Detecting Post-fire Burn Severity Level using Sentinel-2 and MODIS Satellite data

Detecting Post-fire Burn Severity Level using Sentinel-2 and MODIS Satellite data | 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 Detecting Post-fire Burn Severity Level using Sentinel-2 and MODIS Satellite data Aqsa Shabbir, Sahar Zia, Ali Hussain Kazim, Mumraiz Kasi, Muhammad Ali Jamshed, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4091965/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Forest fires adversely affect forest ecosystem by altering its composition, structure, regeneration, and recovery potential of its landscape. The present study investigates forest fire hotspots and examines the relationship between these fire events and deforestation in Tehsil Dhansar, District Sherani, Balochistan. This study proposed a three-step research methodology to achieve its objectives. Firstly, it aims to assess the severity level of the forest burn resulting from the fire event. Secondly, it analyzes the extent of vegetation loss caused by the fire. Thirdly, the study identifies forest fire hotspots using Sentinel-2A images and MODIS Fire Radiative Power (FRP) data. The analysis involves utilizing Normalized Burn Ratio (NBR), Normalized Difference Vegetation Index (NDVI), and Hot Spot Analysis (Getis-Ord Gi*) to gain comprehensive insights into the pre- and post-fire situation accurately. By defining classes, the study achieves a better understanding of the extent of burnt areas and vegetation loss. Results The findings show that 0.03% of Tehsil Dhansar is found to have low to medium burn severity levels during any forest fire event. It is also revealed that the forest remained dominant in the same region and frequency of occurrence of forest fire events is increasing by 1.6% with each passing year. Conclusion The current study's findings in the region famous for the world's oldest forest have significant potential for similar landscapes worldwide, primarily characterized by dry deciduous forests and juniper forests well adapted to arid and semi-arid environments. Given these findings, further studies in the same location should prioritize obtaining precise in-situ measurements to deepen our understanding of the situation. Forest Fire Sentinel-2 Images Normalized burn ratio (NBR) Normalized vegetation Index (NDVI) Fire radioactive power (FRP) Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Background Forest fires are a common and threatening source of disturbance for ecosystems. They often occur due to extreme dry climatic conditions, posing significant risks. Forest fire events lead to substantial economic losses, environmental degradation, and various challenges, such as the loss of nutrients and ground microorganisms. Additionally, they adversely affect social life, animals, and plants (Guan et al. 2022 ; Ryu et al. 2018 ). Regarding Pakistan, Global Forest Watch reports a loss of 5.46 thousand hectares (kha) of tree cover due to fires between the years 2011 and 2021(Pro, Watcher, and Atlases 2022). Additionally, according to the Germanwatch Global Climate Risk Index, Pakistan has been ranked as the eighth most vulnerable country to severe weather events related to climate change, including an increase in forest fire alerts, especially in the provinces of Khyber Pakhtunkhwa (KPK) and Balochistan (Eckstein et al. 2019 ). Many fire events have been studies earlier in Marghala Hill, Pakistan (Tariq et al. 2021 ; Tariq et al. 2023 ; Tariq et al. 2022 ). In the Sherani District, this region is home to the oldest Chalghoza (Pine Nut) forest, dating back 1,500 years in Dhansar. Similarly, District Ziarat in Balochistan is renowned for the world's oldest juniper forest, which dates back 5,000 years. Thus, destruction of the unique aspects of the existing biodiversity, need urgent attention. This research problem would prompt the study to focus on understanding the causes and drivers of biodiversity loss, identifying the specific unique aspects of biodiversity at risk, exploring the potential consequences of their destruction, and proposing urgent and effective strategies for conservation and management to preserve these unique elements for future generations. As a large area of Balochistan suffered from a prolonged drought period, a forest fire began on May 11, 2022, in the Suleiman range forests of Sherani and Musakhail districts, located in the northwest of Balochistan Province(ReliefWeb 2022a ). According to Pakistan Meteorological Department (PMD), the climate of this area is hot and dry in summer (ReliefWeb). On May 18, it began quickly spreading by destroying a significant portion of the old pine nut and olive forest. It covered an area of about 30 square kilometers. Despite several days of effort until 22 May 2022, the forest fire could not be put out (ReliefWeb 2022b ). The fire burned for more than two weeks. Wildfires' devastating impacts resulted in fatalities and significant burn injuries. Moreover, about 4,000 people were relocated with their families. About 35% of this precious pine nut forest (a UNESCO World Heritage Site) had been damaged by the end of May (ReliefWeb 2022b ). Therefore, this study presents burn severity levels of forest fire event in Dhansar, District Sherani, Balochistan. To assess the severity of a fire and determine its temporal dynamics, image classification and fire index methodologies are commonly used. The soil-adjusted vegetation index (Hoy et al. 2008 ; Smith, Eitel, and Hudak 2010), burned area index (Marino et al. 2016 ), differenced Normalized Burn Ratio, and Tasselled-cap brightness and greenness transformations (Epting, Verbyla, and Sorbel 2005), are a few of the reflectance indices that have been developed and analyzed. The most popular index, differenced Normalized Burn Ratio (dNBR), has been shown to accurately depict the spatial variation in intensity in particular fire, across various types of vegetation (Soverel, Perrakis, and Coops 2010). Accuracy can be increased, especially for higher severity classes in varied environments, by relativizing the dNBR with the pre-fire NBR (RdNBR) (Miller et al. 2009 ). A few national fire severity mapping algorithms have been developed using NBR severity classification (Eidenshink et al. 2007 ). Therefore, given the impact of pre-fire vegetation structure, type of soil, and vegetative dampness on the NBR, classifying dNBR or RdNBR into standard intensity classes that seem to be constant between fires throughout the terrain could be challenging (Kolden, Smith, and Abatzoglou 2015). Multiple indices combined can offer more accurate and comprehensive information than any index used alone. For remote sensing change detection approaches, such as fire severity mapping, regions with a forest canopy and significant topographic relief coupled with topographical shadows are recognized to present difficulties (Lydersen et al. 2016). Because the reflectivity signal is masked by a canopy or topographical shades, this might be especially difficult when mapping the burned understory of low severity categories (Lydersen et al. 2016). Areas with steep north-facing perspectives continued to show less pre-fire NBR than some other aspects, due to the direction of the sun angle, according to previous topographical impacts on dNBR that have been observed (Verbyla, Kasischke, and Hoy 2008). Therefore, with suitable image pre-processing modifications for normalized reflectivity, air attenuation, and electromagnetic radiation scattering effects are significantly diminished in topographically complicated regions (Ediriweera et al. 2013 ; Flood et al. 2013 ). Moreover, it has been demonstrated that spectral un-mixing assessments reduce the influence of topology on the accuracy of classifying fire intensity (Rogan and Franklin 2001 ). To interpret and apply remotely sensed fire severity indices correctly in land management, it is crucial to comprehend how they behave across various landscape conditions. Using the SPOT-4 satellite's pre-fire and post-fire images, Xiao et al. (Xiao et al. 2003 ) applied the NDVI and EVI fire indices to the area to investigate how a fire in North Asia affected plant indices. They investigated variations in carbon emission, biomass loss, and ash production by comparing the changes in such image indexes. Therefore, they generated the fire threshold values, which they then used to calculate the fire intensity. On Landsat satellite images, Escuin et al. employed the NBR and NDVI indexes to evaluate three distinct fires that took place in Southern Spain's Cazorla, Nerva, and Anzalcollar regions between 1995 and 2001(Dragozi et al. 2015 ). The NIR and MIR bands are recommended because they are the best bands for revealing the features of fire. Vlassova et al. examined the NDVI time series acquired from Landsat TM to assess the consequences of the Caceres fire and post-fire vegetation in the area (Vlassova et al. 2014). Mallinis et al. analyzed the relationship between field-based indices and indices generated from Sentinel-2 and Landsat-8 (Mallinis, Mitsopoulos, and Chrysafi 2018 ). ThedNBR of Sentinel-2, when compared to Landsat-8, showed the greatest association with in-situ based analysis among the indices. Quintano et al. used Landsat-8 and Sentinel-2 to assess the severity of burns using dNBR, Relativized Burn Ratio (RBR), and Relative dBNR (RdNBR) (Quintano, Fernández-Manso, and Fernández-Manso 2018). The Pléiades image was utilized as a benchmark, and it was determined that RdNBR based on Landsat had the highest accuracy. Using data from the Landsat-8, Sentinel-2, and Deimos-1 satellites, Garcia-Llamas et al. examined a forest fire that took place in August 2017 in the mountain range of Cabrere in northwest Spain. They used reflecting, thermal, and mixed indexes on the images and found that NBR-based indices had a greater association with the site, vegetative cover, and soil burn severity assessments, whereas Sentinel-2 results were marginally better. In various studies, the object-based image classification technique has also been used to identify areas that have been damaged by forest fires. Mitri and Gitas utilized high-resolution Ikonos information to identify areas affected by fire and came to the conclusion that the canopy density most significantly impacted accuracy, which was 87% overall and had a 0.74 kappa coefficient (Mitri and Gitas 2006). Using SPOT images, Polychronaki and Gitas demonstrated good accuracy or about 0.86 kappa coefficient (Polychronaki and Gitas 2012). For the evaluation of burn severity, Dragozi et al. used high-resolution images. They calculated that the two datasets had approximate accuracies of 72% and 82% using GeoEye images (Dragozi et al. 2015 ). The most reliable method for areas with varied tundra cover was found to be dGEMI, according to a recent study that used ten indices to measure the severity of fires on Landsat data. The main objective of this research is to utilize satellite (Sentinel-2 and MODIS) data to evaluate and measure the extent of post-fire burn severity in a particular region. Additionally, the study seeks to analyze the impact of burn severity on vegetation cover in District Sherani, Balochistan Province, Pakistan. 2. Methods 2.1. Study area Sherani district located between 69°31'53"-70°02'55" E longitudes and 31°16'44"-32°04'15" N latitudes (Fig. 1 ). Prior, Sherani was given District status in 2006 before it was part of District Zhob. There is total 1 Tehsil and 7 Union Councils. The district is bordered on the north by South Waziristan, the east by Dera Ismail Khan, the southeast by Musakhel, the south and west by Zhob, and the northwest by the Paktika Province of Afghanistan. It is part of Balochistan Xeric Woodlands ecoregion. The terrain is a mix of lofty hills, valleys and water channels. Its elevation varies from 678–3,356 meters above Mean Sea Level. It is part of the Deserts and Xeric Shrub lands biome. This region has no Intact Forest. The total area is 4,310 km². The majority of the region has semi-arid climate with cold temperatures. Summer weather is hot and dry. The mean maximum and minimum temperatures in January are about 11.5 and 1.9°C, making it the coldest month, The warmest month is July, with average high and low temperatures of about 36.7 and 21.8°C, respectively. Rainy season is mostly in the months of June, July and August and annual rainfall is usually less than 150 mm, falling during the southwest monsoon from June to September. Figure 1 shows the location of the affected area of forest fire event 2022 in Tehsil Dhansar, Sherani district on the map of Pakistan. In Balochistan the peak fire season typically begins in early April and lasts around 15 weeks (Pro, Watcher, and Atlases 2022), and it is also reported that fires were responsible for 100% of tree cover loss in Balochistan between 2001 and 2011. 2.2. Materials This section presents the normalized burn ratio index for the detection of burnt areas in Sentinel-2 multispectral images and outlines the methodological approach that was employed. Sentinel-2, a multi-spectral imaging mission with wide coverage and high resolution, plays a crucial role for Land Monitoring. It captures data in 13 spectral bands, four bands at 10 meters, six bands at 20 meters and three bands at 60 meters spatial resolution. It uses near-infrared (NIR) and shortwave-infrared (SWIR) wavelength for computing Normalized Burn Ratio (NBR) and found advantageous as a phenological indicator, and because its response to various other types of ground cover is simple to interpret (Alcaras et al. 2022 ; Delcourt et al. 2021; Morresi et al. 2022 ). Table 1 Satellite images specification Datasets Sentinel 2 Data Acquisition Date Pre-forest fire image: May 3, 2022 Post-forest fire image: June 22, 2022 Tiles T42RWV T42SWV Spatial resolution B4 10m; NIR 10 m; B8A 20 m; B12 20 m Bands B4-Red, B8-NIR, B8AVegetation Red, and B12-SWIR In this study, Sentinel-2 images acquired on May 3, 2022, and June 22, 2022, as listed in Table 1 , were utilized. Vegetation images captured before the forest fire event (on May 13, 2022) and immediately one month later (on June 22, 2022) were also examined to assess the impact of the fire. Furthermore, the Active fires and fire radiative power (FRP) data used in this study was downloaded from the GFW website (data.globalforestwatch.org/) from Jan 2012- June 2022. This data of VIIRS active fires data (VNP14IMGT) is an open access data, provided by FIRMS (Fire Information for Resource Management System). This datasets only includes the point reference data unlike sentinel images and helps to see the trend over time (Weisse 2019 ; Berhad 2022 ). FIRMS identifies global fire locations, and it provides near real time data and imagery site, every 15 minutes. It is launched by NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) and NOAA-20 satellites at both resolutions 375m and 750 m in October 2011. It flies in sun-synchronous orbit. It provides global coverage every 12 hours. The VIIRS 375 m data are comprised of five distinct single-gain channels extending from the visible to thermal infrared spectral region. VIIRS channel I4 is the primary driver of the fire detection algorithm presented in this study. The spectral response of Channel I4 (ranging from 3.55 to 3.93 µm, centered at 3.74 µm) spans the wavelengths of peak spectral radiance for blackbodies emitting at temperatures between 737 and 817 K. Each fire alert has a confidence value of low, nominal, or high to help users gauge the quality of individual hotspot /fire pixels. Altogether, 59 such fire points were overlaid on the study area to find out the hotspots of forest fires and three-month interval data is selected for each point. 2.3. Methodology The methodological framework for current study designed to find the severity of the forest fire event in Tehsil Dhansar, District Sherani, Balochistan, its effect on vegetation loss and hotspot areas of forest fire in selected area of interest (Fig. 2 ). For identification of assessment of burnt area, the study utilizes pre- and post-forest fire Sentinel-2 satellite images of the Sherani district. Tiles T42RWV and T42SWV with bands B4-Red, B8-NIR, B8A-Vegetation Red, and B12-SWIR have been used as used earlier in previous research studies. By mosaicking different raster datasets together, a single raster dataset is created for further analysis. Then AOI (area of interest) has been clipped. Several indices (normalized difference vegetation index, ratio vegetation index, and Green-Red vegetation index etc) obtained from aerial or remote sensing imagery are very simple and effective methods for quantitative and qualitative evaluations of vegetation cover and associated features among other applications (Xiao et al. 2003 ; Escuin, Navarro, and Fernández 2008; Guerschman et al. 2015 ). Such indices have been widely used in research studies related to agronomy and forestry (Ryu et al. 2018 ; Segarra et al. 2020 ). In this study, NBR, and dNBR were performed as spectral indices of forest fire. To compute NBR, Bands 8A near-infrared (NIR) and Band 12 shortwave-infrared (SWIR) wavelengths of electromagnetic spectrum of sentinel satellite image are used. Recent studies have concluded that using short-wave infrared and thermal bands to analyze burn severity provides highest accuracies. This computes the burnt areas in fire zones in square km and spectral characteristic of vegetation were analyzed immediately prior to the fire and few days later for observing the burned vegetation. NBR is calculated as shown in Eq. 1 (Key, Benson, and system 2006). NBR = \(\frac{\text{N}\text{I}\text{R} - \text{S}\text{W}\text{I}\text{R}}{\text{N}\text{I}\text{R} + \text{S}\text{W}\text{I}\text{R}}\) (1) Where, SWIR = Short-wavelength infrared reflectance; NIR = near-infrared reflectance. Later, difference of pre and post NBR images was computed to observe burn severity. In this study, the dNBR value can be more useful than the NBR alone to determine what is burnt as it shows change from the baseline state. A burnt area will have a positive dNBR value while an unburnt area will have a negative dNBR value or a value close to zero. dNBR is calculated as shown in Eq. 2 . dNBR = NBR pre-fire- NBR post-fire (2) Among the indices used in the study, the pixels in the NBR and dNBR take values between − 1 and + 1. Furthermore, it is categorized into five classes for the mentioned period and location including; (a) Enhanced Regrowth High Post Fire; (b) Enhanced Regrowth Low Post Fire; (c) Unburnt; (d) Low Severity and (e) Moderate to Low Severity class. The classification table below is used to classify the difference raster according to the severity of the burn (Athanasakis, Psomiadis, and Chatziantoniou 2017). These five classes are defined for achieving a better insight into the extent of the burnt area and to compare as accurately as possible (details in Table 2 ). Table 2 Standardized Classes of dNBR on basis of pixel radiance value SEVERITY LEVEL dNBR RANGE Enhanced Regrowth .66 For the assessment of vegetation cover change, the reflectance in the red and near-infrared bands determines the NDVI as shown in Eq. 3 and which is commonly utilized in landscape research studies (Hussain et al. 2022 ; Hussain and Karuppannan 2023). NDVI = \(\frac{\text{N}\text{I}\text{R} - \text{R}}{\text{N}\text{I}\text{R} + \text{R}}\) (3) Where, R = Red band reflectance, NIR = Near-infrared band reflectance. The NDVI approach provides a quantitative evaluation of the changes to the vegetative cover in 2022. Hotspot areas are identified through Active fires and fire radiative power (FRP) using the Getis-OrdGi* hotspot analysis in ArcGIS. Gi* statistics consider every aspect of fire occurrence points under analysis in the perspective to their neighbour values (Scott and Warmerdam 2005). To achieve this objective, the previously mentioned VIIRS data is employed. 3. Results The study reveals forest fire hotspots and burnt areas from Jan 2002 to June 2022, providing insights into the impact of fires on vegetation cover in the study area. Forest fire hotspots. The Getis-Ord Gi* method is utilized to generate a GI-ZScore map, effectively revealing both hot and cold spots of forest fire activity across the study area. Evaluating the spatial pattern of forest fires holds paramount importance as it aids in prevention, mitigation, and control of fire incidents, safeguarding the environment and human communities. Remarkably, the data analysis exposes intriguing trends in fire alerts. Notably, the years 2013 and 2022 witnessed the highest number of reported fire alerts, peaking at around 25 alerts in the selected area. Surprisingly, all 25 alerts from 2013 were later identified as false positives. However, the same 25 alerts in 2022 generated 7 positive alerts, corroborated with a 90 % confidence interval, exemplifying the emerging threat of forest fire events in recent times. Furthermore, the study unveils a gradual increase in the number of positive fire alerts by 0.16 % over time, as evident from the data and depicted in Figure 3 . This upward trend in positive alerts suggests a growing awareness and efficiency in identifying and reporting fire incidents, crucial in ensuring timely intervention and minimizing fire-induced damages to the ecosystem and communities. The spatial pattern of hotspots presented in map below is based on 59 fire alert spots. This pattern is showing statistically significant locations of forest fire alerts with the help of z -score. A minus value of z-score indicates cold spots, while positive value means hotspots. In Figure 4 , the red and blue spots indicate statistically significant hotspot and cold spot respectively. Normalized burnt Ratio (NBR). The study's results, as determined by the Normalized Burn Ratio (NBR), enable the identification of burned areas in Tehsil Dhansar, District Sherani, Balochistan. The NBR serves as a measure of burn severity, allowing for comparisons before and after the catastrophic blaze. Visual inspection of the NBR map, presented in Figure 5 , allows for an assessment of burned areas. As previously discussed in section 5, a higher NBR value signifies healthy vegetation, while a lower value indicates bare ground or recently burnt areas. Non-burnt areas are typically associated with values close to zero. By comparing the image, it becomes evident that some burned areas exist, as radiance values range prominently from -0.3 to -0.2, as highlighted within the blue box. Delta normalized burnt ratio. This section is computed to assess the burn severity and its extent in Tehsil Dhansar, District Sherani Balochistan. As previously mentioned that higher value of dNBR indicates more severe damage, while areas with negative dNBR values may indicate regrowth following a fire Results of dNBR. Values of radiance of dNBR varied from -0.4 to 0.3. Further classes of dNBR are shown in Table 3 : The study's findings revealed that areas of moderate to low severity, accounting for approximately 0.001 % (0.68 hectares), were affected by the forest fire event. This small %age of damage is significant considering because the total forest area in District Sherani is only 3.6 %. Furthermore, the forest fire resulted in approximately 0.03 % (22.92 hectares) of low severe damage within the District Sherani's total area. Notably, forest fires can also positively impact forest productivity. To explore this, two distinct classes were defined: enhanced regrowth high post-fire and enhanced regrowth low post-fire areas ( Figure 6 ). The former class, covering 133.08 hectares, represents areas where vegetation exhibited rapid regrowth after the fire. Meanwhile, the latter class, encompassing 2689.6 hectares (as stated in table 4 ), illustrates areas with enhanced but relatively lower post-fire vegetation regrowth. These findings provide valuable insights into the effects of forest fires on forest productivity in the study area. 6.3. Normalized difference Vegetation Index (NDVI) The NDVI serves as a measure of green vegetation production, detecting changes in vegetation over time. Lower NDVI values are typically observed in areas with less vegetation, likely due to higher soil reflection, resulting in low near-infrared (NIR) values and high red values. The study's analysis, presented in Table 4 and Figure 7 , indicates that the area of bare soil increased by 4 %, expanding from 9029 hectares in May to 9391 hectares in June 2022. Conversely, dense vegetation decreased by 0.08 %, while the area of sparse vegetation increased from 18.29 hectares to 60.2 hectares. These findings align with the dNBR results mentioned in the previous section (6.3), indicating a notable growth of low vegetation after the forest fire. Interestingly, the study concludes that forest fires can have beneficial effects on productivity. They can clear low-growing underbrush, remove debris from the forest surface, and nourish the soil by allowing sunlight to reach it. If managed wisely, fire can be utilized as a means of forestry management, promoting healthier vegetation growth. Table 4: Retrieved statistics of vegetation landcover comparison of area change pre and post fire event 2022 Landcover Classes Area (hectare) May 2022 Area (hectare) June 2022 Bare soil 9029.16 9391.9 Dense vegetation 456061 455656 Sparse vegetation cover 18.29 60.2 (Source: ArcGIS Desktop: Release 10.8.1. Redlands, CA: Environmental Systems Research Institute) 4. Discussion The present study focusses on forest fire hotspots from 2002 to 2022 and offers valuable insights into the dynamics of fire activity within the study area. By employing the Getis-Ord Gi* method to generate a GI-ZScore map, the research effectively delineates both hot and cold spots of forest fire activity. The finding of the study shows the fluctuating trend in fire alerts over the years. However, the study reveals a gradual increase in the number of positive fire alerts over time. Some previous studies presented arguments in the findings that the rise in reported fire incidents may not solely reflect an increase in actual fire occurrence but could also be attributed to factors such as changes in monitoring technology, reporting protocols, and human behavior (Bowman et al. 2009). One another study claimed biases by employing satellite-based detection systems, particularly in remote or inaccessible areas where ground validation is limited (Tansey et al. 2004). Conclusion According to the Burn severity ratio map, computed by dNBR index, it is understood that very small region is affected by forest fire event 2022 however this area is significant due to having only 3.6 % forest in District Sherani, Balochistan. This region comprised of Koh-e-Sulaiman, is also known for being the world’s largest chilghoza (pine nut) forest on higher elevations. The 26,000-hectare forest produces around 640,000 kilograms of chilghozas annually. The forest range is home to pine trees dating back to 1,500 years. Region is famous for its wildlife, environmental and agricultural products. People relay on the forest for their livelihoods in the region. Thus, this assessment is significant to combat the further damages in near future to existing biodiversity conservation. The results of this study conducted in a region which is primarily characterized by dry deciduous forests and juniper forests, are well suited to arid and semi-arid environments and have a greater significant potential for similar landscapes across the globe. Based on the study's results that indicate low to medium burn severity levels in only 0.03 % of the area during forest fire events, along with the observation that the forest remains dominant in the region, and the frequency of fire events is increasing by 1.6 % annually, the subsequent recommendations and future work i.e. implementation of a long-term and continuous monitoring program to track changes in forest burn severity, vegetation cover, and fire occurrence patterns, development of effective fire prevention strategies, including awareness campaigns, firebreaks, controlled burning, and early warning systems. Moreover, future trajectories could also include conducting a thorough evaluation of the ecological consequences of forest fires, encompassing their impacts on flora, fauna, soil, and water quality. This research endeavor is also to comprehensively address several Sustainable Development Goals (SDGs), thereby contributing to the overarching framework of sustainable development. Specifically, it seeks to align with SDGs 13, which pertains to Climate Action, by actively participating in initiatives aimed at mitigating the adverse environmental effects of forest fires. Furthermore, this study aligns with SDGs, concentrating on the preservation, rejuvenation, and sustainable utilization of terrestrial ecosystems, notably forests, in an effort to safeguard these vital ecosystems from the detrimental consequences of forest fires. Furthermore, this research project is closely linked to SDGs 11, which highlights the importance of making communities stronger so they can better handle and reduce the various problems caused by forest fires on the environment and people's lives. Moreover, this research aligns with SDGs 17, which emphasizes collaboration toward shared objectives. This involves establishing strong bonds and alliances among governments, researchers, nonprofit organizations. These sectors play a crucial role in developing effective strategies and disseminating knowledge to collectively achieve our common goals of preventing and responding to forest fires. 10. Availability of data and materials The data sets analysed during the current study available from the corresponding author on reasonable request. Declarations Competent Interest The authors declare that they have no competing interests. Author Contribution declaration A.S. and A.K. devised the project, the main conceptual ideas and the proof outline. A.S. and S.Z. analyzed the results and aided in interpreting the results and worked on the manuscript. M.K. contributed to the writing of the manuscript. M.J., M.R., L.A.W and Q.A. reviewed the findings of this work and Q.A. led the overall project team. All authors reviewed the manuscript. 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Scanning the Present and Resolving the Future. Proceedings. IEEE 2001 International Geoscience and Remote Sensing Symposium (Cat. No. 01CH37217). Ryu, Jae-Hyun, Kyung-Soo Han, Sungwook Hong, No-Wook Park, Yang-Won Lee, and Jaeil Cho. 2018. "Satellite-based evaluation of the post-fire recovery process from the worst forest fire case in South Korea." Remote Sensing 10 (6): 918. Scott, Lauren, and Nathan Warmerdam. 2005. "Extend crime analysis with ArcGIS spatial statistics tools." ArcUser Online April-June . Segarra, Joel, Maria Luisa Buchaillot, Jose Luis Araus, and Shawn C Kefauver. 2020. "Remote sensing for precision agriculture: Sentinel-2 improved features and applications." Agronomy 10 (5): 641. Smith, Alistair MS, Jan UH Eitel, and Andrew T Hudak. 2010. "Spectral analysis of charcoal on soils: Implicationsfor wildland fire severity mapping methods." International Journal of Wildland Fire 19 (7): 976-983. Soverel, Nicholas O, Daniel DB Perrakis, and Nicholas C Coops. 2010. "Estimating burn severity from Landsat dNBR and RdNBR indices across western Canada." Remote Sensing of Environment 114 (9): 1896-1909. Tansey, Kevin, Jean-Marie GrÉgoire, Elisabetta Binaghi, Luigi Boschetti, Pietro Alessandro Brivio, Dmitry Ershov, StÉphane Flasse, Robert Fraser, Dean Graetz, and Marta %J Climatic Change Maggi. 2004. "A global inventory of burned areas at 1 km resolution for the year 2000 derived from SPOT VEGETATION data." 67: 345-377. Tariq, Aqil, Yan Jiango, Linlin Lu, Ahsan Jamil, Ibrahim Al-ashkar, Muhammad Kamran, and Ayman El Sabagh. 2023. "Integrated use of Sentinel-1 and Sentinel-2 data and open-source machine learning algorithms for burnt and unburnt scars." Geomatics, Natural Hazards and Risk 14 (1): 2190856. https://doi.org/10.1080/19475705.2023.2190856. https://doi.org/10.1080/19475705.2023.2190856. Tariq, Aqil, Hong Shu, Alexandre S. Gagnon, Qingting Li, Faisal Mumtaz, Artan Hysa, Muhammad Amir Siddique, and Iqra Munir. 2021. "Assessing Burned Areas in Wildfires and Prescribed Fires with Spectral Indices and SAR Images in the Margalla Hills of Pakistan." 12 (10): 1371. https://www.mdpi.com/1999-4907/12/10/1371. Tariq, Aqil, Hong Shu, Saima Siddiqui, Iqra Munir, Alireza Sharifi, Qingting Li, and Linlin Lu. 2022. "Spatio-temporal analysis of forest fire events in the Margalla Hills, Islamabad, Pakistan using socio-economic and environmental variable data with machine learning methods." Journal of Forestry Research 33 (1): 183-194. https://doi.org/10.1007/s11676-021-01354-4. https://doi.org/10.1007/s11676-021-01354-4. Verbyla, David L, Eric S Kasischke, and Elizabeth E Hoy. 2008. "Seasonal and topographic effects on estimating fire severity from Landsat TM/ETM+ data." International Journal of Wildland Fire 17 (4): 527-534. Vlassova, Lidia, Fernando Pérez-Cabello, Marcos Rodrigues Mimbrero, Raquel Montorio Llovería, and Alberto García-Martín. 2014. "Analysis of the relationship between land surface temperature and wildfire severity in a series of landsat images." Remote Sensing 6 (7): 6136-6162. Weisse, Mikaela. 2019. "What Can Global Forest Watch Tell Us About the Fires in Brazil?". Xiao, Xiangming, Bobby Braswell, Qingyuan Zhang, Stephen Boles, Stephen Frolking, and Berrien Moore III. 2003. "Sensitivity of vegetation indices to atmospheric aerosols: continental-scale observations in Northern Asia." Remote Sensing of Environment 84 (3): 385-392. Table Table 3 is available in the Supplementary Files section. Supplementary Files Table3.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-4091965","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":282360583,"identity":"3238f7ad-a711-490f-8039-c923370bb57e","order_by":0,"name":"Aqsa Shabbir","email":"","orcid":"","institution":"Lahore College for Women University","correspondingAuthor":false,"prefix":"","firstName":"Aqsa","middleName":"","lastName":"Shabbir","suffix":""},{"id":282360584,"identity":"ecfbffd4-6c86-4a95-9e27-8a859c1fe50d","order_by":1,"name":"Sahar Zia","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6UlEQVRIiWNgGAWjYFACHijJ3twAETjAwMDYQJQWnoMkamFgkEgkUot8+9mDnysY6mT4Zz5s/MzbxiDHdyOB7eEMPFoMzuQlS55hYOORuJ3YLA3UYix5I4HdcAM+LQw5BpINDDw8DLcT25hz2xgSNwBtkXyAz2H9b4x/NjBI8MjfPAjWUk9QC8ONHDOgLQY8BjcYwVoSDEBa8DrsxhszywaDBB7DM0C//DknYTjzzMN2Q3zel+/PMb7ZUFFnL3f88MGPM8ps5PmOJx972IPPYRC74CwJIGZsI6gBA7CRrmUUjIJRMAqGMwAAQZxKoNGQHp4AAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-0629-5423","institution":"Lahore College for Women University","correspondingAuthor":true,"prefix":"","firstName":"Sahar","middleName":"","lastName":"Zia","suffix":""},{"id":282360585,"identity":"9e212130-5a86-42b6-911c-8970e6576e05","order_by":2,"name":"Ali Hussain Kazim","email":"","orcid":"","institution":"University of Engineering and Technology","correspondingAuthor":false,"prefix":"","firstName":"Ali","middleName":"Hussain","lastName":"Kazim","suffix":""},{"id":282360586,"identity":"036a6f41-d1a2-4be0-842f-5aa508ca4eb6","order_by":3,"name":"Mumraiz Kasi","email":"","orcid":"","institution":"Balochistan University of Information Technology Engineering and Management Sciences","correspondingAuthor":false,"prefix":"","firstName":"Mumraiz","middleName":"","lastName":"Kasi","suffix":""},{"id":282360587,"identity":"6d8fc309-2262-4c17-b388-eda0b6b8ede1","order_by":4,"name":"Muhammad Ali Jamshed","email":"","orcid":"","institution":"University of Glasgow","correspondingAuthor":false,"prefix":"","firstName":"Muhammad","middleName":"Ali","lastName":"Jamshed","suffix":""},{"id":282360588,"identity":"3e1a10e6-252a-49e7-ab7b-31cc85d7878a","order_by":5,"name":"Liaqat Ali Waseem","email":"","orcid":"","institution":"Government College University Faisalabad","correspondingAuthor":false,"prefix":"","firstName":"Liaqat","middleName":"Ali","lastName":"Waseem","suffix":""},{"id":282360589,"identity":"2b7770df-978b-41c7-9ea1-e5f8620e4e54","order_by":6,"name":"Naveed Iqbal","email":"","orcid":"","institution":"Lahore College for Women University","correspondingAuthor":false,"prefix":"","firstName":"Naveed","middleName":"","lastName":"Iqbal","suffix":""},{"id":282360590,"identity":"2d6e2ea1-ea00-49ef-b9b7-56baf8164947","order_by":7,"name":"Qammer H Abbasi","email":"","orcid":"","institution":"University of Glasgow","correspondingAuthor":false,"prefix":"","firstName":"Qammer","middleName":"H","lastName":"Abbasi","suffix":""},{"id":282360591,"identity":"576fbe29-9151-4a4c-8694-8950a3e609b3","order_by":8,"name":"Masood Ur-Rehman","email":"","orcid":"","institution":"University of Glasgow","correspondingAuthor":false,"prefix":"","firstName":"Masood","middleName":"","lastName":"Ur-Rehman","suffix":""}],"badges":[],"createdAt":"2024-03-13 10:40:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4091965/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4091965/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":53500499,"identity":"f9f4a684-a1f9-407d-b459-eb7c1d003a4e","added_by":"auto","created_at":"2024-03-26 18:11:54","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":974812,"visible":true,"origin":"","legend":"\u003cp\u003eMap of area of interest, Tehsil Dhansar, District Sherani, Balochistan-Pakistan (Source: ArcGIS Desktop: Release 10.8.1. Redlands, CA: Environmental Systems Research Institute)\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4091965/v1/5616018dbeb3c4c59c446fc9.png"},{"id":53501456,"identity":"71b28934-0e4d-44b6-a13f-90bd0713f07e","added_by":"auto","created_at":"2024-03-26 18:19:55","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1108593,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMethodological framework\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4091965/v1/1b46a3217ba47e5a3a9a1350.png"},{"id":53500500,"identity":"9fabeee5-4166-42c3-9f3a-0315a56045de","added_by":"auto","created_at":"2024-03-26 18:11:55","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":65235,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eForestfire reported by NASA's Fire Information for Resource Management System (FIRMS)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(Source: ArcGIS Desktop: Release 10.8.1. Redlands, CA: Environmental Systems Research Institute)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4091965/v1/b658e4b9b7da08ac9db05f19.png"},{"id":53500502,"identity":"efe3269e-8da4-4399-88e7-17b86a547cbd","added_by":"auto","created_at":"2024-03-26 18:11:55","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":990351,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHotspot analysis showing areas of frequent forest fire on basis of 2012-2022 datasets\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData source: NASA's Fire Information for Resource Management System (FIRMS)\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4091965/v1/fdfccfe1db1383ecae53d53e.png"},{"id":53500504,"identity":"351a6f32-cab7-41ea-889d-98f14b1ddf20","added_by":"auto","created_at":"2024-03-26 18:11:55","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1836157,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparison between NBR_before (left) and NBR_After (right) post-burn\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(Source: ArcGIS Desktop: Release 10.8.1. Redlands, CA: Environmental Systems Research Institute)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-4091965/v1/bb66d90647d42760d94dd81b.png"},{"id":53500505,"identity":"74ef3b08-1517-4216-960d-498cf5822758","added_by":"auto","created_at":"2024-03-26 18:11:55","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":659788,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDelta Normalized Burnt Ratio (dNBR) of pre and post fire event 2022 at Tehsil Dhansar, District Sherani, Balochistan\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(Source: ArcGIS Desktop: Release 10.8.1. Redlands, CA: Environmental Systems Research Institute)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-4091965/v1/04ef7cb22e27c6b61b2a5b66.png"},{"id":53500501,"identity":"d33534e4-05e7-460c-81c0-f67be14d325d","added_by":"auto","created_at":"2024-03-26 18:11:55","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1508776,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNormalized Differnnce Vegetation Index (NDVI) of pre and post fire event 2022 at Tehsil Dhansar, District Sherani, Balochistan\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(Source: ArcGIS Desktop: Release 10.8.1. Redlands, CA: Environmental Systems Research Institute)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-4091965/v1/b0aa6862dff34b96d61111b1.png"},{"id":57163404,"identity":"e0d4bd7f-3ded-4a85-9c11-be0b93df107b","added_by":"auto","created_at":"2024-05-26 14:37:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":9106433,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4091965/v1/2a2031c5-19e2-49e9-b3ce-6f380c14b6e1.pdf"},{"id":53500498,"identity":"f992a139-9e0a-4130-8fbd-4655ff534646","added_by":"auto","created_at":"2024-03-26 18:11:54","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":13307,"visible":true,"origin":"","legend":"","description":"","filename":"Table3.docx","url":"https://assets-eu.researchsquare.com/files/rs-4091965/v1/76c6fe1a962a80dbb323ca3e.docx"}],"financialInterests":"","formattedTitle":"Detecting Post-fire Burn Severity Level using Sentinel-2 and MODIS Satellite data","fulltext":[{"header":"1. Background","content":"\u003cp\u003eForest fires are a common and threatening source of disturbance for ecosystems. They often occur due to extreme dry climatic conditions, posing significant risks. Forest fire events lead to substantial economic losses, environmental degradation, and various challenges, such as the loss of nutrients and ground microorganisms. Additionally, they adversely affect social life, animals, and plants (Guan et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Ryu et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRegarding Pakistan, Global Forest Watch reports a loss of 5.46 thousand hectares (kha) of tree cover due to fires between the years 2011 and 2021(Pro, Watcher, and Atlases 2022). Additionally, according to the Germanwatch Global Climate Risk Index, Pakistan has been ranked as the eighth most vulnerable country to severe weather events related to climate change, including an increase in forest fire alerts, especially in the provinces of Khyber Pakhtunkhwa (KPK) and Balochistan (Eckstein et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Many fire events have been studies earlier in Marghala Hill, Pakistan (Tariq et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Tariq et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Tariq et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In the Sherani District, this region is home to the oldest Chalghoza (Pine Nut) forest, dating back 1,500 years in Dhansar. Similarly, District Ziarat in Balochistan is renowned for the world's oldest juniper forest, which dates back 5,000 years. Thus, destruction of the unique aspects of the existing biodiversity, need urgent attention. This research problem would prompt the study to focus on understanding the causes and drivers of biodiversity loss, identifying the specific unique aspects of biodiversity at risk, exploring the potential consequences of their destruction, and proposing urgent and effective strategies for conservation and management to preserve these unique elements for future generations. As a large area of Balochistan suffered from a prolonged drought period, a forest fire began on May 11, 2022, in the Suleiman range forests of Sherani and Musakhail districts, located in the northwest of Balochistan Province(ReliefWeb \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e). According to Pakistan Meteorological Department (PMD), the climate of this area is hot and dry in summer (ReliefWeb). On May 18, it began quickly spreading by destroying a significant portion of the old pine nut and olive forest. It covered an area of about 30 square kilometers. Despite several days of effort until 22 May 2022, the forest fire could not be put out (ReliefWeb \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022b\u003c/span\u003e). The fire burned for more than two weeks. Wildfires' devastating impacts resulted in fatalities and significant burn injuries. Moreover, about 4,000 people were relocated with their families. About 35% of this precious pine nut forest (a UNESCO World Heritage Site) had been damaged by the end of May (ReliefWeb \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022b\u003c/span\u003e). Therefore, this study presents burn severity levels of forest fire event in Dhansar, District Sherani, Balochistan.\u003c/p\u003e \u003cp\u003eTo assess the severity of a fire and determine its temporal dynamics, image classification and fire index methodologies are commonly used. The soil-adjusted vegetation index (Hoy et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Smith, Eitel, and Hudak 2010), burned area index (Marino et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), differenced Normalized Burn Ratio, and Tasselled-cap brightness and greenness transformations (Epting, Verbyla, and Sorbel 2005), are a few of the reflectance indices that have been developed and analyzed. The most popular index, differenced Normalized Burn Ratio (dNBR), has been shown to accurately depict the spatial variation in intensity in particular fire, across various types of vegetation (Soverel, Perrakis, and Coops 2010). Accuracy can be increased, especially for higher severity classes in varied environments, by relativizing the dNBR with the pre-fire NBR (RdNBR) (Miller et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). A few national fire severity mapping algorithms have been developed using NBR severity classification (Eidenshink et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Therefore, given the impact of pre-fire vegetation structure, type of soil, and vegetative dampness on the NBR, classifying dNBR or RdNBR into standard intensity classes that seem to be constant between fires throughout the terrain could be challenging (Kolden, Smith, and Abatzoglou 2015). Multiple indices combined can offer more accurate and comprehensive information than any index used alone.\u003c/p\u003e \u003cp\u003eFor remote sensing change detection approaches, such as fire severity mapping, regions with a forest canopy and significant topographic relief coupled with topographical shadows are recognized to present difficulties (Lydersen et al. 2016). Because the reflectivity signal is masked by a canopy or topographical shades, this might be especially difficult when mapping the burned understory of low severity categories (Lydersen et al. 2016). Areas with steep north-facing perspectives continued to show less pre-fire NBR than some other aspects, due to the direction of the sun angle, according to previous topographical impacts on dNBR that have been observed (Verbyla, Kasischke, and Hoy 2008). Therefore, with suitable image pre-processing modifications for normalized reflectivity, air attenuation, and electromagnetic radiation scattering effects are significantly diminished in topographically complicated regions (Ediriweera et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Flood et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Moreover, it has been demonstrated that spectral un-mixing assessments reduce the influence of topology on the accuracy of classifying fire intensity (Rogan and Franklin \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). To interpret and apply remotely sensed fire severity indices correctly in land management, it is crucial to comprehend how they behave across various landscape conditions.\u003c/p\u003e \u003cp\u003eUsing the SPOT-4 satellite's pre-fire and post-fire images, Xiao et al. (Xiao et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) applied the NDVI and EVI fire indices to the area to investigate how a fire in North Asia affected plant indices. They investigated variations in carbon emission, biomass loss, and ash production by comparing the changes in such image indexes. Therefore, they generated the fire threshold values, which they then used to calculate the fire intensity. On Landsat satellite images, Escuin et al. employed the NBR and NDVI indexes to evaluate three distinct fires that took place in Southern Spain's Cazorla, Nerva, and Anzalcollar regions between 1995 and 2001(Dragozi et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The NIR and MIR bands are recommended because they are the best bands for revealing the features of fire. Vlassova et al. examined the NDVI time series acquired from Landsat TM to assess the consequences of the Caceres fire and post-fire vegetation in the area (Vlassova et al. 2014). Mallinis et al. analyzed the relationship between field-based indices and indices generated from Sentinel-2 and Landsat-8 (Mallinis, Mitsopoulos, and Chrysafi \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). ThedNBR of Sentinel-2, when compared to Landsat-8, showed the greatest association with in-situ based analysis among the indices. Quintano et al. used Landsat-8 and Sentinel-2 to assess the severity of burns using dNBR, Relativized Burn Ratio (RBR), and Relative dBNR (RdNBR) (Quintano, Fern\u0026aacute;ndez-Manso, and Fern\u0026aacute;ndez-Manso 2018). The Pl\u0026eacute;iades image was utilized as a benchmark, and it was determined that RdNBR based on Landsat had the highest accuracy. Using data from the Landsat-8, Sentinel-2, and Deimos-1 satellites, Garcia-Llamas et al. examined a forest fire that took place in August 2017 in the mountain range of Cabrere in northwest Spain. They used reflecting, thermal, and mixed indexes on the images and found that NBR-based indices had a greater association with the site, vegetative cover, and soil burn severity assessments, whereas Sentinel-2 results were marginally better.\u003c/p\u003e \u003cp\u003eIn various studies, the object-based image classification technique has also been used to identify areas that have been damaged by forest fires. Mitri and Gitas utilized high-resolution Ikonos information to identify areas affected by fire and came to the conclusion that the canopy density most significantly impacted accuracy, which was 87% overall and had a 0.74 kappa coefficient (Mitri and Gitas 2006). Using SPOT images, Polychronaki and Gitas demonstrated good accuracy or about 0.86 kappa coefficient (Polychronaki and Gitas 2012). For the evaluation of burn severity, Dragozi et al. used high-resolution images. They calculated that the two datasets had approximate accuracies of 72% and 82% using GeoEye images (Dragozi et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The most reliable method for areas with varied tundra cover was found to be dGEMI, according to a recent study that used ten indices to measure the severity of fires on Landsat data. The main objective of this research is to utilize satellite (Sentinel-2 and MODIS) data to evaluate and measure the extent of post-fire burn severity in a particular region. Additionally, the study seeks to analyze the impact of burn severity on vegetation cover in District Sherani, Balochistan Province, Pakistan.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study area\u003c/h2\u003e \u003cp\u003eSherani district located between 69\u0026deg;31'53\"-70\u0026deg;02'55\" E longitudes and 31\u0026deg;16'44\"-32\u0026deg;04'15\" N latitudes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Prior, Sherani was given District status in 2006 before it was part of District Zhob. There is total 1 Tehsil and 7 Union Councils. The district is bordered on the north by South Waziristan, the east by Dera Ismail Khan, the southeast by Musakhel, the south and west by Zhob, and the northwest by the Paktika Province of Afghanistan. It is part of Balochistan Xeric Woodlands ecoregion. The terrain is a mix of lofty hills, valleys and water channels. Its elevation varies from 678\u0026ndash;3,356 meters above Mean Sea Level. It is part of the Deserts and Xeric Shrub lands biome. This region has no Intact Forest. The total area is 4,310 km\u0026sup2;. The majority of the region has semi-arid climate with cold temperatures. Summer weather is hot and dry. The mean maximum and minimum temperatures in January are about 11.5 and 1.9\u0026deg;C, making it the coldest month,\u003c/p\u003e \u003cp\u003eThe warmest month is July, with average high and low temperatures of about 36.7 and 21.8\u0026deg;C, respectively. Rainy season is mostly in the months of June, July and August and annual rainfall is usually less than 150 mm, falling during the southwest monsoon from June to September. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the location of the affected area of forest fire event 2022 in Tehsil Dhansar, Sherani district on the map of Pakistan. In Balochistan the peak fire season typically begins in early April and lasts around 15 weeks (Pro, Watcher, and Atlases 2022), and it is also reported that fires were responsible for 100% of tree cover loss in Balochistan between 2001 and 2011.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Materials\u003c/h2\u003e \u003cp\u003eThis section presents the normalized burn ratio index for the detection of burnt areas in Sentinel-2 multispectral images and outlines the methodological approach that was employed. Sentinel-2, a multi-spectral imaging mission with wide coverage and high resolution, plays a crucial role for Land Monitoring. It captures data in 13 spectral bands, four bands at 10 meters, six bands at 20 meters and three bands at 60 meters spatial resolution. It uses near-infrared (NIR) and shortwave-infrared (SWIR) wavelength for computing Normalized Burn Ratio (NBR) and found advantageous as a phenological indicator, and because its response to various other types of ground cover is simple to interpret (Alcaras et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Delcourt et al. 2021; Morresi et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\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\u003eSatellite images specification\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\u003eDatasets\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSentinel 2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eData Acquisition Date\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePre-forest fire image: May 3, 2022\u003c/p\u003e \u003cp\u003ePost-forest fire image: June 22, 2022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTiles\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT42RWV\u003c/p\u003e \u003cp\u003eT42SWV\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSpatial resolution\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB4 10m; NIR 10 m; B8A 20 m; B12 20 m\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBands\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB4-Red, B8-NIR, B8AVegetation Red, and B12-SWIR\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\u003eIn this study, Sentinel-2 images acquired on May 3, 2022, and June 22, 2022, as listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, were utilized. Vegetation images captured before the forest fire event (on May 13, 2022) and immediately one month later (on June 22, 2022) were also examined to assess the impact of the fire.\u003c/p\u003e \u003cp\u003eFurthermore, the Active fires and fire radiative power (FRP) data used in this study was downloaded from the GFW website (data.globalforestwatch.org/) from Jan 2012- June 2022. This data of VIIRS active fires data (VNP14IMGT) is an open access data, provided by FIRMS (Fire Information for Resource Management System). This datasets only includes the point reference data unlike sentinel images and helps to see the trend over time (Weisse \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Berhad \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). FIRMS identifies global fire locations, and it provides near real time data and imagery site, every 15 minutes. It is launched by NASA/NOAA Suomi National Polar-orbiting Partnership (Suomi NPP) and NOAA-20 satellites at both resolutions 375m and 750 m in October 2011. It flies in sun-synchronous orbit. It provides global coverage every 12 hours. The VIIRS 375 m data are comprised of five distinct single-gain channels extending from the visible to thermal infrared spectral region. VIIRS channel I4 is the primary driver of the fire detection algorithm presented in this study. The spectral response of Channel I4 (ranging from 3.55 to 3.93 \u0026micro;m, centered at 3.74 \u0026micro;m) spans the wavelengths of peak spectral radiance for blackbodies emitting at temperatures between 737 and 817 K. Each fire alert has a confidence value of low, nominal, or high to help users gauge the quality of individual hotspot /fire pixels. Altogether, 59 such fire points were overlaid on the study area to find out the hotspots of forest fires and three-month interval data is selected for each point.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Methodology\u003c/h2\u003e \u003cp\u003eThe methodological framework for current study designed to find the severity of the forest fire event in Tehsil Dhansar, District Sherani, Balochistan, its effect on vegetation loss and hotspot areas of forest fire in selected area of interest (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor identification of assessment of burnt area, the study utilizes pre- and post-forest fire Sentinel-2 satellite images of the Sherani district. Tiles T42RWV and T42SWV with bands B4-Red, B8-NIR, B8A-Vegetation Red, and B12-SWIR have been used as used earlier in previous research studies. By mosaicking different raster datasets together, a single raster dataset is created for further analysis. Then AOI (area of interest) has been clipped. Several indices (normalized difference vegetation index, ratio vegetation index, and Green-Red vegetation index etc) obtained from aerial or remote sensing imagery are very simple and effective methods for quantitative and qualitative evaluations of vegetation cover and associated features among other applications (Xiao et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Escuin, Navarro, and Fern\u0026aacute;ndez 2008; Guerschman et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Such indices have been widely used in research studies related to agronomy and forestry (Ryu et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Segarra et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In this study, NBR, and dNBR were performed as spectral indices of forest fire. To compute NBR, Bands 8A near-infrared (NIR) and Band 12 shortwave-infrared (SWIR) wavelengths of electromagnetic spectrum of sentinel satellite image are used. Recent studies have concluded that using short-wave infrared and thermal bands to analyze burn severity provides highest accuracies. This computes the burnt areas in fire zones in square km and spectral characteristic of vegetation were analyzed immediately prior to the fire and few days later for observing the burned vegetation. NBR is calculated as shown in \u003cb\u003eEq.\u0026nbsp;1\u003c/b\u003e(Key, Benson, and system 2006).\u003c/p\u003e \u003cp\u003eNBR = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{\\text{N}\\text{I}\\text{R} - \\text{S}\\text{W}\\text{I}\\text{R}}{\\text{N}\\text{I}\\text{R} + \\text{S}\\text{W}\\text{I}\\text{R}}\\)\u003c/span\u003e\u003c/span\u003e (1)\u003c/p\u003e \u003cp\u003eWhere, SWIR\u0026thinsp;=\u0026thinsp;Short-wavelength infrared reflectance; NIR\u0026thinsp;=\u0026thinsp;near-infrared reflectance.\u003c/p\u003e \u003cp\u003eLater, difference of pre and post NBR images was computed to observe burn severity. In this study, the dNBR value can be more useful than the NBR alone to determine what is burnt as it shows change from the baseline state. A burnt area will have a positive dNBR value while an unburnt area will have a negative dNBR value or a value close to zero. dNBR is calculated as shown in \u003cb\u003eEq.\u0026nbsp;2\u003c/b\u003e.\u003c/p\u003e \u003cp\u003edNBR\u0026thinsp;=\u0026thinsp;NBR pre-fire- NBR post-fire (2)\u003c/p\u003e \u003cp\u003eAmong the indices used in the study, the pixels in the NBR and dNBR take values between \u0026minus;\u0026thinsp;1 and +\u0026thinsp;1. Furthermore, it is categorized into five classes for the mentioned period and location including; (a) Enhanced Regrowth High Post Fire; (b) Enhanced Regrowth Low Post Fire; (c) Unburnt; (d) Low Severity and (e) Moderate to Low Severity class. The classification table below is used to classify the difference raster according to the severity of the burn (Athanasakis, Psomiadis, and Chatziantoniou 2017). These five classes are defined for achieving a better insight into the extent of the burnt area and to compare as accurately as possible (details in 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\u003eStandardized Classes of dNBR on basis of pixel radiance value\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\u003eSEVERITY LEVEL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003edNBR RANGE\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnhanced Regrowth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt; \u0026minus;\u0026thinsp;.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnburned\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.1 to +\u0026thinsp;.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow Severity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e+\u0026thinsp;.1 to +\u0026thinsp;.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate Severity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e+\u0026thinsp;.27 to +\u0026thinsp;.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh Severity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;.66\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\u003eFor the assessment of vegetation cover change, the reflectance in the red and near-infrared bands determines the NDVI as shown in \u003cb\u003eEq.\u0026nbsp;3\u003c/b\u003e and which is commonly utilized in landscape research studies (Hussain et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Hussain and Karuppannan 2023).\u003c/p\u003e \u003cp\u003eNDVI = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{\\text{N}\\text{I}\\text{R} - \\text{R}}{\\text{N}\\text{I}\\text{R} + \\text{R}}\\)\u003c/span\u003e\u003c/span\u003e (3)\u003c/p\u003e \u003cp\u003eWhere, R\u0026thinsp;=\u0026thinsp;Red band reflectance, NIR\u0026thinsp;=\u0026thinsp;Near-infrared band reflectance.\u003c/p\u003e \u003cp\u003eThe NDVI approach provides a quantitative evaluation of the changes to the vegetative cover in 2022.\u003c/p\u003e \u003cp\u003eHotspot areas are identified through Active fires and fire radiative power (FRP) using the Getis-OrdGi* hotspot analysis in ArcGIS. Gi* statistics consider every aspect of fire occurrence points under analysis in the perspective to their neighbour values (Scott and Warmerdam 2005). To achieve this objective, the previously mentioned VIIRS data is employed.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eThe study reveals forest fire hotspots and burnt areas from Jan 2002 to June 2022, providing insights into the impact of fires on vegetation cover in the study area.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eForest fire hotspots.\u0026nbsp;\u003c/strong\u003eThe Getis-Ord Gi* method is utilized to generate a GI-ZScore map, effectively revealing both hot and cold spots of forest fire activity across the study area. Evaluating the spatial pattern of forest fires holds paramount importance as it aids in prevention, mitigation, and control of fire incidents, safeguarding the environment and human communities.\u003c/p\u003e\n\u003cp\u003eRemarkably, the data analysis exposes intriguing trends in fire alerts. Notably, the years 2013 and 2022 witnessed the highest number of reported fire alerts, peaking at around 25 alerts in the selected area. Surprisingly, all 25 alerts from 2013 were later identified as false positives.\u003c/p\u003e\n\u003cp\u003eHowever, the same 25 alerts in 2022 generated 7 positive alerts, corroborated with a 90 % confidence interval, exemplifying the emerging threat of forest fire events in recent times. Furthermore, the study unveils a gradual increase in the number of positive fire alerts by 0.16 % over time, as evident from the data and depicted in \u003cstrong\u003eFigure 3\u003c/strong\u003e. This upward trend in positive alerts suggests a growing awareness and efficiency in identifying and reporting fire incidents, crucial in ensuring timely intervention and minimizing fire-induced damages to the ecosystem and communities.\u003c/p\u003e\n\u003cp\u003eThe spatial pattern of hotspots presented in map below is based on 59 fire alert spots. This pattern is showing statistically significant locations of forest fire alerts with the help of \u003cem\u003ez\u003c/em\u003e-score. A minus value of z-score indicates cold spots, while positive value means hotspots. \u0026nbsp;In \u003cstrong\u003eFigure\u0026nbsp;4\u003c/strong\u003e, the red and blue spots indicate statistically significant hotspot and cold spot respectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNormalized burnt Ratio (NBR).\u0026nbsp;\u003c/strong\u003eThe study\u0026apos;s results, as determined by the Normalized Burn Ratio (NBR), enable the identification of burned areas in Tehsil Dhansar, District Sherani, Balochistan. The NBR serves as a measure of burn severity, allowing for comparisons before and after the catastrophic blaze. Visual inspection of the NBR map, presented in \u003cstrong\u003eFigure 5\u003c/strong\u003e, allows for an assessment of burned areas.\u003c/p\u003e\n\u003cp\u003eAs previously discussed in section 5, a higher NBR value signifies healthy vegetation, while a lower value indicates bare ground or recently burnt areas. Non-burnt areas are typically associated with values close to zero. By comparing the image, it becomes evident that some burned areas exist, as radiance values range prominently from -0.3 to -0.2, as highlighted within the blue box.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDelta normalized burnt ratio.\u0026nbsp;\u003c/strong\u003eThis section is computed to assess the burn severity and its extent in Tehsil Dhansar, District Sherani Balochistan. As previously mentioned that higher value of dNBR indicates more severe damage, while areas with negative dNBR values may indicate regrowth following a fire Results of dNBR. Values of radiance of dNBR varied from -0.4 to 0.3. \u0026nbsp;Further classes of dNBR are shown in \u003cstrong\u003eTable 3\u003c/strong\u003e:\u003c/p\u003e\n\u003cp\u003eThe study\u0026apos;s findings revealed that areas of moderate to low severity, accounting for approximately 0.001 % (0.68 hectares), were affected by the forest fire event. This small %age of damage is significant considering because the total forest area in District Sherani is only 3.6 %.\u003c/p\u003e\n\u003cp\u003eFurthermore, the forest fire resulted in approximately 0.03 % (22.92 hectares) of low severe damage within the District Sherani\u0026apos;s total area. Notably, forest fires can also positively impact forest productivity. To explore this, two distinct classes were defined: enhanced regrowth high post-fire and enhanced regrowth low post-fire areas (\u003cstrong\u003eFigure 6\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eThe former class, covering 133.08 hectares, represents areas where vegetation exhibited rapid regrowth after the fire. Meanwhile, the latter class, encompassing 2689.6 hectares (as stated in \u003cstrong\u003etable 4\u003c/strong\u003e), illustrates areas with enhanced but relatively lower post-fire vegetation regrowth. These findings provide valuable insights into the effects of forest fires on forest productivity in the study area.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6.3. Normalized difference Vegetation Index (NDVI)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe NDVI serves as a measure of green vegetation production, detecting changes in vegetation over time. Lower NDVI values are typically observed in areas with less vegetation, likely due to higher soil reflection, resulting in low near-infrared (NIR) values and high red values.\u003c/p\u003e\n\u003cp\u003eThe study\u0026apos;s analysis, presented in \u003cstrong\u003eTable 4\u003c/strong\u003e and \u003cstrong\u003eFigure 7\u003c/strong\u003e, indicates that the area of bare soil increased by 4 %, expanding from 9029 hectares in May to 9391 hectares in June 2022. Conversely, dense vegetation decreased by 0.08 %, while the area of sparse vegetation increased from 18.29 hectares to 60.2 hectares. These findings align with the dNBR results mentioned in the previous section (6.3), indicating a notable growth of low vegetation after the forest fire.\u003c/p\u003e\n\u003cp\u003eInterestingly, the study concludes that forest fires can have beneficial effects on productivity. They can clear low-growing underbrush, remove debris from the forest surface, and nourish the soil by allowing sunlight to reach it. If managed wisely, fire can be utilized as a means of forestry management, promoting healthier vegetation growth.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4: Retrieved statistics of vegetation landcover comparison of area change pre and post fire event 2022\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLandcover Classes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eArea (hectare) May 2022\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eArea (hectare) June 2022\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eBare soil\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e9029.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e9391.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eDense vegetation\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e456061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e455656\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003eSparse vegetation cover\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e18.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"top\"\u003e\n \u003cp\u003e60.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e(Source: ArcGIS Desktop: Release 10.8.1. Redlands, CA: Environmental Systems Research Institute)\u003c/strong\u003e\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe present study focusses on forest fire hotspots from 2002 to 2022 and offers valuable insights into the dynamics of fire activity within the study area. By employing the Getis-Ord Gi* method to generate a GI-ZScore map, the research effectively delineates both hot and cold spots of forest fire activity. The finding of the study shows the fluctuating trend in fire alerts over the years. However, the study reveals a gradual increase in the number of positive fire alerts over time. Some previous studies presented arguments in the findings that the rise in reported fire incidents may not solely reflect an increase in actual fire occurrence but could also be attributed to factors such as changes in monitoring technology, reporting protocols, and human behavior (Bowman et al. 2009). One another study claimed biases by employing satellite-based detection systems, particularly in remote or inaccessible areas where ground validation is limited (Tansey et al. 2004).\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eAccording to the Burn severity ratio map, computed by dNBR index, it is understood that very small region is affected by forest fire event 2022 however this area is significant due to having only 3.6 % forest in District Sherani, Balochistan. This region comprised of Koh-e-Sulaiman, is also known for being the world\u0026rsquo;s largest chilghoza (pine nut) forest on higher elevations. The 26,000-hectare forest produces around 640,000 kilograms of chilghozas annually. The forest range is home to pine trees dating back to 1,500 years. Region is famous for its wildlife, environmental and agricultural products. People relay on the forest for their livelihoods in the region. Thus, this assessment is significant to combat the further damages in near future to existing biodiversity conservation. The results of this study conducted in a region which is primarily characterized by dry deciduous forests and juniper forests, are well suited to arid and semi-arid environments and have a greater significant potential for similar landscapes across the globe.\u003c/p\u003e\n\u003cp\u003eBased on the study\u0026apos;s results that indicate low to medium burn severity levels in only 0.03 % of the area during forest fire events, along with the observation that the forest remains dominant in the region, and the frequency of fire events is increasing by 1.6 % annually, the subsequent recommendations and future work i.e. \u0026nbsp;implementation of a long-term and continuous monitoring program to track changes in forest burn severity, vegetation cover, and fire occurrence patterns, development of effective fire prevention strategies, including awareness campaigns, firebreaks, controlled burning, and early warning systems. Moreover, future trajectories could also include conducting a thorough evaluation of the ecological consequences of forest fires, encompassing their impacts on flora, fauna, soil, and water quality.\u003c/p\u003e\n\u003cp\u003eThis research endeavor is also to comprehensively address several Sustainable Development Goals (SDGs), thereby contributing to the overarching framework of sustainable development.\u0026nbsp;Specifically, it seeks to align with SDGs 13, which pertains to Climate Action, by actively participating in initiatives aimed at mitigating the adverse environmental effects of forest fires. Furthermore, this study aligns with SDGs, concentrating on the preservation, rejuvenation, and sustainable utilization of terrestrial ecosystems, notably forests, in an effort to safeguard these vital ecosystems from the detrimental consequences of forest fires. Furthermore, this research project is closely linked to SDGs 11, which highlights the importance of making communities stronger so they can better handle and reduce the various problems caused by forest fires on the environment and people\u0026apos;s lives.\u0026nbsp;Moreover, this research aligns with SDGs 17, which emphasizes collaboration toward shared objectives. This involves establishing strong bonds and alliances among governments, researchers, nonprofit organizations.\u0026nbsp;These sectors play a crucial role in developing effective strategies and disseminating knowledge to collectively achieve our common goals of preventing and responding to forest fires. 10. Availability of data and materials\u003c/p\u003e\n\u003cp\u003eThe data sets analysed during the current study available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCompetent Interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contribution declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA.S. and A.K. devised the project, the main conceptual ideas and the proof outline. A.S. and S.Z. analyzed the results and aided in interpreting the results and worked on the manuscript. M.K. contributed to the writing of the manuscript. M.J., M.R., L.A.W and Q.A. reviewed the findings of this work and Q.A. led the overall project team. All authors reviewed the manuscript. Furthermore, N.I. \u0026nbsp;played a significant role in the development of digital content for this research project. His contributions greatly enriched the presentation and comprehension of our research findings.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003e\u003cbr\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlcaras, Emanuele, Domenica Costantino, Francesca Guastaferro, Claudio Parente, and Massimiliano %J Remote Sensing Pepe. 2022. \u0026quot;Normalized Burn Ratio Plus (NBR+): a new index for sentinel-2 imagery.\u0026quot; 14 (7): 1727.\u003c/li\u003e\n\u003cli\u003eAthanasakis, George, Emmanouil Psomiadis, and Andromachi Chatziantoniou. 2017. \u003cem\u003eHigh-resolution Earth observation data and spatial analysis for burn severity evaluation and post-fire effects assessment in the Island of Chios, Greece\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eBerhad, Hap Seng Plantations Holdings. 2022. \u0026quot;Fire Hotspot Monitoring Report Jan 2022.\u0026quot;\u003c/li\u003e\n\u003cli\u003eBowman, David MJS, Jennifer K Balch, Paulo Artaxo, William J Bond, Jean M Carlson, Mark A Cochrane, Carla M d\u0026rsquo;Antonio, Ruth S DeFries, John C Doyle, and Sandy P %J science Harrison. 2009. \u0026quot;Fire in the Earth system.\u0026quot; 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Gagnon, Qingting Li, Faisal Mumtaz, Artan Hysa, Muhammad Amir Siddique, and Iqra Munir. 2021. \u0026quot;Assessing Burned Areas in Wildfires and Prescribed Fires with Spectral Indices and SAR Images in the Margalla Hills of Pakistan.\u0026quot; 12 (10): 1371. https://www.mdpi.com/1999-4907/12/10/1371.\u003c/li\u003e\n\u003cli\u003eTariq, Aqil, Hong Shu, Saima Siddiqui, Iqra Munir, Alireza Sharifi, Qingting Li, and Linlin Lu. 2022. \u0026quot;Spatio-temporal analysis of forest fire events in the Margalla Hills, Islamabad, Pakistan using socio-economic and environmental variable data with machine learning methods.\u0026quot; \u003cem\u003eJournal of Forestry Research\u003c/em\u003e 33 (1): 183-194. https://doi.org/10.1007/s11676-021-01354-4. https://doi.org/10.1007/s11676-021-01354-4.\u003c/li\u003e\n\u003cli\u003eVerbyla, David L, Eric S Kasischke, and Elizabeth E Hoy. 2008. \u0026quot;Seasonal and topographic effects on estimating fire severity from Landsat TM/ETM+ data.\u0026quot; \u003cem\u003eInternational Journal of Wildland Fire\u003c/em\u003e 17 (4): 527-534.\u003c/li\u003e\n\u003cli\u003eVlassova, Lidia, Fernando P\u0026eacute;rez-Cabello, Marcos Rodrigues Mimbrero, Raquel Montorio Llover\u0026iacute;a, and Alberto Garc\u0026iacute;a-Mart\u0026iacute;n. 2014. \u0026quot;Analysis of the relationship between land surface temperature and wildfire severity in a series of landsat images.\u0026quot; \u003cem\u003eRemote Sensing\u003c/em\u003e 6 (7): 6136-6162.\u003c/li\u003e\n\u003cli\u003eWeisse, Mikaela. 2019. \u0026quot;What Can Global Forest Watch Tell Us About the Fires in Brazil?\u0026quot;.\u003c/li\u003e\n\u003cli\u003eXiao, Xiangming, Bobby Braswell, Qingyuan Zhang, Stephen Boles, Stephen Frolking, and Berrien Moore III. 2003. \u0026quot;Sensitivity of vegetation indices to atmospheric aerosols: continental-scale observations in Northern Asia.\u0026quot; \u003cem\u003eRemote Sensing of Environment\u003c/em\u003e 84 (3): 385-392.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table","content":"\u003cp\u003eTable 3 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Forest Fire, Sentinel-2 Images, Normalized burn ratio (NBR), Normalized vegetation Index (NDVI), Fire radioactive power (FRP)","lastPublishedDoi":"10.21203/rs.3.rs-4091965/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4091965/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eForest fires adversely affect forest ecosystem by altering its composition, structure, regeneration, and recovery potential of its landscape. The present study investigates forest fire hotspots and examines the relationship between these fire events and deforestation in Tehsil Dhansar, District Sherani, Balochistan. This study proposed a three-step research methodology to achieve its objectives. Firstly, it aims to assess the severity level of the forest burn resulting from the fire event. Secondly, it analyzes the extent of vegetation loss caused by the fire. Thirdly, the study identifies forest fire hotspots using Sentinel-2A images and MODIS Fire Radiative Power (FRP) data. The analysis involves utilizing Normalized Burn Ratio (NBR), Normalized Difference Vegetation Index (NDVI), and Hot Spot Analysis (Getis-Ord Gi*) to gain comprehensive insights into the pre- and post-fire situation accurately. By defining classes, the study achieves a better understanding of the extent of burnt areas and vegetation loss.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe findings show that 0.03% of Tehsil Dhansar is found to have low to medium burn severity levels during any forest fire event. It is also revealed that the forest remained dominant in the same region and frequency of occurrence of forest fire events is increasing by 1.6% with each passing year.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe current study's findings in the region famous for the world's oldest forest have significant potential for similar landscapes worldwide, primarily characterized by dry deciduous forests and juniper forests well adapted to arid and semi-arid environments. Given these findings, further studies in the same location should prioritize obtaining precise in-situ measurements to deepen our understanding of the situation.\u003c/p\u003e","manuscriptTitle":"Detecting Post-fire Burn Severity Level using Sentinel-2 and MODIS Satellite data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-26 18:11:50","doi":"10.21203/rs.3.rs-4091965/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"50ebe202-fc9e-4c16-a79c-5dac311d082c","owner":[],"postedDate":"March 26th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-05-26T14:29:08+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-26 18:11:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4091965","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4091965","identity":"rs-4091965","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}