Indonesia Fire Danger Rating System (Ina-FDRS), a New Algorithm for the Fire Prevention in Indonesia

Hartanto Sanjaya1, G. Fajar Suryono1, Azalea Eugenie1, Mega Novetrishka Putri1, Heri Sadmono1

1 Pusat Teknologi Pengembangan Sumber Daya Wilayah, BPPT

hartanto.sanjaya@bppt.go.id

Abstract. A system named Fire Danger Rating System is a tool using for early warning system of forest fires. Since 1999 Indonesia has this system, adapted from Canadian Forest Service (CFS), and developed by BPPT cooperation with BMG (now is BMKG), and Departemen Kehutanan (KLHK). The main scientific based of this FDRS is meteorology, where it used total precipitation in a day, and noon condition of temperature, relative humidity, and wind speed. The final result is the Fire Weather Index where we may see the four classes of fire danger rating in two-dimension map. The improvement on this weather component is the use of satellite data to replace some parameter of weather from Automatic Weather Station data.

In recent condition, not only weather condition as the primary parameter, but also the human-caused factor, and fuels condition analysis were putting as the same level of weather. Human activities are the main factor of the occurrence of forest fires, that is why the forest, plantation, and horticulture-agriculture land are the main land use that we pay attention more, and it’s related to the people who live on small cities or districts in that areas. The condition of peatland we analyzed is the thickness of the peat and the land cover on the surface. The position of water body also we calculated to see the distance of the source of water for wetting the land.

Using Spatial Multi-Criteria Evaluation is the right way to get some scenarios to have the final composite risk map. Grouping and standardizing the spatial factors based on human-caused and fuels we may know the effect of that component to the final composite map of the Advanced-FDRS. Ina-FDRS is the biggest scenario that includes Natural Resources Accounting as one of the main components, instead of climate, human-caused, and fuels. So, this Advanced-FDRS is the main part of Ina-FDRS (Indonesia Fire Danger Rating System) that ongoing develop by BPPT and the stakeholders for early warning land and forest fire in peatland in Indonesia.

Keywords--- Indonesia Fire Danger Rating System, human-caused, fuels, Fire Weather Index, SMCE

1. IntroductionForest fires are one of the biggest disasters and always hit in many parts of the world. Not only forests are burned but also plantations and community land are part of this fire. Indonesia, like the United States and Australia, always experiences this every year and is a huge loss to the country's economy

and sustainable development. One of the events that became an important milestone in paying attention to the disaster hazards of this forest fire was a weather-induced fire caused by the El Niño Southern Oscillation (ENSO) in 1997/1998 which at that time burned forests of up to 25 million hectares worldwide [1].

Indonesia experienced a very severe fire disaster in 1997/1998, and in the 2000s the Indonesian government through Agency for the Assessment and Application of Technology (BPPT) in cooperation with the Canadian Forest Service developed a special Early Warning System to anticipate fire-prone conditions. BPPT, together with the Ministry of Forestry (now Ministry of Environment and Forestry) and the Meteorology and Geophysics Agency (now BMKG), adopted the Fire Danger Rating System (FDRS) which has been implemented in Canada. BMKG as an institution operating in the field of meteorology continues its operation until now [8].

Figure 1 The flowchart of the Canadian FDRS [10]

FDRS that are being operated rely on weather data and the results are still regional. The unfavorable distribution of weather stations is not yet possible to produce data that reflects local conditions; this is an obstacle to more detailed results. Suryono said that increasing the data source density can improve the accuracy of calculations spatially [2]. For this reason, the use of satellite data or weather radar will greatly help the weather data collection for the future.

Conrad said, from field studies found other factors that affect land fires, namely human activities, among others, the local culture and who have access to certain locations [7]. According to Eugenie et.al. [3], the level of education of the community in the district also has a positive correlation to the occurrence of forest and land fires. Therefore, the development of the FDRS algorithm in this paper will include the factors of the existence of districts and the accessibility that exist in the study area.

According to Wang [14], FDRS in Canada has added studies related to topography and fuel conditions. Topographic data is very necessary if the surface morphology is undulating especially in mountainous areas. Fire distribution can be faster for sharp slope areas. The fuel condition is also the case, and this is a consideration in the development of the FDRS.

Indonesia has an annual fire problem in peat areas, where the consequences can severely damage the ecosystem. Indonesia's peatlands are 50% of the world's total tropical peatlands [5] and have an area of about 20 million hectares (Wetlands International in [5]). The morphology of peatland surface is relatively flat so that topographic factors are not considered related to land/forest fires.

By paying attention to the aforementioned matters, the development of the Advanced-FDRS algorithm is devoted to peatland areas and includes three major components, namely the weather component (via FWI, Fire Weather Index), human activity components, and fuel components.

Figure 3 Study area at Ogan Komering Ilir Regency.

This Advanced-FDRS algorithm is the main structure of Ina-FDRS, where Ina-FDRS has an important component, the component of natural resource accounting, that calculates potential losses economically for each land cover in the event of land and forest fires.

Figure 2 The algorithm of Ina-FDRS [20]

Ina-FDRS is developing based on the local condition on the peatland in Indonesia, so the generic algorithm will be fit on every site where the peatland is the major soil type.

2. Methodology and MaterialThe development of the Advanced-FDRS algorithm prioritizes three components as previously mentioned. All three have a processing flow and with different considerations. The FWI component adopts the algorithm produced by CFS by using adjustments as explained by Wang [14], and by the use of more dense spatial data [2].

Meanwhile, the component of human activity calculates the advantages or disadvantages of the existence of peatlands related to the distance to villages or settlements and road networks. And the fuel component is concerned with the thickness of the peatland, the distance of the peatland to the river and observing the land cover.

The model was built using Spatial Multi-Criteria Evaluation by incorporating spatial factors from the three constituent components. Determination of problem definition is done before the standardization process for each element is carried out. Weighting is carried out with various scenarios from various considerations.

2.1. Study locationPeatlands in Ogan Komering Ilir (OKI), South Sumatra Province, became the study area for the development of this algorithm. This region was chosen because it has a very large peatland area and has a history of the poor land and forest fires.

2.1.1. Data and software. The data used are processed spatial data, which consists of data from the forest fire index (Fire Weather Index), road network, river network, distribution of settlements, and distribution of thickness of peatland, and peatland boundaries in OKI Regency. Preparation of spatial data using Quantum GIS and R Programming software, while for the SMCE model Ilwis software is used. All spatial data formed to coordinate reference system (CRS) “EPSG:32748 - WGS 84 / UTM zone 48S – Projected”.

2.2. Preparation of spatial component

The spatial dimension for spatial analysis is two kilometers, so all spatial data is in raster format with the same dimensions. All spatial data are processed according to requirements as described below.

2.2.1. Weather index component. Weather data that is processed is the result of FWI modeling, where the components of weather data are temperature, humidity, wind speed, and rainfall. The three data in the first order are the data at noon, while the precipitation data is the total rainfall in 24 hours according to the data processing structure for FWI, according to van Wagner [6]. Data were obtained from synoptic monthly mean modeling results based on the data of the last 30 years, and for August 2018. There is five product that we can get before we have the FWI. The first part of FWI is Fine Fuels Moisture Code (FFMC), Duff Moisture Code (DMC), and Drought Code (DC). This part named as fuel moisture code. And the other one is fire behavior indices consist of Initial Spread Index (ISI), Build up Index (BI), and Fire Weather Index (FWI) [6].

2.2.2. Human-caused component. Road and river network and settlement distribution are the main data. Data sources are Indonesian Earth Map (Peta Rupa Bumi Indonesia) published by the Geospatial Information Agency (BIG). These three data are processed using the Map Distance method to determine the distance that originates from a particular object. The three results from the map distance will be the cost or benefit in the calculation in the SMCE model.

2.2.3. Fuels component. This component takes into account the condition of peatland, which is related to the thickness of the peat, while the river network is to calculate the wetness. Land cover is also a concern for the fuel component. Data on peatland distribution is published data from CIFOR in 2016. Data is processed with an area boundary from the KHG (Peatland Hydrological Area) map published by the Ministry of Environment and Forestry. Land cover data is generated from the 2017 Landsat 8 satellite data classification using the Machine Learning with Random Forest method. The resulting classes are settlements, dense forest, medium forest, sparse forest, and water body.

2.3. Building Advanced-FDRSModeling using SMCE requires initial settings for problem definition. In this process three groupings were built, firstly is FWI, then groups for human activities (Human-Caused), and also groups for fuels.

Figure 4 The algorithm of Advanced-FDRS

2.3.1. Human-caused spatial group. As previously explained, this group consists of water body, road network, and river network. All three are processed using a distance map method so that the relative distance is generated for each object. Peat areas that are farther away from the water body will be more secure because the conditions are increasingly not suitable for processing into agriculture by

the surrounding community. This condition reduces the desire for land clearing. This Map Distance of the water body becomes a benefit, the bigger the pixel value the greater the standard value for the SMCE. Likewise, with the Map Distance road network where the farther from the road, the less likely the fire will be due to human activities. The same thing for settlements, where peatlands are farther away from settlements, it will be less likely to open agricultural land. The comparison between these three spatial factors using pairwise comparison where that three have equally important.

2.3.2. Fuels spatial group. Peatland thickness is a major concern because it has great potential as a fuel for land and forest fires. So that in the context of standardization, the thickness of the peat as a cost in which it will be in will be more vulnerable. Land Cover has five classes, namely built-up area, and water body, where both are not considered as objects of fuel. Whereas the other three classes, namely dense, moderate, and sparse vegetation are calculated as fuel materials where dense vegetation has the highest weight, then moderate and sparse vegetation has a lower weight in sequence. Water body is in the form of Map Distance so that it has a greater value when away from water objects. In the context of the fuel material, the area of peatland that is farther away from the water will be more vulnerable because the wetness is reduced. So that the standardization value in this component becomes a cost, where the greater the pixel value indicates the more vulnerable conditions. The weighting of the three components in this group is by putting the highest level of wetness, it’s the distance map from the water body, and then the thickness of the peat and the land cover has a lower level respectively.

2.3.3. Weighting for spatial factors. The three main components in the calculation of the FDRS are weighted according to the selected scenario. All the three: FWI, Human-caused, and Fuels can be given the same weight or the ranking is chosen matches the higher and lower according to the experiment.

3. ResultThe following is the result of the Spatial Multi-Criteria Evaluation modeling in accordance with the process described above.

3.1. Weather index component. Fire Weather Index and its component are processed in R programming and the results are shown below.

Figure 5 The results of all components of Fire Weather Index, where the gradation colors show the values. White colors show the lowest value and dark green show the highest value.

With the input of weather data from the average monthly synoptic model, for August 2018 is produced as shown above. Even though on FWI maps we can see that they have low values and are still in danger range Low, but these results remain the main input in Advanced-FDRS in the following process.

3.2. Human-caused componentThere are three maps as the input of the group of the human-caused component. All of these are processed by map distance methods.

Figure 6 Map distance of water body (left), urban/settlements (center), and roads (right), overlayed by administration boundary.

3.3. Fuels component The thickness of peatland, water network, and the land cover as input for the fuel’s component group, which the water body map processed by map distance method, and the thickness of peat and the land cover as it is. The values of peat thickness are between 1 and 10 meters, according to the publisher of the data. The class of land cover consist of five classes: built-up area, water body, dense vegetation, medium vegetation, and sparse vegetation. The classification was using machine learning with random forest method.

Figure 7 a) The map distance of water body (left); b) the thickness of peatland (center); c) and five classes of the land cover (right).

3.4. The composite index mapsThe three components have resulted as composite index maps as shown below.

Figure 8 a) Map of fire weather index (left); b) Composite index map of human-caused component; c) Composite index map of fuels component.

To get the final composite map, the three composite maps are weighted. Weighting is done using ranking methods, where human-caused is positioned at the top, the next is the weather factor, and the final part is the fuel factor. Figure 8 (a) shows the colors gradation which the dark green represents the higher values and the dark red is for lower values. It shows that FWI has higher values on the middle of the regency. Meanwhile, the gradation of colors of the composite map of human-caused (Figure 8b) shows dark red represents lower values and dark green for the higher values. Lower values mean the area more vulnerable than the higher values. And the color gradation of composite index map of fuels (Figure 8c) shows dark red is more vulnerable than dark green. The composite index map of the Advanced-FDRS shows the scale of risk, which is divided into four classes and represented by colors.

Table 1. The colors of risk mapColors RiskBlue LowGreen MediumYellow HighRed Extreme

The composite map for Advanced-FDRS is as below.

Figure 9 Composite index map of Advanced-FDRS, overlayed with administration and peatland boundary. The colors represent the scale of risk, blue is low, green is medium, yellow for high risk, and the red is for extreme.

From the Figure 9 above we see an extreme area on the southern peatland. In this section there are rivers and there are also roads around the location. This shows that in certain weather conditions, even low in risk for fires, the potential for fire hazards then determined by the conditions of human activities that have access (roads) and close to water sources (for tillage on peatlands).

4. ConclusionThe Advanced-FDRS is a new algorithm for developing forest and land fire early warning system, especially for peatland. This algorithm pays attention to other components as the main input, in addition to weather conditions. Human activities as a cause of the fire are approached by spatial models, as well as the conditions of the fuel that is on peatlands.

The final map results obtained composite from various considerations of the vulnerability of each component that forms this model. Human activities have the higher score to drive the potential fires even the weather data shows the low level of risk. It shows that the human-caused component, together with the fuels condition, is important to put into this algorithm.

The final risk map may be changed if the scenario change. There are many options for the standardization of each item and depends on the situation on study site. The optimum scenario should carry out for the next future by experiencing this algorithm to be related to the occurrence of land and forest fire. This algorithm needs extended time to adjust the threshold values and the scenario of weighting to historical hotspot data and using the real daily weather data before being applied.

In the near future, developing Ina-FDRS by adding the natural resources accounting to this Advanced-FDRS to be one of the independent components, hopefully, will increase the result of this early warning system algorithm, and the result may show the actual local social-economy process related to fires.

5. AcknowledgmentThe 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 primary and secondary data based on their experience and expertise and letting us use it for developing this new algorithm.

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