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The article presents the results of experimental studies of water droplets propagation through the sample of typical forest fuel materials: needles, leaves, and their mixture. Different conditions are considered: without any additional energy supply, with heating, in the course of intensive thermal decomposition and flaming combustion. Three methods of registration are applied: thermocouple measurements, control of the weight of the sample as a whole and of its individual layers, and high-speed video recording. Water-based compositions with special additives (bentonite, bischofite, and foaming agents) typical for forest fire extinguishing systems are used. The experiments are carried out using aerosol and single water drops, as well as a small group of the latter. It is shown that the mechanisms, conditions and characteristics of droplet propagation through the layers of needles, leaves and their mixtures differ significantly. The scientific novelty of the work is the determining of the values of all the key characteristics of these processes in the conditions of intensive pyrolysis of the material, as well as through its inert layers. nema

An adapted bench-scale Mass Loss Calorimeter (MLC) device is proposed for evaluating effective heat of rapid flaming combustion of fine Mediterranean forest fuels. The MLC apparatus uses a calibrated thermopile to quantify heat release rate (HRR) as an alternative to the classical oxygen consumption measurement. A porous holder was used to simulate rapid flaming combustion. Average effective heat of combustion (AEHC) during the flaming phase was related to the classical measurement of gross heat of combustion (GHC) obtained in oxygen bomb calorimeter. Results showed that the effective heat of combustion (oven-dry basis) was between 18% and 44% lower than the gross heat of combustion. A linear regression was obtained (r² = 0.48; SEE = 1.25; p < 0.01; n = 26) to relate AEHC and GHC values. The simple model developed (AEHC = GHC − 6.75) suggests the possibility of reducing the heat of combustion values used in forest fire behaviour models for Mediterranean forest fuels.

In this study, we used data from a thinning trial conducted on 34 different sites and 102 sample plots established in pure and even-aged Pinus radiata and Pinus pinaster stands, to test the potential use of low-density airborne laser scanning (ALS) metrics and terrestrial laser scanning (TLS) metrics to provide accurate estimates of variables related to surface and canopy fires. An exhaustive field inventory was carried out in each plot to estimate the main stand variables and the main variables related to fire hazard: surface fuel loads by layers, fuel strata gap, surface fuel height, stand mean height, canopy base height, canopy fuel load and canopy bulk density. In addition, the point clouds from low-density ALS and single-scan TLS of each sample plot were used to calculate metrics related to the vertical and horizontal distribution of forest fuels. The comparative performance of the following three non-parametric machine learning techniques used to estimate the main stand- and fire-related variables from those metrics was evaluated: (i) multivariate adaptive regression splines (MARS), (ii) support vector machine (SVM), and (iii) random forest (RF). The selection of the best modeling approach was based on a comparison of the root mean square error (RMSE), obtained by optimizing the parameters of each technique and performing cross-validation. Overall, the best results were obtained with the MARS techniques for data from both sensors. The TLS data provided the best results for variables associated with the internal characteristics of canopy structure and understory fuel but were less reliable for estimating variables associated with the upper canopy, due to occlusion by mid-canopy foliage. The combination of ALS and TLS metrics improved the accuracy of estimates for all variables analyzed, except the height and the biomass of the understory shrubs. The variability demonstrated by the combined use of both types of metrics ranged from 43.11% for the biomass of duff litter layers to 94.25% for dominant height. The results suggest that the combination of machine learning techniques and metrics derived from low-density ALS data, drawn from a single-scan TLS or a combination of both metrics, may represent a promising alternative to traditional field inventories for obtaining valuable information about surface and canopy fuel variables at large scales.

The suggestion has been made within the wildland fire community that the rate of spread in the upper portion of the fire danger spectrum is largely independent of the physical fuel characteristics in certain forest ecosystem types. Our review and analysis of the relevant scientific literature on the subject suggest that fuel characteristics have a gradual diminishing effect on the rate of fire spread in forest and shrubland fuel types with increasing fire danger, with the effect not being observable under extreme fire danger conditions. Empirical-based fire spread models with multiplicative fuel functions generally do not capture this effect adequately. The implications of this outcome on fire spread modelling and fuels management are discussed.

A significant increase in treatment pace and scale is needed to restore dry western US forest resilience owing to increasingly frequent and severe wildfire and drought. We propose a pyrosilviculture approach to directly increase large-scale fire use and modify current thinning treatments to optimize future fire incorporation. Recommendations include leveraging wildfire’s “treatment” in areas burned at low and moderate severity with subsequent pyrosilviculture management, identifying managed wildfire zones, and facilitating and financing prescribed fire with “anchor,” “ecosystem asset,” and “revenue” focused thinning treatments. Pyrosilviculture would also expand prescribed-burn and managed-wildfire objectives to include reducing stand density, increasing forest heterogeneity, and selecting for tree species and phenotypes better adapted to changing climate and disturbance regimes. The potential benefits and limitations of this approach are discussed. Fire is inevitable in dry western US forests and pyrosilviculture focuses on proactively shifting more of that fire into managed large-scale burns needed to restore ecosystem resilience.

The accurate classification of forest fuels and the evaluation of the flammability of different forest types are crucial for effective forest fire control and classification management. We aimed to evaluate and classify the flammability of surface forest fuels in the subtropical area of China. The surface forest fuels were collected from 12 typical forest types. The flammability of surface forest fuels was assessed by evaluating their drying time, fuel moisture, ignition point, calorific value, combustion duration, and ash content. The principal component analysis (PCA), entropy weight method, k‐means clustering algorithm, and Pearson correlation coefficient method were employed for the classification of forest fuels and the evaluation of forest flammability. The results revealed that the flammability of surface living fuels across diverse plant families was significantly different. Rutaceae and Cucurbitaceae plants exhibited relatively high flammability, while Arecaceae plants demonstrated characteristics of low flammability. The surface fuels could be categorized into high, moderate, and low flammability. The high flammability fuels mainly consisted of plant leaves and litter components. The forest humus belongs to the low flammability. The forest flammability was classified into three categories according to the ignition forest fire risk index (IRI) and the burning intensity & severity index (BSI). The highest flammability forest types were EPF: Pinus elliottii pure forest, BMF: broad‐leaved mixed forest, CPF: Cunninghamia lanceolata (Lamb.) Hook pure forest, and CBF: coniferous broad‐leaved mixed forest. The lowest flammability was in FPF: Liquidambar formosana Hance pure forest, an optimal forest type with a neatly structured environment, few understory weeds, and less dead fuel loading of only 4.32 tons per hectare. The flammability index method presented in this study contains the key elements of flammability, provides a standardized tool for fire managers to assess and mitigate fire risk, and it also applies to other regions. Flammability of various plant families in surface living fuels varies significantly, and fuels of Rutaceae, Cucurbitaceae exhibited high flammability, while Arecaceae were low. Leaves, litters were high flammability fuels, humus was low flammability fuel. Flammability index including IRI & BSI can be used to classify stand combustibility, which showed that the fewer understory and less dead fuels, the lower flammability the forest type was.

Tropical coastal freshwater forested wetlands in coastal regions are rapidly disappearing as a result of various disturbance agents, mainly wildfires caused by high accumulations of forest fuels. The objective of this study was to characterize the structure and composition of fuel beds in tropical coastal freshwater forested wetlands with three levels of disturbance at El Castaño, La Encrucijada Biosphere Reserve. Seventeen sampling units were used to describe the structure of the forest’s fuel beds (canopy, sub-canopy, and understory). Fallen woody material and litter (surface and fermented) were characterized using the planar intersection technique. Diversity comprised eight species of trees, two shrubs, five lianas, and two herbaceous species. The vertical strata were dominated by trees between 2 and 22 m in height. The horizontal structure had a higher percentage of trees with normal diameter between 2.5 and 7.5 cm (61.4%) of the total. Sites with low disturbance had the highest arboreal density (2686 ind. ha−1). Diversity of species showed that the Fisher, Margalef, Shannon, and Simpson α indices were higher in the low disturbance sites. The Berger–Parker index exhibited greater dominance in the sites with high disturbance. Pachira aquatica Aubl. Showed the highest importance value index and was the largest contributor to fuel beds. Sites with the highest disturbance had the highest dead fuel load (222.18 ± 33.62 Mg ha−1), with woody fuels of classes 1, 10, and 1000 h (rotten) being the most representative. This study contributes to defining areas prone to fire in these ecosystems and designing prevention strategies.

Prescriptions for fuel management are universally applied across the forest types in British Columbia, Canada, to reduce the fire behaviour potential in the wildland–urban interface. Fuel thinning treatments have been advocated as a means of minimizing the likelihood of crown fire development in conifer forests. We hypothesized that these types of prescriptions are inappropriate for the coastal rainforests of the Whistler region of the province. Our study examined the impact of fuel thinning treatments in four stands located in the Whistler community forest. We measured several in-stand microclimatic variables beginning with snow melt in the spring up to the height of fire danger in late summer, at paired thinned and unthinned stand locations. We found that the thinning led to warmer, drier, and windier fire environments. The difference in mean soil moisture, ambient air temperature, and relative humidity between thinned and unthinned stands was significant in the spring with approximate p-values of 0.000217, 9.40 × 10−5, and 4.33 × 10−8, respectively, though there were no discernible differences in the late summer. The difference in mean solar radiation, average wind speed, and average cross wind between thinned and unthinned locations are significant in the spring and late summer (with approximate p-values for spring of 9.54 × 10−7, 0.02101, 1.92 × 10−9, and for late summer of 2.45 × 10−7, 4.08 × 10−6, and 2.45 × 10−5, respectively).

BackgroundAssessment of fuel hazard has become the dominant method of describing Australian forest fuel complexes, despite a lack of evidence supporting the veracity of its underpinning assumptions.AimsTo analyse and discuss the merits of fuel hazard ratings and scores in representing measurable fuel characteristics, such as fuel load and fire behaviour potential.MethodsPublished findings were reviewed, and available data analysed to investigate the validity of the Australian fuel hazard assessment concepts.Key resultsMultiple published studies showed the Australian fuel hazard assessment methods to be subjective and non-replicable. All available evidence shows no relationship between fuel hazard ratings and fuel quantity. No relationship between the ratings and fire behaviour potential was found.ConclusionsThe principles underpinning the use of fuel hazard ratings for fuel assessment were shown to be unfounded. The ratings cannot be converted into physical fuel characteristics or fire behaviour potential, and its application in Australian fire management is unwarranted.

One of the most determining factors in forest fire behaviour is to characterize forest fuel attributes. We investigated a complex Mediterranean forest type—mountainous Abies pinsapo–Pinus–Quercus–Juniperus with distinct structures, such as broadleaf and needleleaf forests—to integrate field data, low density Airborne Laser Scanning (ALS), and multispectral satellite data for estimating forest fuel attributes. The three-step procedure consisted of: (i) estimating three key forest fuel attributes (biomass, structural complexity and hygroscopicity), (ii) proposing a synthetic index that encompasses the three attributes to quantify the potential capacity for fire propagation, and (iii) generating a cartograph of potential propagation capacity. Our main findings showed that Biomass–ALS calibration models performed well for Abies pinsapo (R2 = 0.69), Juniperus spp. (R2 = 0.70), Pinus halepensis (R2 = 0.59), Pinus spp. mixed (R2 = 0.80), and Pinus spp.–Juniperus spp. (R2 = 0.59) forests. The highest values of biomass were obtained for Pinus halepensis forests (190.43 Mg ha−1). The structural complexity of forest fuels was assessed by calculating the LiDAR Height Diversity Index (LHDI) with regard to the distribution and vertical diversity of the vegetation with the highest values of LHDI, which corresponded to Pinus spp.–evergreen (2.56), Quercus suber (2.54), and Pinus mixed (2.49) forests, with the minimum being obtained for Juniperus (1.37) and shrubs (1.11). High values of the Fuel Desiccation Index (IDM) were obtained for those areas dominated by shrubs (−396.71). Potential Behaviour Biomass Index (ICB) values were high or very high for 11.86% of the area and low or very low for 77.07%. The Potential Behaviour Structural Complexity Index (ICE) was high or very high for 37.23% of the area, and low or very low for 46.35%, and the Potential Behaviour Fuel Desiccation Index (ICD) was opposite to the ICB and ICE, with high or very high values for areas with low biomass and low structural complexity. Potential Fire Behaviour Index (ICP) values were high or very high for 38.25% of the area, and low or very low values for 45.96%. High or very high values of ICP were related to Pinus halepensis and Pinus pinaster forests. Remote sensing has been applied to improve fuel attribute characterisation and cartography, highlighting the utility of integrating multispectral and ALS data to estimate those attributes that are more closely related to the spatial organisation of vegetation.