Assessing Spatiotemporal Land Dynamic Using Google Earth Engine and Random Forest: Trends and Drivers of Change in Ethiopia’s Fragile Jema River Basin

Assessing Spatiotemporal Land Dynamic Using Google Earth Engine and Random Forest: Trends and Drivers of Change in Ethiopia’s Fragile Jema River Basin | 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 Spatiotemporal Land Dynamic Using Google Earth Engine and Random Forest: Trends and Drivers of Change in Ethiopia’s Fragile Jema River Basin Tamrat Selamu, Sileshi Degefa, Wakgari Furi, Tegenge Mola, Ram L. Ray This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8366986/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Quantifying land use land cover (LULC) dynamics in vulnerable ecosystems of the Ethiopian highlands is crucial for understanding the drivers of environmental degradation and informing sustainable land management to protect ecosystem integrity. The Jema River Sub-basin, a critical contributor to the Upper Blue Nile, is highly vulnerable to agricultural expansion, the loss of native vegetation, and landscape fragmentation. This study quantified the spatiotemporal dynamics of LULC change and identified their potential drivers between 1994 and 2024, to support evidence-based and climate-resilient land-use planning in this basin. A machine-learning framework was implemented on the Google Earth Engine (GEE) platform to classify LULC. We utilized Landsat 5 TM, Landsat 7 ETM+, Landsat 8 OLI, and Sentinel-2 MSI imageries, which were classified using the Random Forest (RF) algorithm. The classification was optimized by integrating spectral indices, including the Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), and Modified Normalized Difference Water Index (MNDWI) as well as topographic variables (slope and elevation). Classification accuracy, assessed using 3,100 ground-truth samples, exceeded 87% overall accuracy with Kappa coefficients above 83%. Temporal change detection, transition matrices, and trend analyses quantified alterations across seven LULC classes. The results show that Farmland (FL) expanded by 37.41%, and built up area (BA) by 230.45%, while Natural Vegetation (NV) and Grazing Land (GL) declined by 55.98% and 67.42%, respectively. Focus group discussions and key informant interviews identified population pressure, agricultural intensification, and weak governance as the dominant socio-economic drivers. The integration of multi-temporal geospatial analysis with local knowledge indicates substantial anthropogenic modification of the landscape and provides empirical evidence to inform sustainable land-use planning and ecosystem restoration initiatives in the Jema Sub-basin Land use land cover change Random Forest Google Earth Engine Jema River Basin Machine learning Environmental degradation Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 22 Dec, 2025 Editor assigned by journal 21 Dec, 2025 Submission checks completed at journal 21 Dec, 2025 First submitted to journal 15 Dec, 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-8366986","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":563872172,"identity":"9afd3f5e-8e82-4a8b-a9cf-1915cd4f27bf","order_by":0,"name":"Tamrat Selamu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBElEQVRIiWNgGAWjYFADdhBRAWFL4FPIAyYTgJgZxDiD0IJTG6oWxjYitNizn0778PMHgz0/M/PTzbzz7OQNDjAfvM3DYFOH0xae3M0zexIYEmc2s5nd5t2WbLjhAFuyNQ9DGh6H5W5m4ElgSDA4zADSwsy44QCPmTQPw2HcWvjfbmb8k8Bgb3+Y/dtt3jn19hsO8H8DavmPW4tE7mZmoC2MG5h5gLY0HE4E2sIG1HIAt5Ybbzczy6RJJM44zFN2c86x48kzD7MZW84xSJZswKGFvT93M+MbGxt7/vb2bTfe1FTb9h1vfnjjTYUdPy5boADZFeA4NSCgYRSMglEwCkYBXgAASdtLlPNpc4cAAAAASUVORK5CYII=","orcid":"","institution":"Addis Ababa University","correspondingAuthor":true,"prefix":"","firstName":"Tamrat","middleName":"","lastName":"Selamu","suffix":""},{"id":563872173,"identity":"298e03dc-3688-49c8-b295-01f2d4518dfb","order_by":1,"name":"Sileshi Degefa","email":"","orcid":"","institution":"Addis Ababa University","correspondingAuthor":false,"prefix":"","firstName":"Sileshi","middleName":"","lastName":"Degefa","suffix":""},{"id":563872174,"identity":"e9e71257-5cf8-41ee-bce2-7a2b7c1446c4","order_by":2,"name":"Wakgari Furi","email":"","orcid":"","institution":"Addis Ababa University","correspondingAuthor":false,"prefix":"","firstName":"Wakgari","middleName":"","lastName":"Furi","suffix":""},{"id":563872175,"identity":"a23cc043-2503-443a-a40c-e6b3bdc3747a","order_by":3,"name":"Tegenge Mola","email":"","orcid":"","institution":"Bahir Dar University","correspondingAuthor":false,"prefix":"","firstName":"Tegenge","middleName":"","lastName":"Mola","suffix":""},{"id":563872176,"identity":"7859ab0a-214a-4647-af33-23735b3488a9","order_by":4,"name":"Ram L. 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Classification accuracy, assessed using 3,100 ground-truth samples, exceeded 87% overall accuracy with Kappa coefficients above 83%. Temporal change detection, transition matrices, and trend analyses quantified alterations across seven LULC classes. The results show that Farmland (FL) expanded by 37.41%, and built up area (BA) by 230.45%, while Natural Vegetation (NV) and Grazing Land (GL) declined by 55.98% and 67.42%, respectively. Focus group discussions and key informant interviews identified population pressure, agricultural intensification, and weak governance as the dominant socio-economic drivers. 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