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Forests
Special Issues
Ecological Monitoring and Forest Fire Prevention
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Ecological Monitoring and Forest Fire Prevention

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 July 2026 | Viewed by 2906

Editors

Dr. Hao Liang

E-Mail Website
Guest Editor
School of Technology, Beijing Forestry University, Beijing 100083, China
Interests: remote sensing image interpretation
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yili Zheng

E-Mail Website
Guest Editor
School of Technology, Beijing Forestry University, Beijing 10083, China
Interests: sensors and monitoring technologies; forest image recognition; YOLO model; forest parameter detection
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Luis Ángel Ruiz Fernández

E-Mail Website
Guest Editor
GeoEnvironmental Cartography and Remote Sensing Group (CGAT), Universitat Politècnica de València, Camino de Vera s/n, 46022 Valencia, Spain
Interests: lidar for forest structure analysis; 3D fire behavior models; object-based feature extraction and classification; land use/land cover change analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Escalating wildfire activity—driven by climate change, land-use pressures, and expanding wildland–urban interfaces—poses an unprecedented threat to forest ecosystems and the communities that depend on them. This Special Issue seeks contributions that advance our ability to detect, analyze, and mitigate forest fires across spatial and temporal scales. We welcome original research and critical reviews that integrate cutting‑edge monitoring technologies—such as multispectral and hyperspectral remote sensing, UAV‑borne sensors, Internet‑of‑Things (IoT) networks, and machine learning analytics—with ecological knowledge to quantify fuel dynamics, fire behavior, and post‑fire recovery. Equally important are studies that translate monitoring insights into proactive prevention: fuel-management planning, early-warning systems, community-based risk reduction, and policy frameworks that enhance forest resilience while safeguarding biodiversity, carbon stocks, and human health. By bridging disciplinary boundaries—from ecology and climatology to data science, economics, and social sciences—this Special Issue aims to chart actionable pathways for reducing wildfire impacts under a rapidly changing climate. We invite submissions that highlight innovative methodologies, interdisciplinary case studies, and forward-looking strategies that collectively move the needle from reactive firefighting toward anticipatory, ecosystem-centered fire governance.

Submitted manuscripts must be original contributions, not previously published or submitted to other journals.

Dr. Hao Liang
Prof. Dr. Yili Zheng
Prof. Dr. Luis Ángel Ruiz Fernández
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-anonymized 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 fire prevention
  • remote sensing
  • early-warning systems
  • fuel management
  • machine learning models
  • climate change adaptation
  • bio-diversity conservation

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Published Papers (3 papers)

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Research

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19 pages, 13081 KB  
Article
A Spatiotemporal Wildfire Risk Prediction Framework Integrating Density-Based Clustering and GTWR-RFR
by Shaofeng Xie, Huashun Xiao, Gui Zhang and Haizhou Xu
Forests 2025, 16(11), 1632; https://doi.org/10.3390/f16111632 - 26 Oct 2025
Viewed by 1112
Abstract
Accurate wildfire prediction and identification of key environmental drivers are critical for effective wildfire management. We propose a spatiotemporally adaptive framework integrating ST-DBSCAN clustering with GTWR-RFR. In this hybrid model, Random Forest captures local nonlinear relationships, while GTWR assigns adaptive spatiotemporal weights to [...] Read more.
Accurate wildfire prediction and identification of key environmental drivers are critical for effective wildfire management. We propose a spatiotemporally adaptive framework integrating ST-DBSCAN clustering with GTWR-RFR. In this hybrid model, Random Forest captures local nonlinear relationships, while GTWR assigns adaptive spatiotemporal weights to refine predictions. Using historical wildfire records from Hunan Province, China, we first derived wildfire occurrence probabilities via ST-DBSCAN, avoiding the need for artificial non-fire samples. We then benchmarked GTWR-RFR against seven models, finding that our approach achieved the highest accuracy (R2 = 0.969; RMSE = 0.1743). The framework effectively captures spatiotemporal heterogeneity and quantifies dynamic impacts of environmental drivers. Key contributing drivers include DEM, GDP, population density, and distance to roads and water bodies. Risk maps reveal that central and southern Hunan are at high risk during winter and early spring. Our approach enhances both predictive performance and interpretability, offering a replicable methodology for data-driven wildfire risk assessment. Full article
(This article belongs to the Special Issue Ecological Monitoring and Forest Fire Prevention)
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Review

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31 pages, 2508 KB  
Review
From Ecological Monitoring to Prevention Decision Support: A Critical Review of Artificial Intelligence for Forest Fire Prevention
by Shuwei Feng, Hao Liang and Xiaodong Liu
Forests 2026, 17(7), 817; https://doi.org/10.3390/f17070817 - 11 Jul 2026
Viewed by 301
Abstract
Forest fire prevention increasingly depends on translating ecological monitoring into earlier, more reliable decisions about ignition risk, fuel condition, spread potential, and management intervention. This critical review evaluates artificial intelligence (AI) for forest fire prevention through full-text extraction of core studies and contextual [...] Read more.
Forest fire prevention increasingly depends on translating ecological monitoring into earlier, more reliable decisions about ignition risk, fuel condition, spread potential, and management intervention. This critical review evaluates artificial intelligence (AI) for forest fire prevention through full-text extraction of core studies and contextual synthesis of foundational fire-science literature. The evidence base contains 179 unique references, including an AI-focused corpus, classical deterministic and probabilistic fire-danger and spread models, global ignition and lightning studies, remote-sensing and fuel-moisture foundations, decision-support tools, and governance literature. We define prevention-facing AI as systems that support pre-ignition or pre-escalation decisions and compare studies by data source, model design, validation protocol, forecast horizon, transferability, interpretability, and management action. The synthesis shows that AI is most mature for multimodal sensing, smoke/fire detection, susceptibility mapping, and short-horizon forecasting, but less mature for prospective decision-support validation, cross-ecosystem transfer, and operational accountability. AI is therefore most useful when it is hybrid, interpretable, and deployment-aware: it should complement established fire-weather and spread-model baselines while converting ecological observations into timely and actionable prevention judgments. Full article
(This article belongs to the Special Issue Ecological Monitoring and Forest Fire Prevention)
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Other

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23 pages, 4158 KB  
Systematic Review
A Comparative Review of Wildfire Danger Rating Systems: Focus on Fuel Moisture Modeling Frameworks
by Songhee Han, Sujung Heo, Yeeun Lee, Mina Jang, Sungcheol Jung and Sujung Ahn
Forests 2026, 17(4), 486; https://doi.org/10.3390/f17040486 - 15 Apr 2026
Cited by 1 | Viewed by 766
Abstract
As wildfires intensify globally due to climate change, accurate wildfire danger forecasting systems have become essential for effective disaster management and early warning. Fuel Moisture Content (FMC), defined as the ratio of water mass to dry fuel mass, plays a critical [...] Read more.
As wildfires intensify globally due to climate change, accurate wildfire danger forecasting systems have become essential for effective disaster management and early warning. Fuel Moisture Content (FMC), defined as the ratio of water mass to dry fuel mass, plays a critical role in determining ignition probability and fire spread dynamics. This study conducts a comparative analysis of five major national wildfire danger rating systems: the National Fire Danger Rating System (NFDRS, USA), Canadian Forest Fire Danger Rating System (CFFDRS), European Forest Fire Information System (EFFIS), Australian Fire Danger Rating System (AFDRS), and the Korean Forest Fire Danger Rating System (KFDRS). Using a multi-criteria comparative framework, the systems were evaluated based on fuel classification structure, input variables, modeling approach, and spatiotemporal prediction resolution. The results reveal substantial disparities in spatial resolution (100 m to district-level), temporal resolution (hourly vs. daily), and fuel moisture modeling approaches (physics-based, index-based, and hybrid systems). Specifically, NFDRS and AFDRS provide high-frequency forecasting with hourly temporal resolution, operating at spatial resolutions of 1 km and 100 m, respectively, and incorporating dynamic fuel moisture modeling. In contrast, CFFDRS and KFDRS primarily rely on daily index-based predictions. Furthermore, while many global systems increasingly leverage remote sensing and machine learning for real-time FMC estimation, South Korea’s KFDRS remains predominantly empirical and weather-driven. The analysis identifies critical limitations in the KFDRS, including coarse spatial resolution (district-level), limited integration of Live Fuel Moisture Content (LFMC) modeling, and the lack of AI-augmented hybrid approaches. Accordingly, this study proposes a phased three-stage policy roadmap (2026–2035), emphasizing sensor-network expansion, AI–physics fusion modeling, and high-resolution (10 m) FMC mapping to enhance forecasting accuracy in complex terrains. These findings provide strategic insights for improving wildfire risk management and supporting the transition from reactive response to predictive wildfire forecasting under increasing climate variability. Full article
(This article belongs to the Special Issue Ecological Monitoring and Forest Fire Prevention)
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