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Plant flammability experiments
offer limited insight into
vegetation–fire dynamics
interactions
Introduction
Flammability is the general ability of vegetation (fuel) to burn
(Gill & Zylstra, 2005). The concept of flammability can be
narrowed down to distinct aspects of combustion as gauged
by a number of metrics (White & Zipperer, 2010). In this
respect, Anderson (1970) proposed flammability to consist of
ignitibility (ease of ignition), sustainability (how well combustion will proceed) and combustibility (velocity or intensity of
combustion), and Martin et al. (1994) further added consumability, the amount of combusted fuel. Flammability is
experimentally assessed by burning fuels in the laboratory,
either in the form of discrete elements (e.g. a leaf, a twig) for
which the concept has been coined, or as a fuel bed, a homogeneous or heterogeneous assemblage of individual units. In
the real world (the community or stand level), flammability
should be translated in terms of fire behaviour characteristics,
for example, rate of fire spread and fire intensity (Anderson,
1970). However, because flammability is a broad, allencompassing idea, its usage and appraisal know no scale
boundaries and can even extend to the ecosystem level. It is
notable that the scientific and practical relevance of vegetation
flammability have been dismissed (Dickinson & Johnson,
2001), possibly because the term has been used less rigorously
in fire ecology than in combustion science.
Flammability studies have been carried out for various
purposes. A recent trend is to examine plant flammability
descriptors as fire-adaptive traits (Schwilk, 2003; Scarff &
Westoby, 2006; Cowan & Ackerly, 2010; Saura-Mas et al.,
2010; Pausas et al., 2012) in relation to the hypothesis that
flammability has evolved to confer fitness in fire-prone environments (Bond & Midgley, 1995). None of these studies or, for
that matter, any other flammability-related research thoroughly
questions to what extent flammability experiments are adequate
surrogates for real-world, full-scale fire behaviour and dynamics.
White & Zipperer (2010) provide a thorough description and
comparison of the methods used to assess flammability. Here
we discuss the limitations of plant flammability tests regarding
their ability to replicate real-world conditions, with an emphasis
on shrub species.
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Scale limitations of plant flammability experiments
A major shortcoming of flammability tests based on fuel elements
is that these are taken out of the overall fuel context, that is, the
fuel bed or complex (in this case the shrub canopy), as isolated
and aggregated fuel particles have different burn properties.
Combustion behaviour has been shown to vary with fuel particle
mass (Fletcher et al., 2007; Cole et al., 2011), between single and
paired shrub leaves (Pickett et al., 2009), and with shrub leaf orientation (Engstrom et al., 2004). An extreme illustration of
decoupled flammability between fuel elements and their corresponding fuel beds is given by thin and small shrub leaves, which
ignite easily as individuals but when organized as litter display
low heat release rates as a result of poor ventilation (Scarff &
Westoby, 2006). Ignition and combustion are directly affected
by the intrinsic properties of fuel particles, but fuel-driven fire
behaviour is mostly a function of the fuel complex, which essentially is defined by fuel loading, arrangement, size distribution
and dead : live ratio (Chandler et al., 1983). In fact, because of
their minor range of variation, reduced effect or lack of data, certain intrinsic fuel properties (specific gravity, mineral content,
chemical composition) either are not used as inputs to fire spread
models or are assumed constant, as in Rothermel (1972) and in
sophisticated process models based on the solution of equations
describing the conservation of mass, momentum, energy and
species (Larini et al., 1998; Linn et al., 2002).
Fuel types dominated by shrubs can be structurally complex,
which complicates the quantification of each fuel component and
fuel metric contribution to flammability (Fernandes et al., 2000;
Davies & Legg, 2011). The likelihood of fire spread is affected by
wind speed, slope, fuel moisture content and fuel bed depth
when live and implicitly discontinuous fuels predominate, which
very often is the case in shrubland (Zhou et al., 2005; Yedinak
et al., 2010). Hence, the ignition of fuel elements or even of individual shrubs is not synonymous with successful fire spread, as
found by Anderson & Anderson (2010) in field experiments.
Fire behaviour measured in shrub fuel beds reconstructed in the
laboratory can be upscaled to match the results of field-based
models (Marino et al., 2008). However, the flammability of
shrub samples of foliage and branches was poorly correlated with
whole shrub flammability in the laboratory studies of Weise et al.
(2005) and Madrigal et al. (2011).
Limitations of laboratory-scale flammability studies are highlighted when contrasting results from these experiments with
observations of fire dynamics in scaled-up outdoor experiments
in shrub and forest fuel complexes. While live fuel moisture content has been identified as a key flammability variable (Alessio
et al., 2008; Plucinski et al., 2010; Cole et al., 2011; Pausas
et al., 2012), analyses of field experimental data failed to find a
significant effect of this variable on fire spread and intensity in
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shrub ecosystems (Thomas, 1971; Lindenmuth & Davis, 1973;
Van Wilgen et al., 1985; Catchpole et al., 1998; Vega et al.,
1998; Fernandes, 2001; Bilgili & Saglam, 2003; Davies et al.,
2009) and conifer forests (Van Wagner, 1998; Cruz et al., 2004,
2005; Fernandes et al., 2009). Likewise, Fletcher et al. (2007)
were unable to find consistent correlations between moisture content and the ignition behaviour of shrub leaves exposed to fieldlike convective heat fluxes of 80–140 kW m)2. The influence of
live fuel moisture content on flammability is expected to decrease
as the experimental heat source gets larger (White & Zipperer,
2010).
Laboratory flammability studies seldom replicate the heat
transfer mechanisms and combustion processes driving fire propagation. Many studies employ a cone calorimeter (Weise et al.,
2005; Madrigal et al., 2011) or epiradiator as the heat source
(Pausas et al., 2012). Considering an epiradiator radius of
0.05 m and a surface temperature of 650°C, the fuel sample in
Pausas et al. (2012) was heated by a constant radiative flux of
4.6 kW m)2. Comparatively, measurements in shrubland fires
yielded peak radiative heat fluxes between 36 and 150 kW m)2
(Silvani & Morandini, 2009; Cruz et al., 2011). Values
c. 250 kW m)2 were measured in crown fires in conifer forests
(Butler et al., 2004). In epiradiator experiments convection acts
as a cooling mechanism after the fuel particle temperature surpasses the background temperature. Nonetheless, convective heat
transfer mechanisms dominate fire spread in highly porous
fuel complexes (Zhou et al., 2007; Yedinak et al., 2010).
Measurements in moderate intensity wind-driven shrubland fires
yielded peak convective heat fluxes between 40 and 61 kW m)2
(Silvani & Morandini, 2009). Convective heat fluxes peaking at
150 kW m)2 were measured in high-intensity crown fires
(Butler, 2010). As such, the energy levels impinging fuel samples
placed in cone calorimeters and epiradiators are two to three
orders of magnitude lower than those observed in real fires. This
has significant effects on the experimental conclusions as heating
rates determine the rate, pathways and efficiency of pyrolysis of
woody and nonwoody plant materials (Saastamoinen & Richard,
1996; Sullivan & Ball, 2012). Laboratory experiments that rely
on realistic heat fluxes do provide key information to understand
ignition processes at the leaf scale. However, only experiments
that replicate not just the heat flux environment of outdoor fires,
but also plant (fuel-complex) structure result in flammability
metrics that are relevant to fire propagation in shrubland
ecosystems.
Flammability and physiological condition
Live fuels are the subjects of many flammability studies, particularly those that test the behaviour of individual leaves or twigs
(Engstrom et al., 2004; Fletcher et al., 2007; Alessio et al., 2008;
Pickett et al., 2009; Pausas et al., 2012). However, retention of
standing dead fuel in the lower to mid-canopy is a common feature
in fire-prone shrubs, especially in mature and senescent populations (Fernandes et al., 2000; Baeza et al., 2006, 2011; Davies &
Legg, 2011; Madrigal et al., 2011; Pausas et al., 2012). The main
difference between physiologically active biomass and necromass
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in relation to ignition and combustion arises from substantially
different water contents. Combustion of live fuels is often dependent on the combustion of dead fuels (Davies & Legg, 2011),
which work as a catalyst releasing high amounts of energy that will
lead to the sustained combustion of live fuels. Although the effect
of dead : live ratio on shrubland fire behaviour remains to be
experimentally quantified, testing live plant parts alone is a potentially misleading procedure when the objective is to characterize
the flammability of species with high dead : live ratios. Likewise,
the effect of minor differences in live fuel flammability is diluted
and probably made irrelevant in the presence of high dead : live
ratios, such as in the Ulex parviflorus study of Pausas et al. (2012).
Fire modelling as a complement to flammability
experiments
Several authors infer flammability directly from fuel properties
(Dimitrakopoulos, 2001; Behm et al., 2004; Cowan & Ackerly,
2010) or complement the results of flammability tests with fuel
data (Saura-Mas et al., 2010; Pausas et al., 2012). This approach
relies on personal judgement, is largely qualitative, and lacks the
ability to weigh the role of each fuel property and the interactions
involved. Fire behaviour modelling based on fuel characteristics
provides a valuable, objective and quantitative supplement to
flammability experiments.
Fuel bulk density describes the mass of fuel per unit volume
and is a common descriptor of fuel bed porosity, which determines convective and radiative heat transfer through the fuel bed,
combustion efficiency and heat release rates. Empirical and theoretical analysis suggests that fire spread is inversely proportional
to bulk density in some shrublands and dense conifer fuel types
(Thomas, 1971; Butler et al., 2004). The relationship of combustion rate with bulk density is not straightforward and has been
described through a parabolic curve, for example, by Wilson
(1982). In the fire spread model of Rothermel (1972) bulk
density acts as an heat sink, which implies that fire spread rate
can decrease when fuel loading increases for a given fuel depth.
We will resort to the fuel bulk density data in Pausas et al.
(2012) to exemplify the potential of fire modelling in flammability studies. Pausas et al. (2012) tested if previous exposure to fire
had increased the flammability of the Mediterranean shrub
U. parviflorus; two fire regimes were considered, highly frequent
fire (HiFi) and absent fire (NoFi). Ulex parviflorus plants from
the HiFi regime had higher bulk density than plants from populations without a prior history of fire. We used the BehavePlus
5.0 fire-modelling software (Heinsch & Andrews, 2010) based
on Rothermel’s (1972) model to quantify how the bulk density
difference between the NoFi and HiFi populations translates into
fire behaviour. The required fuel data were sourced from Viegas
et al. (2001), Baeza et al. (2006, 2011), Santana et al. (2011)
and Pausas et al. (2012). Two simulations were carried out for
typical Mediterranean climate summer moisture conditions: for a
no-slope and no-wind setting and for a combination of 30%
slope and 10 km h)1 midflame wind speed.
Table 1 indicates that NoFi populations are more flammable
than HiFi populations on average, as the simulated fire spread
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Table 1 Ulex parviflorus fire behaviour characteristics for the two fire
regimes in Pausas et al. (2012), expressed by a HiFi : NoFi (highly frequent
fire regime : absent fire regime) ratio
Simulation
Spread rate
Fireline intensity
No-slope, no wind
Slope, wind
0.63
0.51
0.67
0.53
rate and fireline intensity are substantially higher, especially
under the influence of slope and wind. This reflects distinct relative packing ratio values between NoFi and HiFi, respectively, of
0.614 and 1.109. The relative packing ratio expresses the ratio of
actual to optimum packing ratio for heat transfer and combustion efficiency. Hence, HiFi fuel structure is near optimal for
combustion velocity, although this does not correspond with
increased spread rate and intensity of the flame front (Rothermel,
1972). Rothermel’s model does have a number of problems,
especially in relation to mixed-size and live fuels, to which the
model has been mathematically extended. Nevertheless, its prediction of higher flammability when the bulk density of fine fuel
beds decreases is corroborated by fire experiments in different
shrubland types (Thomas, 1971; Fernandes et al., 2000; Davies
et al., 2009).
This example of fire modelling was simple. Recent advances in
the understanding of fire processes at fine scales allow for the use
of more sophisticated, physical-based models to investigate the
flammability of particular vegetation assemblages (Linn et al.,
2002; Zhou et al., 2005; Parsons et al., 2011), provided that
kinetic parameterization of ignition and combustion processes
with field or laboratory data is possible.
Conclusion
The assessment of flammability in the laboratory is limited by
various factors, mostly related to the scale of experimentation.
Individual elements or fuel beds that do not replicate the natural
fuel-complex structure and composition are often the subjects of
flammability trials. Plant exposure to heat in flammability experiments is frequently not comparable to wildfire conditions. If the
heat transfer processes that operate in the real world cannot be
realistically reproduced in the laboratory then the correspondent
flammability metrics and their interpretation should not be
extrapolated beyond the experimental setting.
Various configurations for flammability experiments are possible. As a minimum requirement, the reconstructed fuel complex
should emulate natural fuel structure as close as possible. The heat
fluxes used in laboratory experiments should approximate the
quantities observed in free-spreading flame fronts, as in the experimental setup of Engstrom et al. (2004) and Fletcher et al. (2007).
Finally, flammability metrics should have field correspondence to
enable scaling up, comparison and linkage with fire behaviour
models. Reconciling these requirements may well be quite difficult, and laboratory flammability may, after all, remain a poor
surrogate for full-scale, properly instrumented experimental fires.
Nonetheless, outdoor experimental fires are often limited by operNew Phytologist (2012) 194: 606–609
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ational, safety and cost constrains. As an alternative or supplement
to experimentation, fire behaviour simulation based on detailed
fuel characterization is useful to examine how flammability
changes with the properties of fuel elements and complexes. Such
a research approach is particularly promising to foster crossdisciplinary collaboration and to further increase our understanding
of fire potential associated with specific vegetation types.
Acknowledgements
The authors thank three anonymous referees for useful comments
on the manuscript.
Paulo M. Fernandes1* and Miguel G. Cruz2
Centro de Investigação e de Tecnologias Agro-Ambientais e
Biológicas (CITAB), Departamento de Ciências Florestais e
Arquitectura Paisagista, Universidade de Trás-os-Montes e Alto
Douro, Apartado 1013, 5001-801 Vila Real, Portugal;
2
Bushfire Dynamics and Applications, CSIRO Ecosystem
Sciences and Climate Adaptation Flagship, GPO Box 1700,
Canberra, ACT 2601, Australia
(*Author for correspondence: tel +351 259 350861;
email [email protected])
1
References
Alessio GA, Peñuelas J, Llusià J, Ogaya R, Estiarte M, De Lillis M. 2008.
Influence of water and terpenes on flammability in some dominant
Mediterranean species. International Journal of Wildland Fire 17: 274–286.
Anderson HE. 1970. Forest fuel ignitibility. Fire Technology 6: 312–319.
Anderson S, Anderson W. 2010. Ignition and fire spread thresholds in gorse
(Ulex europaeus). International Journal of Wildland Fire 19: 589–598.
Baeza J, Santana VM, Pausas JG, Vallejo R. 2011. Successional trends in
standing dead biomass in Mediterranean basin species. Journal of Vegetation
Science 22: 467–474.
Baeza MJ, Raventós J, Escarré A, Vallejo VR. 2006. Fire risk and vegetation
structural dynamics in Mediterranean shrubland. Plant Ecology 187: 189–201.
Behm AL, Duryea ML, Long AJ, Zipperer WC. 2004. Flammability of native
understory species in pine flatwood and hardwood hammock ecosystems and
implications for the wildland–urban interface. International Journal of Wildland
Fire 13: 355–365.
Bilgili E, Saglam B. 2003. Fire behavior in maquis fuels in Turkey. Forest Ecology
and Management 184: 201–207.
Bond WJ, Midgley JJ. 1995. Kill thy neighbour: an individualistic argument for
the evolution of flammability. Oikos 73: 79–85.
Butler B. 2010. Characterization of convective heating in full scale wildland fires.
In: Viegas DX, ed. Proceedings of the VI International Conference on Forest Fire
Research. Coimbra, Portugal: University of Coimbra.
Butler BW, Cohen J, Latham DJ, Schuette RD, Sopko P, Shannon KS, Jimenez
D, Bradshaw LS. 2004. Measurements of radiant emissive power and
temperatures in crown fires. Canadian Journal of Forest Research 34:
1577–1587.
Catchpole WR, Bradstock J, Choate L, Fogarty N, Gellie G, McCarthy L,
McCaw L, Marsden-Smedley J, Pearce G. 1998. Cooperative development of
equations for heathland fire behaviour. In: Viegas DX, ed. Proceedings of the 3rd
International Conference on Forest Fire Research. Coimbra, Portugal: ADAI,
631–645.
Chandler C, Cheney P, Thomas P, Trabaud L, Williams D. 1983. Fire in
forestry. New York, NY, USA: John Wiley & Sons.
Cole WJ, Dennis MH, Fletcher TH, Weise DR. 2011. The effects of wind on
the flame characteristics of individual leaves. International Journal of Wildland
Fire 20: 657–667.
Ó 2012 The Authors
New Phytologist Ó 2012 New Phytologist Trust
New
Phytologist
Cowan PD, Ackerly DD. 2010. Post-fire regeneration strategies and flammability
traits of California chaparral shrubs. International Journal of Wildland Fire 19:
984–989.
Cruz MG, Alexander ME, Wakimoto RH. 2004. Modeling the likelihood of
crown fire occurrence in conifer forest stands. Forest Science 50: 640–658.
Cruz MG, Alexander ME, Wakimoto RH. 2005. Development and testing of
models for predicting crown fire rate of spread in conifer forest stands.
Canadian Journal of Forest Research 35: 1626–1639.
Cruz MG, Butler BM, Viegas DX, Palheiro P. 2011. Characterization of flame
radiosity in shrubland fires. Combustion and Flame 158: 1970–1976.
Davies GM, Legg CJ. 2011. Fuel moisture thresholds in the flammability of
Calluna vulgaris. Fire Technology 47: 421–436.
Davies GM, Legg CJ, Smith AA, MacDonald AJ. 2009. Rate of spread of fires in
Calluna vulgaris-dominated moorlands. Journal of Applied Ecology 46:
1054–1063.
Dickinson MB, Johnson EA. 2001. Fire effects on trees. In: Johnson EA,
Miyanishi K, eds. Forest fires: behavior and ecological effects. San Diego, CA,
USA: Academic Press, 477–525.
Dimitrakopoulos AP. 2001. A statistical classification of Mediterranean species
based on their flammability components. International Journal of Wildland Fire
10: 113–118.
Engstrom JD, Butler JK, Smith SG, Baxter LL, Fletcher TH. 2004. Ignition
behaviour of live California chaparral leaves. Combustion Science and Technology
176: 1577–1591.
Fernandes PM. 2001. Fire spread prediction in shrub fuels in Portugal. Forest
Ecology and Management 144: 67–74.
Fernandes PM, Botelho HS, Rego FC, Loureiro C. 2009. Empirical modelling
of surface fire behaviour in maritime pine stands. International Journal of
Wildland Fire 18: 698–710.
Fernandes PM, Catchpole WR, Rego FC. 2000. Shrubland fire behaviour
modelling with microplot data. Canadian Journal of Forest Research 30:
889–899.
Fletcher TH, Pickett BM, Smith SG, Spittle GS, Woodhouse MM, Haake E.
2007. Effect of moisture on ignition behaviour of moist California chaparral
and Utah leaves. Combustion Science and Technology 179: 1183–1203.
Gill AM, Zylstra P. 2005. Flammability of Australian forests. Australian Forestry
68: 88–94.
Heinsch FA, Andrews PL. 2010. BehavePlus fire modeling system, version 5.0:
design and features. General Technical Report RMRS-GTR-249. Fort Collins,
CO, USA: USDA Forest Service.
Larini M, Giroux F, Porterie P, Loraud JC. 1998. A multiphase formulation for
fire propagation in heterogeneous combustible media. International Journal of
Heat and Mass Transfer 41: 881–897.
Lindenmuth AW, Davis JR. 1973. Predicting fire spread in Arizona’s oak chaparral.
Research paper RM-101. Fort Collins, CO, USA: USDA Forest Service.
Linn R, Reisner J, Colman JJ, Winterkamp J. 2002. Studying wildfire behavior
with FIRETEC. International Journal of Wildland Fire 11: 233–246.
Madrigal J, Marino E, Guijarro M, Hernando C, Dı́ez C. 2011. Evaluation of
the flammability of gorse (Ulex europaeus L.) managed by prescribed burning.
Annals of Forest Science. doi: 10.1007/s13595-011-0165-0.
Marino E, Guijarro M, Madrigal J, Hernando C, Dı́ez C. 2008. Assessing fire
propagation empirical models in shrub fuel complexes using wind tunnel data.
In: de las Heras J, Brebbia CA, Viegas D, Leone V, eds. Modelling, monitoring
and management of forest fires. Southampton, UK: WIT Press, 121–130.
Martin RE, Gordon D, Gutierrez M, Lee D, Molina D, Schroeder R, Sapsis D,
Stephens S, Chambers M. 1994. Assessing the flammability of domestic and
wildland vegetation. In: Proceedings of the 12th Conference on Fire and Forest
Meteorology, SAF Publ. 94-02. Bethesda, MD, USA: SAF, 130–137.
Parsons RA, Mell WE, McCauley P. 2011. Linking 3D spatial models of fuels
and fire: effects of spatial heterogeneity on fire behavior. Ecological Modelling
222: 679–691.
Ó 2012 The Authors
New Phytologist Ó 2012 New Phytologist Trust
Letters
Forum 609
Pausas JG, Alessio GA, Moreira B, Corcobado G. 2012. Fire enhances
flammability in Ulex parviflorus. New Phytologist 193: 18–23.
Pickett BM, Isackson C, Wunder R, Fletcher TH, Butler BW, Weise DR. 2009.
Flame interactions and burning characteristics of two live leaf samples.
International Journal of Wildland Fire 18: 865–874.
Plucinski MP, Anderson WR, Bradstock RA, Gill AM. 2010. The initiation of
fire spread in shrubland fuels recreated in the laboratory. International Journal
of Wildland Fire 19: 512–520.
Rothermel RC. 1972. A mathematical model for predicting fire spread in wildland
fuels. Research paper INT-115. Ogden, UT, USA: USDA Forest Service.
Saastamoinen J, Richard JR. 1996. Simultaneous drying and pyrolysis of solid
fuel particles. Combustion and Flame 106: 288–300.
Santana VM, Baeza MJ, Vallejo VR. 2011. Fuel structural traits modulating soil
temperatures in different species patches of Mediterranean Basin shrublands.
International Journal of Wildland Fire 20: 668–677.
Saura-Mas S, Paula S, Pausas JG, Lloret F. 2010. Fuel loading and flammability
in the Mediterranean Basin woody species with different post-fire regenerative
strategies. International Journal of Wildland Fire 19: 783–794.
Scarff FR, Westoby M. 2006. Leaf litter flammability in some semi-arid
Australian woodlands. Functional Ecology 20: 745–752.
Schwilk DW. 2003. Flammability is a niche construction trait: canopy
architecture affects fire intensity. The American Naturalist 162: 725–733.
Silvani X, Morandini F. 2009. Fire spread experiments in the field: temperature
and heat fluxes measurements. Fire Safety Journal 44: 279–285.
Sullivan AL, Ball R. 2012. Thermal decomposition and combustion chemistry of
cellulosic biomass. Atmospheric Environment 47: 133–141.
Thomas PH. 1971. Rates of spread of some wind-driven fires. Forestry 44:
155–175.
Van Wagner CE. 1998. Modelling logic and the Canadian forest fire behavior
prediction system. Forestry Chronicle 74: 50–52.
Van Wilgen BW, Le Maitre DC, Kruger FJ. 1985. Fire behaviour in South
African fynbos (macchia) vegetation and predictions from Rothermel’s fire
model. Journal of Applied Ecology 22: 207–216.
Vega JA, Cuiñas P, Fontúrbel MT, Pérez-Gorostiaga P, Fernández C. 1998.
Predicting fire behaviour in Galician (NW Spain) shrubland fuel complexes.
In: Viegas DX, ed. Proceedings of the 3rd International Conference on Forest Fire
Research. Coimbra, Portugal: ADAI, 713–728.
Viegas DX, Piñol J, Viegas MT, Ogaya R. 2001. Estimating live fine fuel
moisture content using meteorologically-based indices. International Journal of
Wildland Fire 10: 223–240.
Weise DR, White RH, Beall FC, Etlinger M. 2005. Use of the cone calorimeter
to detect seasonal differences in selected combustion characteristics of
ornamental vegetation. International Journal of Wildland Fire 14: 321–338.
White RH, Zipperer WC. 2010. Testing and classification of individual plants
for fire behaviour: plant selection for the wildland–urban interface.
International Journal of Wildland Fire 19: 213–227.
Wilson RA. 1982. A reexamination of fire spread in no-wind and no-slope. Research
Paper INT-289. Ogden, UT, USA: USDA Forest Service.
Yedinak K, Cohen JD, Forthofer J, Finney M. 2010. An examination of flame
shape related to convection heat transfer in deep-fuel beds. International
Journal of Wildland Fire 19: 171–178.
Zhou X, Mahalingam S, Weise D. 2005. Modeling of marginal burning state of
fire spread in live chaparral shrub fuel bed. Combustion and Flame 143:
183–198.
Zhou X, Mahalingam S, Weise D. 2007. Experimental study and large eddy
simulation of effect of terrain slope on marginal burning in shrub fuel beds.
Proceedings of the Combustion Institute 31: 2547–2555.
Key words: combustion, fire ecology, heat transfer, Mediterranean-type
ecosystems, plant flammability, shrubland.
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