Fire | Special Issue : Advances in the Measurement of Fuels and Fuel Properties

Editorial

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6 pages, 211 KiB

Open AccessEditorial

Preface: Special Issue on Advances in the Measurement of Fuels and Fuel Properties

by Wade T. Tinkham, Lauren E. Lad and Alistair M. S. Smith

Fire 2023, 6(3), 108; https://doi.org/10.3390/fire6030108 - 9 Mar 2023

Viewed by 1601

Abstract

Increasing global temperatures and variability in the timing, quantity, and intensity of precipitation and wind have led to longer fire season lengths, greater fuel availability, and more intense and severe wildfires [...] Full article

(This article belongs to the Special Issue Advances in the Measurement of Fuels and Fuel Properties)

Research

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19 pages, 1944 KiB

Open AccessArticle

A Quantitative Analysis of Fuel Break Effectiveness Drivers in Southern California National Forests

by Benjamin Gannon, Yu Wei, Erin Belval, Jesse Young, Matthew Thompson, Christopher O’Connor, David Calkin and Christopher Dunn

Fire 2023, 6(3), 104; https://doi.org/10.3390/fire6030104 - 7 Mar 2023

Cited by 8 | Viewed by 2951

Abstract

Fuel and wildfire management decisions related to fuel break construction, maintenance, and use in fire suppression suffer from limited information on fuel break success rates and drivers of effectiveness. We built a dataset of fuel break encounters with recent large wildfires in Southern [...] Read more.

Fuel and wildfire management decisions related to fuel break construction, maintenance, and use in fire suppression suffer from limited information on fuel break success rates and drivers of effectiveness. We built a dataset of fuel break encounters with recent large wildfires in Southern California and their associated biophysical, suppression, weather, and fire behavior characteristics to develop statistical models of fuel break effectiveness with boosted regression. Our results suggest that the dominant influences on fuel break effectiveness are suppression, weather, and fire behavior. Variables related to fuel break placement, design, and maintenance were less important but aligned with manager expectations for higher success with wider and better maintained fuel breaks, and prior research findings that fuel break success increases with accessibility. Fuel breaks also held more often if burned by a wildfire during the previous decade, supporting the idea that fuel breaks may be most effective if combined with broader fuel reduction efforts. Full article

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17 pages, 6319 KiB

Open AccessArticle

Indoor Experiments on the Moisture Dynamic Response to Wind Velocity for Fuelbeds with Different Degrees of Compactness

by Yunlin Zhang

Fire 2023, 6(3), 90; https://doi.org/10.3390/fire6030090 - 26 Feb 2023

Cited by 1 | Viewed by 1408

Abstract

The semiphysical method is presently the most widely used for predicting litter moisture content, but it produces some errors. These are mainly due to the simplification of the water loss process and not accounting for the fuelbed structure, which can have a serious [...] Read more.

The semiphysical method is presently the most widely used for predicting litter moisture content, but it produces some errors. These are mainly due to the simplification of the water loss process and not accounting for the fuelbed structure, which can have a serious impact on the accuracy of litter moisture content predictions and, consequently, on forest fire management. As such, in this study, we constructed fuelbeds with different degrees of compactness, and the moisture content is saturated at this time. The drying process is recorded every 10 min under different wind velocity, and the experiment is stopped when the moisture content is not changing. Taking the saturated fibers’ moisture content (30%) as the threshold value, the drying process was artificially divided into two stages (from the initial moisture content to 30%, it is a process of free water drying, and from 30% to the equilibrium moisture content, this is the process of drying of bound water), which is called the distinguishing drying process. The whole drying process (from the initial to the equilibrium moisture content) is called the undistinguishing drying process. Drying coefficient and effect factors were calculated by distinguishing and not distinguishing the drying process, respectively. This established a prediction model based on compactness and wind velocity. The results show that the drying coefficients,

k_{2}

and

k

, of the two litter types were significantly different: the

k_{2}

of the white oak fuelbed was significantly lower than its

k

, with a maximum variation difference of 57.10%. The

k_{2}

in the Masson pine fuelbed was significantly higher than its

k

, with a maximum variation difference of 72.76%. Wind velocity and compactness had significant effects on all the drying coefficients of the two litter types, but with changes in the effect factors. The changes in

k_{2}

were weaker than those of the other drying coefficients. Compared with the model that did not distinguish the drying process, the MRE of the prediction models for white oak and Masson pine decreased by 27.39% and 2.35%, respectively. The prediction accuracy of the model of the drying coefficient obtained by distinguishing the drying loss process was higher than that of the model that did not distinguish the drying process. This study was an indoor simulation experiment that elucidated the drying mechanism of litter and established a prediction model for the drying coefficient based on effect factors. It is of great significance for further field verification and for improving the accuracy of moisture content predictions based on the semiphysical method and will significantly improve the accuracy of fire risk and fire behavior prediction. Full article

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18 pages, 4774 KiB

Open AccessArticle

Vegetation Cover Type Classification Using Cartographic Data for Prediction of Wildfire Behaviour

by Mohammad Tavakol Sadrabadi and Mauro Sebastián Innocente

Fire 2023, 6(2), 76; https://doi.org/10.3390/fire6020076 - 18 Feb 2023

Cited by 4 | Viewed by 2314

Abstract

Predicting the behaviour of wildfires can help save lives and reduce health, socioeconomic, and environmental impacts. Because wildfire behaviour is highly dependent on fuel type and distribution, their accurate estimation is paramount for accurate prediction of the fire propagation dynamics. This paper studies [...] Read more.

Predicting the behaviour of wildfires can help save lives and reduce health, socioeconomic, and environmental impacts. Because wildfire behaviour is highly dependent on fuel type and distribution, their accurate estimation is paramount for accurate prediction of the fire propagation dynamics. This paper studies the effect of combining automated hyperparameter tuning with Bayesian optimisation and recursive feature elimination on the accuracy of three boosting (AdaB, XGB, CatB), two bagging (Random Forest, Extremely Randomised Trees), and three stacking ensemble models with respect to their ability to estimate the vegetation cover type from cartographic data. The models are trained on the University of California Irvine (UCI) cover type dataset using five-fold cross-validation. Feature importance scores are calculated and used in recursive feature elimination analysis to study the sensitivity of model accuracy to the different feature combinations. Our results indicate that the implemented fine-tuning procedure significantly affects the accuracy of all models investigated, with XGB achieving an overall accuracy of 97.1% slightly outperforming the others. Full article

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25 pages, 7981 KiB

Open AccessArticle

A Multimodal Data Fusion and Deep Learning Framework for Large-Scale Wildfire Surface Fuel Mapping

by Mohamad Alipour, Inga La Puma, Joshua Picotte, Kasra Shamsaei, Eric Rowell, Adam Watts, Branko Kosovic, Hamed Ebrahimian and Ertugrul Taciroglu

Fire 2023, 6(2), 36; https://doi.org/10.3390/fire6020036 - 17 Jan 2023

Cited by 21 | Viewed by 5107

Abstract

Accurate estimation of fuels is essential for wildland fire simulations as well as decision-making related to land management. Numerous research efforts have leveraged remote sensing and machine learning for classifying land cover and mapping forest vegetation species. In most cases that focused on [...] Read more.

Accurate estimation of fuels is essential for wildland fire simulations as well as decision-making related to land management. Numerous research efforts have leveraged remote sensing and machine learning for classifying land cover and mapping forest vegetation species. In most cases that focused on surface fuel mapping, the spatial scale of interest was smaller than a few hundred square kilometers; thus, many small-scale site-specific models had to be created to cover the landscape at the national scale. The present work aims to develop a large-scale surface fuel identification model using a custom deep learning framework that can ingest multimodal data. Specifically, we use deep learning to extract information from multispectral signatures, high-resolution imagery, and biophysical climate and terrain data in a way that facilitates their end-to-end training on labeled data. A multi-layer neural network is used with spectral and biophysical data, and a convolutional neural network backbone is used to extract the visual features from high-resolution imagery. A Monte Carlo dropout mechanism was also devised to create a stochastic ensemble of models that can capture classification uncertainties while boosting the prediction performance. To train the system as a proof-of-concept, fuel pseudo-labels were created by a random geospatial sampling of existing fuel maps across California. Application results on independent test sets showed promising fuel identification performance with an overall accuracy ranging from 55% to 75%, depending on the level of granularity of the included fuel types. As expected, including the rare—and possibly less consequential—fuel types reduced the accuracy. On the other hand, the addition of high-resolution imagery improved classification performance at all levels. Full article

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19 pages, 3522 KiB

Open AccessArticle

Quantifying Litter Bed Ignitability: Comparison of a Laboratory and Field Method

by Jamie E. Burton, Alexander I. Filkov, Bianca J. Pickering, Trent D. Penman and Jane G. Cawson

Fire 2023, 6(1), 24; https://doi.org/10.3390/fire6010024 - 10 Jan 2023

Cited by 5 | Viewed by 2000

Abstract

Understanding the conditions when litter beds will ignite from firebrands is critical for predicting spot fire occurrence. Such research is either field- or laboratory-based, with limited analysis to compare the approaches. We examined the ability of a laboratory method to represent field-scale ignitability. [...] Read more.

Understanding the conditions when litter beds will ignite from firebrands is critical for predicting spot fire occurrence. Such research is either field- or laboratory-based, with limited analysis to compare the approaches. We examined the ability of a laboratory method to represent field-scale ignitability. The laboratory method involved collecting litter-bed samples concurrently with the field experiments and then reconstructing and burning the litter-bed samples in the laboratory. We measured the number of successful and sustained ignitions in the laboratory (n = 5) and field (n = 30 attempts). The laboratory and field results were more similar for successful (bias = 0.05) than sustained ignitions (bias = 0.08). Wind, fuel structure (in the field) and near-surface fuel moisture influenced the differences between the methods. Our study highlights the value in conducting simultaneous laboratory and field experiments to understand the scalability of laboratory studies. For our ignitability method, our results suggest that small-scale laboratory experiments could be an effective substitute for field experiments in forests where litter beds are the dominant fuel layer and where the cover of the near-surface fuel is low. Full article

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10 pages, 1542 KiB

Open AccessArticle

Adjustment of the Grass Fuel Moisture Code for Grasslands in Southern Brazil

by João Francisco Labres dos Santos, Bruna Kovalsyki, Tiago de Souza Ferreira, Antonio Carlos Batista and Alexandre França Tetto

Fire 2022, 5(6), 209; https://doi.org/10.3390/fire5060209 - 7 Dec 2022

Cited by 2 | Viewed by 1901

Abstract

Grasslands are one of the vegetation types most widely affected by wildfires in southern Brazil. It is a fire-dependent ecosystem and it is necessary to know the hourly fuel moisture variation for its management. The objective of this work is to fit Grass [...] Read more.

Grasslands are one of the vegetation types most widely affected by wildfires in southern Brazil. It is a fire-dependent ecosystem and it is necessary to know the hourly fuel moisture variation for its management. The objective of this work is to fit Grass Fuel Moisture Code (GFMC) models to estimate the moisture content for the grassland of the State Park of Vila Velha, Paraná, Brazil. Data sampling to determine the hourly moisture content was performed during the winter of 2018 and divided into two campaigns of five days with stable weather conditions. Destructive samples were taken out for the sorption tests on climatic chambers to obtain the equilibrium moisture content and the time lag values. The fitted equilibrium moisture and time lag models were evaluated by residual distribution analysis, mean absolute error (MAE), root mean square error (RSME) and coefficient of determination (R²). The fitted model performed better than the original GFMC model due to the obtained MAE, RSME and R² values. The results showed that the fitted GFMC model is better to predict the fine fuel moisture for the region. Full article

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31 pages, 3280 KiB

Open AccessArticle

Fuel in Tasmanian Dry Eucalypt Forests: Prediction of Fuel Load and Fuel Hazard Rating from Fuel Age

by Jon B. Marsden-Smedley, Wendy R. Anderson and Adrian F. Pyrke

Fire 2022, 5(4), 103; https://doi.org/10.3390/fire5040103 - 19 Jul 2022

Cited by 3 | Viewed by 2787

Abstract

This paper presents equations for fuel load and fuel hazard rating (FHR) models based on the time since last fire for dry eucalypt forests in eastern Tasmania. The fuel load equations predict the load of the surface/near-surface and elevated fine fuel. The FHR [...] Read more.

This paper presents equations for fuel load and fuel hazard rating (FHR) models based on the time since last fire for dry eucalypt forests in eastern Tasmania. The fuel load equations predict the load of the surface/near-surface and elevated fine fuel. The FHR equations predict the surface, near-surface, combined surface and near-surface, bark, and overall FHR. The utility of the “Overall fuel hazard assessment guide” from Victoria, Australia, is assessed for Tasmanian dry eucalypt forests: we conclude that, when fuel strata components are weighted according to their influence on fire behaviour, the Victorian guide provides a rapid, robust, and effective methodology for estimating FHR. The equations in this paper will be used for operational planning and on-the-ground performing of hazard reduction burning, prediction of fire behaviour for fire risk assessments and bushfire control, and providing inputs into the new Australian Fire Danger Rating System. Full article

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20 pages, 6226 KiB

Open AccessArticle

Terrestrial Laser Scanning: An Operational Tool for Fuel Hazard Mapping?

by Luke Wallace, Samuel Hillman, Bryan Hally, Ritu Taneja, Andrew White and James McGlade

Fire 2022, 5(4), 85; https://doi.org/10.3390/fire5040085 - 22 Jun 2022

Cited by 12 | Viewed by 4274

Abstract

Fuel hazard estimates are vital for the prediction of fire behaviour and planning fuel treatment activities. Previous literature has highlighted the potential of Terrestrial Laser Scanning (TLS) to be used to assess fuel properties. However, operational uptake of these systems has been limited [...] Read more.

Fuel hazard estimates are vital for the prediction of fire behaviour and planning fuel treatment activities. Previous literature has highlighted the potential of Terrestrial Laser Scanning (TLS) to be used to assess fuel properties. However, operational uptake of these systems has been limited due to a lack of a sampling approach that balances efficiency and data efficacy. This study aims to assess whether an operational approach utilising Terrestrial Laser Scanning (TLS) to capture fuel information over an area commensurate with current fuel hazard assessment protocols implemented in South-Eastern Australia is feasible. TLS data were captured over various plots in South-Eastern Australia, utilising both low- and high-cost TLS sensors. Results indicate that both scanners provided similar overall representation of the ground, vertical distribution of vegetation and fuel hazard estimates. The analysis of fuel information contained within individual scans clipped to 4 m showed similar results to that of the fully co-registered plot (cover estimates of near-surface vegetation were within 10%, elevated vegetation within 15%, and height estimates of near-surface and elevated strata within 0.05 cm). This study recommends that, to capture a plot in an operational environment (balancing efficiency and data completeness), a sufficient number of non-overlapping individual scans can provide reliable estimates of fuel information at the near-surface and elevated strata, without the need for co-registration in the case study environments. The use of TLS within the rigid structure provided by current fuel observation protocols provides incremental benefit to the measurement of fuel hazard. Future research should leverage the full capability of TLS data and combine it with moisture estimates to gain a full realisation of the fuel hazard. Full article

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14 pages, 3167 KiB

Open AccessArticle

Long-Term Response of Fuel to Mechanical Mastication in South-Eastern Australia

by Bianca J. Pickering, Jamie E. Burton, Trent D. Penman, Madeleine A. Grant and Jane G. Cawson

Fire 2022, 5(3), 76; https://doi.org/10.3390/fire5030076 - 3 Jun 2022

Cited by 4 | Viewed by 3061

Abstract

Mechanical mastication is a fuel management strategy that modifies vegetation structure to reduce the impact of wildfire. Although past research has quantified immediate changes to fuel post-mastication, few studies consider longer-term fuel trajectories and climatic drivers of this change. Our study sought to [...] Read more.

Mechanical mastication is a fuel management strategy that modifies vegetation structure to reduce the impact of wildfire. Although past research has quantified immediate changes to fuel post-mastication, few studies consider longer-term fuel trajectories and climatic drivers of this change. Our study sought to quantify changes to fuel loads and structure over time following mastication and as a function of landscape aridity. Measurements were made at 63 sites in Victoria, Australia. All sites had been masticated within the previous 9 years to remove over-abundant shrubs and small trees. We used generalised additive models to explore trends over time and along an aridity gradient. Surface fuel loads were highest immediately post-mastication and in the most arid sites. The surface fine fuel load declined over time, whereas the surface coarse fuel load remained high; these trends occurred irrespective of landscape aridity. Standing fuel (understorey and midstorey vegetation) regenerated consistently, but shrub cover was still substantially low at 9 years post-mastication. Fire managers need to consider the trade-off between a persistently higher surface coarse fuel load and reduced shrub cover to evaluate the efficacy of mastication for fuel management. Coarse fuel may increase soil heating and smoke emissions, but less shrub cover will likely moderate fire behaviour. Full article

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24 pages, 6499 KiB

Open AccessArticle

Modeling of Wood Surface Ignition by Wildland Firebrands

by Oleg Matvienko, Denis Kasymov, Egor Loboda, Anastasia Lutsenko and Olga Daneyko

Fire 2022, 5(2), 38; https://doi.org/10.3390/fire5020038 - 15 Mar 2022

Cited by 7 | Viewed by 4809

Abstract

The probability of structural ignition is dependent both on physical properties of materials and the fire exposure conditions. In this study, the effect of firebrand characteristics (i.e., firebrand size, number of firebrands) on wood ignition behavior was considered. Mathematical modeling and laboratory experiment [...] Read more.

The probability of structural ignition is dependent both on physical properties of materials and the fire exposure conditions. In this study, the effect of firebrand characteristics (i.e., firebrand size, number of firebrands) on wood ignition behavior was considered. Mathematical modeling and laboratory experiment were conducted to better understand the conditions of wood ignition by a single or group of firebrands with different geometry. This model considers the heat exchange between the firebrands, wood layer and the gas phase, moisture evaporation in the firebrands and the diffusion gases of water vapor in the pyrolysis zone. In order to test and verify the model, a series of experiments to determine probability and conditions for ignition of wood-based materials (plywood, oriented strand board, chipboard) caused by wildland firebrands (pine twigs with a diameter of 6–8 mm and a length of 40 ± 2 mm) were conducted. The experiments investigated the firebrand impact on the wood layer under different parameters, such as firebrand size and quantity, wind speed, and type of wood. The results of experiments showed that the increase in wind speed leads to the increase in probability of wood ignition. Based on the received results, it can be concluded that the ignition curve of wood samples by firebrands is nonlinear and depends on the wind speed and firebrand size as well as their quantity. At the same time, there is no ignition of wood samples in the range of wind speed of 0–1 m/s. The ignition of wood is possible with a decrease in the distance between the firebrands with a decrease in the firebrand length. This result agrees more closely with the model. Full article

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29 pages, 7473 KiB

Open AccessArticle

New In-Flame Flammability Testing Method Applied to Monitor Seasonal Changes in Live Fuel

by Oleg M. Melnik, Stephen A. Paskaluk, Mark Y. Ackerman, Katharine O. Melnik, Dan K. Thompson, Sara S. McAllister and Mike D. Flannigan

Fire 2022, 5(1), 1; https://doi.org/10.3390/fire5010001 - 23 Dec 2021

Cited by 7 | Viewed by 7587

Abstract

Improving the accuracy of fire behavior prediction requires better understanding of live fuel, the dominant component of tree crowns, which dictates the consumption and energy release of the crown fire flame-front. Live fuel flammability is not well represented by existing evaluation methods. High-flammability [...] Read more.

Improving the accuracy of fire behavior prediction requires better understanding of live fuel, the dominant component of tree crowns, which dictates the consumption and energy release of the crown fire flame-front. Live fuel flammability is not well represented by existing evaluation methods. High-flammability live fuel, e.g., in conifers, may maintain or increase the energy release of the advancing crown fire flame-front, while low-flammability live fuel, e.g., in boreal deciduous stands, may reduce or eventually suppress flame-front energy release. To better characterize these fuel–flame-front interactions, we propose a method for quantifying flammability as the fuel’s net effect on (contribution to) the frontal flame energy release, in which the frontal flame is simulated using a methane diffusion flame. The fuel’s energy release contribution to the methane flame was measured using oxygen consumption calorimetry as the difference in energy release between the methane flame interacting with live fuel and the methane flame alone. In-flame testing resulted in fuel ignition and consumption comparable to those in wildfires. The energy release contribution of live fuel was significantly lower than its energy content measured using standard methods, suggesting better sensitivity of the proposed metric to water content- and oxygen deficiency-associated energy release reductions within the combustion zone. Full article

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26 pages, 5912 KiB

Open AccessArticle

Generation and Mapping of Fuel Types for Fire Risk Assessment

by Elena Aragoneses and Emilio Chuvieco

Fire 2021, 4(3), 59; https://doi.org/10.3390/fire4030059 - 6 Sep 2021

Cited by 32 | Viewed by 7386

Abstract

Fuel mapping is key to fire propagation risk assessment and regeneration potential. Previous studies have mapped fuel types using remote sensing data, mainly at local-regional scales, while at smaller scales fuel mapping has been based on general-purpose global databases. This work aims to [...] Read more.

Fuel mapping is key to fire propagation risk assessment and regeneration potential. Previous studies have mapped fuel types using remote sensing data, mainly at local-regional scales, while at smaller scales fuel mapping has been based on general-purpose global databases. This work aims to develop a methodology for producing fuel maps across European regions to improve wildland fire risk assessment. A methodology to map fuel types on a regional-continental scale is proposed, based on Sentinel-3 images, horizontal vegetation continuity, biogeographic regions, and biomass data. A vegetation map for the Iberian Peninsula and the Balearic Islands was generated with 85% overall accuracy (category errors between 3% and 28%). Two fuel maps were generated: (1) with 45 customized fuel types, and (2) with 19 fuel types adapted to the Fire Behaviour Fuel Types (FBFT) system. The mean biomass values of the final parameterized fuels show similarities with other fuel products, but the biomass values do not present a strong correlation with them (maximum Spearman’s rank correlation: 0.45) because of the divergences in the existing products in terms of considering the forest overstory biomass or not. Full article

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23 pages, 4341 KiB

Open AccessArticle

Forest Structure Drives Fuel Moisture Response across Alternative Forest States

by Tegan P. Brown, Assaf Inbar, Thomas J. Duff, Jamie Burton, Philip J. Noske, Patrick N. J. Lane and Gary J. Sheridan

Fire 2021, 4(3), 48; https://doi.org/10.3390/fire4030048 - 15 Aug 2021

Cited by 14 | Viewed by 5344

Abstract

Climate warming is expected to increase fire frequency in many productive obligate seeder forests, where repeated high-intensity fire can initiate stand conversion to alternative states with contrasting structure. These vegetation–fire interactions may modify the direct effects of climate warming on the microclimatic conditions [...] Read more.

Climate warming is expected to increase fire frequency in many productive obligate seeder forests, where repeated high-intensity fire can initiate stand conversion to alternative states with contrasting structure. These vegetation–fire interactions may modify the direct effects of climate warming on the microclimatic conditions that control dead fuel moisture content (FMC), which regulates fire activity in these high-productivity systems. However, despite the well-established role of forest canopies in buffering microclimate, the interaction of FMC, alternative forest states and their role in vegetation–fire feedbacks remain poorly understood. We tested the hypothesis that FMC dynamics across alternative states would vary to an extent meaningful for fire and that FMC differences would be attributable to forest structural variability, with important implications for fire-vegetation feedbacks. FMC was monitored at seven alternative state forested sites that were similar in all aspects except forest type and structure, and two proximate open-weather stations across the Central Highlands in Victoria, Australia. We developed two generalised additive mixed models (GAMMs) using daily independent and autoregressive (i.e., lagged) input data to test the importance of site properties, including lidar-derived forest structure, in predicting FMC from open weather. There were distinct differences in fuel availability (days when FMC < 16%, dry enough to sustain fire) leading to positive and negative fire–vegetation feedbacks across alternative forest states. Both the independent (r² = 0.551) and autoregressive (r² = 0.936) models ably predicted FMC from open weather. However, substantial improvement between models when lagged inputs were included demonstrates nonindependence of the automated fuel sticks at the daily level and that understanding the effects of temporal buffering in wet forests is critical to estimating FMC. We observed significant random effects (an analogue for forest structure effects) in both models (p < 0.001), which correlated with forest density metrics such as light penetration index (LPI). This study demonstrates the importance of forest structure in estimating FMC and that across alternative forest states, differences in fuel availability drive vegetation–fire feedbacks with important implications for forest flammability. Full article

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20 pages, 14294 KiB

Open AccessArticle

Non-Destructive Fuel Volume Measurements Can Estimate Fine-Scale Biomass across Surface Fuel Types in a Frequently Burned Ecosystem

by Quinn A. Hiers, E. Louise Loudermilk, Christie M. Hawley, J. Kevin Hiers, Scott Pokswinski, Chad M. Hoffman and Joseph J. O’Brien

Fire 2021, 4(3), 36; https://doi.org/10.3390/fire4030036 - 14 Jul 2021

Cited by 5 | Viewed by 3016

Abstract

Measuring wildland fuels is at the core of fire science, but many established field methods are not useful for ecosystems characterized by complex surface vegetation. A recently developed sub-meter 3D method applied to southeastern U.S. longleaf pine (Pinus palustris) communities captures [...] Read more.

Measuring wildland fuels is at the core of fire science, but many established field methods are not useful for ecosystems characterized by complex surface vegetation. A recently developed sub-meter 3D method applied to southeastern U.S. longleaf pine (Pinus palustris) communities captures critical heterogeneity, but similar to any destructive sampling measurement, it relies on separate plots for calculating loading and consumption. In this study, we investigated how bulk density differed by 10-cm height increments among three dominant fuel types, tested predictions of consumption based on fuel type, height, and volume, and compared this with other field measurements. The bulk density changed with height for the herbaceous and woody litter fuels (p < 0.001), but live woody litter was consistent across heights (p > 0.05). Our models predicted mass well based on volume and height for herbaceous (RSE = 0.00911) and woody litter (RSE = 0.0123), while only volume was used for live woody (R² = 0.44). These were used to estimate consumption based on our volume-mass predictions, linked pre- and post-fire plots by fuel type, and showed similar results for herbaceous and woody litter when compared to paired plots. This study illustrates an important non-destructive alternative to calculating mass and estimating fuel consumption across vertical volume distributions at fine scales. Full article

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25 pages, 3863 KiB

Open AccessArticle

Ignition of Fuel Beds by Cigarettes: A Conceptual Model to Assess Fuel Bed Moisture Content and Wind Velocity Effect on the Ignition Time and Probability

by Domingos Xavier Viegas, Ricardo Oliveira, Miguel Almeida and Donghyun Kim

Fire 2021, 4(3), 35; https://doi.org/10.3390/fire4030035 - 6 Jul 2021

Cited by 4 | Viewed by 4495

Abstract

A conceptual model based on the balance of energy in a system composed of a burning cigarette, ambient flow and a porous fuel bed is proposed to study the burning of a single cigarette and the process of fuel bed dehydration, pyrolysis and [...] Read more.

A conceptual model based on the balance of energy in a system composed of a burning cigarette, ambient flow and a porous fuel bed is proposed to study the burning of a single cigarette and the process of fuel bed dehydration, pyrolysis and its eventual ignition or combustion extinction. Model predictions of time to ignition and of the probability of ignition as a function of fuel bed moisture content and ambient flow velocity are compared with results obtained in laboratory ignition tests of straw fuel beds for various ambient conditions. According to this study, the main parameters influencing the models developed are the fuel bed and tobacco moisture content, as well as the flow velocity. Full article

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11 pages, 7944 KiB

Open AccessTechnical Note

The Fuel Moisture Index Based on Understorey Hygrochron iButton Humidity and Temperature Measurements Reliably Predicts Fine Fuel Moisture Content in Tasmanian Eucalyptus Forests

by David M. J. S. Bowman, James M. Furlaud, Meagan Porter and Grant J. Williamson

Fire 2022, 5(5), 130; https://doi.org/10.3390/fire5050130 - 30 Aug 2022

Cited by 5 | Viewed by 2353

Abstract

Fine fuel moisture content (FFMC) is a key determinant of wildfire occurrence, behaviour, and pyrogeographic patterns. Accurate determination of FFMC is laborious, hence managers and ecologists have devised a range of empirical and mechanistic measures for FFMC. These FFMC measures, however, have received [...] Read more.

Fine fuel moisture content (FFMC) is a key determinant of wildfire occurrence, behaviour, and pyrogeographic patterns. Accurate determination of FFMC is laborious, hence managers and ecologists have devised a range of empirical and mechanistic measures for FFMC. These FFMC measures, however, have received limited field validation against field-based gravimetric fuel moisture measurements. Using statistical modelling, we evaluate the use of the relationship between gravimetric FFMC and the Fuel Moisture Index (FMI), based on Hygrochron iButton humidity and temperature dataloggers. We do this in Tasmanian wet and dry Eucalyptus forests subjected to strongly contrasting disturbance histories and, hence, percentage of canopy cover. We show that 24 h average FMI based on data from Hygrochron iButtons 0.75 m above the forest floor provides reliable estimates of gravimetric litter fuel moisture (c. 1 h fuels) that are strongly correlated with near surface gravimetric fuel moisture sticks (c. 10 h fuels). We conclude FMI based on Hygrochron iButton data provides ecologists with an economic and effective method to retrospectively measure landscape patterns in fuel moisture in Tasmanian forests. Full article

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