FOREST FIRE VULNERABILITY ASSESSMENT STUDY · PDF fileForest Fire Vulnerability Assessment...

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FOREST FIRE VULNERABILITY ASSESSMENT STUDY USING

REMOTE SENSING AND GIS –

A CASE STUDY IN TIRUPATTUR DIVISION

DINKAR KUMAR, I.F.S.

(District Forest Officer, Tirupattur, TAMILNADU - 635601)

(E-mail: [email protected])

ABSTRACT

Forest Fire has always remained a challenge for the forest managers. A no. of natural and

manmade components decides the vulnerability of a forest area for fire. Often the field

managers and the field staff, lack the basic information regarding fire occurrences, fire

frequency, fire intensity, exact cause of fire and the vulnerable areas. The forest fire is

dealt as and when it occurs and then forgotten. There is no fire vulnerability map to

forecast or preparations done for future occurrences due to lack of scientific study in this

regard. In this study, the MODIS satellite data which gives near real time data regarding

forest fire along with other geographical data of the area in different layers in a

Geographical Information System (GIS) platform is used to create fire vulnerability

model and fire vulnerability map of an area. This map and the details therein give a fairly

good idea of vulnerable areas and forest fire can be effectively managed by the use of this

data. Use of Remote sensing technology and the knowledge of Geographical Information

System facilitate combining different factors responsible for fire and creating maps

showing vulnerable areas can be of immense help for the front line staff.

KEY WORDS

Forest fire, Remote sensing, Risk, Vulnerability, MODIS

Forest Fire Vulnerability Assessment Study using Remote Sensing And GIS – A Case Study in Tirupattur Division

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INTRODUCTION

Forest fires are a ubiquitous phenomenon in Indian forests, and they probably date back

to the first arrival of people on the Indian subcontinent. Humans have long used fire as a

forest management tool, whether for hunting, to clear land for agriculture, to promote the

growth of fodder for grazing livestock, or to facilitate their collection of non-timber forest

products (NTFP). Forest fires cause major damage to the environment, human health and

property, and endanger life. It has long been held that such fires have been inseparably

connected with the development of woody vegetation and grasslands in India. However,

there is also recent evidence to suggest that natural fires in conjunction with grasslands

pre-dated human caused fires on the Indian subcontinent. Fires continue to be a

significant influence on Indian forests even today. At the end of the 19th century Dietrich

Brandis, the then Director General of the Indian Forest Service, had observed that half to

three quarters of mature trees in the plains and lower hills of India were hollow, and he

attributed this to fire. The government estimates were that 35 million ha of Indian forests

(~54%) are affected by fire annually. Forest fires are considered as a major and

permanent threat for the forests in Tamilnadu. Every year, forest fires cause enormous

and irreparable damage to forest ecosystems and in some cases threaten residential

regions. Forest fires can be ignited by a variety of reasons. The cause may be natural or

manmade, intentional or unintentional. There are many factors that contribute to an

increased fire risk, such as high temperatures and prolonged drought periods, the strong

winds, the configuration of the ground and the extremely flammable vegetation. Once a

forest fire has started, many factors contribute to its spread and intensity. Fuel – such as

leaves, needles, grass, branches, and logs, Weather, including temperature, humidity,

precipitation, and wind, Topography, or landscape of the area, as steep slopes offer

greater potential for increased fire intensity and more obstacles for fire fighting. Forest

fire causes imbalances in nature and endangers biodiversity. The menace has been

aggravated with rising human and cattle population and the consequent increase in

demand for Forest products by individuals and communities. Despite the widespread

occurrence of fires in Indian forests, we still lack precise information regarding fire

occurrence, fire frequency, fire intensity, and the exact causes of fire. Even though most

fires today are likely to be human-caused, the reasons could be manifold. By and large,

forest fires are linked to the benefits that people obtain from forests. To a lesser extent,

forest fires are linked to people’s religious beliefs or cultural practices. People have also

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been known to use fires as a form of protest against restrictive forest policies. In addition,

forest managers use fire as a habitat management tool, for instance, to maintain

grasslands for herbivores. The Tamilnadu Forest Act of 1882 specifies setting of fire as a

punishable offence and it is mandatory for all forest dwellers to assist in the prevention of

fire. The National Forest Policy of 1988 has the same spirit although it lays greater

emphasis on the use of modern fire prevention tools while continuing to stress the

involvement of local communities in the prevention of fires. The essential goal has

remained the same over almost a century: to have forests free of the influence of fire.

Forest vulnerability to fire is defined in this study as the combination of the susceptibility

of a given area to natural and human-induced ignition, and the ease in which fire spreads

through an area once present. This working definition of forest fire vulnerability builds

off the work of Gonzalez et al. (2007) who identified natural variables such as slope,

aspect, vegetation type, and presence of surface water as significantly influencing

vulnerability. Anthropogenic variables such as increased proximity to roads and

recreational areas have also been identified as increasing the risk of forest fire ignition

(Sowmya and Somashekar, 2010). Other, more stochastic variables, such as lightening

strikes, wind patterns, and precipitation have also been found to significantly influence

forest fire vulnerability (Arienti et al., 2006). The variables identified above all

experience spatial variability in such a way that different forest landscapes exhibit unique

degrees of fire vulnerability. Therefore, the ability to successfully combine multiple

variables that influence forest fire vulnerability can be of assistance to forest managers as

well as any other stakeholder which is interested in the impacts of forest fire. For

effective Forest fire management early detection, monitoring and assessment is very

necessary. It depends on the terrain, aspect, available dry material and anthropogenic

pressure. All these variables may differ for different place and the degrees fire

vulnerability may also differ. The analysis of multiple variables in Geographical

Information Systems (GIS) environment is crucial for developing a model in evaluating

forests’ vulnerability to fire. GIS has the capacity to use these variables for evaluation

over wide geographical areas. The study of forest fire mapping is made easier by the

advent of MODIS data and active fire maps. The MODIS Active Fire Map provides a

near real-time geospatial overview of the current forest fire situation at regional and

national scales. Locations of current fires and the extent of previous fire activity are

ascertained using satellite imagery acquired by the MODIS sensor. These fire data are


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integrated with various sources of contextual spatial data and information in a suite of

geospatial data and mapping products. The data and products provided by the program are

valuable for numerous fire-related applications. This information is utilized by fire

managers to assess the current fire situation and serves as a decision support tool in

strategic decisions regarding fire suppression resource allocation.

MATERIALS AND METHODS

Study Area

Tirupattur Forest Division is a territorial forest division in Tamilnadu, which is in the

southern part of Vellore district. This division is surrounded by Vellore forest division on

the North, Hosur forest divisions on the South, Tiruvannamalai North forest division on

the East and borders with Andhra Pradesh state on the west. The tract covered by this

division lies between North latitudes 12˚ 15’ 13” to 12˚ 55’ 25” and 78˚ 24’ 18” to 78˚

51’ 43” East longitudes. The tract is mainly grouped under two categories viz., the plains

and the hills. The plains are out ranges of Mysore plateau. The hills are divided into two

main blocks viz., the Javadhi hills and the Yelagiri hills. The Javadhi hills are steeply

forming an undulating plateau on the top about 800M above mean sea level. These are the

principal mountains of this division and runs from South West to North East with an area

of 570 Sq. Km. Yelagiri hills is a compact block with an area of 72 Sq. Km. The total

Reserved Forest (RF) area is 69233.15 ha and 3956.05 ha of Reserved Land (RL).

Drainage

Palar, Pambar and Malatar are the three rivers traversing in this division. The rain water

mainly drains into Palar and to Cheyar river which is a major tributary to Palar River. A

part of water drains into Pambar which is a major tributary to Ponnaiar River. These

rivers are not perennial.

Climate & Rainfall

There is a distinct variation in the climate between the Javadhi hills and plain land in the

division. The climate is little cooler on the western side and progressively warmer

towards East. During March – May it is very hot and gets cold after few summer showers.

On the hills the climate is milder and pleasant. The average temperate varies from 19º to

36˚C. The rainfall in this Division is evenly distributed. The average rainfall is 850mm to

900mm.

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Geology &Soils

The underlying rocks are mostly granite or genesis in composition and the chief

components of the rock are hornblende and feldspar. The soil type is mostly shallow

reddish loamy of varying depth. The soil is grouped into Red, Laterite, Black sandy,

Kalar and Skeletal soil.

Land cover & Forest

The forest types identified in the Division as per the Champion and Seth (1962)

classification of Forest Types in India are:

i. Southern Dry mixed Deciduous Forests (5A/C3)

ii. Southern Dry Deciduous Scrub Forests (5A/DS1)

iii. Southern Dry Tropical Riverine Forests (5A/ISI)

iv. Southern Thorn Forests (6A/C1)


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v. Southern Thorn Scrub Forests (6A/DS1)

vi. Tropical Dry Evergreen Forest-Carnatic Evergreen Forests(G7/C1)

vii. Tropical Dry Evergreen Forests – Hill Evergreen Forests (not classified)

viii. Tropical Dry Evergreen Scrub Forests (G7/DS1)

ix. Tropical Dry Semi Evergreen Forests – Plains Semi Evergreen Forests

x. (Not classified)Man made Forests (Plantation)

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Objective

The objectives of this study are

1. To identify ‘fire sensitive area’ for Tirupattur Forest Division based on the fire

burnt area as assessed using Satellite Images, Past fire occurrences as reported

by field staff and MODIS fire data.

2. To map ‘fire vulnerable areas’ for Tirupattur Forest Division is based on spatial

distribution of past fire occurrences, Terrain, Vegetation type and

anthropogenic pressure.

Data used

Forest Administrative Layer (vector, polygon feature)

Forest fire data (2001 to 2011 except 2003) (raster)

Past fire occurrences as reported by field staff

Road Network (vector, Line feature)

Forest enclosures/settlements (vector, polygon feature)

Location of Villages (vector, point data)

Forest Type Map (raster)

MODIS fire data (geo coordinates)

DEM of Shuttle Radar Topography Mission (SRTM) data (raster)

Software used

ArcGIS

ERDAS Imagine

Approach

The approach for the study involves the following steps

Generation of required data from original data

Data processing

Data analysis and Map generation

Results and Discussions

Introduction to the Forest Fire Vulnerability Model

A Geographical Information Systems (GIS) and Remote Sensing technology based multi-

variable evaluation will be used to map fire vulnerable areas for Tirupattur Forest

Division. Terrain, vegetation type and human influence variables will be used to generate


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a fire vulnerability map for the Reserved forests and Reserved lands of Tirupattur forest

division.

Landscape Variables

The landscape in a region can have several different impacts on a forest fire that may

increase or decrease the likelihood of ignition or spread. The landscape variables

considered in our study are slope, aspect, vegetation and proximity to road.

Aspect: Aspect is a key factor for forest fire ignition in a study area. For instance, in the

northern hemisphere a slope that faces south has a much higher vulnerability to fire than a

north facing slope. This is because the south facing slope is exposed to more sun radiation

throughout the day and thus these slopes are drier than north facing slopes. Therefore the

most vulnerable slopes are those that face south-west and the least vulnerable slopes are

those that face north-east. The aspect also affects the vegetation type that grows in the

area. The drier conditions of a southern facing slope generally provide more favourable

conditions for more vulnerable plant species to grow.

Slope: The slope is a factor in forest fire vulnerability analysis because as a slope

increases in steepness, the chance of fire spread is decreased and thus decreases

vulnerability. However, when slopes have a small inclination rate or there is no slope (the

land is flat) this increases the rate of spread and increases the fire vulnerability in the area

(Morvan, Dupuy, Porterie, & Larini, 2000). Slope also has strong correlation with the

direction of the fire. For instance, if a fire is moving up a slope it will spread much faster

than if the fire was travelling down slope. However, the rate of spread up and down

slopes will not be considered in our model. The slope will only be analysed in our model

to compare with level ground rates of spread.

Vegetation Cover i.e. Forest types: Vegetation is also a key factor in forest fire

vulnerability and spread. The fire ignition factor and the sustaining of fire in different

forest types are different. The dry grassland will be more vulnerable to fire than a moist

deciduous forest.

Proximity to Roads: Roads play both a positive and negative role in forest fire

vulnerability. When considering forest fire spread vulnerability, roads can actually aid in

containing the fire because roads can act as a fire break Another way roads can aid forest

fire spread is that they increase accessibility to the forested area, allowing fire control

staff easier access to it .Thus, forests in close proximity to roads will have a lower risk of

forest fire spread, but forests that are not in close proximity to roads have an increased

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risk of forest fire spread (Arienti et al., 2006). When considering forest fire ignition

vulnerability roads play the opposite role and increase the risk of fire ignition because

they offer more human access to forested areas. This increased access to people, often

leads to more accidental ignition.

DATA PROCESSING AND MAP GENERATION

MODIS fire data

Active fire spots detected using Moderate Resolution Imaging Spectro-Radiometer

(MODIS) sensors are available on daily basis for the entire globe on Fire Information for

Resource Management Systems (FIRMS) website.

The near real time MODIS fire data are good for determining the location of active fires.

As per MODIS fire data the number of fire incidences within reserved forests in

Tirupattur Forest Division is shown in Table 1.

Table 1: Details of fire incidences

Sl. No. Year No. of Fire incidences

1 2001 1

2 2002 0

3 2003 11

4 2004 49


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5 2005 44

6 2006 8

7 2007 15

8 2008 1

9 2009 4

10 2010 4

11 2011 9

Forest Fire field data

From the Forest fire field data, the forest fire burnt area in Tirupattur Forest Division for

the past 10 years is as shown in Table 2.

Table 2: Forest fire burnt area (year wise)

Sl. No. Year Burnt Area (in Ha)

1 2001 789.52

2 2002 367.32

3 2003 0

4 2004 0

5 2005 543.47

6 2006 674.24

7 2007 1315.81

8 2008 0

9 2009 74.46

10 2010 80.01

11 2011 70.21

Fire recurrence analysis is the first step towards fire control planning.

Using ERDAS Imagine software, the 10-year forest fire spatial data is aggregated and

analyzed to generate Fire Recurrence map as shown in Table 3.

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Table 3: Details of fire recurrence

Sl. No Recurrence Area (in Ha)

1 Once 3202.73

2 Twice 343.29

3 Thrice 7.09

4 Four Times 1.79

Forest Fire incidents reported by Field staff

As per fire occurrences as reported by field staff, the number of fire incidences within RF

areas in Tirupattur Forest Division shown in Table 4.

Table 4: Actual fire occurrence

Sl. No. Year No of occurrence

1 2006 21

2 2007 8

3 2008 6

4 2009 20

5 2010 17

6 2011 15

Total 87

Forest Fire Vulnerability Model

The fire model is divided into three sub models namely Terrain Risk model, Human

Influence Model and Fuel Risk Model. The variables, the data layers chosen for the

creation of such models are comprehensively determining factors in Forest Fire. The

variables of every sub model are given quantitative fire risk values depending upon their

capacity to promote fire hazard. The terrain risk sub model has Aspect, Slope and

Elevation and past five histories as its components. Human influence risk has roads and

village location as its components. Fuel risk model has Forest Type as its components.

These models are combined to get the Fire Vulnerability Model.


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Fire Vulnerability Model

Terrain Risk Model

As per the Terrain risk model

1. Areas facing the sun will be drier and hotter hence sensitive to fire.

2. Fire will spread easily and quickly on upward slopes than the plain surface

and also due wind velocity which will more on higher slopes.

3. Higher elevation will be less sensitive to fire due to less oxygen as

compared to lower elevation where there is more oxygen.

4. The past fire history will also be used to determine risk index.

Generation of required data from the original data

Using ArcGIS software, ‘Division boundary’, ‘Range boundary’ and ‘Beat boundary’,

Reserved Forests’ and ‘forest enclosures’ layers are extracted. Also using ArcGIS

software, a 1 km radius buffer is created around the Reserved Forests of the division.

Using Reserved Forests layer and the buffer, the roads falling within the Reserved Forests

Digital Elevation

Model (DEM

Aspect

Slope

Elevation

Aspect Risk

Slope Risk

Elevation Risk

Terrain Risk

Fire

Vulnerability Model

Terrain Risk Model (Aspect, Slope, Elevation

Human Influence Proximity to Forests

Fuel Risk (Forest types)

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and around the Reserved Forests within 1 km distance are extracted from Divisional

‘Road Network’ layer to generate ‘forest roads’ layer. Using Reserved Forests layer and

its 1 km radius buffer, the villages falling within the Reserved Forests and around the

Reserved Forests within 1 km distance are extracted from Divisional ‘Location of

Villages’ layer to generate ‘forest villages’ layer. The MODIS fire data gives only point

location of centre of pixel in which fire occurred. It will not give the extent of fire burnt

area. Using Reserved Forests layer, location of fire spots falling within the Reserved

Forests are extracted from MODIS fire data to generate MODIS fire layer for each year.

Using ERADAS Imagine software, Aspect, Elevation and Slope maps for Tirupattur

Forest Division are generated using SRTM data. in raster format The forest enclosures,

forest roads, forest villages, division fire & MODIS fire layers are in vector format. These

maps/layers are the input data for vulnerability analysis. The 10 year forest fire data and

MODIS fire data are over-layed on Aspect, Elevation and Slope maps to assess and

identify fire sensitive aspects, elevation zones and slope zones. Dry matter availability is

one of the reasons for fire spreading. Forest type map is used to assess and identify fire

sensitive zones based on dry matter availability. In India, 99% of forest fire occurrence is

anthropogenic in nature. Public movement are generally more around forest enclosures,

forest villages and along forest roads. Buffer zones are created around forest enclosures,

forest villages and forest roads to assess and identify fire sensitive zones. To have proper

balance of effect of different factor which are causes of fire, weights are given after

dividing it into different fire sensitive zones like High, Moderate and Low.

Slope Risk:

Topographical factors have a large effect on the spreading speed of fire. The

steepness of slope has a great influence on the spreading of fire. The 11 year forest fire

data is over-layed on Slope Zone and fire occurrence on each Slope zone is studied.

According to area burnt in each slope zone, the weightage for slope is given as in Table5.

Table 5: Slope weightage

Sl. No Slope Zone

(in Degree)

Burnt Area

(in Ha) Weightage

1 0 – 15 1945.09 4

2 15 – 30 1368.52 5

3 30 – 45 202.12 3


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4 45 – 90 37.38 2

Aspect Risk:

Aspect is the direction in which slope faces. The 11 year Fire data and MODIS

data are over layed on Aspect and the intensity of fire was studied. According to fire

occurrence the weightage is given to each aspect is as in Table 6.

Table 6: Aspect weightage

Sl. No Aspect Burnt Area

(in Ha) Weightage

1 North 237.08 3

2 North Eastern 283.33 3

3 East 429.18 4

4 South East 681.93 5

5 South 428.141 4

6 South West 402.51 5

7 West 615.74 5

8 North West 469.21 4

Elevation Risk:

Elevation of an area above the sea level influences fire risk for the fact that fire

spreads quicker uphill than downhill. Availability of fuel like grasslands in higher

elevation also influences fire risk. The 11 year fire data and MODIS data are over layed

to assess the weightage for different elevation zones as in Table 7.

Table 7: Elevation weightage

Sl. No Elevation Zone (in Meters) Burnt Area Weightage

1 0-200 1498.12 4

2 200-500 1424.45 5

3 500-1000 344.909 3

4 > 1000 285.64 2

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Human Influence Model:

Human activities will be more around forest villages, fringe villages and

all along forest roads and the proximity to forests influences forest fire.

The buffers were created for villages and roads falling with in 1 Km radius.

Also buffers were created within and outside Reserve forests. Weightage is given to their

proximity to villages and roads as in Table 8 and 9 respectively.

Table 8: Road weightage

Sl. No Buffer zone Weightage

1 0 – 100 5

2 100 – 300 3

3 300 – 500 1

Table 9: Village weightage

Sl. No Buffer zone Weightage

1 0 – 500 5

2 500 – 1000 3

3 1000 – 1500 1

Forest Roads

Proximity to Forests

Road Risk

Villages Proximity to Forests

Village Risk

Human Influence


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Fuel Risk Model

Dry matter availability in the forests is the major cause for fire and fast fire

spreading. Forest Type is used to assess the dry matter availability within the forests

during the post winter season and pre summer season. The dry matter content will be

more forest types like, Tropical dry deciduous, Tropical thorn forests, Tropical thorn

scrub will be High and for dry grasslands it will be very high. Fire will be moderate in

Tropical moist deciduous and low in Evergreen forests. However Rocky area, Sand,

Barren and water bodies has no weightage. The weightage for different Forest Type is

given in Table 10.

Table 10: Fuel risk weightage

Sl. No Forest Type Weightage

1 Water 0

2 Southern dry mixed deciduous forests 2

3 Dry deciduous scrub 5

4 Dry savannah forest 5

5 Dry grassland 5

6 Dry bamboo brake 5

7 Dry tropical Riverine forest 1

8 Secondary dry deciduous forest 3

9 Southern thorn forest 4

10 Carnatic umbrella thorn forest 4

11 Southern thorn scrub 5

12 Tropical dry evergreen forest 1

13 Tropical dry evergreen scrub 1

14 Eucalyptus 3

Forest Type

Vegetation Dryness

Fuel Risk

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Fire Map 2001 Fire Map 2002



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Fire Map 2011 Fire Recurrence map

(2001 – 2011)


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Forest Fire HOT SPOT (2001 – 2011)

Forest Fire report map 2006-2011

Forest Fire Occurrence (2001-2011)

Elevation Zone

Forest Fire Occurrence (2001- 2011) Slope Zone

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Result and Discussion

This study conducted a GIS based assessment of the Tirupattur Forest Division by

combining vulnerability to ignition and spread variables in multi criteria evaluations.

Using Raster calculation facility, sub models are created by using suitable algorithm. By

integrating slope, aspect, vegetation, roads, human interference into a single analysis,

results show that forested areas in close proximity to anthropogenic features exhibit a

higher vulnerability to forest fire. The generated Terrain risk, Aspect risk, Elevation risk

and Slope risk are combined together. Human influence and Fuel risk are directly used

with the terrain risk to arrive the final vulnerability data for the entire division with

appropriate weightage. These sensitive zones are crossed and/or over-layed to generate

the ‘Forest fire Vulnerability Map’ for the Division. The final data is reclassified into five

classes as Very Low, Low, Moderate, High and Very High. The Final Forest

Vulnerability data was arrived by using the Reserve Forest boundary for Tirupattur

Division. As per the data generated the vulnerable area falling in different category are

sown in Table 11.

Table 11: Results

Sl. No Vulnerability Area (in Ha.)

1 Very Low 114.68

2 Low 2018.43

3 Moderate 9144.98

4 High 22247.52

5 Very High 37818.29


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The Fire Vulnerability map itself serves a great purpose for the field staff in

understanding the vulnerable areas. It shows the spatial distribution of the various high

and very high fire vulnerable areas so that he can station himself at a strategic location for

effective fire monitoring. The map also shows the various routes and the nearby village,

so that necessary help can be sought in case the need arises. The first few hours of forest

fire is very important for the fact that if the fire is noticed and fire control measures are

taken it is much easier to bring it under control than when it not attended and it grows

very fast and becomes out of control. From the data obtained, beat wise vulnerable areas

can be identified. The map also gives beat wise vulnerable areas which are of tremendous

knowledge and use to the frontline field staff. From the data and maps generated it quite

clear that areas near to human impact increased vulnerability to forest fire. The influence

of slope and aspect, although important in affecting forest fire vulnerability did not

display a significant effect in the chosen study area due to a gently sloping terrain in

parts. The other major conclusion was the vegetation type i.e. the grassland and dry

deciduous forest type are highly vulnerable to fire and if these areas are near to habitation

i.e. a village or road the vulnerability is very high and it is also reflected in past data.

The GIS model used for this assessment separated forest fire into two phases and was

able to assess forest fire vulnerability for both fire ignition and fire spread. This type of


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model allows for factors that influence fire in different ways to be incorporated in a way

that accurately reflects the way forest fire is influenced. This model was able to depict the

areas of high, medium and low risk areas and matches with the past history of fire

occurrences. The development of the high, medium and low vulnerability levels allows

for a complete understanding of potential in forest fire in Tirupattur Division. It also helps

in identification of suitable sites for positioning the fire watchers and construction of Fire

watch towers. Strengths of the GIS model include the ability to integrate multiple

variables into a single model, the potential for simple alteration to fit changing needs, and

the potential to apply this model in other areas where data access is adequate. Limitations

of this GIS approach include the absence of variables such as tree species and direct soil

moisture data, as well as the lack of accounting for temperature and precipitation data

which changes over short time periods. The results provide a greater understanding of

forest fire vulnerability and provide a base for forest fire prevention and management

policies.

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References

1. Working plan for Tirupattur Forest Division

2. Tegan Buckingham, Daniel Felicetti and James Stephens, A GIS-Based MCE

Forest Fire Vulnerability Model for the City of Greater Sudbury, Ontario.

http://www.uoguelph.ca/geography/research/geog4480_w2011/Group07/index.sht

ml

3. http://forestfireindia.org/urls/fires_india.html

4. https://earthdata.nasa.gov/data/near-real-time-data/firms/active-fire-data

5. http://activefiremaps.fs.fed.us/gisdata.php

6. Morvan, D., Dupuy, J. L., Porterie, B., & Larini, M. Multiphase Formulation

Applied to the Modelling of Fire Spread Through A Forest Fuel Bed. Proceedings

of the Combustion Institute, 28, 2803-2809, 2000.

7. Sowmya, S. V., & Somashekar, R. K. . . Application of Remote Sensing and

geographical information system in mapping forest fire risk zone at Bhadra

wildlife sanctuary, India. Journal of Environmental Biology , 31 (6), 969-974,

2010

8. Gonzalez, J. R., Kolehmainen, O., & Pukkala, T. Using expert Knowledge to

Model Forest Stand Vulnerability to Fire. Computers and Electronics in

Agriculture , 107-114, 2007

9. Arienti, M. C., Cumming, S. G., & Boutin, S. Empirical models of forest fire

initial attack success probabilities: the effects of fuels, anthropogenic linear

features, fire weather, and management. Canadian Journal of Forest Research , 36,

3155-3166, 2006

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