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# Anthropogenic factors in a forest and peatland fire danger rating system in South Sumatra

Ati Dwi Nurhayati, Bambang Hero Saharjo, Leti Sundawati, Mark A Cochrane, Christelle Hély

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Ati Dwi Nurhayati, Bambang Hero Saharjo, Leti Sundawati, Mark A Cochrane, Christelle Hély. Anthropogenic factors in a forest and peatland fire danger rating system in South Sumatra. Environmental Research Letters, 2025, 20, pp.064018. 10.1088/1748-9326/add609. hal-05201203

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# Anthropogenic factors in a forest and peatland fire danger rating system in South Sumatra

To cite this article: Ati Dwi Nurhayati et al 2025 *Environ. Res. Lett.* 20 064018

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LETTER

# Anthropogenic factors in a forest and peatland fire danger rating system in South Sumatra

Ati Dwi Nurhayati¹,⁶,* ID, Bambang Hero Saharjo¹,⁶, Leti Sundawati², Syartinilia³, Mark A Cochrane⁴ and Christelle Hély⁵

¹ Department of Silviculture, Faculty of Forestry and Environment IPB University, Bogor, Indonesia

² Department of Forest Management, Faculty of Forestry and Environment IPB University, Bogor, Indonesia

³ Department of Landscape Architecture, Faculty of Agriculture, IPB University, Bogor, Indonesia

⁴ Appalachian Laboratory, University of Maryland Center for Environmental Science, Frostburg, MD, United States of America

⁵ ISEM, University Montpellier, CNRS, IRD, EPHE, Montpellier, France

⁶ The Disaster Studies Center, IPB University, Bogor, Indonesia

* Author to whom any correspondence should be addressed.

E-mail: awinur@apps.ipb.ac.id

Keywords: forest fire, FDRS, hazard, risk, Indonesia OKI District

Supplementary material for this article is available online

## Abstract

In an effort to prevent forest and land fires, Indonesia has implemented its fire danger rating system (FDRS). Currently, the FDRS is based upon the Canadian system, which is driven by only weather parameters. However, fires in Indonesia are primarily caused by human activities with most of them occurring on peatlands. Thus, it is necessary to integrate these factors in the development of the next generation FDRS to increase the precision and accuracy in assessing levels of fire danger in various Indonesian regions. This study presents a proof of concept for a new FDRS, applied in forest and peatland in Ogan Komering Ilir (OKI) District, South Sumatra Province, based on three parameters: weather, anthropogenic factors and presence or absence of peat. The anthropogenic and peatland FDRS (AP-FDRS) map was produced using a spatial logistic regression method for incorporating anthropogenic and peat models, using the fire weather index (FWI) model from forest and land fire early warning system together with an overlay method to produce a composite map of forest and peatland fire. In this study, 2 d in 2020 were selected as sample dates: one with hotspot occurrences (23 August 2020) and one without hotspots (28 December 2020). On 23 August 2020, the AP-FDRS map was dominated by high (54%) and extreme (46%) fire hazard levels after combining high FWI values with extreme AP risks in the OKI District. In contrast, on 28 December 2020, although the FWI indicated low fire risk, the integration with AP factors resulted in an AP-FDRS map dominated by medium (34%) and high (60%) fire hazard levels. These results highlight that weather factors alone are not sufficient to predict fire hazard risks in Indonesia. Therefore, the AP-FDRS provides more detailed and accurate predictions compared to current FWI based systems.

## 1. Introduction

Forest and peatland fires in Indonesia contribute greatly to the haze issues that both threaten human health regionally and produce large amount of greenhouse gases. Since 1997/1998, satellite images have shown that the haze not only affected Indonesia, but also crossed borders into neighboring Malaysia,

Singapore, Thailand and the Philippines [1]. Peat fires have a worse impact than fires on mineral soils. Peat fires are typified by smoldering combustion burning underground organic matter with fire spread at a very low rate from a few decimeters to tens of meters per day [2]. These peat fires move slowly because they are less influenced by the wind than by the water table levels and the root systems, so the pattern of
spread is erratic and it is difficult to determine where fires actually occur [3], even though peat fires are usually started by fires on the surface of the peat-land. Due to smoldering propagation, peat fires emit large amounts of particulate matter and both highly toxic and green-house gases that all adversely impact human health, as shown by studies on peat fires in Central Kalimantan [4].

Based on the adverse impacts of forest and peat-land fires, efforts to control forest and land fires must now emphasize prevention. One way to prevent forest and land fires is to monitor the fire danger at a daily time step over the dry-season [5], as well as a few months in advance, before the start of the fire-season [6]. Indeed, several studies have shown that phenomena, such as phases of El Niño Southern Oscillation, significantly affect first fuel load growth during the wet season and then fuel dryness and availability during the (dry) fire-season: heavy loads of biomass produced during the wet season are transformed into heavy loads of fuel available for burning during the dry season [7]. By determining the level of fire danger in an area and at a certain time, efforts to preposition means to suppress forest fires at their early stages could be carefully planned.

FDRSs have long been formally developed in Canada, Australia, and the United States. The Canadian FDRS is the most widely adopted globally due to its adaptability from local to global scales. Indonesia began developing its FDRS in 1999, in cooperation with Canada. The FDRS applied in Indonesia today is still based upon the Canadian FDRS, whose original version is only based on daily weather data (UTM noon air temperature, relative humidity, wind speed and rainfall amount over the last 24 h) [8]. The meteorology and geophysics agency, now called the meteorology, climatology and geophysics agency (BMKG), is a government agency tasked with disseminating FDRS information nationally. The development and use of FDRS in Indonesia from 2005 to 2018 were based solely on a single parameter, weather conditions. However, as forest and land fires in Indonesia are almost 100% human-caused and often occur on peatlands [9]. It is crucial to consider human factors as a cause of wildfires in fire danger rating systems (FDRS). The original version currently in use is not optimal, requires improvement and would benefit from the additional implementation of anthropogenic and peat fuel factors. Therefore, the development of AP-FDRS by integrating anthropogenic and peatland (AP) factors is necessary to prevent forest and peatland fires.

The aim of the present study is to demonstrate how such a new version of FDRS, called the AP-FDRS, increases the precision and accuracy of fire danger assessment. To develop the AP-FDRS, this research focused on a fire-prone area in South

Sumatra Province, and is based on three factor types: weather conditions, anthropogenic activities and peat characteristics.

## 2. Materials and methods

### 2.1. Study area

Based on Terra/Aqua MODIS satellite imagery, a previous study showed that Ogan Komering Ilir (OKI) district in South Sumatra province has experienced the most extensive fires in the last decade [10]. We therefore selected this district to develop the new AP-version of the Indonesian FDRS. The OKI district includes 19 023.47 km² of peat swamp and mangrove ecosystems types that are 0–10 m in elevation and drained by many rivers. Approximately 75% of the OKI District’s area consist of peat swamps, while 25% consists of land. The dry season, characterized by monthly rainfall of less than 100 mm, typically occurs from May to October each year, whereas the rainy season generally spans from November to April [11].

### 2.2. Current operational FDRS

FDRS is a system used to estimate and predict the risk of fire hazards and their spread. The current FDRS relies solely on weather parameters and is designed for mineral soils. The Indonesian FDRS currently used is based on the original Canadian fire weather index (FWI) (figure 1) [12], the most widely used FDRS in the world [13, 14]. However, four FWI classes have been defined according to Indonesian weather conditions and fire occurrences (table 1) in order for the FWI values to be representative of *in situ* conditions for which real fires have been recorded over the same period. The FWI used in this study is sourced from BMKG’s forest and land fire early warning system (SPARTAN) which was developed in July 2020, based on weather parameters [15].

### 2.3. Datasets to calibrate and validate the AP-FDRS

In terms of inputs, beyond the basic weather data required as in the original Canadian and Indonesian FDRS, we needed land cover sources from the Ministry of Environment and Forestry 2022, maturity and depth of peat from National and Ecosystem Function 2011 and burned areas to develop regression logistic models. We used both Landsat 8 images and field data to identify burned areas and validate the identified fire scars.

#### 2.3.1. FWI

FWI values are obtained through several stages. The data used in building the FWI are temperature, relative humidity, wind speed and rain. Basically, FWI consists of three fuel moisture codes and three fire behavior indices [12]. Fuel moisture codes consist of Fine fuel moisture code, Duff moisture code and
<figure><img src="image_8.png" /><figcaption>**Figure 1.** Basic structure of fire danger information system FWI index [12].</figcaption></figure>

<table><caption>**Table 1.** Fire danger classes based on FWI [16].</caption><thead><tr><td>Color</td><td>Ranges</td><td>Description</td></tr></thead><tbody><tr><td>Blue</td><td>0–1</td><td>Fire intensity in the low category</td></tr><tr><td>Green</td><td>2–6</td><td>Fire intensity in the medium category</td></tr><tr><td>Yellow</td><td>7–13</td><td>Fire intensity in the high category</td></tr><tr><td>Red</td><td>>13</td><td>Fire intensity in the extreme category</td></tr></tbody></table>

Drought code. The fire behavior indices consist of initial spread index, build-up index and FWI (figure 1).

Based on the results of this FWI calculation, there are four fire danger classes: low, medium, high and extreme (table 1).

AP-FDRS is a system used to predict fire hazard risks based on weather parameters, anthropogenic factors, and peatland conditions. The AP-FDRS Concept development carried out in this study is to develop FDRS integrating the FWI index with anthropogenic and peat models (figure 2).

## 2.4. Anthropogenic and peat models

We developed the two models, anthropogenic and peat models using logistic regression. The first step is determination of the dependent variable. In both models, the dependent variable is the burned area, where presence (i.e. y = 1) indicates that the burned pixel had either forest or a peatland cover on a given day, and absence (i.e. y = 0) indicates that the pixel did not burn on that day. The second is determination of the independent variables. The independent environmental variables used in the anthropogenic and the peat models are also encounter variables (i.e. presence and absence) as the dependent variable. The total number of points used to construct the model was 60 points (30 presence points; 30 absence points) in the peatlands of three sub districts (Tulung Selapan, Cengal and Air Sugihan). The presence model was obtained from ‘random sampling points’ on the extension Hawths Tool Analysis in ArcGIS 10.5 based on a burned area map with a distance between points of 1 KM. See supplementary figure 1 for additional detail.

After constructing the models, we collected 130 validation points (100 presence points; 30 absence points) for use in evaluating the models. The presence

validation points were obtained from a combination of validation points from field results (2 points), ‘random sampling points’ on the polygon of field results, and ‘random sampling points’ on burned areas in OKI District. The absence points were obtained with the same procedure as the random sampling points made outside the burned area in OKI District. The presence point data from the field has an important role in the formation of the anthropogenic model even though there were only 2 points. These presence points can be an early indication of anthropogenic suitability or as validation points. See supplementary figure 2 for additional detail.

The independent variables used in modeling anthropogenic fire suitability are indicators of where humans are active in burning. This indicator is interpreted in distance, which in this study used independent variables consisting of distances from secondary forests, plantation forests, plantations, shrubs, settlements, population density, roads and rivers. See supplementary figure 3 for additional detail. The independent variables used in peat modeling are peat depth, peat maturity and soil moisture. These independent variables consisted of soil moisture and distances from very shallow peat (<50 cm), shallow peat (50–100 cm), medium peat (100 < 200 cm), hemic peat, and sapric peat. The anthropogenic and peat variables were analyzed using Euclidean Distance in ArcGIS 10.5. Soil moisture was obtained from the FAO system for earth observation data access, processing and analysis for land monitoring platform. This platform is used for peatland monitoring related to peat soil moisture. See supplementary figure 4 for additional detail.

The third step is anthropogenic and peat model analysis. The anthropogenic and peat models were formed using encounter point data (dependent variable) and environmental variables (independent variables). The environmental variables were first tested for multicollinearity to determine the possibility of a perfect linear relationship between the independent variables. One method that can be used to diagnose the presence or absence of multicollinearity is by calculating the tolerance value or variance inflation factor (VIF). If the tolerance value < 0.1 or VIF value > 10, then there is multicollinearity or a perfect linear relationship between the independent variables so that the independent variables must be eliminated. The analysis of anthropogenic and peat modeling used binary logistic regression analysis processed on SPSS 25:

$$Pi = \frac{1}{1 + \text{Exp} \left[ -(\beta_0 + \sum_{j}^{k} \beta_j X_{ji}) \right]}$$

where Pi is opportunity, Xj is variable/independent variables (covariate), I is pixel, β₀ is coefficient of measurement result and k is number of variables.
<figure><img src="image_9.png" /><figcaption>**Figure 2.** Flowchart of the AP-FDRS concept development in OKI District.</figcaption></figure>

<table><caption>**Table 2.** Fire danger class based on anthropogenic model and peat model.</caption><thead><tr><th>Colors</th><th>Ranges (Pi)</th><th>Description</th></tr></thead><tbody><tr><td>Blue</td><td>0.00–0.25</td><td>Fire probability categorized as low</td></tr><tr><td>Green</td><td>0.25–0.50</td><td>Fire probability categorized as medium</td></tr><tr><td>Yellow</td><td>0.50–0.75</td><td>Fire probability categorized as high</td></tr><tr><td>Red</td><td>0.75–1.00</td><td>Fire probability categorized as extreme</td></tr></tbody></table>

<table><caption>**Table 3.** Class of AP-FDRS in OKI District.</caption><thead><tr><th>Color</th><th>Ranges</th><th>Description</th></tr></thead><tbody><tr><td>Blue</td><td>1–3</td><td>Fire danger rating in the low category</td></tr><tr><td>Green</td><td>3–6</td><td>Fire danger rating in the medium category</td></tr><tr><td>Yellow</td><td>7–9</td><td>Fire danger rating in the high category</td></tr><tr><td>Red</td><td>10–12</td><td>Fire danger rating in the extreme category</td></tr></tbody></table>

The fourth step is to determine the likelihood of AP forest and peatland fires. Based on this probability value, it is categorized into four categories (low, medium, high and extreme). The fire danger classes for the anthropogenic and peat models are presented in table 2. The last evaluation of the logistic regression was to test the feasibility of the model using the Hosmer–Lemeshow test and model validation using Kappa Accuracy. Kappa Accuracy obtained from observed agreement and change agreement:

Kappa Accuracy

$$ = \frac{\text{Observed Agreement} - \text{Change Aggrement}}{1 - \text{Change Aggrement}} $$

$$ \text{Observed Agreement} = \frac{B3 + C4}{D5} $$

$$ \text{Change Aggrement} = \frac{B5}{D5} * \frac{D3}{D5} * \frac{C5}{D5} * \frac{D4}{D5} $$

The development of a forest and peatland FDRS in OKI District was carried out by integrating three factors: FWI, anthropogenic and peat. The results of the analysis of each factor were used in the development of the FDRS by overlaying each map produced. The level of forest and peatland fire danger rating in OKI District was measured using a Likert scale. The Likert scale is based on the ranking of each factor (FWI, anthropogenic and peat). In this study,

each factor consisted of 4 ratings: low, medium, high and extreme. The score for the Likert scale is 4 for the extreme category, 3 for the high category, 2 for the medium category and 1 for the low category. The ranking of forest and peatland fire danger in OKI District is based on the total score obtained, categorized as shown in table 3.

## 3. Result

### 3.1. FDRS development in forest and peatland

#### 3.1.1. FWI

The results of the FWI map for OKI District were prepared using the parameters of temperature, humidity, wind speed and rainfall, which were based on the BMKG's SPARTAN platform. SPARTAN has undergone significant improvements in input data resolution. This enhancement was achieved by combining observation data from BMKG stations with remote sensing data. The prediction data now features a higher resolution compared to the previous FDRS, which had a spatial resolution of 27 km. Currently, SPARTAN has a spatial resolution of 10 km. The maps are now more detailed, with data and information available for each province. Furthermore, this higher-resolution observational data is derived from BMKG observation stations integrated with remote sensing (satellite) data. In this study, 2 d in 2020 were selected as samples to analyze the FWI, where the first sample
<figure><img src="image_10.png" /></figure>

<figure><img src="image_11.png" /><figcaption>**Figure 3.** Map of FWI results on peat areas in OKI district (a) FWI dated 23 August 2020; (b) FWI dated 28 December 2020.</figcaption></figure>

was a day with hotspots (23 August 2020) and a day without hotspots (28 December 2020) (figure 3).

The fire danger class based on the FWI on 23 August 2020 (figure 3(a)) is classified as high and extreme. About 54% of the study area is in the high category (yellow). This condition indicates that if a fire occurs, it will produce a high fire intensity, so that the fire will be difficult to control. Around 46% of the study area falls into the extreme category (red). This indicates that the expected fire intensity will be extreme in the event of a fire and very difficult to control. Based on weather parameters obtained from

GSMap, on 23 August 2020 there was no rain at all in OKI District. The temperature ranged from 30 °C to 32 °C, the average air humidity was ~59% and the average wind speed was ~17 m s⁻¹.

The FWI fire danger class on 28 December 2020 (figure 3(b)) is classified as low, where the FWI map is entirely blue. This condition indicates that if a fire occurs, the intensity of the resulting fire will be low and easy to control. Based on the weather parameters obtained from GSMap, on 28 December 2020 there was rain in all areas of OKI District. The temperature ranged from 30 °C to 32 °C, the average air humidity was 72%, and the average wind speed was 4.8 m s⁻¹. The different weather conditions in the two examples produce different FWI maps. This result certainly affects the likelihood of forest and land fires. One of the factors influencing fire behavior is weather. Temperature is one of the weather elements that can affect the ease with which fuel ignites. Air humidity will also affect the sensitivity to fire, where the combustion process will survive in air humidity less than 65% [17]. Wind speed will affect the rate of fire spread. Rainfall has a high negative correlation with the incidence of forest and land fires [9]. The occurrence of forest and land fires is usually related to rainfall patterns, especially drought. When rainfall is low or there is no rain, it will encourage human activity in burning for various purposes. The fire danger rating on the sample day without rain (23 August 2020) was dominated by high and extreme fire danger ratings, while the fire danger rating on the sample day with rain (28 December 2020) was dominated by a low fire danger rating.

### 3.1.2. Anthropogenic factors

The results of the analysis of burned areas in OKI District show that fires often occur in the land cover of shrubs, plantations, and secondary forests. The availability of road access (accessibility) that can be used by the community can increase the chance of fire occurrence. Thus, distance from shrubs, plantation forests, plantations, secondary forests, roads, rivers, population density and settlements are used as independent variables in this anthropogenic model. Based on the results of binary logistic regression analysis with the Backward Stepwise (conditional) method of the eight independent variables used, only 2 independent variables are significant and can be used for this anthropogenic model. These access variables are distance to road and distance to river (table 4). The results of the feasibility test using Hosmer-Lemeshow showed that the model was declared feasible with a value of 0.985 (>0.05). The Nagelkerke R² square value in the anthropogenic model is 0.892, meaning that the independent variables can explain about 89.2% of the anthropogenic model, while
<table><caption>**Table 4.** T-test, Coefficient, Hosmer and Lemeshow, and Nagelkerke R square anthropogenic factors.</caption><thead><tr><th>Variables</th><th>β</th><th>P-value (sig)</th><th>Hosmer–Lemeshow</th><th>Nagelkerke R square</th></tr></thead><tbody><tr><td>Soil moisture</td><td>0.000 61</td><td>0.000</td><td>Sig. 0.674</td><td>0.795</td></tr><tr><td>Distance to shallow peat</td><td>-0.000 41</td><td>0.014</td><td></td><td></td></tr><tr><td>Constant</td><td>5.368 11</td><td>0.004</td><td></td><td></td></tr></tbody></table>

about 10.8% can be explained by other independent variables.

The equation of the anthropogenic model in OKI District is as follows:

$$Pi = \frac{1}{1 + \exp\left[-\left(\frac{0.00462 \text{ Distance From Road} + 0.00419 \text{ Distance From River} - 7.56156}{0.00419 \text{ Distance From River} - 7.56156}\right)\right]}$$

The anthropogenic map of OKI District in figure 4 is dominated by red color, which means that the probability of forest and peatland fires based on anthropogenic factors is extreme. This result indicates that humans have a high role in causing forest and peatland fires. The results of the validation test with Kappa Accuracy showed a value of 73.5%, indicating that the anthropogenic model formed is quite accurate.

### 3.1.3. Biophysical factors (peat)

Based on the results of the binary logistic regression analysis of 6 independent variables, 2 significant

independent variables were obtained and can be used in this peat model. The two variables are soil moisture and distance from shallow peat (table 5). The results of the feasibility test using Hosmer–Lemeshow showed that the model was declared feasible with a value of 0.674 (>0.05). The Nagelkerke R square value in this model is 0.795 which means that about 79.5% of the model results were explained by these independent variables while 20.5% are related to other factors.

The equation of the peat model in OKI District is as follows:

$$Pi = 1 + \exp\left[-\left(\frac{5.36811 + 0.00061 \text{ Soil Moisture} - 0.00041 \text{ Distance From Shallow Peat}}{0.00041 \text{ Distance From Shallow Peat}}\right)\right].$$

It can be seen in figure 5 that the probability of forest and peatland fires in OKI District based on the peat variable is dominated by red color. This condition indicates that the probability of forest and peatland fires based on the peat fuel variable is categorized as extreme. The results of the validation test using Kappa Accuracy yielded a value of 77.5%, which means that the peat model is quite accurate.

### 3.1.4. AP-FDRS in OKI District

The FWI map on 23 August 2020 was predominantly in the high category. The anthropogenic and peat maps in OKI District are predominantly in the extreme category. The overlay of the three maps resulted in AP-FDRS map presented in figure 6(a), with 3 classes of fire danger, namely medium (green), high (yellow) and extreme (red). This date is dominated by red and yellow colors, indicating that the chances of forest and peatland fires occurring are mostly in the

high to extreme classes, and there is no fire danger rating in the low category.

This condition is partly due to the absence of rain on this date throughout the study area. Dry weather conditions like this can encourage high human activity on peatlands to prepare land through burning, because days without rain cause the fuel to dry and ignite faster. Human behavior that coincides with favorable weather conditions will trigger large fires. If a large fire occurs on peatland, it will be easier for the fire to penetrate downward and cause smoldering subsurface peat fires.

On 28 December 2020, AP-FDRS was generated from overlaying the FWI, anthropogenic and peat maps (figure 6(b)). The FWI map on 28 December 2020 is dominated by blue color, which indicates that the chances of fire occurrence based on weather parameters tend to be low throughout the study area. However, after integrating with the anthropogenic
<figure><img src="image_12.png" /><figcaption>**Figure 4.** Modeling the probability of forest and peatland fires in OKI District based on anthropogenic variables.</figcaption></figure>

<figure><img src="image_13.png" /><figcaption>**Figure 5.** Modeling the probability of forest and peatland fires in OKI District based on peat variables.</figcaption></figure>

map and peat map, AP-FDRS map with 3 classes of fire danger was obtained, namely low (blue), medium (green) and high (yellow). This date is dominated by green and yellow colors, which indicates that the chances of forest and land fires are mostly in the medium to high classes and there is no extreme fire danger category. The average rainfall around the study area was about 14 mm. Low rainfall periods during the rainy season still allow people to prepare their land through burning. The moist fuels will smolder and have a very bad impact on the local environment, with the emergence of haze due to incomplete combustion.

<figure><img src="image_14.png" /></figure>

<figure><img src="image_15.png" /><figcaption>**Figure 6.** AP-FDRS in OKI District (a) 23 August 2020; (b) 28 December 2020.</figcaption></figure>

The AP-FDRS map for 23 August 2020 is dominated by red color with an area of 344 713 ha or around 54% included in the extreme fire danger rating category. There were areas in the high fire danger rating category, 246 094 ha (39%). The AP-FDRS map on 28 December 2020 is dominated by high fire danger (yellow) with an area of 381 661 ha or around 60% included in this fire danger rating category. The AP-FDRS validation test on 23 August 2020, with Kappa Accuracy resulted in a value of 87.21%. The model validation test also shows an omission error of 36% and a commission error of 23%. The FDRS validation test on 28 December 2020 with Kappa Accuracy resulted in a value of 89.65%. The model validation test also shows an omission error of 29% and a commission error of 25%.
<table><caption>**Table 5.** T-test, Coefficient, Hosmer and Lemeshow, and Nagelkerke R square peat factor.</caption><thead><tr><th>Variables</th><th>β</th><th>P-value (sig)</th><th>Hosmer–Lemeshow</th><th>Nagelkerke R square</th></tr></thead><tbody><tr><td>Distance to road</td><td>0.004 62</td><td>0.034 73</td><td rowspan="3">Sig. 0.985</td><td rowspan="3">0.892</td></tr><tr><td>Distance to river</td><td>0.004 19</td><td>0.019 16</td></tr><tr><td>Constant</td><td>-7.561 56</td><td>0.012 05</td></tr></tbody></table>

## 4. Discussion

The FDRS is an early warning system that can reduce the danger of fire [8]. Human activity on peatlands can pose a threat to the sustainability of peat ecosystems. The fires are often used in resource utilization activities, which of course leads to a higher chance of peat fires. The high incidence of forest and peatland fires has had a devastating impact on the environment. The area of peatland in South Sumatra Province is 1262 384 ha [18]. OKI District has the largest peatland area in South Sumatra Province, with around 769 000 ha [19]. Community activities that are the main triggers for peatland fires in OKI District are sonor rice cultivation, land clearing by burning for plantation and agricultural purposes, and fishing activities [20].

The independent variables in the anthropogenic model include distance from roads and from rivers or canals. Easy access to roads and waterways facilitates human activities related to natural resource utilization. Increased accessibility often leads to a higher risk of forest and land fires. The independent variables in the peat model include soil moisture and distance from shallow peat. While peat fires are strongly correlated with groundwater level (GWL), the limited availability of GWL data restricts its direct use. Instead, the correlation between field-measured GWL and soil moisture serves as the basis for using soil moisture in peat fire monitoring efforts [21]. GWL is a key indicator of peatland fires. In the mega rice project area, fire peaks when GWL reaches 40 cm and worsens below this threshold [22]. A GWL of 40 cm below the surface is a critical indicator of severe peatland fires [23], while degraded peat should maintain GWL above 20 cm to prevent fires [24]. Peat moisture also affects fire spread, as fires burn underground with limited oxygen and can persist for weeks or longer, independent of wind direction [3, 25]. Shallow peat has a peat thickness less than 100 cm and has a higher pH than deep peat [26]. Shallow peat is often located near rivers and roads, which encourages higher human activity in these areas. This increased activity heightens the vulnerability of shallow peatlands to fire.

On the AP-FDRS map on 28 December 2020, although there were no detected hotspots, the fire danger rating was dominated by the high rating, approximately 60%, indicating fire activity was possible with potential for low intensity, undetectable burning. Weather (FWI) is not the only determining

factor for the likelihood of forest and peatland fires in OKI District. Other determining factors, namely anthropogenic use and peat condition, also influence fire prevalence. FWI in the development of FDRS for forest and peatland in OKI District is dynamic, because FWI is related to the daily weather of the study location. Anthropogenic factors and peat are static in the development of this AP-FDRS.

The reliability of the model was evaluated using three key performance metrics: Kappa Accuracy, Omission Error, and Commission Error. The AP-FDRS validation test resulted Kappa Accuracy of 87.21% (23 August 2020) and 89.65% (28 December 2020) indicating very strong agreement between the model's predictions and the actual data. Given the high Kappa Accuracy, the model can be considered statistically valid, as it shows strong agreement beyond chance. The AP-FDRS on 23 August 2020, resulted in 36% omission error meaning that more than one-third of actual fire occurrences were not predicted by the model. The high omission error suggests that some improvements are needed, especially if missing positive cases are critical. The model can still be used with caution, but further refinement is recommended for better reliability. The AP-FDRS on 28 December 2020 has the lower omission error (29%) compared to the previous model meaning that this model is better at detecting actual fire occurrences, making it more reliable for fire risk prediction. Commission error refers to the percentage of cases where the model incorrectly predicts an area as fire-prone when, in reality, no fires occurred. In this study, the commission error on 23 August 2020 was 23%, meaning that 23% of the areas classified as fire prone did not actually experience any fires. The Commission Error on 28 December 2020 was 25%, indicating that 25% of the areas classified as having fire risk were actually not burned. Based on validation test, this model is valid and can be implemented at other sites with similar characteristics.

This study serves as an initial step in developing an FDRS tailored specifically for peatland areas. Given the exploratory nature of the research, the sample size was limited to two distinct conditions: areas with no detected hotspots and areas where hotspots were observed. This sampling approach was chosen to establish a fundamental understanding of fire risk patterns in peatland environments while recognizing the need for further refinement in future studies. At a minimum, this study provides more detailed insights
into forest and land fire hazards based on their danger
ratings, particularly in peatland areas.

5. Conclusions

The AP-FDRS map for 23 August 2020 (fire period), identified three fire danger classes: medium, high, and extreme with 54% probability of forest and peatland fires in OKI District, predominantly in the extreme danger category. Meanwhile, the AP-FDRS map for 28 December 2020 (non-fire period), displayed three classes: low, medium, and high with 60%, probability, primarily falling into the high danger category. The AP-FDRS generates a highly valid model, achieving Kappa accuracy values of 87.21% on 23 August 2020, and 89.65% on 28 December 2020. These results indicate that the system is reliable and suitable for further development.

Data availability statement

The data cannot be made publicly available upon
publication because they are not available in a format
that is sufficiently accessible or reusable by other
researchers. The data that support the findings of this
study are available upon reasonable request from the
authors.

ORCID iD

Ati Dwi Nurhayati https://orcid.org/0000-0002-0319-6446

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