, LiDAR                                                                   | SRTM, TanDEM-X, ICESat-2, airborne LiDAR                             | Assessing glacier mass balance, detecting ice thinning, and estimating potential outburst volumes. | Accuracy varies with DEM source and terrain complexity; LiDAR provides highest accuracy.                                                        |
| Glacial Lake Area/Volume         | Spectral indices (NDWI, MNDWI), optical imagery, DEM analysis, Area-Volume Relationship | Landsat, Sentinel-2, high-resolution satellites, DEMs                | Tracking lake expansion, identifying unstable lakes, and estimating potential flood volumes.       | Accuracy dependent on water clarity and image resolution; DEMs improve volume estimation. Area-Volume relationships provide useful estimations. |
| Lake Water Level                    | Radar altimetry, optical imagery, DEM analysis                                                      | Sentinel-3, ICESat-2, high-resolution time series.                   | Monitoring lake level fluctuations, identifying rapid changes that may indicate instability.       | Radar altimetry provides good accuracy; optical and DEM methods are less precise.                                                      |
| Ice/Snow Cover                      | Spectral indices (NDSI), optical imagery                                                         | MODIS, Landsat, Sentinel-2                                              | Monitoring snow/ice melt rates, identifying potential triggers for GLOFs.                          | Accuracy affected by atmospheric conditions and debris cover.                                                                                |
| Surface Temperature              | Thermal infrared (TIR) imagery                                                                      | Landsat-8 TIRS, ASTER                                                      | Detecting changes in ice/snow temperature, identifying areas of rapid melt.                        | Accuracy affected by atmospheric correction and emissivity.                                                                                |

Parameter
Measurement Technique
Remote Sensing Tools
Application in GLOF Studies
Accuracy/Reliability


Document: 1. Introduction
Section: 3. Role of Remote Sensing in GLOF Studies > 3.3. Data Analysis Methods > 3.3.2. Estimation of Physical Parameters
Type: table
Table: Table 3. Cont.

| Parameter                          | Measurement Technique                | Remote Sensing Tools | Application in GLOF Studies                                                           | Accuracy/Reliability                                                 |
|------------------------------------|-----------------------------------------|----------------------|------------------------------------------------------------------------------------------|----------------------------------------------------------------------|
| Water Turbid- ity/Sediment Load | Spectral analysis of optical imagery | Sentinel-2, Landsat. | Indicating sediment transport, potential dam weakening, and downstream hazards. | Accuracy varies with water clarity and sediment concentration. |
| Ice Thickness and Mass Balance  | Radar Penetration, Altimetry         | SAR, LiDAR, ICESat-2 | Understanding glacier health and melt dynamics                                     | Reliable in ice thickness, varied in mass balance                 |