Evaluation of Vegetation Conditions for Green Legacy Using Geospatial Technology: A Case of Entoto Natural Park, Addis Ababa, Ethiopia

Evaluation of Vegetation Conditions for Green Legacy Using Geospatial Technology: A Case of Entoto Natural Park, Addis Ababa, 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 Research Article Evaluation of Vegetation Conditions for Green Legacy Using Geospatial Technology: A Case of Entoto Natural Park, Addis Ababa, Ethiopia Amanuel Wolde This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8310185/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Monitoring vegetation condition is essential for ecological sustainability, restoration planning, and climate change adaptation, particularly in urban-adjacent conservation areas such as Entoto Natural Park in Addis Ababa, Ethiopia. However, vegetation condition assessments in the park have been limited and lack quantitative evidence based on geospatial approaches. This study evaluates natural vegetation conditions using multispectral remote sensing, spectral indices, and a Random Forest machine learning model. Landsat imagery from 1995, 2005, 2015, and 2025 was processed to generate NDVI, GNDVI, and NDWI indices, which were used to classify vegetation health and analyze temporal trends. The Random Forest classifier was trained using field-based reference samples and validated using out-of-bag accuracy metrics. Results indicate a general improvement in vegetation condition between 1995 and 2025, with higher chlorophyll content and water availability in recently rehabilitated areas, while eucalyptus-dominated zones exhibited comparatively lower moisture and greenness values. The prediction model also forecasted future vegetation conditions, suggesting continued improvement under ongoing restoration programs. This study demonstrates the effectiveness of spectral indices combined with machine learning for vegetation condition monitoring and provides a geospatial foundation to support sustainable management and restoration efforts under Ethiopia’s Green Legacy Initiative. Ecological Modeling Entoto Natural Park GNDVI NDVI NDWI Random Forest remote sensing spectral indices vegetation condition Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted 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. 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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-8310185","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":557166551,"identity":"e83c6e13-63fe-45dd-87b3-b556fc9de5f2","order_by":0,"name":"Amanuel 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