Spatial and Temporal Analysis of Forest Fire Risk and Propagation in South Sumatra Peatlands: Insights from Remote Sensing and GIS (2014–2023)

Spatial and Temporal Analysis of Forest Fire Risk and Propagation in South Sumatra Peatlands: Insights from Remote Sensing and GIS (2014–2023) | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Spatial and Temporal Analysis of Forest Fire Risk and Propagation in South Sumatra Peatlands: Insights from Remote Sensing and GIS (2014–2023) Cahyo Aji Hapsoro, Syakira Ghina Maulina, Mochamad Khoirul Rifai, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5733851/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 26 Dec, 2025 Read the published version in Natural Hazards → Version 1 posted 5 You are reading this latest preprint version Abstract Forest fires pose a significant threat to ecological sustainability, human health, and economic stability, especially in fire-prone regions like South Sumatra, Indonesia. Peatland areas are particularly vulnerable due to their unique ecological characteristics and human-induced disturbances. The objective of this study is to map, analyze, and investigate forest fire risk and fire spread in peatlands within South Sumatra, Indonesia, from 2014 to 2023 using Remote Sensing and GIS technologies. Key geomorphological, environmental, and human activity parameters were assessed, including slope, elevation, temperature, distance to roads, and land cover. Each parameter was classified into five fire hazard levels, and a comprehensive forest fire risk map was generated using an overlay approach via the intersect method in ArcGIS. The analysis revealed that 82% of South Sumatra's area is classified as having high to very high fire risk, with peatlands being the most vulnerable regions due to their low elevation, gentle slopes, moderate to high temperatures, and proximity to roads, which facilitate human access. Fire propagation was further examined using dNBR (differenced Normalized Burn Ratio) and hotspot data from NASA's FIRMS. The dNBR analysis identified fluctuations in burn severity over the years, with significant damage observed in 2015, 2018, and 2022. However, the accuracy of dNBR in peatland areas was affected by cloud cover. Hotspot density, analyzed using Kernel Density Estimation (KDE), highlighted fire-prone zones, particularly in peatland regions. Findings show that human activity, coupled with climatic phenomena such as El Niño, significantly influences fire risk and propagation in South Sumatra, Indonesia. forest fire risk peatland fires remote sensing and GIS burn severity (dNBR) hotspot density analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 1. Introduction Indonesia experiences forest fires annually (Hein et al., 2022; Nurhayati et al., 2021), with South Sumatra Province recording the highest incidence, according to the Forest Fire Monitoring System (SiPongi) of the Indonesian Ministry of Environment and Forestry. Over the past decade (2014–2023), more than 1.5 million hectares of land have been burned in South Sumatra, with 2014 being the most destructive year, accounting for 644,000 hectares of burned area. Ogan Komering Ilir district is the most affected, experiencing the largest burned area each year, primarily due to human activities (Nurhayati et al., 2021). In addition to forest fires, peat fires are also a recurring issue in Indonesia. Indonesia hosts some of the largest peatlands globally (Maltby & Immirzi, 1993), covering 13.4 million hectares, predominantly in Kalimantan, Papua, and Sumatra (Irfan et al., 2024). South Sumatra Province alone has approximately 1 million hectares of peatland. According to 1990 data, these peatlands are classified as medium-high peat, with depths ranging from 100 to 200 cm (Wahyunto et al., 2005). Peatland fires in Indonesia occur annually and have historically been more frequent on peatlands than on mineral soils (Abriantini et al., 2017). Peatland fires have more severe environmental consequences compared to mineral soil fires. The smoke generated from peat fires contributes significantly to air pollution (Nurhayati et al., 2021) and leads to increased greenhouse gas emissions, fine particulate matter, aerosols, and ecosystem degradation (Hayasaka et al., 2020; Hein et al., 2022; Miettinen et al., 2017). Over half of Indonesia's total peatland area has been cleared and drained, primarily for plantation development (Vetrita & Cochrane, 2019). The conversion of peatlands for forestry, agriculture, plantations, and settlements exacerbates peatland degradation (Sulaeman et al., 2021; Wahyunto et al., 2005). Peatlands are highly susceptible to fires, which are driven by multiple factors, including human activities and weather conditions (Abriantini et al., 2017; Irfan et al., 2024; Yuwati et al., 2021). Human activity is a major contributor to peatland fires, primarily due to land conversion practices that involve burning vegetation (Abriantini et al., 2017; Nurhayati et al., 2021). The human factor is challenging to mitigate, as rising demand for land perpetuates these practices. Peatlands do not naturally burn below the surface unless weather and fuel conditions exacerbate the risk (Nurhayati et al., 2021). Weather events, such as the El Niño phenomenon, further increase fire occurrences (Hayasaka et al., 2020; Miettinen et al., 2017; Sofan et al., 2020), and severe peat fires are typically associated with prolonged dry seasons (Irfan et al., 2024). The high incidence of forest fires in South Sumatra Province is particularly concerning. To address this, mapping fire-prone areas and fire propagation, especially in peatland regions, is essential. Remote Sensing (RS) and Geographic Information Systems (GIS) are critical tools used to assess fire risk and map fire spread. Satellite data from remote sensing is widely used to identify burned areas (Table 1 ) (Sirin & Medvedeva, 2022). Remote sensing is effective for both qualitative and quantitative monitoring of forest ecosystems, making it valuable for forest fire surveillance (Suryabhagavan et al., 2016). It offers advantages such as extensive area coverage, coherence, and reproducibility, allowing access to information from remote locations. Several fire risk parameters, including vegetation, humidity, and temperature, can be monitored using remote sensing techniques (Konkathi et al., 2019; Leblon, 2005; Suresh Babu et al., 2018). Additionally, remote sensing is cost-efficient, as much of the necessary data is publicly available and easily downloadable, reducing both time and effort for data acquisition and processing (Sobrino et al., 2019). In this study, Landsat 8 satellite data is used to identify fire zones, while DEM and MODIS data are utilized to map fire risk levels. Hotspot data is analyzed to determine the distribution of fire points, with parameters such as elevation, slope, temperature, and distance from roads incorporated to map forest fire risk areas. The objective of this study is to map, analyze, and investigate forest fire risk and fire spread in peatlands within South Sumatra. The findings are expected to provide valuable insights for researchers, policymakers, and the wider community about peat fires, particularly in South Sumatra. 2. Material and Methods 2.1. Study Area South Sumatra is one of the Indonesian provinces with the largest forest fire areas. Covering a total area of 91,806.36 km², it is located on the island of Sumatra. The province has approximately 3.4 million hectares of forest, comprising 566,000 hectares of protected forest, 2 million hectares of production forest, and around 250,000 hectares of nature reserves. The topography of South Sumatra varies, with elevations ranging from 400 to 1,700 meters above sea level (ASL). The western region, dominated by the Bukit Barisan Mountains, has an average elevation between 900 and 1,200 meters ASL, while the eastern part consists mainly of coastal areas, swamps, and brackish waters influenced by tidal movements. These coastal areas support plant species such as Palmase and mangroves, and the central region is characterized by extensive plains. South Sumatra also has 1.42 million hectares of peatlands, making it the second-largest peatland province on the island of Sumatra after Riau. Peat thickness in South Sumatra ranges from 50 to 400 cm, classifying it as shallow to deep peatland. These peatlands are primarily distributed in the regencies of Musi Banyuasin, Banyuasin, Muara Enim, Musi Rawas, and Ogan Komering Ilir. The geographical distribution of South Sumatra is shown in Fig. 1 . Table 1 Researches using remote sensing and GIS methods to map fire hazards and burned areas. No. Variables Country Index Authors Result 1. Temperature, precipitation, vegetation, humidity Indonesia LST, NDVI, NDMI, SPI (Rendana et al., 2023) The fire hazard mapping results provide a clear comparison between current and future fire hazard conditions. However, the analysis did not incorporate topographic variables, and the search area was limited, excluding certain regions within the province of South Sumatra. 2. Burned area, hotspot, rainfall Indonesia NBR (Nurhayati et al., 2021) The study results also reveal the impact of rainfall and highlight the distribution of hotspots and burned areas over a five-year period (2015–2019). For a more comprehensive analysis, other climatic factors, such as temperature, could be incorporated to better understand the patterns of hotspot occurrences and burned area distribution. 3. Vegetation, burned area Russia NDVI, NDMI, NBR, MIRBI, BAI (Sirin & Medvedeva, 2022) The results indicated that each variable demonstrated high accuracy in mapping burned areas within peatlands. 4. Hotspot Indonesia Hotspot (Putra et al., 2021) The study utilizes hotspot data to provide insights into the distribution of burned peatlands. For a more comprehensive understanding of burned area distribution, additional satellite data can be incorporated. 5. Vegetation, hotspot Indonesia Land Cover, Hotspot (Putra et al., 2019) The study analyzes hotspot data across various land cover types to explain the distribution of burned areas over two decades (1997–2016). Incorporating additional indicators could further enhance the mapping process, providing a clearer visualization of the burned area distribution. 6. Topography, temperature, hotspot, burned area Indonesia Slope, Elevation, Distance from Road, LST, dNBR, Hotspot This research The results of this study include a wildfire risk map, a hotspot distribution map, and a dNBR map, all of which are developed using climate and topographic variables. These variables provide valuable insights into the distribution of wildfire-prone areas, as well as the spatial patterns of burned areas and hotspots in South Sumatra, particularly within peatland regions. 2.2. Datasets In this study, data analysis relies on several variables, including slope, elevation, temperature, and distance from roads, derived from Digital Elevation Model (DEM) and Moderate Resolution Imaging Spectroradiometer (MODIS) data. Landsat-8 satellite imagery and hotspot data are used to validate forest fire risk maps for wildfires based on temporal data (Table 2 ). Table 2 Parameters used to map and validate forest fire risk of South Sumatera. Parameter Source Spatial Resolution Purpose Elevation ASTER DEM 30 m Inputs to forest fire risk mapping Slope ASTER DEM 30 m Inputs to forest fire risk mapping Temperature MODIS 1 km Inputs to forest fire risk mapping Distance from road RBI Map - Inputs to forest fire risk mapping dNBR Landsat 8 30 m Validate fire risk map Hotspot MODIS 1 km Validate fire risk map 2.2.1. Elevation Fire occurrence is highly dependent on topography, particularly elevation (Maingi & Henry, 2007). Elevation influences forest fires by affecting temperature, humidity, and wind speed (Sivrikaya & Küçük, 2022). Higher elevations generally experience lower temperatures and higher humidity, which reduces the likelihood of fires (Suryabhagavan et al., 2016). In areas with high elevation, the risk of fire is lower compared to low-elevation regions, primarily due to increased rainfall (Adab et al., 2013). In contrast, low-elevation areas tend to have higher temperatures and lower rainfall, causing vegetation to dry out more quickly, which increases fire risk (Kant Sharma et al., 2012; Zahran et al., 2020). Additionally, fires can spread more rapidly in low-elevation areas (Asori et al., 2020). For fire risk assessment, elevation is categorized into five classes, as shown in Table 3 (Sivrikaya & Küçük, 2022). Table 3 Elevation classes. Parameter Class (m) Risk Classes Elevation 2000 Very Low 2.2.2. Slope Slope is a geomorphological parameter controlled by drainage and the stage of development of the soil shape. Slope has a positive correlation with the speed of flame movement. The slope influences the occurrence of wildfires as it determines the speed of fire spread, while the steep slope increases the rate of spread of the fires (Ajin et al., 2015; Asori et al., 2020; Sivrikaya & Küçük, 2022; Suryabhagavan et al., 2016). Based on the level of fire hazard, the slope is divided into 5 classes, which are listed by Table 4 (Ozenen Kavlak et al., 2021; Parajuli et al., 2020). Table 4 Slope classes. Parameter Class Risk Classes Slope > 35° Very High 35°– 15° Very High 15° – 10° High 10° – 5° Moderate 5° – 0° Low 2.2.3. Temperature Temperature is a crucial factor in assessing forest fire risk, as it provides insight into the thermal conditions affecting vegetation (fuel) and topographic drought (Jodhani et al., 2024). Temperature significantly influences fire occurrence by determining the rate at which fuel dries, altering relative humidity, and accelerating evapotranspiration (Asori et al., 2020). High temperatures speed up the drying of biomass, particularly dry grasses, dead leaves, needles, and small trees. Fuel that is preheated by high average temperatures ignites more rapidly than cooler fuel, making it a key indicator of fire occurrence (Prasad et al., 2008). In this study, land surface temperature data derived from MODIS satellite imagery is used. The dataset includes surface temperature records from July to October over a 10-year period (2014–2023). The 10-year average temperature data is utilized to map fire susceptibility. Based on fire hazard levels, the temperature is categorized into five classes, as shown in Table 5 (Parajuli et al., 2020). Table 5 Temperature classes. Parameter Class (°C) Risk Classes Temperature > 35 Very High 35–30 High 30–25 Medium 25–20 Low 20–5 Very Low 2.2.4. Distance from Roads The distance from roads is a critical factor in determining forest fire risk. Roads provide greater human access to forests and grasslands, increasing the likelihood of fires being ignited (Adab et al., 2013; Ajin et al., 2015; Ozenen Kavlak et al., 2021)The closer a road is to a forest, the higher the risk of wildfire occurrence (Asori, 2020). For fire hazard assessment, the distance to roads is categorized into six classes, as shown in Table 6 (Ozenen Kavlak et al., 2021). Table 6 Distance from road classes. Parameter Class (m) Risk Classes Distance from roads 0–50 Very High 50–100 Very High 100–200 High 200–300 Moderate 300–400 Low 2.2.5. Difference Normalized Burnt Ratio (dNBR) The band ratio from satellite imagery is used to assess the severity of fires. The Difference Normalized Burn Ratio (dNBR) is a method that detects changes in an area before and after a fire by analyzing satellite images (Ozenen Kavlak et al., 2021). The dNBR value is calculated as the difference between the Normalized Burn Ratio (NBR) values recorded before and after the fire (Eq. 2 ). The NBR value itself is derived using the near-infrared (NIR) and short-wave infrared (SWIR) bands from Landsat 8 satellite imagery (Eq. 1 ) (Suresh Babu et al., 2018). $$\:\text{NBR=}\frac{\text{NIR-SWIR}}{\text{NIR+SWIR}}$$ 1 $$\text{dNBR=}{\text{NBR}}_{\text{pre-fire}}\text{-}{\text{NBR}}_{\text{post-fire}}$$ 2 In this study, dNBR data is used to determine the severity of burned areas in South Sumatra over the period from 2014 to 2023. The data is analyzed annually to assess the impacts of forest fires in the region. dNBR values are classified into seven categories based on standards from the United States Geological Survey (USGS). The classification of dNBR values is presented in Table 7 (Roy et al., 2006). Table 7 dNBR value classification. Pixel Values Severity Level − 0.50 ≤ dNBR < − 0.25 High regrowth − 0.25 ≤ dNBR < − 0.10 Low regrowth − 0.10 ≤ dNBR < 0.10 Unburned 0.10 ≤ dNBR < 0.27 Low 0.27 ≤ dNBR < 0.44 Moderate-low 0.44 ≤ dNBR < 0.66 Moderate-high 0.66 ≤ dNBR < 1.33 High 2.2.6. Hotspot The detection of land or forest fires at a specific pixel size results in the identification of hotspots, which represent areas with relatively higher surface temperatures compared to their surroundings (Khairani & Sutoyo, 2020). Each hotspot is assigned a confidence level, indicating the likelihood of fire occurrence. A higher confidence level corresponds to a greater fire risk at that point (Khairani & Sutoyo, 2020; Shofiana & Sitanggang, 2021). The hotspot data for this study is derived from MODIS satellite imagery spanning from 2014 to 2023, and it is analyzed to identify areas prone to fire. According to the MODIS Active Fire User Guide, the confidence levels are categorized into three classes, as shown in Table 8 (Giglio et al., 2021). Table 8 Confidence classification. Confidence Class 0% ≤ C < 30% Low 30% ≤ C < 80% Nominal 80% ≤ C ≤ 100% High Based on the hotspot data from 2014 to 2023 (Table 9 ), the regions in South Sumatra with the highest number of hotspots are Ogan Komering Ilir, Musi Banyuasin, and Banyuasin, all of which contain significant peatland areas. In contrast, the regions with the fewest hotspots are Pagar Alam, Prabumulih, and Palembang City. The year with the highest number of hotspots was 2015, with over 34,000 hotspots, while the lowest number was recorded in 2022, with just 366 hotspots. Table 9 Spatial-temporal hotspot distribution of South Sumatera. Regency/City* 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 Total Average OKI 3,187 18,966 110 136 500 5,005 163 206 63 3,941 32,277 3,227.7 MB 365 7,469 96 64 122 2400 47 78 82 577 11,300 1130.0 BA 106 2,230 44 79 165 1,278 27 58 20 396 4,403 440.3 ME 256 1,346 52 86 103 331 29 26 15 145 2,389 238.9 MRU 87 1,018 93 70 43 591 22 35 38 247 2,244 224.4 MR 168 1,102 129 74 95 299 16 45 40 125 2,093 209.3 OKU 100 631 31 55 42 124 15 17 18 98 1,131 113.1 PALI 59 430 60 53 23 265 18 18 26 113 1,065 106.5 OI 74 334 20 45 37 253 13 20 4 238 1,038 103.8 OKUT 19 386 21 35 36 213 4 15 6 165 900 90.0 LA 52 404 32 26 30 100 31 42 22 117 856 85.6 OKUS 57 398 31 31 52 67 8 27 20 43 734 73.4 EL 20 181 16 22 40 104 15 8 8 47 461 46.1 LU 1 30 8 7 4 10 5 3 1 8 77 7.7 PRA 11 30 8 6 5 8 3 0 0 2 73 7.3 PAL 0 9 2 3 3 7 3 3 1 10 41 4.1 PA 3 9 0 2 4 13 3 2 2 0 38 3.8 Total 4,565 34,973 753 794 1,304 11,068 422 603 366 6,272 61,120 6,112 *Abbreviation OKI (Ogan Komering Ilir); MB (Musi Banyuasin); BA (Banyu Asin); ME (Muara Enim); MRU (Musi Rawas Utara); MR (Musi Rawas); OKU (Ogan Komering Ulu); PALI (Penukal Abab Lematang Ilir); OI (Ogan Ilir); OKUT (Ogan Komering Ulu Timur); LA (Lahat); OKUS (Ogan Komering Ulu Selatan); EL (Empat Lawang); LU (Lubuklinggau); PRA (Prabumulih); PAL (Palembang); PA (Pagar Alam). 2.3. Methodology The data for each variable is processed and categorized into several classes, with a scoring method applied to quantify the fire risk associated with each class. This method assigns numerical scores to represent the level of fire risk for each parameter. The scores for each variable are then used to assess the overall fire susceptibility. The flow of the research process is illustrated in Fig. 3 . 2.3.1. Scoring For each parameter, the score reflects the level of fire vulnerability, where a higher score indicates a greater fire potential. The scores range from 1 to 5. The total score, which is the sum of all variables, is then categorized into four fire vulnerability classes: Low, Moderate, High, and Very High. The scoring for each parameter is presented in Table 10 . Table 10 Scores of each forest fire risk parameter. Index Class Risk Level Score Slope > 35° Very High 4 35°– 15° Very High 4 15° – 10° High 3 10° – 5° Moderate 2 5° – 0° Low 1 Distance from roads (m) 0–50 Very High 4 50–100 Very High 4 100–200 High 3 200–300 Moderate 2 300–400 Low 1 > 400 Low 1 Temperature (°C) > 35 Very High 5 35–30 High 4 30–25 Moderate 3 25–20 Low 2 20–5 Very Low 1 Elevation (m) 2000 Very Low 1 2.3.2. Forest Fire Risk Mapping The forest fire risk map is generated by overlaying the maps of each parameter. This is done using the Intersect method in ArcGIS. During the overlay process, the scores of all parameters are summed, and the results are divided into four classes at equal intervals. Eq. 3 is applied to calculate the fire susceptibility value. FS = S + DR + T + E (3) FS is fire susceptibility value, S is score of slope index, DR is score of distance from roads index, T is score of temperature index, and E is score of elevation index. 3. Result and Discussions 3.1. Elevation According to the elevation data, South Sumatra is predominantly composed of low-elevation areas. Regions located at elevations of less than 500 meters above sea level cover approximately 7.7 million hectares, representing about 90% of the province’s total area. In contrast, high-elevation areas (> 2000 meters above sea level) account for only 26.6 thousand hectares. The elevation classifications and their respective areas are shown in Table 11 . Table 11 Area distribution of elevation classes. Elevation Area (ha) Percentage 2000 26605,07 0% The high mountain regions are located in the western part of South Sumatra, specifically within the Bukit Barisan mountain range. The central area consists of large plains, while the eastern part is characterized by marshes and coastal areas. The elevation map is displayed in Fig. 3 . 3.2. Slope South Sumatra is predominantly composed of areas with gentle slopes, as it is largely characterized by vast plains. The classification of slopes and their corresponding surface areas is presented in Table 12 . Table 12 Area distribution of slope classes. Slope Area (ha) Percentage > 35° 40,319.49503 0% 35°– 15° 558,622.6477 6% 15° – 10° 371,959.58 4% 10° – 5° 1,192,651.697 14% 0° – 5° 6,515,046.611 75% Based on the DEM data analysis, regions with steep slopes are concentrated in the western part of the province, where the high elevations of the mountainous areas are located. In contrast, the central and eastern regions fall within the gentle slope category. The slope map is visualized in Fig. 4 . 3.3. Temperature Based on temperature data from 2014 to 2023 (July to October), South Sumatra is predominantly characterized by moderate to high temperatures, with the highest recorded temperature reaching 42.8°C in 2014. Temperature fluctuations in South Sumatra are influenced by climate phenomena such as El Niño, which occurred in 2015, 2019, and 2023, leading to significant temperature increases (Munawar et al., 2023). After El Niño events, the average temperature typically drops due to the La Niña phenomenon, which brings rainfall even during dry months, as observed in 2016 and 2020 (Nurhayati et al., 2021). The average annual temperature data is presented in Fig. 5 . The 10-year average temperature is calculated based on yearly temperature data. The highest average temperature recorded is 37.3°C, while the lowest is 15°C. The temperature classification and corresponding ranges are provided in Table 13 . Table 13 Area distribution of temperature classes. Temperature (°C) Area (ha) Percentage > 35 10,563.35065 0% 35–30 3,163,460.057 37% 30–25 5,083,560.561 59% 25–20 323,853.4604 4% 20–5 52,596.45699 1% Medium to high temperature zones are primarily located in the central and eastern regions of South Sumatra, whereas lower temperatures are found in the western part. The high-temperature category (> 35°C) is concentrated in Palembang, driven by the urban heat island (UHI) effect. This UHI effect, which causes Palembang City (the capital of South Sumatra) to experience higher temperatures than surrounding areas, is due to increased population, human activity, vehicular traffic, and infrastructure development (Adiyanto & Atyanta, 2020; Gaol et al., 2020). The map of average temperatures is shown in Fig. 6 . 3.4. Distance from Roads Human activity is one of the primary factors contributing to wildfires. The presence of roads facilitates human access to forested areas, increasing the likelihood of fires. Heavily congested roads lead to greater human activity, thereby raising the risk of fire occurrences. In South Sumatra, roads are widespread across nearly the entire region, with many areas located in close proximity to roads (0–50 m). The classification of distance to roads and the corresponding areas is provided in Table 14 . Table 14 Area distribution of distance from road. Distance (m) Area (ha) Percentage 0–50 6,228,052.302 72% 50–100 0 0% 100–200 0 0% 200–300 0 0% 300–400 0 0% > 400 2,465,507.418 28% According to the road distance map (Fig. 7 ), areas situated more than 400 meters from roads are predominantly within wildlife sanctuaries, protected forests, and national parks, where human access is more restricted. In contrast, regions classified as “production forests,” “converted production forests,” and “limited production forests” are located very close to roads (< 50 m), providing easier access for people and increasing the fire risk. Additionally, peatlands in South Sumatra are found in regions adjacent to forest areas, further contributing to their vulnerability to fires. 3.5. Forest Fire Risk Map Based on the overlay of parameters, the forest fire risk values were calculated by summing the ratings of each parameter and categorizing the results into four classes. South Sumatera is predominantly covered by areas of high and very high fire risk, each accounting for approximately 3.5 million hectares or 41% of the total area. The classification of fire hazards and corresponding areas is shown in Table 15 . Table 15 Area distribution of forest fire risk classes. Total Sc Level of Fire Risk Area (ha) Percentage 4.00–7.25 Low 47,328.37294 1% 7.25–10.50 Moderate 1,445,181.535 17% 10.50–13.75 High 3,552,288.6 41% 13.75–17.00 Very high 3,524,590.905 41% The forest fire risk map indicates that high and very high-risk zones are concentrated in the central part of South Sumatera, while areas with medium and low risk are primarily found in the western and eastern regions. Areas with high to very high fire risk tend to be in regions characterized by gentle slopes, low elevations, and close proximity to roads, which facilitate human access and thereby increase wildfire risk. Additionally, relatively high temperatures (> 25°C) accelerate the drying of fuel, making it more flammable. Peatlands in South Sumatera fall within the medium to very high forest fire risk levels, as evidenced by the frequent occurrence of peat fires each year. According to the forest fire danger parameters, peatlands are located in areas with low elevations, gentle slopes, moderate to high temperatures, and some proximity to roads. These peatlands are part of protected areas and production forests, where human activity is significant (Usmadi, 2023). The practice of slash-and-burn agriculture to clear peatlands remains widespread, further increasing fire risk (Ardiansyah et al., 2017). This situation renders peatlands in South Sumatera particularly vulnerable to fires, with a fire risk ranging from moderate to very high. Peatlands in the region are dominated by shrubs and ferns, which are highly susceptible to fires, especially during the dry season (Putra et al., 2019). The forest fire risk map is presented in Fig. 8 . 3.6. Sattelite Data Validation 3.6.1. dNBR Analysis The dNBR (Difference Normalized Burn Ratio) data (Fig. 9 ) is analyzed to assess the severity of fire damage in various areas. The dNBR value is calculated as the difference between the NBR (Normalized Burn Ratio) values before and after a fire. This analysis utilizes Landsat 8 satellite imagery, with data collected in August and November from 2014 to 2023. The dNBR is mapped annually to monitor fire spread and changes in the burned area. Analysis of the dNBR data reveals that the extent of damage varies year by year. Areas with low to high damage are typically found in regions with medium to high fire risk, indicating that fires tend to spread in areas with moderate to high risk levels. The highest damage categories were observed in 2015, 2018, and 2022. Peatlands experience significant damage annually, with areas of medium to high damage being relatively few, while areas with low damage are more widespread. However, data on forest fires in South Sumatera indicate that fires in 2018 and 2022 were relatively minor, with the total area burned not exceeding 100,000 hectares. This discrepancy suggests that dNBR data may not accurately represent fire spread in peatlands, potentially due to large cloud cover during satellite observations, which can affect the accuracy of the data. 3.6.2. Hotspot Analysis Hotspot data was obtained from NASA’s FIRMS (Fire Information for Resource Management System) for the period 2014 to 2023. Based on the confidence classification, a confidence score exceeding 70% is considered indicative of a fire (Shofiana & Sitanggang, 2021). Annual hotspot data is mapped to identify the distribution of fire occurrences in South Sumatera for each year. The highest number of hotspots was recorded in 2015, with widespread distribution across the entire region due to the El Niño phenomenon in 2015 and 2019 (Nurhayati et al., 2021). In contrast, the lowest number of hotspots occurred in 2021. The annual distribution of hotspots is visualized in Fig. 10 . Using 10 years of hotspot data, the density of hotspots was analyzed through the Kernel Density Estimation (KDE) method. KDE converts discrete hotspot data into a continuous surface map, illustrating variations in wildfire intensity (Zahran et al., 2020). According to the hotspot density map, the highest density of hotspots is located in the eastern part of South Sumatera, particularly in regions with medium to very high fire potential. High hotspot densities are observed in Ogan Komering Ilir and Musi Banyuasin regencies, areas rich in peatlands. Peatland fires occur at two levels: shallow peat fires and deep peat fires (Hayasaka et al., 2020). The density of hotspots is significantly higher in peatland areas compared to mineral soil areas (Miettinen et al., 2017). This heightened density is largely driven by human activities involving peatlands, such as land clearing and agricultural practices (Nurhayati et al., 2021). The hotspot density map is illustrated in Fig. 11 . 4. Conclusion Remote sensing and GIS methods have been successfully applied to map and analyze fire-prone areas in South Sumatra Province, with a particular focus on peatlands. The resulting forest fire risk map indicates that South Sumatra is predominantly characterized by high- and very high-risk areas concentrated in the central region. In contrast, moderate and low-risk areas are mainly located in the western and eastern regions. Peatlands in South Sumatra are categorized as having medium to very high forest fire risk levels, attributed to their low elevation and slope, moderate to high temperatures, and proximity to roads. The dNBR data highlights annual fluctuations in burn severity, showing that fires typically occur in areas with moderate to high fire risk. In peatlands, areas with moderate to high and high burn severity are limited, while low-severity areas are more widespread. However, the accuracy of dNBR data is compromised by significant cloud cover, which affects the reliability of the results. Hotspot density analysis reveals that peatlands have the highest hotspot densities, primarily driven by human activities such as land clearing and agricultural practices. The occurrence of peat fires is further exacerbated by climatic phenomena such as El Niño. These findings are corroborated by data on annual peatland fires in South Sumatra, underscoring the vulnerability of these areas. The results of this study provide valuable information for the public and the government of South Sumatra, particularly regarding the distribution of fire-prone and burned areas in peatlands. This information serves as a critical step in fire prevention efforts to mitigate the impacts and risks of peatland fires, especially in South Sumatra. Declarations Competing Interests The authors declare that they have no financial interests or personal relationships that could have appeared to influence the work reported in this paper. All analyses and interpretations presented in this study are based solely on academic purposes, without any external influence from commercial, financial, or political entities. The authors have acknowledged and addressed any potential bias that may arise from the data or methodology used, ensuring the integrity of the research findings. Funding This research was supported by funding from Universitas Negeri Malang through the Riset Kolaborasi Indonesia (RKI) grant, under contract number 10.5.48/UN32.20.1/LT/2023 , for the 2023 fiscal year. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5733851","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":399428418,"identity":"520ef00e-5d9b-4395-97a4-c332b86d56ba","order_by":0,"name":"Cahyo Aji Hapsoro","email":"","orcid":"https://orcid.org/0000-0003-0487-1308","institution":"State University of Malang Faculty of Mathematics \u0026 Natural Sciences: Universitas Negeri Malang 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5","display":"","copyAsset":false,"role":"figure","size":867263,"visible":true,"origin":"","legend":"\u003cp\u003eAverage temperature in (a) 2014, (b) 2015, (c) 2016, (d) 2017, (e) 2018, (f) 2019, (g) 2020, (h) 2021, (i) 2022, (j) 2023 of South Sumatera, Indonesia.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-5733851/v1/48f33d0d45ff67e7226e18b2.png"},{"id":73501359,"identity":"0277e0e3-e7e6-43d1-af98-ffb7f1fe6836","added_by":"auto","created_at":"2025-01-10 15:01:11","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":587269,"visible":true,"origin":"","legend":"\u003cp\u003eAverage temperature map (2014–2023) of South 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10","display":"","copyAsset":false,"role":"figure","size":323024,"visible":true,"origin":"","legend":"\u003cp\u003eHotspot distribution in (a) 2014, (b) 2015, (c) 2016, (d) 2017, (e) 2018, (f) 2019, (g) 2020, (h) 2021, (i) 2022, (j) 2023 of South Sumatera, Indonesia.\u003c/p\u003e","description":"","filename":"floatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-5733851/v1/ccba27c830ca8285951f86d2.png"},{"id":73501372,"identity":"c19d7a4b-2950-4a3c-adff-762b2e653139","added_by":"auto","created_at":"2025-01-10 15:01:11","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":131458,"visible":true,"origin":"","legend":"\u003cp\u003eHotspot density map of South Sumatera.\u003c/p\u003e","description":"","filename":"floatimage11.png","url":"https://assets-eu.researchsquare.com/files/rs-5733851/v1/17df5e1a8c88e78ba691979c.png"},{"id":99172361,"identity":"20aff902-d0a4-4aab-ad90-75bde8a26cfa","added_by":"auto","created_at":"2025-12-29 16:08:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7472975,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5733851/v1/2639c126-2c5a-442a-9c10-8fab03f7d297.pdf"}],"financialInterests":"","formattedTitle":"Spatial and Temporal Analysis of Forest Fire Risk and Propagation in South Sumatra Peatlands: Insights from Remote Sensing and GIS (2014–2023)","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eIndonesia experiences forest fires annually (Hein et al., 2022; Nurhayati et al., 2021), with South Sumatra Province recording the highest incidence, according to the Forest Fire Monitoring System (SiPongi) of the Indonesian Ministry of Environment and Forestry. Over the past decade (2014\u0026ndash;2023), more than 1.5\u0026nbsp;million hectares of land have been burned in South Sumatra, with 2014 being the most destructive year, accounting for 644,000 hectares of burned area. Ogan Komering Ilir district is the most affected, experiencing the largest burned area each year, primarily due to human activities (Nurhayati et al., 2021). In addition to forest fires, peat fires are also a recurring issue in Indonesia.\u003c/p\u003e \u003cp\u003eIndonesia hosts some of the largest peatlands globally (Maltby \u0026amp; Immirzi, 1993), covering 13.4\u0026nbsp;million hectares, predominantly in Kalimantan, Papua, and Sumatra (Irfan et al., 2024). South Sumatra Province alone has approximately 1\u0026nbsp;million hectares of peatland. According to 1990 data, these peatlands are classified as medium-high peat, with depths ranging from 100 to 200 cm (Wahyunto et al., 2005).\u003c/p\u003e \u003cp\u003ePeatland fires in Indonesia occur annually and have historically been more frequent on peatlands than on mineral soils (Abriantini et al., 2017). Peatland fires have more severe environmental consequences compared to mineral soil fires. The smoke generated from peat fires contributes significantly to air pollution (Nurhayati et al., 2021) and leads to increased greenhouse gas emissions, fine particulate matter, aerosols, and ecosystem degradation (Hayasaka et al., 2020; Hein et al., 2022; Miettinen et al., 2017). Over half of Indonesia's total peatland area has been cleared and drained, primarily for plantation development (Vetrita \u0026amp; Cochrane, 2019). The conversion of peatlands for forestry, agriculture, plantations, and settlements exacerbates peatland degradation (Sulaeman et al., 2021; Wahyunto et al., 2005).\u003c/p\u003e \u003cp\u003ePeatlands are highly susceptible to fires, which are driven by multiple factors, including human activities and weather conditions (Abriantini et al., 2017; Irfan et al., 2024; Yuwati et al., 2021). Human activity is a major contributor to peatland fires, primarily due to land conversion practices that involve burning vegetation (Abriantini et al., 2017; Nurhayati et al., 2021). The human factor is challenging to mitigate, as rising demand for land perpetuates these practices. Peatlands do not naturally burn below the surface unless weather and fuel conditions exacerbate the risk (Nurhayati et al., 2021). Weather events, such as the El Ni\u0026ntilde;o phenomenon, further increase fire occurrences (Hayasaka et al., 2020; Miettinen et al., 2017; Sofan et al., 2020), and severe peat fires are typically associated with prolonged dry seasons (Irfan et al., 2024).\u003c/p\u003e \u003cp\u003eThe high incidence of forest fires in South Sumatra Province is particularly concerning. To address this, mapping fire-prone areas and fire propagation, especially in peatland regions, is essential. Remote Sensing (RS) and Geographic Information Systems (GIS) are critical tools used to assess fire risk and map fire spread. Satellite data from remote sensing is widely used to identify burned areas (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) (Sirin \u0026amp; Medvedeva, 2022). Remote sensing is effective for both qualitative and quantitative monitoring of forest ecosystems, making it valuable for forest fire surveillance (Suryabhagavan et al., 2016). It offers advantages such as extensive area coverage, coherence, and reproducibility, allowing access to information from remote locations. Several fire risk parameters, including vegetation, humidity, and temperature, can be monitored using remote sensing techniques (Konkathi et al., 2019; Leblon, 2005; Suresh Babu et al., 2018). Additionally, remote sensing is cost-efficient, as much of the necessary data is publicly available and easily downloadable, reducing both time and effort for data acquisition and processing (Sobrino et al., 2019).\u003c/p\u003e \u003cp\u003eIn this study, Landsat 8 satellite data is used to identify fire zones, while DEM and MODIS data are utilized to map fire risk levels. Hotspot data is analyzed to determine the distribution of fire points, with parameters such as elevation, slope, temperature, and distance from roads incorporated to map forest fire risk areas.\u003c/p\u003e \u003cp\u003eThe objective of this study is to map, analyze, and investigate forest fire risk and fire spread in peatlands within South Sumatra. The findings are expected to provide valuable insights for researchers, policymakers, and the wider community about peat fires, particularly in South Sumatra.\u003c/p\u003e"},{"header":"2. Material and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study Area\u003c/h2\u003e \u003cp\u003eSouth Sumatra is one of the Indonesian provinces with the largest forest fire areas. Covering a total area of 91,806.36 km\u0026sup2;, it is located on the island of Sumatra. The province has approximately 3.4\u0026nbsp;million hectares of forest, comprising 566,000 hectares of protected forest, 2\u0026nbsp;million hectares of production forest, and around 250,000 hectares of nature reserves. The topography of South Sumatra varies, with elevations ranging from 400 to 1,700 meters above sea level (ASL). The western region, dominated by the Bukit Barisan Mountains, has an average elevation between 900 and 1,200 meters ASL, while the eastern part consists mainly of coastal areas, swamps, and brackish waters influenced by tidal movements. These coastal areas support plant species such as Palmase and mangroves, and the central region is characterized by extensive plains.\u003c/p\u003e \u003cp\u003eSouth Sumatra also has 1.42\u0026nbsp;million hectares of peatlands, making it the second-largest peatland province on the island of Sumatra after Riau. Peat thickness in South Sumatra ranges from 50 to 400 cm, classifying it as shallow to deep peatland. These peatlands are primarily distributed in the regencies of Musi Banyuasin, Banyuasin, Muara Enim, Musi Rawas, and Ogan Komering Ilir. The geographical distribution of South Sumatra is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResearches using remote sensing and GIS methods to map fire hazards and burned areas.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCountry\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIndex\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAuthors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eResult\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTemperature, precipitation, vegetation, humidity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIndonesia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLST, NDVI, NDMI, SPI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(Rendana et al., 2023)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eThe fire hazard mapping results provide a clear comparison between current and future fire hazard conditions. However, the analysis did not incorporate topographic variables, and the search area was limited, excluding certain regions within the province of South Sumatra.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBurned area, hotspot, rainfall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIndonesia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNBR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(Nurhayati et al., 2021)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eThe study results also reveal the impact of rainfall and highlight the distribution of hotspots and burned areas over a five-year period (2015\u0026ndash;2019). For a more comprehensive analysis, other climatic factors, such as temperature, could be incorporated to better understand the patterns of hotspot occurrences and burned area distribution.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVegetation, burned area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRussia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNDVI, NDMI, NBR, MIRBI, BAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(Sirin \u0026amp; Medvedeva, 2022)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eThe results indicated that each variable demonstrated high accuracy in mapping burned areas within peatlands.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHotspot\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIndonesia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHotspot\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(Putra et al., 2021)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eThe study utilizes hotspot data to provide insights into the distribution of burned peatlands. For a more comprehensive understanding of burned area distribution, additional satellite data can be incorporated.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVegetation, hotspot\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIndonesia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLand Cover, Hotspot\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(Putra et al., 2019)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eThe study analyzes hotspot data across various land cover types to explain the distribution of burned areas over two decades (1997\u0026ndash;2016). Incorporating additional indicators could further enhance the mapping process, providing a clearer visualization of the burned area distribution.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTopography, temperature, hotspot, burned area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIndonesia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSlope, Elevation, Distance from Road, LST, dNBR, Hotspot\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eThis research\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eThe results of this study include a wildfire risk map, a hotspot distribution map, and a dNBR map, all of which are developed using climate and topographic variables. These variables provide valuable insights into the distribution of wildfire-prone areas, as well as the spatial patterns of burned areas and hotspots in South Sumatra, particularly within peatland regions.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Datasets\u003c/h2\u003e \u003cp\u003eIn this study, data analysis relies on several variables, including slope, elevation, temperature, and distance from roads, derived from Digital Elevation Model (DEM) and Moderate Resolution Imaging Spectroradiometer (MODIS) data. Landsat-8 satellite imagery and hotspot data are used to validate forest fire risk maps for wildfires based on temporal data (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eParameters used to map and validate forest fire risk of South Sumatera.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSpatial Resolution\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePurpose\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElevation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eASTER DEM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30 m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInputs to forest fire risk mapping\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSlope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eASTER DEM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30 m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInputs to forest fire risk mapping\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTemperature\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMODIS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 km\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInputs to forest fire risk mapping\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistance from road\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRBI Map\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInputs to forest fire risk mapping\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edNBR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLandsat 8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30 m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eValidate fire risk map\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHotspot\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMODIS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 km\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eValidate fire risk map\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1. Elevation\u003c/h2\u003e \u003cp\u003eFire occurrence is highly dependent on topography, particularly elevation (Maingi \u0026amp; Henry, 2007). Elevation influences forest fires by affecting temperature, humidity, and wind speed (Sivrikaya \u0026amp; K\u0026uuml;\u0026ccedil;\u0026uuml;k, 2022). Higher elevations generally experience lower temperatures and higher humidity, which reduces the likelihood of fires (Suryabhagavan et al., 2016). In areas with high elevation, the risk of fire is lower compared to low-elevation regions, primarily due to increased rainfall (Adab et al., 2013). In contrast, low-elevation areas tend to have higher temperatures and lower rainfall, causing vegetation to dry out more quickly, which increases fire risk (Kant Sharma et al., 2012; Zahran et al., 2020). Additionally, fires can spread more rapidly in low-elevation areas (Asori et al., 2020). For fire risk assessment, elevation is categorized into five classes, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e (Sivrikaya \u0026amp; K\u0026uuml;\u0026ccedil;\u0026uuml;k, 2022).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eElevation classes.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClass (m)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRisk Classes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eElevation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVery High\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e500\u0026ndash;1000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1000\u0026ndash;1500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1500\u0026ndash;2000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;2000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVery Low\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2. Slope\u003c/h2\u003e \u003cp\u003eSlope is a geomorphological parameter controlled by drainage and the stage of development of the soil shape. Slope has a positive correlation with the speed of flame movement. The slope influences the occurrence of wildfires as it determines the speed of fire spread, while the steep slope increases the rate of spread of the fires (Ajin et al., 2015; Asori et al., 2020; Sivrikaya \u0026amp; K\u0026uuml;\u0026ccedil;\u0026uuml;k, 2022; Suryabhagavan et al., 2016). Based on the level of fire hazard, the slope is divided into 5 classes, which are listed by Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e (Ozenen Kavlak et al., 2021; Parajuli et al., 2020).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSlope classes.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClass\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRisk Classes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eSlope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;35\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVery High\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35\u0026deg;\u0026ndash; 15\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVery High\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u0026deg; \u0026ndash; 10\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u0026deg; \u0026ndash; 5\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u0026deg; \u0026ndash; 0\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.2.3. Temperature\u003c/h2\u003e \u003cp\u003eTemperature is a crucial factor in assessing forest fire risk, as it provides insight into the thermal conditions affecting vegetation (fuel) and topographic drought (Jodhani et al., 2024). Temperature significantly influences fire occurrence by determining the rate at which fuel dries, altering relative humidity, and accelerating evapotranspiration (Asori et al., 2020). High temperatures speed up the drying of biomass, particularly dry grasses, dead leaves, needles, and small trees. Fuel that is preheated by high average temperatures ignites more rapidly than cooler fuel, making it a key indicator of fire occurrence (Prasad et al., 2008).\u003c/p\u003e \u003cp\u003eIn this study, land surface temperature data derived from MODIS satellite imagery is used. The dataset includes surface temperature records from July to October over a 10-year period (2014\u0026ndash;2023). The 10-year average temperature data is utilized to map fire susceptibility. Based on fire hazard levels, the temperature is categorized into five classes, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e (Parajuli et al., 2020).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTemperature classes.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClass (\u0026deg;C)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRisk Classes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eTemperature\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVery High\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35\u0026ndash;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30\u0026ndash;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25\u0026ndash;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVery Low\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.2.4. Distance from Roads\u003c/h2\u003e \u003cp\u003eThe distance from roads is a critical factor in determining forest fire risk. Roads provide greater human access to forests and grasslands, increasing the likelihood of fires being ignited (Adab et al., 2013; Ajin et al., 2015; Ozenen Kavlak et al., 2021)The closer a road is to a forest, the higher the risk of wildfire occurrence (Asori, 2020). For fire hazard assessment, the distance to roads is categorized into six classes, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e (Ozenen Kavlak et al., 2021).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDistance from road classes.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClass (m)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRisk Classes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eDistance from roads\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u0026ndash;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVery High\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50\u0026ndash;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVery High\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100\u0026ndash;200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e200\u0026ndash;300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e300\u0026ndash;400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.2.5. Difference Normalized Burnt Ratio (dNBR)\u003c/h2\u003e \u003cp\u003eThe band ratio from satellite imagery is used to assess the severity of fires. The Difference Normalized Burn Ratio (dNBR) is a method that detects changes in an area before and after a fire by analyzing satellite images (Ozenen Kavlak et al., 2021). The dNBR value is calculated as the difference between the Normalized Burn Ratio (NBR) values recorded before and after the fire (Eq.\u0026nbsp;\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The NBR value itself is derived using the near-infrared (NIR) and short-wave infrared (SWIR) bands from Landsat 8 satellite imagery (Eq.\u0026nbsp;\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) (Suresh Babu et al., 2018).\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:\\text{NBR=}\\frac{\\text{NIR-SWIR}}{\\text{NIR+SWIR}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\text{dNBR=}{\\text{NBR}}_{\\text{pre-fire}}\\text{-}{\\text{NBR}}_{\\text{post-fire}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn this study, dNBR data is used to determine the severity of burned areas in South Sumatra over the period from 2014 to 2023. The data is analyzed annually to assess the impacts of forest fires in the region. dNBR values are classified into seven categories based on standards from the United States Geological Survey (USGS). The classification of dNBR values is presented in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e (Roy et al., 2006).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003edNBR value classification.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePixel Values\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSeverity Level\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;0.50\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026le;\u003c/span\u003e\u0026thinsp;dNBR\u0026thinsp;\u0026lt;\u0026thinsp;\u0026minus;\u0026thinsp;0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh regrowth\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;0.25\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026le;\u003c/span\u003e\u0026thinsp;dNBR\u0026thinsp;\u0026lt;\u0026thinsp;\u0026minus;\u0026thinsp;0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow regrowth\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;0.10\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026le;\u003c/span\u003e\u0026thinsp;dNBR\u0026thinsp;\u0026lt;\u0026thinsp;0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnburned\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.10\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026le;\u003c/span\u003e\u0026thinsp;dNBR\u0026thinsp;\u0026lt;\u0026thinsp;0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.27\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026le;\u003c/span\u003e\u0026thinsp;dNBR\u0026thinsp;\u0026lt;\u0026thinsp;0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerate-low\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.44\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026le;\u003c/span\u003e\u0026thinsp;dNBR\u0026thinsp;\u0026lt;\u0026thinsp;0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerate-high\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.66\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026le;\u003c/span\u003e\u0026thinsp;dNBR\u0026thinsp;\u0026lt;\u0026thinsp;1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.2.6. Hotspot\u003c/h2\u003e \u003cp\u003eThe detection of land or forest fires at a specific pixel size results in the identification of hotspots, which represent areas with relatively higher surface temperatures compared to their surroundings (Khairani \u0026amp; Sutoyo, 2020). Each hotspot is assigned a confidence level, indicating the likelihood of fire occurrence. A higher confidence level corresponds to a greater fire risk at that point (Khairani \u0026amp; Sutoyo, 2020; Shofiana \u0026amp; Sitanggang, 2021). The hotspot data for this study is derived from MODIS satellite imagery spanning from 2014 to 2023, and it is analyzed to identify areas prone to fire. According to the MODIS Active Fire User Guide, the confidence levels are categorized into three classes, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e (Giglio et al., 2021).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eConfidence classification.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConfidence\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClass\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0% \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026le;\u003c/span\u003e C\u0026thinsp;\u0026lt;\u0026thinsp;30%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30% \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026le;\u003c/span\u003e C\u0026thinsp;\u0026lt;\u0026thinsp;80%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNominal\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e80% \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026le;\u003c/span\u003e C\u0026thinsp;\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u0026le;\u003c/span\u003e\u0026thinsp;100%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eBased on the hotspot data from 2014 to 2023 (Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e), the regions in South Sumatra with the highest number of hotspots are Ogan Komering Ilir, Musi Banyuasin, and Banyuasin, all of which contain significant peatland areas. In contrast, the regions with the fewest hotspots are Pagar Alam, Prabumulih, and Palembang City. The year with the highest number of hotspots was 2015, with over 34,000 hotspots, while the lowest number was recorded in 2022, with just 366 hotspots.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSpatial-temporal hotspot distribution of South Sumatera.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"13\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegency/City*\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2014\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\"\u003e \u003cp\u003eAverage\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOKI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18,966\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5,005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e3,941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e32,277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e3,227.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e365\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7,469\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e577\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e11,300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e1130.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1,278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e396\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e4,403\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e440.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eME\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,346\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e2,389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e238.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMRU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e591\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e2,244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e224.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e2,093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e209.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOKU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e631\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e1,131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e113.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePALI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e430\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e1,065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e106.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e1,038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e103.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOKUT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e386\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e90.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e404\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e856\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e85.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOKUS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e398\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e734\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e73.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e461\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e46.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e7.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePRA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e7.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePAL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e4.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e3.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e4,565\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e34,973\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e753\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e794\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1,304\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e11,068\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e422\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e603\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e366\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e6,272\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e61,120\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e\u003cb\u003e6,112\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003e*Abbreviation\u003c/strong\u003e \u003cem\u003eOKI (Ogan Komering Ilir); MB (Musi Banyuasin); BA (Banyu Asin); ME (Muara Enim); MRU (Musi Rawas Utara); MR (Musi Rawas); OKU (Ogan Komering Ulu); PALI (Penukal Abab Lematang Ilir); OI (Ogan Ilir); OKUT (Ogan Komering Ulu Timur); LA (Lahat); OKUS (Ogan Komering Ulu Selatan); EL (Empat Lawang); LU (Lubuklinggau); PRA (Prabumulih); PAL (Palembang); PA (Pagar Alam).\u003c/em\u003e \u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Methodology\u003c/h2\u003e \u003cp\u003eThe data for each variable is processed and categorized into several classes, with a scoring method applied to quantify the fire risk associated with each class. This method assigns numerical scores to represent the level of fire risk for each parameter. The scores for each variable are then used to assess the overall fire susceptibility. The flow of the research process is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1. Scoring\u003c/h2\u003e \u003cp\u003eFor each parameter, the score reflects the level of fire vulnerability, where a higher score indicates a greater fire potential. The scores range from 1 to 5. The total score, which is the sum of all variables, is then categorized into four fire vulnerability classes: Low, Moderate, High, and Very High. The scoring for each parameter is presented in Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eScores of each forest fire risk parameter.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndex\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClass\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRisk Level\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eScore\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eSlope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;35\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVery High\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35\u0026deg;\u0026ndash; 15\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVery High\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u0026deg; \u0026ndash; 10\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u0026deg; \u0026ndash; 5\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u0026deg; \u0026ndash; 0\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eDistance from roads (m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u0026ndash;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVery High\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50\u0026ndash;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVery High\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100\u0026ndash;200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e200\u0026ndash;300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e300\u0026ndash;400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eTemperature (\u0026deg;C)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVery High\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35\u0026ndash;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30\u0026ndash;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25\u0026ndash;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVery Low\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eElevation (m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVery High\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e500\u0026ndash;1000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1000\u0026ndash;1500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1500\u0026ndash;2000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;2000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVery Low\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2. Forest Fire Risk Mapping\u003c/h2\u003e \u003cp\u003eThe forest fire risk map is generated by overlaying the maps of each parameter. This is done using the Intersect method in ArcGIS. During the overlay process, the scores of all parameters are summed, and the results are divided into four classes at equal intervals. Eq.\u0026nbsp;3 is applied to calculate the fire susceptibility value.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFS\u0026thinsp;=\u0026thinsp;S\u0026thinsp;+\u0026thinsp;DR\u0026thinsp;+\u0026thinsp;T\u0026thinsp;+\u0026thinsp;E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFS is fire susceptibility value, S is score of slope index, DR is score of distance from roads index, T is score of temperature index, and E is score of elevation index.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. Result and Discussions","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Elevation\u003c/h2\u003e \u003cp\u003eAccording to the elevation data, South Sumatra is predominantly composed of low-elevation areas. Regions located at elevations of less than 500 meters above sea level cover approximately 7.7\u0026nbsp;million hectares, representing about 90% of the province\u0026rsquo;s total area. In contrast, high-elevation areas (\u0026gt;\u0026thinsp;2000 meters above sea level) account for only 26.6 thousand hectares. The elevation classifications and their respective areas are shown in Table\u0026nbsp;\u003cspan refid=\"Tab11\" class=\"InternalRef\"\u003e11\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab11\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 11\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eArea distribution of elevation classes.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElevation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArea (ha)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7792470\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e500\u0026ndash;1000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e526343,7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1000\u0026ndash;1500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e249223,6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1500\u0026ndash;2000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e92290,43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;2000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26605,07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe high mountain regions are located in the western part of South Sumatra, specifically within the Bukit Barisan mountain range. The central area consists of large plains, while the eastern part is characterized by marshes and coastal areas. The elevation map is displayed in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Slope\u003c/h2\u003e \u003cp\u003eSouth Sumatra is predominantly composed of areas with gentle slopes, as it is largely characterized by vast plains. The classification of slopes and their corresponding surface areas is presented in Table\u0026nbsp;\u003cspan refid=\"Tab12\" class=\"InternalRef\"\u003e12\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab12\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 12\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eArea distribution of slope classes.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSlope\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArea (ha)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;35\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e40,319.49503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35\u0026deg;\u0026ndash; 15\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e558,622.6477\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u0026deg; \u0026ndash; 10\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e371,959.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u0026deg; \u0026ndash; 5\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1,192,651.697\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u0026deg; \u0026ndash; 5\u0026deg;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6,515,046.611\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eBased on the DEM data analysis, regions with steep slopes are concentrated in the western part of the province, where the high elevations of the mountainous areas are located. In contrast, the central and eastern regions fall within the gentle slope category. The slope map is visualized in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Temperature\u003c/h2\u003e \u003cp\u003eBased on temperature data from 2014 to 2023 (July to October), South Sumatra is predominantly characterized by moderate to high temperatures, with the highest recorded temperature reaching 42.8\u0026deg;C in 2014. Temperature fluctuations in South Sumatra are influenced by climate phenomena such as El Ni\u0026ntilde;o, which occurred in 2015, 2019, and 2023, leading to significant temperature increases (Munawar et al., 2023). After El Ni\u0026ntilde;o events, the average temperature typically drops due to the La Ni\u0026ntilde;a phenomenon, which brings rainfall even during dry months, as observed in 2016 and 2020 (Nurhayati et al., 2021). The average annual temperature data is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe 10-year average temperature is calculated based on yearly temperature data. The highest average temperature recorded is 37.3\u0026deg;C, while the lowest is 15\u0026deg;C. The temperature classification and corresponding ranges are provided in Table\u0026nbsp;\u003cspan refid=\"Tab13\" class=\"InternalRef\"\u003e13\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab13\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 13\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eArea distribution of temperature classes.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTemperature (\u0026deg;C)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArea (ha)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10,563.35065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35\u0026ndash;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,163,460.057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u0026ndash;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5,083,560.561\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u0026ndash;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e323,853.4604\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e52,596.45699\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eMedium to high temperature zones are primarily located in the central and eastern regions of South Sumatra, whereas lower temperatures are found in the western part. The high-temperature category (\u0026gt;\u0026thinsp;35\u0026deg;C) is concentrated in Palembang, driven by the urban heat island (UHI) effect. This UHI effect, which causes Palembang City (the capital of South Sumatra) to experience higher temperatures than surrounding areas, is due to increased population, human activity, vehicular traffic, and infrastructure development (Adiyanto \u0026amp; Atyanta, 2020; Gaol et al., 2020). The map of average temperatures is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Distance from Roads\u003c/h2\u003e \u003cp\u003eHuman activity is one of the primary factors contributing to wildfires. The presence of roads facilitates human access to forested areas, increasing the likelihood of fires. Heavily congested roads lead to greater human activity, thereby raising the risk of fire occurrences. In South Sumatra, roads are widespread across nearly the entire region, with many areas located in close proximity to roads (0\u0026ndash;50 m). The classification of distance to roads and the corresponding areas is provided in Table\u0026nbsp;\u003cspan refid=\"Tab14\" class=\"InternalRef\"\u003e14\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab14\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 14\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eArea distribution of distance from road.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistance (m)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArea (ha)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u0026ndash;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6,228,052.302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50\u0026ndash;100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e100\u0026ndash;200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e200\u0026ndash;300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e300\u0026ndash;400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,465,507.418\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAccording to the road distance map (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e), areas situated more than 400 meters from roads are predominantly within wildlife sanctuaries, protected forests, and national parks, where human access is more restricted. In contrast, regions classified as \u0026ldquo;production forests,\u0026rdquo; \u0026ldquo;converted production forests,\u0026rdquo; and \u0026ldquo;limited production forests\u0026rdquo; are located very close to roads (\u0026lt;\u0026thinsp;50 m), providing easier access for people and increasing the fire risk. Additionally, peatlands in South Sumatra are found in regions adjacent to forest areas, further contributing to their vulnerability to fires.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Forest Fire Risk Map\u003c/h2\u003e \u003cp\u003eBased on the overlay of parameters, the forest fire risk values were calculated by summing the ratings of each parameter and categorizing the results into four classes. South Sumatera is predominantly covered by areas of high and very high fire risk, each accounting for approximately 3.5\u0026nbsp;million hectares or 41% of the total area. The classification of fire hazards and corresponding areas is shown in Table\u0026nbsp;\u003cspan refid=\"Tab15\" class=\"InternalRef\"\u003e15\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab15\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 15\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eArea distribution of forest fire risk classes.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Sc\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLevel of Fire Risk\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eArea (ha)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercentage\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4.00\u0026ndash;7.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47,328.37294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7.25\u0026ndash;10.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,445,181.535\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10.50\u0026ndash;13.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3,552,288.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13.75\u0026ndash;17.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVery high\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3,524,590.905\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe forest fire risk map indicates that high and very high-risk zones are concentrated in the central part of South Sumatera, while areas with medium and low risk are primarily found in the western and eastern regions. Areas with high to very high fire risk tend to be in regions characterized by gentle slopes, low elevations, and close proximity to roads, which facilitate human access and thereby increase wildfire risk. Additionally, relatively high temperatures (\u0026gt;\u0026thinsp;25\u0026deg;C) accelerate the drying of fuel, making it more flammable.\u003c/p\u003e \u003cp\u003ePeatlands in South Sumatera fall within the medium to very high forest fire risk levels, as evidenced by the frequent occurrence of peat fires each year. According to the forest fire danger parameters, peatlands are located in areas with low elevations, gentle slopes, moderate to high temperatures, and some proximity to roads.\u003c/p\u003e \u003cp\u003eThese peatlands are part of protected areas and production forests, where human activity is significant (Usmadi, 2023). The practice of slash-and-burn agriculture to clear peatlands remains widespread, further increasing fire risk (Ardiansyah et al., 2017). This situation renders peatlands in South Sumatera particularly vulnerable to fires, with a fire risk ranging from moderate to very high. Peatlands in the region are dominated by shrubs and ferns, which are highly susceptible to fires, especially during the dry season (Putra et al., 2019). The forest fire risk map is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.6. Sattelite Data Validation\u003c/h2\u003e \u003cdiv id=\"Sec21\" class=\"Section3\"\u003e \u003ch2\u003e3.6.1. dNBR Analysis\u003c/h2\u003e \u003cp\u003eThe dNBR (Difference Normalized Burn Ratio) data (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e) is analyzed to assess the severity of fire damage in various areas. The dNBR value is calculated as the difference between the NBR (Normalized Burn Ratio) values before and after a fire. This analysis utilizes Landsat 8 satellite imagery, with data collected in August and November from 2014 to 2023. The dNBR is mapped annually to monitor fire spread and changes in the burned area.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAnalysis of the dNBR data reveals that the extent of damage varies year by year. Areas with low to high damage are typically found in regions with medium to high fire risk, indicating that fires tend to spread in areas with moderate to high risk levels. The highest damage categories were observed in 2015, 2018, and 2022. Peatlands experience significant damage annually, with areas of medium to high damage being relatively few, while areas with low damage are more widespread.\u003c/p\u003e \u003cp\u003eHowever, data on forest fires in South Sumatera indicate that fires in 2018 and 2022 were relatively minor, with the total area burned not exceeding 100,000 hectares. This discrepancy suggests that dNBR data may not accurately represent fire spread in peatlands, potentially due to large cloud cover during satellite observations, which can affect the accuracy of the data.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e \u003ch2\u003e3.6.2. Hotspot Analysis\u003c/h2\u003e \u003cp\u003eHotspot data was obtained from NASA\u0026rsquo;s FIRMS (Fire Information for Resource Management System) for the period 2014 to 2023. Based on the confidence classification, a confidence score exceeding 70% is considered indicative of a fire (Shofiana \u0026amp; Sitanggang, 2021). Annual hotspot data is mapped to identify the distribution of fire occurrences in South Sumatera for each year. The highest number of hotspots was recorded in 2015, with widespread distribution across the entire region due to the El Ni\u0026ntilde;o phenomenon in 2015 and 2019 (Nurhayati et al., 2021). In contrast, the lowest number of hotspots occurred in 2021. The annual distribution of hotspots is visualized in Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eUsing 10 years of hotspot data, the density of hotspots was analyzed through the Kernel Density Estimation (KDE) method. KDE converts discrete hotspot data into a continuous surface map, illustrating variations in wildfire intensity (Zahran et al., 2020). According to the hotspot density map, the highest density of hotspots is located in the eastern part of South Sumatera, particularly in regions with medium to very high fire potential. High hotspot densities are observed in Ogan Komering Ilir and Musi Banyuasin regencies, areas rich in peatlands.\u003c/p\u003e \u003cp\u003ePeatland fires occur at two levels: shallow peat fires and deep peat fires (Hayasaka et al., 2020). The density of hotspots is significantly higher in peatland areas compared to mineral soil areas (Miettinen et al., 2017). This heightened density is largely driven by human activities involving peatlands, such as land clearing and agricultural practices (Nurhayati et al., 2021). The hotspot density map is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e.\u003c/p\u003e\u003c/div\u003e \u003c/div\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eRemote sensing and GIS methods have been successfully applied to map and analyze fire-prone areas in South Sumatra Province, with a particular focus on peatlands. The resulting forest fire risk map indicates that South Sumatra is predominantly characterized by high- and very high-risk areas concentrated in the central region. In contrast, moderate and low-risk areas are mainly located in the western and eastern regions. Peatlands in South Sumatra are categorized as having medium to very high forest fire risk levels, attributed to their low elevation and slope, moderate to high temperatures, and proximity to roads. The dNBR data highlights annual fluctuations in burn severity, showing that fires typically occur in areas with moderate to high fire risk. In peatlands, areas with moderate to high and high burn severity are limited, while low-severity areas are more widespread. However, the accuracy of dNBR data is compromised by significant cloud cover, which affects the reliability of the results. Hotspot density analysis reveals that peatlands have the highest hotspot densities, primarily driven by human activities such as land clearing and agricultural practices. The occurrence of peat fires is further exacerbated by climatic phenomena such as El Ni\u0026ntilde;o. These findings are corroborated by data on annual peatland fires in South Sumatra, underscoring the vulnerability of these areas. The results of this study provide valuable information for the public and the government of South Sumatra, particularly regarding the distribution of fire-prone and burned areas in peatlands. This information serves as a critical step in fire prevention efforts to mitigate the impacts and risks of peatland fires, especially in South Sumatra.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting Interests\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no financial interests or personal relationships that could have appeared to influence the work reported in this paper. All analyses and interpretations presented in this study are based solely on academic purposes, without any external influence from commercial, financial, or political entities. The authors have acknowledged and addressed any potential bias that may arise from the data or methodology used, ensuring the integrity of the research findings.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research was supported by funding from Universitas Negeri Malang through the Riset Kolaborasi Indonesia (RKI) grant, under contract number \u003cb\u003e10.5.48/UN32.20.1/LT/2023\u003c/b\u003e, for the 2023 fiscal year.\u003c/p\u003e\u003ch2\u003eAuthor Contributions\u003c/h2\u003e \u003cp\u003e \u003cb\u003eCahyo Aji Hapsoro\u003c/b\u003e: Conceptualization, Methodology, Investigation, Data Curation, Writing (Original Draft Preparation, Review, and Editing); \u003cb\u003eSyakira Ghina Maulina\u003c/b\u003e: Investigation, Data Curation, Writing (Original Draft Preparation); \u003cb\u003eMochamad Khoirul Rifai\u003c/b\u003e: Investigation, Data Curation, Project Administration, Funding Acquisition, Writing (Review \u0026amp; Editing); \u003cb\u003ePakhrur Razi\u003c/b\u003e: Conceptualization, Supervision, Validation; \u003cb\u003eMarzuki\u003c/b\u003e: Formal Analysis, Supervision, Validation.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbriantini, G., Sitanggang, I. 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Validation of forest fire hotspot analysis in GIS using forest fire contributory factors. \u003cem\u003eSystematic Reviews in Pharmacy\u003c/em\u003e, \u003cem\u003e11\u003c/em\u003e(12), 249\u0026ndash;255.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"natural-hazards","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nhaz","sideBox":"Learn more about [Natural Hazards](https://www.springer.com/journal/11069)","snPcode":"11069","submissionUrl":"https://submission.nature.com/new-submission/11069/3","title":"Natural Hazards","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"forest fire risk, peatland fires, remote sensing and GIS, burn severity (dNBR), hotspot density analysis","lastPublishedDoi":"10.21203/rs.3.rs-5733851/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5733851/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eForest fires pose a significant threat to ecological sustainability, human health, and economic stability, especially in fire-prone regions like South Sumatra, Indonesia. Peatland areas are particularly vulnerable due to their unique ecological characteristics and human-induced disturbances. The objective of this study is to map, analyze, and investigate forest fire risk and fire spread in peatlands within South Sumatra, Indonesia, from 2014 to 2023 using Remote Sensing and GIS technologies. Key geomorphological, environmental, and human activity parameters were assessed, including slope, elevation, temperature, distance to roads, and land cover. Each parameter was classified into five fire hazard levels, and a comprehensive forest fire risk map was generated using an overlay approach via the intersect method in ArcGIS. The analysis revealed that 82% of South Sumatra's area is classified as having high to very high fire risk, with peatlands being the most vulnerable regions due to their low elevation, gentle slopes, moderate to high temperatures, and proximity to roads, which facilitate human access. Fire propagation was further examined using dNBR (differenced Normalized Burn Ratio) and hotspot data from NASA's FIRMS. The dNBR analysis identified fluctuations in burn severity over the years, with significant damage observed in 2015, 2018, and 2022. However, the accuracy of dNBR in peatland areas was affected by cloud cover. Hotspot density, analyzed using Kernel Density Estimation (KDE), highlighted fire-prone zones, particularly in peatland regions. Findings show that human activity, coupled with climatic phenomena such as El Ni\u0026ntilde;o, significantly influences fire risk and propagation in South Sumatra, Indonesia.\u003c/p\u003e","manuscriptTitle":"Spatial and Temporal Analysis of Forest Fire Risk and Propagation in South Sumatra Peatlands: Insights from Remote Sensing and GIS (2014–2023)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-10 15:01:04","doi":"10.21203/rs.3.rs-5733851/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revisions","date":"2025-04-02T03:34:35+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2025-01-11T22:03:58+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-01-08T12:51:03+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-12-31T07:22:56+00:00","index":"","fulltext":""},{"type":"submitted","content":"Natural Hazards","date":"2024-12-30T03:16:48+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"natural-hazards","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nhaz","sideBox":"Learn more about [Natural Hazards](https://www.springer.com/journal/11069)","snPcode":"11069","submissionUrl":"https://submission.nature.com/new-submission/11069/3","title":"Natural Hazards","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"df9d798f-b09d-4129-9b01-37d1a4c4fc03","owner":[],"postedDate":"January 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-29T16:02:40+00:00","versionOfRecord":{"articleIdentity":"rs-5733851","link":"https://doi.org/10.1007/s11069-025-07755-6","journal":{"identity":"natural-hazards","isVorOnly":false,"title":"Natural Hazards"},"publishedOn":"2025-12-26 15:58:12","publishedOnDateReadable":"December 26th, 2025"},"versionCreatedAt":"2025-01-10 15:01:04","video":"","vorDoi":"10.1007/s11069-025-07755-6","vorDoiUrl":"https://doi.org/10.1007/s11069-025-07755-6","workflowStages":[]},"version":"v1","identity":"rs-5733851","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5733851","identity":"rs-5733851","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}