Forest Fire Regimes and Forest Fuels: Characterization and Modelling in a Climate Change Scenario

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

Deadline for manuscript submissions: closed (15 March 2024) | Viewed by 759

Special Issue Editor


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Guest Editor
Dipartimento di Scienze e Tecnologie Agrarie, Alimentari, Ambientali e Forestali—DAGRI, Università Degli Studi di Firenze, Via San Bonaventura 13, 50145 Firenze, Italy
Interests: fire ecology; forest fuels dynamics; forest operations in fire prone areas; wildfire models; post fire recovery operations

Special Issue Information

Dear Colleagues,

Forest fire regimes and forest fuels are critical aspects of understanding and managing wildfire risk, particularly in the context of climate change. The Special Issue aims to explore the key points related to the characterization and modeling of forest fire regimes and forest fuels in a climate change scenario.

Forest fire regimes refer to the patterns and characteristics of wildfires within a specific region or ecosystem. They encompass various factors such as fire frequency, intensity, size, severity, and behavior. Fire regimes are influenced by a combination of climate, vegetation, topography, and human activities. In a climate change scenario, forest fire regimes may experience significant shifts due to changing environmental conditions, including altered precipitation patterns, temperature increases, and changes in vegetation composition. These changes can impact the frequency, severity, and behavior of wildfires with important side effects on habitat conservation and species diversity maintenance.

The main component we can interact with, in order to control fire regimes and reduce risk, is related to forest fuel characteristics.

Forest fuels are the materials present in a forest ecosystem that can support and propagate wildfires. They include live and dead vegetation, such as trees, shrubs, grasses, leaves, branches, and accumulated organic matter like fallen logs and woody debris. The spatial distribution, moisture content, and quantity of forest fuels influence fire behavior and its ability to spread. 

In a climate change scenario, shifts in precipitation patterns and increased drought conditions can alter fuel moisture content, leading to drier vegetation and increased fire susceptibility. Additionally, changes in vegetation composition due to factors like insect infestations or invasive species can affect fuel characteristics and modify fire behavior.

Various modeling approaches were developed to understand and predict forest fire regimes and fuel dynamics in a climate change scenario. Additionally, fuel management strategies can be explored using modeling tools to assess the effectiveness of fuel reduction treatments in mitigating fire risks. It is important to note that the accuracy and reliability of fire regime and fuel models depend on the quality and availability of data, as well as the complexity of the ecological systems being studied. Continuous research, monitoring, and improvement of models are necessary to enhance our understanding of forest fire regimes and effectively adapt to changing environmental conditions.

Any novel approach or comparison between different models will provide increased value to the scientific community, thus improving knowledge about this topic.

Dr. Cristiano Foderi
Guest Editor

Manuscript Submission Information

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Keywords

  • wildfire
  • fire regime
  • forest fuel dynamics
  • forest fuel characterization
  • forest fuel models
  • wildfire risk
  • wildfire and climate change

Published Papers (1 paper)

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Research

15 pages, 7485 KiB  
Article
Predicting Fine Dead Fuel Load of Forest Floors Based on Image Euler Numbers
by Yunlin Zhang and Lingling Tian
Forests 2024, 15(4), 726; https://doi.org/10.3390/f15040726 - 21 Apr 2024
Viewed by 519
Abstract
The fine dead fuel load on forest floors is the most critical classification feature in fuel description systems, affecting several parameters in the manifestation of wildland fires. An accurate determination of this fine dead fuel load contributes substantially to effective wildland fire prevention, [...] Read more.
The fine dead fuel load on forest floors is the most critical classification feature in fuel description systems, affecting several parameters in the manifestation of wildland fires. An accurate determination of this fine dead fuel load contributes substantially to effective wildland fire prevention, monitoring, and suppression. This study investigated the viability of incorporating image Euler numbers into predictive models of fine dead fuel load via the taking photos method. Pinus massoniana needles and Quercus fabri broad leaves—typical fuel types in karst areas—served as the research subjects. Accurate field data were collected in the Tianhe Mountain forests, China, while artificial fine dead fuelbeds of differing loads were constructed in the laboratory. Images of the artificial fuelbeds were captured and uniformly digitized according to various conversion thresholds. Thereafter, the Euler numbers were extracted, their relationship with fuel load was analyzed, and this relationship was applied to generate three load-prediction models based on stepwise regression, nonlinear fitting, and random forest algorithms. The Euler number had a significant relationship with both P. massoniana and Q. fabri fuel loads. At low conversion thresholds, the Euler number was negatively correlated with fuel load, whereas a positive correlation was recorded when this threshold exceeded a certain value. The random forest model showed the best prediction performance, with mean relative errors of 9.35% and 14.54% for P. massoniana and Q. fabri, respectively. The nonlinear fitting model displayed the next best performance, while the stepwise regression model exhibited the largest error, which was significantly different from that of the random forest model. This study is the first to propose the use of image features to predict the fine fuel load on a surface. The results are more objective, accurate, and time-saving than current fuel load estimates, benefiting fuel load research and the scientific management of wildland fires. Full article
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