Assessing the role and value of trees outside forests by integration of field plots, remote sensing data, and machine learning algorithms

Assessing the role and value of trees outside forests by integration of field plots, remote sensing data, and machine learning algorithms | 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 the role and value of trees outside forests by integration of field plots, remote sensing data, and machine learning algorithms Luong Nguyen Viet, Tu To Trong, Son Le Mai, Giang Nguyen Vu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7534598/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 15 Nov, 2025 Read the published version in Remote Sensing in Earth Systems Sciences → Version 1 posted 7 You are reading this latest preprint version Abstract This research used field data, remote sensing data, and machine learning algorithms to evaluate the role and value of trees outside forests. The study was conducted in a tropical region - Thai Nguyen Province, Vietnam. Outstanding results include assessing biodiversity value, use value, timber volume and carbon stock of trees outside forests. This study employed satellite images, including optical (Landsat 8, Landsat 9) and radar (ALOS-2 PALSAR-2) data, to build a model to estimate the carbon storage and timber volume of trees outside forests. The results indicated that optical satellite data explain carbon storage and timber volume reaching up to 69% and 42%, respectively, whereas radar satellite data explain up to 69% and 64%, respectively. The combination of optical and radar satellite data has significantly improved the efficiency of assessing carbon and timber, with mapping accuracies reaching up to 81% and 69%, respectively. The study improves TOF assessment through the integration of field data, satellite imagery, and machine learning, enabling more reliable evaluation and management. Tree out forests TOF optical data radar data machine learning Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 15 Nov, 2025 Read the published version in Remote Sensing in Earth Systems Sciences → Version 1 posted Editorial decision: Revision requested 11 Oct, 2025 Reviews received at journal 11 Oct, 2025 Reviewers agreed at journal 18 Sep, 2025 Reviewers invited by journal 18 Sep, 2025 Editor assigned by journal 13 Sep, 2025 Submission checks completed at journal 11 Sep, 2025 First submitted to journal 04 Sep, 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-7534598","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":521752140,"identity":"2ed1665d-43e4-4029-8ee4-e10b0992c75d","order_by":0,"name":"Luong Nguyen 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algorithms","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":"remote-sensing-in-earth-systems-sciences","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"rses","sideBox":"Learn more about [Remote Sensing in Earth Systems Sciences](https://link.springer.com/journal/41976)","snPcode":"41976","submissionUrl":"https://submission.nature.com/new-submission/41976/3","title":"Remote Sensing in Earth Systems Sciences","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Tree out forests, TOF, optical data, radar data, machine learning","lastPublishedDoi":"10.21203/rs.3.rs-7534598/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7534598/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis research used field data, remote sensing data, and machine learning algorithms to evaluate the role and value of trees outside forests. The study was conducted in a tropical region - Thai Nguyen Province, Vietnam. Outstanding results include assessing biodiversity value, use value, timber volume and carbon stock of trees outside forests. This study employed satellite images, including optical (Landsat 8, Landsat 9) and radar (ALOS-2 PALSAR-2) data, to build a model to estimate the carbon storage and timber volume of trees outside forests. The results indicated that optical satellite data explain carbon storage and timber volume reaching up to 69% and 42%, respectively, whereas radar satellite data explain up to 69% and 64%, respectively. The combination of optical and radar satellite data has significantly improved the efficiency of assessing carbon and timber, with mapping accuracies reaching up to 81% and 69%, respectively. The study improves TOF assessment through the integration of field data, satellite imagery, and machine learning, enabling more reliable evaluation and management.\u003c/p\u003e","manuscriptTitle":"Assessing the role and value of trees outside forests by integration of field plots, remote sensing data, and machine learning algorithms","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-30 04:17:46","doi":"10.21203/rs.3.rs-7534598/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-11T14:18:16+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-11T09:26:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"12594268789102804478712005259672805128","date":"2025-09-18T13:50:46+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-18T12:26:57+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-13T23:41:15+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-11T12:07:29+00:00","index":"","fulltext":""},{"type":"submitted","content":"Remote Sensing in Earth Systems Sciences","date":"2025-09-04T09:29:02+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"remote-sensing-in-earth-systems-sciences","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"rses","sideBox":"Learn more about [Remote Sensing in Earth Systems Sciences](https://link.springer.com/journal/41976)","snPcode":"41976","submissionUrl":"https://submission.nature.com/new-submission/41976/3","title":"Remote Sensing in Earth Systems Sciences","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"4007f35a-8152-4aa4-afbf-e8b7b8a00938","owner":[],"postedDate":"September 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-11-17T15:59:31+00:00","versionOfRecord":{"articleIdentity":"rs-7534598","link":"https://doi.org/10.1007/s41976-025-00260-1","journal":{"identity":"remote-sensing-in-earth-systems-sciences","isVorOnly":false,"title":"Remote Sensing in Earth Systems Sciences"},"publishedOn":"2025-11-15 15:57:07","publishedOnDateReadable":"November 15th, 2025"},"versionCreatedAt":"2025-09-30 04:17:46","video":"","vorDoi":"10.1007/s41976-025-00260-1","vorDoiUrl":"https://doi.org/10.1007/s41976-025-00260-1","workflowStages":[]},"version":"v1","identity":"rs-7534598","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7534598","identity":"rs-7534598","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}