Glacier Lake Detection Utilizing Remote Sensing Integration with Satellite Imagery and Advanced Deep Learning Methods

Glacier Lake Detection Utilizing Remote Sensing Integration with Satellite Imagery and Advanced Deep Learning Methods | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Glacier Lake Detection Utilizing Remote Sensing Integration with Satellite Imagery and Advanced Deep Learning Methods Anita Sharma, Chander Prakash, Divyansh Thakur This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3963695/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 19 Sep, 2024 Read the published version in Applied Geomatics → Version 1 posted 12 You are reading this latest preprint version Abstract The Himalayan glaciers are extremely susceptible to global climate change, leading to substantial glacial retreat, the creation and expansion of glacial lakes, and a rise in GLOFs.These alterations have changed the patterns of river flow and moved the borders of glaciers, resulting in significant socio-economic damages. Accurately monitoring glacial lakes is essential for managing GLOF events and evaluating the effects of climate change on the cryosphere. This study utilizes a Deep Learning-based U-net technique to extract glacial lakes from Landsat-8 satellite imagery by propagating characteristics and minimizing information loss. The method improves the importance given to glacial lakes, reduces the influence of low contrast, and handles different pixel categories. We apply this methodology to the Chandra-Bhaga basin, Himachal Pradesh located in NW Indian Himalaya, and successfully extract 107 glacial lakes. The U-net model attains an accuracy of 97.32%, precision of 95.98%, recall of 95.23%, and an IoU of 97.45% during validation with high-resolution photos from Google Earth and a digital elevation model. The suggested approach could be beneficial for precise and effective monitoring of glacial lakes in different areas, assisting in the management of natural disasters and offering vital information on the effects of climate change on the cryosphere. Deep Learning U-Net Satellite imagery climate change Glacial lakes Semantic segmentation Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 19 Sep, 2024 Read the published version in Applied Geomatics → Version 1 posted Editorial decision: Revision requested 10 Aug, 2024 Reviews received at journal 10 Aug, 2024 Reviewers agreed at journal 05 Aug, 2024 Reviewers agreed at journal 05 Aug, 2024 Reviewers agreed at journal 16 Jul, 2024 Reviews received at journal 21 Apr, 2024 Reviewers agreed at journal 02 Apr, 2024 Reviewers agreed at journal 01 Apr, 2024 Reviewers invited by journal 01 Apr, 2024 Editor assigned by journal 19 Feb, 2024 Submission checks completed at journal 19 Feb, 2024 First submitted to journal 17 Feb, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3963695","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":273661755,"identity":"b995f5e3-4c98-429e-a370-528ab9ffda3c","order_by":0,"name":"Anita Sharma","email":"","orcid":"","institution":"National Institute of Technology Hamirpur","correspondingAuthor":false,"prefix":"","firstName":"Anita","middleName":"","lastName":"Sharma","suffix":""},{"id":273661756,"identity":"c815e9b1-98d5-4834-888a-849c936ecf8f","order_by":1,"name":"Chander Prakash","email":"","orcid":"","institution":"National Institute of Technology Hamirpur","correspondingAuthor":false,"prefix":"","firstName":"Chander","middleName":"","lastName":"Prakash","suffix":""},{"id":273661757,"identity":"94473722-aa8f-4030-a076-d954ad66551e","order_by":2,"name":"Divyansh Thakur","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEUlEQVRIiWNgGAWjYBACe2YIncAgwQBi2siBeAce4NFi2AzVwgPRkmYM1pKAR4vBAVQthxMbIJbi0XKcx0zi5466PHvp5sfGvHvS0ueHHX4ItMVOTrcBh5bDPGaSvWcOF/PIHDNO5nlmk7vxdpoBUEuysdkBXFrY0iR42w4k9kgkGB/mOZCWu3F2AkjLgcRteLRI/m2rA2pJ/wzUcjjdcHb6BwJamI9J87YxA7XkAB124HCCvHQOflsMm5kPW8u2HU7suZFTbDjnQJrhBumcggMJBrj9Ys9/sPHmW6DD2mekb5Z4c8BGXn52+uYPHyrs5HBpAQIWCVSnglUa4FQOAswfULjyDXhVj4JRMApGwQgEADLDZKG7xCZFAAAAAElFTkSuQmCC","orcid":"","institution":"Indian Institute of Information Technology Una","correspondingAuthor":true,"prefix":"","firstName":"Divyansh","middleName":"","lastName":"Thakur","suffix":""}],"badges":[],"createdAt":"2024-02-17 09:59:52","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3963695/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3963695/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s12518-024-00594-3","type":"published","date":"2024-09-19T15:57:10+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":65103916,"identity":"903ee377-6d79-4a37-97d7-b96104cea1a9","added_by":"auto","created_at":"2024-09-23 16:09:35","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":527529,"visible":true,"origin":"","legend":"","description":"","filename":"OmShivFinalUpdated.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3963695/v1_covered_6817f76b-a8ed-44b6-bcab-bf1dbf47b3dc.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":" Glacier Lake Detection Utilizing Remote Sensing Integration with Satellite Imagery and Advanced Deep Learning Methods","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"applied-geomatics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"agmj","sideBox":"Learn more about [Applied Geomatics](http://link.springer.com/journal/12518)","snPcode":"12518","submissionUrl":"https://submission.nature.com/new-submission/12518/3","title":"Applied Geomatics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Deep Learning, U-Net, Satellite imagery, climate change, Glacial lakes, Semantic segmentation","lastPublishedDoi":"10.21203/rs.3.rs-3963695/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3963695/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe Himalayan glaciers are extremely susceptible to global climate change, leading to substantial glacial retreat, the creation and expansion of glacial lakes, and a rise in GLOFs.These alterations have changed the patterns of river flow and moved the borders of glaciers, resulting in significant socio-economic damages. 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