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FOREST FIRE RISK ASSESSMENT MODEL AND POST-FIRE EVALUATION USING REMOTE SENSING AND GIS: A Case Study in Riau, West Kalimantan and East Kalimantan Provinces, Indonesia1

I Nengah Surati Jaya2, Edwin Setia Purnama3, Iin Arianti4 and Jaruntorn Boonyanuphap4

Abstract

This paper describes the use of GIS and Remote Sensing technology for developing forest fires risk model and post-fire evaluation. The forest fires models considered human activity as well as environmental factors to derive a single index expressing forest fires prone. In the normal situation, as examined in Riau and West Kalimantan Provinces, the models work quite well providing accuracy assessment more than 80%. However, when the anomaly dry season comes with prolonged drought and ENSO phenomenon, the model does not work well. In general, the study found that the human activity factors tend to contribute higher weight (load) than those environmental factors. The study also shows that the post-fire evaluation methods using medium resolution satellite imageries provide good information for establishing forest rehabilitation and restoration programs. Those images data may provide burnt forest damage classes, such as unburnt, slightly burnt, moderately burnt and or severely burnt forest.

Keywords: forest fires risk, forest fires prone, post-fires evaluation, satellite imageries

INTRODUCTION

In Indonesia, forest and land fire have risen to global attention as environmental and economic issues, especially since the large forest fire occurred in 1982/83. Since that disaster, forest fires are considered have potential threat to sustainable development because of their direct effect on ecosystems, their contribution to carbon emissions and their impact on biodiversity. That is why, the Ministry of Forestry of The Republic of Indonesia (MoF) now paid a high attention to control and minimize the forest fire occurrence. During the 1997/98 ENSO, up to 25 million hectares of land worldwide were affected by fire. Indonesia had the most severe fires in the world with similar problem with the ENSO in 2002. The areas affected by fire in 1997/98 are approximately 11.7 million hectares. During these fires, the forest degradation and deforestation due to

1 Presented at The Forest Restoration and Rehabilitation Training Course and Workshop in the Viiki

Tropical Resources Institute (VITRI) of the University of Helsinki, Finland, 13~19 May 2007 2 Associate Professor at the Laboratory of Forest Resource Inventory, Faculty of Forestry, IPB. Kampus

IPB Darmaga Bogor Indonesia. E-mail: ins-jaya@cbn.net .id or ins-jaya@ipb.ac.id 3 Assistant Professor at the Laboratory of Forest Resource Inventory, Faculty of Forestry, IPB. Kampus

IPB Darmaga Bogor Indonesia. 4 Graduate student at the Post Graduate School of Bogor Agricultural University, Bogor Indonesia

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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 (Table 1).

To reduce the forest fire disaster as well as to prevent and control forest fire, the 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 distribution. The hot spot derived from NOAA AVHRR are 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 (37oC) in the night and 315oK (42oC) 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.

In this paper, the author develops a forest fire models that pertain to the first phase of fire management, i.e., pre-fire planning (fire risk models). 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 this study, the model combines local weather patterns (particularly precipitation), vegetation (fuel type/land cover, biomass density, and moisture level), land use and proximity (distance from stream, road and villages). The concern here is the development of a risk model for use in estimating the frequency of a forest fire taking place at a particular location and time as a function of explanatory variables. Forest fire risk zones are locations where a fire is likely to start, and from where 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.

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Table 1. Number of Hot Spot detected by NOAA AVHRR that received by MoF Year 2004 No. Province Year

2003 Jan Feb Mar Apr May Jun 1 N. Aceh Darussalam 209 10 28 17 10 56 129 2 North Sumatra 1.176 35 26 81 53 297 751 3 West Sumatra 434 5 12 31 34 57 344 4 Riau 5.380 781 345 96 95 594 2.825 5 Jambi 1.678 10 32 10 9 16 217 6 South Sumatra 3.367 1 48 10 12 8 140 7 Bangka Belitung 1.176 3 28 7 16 1 26 8 Bengkulu 174 1 6 7 2 5 96 9 Lampung 968 5 14 6 5 15 42

10 Banten 299 6 18 9 14 - 4 11 DKI Jakarta 47 3 - 1 18 - - 12 West Java 1.103 15 15 8 17 6 45 13 DI.Yogyakarta 32 - 3 3 - - - 14 Central Java 458 - 8 7 17 1 10 15 East Java 1.597 8 9 8 34 15 30 16 Bali 4 - - - 5 - - 17 NTB 31 10 - - 4 - - 18 NTT - 75 - - - - - 19 West Kalimantan 4.860 17 49 106 28 62 340 20 Central Kalimantan 7.341 5 25 89 34 177 225 21 East Kalimantan 1.752 23 13 38 29 23 72 22 South Kalimantan 1.588 - - 7 4 8 41 23 North Sulawesi 102 1 - 4 - 6 3 24 Central Sulawesi 90 2 - 19 - 2 41 25 South Sulawesi 439 - 2 14 8 4 28 26 South East Sulawesi 332 - 1 3 8 - 19 27 Maluku 17 - - - - - - 28 North Maluku 1 2 - - 1 - - 29 Papua - - - - - - - Total 34.655 1.018 682 581 457 1.353 5.428

Source: MoF (2004)

Objective

The study objective is to:

• Develop a forest fire risk model in three several provinces in Indonesia • To map out the forest fire risk zone in the study area • 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

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METHODS

Study Area

Three provinces that include Riau, West Kalimantan and East Kalimantan are selected because of their high rate of fire and their geographic location that represent a wide variety of forest type and socio-economics conditions of the area. The study was performed from 2000 to 2006.

Supporting Data

The data used in each study area are differs slightly to each other and depend to the availability of data and area characteristics. However, in general, the data used include:

a. Satellite imagery

b. Digital Maps of administration border, road network, stream and villages

c. Tabular Data of precipitation and 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 Microsoft Office. All of the analyses were performed using personal computer, digitizer, scanner and printer. To get ground truth condition, we use GPS and digital Camera.

Study methods

The study method includes the following steps:

a. Image pre-processing (rectification, registration, image matching, mosaicking and area selection/image cropping)

b. Image classification (supervised classification, separability and accuracy evaluation)

c. Image transformation:

• Normalized Difference Vegetation Index (NDVI)

NDVI is an index that sensitive to amount of vegetation above ground that computed base on the following formula:

NDVI = (NIR - RED) / (NIR + RED)

This NDVI values are ranking from -1 to 1, then can be classified as follows (Table 2):

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Tabel 2. Normalized Difference Vegetation Index (NDVI) Classes

No NDVI values NDVI classes 1 < 0,05 Non-vegetation 2 0,06 < NDVI < 0,1 Very low NDVI 3 0,11 < NDVI < 0,20 Low NDVI 4 0,21 < NDVI < 0,3 Medium NDVI 5 > 0,3 High NDVI i

Sumber : dimodifikasi dari http://www.lapanrs.com

• Normalized Difference Water Index (NDWI)

The NDWI is also called infrared index. This index expresses the moisture contents or degree of the leaf wetness.

NDWI = (RNIR – RMIR / (RNIR + RMIR)

Where RMIR has wavelength ranging from 1, 2 to 2.5 µm, while RNIR is ranging from 0.78 to 0.90 µm.

d. Hotspot analysis

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 spatially analyzed providing the hotspot density (HS/sq km). Furthermore, the hotspot densities were used to develop mathematical model expressing the area vulnerability.

e. Spatial modeling of forest fire risk

• Identification of Explanatory variables. Among various variables that may affect the forest fire intensity, the study identifies some variables that relevant to each study site. The followings are the variables that used for each study area:

Riau Province: Environmental variables include precipitation, vegetation index (Normalized Difference Vegetation Index/NDVI), wetness index (Normalized Difference Water Index/NDWI), and land cover; human activity variables include: proximity from villages, road network, stream and land use.

West Kalimantan Case study: Environmental variables include precipitation, vegetation index (Normalized Difference Vegetation Index/NDVI), wetness index (Normalized Difference Water Index/NDWI), and land cover; human activity variables includes: proximity from villages, road network, stream and land use.

East Kalimantan Case Study use uses the following variables, i.e., daily temperature, precipitation, daily humidity, agro-climate type, slope, proximity from settlement, road and land cover.

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• Evaluation of correlation among explanatory variables. In this stage, the study only use one of the variables that have very close correlation. This evaluation is intended to reduce redundancy.

f. Scoring of each variables

For Riau and West Kalimantan Provinces, the scoring for each variable are performed as shown in Table 3.

Table 3. Environmental and human factors considered in Riau and West Kalimantan Study area

Variable/ Factor Sub Factor Score Variable/ Factor Sub Factor Score

Bare Land 10 NDVI -1 ~ -0,8 10 Land cover

Primary Mangrove Area 20 -0,8 ~ -0,6 20 Settlement 30 -0,6 ~ -0,4 30 Primary dry land forest 40 -0,4 ~ -0,2 40 Agriculture 50 -0,2 ~ 0 50 Secondary Mangrove 60 0 ~ 0,2 60 Secondary forest 70 0,2 ~ 0,4 70 Shrub/grass land 80 0,4 ~ 0,6 80 Plantation forest 90 0,6 ~ 0,8 90

Estate crop 100 0,8 ~ 1 100

Area for other utilization 10 NDWI 0,8 ~ 1 10 Land use Forest consession area 20 0,6 ~ 0,8 20

Resetlement area (Transmigration) 30 0,4 ~ 0,6 30

Ex forest consession area 40 0,2 ~ 0,4 40 Plantation forest area 50 0 ~ 0,2 50

Estate crop area 60 -0,2 ~ 0 60

> 3000 10 -0,4 ~ -0,2 70 Distance from river (m) 2000 ~ 3000 20 -0,6 ~ -0,4 80 1000 ~ 2000 30 -0,8 ~ -0,6 90

0 ~ 1000 40 -1 ~ -0,8 100

> 4000 10 > 3000 10

3000 ~ 4000 20

Distance from road (m) 2000 ~ 3000 20

2000 ~ 3000 30 1000 ~ 2000 30

Distance from village/setlement (m)

1000 ~ 2000 40 0 ~ 1000 40

0 ~ 1000 50

> 200 10 Precipitation (mm/month) 100 ~ 200 20

< 100 30

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For Sasamba area in East Kalimantan Province, the factors of vulnerability considered are listed in Table 4.

Table 4. Environmental and human factors considered in Sasamba, East Kalimantan Study area

Environmental factors Classes Average of daily temperature < 30o C

30o-31o C 31o-32o C > 32o C

Average of rainfall < 800 mm/year 800 – 900 mm/year > 900 mm/year

Average of daily humidity < 60% 60% - 70% > 70%

Average of daily maximum wind speed < 10 knots 10-12 knots > 12 knots

Agro-climate zone D1: wet month 3-4, dry month < 2 E1: wet month <3, dry month < 2 E2: wet month < 3, dry month 2-3

Slope 0-8%, 8-15%, 15-30%, > 30% Aspects Flat, N, NE,E, SE, S, SW, W, NW Human activity factors Distance to village center < 1 km, 1-2 km, > 2 km Distance to road < 1 km, 1-2 km, > 2 km Distance to river or stream < 1 km, 1-2 km, > 2 km Vegetation cover Bare land, grass land, bush & shrub

Lowland Dipterocarp Mangrove forest Nipa forest Swamp/marsh forest Degraded secondary & plantation forest Settlement area

g. Weight Determination

The method used to determine weight of each variables is Composite Mapping Analysis (CMA). In this study, the relationship between hotspot and wildfire risk factors were analyzed to derive a vulnerability value. The weights were divided into two groups, namely macro and micro weights. The composite vulnerability value for a spatial unit was calculated using the following equation:

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( )∑ ∑+= piziLqiyiMV

where: V is vulnerability value (forest and land fire risk); M is weight of macro variable of human activity; L is weight of macro variable of environmental factor; qi is weight of micro variable of human activities; pi is weight of micro variable of bio-physical variables; yi is a score for human activity sub-factors; zi is a score for environmental sub-factors.

The macro weight expresses the degree of environmental and human activities effects, while the micro weight express the relative weight of each variables within either environmental or human factor. .

Score computation

The score of each sub-factor is computed as follows:

∑ ⎟⎠⎞

⎜⎝⎛

⎟⎠⎞

⎜⎝⎛=

EiOi

xEiOiXi 100

⎟⎠⎞

⎜⎝⎛=

100iTxF

Ei

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

Equation for computing micro weight are:

( )∑ +

=NiMi

Mipi ; ( )∑ +

=NiMi

Niqi

Where Pi is the relative weight of biophysical factor (environmental factor) Quiz is the relative weight for human activities factors; Mi is percentage of hotspot of each environmental factor; Ni is percentage of hotspot of each human activities factor;

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Equation for computing macro weight:

⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢

⎣

⎡

⎟⎠⎞

⎜⎝⎛+⎟

⎠⎞

⎜⎝⎛

⎟⎠⎞

⎜⎝⎛

=

EhOh

EnOn

EhOh

M ;

⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢

⎣

⎡

⎟⎠⎞

⎜⎝⎛+⎟

⎠⎞

⎜⎝⎛

⎟⎠⎞

⎜⎝⎛

=

EhOh

EnOn

EnOn

L

Where M is relative weight of human factor L is relative weight of environmental factor Oh is observed hotspot for human factor Eh is expected hotspot for human factor; On is observed hotspot for environmental factor En is expected hotspot for environmental factor

h. Model Development

Based on the weights derived from the CMA method, the spatial distribution of forest fire prone can be developed. The zone of vulnerability can be divided into several classes, namely, low risk, medium risk and high risk.

3ln ValueMinValueMaxclasserabilityVuFireForest −

=

i. Model verification

Model verification was done to evaluate the accuracy or coincidence value of the model. The verification plot having size 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. For Riau and West Kalimantan study area, the reference map were the hot spot density that derived from hotspot density in June 2003. For the East Kalimantan study area, the reference map was based upon damage level map that derived from ground measurement.

j. Post-fire detection system

When the fire burnt out the forest, what kind of action should be done. Many questions will come. The frequent questions are:

• How much forest has been lost over the fire? • What is the condition of burnt forest?

Most of forest managers, environmentalist and or biologist perform post-fire evaluation. Nowadays, in line with the advent of new satellite technology, detecting and monitoring burnt forest immediately after fire can be done using satellite imageries. As known,

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there are a lot of satellite data source that possible to be used as an analytical tools to derive the information regarding burnt forest condition. There are also some reliable analytical method for detecting the level of damage of burnt forest.

In this study, the authors introduce some methods, and the data used mostly TM, SPOT MOS MESSR. The followings are some method that had been evaluated in some places in Indonesia.

Post-fire evaluation using SPOT 4 imagery in East Kalimantan.

1. Detecting burnt forest condition using SPOT Imagery

For detecting burnt forest condition in East Kalimantan (ITCI concession area), the authors used SPOT imagery. The following are the class category developed during implementation of the study.

• Slightly burnt forest: is a forest area that has more than 75% healthy life-trees. • Moderately burnt forest: is a forest class having healthy life-trees ranging from

50% to 75%. • Severely burnt forest: is a forest class, which has healthy life-trees ranging from

25% to 50% • Extremely burnt forest: is a forest class, which has very few healthy life trees, i.e.,

less than 25%. • Unburnt tropical forest: This class includes of unburnt natural and logged-over

tropical forests as well as mangrove forest. The predominant tree species are Shorea spp (meranti), Dipterocarpus spp (keruing) and Dryobalanops spp (kapur). In specific and limited sites, Agathis borneensis was also found.

• Unburnt plantation forest, includes unburnt plantation forest of Acacia Mangium, Eucalyptus sp, rubber plantation and agricultural lands.

• Bare land: is a land without vegetation or land with very sparse vegetation cover includes roads base camps, log yards, gravel pit, paved surface in Eastern part of ITCI Ltd. and other forms of bare lands.

• Slightly burnt forest: is a forest area that has more than 75% healthy life-trees. • Moderately burnt forest: is a forest class having healthy life-trees ranging from

50% to 75%. • Severely burnt forest: is a forest class, which has healthy life-trees ranging from

25% to 50% • Extremely burnt forest: is a forest class, which has very few healthy life trees, i.e.,

less than 25%. • Unburnt tropical forest: includes of unburnt natural and logged-over tropical

forests as well as mangrove forest. The predominant tree species are Shorea spp (meranti), Dipterocarpus spp (keruing) and Dryobalanops spp (kapur). In specific and limited sites, Agathis borneensis was also found.

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• Unburnt plantation forest: This class includes unburnt plantation forest of Acacia Mangium, Eucalyptus sp, rubber plantation and agricultural lands.

• Bare land: is a land without vegetation or land with very sparse vegetation cover. This class includes roads base camps, log yards, gravel pit, paved surface in Eastern part of ITCI Ltd. and other forms of bare lands.

2. Technique for detecting post-forest fire conditions in Sumatra Island. Some remote sensing techniques are available. Jaya et al (2000) perform a study to examine some techniques for detecting forest fire using Landsat TM data. Of the three-change detection techniques evaluated, i.e., post-classification comparison (PCC), multitemporal principal component (MPC) and direct multitemporal classification (MDC), the MPC, particularly which was derived from variance-covariance matrix (unstandardized principal component) was recognized to be suitable in detecting changes due to forest and land fires. His study found that the delta brightness (DB), delta greenness (DG), stable brightness (SB) and stable greenness (SG) indices derived from unstandardized multitemporal principal component analysis effectively summarized burnt-forest information. In this study, it was shown that Landsat TM provides information of totally and moderately burnt logged-over forest as well as burnt bush/shrub. In their study, the following conclusions were presented:

a) Landsat TM data are feasible to be used for detecting forest changes and forest condition after fire. In Riau study area, it was recognized that land/forest cover changes mainly due to fire in recently logged over area, estate area and land preparation for estate area. Fire also occurred in shrub/bush land. In South Sumatra, forest fires were found either in old or newly logged over forest.

b) As shown using SPOT data, the Landsat TM data could also detect the intensity of forest damage caused by fire into moderately and totally damaged forests (c.f the SPOT results described previously).

c) Of the several methods examined (the PCC, 12-d SMPC, 12-d UMPC and DMC), the 12-d UMPC method effectively detect forest and land fires providing incredibly high accuracy of 98,14% for Riau and 99,04% for South Sumatra (Palembang). This method concisely summarizes the information of forest changes caused by fire.

d) In comparison with the 12-d SMPC, the 12-d UMPC is much better providing more various characteristics of new axes. For Riau, 12-d SMPC only provides three indices while 12-d UMPC has four indices (complete indices). For South Sumatra, 12-d SMPC provides only two indices, while 12-d UMPC provides three indices.

3. Detecting Post-Fire Forest Condition using Multi-Sensor MOS-MESSR and Landsat TM.

Jaya, Pujiastuti and Saleh (2000) describe how the multi-sensor MOS-MESSR and Landsat Thematic Mapper (TM) should be manipulated as tools for detecting land cover changes. Radiometric correction using image regression was recognized as useful

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approach to adjust pixel brightness value of MOS-MESSR. They found that the standardized MPC showed comparable high accuracy, similar to DMC method. Using the MPC technique, forest changes due to fire as well as land clearing were well recognized. Some recommendations and suggestions for improving classification accuracy of change detection using multisensor MOS-MESSR and Landsat TM were drawn up from this study.

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RESULT AND DISCUSSION

Factors Affecting Forest Fire Risk

As mentioned previously, the variables identified in developing fire risk assessment models are grouped into (a) environmental factors and (b) human activity factors. Based on the best coincidence value and lesser number of variables, it seems clear that fire occurrence depends on local conditions such as location, agro-climate zone, precipitation, air temperature, air humidity, litter/vegetation type, land use, land cover, amount of green biomass (NDVI), and moisture content of green biomass (NDWI). Besides, in these studies, the source of fire ignition that closely related to human activities are also considered. Slope is an important physiographic factor, which is related to wind behaviour, and hence affects the fire proneness of the area. Fire travels most rapidly up slopes and the least rapidly down slopes.

Forest Fire Vulnerability Models

Riau Province

From the data analysis, we found that no significant increase of accuracy was provided by the model when land cover variable was included. We also found that land cover variable exhibit very close correlation with the NDVI. Since the NDVI is easier to be measure, more relevant in describing the amount of biomass and provides a consistent result in comparison with the land cover variable, the NDVI was included in the model development. The vulnerability model expresses that human activity factors give significant effect on vulnerability score (51.4%), slightly higher than those provided by environmental factors of only 48.6%.

Among four human activity variables examined, type of land use gives the highest weight (0.538). It means that 53.8% of the human activity variables are affected by land use types. The second and third highest of human activity factors are distance from river or stream and distance from road having weight of 0.247 and 0.161. Referring to the field check and interviews with local people, human activities such as land preparation for agriculture and settlement using fire are frequently found in the area where the accessibility is good. The study also found that rivers are used as a transportation infrastructure. People use the river as access way to their agriculture areas (shifting cultivation area) or to their hunting or fishing area.

Movements of humans, animals and vehicles may caused accidental forests fires. Thus, forests that are near roads are fire prone. Many roads traverse the study area. This makes people and animals grazing there the cause of fire in the forest.

Theoretically, forests located near settlements can be said to be more fire prone since the people living there can cause an accidental fire. Crowded settlements are located within the forest in the study area, so they can cause forest fires.

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The environmental factors that significantly affect the vulnerability score are precipitation, wetness and amount of green biomass as represented by NDVI value. During the dry seasons, in March and from June to August, the hot spots density was increase significantly. In the model developed, we found that precipitation variable contributes 0.476 (47.6% from the total environmental weight). The second largest weight is provided by wetness index (NDWI), namely 32.2%. This index expresses the moisture content of the vegetation cover. The amount of vegetation (NDVI) contribute 20.2% of the total environmental weight. Mathematically, the equation of forest fire vulnerability in Riau Province is as follows:

Vulnerability score = 0,514 [0,054 distance from village or settlement + 0,161 distance from road + 0,247 distance from stream + 0,538 land use] + 0,486 [0,476 precipitation + 0,202 NDVI + 0,322 NDWI]

West Kalimantan Province

Similar to the research finding Riau Province, the model obtained in West Kalimantan study area has a similar pattern, in which the human factor contribute approximately 59% of the total weight, while the rest weight is from environmental factors (0.41%). Of the four human activity factors, the land use type contribute and distance from settlement contribute the same weight, each 27%. The distance from road and river contribute respectively 24% and 22%. In the study area, the human activity is found intensively within the distance ranging from zero to 1 km from the road and or from the stream/river.

In West Kalimantan study area, the consecutive environmental factors that influence forest fire most are NDVI, NDWI and precipitation. This pattern slightly differs from the weight that obtained in Riau. In West Kalimantan, the NDVI was found to have a highest influence on forest fire compared to NDWI and precipitation. In dry season, the vulnerability of forest fire is depends on the amount of biomass either quantity or quality. We may conclude that, in the area where human activities are exist, the higher hotspot densities are found.

Vulnerability score = 0.59 [ 0.22 distance from stream/river+ 0.24 distance from road network + 0.27 distance from settlement + 0.27 land use type] + 0,41 [0,54 NDVI + 0.40 NDWI + 0.06 precipitation]

East Kalimantan Province:

In East Kalimantan, the analyzed hotspots were hotspot that detected in May 1997 and May 1998, during abnormality of long drought associated with the El-Nino-Southern Oscillation (ENSO) phenomenon. As shown in the following model, the weight of human factor almost twice as much as the weight of environmental factors. Of the three human factors considered, each factor, namely, distance from village or settlement, distance from road and land use gave almost the same weight, i.e., 0.33.

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During this long drought, the rank of environmental weight are, daily humidity (0.23) , daily temperature (0.21), agro-climate (0.21), precipitation (0.19) and slope (0.16). In the anomaly season as described in this study, each of the rainfall (precipitation) and the steepness of slope only contribute less than 19% of the total weight of human factor. This means that when wildfire breaks out in such condition, the fire vulnerability score (risky area) will depend significantly on proximity to road, villages and type of land use. During that time, the air temperatures are ranging from 28.8o C and 31o C, while the rainfall is less than 900 mm per year. The areas that have temperature less than 30oC only 5.77% of the study area or about 14.410 Ha (Total areas are about 249,532 Ha). Only small part of the study area, i.e., 7.09% or about 17, 685 Ha that has rainfall more than 900 mm per year.

From the agro-climate zone and slope point of view, 47.74% of the study areas belong to E1 agro-climate zone, where wet seasons are 3~4 months and dry season are less than 2 months. Almost 71.77% of the area (179,079 Ha) are flat, while the steep slopes were found very small, not more than 0.37%

Based on the spatial analysis we can say that the study areas are very accessible. The areas are very close to villages, road and river networks. Almost 49% of the areas are close villages, while more than 71% of the areas are close to road and river networks.

From the land cover types, the study shows that about 43% of the areas are secondary and plantation forest covers, while 21% are in the form of shrub, grass and bush and bare land. Only 25% of the area are low land forest . Mangrove, Nipa and swamp forest are not exceed than 5%. This condition, describe that most of the area contains flammable fuel wood. The vulnerability model of forest fire in East Kalimantan,

Vulnerability score = 0,66 [0,34 distance from village/settlement + 0,33 distance from road + 0,33 land use] + 0,34 [0,21 daily temperature + 0,19 precipitation + 0,23 daily humidity + 0,21 agro-climate + 0,16 slope]

The spatial distribution of forest fire prone for the study area in Riau, West Kalimantan and East Kalimantan Provinces are shown respectively in Figure 1 to Figure 3.

Model accuracy

In the normal condition, the model developed in Riau and West Kalimantan are promising giving model accuracy more than 80%. However, when the condition is anomaly when long drought associated with ENSO phenomenon occurred, such as in East Kalimantan, it seems that the model does not work well, having accuracy of only 44%.

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Table 5. Accuracy assessment of the model.

Study area Risk classes Model accuracy

Average (%)

Riau Low risk 95.4 90.9 Medium risk 83.1 Very risk 93.8 West Kalimantan Low risk 77.6 83. Medium risk 74.5 Very risk 99.5 East Kalimantan Low risk 22.1 44.1 Medium risk 34.2 Very risk 76.1

Post-fire Evaluation for Forest Restoration and Rehabilitation

From our previous study as mentioned in chapter methodology, it is possible to detect and monitor post-fire condition using satellite imageries. Using medium size resolution (namely SPOT 4 image) such as conducted in ITCI Concession area, East Kalimantan, the information related to stand condition could be detected in reliable, faster and might in cheaper manner. The study results using these images may provide useful information related to forest restoration and rehabilitation action programs. Post-fire information that could be derived from medium satellite imageries may include burnt forest damage classes, such as unburnt, slightly burnt, moderately burnt and severely burnt forest. The study also shows that the classification accuracy for this classification is quite high, having accuracy more than 90%. Figure 4 and 5 respectively depict the original burnt forest recorded from SPOT 4 imageries and classified burnt and unburnt forest classes. The more detail condition of burnt and unburnt forests taken from helicopter and ground survey are shown in Figure 6.

When multi-date imageries are available, the multi-date principal component (MPC) had been proven to provide better accuracy, even more than 95%. By using this method with TM imagery, this methods are capable to detect the intensity of forest damage into, moderately and extremely (totally) damaged.

Using coarser spatial resolution such as MOS-MESSR in the plantation forest area of Musi Hutan Persada, South Sumatra, the MPC techniques were also promising.

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CONCLUDING REMARKS

From the foregoing discussion, summarize some remarks:

1. GIS technology is powerful tool for establishing spatial modeling of forest fires prone

2. In the forest fires risk model developed, commonly, the human activity factors tend to contribute higher weight than those environmental factors.

3. In the normal condition, the forest fires risk models seem work quite well, giving accuracy more than 80%

4. In the anomaly condition, when the prolonged drought comes with ENSO phenomenon, the human factors become only a triggering factor of fire ignition. In such condition, the models seem does not work.

5. Forest Fires risk model will provide a useful information during the dry season, particularly to provide information regarding, the location of the most risky areas, watchtower establishment, mobilization of forest fire brigade (fire extinguisher) including estimate of financial needs.

6. The satellite data may provide a variety of information, from early detection system to post-fire monitoring system. For national level, the NOAA AVHRR imagery had been utilized as a routine operational tool to detect fire occurrence. This is also being used to establish the early detection system.

7. For detecting and monitoring post-fire condition, the medium resolution satellite imagery could be used to detect and monitor burnt forest immediately (SPOT, TM, ASTER etc). Since the vastness of burnt areas, the use of satellite imageries seems to be prospective because it will be more efficient, faster, timely and reliable.

8. For wide areas, the medium resolution satellite imageries might be more suitable because they can cover larger areas and may provide useful information that needed for establishing forest restoration and rehabilitation programs.

9. Post-fire information that could be derived from medium may include burnt forest damage classes, such as unburnt, slightly burnt, moderately burnt and severely burnt forest.

10. To select the proper images to be used; the user or analyst should consider some factors; such as the minimum size of forest feature to be resolved, the spectral wavelength to be used; time of the year that would provide the best imagery for discriminating the area of interest; and the size of an area that would be analyzed

LITERATURES CITED

Departemen Kehutanan, 2002. Informasi Umum Kehutanan. Departemen Kehutanan.

Departemen Kehutanan, 2004. Data Strategis Kehutanan: Eksekutif.

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Departemen Kehutanan, 2005. Rencana Strategis. Departemen Kehutanan

Jaya, I NS, 2000a. Detecting Burnt Forest Damage Using Digital SPOT Imagery. Tropical Forest Management Journal, Volume 6, No 1, 7 – 23.

Jaya, I NS, 2000b. Monitoring Vegetation Changes in Urban Area Using Landsat TM Imagery, Tropical Forest Management Journal, Volume 6, No 1, 33-42.

Jaya, I N.S., 2000. Monitoring Vegetation Changes In Urban Area Using Landsat Tm Imagery. Tropical Forest Management Journal, Vol. 6 No. 1 : 33-42

Jaya, I N.S., E. Pujiastuti, and M.B. Saleh, 2000, Detection Post-Fire Forest Condition By Using Multisensor Mos-Messr and Landsat TM: A Case Study In The Area of Musi Hutan Persada Co.Ltd., South Sumatra). Tropical Forest Management Journal, Vol. 6 No. 2 : 55-70

Susilawati and I N. S. Jaya, 2003. Evaluating Logged Over Stand Using Landsat 7 ETM+ in Sri Buana Dumai Co. Ltd. Concession Area, Riau Province. Tropical Forest Management Journal, Vol. 9 No. 2: 1-16.

Tacconi, L. 2003. Fires in Indonesia: Causes, Cost and Policy Implication. CIFOR Occasional Paper No. 38

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Figure 1. Forest fire risk map (vulnerability map) derived from the spatial model developed for Riau Province

FOREST FIRE RISK MAP OF RIAU PROVINCE

VERY RISK MEDIUM RISK NO RISK

LEGEND

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Figure 2. Forest fire risk map (vulnerability map) derived from the spatial model developed for West Kalimantan Province

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Figure3. Forest fire risk map (vulnerability map) derived from the spatial model developed for East Kalimantan Province

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Figure 4. Burnt Forest Imageries in East Kalimantan (SPOT 4 with 20 m x 20m recorded on June 1998 ): (a) false color (NIR-RED-GREEN), (b) RED-NIR-GREEN)

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Figure5. Map of Forest Damage caused by 98-fire using SPOT imagery.

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2.1 Vegetation Type

The Gallipoli Peninsula is located over an area of 1684.02 km2 and is rich in forest resources. 23% of the total area in the forest is consisted mostly of pine trees together with other species

with leaves and groups of bushes. This kind of vegetation is especially susceptible to fire.

2.2 Climate

Slightly burnt forest Moderately burnt forest

Severely burnt forest Extremely burnt forest

a b

c d

e f

g hExtremely burnt forest

Moderately burnt forest Severely burnt forest

Extremely burnt forest

Figure 6. Forest condition after 98-fires: a~ d are recorded from helicopter and e~h are photos of ground condition.