Estimation of aboveground biomass and carbon stocks in different agro-climatic zones of Manipur using GIS and remote sensing

Estimation of aboveground biomass and carbon stocks in different agro-climatic zones of Manipur using GIS and remote sensing | 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 Estimation of aboveground biomass and carbon stocks in different agro-climatic zones of Manipur using GIS and remote sensing Asha Gupta, Ng Niirou, Nahakpam Santosh Singh This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8456615/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract A study was conducted to estimate the aboveground biomass and carbon pools in different agro-climatic zones of Manipur, NE India using satellite images combined with field data. DBH (diameter at breast height) and height of all trees > 10 cm in sample plots were measured. Estimation of herbs and shrubs biomass was done through harvest method. Previously developed relevant allometric equations were used to estimate biomass using DBH of each species present in all the plots. The relationship between Normalized Difference Vegetation Index (NDVI) and forest stands biomass showed considerable significance for estimating the forest carbon stocks. The ability of NDVI to capture structural differences in canopy greenness, particularly during the winter season when understory interference is minimal, is highlighted by the NDVI–AGB exponential relationship (R² = 0.705). NDVI–AGB classes allow for remote biomass zone classification, differentiating between vegetation structures from degraded forests (NDVI 0.70). The spectral model helps in reliable carbon accounting and highlights the necessity of community-based management and zonal restoration techniques in areas where NDVI signals show ongoing low biomass conditions by estimating forest carbon stocks. Aboveground biomass and carbon map were prepared through spectral modelling which could be used to predict the distribution of forest biomass. Maximum estimated carbon density in subtropical pine zone emphasize the importance of pine forest in capturing large amount of carbon in forests of Manipur..Enhanced vegetation indices (EVI) were suggested for future research due to NDVI's saturation effects in dense forests. Aboveground biomass (AGB) Carbon stocks Agro-climatic zones Remote sensing GIS (Geographic Information Systems) NDVI Spectral modellingClimate change mitigation Full Text Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterial.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 28 Apr, 2026 Reviews received at journal 13 Mar, 2026 Reviews received at journal 09 Mar, 2026 Reviewers agreed at journal 09 Mar, 2026 Reviews received at journal 08 Mar, 2026 Reviewers agreed at journal 06 Mar, 2026 Reviewers agreed at journal 05 Mar, 2026 Reviewers invited by journal 10 Feb, 2026 Editor invited by journal 29 Jan, 2026 Editor assigned by journal 14 Jan, 2026 Submission checks completed at journal 14 Jan, 2026 First submitted to journal 26 Dec, 2025 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-8456615","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":590298833,"identity":"81bf24e8-cbd4-4bb1-83ef-39fb97b2b2d7","order_by":0,"name":"Asha Gupta","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIiWNgGAWjYFACHjiL8QGIy0esFgkgZjYAcdlI0cIGIhgIauHvP3vw49e2O3X8086YVX7NsZNhY2B++OgGHi0SN/KSpWXbnklI3M4xuy27LRnoMDZj4xx81tzgMZCWbDsswQDSIrmNGaiFh00anxb582eMf4O0yAO1FEtuqyesxeBAjpnkR6AWA6AWxo/bDhPWYngjx8ya4dwzyY2304qlGbcd52FjJuAXOaDDbv4ou8Mvdzt548ef26rt+dmbHz7G630gYOZhOABjgEgCykGA8QdUC5AxCkbBKBgFowATAADDaUdeaFVkrAAAAABJRU5ErkJggg==","orcid":"","institution":"Manipur University","correspondingAuthor":true,"prefix":"","firstName":"Asha","middleName":"","lastName":"Gupta","suffix":""},{"id":590298834,"identity":"5013e9a2-d071-432f-b17c-0f999875d6ea","order_by":1,"name":"Ng Niirou","email":"","orcid":"","institution":"Manipur University","correspondingAuthor":false,"prefix":"","firstName":"Ng","middleName":"","lastName":"Niirou","suffix":""},{"id":590298835,"identity":"892a4a9b-e4cc-4ae6-985c-5671024b7b1c","order_by":2,"name":"Nahakpam Santosh Singh","email":"","orcid":"","institution":"Manipur University","correspondingAuthor":false,"prefix":"","firstName":"Nahakpam","middleName":"Santosh","lastName":"Singh","suffix":""}],"badges":[],"createdAt":"2025-12-26 16:23:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8456615/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8456615/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102749080,"identity":"f3d6b274-19b7-4be9-9a24-df50a72b3417","added_by":"auto","created_at":"2026-02-16 09:11:56","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1350182,"visible":true,"origin":"","legend":"","description":"","filename":"GISpaperFinal1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8456615/v1_covered_aa93b33b-02f3-40e9-a43d-31ac147fe902.pdf"},{"id":102709803,"identity":"76709633-2afb-49e9-beae-42b26a434f88","added_by":"auto","created_at":"2026-02-15 14:54:54","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":19492,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-8456615/v1/ac5722a5e8978ad028aebab6.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Estimation of aboveground biomass and carbon stocks in different agro-climatic zones of Manipur using GIS and remote sensing","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"discover-forests","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Forests](https://link.springer.com/journal/44415)","snPcode":"44415","submissionUrl":"https://submission.nature.com/new-submission/44415/3","title":"Discover Forests","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Aboveground biomass (AGB), Carbon stocks, Agro-climatic zones, Remote sensing, GIS (Geographic Information Systems), NDVI, Spectral modellingClimate change mitigation","lastPublishedDoi":"10.21203/rs.3.rs-8456615/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8456615/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eA study was conducted to estimate the aboveground biomass and carbon pools in different agro-climatic zones of Manipur, NE India using satellite images combined with field data. DBH (diameter at breast height) and height of all trees\u0026thinsp;\u0026gt;\u0026thinsp;10 cm in sample plots were measured. Estimation of herbs and shrubs biomass was done through harvest method. Previously developed relevant allometric equations were used to estimate biomass using DBH of each species present in all the plots. The relationship between Normalized Difference Vegetation Index (NDVI) and forest stands biomass showed considerable significance for estimating the forest carbon stocks. The ability of NDVI to capture structural differences in canopy greenness, particularly during the winter season when understory interference is minimal, is highlighted by the NDVI\u0026ndash;AGB exponential relationship (R\u0026sup2; = 0.705). NDVI\u0026ndash;AGB classes allow for remote biomass zone classification, differentiating between vegetation structures from degraded forests (NDVI\u0026thinsp;\u0026lt;\u0026thinsp;0.45) to temperate forests (NDVI\u0026thinsp;\u0026gt;\u0026thinsp;0.70). The spectral model helps in reliable carbon accounting and highlights the necessity of community-based management and zonal restoration techniques in areas where NDVI signals show ongoing low biomass conditions by estimating forest carbon stocks. Aboveground biomass and carbon map were prepared through spectral modelling which could be used to predict the distribution of forest biomass. 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