This document is a thesis submitted by Jose Francisco Lafragueta in partial fulfillment of a Master of Science degree in Energy and Environmental Management at Glasgow Caledonian University. The thesis develops a GIS-based model to create a forest fire risk map for an area in Huesca, Spain. Eight factors are used - vegetation, slope, aspect, elevation, distance to roads, railroads, camping sites and settlements. MCE techniques, including the AHP method, are used to rate the factors, combine them and generate a weighting scheme to produce the final forest fire risk map. The results are validated using a 10-year fire record, showing most previous fires occurred in high or very high risk areas.

1. School of Engineering and the Built Environment
MSc Energy and Environmental Management
GIS and MCE-based forest fire risk assessment and
mapping -
A case study in Huesca, Aragon.
Spain
Jose Francisco Lafragueta
December 2013

2. GIS and MCE-based forest fire risk assessment and mapping
A case study in Huesca, Aragon.
Spain.
Jose Francisco Lafragueta
Submitted in partial fulfilment for the
Degree of Master of Science
in Energy and Environmental Management
School of Engineering and the Built Environment
Glasgow Caledonian University
Cowcaddens Road, Glasgow, G4 OBA
Supervisor: Dr C. Gallagher
December 2013

3. Author’s declaration
This dissertation is my own original work and has not been submitted elsewhere in fulfilment of the
requirements of this or any other award.
……………………………………………………………………

4. iv
Abstract
Forests are one of the most important natural resources on earth, covering 31 per cent of the land
use surface of the planet. Fires have historically been the major threat to forested land, causing
countless damaging effects locally and globally regardless of being ignited by natural forces or
human activity. Spain suffers from forest fires, being one the most affected amongst European
state members. Geographic information systems (GIS) and remote sensing data can aid forest
management to minimize the devastating effects of fire by mapping the zones where fires are
more prone to occur and easily spread from.
In this particular study, a GIS-based model was developed to create a forest fire risk map for an
area of Huesca (Aragon), located in the northeast of Spain. Eight influencing factors were
selected to create the final map, namely vegetation, slope, aspect, elevation, distance to road,
distance to railroad, distance to camping sites, and distance to settlements. With the help of two
GIS-based software platforms, ArcGIS 10.1 and IDRISI Selva, all the factors were rated and
combined by the means of multi-criteria evaluation techniques (MCE). In order to generate a
factor weighting scheme, statistics on causative forest fire factor of the fires occurred during the
last year (2012) were used carry out the Analytic Hierarchy Process (AHP) method. A scale of
very high, high, moderate and low risk of fire was used to value the different boundaries shown
in the final forest fire risk map.
In the last part of the project, the record of fires occurred in the study areas, during the last
decade was used to validate the results in terms of reliability. The results show that a great
percentage of previous fires incidents are located in areas labelled as very high or high risk.
Therefore, the reliability of the final map can be rewarded as very satisfactory. To sum up, local
authorities and members involved in the forest fire management services could, therefore, make
use of this model in order to mitigate future fire incidents or as based model for future
improvements. Some of these possible improvements are suggested in the final part of the paper.
Keywords: GIS, Forest fire risk assessment, Multi-criteria evaluation (MCE), Analytical
Hierarchy Process (AHP).

5. v
TO MY FAMILY

6. vi
Acknowledgements
At this point, I would like to acknowledge some people that have specially contributed in the
achievement of this dissertation work.
Firstly, I would like to thank my supervisor Caroline Gallagher for her willingness to help me
solve any difficulties met during the practical phase of the project despite being always
extremely busy with university work.
My sincerely thankfulness goes to the Geographic National Institute of Spain for providing me
with all the raw data without which this project would never have been possible and specially to
one of its employees, Ramon Sanchez for the priceless information about maps that he gave me.
I feel deeply grateful to my friends for their support and especially to Cipry for all his technical
support. Without his help, this project would have never got started.
I would like to thank my beloved girlfriend Olga Samayoa who has always been encouraging,
caring and supportive throughout this thesis and beyond. I do extend my thanks to my also
beloved dog, Sam, who has stayed with me every second I have spent in front of my laptop, this
thesis is also yours.
Last but not the least, I would like to acknowledge all the member of my family and in special
my mum, father and brother who have motivated me during this time and without their courage, I
would have never been studying abroad. I am proud of you.

7. vii
Table of contents
Abstract………………………………………………………………………………................. iv
Dedicatory……………………………………………………………………………................ v
Acknowledgement……………………………………………………………………………… vi
Table of contents……………………………………………………………………………….. vii
Table of figures……...………………………………………………………………………… viii
Table of tables…………………………………………………………………………………. x
Chapters:
1. Introduction…..………………………………………………………………................1
1.1 Background…………………………………………………………………………1
1.2 Aims and objectives……..………………………………………………………… 2
1.3 Organization of the thesis……..…………………………………………………… 3
2. Theoretical framework.………………………………………………………………. 4
2.1 Forest Fires.……………………………………………………………………….. 4
2.2 Risk assessment……………………………………………………………………. 6
2.3 MCE-GIS based forest fire management…………….……………………………..7
2.4 Previous MCE-GIS based forest fire management studies………………………... 8
3. Case study…...…………………………………………………………………….........9
3.1 Location…..………………………………………………………………………... 9
3.2 Statistics…………………………………………………………………………….10
4. Methodology…….……………………………………………………………………...12
4.1 Programs and applications……………………….………………………………… 12
4.2 Data collection and preparedness…………….…………………………………… 13
4.3 Influencing factors…………………………………………………………………. 14
4.4 MCE and weighting scheme………..………………………………………………22
4.5 Final forest fires risk map….…………..………………………………………….. 34
5. Findings….…………………………………………………………………………….. 37
6. Discussion…..…………………………………………………………………………..39
6.1 Influencing factors selection………………………………………………………..39
6.2 Weighting scheme selection…………..…………………………………………… 40
6.3 Validation model selection………………………………………………………… 40
7. Conclusions…….……………………………………………………………………… 42
References…………………………………………………………………………............ 43
Appendix 1: Fuel content map……………………………………………………………. 49
Appendix 2: AHP matrix …………………………………………………………………. 49

8. viii
Table of figures
Figure 1: Burnt area (ha) in Spain (1980-2011)………….……………………. 5
Figure 2: Number of fires in Spain (1980-2011)..........................................…... 5
Figure 3: Map of Spain…………………………………………………………. 9
Figure 4: Satellite image of the study area……………………………………... 9
Figure 5: Statistics of Aragon (2018-2012).......................................................... 10
Figure 6: Causative factors of forest fires in Aragon (2012)………………….... 11
Figure 7: Record of forest fires per month in Aragon (2012)…………………... 11
Figure 8: Elevation map……………….……………..………………………….. 15
Figure 9: Aspect map…………………………………………………………… 16
Figure 10: Slope map…………………………………………………………… 17
Figure 11: Vegetation/land use map……………………………………………. 18
Figure 12: Roads vector map…………………………………………………….. 19
Figure 13: Railroad vector map………………………………………………….. 20
Figure 14: Settlement vector map………………………………………………... 21
Figure 15: Camping sites vector map…………………………………………….. 22
Figure 16: Multi-criteria evaluation (MCE) model…………………………….. 23
Figure 17: Monotonically decreasing function…………………………………… 25
Figure 18: Standardized vegetation map……………………………………….. 28
Figure 19: Standardized aspect map……………………………………………. 28
Figure 20: Standardized slopes map……………………………………………. 29
Figure 21: Standardized elevation map………………………………………… 29
Figure 22: Standardized distance to road map………………………………….. 30
Figure 23: Standardized distance to railroad map……………………………… 30
Figure 24: Standardized distance to settlements map…………………………… 31

9. ix
Figure 25: Standardized distance to camping sites map………………………… 31
Figure 26: Final forest fire risk map…………………………………………….. 34
Figure 27: Validation of forest fire risk map……………………………………. 36

10. x
Table of tables
Table 1: Rating scheme for factors…………………………………………… 27
Table 2: Analytic Hierarchy Process (AHP)…………………………………. 33
Table 3: Weighting scheme for factors………………………………………. 33
Table 4: Forest fires record…………………………………………………… 35

11. 1
Chapter 1
1 INTRODUCTION
This chapter introduces the context and purpose of the thesis. Firstly, a brief summary of the
background of the concept and also a description of actual problems in the subject of Forest Fire
Management is introduced. Secondary. Following to this, the aim of this chapter is to create a
clear understanding of the objectives and framework of this thesis. Finally, an overview of the
outline of the thesis ends this chapter.
1.1 BACKGROUND
In today’s world, sustainable development should be one of the main topic in every country’s
agenda. This is due to the interaction between human activity and the current climate change
(Intergovernmental Panel on Climate Change, 2007). The search for the correct balance between
economic development and sustainable use of natural resources is one of the main priorities that
humanity is currently facing. The Forest ecosystem is one of the most important renewable
resources which, if well managed, could play an important part to reverse this situation. Forests
cover 31 percent of the land use surface of the planet (World Forest Organization, 2013) and the
human race has been using forests as a source of raw material for building, transportation, food,
fuel, etc. Moreover, when forests are cleared, the land can be used as farming areas or for
building cities (Food and Agriculture Organization, 2010). Furthermore, forests are important to
preserve life in the planet at all scales by providing a wide range of services such as buffering
floods and droughts, harboring biodiversity and mitigating the effects of greenhouse gases
(GHG) (FAO, 2012). Therefore, sustainable forest management is necessary to maintain this
basic resource.
Forest fires are the biggest threat to forested land (FAO, 2012). Deforestation and desertification
are amongst the most damaging effects of wild fires on forested areas (Adab et al., 2013). The
last report issued by the European Forest Fire Information System (EFFIS, 2011) found that
around 269,000 ha were consumed by fire, especially in Southerner countries such as Spain,
Portugal, Greece, etc. and about 55,543 fires were recorded. These figures underpin the need to
improve forest fire management plans in order to minimize this threat. Successful fire
management is based on the ability to assess and map the areas where fires are more prone to
occur and they can easily spread to other areas (Xu et al., 2005). Forest fire risk map is therefore
the first step to preventing and forecasting fire incidents and successfully react in the event of
one (Jaiswal et al., 2002).
Since fire and weather are closely linked (Teodoro and Duarte, 2013), fire management has
traditionally been carried out based mainly on weather/climate patterns (Chuvieco et al., 2010).
Some of these meteorological-based projects were aimed to predict the forest fire prone areas
(Lazaros et al., 2002 Alonso-Betanzos et al., 2003). However, there are a variety of other
influencing factors such as ignition agents, topography, vegetation, landscape, distance to

12. 2
settlements, distance to roads, etc. that are sometimes very complex to assess and interconnect
(Castro and Chuvieco, 1998). Thus, there has always been an air of uncertainty surrounding the
decision making process (Thompson and Calkin, 2011).
Fortunately, with the development, in recent years, of satellite remote sensing and Geographic
Information Systems technology (GIS) an opportunity for quantitative analysis of those
influencing factors has opened up to be applied as a decision making tool on risk assessment of
natural hazard (Van Westen, 2013). Geographical Information System (GIS) is a computer based
system that captures, incorporates, stores, manages, analyses and interprets data of a location, a
mapping software that presents spatial data by interlinking location with available data, a tool
that helps combine graphical features with tabular data in order to evaluate real world problems
(Ogunbadewa, 2012). Because of the contribution of GIS technology, more influencing factors
can be taken into consideration in order to make more detailed and accurate models which
properly evaluate the likelihood of a disaster. Furthermore, along with the development of GIS
technology, multi-criteria evaluation (MCE) is becoming more and more popular in GIS
processing for designing models. In MCE, several different criteria are taken into consideration
and weighted against each other in order to produce the optimal result (Chuvieco, 2010).
Several GIS-based projects on fire risk mapping have been carried out in recent years (Chuvieco
and Congalton 1989, Chou 1992, Chuvieco and Salas 1996, Castro and Chuvieco 1998). The
majority of these projects are locally orientated, specifically in Mediterranean countries where
the risk of fire has historically been greater.
1.2 AIMS AND OBJECTIVES
The aim of this thesis is to develop a GIS-based forest fire risk model which might help public
authorities and public with the prevention and management of forest fires. This study is based on
an area located in Spain, which is one of the most affected countries by wildfires amongst the
other southern member states (Portugal, Italy, Greece and France), recording the greatest number
of burned area in 2011 with 84,490 ha and the second biggest number of fire accidents with
16,028 (EFFIS, 2011). The dissertation will apply the following objectives:
 Finding out the main factors, both natural and human, influencing the occurrence and
spreading of fires in forester areas through a literature review of the case studies carried
out in Mediterranean countries
 Obtaining the maps from public sources which are the base to generate the factor and
constrain maps involved in the multi-criteria evaluation (MCE).
 Creating the final forest fire risk map of the location by applying multi-criteria techniques
(WIZARD) available in GIS-based software IDRISI.
 Validation of the result by comparing the yielded forest fire risk map with the record of
forest fire occurred within the study area during the last decade in order to assess it in
terms of efficiency and reliability.

13. 3
1.3 ORGANIZATION OF THE THESIS
In order to clarify the outline of the thesis, the structure of the report is presented below.
Chapter 1 – Introduction
The background, purpose and the objectives of the thesis followed by an explanation of the
structure of the project.
Chapter 2 – Theoretical framework
This chapter describes the theory that has been studied in order to perform the thesis study. The
theoretical framework is generally based on the different projects carried out by a variety of
researchers in the GIS field.
Chapter 3 – Case study
This chapter describes the location of the area which is the base for the thesis. A map of this zone
is illustrated and records of fires area are also shown.
Chapter 4 –Methodology
This chapter describes the system boundaries for the thesis and the chosen process to be followed
in order to create the final map showing the areas where fires are more prone to occur. This
chapter is divided in two different parts; the first shows how to create the maps of the variety of
factors involved in the event of forest fire and the second parts focuses on the MCE process used
to create the final map.
Chapter 5 – Final Map analysis and findings
This chapter shows the final map of the chosen area and the validation technique chosen to
assess the effectiveness of the results.
Chapter 6 – Discussion
This chapter presents the analysis of this thesis and the relationship between the methods, the
theoretical framework and the final result. It also analyses the problems faced during the creation
of the map.
Chapter 7 – Conclusion
This chapter gives a further discussion about the analysis and different aspects that are important
to take into account. Moreover, the chapter summarizes the conclusions of the thesis which are
based on the aim set at the beginning of the thesis.

14. 4
Chapter 2
2 THEORETICAL FRAMEWORK
This chapter provides a frame of reference in order to understand the issues of forest fire and
how Geographic Information System (GIS) techniques could be used to improve the successful
management of forest fires.
2.1 FOREST FIRES
Teodoro and Duarte (2013) describe forest fires as any wildfire that is burning in areas of
vegetation or forest. Fires are caused by either natural processes or human activities (Vasilakos
et al., 2009). Fire provides both positive and negative consequences to nature and human beings
(Chuvieco, 2010). On one hand, fire has been playing an important part of the creation and
shaping of earth as we know it nowadays, especially by maintaining the health and diversity of
many forest ecosystems and it has been a powerful tool for humans in their evolution both social
and economically. On the other hand, fire can deliver devastating socio-economic impacts and
also endanger public health and safety, property and natural resources (Chuvieco et al., 2010)
either, in a global or local scale.
Amongst the global effects, since trees play an important role in the natural carbon circle by
absorbing and storing carbon dioxide (CO2) from the atmosphere (FAO, 2012), the combustion
of large amount of forested areas leads to its release. Thus, it would worsen the actual peccary
situation of global warming. This could be seen from the other point of view, as Flannigan et al.
(2005) predicts the increase of frequency and intensity of forest fires due to the global warming
of the planet. Moreover, in a local scale, fires cause devastating effects such as soil degradation,
soil erosion, loss of lives infrastructures, and economic loss relative to the land use (Adab et al.
2013). In addition to this, the current trend of abandonment of rural practices in developed
countries has produced an of fuel accumulation in forested areas that implies more intense and
damaging fires (Chuvieco et al, 2010). In this context, knowing the causing factors of fire and
how fires behave is essential for adverting their occurrence and lessening their damaging effects
(Chuvieco & Congalton, 1989).
In Spain, forest fires have always been one of the most dangerous environmental hazards. As it
can be seen from figure1, the area burned by forest fire has decreased for the last 20 years in a
remarkable rate. This could be due to the investment made by the public authorities in
technology and human forces involved in the fighting against forest fires. Although, the number
of fires has also decreased steady in the same period of time (figure 2), the number of forest fires
is still very high. The difference between these two trends could be understood as the differences
between the money invest in post-fire and pre-fire technology. Thus, more effort is necessary in
trying to understand the causing factors and how they can be analyzed to prevent fire at first
instance. GIS technology and satellite data can aid to the fire risk mapping process.

15. 5
Figure 1: Burned area (ha) in Spain (1980-2011). Source: (EFFIS, 2011).
Figure 2: Number of fires in Spain (1980-2011). Source: (EFFIS, 2011).

16. 6
2.2 RISK ASSESSMENT
Basically, risk assessment process is structured in three main phases, namely identifying the risk
factor, analyzing the characteristics of the risk and following steps to reduce or eliminate the
danger faced by an organization or person. Quantitative and qualitative risk analysis make use of
risk assessment, in fact it is a basic component of these type of analysis. Risk assessment
methodology is based on two main components, hazard and vulnerability. The former is used to
measure the physical intensity of the risk factor at a given point or location and relates to the
likelihood of occurrence of the risk. On the other hand, vulnerability relates to the severity of
damage caused by the hazard (Chen et al, 2012).
In environmental terms, three different stages form the risk assessment, namely probability risk
assessment, real time assessment and consequence assessment (Jiang et al., 2012). The first stage
is performed before the hazard has been generated, it is used as a preventing measurement. Real
time assessment is carried out when the hazard is present and it involves how to respond and
taking adaptive measures to face the risk. The final stage is performed after the occurrence of the
incident or accident, consequence assessment is used to find the measures to avoid similar
accidents in the future (Khadam & Kaluarachi, 2003).
Within the forest fire context, the terms of risk and hazard have been used confusingly since the
beginning of modern fire science in 1920 (Hardy 2005). Nowadays, international organizations
such as the Food and Agriculture Organization (FAO), the Canadian Committee on Forest Fire
Management (CCFFM), the Society of American Foresters (SAF) have finally agreed on a global
definition for fire risk- the chance that a fire might start, as affected by the nature and incidence
of causative agents. In paper, most researchers used the definition of risk as the combination of
hazard and potential damage to map the forest fire risk zones (Adab et al, 2012, Jaiswal et al,
2002; Xu et al, 2005). This forest fire zones are the result of assessing the individual and
combined influencing factor of ignition and spread of fire and is the first step necessary for a
successful forest fire management (Chuvieco, et al., 2010).
A variety of factors have to be considered in the forest fire risk assessment. These factors are
usually divided into three main groups, natural (fuel and topography), anthropogenic (distance to
roads, settlements) and climatic factors. Fuel, also known as moister represents the material
necessary for ignition and combustion. This is commonly recognized as the amount, type and
characteristics of the vegetation in a specific location (Chuvieco et al., 2010). Along with fuel,
topography also influences the risk of occurrence and spread of fires by generating different
ranges of wind and microclimate. The main topographic factors are slope, aspect or insolation
and elevation (Chuvieco et al., 2010). Climatic factors are considered Anthropogenic factors
refers to the spatial distribution of some man-made infrastructures, for instance, roads,
farmlands, camping sites and settlements which are known as potential points of fire ignition
(Abad et al., 2013).
Since fire risk is a spatial and temporal process, the causative factors should be assessed and then
managed spatially and temporally. In recent years, GIS technique has been applied in forest risk
assessment. This is due to the capability of create, transform and combine multiple geographical

17. 7
variables (Teodoro and Duarte, 2013).Although, no specific approach has yet been developed to
assess the interaction of all these factors, many projects have been carried out in a local scale
creating a framework that could be used worldwide, leaving space for future improvements.
2.6 MCE-GIS BASED FIRE RISK MANAGEMENT
Decision-making processes based on the risk of analysis of natural hazard is recognized as a
multidimensional and multidisciplinary activity. In fact, management, environmental and socio-
economic factors are involved at different spatial and temporal scales (Chen et al., 2001).
Combining or coupling these usually conflicting factors is the major problem faced during these
type of processes. In this context, the use of multi-criteria evaluation (MCE) linked with
geographic information systems (GIS) is feasible, permitting decision makers to make value
judgments and identify different levels of risk in a rational, interpretable and systematic way.
With the improvement of the GIS technique, several risk management studies have been
performed incorporating spatial analysis to identify different kinds of ecological, environmental
and geological (Bhuiyan, 2012). For instance, Partington (2010) used this technology to evaluate
the risk in a gold mineral exploration. GIS technique was also used in a project carried out in
Mexico to predict the vulnerability of the areas to hurricanes and set up emergency plans
(Krishnamurty et al., 2011). Other example is found in Italy, where Poggio and Vrscaj (2009)
used GIS technology to carry out a quantitative risk assessment on contaminated soil in order to
be reflected into urban planning.
As the forest fire risk management is based on the evaluation and aggregation of combined
factors, the use of MCE becomes necessary in order to analyze and rank the different alternatives
from the most to the least preferable through the use of an organized approach. (Jaiswal et al.,
2002). This approach is divided into the following steps:
Firstly, all the influencing factors must be transformed into raster and vector-based data.
Secondly, standardization must be applied in order to allow inter-attribute and intra-attribute
comparing. Different functions could be applied to convert the raw data such as, triangular,
trapezoidal, Gaussian, generalized bell, sigmoidal and left-right functions (Jiang and Eastman,
2000). Next, in decision making processes using MCE-GIS application, weighting schemes are
applied to express the different grades of importance and preference of each factor with respect
to the others, and usually depends on the decision maker subjective point of view. Alternatively
to this subjective method, which is the most used amongst the wide amount of studies published
on forest fire risk mapping, statistics methods were used for the calculation of forest fire risk
such as linear and logistic techniques (Kalabokidis et al., 2007, Vasconcelos et al., 2001). The
choice of methodologies for the calculation of these weights varies from text to text (Chuvieco et
al., 2010). Different assumptions may generate different result, so validation is often applied to
assess the results (Begueria, 2006). Finally, the final maps is created by multiplying each factor
map and their weight and sum them together in order to generate a map showing the risk and
potential damage which may help decision makers. This information can be used for prevention,
emergency preparedness, and also avoid future hazard (Vadrevu et al, 2010).

18. 8
As said by Chen (2001), few programs incorporate MCE components; one of them is IDRISI
Selva which was used in this project. IDRISI also offers different MCE methods such as
Analytic Hierarchy Process (AHP), Weighted Linear Combination (WLC), and Ordered
Weighted Averaging (OWA). The AHP method is explained in more detail in the practical phase
of this project.
2.8 PREVIOUS MCE-GIS BASED FOREST FIRE MANAGEMENT STUDIES
There is a lot of available studies amongst scientific papers focused on forest fire mapping. The
main of this literature research is to find out the most influencing factors and the most popular
weighting schemes used in Mediterranean-based studies.
One of the earliest studies carried out in Spain is presented by Chuvieco and Congalton (1989),
the authors used remote sensing and GIS technology to develop a forest fire risk map to aid in
the management of future hazards. Amongst the forest fire influencing factors, vegetation type,
elevation, aspect, slope and distance to roads are the chosen for the authors to create the final
map. Vegetation and distance to roads factors are taken form high resolution satellite image
(Landsat TM). Author considered the type of vegetation as the most influencing factor, following
in order of importance for slope, aspect, and distance to roads and elevation. This means that
topographic factors are of mayor influence than anthropogenic criteria.
Bonazountas et al. (2007) published a study based on an area of Greece (Attica). This study aims
to create a simulation tool to be used as decision support system (DDS). It simulates fire
behavior based in criteria taken from satellite image. Terrain and meteorological characteristics
and vegetation type are used as part of the function to estimate fire spread. This is achieved by
the use of fuzzy logic with neural network.
Another interesting study carried out in Southern India by Vadrevu et al. (2010) is worth
mentioning. In their study, the integration of fussy data in GIS-MCE is used to process four
groups of factors, named topography, vegetation, climate and socioeconomic. It is considered
very detailed, for example, temperature is divided into intervals by every two degree centigrade.
The authors developed an Analytic Hierarchy Process (AHP) to give the weights to the criteria
using fuzzy set technology. In the process, the highest weights are applied to socioeconomic
factors and the lowest weights to topography criteria. This approach means that the most
important factors of causing forest fire are the man-made activities.
One of the latest studies in this field is published by Chuvieco et al, (2010), the occurrence of fire
is quantified using a multi-criteria method where ignition factors (lightning, anthropogenic) and
propagation factors (moisture, slope, etc.) are linked to the vulnerability of the area (socio-
economic value, potential degradation, landscape value) to assess the overall risk. In this study,
the total risk is made of different variables or indices associated with each factor. Socioeconomic
factors such as recreational and touristic resources, properties and hunting revenues are included
in the total risk by consulting market prices and expert opinion. This project called FIREMAP is
meant to encourage the participation of end-users due to its public dominant.

19. 9
Chapter 3
3 CASE STUDY
This chapters tries to familiarize the reader with the area chosen to carry out the study by
describing its location and characteristics.
3.1 LOCATION
The study is based on an area of the region of Aragon which is located in the northeastern of
Spain (Figure 3). In order to understand better the location of the study area and the statistics
explained in the next section, it has to be said that Aragon is divided into three provinces
(Zaragoza, Huesca and Teruel); this project focuses on an area located in the province of Huesca,
situated in the north area of the region. The study area lies between latitude 42º 03’37’’ and 42º
16’N, longitude 0º 08’ 59’’ and 0º 05’12’’W (Figure 4). The climate of this area responds to the
Mediterranean conditions with warm and dry summers with picks of 32°C but due to its north
location and geographical formation, it suffers from cold and rainy winters too (-1C). The annual
temperature is around 12°C and annual precipitation average is 650mm (AEMET, 2013). The
terrain of the area is highly hilly and rough, with altitudes between 380.557 and 1,563.88 m
above sea. The land of this area is cultivated with crops such as vines and olives and the
predominant type of vegetation is pine.
Figure 3: Map of Spain. Figure 4: Satellite image of the study area.

20. 10
3.2 STATISTICS
The region of Aragon is one of the most affected areas by forest fires in Spain. In 2012, a total of
541 forest fires were recorded in this region burning 8,245ha of land (figure 5). Both, the number
of fires and burned area have increased dramatically from 2011. This total of fires is divided
between the three former provinces, Zaragoza recorded the highest number of fires with 266,
followed by Huesca with 153 and Teruel registered the lowest number with 122. According to
burned area, Zaragoza presented the higher number with 5,172 ha, within the boundaries of
Huesca, a total of 2,942 ha were burned and finally Teruel recorded 130 ha. (Gobierno de
Aragon, 2012). The fact is that Huesca includes most of the forested land in Aragon, and
therefore, the majority of forest fires amongst the total of fires shown in figure 4 were recorded
in this area. Thus, this project focuses in an area of this province.
Figure 5: Statistics of Aragon (2008-2012). Source: Gobierno de Aragon, 2012
The main causes of these fires have been recorded and are shown in Figure 5, human negligence
or accidents make up 52% of the total fires, followed with 18.1% and 16.1% representing fires
ignited deliberately by human hand and lightening, respectively. Next is unknown causes
recording 13.1% and finally with 0.6% were caused by the reproduction of previous fires. The
difference of percentage from the former group to the other could be explained by the use of fire
to clear areas in order to harvest the land. When this fires lose control and cause farther damage
are considered as part of this group. This is common technique used in this particular region
where agriculture plays an important role in its economy. This data underpins the decisive
participation of human action in the likelihood of fires appearance in this area. All this

21. 11
information is taken into consideration lately for the development of the factor weighting
scheme.
Figure 6: Causative factors of forest fires in Aragon (2012). Source: Gobierno de Aragon, 2012
Finally, it is also worth mentioning the importance of climatological condition in the grade of
occurrence of fire. This can be seen in the next graph (Figure 7) showing the record of fires
suffered in this location in each month of the year. It can be seen that the maximum number of
fires have historically been during summer season where the highest temperatures are registered.
Strangely, the maximum number of fires was recorded in February in 2012. Information about
the weather during that month would be necessary to clarify this event.
Figure 7: Record of forest fires per month in Aragon (2012). Source: Gobierno de Aragon, 2012

22. 12
Chapter 4
4 METHODOLOGY
In this chapter, all the applications used to carry out the study are explained. Furthermore, the
method followed to create the final map is explained step by step, the collection of the data, the
transformation of it and weighting scheme chosen due to the characteristics of the location. This
chapter shows the process of building up the map which will show the different grades of forest
fire occurrence across the selected area.
4.1 PROGRAMS AND APPLICATIONS
In this particular study, the combination of two software were used, namely ArcGIS 10.1 and
IDRISI Selva. Each of them was used to specific tasks and played an important part in the
creation of the final map.
4.1.1 ARCGIS 10.1
ArcGIS 10.1 is one of the latest versions of ArcGIS which is considered worldwide as the
principal software in GIS work. It is designed by Environmental System Research Institute
(ESRI). Basically, ArcGIS is software that integrates stores, edits, analyzes shares and displays
geographical information (ESRI, 2013). Thus, it is an effective tool for decision makers to
integrate different and complex factors regarding geographical characteristics. For instance,
wildfire managing could benefit from its use on hazard assessment in order to easily visualize
potential high risk areas to apply mitigation programs. In this particular study, ArcGIS was
firstly used to analyze and work on a Digital Elevation Map (DEM) of the study area in order to
develop the maps of the other factors such as aspect, and slopes. Secondly, was used to digitalize
the fires recorded across this specific area for the last decade by geo-processing their coordinate
points extracted from the Global Fire Information Management System (FAO, 2013). Finally,
ArcGIS was also applied to import information in form of vector layers and generate different
files. All the information was downloaded from the Instituto Geografico Nacional of Spain (IGN,
2013).
4.1.2 IDRISI SELVA
IDRISI Selva is also an integrated GIS and remote sensing software for advanced spatial analysis
alike ArcGIS. It was developed by students at Clark University in United States. The election of
this software as the principal work space for this project is its application model WIZARD which
is explained deeply in the next point. There is a lack of studies carried out with this software on
forest fire mapping, just few researchers such as Chen et al., (2001) chose IDRISI upon ArcGIS.

23. 13
All the previously processed maps were transferred from ArcGIS to IDRISI for further
processing and analysis. A variety of IDRISI tools were used in this second part of the project,
named, RASTERVECTOR, DISTANCE, BUFFER and FUZZY in order to transform the vector
maps into raster maps, calculate and valuate the distance between different parameters, create
constraint maps and standardize all the maps to a common pixel measurement, respectively. The
process of standardization of the factors map to a common scale of measurement was also
performed by this software.
4.1.3 WIZARD TOOL
The final part of the project or multi-criteria evaluation was carried out by using one of the
IDRISI decision support tools called WIZARD. MCE is a process in which multiple layers or
maps are aggregated to yield a single output map, in this case, a map of the study location
showing the areas where wildfires are more prone to occur and easily spread from. WIZARD
simplifies this complex process by taking the user through a set of steps. Moreover, in order to
ease the decision making process, WIZARD includes the option of Analytic Hierarchy Process
(AHP), which was used to calculate weights for the different factors taken part in the
development of the risk map. This method is based on a pairwise comparison matrix developed
by Saaty (1980). Thus, this project made use of this tool at the final stages of the creation of the
forest fire risk map and is explained posteriorly.
4.2 DATA COLLECTION AND PREPAREDNESS
GIS technology uses the concept of map layers as the fundamental units of analysis and display.
Map layers represent a single feature that can be mapped across space. They can be basically of
two types, raster layers and vector layers. Raster layers show a region of space by dividing the
world into cells laid out in a grid. Each cell has a value that is used to represent some
characteristics of that location such as temperature, elevation or type of vegetation. On the other
hand, vector layers describe the location and characteristics of a feature in space, such as road,
rivers, or cities. Moreover, vector layers are grouped in three different types, point files, line files
and polygon files. Basically, files represent the location of features such as buildings or cash
points in a city, line files show lineal features such as rivers or roads and polygon files display
areal features such as the different uses of the land in a location. All these features in each type
of file describe an attribute value which is represented by one or series of X, Y coordinate points
on the layer.
Ones the location was chosen, in order to develop the fire risk map, mainly three source of
information were used. Firstly, a Digital Elevation Model (DEM), shown in Figure 8, in raster
format was the base to create the rest of topographic factor maps by the use of ArcGIS surface
tools such as SLOPE and ASPECT. Secondly, a land use map of the study area, shown is figure
11, was used to describe and assess the different types of forest and harvested land. Finally, a
series of vector files, shown in figures 12, 13, 14 y 15 were used to analyze the anthropologic

24. 14
factors and then transform them in raster format maps by the use of IDRISI converting tools such
as RASTERVECTOR. This is a necessary step since WIZARD works only with raster format
maps.
All the needed information was downloaded from the Spanish National Geographic institute
(IGN). IGN divides the entire terrain of Spain into a series of equal quadrants, representing
different areas in a scale of 1:50000, the study area lies in quadrant 247. DEM is spatial image
taken from satellite of this quadrant showing the different altitude of the land by using an optical
remote-sensing technique called LIDAR (light detection and ranging). The vegetation map
chosen for the project was a map of the land use (CORINE) developed by European Corine Land
Cover project which was also downloaded from IGN. It is a shape file showing the different use
of land in the entire Spanish territory in a scale of 1:100000. This map was adapted to the
geographical space and scale of the DEM map by using the ArcGIS tool CLIP. Posteriorly, this
map was transformed to a raster map within IDRISI. Finally, vector files, unlike DEM map, were
not available in the scale of 1:50000. Fortunately, IGN subdivide each quadrant into 1:25000
areas which include different vector files showing different features. The vector layers needed
for the project were extracted in this format and then merge together to create the vector file of
the entire location by using ArcGIS tool MERGE.
In addition to this, in order to display this data sets in ArcGIS, it is necessary to establish the
correct spatial reference system. Geographical coordinate system is a method for describing the
position of geographic location on the earth’s surface using spherical measures of latitude and
longitude. There are many types of coordinate systems; each is defined by its characteristics,
such as its measurement framework (geographic or plan-metric) or the unit of measurement
(meters, decimal degrees). For this particular study, ETRS89 is the coordinate system to be used
with all the information of this specific location, more specifically, ETRS89/TM30. This
information was collected from IGN.
4.3 INFLUENCING FACTORS
Forest fires are difficult to predict in terms of occurrence and behavior. This is due to the
complex interaction of a variety of factors involved. Based on previous studies in the field
(Chuvieco & Congalton 1989, Chen et al., 2001, Jaiswal et al., 2002, Xu et al., 2005,
Bonazountas et al., 2007, Vadrevu et al., 2010, Chuvieco et al., 2010) the following factors are
considered as the most influencing for forest fires: elevation, aspect, slope, vegetation and
distance to roads, railroad, settlements and camping sites. For this particular project, all these
factors are grouped into natural and human factors and shown below.

25. 15
1. NATURAL FACTORS
 ELEVATION
Elevation is regarded as one of the most influencing factors of forest fire because it is closely
linked to precipitation and temperature and also relates to vegetation structure (Adab et al.,
2013). Basically, precipitation increases as elevation increases (Sen and Habib, 2000). Therefore,
the probability of fire is less in higher elevation areas. Most researchers have taken a similar use
of elevation like Castro and Chuvieco (1998) or Yin et al. (2004). Arguably, Hernandez-Leal et
al (2006), performed this factor in the opposite way, by ranking higher areas with high risk.
Temperature wise, higher elevation leads to lower temperature, which adds to the former theory
of lower probability of fire to occur in areas with higher elevation. In this project, elevation as
influenced by these two meteorological components is considered as a negative factor for the
likelihood of forest fires. Higher rates of appearance are given to lower areas. The elevation of
the study area ranges from 380 to 1 570 meters. The elevation map used for this study is shown
below in figure 7.
Figure 8: Elevation Map. Source: Instituto Geografico Nacional (IGN)

26. 16
 ASPECTS
Aspect is also considered as an influencing factor because it shows the relationship of the terrain
with sunlight and wind (Jaiswal et al. 2002). In the North Hemisphere, south facing slopes
experience more sun light, higher temperatures, low humidity and strong winds (Adab et al.
2013). The rule here is clear; more time of sunlight means higher temperatures. Moreover, the
amount of sunlight projected on certain areas is linked to the dryness of the vegetation covering
the terrain which eases the appearance of fire. Therefore, south facing slopes present drier and
less dense vegetation than north facing. In addition to this, east facing aspects experience more
ultraviolet and direct sunlight than west aspects (Anderson, 1982). The same approach to this
factor was performed by Vadrevu et al. (2010). The aspect map used in this project is shown
below in figure 8. It shows the areas facing the eight cardinal points.
Figure 9: Aspect map. Source: Instituto Geografico Nacionla (IGN)

27. 17
 SLOPE
Slope is regarded as an important factor because it has a large effect on the speed of fire spread.
Kushla and Ripple (1997) concluded that fire always spreads faster up-slope than down-slope.
Therefore, steep slopes increase the spread of fire. This approach is wide adopted by the
researchers of this field. The slope map used for this study is presented in figure 8. This map
shows the different grades of inclination of the slopes located in the study areas ranging from 0
to about 86 grades. How these grades were valuated is explained in the next stage of the project.
Figure 10: Slopes map. Source: Instituto Geografico Nacional
(IGN)

28. 18
 VEGETATION
Vegetation is considered as the most important factor for most of the researchers in the field.
This is because fire needs fuel in order to be set, vegetation of any terrain is the fuel needed for
fire to catch fire, without fuel, no fire can survive. A variety of approaches have been performed
by researcher, some classified the different types of forest by their grade of inflammability
depending on the characteristics of each forest (Jaiswal et al. 2002, Yin et al. 2004, Hernandez-
Leal et al. 2006). Different types of vegetation have different kinds of combustibility. Generally,
coniferous forest has a higher probability for fire risk than deciduous forest, because coniferous
trees contain less water and higher oiliness (Li, 1998). Other researcher projected the moisture
content of the different areas to measure the combustibility by the use of satellite image
techniques (Adab et al. 2013). In this project, a land use map was used to extract the types of
vegetation present in the study area. (Figure10).
Figure 11: land use map. Source: Instituto Geogrfico Nacional
(IGN)

29. 19
2. HUMAN FACTORS
 PROXIMITY TO TRANSPORTATION
Transportation is considered as an influencing factor amongst researchers. Areas close to
transportation systems present more human activity rates (Chuvieco & Congalton, 1989). Human
activity leads to unexpected man-made fire ignition accidents. Thus, increases fire risk. Forested
areas close to transportation systems should be considered more prone to fires because the
accessibility for humans is greater. The traffic density of the roads is also considered an
important parameter because the movement of vehicles creates air flow which aids the spread of
fire in the areas proxy to the road. The map presented below in figure 11 shows the main road
situated in the study area and it was created by bounding together different vector files by the use
of ArcGIS. It is the base for the development of the distance to roads factor.
Figure 12: Road vector file. Source: Instituto Geografico Nacional (IGN).

30. 20
 PROXIMITT Y TO RAILROAD
Train transportation is also considered as an influencing factor of forest fires for some
researchers as a point of ignition due to sparks coming from the friction created by the train and
the railway. Alike distance to roads, the air flow created by the train aids to the spreading of
fires. The maps shown in Figure 12, draws the location of the railroad crossing the study area.
The distance to railroad factor was created from this file.
Figure 13: Railroad vector file. Source: Instituto Geografico Nacional (IGN)

31. 21
 PROXIMITY TO URBAN AREA
Alike proximity to roads, proximity to urban areas means an increase of the intensity of
human activities. Several researchers have considered this factor in their projects (Chuvieco
and Congalton, 1989, Jaiswal et al., 2002), but not with the same grade of importance than
the study carried out by Vadrevu et al. (2010), they made an attempt to evaluate the
correlation between population density and grade of fire risk based on the dependency of the
population on the forest resource. Therefore, population density is considered as an
individual variable. The former approach is followed in this study. This polygon file,
represented in Figure 13, shows the position of the cities and small towns located in the study
area. It was used as the base to create the factor map of distance to settlements.
Figure 14: Settlement vector file. Source: Instituto Geografico Nacional (IGN)

32. 22
 PROXIMITY TO CAMPING SITES
Camping sites are considered for some researchers as an influencing factor (Loboda and Csiszar
2007). Outdoor fires are normally performed in these locations for heating and cooking purposes.
This could generate wild fire when incorrectly suffocated. Figure 15 shows the three camping
sites located within the study area in vector format. This was the foundation for the creation of
the factor file distance to camping sites.
Figure 15: Camping sites vector file. Source: Instituto Geografico Nacional (IGN)
4.4 MULTICRITERIA EVALUATION AND WEIGHTING SCHEME
As mention in the introduction, MCE is a technology to support decision making processes by
combining different factor influencing the final decision. Decisions are based on criteria which
may be of two different types, factors and constraints. In one hand, factors are criteria that
enhance or detract the suitability of a specific feature regarding the final objective and usually
measured on a continuous scale. In this case, factors represent the vulnerability of a location to
catch fire. On the other hand, constraint criteria function is to limit the alternatives to consider.
This is performed by applying values of 0s to the areas to be excluded and 1s to areas to be

33. 23
considered. For this reason, constraint criteria take the form of Boolean (logical) maps. In this
particular project, constraint areas are those where fires are not to occur, such water bodies.
Ones the factors map are created and assessed, weights are assigned for each of them to specify
the relative importance between them in regards to the final objective in consideration, in this
case the risk of an area to catch fire. These weights given to each factor must sum to one and
they can be assigned following the wise opinion of specialist in the field or calculated by using
AHP matrix tool available in the IDRISI software. The latter is the chosen method for this
particular project.
A flow chart detailing the structure of the MCE model used for this project is shown below in
figure 16. The final map is created by the sum of factor and constraint criteria which contains all
the influencing parameters of wildfires found in the literature research, divided into topographic
and anthropogenic.
Figure 16: Multi-criteria evaluation (MCE) model.
VEGETATION
ELEVATION
SLOPES
ASPECT
FACTORS
TOPOGRAPHIC
FACTORS
ANTHROPOG
ENIC
FACTORS
DISTANCE TO
ROADS
DISTANCE TO
RAILROAD
DISTANCE TO
SETTLEMENTS
DISTANCE TO
CAMPING SITES
FACTOR
MAP
FIRE
RISK
MAP
CONSTRAI
NTS
WATER BODIES
SETTLEMENTS
CONSTRA
INT MAP

34. 24
At this stage, the project has covered the first two steps (identification of the decision making
problem and the criteria relevant), in the common approach of any decision making process
using MCE-GIS method. The next steps are standardization of criteria values, determining
weights between criteria, linking criteria and weights, performing validation and interpreting the
results (Chen et al., 2001). These steps are covered in the next section of the project.
 Standardization of criteria values
Maps are formed of pixels which carry information about the value attached to the feature
represented by them. Since the final map is a combination of maps, it is necessary to standardize
the images to a consistent numeric scale before any comparison or calculation is made. In this
project that standardization was automatically made by IDRISI when importing the maps from
ArcGIS. All the maps were stretched to a scale ranging between 0 and 255. Thus, pixels with low
values or close to 0 represent areas where fires are no prone to occur and the terrain is no
favorable for fire to spread over, and by contrast, pixels with high values or close to 255
represents areas where the likelihood of occurrence is high and fire could easily be spread to
neighboring areas. This process was carried with each topographic factor map named,
vegetation, slope, aspect and elevation. This yielded a problem, because that transformation was
made on real values such as meters or degrees which no represent the grade of risk in reality.
Then, a reclassification process was developed to convert those into real values of the
vulnerability of the pixel to fire. The rated used to carry out this conversion on each topographic
maps are explained below and shown in table 1. The final standardized maps are also displayed
in figure 18, 19, 20 and 21. By contrast, a different standardization process was needed for the
human factor maps. Firstly, a conversion process was used to transform this vector layers into
raster format maps by running the RASTERVECTOR tool in IDRISI. Secondly, the spatial
processing tool DISTANCE was run to calculate the distance in meters between features, in this
case, distance to road, railroad, camping sites and settlements. Finally, the standardization of
these four human factors was carried out by applying the analysis tool FUZZY which, by
applying membership function, established the grades of fire risk to each pixel.
The same monotonically decreasing lineal function (figure 17) was selected for each of the four
human factors. This function gives the highest grade of risk to the areas close to the features,
grades decrees as the distance to the features increases, in a 0-255 scale. This function explains
well the reality on each factors scenario, areas closer to roads, railways, camping sites and
settlements are rated with grades around 255 and the more distant areas are rated with low grades
of close to 0. The final human factor maps are shown in figures 22, 23, 24 and 25. As seen in the
table 1, all maps were rated according to their sensitivity to forest fire as very high, high,
moderate and low using a 0-255 scale. Similar classification was used by Yin et al. (2004).

35. 25
Figure 17: Monotonically decreasing lineal function. Source: IDRISI.
In order to rate the different types of forest shown in the vegetation or land use map, another map
showing the fuel composition of the study area was required (Appendix 1). By matching the
level of fuel composition with the 8 types of land use shown in our map, the following rating
scheme was developed: Pine tree forest, bush presents the highest fuel content (20-35 t/ha of dry
matter), therefore they are regarded with very high risk grades, 255 and 230 respectively.
Followed by mixed forest with 10-15 t/ha of dry matter, considered as high risk area (200).
Caducifolias is type of forest commonly found in Spanish territory, it does not belong to the
Mediterranean family. Thus, less fuel content is present (8-12) and is classified as moderate fire
risk area (120). Also, annual agriculture land surrounded with thin forest is considered as
moderate risk area (100), presenting a dry matter content of 4-6 t/ha. Finally, low risk areas such
as built-up land and water and rock land were labeled with the lowest rate. Fires are not likely to
occur in these areas due to the lack of vegetation.
Aspect map shows 8 groups representing the 8 different coordinate points, named North (N),
Northeast (NE), East (E), Southeast (SE), South (S), Southwest (SW), West (W) and Northwest
(NW). Due to the geographic location of the study area, higher rates were given to areas facing
South and East. This areas present a greater time period of solar insolation per day that those
facing North and West. Similar approach was used in the different projects carried out in Spain
by Chuvieco and Congalton (1989), Hernandez-Leal et al. (2006). So, very high risk zones
correspond to S (255) and SE (235), high risk zones match SW (200), moderate values are given
to E (175), followed by NE (75), W (50), NW (10) and N (0) as low risk zones.

36. 26
The slope map is classified into 8 quartiles showing the different steep grades of the slopes
ranging from 0 to 85. Higher rates were assigned to steepest slopes. The steep of the slope does
not affect the likelihood of fire appearance but it does contribute to the risk of spreading. This is
because fires spread faster under these conditions than in flat areas. This type of classification is
used for the majority of researchers in the field (Jaiswal et al. 2002; Xu et al. 2005). Following
this approach, very high risk values (255) are given to slopes ranging between 46 and 85 grades
of inclination, high values (200) to slopes of 34-46 grades, followed by moderate risk values of
150 and 100 given to slopes ranging between 28-34 and 13-28 grades. Finally, areas presenting
slopes of 3-13 and 0-13 grades were considered as low risk areas with values of 50 and 0,
respectively.
Elevation map shows a continuous metrical scale of the study area’s altitude in real values.
Higher rates were labeled to areas at low altitude. This rating is also used for the majority of
researchers, whereas other used the opposite approach such as Hernandez-Leal et al 2006 who
gave higher rates to higher altitude zones. By following this classification, climate is taken into
consideration because areas at high altitude record higher annual average of rainfall. So, areas
between 0 and 400 m above sea level are labeled as extremely high risk zones, followed as high
risk areas, the locations situated between 400 and 600 m. Followed by moderate and low risk
zones for locations presenting altitudes between 600 and 1200 and higher than 1200, respectively
In the case of human factors, as it was said previously, a common methodology was applied
yielding a similar result for all of them. Areas located within 100 m of distance from the features
were valuated as very high risk zones (255), next areas within the range 100-500 m of the feature
were branded as high risk zones (200), between 500 and 1000 m relates to moderate risk zones,
and finally, areas further than 1000 m were labeled as low risk zones.
The rating scheme is illustrated below in Table 1: ratings assigned to the factors for forest fire
mapping.

37. 27
FACTOR CLASS RATING FIRE RISK
Vegetation Pine forest 255 very high
Bush 230 very high
Mixed forest 200 high
Caducifolias 120 moderate
Annual agriculture and forest 100 moderate
Harvested land 40 low
Wet and rocky land 10 low
Built-up 0 low
Aspect North (N) 0 low
Northeast (NE) 75 low
East (E) 175 moderate
Southeast (SE) 230 very high
South (S) 255 very high
Southwest (SW) 200 High
West (W) 50 low
Northwest (NW) 10 low
Slope
(%)
0-3 0 Low
3-13 50 Low
13-28 100 Moderate
28-34 150 Moderate
34-46 200 High
46-86 255 very high
Elevation
(m)
0-400 255 very high
400-600 200 High
600-1200 100 moderate
>1200 0 Low
Distance to road
(m)
<100 255 very high
100-500 200 High
500-1000 100 moderate
>1000 0 Low
Distance to railroad
(m)
<100 255 very high
100-500 200 High
500-1000 100 moderate
>1000 0 Low
Distance to camping
(m)
<100 255 very high
100-500 200 High
500-1000 100 moderate
>1000 0 Low
Distance to
settlement
(m)
<100 255 very high
100-500 200 High
500-1000 100 moderate
>1000 0 Low

38. 28
Figure 18: Standardized vegetation map. Source: IDRISI
Figure 19: Standardized Aspect map. Source: IDRISI.

39. 29
Figure 20: Standardized Slopes map. Source: IDRISI.
Figure 21: Standardized elevation map. Source: IDRISI.

40. 30
Figure 22: Standardized distance to roads map.. Source: IDRISI.
Figure 23: Standardized distance to railroad map. Source: IDRISI.

41. 31
Figure 24: Standardized distance to settlements map. Source: IDRISI.
Figure 25: Standardized distance to camping sites map. Source: IDRISI.

42. 32
 Determining weights between criteria
All the resultant maps form the processing and standardization process were combined by using
the IDRISI tool WIZARD. This a support tool that guides you through a set of building models
to solve multi-criteria decision problems. At this point, the multiple factors that comprise the
final fire risk map are of equal importance. WIZARD incorporates a technique to calculate and
assign weight to the factor maps to adjust the relative importance of each of them in determining
the final risk factor. In fact, factor weights serve to define to what extent high score on one factor
can compensate a low score on another factor.
One of the possibilities that WIZARD offers is the Analytical Hierarchy Process (AHP). This
technique helps to define the weights through a pairwise comparison process. In order to proceed
with this comparison, a scale from 1 to 9 is used, where the values of 1, 3 5, 7 and 9 mean that
one factor is equal, moderately, strongly, very strongly and extremely important than the other
factor, respectively. Intermediate values such as 2, 4, 6 and 8 are also valid. In case that the
factor is less important than the other, fraction figures of the reciprocal scale are used from 1/1 to
1/9. This comparison permit decision maker to set the weighting scheme. The sum of all the
weights has to be 1. In addition to this, to determine if the evaluation is successful or not, a
consistence ratio (CR) is calculated. Saaty (1980) stated that a CR less than 0.1 is considerate
acceptable, a CR superior to 0.1 means inconsistency, then the AHP should be reanalyzed and
reassessed. This project made use of this technique to calculate the final factor weights.
There is no a common approach amongst researchers when it comes to weighting the factors,
some researchers like Vadrevu et al. (2010) gave more importance to the natural factors whereas
others like Chuvieco et al (2010) treated the anthropologic factors as proximity to cities as more
influencing factors than the topographic factors.
By analyzing the statistics of the forest fires recorded in the study area (figure 3), it can be said
that the human caused factors prevailed from the topographic as 52% were caused by negligence
or accidents. This type of cause includes fires that were no suffocated correctly and lit up freely
again such as fires used to clean agricultural fields or fires used in camping sites as a source for
heating and cooking. This confirms the importance of the human hand as a point of ignition.
Moreover, 18.1% of the recorded forest fires were pre-meditated which adds to the theory of
considering the anthropologic factors as the most important for this specific location. Although,
human factors seem to be of great importance, for this project, vegetation type was considered as
the most influencing factor of them all, then distance to roads and railways. Distance to camping
sites and settlements were not consider as important as the other two human factors due to low
population density in the settlements of the area. Similar point of view was adopted for
Bonazountas et al. (2007). Based on the statistics, values used for the AHP matrix and the
weights generated are shown below in table 2 and 3, respectively.

43. 33
Table 2: Analytic Hierarchy Process.
Pairwise composition 9 point continuous scale
1/9 1/7 1/5 1/3 1 3 5 7 9
less important more important
Aspect Camp. Railroad Roads Settle Slopes Vegeta. Elevat.
Aspect 1
D. camping 3 1
D. railroad 3 1 1
D. roads 3 1 1 1
D. settlemen. 0.5 0.2 0.2 0.2 1
Slopes 0.8 0.5 0.5 0.5 5 1
Vegetation 7 5 5 5 9 9 1
Elevation 0.4 0.9 0.9 0.9 5 3 0.5 1
Table 3: weights generated.
FACTORS WEIGHTS
Vegetation 0.36
Aspect 0.1
Slopes 0.09
Elevation 0.09
Distance to roads 0.11
Distance to railways 0.11
Distance to settlements 0.07
Distance to camping sites 0.07
Consistency ratio (CR): 0.08 (Acceptable)
The fire risk index calculated for each pixel of the final map follows this formula:
FR= 0.36Vi + 0.09Sj + 0.1Ak + 0.09El + 0.11DRm + 0.11DRRn + 0.07DSo + 0.07DCp
Where FR is the fire risk index, V is the vegetation factor (i=8 classes), A refers to the aspect
factor (k=4 classes), E refers to elevation factor (l=4 classes), DR meaning distance to roads
factor (m=4 classes), DRR is the distance to railroad factor (n=4classes), DS refers to distance to
settlements (o=4 classes) and DC meaning distance to camping sites (p= 4 classes).

44. 34
4.5 FINAL MAP
In the last part of the MCE process, the final map was generated (figure 26) on a 0-255 scale.
Figure 26: Final forest risk map. Source: IDRISI.
 Validation.
As Begueria (2006) stated, validation is an important process of every assessment on natural
hazards as it compares the project prediction to real data sets. By this means, the project can be
assessed with regards to its accuracy. In order to validate the final forest fire risk map, the chosen
approach was to compare it to the record of fires occurred in the location for the last ten years.
The required data was collected from the Global Fire Information Management System
developed by FAO. This website allows users to check out either currently active fires or the
historic record of fires in any given location. The fires are recorded via satellite (MODIS).
For this particular project, the forest fire data was firstly extracted to an excel file shown below
in table 4 and then converted to a vector layer (point file) via ArcGIS. Finally this shape file was
transposed to IDRISI software and blended into the final map in order to assess the accuracy of
the risk map (figure 27). This assessing methodology is simple, if most of the previous forest

45. 35
fires are located in areas of high risk, it could be said that the risk map is effective and could help
in the prediction of future fire accidents. On the other hand, if the opposite scenario is given, it
would mean that the risk map is not acquired and the methodology should be reviewed and
reassessed, specially the factor weighting scheme.
Table 4: forest fires record.
Forest fires record
RANK DATE TIME LATITUDE LONGITUDE
1 8/31/2013 2:05 42.302 -0.651
2 8/31/2013 13:10 42.296 -0.656
3 6/29/2012 11:05 42.311 -0.749
4 9/22/2011 12:50 42.229 -0.609
5 9/22/2011 11:10 42.228 -0.601
6 9/22/2011 11:10 42.23 -0.614
7 6/9/2006 13:10 42.188 -0.564
8 8/3/2001 10:15 42.326 -0.677
9 8/3/2001 10:15 42.332 -0.669
10 8/3/2001 22:20 42.327 -0.658
11 8/2/2001 22:20 42.354 -0.678
12 8/2/2001 22:20 42.307 -0.7
13 8/2/2001 22:20 42.295 -0.724
14 8/2/2001 22:20 42.296 -0.711
15 8/2/2001 22:20 42.304 -0.726
16 8/2/2001 22:20 42.315 -0.716
17 8/2/2001 22:20 42.305 -0.713
18 8/2/2001 22:20 42.321 -0.723
19 8/2/2001 22:20 42.318 -0.736
20 8/2/2001 11:10 42.337 0.692
21 8/2/2001 11:10 42.336 -0.742

46. 36
In order to analyze the result in more detail, a scale of low, medium, high and very high risk was
applied to the previous final map, where values from 0 to 79 are considered low risk (black),
medium risk (green) includes values from 80 to 119, high risk values (orange) rank from 120 to
160, and very high risk areas (purple) record values from 161 to 255.
Figure 27: Validation map. Source: IDRISI.
As it can be seen in the validation fire risk map (figure 27), about 80% of the fires registered in
this study area (16 out of 21) are located in zones of high risk. This figure implies that the final
map is very acquired at showing areas where fires are more prone to occur and spread as a result
of the combination of topographic and anthropogenic factors.

47. 37
Chapter 5
5 FINDINGS
Firstly, before analyzing the results yielded in this project, it is worth mentioning again the
concept of risk evaluated in this project. As mentioned before, high risk areas are not only zones
where fires might start but also the areas where fires might be easily spread.
As it can be seen in the final map, high risk areas are concentrated in the top area of the map,
although this geographic area has a medium-high grade of elevation, it is regarded as the most
problematic zones for fire accidents. This is due to the compensation drawn for the other factors
influencing the risk. For instance, it has the type of vegetation rated as more favorable for fire
accidents (pine tree forest) and settlements surrounding the area. Moreover, the railroad goes
across the area and it can be accessed by car. These factors contribute to valuate this zone as a
high forest fire risk area.
The geographic location of the majority of occurred fires during the last decade also contributes
to the understanding that this area must be labeled as high risk area. The amount of human
activity around this area is one of the most important factors for this consideration, but also, the
favorable characteristics of the terrain for fire spreading has to be taken into consideration. This
theory is also supported by the accidents occurred. Looking at the fires record, it can be seen that
in 2001, during the 2nd
and 3th of August, a total of 14 fires were recorded in this area close one
to another. Obviously, all these fires are treated as the spreading of one fire into other locations.
This incident also verifies that the topographic characteristics of this specific area adds to the
spreading of fires and therefore to the overall risk as it is demonstrated by the high grade of risk
given to this boundary in the final map.
A similar incident happened in 2011, a forest fire was ignited in the same high risk area and
after, it spread into 3 different points. Although, these fires were located in a boundary labeled as
low/moderate risk zone, the succession of fires in the same location in a short period of time adds
to the theory that the characteristics of the terrain in the study area are favorable to the spreading
of fires. Adding to this, the most recent fire accidents suffered dated in 2012 and 2013 were
recorded in the boundaries high risk areas, which demonstrates that the methodology and the
weighting factor scheme used in this project are acquired. Whereas, more information about the
fires should be taken into consideration to reach a more relaying result.
It is also worth taking a look closely at the date and timing of the fire incidents recorded in the
spreadsheet (Table 4). Basically, the period of time with more activity of fire accidents match
with the summer season. All the incidents were embedded between Jun and September, when the
highest temperatures are registered in this location. This corroborates the existing link between
forest fires and climate, as Perry (1998) stated, climate is considerate as one the most important
influencing factors amongst vegetation and topography. High temperatures contribute to dry the

48. 38
moisture present in the different types of vegetation, making more fictile fire ignition and
spreading.
In terms of accuracy of the AHP matrix, the majority of the fires were occurred in locations such
as pine tree forest, mixed forest and bush. All these types of forest possess Mediterranean
characteristics meaning basically a faster dry out of the water content when warm climate. For
this reason, the consideration of vegetation as the most important factor seems to be accurate.
Moreover, the location of the fires occurred in 2001, close to the railroad trajectory suggest that
this factor could have played an important role in the spread of the initial fire and therefore, its
consideration as the second most influencing factor also seems acceptable. The value given to
the other factors is difficult to valued individually, more analysis of the final map should be
necessary.
To sum up, the final forest fire risk map shows very correctly both parameters of risk, ignition
and spread for this study area.

49. 39
Chapter 6
6 DISCUSSION
In this section, the points open to discussion correspond to the dilemmas met during the different
phases of this project. Firstly, the selection of the influencing factors was based on previous
studies where authors present a variety of approaching ways. Secondly, the rating and weighting
schemes used to carry out the multi-criteria evaluation. Finally, the use of the forest fire record of
the last decade to assess the effectiveness of the final forest fire risk map.
6.1 Influencing factors selection
After the literature review, the first selection of influencing factors such as vegetation type,
aspect, slope, elevation, and distance from roads settlements was based on two major projects
developed by Chuvieco and Congalton (1998) and Jaiswal et al. (2002). Although, the latter used
less number of factors, authors took into consideration the proximity to settlement. Thus, a
combination of the factors form both project was used in this study as main influencing factors.
This was later supported for farther review of more studies where a similar approach was used
(Chen, et al., 2001, Hernandez-Leal et al, 2006, Yin et al., 2004, Adab et al., 2013). The
inclusion of the last two factors, namely distance from camping sites and railroad, was based on
the use of farm distance as a factor by Xu et al., (2005). Thus, vegetation, topography and human
activity were covered as the mayor influencing groups in forest fire appearing and spreading, just
leaving aside climate.
The difficulty to collect climate data of the study area was overcome by considering it together
with topographic factors, following the ideas of some of the studies mentioned in this section. By
doing this, climate was considered indirectly in this particular project. This can be seen as an
uncertainty that might lead to unreliability of the results. Some authors have tried to include
climate data amongst the factors involved in the creation of the final map by developing indexes
such as Land surface Temperature (LST) that combine with meteorological data from ground
station (wind, air temperature, humidity, etc.) could add in the prediction of fires (Hernandez-
Leal et al. 2006,). Yet no clear results are shown. If climate data could be collected and applied
in this project, the result would be more acquired and representative of the vulnerability to fire.
Moreover, another factor that is considered and treated differently amongst the researchers is the
use of fire by farmers to clear forested areas in order to broaden the productive land. This
technique has been in use for many years in Spain and has caused multiple forest fires
throughout the history. This is due to the facility of these fires to lose control. Thus, the
technique has been banned in other countries and in Spain; it has to be carried out under
supervision of the authorities. Although researchers like Vadrevu et al., (2010) or Xu et al.,
(2005) treated agriculture lands as one of the anthropogenic factors, in this study, this factor is
treated together with vegetation by delimiting the boundary of land where this technique is used

50. 40
and rated it accordingly. More data about the seasonality of this technique and how it is carry out
would be necessary in order to specifically and effectively valuate the role of this factor in the
overall.
Amongst researchers, there is a clear agreement on treating vegetation as the most important
influencing factor. However, there is not a unique approach to represent it. Some of them used
satellite images to produce land use maps of the locations showing different types of vegetation
(Xu, et al. 2005; Jaiswal et al. 2002), whereas other researcher take a more technical approach by
evaluating the calorific value of the different types of vegetation or even the wet content by using
satellite images and the calculation of indexes (Vadrevu 2010; Adab, et al. 2013, Yebra &
Chuvieco, 2009). In this project, the former approach was chosen due to the accessibility to the
land use map of the location. Other option were considered but not taken due to the difficulty of
collecting the data and the incompatibility of it with the software used. Further research in this
area would be beneficial in order to improve the final fire risk map.
Finally, in contrast with human action, lightning is regarded as the main natural ignition agent of
forest fires. As seen in the statistic section, this is the third more important causing factor in the
region of the study area (Figure 6). Therefore, it should be taken into consideration somehow.
There are not previous studies taken into consideration lightning as a factor due to the difficulty
to predict where and when these natural phenomena will occur. On the other hand, existing data
of previous fires ignited by lightning could be used to predict future appearances.
6.2 Weighting scheme selection
The process of MCE and its results basically depend on the weighting scheme used. In previous
studies, some researchers used the opinion of experts or literature research to directly apply a
weight to each factor (Chuvieco and Congalton 1989; Jaiswal et al. 2002; Xu et al. 2005).
Whereas, others authors preferred the use of AHP matrix tool to calculate the weights. This can
be used in two different ways, comparing to different AHP matrix to see which one is more
representative of the reality (Chen et al. 2001) or applying knowledge or data of the location
directly into the matrix to calculate the weights (Yin et al. 2004). The latter approach was
chosen to be applied in this project. Record, statistics and informs of previous fires were the base
to extract the data needed for the AHP matrix. This could be also considered as a point of debate
because those statistics are about fires registered in the entire region, not just of the study area. A
deeper research on these previous fires might yield a different weighting scheme and therefore
the final map would be modified.
6.3 Validation model selection
The validation model used in this project was to compare the final prediction to the record of
fires suffered within the study area during the last decade. A similar approach was used by Xu et
al., (2005), where the authors compare their results to the fires occurred between 1974 and 2001.

51. 41
Other approach models were found during the literature review, Jaiswal et al., (2002) or Yin et
al., (2004,) used the actual fire-affected sites to validate their predictions. Similar approach to
this project was by Adab et al. (2013), who used hot spot data obtained from the moderate
resolution imaging Spector-radiometer (MODIS) fire product developed by NASA to assess their
three different forest fire index. Anther recently research done by Giriraj et al., (2010) used
scanning radiometer ATRS data to track fires.
In this project, the access to the needed data through the use of Fire Mapper tool developed by
FAO (2013) was the major factor for selecting this validation model. To sum up, as seen above,
there are different models to validate the accuracy of the project, and depending on which one is
selected the reliability of the prediction might change.

52. 42
Chapter7
7 CONCLUSION
An attempt to develop a forest fire risk map of an area located in the northeast of Spain was
made in this study. Multi-criteria evaluation techniques included in GIS software platforms such
as ArcGIS 10.1 and IDRISI Selva were selected to perform the activities. According to the
literature review of previous studies in the field, eight factors maps were chosen. A forest fire
risk map was created dividing the study area between very high, high, moderate and low risk
boundaries. Although, some uncertainties might be found, the results obtained in this project
show a high reliability. Thus, the forest fire map developed could be used as a base for the
prediction of future fire events in this location, and be used by public authorities and forest
management members as supporting tool in decision making processes.
It can be said that the objectives and aims of the study have been achieved but there is still space
for improvement. More detailed data about vegetation types and fuel estimation, more
professional estimations for the weighting scheme, and the integration of accurate climate data
directly in the project could be regarded as suggestion for farther improvement in future projects.
According to software selection, although no many projects have been carried out using IDRISI
Selva and its MCE tool WIZARD, their use have been proved feasible. In addition to this, the
use of ArcGIS was not planned at the beginning but very needed previously for the
incompatibility of IDRISI Selva of importing and working with type of data found for the study
area. This could be a reason for the election of ArcGIS as the software to develop the projects by
the majority of researchers.
This project was developed in the hope of adding new ideas or views of the use of GIS and MCE
techniques to reduce or at least minimize the number of fires affecting forested areas which
cause an immense damage to us and our planet.

53. 43
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59. 49
Appendix 1: Fuel content map of the study area.
Source: (pcnacional.meteologica.com).
Appendix 2: AHP matrix used in the MCE process-WIZARD