AI Applications in Forest Fires
A special issue of Forests (ISSN 1999-4907). This special issue belongs to the section "Natural Hazards and Risk Management".
Deadline for manuscript submissions: 31 May 2026 | Viewed by 13
Special Issue Editors
Interests: forest fires; remote sensing; machine learning; artificial intelligence
Interests: remote sensing; GIS; machine learning; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Interests: remote sensing; GIS; artificial intelligence; air pollution
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Forest fires have become a critically pressing global challenge, exacerbated by climate change and expanding human encroachment into forested areas. To address this complex and escalating threat, there is an urgent demand for more innovative, adaptive and intelligent solutions. In recent years, artificial intelligence (AI) has become a pivotal tool in modern forest fire management. By processing vast amounts of heterogeneous data sourced from satellites, weather models, ground-based sensors, and air quality monitoring networks, AI-driven systems significantly enhance predictive accuracy for fire risk assessment, enable autonomous early warning, optimize real-time decision-making during firefighting operations, and improve post-event environmental impact analysis. These capabilities allow authorities to carry out more precise and effective interventions. To advance these technologies, this Special Issue seeks interdisciplinary studies that bridge AI technology with practical forest fire management needs, offering cutting-edge computational insights, integrated frameworks, and scalable tools across all phases of the fire management cycle. We welcome the contribution of original research to this Special Issue titled “AI Applications in Forest Fires”, covering topics including (but not limited to) the following:
- AI-powered Fire Prediction and Risk Modeling;
- Intelligent Early Detection Systems;
- Real-time Monitoring and Adaptive Response;
- AI-assisted Damage Assessment and Ecosystem Recovery;
- Predictive Analytics for Fire-derived Air Pollution.
Dr. Zhong Zheng
Prof. Dr. Jinfei Wang
Prof. Dr. Bin Zou
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 100 words) can be sent to the Editorial Office for announcement on this website.
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
- forest fires
- machine learning analysis
- geospatial deep learning
- artificial intelligence
- geospatial data
- AI-driven system
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