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Fire
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Fire Patterns, Driving Factors, and Multidimensional Impacts Under Climate Change...
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Fire Patterns, Driving Factors, and Multidimensional Impacts Under Climate Change and Human Activities

A special issue of Fire (ISSN 2571-6255). This special issue belongs to the section "Fire Research at the Science–Policy–Practitioner Interface".

Deadline for manuscript submissions: 31 January 2027 | Viewed by 13138

Special Issue Editors

Prof. Dr. Guang Yang

E-Mail Website
Guest Editor
Key Laboratory of Sustainable Forest Ecosystem Management-Ministry of Education, Northern Forest Fire Management Key Laboratory of State Forestry and Grassland Administration, College of Forestry, Northeast Forestry University, Harbin 150040, China
Interests: forest fire prediction and forecast; forest fire ecology; forest fire behavior
Special Issues, Collections and Topics in MDPI journals
Dr. Fengjun Zhao

E-Mail Website
Guest Editor
Key Laboratory of Forest Protection of National Forestry and Grassland Administration, Ecology and Nature Conservation Institute, Chinese Academy of Forestry, National Forestry and Grassland Fire Monitoring, Early Warning and Prevention Engineering Technology Research Center, Beijing 100091, China
Interests: forest fire; forest fuel regulation; lightning fire; fire behavior; fire danger forecast
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yanlong Shan

E-Mail Website
Guest Editor
Science and Technology Innovation Center of Wildland Fire Prevention and Control of Beihua University, Forestry College, Beihua University, Jilin 132013, China
Interests: forest protection; forest fire ecology; fire management
Prof. Dr. Qiuhua Wang

E-Mail Website
Guest Editor
Yunnan Key Laboratory of Forest Disaster Warning and Control, College of Civil Engineering, Southwest Forestry University, Kunming 650233, China
Interests: fire remote sensing; fire risk modeling; post-fire vegetation recovery; landscape ecology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Forest fires have emerged as a critical global challenge to ecological security due to their characteristics of sudden onset, high randomness, and devastating destructive potential. Under the combined influence of climate change and human activities, both the frequency and intensity of forest fires have risen dramatically. Compounding this issue, the substantial carbon emissions generated by these fires further exacerbate the climate crisis, creating a dangerous feedback loop between forest fires and global warming. A scientific understanding of fire patterns, driving factors, and ecological impacts forms the foundation for effective fire prevention, control strategies, and post-fire recovery management.

Analyzing the dynamic changes in forest fires across temporal scales (annual and fire season variations) provides essential data for fire risk zoning and preventive policymaking. The occurrence of forest fires is driven by complex interactions between multiple factors, including fire management policies, ignition sources, climatic conditions, vegetation types, terrain features, and fuel characteristics. These factors range from stable variables to the semi-stable and highly unstable, all of which collectively influence fire ignition, spread, and behavior. Different factor combinations determine fire severity—low-intensity fires may enhance nutrient cycling, whereas high-intensity fires may cause catastrophic forest loss and irreversible ecological damage. High-intensity forest fires with high concentrations of smoke emissions can also cause surrounding towns to be shrouded in smoke, leading to serious social impacts such as panic among residents.

Presently, due to the impact of global climate change and human activities, forest fire prevention and post-fire management face unprecedented challenges. The comprehensive scientific analysis of spatiotemporal fire distribution patterns, driving factors, and multidimensional impacts (economic, ecological, and societal) can help mitigate fire risks and consequences, and provide important reference for the formulation of forest fire prevention management policies.

In this Special Issue, original research articles and reviews are welcome, and research areas may include, but are not limited to, the following:

  1. Climate change and forest fires;
  2. Lightning-caused fire risk;
  3. Distribution of human fire sources;
  4. The factors driving forest fires;
  5. The spatial and temporal distribution of forest fires;
  6. Forest fire risk assessment and zoning;
  7. The ecological, economic, and social impacts of forest fire;
  8. Forest burning and fire behavior;
  9. Forest fire smoke emissions;
  10. Fuel management and treatment.

Prof. Dr. Guang Yang
Dr. Fengjun Zhao
Prof. Dr. Yanlong Shan
Prof. Dr. Qiuhua Wang
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. Fire 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 2400 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

  • wildfires
  • climate change
  • human activity
  • fire prediction and forecasting
  • fire prevention and control
  • fire ecology
  • fuel management
  • fire behavior
  • smoke emissions

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Published Papers (5 papers)

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Research

25 pages, 3501 KB  
Article
Characterisation and Analysis of Large Forest Fires (LFFs) in the Canary Islands, 2012–2024
by Nerea Martín-Raya, Abel López-Díez and Álvaro Lillo Ezquerra
Fire 2026, 9(1), 7; https://doi.org/10.3390/fire9010007 - 23 Dec 2025
Cited by 1 | Viewed by 4917
Abstract
In recent decades, forest fires have become one of the most disruptive and complex natural hazards from both environmental and territorial perspectives. The Canary Islands represent a particularly suitable setting for analysing wildfire risk. This study aims to characterise the Large Forest Fires [...] Read more.
In recent decades, forest fires have become one of the most disruptive and complex natural hazards from both environmental and territorial perspectives. The Canary Islands represent a particularly suitable setting for analysing wildfire risk. This study aims to characterise the Large Forest Fires (LFFs) that occurred across the archipelago between 2012 and 2024 through an integrative approach combining geospatial, meteorological, and socio-environmental information. A total of 13 LFFs were identified in Tenerife, Gran Canaria, La Palma, and La Gomera, affecting 55,167 hectares—equivalent to 7.4% of the islands’ total land area. The results indicate a temporal concentration during the summer months and an altitudinal range between 750 and 1500 m, corresponding to transitional zones between laurel forest and Canary pine woodland. Meteorological conditions showed average temperatures of 24.3 °C, minimum relative humidity of 23.7%, and thermal inversion layers at around 270 m a.s.l., creating an environment conducive to fire spread. Approximately 81% of the affected area lies within protected natural spaces, highlighting a high level of ecological vulnerability. Analysis of the Normalized Burn Ratio (NBR) index reveals a growing trend in fire severity, while social impacts include the evacuation of more than 43,000 people. These findings underscore the urgency of moving towards proactive territorial management that integrates prevention, ecological restoration, and climate change adaptation as fundamental pillars of any disaster risk reduction strategy. Full article
(This article belongs to the Special Issue Fire Patterns, Driving Factors, and Multidimensional Impacts Under Climate Change and Human Activities)
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18 pages, 3006 KB  
Article
A Forest Fire Occurrence Prediction Method for Guizhou Province, China, Based on the Ignition Component
by Guangyuan Wu, Yunlin Zhang, Aixia Luo, Jibin Ning, Lingling Tian and Guang Yang
Fire 2025, 8(11), 439; https://doi.org/10.3390/fire8110439 - 9 Nov 2025
Viewed by 1255
Abstract
Guizhou Province in China exhibits a distinctive agroforestry mosaic landscape with frequent human activity in forested areas. This region experiences recurrent forest fires, characterized by significant difficulties in suppression and high risks. Research on the prediction of forest fire occurrences holds crucial practical [...] Read more.
Guizhou Province in China exhibits a distinctive agroforestry mosaic landscape with frequent human activity in forested areas. This region experiences recurrent forest fires, characterized by significant difficulties in suppression and high risks. Research on the prediction of forest fire occurrences holds crucial practical significance in terms of enhancing regional forest fire prevention capabilities. However, the current fire risk forecasting methods in the area consider only meteorological factors, neglecting firebrands and fuel conditions, which results in deviations between forecasted and actual fire occurrences. Therefore, this study proposes a novel fire occurrence prediction method that utilizes the ignition component (IC) from the National Fire Danger Rating System (NFDRS) to characterize the weather–fuel complex while integrating the firebrand occurrence probability to construct a predictive model. The applicability and accuracy of this method are also evaluated. The results show that, firstly, the probability of at least one daily forest fire occurrence in the study area can be expressed as a nonlinear function based on the IC. Secondly, as time progresses, the correlation between the forest fire occurrence probability and the IC shows a decreasing trend, although the differences across different time spans are not statistically significant. Thirdly, when a 5-year time span is adopted, the error in calculating the forest fire occurrence probability based on the IC is significantly lower than at other time spans. Finally, a predictive model for the forest fire occurrence probability based on the IC is established, where P = (100*IC)/(4.06 + IC), with a mean absolute error (MAE) of 4.83% and mean relative error (MRE) of 14.87%. Based on this research, the IC enables the calculation of forest fire occurrence probabilities, assessment of fire risk ratings, and guidance for fire preparedness and planning. This work also provides theoretical support and a methodological reference for conducting forest fire probability studies in other regions. Full article
(This article belongs to the Special Issue Fire Patterns, Driving Factors, and Multidimensional Impacts Under Climate Change and Human Activities)
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18 pages, 1154 KB  
Article
Explainable AI-Driven Wildfire Prediction in Australia: SHAP and Feature Importance to Identify Environmental Drivers in the Age of Climate Change
by Zina Abohaia, Abeer Elkhouly, May El Barachi and Obada Al-Khatib
Fire 2025, 8(11), 421; https://doi.org/10.3390/fire8110421 - 30 Oct 2025
Cited by 2 | Viewed by 1878
Abstract
This study develops an explainable machine learning framework for wildfire prediction across Australia, integrating region-specific models and feature attribution to identify key environmental drivers. Three wildfire indicators, Estimated Fire Area (FA), Mean Fire Brightness Temperature (FBT), and Fire Radiative Power (FRP), were modeled [...] Read more.
This study develops an explainable machine learning framework for wildfire prediction across Australia, integrating region-specific models and feature attribution to identify key environmental drivers. Three wildfire indicators, Estimated Fire Area (FA), Mean Fire Brightness Temperature (FBT), and Fire Radiative Power (FRP), were modeled using Lasso, Random Forest, LightGBM, and XGBoost. Performance metrics (RMSEC, RMSECV, RMSEP) confirmed strong calibration and generalization, with Tasmania and Queensland achieving the lowest prediction errors for FA and FRP, respectively. Feature importance and SHAP analyses revealed that soil moisture, solar radiation, precipitation, and humidity variability are dominant predictors. Extremes and variance-based measures proved more influential than mean climatic values, indicating that fire dynamics respond non-linearly to environmental fluctuations. Lasso models captured stable linear dependencies in arid regions, while ensemble models effectively represented complex interactions in tropical climates. The results highlight a hierarchical process where cumulative soil and radiation stress establish fire potential, and short-term meteorological variability drives ignition and spread. Projected climate shifts, declining soil water and increased radiative load, are likely to intensify these drivers. The framework supports interpretable, region-specific mitigation planning and paves the way for incorporating generative AI and multi-source data fusion to enhance real-time wildfire forecasting. Full article
(This article belongs to the Special Issue Fire Patterns, Driving Factors, and Multidimensional Impacts Under Climate Change and Human Activities)
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23 pages, 8920 KB  
Article
All-Weather Forest Fire Automatic Monitoring and Early Warning Application Based on Multi-Source Remote Sensing Data: Case Study of Yunnan
by Boyang Gao, Weiwei Jia, Qiang Wang and Guang Yang
Fire 2025, 8(9), 344; https://doi.org/10.3390/fire8090344 - 27 Aug 2025
Cited by 4 | Viewed by 3027
Abstract
Forest fires pose severe ecological, climatic, and socio-economic threats, destroying habitats and emitting greenhouse gases. Early and timely warning is particularly challenging because fires often originate from small-scale, low-temperature ignition sources. Traditional monitoring approaches primarily rely on single-source satellite imagery and empirical threshold [...] Read more.
Forest fires pose severe ecological, climatic, and socio-economic threats, destroying habitats and emitting greenhouse gases. Early and timely warning is particularly challenging because fires often originate from small-scale, low-temperature ignition sources. Traditional monitoring approaches primarily rely on single-source satellite imagery and empirical threshold algorithms, and most forest fire monitoring tasks remain human-driven. Existing frameworks have yet to effectively integrate multiple data sources and detection algorithms, lacking the capability to provide continuous, automated, and generalizable fire monitoring across diverse fire scenarios. To address these challenges, this study first improves multiple monitoring algorithms for forest fire detection, including a statistically enhanced automatic thresholding method; data augmentation to expand the U-Net deep learning dataset; and the application of a freeze–unfreeze transfer learning strategy to the U-Net transfer model. Multiple algorithms are systematically evaluated across varying fire scales, showing that the improved automatic threshold method achieves the best performance on GF-4 imagery with an F-score of 0.915 (95% CI: 0.8725–0.9524), while the U-Net deep learning algorithm yields the highest F-score of 0.921 (95% CI: 0.8537–0.9739) on Landsat 8 imagery. All methods demonstrate robust performance and generalizability across diverse scenarios. Second, data-driven scheduling technology is developed to automatically initiate preprocessing and fire detection tasks, significantly reducing fire discovery time. Finally, an integrated framework of multi-source remote sensing data, advanced detection algorithms, and a user-friendly visualization interface is proposed. This framework enables all-weather, fully automated forest fire monitoring and early warning, facilitating dynamic tracking of fire evolution and precise fire line localization through the cross-application of heterogeneous data sources. The framework’s effectiveness and practicality are validated through wildfire cases in two regions of Yunnan Province, offering scalable technical support for improving early detection of and rapid response to forest fires. Full article
(This article belongs to the Special Issue Fire Patterns, Driving Factors, and Multidimensional Impacts Under Climate Change and Human Activities)
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7 pages, 1359 KB  
Article
Using Count Regression to Investigate Millennial-Scale Vegetation and Fire Response from Multiple Sites Across the Northern Rocky Mountains, USA
by Jennifer Watt, Brian F. Codding, Jordin Hartley, Carlie Murphy and Andrea Brunelle
Fire 2025, 8(8), 321; https://doi.org/10.3390/fire8080321 - 14 Aug 2025
Viewed by 1044
Abstract
The Northern Rocky Mountains, USA contain a vast forested landscape, managed primarily by the federal government. This region contains some of the highest elevations forests and most iconic endangered and threatened species in the contiguous United States. The influence of human impacts and [...] Read more.
The Northern Rocky Mountains, USA contain a vast forested landscape, managed primarily by the federal government. This region contains some of the highest elevations forests and most iconic endangered and threatened species in the contiguous United States. The influence of human impacts and climate change are evident on the landscape today, with larger and more frequent fires impacting vegetation composition and recovery. This project uses paleoecological data from six lake sediment cores to investigate what drives fire across this region over the Holocene. Count regression was used to predict charcoal influx as a function of Pinus pollen accumulation rates (PAR) and percent. The results show that fire activity increases significantly with Pinus pollen, and that baseline fire activity varies significantly across sites, largely following an elevation gradient. The results of this analysis illustrate a novel way to use paleoecological data to provide valuable information to federal agencies as they prepare for future management of these ecologically valuable areas. Full article
(This article belongs to the Special Issue Fire Patterns, Driving Factors, and Multidimensional Impacts Under Climate Change and Human Activities)
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