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The views expressed are purely those of the authors and may not, in any circumstances, be regarded as stating an official position of the European Commission
EUFIRELAB
EVR1-CT-2002-40028
D-08-07
http://www.eufirelab.org/
EUFIRELAB: Euro-Mediterranean Wildland Fire Laboratory,
a “wall-less” Laboratory for Wildland Fire Sciences and Technologies
in the Euro-Mediterranean Region
Deliverable D-08-07
Wildland Fire Danger and Hazards: a state of the art, final version
P014: Raffaella MARZANO, Giovanni BOVIO, Elisa GUGLIELMET, Andrea CAMIA P016: Michel DESHAYES, Corinne LAMPIN
P023: Javier SALAS, Jesús MARTÍNEZ P024: Domingo MOLINA
P027: Nuno GERONIMO, Pierre CARREGA, Dennis FOX P028: Santi SABATÉ, Jordi VAYREDA
P030: Pilar MARTÍN, Javier MARTÍNEZ, Lara VILAR P035: Claudio CONESE, Laura BONORA
P036: Spiros TSAKALIDIS, Ioannis GITAS, Michael KARTERIS
December 2006
EUFIRELAB
D-08-07.doc
CONTENT LIST
1 Scope and objectives .........................................................................................................................................1 2 Wildland fire terminology on fire risk ..................................................................................................................2
2.1 Introduction....................................................................................................................................................2 2.2 Review of existing wildland fire risk terminology...........................................................................................2
2.2.1 Risk and Fire Risk ....................................................................................................................................2 2.2.2 Fire Danger ..............................................................................................................................................2 2.2.3 Hazard and Fire Risk................................................................................................................................3
2.3 Proposed wildland fire risk structure and terminology...................................................................................5 2.3.1 Danger......................................................................................................................................................5 2.3.2 Vulnerability..............................................................................................................................................5 2.3.3 Tables and Figures...................................................................................................................................6
3 Fire risk issues..................................................................................................................................................11 3.1 Temporal and spatial issues........................................................................................................................11 3.2 Fire management and risk assessment ......................................................................................................12
4 Risk variables: definition, estimation and mapping ..........................................................................................13 4.1 Vegetation ...................................................................................................................................................13
4.1.1 Wildland fuels .........................................................................................................................................13 4.1.2 Fuel moisture content and flammability..................................................................................................23 4.1.3 Figures and tables..................................................................................................................................28
4.2 Climatic and meteorological variables.........................................................................................................37 4.2.1 A study case in the Department of Alpes-Maritimes – France...............................................................38 4.2.2 Figures ...................................................................................................................................................41
4.3 Topography .................................................................................................................................................44 4.3.1 The role of topography in forest fires .....................................................................................................44 4.3.2 The use of topographical variables in forest fire danger indices............................................................44 4.3.3 Digital terrain models..............................................................................................................................45 4.3.4 Tables.....................................................................................................................................................47
4.4 Anthropogenic variables..............................................................................................................................48 4.4.1 Rationale ................................................................................................................................................48 4.4.2 Factors in relation to socio-economic transformations...........................................................................50 4.4.3 Factors related to traditional economic activities in rural areas .............................................................52 4.4.4 Factors which could cause fires by accident or negligence...................................................................52 4.4.5 Factors that generate conflicts that could lead to the intentional start of a fire and/or facilitate its propagation.......................................................................................................................................................53
5 References .......................................................................................................................................................55
EUFIRELAB
D-08-07
SUMMARY
The deliverable addresses the analysis of all the environmental and anthropogenic variables from which fire danger and hazard are dependent upon.
Fire danger is the resultant of many factors affecting fire occurrence (and therefore fire ignition and spread).
Such factors are meant as variables that have to be properly mapped in order to feed a given model and produce a fire risk map.
The deliverable is focused on the detailed description of such variables, addressing definitions, estimation and mapping issues for each one of them.
The methods to combine the variables and derive risk maps are addressed in another deliverable of EUFIRELAB (D-08-05) which is strictly linked to the present one.
Since fire danger, hazard and risk are terms largely prone to different interpretations and consequently misunderstanding both within the operational and scientific wildland fire communities, chapter 2 is devoted to review the different meanings given to such terms in the literature, taking also into account recent reviews that have been done in other projects.
In addition, since the analysis of risk variables, and therefore the derived fire risk assessment, is strongly dependent on the time and spatial scale addressed and on the fire management context in which ultimately the fire risk information has to be used, chapter 3 is devoted to provide a general reference framework in this respect.
In this chapter the relevant time and spatial frames for fire risk assessment and the operational background where the risk information has to be used are analysed.
In chapter 4 a detailed description of individual variables is given, followed by bibliographical references.
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1 SCOPE AND OBJECTIVES
The main objective of the present deliverable is to produce a review on fire danger and hazard assessment and mapping.
This review was carried on within Unit08 in the frame of the EUFIRELAB Project.
The deliverable is meant to be strongly connected with deliverable D-08-05 (Common methods for mapping the wildland fire danger), which completes the review.
The state of the art on fire danger and hazard assessment and mapping carried out within Unit08 is logically divided into two major parts: - a first one, with the analysis of the individual
environmental and anthropogenic variables from which fire danger and hazard are dependent upon,
- and a second one that describes the models and methods currently used to combine those variables to provide estimates of the level of fire danger and hazard and maps of their spatial distribution.
In this document only the first part will be addressed, together with the introduction of the general framework and some general concepts and basic terminology on fire risk, danger and hazard.
A specific focus is also given to the generation of basic individual data layers (data layers of risk variables) that are to be integrated according to the methods that are described in deliverable D-08-05.
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2 WILDLAND FIRE TERMINOLOGY ON FIRE RISK
2.1 INTRODUCTION
When looking through wildfire risk related literature one notices a great confusion on the proper use of terminology.
Various terms, like ‘danger,’ ‘hazard,’ and ‘risk’ are being used reciprocally.
This fact may result in misunderstandings and inability of cooperation between scientists and services or operational forces.
Both, managers and researchers depend on comprehensive, reliable communication facilities.
What is more the global scale of the problem makes the need of a world-wide adopted terminology quite demanding.
So far the results for the existence of such a consistent terminology are more or less unsatisfactory (BACHMANN and ALLGÖWER, 2001).
The main reasons for this, according to the aforementioned authors, are the following: - The term ‘risk’ is part of ‘everyday life’ where, de-
pending on the context, a wide range of notions is assigned to it.
- Terminology is always to some extent a ‘linguistic’ and / or ‘cultural’ issue. Every language has its own words and meanings, e.g. the terms ‘hazard’ and ‘danger ’ are the same word in German (i.e. ‘Gefahr’).
- The phenomenon ‘fire’ has as many aspects as people who are dealing with it: Fire managers and fighters, environmentalists, foresters, house and land owners, scientists, land planing organizations, etc. Based on their primary interests, each of these ‘communities’ has different notions of the term ‘wildfire risk.’
Due to the lack of a consistent and widely accepted fire risk terminology several problems arise: - misunderstandings among the scientific community
concerning the risk models and indices developed and hence diminished evolution and amelioration of these models,
- inability of the managers and the authorities to apply the proper models to the analogous scale and thus making them inadequate operationally,
- inability to quantify and map wildfire risk, and - insufficient analysis and understanding in depth of
the components and parameters related to fire events (both ignition and behaviour).
The aim of this work is to review existing terminology quoted by several authors and to propose definitions that can be used in order to carry out a quantitative risk analysis in the context of wildfire management.
Following a number of different approaches and definitions about risk-related issues, adopted so far in wildfire management are summarised.
2.2 REVIEW OF EXISTING WILDLAND FIRE RISK TERMINOLOGY
A review of definitions and terminology used by researchers is needed in order to assess the problem of understanding the variety of notions that have been ascribed to each wildfire risk term and to accept a multitude of concepts that can help us build a complex yet operationally useful Euro-Mediterranean Wildland Fire Risk assessment method.
2.2.1 Risk and Fire Risk
Below several definitions are quoted in relation to “risk” and “fire risk” terms.
Surveying these definitions we deduce that the term “fire risk” is constituted by two notions (following definitions of BACHMAN and ALLGÖWER (2001), BLANCHI et al. (2002) and HALL (1992)).
Firstly the chance and probability of a fire occurring and secondly the expected outcome as defined by the fire impact on the objects it affects (vulnerability).
It is widely agreed that these two concepts (probability of an event occurrence and its consequences) are used to assess natural risks (BACHMANN, 1998). (Tables 2-1 and 2-2)
2.2.2 Fire Danger
According to BACHMAN and ALLGÖWER (1999), the term “danger” is an abstract concept based on perception.
Danger per se does not exist. It is defined by the subjective and societal
perception and assessment of factors (of the physical and non-physical environment) that are considered harmful.
Examining the below definitions we conclude that danger arises by the synergy of constant and variable factors which create adverse conditions based on human and societal perception.
Consequently in the case of wildfires, danger is the result of both constant and variable fire danger factors which affect the inception, spread, difficulty of fire control and fire impact.
Factors that can be considered as constant or static along at least a fire season are e.g. land use, fuel types, topography and climatic patterns whereas as variable or dynamic factors can be considered, fuel moisture content, temperature, relative humidity, wind, precipitation, etc.
In this context we can classify wildfire danger into categories based on the factors that affect each one of them.
EUFIRELAB
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Accordingly the following types of danger can arise: - Ignition Danger. It is the danger that arises due to
the combination of factors that can lead to the inception of a wildfire.
- Propagation Danger. It can be defined also as Fire Spread Danger. This type of danger arises due to the combined existence or emergence of factors that favour the spread of a Wildfire.
- Vulnerability. This type of danger is related to the potential fire impact or else potential damages on environmental and socio-economical elements and is defined by the factors that can favour such a process.
The aforementioned classification can be considered crucial towards the scope of a quantitative risk analysis since each type of danger is closely related to factors that have to be analysed.
This classification scheme (Ignition danger, Propagation danger and Vulnerability) has been followed in D-08-03 deliverable in order to develop a Euro-Mediterranean Wildland Fire Risk Index (EM-WFRI). (Table 2-3(.
2.2.3 Hazard and Fire Risk
Hazard can be considered as a phenomenon that can result to undesirable outcomes. Based on this definition, Wildfire Hazard is a hazard just like e.g. an avalanche or a mudslide. (Tables 2-4 and 2-5).
Further below several approaches concerning the components and the structure of wildfire terminology are shown schematically. (Figure 2-1)
It has also to be referred that other European projects have already dealt with fire risk issues and relevant definitions have been proposed.
In Deliverable D161 of SPREAD project the following structure, that is presented schematically, has been suggested. (Figure 2-2).
Within the above proposed structure we easily identify various terms that have already been mentioned before like Ignition Danger, Propagation (Fire Spread) Danger and Vulnerability.
According to this, it follows that Wildland Fire Risk is constituted from danger, as defined by the probability of having a fire somewhere and vulnerability, which expresses the potential effects of fire on humans and ecosystems.
Further below various terms that are referred in the Glossary of Wildland Fire Terminology of the National Wildfire Coordinating Group in USA, in relation to fire risk, are presented together with their definitions.
A table has been constructed also with Wildland Fire Terminology in several languages (Table 2-6).
FIRE HAZARD INDEX
A numerical rating for specific fuel types, indicating the relative probability of fires starting and spreading, and the probable degree of resistance to control; similar to burning index, but without effects of wind speed.
FIRE DANGER INDEX
A relative number indicating the severity of wildland fire danger as determined from burning conditions and other variable factors of fire danger.
RISK INDEX
A number related to the probability of a firebrand igniting a fire.
FIRE DANGER RATING
A fire management system that integrates the effects of selected fire danger factors into one or more qualitative or numerical indices of current protection needs.
FIRE DANGER RATING AREA
Geographical area within which climate, fuel, and topography are relatively homogeneous, hence fire danger can be assumed to be uniform.
IGNITION PROBABILITY
Chance that a firebrand will cause an ignition when it lands on receptive fuels. (Syn. IGNITION INDEX)
ACCEPTABLE DAMAGE
Damage which does not seriously impair the flow of economic and social benefits from wildlands.
ACCEPTABLE FIRE RISK
The potential fire loss a community is willing to accept rather than provide resources to reduce such losses.
HAZARD MAP
Map of the area of operations that shows all of the known aerial hazards, including but not limited to power lines, military training areas, hang gliding areas, etc.
HAZARDOUS AREAS
Those wildland areas where the combination of vegetation, topography, weather, and the threat of fire to life and property create difficult and dangerous problems.
HAZARD REDUCTION
Any treatment of living and dead fuels that reduces the threat of ignition and spread of fire.
HUMAN-CAUSED RISK
Part of the National Fire Danger Rating System (NFDRS).
A model for predicting the average number of reportable human caused fires from a given ignition component value.
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HUMAN-CAUSED RISK SCALING FACTOR
Part of the National Fire Danger Rating System (NFDRS).
Number relating human-caused fire incidence to the ignition component in a fire danger rating area.
It is based on three to five years of fire occurrence and fire weather data that adjusts the prediction of the basic human-caused fire occurrence model to fit local experience.
LIGHTNING RISK (LR)
Part of the National Fire Danger Rating System (NFDRS).
A number related to the expected number of cloud-to-ground lightning strokes to which a protection unit is expected to be exposed during the rating period; the LR value used in the occurrence index includes an adjustment for lightning activity experienced during the previous day to account for possible holdover fires.
LIGHTNING RISK SCALING FACTOR
Part of the National Fire Danger Rating System (NFDRS).
Factor derived from local thunderstorm and lightning-caused fire records that adjusts predictions of the basic lightning fire occurrence model to local experience, accounting for factors not addressed directly by the model (e.g., susceptibility of local fuels to ignition by lightning, fuel continuity, topography, regional characteristics of thunderstorms).
PARTIAL RISK
Part of the National Fire Danger Rating System (NFDRS).
Contribution of a specific source to human-caused risk, derived from the daily activity level assigned a risk source and its risk source ratio.
PARTIAL RISK FACTOR
Part of the National Fire Danger Rating System (NFDRS).
Contribution to human-caused risk made by a specific risk source; a function of the daily activity level assigned that risk source and the appropriate risk source ratio.
RISK SOURCE
Identifiable human activity that historically has been a major cause of wildfires on a protection unit; one of the eight general causes listed on the standard fire report.
RISK SOURCE RATIO
Portion of human-caused fires that have occurred on a protection unit chargeable to a specific risk source; calculated for each day of the week for each risk source.
TOTAL RISK
Part of the National Fire Danger Rating System (NFDRS).
Sum of lightning and human-caused risk values; cannot exceed a value of 100.
UNACCEPTABLE FIRE RISK
Level of fire risk above which specific action is deemed necessary to protect life, property and resources.
VARIABLE DANGER
Resultant of all fire danger factors that vary from day to day, month to month, or year to year (e.g., fire weather, fuel moisture content, condition of vegetation, variable risk)
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2.3 PROPOSED WILDLAND FIRE RISK STRUCTURE AND TERMINOLOGY
Surveying the above quoted multitude of definitions, we deduce that there is a puzzlement of notions that are ascribed to each fire risk term and as a result the proper use for each one of them under the proper circumstances becomes problematic.
Definitions are constructed and become valid only within a given scope (SEIFFERT, 1997).
The aim of this work is to identify definitions that can be used in the context of a wildland fire risk analysis.
The chosen fire risk definitions apart from their theoretical basis they provide, can also guide us through the different aspects of a wildfire risk analysis.
This become obvious considering the above mentioned terms Ignition Danger, Propagation Danger and Vulnerability that have already been used in D-08-03 deliverable.
The solution of the problem is not the formation and the invention of another set of definitions, but it should rather focus on the choice and the adoption of a widely accepted terminology from the existed ones.
We believe that the definitions proposed in the Deliverable D161 “Fire risk mapping (I): Methodology, selected examples and evaluation of user requirements” under EC research programme SPREAD (Figure 2-2) should be adopted also in the EUFIRELAB frame.
Following the structure referred in Figure 2-2, the major components of this methodology are presented further below.
2.3.1 Danger
The danger component will be considered in a broad perspective, covering the probability of a fuel ignites (ignition danger) and the potential hazard that this fire propagates in space and time (propagation danger)
2.3.1.1 Ignition danger
In opposition with other natural phenomena, fires can theoretically start in any point of space (in the zones covered with vegetation).
The probability of ignition is the probability of starting a fire in a given place.
It depends primarily on the fuel conditions (flammability, moisture content) and on some fuel properties (i.e. particle size distribution), as well as on the action of causal agents (human or natural).
2.3.1.2 Fire Spread Danger
This term will refer to the chances for a fire of being spread over an area in a given place (regardless the place of ignition).
For the Spread project, this term will be related to the fire rate of spread, which is the main factor influencing the final extension of the burned area.
2.3.2 Vulnerability
The vulnerability component of wildland fire risk includes two issues: the effects of fire as a result of the fire behaviour and ecosystem characteristics and on the other hand the value of the affected resources.
2.3.2.1 Fire characteristics
The joint expression of fire intensity and duration. It is actually given by the product of these two
parameters and provides an estimate of the rate of damage that a fire can determine onto the exposed elements (soil and vegetation) independently from their characteristics.
2.3.2.2 Ecosystem response
The potential ability of an ecosystem to absorb the perturbations produced by a fire of certain characteristics (defined in the fire characteristics parameter).
The vegetation can absorb the disturbance either passively (plant resistance) or though post fire recovery (regeneration and resilience of the ecosystem).
For given fire characteristics, soil erosion can also be modified differently according to relevant soil properties (soil structure, organic matter, slope etc.).
2.3.2.3 Value of affected resources
Values at stake are people and/or goods that are exposed to natural or man-made hazards.
It includes losses caused by reduction in timber production, soil protection, recreational use, wildlife and livestock, conservation of nature, as well as building or infrastructure destruction. (Table 2-6).
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2.3.3 Tables and Figures
a/a DEFINITION SOURCE 1 The probability of harmful consequences, or expected losses (deaths,
injuries, property, livelihoods, economic activity disrupted or environment damaged) resulting from interactions between natural or human induced hazards and vulnerable conditions. Conventionally risk is expressed by the notation Risk = Hazards x Vulnerability.
International Strategy for Disaster Reduction
2 The chance of something happening that will have an impact on objectives. Measured in terms of consequences and likelihood.
New Zealand’s project of Wildfire Threat Analysis
3 The probability of an undesirable event occurring within a specified period of time. With regard to insect populations, risk involves components to evaluate the likelihood of an outbreak, the likelihood of trees being attacked (susceptibility) or the likelihood of trees being damaged (vulnerability). In fire prevention, risk involves those things or events that cause fires to start (including the physical igniting agents and people).
Glossary of Forestry Terms, Ministry of Forestry (MOF), British Columbia (1997)
Table 2-1: “Risk” definitions
a/a DEFINITION SOURCE 1 (1) The chance of a fire starting as affected by the nature and incidence of
causative agencies; an element of the fire danger in any area. (2) Any causative agency.
FAO’s terminology (FAO 1986)
2 Distinguish fire risk between the concepts of risk associated to the beginning of a fire (fire ignition risk or flammability) and to the spreading of an active fire (fire behaviour risk or fire hazard).
VASCONCELOS (1995)
3 Fire-risk prediction demands an answer to the following questions (thus he describes fire risk with the components of occurrence and spread) - Where will fire break out? - When will it occur? - How will it develop?
VELEZ (1988)
4 The probability of a wildland fire occurring at a specified location and under specific circumstances, together with its expected outcome as defined by its impacts on the objects it affects.
BACHMAN and ALLGÖWER (2001)
5 They subdivide wildfire risk to hazard or “alea” (related to occurrence and intensity) and vulnerability (consequences of the event) component.
BLANCHI et al. (2002)
6 Another component should be considered among the hazard and the vulnerability components, namely the risk potential or susceptibility with more long term prospectus, contrary to the probability of occurrence.
CARREGA (1997, 2003)
7 The probability or chance of a fire starting determined by the presence and activities of causative agents (i.e. potential number of ignition sources)
GFFMT glossary (1987) and MOF (1997)
8 (1) The chance of fire starting, as determined by the presence and activity of causative agents.
(2) A causative agent. A number related to the potential number of firebrands to which a given area will be exposed during the rating day (National Fire Danger Rating System).
Glossary of the US’s National Wildfire Co-ordinating Group
9 A measure of fire risk has two parties: (1) a measure of the expected severity (e.g., how many deaths, injuries,
dollars of damage per fire) for all fires or for a particular type of fire, and
(2) a measure of the probability of occurrence of all fires or of that particular type of fire. In general, a fire risk measure will be a product of an expected severity term and a probability term or a sum of such products.
HALL (1992)
10 Fire risk is the union of two components: fire hazard and fire ignition CHUVIECO and CONGALTON (1989)
11 The concept of fire risk embraces three aspects:Hazard, Threat and Vulnerability.
ZENG et al. (2003)
Table 2-2: “Fire Risk” definitions
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a/a DEFINITION SOURCE 1 Fire danger represents the combined probability of the onset of fire, its
spreading, and resulting damage at a given time, and can be assessed from vegetation conditions, environmental and topographic factors that influence the combustion of vegetation.
FORBES and MEYER (1961)
2 The resultant of both constant and variable fire danger factors, which affect the inception, spread, and difficulty of control of fires and damage they cause
DAVIS (1959)
3 Fire danger is the result of both constant (fuel types and topography) and variable (weather conditions) fire danger factors affecting the inception, spread and difficulty of control of fires and the damage they cause.
CHANDLER (1983)
4 The resultant, often expressed as an index, of both constant and variable factors affecting the inception, spread, and difficulty of control of fires and the damage they cause.
FAO (1986)
5 An assessment of both fixed and variable factors of the fire environment, which determine the ease of ignition, rate of spread, difficulty of control, and the fire impact
Glossary of Forestry Terms, Ministry of Forestry (MOF), British Columbia (1997)
6 A general term used to express an assessment of both fixed and variable factors of the fire environment that determines the ease of ignition, rate of spread, difficulty of control, and fire impact.
Canada’ s GFFMT (1987)
7 Sum of constant danger and variable danger factors affecting the inception, spread, and resistance to control, and subsequent fire damage; often expressed as an index.
US’s National Wildfire Co-ordinating Group (NWCG, 1996)
Table 2-3: “Fire Danger” definitions
a/a DEFINITION SOURCE 1 A potentially damaging physical event, phenomenon and/or human
activity, which may cause the loss of life or injury, property damage, social and economic disruption or environmental degradation.
International Strategy for Disaster Reduction
2 A process with undesirable outcomes
BACHMAN and ALLGÖWER (2001)
3 A physical situation with a potential for human injury, damage to property, damage to the environment or some combination of these.
ALLEN (1992)
Table 2-4: “Hazard” definitions
a/a DEFINITION SOURCE 1 A general term to describe the potential fire behaviour, with-out regard to
the state of weather influenced fuel moisture content and / or resistance to fireguard construction for a given fuel type.
CCFFM glossary (1987)
2 The potential fire behaviour for a fuel type, regardless of the fuel type’s weather influenced fuel moisture content or its resistance to fireguard construction. Assessment is based on physical fuel characteristics, such as fuel arrangement, fuel load, condition of herbaceous vegetation, and presence of elevated fuels.
Glossary of Forestry Terms, Ministry of Forestry (MOF), British Columbia (1997)
3 A fuel complex, defined by volume, type condition, arrangement, and location, that determines the degree both of ease of ignition and of fire suppression difficulty.
FAO (1986)
4 A wildland fire with undesirable outcomes. BACHMAN and ALLGÖWER (2001)
5 A fuel complex, defined by volume, type condition, arrangement, that determines the degree of ease of ignition and of resistance to control.
US’s National Wildfire Co-ordinating Group
6 States that hazard includes both risk and danger components (risk is associated to prevention and ignition, danger corresponds to spread and fighting actions.
WYBO (1995)
7 Potential fire behaviour based on physical fuel characteristics (e.g. fuel arrangement, fuel load, condition of herbaceous vegetation, presence of ladder fuels).
Canadian Interagency Forest Fire Centre’s Glossary of Forest Fire Management Terms (CIFFC 1999)
8 The probability that a forest fire might occur in a given place at a given intensity
BLANCHI et al. (2002)
9 The potential severity of the fire that is influenced primarily by the vegetation type, slope and weather conditions.
ZENG et al. (2003)
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Table 2-5: “Fire Hazard” definitions
Fire risk
Fire ignition risk or
flammability
Fire behavior risk or
Fire hazard
Fire risk
Fire hazard“alea” Vulnerability
IntensityOccurence
Risk PotentialSusceptibility
Wildfire risk
Probability of occurence Outcome
Wildfire hazard
Risk Danger
Vasconcelos 1995, Chuvieco and Congalton 1989
Blanchi et. al. 2002, Carrega 2004
Bachmann and Allgöwer 2001
Wybo 1995
Figure 2-1: Approaches found in the literature concerning the components and the structure of wildfire terminology
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Wildland fire risk
Danger: Probability of occurrence Vulnerability : Potential damge
Fire spread dangerIgnition danger Value of affected resources
Potential Fire effects :(Fire characteristics
and Ecosystem response )
Figure 2-2 Structure and major components of wildland fire risk (after Deliverable D161-SPREAD project)
EU
FIR
ELA
B
D-0
8-07
10
Eng
lish
Spa
nish
Ita
lian
Ger
man
Fr
ench
P
ortu
gues
e
Fire
Dan
ger
Pel
igro
de
ince
ndio
P
eric
olos
itá d
i inc
endi
o B
rand
gefa
hr
Ris
que
d' in
cend
ie
Per
igo
de in
cênd
io
Fire
Haz
ard
(1) P
elig
rosi
dad
del
com
bust
ible
,
(2) I
nfla
mab
ilida
d
(1) T
ipo
di c
ombu
stib
ile,
(2) G
rado
di P
redi
spos
izio
ne
agli
ince
ndi
(1) F
euer
gefa
erlic
hkei
t,
(2
) Feu
erge
faer
dung
R
isqu
e d'
ince
ndie
-«a
lea»
R
isco
loca
l
(1) A
fuel
com
plex
, def
ined
by
volu
me,
type
con
ditio
n, a
rran
gem
ent,
and
loca
tion,
that
det
erm
ines
the
degr
ee o
f bot
h of
eas
e of
igni
tion
and
of fi
re s
uppr
essi
on.
(2) A
mea
sure
of t
hat p
art o
f the
fire
dan
ger c
ontri
bute
d by
the
fuel
s av
aila
ble
for b
urni
ng
Fire
Ris
k R
iesg
o de
ince
ndio
(1
) R
isch
io d
i inc
endi
o
(2)
Cau
sa d
i inc
endi
o W
aldb
rand
risik
o (1
) Ris
que
d' e
clos
ion,
(2
) Cau
se d
' Inc
endi
e R
isco
de
incê
ndio
(1) T
he c
hanc
e of
fire
sta
rting
, as
affe
cted
by
the
natu
re a
nd in
cide
nce
of c
ausa
tive
agen
ts; a
n el
emen
t of t
he fi
re d
ange
r in
any
area
.
(2) A
ny c
ausa
tive
agen
t.
Tabl
e 2-
6: W
ildla
nd fi
re m
anag
emen
t ter
min
olog
y in
sev
eral
lang
uage
s (a
fter V
iega
s 19
97).
EUFIRELAB
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3 FIRE RISK ISSUES
In fire risk studies many factors affecting fire occurrence (and therefore fire ignition and spread) and fire damage have to be considered.
Such factors can be meant as variables that have to be properly mapped in order to feed a given fire risk model and produce a fire risk map.
The analysis of risk variables, and therefore the derived fire risk assessment, is strongly dependent on the time and spatial scale addressed and on the fire management context in which ultimately the fire risk information has to be used.
In this chapter the relevant time and spatial frames for fire risk assessment are addressed and the operational background where the risk information has to be used is considered.
It is meant to provide a reference framework to which the risk variables description given in chapter 4 will be referred to.
3.1 TEMPORAL AND SPATIAL ISSUES
Although the risk of wildland fires changes in a continuous way both in time and space, for practical purposes, in the assessment of fire risk it is common to distinguish different temporal and spatial scales.
Based on the scales considered, both the assessment procedures or methods, and the fire management objectives or context supported by the estimates of fire risk distribution are ultimately quite different.
With reference to the spatial scale, the global approach involves territories of millions square kilometres (CHUVIECO et al. 2003), and the resulting maps of the global (continental or world wide) distribution of fire risk have scales of the range of 1:1,000,000 or less.
Fire risk assessment at this scale is mainly undertaken for establishing general guidelines or strategic purposes and for enhancing international co-operation.
On the other hand local scale is referred to areas extended from hundreds up to few thousands square kilometres, with related thematic maps of 1:10,000 to 1:250,000 scale, addressing various fire management issues at regional or lower level.
It must be taken into account that according to the spatial scale, the risk variables to be considered and their role can change.
From one part because of the different explanation they can provide to the spatial distribution of the wildfire phenomenon, but also because of the different purposes of the two different risk assessment perspectives and because of data availability at the different scales.
Within the same category of variables it is possible to identify different aspects at global or local scale.
Considering for example the risk variables related to human activities, at global scale an important focus is given to socio-economical context, and their spatial distribution is often analysed using administrative boundaries to define geographical units, on the other hand at local scale it is easier to go into more detailed analysis considering for example the location of anthropogenic infrastructures, such as roads or railway, that can be correlated with the spatial distribution of fire ignition sources.
Two temporal scales are commonly identified in fire risk estimation: short-term and long-term.
Short-term fire risk estimations refer to the most dynamic factors of fire ignition or fire behaviour, mainly those based on the estimation of vegetation moisture content (either dead or live fuels) and the effect of meteorological variables on fire behaviour.
Therefore short term risk estimation requires daily or also hourly information on fuel moisture content, weather variables as temperature, relative humidity, wind, and precipitation.
This kind of estimation allows to organise the activity of fire pre-suppression, detection and suppression and update decisions according to changes in the fire risk level.
Therefore this temporal scale has a main practical use in the update of the level of alert and in the organisation of the firefighters actions directly on the flaming front.
Long-term fire risk includes the fire risk that does not change, or changes very slowly over time, in practical term the risk level determined by factors that can be considered, for the purpose of risk assessment, static along at least a fire season.
Therefore in the long term trends of fire risk the variables involved are mostly related to the structural factors that affect fire ignition and propagation in a given site (CHUVIECO, 1999).
Examples of such factors can be fuel types, topography or climatic patterns.
In general the long term temporal scale estimation provides information for the wildfire defence plan and so for the distribution of structural protection resources and the prevention activities.
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3.2 FIRE MANAGEMENT AND RISK ASSESSMENT
Fire risk assessment is mostly performed in order to provide forest services and fire protection agencies with information to support their activities, enhance wildfire protection actions and optimise fire management plans.
Therefore, in addition to the temporal and spatial scale issues illustrated in the previous section, the specific fire protection activity that has to be supported must also be identified, since this one may strongly influence the approach to the fire risk assessment procedure.
In fact the context and the related fire protection tasks for which the information on fire risk is needed can be quite different.
All fire risk studies address a specific requirement, the reason why the information on fire risk is needed, and consequently who and for what purpose will have to use the information on fire risk estimation provided, are all relevant issues.
The rationale behind is that the different domains of fire management have specific problems to address from which the components of fire risk may result with different emphasis, and also the proper temporal and spatial scales have to be selected accordingly.
The following fire management contexts can be considered: - Fire prevention - Fire pre-suppression - Fire detection - Fire fighting - Post-fire
In the context of fire prevention, information about the most fire prone areas and their location are required.
Long term fire risk estimation is typically needed in order to set up proper fire management plans at the beginning at the fire season.
Typical management actions that count on the spatial distribution of fire risk estimates are for example silvicultural interventions, prescribed fire, viability.
At global scale, long-term fire risk estimation done as a fire prevention task, can be used to support strategic and political decisions.
Both long-term and short-term fire risk estimation are applied to support decisions in the fire pre-suppression context.
In fact the allocation of fire fighting personnel, funds and equipment has to be defined also on the base of a long-term fire risk analysis, but these decisions can be changed in the light of short-term risk information (CHUVIECO, 2003).
The fire pre-suppression activities, concerned with real time allocation of protection resources to optimise the preparedness level of the fire protection organisation following the changing fire danger conditions with time, are typically local ones.
At global level the monitoring of fire danger conditions is mostly relevant for the displacement of heavy fire fighting means and pre-alert of protection agencies.
While in prevention and pre-suppression both fire occurrence and behaviour estimates are concerned, in fire detection the focus is mostly on the probability of fire occurrence.
The applications are basically at local scale. In this case the short-term fire risk evaluation combined with information derived from long-term fire risk assessment, permit to define the areas in which telecameras are more useful (ARRUE et al., 2000).
In addition short-term fire risk maps can be used as a criterion to evaluate the alarms given by automatic fire detection systems in order to reduce false alarm.
In any case, the focus is basically on fire occurrence, while fire spreading is relatively less important.
Fire fighting (suppression) activities require information derived from short-term fire risk assessment to evaluate the behaviour of the wildfires and define the most appropriated attack strategies and means in real time.
Weather conditions and their changing over time are, for example, important data on which suppression decisions must be based on.
Within the post-fire context, restoration is the task that is relevant and the main data required are related with long-term fire risk assessment.
In particular, it is necessary to consider the structural properties of fuel, topography and climatic conditions of the site in which is applied in order to support the restoration work with guidelines and priorities also based on fire risk criteria.
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4 RISK VARIABLES: DEFINITION, ESTIMATION AND MAPPING
Taking into account the terminology proposed in chapter 2 of this deliverable, wildland fire risk is defined by the probability of occurrence and vulnerability.
Only probability of occurrence will be reviewed here, since it has been more widely studied and holds a greater consensus.
Some elements of the term vulnerability are subject to opinion (as for instance assessing the functions of a forest -ecological, recreational...-) and in other elements we lack timely standard data (fire characteristics, fire severity...).
This explains why there are hardly any global studies on vulnerability, although it is beginning to draw an interest in the scientific community.
In accordance with this proposal, the probability of occurrence will be considered in a broad perspective, covering the ignition danger and the propagation danger.
Ignition danger is restricted to the fire triangle composed of oxygen, ignition source, and fuel (conditions).
Assuming oxygen is always available in a fire, the study should focus on the other two variables.
The ignition source is made of the causal agents of fire, which can be split into natural (lightning) and human (related to socioeconomic factors).
The two most important features in fuel at the beginning of a fire are flammability and moisture content, which has been traditionally estimated from meteorological variables (temperature, relative humidity, rainfall and wind) and in recent years, by using satellite imagery.
Propagation danger is defined by the so-called fire behaviour triangle, which is made of fuel, weather conditions, and topography.
In this case the main focus is on fuel properties (loads, geometrical arrangement, and physical characteristics) which largely determine the probability of a fire spreading over an area.
Although several meteorological variables affect fuel status, in propagation processes the most important is wind, which is included in all fire simulation programmes.
Finally, as in the previous variable, of all topographical factors that directly or indirectly influence propagation, slope is the most important one.
Bearing in mind these comments, what follows is a detailed analysis of the variables that should be included in a probability of occurrence index, which must cover the two sides mentioned: ignition danger and propagation danger.
4.1 VEGETATION
4.1.1 Wildland fuels
4.1.1.1 Fuels definition
A state of the art on wildland fuel description and modelling has been prepared as two deliverables of EUFIRELAB project (D-02-01 and D-02-06), that is therefore to be considered as a major reference for a detailed analysis of such important variable.
In this chapter only some concepts will be recalled, which are more related to the mapping issue, and some fuel maps examples will be given.
Vegetation plays a key role on fire propagation, and in this context can be defined by its structure both vertical and horizontal (slow temporal dynamic) and by its moisture content (intermediate temporal dynamic and related to meteorological conditions).
Vegetation is the main component that constitutes wildland fuel.
Fuel, in the context of wildland fire, refers to all combustible material available to burn (i.e. includes all dead and alive material present in the area).
One of the main factors that determine fire spread is the fuel load present in a given area and its physical and chemical traits.
As a rule, the higher the fuel amount the higher the energy released.
Nevertheless, this relation varies depending on fuel traits, e.g. the ratio of dead/alive material, the amount of material characterise by different size classes and components (leaves, branches, etc.), the presence of volatile substances and its moisture content.
These traits are the key factors determining the spreading of fire, as it is how wildland fuel is distributed on the area defining continuities and discontinuities both horizontal and vertical.
4.1.1.2 Fuel classification
To deal with the above fuel traits and use the fuel information as variable in a fire risk map, some way of classifying fuel properties must be applied.
Many fuel classification systems exist and can be applied to the fire risk mapping exercise at different scales.
When fuel properties are related with burning and spreading processes, and their relevant parameters used to implement some kind of fire behaviour model, than the concept of fuel model is applied (FAO 1986).
Fuel models developed by the US Northern Forest Fire Laboratory (ROTHERMEL 1972, ALBINI 1976, ANDERSON 1982) have been widely used in many areas in Europe during the last decades.
ANDERSON (1982) gave photographic examples of USA vegetation landscapes accompanying each fuel model description.
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These fuel models can be properly defined as fuel numerical arrays, i.e. standardised descriptions of fuel complexes physical properties, ranging from total load, to surface/volume ratio, to amount of fuel per size classes, to fuel bed depth, to extinction moisture.
Fuels have been classified into four groups (grasses, brush, timber and slash) and grouped into 13 models, with a numerical code from 1 to 13, which roughly correspond to vegetation typologies, namely: - Grasses (1, 2, 3) - Brushes (4, 5, 6, 7) - Timber (8, 9, 10) - Slash (11, 12, 13)
An adaptation of the 13 USA standard fuel models described above has been done in Spain by VELEZ (2000), in this case with typical photographic examples of Spanish vegetation landscapes.
When the standard 13 fire behaviour fuel models (ANDERSON 1982) are not adequate, there are other ways to build ad hoc fuel models through specific programs as the NEWMDL and TSTMDL both components of BEHAVE system (BURGAN and ROTHERMEL 1984, BURGAN 1987).
4.1.1.3 Fuels mapping
The use of fuel models to describe the structure of vegetation can be applied locally.
At this level, vegetation-type maps are often available in European fire prone areas, i.e. maps that plot the distribution area of different groups of species that define a vegetation type, e.g. grasslands, heathland, maquis, garrigue, holm oak forest, Aleppo pine forest, etc.
Then, the most common approach to map fuel models is to assign each vegetation type available on the maps to one fuel model class (one of the 13 defined above).
Nevertheless, direct assignment is not always satisfactory due to temporal changes of the vegetation, i.e. because of increasing fuel load, the ratio surface/volume and dead/alive material during vegetation development.
For instance, Ulex parviflorus dramatically changes the amount of dead material with age. Only, in few cases, a direct assignment would be correct, e.g. Chestnut forest to fuel model 9.
The types of cartographic information available at present are: satellite imagery of high resolution and periodicity, aerial photograph and orthophotomaps at different scales, in black and white, colour or infrared.
These cartographic information types are widely used to produce fuel model maps by means of photo-interpretation or alternatively by using automatic classification techniques or supervised classification techniques trained on specific areas.
Nevertheless, difficulties increase when several vegetation layers are present since only the top one is on view.
This is very common in Mediterranean conditions where vegetation has different layers that can be grouped in two: overstory and understory layers.
In this case, additional field information to address the complexity of fuel distribution is required.
If spatial distribution of vegetation is very heterogeneous the intensity of field sampling effort has to be increased, and thus the economic costs of this information, i.e. risking its economic viability.
Obtaining maps of these parameters using only field data can be done by means of spatial techniques (Lam 1983) into a Geographic Information Systems (GIS).
Several methods exist, both for continuous data and for categorical data.
In the former case, numerical interpolation is a usual technique (OLEA 1974, BURROUGH 1986), whereas Thiessen polygons are commonly used in the second case (BRASSEL and REIF 1979, GOODCHILD and LAM 1980, GASSON 1983, Mark 1987).
Nevertheless, these techniques usually assume no important changes in spatial distribution and pay little attention to the existence of real islands and corridors of vegetation inside other kind of vegetation.
Thus, doing an interpolation based on vegetation patches gives a more realistic result and it can better predict fire risk and propagation.
In summary, it is possible to combine a vegetation map (often not enough to obtain a fuel model map) with other information such as Forest Inventory data with geo-referenced plots that contain information to build fuel models.
This can be done by means of interpolation methods to generate a fuel model GIS layer and thus improving the quality and resolution of this mapping.
4.1.1.4 Current fuel data availability
Forest inventories are available from most of the European countries at national level, and are repeated every 10 years.
What it is not so general for these forest inventories is the sampling of information necessary to quantify wildland fuel.
In Spain the 3rd National Forest Inventory (1997-2006) includes Fuel Models classification of each sampled plot, according to VÉLEZ (2000).
In Catalonia this sampling was complemented with extra-information relevant for crown fire behaviour.
National Forest Inventories usually are accompanied by land use maps that are useful to quantify land use changes as well as to account for forested areas and their stocks.
The point is whether these maps are enough detailed, in terms of species composition and relative presence, canopy cover, etc to generate wildland fuel maps.
There is available a common European legend of land use cover classification thanks to CORINE Land Cover project.
This is important to harmonise the information all over Europe.
The output scale in this case is 1:100000 and the map is updated every 10 years.
Nevertheless the legend of CORINE is not enough detailed to generate a good wildland forest fuel map.
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This legend distinguish from forests: broad-leaved forests, coniferous forests and mixed forests, and from the shrub vegetation: moors and heathland, sclerophyllous vegetation and transitional woodland shrubs, but to be useful for wildland fuel mapping it would be necessary to distinguish riparian vegetation and sclerophyllous forest from the other forests.
On the other hand updating every 10 years should be enough to monitor the evolution of wildland fuels.
Changes of vegetation fuel load along 10 years is slow when vegetation is growing; only disturbances, e.g. after forest fires, can quickly affect the values of the area.
Thus, on those areas where dramatic changes occur, an extra assessment effort is needed.
4.1.1.5 Selected examples in fuel mapping
Fuel map of Catalonia: a study case
In Catalonia (NE Spain) there were available in 1995 two independent GIS layers: - The Forest Map of Catalonia (Scale 1:100000), with
polygons representing detailed forest species distribution and ordered by the degree of presence.
- The Ecological Forest Inventory of Catalonia (IEFC) with 10644 geo-referenced plots where species composition and vertical and horizontal structure was sampled for both understory and overstory forest layers.
From these data, the following methodology was applied: - Labelling each field plot. It was defined the value for
each field plot according to the 13 fuel models according to Vélez (2000) adapted to Spain from Anderson (1982).
- Reclassifying Forest Map. Since Forest Map has more than 100 categories in the map legend, these categories were re-grouped into 13, considering the main forest types relevant for fuel model.
- Mapping. This was done for each main forest type. The interpolation procedure was done by growing to connect field plots through vegetation patches.
This technique provides realistic results because it avoids filling areas with impossible values coming from a near plot.
For example, riparian vegetation often needs to be considered from relatively far plots, because the nearest plots can be very different in terms of combustibility (Figure 4-1).
The current Fuel Model Map of Catalonia (see Figure 4-2) is available for end-users in raster format (25x25 m pixel size) on the website http://mediambient.gencat.net.
These Fuel Model Maps are to be updated with more recent data such as the 3rd National Forest Inventory of Spain (IFN3) and new Forest Map of Spain (Scale 1:50000) during 2004.
Advantages - Cheap. Since pre-existing field information is used
to build Fuel Model Maps, obtained for other purposes such as Forest Inventories, the cost of mapping is dramatically reduced, and no new specific sampling initiative is required. The only cost is to process the information. This process is difficult to make it automatic and needs training to build-up the expertise.
- Fast. Computer processing is growing nowadays, and in this case the process of Fuel Model assignment to each forest plot limits the mapping.
- Easily updated.
Disadvantages - Accuracy. The resolution of the maps is limited by
the original GIS layers resolution. Forest Inventories at national scale are not designed to produce these information layers due to their low sampling intensity (usually 1 plot per square kilometre).
- Reliability. There are two factors affecting the map reliability: the spatial heterogeneity of the current mapped variable and the patch size of the represented vegetation type. Reliability is reduced when heterogeneity increases and/or vegetation patch size is small (for instance as the case of riparian vegetation). In Catalonia the study show that these factors are highly relevant in the Mediterranean forests of the area. These restrictions could be overcome by stratifying the sampling according to the representatively and heterogeneity of the sampled vegetation.
- Applicability. These Fuel Maps are useful at landscape level where software such as BEHAVE (ROTHERMEL, 1983) or FARSITE (FINNEY, 1998) are applied, but not enough to produce inputs to these models at local scale, e.g. to test the effectiveness of fuel-breaks on fire behaviour.
A model for automatically refreshing fuel type raster layer by means of interacting Structural Vegetation Types & Fire Behaviour Factor
Wildland firefighters (as fuel map end users) face today two unresolved issues.
First one (mentioned before), “updating fuel type maps”.
Second, and not less important, “the fact that a given structural vegetation type (SVT thereafter) may behave differently under diverse fire behaviour factors”.
Therefore, a given SVT may correspond to different fuel models at different situations.
To address these issues, close collaboration between GRAF-DGESC (Catalonia Regional Firefighting Agency) and University of Lleida (EUFIRELAB partner 024) was established.
A model is being developed. MARF (model for automatically refreshing fuel, MoLina 2000, MOLINA & CASTELLNOU 2000) is a qualitative simulation model that will automatically generate a Fuel Type raster layer by means of interacting SVT & Fire Behaviour Factors.
The ultimate goal is to improve the FARSITE (FINNEY 1998) simulations to help in wildland fire analysis.
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The FARSITE fire growth model is increasingly used as a planning tool for prospecting consequences of fuel management options on fire growth (MOLINA & CASTELLNOU 2000).
It has also been used to illustrate effects of fire behaviour of specific fuel treatments (VAN WAGTENDONK 1996).
The benefit of using FARSITE is its capability to mechanistically model fire growth with intricate fuels, weather, and topography.
FARSITE applies the same fire behaviour models most fire managers are used to in the BEHAVE program (ANDREWS 1986) and displays colour maps of fire behaviour across a landscape.
The deterministic nature of FARSITE simulations allows the results to be directly related to the causative factors (FINNEY et al. 1999).
Forest vegetation as fuel type is a major piece of information to mechanistically model fire growth in Farsite.
Today state-of-the-art in Farsite does allow for a fast conversion of fuel types following the same rule of conversion for the whole landscape (FINNEY 1998).
However it is not possible to accomplish a rapid and fine scale refreshment of fuel types taking into account that fire behaviour varies under different fire environments and diverse landscape features (MOLINA & CASTELLNOU 2000).
The elaboration of appropriate management practices at the landscape level may be achieved by testing and ranking “what if” scenarios.
New tools such as spatially explicit ecosystem models may aid in the decision making process in land management and bio-regional planning (PLANT et al. 1999).
To be workable to land managers, spatially explicit models must be frugal in their data needs, clear about uncertainties and assumptions and specific about the degree of accuracy of particular forecasts they make (PLANT et al. 1999)
WESTOBY et al. (1989) developed the state-and-transition model for rangeland vegetation dynamics.
They distinguish transitional states in which a site may not last forever, but rather may turn into one or another of the persisting states, depending on incidents while the system is in a transient state.
WESTOBY et al. (1989) envisioned the state-and-transition model as a conceptual management aid that could be implemented through a flowchart diagram.
This methodology can also be implemented on a computer; i.e., qualitative simulation models (PLANT 1997) by means of a rule-based representation of a state-and-transition model.
By linking this models with a geographical information system it is possible to generate a spatially explicit representation of the ecosystem dynamics at the landscape level.
PLANT et al. (1999) presented the study of a qualitative simulation study on an oak woodland site in the Sierra Nevada foothills (California).
PLANT et al. (1999) used the QTIP (Qualitative Temporal Inference Program) expert system (Plant 1997) to encode the model’s transition rules.
In MARF, we present a qualitative simulation model to automatically refresh the fuel model layer by means of interacting Structural Vegetation Types (SVT) & Fire Behaviour Factors (FBF - fire behaviour under different fire environments and diverse landscape features).
Additionally, this model allows for the update of Structural Vegetation Types.
In doing so, we should improve Wildland Fire Analysis and Fire Area Growth Forecast (i.e., when using FARSITE simulations).
The system that we propose functions by applying a succession of two models, first one to determine the vegetation structure and second one to determine the fuel state based on the vegetation structure.
We pursue to facilitate wildfire managers to refresh their landscape fuel type raster layer because we recognise the dynamic nature of both vegetation succession and forest fuel availability to burn under ever changing environments.
We present a catalogue of transition rules that enumerate the circumstances causing a transition from one state to another.
Firstly, states are Structural Vegetation Types (SVT) and transition rules account for both successional change and seasonal development of vegetation (fenology); both of them temporal changes.
Later, states are the forest fuel types derived from SVT under both spatial (fire spread direction) and temporal (meteorology) variations.
This work has been made for a NE Spain forest region, which includes 35000 ha of forests and rangelands, and with some minor modification it may fit other regions not only in Spain but in the Mediterranean basin.
Simulation methodology
The simulation methodology establishes an equivalence among the rules of a rule based expert system (NOBLE 1987, PLANT and STONE 1991) and the transition rules of a state-and-transition model (PLANT 1997).
We use QTIP (Qualitative Temporal Interface Program) expert system to encode the model’s transition rules.
The QTIP incorporates qualitative (i.e., non-numerical) simulation.
The most important aspect of qualitative simulation is that variables take on ordinal rather than rational or interval values (PLANT et al. 1999).
The QTIP was originally developed for the qualitative modelling of crop production systems (PLANT and LOOMIS 1991) and was later used for natural systems (i.e., oak woodlands) (PLANT et al. 1999).
The important feature of this program for application to state-and-transition modelling is that it combines an expert system with dynamic simulation of system behaviour (PLANT et al. 1999).
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The state-and-transition model is linked to a GIS through an algorithm that alternates among spatial steps and dynamic steps.
To accomplish a rapid, fine scale refreshment of fuel types taking into account that fire behaviour varies under different fire environments and diverse landscape features, our approach is to build a Structural Vegetation Types (SVT) raster layer instead of a Fuel Type raster layer – which is the present approach to fire simulations.
Our qualitative simulation model will automatically generate a Fuel Type raster layer by means of interacting SVT & Fire Behaviour Factors.
This methodology requires four steps: - Step 1. To select and list those variables &
parameters to be used in the steady-and-transition model for automatically refreshing the fuel layer (MARF). See below and table 4-1 for more detail.
- Step 2. To establish the appropriate correspondence between Structural Vegetation Type (SVT) and plant cover variables. A SVT is discrete vegetation type in terms of fuel structure, fuel availability and successional trend. Plant cover variables are available in raster layers. See below and table 4-2 for more detail.
- Step 3. To write down rules to enable the update of the Structural Vegetation Types raster layer after management actions or natural disturbance. To do so, we present MARV (model for automatically refreshing vegetation) or set of steady-and-transition rules about the dynamics of SVT. MARV is a qualitative simulation model that will automatically generate a SVT raster layer by means of interacting SVT & management actions and disturbances. Structural Vegetation Types (SVT) & theirs successional and seasonal trends under different management actions and natural disturbances can be seen in table 4-3
- Step 4. MARF (model for automatically refreshing fuel) is a qualitative simulation model that will automatically generate a Fuel Type raster layer by means of interacting SVT & Fire Behaviour Factors.
In table 4-1, there is a list of variables & parameters used in the steady-and-transition model for automatically refreshing the fuel layer (MARF).
If we focus only on the dynamic structural vegetation types, a simpler model of automatically refreshing vegetation types is constructed (MARV).
In this later case (MARV), no data of fuel moisture contents (FMC), neither fire spread direction is considered.
Those external inputs are important in MARF because they play a major role in determine fuel availability to the fire.
“Structural” vegetation type stands for vegetation as a fuel complex; it is not only a vegetation classification.
Fuel spatial arrangement, fuel bulk density, fuel complex ignitability, degree of shading of surface fuels is major items in this classification.
Variables characterising fuel structure. 5 variables 1. % cover of mature trees - <10, 11-20, 21-40, >41 2. % cover of immature trees - <30, >31 3. % cover of shrubs - <10, 11-20, 21-40, >41 4. % cover of grass, herbs & their litter - <10, 11-20,
21-40, >41 5. overstory/understory fuel continuity level – without
continuity, rare continuity, continuity almost everywhere
Variables characterising external inputs. 7 variables 1. fire level – Stand replacement wildfire (3), backing
wildfire (2), understory prescribed fire (2), Prescribed fire to enhance grazing (2), no fire (1) – 3 levels in five management /disturbance scenarios
2. grazing level – no grazing (1), proper grazing (2), overgrazing (3) – 3 levels
3. tree cutting – clearcut, stripcut (5), selective cutting (only removing large commercial trees) (4), light thinning (dense stands) (3), strong thinning (leaving few trees) (3), thinning pruning & removing shrubs (fire hazard reduction) (2), no cutting (1) – 6 levels
4. FMC 1h (%) – Fuel moisture content of 1 hour time lag (dead) fuels - <4, 5-8, 8-13, >13
5. FMC 10h (%) - Fuel moisture content of 10 hour time lag (dead) fuels - <4, 5-8, 8-13, >13
6. FMC live (%) - Fuel moisture content of live fuels - <55, 56-80, 81-110, 110-190, >190
7. Fire spread – downhill, uphill – 2 levels
Note than 1, 2, & 3 are management issues
Note than 4,5, & 6 are Fire Behaviour FACTORS (to obtain fuel model from vegetation structure i.e., Fire Behaviour under different fire environments - DFE)
Fixed Parameters. 1 variable 1. Soil depth parameter: Deep soil (3), medium deep
soil (2), and shallow soil (1) - linked to soil survey published or based on location on slope hilltop, hillside or piedmont
Table 4-2: Note: There are three levels: low, medium and high. The value 0 falls within low class, it is used in this table only to highlight differences between different SVT and to avoid showing water surfaces have low seedlings or tree canopy cover and alike
NRS – Non resprouting shrub - Shrubs without vegetative reproduction
Some species (i.e., Pinus halepensis with its serotinous cones) require a special SVT because to their uniqueness regarding their major role in controlling the vegetation dynamics in this fire prone ecosystems.
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Table 4-3: Note: Forest / woodland with little understory and high lower branches corresponds with SVT 11 (Even-aged pine stand, without fuel layer) and SVT 13 (Closed oak stand, without fuel layer) 1. fire level – Stand replacement wildfire (3), backing
wildfire (2), understory prescribed fire (2), Prescribed fire to enhance grazing (2), no fire (1) – 3 levels in five management /disturbance scenarios
2. grazing level – no grazing (1), proper grazing (2), overgrazing (3) – 3 levels
3. tree cutting – clearcut, stripcut (5), selective cutting (only removing large commercial trees) (4), light thinning (dense stands) (3), strong thinning (leaving few trees) (3), thinning pruning & removing shrubs (fire hazard reduction) (2), no cutting (1) – 6 levels
Soil depth parameter: Deep soil (3), medium deep soil (2), and shallow soil (1)
Fuel types in fire risk analysis using Remote Sensing data in Elba island (Italy)
Temporal changes in state and cover of vegetation and soil can be detected in different parts of the electromagnetic spectrum or combinations thereof.
Most vegetation types with green leaves show a similar spectral behaviour with relatively low reflectance in the visible part of the spectrum and high values in near infrared (“red edge”) (LILLESAND & KIEFER, 1987).
Changes of vegetation vitality have drastic impact on the spectral behaviour, especially concerning the visible red and near infrared bands.
An arithmetic combination of these bands known as Normalised Differential Vegetation Index (NDVI) is strongly correlated with the vitality of green vegetation.
Burnt or very dry areas, having a low NDVI-value, can be easily detected by a drastic change.
Thermal remote sensing data, e.g. from Landsat’s band 6, can also be used as an indicator for changes in vegetation cover.
Black-burned areas radiate more energy in the thermal infrared band than a green vegetation cover.
Unfortunately, optical imaging can be hampered by clouds, aerosols, dust, haze and smoke and is dependent of solar illumination, meaning that no optical remote sensing images can be taken during night, in clouded areas and also during wildfires when there is much smoke.
Passive microwaves penetrate smoke and bushes; active microwave imaging is not dependent of daylight and atmospheric conditions and is in almost all aspects complementary to optical imaging.
Microwave remote sensing is very sensitive to soil/vegetation moisture and to roughness of the illuminated surface.
Visible, near- and mid-infrared bands can be used for analysis of vegetation condition and cover, while hydrological and thermal conditions can be determined using mid- and thermal-infrared and SAR data.
In order to compare these conditions before and after a fire, multi-temporal analysis is to be applied, showing changes in a very direct way.
Also combining optical and SAR data highlights certain phenomena concerning vegetation cover and hydrology.
Map of vegetation can be obtained from air photos; by visual interpretation and accurate ground surveys, detailed and updated map of vegetation can be obtained, useful also as ground control areas for the remote sensing image processing and classification. (Figures 4-3 and 4-4).
Evaluation of forest fire risk by the analysis of environmental data and TM images
Spectral indices are by now standard procedures in remote sensing image analysis and interpretation.
In particular, vegetation indices derived from the visible and near infrared channels of airborne and satellite sensors are widely used for the discrimination and study of vegetation cover types.
These indices, among which the Normalised Difference Vegetation Index (NDVI) is probably the most common, are generally related to active green biomass and Leaf Area Index, and can be indirectly employed to assess plant conditions. - An analysis was conducted to assess the value of
an usual vegetation index as an indicator of fire risk. The rationale for this is that vegetation indices are
strongly related to the quantity of active green biomass, which, in turn, is an indicator of vegetation density and health.
In practice, it can be reasonably hypothesised that, at the peak of the arid season, vegetation activity is mainly controlled by water availability, so that dense, healthy vegetation is less subjected to fire hazard.
A NDVI was generated from the atmospherically and topographically corrected TM bands 3 and 4.
Only small fires had occurred in the two years prior to the scene acquisition, so that it could be assumed that vegetation conditions were mainly controlled by environmental factors.
The NDVI image obtained was divided into eight levels and statistically analysed as above.
A correlation coefficient slightly lower than that from the previous analysis was obtained (r = -0.854), indicating the substantial validity of the hypothesis formulated.
Evaluation of forest fire risk by means of classification of Mediterranean vegetation by remote sensing and ancillary data
Since different vegetation types can be associated to different fire risk levels, a classification approach based on the use of different types of remote sensing data can be proposed for the generation of maps related to fire risk.
Hard and fuzzy classifications have been tested for this purpose in study areas, taking into account the effects deriving from the use of scenes from different periods and of ancillary data.
The evaluation of the risk images produced is carried out by comparison with the fire events occurred in the study area for at least one decade.
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The results show that while the acquisition period has only minor effects on classification accuracy, this is strongly dependent on the inclusion of ancillary data.
As regards the estimation capacity of fire risk, the fuzzy approach better exploits the information of the integrated data sets, producing maps, which are temporally stable and highly indicative of this risk in the study area.
Unsupervised and supervised spectral indices related to fire risk could be derived from Landsat Thematic Mapper (TM) images taken in a Mediterranean area during the arid season.
Several investigations showed only a slight superiority of the supervised method, but this can be partly attributed to the image acquisition date corresponding to the peak of the arid season.
Also, that approach partly obscured the relationship between vegetation types and ecological factors, which could be decisive for the general applicability of the method.
A more deterministic approach can be proposed based on supervised classifications of TM images and ancillary data taken in the same study area.
The rationale for this approach is that each vegetation class can be associated to a different level of fire risk.
Consequently, risk maps are produced by assigning to these classes risk levels derived from existing literature and personal knowledge on local environmental characteristics.
The effects of hard and fuzzy processing of the data have been investigated, as well as those of different image acquisition periods during the growing season (spring and summer).
The evaluation of the maps produced has been performed by comparison to the frequencies of fires really occurred in the study areas, in order to identify an optimum map which is offered as a practical product for the management of forest resources.
At the same time, neural network classification and analysis of texture by ERS- 1 data methodologies have been applied.
It was evident from inspection of the imagery that the detailed classification based on field information would be beyond the capabilities of computer classification of the imagery.
The aim of the work was therefore to investigate the land cover classes that it may be possible to differentiate in the imagery either using a single image or, for the more complex classification schemes, using more than one image.
The classifications were mainly based on an analysis of the spectral reflectance values, supplemented by interpretation where appropriate.
Data Used
For the study area, the following data layers were utilised: - Digital Terrain Model (DTM) - Land Unit map at 1:50.000 scale, consisting of five
distinct layers (land suitability, vegetation, natural value, soils and land units), produced by direct ground surveys and interpretation of air photos.
- Information on the distribution and size of forest fires occurred (historical series).
- Land cover references derived from the visual interpretation of air photos and direct ground surveys. 16 vegetation classes were considered, which were representative for the land cover of the area (Mediterranean characteristics). (Table 4-4).
Classifications of Satellite and Ancillary Data
Supervised classifications can be carried out using TM scenes and ancillary data layers, as elevation, slope, aspect and soil type.
A quite classical, but effective, procedure consists in carrying out the classification with a modified maximum likelihood algorithm, which is capable of considering non-parametric ancillary layers.
Since to work properly the algorithm must be applied to nearly independent channels, the TM scenes (at least two) were subjected to a preliminary Principal Component Transformation.
The first four principal components of each scene, which accounted for about 99% of the total variance, were retained for the classifications.
The conventional (mean vectors and variance-covariance matrices) and non-parametric (frequency histograms) statistics for the training phase were derived from the relevant pixels for the spectral and ancillary data.
The algorithm is first applied in a conventional "hard" way, using only each TM scene and these plus the ancillary layers.
In this way four usual "hard" maps were produced reporting the 16 study classes.
A mode filtering is generally applied to the classified images to reduce excessive spatial irregularities.
The accuracy assessment is carried out by confusion matrices compared to the test pixels and summarised by the Kappa coefficient of agreement.
The modified maximum likelihood algorithm can also work in a fuzzy way. 16 fuzzy membership grade images are produced for each date, expressing the maximum likelihood "a posteriori" probability of the pixel attribution to the respective classes.
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In this example, a total of three alternative classification schemes are put forward and tested to see how well they performed.
The differences between the schemes are mainly based on the way the original classes have been grouped together based on their spectral/spatial differentiation to form new classes.
For testing, unsupervised classification of the Spot XS image has been carried out.
The resulting classes are based initially only on natural spectral clusters of pixels but the classes have been subsequently modified by considering the ground data also.
The suggested new schedules indicate the new class names for classes identified in the fieldwork. (Table 4-5).
Schedule 1 - represents the original classification scheme.
This has been derived on the basis of fieldwork and represents the classes that can be defined if one is on the ground.
This 26 class classification was used as a starting point for further investigation of the spectral classes observed in the imagery.
The classification is based on 8 primary cover types each with a small number of variants.
The different schedules are based on the fact that these subclasses cannot in every case be distinguished from each other.
Schedule 2 - this represents the worst possible case where only the main classes can be identified based on a forest/non forest classification.
This scheme has been derived using unsupervised clustering and subsequent merging of similar spectral classes based partially on their context and partially on the knowledge gained from the available ground data.
The degree to which they can be distinguished depends on the spectral and spatial resolution of the imagery, the number of images used and the approach adopted to classification.
In the case where spectral characteristics alone are used for classification there is possible confusion between deciduous trees and agriculture.
However, if texture/pattern is used as well, this may be overcome and indeed very little confusion is observed.
In most cases confusion can be reduced by increasing the number of classes that are automatically derived by the program and subsequent careful merging of spectrally similar classes.
Assuming that the first item named in the 26-class field derived data is in fact the predominant cover type, it was possible to identify 8 principal cover types as indicated below.
These are subsequently compared with the field data in order to ascertain how well the unsupervised classification had performed:
Deciduous - this takes into account all the classes with a predominant deciduous species such as chestnut, coppice (which has been assumed to mean small wooded, predominantly mixed species).
It includes all 5 coppice classes identified on the g round assuming that the predominant cover type is the wooded coppice.
Reforestation areas also appeared to fall into this class.
This may be due to the fact that most young trees contain a fairly large proportion of chlorophyll and in summer more closely resemble deciduous than coniferous trees regardless of the species involved.
There was, however, some confusion with agriculture which was assumed to occur in areas where the trees were still very young, leafy and not very tall;
Coniferous - the coniferous class appeared to include all defined coniferous classes, irrespective of the proportions of trees to other named covertypes.
In the majority of cases the assumption that pine is the predominant class appeared to be correct;
U/C - unclassified. It was assumed that this class also contain any paths, which stand out against burnt areas.
Maquis - Only one class of maquis was immediately identifiable based on unsupervised spectral clustering.
Therefore all maquis classes based on field mapping were assigned to this class.
There was some confusion with areas identified as agriculture;
Agriculture - on the basis of a spectral classification only a single agricultural class was found.
This therefore was assumed to include agriculture, agriculture with trees and pasture.
However, given the nature of agriculture on the island it may be possible to identify the pasture areas quite easily.
This is reflected in Schedule 3, which was derived using a supervised classification;
Rocks - areas with a predominant cover type of rocks, as exhibited, for example, by the upper areas of most of the mountain regions.
There was however, some obvious confusion with other non vegetation cover types such as urban;
Water - main water courses - although it should be noted that none were identified;
Urban - principal urban areas;
It should be noted that maquis and deciduous woodland could be differentiated in areas with a high percentage of one or other of the cover types.
However, it is clear that there is a continuum from maquis to deciduous woodland and there is considerable confusion in marginal cases.
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Schedule 3 - this represents classification nomenclature based on supervised classification.
Two additional primary classes were identified, coppice and pasture.
In addition it proved possible to make subdivisions of the initial eight cover types into a number of the identified classes.
However, it is not entirely clear what the most appropriate descriptions are for these classes.
The class names chosen are dictated mainly by the names identified on the field data maps.
This approach allows the chestnut wood to be clearly identified.
Since there are no other significant deciduous woodland stands it is not clear if it is possible to differentiate between chestnut and other deciduous species.
Two classes of coniferous woodland were identified. These have been called Coniferous and Sparse
Coniferous (sp. Conif.) and correspond to a full canopy class (2) - pine wood in the field based classification and a sparse class (2a) corresponding to both sparse pine wood and sparse pine wood with rocks in the ground based classification.
The reason for including the areas with rocks in this class is due to the fact that the rocks are well below the canopy and it is, generally, not possible to discern these areas as a separate class.
Only 2 classes of maquis are identified corresponding to a full cover class and a sparse class.
The full cover class (4) includes the low maquis, low maquis with rocks, tall maquis and tall maquis with rocks classes in the ground based classification (classes 6, 8, 15, and 17).
Where full canopy cover exists it is not be possible to see rocks below the maquis canopy.
The second maquis class (4a) combines all the sparse maquis classes identified in the ground based classification and, again it comprises both the tall and low maquis classes.
Pasture is identified on the basis of the absence of an obvious field pattern.
In addition, in most cases it exhibited a less intense spectral response than that resulting from agriculture, which in this area tends to be fairly intense and confined to small terraced fields closely following the topography of the area.
Deciduous - In this case it was possible to identify the area shown to be chestnut wood as a separate class.
However, the class name retained is still the more generic "deciduous" since it was not possible to say with confidence which other deciduous species were present if any, nor was it possible to differentiate between actual species;
Coniferous - with the benefit of training data it was possible to identify two coniferous classes, which have been called Coniferous and Sparse Coniferous (sp. conif).
These are taken to correspond to a full canopy class (2) corresponding to pine wood in the field-based classification and a sparse class (2a) corresponding to both sparse pine wood and sparse pine wood with rocks in the field based classification.
Taking into account that the rocks are likely to be well below the canopy it is not surprising that a pine class with rocks could not be differentiated.
U/C - unclassified;
Maquis - Only 2 classes of maquis were identified, which appeared to correspond to a full cover class and a sparse class.
The full cover class (4) was mainly formed by the low maquis, low maquis with rocks, tall maquis and tall maquis with rocks classes based on fieldwork (classes 6, 8, 15, and 17).
It is almost certain that in the case of a full canopy cover it was not possible to see rocks below the maquis canopy.
The second maquis class (4a) combines all the sparse maquis classes identified in the field based classification.
Coppice - depending on the species and stage of development it was possible, in some cases, to discern a difference between deciduous and coppice classes.
When choosing training areas, it was possible to distinguish two classes of coppice.
As with the other main cover classes these were found to correspond to a full cover class (5) and a partial cover class (5a), which has been called sparse coppice.
This latter class includes the class identified in the field as sparse coppice with rocks;
Pasture - pasture is confused with agriculture by the classifier and could be identified with any confidence only on the basis of the absence of an obvious field pattern.
It was, however, observed that areas identified as pasture exhibited a less intense spectral response in the near infrared bands than that resulting from agriculture.
Reforestation - the reforestation class could not be identified as a class on its own but, depending on the species, was classed together with coppice (5) or with sparse coniferous (2a);
Rocks - areas with a predominant covertype of rock, as exhibited, for example, by the upper slopes of most of the mountains;
Water - main water courses;
Urban - principal urban areas;
Agriculture - only a single agricultural class was identified;
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Schedule 4 - shows the best classification that is achieved using a large number of training sites, and with a significant element of manual intervention in areas of high confusion.
However, it is still not possible to differentiate between cover types with and without rocks mainly it is believed because rocks tended to occur underneath canopies.
However, where the spectral characterisation of the "rocks" class was tightly defined, the rock pixels tended to form very small areas within other cover types.
This was the case particularly with sparse vegetation cover classes, which occurred with rocks in the Schedule 1 classification.
This schedule differs from Schedule 3 mainly because it is possible to differentiate between the low and tall maquis covertypes through the use of a texture measure.
Chestnut - In this case is possible to clearly identify the chestnut wood as a separate class and the class name could be used.
Where areas identified as coppice were found to have similar spectral characteristics these were renamed chestnut or the whole class could be given the more generic "deciduous" name;
Pine - as in Schedule 3, two classes of coniferous woodland could be identified.
These have been called Pine and Sparse Pine. These correspond to a full canopy class (2) now
identified as pine wood and a sparse class (2a) corresponding to both sparse pine wood and sparse pine wood with rocks.
As explained above, a class clearly identifying pine with rocks was not distinguished, although it is possible that in some circumstances a Pine II class (class 2b) was identified;
U/C - unclassified;
Maquis - in this classification a number of Maquis classes were identified: maquis and sparse maquis.
The full cover class (4) was mainly formed by the low maquis, low maquis with rocks, tall maquis and tall maquis with rocks classes in the field derived classification (classes 6, 8, 15, and 17).
The second maquis class (4a) combines all the sparse maquis classes identified in the field based classification, again comprising both the tall and low maquis classes combining the following original class numbers: 7, 9, 16 and 18.
The principal difference between this schedule and Schedule 3 was that with careful training and the use of several image types it is possible to differentiate between the low and tall maquis covertypes.
Coppice - it was possible to discern a difference between deciduous and coppice and subsequently two classes of coppice were identified.
As with the other main cover classes these were a full cover class (5) and a partial cover class (5a) called sparse coppice;
Pasture - pasture was identified as separate class;
Reforestation - see Schedule 3;
Rocks - areas with a predominant cover type of rocks;
Water - main water courses;
Urban - principal urban areas;
Agriculture - two agricultural classes were distinguished.
However, since there was no indication of species in the field based classification these classes were amalgamated into the single agricultural class;
Subsequently to the completion of these tests a further classification scheme was suggested and tested.
This Schedule 5 is presented together with Schedules 2 and 3 for comparison below.
With the exception of being able to discern tall Maquis this classification is most similar to Schedule 3. (Table 4-6).
Land classification by Artificial Neural Networks
The effectiveness of applying Artificial Neural networks (ANNs) to classify multisensor remote-sensing data has been documented in various works.
The main characteristics of ANNs are: - the capability of adapting connections weights to
improve performances based on current results; - the intrinsic parallelism which provides high
computation rates; - the robustness to noise overall when distributions
are generated by non-linear processes and are strongly non-Gaussian;
- the capability to take into account the importance and reliability of different sensors.
A critical analysis of the different approaches to classification of multi-source remotely-sensed data based on ANNs was conducted.
On the basis of this analysis, the choice felt on the Multi-Layer Perceptron networks (MLPs), trained by means of the Error Back Propagation (EBP) algorithm as a very effective approach to classification of multi-source remote-sensing images.
MLPs are feed-forward neural networks with one or more layers of nodes (neurons) among the input and the output nodes.
The principal characteristic of MPLs is an intrinsic flexibility that, choosing appropriate network architecture, allows them to solve different classification problems.
The success of MLPs is principally due to the adoption of the error back-propagation learning algorithm.
The EBP algorithm is a supervised learning algorithm where a mean square error function is minimised.
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The classifier has been developed using a very general software tool, which allows to select different networks topologies (i.e., by choosing the number of layers, the number of neuron per layer, etc.) and to control the EPB learning algorithm (i.e., by setting the error function to be minimised, the learning rate, the type of training, etc.).
A crucial task of a neural classifier is the choice of network architecture and the definition of the parameters of the EBP learning algorithm.
Several MLP architecture have been considered to find the best one
The training of all the considered neural networks has been carried out by the EBP learning algorithm with both ”training by pattern" and a “training by epoch” using different "learning rates".
As input to the neural-network classifier we used 6 TM bands (i.e. all TM bands except thermal one), the best 5 textural features derived from the GLCM (i.e., sum average, difference of variance, correlation, contrast, difference of entropy) and the 3 DTM features.
The proportion of training and test samples in the considered data set are shown in the following table 4-7.
The best performances were obtained by exploiting an architecture with 14 input units (i.e. as many units as the number of input features), one hidden layer with 25 units, and 16 units in the output layer (i.e. as many units as the number of data classes).
The training of the network was carried out by the EBP algorithm (15.000 epochs).
The errors of classification obtained are reported in the following table 4-8.
Generation and evaluation of fire risk maps
Fire risk maps were obtained from the hard and fuzzy classifications by assigning to each vegetation class a risk value derived from the literature, slightly modified to adjust to the specific environmental situation.
In particular, the fuel types used in the BEHAVE model were taken as a reference but, since the model did not specifically mentioned most Mediterranean vegetation types, the relevant values were derived on the basis of our knowledge of local environmental characteristics.
Table 4-4 shows the results of this adaptation process in a scale which was kept the same as that of the BEHAVE fuel types with decreasing levels of risk from 1 to 8 (urban/agricultural land was obviously excluded from this analysis).
In the case of the hard classifications, the risk images were simply derived from the two classifications with the complete data sets by assigning the risk value of the relevant class to each pixel.
The derivation of the risk images from the output of the fuzzy procedures was more complex, since it was aimed at utilising all class membership information produced.
A method based on a weighted average strategy was therefore utilised.
In practice, the risk value was computed for each pixel as:
where: - NC = number of classes; - R' = estimated risk value for the pixel considered; - Pri = fuzzy probability of attribution of the pixel to
class i; - Ri = risk value of class i.
In this way all information produced by the fuzzy classification processes was conveyed into the risk images.
As previously, the urban/agricultural land was excluded from the computation of the risk images.
Also, their excessive spatial variability was reduced by the final application of a mean filtering with a 3x3 pixels neighbourhood function.
The risk images produced by the hard and fuzzy approaches from the two complete data sets were finally evaluated by comparison with the frequencies of fire events occurred in the study area during the last decade (1984-1994), as already carried out in the preliminary work.
This evaluation relied on the assumption that such frequencies were indicative of the natural distribution of fires in the area.
The frequencies of fires events were computed for each of the 8 risk levels considered for both hard and fuzzy classifications.
To obtain risk images with 8 levels from the fuzzy outputs, which were continuous, they were divided into 8 equally spaced intervals.
The agreement between estimated risk values and frequencies of fires actually occurred was evaluated by usual regression analysis.
4.1.2 Fuel moisture content and flammability
The methods used to estimate short-term fire danger and mainly those based on Foliage Moisture Content (FMC) assessment are reviewed.
The most sensitive variables for FMC estimation are based on short-wave infrared bands and the combination of vegetation indices and surface temperature.
The applications using low resolution data (NOAA-AVHRR, SPOT VGT, TERRA MODIS) and high resolution data (LANDSAT TM) are examined through recent works
4.1.2.1 Role of foliage moisture content in the short-term estimation of fire danger
Fuel water content is one of the critical factors affecting fire ignition and fire propagation and therefore is taken into account in most fire danger and fire behaviour modelling systems (ALBINI 1985, VAN WAGNER 1985, ROTHERMEL et al. 1986, ANDREWS and CHASE 1990, HARTFORD and ROthermel 1991, CHUVIECO et al. 2002).
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VALETTE and MORO (1990) have estimated the influence of FMC on fire ignition and propagation for several Mediterranean species.
Frequency and duration of the flammability were compared to FMC (Table 4-9).
At present time FMC is currently obtained from field campaigns but this method is limited at a temporal and spatial local scale.
When they prove efficient for detecting vegetation water content remote sensing techniques represent a very convenient method of data acquisition because it is possible to cover large areas several times during the season (FOURTY and BARET, 1998).
4.1.2.2 Estimation of foliage moisture content
Leaves represent the main area seen by plane or satellite borne sensors.
At leaf level, chlorophyll content is assessed by spectrometry with good accuracy.
More recently, FOURTY and BARET (1998) has also demonstrated that leaf water and dry matter content are robustly accessible from fresh leaf reflectance or transmittance measurements.
FOURTY (1996) shows good estimates for water and dry matter contents through model inversion.
JACQUEMOUD and BARET (1990) using the same type of leaf model conclude similarly that water content can be estimated accurately from fresh leaf reflectance spectra.
The accuracy of the estimates is very sensitive to the radiometric resolution of the sensor used to measure reflectance or transmittance spectra.
Due to the former reasons, leaves are the vegetation components most appropriate for correlating their water content with the information obtained from Earth-Observation (EO) data.
Although FMC refers to the whole plant, moisture content is commonly measured only from the leaves, at least in live fuels, since wooden parts of the plant are less sensitive to variations.
Several measures of FMC have been proposed, the most common being the proportion of fresh weight versus dry weight of the sample (BURGAN 1996, DESBOIS et al. 1997, CHUVIECO et al. 1999).
100×−
=f
df
W
WWFMC
Wf= fresh weight (g)
Wd= dry weight (g) obtained after drying the leaf in an oven
This index is the most commonly used in the forestry ambit to measure plant water stress and it has been recommended by several scientists (BlackmArr and FLANNER 1968, SIMARD 1968, OLson 1980, VINEY 1991).
It is easy to obtain and very sensitive to changes of fresh weight (CHATTO, 1997).
In some other scientific domains, such as plant physiology, other measurements of plant water content are more common than FMC, such as the Relative Water Content, RWC (a function of actual versus potential maximum moisture content).
In plant physiology, RWC is used to determine plant water stress (GRACE, 1983).
However, FMC is the most common in fire-related studies (BROWN et al. 1989, VIEGAS et al. 1990), since the actual amount of water is the critical parameter in fire behaviour.
Different spatial sampling techniques have been proposed for FMC measurement, the most common being transects and quadrates.
Samples are commonly composed of leaves for trees and shrubs, while for grasslands the whole plant is measured.
Samples are weighed on the field with portable balances or introduced in ice bags to be weighed at the laboratory.
Then, these samples are dried in an oven and weighed again to compute FMC.
The length and temperature of the drying process vary among the authors.
Some suggest 24 or 48 hours at 60º C, while some others propose 24 hours at 100º C.
The former is more secure to avoid loss of oils and essentials, while the latter is less tedious (VIEGAS et al., 1998).
Detailed measurements of FMC and canopy structure for canopy-reflectance modelling are registered in the sampling sites selected inside each study area.
The main constraint to choose them is the spatial resolution of the satellites.
Indeed, these sites must be as homogeneous as possible concerning the type of vegetation and the topography, i.e. 2 by 2 kilometres.
For ensuring the collection of more field samples in a short time window, an easier access to leaves has been preferred.
As a consequence, sites have been chosen mainly in shrublands and in forests with trees of low height, formations common in the Mediterranean basin. (Figure 4-5).
General constraints for site selection are the following: (1) Vegetation must present a sufficiently dense cover
(> 60 %) to prevent the soil from having too much influence on signal;
(2) Topography must be as homogeneous and flat as possible, to avoid the influence of variability linked to aspect;
(3) The number of sites and the distance between them must be adjusted so that the tour can be done in a short period of time, during the most dangerous conditions.
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In a first step, the identification of the areas responding to these constraints can be done by combining a vegetation map and a digital terrain model (aspect and slope) or using a high resolution satellite image to identify homogeneous areas.
Then, a field trip is necessary to check and to adjust the location of the sampling sites.
The campaign of measurements should run from May to the end of September in order to have a wide range of fuel moisture contents.
But, depending on the countries, the drought period is not the same.
For example, in Spain it can start early April, whereas in South of France it is rarely so before early July.
The moisture measurements should thus start after Spring rains, in order to have the highest rate of moisture content, and continue until the beginning of Autumn rains, to have the complete evolution of the vegetation water status.
Measurements are to be performed between 11:00 and 16:00 Local Solar Time (LST), when the air temperature is the highest and the most steady (the air temperature is maximal at 15:00-16:00 LST and the air humidity is minimal at about 16:00 LST).
This is also the period when vegetation is mostly affected by water stress and when the number of fire ignitions is maximal.
Measurements should not be done when it rains or when the foliage is wet (after rainfall).
Measurements will be done every week. The protocol for sampling and measuring moisture
content is derived from the one proposed by VALETTE and MORO (1990) in France.
To be precise, each test site is a homogeneous area of 1200 m² and every sample is a mean of 5 collections of 15 to 25 g of fresh leaves.
The sampling protocol at the plant level has to take into account different variabilities within the canopy, for example related to age or to position in the crown.
As it was not possible to devise the sampling according to every possible source of variability, the solution taken has been to adopt a simple, reproducible and coherent sampling, based on terminal shoots.
Only these shoots (leaves and branch) are collected on individuals taken at random (at least 3 shoots on each individual).
On shrubs, the shoots of the year are well identifiable and, as the branch is not yet woody, they do not present a great difference of moisture content with the leaves.
The fact to take only terminal shoots eliminates the variability due to the age and the place of the shoots in the plant (for shrubs).
Thus, the measurements will be comparable from one day to another and from one site to another.
It is however clear that the measured changes will be larger than what should be expected at the whole plant level.
4.1.2.3 Effect of moisture content on reflectance and temperature
To contribute to a better understanding of the possible applications of remote sensing for FMC estimation, several laboratory and field analyses have been conducted in the last decade to improve our knowledge on the effects of water content on vegetation reflectance and temperature.
Detection of plant water stress caused by drought is a major goal for remote sensing.
The interaction of vegetation with radiation depends on the amount of water in leaf cells.
This water is particularly linked to wavelengths in the near-infrared (NIR, 0.7-1.3 mm) and in the Short-Wave Infrared Reflectance (SWIR, 1.3-2.5 mm) (Cf. Figure 4-6).
Water strongly absorbs in SWIR region and is a major factor controlling leaf spectral properties.
The use of SWIR bands is suggested for remotely sensing leaf water contents since the 80’s (TUCKER 1980, HARDISKY et al. 1983, ELVIDGE and Lyon 1985, Hunt et al. 1987).
Reflectance in the SWIR is highly sensitive to vegetation water content, while reflectance in the NIR is only weakly so (GATES et al. 1965, WOOLLEY 1971, CARTER 1991).
Over the years many attempts have been made to estimate vegetation water content from reflectance data (THOMAS et al. 1971, JACKSON and EZRA 1985, RIPPLE 1986, BOWMAN 1989, COHEN 1991, HARDY and BURGAN 1999, CECCATO et al. 2002a) and methods for detection of water stress by remote sensing are desirable because they can be readily used on different vegetation types with little adjustment (HUNT et al., 1987).
The development of physically-based canopy reflectance models for estimating FMC, can explicitly incorporate knowledge on the fractional vegetation cover (Fcov), leaf area index (LAI) and the solar zenith angle (SZA), such that they may provide more accurate estimation of FMC, than a simple spectral index approach.
Whichever method is used, reflectance data from the SWIR and NIR regions is likely to be essential. (Figure 4-6).
However, it must be pointed out that physiological variables as chlorophyll content or stomatal conductance, are not directly related to plant water content (CECCATO et al., 2001).
Variations in chlorophyll content can be caused by water stress but also by phenological status of the plant, atmospheric pollution, nutrient deficiency, toxicity, plant disease, and radiation stress (LARCHER, 1995).
The stress can be due to a lack of water that restricts transpiration, inducing closure of stomata and resulting in less water evaporation in the leaf surface.
Because less cooling occurs due to water evaporation, the temperature of the leaves increases (JACKSON, 1986).
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As an alternative to measurements of vegetation water content to assess short-term fire risk, some authors investigated the opportunity of using this thermal dynamic of vegetation.
They assumed that differences between the air and surface temperatures were related to plant water content and to water stress (CECCATO et al., 2001).
These indices estimate vegetation status by measuring evapotranspiration. However, this is not a direct measurement of vegetation water content.
4.1.2.4 Use of remotely sensed data for foliage moisture estimation
The use of remote sensing methods may overcome some difficulties, since it provides temporal and spatial coverage.
Until now, studies have generally relied on establishing an empirical relationship between reflectance in one or more wavebands sometimes combined in an index and FMC.
Perhaps the first attempt to estimate FMC using reflectance data was made by PALTRIDGE and BARBER (1988).
They used a modified version of the NDVI for estimating FMC from AVHRR data in Australian grasslands.
Later, this work was improved by PALTRIDGE and MITCHELL (1990) to more explicitly account for the atmospheric and BRDF effects in red reflectance data, as a function of variable solar and viewing zenith angles.
In Mediterranean shrublands, PEÑUELAS et al. (1997) and PIÑOL et al. (1998) have found reasonable correlations between FMC and the ratio of reflectance at 900nm to that at 970nm, using ground based hyperspectral data.
CHUVIECO et al. (2002) showed that strong correlation could be established between FMC and reflectance data if applied to a specific vegetation type.
If the vegetation type was not known then a more general relationship would have to be used, which produced less precise results.
Using model simulated reflectance data, DAWSON et al. (1999) report strong correlations between FMC and two hyperspectral water indices, the Water Index (WI) (PEÑUELAS et al. 1993), and the Normalised Difference Water Index (NDWI) (GAO, 1996).
Overall, empirical relations have not been extensively tested and estimation of FMC from reflectance data lacks generality.
Good results may be obtained in certain particular ecosystems, e.g. grasslands, while in others rather poor results may be obtained.
Furthermore, even in the best planned fieldwork, relationships may not be fully tested or established between FMC and reflectance data, owing to the fact that vegetation may not display the complete range of drying effects because of variability in the local climate (PIÑOL et al. 1998).
In addition, hyperspectral indices, which have shown promise in estimating FMC, remain still untested, but this may change as results using TERRA sensors should appear soon.
Moreover, the competing influences of variable soil reflectance, Leaf Area Index (LAI), solar and sensor viewing angles make the empirical approach site-specific and only applicable to the site where relationships were established.
The estimation of FMC over the whole Mediterranean may need a prior classification of vegetation types.
With NOAA-AVHRR data, several studies have shown strong correlations between vegetation indices and critical physiological variables.
CHUVIECO et al. (1994) showed that vegetation moisture is a particularly difficult parameter to estimate as it accounts for little spectral variation with respect to other environmental factors.
However, spectral characterisation of vegetation stress is possible if temporal profiles are derived.
The most common vegetation stress estimation has been the analysis of NDVI multitemporal series (GONZÁLEZ et al. 1997, ILLERA et al. 1996, LÓPEZ et al. 1991, PROSPER-LAGET et al. 1994, VIDAL et al. 1994).
An alternative to this approach has been to monitor the thermal dynamism of the vegetation cover (BARTHOLIC et al., 1972).
ALONSO et al. (1996) have developed the ratio NDVI / ST to estimate temporal dynamics of FMC from NOAA data.
DESHAYES et al. (1998) have shown that vegetation stress can be monitored using the same ratio (r² = 0.79 for grassland).
In Spain, Casanova et al. (1998) have computed daily fire danger maps with a model using the state of vegetation degradation and the relation between surface temperature (ST) and NDVI.
Nevertheless, here it still consists in an estimation of vegetation stress not on an assessment of vegetation water budget.
With SPOT-VEGETATION data, CECCATO et al. 2002b have investigated the Global Vegetation Moisture Index (GVMI) to retrieve vegetation water content from radiative transfer models.
This index was validated for savanna in Senegal and is linked to the Equivalent Water Thickness of canopy (EWTcanopy) that is to the amount of water per vegetated area.
According to CECCATO EWTcanopy index is not corrupted by dry matter of leaves on the contrary of FMC and the correlations with EO data are better.
Moreover the spatial ratio of moisture content provided by EWT canopy seems to be probably most suited to the low resolution sensor sensitivity.
At local scale, CHUVIECO et al. (2002) have carried out a multitemporal analysis of Landsat Thematic Mapper reflectance data in order to estimate FMC.
EUFIRELAB
D-08-07 27
This study using six sites in Central Spain has produced good empirical relations of FMC and indices derived from Landsat TM reflectances for several types of Mediterranean fuels including grassland and shrubs.
It has shown the utility of including bands located in the SWIR region to improve the estimations of FMC.
Since Landsat TM data are not very appropriate for operational fire danger estimation, this experience emphasizes the need of including SWIR bands in other sensors with a better temporal resolution, such as TERRA-MODIS.
A very sound approach to integrated analysis of fire danger is based on the combination of satellite data and meteorological danger indices (CHUVIECO et al., 1999).
The former would inform on live fuels conditions, while the latter would provide an estimation of FMC for dead fuels.
Theoretical frameworks are available (BURGAN et al., 1998), but additional research is required to obtain a proper integration of these two sources of information (AGUADO et al., 1998).
This approach may be considered an indirect validation of the fire danger index, since fire occurrence is not always related to FMC.
Fire danger is a conjunction of different factors, both physical and human caused.
Satellite data can only assess vegetation dryness (or, more precisely, moisture content), but other factors related to fire ignition or fire propagation cannot be directly derived from satellite observations.
Fire only occurs when an ignition cause is present, even if FMC is not critically low.
On the other hand, critical levels of FMC may not necessarily lead to fires, if other factors of risk do not appear.
4.1.2.5 Canopy foliar moisture content in crown fire initiation and spread
Canopy foliar moisture content (CFMC) has less influence over crown fire initiation than Crown bulk density.
However, its theoretical effects on crown fire spread rate in much stronger (SCOTT 1999, SCOTT and REINHARDT, 2001).
In old foliage (at least 1-year-old), moisture content varies from 75 % to nearly 150% (referred to oven dry weight) as literature review is presented in Scott and REINHARDT (2001) for North American conifer species.
In computer programs like Nexus (SCOTT 1999) and Farsite (FINNEY 1998), as many species have CFMC around 100%, this value has been chosen as a default value if no other information is available.
Therefore, a research gap is to characterise CFMC for major conifer and non-conifer species.
Conducting extensive field research is needed to make sound estimations of CFMC of major European species to assess and classify forest stands by their relative susceptibility to crown fire as well as to evaluate the effectiveness of different crown fire alleviation treatments.
A better understanding of the thresholds in the transition from surface to crown fires in vital.
This is especially true because, today, fire managers are increasingly concern about the threat of crown fires.
In addition, considerable errors in estimating CFMC could come from the existence of variable amounts of both dry dead fuels and lichens in the canopy.
At a different scale than field research at the stand, remote sensing offers the potential for providing spatially distributed information CFMC, as well as, on biomass and canopy structure help to assess the fire risk and to mitigate the impact of forest fires (Chuvieco et al. 2002, CONGALTON 2001).
For crown fire hazard assessment, two indices – the Torching Index and the Crowning Index- are developed in Nexus (SCOTT & REINHARDT 2001) from existing mathematical models of fire spread.
These indices may be used to classify forest stands by their relative susceptibility to crown fire.
The crown hazard indices and fire behaviour simulations in Nexus include the effects of slope steepness, canopy base height, canopy bulk density, surface fuel characteristics, wind reduction factor by the canopy, and dead and live fuel moistures (both surface and crown).
Nexus is an Excel spreadsheet-linking surface and crown fire prediction models.
EUFIRELAB
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4.1.3 Figures and tables
Quercus ilex
Pinus halepensis
4
44
55
Riparian vegetation
Model 4
Model 5
Model 8
Model 9
Model 6
Model 7
67
6
44
5
8
9
Figure 4-1: Example of interpolation procedure
(Left: Forest Map and IEFC plots with assigned fuel model, right: Resulting Fuel Model Map after interpolation procedure).
Figure 4-2: Fuel Model Map resulted from the IEFC and the Forest Map in Catalonia
EUFIRELAB
D-08-07 29
Vegetation layers External inputs Fixed parameters C1 - cover of mature trees fire level soil depth C2 - cover of immature trees grazing level C3 - cover of shrubs tree cutting C4 - cover of grass, herbs & their litter FMC 1h (%) FCL - overstory/understory fuel continuity level FMC 10h (%) FMC live (%) Fire spread
Table 4-1 List of variables & parameters used in the steady-and-transition model for automatically refreshing the fuel layer (MARF)
Structural Vegetation Type (SVT) SVT Code
C1 tree C2 seedling
C3 shrub
C4 grass
FCL Species
grassland 1 low low low high 0 short, green shrub 2 low low high low 0 tall, mature shrub 3 low low high low 0 Grassland – with pine seedlings 4 low low low high 0 short, green shrub – with pine seedlings 5 low low high low 0 tall, mature shrub – with pine seedlings 6 low low high low 0 Open pine stand – grass understory 7 medium low low high low Pine stand with strong shrub understory 8 high low low high hig
h
Short pine forest with strong shrub understory
9 high low high low high
Uneven-aged pine stand, with fuel layer 10 high high low low high
Even-aged pine stand, without fuel layer 11 high low low low low Open oak stand – grass understory 12 medium low low high low Closed oak stand, without fuel layer 13 high low low mediu
m low
Oak stand with strong shrub understory 14 medium low high low high
Unhealthy (or overstocked) forest 15 high low low low low Semi-desert or rock 16 low low low low low Cereal crops (agriculture) 17 0 0 0 high 0 short, green shrub – without vegetative reproduction
18 NRS
tall, mature shrub – without vegetative reproduction
19 NRS
water 99 0 0 0 0 0 Urban or agriculture – non flammable 0
Table 4-2 Correspondence between Structural Vegetation Type (SVT) and cover variables
EU
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ELA
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Old
Stru
ctur
al V
eget
atio
n Ty
pe (S
VT)
S
VT
Cod
e “If
” ru
le
code
Fire
Le
vel
Gra
zing
le
vel
Tree
cu
tting
S
oil d
epth
#
year
s re
quire
d fo
r the
ch
ange
Pro
babi
lity
of
SV
T m
odifi
catio
n (0
to 1
)
New
Stru
ctur
al V
eget
atio
n Ty
pe (S
VT)
S
VT
Cod
e
gras
slan
d 1
1a
1,2
2 1
med
ium
0
0.2
gras
slan
d 1
gras
slan
d 1
1b
1,2
3 1
med
ium
10
0.
2 sh
ort,
gree
n sh
rub
2 gr
assl
and
1 1c
1
1 1
med
ium
10
0.
2 G
rass
land
– w
ith p
ine
seed
lings
4
shor
t, gr
een
shru
b 2
2a
1 1,
2 1
low
10
0.
2 ta
ll, m
atur
e sh
rub
3 sh
ort,
gree
n sh
rub
2 2b
2
2 1
med
ium
10
0.
2 gr
assl
and
1 sh
ort,
gree
n sh
rub
2 2c
2
2 1
low
0
0.2
shor
t, gr
een
shru
b 2
tall,
mat
ure
shru
b 3
3a
1 1,
2 1
low
0
0.2
tall,
mat
ure
shru
b 3
tall,
mat
ure
shru
b 3
3b
2 2
1 m
ediu
m
10
0.2
gras
slan
d 1
tall,
mat
ure
shru
b 3
3c
2 2
1 lo
w
1 0.
2 sh
ort,
gree
n sh
rub
2 G
rass
land
– w
ith p
ine
seed
lings
4
4a
1 1
1 m
ediu
m, h
igh
30
0.2
Eve
n-ag
ed p
ine
stan
d, w
ithou
t fue
l lay
er
11
Gra
ssla
nd –
with
pin
e se
edlin
gs
4 4b
1,
2 2
1 m
ediu
m, h
igh
10
0.2
gras
slan
d 1
Gra
ssla
nd –
with
pin
e se
edlin
gs
4 4c
1,
2 3
1 m
ediu
m, h
igh
10
0.2
shor
t, gr
een
shru
b 2
shor
t, gr
een
shru
b –
with
pin
e se
edlin
gs
5 5a
1
1 1
med
ium
, hig
h 10
0.
2 S
hort
pine
fore
st w
ith s
trong
shr
ub
unde
rsto
ry
9
shor
t, gr
een
shru
b –
with
pin
e se
edlin
gs
5 5b
2,
3 3
1 m
ediu
m, h
igh
10
0.2
gras
slan
d 1
shor
t, gr
een
shru
b –
with
pin
e se
edlin
gs
5 5c
2,
3 1,
2 1
med
ium
, hig
h 1
0.2
shor
t, gr
een
shru
b 2
tall,
mat
ure
shru
b –
with
pin
e se
edlin
gs
6 6a
1
1 1
med
ium
, hig
h 10
0.
2 S
hort
pine
fore
st w
ith s
trong
shr
ub
unde
rsto
ry
9
tall,
mat
ure
shru
b –
with
pin
e se
edlin
gs
6 6b
2,
3 3
1 m
ediu
m, h
igh
10
0.2
gras
slan
d 1
tall,
mat
ure
shru
b –
with
pin
e se
edlin
gs
6 6c
2,
3 1,
2 1
med
ium
, hig
h 1
0.2
shor
t, gr
een
shru
b 2
Ope
n pi
ne s
tand
– g
rass
und
erst
ory
7 7b
1
1 4
med
ium
, hig
h 10
0.
2 U
neve
n-ag
ed p
ine
stan
d, w
ith fu
el la
yer
10
Ope
n pi
ne s
tand
– g
rass
und
erst
ory
7 7a
2
1,2
1 m
ediu
m, h
igh
0 0.
2 O
pen
pine
sta
nd –
gra
ss u
nder
stor
y 7
Ope
n pi
ne s
tand
– g
rass
und
erst
ory
7 7b
1
1 1
med
ium
, hig
h 10
0.
2 P
ine
stan
d w
ith s
trong
shr
ub u
nder
stor
y
8 O
pen
pine
sta
nd –
gra
ss u
nder
stor
y 7
7c
2 3
1 m
ediu
m, h
igh
10
0.2
Eve
n-ag
ed p
ine
stan
d, w
ithou
t fue
l lay
er
11
Ope
n pi
ne s
tand
– g
rass
und
erst
ory
7 7d
3
1 1
med
ium
, hig
h 1
0.2
shor
t, gr
een
shru
b –
with
pin
e se
edlin
gs
5 P
ine
stan
d w
ith s
trong
shr
ub u
nder
stor
y
8 8a
2
1,2,
3 1
med
ium
, hig
h 10
0.
2 E
ven-
aged
pin
e st
and,
with
out f
uel l
ayer
8
Pin
e st
and
with
stro
ng s
hrub
und
erst
ory
8
8b
1 1
1 m
ediu
m, h
igh
0 0.
2 P
ine
stan
d w
ith s
trong
shr
ub u
nder
stor
y
8 P
ine
stan
d w
ith s
trong
shr
ub u
nder
stor
y
8 8c
3
1 1
med
ium
, hig
h 10
0.
2 ta
ll, m
atur
e sh
rub
– w
ith p
ine
seed
lings
6
Sho
rt pi
ne fo
rest
with
stro
ng s
hrub
un
ders
tory
9
9a
1 1
1 m
ediu
m, h
igh
10
0.2
Pin
e st
and
with
stro
ng s
hrub
und
erst
ory
8
Sho
rt pi
ne fo
rest
with
stro
ng s
hrub
un
ders
tory
9
9b
3 1
1 m
ediu
m, h
igh
10
0.2
tall,
mat
ure
shru
b –
with
pin
e se
edlin
gs
6
Sho
rt pi
ne fo
rest
with
stro
ng s
hrub
un
ders
tory
9
9c
1 1
2 m
ediu
m, h
igh
10
0.2
Ope
n pi
ne s
tand
– g
rass
und
erst
ory
7
Une
ven-
aged
pin
e st
and,
with
fuel
ladd
er
10
10a
1 1
1 m
ediu
m, h
igh
0 0.
2 U
neve
n-ag
ed p
ine
stan
d, w
ith fu
el la
yer
10
Une
ven-
aged
pin
e st
and,
with
fuel
ladd
er
10
10b
3 1
1 m
ediu
m, h
igh
10
0.2
tall,
mat
ure
shru
b –
with
pin
e se
edlin
gs
10
Une
ven-
aged
pin
e st
and,
with
fuel
ladd
er
10
10c
1 1
2 m
ediu
m, h
igh
1 0.
2 O
pen
pine
sta
nd –
gra
ss u
nder
stor
y 7
Une
ven-
aged
pin
e st
and,
with
fuel
ladd
er
10
10d
2 1
1 m
ediu
m, h
igh
10
0.2
Pin
e st
and
with
stro
ng s
hrub
und
erst
ory
8
Eve
n-ag
ed p
ine
stan
d, w
ithou
t fue
l lad
der
11
11a
1 1
1 m
ediu
m, h
igh
0 0.
2 E
ven-
aged
pin
e st
and,
with
out f
uel l
ayer
11
E
ven-
aged
pin
e st
and,
with
out f
uel l
adde
r 11
11
b 1
1 3
med
ium
, hig
h 0
0.2
Eve
n-ag
ed p
ine
stan
d, w
ithou
t fue
l lay
er
11
EU
FIR
ELA
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D-0
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31
Old
Stru
ctur
al V
eget
atio
n Ty
pe (S
VT)
S
VT
Cod
e “If
” ru
le
code
Fire
Le
vel
Gra
zing
le
vel
Tree
cu
tting
S
oil d
epth
#
year
s re
quire
d fo
r the
ch
ange
Pro
babi
lity
of
SV
T m
odifi
catio
n (0
to 1
)
New
Stru
ctur
al V
eget
atio
n Ty
pe (S
VT)
S
VT
Cod
e
Eve
n-ag
ed p
ine
stan
d, w
ithou
t fue
l lay
er
11
11c
1 1
5 m
ediu
m, h
igh
10
0.2
Sho
rt pi
ne fo
rest
with
stro
ng s
hrub
un
ders
tory
9
Eve
n-ag
ed p
ine
stan
d, w
ithou
t fue
l lad
der
11
11d
2 1
1 m
ediu
m, h
igh
0 0.
2 E
ven-
aged
pin
e st
and,
with
out f
uel l
ayer
11
O
pen
oak
stan
d –
gras
s un
ders
tory
12
12
a 2
1,2
1 m
ediu
m, h
igh
0 0.
2 O
pen
oak
stan
d –
gras
s un
ders
tory
12
O
pen
oak
stan
d –
gras
s un
ders
tory
12
12
b 1
1 1
med
ium
, hig
h 10
0.
2 O
ak s
tand
with
stro
ng s
hrub
und
erst
ory
14
C
lose
d oa
k st
and,
with
out f
uel l
adde
r 13
13
a 2
1,2,
3 1
high
0
0.2
Clo
sed
oak
stan
d, w
ithou
t fue
l lay
er
13
Clo
sed
oak
stan
d, w
ithou
t fue
l lad
der
13
13b
1 1,
2 1
high
0
0.2
Clo
sed
oak
stan
d, w
ithou
t fue
l lay
er
13
Clo
sed
oak
stan
d, w
ithou
t fue
l lad
der
13
13c
3 1
1 hi
gh
30
0.2
Ope
n oa
k st
and
– gr
ass
unde
rsto
ry
12
Clo
sed
oak
stan
d, w
ithou
t fue
l lad
der
13
13d
3 2,
3 1
high
30
0.
2 gr
assl
and
1 O
ak s
tand
with
stro
ng s
hrub
und
erst
ory
14
14
a 2
1 1
med
ium
, hig
h 1
0.2
Clo
sed
oak
stan
d, w
ithou
t fue
l lad
der
13
Oak
sta
nd w
ith s
trong
shr
ub u
nder
stor
y
14
14b
1,2
2 2
med
ium
, hig
h 10
0.
2 O
pen
oak
stan
d –
gras
s un
ders
tory
12
O
ak s
tand
with
stro
ng s
hrub
und
erst
ory
14
14
c 1
1 1
med
ium
, hig
h 0
0.2
Oak
sta
nd w
ith s
trong
shr
ub u
nder
stor
y
14
Oak
sta
nd w
ith s
trong
shr
ub u
nder
stor
y
14
14d
3 1
1 m
ediu
m, h
igh
10
0.2
Oak
sta
nd w
ith s
trong
shr
ub u
nder
stor
y
14
Unh
ealth
y (o
r ove
rsto
cked
) for
est
15
15a
1 1
1 m
ediu
m, h
igh
0 0.
2 U
nhea
lthy
(or o
vers
tock
ed) f
ores
t 15
U
nhea
lthy
(or o
vers
tock
ed) f
ores
t 15
15
b 3
1 1
med
ium
, hig
h 1
0.2
Gra
ssla
nd –
with
pin
e se
edlin
gs
4 U
nhea
lthy
(or o
vers
tock
ed) f
ores
t 15
15
b 1
1 5
med
ium
, hig
h 1
0.2
Gra
ssla
nd –
with
pin
e se
edlin
gs
4 U
nhea
lthy
(or o
vers
tock
ed) f
ores
t 15
15
c 1
1 2
med
ium
, hig
h 1
0.2
Eve
n-ag
ed p
ine
stan
d, w
ithou
t fue
l lad
der
11
Unh
ealth
y (o
r ove
rsto
cked
) for
est
15
15d
1 1
3,4
med
ium
, hig
h 10
0.
2 U
neve
n-ag
ed p
ine
stan
d, w
ithou
t fue
l lad
der
10
Tabl
e 4-
3: S
tead
y-an
d-tra
nsiti
on ru
les
for s
truct
ural
veg
etat
ion
type
s in
MA
RV
(mod
el fo
r aut
omat
ical
ly re
fresh
ing
vege
tatio
n).
Stru
ctur
al V
eget
atio
n Ty
pes
(SV
T) &
thei
rs s
ucce
ssio
nal t
rend
s un
der d
iffer
ent m
anag
emen
t act
ions
or n
atur
al d
istu
rban
ces.
Th
e nu
mbe
r of y
ears
requ
ired
for t
he c
hang
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Class Number of training pixels Number of test pixels Risk level 1) Chestnut forest 113 307 8 2) Dense pine forest 104 323 5 3) Thin pine forest 136 376 4 4) Dense low maquis 276 740 3 5) Thin low maquis 118 272 2 6) Thin low maquis with rocks 150 376 2 7) Dense coppice 69 108 6 8) Mixed coppice-pine forest 678 2209 7 9) Thin coppice 136 354 5 10) Dense tall maquis 137 320 5 11) Thin tall maquis 101 251 4 12) Reforested land 53 124 6 13) Pasture with rocks 393 1212 1 14) Pasture 669 1912 1 15) Pasture with trees 168 485 1 16) Urban and agricultural land 71 197 / Table 4-4: Vegetation types considered in the study with numbers of training, test pixels and risk levels assigned.
Figure 4-3: Example of Vegetation map of a Mediterranean area
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Frequency of fires on land use
0
5
10
15
20
25
30
35pine wood
low maquis
low sparse maquis
coppice
sparse coppice
tall maquis
tall sparse maquis
others
rocks
riparian wood
urban area
crops
observed frequency (%)expected frequency (%)
χ2 =30.37 d.f. 11 P<0.01
Figure 4-4: Example of fire frequency analysis versus some vegetation cover classes
Schedule 1 Schedule 2 Schedule 3 Schedule 4 Class name no Name no Name no Name no
Chestnut wood 1 Deciduous 1 Deciduous 1 Chestnut 1 Pine wood 2 Coniferous 2 Coniferous 2 Pine 2
Sparse pine wood 3 Coniferous 2 Sp. Conif. 2a Sp. Pine 2a Unclassified 4 U/C 3 U/C 3 U/C 3
Sparse pine wood with rocks 5 Coniferous 2 Sp. Conif. 2a Sp. Pine II 2a Low maquis 6 Maquis 4 Maquis 4 Maquis 4
Sparse low maquis 7 Maquis 4 Sp. Maquis 4a Sp. Maquis 4a Low maquis with rocks 8 Maquis 4 Maquis 4 Maquis 4
Sparse low maquis with rocks 9 Maquis 4 Sp. Maquis 4a Sp. Maquis 4a Coppice with conifers 10 Deciduous 1 Coppice 5 Coppice II 5b
Coppice 11 Deciduous 1 Coppice 5 Coppice 5 Sparse Coppice 12 Deciduous 1 Sp. Coppice 5a Sp. Coppice 5b
Coppice with rocks 13 Deciduous 1 Coppice 5 Coppice 5 Sparse coppice with rocks 14 Deciduous 1 Sp. Coppice 5a Sp. Coppice 5b
Tall maquis 15 Maquis 4 Maquis 4 Maquis II 4b Sparse tall maquis 16 Maquis 4 Sp. Maquis 4a Sp. Maquis II 4c
Tall maquis with rocks 17 Maquis 4 Maquis 4 Maquis II 4b Sparse tall maquis with rocks 18 Maquis 4 Sp. Maquis 4a Sp. Maquis II 4c
Pasture 19 Agriculture 5 Pasture 6 Pasture 6 Reforestation 20 Deciduous 2 Reforestation 2a Reforestation 2b
Rocks 21 Rocks 6 Rocks 7 Rocks 7 River 22 Water 7 Water 8 Water 8
Urban Area 23 Urban 8 Urban 9 Urban 9 Agriculture 24 Agriculture 5 Agriculture 10 Agriculture 10
Agriculture with trees 25 Agriculture 5 Agriculture 10 Agriculture II 10aPaths against fire 26 U/C 3 Urban 9 Urban 9
Table 4-5: Suggestion for a Revised Classification Scheme
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Schedule 1 Schedule 2 Schedule 3 Schedule 5 Class name no Name no Name no Name no
Chestnut wood 1 Deciduous 1 Deciduous 1 Chestnut 1 Pine wood 2 Coniferous 2 Coniferous 2 Pine 2 Sparse pine wood 3 Coniferous 2 Sp. Conif. 2a Sp. Pine 2a Unclassified 4 U/C 3 U/C 3 not used Sparse pine wood with rocks 5 Coniferous 2 Sp. Conif. 2a not used Low maquis 6 Maquis 4 Maquis 4 low Maquis 4 Sparse low maquis 7 Maquis 4 Sp. Maquis 4a Sp. Maquis 4a Low maquis with rocks 8 Maquis 4 Maquis 4 Maquis 4b Sparse low maquis with rocks 9 Maquis 4 Sp. Maquis 4a not used Coppice with conifers 10 Deciduous 1 Coppice 5 Coppice II 5b Coppice 11 Deciduous 1 Coppice 5 Coppice 5 Sparse Coppice 12 Deciduous 1 Sp. Coppice 5a Sp. Coppice 5a Coppice with rocks 13 Deciduous 1 Coppice 5 not used Sparse coppice with rocks 14 deciduous 1 Sp. Coppice 5a Sp. Coppice 5c Tall maquis 15 Maquis 4 Maquis 4 Tall Maquis 6 Sparse tall maquis 16 Maquis 4 Sp. Maquis 4a Sp. Maquis II 6a Tall maquis with rocks 17 Maquis 4 Maquis 4 not used Sparse tall maquis with rocks 18 Maquis 4 Sp. Maquis 4a Sp. Maquis II 6b Pasture 19 Agriculture 5 Pasture 6 Pasture 7 Reforestation 20 Deciduous 2 Reforestation 2a Reforestation 8 Rocks 21 Rocks 6 Rocks 7 Rocks 10 River 22 Water 7 Water 8 not used Urban Area 23 Urban 8 Urban 9 Urban 11 Agriculture 24 Agriculture 5 Agriculture 10 Agriculture 12 Agriculture with trees 25 Agriculture 5 Agriculture 10 Agriculture II 12aPaths against fire 26 U/C 3 Urban 9 not used
Table 4-6: Comparison below Schedule 1, 2, 3 and 5
CLASS TRAINING SET TEST SET Chestnut wood 113 307 Pine wood 104 323 Sparse pine wood 136 376 Low maquis 276 740 Sparse low maquis 118 272 Low maquis with rocks 150 376 Coppice with conifers 69 108 Coppice 678 2209 Sparse coppice with rocks 136 354 Tall maquis 137 320 Sparse tall maquis 101 251 Reforestation 53 124 Rocks 669 1212 Crop land 168 485 Crop land with trees 71 197 Others 393 1912 Total 3372 9566
Table 4-7: Proportion of training and test samples
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CLASS TRAINING SET TEST SET Chestnut wood 46.90 % 51.79 % Pine wood 42.30 % 46.43 % Sparse pine wood 13.97 % 19.14 % Low maquis 10.50 % 14.45 % Sparse low maquis 4.23 % 9.55 % Low maquis with rocks 5.33 % 8.77 % Coppice with conifers 10.14 % 17.59 % Coppice 4.71 % 7.19 % Sparse coppice with rocks 15.44 % 20.00 % Tall maquis 18.97 % 28.12 % Sparse tall maquis 24.75 % 35.05 % Reforestation 47.16 % 65.32 % Rocks 0.25 % 8.99 % Crop land 8.33 % 16.49 % Crop land with trees 46.47 % 52.28 % Others 1.49 % 3.92 % OVERALL ERROR 10.8% 14.9%
Table 4-8: Error of classification
Species r²FMC-freq flam r²FMC-dur flam Calycotome spinosa
Cistus monspelliensis Cytisus triflorus Erica arborea
Phyllirea latifolia Quercus coccifera
Ulex parviflorus Pinus pinaster Quercus ilex
Quercus suber
0.98 0.49 0.88 0.67 0.87 0.67 0.73 0.72 0.64 0.85
0.44 0.88 0.73 0.69 0.86 0.77 0.64 0.68 0.64 0.33
r² FMC-freq flam: coefficient of determination r² between FMC and frequency of flammability r² FMC-dur flam: coefficient of determination r² between FMC and duration of flammability Table 4-9: Example of the influence of FMC on fire ignition (from Valette and Moro, 1990)
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40
42
44
46
48
50
52
08-07 21-07 03-08 16-08 29-08 11-09 24-09 07-10
FMC
(% fr
esh
wei
ght)
0
20
40
60
80
100
120
140
160 Rainfall (m
m)
Rainfall Quercus cocciferaQuercus pubescens Quercus ilex
Figure 4-5: Example of the evolution of FMC for 3 species during summer 2002 (Cemagref, Montpellier, France)
Figure 4-6: Typical examples of in situ leaf reflectance profiles at different physiologic plant status
(Girard and Girard, 1999)
Ref
lect
ance
in %
Dry
Scenescent
Green
Wavelength (µm)
Canopy Types
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4.2 CLIMATIC AND METEOROLOGICAL VARIABLES
Weather and its changes over time affect both fire ignition and fire behaviour potentials, mostly through the fuel moisture content variation and the wind field properties, which are the basic variables involved in fire weather related processes.
In fact fuel dryness is strongly related to its flammability and combustibility, and consequently to fire occurrence and behaviour (VIEGAS et al. 1991), and wind plays a generally recognised key role during the flame front propagation (ROTHERMEL 1972).
On the other hand the climate, as expression of long-term weather conditions in a given site, influences vegetation features and therefore has an indirect effect on fuel types and the corresponding fire hazard.
In addition climatic conditions determine different follow up of the fire season, i.e. the season where a peak of fire activity is observed, in most cases coincident with the driest season.
According to the climate type we can have summer fire seasons, with wildland fires mostly concentrated during the summer months as in the Mediterranean area, or also winter-spring fire seasons, which are more frequent in temperate climate, as for example in the Alpine region.
These are the two most typical situations in Europe.
Climate assessment is referred to the long term risk assessment and it is mostly useful when working at global scale.
Several climatic classification systems exist, some of them take into account the climatic conditions for ecosystems life in general (bioclimatic classification), or more specifically for vegetation (phytoclimatic classification).
In addition climatic classification of general scope can be applicable to different spatial scales, from the global one, such as for example the KÖPPEN system (KÖPPEN 1936), which is a worldwide climatic classification system, down to local climate characterisations.
Since the climate provides indirect information upon the general conditions that ultimately determine on the long run many characteristics of the fire environment, they are rarely used explicitly in fire risk mapping, as it is generally preferred to analyse and describe directly the fire environment characteristics that results from the action of climatic variables.
Nevertheless, when working at global scale, it can be useful to apply a climatic classification system, which accounts for the macro differences that can be found in terms of fire season and vegetation conditions mainly (CAMIA et al., 1999).
Meteorological variables are clearly referred to the short-term fire risk estimation.
The way meteorological variables are jointly considered in a fire danger assessment context is through the implementation of meteorological fire danger indices.
Such indices are in fact designed to rate the component of fire danger that changes with weather conditions, using a numerical or qualitative indicator derived by a combination of meteorological variables.
According to the way it is designed, a fire danger index can be more suited to rate fire ignition potential, for example focusing on those particles of the fuel complexes most prone to be initially ignited when a fire start, or to address some of the other fire danger components, for example attempting to model the influence of meteorological variables on fire behaviour potential.
In the most complex fire weather indices, research has resulted in the definition of fire danger rating systems composed of various sub-models addressing specific components of the fire environment, thus providing estimates of both fire occurrence and fire behaviour potential.
The longest response time components of meteorological fire danger indices refer to the concept of drought, a condition of long term moisture deficiency.
The drought indices provide an insight on the seasonal trend of dryness, highlighting long term moisture deficiency periods and disregarding the day to day variation.
In some cases those indices include empirical based moisture content estimates at various soil depths or more or less complex soil water balance models.
Drought is then related to the moisture content of longer response time fuel, organic matter in the deeper soil layers and heavier woody fuels, that ultimately are mostly closed to the fuel loading, which is to the amount of fuel available for combustion.
In fact, while fine fuels are mainly responsible for carrying the fire front, and thus more related to fire spread, heavier fuels affect mostly the total amount of energy released by a fire (ROTHERMEL 1972).
Meteorological danger indices that focus on fire spread potential rating, add to the fuel moisture content estimation, in this case mostly referred to fine fuels, a stress on the wind effect on fire propagation.
In Europe there is not a uniform approach to fire danger rating and different methods have been developed in the different Mediterranean countries.
A review of the most commonly applied indices can be found in CAMIA et al. (1999).
Studies were made in the European research projects Minerve I and II to compare fire danger rating capabilities of different meteorological danger indices, trying to find out a common model to be applied in the European Mediterranean area, but the final results left some uncertainty about the best model to use (BOVIO et al. 1994; VIEGAS et al. 1996).
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Weather conditions and fuel dryness have complex relationships that depend not only on past and present status of meteorological variables, but also on the type and structure of vegetation and fuel layers and, furthermore, they can depend on other site specific features, such as topography or some soil physical properties.
Thus, the attempt to estimate dryness with weather variables has lead in some cases to the development of indices that emphasise the modelling of specific phenomena, such as for example the moisture content of dead or of living material, the moisture content of specific fuel layers with a defined position in the vertical structure of the fuel complex or also the soil water deficit.
Fire danger indices normally account for local meteorological variables that influence through different mechanisms the burning condition of the site.
In addition to these, synoptic meteorological conditions can also play an important role, especially in relation to extreme fire events, which have been associated with typical wind profiles (BROTAK and REIFSNYDER 1976; BROTAK and REIFSNYDER 1977; BROTAK 1991) and atmospheric instability (HAINES 1988).
4.2.1 A study case in the Department of Alpes-Maritimes – France.
As we know, the destructions due to forest fires depend upon many parameters and different steps that can be detailed in: hazard, susceptibility (to develop a fire, i.e. slope value, forest or vegetation density at different levels), vulnerability (human).
Efforts can be made concerning susceptibility and especially vulnerability, but not really concerning the hazards (D08-02).
The hazard is in fact the combination of a flame (natural or anthropic origin) and meteorological conditions that allow it to grow and develop (or not).
Thus it is possible for a given region to evaluate the meteorological risk of fire (beginning and propagation).
There are a lot of indices, which were tested in previous European projects as “Minerve”, comparing their value with the importance of real wildfires.
In fact, the best way to really check the capacity of an index is to realize experimental fires, and to verify that the index gives a good forecast of the fire behaviour (rate of spread, heat, etc).
Since the future of a real wild fire is too much concerned by non-physical and non meteorological parameters (presence of road to access, number of planes or firemen, quantity of water available, etc…) all validations on non experimental fires are not sure.
In fact the type of index is may be less important than the spatial information, which is available about it!
As a matter of fact, the indices are generally computed for one or some different points corresponding to a meteorological station, and between these stations, no information is available.
If the relief is homogeneous, as a plain for instance, this is not really a problem.
But all around the Mediterranean Sea, there are a lot of mountains that produce a very heterogeneous environment, so that the spatial variation of the risk is very important.
In these conditions it is really essential to obtain the most probable information on all the area, and not only on some points that may be separated on more than 5 or 10 kilometres distant from each others.
There are two ways to fill a surface with information: - to attribute the value of a single point to a larger
area, or to compute a virtual value for an area from the average on two or three stations; this method is used by MeteoFrance which has separated the department of Alpes-Maritimes (4400 km2) in 7 different areas (zones). Thus some areas may exceed 1000 km2 in an heterogeneous region…
- to find a way to “guess“ what is the more probable value of a given meteorological variable or of a forest fire index, that is to say to find why and how they are spatially varying.
We have worked on the South part of the Alpes-Maritimes department, near the sea, with Mediterranean vegetation, and forest fires occurring every year, with a station network of 20 stations.
The DTM used is accurate with 50 m definition for each pixel, allowing to create different maps corresponding to various topographic parameters which can “explain” the spatial variation of some meteorological phenomena like air temperature, humidity, etc.
The parameters considered are: - Altitude (figure 4-7); - Relative altitude: We obtained also a map of relative
altitude, indicating if a given pixel is at a lower position (bottom of a valley) or dominating its neighbors (figure 4-8) ;
- Aspect: with a value varying from 1° (North) to 180° (South) that is the maximum, East and West being the same (figure 4-9) ;
- Slope, computed above the given pixel, in degrees (figure 4-10) ;
- Sea distance (figure 4-11).
All these topographic parameters are assembled and named « environmental » parameters, so that each pixel in a Geographical Information System (GIS) is described with a different layer for each parameter. Here, we use” Arc View” soft.
Then, there are 2 possibilities, corresponding to 2 logics: - To interpolate the index (initially computed only for
each station) on every pixels, when a statistic law linking the index value to the environmental parameters with multiple regressions.
- To interpolate for each pixel meteorological variables which constitute the risk index, and to compute then the index for each pixel.
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4.2.1.1 The interpolation of meteorological variables in order to compute the risk index
Different previous studies have shown that the environmental regressions give good results especially for temperature and rain, and also for humidity.
Wind spatial interpolation
But this environmental method can not be used for wind.
The best method consists in a physical model using not only fluids mechanic laws but also able to produce radioactive budgets which induce thermal breezes.
Thus, the more efficient wind speed and direction mapping models are non linear and non hydrostatic, that is to say very complex to feed and very heavy to use, especially at fine spatial scale.
That is why a simpler method consists in using kriging, i.e. a method based on spatial autocorrelation.
Figure 4-12 and 4-13 show a wind speed interpolation on 5 July 2005 at 03:00 and 15:00.
The influence of each station (either very windy or less windy) is obvious.
The change between 3 :00 and 15 :00 is easy to observe: during the night there is a north thermal breeze (relatively cold layer) upon the region and all wind speeds are very low or low, excepted in the south western part of the department where synoptic (general scale) wind is yet reaching ground level (+10m is standard level for wind measurement).
At 15:00, West wind is blowing on all the region, and is higher especially on the coast, with 8-10 m/s average wind speeds (16-20 knots), increasing the meteorological risk of fire.
The meteorological situation of 5 July 2005 is a classical one, very well known to produce forest fires: there is a west circulation with a Foehn effect inducing moderate or high speed wind, high temperatures, low relative air humidity.
On the 7 of July, the situation is quite different with more anticyclonic conditions and a thermal breezes system.
In this case, wind is blowing lightly from North to South during night, and inversely from South to North during day (figure 4-14) the the only extreme South-Western part of the region is affected by high speed.
Relative humidity and soil water reserve interpolation
Other physical variables can be interpolated using environmental regressions method.
As explained in the previous deliverable D-08-03, there is generally (especially for air temperature) a very good multiple coefficient of correlation (r=0.85 to 0.98) with environment parameters as altitude, relative altitude, aspect, slope and sea distance.
The coefficient of correlation “ r “ is less good for relative air humidity (0.60 to 0.85) which is a complex variable resulting at the same time from absolute humidity (dew point) and temperature. “r “ is very good also for water soil reserve interpolation (0.88 to 0.93).
The maps of figure 4-15 show high humidity sometimes > 80%, during the night, because of general temperature decreasing, except on strong slopes, especially in the East part of the region. Most high values are located in some internal valleys (air temperature inversion) or on the higher mountains.
During the day, air humidity is lower everywhere due to temperature increase.
The higher values are near the sea and especially at higher altitudes.
Very low values due to Foehn effect are noticeable, < 30% or even <20%.
The soil water content has been first computed for each station.
This ground water is computed every day for each station according to the following method (from Thornthwaite): it is considered as a tank (maximum possible capacity=150 mm or 150 litters for 1m2 area) the water of which decreases with daily ETP (Potential EvapoTranspiration) and increases with eventual rainfalls.
This “groundwater” is interpolated according to environmental regressions with very good results: for instance “r “= 0.91 on 5 July 2006 and 0.92 on 7 July (figure 4-16).
Time evolution of groundwater is a very slow process excepted in case of heavy rain, necessitating to follow weather every day during weeks and months.
Computing the meteorological risk index for every pixel (50m side) from meteorological data layers
The index used here is Carrega I85/90, but the principle is the same for all indices: to cover an area with numerous pixels and not use only 15 or 20 points.
Here is the index formula which is used:
I85/90 = (500 – I85)/25
(To obtain a value included between 0 (no risk) and 20 maximum, for operational reasons)
I85 = (r^0.5 * H) / V
With H = Air relative humidity in %, and V = Wind speed in m/s
Examining maps of figure 4-17 it is immediately evident that risk value changes very much from one moment to another one: risk index is generally about 8 to 12 /20 during the night, and 17-19 in the afternoon; this is due to lower humidity and higher wind speed.
The local influence of some stations on the interpolation phase can be found, as for example the higher wind speed at 3:00 in the SW part of the department, which induces the yellow colour spot of higher risk at the same time.
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4.2.1.2 The direct spatial interpolation of meteorological risk index
In this case, the work is simpler because the 2 steps are different: in the previous case, there is meteorological variable interpolation + risk index computing.
Here, there is at first index computing for every meteorological station, and then interpolation of the index for each pixel.
Figure 4-18 shows both the risk index maps obtained with this method.
As in previous maps, there is an important difference between both examples (night and day).
Comparing the maps issued from both methods, there are also some differences due to the way the maps are built:
Method 1 (meteo interpolation + index calculation) gives more place to big areas or spots influenced by one meteorological variable, especially wind, which is probably unlikely sometimes, but it is more due to the interpolation method (kriging) than to the variable itself.
Method 2 (index calculation + index interpolation) does not smooth the differences between pixels as method 1 does: there is not compensation between variables as for method 1.
There are more homogeneous cells of some kilometres, probably due to direct influence of environmental parameters weight.
The interest of obtaining a map with accurate evaluation of risk is obvious.
It allows to get a better adequation between real risk and fight means, than with risk values available only on some rare points.
To decide which is the best method numerous examples are required, but there are generally not very important differences concerning the result. Method 1 is longer but more elegant…
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4.2.2 Figures
Figure 4-7: Topography of the Department of Alpes-Maritimes: altitude. DTM with Pixel width: 50 m.
Figure 4-8: Relative altitude of each pixel. (It is 0m if the pixel is at the lower place compared with the others all around.)
Figure 4-9: Aspect of each 50m pixel and Figure 4-10: Slope above each 50m pixel (°)
Figure 4-11: Sea distance of each pixel, in meters
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Figure 4-12 and 4-13-: Wind speed on Alpes-Maritimes on 5 July 2005 at 3:00 and 15:00
Figure 4-14: Wind speed on Alpes-Maritimes on 7 July 2005 at 3:00 and 15 :00
Figure 4-15: Air relative humidity on 5 July 2005 at 3:00 and 15:00
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Figure 4-16: Soil water reserve (max 150 mm) interpolated with environmental regression on 5 July 2005
(Value remains the same during all the day).
Figure 4-17: Risk index computed after meteorological variable interpolation at 3:00 and 15:00.
Figure 4-18: Risk index directly interpolated at 3:00 and 15:00 on 5 July 2006.
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4.3 TOPOGRAPHY
The development of forest fires is influenced by what is known as the fire triangle consisting in: local meteorological conditions, topography and vegetation cover features.
Among these, topography is the most constant and has a strong influence on alterations in the other two components.
4.3.1 The role of topography in forest fires
Topography acts upon mainly three key factors in forest fires: atmospheric conditions, vegetation and fire behaviour.
The topographic variables that shape the development of forest fires are four: altitude, slope, aspect and shape.
4.3.1.1 Altitude
Altitude has a strong effect on atmospheric conditions: rainfall, temperature and moisture.
Generally, high altitudes cause a decrease in temperature and an increase in rainfall, which determines moisture content, which is drier in the lower areas.
Altitude also acts upon vegetation development. As a rule, fuel quantity decreases with altitude.
However, vegetation moisture content increases in higher altitudes, therefore the influence of atmospheric conditions is again important.
As a result of the combined effect of these two factors, highlands have lower temperatures, higher moisture content and a lower fuel load, all of which slow down fire propagation.
Lowlands tend to be hotter, drier and have heavier fuel loads, thus fire intensities are higher.
4.3.1.2 Aspect
As in the previous case, aspect acts upon atmospheric conditions.
South facing slopes have lower water content, higher temperatures -and therefore a lower moisture-, and due to their dryness, fuel loads tends to be smaller.
4.3.1.3 Slope
Slope gradient is directly proportional to fire intensity and spread rate.
Slope gradient causes flames to be nearer the drying line, which increases heat transfer since the fuel preheating is faster, thus accelerating fire spread.
Slope also accelerates wind speed. In general, spread rate is twice as fast on a 10º
slope and four times faster on a 20º slope (MCARTHUR, 1967).
Propagation speed is significantly reduced when the fire front advances downslope.
Fire spread is therefore favoured by slope gradient, and propagation speed increase can be estimated according to table 4-10 (ICONA, 1990) (Table 4-10)
When a slope is ignited, the fire tends to work upslope since the flames go upwards causing convection movements, which place the drying line upslope -instead of downslope- of the fire front.
When the fire reaches the summit, the ascending hot air causes a low pressure that attracts cold air currents from the opposite slope, which in turn repels the fire front.
Slope and wind tend to work together. In fires, which are not influenced by these factors
fuel temperature is higher than air temperature during the preheating phase, which means there is no convection and therefore direct contact with flames does not occur until the fire reaches the particles, thus radiation is the only means of heat transmission.
When wind and/or slope effects are present, air temperature can be greater than fuel surface temperature, which allows propagation by convection to occur.
In addition, due to the gradient the distance between the flames and the potential fuel decreases, causing radiative heat transfer to increase, all of which helps propagation.
4.3.1.4 Shape
Terrain features determine fire behaviour, especially due to its influence on wind effects.
Within forest fires, the relief modifies wind behaviour mainly in the following situations: - round shaped summits hardly alter air fluxes
whereas peak shaped summits cause turbulence and leeward whirlwinds and
- gorges and canyons give way to intense ascending winds.
The relief itself also regulates fire behaviour. For instance, it enables a quicker preheating effect
in narrow valley slopes and in fire recurrence produced by thermal inversions.
4.3.2 The use of topographical variables in forest fire danger indices
Many authors have underlined the key role of topography in fire behaviour, and consequently in fire extinction.
Davis and Match (1987) show a detailed relationship among the physical factors, which determine a higher propagation risk in a forest fire.
Within topographical factors they highlight - steep slopes, - south and southeast facing slopes, and - gorges, passes, and narrow canyons.
Slope, aspect and altitude act upon fire behaviour either directly or via their influence on meteorology and fuel conditions.
The importance of the topographical factors explains why it is included in most of the available danger models.
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It is also an input factor in forest fire simulation programmes, which use all topographical variables (such as FARSITE –FINNEY, 1998-) or only some of them (slope, in the case of BEHAVE –BURGAN and ROTHERMEL, 1984-).
In both global and daily danger indices some kind of topographical variable is usually used to calculate some of the subindexes.
For instance the NFDRS (National Fire Danger Rating System –Deeming et al., 1977) and the Burning Index, which show an appraisal of the difficulty in suppressing fires, use slope together with other variables (wind, fuel properties, etc.).
The same applies to the Fire Behaviour Prediction System (FBP) of the Canadian Forest Fire Danger Rating System (CFFDRS –STOCKS et al., 1989).
In the case of the Spanish Forestry Service and its Meteorological Degree of Danger (VéLez, 1985), slope and aspect are used to calculate ignition probability.
Long-term risk models have always taken into account several topographical variables (ABHINEET et al., 1996; AHMAD and MCDOUGALL, 1981; ALCÁZAR et al., 1998; BRADSHAW et al., 1987; BRASS et al., 1983; BURGAN and SHASBY, 1984; Castro and CHUVIECO, 1998; Chou, 1992; CHUVIECO and CONGALTON, 1989; CHUVIECO and SALAS, 1996; CHUVIECO et al., 1999; DAGORNE et al., 1994; GOUMA and CHRONOPOULOU-Sereli, 1998; GUM, 1985; HELM et al., 1973; RADKE, 1995; ROOT et al., 1986; SALAS and CHUVIECO, 1994; SALAS et al., 1994; SALAZAR, 1987; SHASBY et al., 1981; THOMPSON et al., 2000; VASCONCELOS and GUERTIN, 1992; VEGA-GARCÍA et al, 1995; VLIEGHER et al., 1993; WOODS and GOSSETTE, 1992; YOOL et al., 1985).
However unusual, some of these risk models, which are made up by various subindices, include a topographical index, that combines information on elevation, slope, and aspect (CASTRO and CHUVIECO, 1998; DAGORNE et al., 1994).
Slope is the most frequently used and appeared in the 70's (Helm et al., 1973).
Fire intensity and spread rate are directly proportional to slope gradient.
Slope also accelerates wind speed. For these reasons slope is present in virtually all risk
models, being absent in very rare cases. This variable is especially important in models that
are based on fire behaviour (WOODS and GOSSETTE, 1992; RADKE, 1995; VASCONCELOS and GUERTIN, 1992). In addition, slope gradient can limit mechanical operations available for fire extinction purposes (Brass et al., 1983) (Tab. 4-11).
As well as the slope, several other variables have been proposed, namely altitude, aspect, hillshading (or illumination) and geoshape.
Altitude and aspect determine the distribution of meteorological variables in the area, mainly wind speed and direction, temperature, and relative humidity, which determine fire occurrence and propagation speed.
They also determine vegetation distribution and conditions.
These two variables, together with the slope, have frequently been used directly as risk variables and most studies mentioned do just that.
The weighting factors assigned to these elements vary from, hardly significant to very important, due to which it is not possible to establish a clear trend.
Regression models that consider variables of fire occurrence have shown significant relationships between elevation, slope and aspect, and fire incidence (CASTRO and CHUVIECO, 1998; CHOU, 1992).
The hillshading effect, together with the elevation and aspect, has been used mainly as an input to obtain some of the risk variables -temperature estimation or relative humidity, fuel model mapping, etc.- (BURGAN and SHASBY, 1984; CASTRO and CHUVIECO, 1998; CHUVIECO and SALAS, 1996).
Models in which the hillshading effect has been used directly as a risk variable are rare, and in the event, a very low weight has been assigned (SALAS and CHUVIECO, 1994).
Finally, geoshape is present in only some of the risk models and even so, its specific weight is very low.
In view of the studies mentioned it seems evident that wildland fire risk experts consider topography to be a fundamental variable, since most of them include it in their proposals.
Within probability of occurrence indices, slope is the most important topographic factor concerning the danger of fire spread.
In the case of ignition danger, slope is in unison with elevation and aspect.
Although its importance is clear, there is no consensus on the weight that each of these variables must have in the overall risk model, although slope is more or less predominant over elevation and aspect, resulting from the role of slope in fire propagation.
Its weight is clear only when fire simulation programmes are used, where each factor function is well defined.
In spite of this lack of consensus, most studies that were consulted include several topographic variables when it comes to defining its risk model.
4.3.3 Digital terrain models
It is easy nowadays to obtain topographical information in digital format for forest fire risk purposes.
The product, which includes this information, is known as a digital terrain model (DTM) or digital elevation model (DEM).
These products store the altitudes of every land point, from which, by means of specific programmes (Geographical Information Systems -GIS-, mainly), slope and aspect can be automatically calculated as well as other variables derived from the topography.
Prior to the 90's, topographical information was difficult to obtain in digital format.
Up to then, the main format used for this information was traditional topographic maps (on paper), produced by each country's civil and/or military geographical services, on a wide variety of scales (generally ranging from 1:25000 to 1:1000000).
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Such maps only show direct information on elevations, which is used as an input to obtain the slope, aspect and shape of the terrain.
In the first risk models (in the 70's) all topographic information was produced manually.
This information was integrated manually also with the rest of variables.
In a second stage (in the 80's), topographic information may not have been available in digital format, but computer programmes were already being used to process it.
In such cases, the topographic information on the map was digitised, and interpolation methods were used to produce a continuous layer of altitudes, hence a digital terrain model.
The rest of topographical variables could be obtained automatically from the DTM, and could be combined with the rest of the risk variables automatically also.
Both options are now out of question due to the broad supply of digital products containing topographical information.
In the 90's European Union countries had a wide variety of digital topographical information and their civil or military geographical services had produced many databases containing surface height at resolutions ranging from high detail (pixel size 10-50m), medium (pixel size 100-500m), and global resolutions (pixel size 1,000m or more).
It is common practice for all countries to have products in these three types of categories, but this does not mean that the resolutions used are the same.
This is a drawback when working with data from several countries since some kind of adjustment is necessary to level the resolution.
The information is accessible, usually for sale, via the data distribution networks of each geographical service.
Although the information available may be quite varied (from contour lines and height points extracted from aerial restitution photography, to three-dimensional end products), the specific data which is considered of greatest interest is the grid that includes the height value of each point (or area) in space, known as a digital terrain model.
Besides the array of resolutions and products, there is also a long list of data storage formats, which may also be a problem when combining data from different countries.
However, this inconvenience is less so nowadays as the use of standard exchange formats is becoming more widespread.
In any case, it would helpful if the European Union considered creating topographical databases with common features for all member countries.
Finally, there is a series of digital elevation models produced mainly by the USA, which gather information of the European Union as a whole, most of which can be freely acquired.
Although they are generally produced on a global scale, some of these products have a certain amount of detail (90 or 100 meter pixels).
The most relevant feature of these DTMs is the homogeneity of the data due to the standardised production criteria.
Another advantage is they are generally free (although not always), which is rarely the case in European Union countries.
Consequently, obtaining digital topographical information at different resolutions is a relatively simple task, but not always cheap.
Therefore it should not be a mayor obstacle to include DTMs for generating probability of occurrence indexes, whether local o global, daily or long-term.
The use of GIS helps to simplify the analysis of the information, to produce derived variables (slope, aspect, etc.), and to integrate the variables, which make up a probability of occurrence index.
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4.3.4 Tables Actual slope (%) Next slope (%) Actual speed multiplying factor
0 0 1.0 0 10 2.2 0 30 3.0 0 60 6.0 10 0 0.5 10 10 1.0 10 30 1.4 10 60 3.0 60 0 0.2 60 10 0.5 60 30 1.0 60 60 7.0
Table 4-10: Relation between fire spread and slope (ICONA, 1990
SLOPE (%) MECHANICAL OPERATIONS 0 – 9 Excellent for wheeled vehicle operations. 10 – 19 Marginal operations for wheeled vehicles. 20 – 29 Good caterpillar operations. Poor for wheeled vehicles. 30 – 39 Marginal caterpillar operations – fire line construction. > 40 No mechanical operations.
Table 4-11: Slope and Mechanical operations
Resolution Dataset Dem (1) Institution (2)
Etopo-5 Fnoc – Noaa/Ngdc
Global Dtm5 Getech ≈ 10 Km
Terrainbase Noaa/Ngdc
≈ 3,5 Km Etopo-2 Noaa/Ngdc
Globe Usgs-Edc And Ngdc
Gtopo30 Usgs-Edc
Hydro1k Usgs-Edc
Dcw-Dem Dma And Usgs-Edc
≈ 1 Km
Dted Level 0 Nima
≈ 90 Or 100 M Dted Level 1 For Global Coverage Nima And Others
Table 4-12: DEMs available for the considered area
(1) GLOBE: Global Land One-kilometre Base Elevation Database Project; GTOPO30: Global 30 Arc-Second Elevation Model; DCW-DEM: The Digital Chart of the World DEM.
(2) FNOC: Fleet Numeric Oceanographic Center, US Navy; NOAA/NGDC: National Oceanic and Atmospheric Administration/National Geophysical Data Center, USA; GETECH: Geophysical Exploration Technology, consultancy based at the University of Leeds, United Kingdom; USGS-EDC: US Geological Survey – EROS Data Center, USA; DMA: Defence Mapping Agency, USA; NIMA: National Imagery and Mapping Agency, USA.
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4.4 ANTHROPOGENIC VARIABLES
4.4.1 Rationale
Archaeological and ethnological findings seem to confirm the theory of the fire being used by the humans to modify their geographical environment all through the history.
The practice of burning vegetation with different purposes has very far-off origins.
Throughout the history fire has been used in many socio-economic systems to regenerate pastures, eliminate dangerous faunal species, replace natural vegetation with crops, etc.
Some of those techniques such as the slash-and-burn agriculture are still being applied in different areas (mainly in tropical ecosystems) by small farmers and native population, for whom burning the forest to conquer new suitable areas for farming has a long tradition (FUJISAKA et al., 1996; TINKER et al., 1996).
The negative impacts of those practices range from loose of bio-diversity to contribution to the greenhouse effect.
Those impacts take on global proportions and, as a consequence, worry the international scientific community who has activated several research programmes devoted to evaluate the problem and its main effects as well as to propose methodologies to prevent and fight against fires.
In Europe wildfires are also quite related to human activity.
Nowadays we can affirm that wildfires in Europe are mainly result from the socio-economic development and the consequent change in life habits.
It can be considered a phenomenon linked to modern models of life, increased mobility, tourism and recreational activities, which draw increasing numbers of visitors to the forest (MORANDINI, 1976), etc.
But fire is also a traditional instrument for the management of Mediterranean ecosystems and the long-established use of fire in agriculture, silviculture and livestock breeding is well documented.
From different statistical sources we know that most of the wildfires in Europe (above 90%) occur as a consequence of human activities that can directly act as fire ignition sources or indirectly create the conditions that favours fire ignition and fire propagation.
In spite of that, we do not yet know enough about Who start wildfires and Why.
We do not know enough about human-caused fires and how to prevent them (LEONE et al., 2003).
Despite this gap, European research has made little effort to give better information about causes.
Under the Fourth Framework Programme (1994-1998) special attention was given to the natural role of forest fires, to the development and validation of fire-behaviour and fire-fighting models as well as the prediction of fire danger.
Undoubtedly, European forest fire research has covered much ground in the last ten years, but while the understanding of fire-spread mechanisms has improved markedly, additional areas also require research, such as cross-border socio-economic effects of fire (LEONE et al., 2003).
The Fifth Framework Programme (1998-2002) continues the research work on forest fires focusing on the fight against major natural hazards, such as wildfires, through the development of forecasting, prevention, impact assessment and mitigation techniques.
None of the ongoing Projects, namely SPREAD, ERAS, FIRE STAR, WARM, AUTOHAZARD PRO, COMETS, FIREGUARD, puts fire causes as its main topic although most of them include some sort of analysis related with socio-economic factors and its relation with fire occurrence, fire prevention and fire effects at different scales.
Knowledge on the mechanisms behind fire occurrence is expected to improve wildland fire management.
If we could reliably predict where, when and why how many fires are most likely to occur, fire and landscape management could take on a more ‘pro-active’, planning approach than today where it is usually a ‘reactive’ process.
If we manage to quantify the likelihood and cause for having a fire and to geo-reference its potential ignition location, we would gain a key element for fire risk mapping and thus foreseeing and sustainable wildland fire risk management. (ALGÖWER et al. 2003).
It is blindingly obvious the interest of considering the human factor in forest management and protection against fire.
However, these fact contrast with the limited importance that has been granted to human factors in quantitative risk analysis.
The main reason being that the human component of fire risk is quite complex to model.
First of all, because we will never be able to account for some of the particularities in human behaviour.
Thereby, it is not surprising that there is a great degree of randomness associated in the fire occurrence prediction process.
Secondly, the complexity is due also to the difficulty in representing spatially some human activities related to fire, such as arson, which frequently lack a clear spatial pattern.
However, in spite of these difficulties, we can find many human risk factors that can be measured through spatially represented variables, such as recreational activities and burning of shrub land for pastures, which tend to be associated to particular areas.
The human fire risk modelling implies considering a wide number of factors associated to the beginning of a fire (fire ignition risk) and to the spreading of an active fire (fire propagation risk).
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In both cases, different variables and different risk weights should be considered, depending on the specific characteristics and causes of fire events in the different test sites.
In any case, both approaches (ignition and propagation) require being capable of integrate different spatial variables (LEONE et al., 2003) coming from different sources at different scales.
Geographic Information Systems (GIS) are appropriate tools to create, transform, combine and integrate geographical variables related to fire risk, in order to supply the geographical location of those areas where risk factors are most severe (CHOU, 1990; CHUVIECO & SALAS, 1996; DAGORNE et al., 1994; MASELLI et al., 1996; NUNES et al., 1996; SALAS & CHUVIECO, 1994)).
This integration is done using GIS after building a fire occurrence prediction model.
Many studies that has been performed to quantify fire risk related with human activity were developed based on the abundant geographic data generated as GIS evolved, focusing on where historical fire records were located in relation to roads, trails, towns, vegetation, rivers, topographic (elevation, slope, aspect) data, power lines, train ways, industries, forestry operation sites and many more geographic variables (CHOU et al., 1990; VEGA-GARCÍA et al., 1993).
Usually, they would find valid relations over long periods of time (17 years, CHOU, 1990; 5 years, VEGA-GARCÍA et al., 1993) for reduced geographic units.
Other studies focussed on the temporal dimension of fire occurrence (MARTELL et al., 1987; TODD & KOURTZ 1991; LOFTSGAARDEN & ANDREWS 1992): daily, monthly or seasonal, usually for prediction units encompassing large tracks of land, typically districts or provinces.
A study by VEGA-GARCÍA et al. (1995) identified the following significant geographic variables: distance to nearest road, distance to nearest town, distance to nearest campsite, elevation, fuels, forest commerciality, forest district, and the following significant temporal variables: relative humidity, wind speed, month, codes and indices in the Fire Weather Index (VAN WAGNER, 1987).
Basically, the human risk factor may be considered at two temporal resolutions: short-term and long-term.
The short-term risk factor is rather difficult to estimate, because normally we lack the temporal information on human activities.
For instance, it results very difficult to know how many people will visit a forested area next weekend, or what kind of activity they will engage in.
For this reason, most application related to human risk uses structural indicators for a long-term estimation, i.e. using variables related to the most permanent factors of a territory and its population.
These factors do not change daily but on a long-term basis, and at least can be considered stable during a whole fire season (CHUVIECO et al., 1999).
When the goal is to obtain a long-term fire risk index, the different variables related to human activity should be weighted according to their importance on the fire occurrence (ignition or propagation) and then combined into a single index.
This can be done in many ways, but one of the more common approaches consists in using local adjustments, which may be based on regression analysis (ANDERSON et al., 2000; CARDILLE et al., 2000; CARVACHO, 1998; CASTRO & CHUVIECO, 1998; Chou, 1992; KOUTSIAS et al., 2002b; LEONE et al., 2002; LOFTSGAARDEN & ANDREWS, 1992; MARTELL et al., 1987; PEW & LARSEN, 2001; VASCONCELOS et al., 2001; VEGA-GARCÍA et al., 1995), where historic fire occurrence is the dependent variable, while fire risk variables are the independent ones.
These kinds of models offer two relevant contributions.
The first is a predictive function of forest fire occurrence, measured by the number of fires per unit area or as probability of occurrence.
The information offered by this function becomes an essential instrument, in order to distribute the available fire fighting resources throughout the fire season and throughout the areas at risk.
The second contribution is an explanatory function, which calculates the impact of each socio-economic factor on fire occurrence, identifying statistics associations and potential causal relations.
The main contribution of this analysis is to offer a comparative description of the relative importance of each risk factor in a given point in time and in the different study areas.
In any case, if most fires are the result of human activity and behaviour, it is clear that their analysis must be based on the signs left in the territory by those who are held responsible.
The selection of the spatial-risk indicators (agroforestry and socio-economic parameters) has to be carried out after studying the human factors related with fire ignition and propagation in a specific location, analysis of historical fire records and consultation of fire management authorities.
After identifying those factors the next step is to acquire, compile and homogenize in a spatial database the variables that best represent those factors.
They will be used as predictors or independent variables in the development of models to estimate probability of occurrence.
Two kinds of variables can be distinguished depending on its source:
Statistical: Collected from various official sources (demographic and agrarian census, forestry inventory, other bibliographic sources) and from unpublished or unofficial documents (internal reports of the different public administrations concerning livestock, hunting activities, surveys, etc.).
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Cartographic: These spatial variables such as location of recreational areas, natural protected areas, roads, land use maps, administrative boundaries, etc., are obtained from regional, national or international government institutions.
Beside, in order to obtain new derived variables more directly related to fire occurrence, it is necessary to perform some spatial analysis using a variety of GIS tools.
These analysis include format conversion, re-projection, edge matching, overlay, calculation of areas, buffer zones and other distance operations, landscape indices, quantification of different land use interfaces (i.e. urban-forest), etc.
One of the crucial dimensions of these quantitative models is to have access to available and reliable data.
This is the biggest difficulty in order to have an operational risk model, because there is a scarcity of regular and standardised information sources, at least when the study area is on a regional or global scale.
This is applicable, not only to the set of socio-economic variables related with fire occurrence, but also to the historical fire records which in most cases do not offer accurate information on the location of the fire ignition points
On large spatial resolution in particular, the selection of variables has to be performed considering the limitations of data among the different areas (regions, countries, etc.).
This is the case of census data, which might present certain limitations regarding the geographic unit of analysis.
Often, the main consequence could be that some variables, theoretically well related to human risk, would need to be discarded, either because they are not available in all the spatial units (regions, countries, etc) or because their meaning is not completely compatible and therefore comparisons would be meaningless.
For instance, in a study performed on a European scale (CHUVIECO et al., 1998), it was decided to use the province (UE level NUT-3), instead of council, as spatial unit of statistic aggregation, in order to assure the biggest data availability.
Another typical problem is the lack of updated information for some statistical and cartographic variables.
In some specific cases this problem can be overcome using auxiliary information such as remote sensing images (i.e. update land cover maps to derive the wild land-urban interface).
Whichever the case, we must be well aware of the fact that ideal data sets are rarely available – be it for technical and/or economical reasons.
Based on previous literature reviews and our experience in some recent European projects (SPREAD, MEGAFIRES), we include here a list of human factors affecting fire ignition or fire propagation, briefly describing their relationships with forest and wildland fires and the possible derived variables to measure them with references to previous works (if any).
The list proposed by MARTÍN et al. 2003 (submitted) is not meant to be exhaustive, but to give a general orientation of information needed for human risk assessment of forest fires.
This list reflects the situation regarding forest fires in Spain mainly, but may be considered applicable for most European Mediterranean countries.
4.4.2 Factors in relation to socio-economic transformations
4.4.2.1 Abandonment of traditional activities in wildland/rural areas
Socio-economic development experienced in most European countries during the last decades has caused a generalised reduction of traditional activities in forested rural areas.
Abandonment of activities such as cattle grazing, firewood extraction and forest cleaning has notably increased the fuel load.
In a situation like that, fire could find a favourable environment for ignition and propagation.
This problem could be especially important in privately owned forests with no prospect for economic profit.
Effect: Increase of forest fuel accumulation.
Variables: Temporal evolution of agrarian active population, temporal variation in agricultural area (CARVACHO, 1998; CHUVIECO et al., 1999; CHUVIECO et al., 1998).
Forest area and private forest area with low potential forest productivity; private forest area (owned by individual or collective legal entities); public forest area (owned by the State, federal or regional, or by local governments); forest area managed by the Forest Administration and forest area not managed by the Forest Administration; forest commercial value (Aerial information systems, 1981; CHOU, 1992; PÉREZ & DELGADO, 1995; VEGA-GARCÍA et al., 1993; VEGA-GARCÍA et al., 1995).
4.4.2.2 Depopulation of rural areas.
Rural population drift caused by migration to urban areas and ageing of inhabitants has promoted abandonment of agricultural activities in marginal land (the less productive areas), especially in mountainous regions.
In some cases abandonment is spontaneous due to the lack of profitability or labor, but in some other cases is encouraged by the European Agricultural Policy against surplus production of certain crops.
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This abandonment lead to an invasion of those areas with natural vegetation (mainly shrubs species but also some trees) in a process, which would lead to the future regeneration of the forest.
Increase of forest surface implies accumulation of light fuels classified as very dangerous because they offer a high rate of spread.
In this situation a fire occurring in those areas could spread very quickly and turn into a large and uncontrolled fire.
Effect: Increase of forest fuel accumulation.
Variables: Measure of the temporal change on forest surfaces using land use and/or forestry maps, e.g. change indexes from agricultural uses to forest uses (ROMERO-CALCERRADA & PERRY, 2002).
4.4.2.3 Increasing use of forest as a recreational resource.
Increasing presence of people in forest areas as a consequence of the expansion of recreational activities in rural environments (hunting, fishing, hiking, etc) results in a larger probability of negligence in the use of fire (bonfires, cigarette butts, matches, etc).
Effect: Possible ignition sources by accident or negligence (campfires, smokers, and picnicker’s carelessness).
Variables: Proximity buffers from recreational areas in forest (if possible weighted per visit frequencies); distances and accessibility to natural elements of tourist attraction such as rivers, beaches, lakes, camping, etc.; density and average distance to roadways and ways (weighted by types of roads, etc.); proximity buffers from roads, ways and tracks, and considering forest environment; proximity or distance to urban areas, towns and other disseminated residences, weighted by number of inhabitants; number of tourist arrivals, hotel places-rooms, camping, etc.
(ABHINEET et al., 1996; ALCAZAR et al., 1998; BENVENUTI et al., 2002; BRADSHAW et al., 1987; BRASS et al., 1983; CASTRO & CHUVIECO, 1998; CHOU, 1992; CHOU et al., 1993; CHUVIECO & CONGALTON, 1989; JOHNSON et al., 2003; MARCHETTI, 1990; MILANI et al., 2002; OLIVEIRA et al., 2002; VEGA-GARCIA et al., 1996; VLIEGHER, 1992).
4.4.2.4 Human presence, population increase and urban growth.
During the last decades, urban areas have growth quite rapidly, taking up, first, the neighbouring agricultural land and later the more distant forest areas.
The efficient transport systems that bring current technologies make distances between urban and peri-urban areas irrelevant.
That turns into potentially building a large proportion of the territory.
It is evident the increasing fire risk associated with the growth of urban-wild land interface.
In these areas the probability of a fire to be ignited in an urban area and spread afterwards to the forest land due to negligence or carelessness is much higher.
On the contrary, is quite frequent that a wildfire threat to spread into urban areas in contact with forest land endangering houses and people.
Recent growth of residential areas within forest land is becoming one of the mayor causes of forest fire occurrence in developed countries.
Fire behaviour is quite erratic in that urban-wild land interface due to the presence of different types of fuel (natural vegetation, ornamental plants and houses) and the unusual complexity of wind behaviour.
Effect: Possible ignition sources.
Variables: Increase of the urban/wildland interface (temporal evolution); length of the contact area between urban/forest land (JAPPIOT et al., 2002; JOHNSON et al., 2003; RADKE, 1995); population number and density (CARDILLE et al., 2000;CHUVIECO et al., 1998; DAGORNE et al., 1994; DONOGHUE & MAIN, 1985; LEONE et al., 2002; LYNHAM & MARTELL, 1985; TERRADAS & PIÑOL, 1996); temporal variation of population; urban area; proximity to urban areas, distance to nearest city larger than 10000 people, city lights density obtained from satellite images (CARDILLE et al., 2000; CHUVIECO et al., 1999; JOHNSON et al., 2003; PÉREZ & DELGADO, 1995).
4.4.2.5 Population increase in rural villages during summer holidays
Population increase in rural villages, especially during summer, has promoted a large increase in solid waste production in those areas.
Nowadays most of the cities eliminate those wastes in specific places under control.
However, in some rural areas more traditional methods consisting in waste accumulation and burning in uncontrolled rubbish’s dumps are still used.
If the security measures have not been correctly applied, the wind can spread the fire to the forest areas.
Effect: Possible ignition sources.
Variables: Localisation of uncontrolled rubbish’s dumps (ALCÁZAR et al., 1998).
Optionally any variable related with the stationary population increase (e.g. water consumption).
4.4.2.6 Aged rural population.
Most rural areas in Mediterranean Europe have very aged population as a consequence of the emigration process suffered in the last decades.
This aged population, when working in the forest or cultivating small farms, use to apply traditional agricultural and grazing practices which include burning dead vegetation and brushwood, and the use of the fire to maintain pastures and get rid of ligneous vegetation.
Those practices, very dangerous by themselves, could be even more risky when the people who manage the fire have reduced physical capacity to control the fire and/or adopt prevention measures.
Effect: Probability of fire propagation to forest land and fire extinction difficulties by old population.
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Variables: Aged rate (proportion of old population, older than 55) specially referred to owners of holdings (CHUVIECO et al., 1999).
4.4.3 Factors related to traditional economic activities in rural areas
4.4.3.1 Agriculture
The importance of agriculture in relation to forest fire risk arises from the use of fire to get rid of harvest remains (stubble burning) and to prepare the land for further sowing.
This is a traditional management method in cereal crops.
Fire is also used for brushwood cleaning in croplands borders and abandoned agrarian plots.
Effect: Probability of propagation to forest lands.
Variables: Agricultural areas/forest interface (MILANI et al., 2002; OLIVEIRA et al., 2002; PÉREZ & DELGADO, 1995); agricultural area; agrarian active population (Chuvieco et al., 1999); number of electric connections in agriculture; energy consumption in agriculture (LEONE et al., 2002).
4.4.3.2 Cattle grazing.
Two facts, in certain way opposing, and related with cattle grazing, exert a noticeable influence in forest fire risk.
The first one is related with traditional burns to maintain herbaceous vegetation and eliminate non-grazed and shrub species.
The other refers to the prevention effect of grazing that contributes to shrub control and maintains a clean forest without dangerous concentration of light fuels, therefore, reducing the probability of fire ignition and specially fire propagation.
The use of in areas, which have a strong deficit in fodder production, represents an archaic agronomic practice, which is blameable but certainly low cost.
It is able to ensure both the control of infesting species, in areas where it would appear impracticable to resort to mechanical mowing, and stimulate the growth of tender, young shoots.
On a more thorough evaluation, we find that the use of fire also expresses a predatory attitude, as well as indicating a state of latent social conflict among shepherds, owners of grazing land and rural dwellers.
Apart from its obvious function of land clearing, it seems likely that fire constitutes a form of warning or a latent threat, which aims at underlining the agricultural and grazing use of the territory, linked to the craving for land for shifting sheep-farming. (LEONE et al., 1989; LEONE et al., 2002).
In some circumstances, such fires may be ascribable to conflict between antagonist groups for grazing opportunities or watering places and for controversies linked to the archaic world of sheep farming.
Effect 1: Possible propagation of fire from pastures to forest.
Effect 2: Wildland cleaning by livestock (especially goats) ® Reduce fuel load ® Reduce fire risk.
Variables: Density of livestock (especially goats & sheep) (CHUVIECO et al., 1999; Leone et al., 2002; PÉREZ & DELGADO, 1995) and if is possible weighted by cattle type and regimen type (intensive or extensive); grassland/forest interface; distance to livestock (KALABOKIDIS et al., 2002; MILANI et al., 2002; OLIVEIRA et al., 2002).
4.4.4 Factors which could cause fires by accident or negligence
Many accidents leading to forest fires stem from negligence coupled with the lack of understanding the risks of, for example, dropping cigarette ends or starting fires for camping and barbecues.
The risk of fire has been further increased by the greater mobility of urban dwellers, who visit forest areas more regularly, but who may not understand the risks of starting a fire, also because much of the traditional understanding of fire management has also been lost because of the depopulation of many rural areas.
Analysis of UNECE-FAO fire statistics suggests that various types of negligence are an important cause of fire in most European countries, but generally less than arson (although the causes of the majority of such fires remain undetected).
For example, in 1987 accidental fires accounted for some 59 per cent of fires in Turkey, 50 per cent in Austria, 32 per cent in Portugal and 25 per cent in Spain (UNECE/FAO, 1990).
Arson remains the most important known cause of fire in much of Europe.
For example, combined figures for the whole of the Mediterranean for 1981-85 indicate that negligence was the cause of 23 per cent of fires, arson accounted for 32 per cent, while the cause of 40 per cent of fires was unknown (WWF, 1992).
Arson is said to be the easiest crime to commit (even young children can do it), but the most difficult to detect and prove.
It needs to be combated by finding and prosecuting those responsible (Cafe & Stern, 1989).
Among the factors that can accidentally start a fire we highlight the following:
4.4.4.1 Electric lines
Effect: Possible cause of ignition by accident (voltaic arc).
Variables: Average distance to electric lines; proximity buffers round electric lines considering forest environment and conservation statement of the security borders (ALCÁZAR et al., 1998; BRADSHAW et al., 1987; OLIVEIRA et al., 2002).
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4.4.4.2 Engines and machines working in or close to the forest areas
Effect: Possible cause of ignition by accident or negligence.
Variables: Agrarian machinery density (and others types if it is possible).
4.4.4.3 Hunting activity.
Effect: Possible cause of ignition by accident or negligence by hunters; sparks produced by the ammunition or stubs badly extinguished; fire use to control or to facilitate hunting; burning to clean forest vegetation and recover grassland for the animals in hunting areas, abandoned campfires.
Variables: Hunting licenses; hunting reserve areas.
4.4.4.4 Presence of roads, railways, tracks and accessibility.
Effect: More human pressure on wildland Possible cause of ignition by accident and negligence.
Variables: Length and density of roads and railways; average distance to roads and railways, weighted by types (only roads); proximity buffers from roads, ways and railways (ABHINEET et al., 1996; AHMAD & MCDOUGALL, 1992; CARDILLE et al., 2000; DAGORNE et al., 1994; EFTICHIDIS et al., 1991; JOHNSON et al., 2003; KALABOKIDIS et al., 2002; MILANI et al., 2002; PEW & LARSEN, 2001; Thompson, 2000; VASCONCELOS et al., 2001; VLIEGHER et al., 1993), considering forest environment, and in the case of railways, considering braking areas, for its special risk.
For example, in a recent study in France (LAMPIN 2003), it has been shown that about 85% of the fires that occurred in the Bouches-du-Rhône département in the 1997-2002 period have started mostly from roads of local importance and in three fourths of the cases at a distance of less than 20m from the road.
4.4.4.5 Military manoeuvres and quarries explosions.
Effect: Possible cause of ignition by accident or negligence.
Variables: Military areas, quarries / total area.
4.4.4.6 Proximity to forest areas
It is expected that the distance to a nonforest area has an influence in the time that a fire is discover thus like in the accessibility for its extinction. Also the areas that are limiting with the forest zones will be more exposed to the human activities.
Effect: Possible cause of ignition by accident or negligence due to the human activity.
Variables: Distance to nonforest areas (CARDILLE et al., 2000; JOHNSON et al., 2003)
4.4.5 Factors that generate conflicts that could lead to the intentional start of a fire and/or facilitate its propagation
After VELEZ (1990), author of a sharp and acute analysis about forest fires in the Mediterranean Basin, socio-economic reasons are bringing about changes in the influences between rural and forest use (forest/farmland interface) and between urban and forest activities (forest/urban land interface).
The resulting relationships are not established harmoniously.
Further conflicts arise or old ones are modified. These conflicts are manifested in several ways: one
of these is fire, which, as statistics show, becomes more frequent and more violent as the process advances.
Most of these factors are site-specific and, therefore, cannot be applied to the whole Mediterranean Europe, but we include here a list of the most frequently cited in statistical sources and also mentioned by fire experts.
4.4.5.1 Changes from forest use to urban use.
Effect: Speculation on land prices, retaliatory actions and possible cause of intentional ignition (construction/building speculation).
Variables: Land-use changes to building areas; number of denied request to change to urban uses.
4.4.5.2 Establishing new protected areas.
Effect: Restrictions of traditional uses and possible cause of arson as a symptom of unrest, grudge or protest of the local population.
Variables: The ratio protected area / total area (weighted by the time from the declaration); protected areas perimeters (ALCÁZAR et al., 1998; ERVITI & ERVITI, 1994).
4.4.5.3 Jobs in wildland fire suppression and restoration (“Fire industry”).
Effect: Fire started to get incomes, job, pays or subsidies in extinction workings and in restoration of land affected by fires; reprisals because of decrease of public investments or economics subside on wild lands.
Variables: Unemployment rate (CHUVIECO et al., 1999); number of days of work in fire control activity of seasonal fire fighters (LEONE & SARACINO, 1990; LEONE et al., 2002).
4.4.5.4 Land tenure disputes.
Effect: Disputes for land tenure, restriction and conflict of uses, etc.
Variables: Area covered by any kind of forest property that includes a restriction of use or it can be cause of land tenure conflicts (PÉREZ & DELGADO, 1995).
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In areas like NW Spain (MOLINA 1997), reforestation programs in the 40’s & 50’s did originate a social discontent that moved some people to arson both as an attempt to get back the resource to local control and as an act of protest (in the sense of GUHA 1990).
This social discontent come mainly from a perceived lose in grazing area (sometimes there was only a relocation of grazing areas).
4.4.5.5 Hunting activity.
Effect: Hunting control; possible conflict with poachers.
Variables: Hunting licenses; hunting reserve areas.
4.4.5.6 Public forest management.
Effect: Possible cause of arson as a symptom of unrest, grudge or protest of the local population.
Variables: Number of public rentals, number of pasture licenses.
4.4.5.7 Speculation with timber prices.
Effects: Arson with the intention of reducing the price of the wood in auction, or to provoke the immediate sale, overcoming in this way opposition of businessmen, ownership’s or Public Administration.
Variables: Average market price of wood, varying wood price.
4.4.5.8 Reprisals because of decrease of public investments or economics subside on wild lands.
Effect: Possible cause of arson as a symptom of unrest, grudge or protest of the local population.
Variables: Data about investment or economic budget. Temporal tendencies (yearly variations).
4.4.5.9 Transformations from forest use to agricultural use.
Effect: Use of fire to foster illegal or not allowed land-use changes.
Variables: Land-use changes to intensive crops (Leone et al., 2002).
4.4.5.10 Vengeance against the Public Administration for expropriations (land properties claims).
Effect: Possible cause of arson as a symptom of grudge or protest of the local population.
Variables: Number of expropriation expedients (or actions).
4.4.5.11 Vengeance against individuals (land properties claims).
Effect: Possible cause of arson as a symptom of grudge or protest of the local population
Variables: Number of expropriation expedients (or actions).
There are other factors related with fire occurrence such as vandalism, crime concealment, terrorism, mental disorder, etc.
The difficulty of including these factors in fire risk models is quite evident due to their lack of spatial patterns.
4.4.5.12 Property regime of foret areas
The objectives of management based on the property regime can influence directly in the strategies of handling, affecting to the amount of accumulated fuel and the connectivity between lands (CARDILLE et al., 2000).
Effect: Possible variation in fire ignition and propagation .
Variables: Area of public/private forest (MARTINEZ et al., 2004), area of national forest, area of forest from the state (CARDILLE et al., 2000).
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