Modeling of Land Use Land Cover Change Dynamics Using Google Earth Engine and Machine Learning Techniques in the Abaya-Chamo Sub-Basin, Ethiopia

Modeling of Land Use Land Cover Change Dynamics Using Google Earth Engine and Machine Learning Techniques in the Abaya-Chamo Sub-Basin, Ethiopia | 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 Modeling of Land Use Land Cover Change Dynamics Using Google Earth Engine and Machine Learning Techniques in the Abaya-Chamo Sub-Basin, Ethiopia Desalegn Laelago Ersado, Admasu Gebeyehu Awoke, Mihret Dananto Ulsido This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8785004/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Modelling land use and land cover change analysis is employed to categorize historical and predicted spatial-temporal land cover dynamics at regional and local scales. This research examined historical and projected future land use and land cover (LULC) dynamics utilizing machine learning methods within Google Earth Engine (GEE) and a Cellular Automata-Markov model. The transition potential maps were modelled using the multi-layer perceptron (MLP) neural network in the Land Change Modeler. The study applied Remote sensing datasets collected from Landsat-7 ETM+, Landsat-8 OLI-TIRS, and Landsat-8 OLI-TIRS for the study years. Although potential driver factors, including digital elevation models, road networks, river networks, and slope maps, were considered for predicting future land use and land cover. The findings reveal that the seven categorized LULC maps achieved overall accuracy and Kappa coefficient values of more than 97% and 0.9, respectively. Owing to the spatial pattern of the historically classified mapping, water bodies and agricultural land represent the majority and minority of land, respectively. Over the past 30 years, forests and barren areas have shown a declining tendency, whereas agricultural land, built-up areas, and aquatic bodies have shown expansion trends. By the end of the projected time (2065), built-up areas, waterbodies, and agricultural land are expected to expand gradually, while forest land and barren land exhibit a pronounced and continuous decline. The predicted future LULC patterns suggest significant implications for sustainable land management, food security, and ecosystem conservation. Biological sciences/Ecology Earth and environmental sciences/Ecology Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Environmental social sciences Google Earth Engine (GEE) CA- Markov model LULC Machine Learning Remote Sensing Gidabo Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 13 Feb, 2026 Reviewers agreed at journal 12 Feb, 2026 Reviewers invited by journal 11 Feb, 2026 Editor assigned by journal 11 Feb, 2026 Editor invited by journal 11 Feb, 2026 Submission checks completed at journal 10 Feb, 2026 First submitted to journal 10 Feb, 2026 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-8785004","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":590655129,"identity":"40a6c025-b867-44f9-be10-7461d0793c9b","order_by":0,"name":"Desalegn Laelago Ersado","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCUlEQVRIiWNgGAWjYDCCA0D8AMbhYbBJANESBLUkILSkwbXg1oam5TBhLXzH2x9+SKiozeOXPv7ww5uK83kGB5gP3ga6sA6XFskzZ4wlEs4cL5bsyzGWnHPmdrHBAbZka6ALcdpicCOHQSKx7VjihjM8DNK8bbcTNxzgMZMGuhC3lvvPH/9I/Hcscf8Z9se/edvOAbXwfwNq+Y/HFgYzicSGmsQNPAxmQFsOgGxhA2o5gFOL5JkcM4uEYwcSZ5zhMbOccyY5ceZhNmPLOQbJkg04tPAdP/74xoeausT+HvbHN95U2CX2HW9+CGLw47IFCg4jsZnBDiaggYEBZySMglEwCkbBKGBgAAB0qF1wh5MZXQAAAABJRU5ErkJggg==","orcid":"","institution":"Addis Ababa Institute of Technology(AAiT), Addis Ababa University","correspondingAuthor":true,"prefix":"","firstName":"Desalegn","middleName":"Laelago","lastName":"Ersado","suffix":""},{"id":590655131,"identity":"c70a0de4-efea-45ea-9835-34affc8edfeb","order_by":1,"name":"Admasu Gebeyehu Awoke","email":"","orcid":"","institution":"Addis Ababa Institute of Technology(AAiT), Addis Ababa University","correspondingAuthor":false,"prefix":"","firstName":"Admasu","middleName":"Gebeyehu","lastName":"Awoke","suffix":""},{"id":590655132,"identity":"5527d138-1d3a-43bd-925e-8a8bd5f3b561","order_by":2,"name":"Mihret Dananto Ulsido","email":"","orcid":"","institution":"Hawassa University","correspondingAuthor":false,"prefix":"","firstName":"Mihret","middleName":"Dananto","lastName":"Ulsido","suffix":""}],"badges":[],"createdAt":"2026-02-04 10:25:52","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8785004/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8785004/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103504241,"identity":"c5a9af3a-b95f-44f5-91f1-84c9862fce07","added_by":"auto","created_at":"2026-02-26 13:18:37","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1503218,"visible":true,"origin":"","legend":"","description":"","filename":"ModellingLULCoverAbayaChamoID9262b17655a84e6f80b25e0583d57cdf.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8785004/v1_covered_d6302db1-a10d-4f08-a776-eff6674d0bb2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Modeling of Land Use Land Cover Change Dynamics Using Google Earth Engine and Machine Learning Techniques in the Abaya-Chamo Sub-Basin, Ethiopia","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":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":"Google Earth Engine (GEE), CA- Markov model, LULC, Machine Learning, Remote Sensing, Gidabo","lastPublishedDoi":"10.21203/rs.3.rs-8785004/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8785004/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eModelling land use and land cover change analysis is employed to categorize historical and predicted spatial-temporal land cover dynamics at regional and local scales. This research examined historical and projected future land use and land cover (LULC) dynamics utilizing machine learning methods within Google Earth Engine (GEE) and a Cellular Automata-Markov model. The transition potential maps were modelled using the multi-layer perceptron (MLP) neural network in the Land Change Modeler. The study applied Remote sensing datasets collected from Landsat-7 ETM+, Landsat-8 OLI-TIRS, and Landsat-8 OLI-TIRS for the study years. Although potential driver factors, including digital elevation models, road networks, river networks, and slope maps, were considered for predicting future land use and land cover. The findings reveal that the seven categorized LULC maps achieved overall accuracy and Kappa coefficient values of more than 97% and 0.9, respectively. Owing to the spatial pattern of the historically classified mapping, water bodies and agricultural land represent the majority and minority of land, respectively. Over the past 30 years, forests and barren areas have shown a declining tendency, whereas agricultural land, built-up areas, and aquatic bodies have shown expansion trends. By the end of the projected time (2065), built-up areas, waterbodies, and agricultural land are expected to expand gradually, while forest land and barren land exhibit a pronounced and continuous decline. The predicted future LULC patterns suggest significant implications for sustainable land management, food security, and ecosystem conservation.\u003c/p\u003e","manuscriptTitle":"Modeling of Land Use Land Cover Change Dynamics Using Google Earth Engine and Machine Learning Techniques in the Abaya-Chamo Sub-Basin, Ethiopia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-17 06:16:10","doi":"10.21203/rs.3.rs-8785004/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"245113593613701142087185653670429616309","date":"2026-02-13T05:38:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"117800835357215599514343344752637646250","date":"2026-02-12T05:50:25+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-12T04:41:39+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-12T04:37:48+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-11T13:13:48+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-10T20:33:48+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-02-10T20:27:01+00:00","index":"","fulltext":""}],"status":"published","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}}],"origin":"","ownerIdentity":"27fdd1cc-a98b-4818-9848-186f46aff99d","owner":[],"postedDate":"February 17th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":62853227,"name":"Biological sciences/Ecology"},{"id":62853229,"name":"Earth and environmental sciences/Ecology"},{"id":62853231,"name":"Earth and environmental sciences/Environmental sciences"},{"id":62853233,"name":"Earth and environmental sciences/Environmental social sciences"}],"tags":[],"updatedAt":"2026-05-19T04:38:33+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-17 06:16:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8785004","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8785004","identity":"rs-8785004","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}