Impact of the full-scale Russian invasion on landscape sustainability in Eastern Ukraine: Insights from the Earth observation foundation model and social media

Impact of the full-scale Russian invasion on landscape sustainability in Eastern Ukraine: Insights from the Earth observation foundation model and social media | 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 Impact of the full-scale Russian invasion on landscape sustainability in Eastern Ukraine: Insights from the Earth observation foundation model and social media Oleksandr Karasov, Usama Humayun, Oleh Prylutskyi, Henrikki Tenkanen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9472717/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 Among the various disruptions to landscape sustainability caused by the full-scale invasion of Ukraine, the war-related impacts on the supply of cultural ecosystem services (CES) remain understudied. We employed a novel GeoAI framework to spatially assess landscape suitability for outdoor recreation in Eastern Ukraine (Luhansk, Donetsk, and Kharkiv oblasts). We trained a Random Forest model on 20,124 spatially unique outdoors visitation locations obtained from georeferenced records from Flickr, Twitter, and VKontakte made in 2015–2022, as a proxy for pre-war met recreational demand, with AlphaEarth satellite embeddings representing environmental conditions to predict environmental (not accounting for accessibility or safety) landscape suitability for recreation in the areas with no detected visitation. We achieved good predictive quality (mean spatially blocked cross-validation AUC = 0.888) in mapping baseline landscape suitability. By applying the same model to wartime satellite embeddings (2022–2024) and isolating war-related effects from climatic and seasonal noise, we detected war-related anomalies in landscape suitability. Our results reveal the presence of both anomaly directions: direct landscape damage (suitability decline, 2,834.25 km²), concentrated near known battlefield zones, and war-wilding (suitability increase, 10,736.75 km²), driven by land abandonment and spontaneous vegetation dynamics. Revealed discrepancies demonstrate a low-to-moderate Spearman’s correlation with war intensity, mapped from open sources, with grasslands exhibiting a correlation up to 0.46. This study is the first of its kind: a regional-scale, spatially explicit assessment of the loss of cultural ecosystem service supply under war pressures, offering a transferable methodology for monitoring landscape sustainability for outdoor recreation in other conflict areas worldwide. Geographic Information Systems Environmental Policy Behavioral Geography cultural ecosystem services outdoor recreation AlphaEarth location-based social media GeoAI war Full Text Additional Declarations The authors declare no competing interests. Supplementary Files Supplementarymaterials2.docx 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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We employed a novel GeoAI framework to spatially assess landscape suitability for outdoor recreation in Eastern Ukraine (Luhansk, Donetsk, and Kharkiv oblasts). We trained a Random Forest model on 20,124 spatially unique outdoors visitation locations obtained from georeferenced records from Flickr, Twitter, and VKontakte made in 2015\u0026ndash;2022, as a proxy for pre-war met recreational demand, with AlphaEarth satellite embeddings representing environmental conditions to predict environmental (not accounting for accessibility or safety) landscape suitability for recreation in the areas with no detected visitation. We achieved good predictive quality (mean spatially blocked cross-validation AUC\u0026thinsp;=\u0026thinsp;0.888) in mapping baseline landscape suitability. By applying the same model to wartime satellite embeddings (2022\u0026ndash;2024) and isolating war-related effects from climatic and seasonal noise, we detected war-related anomalies in landscape suitability. 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