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Forests
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AI-Enabled Wildfire Risk Management: Toward Fire-Smart WUI Communities
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AI-Enabled Wildfire Risk Management: Toward Fire-Smart WUI Communities

A special issue of Forests (ISSN 1999-4907). This special issue belongs to the section "Natural Hazards and Risk Management".

Deadline for manuscript submissions: 30 June 2026 | Viewed by 1153

Special Issue Editors

Dr. Krishna Chandramouli

E-Mail Website
Guest Editor
VenakaTreleaf GbR, Berlin, Germany
Interests: wildfire risk communication; generative AI; scenario-based fire simulation; geospatial AI & earth observation; multimodal data fusion; digital twins for ecosystems; citizen science & participatory design; on-device/mobile AI
Dr. Konstantinos Demestichas

E-Mail Website
Guest Editor
Informatics Laboratory, Agricultural University of Athens, 11855 Athens, Greece
Interests: artificial intelligence; digital technologies for agriculture and forestry; machine learning models; security research
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Wildfire risk communication is a critical lever for climate adaptation in forested landscapes and wildland–urban interfaces (WUI), where climate-driven volatility and ecological degradation intensify exposure. This Special Issue invites research that advances accessible, actionable, and participatory approaches to risk communication, with a focus on hybrid digital–ecological tools. Building on European Union Green Deal initiatives, this Special Issue aims to address the impact of AI-enabled wildfire risk communication—via generative AI for localized, scenario-based fire simulations and lightweight, on-device plant species classification—on community preparedness, ecological literacy, and close-to-nature forestry and nature-based solutions. Contributions are sought that design, deploy, and rigorously evaluate AI-driven interfaces for preparedness; integrate citizen-science workflows with fire-behaviour insights and habitat data; assess usability, trust, and behaviour change in WUI populations; and address data governance, model transparency, accessibility, and inclusivity. Comparative case studies, methodological advances in multimodal data fusion, and frameworks that align FAIR and WCAG standards, support provenance tracking, and address ethical AI are particularly welcome. By synthesizing evidence across regions and governance contexts, this Special Issue aims to establish robust practices for co-adaptive risk communication, demonstrate measurable preparedness gains, and articulate a translational roadmap from prototypes to policy-relevant, scalable deployments in time for the 2026 fire season.

Dr. Krishna Chandramouli
Dr. Konstantinos Demestichas
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Forests is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • wildfire risk management
  • wildland–urban interface (WUI)
  • generative AI
  • scenario-based simulations
  • citizen science
  • nature-based solutions (NbS)
  • plant species classification
  • community preparedness and behaviour change
  • FAIR data and accessibility (WCAG)

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Published Papers (2 papers)

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Research

22 pages, 7273 KB  
Article
Wildfire Risk Assessment of a Restricted Military–Civilian Interface: A Multi-Model Analytical Framework from the Korean DMZ
by Sujung Heo, Sujung Ahn, Song Hee Han, Sungeun Cha, Mi Na Jang, Hyunsu Kim, Sung Cheol Jung, Minjeong Heo and Junsoo Kim
Forests 2026, 17(3), 289; https://doi.org/10.3390/f17030289 - 24 Feb 2026
Viewed by 375
Abstract
Military–civilian interface zones (MCIZs) adjacent to the Korean Demilitarized Zone (DMZ) represent complex wildfire environments shaped by restricted access, intensive military activities, and adjacent civilian land use. This study develops a spatially explicit wildfire ignition risk assessment framework for the DMZ and Civilian [...] Read more.
Military–civilian interface zones (MCIZs) adjacent to the Korean Demilitarized Zone (DMZ) represent complex wildfire environments shaped by restricted access, intensive military activities, and adjacent civilian land use. This study develops a spatially explicit wildfire ignition risk assessment framework for the DMZ and Civilian Control Zone (CCZ) in Paju, South Korea, employing Random Forest (RF), Generalized Additive Models (GAM), and Geographically Weighted Regression (GWR) in a complementary analytical design. A dataset of 318 wildfire ignition events (2001–2024), including 78 associated with military activities, was analyzed. The RF model achieved high predictive accuracy (AUC = 0.81), identifying proximity to military training zones, relative humidity, wind speed, and proximity to built infrastructure as dominant ignition drivers. GAM revealed narrow nonlinear thresholds—relative humidity at 13.8%–14.0% and wind speed at 13.5–14.0 m/s—corresponding to peak ignition probabilities. GWR demonstrated pronounced spatial heterogeneity, with military proximity exerting a stronger influence in the eastern and northern sectors, while the meteorological effects varied geographically. Based on these outputs, a unified analytical framework was established in which RF-derived ignition probabilities were interpreted alongside GAM- and GWR-based explanatory layers to provide spatially explicit ignition susceptibility assessments without numerical map fusion. The proposed approach provides a scientifically rigorous and operationally applicable method for quantifying ignition risk in politically sensitive, access-restricted landscapes, offering valuable insights for adaptive wildfire prevention and spatially informed governance of transboundary fire risk. Full article
(This article belongs to the Special Issue AI-Enabled Wildfire Risk Management: Toward Fire-Smart WUI Communities)
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30 pages, 16835 KB  
Article
Bridging Climate and Socio-Environmental Vulnerability for Wildfire Risk Assessment Using Explainable Machine Learning: Evidence from the 2025 Wildfire in Korea
by Sujung Heo, Sujung Ahn, Ye-Eun Lee, Sung-Cheol Jung and Mina Jang
Forests 2026, 17(2), 182; https://doi.org/10.3390/f17020182 - 29 Jan 2026
Cited by 1 | Viewed by 501
Abstract
Wildfire activity is intensifying under climate change, particularly in temperate East Asia where human-driven ignitions interact with extreme fire-weather conditions. This study examines wildfire risk during the March 2025 large wildfire event in Korea by applying explainable machine-learning models to assess ignition-prone environments [...] Read more.
Wildfire activity is intensifying under climate change, particularly in temperate East Asia where human-driven ignitions interact with extreme fire-weather conditions. This study examines wildfire risk during the March 2025 large wildfire event in Korea by applying explainable machine-learning models to assess ignition-prone environments and their spatial relationship with socio-environmental features relevant to exposure and management. CatBoost and LightGBM models were used to estimate wildfire susceptibility based on climatic, topographic, vegetation, and anthropogenic predictors, with SHAP analysis employed to interpret variable contributions. Both models showed strong predictive performance (CatBoost AUC = 0.910; LightGBM AUC = 0.907). Temperature, relative humidity, and wind speed emerged as the dominant climatic drivers, with ignition probability increasing under hot (>25 °C), dry (<25%), and windy (>6 m s−1) conditions. Anthropogenic factors—including proximity to graves, mountain trails, forest roads, and contiguous coniferous stands (≥30 ha)—were consistently associated with elevated ignition likelihood, reflecting the role of human accessibility within pine-dominated landscapes. The socio-environmental overlay analysis further indicated that high-susceptibility zones were spatially aligned with arboreta, private commercial forests, and campsites, highlighting areas where ignition-prone environments coincide with active human use and forest management. These results suggest that wildfire risk in Korea is shaped by the spatial concurrence of climatic extremes, fuel continuity, and socio-environmental exposure. By situating explainable susceptibility modeling within an event-conditioned risk perspective, this study provides practical insights for identifying Wildfire Priority Management Areas (WPMAs) and supporting risk-informed prevention, preparedness, and spatial decision-making under ongoing climate change. Full article
(This article belongs to the Special Issue AI-Enabled Wildfire Risk Management: Toward Fire-Smart WUI Communities)
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