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

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

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.

……………………………………………………………………

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).

v

TO MY FAMILY

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.

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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

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

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

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

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

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.

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.

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.

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).

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

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).

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.

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.

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

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

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.

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

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.

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)

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)

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)

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)

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).

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)

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)

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

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

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).

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.

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.

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

28

Figure 18: Standardized vegetation map. Source: IDRISI

Figure 19: Standardized Aspect map. Source: IDRISI.

29

Figure 20: Standardized Slopes map. Source: IDRISI.

Figure 21: Standardized elevation map. Source: IDRISI.

30

Figure 22: Standardized distance to roads map.. Source: IDRISI.

Figure 23: Standardized distance to railroad map. Source: IDRISI.

31

Figure 24: Standardized distance to settlements map. Source: IDRISI.

Figure 25: Standardized distance to camping sites map. Source: IDRISI.

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.

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).

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

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

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.

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

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.

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

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.

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.

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.

43

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49

Appendix 1: Fuel content map of the study area.

Source: (pcnacional.meteologica.com).

Appendix 2: AHP matrix used in the MCE process-WIZARD