Assessment of crown fire initiation and spread models in Mediterranean conifer forests by using data from field and laboratory experiments

Silva, Francisco Rodríguez y; Guijarro, Mercedes; Madrigal, Javier; Jiménez, Enrique; Molina, Juan Ramón; Hernando, Carmen; Vélez, Ricardo; Vega, José A.

doi:10.5424/fs/2017262-10652

Cited by 10 publications

(3 citation statements)

References 43 publications

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“…where U 10 is the 10 m open wind speed (km h −1 ), CBD is the canopy bulk density (kg m −3 ), EFFM is the estimated fine dead fuel moisture (%), and model efficiency is the percentage of observed variance explained by the model. The models selected to simulate crown fire behavior have been shown to be reasonably reliable when they were evaluated against independent datasets [2,60,61], and they have been also used in similar studies [62,63].…”

Section: Crown Fire Behavior Simulationmentioning

confidence: 99%

Potential of Sentinel-2A Data to Model Surface and Canopy Fuel Characteristics in Relation to Crown Fire Hazard

et al. 2018

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Background: Crown fires are often intense and fast spreading and hence can have serious impacts on soil, vegetation, and wildlife habitats. Fire managers try to prevent the initiation and spread of crown fires in forested landscapes through fuel management. The minimum fuel conditions necessary to initiate and propagate crown fires are known to be strongly influenced by four stand structural variables: surface fuel load (SFL), fuel strata gap (FSG), canopy base height (CBH), and canopy bulk density (CBD). However, there is often a lack of quantitative data about these variables, especially at the landscape scale. Methods: In this study, data from 123 sample plots established in pure, even-aged, Pinus radiata and Pinus pinaster stands in northwest Spain were analyzed. In each plot, an intensive field inventory was used to characterize surface and canopy fuels load and structure, and to estimate SFL, FSG, CBH, and CBD. Equations relating these variables to Sentinel-2A (S-2A) bands and vegetation indices were obtained using two non-parametric techniques: Random Forest (RF) and Multivariate Adaptive Regression Splines (MARS). Results: According to the goodness-of-fit statistics, RF models provided the most accurate estimates, explaining more than 12%, 37%, 47%, and 31% of the observed variability in SFL, FSG, CBH, and CBD, respectively. To evaluate the performance of the four equations considered, the observed and estimated values of the four fuel variables were used separately to predict the potential type of wildfire (surface fire, passive crown fire, or active crown fire) for each plot, considering three different burning conditions (low, moderate, and extreme). The results of the confusion matrix indicated that 79.8% of the surface fires and 93.1% of the active crown fires were correctly classified; meanwhile, the highest rate of misclassification was observed for passive crown fire, with 75.6% of the samples correctly classified. Conclusions: The results highlight that the combination of medium resolution imagery and machine learning techniques may add valuable information about surface and canopy fuel variables at large scales, whereby crown fire potential and the potential type of wildfire can be classified.

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Section: Crown Fire Behavior Simulationmentioning

confidence: 99%

Potential of Sentinel-2A Data to Model Surface and Canopy Fuel Characteristics in Relation to Crown Fire Hazard

et al. 2018

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“…Fire risk and wildfire damage can be reduced by removing or modifying fuels in strategic locations (Finney 2006;Ager et al 2012). In this context, crown fire behaviour considerations are important when planning or evaluating the effectiveness of fuel treatments (Agee and Skinner 2005;González-Olabarria et al 2012;Jiménez et al 2016;Rodríguez y Silva et al 2017). This has prompted the application of fire behaviour modelling software for both research and operational applications, e.g.…”

Section: Introductionmentioning

confidence: 99%

Improving silvicultural practices for Mediterranean forests through fire behaviour modelling using LiDAR-derived canopy fuel characteristics

Botequim

Fernandes

Borges

et al. 2019

Int. J. Wildland Fire

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Wildfires cause substantial environmental and socioeconomic impacts and threaten many Spanish forested landscapes. We describe how LiDAR-derived canopy fuel characteristics and spatial fire simulation can be integrated with stand metrics to derive models describing fire behaviour. We assessed the potential use of very-low-density airborne LiDAR (light detection and ranging) data to estimate canopy fuel characteristics in south-western Spain Mediterranean forests. Forest type-specific equations were used to estimate canopy fuel attributes, namely stand height, canopy base height, fuel load, bulk density and cover. Regressions explained 61–85, 70–85, 38–96 and 75–95% of the variability in field estimated stand height, canopy fuel load, crown bulk density and canopy base height, respectively. The weakest relationships were found for mixed forests, where fuel loading variability was highest. Potential fire behaviour for typical wildfire conditions was predicted with FlamMap using LiDAR-derived canopy fuel characteristics and custom fuel models. Classification tree analysis was used to identify stand structures in relation to crown fire likelihood and fire suppression difficulty levels. The results of the research are useful for integrating multi-objective fire management decisions and effective fire prevention strategies within forest ecosystem management planning.

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“…By employing the combustion facility, we were able to produce charcoal in an environment more representative of natural conditions. Flame temperatures within the combustion facility often exceeded 1,000°C, albeit briefly, consistent with temperatures observed during forest crown fires (Rodriguez y Silva et al, 2017). Replicating pyrolysis temperatures over 800°C, characteristic of vegetation fires or prescribed fires, proves challenging in a muffle furnace, with most of the sample turning to ash during the long time it took for the muffle furnace to reach such temperatures.…”

Section: Discussionmentioning

confidence: 52%

Charcoal analysis for temperature reconstruction with infrared spectroscopy

Minatre,

Arienzo,

Moosmüller

et al. 2024

Front. Earth Sci.

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The duration and maximum combustion temperature of vegetation fires are important fire properties with implications for ecology, hydrology, hazard potential, and many other processes. Directly measuring maximum combustion temperature during vegetation fires is difficult. However, chemical transformations associated with temperature are reflected in the chemical properties of charcoals (a by-product of fire). Therefore, charcoal could be used indirectly to determine the maximum combustion temperature of vegetation fires with application to palaeoecological charcoal records. To evaluate the reliability of charcoal chemistry as an indicator of maximum combustion temperature, we studied the chemical properties of charcoal formed through two laboratory methods at measured temperatures. Using a muffle furnace, we generated charcoal from the woody material of ten different tree and shrub species at seven distinct peak temperatures (from 200°C to 800°C in 100°C increments). Additionally, we simulated more natural combustion conditions by burning woody material and leaves of four tree species in a combustion facility instrumented with thermocouples, including thermocouples inside and outside of tree branches. Charcoal samples generated in these controlled settings were analyzed using Fourier Transform Infrared (FTIR) spectroscopy to characterize their chemical properties. The Modern Analogue Technique (MAT) was employed on FTIR spectra of muffle furnace charcoal to assess the accuracy of inferring maximum pyrolysis temperature. The MAT model temperature matching accuracy improved from 46% for all analogues to 81% when including ±100°C. Furthermore, we used MAT to compare charcoal created in the combustion facility with muffle furnace charcoal. Our findings indicate that the spectra of charcoals generated in a combustion facility can be accurately matched with muffle furnace-created charcoals of similar temperatures using MAT, and the accuracy improved when comparing the maximum pyrolysis temperature from muffle furnace charcoal with the maximum inner temperature of the combustion facility charcoal. This suggests that charcoal produced in a muffle furnace may be representative of the inner maximum temperatures for vegetation fire-produced charcoals.

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Assessment of crown fire initiation and spread models in Mediterranean conifer forests by using data from field and laboratory experiments

Cited by 10 publications

References 43 publications

Potential of Sentinel-2A Data to Model Surface and Canopy Fuel Characteristics in Relation to Crown Fire Hazard

Potential of Sentinel-2A Data to Model Surface and Canopy Fuel Characteristics in Relation to Crown Fire Hazard

Improving silvicultural practices for Mediterranean forests through fire behaviour modelling using LiDAR-derived canopy fuel characteristics

Charcoal analysis for temperature reconstruction with infrared spectroscopy

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