Increasing the Scale of Spatial Weather Information for Indonesia Peatland Fire Danger Rating System (Ina-FDRS)

G Fajar Suryono1, H Sanjaya1, A Eugenie1, M N Putri1

1 Pusat Teknologi Pengembangan Sumber Daya Wilayah, BPPT

gatot.fajar@bppt.go.id.

Abstract--- The Forest Danger Rating System (FDRS) in Indonesia begins with the experience of handling forest and land fires in 1997. After that activity the FDRS construction began, marked by the signing of a memorandum of understanding (MoU) between the Government of Indonesia (BPPT, BMKG, and the Ministry of Forestry) and the Government of Canada (Canadian Forest Service), in 1999. Development of FDRS in Indonesia has been carried out by BPPT, LAPAN, and BMKG. Each with a different scale and usually in regional scale coverage. Currently, data is needed to support the development of a more tight and accurate system, as well as local, with daily frequency. The AWS (Automatic Weather Station) station network that has not been meeting does not allow producing good meteorological spatial data for a wide range. So that for local analysis is not possible. At present, with the availability of meteorological satellite data, such as Himawari, and also weather radar data, it is expected that there will be better distribution of meteorological data. The use of satellite data and/or weather radar data is an alternative to meteorological data recorded by AWS. Spatial coverage with satellite data grids or weather radar will reduce the error value of the interpolation process at the current AWS point data. Using this data is expected to produce a local scale FDRS map, details, which are more accurate. The Fire Weather Index (FWI) is the main input for the new FDRS algorithm called the Indonesia Fire Danger Rating System or Ina-FDRS. Keywords--- Indonesia Fire Danger Rating System, Fire Weather Index (FWI), Automatic Weather Station (AWS), weather radar, weather satellite.

I. INTRODUCTION

I.1. General

One type of disaster that can occured and is closely related to the changes in climate dynamics is a Forest Fire Disaster where the weather dynamics in it greatly determines the level of potential trigger of the disaster and also how much the disaster will occur. Efforts to monitor and predict the potential for disasters and how big the disaster will occur will be carried out by the Indonesian government through intensive research and produce a detection system for potential (forest/land) fires. The study and research has begun after the forest/land fires in year 1997/1998 were almost evenly distributed in Sumatera, Kalimantan, Sulawesi, parts of Java, and a small part to the west of Irian Island. These forest/land fire disasters eventually became a national problem and even emerged as an international issues that attracted the attention of international research institutions to study and understand it.

1

With responsiveness, after the emergence of that national and international issues, BPPT began pioneering research, assessment and application of technology through monitoring system engineering. One of the products from the results of national and international cooperation was then named Fire Danger Rating System (FDRS) or also called in Bahasa Indonesia as a Sistem Pemeringkatan Bahaya Kebakaran (SPBK). This system monitors weather conditions and the condition of combustible materials as fuel (fuel) that can trigg the potential for burning into forest and land fires. If weather conditions support and the characteristics of combustible material in the long run are more flammable, then the fire danger rating will increase. The increasing rank of fire danger will not necessarily result in a fire if there is no trigger for fires.

I.2. Background

What is a Fire Danger Rating System (FDRS)? The Forest/Land Fire Danger Rating System (FDRS) is an early warning system that focuses on the possibility of a fire or not, in a forest/land. This system is made on the basis of an indicator of the causes of fire, such as: fuel humidity, drought levels, etc. The basic concept of FDRS is weather observation, such as temperature, humidity, wind speed, and rain fall, as well as fuel. The intended fuel materials include: fire behavior prediction (FBP), and fire weather index (FWI). From these observations it is expected that the level of forest/land fire danger will be known, whether low (low), moderate (high), or extreme (extreme).

Actually, FDRS or SPBK or fire danger ratings are arranged based on the forecast of local weather conditions and then provide information about the level of fire hazard at certain times. When the ranking is high, then the threat of fire danger will also increase. When FDR shows an extreme or catastrophic level, this means that the fires that will occur tend to be very powerful. Or in other words, FDRS is a forecast tool that measures the risk when forest fires begin to occur and spread. Forecasts are based on daily meteorological observations, which are modified by vegetation analysis as potential fuels. FDRS also provides an early warning system to reduce fire danger, as well as smoke haze.

The approach of this system is based on the Forest Fire Danger Rating System in Canada, which has been developed by the Canadian Forest Service (CFS) and widely adopted including in the ASEAN Region (including Indonesia). Until now, FDRS have been arranged in several countries other than Canada, such as: the United States, Australia, several European countries, China, Indonesia, Malaysia, and others. So SPBK or FDRS is a predictive system, which provides an early warning of the land/forest fires, so that the authorities can take action to minimize the risk of fires.

I.3. Fires that occurs in Peatlands

Large forest fires in Indonesia in 1997-1998 attracted international attention and also triggered the improvements in fire and peatlands sustainability management in Indonesia. After that, a major fire broke out again in 2013-2015, which is said to be the most severe in 2015 and triggered a humanitarian crisis and an ecological crisis. Actually since 1998, forest fires usually only occur once a year, but become more frequent because they were triggered by climate anomalies. Regarding to the causes of smoke pollution and land/forest fires, the type of soil (peat or mineral) has important implications for the fires and the spread of these fires, not to mention other issues, such as smoke to carbon emissions.

According to a recent study from the Center for International Forestry Research (CIFOR), in collaboration with Lancaster, Cambridge and Florida Universities, perceptions of peatland fires in Indonesia are very different, particularly in relation to actors, behavior, actions and environmental impacts in the field. Some of the things highlighted were mapping perceptions of stakeholders, ranging from policy makers to local farmers and landowners. They all play a role in the utilization, management and future of peatlands. By using a particular methodology, the research team was able to

2

absorb information about how various groups perceive the advantages and disadvantages of peat fires, as well as toxic fumes, as well as perceptions about the effectiveness of potential solutions.

In general, peatland transformation is rapid, and landuse changes sometimes occured in a short time. For some people these changes are positive, partly because of the rapid increase in their income. While the others complained of drastic changes, for example, due to degradation of ecosystem services and related public health impacts, that many have not been quantified. A better understanding of fire losses in various sectors, such as health, education and the environment, will improve the understanding of real reciprocity from the plantation sector, so it is hoped that will become the foundation for future knowledge in planning and management.

It is said that in a certain area, the use of fire for land clearing has taken root, both intentionally and driven by conditions that make the fire spread accidentally. For example, the reasons for deliberate burning include land preparation, or against land and resource disputes. Indirect causes of fires include draining peatlands for the needs of plantation expansion on peatlands, which will increase vulnerability. Accidental fires (fire extends beyond borders) are affected by dry peat conditions, which encourage the spread of fires, and tenurial conflicts, which means fire management incentives are not ideal, said the CIFOR Research Team.

II. HISTORY OF FOREST FIRE AND FDRS IN INDONESIA

II.1. History of Forest/Land Fire in Indonesia

Forests in Indonesia are wet tropical forests, which have a long history in terms of forest fires. Forest fires (in Indonesia) are said to have been known to have occurred since the 19th century, precisely around 1877, which occurred in the area between the Kalinaman River and Cempaka River (now the Sampit River and the Katingan River) in Central Kalimantan Province. According to available data, the incidence of forest fires in Indonesia has been recorded since 1978. However, a large fires which was known by the public occurred in 1982/1983 which at that time burned 3.6 million hectares of forest including about 500,000 ha of peatland in East Kalimantan ( Page et al., 2000; Parish, 2002).

Forest and land fires (Karhutla) occurred again in 1987, this time bigger and wider, which hit 21 provinces in Indonesia, especially East Kalimantan Province. These fires occured during the dry season which is affected by the emergence of the hot climate periods or as known as El Nino-Southern Oscillation (ENSO). Furthermore, the phenomenon of long and dry drought due to the emergence of ENSO is then considered responsible for the occurrence of forest/land fires disasters in Indonesia, such as the occurrence of major fires in 1991, 1994 and 1997 in 24 provinces in Indonesia. Fires during the dry season of 1997 burned about 1.5 million hectares of peatland in Indonesia (BAPPENAS, 1998), including 750,000 ha of peatland in Kalimantan. It is said that Forest/Land Fires (so called Karhutla) in 1997 was declared the worst natural fires disasters in the last 20 years. From the historical record mentioned above, “Karhutla” in Indonesia turns out to be repeated every 5 (five) years. This seems to fit the period of the emergence of the ENSO hot climate which also has a 5 (five) years average repetition.

II.2. History of FDRS Development in Indonesia

Development of the Forest/Land Fire Danger Rating System (FDRS) in Indonesia also has a long history, from the initial construction to its development to date. There are several milestones in the development of FDRS in Indonesia, as follows:

In 1997 there were significant forest/land fires in Lampung, Kalimantan and East Java. Related Ministries/Institutions (K/L), such as: Agency for the Assessment and Application of

3

Technology (BPPT), Ministry of Forestry (Dephut), Regional Governments (Pemda) jointly conduct in forest/land fires suppression operations, under the coordination of the Coordinating Ministry for Welfare People (Kemenko Kesra). Then in 1999 - 2002 drafting and signing of a Government to Government Memorandum of Understanding (MOU) between the Government of Indonesia, which in this case was represented by BPPT, the Meteorology and Geophysics Agency (BMG), and the Ministry of Forestry, with the Government of Canada, in this case represented by Canadian Forest Services. The scope of the MOU is: adaptation, operational, application, and regional system haze.

In 2003 - 2005 the MOU was drafted and signed between 3 (three) institutions in Indonesia related to forest/land fire issues, namely: BPPT, Ministry of Forestry and BMG concerning Indonesian FDRS, including: operationalization of FDR, capacity building at the Ministry of Forestry, and product dissemination. The following year, 2004 the Ministry of Forestry began installing Automatic Water Level (AWS) and XLFWI FDR Calculation devices in 29 locations in 8 (eight) Provinces in Indonesia. On the other hand, in 2005 the National Institute of Aviation and Space (LAPAN) began monitoring forest/land fires based on Remote Sensing. In the same year BMG conducted a training of trainers (TOT) and increased capacity to use XLFWI Calculation in Riau and West Kalimantan (West Kalimantan).

Furthermore, in 2006 BMG began to develop predictions of FDR (Fire Danger Rating) within 3 (three) days. This is a synergy module of collaboration between BMG and Meteo France International (MFI). In the same year BMG also continued its TOT activities and increased capacity utilization of XLFWI Calculation in 3 (three) provinces, namely: Jambi, Lampung and Central Kalimantan. In 2007 the Center for Research and Development BMG began to develop predictions of FDR for the next 7 (seven) days and TAPM (The Air Pollutant Model) including smoke from forest fires based on Numerical Weather Prediction (NWP), collaboration between BMG and CSIRO-Australia. The air pollutant model for smoke trajectories is carried out using hotspot information from the Ministry of Forestry (Sipongi Output Program). Finally in 2008 BMG began to install and use the Forest/Land Fire Danger Rating System (FDRS), as well as the National Smoke Trajectory, for the regions of West Kalimantan, Central Kalimantan, Jambi, South Sumatera, Riau and Lampung.

III. BASIC THEORY

III.1. Definition of Climate and Weather

Climate and weather have different definitions. Weather parameters are observed through atmospheric observation equipment for a short period of time (momentary), while the climate term will be used from a collection of data from weather observations over a long period of time. Weather conditions are observed from hours to hours to day to day. The results of a collection of weather data over a long period of more than 30 years results in climatic pattern conditions in a particular area. The output of weather information is in the form of synoptic data (times of the day) and is used to forecast short-term weather in the following hours or days. Meanwhile, the outcome of climate information is the value of climate patterns, so that it is known that normal climate conditions in a particular area.

4

Figure 1. Weather and Climate Time Range and its links betweenLand-Sea-Atmospheric Conditions [1]

Weather and climate parameters are strongly influenced by land-sea-atmospheric interactions. Weather parameters are observed from atmospheric condition data while climate parameters are observed from changes in weather conditions and also the condition of the sea layers. Therefore, the weather can be defined as a momentary condition of the atmosphere somewhere and at a certain time, while the climate can be defined as normal weather conditions from the average weather conditions in a certain period of time (long term). The cycle of weather parameters observed consists of hours (hourly), 3 hours (synoptic), daily (diurnal), 5 daily (pentad) and 10 daily (dasarian). Climate parameters have a cycle of the results of the average climate parameters, namely monthly, intraseasonal, seasonal, annual, and inter-annual. Meanwh Air humidity is used in FDRS + because if the humidity increases, the ability of the atmosphere to accommodate the vapor content of water that evaporates from combustible material on peatlands decreases. If the moisture content in combustible material on peatlands is difficult to evaporate, then the combustible material is difficult to burn.ile, the decadal cycle (10 years) and hundreds of years (centennial) tend to be defined in terms of global climate change. The study of weather behavior is called meterology and the study of climate characteristics is called climatology [1].

Either the weather phenomenon, climate and global climate change greatly influence the dynamics that occur between in the earth-sky and its entire. Various human activities are closely influenced and depend on the dynamics of change or climate and weather variability. The weather phenomenon is generally associated with momentary disasters and impacts for a moment. As with the climate phenomenon, the impact in general is more related to long-term phases/conditions in a particular region although if this long-term impact continues to exceed its normal conditions it can also cause disasters and even the impact of the disaster can be felt longer. Weather dynamics are more difficult to predict in a long period of time in the future, but the dynamics of weather changes can be easier to observe in the short term the tendency of weather phenomena to emerge. On the contrary, climate change dynamics are more difficult to predict in short-term observations, but it can be more easily observed the trend of changes in the opportunities for climate phenomena.

III.2. Climate and Weather Parameters used in FDRS

The parameters used in the study of the development of the FDRS+ are 5 (five) parameters consisting of air temperature, air pressure, air humidity, wind speed and direction, and rainfall. The

5

five parameters are obtained from the Era Interim Database with naming and calculation approaches which can be seen in the table below.

Table 1. Comparison of climate parameters in FDRS+ with Interim ERA parameters [2]FDRS + Parameters ERA Interim Parameters

Air temperature [°C] 2 meters temperature Air pressure [Pa] Surface pressure

Humidity [%] = 100 * (2 meters dewpoint temperature [°C]/2 meter temperature [°]) [%]

Speed [m/s] and Wind Direction [°] 10 meters U wind component [m/s] dan 10 meters V wind component [m/s]

Rainfall [mm] Total precipitation [m]

Air temperature parameters are used as input data in the FDRS+ system with a thinking approach that if the air temperature rises, then the evaporation level of combustible materials on peatlands are increasing. If the evaporation rate of combustible materials increases, the water content in the combustible material will decrease. If the water content in the combustible materials decreases, the material will be flammable. Air pressure parameters are used in FDRS+, because if the air pressure decreases, then the air's ability to accommodate the moisture content of the combustible materials will be greater. In addition, if the air pressure decreases, then the potential for wind with high speeds will be even greater. If the wind speed increases, then it will make greater potential for the spread of fire if there is a fire on peatland.

Air humidity is used in FDRS+, because if the humidity increases, the ability of the atmosphere to accommodate the vapor content of water that evaporates from combustible materials on peatlands decreases. If the moisture content in combustible materials on peatlands is difficult to evaporate, then the combustible materials is difficult to burn.

Rainfall is used in FDRS+, because if the rainfall decreases, the combustible materials on the peatland will not receive water input that is absorbed into the combustible materials. More less water content in the flammable material, then it will make easier for the materials to burn if there is fire around it. Drier of the flammable materials, then it will easier fire to propagate.

III.3. Fire Weather Index (FWI)

The Fire Weather Index (FWI) is a sub system of the Canadian Forest Fire Danger Rating System (CFFDRS) which was introduced in 1970. The function of the FWI is to calculate the impact of weather parameters related to forest fire fuel and evaluate weather conditions. The Canadian Forest Fire Weather Index (FWI) System consists of 6 (six) components that explain the effects of fuel humidity and wind behavior, namely: 3 (three) components for fuel moisture codes (Fine Fuel Moisture Code, Duff Moisture Code and Drought Code), and 3 (three) other components are for the Fire Behavior Index (Initial Spread Index, Build-up Index and Fire Weather Index). Basically, high values for each code and index reflect the ease of a fire burning, or in other words their values increase when the fire danger increases.

So basically, the FWI system is a set of computerized formulations that can be calculated easily and sophisticatedly, in contrast to the first version of FWI and calculated manually. The FWI system develops to provide maximum information from minimum weather parameter input, depending on the input of weather parameters (hourly, daily and monthly). The FWI system can also be used in separate components and a combination of several components as FWI output. Figure 2 (below) shows the components of the FWI System. Component calculations are based on daily observations of

6

temperature, relative humidity, wind speed, and 24-hour rainfall. The six standard components provide relative potential numerical ratings for forest fires. The explanation of the components of the FWI System are as follows:

a) Fine Fuel Moisture Code (FFMC), the code for Smooth Fuel Moisture, is a numerical rating of the littered water levels and other refined fuels. This code is an indicator of the relative ease of ignition and easy burning of fuel.

b) Duff Moisture Code (DMC), is a numerical rating of the average water content of semi-solid organic layers with moderate depth. This code gives an indication of fuel consumption in the medium duff layer and the medium sized of wood materials.

c) Drought Code (DC), is a numerical rating of the average moisture content of a deep and dense organic layer. This code is a good indicator of the effects of seasonal drought on forest fuel and the amount of coals in the thick duff and large round wood.

d) Initial Spread Index (ISI), is a numerical rating of the rate of fires spread. This combines the effects of wind and FFMC on the dispersion level without the influence of the variable amount of fuels.

e) Build-up Index (BUI), is a numerical rating of the total amount of available fuels. This BUI combines DMC and DC.

f) Fire Weather Index (FWI), is a numerical rating of fire intensity. This FWI is a combination of Initial Spread Index (ISI) and Build-up Index (BUI). With the FWI system it is very suitable as a general index of fire danger in all forest areas in Canada.

g) Daily Severity Rating (DSR), is a numerical rating of the difficulty of controlling fires. This is based on the Fire Weather Index but more accurately reflects the expected effort for fire suppression.

Figure 2. Structure of the Fire Weather Index (FWI) System [7].

IV. PROBLEMS

Conducting to this research, some of the problems that arise are the need for accurate and local data. While the constraints faced in terms of data sources, such as: lack of a network of tightly Automatic Water Station (AWS), and the use of data models (which are more tight) for spatial approaches. The data needed in this study are weather observation data, such as: temperature, humidity, wind speed, and rainfall, which are spatially and locally based.Why is this research or study done? This research was conducted with the aim to support the

7

development of the Indonesian Fire Danger Rating System (Ina-FDRS) algorithm.

V. HIPOTHESIS

Ideally using of the Satellite or Radar Data is needed to get the data more detail. This detailing of the data is carried out with the aim of increasing the information scale on the weather index (FWI) of fire danger rating system (FDRS) on peatlands.

VI. DISCUSSION

VI.1. Research Area

The research area is located in the Tropical Peatland Area, Ogan Komering Ilir (OKI) District, South Sumatera Province, as shown below:

Figure 3. Map of Research Area Locations.

VI.2. USE OF DATA

In relation to this research on increasing the scale of spatial weather information for the Indonesian Peatland Danger Rating System (Ina-FDRS), sufficient, accurate and local data is needed. For this reason, climate and weather parameter data are used, namely the ECMWF Reanalysis model data, issued by the ERA Interim - ECMWF (European Center for Medium-Range Weather Forecasts). The data selected and used is the Monthly Normal Sinoptic Model Data, in August 2018. August is chosen, because it represents the dry month in 2018.

ECMWF is a research institute and operates 24-hour and 7-day-a-week (24/7) operational services, resulting in global numerical weather forecasts and other data for the needs of Member States and Collaboration and the wider community. ECMWF has one of the largest supercomputer facilities and meteorological data archives in the world. ECMWF is located in Reading City, United Kingdom.

8

ERA-Interim is a global atmospheric reanalysis from 1979, continuously updated in real time (https://www.ecmwf.int., ECMWF, European Union).

VI.3. Previous Research

Because the land/forest Fire Danger Rating System (FDRS) is an indispensable information in order to anticipate forest fires that often occur in Indonesia, the FDRS is also developed in Indonesia which refers to a model that already exists, namely from Canadian Forest Fire Danger Rating System (CFFDRS). In this research, also conducted a study of the results of previous studies related to the comparison of information between forest/land FDRS resulting from meteorological station and forest/land FDRS data from remote sensing data. Comparisons were made on the parameters of Fine Fuel Moisture Code (FFMC) and Fire Weather Index (FWI). The research was carried out in areas in Sumatera, Java and Kalimantan, then modified parameters of air temperature, rainfall and wind speed (Febrianti, 2015) [4].

Some satellite image data are used in the study, such as: NOAA AVHRR, Qmorph, and TXPLAPS. Then compared with data from 70 meteorology stations spread across several research areas. However, the meteorology station was limited in number (not so tight), so that 1 (one) station represented a fairly wide area. Therefore the research results are not enough to provide a more detailed picture of weather conditions in an area. Data processing results of meteorological stations tend to form global patterns. Based on the results of the analysis obtained information that FFMC and FWI from remote sensing data tend to be higher in some areas compared to using meteorology station data, but the information produced is more detailed. Modified results of air temperature data indicate that a small portion of FFMC in Sumatera has increased, and FWI in Kalimantan has declined. Rainfall modification causes the FFMC and FWI in Sumatra to decline. While modification of wind speed only causes small changes in Kalimantan.

VII. RAINFALL DATA FROM SATELLITE HIMAWARI FOR FDRS

Basically, forest fires are closely related to the climate and weather phenomena. For example, during Seasonal Winter (ITCZ in the southern hemisphere), usually Forest Fires will occur in the Indochina region. In contrast during Seasonal Summer (ITCZ in the northern hemisphere), forest fires will occur in the Indonesian Maritime Continent. During the dry season, many forest fires occur on peatlands in lowland forests (Sumatera, Kalimantan, Sulawesi). Then during El-Nino, forest fires will usually be more active.

For the development of FDRS, accurate meteorological data is needed. Besides that, it is also necessary to increase the early warning system for information on the Fire Danger Rating System (FDRS). But the existing surface meteorological station is very limited. The purpose of preparing meteorological data, especially rainfall data, is to: understand the role of rainfall data for the Fire Weather Index (FWI), the comparison of the physical conditions of rainfall data in the fire and non-fire seasons in the targeted areas of South Sumatera, and the application of rainfall data satellite (HIMAWARI-8) for peatland areas.

VIII. ANALYSIS RESULTS

As mentioned in the previous subchapter, that to conduct this research in order to increase the Scale of Spatial Weather Information for Indonesia Peatland Fire Danger Rating System (Ina-FDRS), an analysis of existing data is considered sufficiently representative. The data which selected and used in this study are the Monthly Normal Synoptic Model Data, in August 2018. The data is derived from

9

the ERA Interim, ECMWF (European Center for Medium-Range Weather Forecasts), based in Reading City, United Kingdom.

The month of August is chosen, because it is considered to represent the dry month in 2018, then it is expected that the Land/Forest Fire Danger Rating will be seen. In conducting spatial data analysis on weather, the "R" programming language is used. The obtained of analysis results, are as follows:

a). Fine Fuel Moisture Code (FFMC), is a numerical rating of litter water levels and other refined fuels:

Figure 4. Fine Fuel Moisture Code (FFMC)

Figure 4 above, it can be seen that in the northern part of the study area is the highest FFMC or litter content and other fine fuel materials (48 - 50) were found. This indicates that the area is prone to land/forest fires (easy burning of fuels).

b). Duff Moisture Code (DMC), is a numerical rating of the average water content of semi-solid organic layers with moderate depth.

Figure 5. Duff Moisture Code (DMC)

10

From figure 5 above it can be seen, that in the west to north the study area has a high DMC value or average water content of semi-solid organic layers with moderate depth (3,3 - 3,4). This indicates that the region has a moderate level of fire vulnerability.

c). Drought Code (DC), is a numerical rating of the average moisture content of a deep and dense organic layer.

Figure 6. Drought Code (DC)

From figure 6 above it can be seen, that in the west to north the study area has a high DC value or the average moisture content of the deep and solid organic layers (8.15 - 8.30).This is the impact of seasonal droughts on forest fuels, and the number of flames in deep organic layers and large logs. This code is used as an indicator of the potential for fire in a land/forest fire and the potential for smoke haze.

d). Initial Spread Index (ISI), is a numerical rating of the rate of spread of fire. ISI combines the effects of wind and FFMC on the dispersion level without the influence of the variable amount of fuel.

Figure 7. Initial Spread Index (ISI)

From figure 7 above it can be seen, that almost all study areas have medium - low ISI values (0.15 - 0.25), index values spread and enlarge from the south to the north, west, and east. The ISI value

11

combines the effects of wind and FFMC at the dispersion level without the influence of the variable fuel quantity. This code shows how fires will spread/propagate after the ignition occurs.

e). Build-up Index (BUI), is a numerical rating of the total amount of fuel available. This BUI

combines DMC and DC.

Figure 8. Build-Up Index (BUI)

From figure 8 above it can be seen, that in the eastern, centre and south part of the study area have the medium - low BUI values (3.30 - 3.10), while the west - north have medium - high BUI index values (3.30 - 3 , 45). The BUI value is the total amount of fuel available, and is a combination of DMC and DC.

f). Fire Weather Index (FWI), is a numerical rating of fire intensity. This FWI is a combination of Initial Spread Index (ISI) and Build-up Index (BUI). With this FWI system it is very suitable as a general index of fire danger in all forest areas in Canada.

Figure 9. Fire Weather Index (FWI)

12

From figure 9 above it can be seen, that almost all study areas had moderate - low FWI values (0.11 - 0.06), with relatively low in the middle parts spreading out to all directions to moderate values.

This FWI index value is a numerical rating of fire intensity. This index can generally be referred to as a fire danger index in terms of weather. Fire Danger is a general indication of all factors that affect the ease of burning, the spread of fire, the physical impact of fire and the degree of difficulty of fire control. This code is used as an indicator of forecasting fire control difficulties.

IX. CONCLUSION

Furthermore, from the results of data analysis, review of various previous studies, and studies related to the development of Fire Danger Rating System (FDRS) in Indonesia, as well as the research that the author did, it is concluded as follows:

1) Information on weather data has a very important role in the development of FDRS in Indonesia in the future (Ina-FDRS).

2) To understand the dynamics phenomenon of peat land fires, we must first have sufficient knowledge about the parameters needed, such as: rainfall (CH), temperature (Temp), relative humidity (RH), and wind speed (WS).

3) Development of Ina-FDRS for disaster mitigation of forest/land fires requires real-time data, and high resolution both spatially and temporally.

4) The recent FDRS uses only surface meteorological stations, therefore the spatial resolution is very limited.

5) Ideally for Rainfall Data, we use Satellite Data, so we will get data with high spatial and temporal resolution. This can be used as an input for Ina-FDRS, so the resulting system will have a very high resolution as well.

6) Coverage of forest/land fires, especially on very local peatlands, so that it will have better results if we use high resolution data.

7) From the results of the Fire Weather Index (FWI) analysis in the study area, it can be seen that: almost all study areas have low - medium FWI values (0.06 - 0.11), with relatively low FWI values in the center of the study area, spread out all directions towards the medium FWI value. It can be interpreted that in the study area (OKI District, South Sumatera) in August 2018 was not too prone to forest/land fires even in dry months.

8) The higher of the FWI (Fire Weather Index) level index in an area, the higher danger of forest fires that might occur. Conversely, the lower of the numerical value of the FWI index level of a region, the lower too the danger of forest fires that might occur.

9) By utilizing the FDR System for early warning information the level of danger of forest/land fires will be more effective and efficient in preventing early land/forest fires.

X. ACKNOWLEDGMENT

The authors wish to thank the team of STKK PPPTPSW (the Program of the Assessment and Application of Technology for Regional Resource Development at BPPT) who provided weather data and letting us use it for writing this paper.

XI. REFERENCE

1) Afifuddin, R. Ardiyanto, dan A. Purwandani, 2018, “Prosedur Operasi Standar (SOP) untuk Pembuatan Data Normal Sinoptik Bulanan Parameter Iklim Menggunakan Data Reanalisis ECMWF”, Pusat Teknologi Pengembangan Sumberdaya Wilayah (PTPSW), TPSA, BPPT, Jakarta.

13

2) Guswanto, E. Heriyanto, 2009, “Operational Weather Systems for National Fire Danger Rating”, Jurnal Meteorologi dan Geofisika, Vol. 10, No. 2 Tahun 2009, Research and Development Center, Meteorological Climatological and Geophysical Agency (BMKG),

3) Lawson, B.D. and O.B. Armitage, 2008, “Weather Guide for the Canadian Forest Fire Danger Rating System”, Natural Resources Canada, Can. For. Serv., North. For. Cent., Edmonton, AB. (http://cfs.nrcan.gc.ca/pubwarehouse/pdfs/29152.pdf.)

4) N. Febrianti, S. Sulma, Lintang, 2015, “Analisis Awal Perbandingan Sistem Peringkat Bahaya Kebakaran Hutan dari Data Penginderaan Jauh dan Stasiun Meteorologi”, Pusat Pemanfaatan Penginderaan Jauh, Lembaga Penerbangan dan Antariksa Nasional (LAPAN), Prosiding Pertemuan Ilmiah Tahunan (PIT) MAPIN XX 2015, Jakarta. (https://www.researchgate.net/publication/323799961).

5) N.N., 2017, “Canadian Forest Fire Weather Index (FWI) System”, Natural Resources of Canada, Government of Canada, http://cwfis.cfs.nrcan.gc.ca/background/summary/fwi.

6) N.N., “CFFDRS System Overview”, (http://www.fbfrg.org/cffdrs/cffdrs-system-overview).7) N.N., 2009, “Fire Danger Technote 03: Fire Environment Working Group (FEWG)”, A

Working Group for North Caroline Fire Danger Rating System (NCFDRS), NCDFR MTM, USA.

8) N.N., “Canadian Forest Fire Weather Index (FWI) System”, Natural Resources of Canada, Government of Canada, http://cwfis.cfs.nrcan.gc.ca/background/summary/fwi.

9) R. Sulistyowati, et. All., 2018, “The Role of Himawari Rainfall Data for Indonesia Fire Danger Rating System (Ina-FDRS)”, BPPT – BMKG, Paper of the 2018 IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS) – IEEE Conference No. #44946, BPPT Jakarta.

10) Suciarti, 2011, “Sistem Informasi Tingkat Bahaya Kebakaran Hutan dan Lahan dengan menggunakan Fire Weather Index (FWI) dan SIG Arcview”, Program Studi Teknik Elektro, Fakultas Teknik Universitas Tanjungpura, Pontianak.

11) Van Wagner, C.E. and T.L. Pickett. 1985, “Equations and FORTRAN program for the Canadian Forest Fire Weather Index System”, Canadian Forestry Service, Ottawa, Ont. For. Tech. Rep. 33. 18 p. http://cfs.nrcan.gc.ca/pubwarehouse/pdfs/19973.pdf.

12) Van Wagner, C.E. 1987, “Development and Structure of the Canadian Forest Fire Weather Index System”, Forest Technology Report 35, Canadian Forestry Service: Ottawa, http://cfs.nrcan.gc.ca/pubwarehouse/pdfs/19927.pdf.

13) Xianli Wang, Alan Cantin, Marc-Andre Parisien, Mike Wotton, Kerry Anderson, and Mike Flannigan, 2017, “Package Canadian Forest Fire Danger Rating System (‘cffdrs’)”, Version 1.7.6., (https://cran.r-project.org/web/packages/cffdrs/cffdrs.pdf).

14