Assessing Climate-Induced Vegetation Dynamics in the Kullu Valley Watershed, Western Himalayas, through Remote Sensing and Machine Learning: Statistical Analysis spanning 2000–2020

Assessing Climate-Induced Vegetation Dynamics in the Kullu Valley Watershed, Western Himalayas, through Remote Sensing and Machine Learning: Statistical Analysis spanning 2000–2020 | 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 Assessing Climate-Induced Vegetation Dynamics in the Kullu Valley Watershed, Western Himalayas, through Remote Sensing and Machine Learning: Statistical Analysis spanning 2000–2020 Akash Kashyap, Ashwani Kumar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4198333/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The dynamics of vegetation are critical for identifying climate change trends. The current study aims to examine the spatio-temporal changes in vegetation cover in the Kullu valley (Western Himalaya) during the decadal 2000, 2010, and 2020. The objective of the study includes the computation of normalized difference vegetation index (NDVI) vegetation spectral indices, the extraction of various classes of vegetation, and statistical analysis of the sequential Mann Kendall test and Mann-Kendall (MK) test on historical metrological data from the study site (specifically precipitation, relative humidity, and temperature). Power data access viewer (NASA) datasets have been used in the statistical analysis of climatological data. The primary feature classes in the study are forest cover, snow, river, and grassland/scrubland. The result indicated that the region's grassland cover declined by 120.57 km2 and its forest cover decreased by 40.6 km2 between 2000 and 2020. The results demonstrate that climatic variables like slopes and increased minimum temperatures by two meters (A), minimum temperatures (B), maximum temperatures (C), relative humidity (D), and annual mean precipitation (E) from 2000-2020 are the main factors limiting vegetation growth. The determined NDVI displays significant variations across the study area. The annual maximum temperature was falling. The study's objectives are to: 1) analyse the spatiotemporal variation of vegetation cover, 2) identify its primary drivers, and 3) examine statistical trends in long-term metrological data. The result of the research presented will be useful in properly managing and monitoring the forest ecosystem. Vegetation indices Remote sensing techniques Climatological variations Maan Kendall test. Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-4198333","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":306688731,"identity":"39d7b98a-813d-4fcc-a6e1-d8abcf27642c","order_by":0,"name":"Akash Kashyap","email":"data:image/png;base64,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","orcid":"","institution":"Soban Singh Jeena University","correspondingAuthor":true,"prefix":"","firstName":"Akash","middleName":"","lastName":"Kashyap","suffix":""},{"id":306688732,"identity":"a4c4ad19-3e3a-480b-9fe5-249bc7a552ec","order_by":1,"name":"Ashwani Kumar","email":"","orcid":"","institution":"Soban Singh Jeena University","correspondingAuthor":false,"prefix":"","firstName":"Ashwani","middleName":"","lastName":"Kumar","suffix":""}],"badges":[],"createdAt":"2024-04-01 05:44:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4198333/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4198333/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":59735254,"identity":"5261aae4-13f1-49d1-8ecd-eff48057d4df","added_by":"auto","created_at":"2024-07-05 13:26:59","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":749025,"visible":true,"origin":"","legend":"","description":"","filename":"Vegetationreserachpaper07052024.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4198333/v1_covered_ae1057c5-3ac1-4331-9bc8-74dc74a985b8.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Assessing Climate-Induced Vegetation Dynamics in the Kullu Valley Watershed, Western Himalayas, through Remote Sensing and Machine Learning: Statistical Analysis spanning 2000–2020","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Vegetation indices, Remote sensing techniques, Climatological variations, Maan Kendall test.","lastPublishedDoi":"10.21203/rs.3.rs-4198333/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4198333/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe dynamics of vegetation are critical for identifying climate change trends. 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