Integrating Remote Sensing, Field-Measured Tree Heights, and Machine Learning to Enhance Mangrove Above-Ground Carbon Estimation in Baluran National Park, Indonesia

Integrating Remote Sensing, Field-Measured Tree Heights, and Machine Learning to Enhance Mangrove Above-Ground Carbon Estimation in Baluran National Park, Indonesia | 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 Integrating Remote Sensing, Field-Measured Tree Heights, and Machine Learning to Enhance Mangrove Above-Ground Carbon Estimation in Baluran National Park, Indonesia Seftiawan Samsu Rijal, Neil Saintilan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7359399/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Mangroves make an important contribution to coastal ecosystem services, including the absorption of large amounts of atmospheric carbon, thereby contributing to climate change mitigation. Developing a mangrove carbon model to better understand and monitor mangrove condition and carbon stores at relevant scales is crucial. The study purpose is to estimate mangrove Above-Ground Carbon (AGC) at Baluran National Park using variables from field measurements and remote sensing combined with a Machine Learning (ML) approach. The study utilised an extensive field data collection programme of 60 sampling plots of girth at breast height, canopy cover, tree height, and tree density. Mangrove AGC was estimated using allometric equations. AGC was also modelled by processing satellite images, conducting statistical analyses, developing models with ML algorithms (Random Forest (RF), Support Vector Machine (SVM), Decision Tree (DT), k-Nearest Neighbour (k-NN), and Gradient Boost (GB)), and checking accuracy using 5-fold cross-validation (CV) of Root Mean Square Error (RMSE). The RF model using Red Edge 3 band, Green Normalised Difference Vegetation Index (GNDVI) from Sentinel-2A (S2A), and field-measured tree height achieved the best estimation of sampled AGC (R² training = 0.93, R² testing = 0.86, 5-fold CV RMSE = 8.27 Mg C ha⁻¹). We estimate mangrove AGC values ranging from 8.89 to 46.20 Mg C ha⁻¹ (average value: 27.42 ± 10.47 Mg C ha⁻¹), which is more accurate than global datasets representing the same locations. This study demonstrates that combining field-measured tree height with high-resolution satellite imagery and appropriate ML algorithms significantly improves mangrove AGC estimations. Above-Ground Carbon (AGC) Indonesia Machine Learning Sentinel-2A mangroves Full Text Supplementary Files PaperBaluranSupplementaryFileSubmit.pdf Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 30 Oct, 2025 Reviewers invited by journal 21 Aug, 2025 Editor invited by journal 15 Aug, 2025 Editor assigned by journal 12 Aug, 2025 First submitted to journal 12 Aug, 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. 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-7359399","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":503680944,"identity":"c96eda90-2216-47eb-ba2b-b3d791c76e62","order_by":0,"name":"Seftiawan Samsu Rijal","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0002-5684-5817","institution":"Macquarie University","correspondingAuthor":true,"prefix":"","firstName":"Seftiawan","middleName":"Samsu","lastName":"Rijal","suffix":""},{"id":503680945,"identity":"67c3c32b-5493-4afd-b06a-52fc12f85c24","order_by":1,"name":"Neil Saintilan","email":"","orcid":"","institution":"Macquarie University","correspondingAuthor":false,"prefix":"","firstName":"Neil","middleName":"","lastName":"Saintilan","suffix":""}],"badges":[],"createdAt":"2025-08-12 22:55:45","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7359399/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7359399/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90153931,"identity":"5ad61c87-7acc-4a56-a7c5-f745a88efb11","added_by":"auto","created_at":"2025-08-29 07:44:28","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1158703,"visible":true,"origin":"","legend":"","description":"","filename":"PaperBaluranSubmit.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7359399/v1_covered_f77093f6-f79c-453c-ba25-24aca77110c6.pdf"},{"id":90152955,"identity":"5266ee4b-27da-47eb-bc11-7c39a36b1523","added_by":"auto","created_at":"2025-08-29 07:36:23","extension":"pdf","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":53944,"visible":true,"origin":"","legend":"","description":"","filename":"PaperBaluranSupplementaryFileSubmit.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7359399/v1/8c4e916752d502ca5d8a397c.pdf"}],"financialInterests":"","formattedTitle":"Integrating Remote Sensing, Field-Measured Tree Heights, and Machine Learning to Enhance Mangrove Above-Ground Carbon Estimation in Baluran National Park, Indonesia","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":true,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"estuaries-and-coasts","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"esco","sideBox":"Learn more about [Estuaries and Coasts](https://www.springer.com/journal/12237)","snPcode":"12237","submissionUrl":"https://www.editorialmanager.com/esco/","title":"Estuaries and Coasts","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Above-Ground Carbon (AGC), Indonesia, Machine Learning, Sentinel-2A, mangroves","lastPublishedDoi":"10.21203/rs.3.rs-7359399/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7359399/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Mangroves make an important contribution to coastal ecosystem services, including the absorption of large amounts of atmospheric carbon, thereby contributing to climate change mitigation. Developing a mangrove carbon model to better understand and monitor mangrove condition and carbon stores at relevant scales is crucial. The study purpose is to estimate mangrove Above-Ground Carbon (AGC) at Baluran National Park using variables from field measurements and remote sensing combined with a Machine Learning (ML) approach. The study utilised an extensive field data collection programme of 60 sampling plots of girth at breast height, canopy cover, tree height, and tree density. Mangrove AGC was estimated using allometric equations. AGC was also modelled by processing satellite images, conducting statistical analyses, developing models with ML algorithms (Random Forest (RF), Support Vector Machine (SVM), Decision Tree (DT), k-Nearest Neighbour (k-NN), and Gradient Boost (GB)), and checking accuracy using 5-fold cross-validation (CV) of Root Mean Square Error (RMSE). The RF model using Red Edge 3 band, Green Normalised Difference Vegetation Index (GNDVI) from Sentinel-2A (S2A), and field-measured tree height achieved the best estimation of sampled AGC (R² training = 0.93, R² testing = 0.86, 5-fold CV RMSE = 8.27 Mg C ha⁻¹). We estimate mangrove AGC values ranging from 8.89 to 46.20 Mg C ha⁻¹ (average value: 27.42 ± 10.47 Mg C ha⁻¹), which is more accurate than global datasets representing the same locations. This study demonstrates that combining field-measured tree height with high-resolution satellite imagery and appropriate ML algorithms significantly improves mangrove AGC estimations.","manuscriptTitle":"Integrating Remote Sensing, Field-Measured Tree Heights, and Machine Learning to Enhance Mangrove Above-Ground Carbon Estimation in Baluran National Park, Indonesia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-29 07:28:19","doi":"10.21203/rs.3.rs-7359399/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2025-10-30T15:32:26+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-21T12:57:00+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Estuaries and Coasts","date":"2025-08-15T23:25:47+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-13T03:25:12+00:00","index":"","fulltext":""},{"type":"submitted","content":"Estuaries and Coasts","date":"2025-08-12T18:55:20+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"estuaries-and-coasts","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"esco","sideBox":"Learn more about [Estuaries and Coasts](https://www.springer.com/journal/12237)","snPcode":"12237","submissionUrl":"https://www.editorialmanager.com/esco/","title":"Estuaries and Coasts","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"0c999817-7e76-42ea-a497-52215e94d24f","owner":[],"postedDate":"August 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-06T00:40:10+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-29 07:28:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7359399","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7359399","identity":"rs-7359399","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}