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Forests
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Wildfire Behavior and Risk: From Fundamental Research to Pioneering Modeling...
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Wildfire Behavior and Risk: From Fundamental Research to Pioneering Modeling Approaches

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 (30 June 2023) | Viewed by 14093

Special Issue Editors

Dr. Michele Salis

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Guest Editor
National Research Council of Italy, Institute of BioEconomy (CNR-IBE), Traversa La Crucca 3, 07100 Sassari, Sardinia, Italy
Interests: forest fires; fire exposure and risk; fire spread and behavior modeling; fire regime; fire-prone Mediterranean areas
Special Issues, Collections and Topics in MDPI journals
Dr. Grazia Pellizzaro

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Guest Editor
Institute of BioEconomy, National Research Council, Traversa La Crucca 3, 07100 Sassari, Italy
Interests: drought stress; weather; fire; environmental science; climate; abiotic stress; wildland fire; climate change; climate change impacts; drought; plant physiology; meteorology
Dr. Bachisio Arca

E-Mail Website
Guest Editor
Institute of BioEconomy, National Research Council, Traversa La Crucca 3, 07100 Sassari, Italy
Interests: environmental science; irrigation and water management; agronomy; climatology; meteorology
Dr. Pierpaolo Duce

E-Mail Website
Guest Editor
Institute of BioEconomy—National Research Council of Italy (IBE-CNR), 10 Via Madonna del Piano, 50019 Sesto Fiorentino, Italy
Interests: bioeconomy; LCA; agricultural byproducts
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Donatella Spano

E-Mail Website
Guest Editor
1. Fondazione CMCC—Centro Euro-Mediterraneo sui Cambiamenti Climatici, Impacts on Agriculture, Forests and Ecosystem Services (IAFES) Division, 07100 Sassari, Italy
2. Department of Agriculture Sciences, University of Sassari, 07100 Sassari, Italy
Prof. Dr. Costantino Sirca

E-Mail Website
Guest Editor
Dipartimento di Agraria, University of Sassari, Viale Italia, 39-07100 Sassari, Italy
Interests: climate change impacts; agrometeorology; plant–water relationship; soil respiration and carbon dynamics
Special Issues, Collections and Topics in MDPI journals
Dr. Valentina Bacciu

E-Mail Website
Guest Editor
Fondazione CMCC—Centro Euro-Mediterraneo sui Cambiamenti Climatici, Impacts on Agriculture, Forests and Ecosystem Services (IAFES) Division, 07100 Sassari, Italy
Interests: fire emissions; climate change; wildland–urban interface fire risk mapping; forest management adaptation options; fuel modeling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Wildfire regimes have shaped ecosystem dynamics in many bio-climatic regions of the world. There is evidence that climate, land use, and socio-economic changes are altering fire regimes and will affect future wildfire patterns in terms of frequency, size, intensity, and seasonality. Accurate predictions of wildfire behavior, spread, and risk are used by land managers and policy-makers to target and optimize prevention, management, and suppression activities and resources. In this Special Issue, we encourage the submission of manuscripts on any field of wildfire behavior and risk, including the effects of environmental factors on wildfire spread and hazards, the strategies for optimizing wildfire management, and the development of wildfire simulation and monitoring techniques and systems, along with new findings and insights that may contribute to expanding our knowledge of wildfire behavior and risk evaluation.

Please note that this Special Issue is organized in cooperation with the International Conference on Fire Behavior and Risk (“ICFBR2022”) and welcomes submissions from participants to this Conference. Further information on ICFBR2022 are available at this link: http://www.icfbr2022.it/.

Dr. Michele Salis

Dr. Grazia Pellizzaro
Dr. Bachisio Arca
Dr. Pierpaolo Duce
Prof. Donatella Spano
Dr. Costantino Sirca
Dr. Valentina Bacciu
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

  • wildfire behavior
  • wildfire risk
  • wildfire spread
  • large wildfires
  • fuels
  • weather and climate
  • topography
  • decision support systems
  • wildfire management.

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Published Papers (4 papers)

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Research

22 pages, 2934 KiB  
Article
Limitations and Opportunities of Spatial Planning to Enhance Wildfire Risk Reduction: Evidences from Portugal
by Fantina Tedim, André Samora-Arvela, Catarina Coimbra, José Aranha, Fernando Correia, Diogo M. Pinto, Célia Figueiras and Cláudia Magalhães
Forests 2023, 14(2), 303; https://doi.org/10.3390/f14020303 - 3 Feb 2023
Cited by 5 | Viewed by 2420
Abstract
Spatial planning potential for reducing natural risks including wildfires is widely recognized. This research is focused on Portugal, a wildfire-prone country in southern Europe, where the competencies for spatial planning lie on four geographical levels: (i) the national and regional levels, with a [...] Read more.
Spatial planning potential for reducing natural risks including wildfires is widely recognized. This research is focused on Portugal, a wildfire-prone country in southern Europe, where the competencies for spatial planning lie on four geographical levels: (i) the national and regional levels, with a strategic nature, set the general goals or the agenda of principles for spatial planning and (ii) the inter-municipal and municipal levels use regulative land-use planning instruments. There is a trend to bring together spatial planning and wildfire management policies. Thus, this paper aims to identify which are the main difficulties and which are the major opportunities, regarding the implementation of the new Integrated Management System for Rural Fires (IMSRF) and the challenge of integrating wildfire risk reduction in the Portuguese spatial planning framework. Through a survey of municipal professionals with experience in applying the legislation of both policies, the major difficulties and the opportunities of alignment of these two spheres are identified, which can be extrapolated for the whole country or countries in a similar context. Full article
(This article belongs to the Special Issue Wildfire Behavior and Risk: From Fundamental Research to Pioneering Modeling Approaches)
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40 pages, 8419 KiB  
Article
Design and Conceptual Development of a Novel Hybrid Intelligent Decision Support System Applied towards the Prevention and Early Detection of Forest Fires
by Manuel Casal-Guisande, José-Benito Bouza-Rodríguez, Jorge Cerqueiro-Pequeño and Alberto Comesaña-Campos
Forests 2023, 14(2), 172; https://doi.org/10.3390/f14020172 - 17 Jan 2023
Cited by 9 | Viewed by 3833
Abstract
Forest fires have become a major problem that every year has devastating consequences at the environmental level, negatively impacting the social and economic spheres of the affected regions. Aiming to mitigate these terrible effects, intelligent prediction models focused on early fire detection are [...] Read more.
Forest fires have become a major problem that every year has devastating consequences at the environmental level, negatively impacting the social and economic spheres of the affected regions. Aiming to mitigate these terrible effects, intelligent prediction models focused on early fire detection are becoming common practice. Considering mainly a preventive approach, these models often use tools that indifferently apply statistical or symbolic inference techniques. However, exploring the potential for the hybrid use of both, as is already being done in other research areas, is a significant novelty with direct application to early fire detection. In this line, this work proposes the design, development, and proof of concept of a new intelligent hybrid system that aims to provide support to the decisions of the teams responsible for defining strategies for the prevention, detection, and extinction of forest fires. The system determines three risk levels: a general one called Objective Technical Fire Risk, based on machine learning algorithms, which determines the global danger of a fire in some area of the region under study, and two more specific others which indicate the risk over a limited area of the region. These last two risk levels, expressed in matrix form and called Technical Risk Matrix and Expert Risk Matrix, are calculated through a convolutional neural network and an expert system, respectively. After that, they are combined by means of another expert system to determine the Global Risk Matrix that quantifies the risk of fire in each of the study regions and generates a visual representation of these results through a color map of the region itself. The proof of concept of the system has been carried out on a set of historical data from fires that occurred in the Montesinho Natural Park (Portugal), demonstrating its potential utility as a tool for the prevention and early detection of forest fires. The intelligent hybrid system designed has demonstrated excellent predictive capabilities in such a complex environment as forest fires, which are conditioned by multiple factors. Future improvements associated with data integration and the formalization of knowledge bases will make it possible to obtain a standard tool that could be used and validated in real time in different forest areas. Full article
(This article belongs to the Special Issue Wildfire Behavior and Risk: From Fundamental Research to Pioneering Modeling Approaches)
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19 pages, 3545 KiB  
Article
Individual-Tree and Stand-Level Models for Estimating Ladder Fuel Biomass Fractions in Unpruned Pinus radiata Plantations
by Cecilia Alonso-Rego, Paulo Fernandes, Juan Gabriel Álvarez-González, Stefano Arellano-Pérez and Ana Daría Ruiz-González
Forests 2022, 13(10), 1697; https://doi.org/10.3390/f13101697 - 15 Oct 2022
Cited by 1 | Viewed by 1545
Abstract
The mild climate and, in recent decades, the increased demand for timber have favoured the establishment of extensive plantations of fast-growing species such as Pinus radiata in Galicia (a fire-prone region in northwestern Spain). This species is characterised by very poor self-pruning; unmanaged [...] Read more.
The mild climate and, in recent decades, the increased demand for timber have favoured the establishment of extensive plantations of fast-growing species such as Pinus radiata in Galicia (a fire-prone region in northwestern Spain). This species is characterised by very poor self-pruning; unmanaged pine stands have a worrying vertical continuity of fuels after crown closure because the dead lower branches accumulate large amounts of fine dead biomass including twigs and suspended needles. Despite the important contribution of these dead ladder fuels to the overall canopy biomass and to crown-fire hazards, equations for estimating these fuels have not yet been developed. In this study, two systems of equations for estimating dead ladder fuel according to size class and the vertical distribution in the first 6 m of the crown were fitted: a tree-level system based on individual tree and stand variables and a stand-level system based only on stand variables. The goodness-of-fit statistics for both model systems indicated that the estimates were robust and accurate. At the tree level, fuel biomass models explained between 35% and 59% of the observed variability, whereas cumulative fuel biomass models explained between 62% and 81% of the observed variability. On the other hand, at the stand level, fuel-load models explained between 88% and 98% of the observed variability, whereas cumulative fuel-load models explained more than 98% of the total observed variability. These systems will therefore allow managers to adequately quantify the dead ladder fuels in pure Pinus radiata stands and to identify the treatments required to reduce crown-fire hazard. Full article
(This article belongs to the Special Issue Wildfire Behavior and Risk: From Fundamental Research to Pioneering Modeling Approaches)
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13 pages, 1883 KiB  
Article
Forest Fire Forecasting Using Fuzzy Logic Models
by Àngela Nebot and Francisco Mugica
Forests 2021, 12(8), 1005; https://doi.org/10.3390/f12081005 - 29 Jul 2021
Cited by 17 | Viewed by 3971
Abstract
In this study, we explored hybrid fuzzy logic modelling techniques to predict the burned area of forest fires. Fast detection is crucial for successful firefighting, and a model with an accurate prediction ability is extremely useful for optimizing fire management. Fuzzy Inductive Reasoning [...] Read more.
In this study, we explored hybrid fuzzy logic modelling techniques to predict the burned area of forest fires. Fast detection is crucial for successful firefighting, and a model with an accurate prediction ability is extremely useful for optimizing fire management. Fuzzy Inductive Reasoning (FIR) and the Adaptive Neuro-Fuzzy Inference System (ANFIS) are two powerful fuzzy techniques for modelling burned areas of forests in Portugal. The results obtained from them were compared with those of other artificial intelligence techniques applied to the same datasets found in the literature. Full article
(This article belongs to the Special Issue Wildfire Behavior and Risk: From Fundamental Research to Pioneering Modeling Approaches)
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