Forest Fuel Drying, Pyrolysis and Ignition Processes during Forest Fire: A Review

Baranovskiy, Nikolay V.; Kirienko, Viktoriya Andreevna

doi:10.3390/pr10010089

Cited by 16 publications

(7 citation statements)

References 155 publications

(183 reference statements)

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“…The observed relationships between ignition drivers and predicted likelihood are in line with the expected behavior, i.e., fuel moisture as the most influential factor modulating ignition followed by topography. The important role of the moisture of the live/dead fuels causing the ignition and propagation of new fire events have been documented in many works previously (Baranovskiy & Kirienko, 2022;Resco de Dios et al, 2021Rodrigues et al, 2021). However, our findings were non-conclusive in terms of the role of the characteristics of the fire-prone flash.…”

Section: Discussioncontrasting

confidence: 76%

Modeling daily natural-caused ignition probability in the Iberian Peninsula

Jiménez-Ruano¹,

Gelabert²,

Dios³

et al. 2022

Advances in Forest Fire Research 2022

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In the European Mediterranean region natural-caused wildfires are a small fraction of total ignitions. Lightning strikes are the most common source of non-human fires, being strongly tied to specific synoptic conditions and patterns associated with atmospheric instability, such as dry thunderstorms. Likewise, lightning-related ignitions often associate with dry fuels and dense vegetation layers. In the case of Iberian Peninsula, the confluence of these factors favors recurrent lightning fires in the eastern Mediterranean mountain ranges and the. However, under appropriate conditions lightning fires can start elsewhere, holding the potential to propagate over vast distances. In this work, we assessed the likelihood of ignition leveraging a large dataset of lightning strikes and historical fires available in Spain. We trained and tested a machine learning model to evaluate the probability of ignition provided that a lightning strikes the ground. Our model was calibrated in the period 2009-2015 using data for mainland Spain plus the Balearic Islands. To build the binary response variable we classified lightning strikes between that triggered a fire event. For each lightning strike we extracted a set of covariates relating fuel moisture conditions, the presence and density of the vegetation layer and the shape of the relief. The final model was subsequently applied to forecast daily probabilities at 1x1 km resolution for the entire Iberian Peninsula. Although the model was originally calibrated in Spain, we extended the predictions to the entire Iberian Peninsula. By doing so we were able to validate in the future our outputs against the Portuguese dataset of recent natural-caused fires (bigger than 1 ha) from 2001 to 2021. Overall, the model attained a great predictive performance with a median AUC of 0.82. Natural-caused ignitions triggered mainly in low dead (dFMC 250) fuel moisture conditions. Lightning strikes with negative polarity seem to trigger fires more frequently when the mean density of discharger was greater than 5. Finally, natural wildfires usually started at higher elevations (above 500 m.a.s.l.).

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Section: Discussioncontrasting

confidence: 76%

Modeling daily natural-caused ignition probability in the Iberian Peninsula

Jiménez-Ruano¹,

Gelabert²,

Dios³

et al. 2022

Advances in Forest Fire Research 2022

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“…This explain the length of exposure of users by inhaling the smoke released during the ignition of solid fuels fire by plastics. The ignition time depends on the type of fuel and the environment conditions [ 38 ] and on mass of fuel [ 34 ].…”

Section: Resultsmentioning

confidence: 99%

Production and characterization of ecological fire starter from sawdust and vegetable oil

Kodji

Joel

Soulouknga

et al. 2023

Heliyon

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“…This was because the surface dead fuel moisture content is composed of water within the leaf structure and surface free water. At the same time, fuels have a large space for the absorption of precipitation under dry conditions, making them prone to sharp peak changes [36,37], which is consistent with the Baksic study on the temporal dynamic changes in fuel moisture content [38]. The difference in moisture content between the two types of surface fuels is mainly due to the larger leaf area and higher decomposition degree of Quercus mongolica in terms of the morphology and structure of fuels, resulting in a higher overall moisture content.…”

Section: The Influence Of Rainfall On the Construction Of A Model For...mentioning

confidence: 99%

Modification and Comparison of Methods for Predicting the Moisture Content of Dead Fuel on the Surface of Quercus mongolica and Pinus sylvestris var. mongolica under Rainfall Conditions

Hu,

Ma,

Gao

et al. 2023

Fire

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The surface fine dead fuel moisture content (FFMC) is an important factor in predicting forest fire risk and is influenced by various meteorological factors. Many prediction methods rely on temperature and humidity as factors, resulting in poor model prediction accuracy under rainfall conditions. At the same time, there is an increasing number of methods based on machine learning, but there is still a lack of comparison with traditional models. Therefore, this paper selected the broad-leaved forest tree species Quercus mongolica and the coniferous forest species Pinus sylvestris var. mongolica in Northeast China. Taking surface dead fine fuel as the research object, we used indoor simulated rainfall experiments to explore the impact of rainfall on the surface dead fuel moisture content. The prediction model for surface dead fuel moisture content was modified by the direct estimation method. Finally, using field data, the direct estimation method and convolution neural network (CNN) model were used in the comparison. The rainfall simulation results showed that the indoor fuel moisture content had a logarithmic increasing trend. Rainfall and previous fuel moisture content had a significant impact on the fuel moisture content prediction model, and both the relational model and nonlinear model performed well in predicting fuel moisture content under indoor rainfall conditions. Under field conditions, humidity, temperature and rainfall played a significant role in fuel moisture content. Compared with the unmodified direct estimation method, the modified direct estimation method significantly improved the prediction accuracy and the goodness of fit (R2) increased from 0.85–0.94 to 0.94–0.96. Mean absolute error (MAE) decreased from 9.18–18.33% to 6.86–10.74%, and mean relative error (MRE) decreased from 3.97–17.18% to 3.53–14.48%. The modified direct estimation method has higher prediction accuracy compared with the convolutional neural network model; the R2 value was above 0.90, MAE was below 8.11%, and MRE was below 8.87%. The modified direct estimation method had the best prediction effect among them. This study has a certain reference value for the prediction model of surface fuel moisture content in post-rainfall fire risk assessment and is also of great significance for forest fire management in Northeast China.

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Forest Fuel Drying, Pyrolysis and Ignition Processes during Forest Fire: A Review

Cited by 16 publications

References 155 publications

Modeling daily natural-caused ignition probability in the Iberian Peninsula

Modeling daily natural-caused ignition probability in the Iberian Peninsula

Production and characterization of ecological fire starter from sawdust and vegetable oil

Modification and Comparison of Methods for Predicting the Moisture Content of Dead Fuel on the Surface of Quercus mongolica and Pinus sylvestris var. mongolica under Rainfall Conditions

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