2008 Forest Fire Vulnerability in Central Kalimantan

7/30/2019 2008 Forest Fire Vulnerability in Central Kalimantan

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Wildfire Vulnerability index - 1

DEVELOPMENT OF WILDFIRE VULNERABILITY INDEX IN

CENTRAL KALIMANTAN

I Nengah Surati Jaya1, Rizaldi Boer

1, Samsuri

1, and Fathurakhman

2

1Bogor Agricultural University,

2CARE - Indonesia

INTRODUCTION

Since the first largest forest fire destroyed approximately 3 million Ha of East Kalimantan

Forest in 1982/83, the fires had been raised to global attention as environmental and

economic issues. The fires had caused direct effect on ecosystems, particularly on their

contribution to carbon emissions and their impact on biodiversity. During the 1997/98

ENSO, up to 25 million hectares of land worldwide were affected by fire. The areas

affected by fire in 1997/98 are approximately 11.7 million hectares. Indonesia had the

most severe fires in the world with similar problem with the ENSO in 2002. During these

fires, the forest degradation and deforestation due to fire had caused economic cost in the

range of USD 1.6~2.7 billion. The cost of smoke haze pollution was in the range of USD

674~799 million.

During 2003, approximately 34,655 hotspots were detected. These hotspots were spread

out to almost whole islands in the country. The largest number of hotspots were found in

Central Kalimantan Province (7341), and then followed by Riau Province (5380), West

Kalimantan Province (4860), and South Sumatra Province (3367). Almost no hotspot was

found in Bali, NTT, North Maluku and Papua (hotspot less than 10). Up to the present, the

rate of forest fire damage are ranging from 0.1 to 0.5 million hectares per year. As

reported by JICA Project in 2002, the months having frequent hot spot findings are starting

from July to November then continuously decrease from December to May of the

following year. As described in Figures 1 ~ 5, large amount of hotspot in Central

Kalimantan commonly occurred between July and October.

To reduce the forest fire disaster as well as to prevent and control forest fire, the Ministry

of Forestry of Indonesia (MoF) developed collaboration with some International Donor

Countries such as JICA (Japan International Cooperation Agency), European Union (EU)

and the Government of Germany (GTZ) for establishing an early detection system using

NOAA AVHHR satellite imagery. The data obtained from NOAA are mainly used for

detecting hot spot and or smoke/haze coverage. The hot spots derived from NOAA

AVHRR are mainly used as a component of early detection system. The hot spot is an

early indication of forest fire occurrence. The hot spot recorded as one pixel is not

absolutely fire, but it express the temperature that relatively higher than its surrounding

pixels (areas). The temperature detected are ranging from 310oK (37

oC) in the night and

315oK (42

oC) in the daytime. The geographical coordinates of the hotspots are recorded by

the system then sent to the MoF and forest manager. The same hotspot that continuously

recorded more than 3 day respectively would be predicted as fire.

Up to now, although the forest fire occurrence are getting worsen, we have no information

yet regarding forest fire prone area. That is why, the development of integrated forest fire

sensitive zoning by taking into consideration the human and biophysical factors is needed.

Wild fire may occur in any vegetation cover type when conditions are favorable for

burning. However, every fire need some spark or flame to start it.


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In this report, the authors develops forest fire models that pertain to the first phase of fire

management, i.e., pre-fire planning (fire risk models) through developing wildfire

vulnerability index. This index is needed for during program development for forest fire

prevention and suppression. At the beginning of any fire protection works it is important to

establish the source of sparks of flames which under favourable condition could start a

forest fire. The fire prevention is one of the most important functions of the fire control

service. Prevention activities are very often the most economical way of reducing firedamage and losses. Fire prevention can be started without any expensive equipment.

One part of forest fire prevention planning is to make an analysis of the fire risk and causes.

The various types of risks and hazards in the area should be considered in a wildfire

prevention analysis. In organizing wildfire prevention in particular area, one must first

know what the usual causes of fire are, and the risk and hazard involved. Risk is defined

as the chance of fire starting from one cause or another such as people, lighting, electricity

etc. While, hazard is the fuel complex by type, arrangement, volume, condition and

location that forms a special threat of ignition or difficulty in suppression (Heikkila,

Gronqvist and Jurvelius, 1993). Area covered with grass, brush and forest fuels are

examples of hazards.

In fire prevention programs, it would be helpful if the organizer can make a summary of

the main problems, such as what are the main causes of wildfire, location of very risk area,

location of the area that should be protected and what are the main objective and method

for fire protection. General guideline of a wild fire prevention plan may include basic

data and information that consisted of fire occurrence map, fire statistics graph, fire risk

area map, hazards area map and forest operation map.

Forest fire risk may be considered at different spatial and temporal resolutions: global and

local; short-term and long-term; These forest fire risks are very important in forest and

land fire management. Comprehensive consideration for forest fire risk implies taking

into account a wide range of variables. Chuvieco and Falas (1999) distinguished between

the concept of risk associated to the beginning of a fire (fire ignition risk or flammability)and the spreading of active fire (fire behaviour risk or fire hazard).

The fire suppression (fire behavior models) and post-fire evaluation (fire effects and

economics models) are the remaining models that did not describe in this study. In each

case, different variables and different risk weight should be considered. In this study, the

model considers variables that related to fuel type such as land cover type, soil type

(peat/non-peat) and source of fire from human activities such as land use (and land status)

and proximity (distance from streams/rivers, road, villages and city). The concern here is

the development of wildfire vulnerability for estimating the frequency of a forest fire

taking place at a particular location and time as a function of explanatory variables. In this

study, the authors used hotspot data as variable that express the frequency of fire

occurrence. Forest fire risk zones are locations where a fire is likely to start, and fromwhere it can easily spread to other areas. This fire risk zone map, furthermore, can be used

to make a precise evaluation of forest fire problems and decision on solutions. Recently,

most application of GIS to forest fire risk mapping have been developed at the local and

global level. In this study, the study area cover a relatively wide area (Kalimantan Tengah

Province) at medium resolution (500 m grid size).


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Objective

The study objective is to:

Develop a forest fire vulnerability index in Central Kalimantan To identify the explanatory variables that contributes forest fire prone significantly. To develop a reliable technique to detect post-fire condition using remote sensing data

METHODS

Study Area

The study was carried out in Central Kalimantan Province, Indonesia that represents a wide

variety of forest cover, forest type and socio-economics conditions (accessibility) of the

area. The study was focused to analyze the fire occurred from 2000 to 2004. Howeversince the land cover data used in this study was made based on the satellite data that

acquired in 2001/2002, vulnerability index model was developed using the hotspot

acquired in 2003 (particularly that recorded in October)

Supporting Data

To support the analysis, the data used in this study include digital forms of:

a. Administrative border map,b. Villages and cities center mapc. Road networksd. Stream or riverse. Land coverf. Land use (status)g. Land systemh. Soil typei. Hotspots coordinate.Software and Hardware

The main software that used to perform the study are ERDAS IMAGINE ver. 8.7., ArcView

GIS ver.3.2., and statistical software SPSS. All of the analyses were performed using

personal computer, digitizer, scanner and printer. To get ground truth condition, we use

GPS, Hagameter, phiband and, Spiegel Relascope

A long-term statistic of fire occurrence, predicted with hotspot (HS) may help in

developing forest fire risk (see Figures 1 ~ 5). Although the hot spot data are not

absolutely fire, however, the hotspot data are closely related to forest and land fire


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occurrence and smoke fog. Therefore, these data could be used to develop wildfire risk

map that then referred to as fire vulnerability map. Syaufina et al. (2004) mentioned that

prevention activities should be performed before water deficit starting to occur in the area

(before January) and during the dry season (May September).,

Kurva Sebaran Temporal Hotspot Tahun 2001

0

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

Jan Feb Mrt Apr Mei Jun Jul Ags Sep Okt Nop

mlah Hotspot

Aceh

Bali

Bangka Belitung

Banten

Bengkulu

Brunei

DIY

DKIJak

Jabar

Jateng

Jatim

Kalbar

Kalsel

Kalteng

Kaltim

Maluku

NTB

Papua

Sulsel

SultengSultra

Sulut

Lampung

Riau

Sumbar

Sumsel

Sumut

Jambi

Thailand

Peninsula Malaysia

Philippines

Serawak & Sabah


0

200

400

600

800

1000

1200

1400

1600

Jan

Feb

Mrt

Aprl

Mei

Jun

Jul

Ags

Sept

Okt

Nop

Des

Bulan

mlah Hotspot

ce

Jambi

Sumsel

Sumut

Bengkulu

Riau

Sumbar

Lampung

Jabar

Jateng

DIY

Jatim

Kalbar

Kalsel

Kalteng

KaltimSulsel

Sulteng

Sultra

Bali

Brunei

Serawak & S

Singapore

Malaysia

Phillipines

Thailand

Number of hotspot

Month

Figure 1 Hotspot frequency during the year 2000

Figure 2. Hotspot frequency during the year 2001

Number of hotspot


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-500

500

1500

2500

3500

4500

5500

6500

7500

Jan Feb Mrt Apr Mei Jun Jul Ags Sep Okt Nop Des

Bulan

mlah Hotspot

Aceh

Bangka Balitung

Banten

Bengkulu

DI Yogyakarta

DKI Jakarta

Jabar

Jambi

Jateng

Jatim

Kalbar

Kalsel

Kalteng

Kaltim

Lampung

Maluku

Papua

Riau

Sulsel

Sulteng

Sultra

Sulut

Sumbar

Sumsel

Sumut

ThailandVietnam

Brunei

Sarawak & Sabah

Singapura

Philippines

Paninsula Malaysia


Number of hotspot


0

1000

2000

3000

4000

5000

6000

7000


Bulan

mlah Hotspot

Aceh

Bali

Bangka Balitung

Banten

Bengklu

DIY

DKI

Jambi

Jateng

Jatim

Jawa Barat

Kalbar

Kalsel

Kalteng

Kaltim

Lampung

Maluku

Maluku utara

NTT

Riau

Sulsel

SultengSultra

Sulut

Sumbar

Sumsel

Sumut

Thailand

Brunei

Paninsula Malaysia

Philippines

Sarawak & Sabah


Number of hotspot


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Study methods

The study method includes the following steps:

1. Data pre-processing that includes image rectification, registration and geo-referencingand data cropping

2. Data Processing that includes hotspot density computation (spatial analysis), andspatial operation (identity, intersect and buffering). Assuming that each hotspot (HS)

was resampled to represent an area of 1 km x 1 km, the radius for computing the HD

will be optimally interpolated using 2-km radius. In this study, the Hotspot density

(HD) was computed using Kernel method with 2-km radius. To provide information

that is more precise the cell size during the HD development is 500 m x 500 m. .

The Data hotspot derived from several agencies such as JICA, EU and GTZ then

uploaded to the GIS software. In this GIS software, the hotspots are then spatiallyanalyzed providing the hotspot density (number of hot spot per squared kilometer;

HS/sq km). Furthermore, the hotspot densities were used to develop mathematical

model expressing the area vulnerability.


0

1000

2000

3000

4000

5000

6000

7000

8000


Bulan

Jumlah hotspot

Aceh

Bali

Bangka Balitung

Banten

Bengkulu

Brunei

DIY

DKI

Jabar

Jambi

Jateng

Jatim

Jatim

Kalbar

Kalsel

Kalteng

Kaltim

Lampung

Maluku

Maluku utara

NTB

NTT

Riau

Sulsel

Sulteng

Sultra

Sumbar

Sumsel

Sumut

ThailandPaninsula Malaysia

Philippines

Sarawak & Sabah

Singapura


Number of hotspot


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3. Identification of Explanatory variables. Among various variables that may affect theforest fire intensity, the study identifies some variables that relevant to each study site.

The followings are the variables that used for each study area:

(a)Proximity from village center (X1)(b)Distance from road network (X2)(c)Distance from stream or river networks (X3)(d)Distance from city centers (X4)(e)Land use/status (X5)(f) Land cover (X6)(g)Soil type (X7)(h)Land system (X8)All fires are the result of chemical process that occur when fuel, heat and oxygen

are brought together in the necessary combination that support combustion. In a forest,

there is abundance of fuel and air (oxygen) always present. The fire can be put out, if

we can eliminate one of these three variables. Thus, fuel and heat would be the most

important factors that should be considered in affecting wildfire. As mentioned above,

variables X1 ~ X4 are mostly related to a source of ignition, while X6 and X7 are

related to fuel. Some of the sources of the heat which may cause forest or land fire are

flames (e.g. match), ember (e.g. cigar), electrical sparks from man made sources,

lighting, and friction (e.g., forest machines, trains etc).

4. Development of classes for each variablesThe factors and variable-classes considered in the study are shown in Table 1.

Table 1 Variable classes used in this study.Variables Classes

Distance to village centers Buffer with 1000-m interval

Distance to road networks Buffer with 1000-m interval

Distance to streams/river networks Buffer with 1000-m interval

Distance to city centers Buffer with 1000-m interval

Status of Land use*) Timber estateArea for conservation of hydrology

Deep peat conservation

Area for production development

Area for flora and fauna conservationProduction forest

Water bodies

Limited production forest

Area for settlementsAgric land "handil"

Area for forestry training and education

Black water conservationNatural Park

Transmigration

Forest Protection and conservation area

Land cover Shrubs

Dry land agriculture (paddy field)Lowland forest


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Bare landEstate

Secondary forest

Mountain forest

Soil type Peat

Non-peat

Land system Swampy floodplainsShallow peat

Deeper peat swampsPermanently waterlogged

Undulating sandyMeander belt

Shallower peat

Coalescent estuarine

Minor valley floorsSedimentary ridges

Rolling plains

Alluvial fans and mountain

*) These classes are derived from Spatial land-use planning of Kalimantan Tengah

Province.

5. Weight DeterminationThe method used to determine weight of each variable is Composite Mapping Analysis

(CMA). This method had been successfully implemented in several research conducted

previously in East Kalimantan, Riau and West Kalimantan (Boonyanuphap et al, 2001;

Purnama and Jaya, 2007). In this study, the relationship between hotspot density and

wildfire risk factors were analyzed to derive a vulnerability scores. The variables that have

close correlations were selected then composed to develop linear multiple regression. The

weight of each factor is the proportion (ratio) of each coefficient of regression to the total

of all coefficients of the regression

6. Score developmenta. Actual score (SC)The actual score of each sub-factor is computed as follows:

=

Ei

Oix

Ei

OiXi

100 Equation 1

=

100

iTxFEi Equation 2

Where

Xi is score for each factor;

Oi is the number of hotspot that observed in each class (observed hotspot);

Ei is the number of hotspot that expected to be found in each class (expected

hotspot),

T is the total hotspot;

F is the percentage of area in each sub-factor


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b. Estimated score (ES)Computing the score estimate (ESC). The actual scores (SC) obtained are usually in

irregular pattern. Thus, to smooth out the score systematically, the authors evaluate

the relationship between the class of each variables and hotspot density. The

estimate of scores (estimated scores) were derived using the regression equationmodel developed.

c. Rescaled Score (RSC)Since the developed vulnerability index was composed using several variables

having different unit and different scale, it is needed to standardize the score of

each variables. The standardization of each score was done by rescaling the range

of estimated score into the range between 10 to 100. The rescaling was done using

the following formula:

( ) MinRMinRMaxREMaxE

EinputE

outR ScoreScoreScoreScoreScore

ScoreScoreScore ...

min..

min..

. +

=

Equation 3

Where:

ScoreR.out = Output of Rescaled score that would have values ranging from

ScoreR.min andScore.R.Max.

Score E.input = Score derived using regression line (estimated score)

Score E.Min = minimum value of estimated score

Score E.Max = maximum value of estimated score

If the Score R.min and score R.max are set to have a range between 10 and 100, the

rescaling equation can be simplified into Equation 4

( ) 1090min..

min..

. +

=

EMaxE

EinputE

outRScoreScore

ScoreScoreScore Equation 4

7. Vulnerability classesIn this study, the vulnerability classes were developed using the spatial resolution of

the hotspot having size 1 km x 1 km. Theoretically, during dry season when wildfire

occurred, one hotspot will rapidly spread within the radius of 1 km. This means that

the very high risk area will be within radius 1 km representing the area of

approximately 3.141 km2. In this case, the hotspot density would be 0.3183. Within

the radius from zero to 0.5 km, which is represent the area of 0.196 sq km, the author

categorize it into extremely high risk, while from the radius of 0.5 km to 1 km is

referred to as very high risk. Farther distance from fire center would have lower


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risky areas. Comprehensively, the authors classify the vulnerability classes into five

classes as described in Table1. Based on the weights derived from the CMA method,

the spatial distribution of forest fire prone can be developed.

Table 2. Vulnerability classes based on hotspot density and radius from HS

Radius fromthe HS (fire)

center (km)

Hotspot density(HS/ sq km)

Area represented byeach hot spot

(sq km)

Vulnerability classes

< 0.5 > 1.273 < 0.785 Extremely high risk

0.5 < 1.0 0.318 - < 1.273 0.785 - 3.141 Very high risk

1.0 - < 1.5 0.141 < 0.318 3.141 - 7.069 High risk

1.5 - < 2.0 0.080 - < 0.141 7.069 - 12.566 Medium risk

> 2.0 < 0.080 > 12.566 Low risk

8. Model verificationModel verification was done to evaluate the coincidence between vulnerability score and

fire incidence (HD). A number of verification plots having size 0.1 Ha 1 km x 1 km were

selected randomly and represent all forest and land fire risk classes (low, medium and high

risks). The map that used as reference is hotspot density map that derived from

selected/available hotspot information.

In this study, sample area representing all vulnerability classes were selected. The map

representing vulnerability values and HD were then superimposed Coincidence was

evaluated by examining the correlation between vulnerability score and HD.


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RESULTS AND DISCUSSION

A. Spatial Distribution of Hotspot Density

Forest fire vulnerability map was created based upon hotspot distribution occurred in

Central Kalimantan Province, particularly in the months which has densest hotpot density.Since we used the land cover digital map that published in 2003 (which probably

developed from 2001/2002 satellite data), then the hotspot we used was the hotspot that

recorded in 2002, particularly in September 2002 (see Fig 6). From this hotspot data, the

hotspot density was derived using Kernel method having radius 2 km and 500 m cell size.

As shown in Figure 7, the dense hot spot having more than 0.5 hotspot per km2 are found

in Seruyan, Kota Waringin Timur and Pulang Pisau Regencies as well as in Palangkaraya

City. The data also show that denser hotspot commonly found in peat land areas. For

Comparison purposes, the hotspot distribution recorded in September 2004 and its spatial

density are depicted in Figures 8 and 9.

Figure 6 Hotspot distribution occurred in October 2002


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Figure 7 Hotspot density distribution in October 2002

Figure 8 Hotspot distribution occurred in September 2004


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Figure 9 Hotspot density distribution in September 2004

B. Hotspot Density Evaluation

As mentioned previously, since the land cover data that used in the study was published in

2003 and probably made from satellite image acquired in 2001/2002, and then the hotspot

data that analyzed for score development are the hotspots, which recorded in October 2002.This study considers 8 (eight) factors or variables that affect the forest fire vulnerability,

i.e., distance from surrounding villages (X1), distance from road (X2), distance from

stream/rivers (X3), distance from surrounding cities (X4), land status or land use (X5),

land cover (X6), soil type (X6) and land system (X7). The following are the analysis

results of each variable.


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1. Distance from Village centersTable 3 Data of HD in various distances from village centers

Village

Distance

(km)

Area

(Ha)

Total

Hospot *)

Hotspot

density

(HS/km2)

Village

Distance

(km)

Area

(Ha)

Total

Hospot *)

Hotspot

density

(HS/km2)

1 2,311.2 3.497 0.151 21 2,735.8 3.761 0.137

2 5,941.7 9.362 0.158 22 2,526.0 3.439 0.136

3 7,675.6 13.142 0.171 23 2,264.5 3.163 0.140

4 8,389.0 16.242 0.194 24 2,059.8 3.194 0.155

5 8,589.5 17.809 0.207 25 1,819.3 2.956 0.162

6 8,489.9 17.915 0.211 26 1,638.0 2.499 0.153

7 8,063.1 18.294 0.227 27 1,505.9 2.018 0.134

8 7,859.2 19.227 0.245 28 1,342.7 1.676 0.125

9 7,533.8 18.727 0.249 29 1,109.8 1.378 0.124

10 6,810.6 16.865 0.248 30 864.4 1.065 0.123

11 6,101.6 14.693 0.241 31 705.0 0.872 0.124

12 5,478.0 12.252 0.224 32 566.2 0.703 0.124

13 4,969.8 10.547 0.212 33 499.3 0.614 0.12314 4,532.2 9.109 0.201 34 463.9 0.578 0.125

15 4,210.4 7.761 0.184 35 428.9 0.534 0.125

16 3,965.6 6.934 0.175 36 394.2 0.490 0.124

17 3,720.8 6.543 0.176 37 350.1 0.429 0.123

18 3,469.6 5.865 0.169 38 157.5 0.195 0.124

19 3,306.9 4.921 0.149 39 41.4 0.048 0.116

20 3,022.2 4.257 0.141 40 0.9 0.001 0.117

Between the ranges from 1 km to 40 km, there is significant relationship between hotspot

density (HD) and distance from village center. With polynomial form, 82% variation ofdistance from village center will affect the variation in HD. In Figure 10, shows that

between the buffer range of 0 to 9 km from the village centers, the HD tend to decrease

when the distance getting closer. Within this buffer zone, the local people may control the

fire occurrence. Inversely, in the area of the village buffer located further than 9 km, the

HD tend to increase as the distance become closer. For the analysis, the HD distribution

was cut off at the buffer 7 km (Figure 11).

By using polynomial order 3 equation, the correlation between HD and distances from

village center are very close having coefficient of determination of 88.25%.

,

Remarks: *) derived by multiplying mean value of hotspot density in each

ol on b the area of each ol on.


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Hotspot distribution

y = 0.2534x-0.1697

R2= 0.3618

y = 2E-05x3- 0.001x

2+ 0.0147x + 0.1524

R2= 0.8234

0.1

0.12

0.14

0.16

0.18

0.2

0.22

0.24

0.26

0.28

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39

Distance fromsurrounding villages (km)

Number of hotspots per km2

Figure 10 Hotspot density distribution from the village centers (further than one km)

Hotspot Distribution by Distance fromVillages

y = 0.3018x-0.2572

R2= 0.7867

y = 2E-06x3+ 7E-05x

2- 0.0078x + 0.254

R2= 0.8825

0.10

0.120.14

0.16

0.18

0.20

0.22

0.24

0.26

0.28

0.30

7.00 10.00 13.00 16.00 19.00 22.00 25.00 28.00 31.00 34.00 37.00 40.00


HD

Power (HD)

Poly. (HD)

Number of hotspot per km2

Figure 11 Hotspot density distribution from the village centers (further than 7 km)

2. Distance from road networksSimilar to the HD from the village centers, the HD had systematical distribution form in

relationship with the distance from the road networks. In the range between 1 and 7 km,

local people may control the fire occurrence (see Figure 12). Thus for the vulnerability

analysis, the distribution of the HD was cut in the buffer of 7 km (see Figure 13). By

using the equation of polynomial order 3, the coefficient determination provided is 85%.


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Table 4 Data of HD in various distances from road network

Distance

fromroad

networks

(km)

Area

(Ha)

Total

Hotspot *)

Hot spot

density(HS/

km2)

Distance

fromroad

networks

(km)

Area

(Ha)

Total

Hotspot *)

Hot spotdensity

(HS/ km2)1 10673.587 18.1770 0.1703 23 1623.221 2.2640 0.1395

2 10011.215 17.7730 0.1775 24 1469.358 2.0940 0.1425

3 9345.955 17.8680 0.1912 25 1346.743 1.8510 0.1374

4 8810.032 18.2410 0.2070 26 1228.754 1.5940 0.1297

5 8427.065 18.0780 0.2145 27 1101.524 1.3780 0.1251

6 8122.028 18.7370 0.2307 28 980.480 1.2100 0.1234

7 7747.782 19.7390 0.2548 29 747.945 0.9330 0.1247

8 7122.900 18.5140 0.2599 30 607.772 0.7530 0.1239

9 6527.433 16.3590 0.2506 31 569.001 0.7060 0.1241

10 6074.169 13.4790 0.2219 32 530.800 0.6590 0.1242

11 5592.849 10.9290 0.1954 33 494.978 0.6130 0.1238

12 5144.133 9.3000 0.1808 34 467.547 0.5710 0.1221

13 4706.078 8.0190 0.1704 35 436.250 0.5420 0.124214 4282.310 7.5530 0.1764 36 384.384 0.4740 0.1233

15 3652.314 7.0230 0.1923 37 346.760 0.4280 0.1234

16 2933.171 6.1940 0.2112 38 310.040 0.3840 0.1239

17 2712.141 5.2700 0.1943 39 271.906 0.3380 0.1243

18 2573.570 4.2240 0.1641 40 180.175 0.2190 0.1215

19 2357.407 3.3480 0.1420 41 104.584 0.1300 0.1243

20 2123.107 2.7500 0.1295 42 55.871 0.0690 0.1235

21 1930.630 2.4230 0.1255 43 10.334 0.0130 0.1258

22 1775.899 2.3540 0.1326


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Distribution of Hotspots fromroads

y = 4E-05x2- 0.0047x + 0.2346

R2= 0.6858

0.1000

0.1200

0.1400

0.1600

0.1800

0.2000

0.2200

0.2400

0.2600

0.2800

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43

Distancefromroads (km)

HD

Poly. (HD)

Hotspot number per km2

Figure 12 Hotspot density distribution from the road networks (further than one km)

Hotspot distribution by fromdistance

y = 0.2804x-0.2398

R2= 0.8051

y = 0.0002x2- 0.0091x + 0.2479

R2= 0.8572

0.10

0.12

0.14

0.16

0.18

0.20

0.22

0.24

0.26

0.28

7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43

Distance fromroads (km), > 7 k

HD

Power (HD)

Poly. (HD)


Figure 13 Hotspot density distribution from the road networks (further than 7 km)

3. Distance from rivers/streamsIn relation with the stream/rivers buffers, the HD distribution shows very systematic

pattern. Densest HDs were found in the river buffer of 1 km to 6 km having HD of

approximately 0.17 ~ 0.2 HS/km2. The HD gradually decreased as the distance increase.

From the buffer 10 km and more the HD is relatively constant (see Table 5 and Figure 14).

The polynomial equation provide 95% of coefficient of determination.


18/48


Table 5 Data of HD in various distances from river/stream networks

Distance from River/

stream networks(km)

Area

(Ha)S

Total

Hotspot

Hotspot density

(HS/km2)

1 52233.689 100.200 0.19183

2 28721.011 58.800 0.204728

3 18602.728 37.751 0.202933

4 12729.521 25.108 0.197242

5 8422.257 16.542 0.196408

6 5174.005 10.071 0.194646

7 3663.532 6.566 0.179226

8 2528.572 3.743 0.148028

9 1245.889 1.593 0.127861

10 765.975 0.942 0.122981

11 616.595 0.761 0.12342

12 575.372 0.713 0.12392

13 444.954 0.551 0.123833

14 171.034 0.212 0.123952

15 19.068 0.022 0.115377

Hotspot Density Distribution fromrivers/streams

y = 0.0002x3- 0.0041x

2+ 0.0191x + 0.1794

R2= 0.9459

0.10

0.12

0.14

0.16

0.18

0.20

0.22

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Distance fromrivers/streams (km)

HD

Poly. (HD)


Figure 14 Hotspot density distribution from the road networks (further than one km)


19/48


4. Distance from citiesTable 6 Data of HD in various distances from city centers

Distance

fromRiver/

stream

networks(km)

Area(Ha)

TotalHotspot

Hotspot

density(HS/km2)

Distance

fromRiver/

stream

networks(km)

Area(Ha)

TotalHotspot

Hotspot

density(HS/km2)

1 210.280 0.254 0.12079 29 3668.126 7.251 0.19768

2 671.245 0.833 0.12410 30 3563.137 6.798 0.19079

3 1156.268 1.539 0.13310 31 3456.727 6.442 0.18636

4 1605.341 2.409 0.15006 32 3407.552 6.180 0.18136

5 2148.367 3.347 0.15579 33 3380.495 6.192 0.18317

6 2637.160 4.157 0.15763 34 3331.509 6.455 0.19376

7 3054.601 5.004 0.16382 35 3061.408 6.212 0.20291

8 3184.147 5.528 0.17361 36 2573.594 5.570 0.21643

9 3363.195 6.167 0.18337 37 2058.020 4.333 0.21054

10 3555.969 7.044 0.19809 38 1618.861 3.054 0.18865

11 3771.988 8.168 0.21654 39 1378.852 2.538 0.18407

12 3883.636 9.004 0.23184 40 1230.244 2.402 0.19525

13 3946.344 9.424 0.23880 41 1081.313 2.270 0.20993

14 4028.006 9.664 0.23992 42 869.298 1.430 0.16450

15 4162.060 10.009 0.24048 43 718.625 0.938 0.13053

16 4305.801 9.954 0.23118 44 586.140 0.721 0.12301

17 4468.451 9.386 0.21005 45 469.104 0.577 0.12300

18 4613.248 9.259 0.20070 46 410.083 0.508 0.12388

19 4794.754 9.554 0.19926 47 389.801 0.479 0.12288

20 4651.192 9.484 0.20390 48 370.515 0.523 0.14115

21 4422.756 9.065 0.20496 49 351.213 0.636 0.18109

22 4424.298 8.535 0.19291 50 331.911 0.674 0.20307

23 4277.995 8.072 0.18869 51 303.791 0.558 0.18368

24 4192.537 7.572 0.18061 52 248.828 0.326 0.13101

25 3969.132 6.878 0.17329 53 200.741 0.246 0.12255

26 3792.049 6.375 0.16811 54 169.045 0.208 0.12304

27 3684.386 6.340 0.17208 55 73.574 0.091 0.12368

28 3636.064 6.938 0.19081 56 0.425 0.000 -

As described in Figure 15, the occurrence of HS also decrease in the range of 1 to 10 km

buffer distance from the surrounding city centers. It seems that the government authority

successfully control the fire in surrounding cities, particularly in the distance less than 13

km. In this case, the analysis of HD for wildfire analysis use the buffer of more than 10

km as shown in Figure 16. Statistical analysis shows that between the ranges of 10 km to

more than 55 km, the HD density increase as the distance getting closer


20/48


Hotspot distribution by distance fromneighbouring cities

0.10

0.12

0.14

0.16

0.18

0.200.22

0.24

0.26

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55

Distance fromneighbouring cities (km)

HD


Figure 15 Hotspot density distribution from the city centers (further than one km)

Hotspot distribution by distance fromneighbouring cities

y = -0.0019x + 0.2262

R2= 0.5111

0.10

0.12

0.14

0.16

0.18

0.20

0.22

0.24

0.26

10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55

Distance fromneighbouring cities (km)

HD

Linear (HD)


Figure 16 Hotspot density distribution from the city centers (further than 10 km)


21/48


5. Land useAS tabulated in Table 7 and Figure 17. The very dense HS of approximately 0.72/ km2 is

found in area that determined as timber estate. The lowest HD is found in the area

devoted as forest protection and conservation. This condition is very logic if land

preparation for establishing timber estate uses fire as major tool for land clearing.

Table 7 Data of HD in several land use

Status on land use Abbreviation

Area

(Ha)

Total

Hotspot

Hotspot density

(HS/km2)

Timber estate HTI 240.066 1.7410 0.7252

Area for hydrology conservation KH 14479.832 38.3710 0.2650

Deep peat conservation KGT 16042.780 39.0950 0.2437

Area for production development KPP 22551.894 43.7480 0.1940

Area for flora and fauna conservation KFF 5300.660 9.9640 0.1880

Production forest HP 39299.689 71.2270 0.1812

Water bodies DS 1503.355 2.4120 0.1604

Limited production forest HPT 23125.908 36.9280 0.1597

Area for settlements KPPL 9594.281 14.9550 0.1559Agric land "handil" KHR 1172.851 1.7530 0.1495

Area for forestry training and education PPK 1.505 0.0020 0.1329

Black water conservation KEAH 2181.446 2.8660 0.1314

Natural Park TW 280.947 0.3480 0.1239

Transmigration T1 67.301 0.0800 0.1189

Forest Protection and conservation area PPH 71.687 0.0850 0.1186

Hotspot distribution by land use status

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

HTI KH KG

TKPP

KFF

HP

DS

HPT

KPPL

KHR

PPK

KEAH TW T

1PPH

Land use

HD


Figure 17 Hotspot density distribution by land use status


22/48


6. Soil TypeTable 8 Data of HD by soil type

Soil typeArea

(Ha)

Total

Hotspot

Hotspot

density

(HS/km2)

non-peat (np) 55366 94 0.169683

peat 80549 170 0.210592

Total 35,914.2 63.6 0.4

Average ,957.1010 1.7875 0.1901

Table 8 shows that the hot spot density in peat areas is higher than in non-peat areas. As

described by Syaufina et al. (2004), peat area is a fragile ecosystem. When the peat forest

is cleared, the exposed peat will dry out quickly and easily ignited.

7. LandoverBeside weather and topography, fuel is one of the major factors, which influence fire

behavior. It is important to be familiar with certain properties and characteristics of the

fuels, which can include size of fuel, fuel arrangement, volume fuel, fuel type and fuel

pattern and condition. The size of fuel has an important role in determining the rate of

combustion of the fuel.

Based on the vegetation cover, the highest number of hotspot density is found in shrubs,

and then followed by dry land paddy field (lading) and lowland forest. During dry season,

dried matter of shrubs vegetation is sensitively combustible. In this coverage, the hotspot

density is approximately 0.30 HS/km2. As we all may aware, shrubs have large amount

of light (fine) fuels such as leaves, grass, small branches, twigs etc. This fuel needs very

little heat to reach ignition temperature. Once the grass begins to burn, it will burn very

quickly. Shrubs are frequently called to as fast-burning fuels.

In dry land paddy field (ladang), the human factor seem to have significant role. In this

land cover or land use, people use fire for land preparation. Thus, medium to small

branches, twigs, leaves are there. This land cover occupies the second largest hot spot

density.

In lowland forest area, usually has large amount of heavy (coarse) fuel such as logs,

stumps and standing trees. These fuels need more heat to reach ignition temperature in

comparison with the light fuel. The heavy fuels are also referred to as being slow-burning

fuels.

Table 9 Data of HD in several land cover

Land cover

Area

(Ha)S

Total

Hotspot

Hotspot density

(HS/km2)

Shrubs 5018.764 15.408 0.307008

Dry land paddy field 44587.291 94.144 0.211145

Lowland forest 78164.136 142.263 0.182005

Bare land 2524.350 3.810 0.150930

Estate 5596.256 7.922 0.141559

Secondary forest 23.405 0.028 0.119633


23/48


Hotspot distribution by Landcover

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

Shrubs Dryland agric. Lowland forest Bare land Estate Secondary

forestLand cover


Figure 18 Hotspot density distribution by land cover

8. Land SystemBased on land system, the more HSs were found in swampy floodplains, then shallow peat,

deeper peat having HD more than 0.21. During the dry season (July to October), these

land system areas have large amount of combustible materials Dried peat either in

shallow and deeper peat areas are very easy to be ignited (se Table 10 and Figure 19)

Table 10 Data of HD in several land system

Land system Area(Ha) TotalHotspot Hotspot density(HS / km2)

Swampy floodplains 2334.513 5.324 0.228056

Shallow peat 45690.828 99.556 0.217891

Deeper peat swamps 23117.630 49.495 0.214101

Permanently waterlogged 2034.532 4.068 0.199948

Undulating sandy 35811.998 66.186 0.184815

Meander belt 2869.327 4.486 0.156343

Shallower peat 7371.169 11.186 0.151753

Coalescent estuarine 13062.246 18.569 0.142158

Minor valley floors 2121.034 2.856 0.134651

Sedimentary ridges 50.568 0.063 0.124585

Rolling plains 1433.816 1.767 0.123238

Alluvial fans and mountain 16.541 0.019 0.114866


24/48


Distribution of Hotspot by Landsystem

0.10

0.12

0.14

0.16

0.18

0.20

0.22

0.24

Swampyfloodplain

Shallowpeat

Deeperpeatswamp

Permanentlywaterlogg

Undulatingsand

Meanderbelt

Shallowerpeat

Coalescentestuarin

Minorvalleyfloors

Sedimentaryridge

Rollingplains

Alluvialfansandmounta

Land system

HD

Hotspot density (Hs/km2)

Figure 19 Hotspot density distribution by land system

C. Score DevelopmentBase on the HD and variable data described previously, the actual score (SC), estimated

score (ESC) and rescaled score (RSC) were calculated and summarized in Table 11 ~ 18.

The actual score was computed using the ratio observed and expected hotspot, while the

estimated score was computed using the regression model derived from each variable. The

rescaled score was computed using the Equation 3.


25/48


1. Score for Village DistancesTable 11 Actual, estimated and rescaled score for village distance

VillageDistance

(km) Area (Ha)

Observed

HS (O)

HD

(HS/km2)

Expected

HS (E) O/E

Actual

Score

Estimated

score

Rescaled

score

7 49,460 96.261 0.1946 95.92 1.0036 3.67 3.45 100.008 7,859 19.227 0.2446 15.24 1.2615 4.61 3.34 91.60

9 7,534 18.727 0.2486 14.61 1.2818 4.69 3.24 84.42

10 6,811 16.865 0.2476 13.21 1.2769 4.67 3.15 78.19

11 6,102 14.693 0.2408 11.83 1.2417 4.54 3.07 72.69

12 5,478 12.252 0.2237 10.62 1.1533 4.22 3.01 67.78

13 4,970 10.547 0.2122 9.64 1.0943 4.00 2.95 63.36

14 4,532 9.109 0.2010 8.79 1.0364 3.79 2.89 59.35

15 4,210 7.761 0.1843 8.17 0.9505 3.48 2.84 55.69

16 3,966 6.934 0.1749 7.69 0.9016 3.30 2.79 52.32

17 3,721 6.543 0.1759 7.22 0.9068 3.32 2.75 49.20

18 3,470 5.865 0.1488 6.73 0.8717 3.19 2.71 46.31

19 3,307 4.921 0.1409 6.41 0.7673 2.81 2.67 43.61

20 3,022 4.257 0.1375 5.86 0.7263 2.66 2.64 41.08

21 2,736 3.761 0.1361 5.31 0.7089 2.59 2.61 38.71

22 2,526 3.439 0.1397 4.90 0.7020 2.57 2.58 36.47

23 2,264 3.163 0.1551 4.39 0.7203 2.63 2.55 34.36

24 2,060 3.194 0.1625 3.99 0.7996 2.92 2.52 32.36

25 1,819 2.956 0.1526 3.53 0.8378 3.06 2.49 30.47

26 1,638 2.499 0.1340 3.18 0.7867 2.88 2.47 28.66

27 1,506 2.018 0.1248 2.92 0.6910 2.53 2.44 26.95

28 1,343 1.676 0.1242 2.60 0.6437 2.35 2.42 25.31

29 1,110 1.378 0.1232 2.15 0.6403 2.34 2.40 23.74

30 864 1.065 0.1237 1.68 0.6354 2.32 2.38 22.24

31 705 0.872 0.1242 1.37 0.6378 2.33 2.36 20.79

32 566 0.703 0.1230 1.10 0.6402 2.34 2.34 19.4133 499 0.614 0.1246 0.97 0.6342 2.32 2.32 18.08

34 464 0.578 0.1245 0.90 0.6425 2.35 2.30 16.80

35 429 0.534 0.1243 0.83 0.6420 2.35 2.29 15.57

36 394 0.490 0.1225 0.76 0.6410 2.34 2.27 14.38

37 350 0.429 0.1238 0.68 0.6318 2.31 2.25 13.23

38 158 0.195 0.1160 0.31 0.6383 2.33 2.24 12.12

39 41 0.048 0.1171 0.08 0.5984 2.19 2.22 11.04

40 2.21 10.00

Total 135,913 263.574 5.251 263.574 27.346 100

Average 4,118.59 7.987 0.159 7.987 0.829


26/48


Score for village distance

y = 5.6749x-0.2556

R2= 0.779

2.00

2.50

3.00

3.50

4.00

4.50

5.00

5.50

6.00

7 10 13 16 19 22 25 28 31 34 37


SC_VD

Power (SC_VD)

Score for village distance

Figure 20 Model for estimating score of village distance

2. Score for Road DistancesTable 12 Actual, estimated and rescaled score for road distance

VillageDistance (km) Area (Ha)

ObservedHS (O)

HD(HS/km2)

ExpectedHS (E) O/E

ActualScore

Estimatedscore

Rescaledscore

7 63137.664 128.613 0.2037025 122.44 1.0504 3.63 3.14 100.008 7122.9 18.514 0.2599222 13.81 1.3403 4.64 3.04 92.12

9 6527.433 16.359 0.2506192 12.66 1.2923 4.47 2.95 85.38

10 6074.169 13.479 0.2219069 11.78 1.1443 3.96 2.88 79.50

11 5592.849 10.929 0.1954102 10.85 1.0076 3.49 2.81 74.32

12 5144.133 9.3 0.1807885 9.98 0.9322 3.23 2.76 69.68

13 4706.078 8.019 0.1703967 9.13 0.8787 3.04 2.70 65.51

14 4282.31 7.553 0.1763768 8.30 0.9095 3.15 2.66 61.71

15 3652.314 7.023 0.1922891 7.08 0.9916 3.43 2.61 58.24

16 2933.171 6.194 0.2111708 5.69 1.0889 3.77 2.57 55.04

17 2712.141 5.27 0.1943114 5.26 1.0020 3.47 2.54 52.08

18 2573.57 4.224 0.16413 4.99 0.8463 2.93 2.50 49.33

19 2357.407 3.348 0.1420204 4.57 0.7323 2.53 2.47 46.76

20 2123.107 2.75 0.1295272 4.12 0.6679 2.31 2.44 44.35

21 1930.63 2.423 0.1255031 3.74 0.6472 2.24 2.41 42.09

22 1775.899 2.354 0.1325526 3.44 0.6835 2.36 2.38 39.96

23 1623.221 2.264 0.1394758 3.15 0.7192 2.49 2.36 37.95

24 1469.358 2.094 0.1425112 2.85 0.7349 2.54 2.33 36.04

25 1346.743 1.851 0.1374427 2.61 0.7087 2.45 2.31 34.22

26 1228.754 1.594 0.1297249 2.38 0.6689 2.31 2.29 32.50

27 1101.524 1.378 0.1250994 2.14 0.6451 2.23 2.27 30.85

28 980.48 1.21 0.1234089 1.90 0.6364 2.20 2.25 29.28


27/48


Table 12. (continued)VillageDistance Area (Ha)

ObservedHS (O)

HD(HS/km2)

ExpectedHS (E) O/E

ActualScore

Estimatedscore

Rescaledscore

29 747.945 0.933 0.1247418 1.45 0.6432 2.23 2.23 27.78

30 607.772 0.753 0.1238951 1.18 0.6389 2.21 2.21 26.34

31 569.001 0.706 0.1240771 1.10 0.6398 2.21 2.20 24.96

32 530.8 0.659 0.1241522 1.03 0.6402 2.21 2.18 23.63

33 494.978 0.613 0.1238439 0.96 0.6386 2.21 2.16 22.36

34 467.547 0.571 0.1221268 0.91 0.6298 2.18 2.15 21.13

35 436.25 0.542 0.1242407 0.85 0.6407 2.22 2.13 19.94

36 384.384 0.474 0.1233142 0.75 0.6359 2.20 2.12 18.80

37 346.76 0.428 0.1234283 0.67 0.6365 2.20 2.10 17.69

38 310.04 0.384 0.123855 0.60 0.6387 2.21 2.09 16.62

39 271.906 0.338 0.1243077 0.53 0.6410 2.22 2.08 15.59

40 180.175 0.219 0.1215485 0.35 0.6268 2.17 2.06 14.58

41 104.584 0.13 0.124302 0.20 0.6410 2.22 2.05 13.61

42 55.871 0.069 0.1234988 0.11 0.6368 2.20 2.04 12.67

43 10.334 0.013 0.1257983 0.02 0.6487 2.24 2.03 11.75

< 44 2.24 2.02 10.00

Total 135914.202 263.575 5.605 263.575 28.905 104.488Average 3,673.357 7.124 0.151 7.124 0.781 2.703

y = 5.0014x-0.2398

R2 = 0.8051

1.80

2.30

2.80

3.30

3.80

4.30

4.80

5.30

7 10 13 16 19 22 25 28 31 34 37 40 43

Distance from road (km)

score road D

Power (score road D)

Score value for road distances

Figure 21 Model for estimating score of road distance


28/48


3. Score for River/Stream DistancesTable 13 Actual, estimated and rescaled score for stream/river distance

Village

Distance Area (Ha)

Observed

HS (O)

HD

(HS/km2)

Expected

HS (E) O/E

Actual

Score

Estimated

score

Rescaled

score

1 52233.689 100.2 0.1918302 101.30 0.9892 8.07 8.18 100.00

2 28721.011 58.8 0.2047282 55.70 1.0557 8.62 8.51 108.96

3 18602.728 37.751 0.2029326 36.08 1.0464 8.54 8.59 110.94

4 12729.521 25.108 0.1972423 24.69 1.0171 8.30 8.45 107.14

5 8422.257 16.542 0.1964082 16.33 1.0128 8.27 8.14 98.76

6 5174.005 10.071 0.1946461 10.03 1.0037 8.19 7.70 86.99

7 3663.532 6.566 0.1792259 7.10 0.9242 7.54 7.18 73.02

8 2528.572 3.743 0.1480282 4.90 0.7633 6.23 6.62 58.05

9 1245.889 1.593 0.1278605 2.42 0.6593 5.38 6.07 43.27

10 765.975 0.942 0.1229805 1.49 0.6342 5.18 5.58 29.88

11 616.595 0.761 0.1234197 1.20 0.6364 5.19 5.17 19.08

12 575.372 0.713 0.1239198 1.12 0.6390 5.21 4.91 12.05

13 444.954 0.551 0.123833 0.86 0.6386 5.21 4.84 10.00

14 171.034 0.212 0.123952 0.33 0.6392 5.22 4.84 10.00< 15 19.068 0.022 0.1153765 0.04 0.5949 4.86 4.84 10.00

Total 135914.202 263.575 2.3763839 263.575 12.25398 100

Average 9,060.947 17.572 0.158 17.572 0.817

Score for Stream/River Distance

y = 0.0074x3- 0.174x

2+ 0.8031x + 7.5474

R2= 0.9459

4.00

4.50

5.00

5.50

6.00

6.50

7.00

7.50

8.00

8.50

9.00

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Distance fromstream/river (km)

SCRV

Poly. (SCRV)

Score for Distance fromstream/river

Figure 22 Model for estimating score of stream/river distance


29/48


4. Score for City DistancesTable 14 Actual, estimated and rescaled score for city distance

City

Distance Area (Ha)

Observed

HS (O)

HD

(HS/km2)

Expected

HS (E) O/E

Actual

Score

Estimated

score

Rescaled

score

10 21586.573 36.282 0.1680767 41.86 0.8667 2.00 2.4743 100.00

11 3771.988 8.168 0.2165436 7.31 1.1166 2.58 2.4521 98.20

12 3883.636 9.004 0.2318446 7.53 1.1955 2.76 2.4299 96.40

13 3946.344 9.424 0.2388033 7.65 1.2314 2.85 2.4077 94.60

14 4028.006 9.664 0.2399202 7.81 1.2372 2.86 2.3855 92.80

15 4162.06 10.009 0.2404819 8.07 1.2401 2.87 2.3633 91.00

16 4305.801 9.954 0.2311765 8.35 1.1921 2.76 2.3411 89.20

17 4468.451 9.386 0.2100504 8.67 1.0831 2.50 2.3189 87.40

18 4613.248 9.259 0.2007046 8.95 1.0349 2.39 2.2967 85.60

19 4794.754 9.554 0.1992594 9.30 1.0275 2.37 2.2745 83.80

20 4651.192 9.484 0.2039047 9.02 1.0514 2.43 2.2523 82.00

21 4422.756 9.065 0.2049627 8.58 1.0569 2.44 2.2301 80.20

22 4424.298 8.535 0.192912 8.58 0.9948 2.30 2.2079 78.40

23 4277.995 8.072 0.1886865 8.30 0.9730 2.25 2.1857 76.60

24 4192.537 7.572 0.1806066 8.13 0.9313 2.15 2.1635 74.8025 3969.132 6.878 0.1732873 7.70 0.8936 2.07 2.1413 73.00

26 3792.049 6.375 0.1681149 7.35 0.8669 2.00 2.1191 71.20

27 3684.386 6.34 0.1720775 7.15 0.8873 2.05 2.0969 69.40

28 3636.064 6.938 0.1908107 7.05 0.9839 2.27 2.0747 67.60

29 3668.126 7.251 0.1976759 7.11 1.0193 2.36 2.0525 65.80

30 3563.137 6.798 0.1907869 6.91 0.9838 2.27 2.0303 64.00

31 3456.727 6.442 0.1863613 6.70 0.9610 2.22 2.0081 62.20

32 3407.552 6.18 0.1813619 6.61 0.9352 2.16 1.9859 60.40

33 3380.495 6.192 0.1831684 6.56 0.9445 2.18 1.9637 58.60

34 3331.509 6.455 0.193756 6.46 0.9991 2.31 1.9415 56.80

35 3061.408 6.212 0.2029132 5.94 1.0463 2.42 1.9193 55.00

36 2573.594 5.57 0.2164289 4.99 1.1160 2.58 1.8971 53.20

37 2058.02 4.333 0.2105422 3.99 1.0857 2.51 1.8749 51.4038 1618.861 3.054 0.1886512 3.14 0.9728 2.25 1.8527 49.60

39 1378.852 2.538 0.1840662 2.67 0.9491 2.19 1.8305 47.80

40 1230.244 2.402 0.1952458 2.39 1.0068 2.33 1.8083 46.00

41 1081.313 2.27 0.20993 2.10 1.0825 2.50 1.7861 44.20

42 869.298 1.43 0.1645006 1.69 0.8483 1.96 1.7639 42.40

43 718.625 0.938 0.130527 1.39 0.6731 1.56 1.7417 40.60

44 586.14 0.721 0.1230082 1.14 0.6343 1.47 1.7195 38.80

45 469.104 0.577 0.1230004 0.91 0.6343 1.47 1.6973 37.00

46 410.083 0.508 0.1238774 0.80 0.6388 1.48 1.6751 35.20

47 389.801 0.479 0.1228832 0.76 0.6337 1.46 1.6529 33.40

48 370.515 0.523 0.1411549 0.72 0.7279 1.68 1.6307 31.60

49 351.213 0.636 0.1810867 0.68 0.9338 2.16 1.6085 29.80

50 331.911 0.674 0.2030665 0.64 1.0471 2.42 1.5863 28.0051 303.791 0.558 0.1836789 0.59 0.9471 2.19 1.5641 26.20

52 248.828 0.326 0.1310142 0.48 0.6756 1.56 1.5419 24.40

53 200.741 0.246 0.122546 0.39 0.6319 1.46 1.5197 22.60

54 169.045 0.208 0.1230442 0.33 0.6345 1.47 1.4975 20.80

55 73.574 0.091 0.123685 0.14 0.6378 1.47 1.4753 19.00

> 56 1.45 1.4531 10.00

Total 135913.777 263.575 8.390185 263.575 43.26441

Average 2,954.6473 5.7299 0.1824 5.7299 0.9405


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y = -0.0243x + 2.7305

R2 = 0.622

1.00

1.20

1.40

1.60

1.80

2.00

2.20

2.40

2.60

2.80

3.00

10 14 18 22 26 30 34 38 42 46 50 54 58

Distance from surroundig cities

SC CT

Linear (SC CT)

Score

Figure 23 Model for estimating score of city distance

5. Score for Land StatusScore

y = 16.513x-0.5593

R2=0.9083

-

5.00

10.00

15.00

20.00

25.00

HTI

KGT

KFF DS KP

PL PPK TW PP

H

Land use (status)

SCSTS

Power (SCSTS)

Figure 24 Model for estimating score of land use


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Table 15 Actual, estimated and rescaled score for land status/land use

Land

status Area (Ha)

Observed

HS (O)

HD

(HS/km2)

Expected

HS (E) O/E

Actual

Score

Estimated

score

Rescaled

score

HTI 240 1.74 0.72522 0.47 3.7396 23.79 16.5 100.0

KH 14480 38.37 0.26500 28.08 1.3665 8.69 11.2 62.9

KGT 16043 39.10 0.24369 31.11 1.2566 7.99 8.9 47.0

KPP 22552 43.75 0.19399 43.73 1.0003 6.36 7.6 37.8

KFF 5301 9.96 0.18798 10.28 0.9693 6.17 6.7 31.5HP 39300 71.23 0.18124 76.21 0.9346 5.95 6.1 27.0

DS 1503 2.41 0.16044 2.92 0.8273 5.26 5.6 23.5

HPT 23126 36.93 0.15968 44.85 0.8234 5.24 5.2 20.7

KPPL 9594 14.96 0.15587 18.61 0.8038 5.11 4.8 18.4

KHR 1173 1.75 0.14946 2.27 0.7707 4.90 4.6 16.5

PPK 2 0.00 0.13289 0.00 0.6853 4.36 4.3 14.8

KEAH 2181 2.87 0.13138 4.23 0.6775 4.31 4.1 13.4

TW 281 0.35 0.12387 0.54 0.6387 4.06 3.9 12.1

T1 67 0.08 0.11887 0.13 0.6130 3.90 3.8 11.0

PPH 72 0.09 0.11857 0.14 0.6114 3.89 3.6 10.0

Total 135914 264 3 264 16 100

Average 9,060.947 17.572 0.203

Remarks:Timber estate (HTI) Area for settlements (KPPL)

Area for conservation of hydrology. (KH) Agric land "handil" (KHR)

Deep peat conservation (KGT) Area for forestry training and education (PPK)

Area for production development (KPP) Black water conservation (KEAH)Area for conservation of flora andfauna. (KFF) Natural Park (TW)

Production forest (HP) Transmigration (T1)

Water bodies (DS) Forest Protection and conservation area (PPH)

Limited production forest (HPT)

6. Score for Land CoverTable 16 Actual, estimated and rescaled score for land cover

Land cover Area (Ha)

Observed

HS (O)

HD

(HS/km2)

Expected

HS (E) O/E

Actual

Score

Estimated

score

Rescaled

score

Shrubs 5019 15 0.307 9.73 1.5831 27.60 27.59 100.00

Dry land agric. 44587 94 0.211 86.47 1.0888 18.98 19.44 55.36

Lowland forest 78164 142 0.182 151.58 0.9385 16.36 15.85 35.64

Bare land 2524 4 0.151 4.90 0.7783 13.57 13.70 23.90

Estate crop 5596 8 0.142 10.85 0.7300 12.73 12.24 15.90

Sec. forest 23 0 0.120 0.05 0.6169 10.76 11.17 10.00

Total 135914 264 1.1123 263.5750 5.7355

Average 22,652.37 43.929 0.185


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Score

y = 27.587x-0.5047

R2= 0.9918

10

15

20

25

30

Shrubs Dryland agric. Lowland forest Bare land Estate crop Sec. forest

Land cover

score lcover

Power (score lcover)

Figure 25 Model for estimating score of land cover

7.

Score for Soil ClassTable 17 Actual, estimated and rescaled score for soil type

Soil type Area (Ha)

Observed

HS (O)

HD

(HS/km2)

Expected

HS (E) O/E

Actual

ScoreRescaled

score

Non-peat 55366 94 0.169683 107 0.8750 45 81

peat 80549 170 0.210592 156 1.0859 55 100

Total 135,914.2 263.6 0.4 263.6 2.0 100

Average 67,957.1010 131.7875 0.1901 131.7875

8. Score for Land SystemTable 18 Actual, estimated and rescaled score for land system

Land system*) Area (Ha)Observed

HS (O)

HD

(HS/km2)

Expected

HS (E) O/E

Actual

Score

Estimated

score

Rescaled

score

SFP 2,334.5 5.32 0.23 4.53 1.1760 11.45 11.41 100.00

SPW 45,690.8 99.56 0.22 88.61 1.1236 10.94 10.85 91.82

DPW 23,117.6 49.50 0.21 44.83 1.1040 10.75 10.29 83.64

PWL 2,034.5 4.07 0.20 3.95 1.0310 10.04 9.73 75.45

USD 35,812.0 66.19 0.18 69.45 0.9530 9.28 9.17 67.27

MBL 2,869.3 4.49 0.16 5.56 0.8062 7.85 8.61 59.09

SLPW 7,371.2 11.19 0.15 14.29 0.7825 7.62 8.05 50.91

CES 13,062.2 18.57 0.14 25.33 0.7330 7.13 7.50 42.73

MVF 2,121.0 2.86 0.13 4.11 0.6943 6.76 6.94 34.55SRG 50.6 0.06 0.12 0.10 0.6424 6.25 6.38 26.36

RP 1,433.8 1.77 0.12 2.78 0.6355 6.19 5.82 18.18

AFM 16.5 0.02 0.11 0.03 0.5923 5.77 5.26 10.00

135,914.2 263.6 1.99 10.27 100.00

21.9646 0.1660


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Remarks: *)

SFP: Swampy floodplains SLPW: Shallower peat

SPW: Shallow peat CES: Coalescent estuarine

DPW: Deeper peat swamps MVF: Minor valley floors

PWL: Permanently waterlogged SRG: Sedimentary ridges

USD: Undulating sandy RP: Rolling plains

MBL: Meander belt AFM: Alluvial fans and mountain

Score

y = -0.5585x + 11.964

R2= 0.964

4.0

5.0

6.0

7.0

8.0

9.0

10.0

11.0

12.0

SFP SPW DPW PWL USD MBL SLPW CES MVF SRG RP AFM

Land System

SCLSYS

Linear (SCLSYS)

Figure 26 Model for estimating score of land system

D. Score Verification of score using hotspot densityTo evaluate the coincidence between the vulnerability score and fire occurrence (HD), the

authors randomly select 999 polygons that cover all vulnerability classes and HD. By

evaluating the correlation between HD and score of each variable, the authors then select

the factors that potentially affect the wildfire risk. The selection was based upon the

value of the R2

(coefficient of determination). From Figures 27 to 33, the best coincidence

is given by village distance score (622%), followed by road distance (41.2%) and land

cover (39.8%). Base on this fact, the authors then evaluated the relationship between HD

and these three variables simultaneously (with multiple regression). As shown in Figure34, it is found that R2

is about 66.6%. Thus, vulnerability map was developed using this

three variables.


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1. Hotspot density versus score of Village Distance (X1)X1 Line Fit Plot

y = 5E-07x3.2205

R2= 0.6221

y = 0.0234e0.0419x

R2= 0.5849

y = 0.025x - 1.0596

R2= 0.4259

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

0.0 20.0 40.0 60.0 80.0 100.0 120.0

Score of Village Distance (X1)

HD_RAD2KM

Power (HD_RAD2KM)

Expon. (HD_RAD2KM)

Linear (HD_RAD2KM)

Hotspot density (per km2)

Figure 27 the relationship between village distance score and HD

2. Hotspot density versus score of Road Distance (X2)

y = 0.0067e0.0509x

R2= 0.4052

y = 5E-09x4.1699

R2= 0.4115

y = 2.1277Ln(x) - 8.6063

R2= 0.2185

y = 0.026x - 1.3959

R2= 0.2151

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

0 20 40 60 80 100 120

Score of Road Distance (X2)

HD_RAD2KM

Expon. (HD_RAD2KM)

Power (HD_RAD2KM)

Log. (HD_RAD2KM)

Linear (HD_RAD2KM)

Figure 28 the relationship between road distance score and HD


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3. Hotspot density versus score of River/stream distance (X3)

y = 0.0063e0.0455x

R2= 0.0586

y = 2E-10x4.7645

R2= 0.0585

y = 0.0271x - 1.8365

R2= 0.0424

0.0

0.5

1.0

1.5

2.0

2.53.0

3.5

98 100 102 104 106 108 110 112

Score for River/streamdistance (X3)

HD_RAD2KM

Expon. (HD_RAD2KM)

Power (HD_RAD2KM)

Linear (HD_RAD2KM)

hotspot density (hs per km2)

Figure 29 the relationship between river/stream distance score and HD

4. Hotspot density versus score of City Distance (X4)

Hotspot density (HS/km2)

y = 0.3878e0.0098x

R2= 0.0172

y = 0.001x2- 0.1401x + 5.6972

R2= 0.0316

y = -7E-05x3+ 0.0161x

2- 1.1762x + 28.978

R2= 0.0528

0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

0 20 40 60 80 100

Score for city distance (X4)

HD_RAD2KM

Expon. (HD_RAD2KM)

Poly. (HD_RAD2KM)

Poly. (HD_RAD2KM)

Figure 30 the relationship between city distance score and HD


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5. Hotspot density versus score of Land use status (X5)Hotspot density (HS/km2)

y = 0.4205e0.0166x

R2= 0.0752

y = 0.0609x0.7214

R2= 0.0838

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0

Score for Land Status/Use (X5)

HD_R

AD2

HD_RAD2KM

Expon. (HD_RAD2KM)

Power (HD_RAD2KM)

Figure 31 the relationship between land use score and HD

6. Hotspot density versus score of Land Cover (X6)Hotspot density (HS/km2)

y = 0.0001x2.2665

R2= 0.3977

y = 0.063e0.0527x

R2= 0.3842

y = 0.0361x - 0.6808

R2= 0.3662

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

0.0 10.0 20.0 30.0 40.0 50.0 60.0Score for Land Cover (X6)

HD_RAD2KM

Power (HD_RAD2KM)

Expon. (HD_RAD2KM)

Linear (HD_RAD2KM)

Figure 32 the relationship between land cover score and HD


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7. Hotspot density versus score of Soil (X7)

y = 0.023x - 1.0891

R2 = 0.1192

0.0

0.5

1.0

1.5

2.0

2.53.0

3.5

0 50 100 150

soil type score (X7)

HD_RAD2KM(Y)

Linear

(HD_RAD2KM(Y))

Hot spot density (HS/sq km)

Figure 33 the relationship between soil type score and HD

The actual score was directly rescaled into 81 for non-peat and 100 for peat area. The

correlation of determination between soil type score and hot spot density is approximately

12%.

8. Hotspot density versus score of Land System (X8)Hotspot density (HS/km2)

y = 0.0156x - 0.2344

R2 = 0.088

y = 2E-05x2.4327

R2 = 0.168

y = 0.0661e0.0303x

R2 = 0.1634

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

0.0 20.0 40.0 60.0 80.0 100.0 120.0Score for Land System (X8)

HD_RAD2KM

Linear (HD_RAD2KM)Power (HD_RAD2KM)

Expon. (HD_RAD2KM)

Figure 34 The relationship between village distance score and HD


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9. Hotspot density versus Composite Score of (Village, Road and Land Cover)a. The composite score of village distance, road distance and land cover (Z1)

As mentioned previously, three variables, i.e. distance from village, distance from road and

land cover show potential correlation with the HD. Thus, by using multiple linear

regressions the weight for each variable are as follows:

Table 19. Weight of variable village distance, road distance and land cover

Variables Coefficients*) Weight

Village distance (X1) 0.014379625 0.35467

Road distance (X2) 0.006645865 0.16392

Land cover (X6) 0.019517849 0.48141

*) The constant (elevation) of the regression equation is ignored during calculating weight

for each variable, since the variation of the value of independent variable is influenced by

the only coefficient of regression. The best correlation between HD and Z1 is given by

the power model having R2

of 66.57%

By using the weight of each factor, the composite score obtained from the use of villagedistance, road distance and land cover was computed. The correlation between this

composite score with their corresponding HD is depicted in Figure 35.

Hotspot density (HS/km2)

y = 2E-08x4.1154

R2= 0.6657

y = 0.009e0.0657x

R2= 0.6416

y = -0.0002x2+ 0.0685x - 2.5632

R2= 0.4993

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0 90.0

Coposite score using Village Distance, Road Distance &Land Cover

HD_RAD2KM

Power (HD_RAD2KM)

Expon. (HD_RAD2KM)

Poly. (HD_RAD2KM)

Figure 35 The relationship between the HD and the composite score of village distance, road distance

and land cover (Z1)


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b. The composite score of village distance, road distance, river/stream distance, landcover and soil type (Z2)

c. For comparison and comprehensive analysis, the author also performed correlationanalysis between HD and the composite score of village distance, road distance,

river/stream distance, land cover and soil type (Z2). This is intended to figure out

the role of each variable on creating vulnerability score. Using the multiple linearregression, the weight of each variable considered are tabulated in Table 20. The

prediction line of this multiple regression is drawn in Figure 36.

Table 20 Weight of variable village distance, road distance, land cover, river/stream distance

and soil type

Variables Coefficients Weight

Village distance (X1) 0.013845 0.269

Road distance (X2) 0.004997 0.097

River/stream distance (X3) 0.005612 0.109

Land cover (X6) 0.018459 0.359

soil type (X7) 0.008524 0.166

y = 3E-11x5.6881

R2= 0.6857

y = 0.0019e0.086x

R2= 0.67

0.0

0.5

1.0

1.5

2.0

2.5

3.0

0.0 20.0 40.0 60.0 80.0 100.0

COMPOSITESCOREVARX1,X2,X6,X3,X7)

HD_RAD2KM(Y)

Power (HD_RAD2KM(Y))

Expon. (HD_RAD2KM(Y))

HD(HS/km2)

Figure 36 The relationship between HD and the composite score of village distance, road distance,

stream/river distance, land cover and soil type (Z2)

E. Vulnerability Scores and Map1. Based on the composite scores of Z1 (score of village distance, road distance, and

land cover)


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Based on the randomly sampled 999 polygons evaluated, the study found very close

correlation having about 69% of R2. The model that used to classify the vulnerability score

classes is as follows:

HD = 0.00000002407 Z14.115357533

Where Z1 is the composite score derived from village distance, road distance and land

cover

By adopting Table 2, the score for each vulnerability classes is tabulated in Table 21. The

graph that convert the vulnerability score into HD is depicted in Figure 37. The map that

expresses the vulnerability risk based on this classes is shown in Figure 39.

Table 21 Vulnerability score classes based on X1, X 2 and X6 ( score of village distance, road distance,

and land cover)

Vulnerability score Hotspot density

(HS/ sq km)

Radius from the HS

(fire) center (km)


> 75.28 > 1.273 < 0.5 Extremely high risk53.75 - < 75.28 0.318 - < 1.273 0.5 < 1.0 Very high risk

44.14 - < 53.75 0.141 < 0.318 1.0 - < 1.5 High risk

38.38 - < 44.14 0.080 - < 0.141 1.5 - < 2.0 Medium risk

< 38.38 < 0.080 > 2.0 Low risk

Figure 37 The graph expressing the relationship between vulnerability score classes (Z1) and HD

0.06 0.26 0.46 0.66 0.86 1.06 1.26

36

42

48

54

60

66

72

Hot spot density (HS/km2)

Vulnerability score (Z1)


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2. Based on the composite scores of Z2 (score of village distance, road distance,river/stream distance, land cover and soil type)

Using the same approach as mentioned previously, the best model that used to classify the

vulnerability score class based on Z2 is as follows:

HD = 0.00000000003 Z25.6881

Where Z2 is the composite score derived from village distance, road distance, stream

distance, land cover and soil type. The vulnerability classes and their corresponding HD

and radius from the fire center is described in Table 22 and Figure 38. The vulnerability

map using this classes is described in Figure 40.

Table 22 Vulnerability score classes based on X1, X 2, X3, X6 and X7 ( score of village distance, road

distance, stream distance, land cover and soil type)

Vulnerability score Hotspot density

(HS/ sq km)

Radius from the HS

(fire) center (km)


> 81.12 > 1.273 < 0.5 Extremely high risk

64.37 - < 81.12 0.318 - < 1.273 0.5 < 1.0 Very high risk

56.22 - < 64.37 0.141 < 0.318 1.0 - < 1.5 High risk

51.07 - < 56.22 0.080 - < 0.141 1.5 - < 2.0 Medium risk

< 51.07 < 0.080 > 2.0 Low risk

Figure 38 The graph expressing the relationship between vulnerability score classes (Z1) and HD

0.00 0.50 1.00 1.50 2.00 2.50 3.00

45.50

50.00

54.50

59.00

63.50

68.00

72.50

77.00

81.50

Hot spot density (HS/ sq km)

Vulnerability score (Z2)


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F. Model Verification:

Using two-sample means test, between the actual HD and predicted HD based on the

composite values of Z1 and Z2, the study found the following:

a) No difference was found between the actual HD and predicted HD based either onZ1 or on Z2. These mean that the vulnerability model provide good estimation

using only village proximity, road distance and land cover

b) Land cover contribute 48% on the risk (vulnerability) classes, while the road andvillage proximity give 52%. This describe that the amount of fuel, type of fuel, fuel

arrangement and human activities in surrounding villages and roads significantly

determine the risky areas.

c) Using the model with only 3 variables (village, road and land cover), thecoincidence value (also called model accuracy) of the model verification between

actual and predicted HD is only 55.95% . Using 5 variables (village, road, city, soil

type and land cover), the accuracy is slightly increased, i.e., 56.36%. This slightly

low accuracy is caused by confusion among the low, medium and high risk areas.d) To increase the model accuracy, the classes with contribute more confusion ( the low,

medium and high risk) are then merged into only one class. The merged class is

called as low/medium/high risk). Therefore, the final class with 3 classes provide

model accuracy of 65.27% and 66.07% for 3 and 5 variables respectively. The

accuracy of the model is summarized in Table 23. The final maps that express the

vulnerability using 3 and 5 variables are shown in Figures 41 and 42.

Table 23 Z-test two sample for means

Estimated HD by Z1 Estimated HD by Z3

Mean 0.927459205 0.936573985

Known Variance 0.23 0.251

Observations 999 999Hypothesized Mean Difference 0

Z-cal -0.415390351

Remarks: Z < Z-critical means

that these two mean values are

not significantly different.

P(Z


43/48

Wi

Figure 39 Wildfire Vulnerability Map in Central Kalimantan based on village proximity, road proximity and land cover (5 cl


44/48

Wi

Figure 40 Wildfire Vulnerability Map in Central Kalimantan based on village proximity, road proximity and land cover (5 cla


45/48

Wi

Figure 41 Wildfire Vulnerability Map in Central Kalimantan based on village proximity, road proximity and land cover (3 cl


46/48

Wi

Figure 42 Wildfire Vulnerability Map in Central Kalimantan based on village proximity, road proximity and land cover (3 cla


47/48


As shown in Figure 41, the major parts of the areas of Central Kalimantan Province are

belonging to extremely and very high risk classes. The very high risk areas almost found

in all regencies. However, the extremely risk areas area are mostly found in Pulang Pisau

and Kapuas Regencies. A scattered extremely areas are also exist in Lamandau,

Palangkaraya,Barito Selatan and Barito Utara Regencies.

CONCLUDING REMARKS

From the foregoing discussion, the authors can conclude the following:

1. There are systematical relationship between human activity factors (accessibility &proximity) and the occurrence of the wildfire.

2. Of the 8 variables examined, the major factors that affect the wildfire risk in CentralKalimantan Province are proximity of village, proximity of road, proximity of city,

soil type and land cover.

3. The wildfire risk maps established using all 5 significant variables and 3 variables(village, road and land cover) are not significantly different. Therefore, using only 3variables vulnerability could be developed.

4. Using the model with only 3 variables (village, road and land cover) with 3vulnerability classes, the model accuracy is approximately 65%. Using all 5 significant

variables, the model accuracy is not significantly increased having only 1% higher than

using 3 variables.

5. Human factors (village distance and road distance) contribute approximately 52% tothe wildfire, while the rest of about 48% is contributed by land cover.

6. The major parts of the areas of Central Kalimantan Province are belonging toextremely and very high risk classes. The very high risk areas almost found in all

regencies. The extremely risk areas area are mostly found in Pulang Pisau and Kapuas

Regencies.

7. Since the wildfire risk model had very strong relationship with the human activities,this vulnerability map may be used as a tool during forest and land-fire prevention

programs.

References:

Chuvieco, E. and F.J. Falas, 1999. Integrated Fire Risk Mapping. In Remote Sensing of

Large Wildfires in the European Mediterrannean Basin (Chuvieco, E. Ed.).

Springer. Berlin

Heikkila, T.V., R. Gronqvist, M. Jurvelius, 1993. Handbook on Forest Fire Control.Forestry Training Programme Publication 21. National Board of Education of

Government of Finland. Helsinki.

Jaruntorn Boonyanuphap, FG Suratmo, I N.S. Jaya, F. Amhar, 2001. GIS-based method in

developing wildfire risk model (Case study in Sasamba, East Kalimantan,

Indonesia. Journal of Tropical Forest Management, Vol VII No 2, 33-45.


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Purnama, E.S. and I N.S. Jaya, 2007. Pemodelan Spasial Kerawanan Kebakaran Hutan

dan Lahan Menggunakan Teknologi Sistim Informasi Geografis (SIG) dan

Penginderaan Jauh di Propinsi Riau (Modeling Forest and Land Fire Risk using

Geographic Information System (GIS) and Remote Sensing Technology in Riau

Province). Journal of Tropical Forest Management, Vol XIII, No 1, 85-97.

State Ministry for Environment and United Nations Development Programme (UNDP),1998. Forest and Land Fires in Indonesia. Executive Summary.

Syaufina, L., A.A. Nurudin, J. Basharuddin, L.F. See and M. R. M. Yusof., 2004. The

Effect of Climatic Variations on Peat Swamp Forest Condition and Peat

Combustibility. Journal of Tropical Forest Management, Vol X, No 1, 1-14. .

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2008 Forest Fire Vulnerability in Central Kalimantan

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