The Use of Very High-Resolution Satellite Data and the InVEST Model to Analyse Carbon Stock in the Budongo Forest Reserve, Uganda

The Use of Very High-Resolution Satellite Data and the InVEST Model to Analyse Carbon Stock in the Budongo Forest Reserve, Uganda | 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 The Use of Very High-Resolution Satellite Data and the InVEST Model to Analyse Carbon Stock in the Budongo Forest Reserve, Uganda Sahar Sharifi, Ana Andries, Stephen Morse, Richard Murphy, Jim Lynch, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6775704/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 Background: Forests, especially tropical forests, act as major carbon sinks and regulate the atmospheric carbon content. Conventional methods for quantifying carbon stocks are highly dependent on the accuracy of spatial mapping of land use and land cover (LULC). Recent developments in high-resolution remote sensing technology have increased the potential to produce accurate LULC classifications as a prerequisite for assessing such carbon stocks. This research examines the advantages of remote sensing techniques to support accurate LULC mapping via satellite imagery at 3 m spatial resolution with 8 spectral bands. Results: PlanetScope images were used for the LULC classification for the Budongo Forest Reserve area in Uganda. The classification performance was validated for accuracy based on ground truth data, yielding a kappa coefficient of 0.80. Using the detailed LULC map, the InVEST model was used to determine carbon stock estimates. Aboveground biomass estimation was achieved by combining GEDI LiDAR data with vegetation indices derived from PlanetScope imagery. The total carbon stock estimate from these approaches for the Budongo Forest Reserve area was 11,120,727 MgC, and the average density was 136 MgC/ha. Conclusions: These findings highlight the value of high spatial resolution satellite data for improving our understanding of carbon stock estimation and can be used to facilitate comprehensive strategies for the effective management of terrestrial carbon stocks. Carbon Stock GEDI Remote Sensing GIS LULC maps InVEST model Full Text Additional Declarations No competing interests reported. Supplementary Files DocS1.docx TableS1.csv 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. 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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-6775704","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":466318747,"identity":"0cb6757e-5215-4311-8cc6-2de408f7cb12","order_by":0,"name":"Sahar Sharifi","email":"","orcid":"","institution":"University of Surrey","correspondingAuthor":false,"prefix":"","firstName":"Sahar","middleName":"","lastName":"Sharifi","suffix":""},{"id":466318748,"identity":"52e86563-1b27-4734-af43-01d65447c313","order_by":1,"name":"Ana 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