Modeling Multi-Temporal Fire Niches for Wildfire Susceptibility Mapping Using Active Fire Data

Modeling Multi-Temporal Fire Niches for Wildfire Susceptibility Mapping Using Active Fire Data | 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 Modeling Multi-Temporal Fire Niches for Wildfire Susceptibility Mapping Using Active Fire Data Narges Bagheri, Hamidreza Keshtkar, Mohammad Ali Zare Chahouki, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7887745/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Rising global wildfires threaten semi-arid ecosystems, necessitating advanced predictive mapping for mitigation. This study employs a comparative machine learning approach to develop seasonal wildfire susceptibility maps, capturing dynamic fire drivers for effective management in semi-arid regions. This study specifically investigated seasonal effects on wildfire patterns by evaluating four machine learning techniques (ANN, GLM, XGBoost, and MaxEnt) across distinct temporal scales. A comprehensive fire occurrence database was established using 12 years of active fire data. Twelve predictors, spanning four categories: meteorological parameters, satellite-derived variables, topographic features, and anthropogenic indicators were developed to capture seasonal variations in fire drivers. The results indicated that MaxNet and XGBoost consistently outperformed other models in multi-season wildfire risk prediction, achieving remarkable accuracy. Land cover emerged as the most critical wildfire predictor (> 60% influence). Models highlighted human activity as the dominant driver in warm seasons, while climatic and vegetation factors dominated in colder periods. The central and northern high-elevation regions (> 3000 m) are consistently identified as the highest-risk areas across models and seasons, especially during the warm season. Our findings highlight the crucial role of seasonal dynamics in wildfire risk. Accurate wildfire risk mapping relies on advanced ensemble models that precisely identify hotspots while balancing sensitivity, specificity, and spatial interpretability to guide mitigation strategies. This study provides practical tools for seasonally-targeted wildfire management. Wildfire modelling Fire database Machine learning Remote sensing XGBoost Full Text Additional Declarations No competing interests reported. Supplementary Files Supplementary.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 12 Jan, 2026 Reviewers agreed at journal 05 Jan, 2026 Reviewers invited by journal 28 Oct, 2025 Editor assigned by journal 23 Oct, 2025 Submission checks completed at journal 22 Oct, 2025 First submitted to journal 17 Oct, 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-7887745","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":536480661,"identity":"72919d53-e7ca-486e-aa31-182be0fd3171","order_by":0,"name":"Narges Bagheri","email":"","orcid":"","institution":"University of Tehran","correspondingAuthor":false,"prefix":"","firstName":"Narges","middleName":"","lastName":"Bagheri","suffix":""},{"id":536480662,"identity":"b8a45517-1ee6-4992-8ad8-6b7dc327eef4","order_by":1,"name":"Hamidreza 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