GIs-Based Method in Developing Wildfire Risk Model: A Case Study in Sasamba, East Kalimantan, Indonesia

THESIS

GIs-BASED METHOD

IN

DEVELOPING

WILDFIRE

RISK MODEL:

A CASE

STUDY

IN

SASAMBA, EAST KALIMANTAN,

INDONESIA

Jaruntorn

Boonyanuphap

99?39/MI~

Graduate

Program

Thesis

GIs-BASED

METHOD

IN

DEVELOPING

WILDFIRE RISK MODEL:

A

CASE STUDY

IN SASAMBA, EAST

KALIMANTAN,

INDONESIA

Jaruntom

Boonyanup hap

997391MIT

A thesis Submitted to

Graduate School of Bogor Agricultural University, Indonesia In Fulfill of the requirement for the degree of

Master of Science in Information Technology for

Natural Resources Management

Graduate Program

Bogor

Agricultural

University

Thesis Title : GIs-Based Method in Developing Wildfire

Risk

_Model:

A Case Study in Sasamba, East Kalimantan, Indonesia

Student Name : Jaruntorn Boonyanuphap Student ID : 99739

Study Program : Master of _{Science in Information Technology for}

Natural Resources Management

This thesis proposal is approved by the Advisory Board:

Prof. Dr. Ir.

F.

_{Gunaman Suratmo, MF}

Supervisor

Dr. Ir. Neneah Surati Java, MAP^ Co-supervisor

Dr.-Ing. Fahmi Arnbar

Chairman of _{Study Program}

v-

Dr. Ir. Handoko, M.Sc

Abstract

GIs-Based Method In Developing Wildfire h s k Model:

A _{Case Study} In _{Sasamba, East Kalimantan, Indonesia}

by

Jaruntorn Boonyanuphap

Master of Science in Information Technology for Natural Resources Management Bogor Agricultural University, Indonesia

July 200 1

The modem CIS technology i s _capable of assigning and weighing the vulnerability value of each wildfire risk factor in order to develop the wildfire risk model and map the wildfire risk class. The study area is located in SASAMBA, East Kalin~antan province, Indonesia. Physical- environmental and human activity factors were the two major groups of wildfire risk factors: the physical-environmental factors consist of the average daily rnaxin~um temperatures, the total daily rainfall, the average daily 1300 relative humidity, and the average daily maximum wind speed, agro-climatic zone, slope, and aspect. The village center, road network, river network, and vegetation and land cover types were considered as the human activity factors.

The results showed that all factors had a relationship to the probability of fire starting.

Only the average of daily maximum wind speed, aspect, and river network did not influence the start of fire in the study area. The relative humidity factor and the location of the village center

were the most important of all physical-environmental and human activity factors for starting

wildfire, respectively. Because small agncultural fires caused most of the wildfires in the study

area from fanners or shifting cultivators, it was evident that the human activities had more

influence than physical-environmental factors for a wildfire to start.

The range of wildfire vulnerability value was classified into three severity classes of

wildfire risk zone (low, moderate and high). The high wildfire risk class covered the largest area while the low wildfire risk class was the smallest area. Additionally, the calculation of the coincided area between each class of the wildfire risk and the actual burnt area in 1997198 was used to assess the accuracy of the wildfire risk model. The high wildfire risk class had the highest coincided value at 76.05 %, which was the value used to represent the accuracy of the model.

This research expands the basic function of GIs technology integrated with environmental modeling to model the wildfire risk zone, which provide the information of the

area at risk to wildfire in terms of different severity levels. This map could be an effective early

Summary

Jaruntorn Boonyanuphap

Master of Science in Information Technology for Natural Resources Management Bogor Agricultural University, Indonesia

Supenlisor: Prof. Dr. Ir. F. Gunarwan Suratmo, MF Co-supervisor: Dr. I Nengah Surati Jaya. MAgr

: Dr.-Ing. Fahmi Amhar

Forest and land fire is not a new phenomenon in Indonesia. It has been reported that within the past two decades, the largest tropical wildfire episode ever recorded in Indonesia occurred in 1982/1983, where an estimated 3.7 million hectares of land was burned in East Kalimantan. Other serious fires have occurred in 1986, 1991, 1994, and a

prolonged and extremely severe fire season occurred during the last ENS0 (El-Nifio- Southern Oscillation) of 1997198 in Southeast Asia. Therefore, the prevention of fire should be considered as a high priority because it is key to solving the fire problem. The fire risk map is an alternative information showing locations where activities and improvements might increase the chance of a fire to start. Likewise, the more effective fire prevention, an understanding of the causes of fire, the risk factors and interactions among them is necessary.

The study area in this research represents approximately 249,532 hectares or 79 percent of the Integrated Economic Development Zone (KAPET) in SASAMBA region. It is situated in equatorial area between 1 _{16' 43' 30" and 1 17" 18' 30" East Longitude}

and -0" _{33' 30" and -lo 13' 30" South Latitude. Approximately 219,487 hectares or}

87.96 % of the study area were affected by fire, which showed that the effort to minimize wildfires in the study area had been low due to lack of institutional capacity, personnel and guidelines. It is highly important and necessary to have an effective and eficient wildfire prevention because the El Nifio drought event may occur more frequently than in the past, creating conditions that could trigger even more fires in the future.

The overall objective of this study is to develop a wildfire risk model through the study of the spatial dimensions of interacting factors associated with the likelihood of wildfires using GIS application. Furthermore, the specific purpose is to assign and analyze the Physical-environmental and human activity factors that are associated with the location of wildfire starting in the study area, and map the severity class of the

The wildfire risk factors were grouped into two major groups: Physical- environmental and Human Activity factors. The physical-environmental factors consist of the average daily maximum temperatures, the total daily rainfall, the average daily 1300 relative humidity, the average daily maximum wind speed, agro-climatic zone, slope, and aspect. The human activity factors consist of village location, road network, river network, and vegetation and land cover types.

The location of NOAA-AVI1RR _{IIot Spot} in _{1997198 was overlaid with all of the}

wildfire risk factors in order to analyze the spatial Hot Spots pattern, which showed that, except for the average of daily maximum wind speed, aspect and distance to river network, all of the factors had reasonable relationship to the probability of fire starting. The result of wildfire vulnerability value revealed that the relative humidity factor was the highest important of all physical-environmental factors in the start of wildfires.

For the human activity factors, the location of village center was _{the most}

important, followed by the road network and the vegetation and land cover types, which had the same influence to causing a wildfire to start. Likewise, it was evident that the human activity factors had stronger influence in causing a wildfire than physical- environmental factors.

The relationship of hot spots to some factors gave illogical result. These factors include the average daily maximum wind speed, aspect, and river network. That is the weakness of GIs-based model in comparison with the system analysis model. Nevertheless, GIS has the advantage of manipulating, analyzing, and representing the spatial dataset.

The composite wildfire vulnerability value was grouped into three severity classes of wildfire risk zones. The low wildfire risk zone was 30,709 hectares or 12.3 1% of the study area. This zone was mostly affected by physical-environmental factors, which covered most of the mangrove forest and the protected area. The moderate wildfire risk zone covered 59,422 hectares or 23.81% of the study area, which was mostly disseminated in the lowland dipterocarp forest. The high wildfire risk zone covered the largest area of 159,401 hectares or 63.88% of the study area. Open land, Alang-Alang,

potential to start wildfire. Moreover, the weather condition of this zone evidently contributed to wildfire during the abnormally long drought associated with the El-NiRo phenomenon.

The calculation of the coincided area between each class of the wildfire risk and the actual burnt area in 1997198 was used to assess the accuracy of the wildfire risk model. It gave the coincided values of 76.05 %, 34.24 %, and 22.12 %, for the high,

moderate, and low wildfire risk classes, respectively. Another technique of modcl

This research report i s a _{requirement for the degree Master of Science in}

Information Technology for Natural Resources Management at Bogor Agricultural University. This research concluded a study on developing a wildfire risk rnodel using GIs technology. Concerning the Indonesia Great Fire in 1997198, where approximately 87.96 percent of the study area was _{affected by fire. It evidently indicates that the efforl to}

minimize wildfires had been low due to lack of institutional capacity, personnel and guidelines. Therefore, this research concerns the integration of geographic information system (GIs) and environmental modeling for developing a wildfire risk model. The objective of this study was _{to assign and analyze the physical-environmental and human}

activity factors that area associated to location of wildfire starting as well as to _develops

the model of wildfire risk in term of the severity class of wildfire risk, which is concerned

with the probability of occurrence in future fire.

July, 2001

Acknowledgment

First and foremost, I wish to express my sincere appreciation and gratitude to my supervisor, Prof. Dr. Ir. F. Gunanvan Suratmo, and my co-supervisors, Dr. Nengah Surati Jaya and Dr. Fahmi Amhar for their meaningful support and generous time.

My entire study in Bogor Agricultural University was made possible with the

financial support given by the SEAMEO Development Education Fund (SEDF). It is

gratefully acknowledged. I would also like to extend my special thanks to Dr. Handoko for giving me the opportunity to study in the MIT program.

I would like to gratefully acknowledge the Integrated Forest Fire Management (IFFM) project, GTZ Samarinda Office for providing the weather and fire occurrence data. I am also indebted to Drs. A. Ari Dartoyo and Ir. Sumaryono, Center for Survey of Natural Resources (PUSSUSDA), BAKOSU RTANAL, for providing the thematic vector data as well as Mr. Guswanto, BPP Teknologi, for the weather data.

Special thanks to Mr. Redhahari, the great person from Mulawarman University, who gave me the opportunity to meet so many nice people and have the chance to build up a great friendship. This is include, Ms. Lenny Christy, junior expert of IFFM Project, who kindly gave me a valuable discussion and wonderful time during my visit in

Samarinda.

Special thanks to Rona Dennis, the remote sensing and GIs analyst of the Center for International Forestry Research (CIFOR), as well as Pak Johnnie Hadi Prakoso, early warning and detection system specialist of the Forest Fire Prevention Management Project (JICA), for sharing their valuable forest fire knowledge.

I am also especially grateful to Ms. Arum Rahayu Nusawana Azhar, Faculty of Forestry, Bogor Agricultural University (IPB), for Bahasa Indonesia-English translation of this report, and the other invaluable support during my research time.

1 _{wish to acknowledge and thank all my instructors who gave the very important}

information technology knowledge. I would also like to thank my classmates and all MIT

students, who

were

all

hard

working,

pleasure

of working,

with

gave me

a

happy

time

to

thank, of course, extended to all my friends who helped out when the pressure become

too _much.

To Arporn Khunnachak, who has always been by my side and make me feel comfortable wherever I am. Especially, you have been able to bring out the best in me.