Land Cover/use Classification Optimization Model (LC-COM): new fusion model by considering spatial heterogeneity

Land Cover/use Classification Optimization Model (LC-COM): new fusion model by considering spatial heterogeneity | 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 Land Cover/use Classification Optimization Model (LC-COM): new fusion model by considering spatial heterogeneity Li Ma, Xuan Li, Jianwei Hou This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4894998/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 21 Mar, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract The Land use/Cover Classification Optimization Model (LC-COM) is designed to integrate the strengths of the classification results from multiple classifiers and existing products. In LC-COM, the reconciliation index was developed to align the existing LULC products with the composite approach of Landsat images to be classified. Training samples were then auto-generated from these LC products and refined by the spectral indices to further match the selected Landsat images. Six classifiers provided by the Google Earth Engine platform were applied to make their classification to fully explore the detailed and specific information from the Landsat images. The results of these classifiers with the five LULC products were then integrated into an accuracy-weighted hybrid map by using producer accuracy, user accuracy and the especially designed index of matching accuracy reflecting spatial heterogeneity. The results show that the optimized land-cover classification after fusion effectively improved the overall accuracy by integrating all the strengths from each individual result, and the classification performance could be significantly improved when spatial heterogeneity considered. Earth and environmental sciences/Climate sciences Earth and environmental sciences/Ecology Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Planetary science Earth and environmental sciences/Space physics land cover classification integration optimization Google Earth Engine Full Text Additional Declarations No competing interests reported. Supplementary Files Supplementarydocument.docx Cite Share Download PDF Status: Published Journal Publication published 21 Mar, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 18 Nov, 2024 Reviews received at journal 15 Nov, 2024 Reviews received at journal 11 Nov, 2024 Reviewers agreed at journal 03 Nov, 2024 Reviewers agreed at journal 01 Nov, 2024 Reviewers invited by journal 01 Nov, 2024 Editor assigned by journal 01 Nov, 2024 Editor invited by journal 22 Aug, 2024 Submission checks completed at journal 21 Aug, 2024 First submitted to journal 11 Aug, 2024 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-4894998","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":356081293,"identity":"0e04c050-40be-479c-97f8-4f0a56734254","order_by":0,"name":"Li Ma","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwElEQVRIiWNgGAWjYFAC5oYDHxsYEsBsHuK0MDYcnEmyFmZekrToth9sPGy7wyaPXyKB8cHbNgZ5c0JazM4kNhzOPZNWLDkjgdlwbhuD4c4GQloOgLS0HU7ccCOBTZq3jSHB4AAhLecfNhy2bPufuP9GAvtv4rTcANrC2HYgcYNEAhszkVoeNhzsbUsuljjzsFlyzjkJww2EHZZ8+MPPNrs8/vbkgx/elNnIE7QFCTA2AAkJ4tWPglEwCkbBKMANAG/WR3pFVBF3AAAAAElFTkSuQmCC","orcid":"","institution":"Hebei University of Engineering","correspondingAuthor":true,"prefix":"","firstName":"Li","middleName":"","lastName":"Ma","suffix":""},{"id":356081294,"identity":"be5ffe39-2c10-40af-afb8-045e63aa7209","order_by":1,"name":"Xuan Li","email":"","orcid":"","institution":"Hebei University of Engineering","correspondingAuthor":false,"prefix":"","firstName":"Xuan","middleName":"","lastName":"Li","suffix":""},{"id":356081295,"identity":"d07d8e31-8166-4804-ad31-1cc1ff379c28","order_by":2,"name":"Jianwei Hou","email":"","orcid":"","institution":"Hebei University of Engineering","correspondingAuthor":false,"prefix":"","firstName":"Jianwei","middleName":"","lastName":"Hou","suffix":""}],"badges":[],"createdAt":"2024-08-11 11:42:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4894998/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4894998/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-91260-0","type":"published","date":"2025-03-21T15:57:47+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":79120497,"identity":"71af5a31-0447-49e6-8024-97c750d535af","added_by":"auto","created_at":"2025-03-24 16:08:59","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2367776,"visible":true,"origin":"","legend":"","description":"","filename":"LandCoveruseClassificationOptimizationModelLCCOMrevisedversion.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4894998/v1_covered_5d8c45a1-51b3-43b3-a112-149823c3350f.pdf"},{"id":64892548,"identity":"30abaf61-61ef-4306-bc18-28bcc57cbc25","added_by":"auto","created_at":"2024-09-20 06:26:16","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":5444288,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarydocument.docx","url":"https://assets-eu.researchsquare.com/files/rs-4894998/v1/26496f9cf9cade94ddd7848e.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Land Cover/use Classification Optimization Model (LC-COM): new fusion model by considering spatial heterogeneity","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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"land cover, classification, integration, optimization, Google Earth Engine","lastPublishedDoi":"10.21203/rs.3.rs-4894998/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4894998/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eThe Land use/Cover Classification Optimization Model (LC-COM) is designed to integrate the strengths of the classification results from multiple classifiers and existing products. 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