Project no. FP6-018505
Project Acronym FIRE PARADOX
Project Title FIRE PARADOX: An Innovative Approach of Integrated Wildland Fire
Management Regulating the Wildfire Problem by the Wise Use of Fire: Solving the Fire
Paradox
Instrument Integrated Project (IP)
Thematic Priority Sustainable development, global change and ecosystems
D3.1-5 Probability of ignition modelling in forest fuels – First results
Due date of deliverable: Month 18
Actual submission date: Month 19
Start date of project: 1st March 2006
Duration: 48 months
Organisation name of lead contractor for this deliverable: Mediterranean Agronomic
Institute of Chania (P04)
Revision (1000)
Project co-funded by the European Commission within the Sixth Framework Programme
(2002-2006)
Dissemination Level
PU
Public
PP
Restricted to other programme participants (including the Commission Services)
RE
Restricted to a group specified by the consortium (including the Commission Services)
CO
Confidential, only for members of the consortium (including the Commission Services)
X
Table of contents
1. Introduction (State of the art) ................................................................................. 3
General objectives ...................................................................................................... 5
PART I – Ignition probability in dead fuels (Ex Situ), MAICh ........................................... 7
Laboratory ignition data collection ............................................................................... 7
Materials and methods ............................................................................................... 7
Experiment 1: ............................................................................................................ 9
Experiment 2: .......................................................................................................... 12
Experiment 3: .......................................................................................................... 14
Results and Discussion ............................................................................................. 15
Experiment 1: .......................................................................................................... 15
Experiment 2: .......................................................................................................... 17
Experiment 3: .......................................................................................................... 19
Additional tests: ....................................................................................................... 22
Future work ............................................................................................................. 23
PART II – Ignition probability in live fuels (In situ), AUTh ............................................ 24
Objectives ............................................................................................................... 24
Methods .................................................................................................................. 24
Results .................................................................................................................... 27
References .............................................................................................................. 31
ANNEX I ................................................................................................................. 37
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1. Introduction (State of the art)
Ignition is required for the initiation of any fire, large or small. Ignition can be described as
“the process by which a rapid, exothermic reaction is initiated, which then propagates and
causes the material involved to undergo change” (Drysdale, 1985). In this process fuel is
transformed from “a non reactive equilibrium state to a self-sustaining reactive state”
(Atreya, 1998).
There are two types of ignition, spontaneous and piloted. Spontaneous ignition occurs
without the aid of a pilot source (Drysdale, 1985). In forest fires, however, piloted ignition
is the usual procedure for initiation and spread of the fire. Pilot sources are generally small
in comparison to the size of the fuel and have locally high temperatures in order to initiate
combustion (Atreya, 1998). Piloted ignition occurs at a lower temperature than
spontaneous ignition and it is the mechanism responsible for fire growth (Atreya, 1998).
The first phase of ignition is called preignition (Susott, 1984). During preignition a heat
source provides heat to the fuel. The temperature of the fuel starts rising. During this
endothermic reaction the energy that is absorbed from the ignition source is used to
increase the temperature of the fuel and the moisture it contains. In the process volatiles
start being released in the gas phase. At the temperature of 100oC a significant amount of
energy is consumed for evaporation of the moisture in the fuel. The amount of heat
required to convert a unit mass of water into vapor without a change in temperature is
2257 kJ/kg and is called “the specific latent heat of vaporization”.
The amount of heat necessary to ignite 1 kg of fuel, measured in kJ/kg, is called the “heat
of preignition”. Obviously, it is dependent on the amount of moisture present in the fuel.
The more moisture in the fuel, the more energy is needed to evaporate the water and
then bring the dry fuel to ignition temperature. Furthermore, the presence of moisture
dilutes any flammable volatiles evolved, limiting their ignitability (Atreya, 1998).
Once the density of flammable volatiles is sufficient, ignition can occur providing there is
enough oxygen present. Ignition is marked by the onset of flaming. Ignition temperature
has been defined as the critical temperature that the fuel needs for burning to begin
(Williams, 1982; Saito, 2001). A number of temperatures have been reported in the
literature for the piloted ignition of cellulosic fuels. The most commonly reported ignition
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temperature is 320°C (Anderson, 1969; Schroeder, 1969; Rothermel, 1972; Frandsen,
1973; Wilson, 1990), with other temperatures including 325°C (Stockstad, 1975), 327°C
(600K) (Albini, 1993; Albini and Reinhardt, 1995), 350°C (Simms, 1963; Williams, 1982;
Drysdale, 1985; Saito, 2001) and 384°C (Atreya, 1998) also used. Many authors report a
range of ignition temperatures (eg: Anderson, 1970; Susott, 1984; Wilson, 1985; Liodakis
et al., 2002), due to its dependence on fuel type (Susott, 1984). Moisture content and
amount of fuel being heated also appear to affect ignition temperature (Xanthopoulos
1990). The ignition probability for a given fuel is the probability that an ignition source has
an effective heat equal to or greater than the heat required for pre-ignition (Schroeder,
1969).
Fuel moisture content (FMC) is recognised as the variable with the greatest influence on
fuel ignitability (e.g. Gisbourne, 1928; Byram, 1959; Pompe and Vines, 1966; Anderson,
1969; Schroeder, 1969; Blackmarr, 1972; Luke and McArthur, 1978; Green, 1981;
Xanthopoulos and Wakimoto 1993, Cheney and Sullivan, 1997; Atreya, 1998; Nelson,
2001, Plucinski, 2003, Larjavaara et al. 2004). It has been used as the primary variable in
ignition and flammability studies by Pompe and Vines (1966), Anderson (1969),
Montgomery and Cheo (1969), Schroeder (1969), Parrot and Donald (1970), Blackmarr
(1972), Muraszew and Fedele (1976), Trabaud, (1976); Gill et al. (1978), Countryman
(1980a), Wilson (1985; 1990), Latham and Schlieter (1989), Weber (1989), Fransden
(1991; 1997), Lawson et al. (1997a), Anderson, (2000), and Fernandes et al. (2002).
Most of the wildland fires around the world are human caused (Kruger and Bigalke, 1984;
Whelan, 1995; Perry, 1998; Weber, 2000). Natural causes such as lightning or volcanoes
are only prominent in specific regions. For example, lightning ignitions are most likely to
occur in high-elevation areas (McRae, 1992; Latham and Williams, 2001; Anderson, 2002).
Anthropogenic ignitions are of two types: deliberate and accidental. Deliberate ignition is
used for prescribed fire or arson. Accidental ignitions are usually a result of negligence.
Determining the probability of an ignition source is practically impossible due to the variety
of sources and their causes. The probability of ignition sources varies spatially, depending
on land use, and temporally due to weather conditions, season and day of the week
(Catchpole, 2001). Some effort has been devoted recently to the prediction of specific
causes such as cigarettes that can be used for prevention planning (Dainier, 2003,
Xanthopoulos et al., 2006).
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FMC is not the only fuel property that affects ease-of-ignition. Other fuel characteristics
such as fuel particle thickness (expressed as surface-area-to-volume ratio), chemical
composition, density, arrangement (such as packing ratio), continuity and quantity, also
influence ignitability. Each of these characteristics can be evaluated separately
experimentally, allowing comparisons between fuels (Liodakis et al., 2005), but using such
information for practical purposes is not easy. As a result, many researchers have focused
on experimenting with fuel types of interest and selected heat sources, developing
empirical estimates of ignition probabilities (Countryman, 1980a; Markalas, 1985).
Accidental ignition sources are numerous. They may find their way on the fuels either
truly accidentally or due to human negligence. They include sparks, matches, cigarettes
and car exhausts. Studies using discarded cigarettes as firebrands dropped on pine
needles and dead grass have found that ignition seldom occurs, even when the FMC is
very low (Countryman, 1980a; Markalas, 1985). Car exhaust systems have been found to
ignite grass by direct contact for varied periods of time (Knight and Hutchings, 1987).
Spark ignitions can come from mechanical sources, such as bulldozers and slashers
scraping rocks and careless use of welding and grinding equipment (Luke and McArthur,
1978), and electrical sources, including spark ignitions from power lines (Rowntree and
Stokes, 1994; Conroy, 1996). They can also come from other fires such as fireplaces or
barbeque fires. Sparks need to come into contact with dry fine fuel for ignition to succeed
as they only produce small amounts of heat for short times (Cheney and Sullivan, 1997).
Finally, it should be pointed out that the properties of all ignition sources and the heat
transfer to the fuel are affected by the presence of wind. This is especially true when the
heat source itself, such as a cigarette, is combustible. The wind may favour this
combustion but if it is too strong it may even lead to extinction of the heat source (Satoh
et al., 2003).
General objectives
Following a similar approach to the researchers cited above, and aiming to cover gaps in
the literature on ignition, especially in regard to Mediterranean conditions, the present
study focuses on the following:

To evaluate the threshold of fuel moisture content that allows a fire to start. A
range of fuel types (pine needles, grass and Quercus coccifera leaves) are
examined, as are different ignition sources (kitchen matches, cigarettes,
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machinery sparks and electrical discharge), with the presence/absence of wind in
order to determine the probability of ignition of selected natural dead fuels
(MAICh).

To measure the probability of ignition of annual live herbaceous fuels, at various
moisture contents, in the field (AUTh).
Fuel moisture content (FMC) is used as the main predictor variable throughout these
experiments because it has a major effect on fuel ignitability, is easily altered and can be
accurately measured.
Only the first results of the study are presented within this deliverable:

Probability of ignition of pine needles (Pinus halepensis) with kitchen matches and
cigarettes as ignition sources conducted in the wind tunnel of MAICh.

Probability of ignition of grass (Hyparrhenia hirta) with kitchen matches as an
ignition source conducted in the wind tunnel of MAICh.

Some preliminary test of pine needles with machinery sparks as well as grass with
cigarettes conducted in the wind tunnel of MAICh.

Fuel moisture extinction in live annual herbaceous plants (Avena barbata) in the
field (AUTh).
The final results will be presented within deliverable D 3.1-10 due in month 46 of the
project.
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PART I – Ignition probability in dead fuels (Ex Situ), MAICh
Laboratory ignition data collection
There were many advantages for experiments to be conducted in the laboratory.
Laboratory experiments allow control over fuel and environmental variables and precise
measurement of fuel and fire behaviour. Many of these would be impossible or impractical
to measure in the field and are subject to a great degree of variation, which can be
avoided in the laboratory. Gill et al. (1978) used the laboratory for an investigation into
the role of moisture in flammability to avoid fuel and weather variation in the field. The
three processes investigated in the laboratory experiments would not be able to be
investigated separately in field ignition experiments without excessive site preparation.
Laboratory experiments are also safer and less expensive than field experiments to
conduct (Burrows, 1999a) and allow for more replicates to be accomplished and fewer
gaps in datasets. The laboratory used for all experiments is located at the Mediterranean
Agronomic Institute of Chania. The laboratory was a well ventilated concrete room. All
fuels were stored and prepared in the laboratory. The experiments were conducted inside
a wind tunnel
Materials and methods
Experimental ignitions were attempted in the Forest fire laboratory of MAICh using
reconstructed fuel beds made with fuel of uniform moisture content.
Fuel was collected by hand, with caution taken to avoid collecting fallen leaves or other
photosynthetic organs from the desired species, twigs, rocks and soil. The pine needles of
Pinus halepensis were collected from sites near the Mediterranean Agronomic Institute of
Chania.
The selected grass species was Hyparrhenia hirta a perennial native species, which is very
common next to the roads (Pict. 1). The samples were collected from the area of Elafonisi
on the western part of the prefecture of Chania. The fuels were transported to the
laboratory in garbage bags, where they were subjected to different treatments,
depending on the desired moisture content.
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Picture 1. Hyparrhenia hirta growing next to the road in the area of Elafonisi
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Experiment 1:
Fuel type: Pine needles (Pinus halepensis), Ignition source: kitchen matches.
Dead pine needles of Pinus halepensis were collected, cleaned and put in the oven at
70C for 48 hours to dry. Two different methodologies were used to prepare samples at
different moisture contents as follows:
1. Samples preparation (tests 1 to 44):
Samples of 50 g dry needles were put in plastic bags and sealed. The desired level of
moisture content (percent) was achieved by adding the appropriate amount of moisture
to the dry fuel: The dry sample was placed on a balance and then water was added using
a squeeze-type sprayer bottle to evenly distribute the moisture over the sample until the
desired total weight (based on the initial oven dry weight and the desired moisture
content) was reached. The plastic bags were then quickly sealed and placed in a 50C
drying oven for 24 hours to ensure equal distribution of the moisture within the sample
(moisture equilibrium) (De Groot et al. 2005).
(Pict. 2)
Picture 2. Samples of pine needles sealed in plastic bags.
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2. Samples preparation (tests 45 to115):
Pine needles were put on a fuel bed. Water was added using a squeeze-type sprayer
bottle and the fuel bed was exposed to the sun. Pine needles were mixed continuously to
evenly distribute the moisture over the fuel bed (Pict. 3). Around 50 g of Pine needles
were placed in plastic bags at different time intervals (1 to 5 minutes). Plastic bags were
quickly sealed and placed for at least 2 hours in room at ambient conditions to ensure
equal distribution of the moisture within the sample (Pict. 4).
Picture 3. Pine needles mixed
continuously to evenly distribute the
moisture over the fuel bed.
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Picture 4. Plastic bags with pine
needles placed in the lab.
Page 10
Experiments
All ignition tests (115 experiments) were completed by
removing about 30 g of pine needles from the plastic bag
and placing them in an aluminium disk. The aluminium disk
had a diameter of 20 cm and the fuel depth was 3 to 3.5
cm. The bulk density of the samples was 27 Kg/m3 to 31
Kg/m3. The remaining 20 g of pine needles were placed in a
small aluminium disk and oven dried at 70C for 48 hours to
calculate the real moisture content of the sample.
A wooden match was lit and randomly dropped on the pine
needle sample from a height of approximately 10 cm. If the
match started a fire, it was considered a positive test and
classed as “burn”. If the initially dropped match did not
result in a positive test, a second match was dropped. A
third match was dropped if the second match did not result
in a positive test. The ignition test was classed as “non
burn” if all three matches failed to provide a positive test
(De Groot et al. 2005). During the experiments air RH (%),
air temperature and fuel temperature were recorded. All the
experiments were done indoors in the wind tunnel with no
presence of wind. The RH was in the range of 57% to 71%,
the air temperature in the range of 25C to 26C and the
fuel temperature, measured by a thermocouple in contact
with the needles, in the range of 23C to 27.7C.
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Experiment 2:
Fuel type: Pine needles (Pinus halepensis), Ignition source: Cigarettes.
Dead pine needles of Pinus halepensis were collected and cleaned. Four different
methodologies were used to prepare samples at different moisture contents as follows:
1. for FMC between 1 % and 3 %:
Needles were dried in the oven at 70C for 48 hours and then left in the open air inside
the wind tunnel (RH around 62%) for different time intervals for moisture absorption.
2. for FMC between 3 % and 8 %:
Pine needles were taken from an open big box stored indoors. Their moisture content was
around 12%. Samples of 50 g were prepared in disks and placed in the oven at 70C for
5-20 minutes. The samples were removed from the oven at different time intervals (1 to 2
minutes) and were placed in plastic bags. Plastic bags were quickly sealed and placed for
1 hour in a room at ambient conditions to ensure equal distribution of the moisture within
the sample.
3. for FMC between 8 % and 10 %:
Samples were used directly from the needles kept indoors, inside an open big box, at a
relative humidity higher than 60 %.
4. for FMC higher than 10 %:
Water was added using a squeeze-type sprayer bottle and the needles were mixed to
evenly distribute the moisture over the sample.
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Experiments:
All ignition tests (116 trials) were completed by removing about
30 g of pine needles from the plastic bag and placing them on
an aluminium tray. The sample had a diameter of 16 cm and
the fuel depth was 5 cm. The bulk density of the samples was
30 Kg/m3. The remaining 20 g were placed in the oven at 70C
for 48 hours to calculate the real moisture content of the
sample.
A cigarette was lit and when it had a remaining length of 4 cm,
its tip was placed at 1 cm depth inside the fuel (Satoh et al.
2003) with a 45 degree angle.
If the cigarette started a fire, it was considered a positive test
and recorded as “burn”. The ignition test was recorded as “non
burn” if the cigarette failed to start a fire.
All experiments were done indoors in the wind tunnel with no
presence of wind. The RH was in the range of 57% to 67%, the
air temperature in the range of 23C to 26C and the fuel
temperature in the range of 24.5C to 26.5C.
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Experiment 3:
Fuel type: Perennial grass (Hyparrhenia hirta), Ignition source: kitchen matches.
Fresh material of Hyparrhenia hirta were collected on June 8, 2007 and was air dried for 1
week in the laboratory. The material was cut into small pieces about 8-10 cm then it was
oven dried at 70oC for 48 hours. The material was put on a large tray, and was sprayed
with enough water to bring it moisture content to more than 35%. It was then exposed to
the sun and was continuously mixed. Every 1 or 2 minutes, samples of about 50 g were
taken and put in plastic bags. The samples were stored in the laboratory for at least 2
hours to achieve moisture equilibrium. Before the experiment, a sub-sample was taken for
fuel moisture content measurement. The sub-samples were oven dried at 70oC for 48
hours.
Experiments:
All ignition tests (161) were completed by removing about 25 g
of grass from the plastic bag and placing them in an aluminium
disk. The aluminium disk had a diameter of 20 cm. The fuel
depth was 4 to 4.5 cm and the bulk density of the samples was
14 to 17 Kg/m3. The remaining 20 g of the fuel were placed in a
small aluminium disk and oven dried at 70C for 48 hours to
measure the real moisture content of the sample. A wooden
match was lit and randomly dropped on the fuel sample from a
height of ~ 10 cm. If the match started a fire, it was considered
as a positive test and classed as “burn”. If the initially dropped
match did not result in a positive test, a second match was
dropped. A third match was dropped if the second match did
not result in a positive test. The ignition test was classed as
“non burn” if all three matches failed to provide a positive test.
(De Groot et al. 2005). All tests were done indoors in the wind
tunnel with no presence of wind. The RH was in the range of 67% to 84%, the air
temperature in the range of 20C to 22C and the fuel temperature in the range of 15C
to 22C.
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Results and Discussion
Experiment 1:
The first set of experiments was carried out with needles of Pinus halepensis as fuel and
kitchen matches as ignition source. It resulted in a total of 115 experiments of which 52
resulted in “ignition” and 63 in “no ignition” (Fig.1).
Pine needles - kitchen matches
classification of the 115 experiments
No ignition
Ignition
35
No of experiments
30
3
25
20
26
15
10
21
26
25
5
8
6
0
5-12
12-18
18-24
24-30
>30
Fuel Moisture Content %
Figure 1. Number of experiments at different classes of fuel moisture content.
The results were analysed using logistic regression. The estimated parameters, their
standard errors and significance levels are presented in table 1.
Table 1. The estimated parameters and statistics associated with the logistic model.
Variable
β
S.E.
S.L.
FMC (%)
-69.174
14.133
0.000002
Constant
12.650
2.681
0.000002
The model based on the dataset with FMC (%) as the only independent variable is:
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Ln(Pign/1-Pign) = 12.650 - 69.174*FMC
where Pign = is the estimated probability of ignition and FMC is the fuel moisture content
(%). For this model the following statistics were computed: -2 Log Likelhood, 45.304;
Nagelkerke R2, 0.837.
The model classifies correctly 89.6% of the values in the dataset. It can also be written
as:
Pign=1/(1+exp(-(12.650+(-69.174*FMC))))
A plot of the data and the equation is shown in Figure 2
Figure 2. Dataset points and logistic regression model (solid line) showing ignition
probability of pine needles by kitchen matches as a function of needle moisture content
(%).
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Experiment 2:
The second set of experiments was carried out with needles of Pinus halepensis as fuel
and cigarette butts as ignition source and resulted in a total of 116 experiments of which
67 resulted in “ignition” and 49 in “No ignition” (Fig.3).
Pine needles - cigarette butts
classification of the 116 experiments
No ignition
Ignition
No of experiments
35
30
8
25
20
30
15
25
10
5
0
13
8
2
2
0-1
1-2
2-3
3-4
4-5
9
5-6
12
5
6-7
2
7-8
>8
Fuel Moisture Content %
Figure 3. Number of experiments at different classes of fuel moisture content.
The results were analysed using logistic regression. The estimated parameters, their
standard errors and significance levels are presented in table 2.
Table 2. The estimated parameters and statistics associated with the logistic model.
Variable
β
S.E.
S.L.
FMC (%)
-249.185
53.537
0.00003
Constant
10.268
2.120
0.00001
The model based on the dataset with FMC% as the only independent variable is:
Ln(Pign/1-Pign) = 10.268 - 249.185*FMC
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where Pign = is the estimated probability of ignition and FMC is the fuel moisture content
(%). For this model the following statistics were computed: -2 Log Likelihood, 67.770;
Nagelkerke R2, 0.727.
The model classifies correctly 83.6% of the values in the dataset. It can also be written
as:
Pign=1/(1+exp(-(10.268+(-249.185*FMC))))
A plot of the data and the equation is shown in Figure 4.
Figure 4.
Dataset points and logistic regression model (solid line) showing ignition
probability of pine needles cigarette butts as a function of needle moisture content (%)
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Experiment 3:
The third set of experiments was carried out with the grass Hyparrhenia hirta as fuel and
kitchen matches as ignition source and resulted in a total of 161 experiments of which 55
resulted in “ignition” and 106 in “No ignition” (Fig. 5).
Grass - kitchen matches
classification of the 161 experiments
No ignition
Ignition
No of experiments
60
50
40
32
30
20
5
10
0
18
14
5-10
10-15
29
20
27
13
3
0-5
15-20
20-25
25-30
>30
Fuel Moisture Content %
Figure 5. Number of experiments at different classes of fuel moisture content.
The results were analysed using logistic regression. The estimated parameters, their
standard errors and significance levels are presented in table 3.
Table 3. The estimated parameters and statistics associated with the logistic model.
Variable
β
S.E.
S.L.
FMC (%)
-56.673
11.284
0.000001
Constant
8.942
1.842
0.000001
The model based on the dataset with FMC% as the only independent variable is:
Ln(Pign/1-Pign) = 8.942 - 56.673*FMC
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where Pign = is the estimated probability of ignition and FMC is the fuel moisture content
(%). For this model the following statistics were computed: -2 Log Likelihood, 90.918;
Nagelkerke R2, 0.709.
The model classifies correctly 83.9% of the values in the dataset. It can also be written
as:
Pign=1/(1+exp(-(8.942+(-56.673*FMC))))
A plot of the data and the equation is shown in Figure 6.
Figure 6. Dataset points and logistic regression model (solid line) showing ignition
probability of grass by kitchen matches as a function of grass moisture content (%).
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The results of the experiments until now do not present any surprises. More specifically:

The role of fuel moisture content as one of the most important fuel characteristics
affecting ignition is confirmed and quantified.

Fuel type also has an influence on ignition probability. For 50% ignition probability
with matches the corresponding FMC is 18.25% and 15.75% for pine needles and
grass respectively.

The type of the ignition source and its heat release rate is also a very important
parameter for fire ignition. In pine needles, for 50% ignition probability the FMC
should be 18.25% with matches, while with cigarettes it should be 4.11%. In
grass, for 50% ignition probability the FMC should be 15.75% with matches while
with cigarettes no ignition occurs even on completely dry fuels.
Obviously, these findings will be augmented further by the experiments that will follow. A
complete discussion, including some investigation on the general principles affecting
ignition probabilities, will be provided after completion of all the experiments, within D3.110, which is due in month 46 of the project.
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Additional tests:
Two additional tests were carried out. The first test consisted of Hyparrhenia hirta grass
as fuel and cigarette butts as ignition source with and without wind following the same
methodology as that reported for experiment 2. About thirty, (30) trials with completely
dry fuel (FMC=0%) resulted in no ignition (Pict. 5).
Picture 5. Fuel bed of the grass Hyparrhenia hirta and cigarette butt as ignition source
The second test was carried out with Pinus halepensis needles as fuel and machinery
sparks as ignition source. An electrical grinding disk was applied on a plumbing tube and
on iron stick for the production of machinery sparks (Pict. 6)
Picture 6. Machinery sparks applied on completely dry pine needles
The fuels were completely dry (FMC=0%) The sparks that were produced although they
fell immediately on the fuel bed that was less than 10 cm away, did not cause any
ignitions. The fuel temperature was measured with a thermocouple in contact with the
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needles during the exposure of the fuel bed to the sparks. The fuel temperature increased
slightly by only 2-3oC.
Future work
The ignition experiments will be continued until the end of the project as shown in the
following table:
Ignition source
Machinery
Electrical
sparks
discharge
V
T
X
V
T
X
X
Quercus coccifera leaves
X
X
X
X
Grass (Avena barbata)
X
X
X
X
Pine needles
-
X
X
X
Grass (Hyparrhenia hirta)
-
T
X
X
Quercus coccifera leaves
-
X
X
X
Grass (Avena barbata)
-
X
X
X
Matches
Cigarettes
Pine needles
V
Grass (Hyparrhenia hirta)
Fuel type
No
Wind
Wind
V= Completed,
X= Will be done,
T= Tested no ignition
The final results will be presented in deliverable D 3.1-10 due in the month 46 of the
project.
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PART II – Ignition probability in live fuels (In situ), AUTh
A probabilistic model of moisture of extinction of live fuels in the field
Objectives
The objective of this study was to measure the probability of ignition under various
moisture contents of annual herbaceous fuels in the field. Thus, threshold values of fuel
moisture for fire extinction (non-ignition) can be determined.
This study presents two novelties:

It measures fuel moisture of extinction in live annual herbaceous plants
throughout their life cycle.

The experiments are conducted in the field (in situ), taking into account all the
environmental conditions (air temperature, air relative humidity, and wind speed)
that were present during the time of the ignition tests. Thus, a more realistic
assessment of the fuel moisture of extinction of live herbaceous plants through
their life cycle is achieved.
Methods
The annual herbaceous plant was Avena barbata. The plots were 100 m2 each; with plant
cover > 90% and no litter understory. The firing method was drip torch in a straight line
on the ground, with five trials per test (Pict. 1-4). The firing line was 3 m long and the
criterion for ignition was vivid burning for 45 seconds. During this period, the fire was
spreading at various distances (depending on the windspeed and the other environmental
parameters) but this was not considered a criterion for ignition. In all cases, it was very
obvious after the five trials in each test, whether it was successful ignition or not.
The experiment consisted of a live herbaceous plant, whose moisture content was
monitored through its life cycle. Of course, at the withering stage, it was more dry then
fresh, but still there was no mixture of live and dead plants together, since we dealt with
only one species (Avena barbata). Moisture content was determined by oven-drying at
105o C until constant weight. Plant samples were collected just before the tests. A sling
psychrometer
and
a
portable
wind
meter
were
used
for
the
meteorological
measurements. The wind speed was measured at 2 m height.
The detailed results are given in the Annex I.
D3.1-5-18-1000-1
Page 24
Due to the fact that the dependent variable (ignition or non ignition) has a dichotomous
outcome, a stepwise logistic regression approach that allows the estimation of the
probability of occurrence of event from a combination of fire weather and fire
environmental factors was chosen (Hosmer and Lemeshow 2000). The SPSS 14.0
statistical package was used for the calculations (Norusis1997). The logistic regression
model had the following form (Hosmer and Lemeshow 2000):
e g ( x)
P( yi  1) 
1  e g ( x)
Being the logit given by the equation:
g ( x)   0  1 x1   2 x2  ... i xi
where, P ( yi  1) is the probability of fire ignition, xi are the independent variables, and βι
are the coefficients estimated through the maximum likelihood method, which will
produce coefficients that maximize the probability density as a function of the original set
of data. A decision criterion of 0.5 as the threshold of fire ignition was assumed. This
cutoff value has been commonly used to discriminate the continuous model response in
the interval between 0 (0%) and 1 (100%) (Wilson and Ferguson 1986, Ryan and
Reinhardt 1988, Cruz et al. 2004)
The model was analyzed through the rationality of the explanatory variables, the
significance of the regression coefficients, the goodness of fit statistics and the
discrimination capacity as measured by the area under a relative operating characteristic
(ROC) curve (Wilson 1987).
D3.1-5-18-1000-1
Page 25
Pictures 1-4. Ignition trials in the field
D3.1-5-18-1000-1
Page 26
Results
Table 1 presents basic descriptive statistics associated with the data set used in the
development of the logistic fire ignition model.
Table 1. Basic descriptive statistics associated with the data set used in the development
of the logistic fire ignition model.
Independent variables
n
Minimum
Maximum
Mean
SD
Air Temperature (0C)
108
11
33
26.088
5.099
Relative Humidity (%)
108
35
87
58.213
12.690
Wind Speed (km/hr)
108
0
24
8.537
4.506
Fuel Moisture Content (%)
108
8
200
34.648
46.099
Stepwise logistic regression indicated that only the moisture content was statistically
significant (p<0.001), and consequently, it was the variable that was kept in the model.
The estimated parameters, their standard errors, and significance levels are presented in
table 2.
Table 2. Estimated parameters and statistics associated with the probabilistic fire ignition
logistic model
Variable
β
S.E.
S.L.
Air Temperature (0C)
-0.182
0.178
0.308
Relative Humidity (%)
-0.116
0.080
0.146
Wind Speed (km/hr)
0.109
0.133
0.415
Fuel Moisture Content (%)
-0.127
0.034
0.001
Constant
6.704
1.552
0.001
S.E. Standard Error, S.L. Significance Level
Thus, only the fuel moisture content was kept as a significant independent variable in the
model. Based on the complete data set (n=108) the model form is:
D3.1-5-18-1000-1
Page 27
Probability of ignition = 1 / (1 + exp(-(6.704-0.127*Moisture Content))
A plot of the data and the equation is shown in Figure 1
Figure 1.
Dataset points and logistic regression model (solid line) showing ignition
probability of live grass as a function of grass moisture content (%).
Goodness of fit statistics is presented in table 3. The Negelkerke R2 associated with the
above equation is 0.902, while the -2 Log(Likelihood) was relative low (15.416). The
McFadden R² which is the likelihood ratio index presented a value of 0.85. As the name
suggests, this is an analog to the R² reported in linear regression models. The Hosmer
and Lemeshow's goodness of fit test was not significant indicated that the model has an
adequate fit.
D3.1-5-18-1000-1
Page 28
Table 3. Goodness of fit statistics
Descriptive statistic
Values
Observations
108
-2 Log(Likelihood)
15.416
R²(McFadden)
0.846
R²(Nagelkerke)
0.902
Hosmer and Lemeshow's goodness of fit test
0.812 (sig. 0.997)
The Receiver Operator Characteristic (ROC) curve is often used as an index of how well a
scoring system is able to classify events into one of the two alternatives (an area of 1 is
perfect, 0.5 is non-informative) such as: fire ignition or non fire ignition. The logistic
model yields an area under the ROC curve of 0.99. Overall, the logistic model correctly
predicted fire ignition probability 97.2% of the time in the data set (table 4).
Table 4. Classification table comparing observed and predicted fire ignition probability
through the application of the logistic model to the data.
Observed
Non ignition
Ignition
Percent correct (%)
Non ignition
17
2
89.47
Ignition
1
87
98.86
Overall
97.20
Probability analysis of fire occurrence as a function of moisture content with the logistic
model is presented in table 5.
D3.1-5-18-1000-1
Page 29
Table 5. Fire ignition probability with the fitted model based on lower and upper brackets
(95%) of fuel moisture content values.
Probability of ignition
Lower bound 95%
Upper bound 95%
0.01
0.05
71.326
moisture content
63.941
131.724
moisture content
113.062
0.10
59.895
103.222
0.20
54.830
91.472
0.30
51.005
83.174
0.40
47.562
76.256
0.50
44.139
69.996
0.60
40.443
64.009
0.70
36.091
58.002
0.80
30.350
51.619
0.90
21.158
43.995
0.95
12.664
38.605
0.99
-4.616
29.838
D3.1-5-18-1000-1
Page 30
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D3.1-5-18-1000-1
Page 36
ANNEX I
Moisture of extinction data from PART II
Ignition probability in live fuels (In situ)
D3.1-5-18-1000-1
Page 37
Date
25/5/2006
29/5/2006
30/5/2006
31/5/2006
2/6/2006
5/6/2006
6/6/2006
7/6/2006
8/6/2006
9/6/2006
12/6/2006
13/6/2006
14/6/2006
15/6/2006
16/6/2006
19/6/2006
20/6/2006
21/6/2006
22/6/2006
23/6/2006
26/6/2006
27/6/2006
28/6/2006
29/6/2006
30/6/2006
3/7/2006
4/7/2006
Time
Air
Temperature
(0C)
Relative
Humidity (%)
Precipitation
(mm)
Wind
speed
1
2
3
4
5
Μoisture Content
%
IGNITION
10:50
11:30
11:40
13:30
11:50
11:45
12:10
12:05
11:40
11:45
13:10
12:05
12:15
11:50
12:00
11:30
12:10
11:50
11:40
11:25
11:40
12:05
11:10
11:40
11:20
13:30
11:20
27
28
27.5
30
29.5
27
28
26
22
23.5
26
25
24.5
27
30
29.5
33
31.5
31
28.5
31
32.5
32
31.5
33
25
25.5
66
57
53
59
55
47
45
49
62
52
42
54
57
44
36
58
45
51
51
67
63
55
58
63
50
84
84
0.8
4.8
6.9
1.4
1.1
12.4
-
4
6
8
8
4
10
4
7
13
11
7
6
9
17
11
0
0
4
0
6
5
6
10
4
7
5
10
X
X
X
X
X
X
X
X
X
X
X
X
X
V
V
V
V
V
V
V
V
V
V
V
V
V
V
X
X
X
X
X
X
X
X
X
X
X
X
X
V
V
V
V
V
V
V
V
V
V
V
V
V
V
X
X
X
X
X
X
X
X
X
X
X
X
X
V
V
V
V
V
V
V
V
V
V
V
V
V
V
X
X
X
X
X
X
X
X
X
X
X
X
X
V
V
V
V
V
V
V
V
V
V
V
V
V
V
X
X
X
X
X
X
X
X
X
X
X
X
X
V
V
V
V
V
V
V
V
V
V
V
V
V
V
186
200
191
189
178
164
132
103
158
138
106
89
63
71
44
16
12
34
16
16
17
12
14
17
13
20
24
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
D3.1-5-18-1000-1
Page 38
Date
Time
Air
Temperature
(0C)
Relative
Humidity (%)
Precipitation
(mm)
Wind
speed
1
2
3
4
5
Μoisture Content
%
IGNITION
5/7/2006
6/7/2006
7/7/2006
10/7/2006
11/7/2006
12/7/2006
13/7/2006
14/7/2006
17/7/2006
18/7/2006
19/7/2006
20/7/2006
21/7/2006
24/7/2006
25/7/2006
26/7/2006
27/7/2006
28/7/2006
31/7/2006
1/7/2006
2/7/2006
3/8/2006
4/8/2006
7/8/2006
8/8/2006
9/8/2006
10/8/2006
12:15
11:50
12:40
11:40
11:50
11:10
12:10
12:40
11:45
11:45
11:50
11:35
11:15
11:50
11:35
11:45
11:30
11:55
11:35
11:50
11:25
11:30
11:45
11:55
11:50
11:50
11:40
29
28.5
29.5
28
29.5
28.5
31
32
28
27
27
31
28.5
30
29.5
29.5
30
30
30.5
33
30.5
30
29.5
31
30
31
29
35
60
55
60
52
60
51
42
40
50
53
48
57
68
65
61
53
62
50
50
59
62
61
48
44
38
65
3.5
-
15
9
11
12
13
11
13
8
13
9
9
15
13
8
7
12
9
7
13
12
7
12
8
14
13
15
12
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
11
14
12
20
13
14
13
11
10
12
12
12
12
11
11
15
12
12
11
10
12
15
11
8
8
9
10
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
D3.1-5-18-1000-1
Page 39
Date
Time
Air
Temperature
(0C)
Relative
Humidity (%)
Precipitation
(mm)
Wind
speed
1
2
3
4
5
Μoisture Content
%
IGNITION
21/8/2006
22/8/2006
23/8/2006
24/8/2006
25/8/2006
28/8/2006
29/8/2006
30/8/2006
31/8/2006
1/9/2006
4/9/2006
5/9/2006
6/9/2006
7/9/2006
8/9/2006
11/9/2006
12/9/2006
13/9/2006
14/9/2006
15/9/2006
18/9/2006
19/9/2006
20/9/2006
21/9/2006
22/9/2006
25/9/2006
26/9/2006
11:40
11:30
11:20
11:25
12:00
11:30
10:55
11:50
11:30
11:25
11:55
11:30
12:20
11:30
11:15
12:35
12:00
11:20
12:05
11:30
11:45
11:50
11:45
11:50
11:35
11:30
11:35
32
32
31
30
29
29.5
26.5
31
23
25.5
27
28.5
31
28
27
28
26
23.5
26.5
25
24
26
26
25.5
24
23
23.5
61
39
39
53
52
49
52
48
38
72
63
54
39
57
56
51
55
48
46
54
60
76
62
51
64
67
63
3.7
0.6
-
6
11
13
9
5
20
6
9
24
15
4
5
19
10
6
8
7
5
7
5
4
7
11
6
11
8
4
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
V
11
10
9
13
11
12
10
12
9
9
12
13
10
10
12
19
11
12
10
12
14
13
26
13
19
16
15
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
D3.1-5-18-1000-1
Page 40
Date
Time
Air
Temperature
(0C)
Relative
Humidity (%)
Precipitation
(mm)
Wind
speed
1
2
3
4
5
Μoisture Content
%
IGNITION
27/9/2006
28/9/2006
29/9/2006
2/10/2006
3/10/2006
4/10/2006
5/10/2006
6/10/2006
9/10/2006
10/10/2006
11/10/2006
12/10/2006
13/10/2006
16/10/2006
17/10/2006
18/10/2006
19/10/2006
20/10/2006
23/10/2006
24/10/2006
25/10/2006
1/11/2006
1/11/2006
13/11/2006
13/11/2006
13/11/2006
15/11/2006
11:55
11:35
11:30
11:55
12:05
11:40
14:10
11:50
11:50
11:30
11:35
11:20
11:45
12:25
11:35
11:20
11:50
11:45
11:55
11:15
11:50
9:00
12:00
11:40
12:30
12:45
12:10
23.5
22
25
24.5
23.5
26.5
24.5
24
19
17.5
19
18
20
20.5
16.5
15.5
15.5
19
21.5
19
22
14
16
11
11
11
20
71
87
58
68
71
52
68
71
72
75
76
76
64
52
46
39
53
42
65
81
66
83
84
87
87
87
81
1.6
3
-1
2
-
7
8
16
9
7
10
8
6
12
4
0
0
0
14
5
11
0
7
9
0
4
4
6
12
11
14
11
X
X
V
V
V
V
V
V
X
X
X
V
V
V
V
V
V
V
V
V
V
V
V
X
V
V
V
X
X
V
V
V
V
V
V
X
X
X
V
V
V
V
V
V
V
V
V
V
V
V
X
V
V
V
X
X
V
V
V
V
V
V
X
X
X
V
V
V
V
V
V
V
V
V
V
V
V
X
V
V
V
X
X
V
V
V
V
V
V
X
X
X
V
V
V
V
V
V
V
V
V
V
V
V
X
V
V
V
X
X
V
V
V
V
V
V
X
X
X
V
V
V
V
V
V
V
V
V
V
V
V
X
V
V
V
51
59
32
16
20
18
16
19
44
82
55
28
28
16
18
17
18
16
18
28
18
39
17
78
35
48
21
NO
NO
YES
YES
YES
YES
YES
YES
NO
NO
NO
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
YES
NO
YES
YES
YES
D3.1-5-18-1000-1
Page 41