Vegetation load, structure, composition and moisture status play a key role in wildfire ignition and spread: their characterisation and mapping are therefore of paramount importance to fire risk assessment and reduction. Wildfire fuels can be described in terms of chemical characteristics, flammability and physical properties, which affect the combustion process, and include quantity, size and shape, compactness and arrangement [1] [2].
When addressing wildfires, natural vegetation is usually considered as the “fuel” necessary for the combustion processes. The physical and chemical properties of any specific fuel determine its ease of ignition and sustaining combustion. This section describes the main properties that characterise the fuel particles and the fuel complexes.
In a generic approach, fuels are usually classified according to two parameters: state, dividing them into live fuels (herbs, shrubs, trees) and dead fuels (dry or fallen leaves, herbs, fallen branches, etc.) and vertical strata, which considers their vertical distribution. i.e., ground fuels, surface fuels and aerial fuels, which present different fire behaviour patterns.
As there are several potential combinations of vegetation types, characteristics and succession stages, and describing all these possible combinations is very challenging, the most common approach is to generalise and characterise fuels into a finite number of fuel models [3] [4]. Overall, a fuel model is an identifiable association of fuel components of distinctive species, form, size, arrangement and continuity that exhibits a characteristic fire behaviour under defined burning conditions [5] [6]. The parameters that compose each fuel model are dependent on the input variables required by the fire behaviour models they were created for. For example, the fuel modelling system developed by Rothermel (1972)[7] considers four strata for surface fire propagation (litter, herbs, shrubs, slash) organised in 13 fuel models, and provides a simplified set of fuel parameters that are inputs for the equations of fire spread developed at the Northern Forest Fire Laboratory of the US Forest Service [8] [9]. Scott and Burgan (2005)[6:1] expanded those fuel models to 40, adding fuels representing those existing in high-humidity areas, and in forests with different kinds of understorey vegetation. A more detailed fuel model characterisation was the one developed as part of the Fuel Characteristic Classification System (FCCS) [10]. This system allows for the creation of as many fuel models as necessary, organized in six strata: canopy, shrubs, non-woody vegetation, woody fuels, litter-lichen-moss and ground fuels. For each of these strata, a set of parameters needs to be assigned, representing strata characteristics such as percent cover, depth, fuel load, etc. [2:1]. These fuel models are then used to run the FCCS fire behaviour and emission models [11], based on a reformulation of the Rothermel equations [12]. Complementary, several customised fuel models have also been developed to represent better the fuel characteristics and to estimate observed fire behavior characteristics in many other countries, including European regions [13] [14] [15] [16] and Australia [17].
Custom fuel models are often needed for particular regions, for instance, the set of 14 fuel models developed for Central Portugal by Cruz (2005)[18] or the set of 18 developed by Fernandes et al. (2009)[19] for Continental Portugal. This development includes the quantification of the properties of the fuelbed and fuel particles identified earlier, but, more importantly, the calibration of the fuel models against observed fire behaviour [20]. In terms of practical application, other than the use of fire simulation tools, fuel models are usually accompanied by estimates of fire behaviour propagation for an easy evaluation in the field, many times with photo guides to allow for a visual correspondence of fuel models to observed fuel complexes [5:1] [18:1] [21].
The main parameters that these fuel models try to classify, as they are the most important in terms of fire behaviour are:
Particle size class: This is one of the fuel properties that most affect combustion and fire behaviour [22], as smaller particles require a smaller amount of energy or time to ignite. The relation between the particle size and fuel moisture content is very marked. Following the original unpublished classification from George Byram in 1963 [23], later adopted for the US National Fire-Danger Rating System [24], fuels are divided according to their “time-lag interval.” This is defined as “the time required for fuels to lose approximately two-thirds of their initial moisture content”. In simple terms, if the moisture of a fuel particle is in equilibrium with the environment, i.e., no gains or losses, the time-lag of that particle corresponds to the average time it would take it to reach that equilibrium again in case there was a change in the environmental conditions. The classes considered are named after the average time interval needed: 1 hour, 10 hours, 100 hours, and 1000 hours. The reasoning behind the concept is that, under constant ambient conditions, the fuel drying rate depends on particle thickness [23:1]: thinner particles lose moisture faster than thicker particles. These classes represent the following fuel dimensions, respectively:
a) 1 hr : thin or light, with a diameter less than 6 mm (needles, leaves, herbs);
b) 10 hr : regular, with a diameter between 6 and 25 mm (thin branches and shrub stems);
c) 100 hr : medium, with diameter between 25 and 75 mm (branches);
d) 1000 hr : thick or heavy, with a diameter greater than 75 mm (thick branches and trunks).
Shape: This property is expressed as the surface area-to-volume (SAV) ratio, i.e., the ratio between the exterior surface of a particle and its volume, usually in cm2/cm3 (cm-1), or in m2/m3 (m-1). It translates the suitability of a particle for combustion, as the amount of energy it can receive per unit of time increases with the exposed surface [7:1]. Fuel particles with large SAV will ignite more readily than those with relatively small SAV. For instance, large logs have low SAV values, and pine needles or grasses have high SAV values.
Chemical composition: The combustion process can be influenced by the chemical composition of fuels, to a greater or lesser extent, speeding up or slowing down combustion. Bradshaw et al. (1984)[24:1] consider three important chemical properties: a) heat content, or the energy available per unit mass of fuel through combustion; b) total mineral content, the fraction of a fuel mass composed of inorganic minerals (only organic minerals can sustain combustion); and c) effective mineral content, or the active mineral content in a fuel particle that interferes with the chemical processes of combustion, namely the release of volatile gasses.
Density: The density of a fuelbed refers to the available fuel per unit volume and is incorporated in fire behaviour prediction as the bulk density (kg/m3). Density affects ignition and the rate of spread [25]. Together with moisture content it strongly impacts the thermal conductivity of a fuel [26]. Fuels with a lower density ignite in less time for the same amount of heat or require less heat for the same amount of time. Less density means more oxygen is available for combustion. However, if the fuel particles are too far apart, there is no longer enough proximity for the necessary heat exchange and the consequent ignition of adjacent particles. All size classes have an ideal density point that maximizes heat transfer and the presence of oxygen and, therefore, combustion.
Fuel load: The fuel load expresses the mass of combustible vegetation material per unit area and is usually expressed in kg/m2 or t/ha. Depending on the fire behaviour modelling needs, this parameter can be presented as a total value or divided by stratum (ground, surface, canopy), size class (fine, regular, medium, and heavy) or vegetative state (live and dead). The fuel load and moisture content of a specific fuelbed determine the amount of fuel consumed and the heat released during combustion. The amount of fuel that is consumed directly influences fire effects in vegetation and soils [20:1], fire spread and intensity [27], and the potential fire emissions and smoke impacts [28] [29].
Fuel height: Also called depth, this is an estimate of the vertical dimension of the combustion zone and is required to estimate the fuelbed compactness [30]. Although it may require some training in defining the top of the fuelbed, this parameter can be measured easily and quickly and is, therefore, commonly used in double sampling techniques to estimate fuel load.
Continuity: Fuel continuity expresses “the degree or extent of continuous or uninterrupted distribution of fuel particles in a fuel bed, thus affecting a fire’s ability to sustain combustion and spread” [31]. Continuity exists when fuels are within each other's combustion zone, i.e., when the combustion of one has a preheating effect on the other. This term is used for surface fuels (horizontal continuity) and ladder fuels (horizontal continuity). Ladder fuels are the designation of the more or less continuous fuelbed that can support fire propagation from the surface fuels to the canopy or crown fuels.
Fuel moisture content (FMC): FMC is defined as the ratio of the water content to dry mass within the fuel and is usually expressed in percentage [32]. It is commonly divided into live and dead fuel moisture content (LFMC and DFMC), which relate to the FMC of living (e.g., grass, foliage) and dead (e.g., litter, woody debris) vegetation components, respectively [33]. While dead fuel moisture responds to short-term weather conditions [34], live fuel is more responsive to longer-term weather events, such as dry spells, and plant species-specific water use strategies [35], although it has also been demonstrated to respond to short heatwave events as well [36]. DFMC determines the amount of energy and time required to vaporise moisture before fuels are ignited [37], which means that it greatly affects fire ignition, intensity, rate of spread and extent [38]. It determines whether a fire will start, spread and the amount of fuel available to burn that translates into the potential intensity. High DFMC values reduce ignitability and fire spread, while low DFMC values are associated with high fuel flammability and, subsequently, higher risk of fire ignition and spread [39]. In particular, large fires occur once moisture decreases below a certain threshold value, depending on the different forest environments (e.g., DFMC of 12% or lower for the Mediterranean and temperate broadleaved and mixed forests) [40]. Given its importance as one of the key drivers of fire danger, the estimation of DFMC is one of the key components of meteorological danger indices [41].The role of LFMC in wildfire ignition and spread is more complex. Laboratory experiments have shown a positive correlation between LFMC and time to ignition [42] in foliage from both needle-leaved and broad-leaved trees, as well as a negative correlation with the rate of spread in shrub and tree species [43] and the temperature reached and the heat released during the combustion. However, these interactions are not as clear from field experiments likely due to confounding factors, the different magnitudes of heat flux generated by wildfires or the range of LFMC used being too moist to reveal a significant effect. Alexander and Cruz (2013)[44] found no statistically significant relationship between LFMC and crown fire rate of spread in conifer forests and shrublands, but Rossa (2017)[45] demonstrated that in the absence of wind and slope, fuel moisture can have a significant impact on the rate of spread. Nevertheless, several broad scale empirical studies using remote sensing data and GIS have proven LFMC influences fire activity [46]. This highlights the need to improve our understanding of the relationship between LFMC and fire behaviour to refine fire risk models [47]. Traditionally, both DFMC and LFMC is estimated through field measurements [48]. Even though these measurements are fairly accurate [49], they are very local and extremely time-consuming and labour-intensive, rendering FMC estimation over a large spatial and/or temporal scale nearly impossible [33:1]. The use of meteorological indices and satellite imagery constitute the two alternative methods which avoid the limitations of ground field measurements [33:2] [50] [51] [52]. Nevertheless, due to the usual inadequacy of existing meteorological stations and subsequent spatial incomprehensiveness of the respective meteorological data, FMC estimation is performed through interpolation techniques, which result in significant computational errors [34:1]. Utilisation of weather forecasting models alleviates the problem of limited representativeness of individual stations, but is not free from caveats either, the most important being the quality of precipitation predictions. Moreover, the meteorological data-based estimation of LFMC is additionally challenged since live fuels have multiple drought adaptation strategies [36:1]. Among the meteorological indices used for DFMC estimation, the Keetch-Byram Drought Index and the Cumulative Water Balance Index are the ones most widely employed in fire risk systems [41:1] [50:1] [53], although some authors have found saturation problems in dry regions [54].
Moisture of extinction: This property represents the fuel moisture content of a fuelbed at which a fire will not propagate. It is an essential parameter for applying Rothermel’s model, who initially considered it constant among all fuel types (30%). It was soon found that it could vary with fuel bed compactness, fuel particle size, wind speed, and slope [24:2] or even the location on the fire perimeter [55]. The moisture of extinction values used in fire behaviour fuel models (see below) can range from 12% to 40%, depending on the aforementioned conditions. On live fuels, the values may be higher.
Satellite remote sensing data can overcome most of those limitations, providing estimations at various spatial and temporal scales. The remote sensing based LFMC estimation methods can be categorised into two approaches, depending on whether they rely on radiative transfer models (RTM) [56] [57] or on empirical methods [58] [59] [60]. The former models depend on the physical associations between canopy properties and its spectral reflectance, while the latter on statistical approaches comparing ground observations and satellite estimations. The RTM methods are more robust than the empirical ones since they are independent of sensor and environmental conditions [61], yet they are more complex to parameterise [56:1]. On the contrary, empirical approaches are usually easier to parameterize but are more local and difficult to generalize. Some authors have shown similar accuracy values between the two approaches [62] [63]. Coarse and medium resolution multispectral satellite data, such as those acquired by Terra’s Moderate Resolution Imaging Spectroradiometer (MODIS), Landsat TM/ETM, MSG-Spinning Enhanced Visible and Infrared Imager (SEVIRI) and Sentinel-2 MSI have been employed for LFMC estimation, since they provide either the required frequent temporal or spatial detail coverage [33:3] [59:1] [64]. However, cloud coverage and smoke can hinder the retrieval of spectral information over extended areas. Microwave wavelengths have been presented as an alternative data source to overcome such limitations [65] [66]. Being sensitive to moisture of certain vegetation parts (e.g., crown, stems), different Synthetic Aperture Radar (SAR) bands from sensors, such as the RADARSAT-1 and most recently the Sentinel-1, have been used to reliably estimate FMC in various environments [67] [68]. Current advances in satellite technology are expected to introduce data from new sensors, such as the upcoming Meteosat Third Generation (MTG)-Flexible Combined Imager (FCI), Biomass SAR and OzFuel [69], providing new insights and additional possibilities for LFMC retrieval.
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