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Fire
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Experimentation and Physics-Based Modeling to Support Prescribed Burning
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Experimentation and Physics-Based Modeling to Support Prescribed Burning

A special issue of Fire (ISSN 2571-6255). This special issue belongs to the section "Fire Science Models, Remote Sensing, and Data".

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 17050

Special Issue Editors

Dr. Michael Gallagher

E-Mail Website
Guest Editor
USDA Forest Service, Northern Research Station, 501 Four Mile Road, New Lisbon, NJ 08064, USA
Interests: wildland fire; prescribed fire; remote sensing; fire behavior; fire effects; fire severity
Dr. Nicholas Skowronski

E-Mail Website
Guest Editor
USDA Forest Service, Northern Research Station, Morgantown, WV 26505, USA
Interests: wildfire; ecology; remote sensing; LiDAR; wildland fire
Dr. Alexander I. Filkov

E-Mail Website
Guest Editor
School of Ecosystem and Forest Sciences, Faculty of Science, University of Melbourne, Creswick, VIC 3363, Australia
Interests: extreme fires; fire behavior; fire risk; WUI fires
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Prescribed fire is used extensively worldwide as a tool for meeting land management objectives, including through hazardous fuel mitigation, ecological maintenance and restoration, silvicultural prescriptions, and others. Policy makers and practitioners are progressively working to increase the rate and geographic extent of prescribed fire implementation. In response, recent research efforts have aimed to develop the knowledge and tools that prescribed fire managers need for safe implementation of burns and to increase the probability of achieving the management objective.

A major focus of recent research on prescribed fire has been to increase our underlying knowledge about the physical processes that control fire behavior and related fire effects and then to integrate these findings into numeric models. These studies span scales from laboratory experimentation to operational prescribed burn monitoring and attempt to decompose individual and coupled processes and then reconstruct them in a computational environment.

This call for papers encourages the submission of articles designed to improve the physical understanding of fire behavior with application to prescribed burning. It focuses on wildland fuel characteristics, air flow dynamics, heat transfer mechanisms, plume dynamics, and atmospheric processes. Notably, these questions can be approached through field, laboratory, and numerical modeling investigations, and we are open to a variety of methodological and philosophical perspectives as well as novel experimental approaches. We anticipate this Special Issue will include experts from a diverse backgrounds and strongly encourage participation from all subdisciplines of plant biology, engineering, fire ecology, and numerical modeling. Examples of topics include, but are not limited to:

  • Fire processes (e.g., ignition, combustion, heat transfer, and development of fire-induced air flow) and the importance of spatial scaling in predicting fire behavior;
  • Feedback effects between vegetation structure and composition, air flow, fire behavior and plume development;
  • Disentangling the influences of fuel moisture dynamics and structural heterogeneity on fire intensity, ember production, emissions, and crown fire development;
  • Combined influences of ignition patterns/rates and fuel structure on fire behavior-driven patterns;
  • Novel tools or techniques for quantifying fuels and fire behavior characteristics.

Dr. Michael R. Gallagher
Dr. Nicholas Skowronski
Dr. Alexander I. Filkov
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. 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

  • physics-based approach
  • prescribed fire
  • fire behavior
  • atmospheric dynamics
  • emissions
  • plume dynamics
  • fuels
  • numerical modeling

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

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Research

22 pages, 18099 KiB  
Article
Remotely Sensed Fine-Fuel Changes from Wildfire and Prescribed Fire in a Semi-Arid Grassland
by Adam G. Wells, Seth M. Munson, Steven E. Sesnie and Miguel L. Villarreal
Fire 2021, 4(4), 84; https://doi.org/10.3390/fire4040084 - 11 Nov 2021
Cited by 11 | Viewed by 4781 | Correction
Abstract
The spread of flammable invasive grasses, woody plant encroachment, and enhanced aridity have interacted in many grasslands globally to increase wildfire activity and risk to valued assets. Annual variation in the abundance and distribution of fine-fuel present challenges to land managers implementing prescribed [...] Read more.
The spread of flammable invasive grasses, woody plant encroachment, and enhanced aridity have interacted in many grasslands globally to increase wildfire activity and risk to valued assets. Annual variation in the abundance and distribution of fine-fuel present challenges to land managers implementing prescribed burns and mitigating wildfire, although methods to produce high-resolution fuel estimates are still under development. To further understand how prescribed fire and wildfire influence fine-fuels in a semi-arid grassland invaded by non-native perennial grasses, we combined high-resolution Sentinel-2A imagery with in situ vegetation data and machine learning to estimate yearly fine-fuel loads from 2015 to 2020. The resulting model of fine-fuel corresponded to field-based validation measurements taken in the first (R2 = 0.52, RMSE = 218 kg/ha) and last year (R2 = 0.63, RMSE = 196 kg/ha) of this 6-year study. Serial prediction of the fine-fuel model allowed for an assessment of the effect of prescribed fire (average reduction of −80 kg/ha 1-year post fire) and wildfire (−260 kg/ha 1-year post fire) on fuel conditions. Post-fire fine-fuel loads were significantly lower than in unburned control areas sampled just outside fire perimeters from 2015 to 2020 across all fires (t = 1.67, p < 0.0001); however, fine-fuel recovery occurred within 3–5 years, depending upon burn and climate conditions. When coupled with detailed fuels data from field measurements, Sentinel-2A imagery provided a means for evaluating grassland fine-fuels at yearly time steps and shows high potential for extended monitoring of dryland fuels. Our approach provides land managers with a systematic analysis of the effects of fire management treatments on fine-fuel conditions and provides an accurate, updateable, and expandable solution for mapping fine-fuels over yearly time steps across drylands throughout the world. Full article
(This article belongs to the Special Issue Experimentation and Physics-Based Modeling to Support Prescribed Burning)
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17 pages, 4936 KiB  
Article
Reconstruction of the Spring Hill Wildfire and Exploration of Alternate Management Scenarios Using QUIC-Fire
by Michael R. Gallagher, Zachary Cope, Daniel Rosales Giron, Nicholas S. Skowronski, Trevor Raynor, Thomas Gerber, Rodman R. Linn and John Kevin Hiers
Fire 2021, 4(4), 72; https://doi.org/10.3390/fire4040072 - 15 Oct 2021
Cited by 7 | Viewed by 4342
Abstract
New physics-based fire behavior models are poised to revolutionize wildland fire planning and training; however, model testing against field conditions remains limited. We tested the ability of QUIC-Fire, a fast-running and computationally inexpensive physics-based fire behavior model to numerically reconstruct a large wildfire [...] Read more.
New physics-based fire behavior models are poised to revolutionize wildland fire planning and training; however, model testing against field conditions remains limited. We tested the ability of QUIC-Fire, a fast-running and computationally inexpensive physics-based fire behavior model to numerically reconstruct a large wildfire that burned in a fire-excluded area within the New York–Philadelphia metropolitan area in 2019. We then used QUIC-Fire as a tool to explore how alternate hypothetical management scenarios, such as prescribed burning, could have affected fire behavior. The results of our reconstruction provide a strong demonstration of how QUIC-Fire can be used to simulate actual wildfire scenarios with the integration of local weather and fuel information, as well as to efficiently explore how fire management can influence fire behavior in specific burn units. Our results illustrate how both reductions of fuel load and specific modification of fuel structure associated with frequent prescribed fire are critical to reducing fire intensity and size. We discuss how simulations such as this can be important in planning and training tools for wildland firefighters, and for avenues of future research and fuel monitoring that can accelerate the incorporation of models like QUIC-Fire into fire management strategies. Full article
(This article belongs to the Special Issue Experimentation and Physics-Based Modeling to Support Prescribed Burning)
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21 pages, 6915 KiB  
Article
Fine-Scale Fire Spread in Pine Straw
by Daryn Sagel, Kevin Speer, Scott Pokswinski and Bryan Quaife
Fire 2021, 4(4), 69; https://doi.org/10.3390/fire4040069 - 10 Oct 2021
Cited by 7 | Viewed by 3318
Abstract
Most wildland and prescribed fire spread occurs through ground fuels, and the rate of spread (RoS) in such environments is often summarized with empirical models that assume uniform environmental conditions and produce a unique RoS. On the other hand, representing the effects of [...] Read more.
Most wildland and prescribed fire spread occurs through ground fuels, and the rate of spread (RoS) in such environments is often summarized with empirical models that assume uniform environmental conditions and produce a unique RoS. On the other hand, representing the effects of local, small-scale variations of fuel and wind experienced in the field is challenging and, for landscape-scale models, impractical. Moreover, the level of uncertainty associated with characterizing RoS and flame dynamics in the presence of turbulent flow demonstrates the need for further understanding of fire dynamics at small scales in realistic settings. This work describes adapted computer vision techniques used to form fine-scale measurements of the spatially and temporally varying RoS in a natural setting. These algorithms are applied to infrared and visible images of a small-scale prescribed burn of a quasi-homogeneous pine needle bed under stationary wind conditions. A large number of distinct fire front displacements are then used statistically to analyze the fire spread. We find that the fine-scale forward RoS is characterized by an exponential distribution, suggesting a model for fire spread as a random process at this scale. Full article
(This article belongs to the Special Issue Experimentation and Physics-Based Modeling to Support Prescribed Burning)
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17 pages, 3596 KiB  
Article
Determination of Firebrand Characteristics Using Thermal Videos
by Sergey Prohanov, Alexander Filkov, Denis Kasymov, Mikhail Agafontsev and Vladimir Reyno
Fire 2020, 3(4), 68; https://doi.org/10.3390/fire3040068 - 26 Nov 2020
Cited by 11 | Viewed by 3899
Abstract
Burning firebrands generated by wildland or prescribed fires may lead to the initiation of spot fires and fire escapes. At the present time, there are no methods that provide information on the thermal characteristics and number of such firebrands with high spatial and [...] Read more.
Burning firebrands generated by wildland or prescribed fires may lead to the initiation of spot fires and fire escapes. At the present time, there are no methods that provide information on the thermal characteristics and number of such firebrands with high spatial and temporal resolution. A number of algorithms have been developed to detect and track firebrands in field conditions in our previous study; however, each holds particular disadvantages. This work is devoted to the development of new algorithms and their testing and, as such, several laboratory experiments were conducted. Wood pellets, bark, and twigs of pine were used to generate firebrands. An infrared camera (JADE J530SB) was used to obtain the necessary thermal video files. The thermograms were then processed to create an annotated IR video database that was used to test both the detector and the tracker. Following these studies, the analysis showed that the Difference of Gaussians detection algorithm and the Hungarian tracking algorithm upheld the highest level of accuracy and were the easiest to implement. The study also indicated that further development of detection and tracking algorithms using the current approach will not significantly improve their accuracy. As such, convolutional neural networks hold high potential to be used as an alternative approach. Full article
(This article belongs to the Special Issue Experimentation and Physics-Based Modeling to Support Prescribed Burning)
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