Estimating tropical woody species diversity using Sentinel-2A and Random Forest: A case study in Dak Nong, Vietnam

Estimating tropical woody species diversity using Sentinel-2A and Random Forest: A case study in Dak Nong, Vietnam | 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 Article Estimating tropical woody species diversity using Sentinel-2A and Random Forest: A case study in Dak Nong, Vietnam Nguyen Thi Thanh Huong, Phan Thi Hang, Nguyen Duc Dinh This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7979943/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 Mapping woody plant species (WPS) diversity in tropical forests is vital for biodiversity conservation and management, yet field surveys are labor-intensive and difficult to update. This study evaluates the capability of freely available Sentinel-2A multispectral imagery combined with Random Forest (RF) regression to estimate three alpha - diversity indices including Margalef, Shannon, and Simpson in evergreen forests of Dak Nong Province, Central Highlands of Vietnam. Field data from 202 plots were integrated with spectral bands and vegetation indices for model development. RF models achieved moderate predictive capacity, with relative RMSE (rRMSE) < 30% for all indices: Simpson (5.9%), Shannon (12.2%), and Margalef (24.6%). Although independent test R² values were low (0.045–0.234), reflecting the challenges of capturing biodiversity in structurally complex tropical forests, the estimates remained ecologically meaningful and revealed spatial diversity gradients. Variable importance analysis identified NDVI8a, NDWI, and MSI as consistently influential, emphasizing the relevance of red-edge and water-sensitive spectral features. The moving-window NDVI approach supported the Spectral Variability Hypothesis exhibited reduced predictive reliability compared to RF, indicating a trade-off between efficiency and accuracy. Overall, multispectral data and machine learning provide cost-effective and ecologically meaningful estimates that support conservation planning, hotspot identification, and long-term forest monitoring. Future improvements are expected through the integration of higher-resolution optical, radar, or LiDAR data with advanced machine-learning algorithms. Biological sciences/Ecology Earth and environmental sciences/Ecology Earth and environmental sciences/Environmental sciences Woody plant species Biodiversity indices Random Forest Sentinel-2A Remote sensing Tropical forests 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-7979943","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":544412457,"identity":"af81b16c-f003-456d-80df-7783449f1080","order_by":0,"name":"Nguyen Thi Thanh 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Vietnam","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":"Woody plant species, Biodiversity indices, Random Forest, Sentinel-2A, Remote sensing, Tropical 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Variable importance analysis identified NDVI8a, NDWI, and MSI as consistently influential, emphasizing the relevance of red-edge and water-sensitive spectral features. The moving-window NDVI approach supported the Spectral Variability Hypothesis exhibited reduced predictive reliability compared to RF, indicating a trade-off between efficiency and accuracy. Overall, multispectral data and machine learning provide cost-effective and ecologically meaningful estimates that support conservation planning, hotspot identification, and long-term forest monitoring. 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