{"paper_id":"07debc89-2fa1-4bee-957e-9c0b9d2bc04a","body_text":"Forest Fire Risk Mapping Using GIS Based Weighted Overlay Analysis in Lamjung District, Nepal | 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 Forest Fire Risk Mapping Using GIS Based Weighted Overlay Analysis in Lamjung District, Nepal Prabin Gauli This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9525250/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 Forest fires represent one of the most severe natural disturbances threatening Nepal's mid hill ecosystems, yet district level fire risk mapping remains critically absent for the majority of the country's Middle Mountain physiographic zone. This study presents the first comprehensive GIS based forest fire risk map for Lamjung District, Gandaki Province, Nepal, using a weighted overlay analysis approach integrated with multi source remote sensing and open access spatial data. Five conditioning factors slope, aspect, land cover, distance from roads, and distance from settlements were derived from freely available datasets including the SRTM Digital Elevation Model (30 m), ESA WorldCover 2021 (10 m), and OpenStreetMap vector layers. Each factor was reclassified to a standardised risk scale of 1 (very low) to 5 (very high) and combined using Analytic Hierarchy Process (AHP) derived weights within QGIS 3.x. A total of 10,651 quality filtered active fire detections from NASA FIRMS (MODIS Collection 6.1: n = 931; VIIRS S-NPP 375 m: n = 9,720) spanning 2012–2024 were used for temporal trend analysis and spatial model validation. Results show that 64.55% of the total district area falls within the High (35.67%, 594.29 km²) and Very High (28.88%, 481.09 km²) fire risk zones, predominantly concentrated in mid elevation broadleaf and Chir pine forest belts in proximity to roads and settlements. Spatial validation confirmed that 47.7% of matched VIIRS fire detections (n = 3,251) fell within High and Very High risk zones, with an Area Under the ROC Curve (AUC) of 0.687, indicating acceptable model performance. The Mann Kendall trend test revealed no statistically significant trend in annual fire frequency over the study period (tau = 0.156, p = 0.501), suggesting high inter annual variability. This study fills a critical research gap in Nepal's Mid hills fire risk literature and provides a reproducible, open-data framework applicable to comparable under studied districts across the Gandaki and other mid hill provinces. Forestry forest fire risk mapping GIS weighted overlay Lamjung Nepal MODIS VIIRS Analytic Hierarchy Process Mid-hills Mann-Kendall Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction Forest fires are among the most widespread and destructive natural disturbances globally, causing irreversible damage to ecosystems, biodiversity, carbon stocks, soil stability, and human livelihoods (Jones et al., 2022 ). Climate change has intensified fire weather conditions worldwide, with the probability of extreme fire events estimated to have increased by 88–152% compared to pre industrial baselines in vulnerable mountain forest regions (Abatzoglou et al., 2019 ). In South and Southeast Asia, the Hindu Kush Himalaya (HKH) region has emerged as a particularly fire sensitive zone, where the convergence of complex terrain, diverse and flammable vegetation, prolonged pre monsoon drought, and dense rural populations creates conditions highly conducive to repeated and severe fire events (Mishra et al., 2023 ). Nepal, situated at the heart of the HKH region, has experienced a dramatic escalation in forest fire frequency and severity over the past two decades. The country recorded over 375,000 hectares of burned forest between 2000 and 2014, with a dramatic spike in fire incidents documented in 2023 (Mishra et al., 2023 ; Pokharel et al., 2023 ). Nepal's mean annual temperature is rising at nearly double the global average rate approximately 0.056°C per year extending the fire season and intensifying the drying of forest fuels (Ministry of Forest and Environment, 2023 ). The socioeconomic consequences have been severe: between 2013 and 2023, Nepal recorded 18,791 wildfires resulting in 769 deaths, 2,568 injuries, and financial losses exceeding NPR 22 billion (NDRRMA, 2023 ). Despite this escalating threat, Nepal's fire risk research and monitoring systems remain heavily concentrated in the Terai and Chure physiographic zones, where fire activity is most visible and politically prominent (Matin et al., 2017 ; Parajuli et al., 2020 ). The Middle Mountains zone locally known as the mid hills has received disproportionately less scientific attention, despite documented evidence that fire incidents and burned area have increased significantly in this zone over the past two decades (Joshi et al., 2023 ). The mid hills are characterised by dense broadleaf and coniferous forest cover, high rural population density, extensive community forest management, and a network of roads and agricultural settlements all of which interact to create complex fire risk dynamics that differ fundamentally from the Terai. Lamjung District, located in Gandaki Province in the central mid hills of Nepal, exemplifies this research and management gap. The district spans five ecological zones from subtropical lowlands at approximately 500 m elevation to trans Himalayan landscapes at over 7,690 m, encompassing some of the most diverse forest cover in the country. Notably, the Lamjung Division Forest Office has been identified as one of the most fire active district forest offices in Nepal's Mid hills zone (Nepali Times, 2023 ). Despite this, no dedicated district level fire risk mapping study exists for Lamjung, representing a critical evidence gap for local forest management planning, early warning systems, and community forest governance. GIS based weighted overlay analysis has been widely applied for fire risk mapping in comparable mountain environments globally, including the Indian Himalaya, Mediterranean Europe, Turkey, and Indonesia, demonstrating consistent accuracy in identifying high risk zones using terrain, vegetation, and anthropogenic proximity variables (Jaiswal et al., 2002 ; Chuvieco and Congalton, 1989 ). Within Nepal, Parajuli et al. ( 2020 ) and Joshi et al. ( 2023 ) have applied similar methodologies in the Terai Arc and Doti District respectively, confirming the utility of the approach. However, no equivalent study exists for Lamjung or the broader Gandaki Province mid hills. This study therefore aims to: (1) produce the first GIS based forest fire risk map for Lamjung District using weighted overlay analysis; (2) analyse the spatiotemporal patterns and trends in fire occurrence over the period 2012–2024 using NASA FIRMS satellite data; and (3) validate the risk map using historical fire point data and ROC/AUC analysis. The outputs are intended to directly support district level forest fire management planning and community forest risk prioritisation. 2. Study Area Lamjung District is located in Gandaki Province, central Nepal, lying between approximately 27°55'N to 28°32'N latitude and 84°00'E to 84°48'E longitude (Fig. 1 ). The district covers a total area of approximately 1,692 km², with Besisahar as its administrative headquarters. Lamjung is bounded to the north by Manang District, to the east by Gorkha District, to the south by Tanahun District, and to the west by Kaski District. The district is characterised by one of the most dramatic elevation gradients in Nepal, rising from approximately 500 m above sea level in the southern Marsyangdi River valley to 7,690 m at the summit of Lamjung Himal, within a horizontal distance of approximately 50 km. This exceptional vertical range encompasses three major physiographic zones: the Middle Hills, the High Mountains, and the Himalaya. The district forms part of the gateway to the Annapurna Conservation Area and the internationally renowned Annapurna Circuit trekking route. The climate of Lamjung varies markedly with elevation. Lower valleys experience a subtropical to warm temperate climate (mean annual temperature 18–25°C), while mid elevation areas are temperate (10–18°C) and higher zones are alpine and nival. The pre monsoon period (February-May) is characterised by low relative humidity, elevated temperatures, and strong up-valley foehn winds conditions that create peak fire weather in the district. The monsoon season (June-September) delivers the majority of annual precipitation, effectively suppressing fire activity for approximately four months each year. The vegetation of Lamjung reflects its altitudinal diversity. Mid elevation slopes (approximately 1,000–2,500 m) are dominated by Chir pine (Pinus roxburghii) on south facing aspects a species particularly prone to ground fire due to accumulation of dry needle litter and broadleaf forests of oak (Quercus spp.) and rhododendron on cooler north facing slopes. Upper elevations support subalpine forests of fir (Abies spectabilis) and birch (Betula utilis), transitioning to alpine meadows and permanent ice above the treeline. Based on ESA WorldCover 2021 analysis, tree cover constitutes the dominant land cover class in Lamjung, concentrated in the mid elevation belt where fire risk is highest. The population of Lamjung was recorded at approximately 155,852 in the 2021 Nepal Census, comprising Gurung, Magar, Chhetri, and Brahmin communities. The district has 8 local government units comprising 4 municipalities and 4 rural municipalities. Forest dependency is high across rural communities, with widespread reliance on forests for fuelwood, fodder, and timber. Community Forest User Groups (CFUGs) manage significant portions of the district's forest area, playing a central role in local forest governance. 3. Data and Methods 3.1 Data Sources All datasets used in this study were obtained from freely available, open access repositories, ensuring full reproducibility of the methodology. Table 1 summarises the data layers, their sources, spatial resolution, and acquisition year. Table 1 Summary of data sources used in this study. Data Layer Source Resolution Year URL Active fire points (MODIS) NASA FIRMS, Collection 6.1 1 km 2012–2024 www.firms.modaps.eosdis.nasa.gov Active fire points (VIIRS) NASA FIRMS, S-NPP 375 m 375 m 2012–2024 www.firms.modaps.eosdis.nasa.gov Digital Elevation Model SRTM 1 Arc-Second Global (USGS) 30 m 2000 www.earthexplorer.usgs.gov Land cover ESA WorldCover 2021 (v200) 10 m 2021 www.esa-worldcover.org Road network OpenStreetMap via Geofabrik Vector 2024 www.download.geofabrik.de Settlement points OpenStreetMap via Geofabrik Vector 2024 www.download.geofabrik.de District boundary Survey Department Nepal / HDX Vector 2020 www.data.humdata.org 3.2 Active Fire Data Preparation Active fire point data were obtained from NASA's Fire Information for Resource Management System (FIRMS) for the period 2012–2024. Both MODIS Collection 6.1 (1 km resolution) and VIIRS S-NPP 375 m datasets were downloaded for Nepal via the FIRMS Country Yearly Summary portal ( firms.modaps.eosdis.nasa.gov/country/ ). Fire detections with confidence levels below 50% (MODIS) or classified as low quality ('l') for VIIRS were excluded to minimise false detections. The retained datasets were spatially filtered to the Lamjung district bounding box (latitude: 27.90°-28.55°N; longitude: 83.90°-84.85°E) and subsequently clipped to the exact district boundary in QGIS using the Select by Location tool. All data processing was performed in R (version 4.5.1) using the dplyr and readr packages. Following quality filtering, a total of 931 MODIS and 9,720 VIIRS fire detections were retained, yielding a combined dataset of 10,651 fire points spanning 2012 to 2024. 3.3 Risk Factor Layer Preparation Five conditioning factors for forest fire risk were selected based on their established influence in published fire risk mapping studies in Nepal and comparable Himalayan environments (Parajuli et al., 2020 ; Joshi et al., 2023 ; Matin et al., 2017 ). All raster layers were projected to WGS 84 / UTM Zone 44N (EPSG:32644) and processed in QGIS (version 4.0.1). Following derivation and reclassification, all five layers were aligned to a common 30 m spatial resolution and identical extent using the QGIS Align Rasters tool with nearest neighbour resampling, using the slope raster as the reference layer. 3.3.1 Slope Two SRTM 1 Arc Second GeoTIFF tiles (N27E084 and N28E084) were downloaded from USGS EarthExplorer, merged using the QGIS Raster Merge tool, and clipped to the Lamjung district boundary. Slope was derived in degrees from the merged DEM using the QGIS Slope tool (Raster → Analysis → Slope). Slope was reclassified into five fire risk classes (Table 2 ) following Joshi et al. ( 2023 ) and Parajuli et al. ( 2020 ). Steeper slopes accelerate fire spread rate (Rothermel, 1972 ) and impede suppression access, justifying their assignment of higher risk values. Table 2 Reclassification scheme for slope. Slope (degrees) Risk Class Risk Value 0–10 Very Low 1 10–20 Low 2 20–30 Moderate 3 30–40 High 4 > 40 Very High 5 3.3.2 Aspect Aspect was derived from the merged DEM using the QGIS Aspect tool (Raster → Analysis → Aspect), producing a raster with values in degrees (0-360°). Aspect was reclassified based on directional solar exposure, with south facing slopes assigned the highest risk values due to greater insolation, lower relative humidity, and drier fuel conditions (Parajuli et al., 2020 ). Table 3 shows the reclassification scheme applied. Table 3 Reclassification scheme for aspect. Aspect (degrees) Direction Risk Value 315–360 or 0–45 North 1 45–90 or 270–315 NE / NW 2 90–135 or 225–270 East / West 3 135–180 or 180–225 SE / SW 4 ~ 180 (South-facing) South 5 3.3.3 Land Cover Land cover data were obtained from the ESA WorldCover 2021 product (version 200), a 10 m resolution global land cover map derived from Sentinel-1 and Sentinel-2 satellite imagery (ESA, 2021 ). The dataset was clipped to the Lamjung district boundary and reclassified from 11 original ESA classes into five fire risk classes based on vegetation flammability and fuel load characteristics (Table 4 ). Coniferous and pine dominated forests, which are particularly abundant on south facing mid elevation slopes in Lamjung due to the presence of Chir pine (Pinus roxburghii), were assigned the highest risk value due to the flammability of dry needle litter accumulation (Matin et al., 2017 ). Table 4 Reclassification scheme for land cover. ESA WorldCover Class ESA Code Risk Value Justification Tree cover (conifer/pine) 10 5 Highest fuel load, Chir pine litter Shrubland 20 3 Moderate dry biomass Grassland 30 3 Moderate dry fuel Cropland 40 2 Low fuel load Built-up 50 2 Ignition source, low fuel Bare / sparse vegetation 60 1 Negligible fuel Snow and ice 70 1 Cannot burn Permanent water bodies 80 1 Cannot burn Herbaceous wetland 90 1 Very low fuel Moss and lichen 100 2 Low fuel load 3.3.4 Distance from Roads Road network data were obtained from OpenStreetMap (OSM) via the Geofabrik Nepal download server and clipped to the Lamjung district boundary. The road shapefile was reprojected to EPSG:32644, rasterised to 30 m resolution using the QGIS Rasterize (Vector to Raster) tool, and Euclidean distance from each pixel to the nearest road was calculated using the QGIS Proximity (Raster Distance) tool. Distance to road was reclassified inversely pixels closer to roads were assigned higher risk values (Table 5 ) reflecting the established role of road networks as conduits for human ignition in Nepal, where over 40% of fires have been recorded within 1 km of roads (Matin et al., 2017 ). Table 5 shows the reclassification scheme applied. Table 5 Reclassification scheme for distance from roads. Distance from Road (m) Risk Value 0–499 5 500–999 4 1,000–1,999 3 2,000–2,999 2 ≥ 3,000 1 3.3.5 Distance from Settlements OpenStreetMap building footprint data (gis_osm_buildings_a_free_1) were used as a proxy for human settlement distribution, providing denser and more spatially comprehensive coverage than point based settlement data in Lamjung. The building layer was clipped to the district boundary, reprojected to EPSG:32644, rasterised to 30 m resolution, and distance rasters were produced using the Proximity tool, following the same procedure as for roads. Inverse distance weighting was applied (Table 6 ), with areas closer to buildings assigned higher risk values, reflecting the dominant role of human activities including deliberate burning for pasture renewal and agricultural clearance as the primary ignition sources in Nepal's mid hills (Matin et al., 2017 ). Table 6 . Reclassification scheme for distance from settlements. Table 6 shows the reclassification scheme. Table 6 Reclassification scheme for distance from settlements. Distance from Settlement (m) Risk Value 0–499 5 500–999 4 1,000–2,999 3 3,000–4,999 2 ≥ 5,000 1 3.4 Weighted Overlay Analysis and AHP Weight Determination The five reclassified risk factor layers were combined into a single composite fire risk map using the Weighted Overlay method implemented in the QGIS Raster Calculator. Relative weights were determined using the Analytic Hierarchy Process (AHP) (Saaty, 1980 ), a multi criteria decision making technique widely used in GIS based natural hazard mapping. Weights were assigned based on the relative importance of each factor as established in published fire risk studies in Nepal and comparable Himalayan settings (Parajuli et al., 2020 ; Joshi et al., 2023 ), as shown in Table 7 . Table 7 presents the final weights applied. Table 7 AHP derived weights for weighted overlay analysis. Risk Factor Weight (%) Justification Land Cover 30 Primary determinant of fuel availability and flammability Distance from Settlements 25 Dominant ignition source in Nepal's mid hills Distance from Roads 20 Secondary human access and ignition factor Slope 15 Directly controls fire spread rate Aspect 10 Indirectly controls fuel moisture and dryness Total 100 The composite risk score for each pixel was computed using the following Raster Calculator expression in QGIS: Fire_Risk = (LandCover_Reclass × 0.30) + (Settlement_Dist_Reclass × 0.25) + (Road_Dist_Reclass × 0.20) + (Slope_Reclass × 0.15) + (Aspect_Reclass × 0.10) The resulting continuous raster with values ranging from 1.0 to 5.0 was reclassified into five discrete fire risk zones using equal interval thresholds (Table 8 ) to produce the final fire risk map. Table 8 Final fire risk zone classification. Raw Score Risk Zone 0–1.79 Very Low 1.80–2.59 Low 2.60–3.39 Moderate 3.40–4.19 High 4.20–5.00 Very High 3.5 Temporal Fire Trend Analysis Annual and monthly fire frequency distributions were analysed using the MODIS fire point dataset (2012–2024), as MODIS provides a consistent, uninterrupted sensor record suitable for temporal trend analysis. The Mann Kendall non parametric trend test was applied to annual MODIS fire counts using the Kendall package in R (version 4.5.1) to determine whether fire frequency exhibited a statistically significant increasing or decreasing trend over the study period. The Mann Kendall tau statistic and associated p-value were reported. A p value < 0.05 was considered statistically significant. 3.6 Model Validation Two complementary validation approaches were employed to assess the accuracy of the fire risk map. Fire detections falling outside valid risk zone pixels (n = 6,469, 66.6%) were excluded from percentage-based validation calculations but are noted in the results. Method 1 - Spatial Overlay Validation The VIIRS S-NPP fire point dataset (9,720 points, 2012–2024) was selected for spatial validation due to its higher spatial resolution (375 m) compared to MODIS (1 km). Each VIIRS fire point was assigned the underlying risk zone value from the final fire risk raster using the QGIS Sample Raster Values tool. The percentage of fire points falling within each risk zone was calculated. A model was considered satisfactory if ≥ 40% of fire points fell within the Moderate, High, or Very High risk zones, consistent with benchmarks established in comparable Nepal studies (Parajuli et al., 2020 ; Joshi et al., 2023 ). Method 2 - ROC / AUC Analysis : The Area Under the Receiver Operating Characteristic Curve (AUC) was calculated using the pROC package in R. Background absence points (n = 500) were generated within the Lamjung district boundary, weighted according to the actual spatial distribution of risk zone proportions derived from the final risk map. AUC values were interpreted as: < 0.6 = poor; 0.6–0.7 = acceptable; 0.7–0.8 = good; > 0.8 = excellent (Hosmer et al., 2013 ). 4. Results 4.1 Spatiotemporal Patterns of Fire Occurrence (2012–2024) A total of 10,651 quality assured fire detections were recorded in Lamjung District between 2012 and 2024. On the comprising of 931 MODIS and 9,720 VIIRS S-NPP detections. The VIIRS dataset produced approximately 10.4 times more detections than MODIS for the same study period, reflecting its superior spatial resolution (375 m vs. 1 km) and greater sensitivity to smaller fire events. Annual MODIS fire counts showed considerable inter annual variability over the study period (Fig. 2 ). The Mann Kendall trend test revealed no statistically significant trend in annual fire frequency over 2012–2024 (tau = 0.156, p = 0.501). Although a slight positive direction was observed, the result indicates that fire frequency fluctuated without a consistent directional change during the 13 year study period. Monthly fire distribution (Fig. 3 ) revealed a pronounced seasonal pattern, with fire activity strongly concentrated in the pre monsoon period. This aligns with the national pattern identified by Mishra et al. ( 2023 ), where more than 78% of burned area was recorded between March and May. Fire activity was near absent during the monsoon season (June-September) and reached its minimum in the winter months (December-January), reflecting the role of monsoon precipitation in suppressing fire conditions. Analysis of the temporal distribution of fire detections by time of day (Fig. 4 ) revealed that 50.2% of detections were recorded during daytime and 49.8% at night time, indicating that the majority of fires in Lamjung are associated with daytime human activity, consistent with anthropogenic ignition patterns documented elsewhere in Nepal's mid hills (Kunwar and Khaling, 2006 ). Comparison of MODIS and VIIRS annual fire counts (Fig. 5 ) demonstrated consistently higher detection rates by VIIRS across all years of the study period, confirming the value of using both sensors for comprehensive fire monitoring. 4.2 Fire Risk Map The weighted overlay analysis produced a continuous fire risk surface that was classified into five discrete risk zones across the mapped area of Lamjung District (Fig. 6 ). Table 9 presents the area statistics and fire point distribution for each risk zone. Table 9 Area distribution of fire risk zones and VIIRS fire point distribution in Lamjung District. Risk Zone Pixel Count Area (km²) % of Total Area Fire Points (VIIRS) % of Fire Points Very Low 147,391 124.04 7.45 1,090 33.5 Low 239,246 201.34 12.09 293 9.0 Moderate 315,166 265.23 15.92 244 7.5 High 706,178 594.29 35.67 701 21.6 Very High 571,661 481.09 28.88 923 28.4 Total 1,979,642 1,665.99 100.0 3,251 100.0 ¹ Mapped area = 1,665.99 km² (98.46% of total district area of 1,692 km²). Remaining area consists of NoData pixels along district boundary margins and high-altitude zones where one or more input layers lacked valid coverage. ² Fire points validated = 3,251 of 9,720 total VIIRS detections falling within valid mapped pixels. The combined High and Very High risk zones covered 64.55% of the total mapped area (1,075.38 km²). The Very High risk zone alone accounted for 28.88% of the mapped area (481.09 km²), predominantly concentrated in the mid elevation forest belt of the central and southern portions of the district. The Very Low risk zone (7.45%, 124.04 km²) was confined to the northern high altitude areas covered by permanent snow, ice, and bare rock, as well as major river channels in valley floors. 4.3 Model Validation Results Spatial Overlay Validation : Spatial Overlay Validation: Of the 3,251 VIIRS fire detections that could be matched to valid risk zone pixels, 47.7% fell within the High and Very High risk zones (High: 21.6%, Very High: 28.4%), and 55.2% fell within the Moderate to Very High risk zones combined (Moderate: 7.5%, High: 21.6%, Very High: 28.4%). A total of 6,469 VIIRS points (66.6%) fell outside the valid mapped extent and were excluded from percentage calculations. ROC / AUC Validation The ROC curve analysis (Fig. 8 ) yielded an AUC value of 0.687, indicating acceptable discriminatory ability of the fire risk model. This result is consistent with the AUC of 0.787 reported by Joshi et al. ( 2023 ) for a comparable weighted overlay fire risk model in Doti District, Nepal's Mid hills. 5. Discussion 5.1 Fire Risk Patterns and Their Drivers The fire risk map produced in this study reveals that 64.55% of Lamjung District falls within High and Very High fire risk zones, with the greatest concentration in the mid elevation Chir pine and broadleaf forest belt. This finding is consistent with national level assessments indicating that Nepal's Middle Mountains zone has experienced a significant increase in fire incidents, driven by the combined effects of climate warming, fuel accumulation, and anthropogenic pressures (Mishra et al., 2023 ). The dominance of land cover as the highest weighted risk factor (30%) reflects the critical role of vegetation type in determining fire susceptibility in Lamjung. The prevalence of Chir pine (Pinus roxburghii) on south facing mid elevation slopes creates concentrated zones of highly flammable dry needle litter, particularly during the pre monsoon season (Matin et al., 2017 ). This finding aligns with comparable mid hill studies, where pine dominated forests were identified as the most significant contributors to fire activity (Joshi et al., 2023 ). The proximity of high-risk zones to roads and settlements confirms the predominantly anthropogenic nature of fire ignition in Lamjung. Studies conducted across Nepal's Mid-hills have consistently documented that the majority of fires are initiated by deliberate human actions, including burning for pasture renewal, agricultural residue clearance, and hunting, and that fire incidence rates are significantly elevated within 1 km of roads and settlements (Matin et al., 2017 ; Kunwar and Khaling, 2006 ). The outmigration of younger populations from Lamjung's villages a well documented trend in Nepal's mid hills has reduced labour available for traditional fire management practices, further increasing vulnerability to uncontrolled fire spread (Nepal Economic Forum, 2023 ). The weights assigned to distance from settlements (25%) and roads (20%) collectively represent 45% of the final risk model, reflecting this anthropogenic dominance. Topographic factors (slope and aspect), while assigned lower individual weights in the AHP scheme, play a critical amplifying role. South facing slopes in Lamjung's mid elevation zones tend to be both drier (due to greater solar radiation) and dominated by Chir pine creating a multiplicative interaction between aspect, vegetation, and fire risk that is well captured by the weighted overlay approach. 5.2 Interpretation of Validation Results: The AUC of 0.687 indicates acceptable model performance consistent with the complexity of fire occurrence patterns in Lamjung's diverse landscape. A notable proportion of fire detections (33.5%) were recorded within Very Low risk zones, which may be attributed to several factors. First, the VIIRS dataset captures all heat sources including agricultural burning, cooking fires, and industrial heat in addition to forest fires non forest fire events tend to occur in valley areas near settlements classified as low risk by vegetation-based factors. Second, the OSM building footprint data used as a settlement proxy, while more comprehensive than point-based data, may not fully capture the spatial extent of human activity in remote rural areas of Lamjung. Third, minor positional uncertainty in VIIRS fire detections (± 375 m) may result in some points being assigned to adjacent risk zones. These limitations are consistent with those reported by Parajuli et al. ( 2020 ) and Joshi et al. ( 2023 ) in comparable Nepal studies. 5.3 Temporal Trends and Climate Implications The Mann Kendall trend test revealed no statistically significant trend in annual fire frequency over the 2012–2024 study period (tau = 0.156, p = 0.501). This result should be interpreted carefully for several reasons. The 13 year VIIRS record, while valuable, may be insufficient to resolve decadal scale trends in the presence of high inter annual climate variability. The slight positive tau value (0.156) does suggest a weak increasing direction, consistent with national level observations, but this did not reach statistical significance at the district scale. This finding contrasts with national level studies reporting significant increasing trends in burned area across Nepal (Mishra et al., 2023 ), suggesting that district level dynamics may be influenced by local factors including community forest management effectiveness and inter annual monsoon variability. Extension of the time series to include MODIS data from 2000 onwards is recommended to better characterise decadal fire trends in Lamjung. 5.4 Comparison with Previous Studies This study represents the first dedicated fire risk mapping exercise for Lamjung District and contributes to a growing body of district level fire risk assessments in Nepal's under-studied Mid-hills. The methodology applied here is most directly comparable to Joshi et al. ( 2023 ), who produced a fire risk map for Doti District another mid hill district using a similar weighted overlay framework and reported an AUC of 0.787 and that 42.9% and 46.03% of the total forested area falling within High and Moderate risk zones respectively. The present study extends this approach to the Gandaki Province mid hills, where no equivalent study existed, and incorporates the higher resolution ESA WorldCover 10 m land cover product not used in previous Nepal district level studies. The present study's AUC of 0.687 is somewhat lower, potentially reflecting the greater landscape complexity of Lamjung's five zone altitudinal gradient compared to Doti's more uniform terrain. At the national scale, Parajuli et al. ( 2020 ) confirmed that the majority of fire detections in Nepal fall within Moderate to High risk zones a pattern partially replicated in the present study, where 55.2% of matched fire detections fell within Moderate to Very High risk zones. Compared to Matin et al. ( 2017 ), who conducted a national scale analysis across all physiographic zones, the present study provides substantially finer spatial detail at the district level, enabling identification of specific fire prone localities within Lamjung suitable for targeted management intervention. 5.5 Limitations Several limitations of this study should be acknowledged. First, the MODIS fire detection dataset has a spatial resolution of 1 km, which may underrepresent small fires and introduce positional uncertainty in validation analyses. Although the higher resolution VIIRS dataset (375 m) was used for validation to mitigate this concern, sub pixel fire events remain undetectable. Second, the weighted overlay approach assumes linear relationships between risk factors and fire probability and does not capture complex non linear interactions that machine learning approaches may better represent (Mishra et al., 2023 ). Third, no field based validation was conducted due to logistical constraints, and the results should be interpreted with this caveat. Fourth, climatic variables including temperature, precipitation, and wind speed were not incorporated as explicit risk factors in the present study, representing an important avenue for future refinement. Fifth, the AHP weights, retain an element of subjectivity, and sensitivity analysis of alternative weight combinations is recommended in future work. 5.5 Implications for Forest Fire Management The fire risk map produced in this study has direct practical applications for forest fire management in Lamjung District. High and Very High risk zones should be prioritised for pre monsoon fire surveillance and early warning system coverage by the Lamjung Division Forest Office. Community Forest User Groups operating in high risk zones should receive targeted training and equipment for fire prevention and suppression. Road corridor management including roadside vegetation clearing and public awareness campaigns should be emphasised in road proximate high risk areas. The seasonal analysis, confirming peak fire occurrence in the pre monsoon period, provides a scientific basis for determining the optimal timing of seasonal fire management interventions. 6. Conclusion This study produced the first GIS based forest fire risk map for Lamjung District, Nepal, using a weighted overlay analysis approach combining five terrain, land cover, and anthropogenic proximity factors derived from freely available satellite and open access spatial datasets. A total of 10,651 quality filtered NASA FIRMS fire detections (MODIS: 931; VIIRS: 9,720) spanning 2012–2024 were used for temporal analysis and model validation. The combined High and Very High risk zones covered 64.55% of the total mapped area (1,075.38 km²), with the highest risk areas concentrated in the mid elevation Chir pine and broadleaf forest belt in proximity to roads and settlements. The fire risk model demonstrated acceptable performance with an AUC of 0.687, and 47.7% of matched VIIRS fire detections fell within High and Very High risk zones, with 55.2% in Moderate to Very High zones (n = 3,251 matched points). The Mann Kendall trend test revealed no statistically significant trend in annual fire frequency over the study period (tau = 0.156, p = 0.501), though a slight positive direction was observed. This study fills a critical research gap in Nepal's Mid hills fire risk literature, provides a reproducible open data framework applicable to comparable under studied districts, and offers actionable spatial outputs directly relevant to forest fire management planning, community forest governance, and disaster risk reduction in Lamjung District and the broader Gandaki Province mid-hills. Future research should incorporate climatic variables and field based validation to further improve model accuracy. Declarations Acknowledgements The authors acknowledge NASA's Fire Information for Resource Management System (FIRMS) for providing open access fire detection data, the European Space Agency (ESA) for the WorldCover 2021 land cover product, the United States Geological Survey (USGS) for SRTM DEM data, and OpenStreetMap contributors via Geofabrik for road and settlement data. All spatial analysis was conducted using QGIS (open source) and R (open source). References Abatzoglou JT, Williams AP, Barbero R (2019) Global emergence of anthropogenic climate change in fire weather indices. Geophys Res Lett 46(1):326–336. https://doi.org/10.1029/2018GL080959 Chuvieco E, Congalton RG (1989) Application of remote sensing and geographic information systems to forest fire hazard mapping. Remote Sens Environ 29(2):147–159. https://doi.org/10.1016/0034-4257(89)90023-0 ESA (2021) WorldCover 2021 Product User Manual v2.0. European Space Agency. https://esa-worldcover.org Geofabrik (2024) OpenStreetMap Data Extracts: Nepal . https://download.geofabrik.de/asia/nepal.html Hosmer DW, Lemeshow S, Sturdivant RX (2013) Applied Logistic Regression (3rd ed.). Wiley. https://doi.org/10.1002/9781118548387 Jaiswal RK, Mukherjee S, Raju KD, Saxena R (2002) Forest fire risk zone mapping from satellite imagery and GIS. Int J Appl Earth Obs Geoinf 4(1):1–10. https://doi.org/10.1016/S0303-2434(02)00006-5 Jones MW et al (2022) Global and regional trends and drivers of fire under climate change. Rev Geophys 60. https://doi.org/10.1029/2020RG000726 . e2020RG000726 Joshi KP, Parajuli A, Ayer K, Bhatt R, Gautam AP (2023) Forest fire dynamics in Nepal's Mid-hills: Insights from spatial analysis and risk mapping of Doti District. Forestry: J Inst Forestry Nepal 20(1):61–78. https://doi.org/10.3126/forestry.v20i1.54338 Kunwar RM, Khaling S (2006) Wildfire management in the Himalayas: Learning from local and traditional practices. International Forest Fire News , 34, 46–54. (No DOI — grey literature) Matin MA, Chitale VS, Murthy MSR, Uddin K, Bajracharya B, Pradhan S (2017) Understanding forest fire patterns and risk in Nepal using remote sensing, geographic information system and historical fire data. Int J Wildland Fire 26(4):276–286. https://doi.org/10.1071/WF16056 Ministry of Forest and Environment (2023) Nepal's Forest Fire Management Strategy 2023. Government of Nepal, Kathmandu. (No DOI — government document) Mishra B, Panthi S, Poudel S, Ghimire BR (2023) Forest fire pattern and vulnerability mapping using deep learning in Nepal. Fire Ecol 19(1):3. https://doi.org/10.1186/s42408-022-00162-3 NASA FIRMS (2024) Fire Information for Resource Management System . NASA/EOSDIS. https://firms.modaps.eosdis.nasa.gov/ NDRRMA (2023) National Disaster Risk Reduction and Management Authority Annual Report 2023 . Government of Nepal, Kathmandu Nepal Economic Forum (2023) What is flaring Nepal's issue of forest fires? https://nepaleconomicforum.org Nepali Times (2023) More pre-monsoon forest fires in Nepal. https://nepalitimes.com Parajuli A, Gautam AP, Sharma SP, Bhujel KB, Sharma G, Thapa PB, Bist BS, Poudel S (2020) Forest fire risk mapping using GIS and remote sensing in two major landscapes of Nepal. Geomatics Nat Hazards Risk 11(1):2569–2586. https://doi.org/10.1080/19475705.2020.1853251 Pokharel B et al (2023) Forest fire dynamics in Nepal's Mid-hills: Regional trends and socio-ecological drivers. Environmental Development Rothermel RC (1972) A Mathematical Model for Predicting Fire Spread in Wildland Fuels. USDA Forest Service Research Paper INT-115 Saaty TL (1980) The Analytic Hierarchy Process. McGraw-Hill, New York USGS (2000) Shuttle Radar Topography Mission (SRTM) 1 Arc-Second Global. U.S. Geological Survey. https://earthexplorer.usgs.gov Additional Declarations The authors declare no competing interests. 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-9525250\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":629442318,\"identity\":\"d714465f-0bd2-4868-98be-1e1dd3d48719\",\"order_by\":0,\"name\":\"Prabin Gauli\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+UlEQVRIiWNgGAWjYHACNoYEEMXO2P5BogLIYGZuIFILM2Mbg8UZMIMILWDADGRUtoFYBLSYSx9+9uDhjlp5g8PMbQ9uzquN5m8HavlRsQ2nFsu+NHODxDPHDTccZmw3nLnteO6Mw4wNjD1nbuPUYnCGwUwise0YI1BLg7TktmO5DUAG0F/4tLB/A2mxB2v5O+dY7nzCWnhAttQkArW0SUg21ORuIKTFsoen3CCx7UDyzMOMzQYSxw7kbgRqOYjPL+Y87Nse/myrs+073v7wgURNXe6884cPPvhRgcdhEOowjA9hHMCpHqGlDsavw6VwFIyCUTAKRjAAAL8ZYEe0nkv5AAAAAElFTkSuQmCC\",\"orcid\":\"https://orcid.org/0009-0009-8216-3949\",\"institution\":\"Beijing Forestry University\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Prabin\",\"middleName\":\"\",\"lastName\":\"Gauli\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2026-04-25 11:30:44\",\"currentVersionCode\":1,\"declarations\":{\"humanSubjects\":false,\"vertebrateSubjects\":true,\"conflictsOfInterestStatement\":false,\"humanSubjectEthicalGuidelines\":false,\"humanSubjectConsent\":false,\"humanSubjectClinicalTrial\":false,\"humanSubjectCaseReport\":false,\"vertebrateSubjectEthicalGuidelines\":true},\"doi\":\"10.21203/rs.3.rs-9525250/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-9525250/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":108047157,\"identity\":\"7d7a4125-8f63-4934-a09b-acf9e488764a\",\"added_by\":\"auto\",\"created_at\":\"2026-04-28 19:58:51\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":1047945,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eSpatial distribution of NASA FIRMS active fire detections in Lamjung District, Nepal (2012–2024). Orange points represent MODIS Collection 6.1 detections (n = 931) and dark red points represent VIIRS S-NPP detections (n = 9,720).\\u003c/strong\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9525250/v1/3576e8537c9ab696ce23582f.png\"},{\"id\":108047158,\"identity\":\"fcce3432-99de-43e4-b6e4-7e04ae64d9b6\",\"added_by\":\"auto\",\"created_at\":\"2026-04-28 19:58:51\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":91203,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eAnnual forest fire frequency in Lamjung District, Nepal (2012–2024), based on NASA FIRMS MODIS Collection 6.1 data (n = 931 detections). The dashed line represents the linear trend. The Mann-Kendall test indicated no statistically significant trend (tau = 0.156, p = 0.501).\\u003c/strong\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9525250/v1/cb8c44cec21ec4b54350ada8.png\"},{\"id\":108181521,\"identity\":\"de11e685-591d-400b-937f-58082bee2194\",\"added_by\":\"auto\",\"created_at\":\"2026-04-30 08:58:44\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":70406,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eMonthly distribution of forest fire detections in Lamjung District (2012–2024), based on combined NASA FIRMS MODIS and VIIRS S-NPP datasets (n = 10,651 detections). Fire activity is strongly concentrated in the pre-monsoon period.\\u003c/strong\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9525250/v1/5936fb2803c4cb7e3a14b8e8.png\"},{\"id\":108047160,\"identity\":\"201bf12a-7df9-4cbd-a215-d78ebd6aa250\",\"added_by\":\"auto\",\"created_at\":\"2026-04-28 19:58:51\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":15626,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eDistribution of VIIRS S-NPP fire detections (2012–2024) across fire risk zones in Lamjung District, showing the percentage and absolute count of fire points within each risk zone.\\u003c/strong\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9525250/v1/189eeb1ee20efe61d87e89ea.png\"},{\"id\":108803519,\"identity\":\"14c4c463-0abf-478a-a4b7-1d48ce507a72\",\"added_by\":\"auto\",\"created_at\":\"2026-05-08 14:58:04\",\"extension\":\"png\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":114195,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eAnnual fire detections by satellite sensor (MODIS and VIIRS S-NPP) in Lamjung District (2012–2024), illustrating the higher detection sensitivity of VIIRS due to its finer spatial resolution (375 m vs. 1 km).\\u003c/strong\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9525250/v1/ff1ec1e3b0fcbc07dbbe4f68.png\"},{\"id\":108181525,\"identity\":\"25916437-ace0-4b65-af80-045350b97577\",\"added_by\":\"auto\",\"created_at\":\"2026-04-30 08:58:44\",\"extension\":\"png\",\"order_by\":6,\"title\":\"Figure 6\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":665874,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eGIS-based forest fire risk map of Lamjung District, Nepal, produced using weighted overlay analysis of five conditioning factors. Coordinate grid in WGS 84 / UTM Zone 44N (EPSG:32644).\\u003c/strong\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage6.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9525250/v1/a5cb7ef4dd06d22d57c91065.png\"},{\"id\":108047163,\"identity\":\"221068fe-362d-4dee-af51-ddc1ba41d6a5\",\"added_by\":\"auto\",\"created_at\":\"2026-04-28 19:58:51\",\"extension\":\"png\",\"order_by\":7,\"title\":\"Figure 7\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":75638,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eDistribution of VIIRS S-NPP fire detections (n = 9,720) across fire risk zones in Lamjung District (2012–2024). NA category (n = 158, 4.6%) represents fire detections falling outside valid risk zone pixels, likely along district boundary margins.\\u003c/strong\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage7.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9525250/v1/2627f4fd8bc62142a8bfd368.png\"},{\"id\":108047164,\"identity\":\"1fd4b469-9542-484d-ad7b-82e4b93598ec\",\"added_by\":\"auto\",\"created_at\":\"2026-04-28 19:58:51\",\"extension\":\"png\",\"order_by\":8,\"title\":\"Figure 8\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":43418,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eReceiver Operating Characteristic (ROC) curve for the fire risk model of Lamjung District. AUC = 0.687, indicating acceptable model performance.\\u003c/strong\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage8.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9525250/v1/ae77da76fd7e83bb74697b7f.png\"},{\"id\":108809008,\"identity\":\"5a7cc63e-2bb4-40d3-8ccd-c39cfb7e9915\",\"added_by\":\"auto\",\"created_at\":\"2026-05-08 15:48:43\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":2471912,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9525250/v1/93959916-2e45-410a-a534-ee90eaf15f9c.pdf\"}],\"financialInterests\":\"The authors declare no competing interests.\",\"formattedTitle\":\"\\u003cp\\u003e\\u003cstrong\\u003eForest Fire Risk Mapping Using GIS Based Weighted Overlay Analysis in Lamjung District, Nepal\\u003c/strong\\u003e\\u003c/p\\u003e\",\"fulltext\":[{\"header\":\"1. Introduction\",\"content\":\"\\u003cp\\u003eForest fires are among the most widespread and destructive natural disturbances globally, causing irreversible damage to ecosystems, biodiversity, carbon stocks, soil stability, and human livelihoods (Jones et al., \\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). Climate change has intensified fire weather conditions worldwide, with the probability of extreme fire events estimated to have increased by 88\\u0026ndash;152% compared to pre industrial baselines in vulnerable mountain forest regions (Abatzoglou et al., \\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e). In South and Southeast Asia, the Hindu Kush Himalaya (HKH) region has emerged as a particularly fire sensitive zone, where the convergence of complex terrain, diverse and flammable vegetation, prolonged pre monsoon drought, and dense rural populations creates conditions highly conducive to repeated and severe fire events (Mishra et al., \\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). Nepal, situated at the heart of the HKH region, has experienced a dramatic escalation in forest fire frequency and severity over the past two decades. The country recorded over 375,000 hectares of burned forest between 2000 and 2014, with a dramatic spike in fire incidents documented in 2023 (Mishra et al., \\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e; Pokharel et al., \\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). Nepal's mean annual temperature is rising at nearly double the global average rate approximately 0.056\\u0026deg;C per year extending the fire season and intensifying the drying of forest fuels (Ministry of Forest and Environment, \\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). The socioeconomic consequences have been severe: between 2013 and 2023, Nepal recorded 18,791 wildfires resulting in 769 deaths, 2,568 injuries, and financial losses exceeding NPR 22\\u0026nbsp;billion (NDRRMA, \\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). Despite this escalating threat, Nepal's fire risk research and monitoring systems remain heavily concentrated in the Terai and Chure physiographic zones, where fire activity is most visible and politically prominent (Matin et al., \\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e; Parajuli et al., \\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e). The Middle Mountains zone locally known as the mid hills has received disproportionately less scientific attention, despite documented evidence that fire incidents and burned area have increased significantly in this zone over the past two decades (Joshi et al., \\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). The mid hills are characterised by dense broadleaf and coniferous forest cover, high rural population density, extensive community forest management, and a network of roads and agricultural settlements all of which interact to create complex fire risk dynamics that differ fundamentally from the Terai.\\u003c/p\\u003e \\u003cp\\u003eLamjung District, located in Gandaki Province in the central mid hills of Nepal, exemplifies this research and management gap. The district spans five ecological zones from subtropical lowlands at approximately 500 m elevation to trans Himalayan landscapes at over 7,690 m, encompassing some of the most diverse forest cover in the country. Notably, the Lamjung Division Forest Office has been identified as one of the most fire active district forest offices in Nepal's Mid hills zone (Nepali Times, \\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). Despite this, no dedicated district level fire risk mapping study exists for Lamjung, representing a critical evidence gap for local forest management planning, early warning systems, and community forest governance. GIS based weighted overlay analysis has been widely applied for fire risk mapping in comparable mountain environments globally, including the Indian Himalaya, Mediterranean Europe, Turkey, and Indonesia, demonstrating consistent accuracy in identifying high risk zones using terrain, vegetation, and anthropogenic proximity variables (Jaiswal et al., \\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e2002\\u003c/span\\u003e; Chuvieco and Congalton, \\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e1989\\u003c/span\\u003e). Within Nepal, Parajuli et al. (\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e) and Joshi et al. (\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e) have applied similar methodologies in the Terai Arc and Doti District respectively, confirming the utility of the approach. However, no equivalent study exists for Lamjung or the broader Gandaki Province mid hills. This study therefore aims to: (1) produce the first GIS based forest fire risk map for Lamjung District using weighted overlay analysis; (2) analyse the spatiotemporal patterns and trends in fire occurrence over the period 2012\\u0026ndash;2024 using NASA FIRMS satellite data; and (3) validate the risk map using historical fire point data and ROC/AUC analysis. The outputs are intended to directly support district level forest fire management planning and community forest risk prioritisation.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e\"},{\"header\":\"2. Study Area\",\"content\":\"\\u003cp\\u003eLamjung District is located in Gandaki Province, central Nepal, lying between approximately 27\\u0026deg;55'N to 28\\u0026deg;32'N latitude and 84\\u0026deg;00'E to 84\\u0026deg;48'E longitude (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). The district covers a total area of approximately 1,692 km\\u0026sup2;, with Besisahar as its administrative headquarters. Lamjung is bounded to the north by Manang District, to the east by Gorkha District, to the south by Tanahun District, and to the west by Kaski District. The district is characterised by one of the most dramatic elevation gradients in Nepal, rising from approximately 500 m above sea level in the southern Marsyangdi River valley to 7,690 m at the summit of Lamjung Himal, within a horizontal distance of approximately 50 km. This exceptional vertical range encompasses three major physiographic zones: the Middle Hills, the High Mountains, and the Himalaya. The district forms part of the gateway to the Annapurna Conservation Area and the internationally renowned Annapurna Circuit trekking route. The climate of Lamjung varies markedly with elevation. Lower valleys experience a subtropical to warm temperate climate (mean annual temperature 18\\u0026ndash;25\\u0026deg;C), while mid elevation areas are temperate (10\\u0026ndash;18\\u0026deg;C) and higher zones are alpine and nival. The pre monsoon period (February-May) is characterised by low relative humidity, elevated temperatures, and strong up-valley foehn winds conditions that create peak fire weather in the district. The monsoon season (June-September) delivers the majority of annual precipitation, effectively suppressing fire activity for approximately four months each year. The vegetation of Lamjung reflects its altitudinal diversity. Mid elevation slopes (approximately 1,000\\u0026ndash;2,500 m) are dominated by Chir pine (Pinus roxburghii) on south facing aspects a species particularly prone to ground fire due to accumulation of dry needle litter and broadleaf forests of oak (Quercus spp.) and rhododendron on cooler north facing slopes. Upper elevations support subalpine forests of fir (Abies spectabilis) and birch (Betula utilis), transitioning to alpine meadows and permanent ice above the treeline. Based on ESA WorldCover 2021 analysis, tree cover constitutes the dominant land cover class in Lamjung, concentrated in the mid elevation belt where fire risk is highest. The population of Lamjung was recorded at approximately 155,852 in the 2021 Nepal Census, comprising Gurung, Magar, Chhetri, and Brahmin communities. The district has 8 local government units comprising 4 municipalities and 4 rural municipalities. Forest dependency is high across rural communities, with widespread reliance on forests for fuelwood, fodder, and timber. Community Forest User Groups (CFUGs) manage significant portions of the district's forest area, playing a central role in local forest governance.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e\"},{\"header\":\"3. Data and Methods\",\"content\":\"\\u003cdiv id=\\\"Sec4\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.1 Data Sources\\u003c/h2\\u003e \\u003cp\\u003eAll datasets used in this study were obtained from freely available, open access repositories, ensuring full reproducibility of the methodology. Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e summarises the data layers, their sources, spatial resolution, and acquisition year.\\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\\u003eSummary of data sources used in this study.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"5\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eData Layer\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eSource\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eResolution\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eYear\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eURL\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eActive fire points (MODIS)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNASA FIRMS, Collection 6.1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1 km\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2012\\u0026ndash;2024\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e\\u003ca href=\\\"http://www.firms.modaps.eosdis.nasa.gov\\\" target=\\\"_blank\\\"\\u003ewww.firms.modaps.eosdis.nasa.gov\\u003c/a\\u003e\\u003c/span\\u003e\\u003cspan address=\\\"http://www.firms.modaps.eosdis.nasa.gov\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eActive fire points (VIIRS)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNASA FIRMS, S-NPP 375 m\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e375 m\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2012\\u0026ndash;2024\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e\\u003ca href=\\\"http://www.firms.modaps.eosdis.nasa.gov\\\" target=\\\"_blank\\\"\\u003ewww.firms.modaps.eosdis.nasa.gov\\u003c/a\\u003e\\u003c/span\\u003e\\u003cspan address=\\\"http://www.firms.modaps.eosdis.nasa.gov\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDigital Elevation Model\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eSRTM 1 Arc-Second Global (USGS)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e30 m\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e\\u003ca href=\\\"http://www.firms.modaps.eosdis.nasa.gov\\\" target=\\\"_blank\\\"\\u003ewww.earthexplorer.usgs.gov\\u003c/a\\u003e\\u003c/span\\u003e\\u003cspan address=\\\"http://www.earthexplorer.usgs.gov\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLand cover\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eESA WorldCover 2021 (v200)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e10 m\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2021\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e\\u003ca href=\\\"http://www.firms.modaps.eosdis.nasa.gov\\\" target=\\\"_blank\\\"\\u003ewww.esa-worldcover.org\\u003c/a\\u003e\\u003c/span\\u003e\\u003cspan address=\\\"http://www.esa-worldcover.org\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eRoad network\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eOpenStreetMap via Geofabrik\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eVector\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2024\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e\\u003ca href=\\\"http://www.firms.modaps.eosdis.nasa.gov\\\" target=\\\"_blank\\\"\\u003ewww.download.geofabrik.de\\u003c/a\\u003e\\u003c/span\\u003e\\u003cspan address=\\\"http://www.download.geofabrik.de\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSettlement points\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eOpenStreetMap via Geofabrik\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eVector\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2024\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e\\u003ca href=\\\"http://www.firms.modaps.eosdis.nasa.gov\\\" target=\\\"_blank\\\"\\u003ewww.download.geofabrik.de\\u003c/a\\u003e\\u003c/span\\u003e\\u003cspan address=\\\"http://www.download.geofabrik.de\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDistrict boundary\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eSurvey Department Nepal / HDX\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eVector\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2020\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003e\\u003ca href=\\\"http://www.firms.modaps.eosdis.nasa.gov\\\" target=\\\"_blank\\\"\\u003ewww.data.humdata.org\\u003c/a\\u003e\\u003c/span\\u003e\\u003cspan address=\\\"http://www.data.humdata.org\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec5\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.2 Active Fire Data Preparation\\u003c/h2\\u003e \\u003cp\\u003eActive fire point data were obtained from NASA's Fire Information for Resource Management System (FIRMS) for the period 2012\\u0026ndash;2024. Both MODIS Collection 6.1 (1 km resolution) and VIIRS S-NPP 375 m datasets were downloaded for Nepal via the FIRMS Country Yearly Summary portal (\\u003cb\\u003efirms.modaps.eosdis.nasa.gov/country/\\u003c/b\\u003e). Fire detections with confidence levels below 50% (MODIS) or classified as low quality ('l') for VIIRS were excluded to minimise false detections. The retained datasets were spatially filtered to the Lamjung district bounding box (latitude: 27.90\\u0026deg;-28.55\\u0026deg;N; longitude: 83.90\\u0026deg;-84.85\\u0026deg;E) and subsequently clipped to the exact district boundary in QGIS using the Select by Location tool. All data processing was performed in R (version 4.5.1) using the dplyr and readr packages. Following quality filtering, a total of 931 MODIS and 9,720 VIIRS fire detections were retained, yielding a combined dataset of 10,651 fire points spanning 2012 to 2024.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec6\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.3 Risk Factor Layer Preparation\\u003c/h2\\u003e \\u003cp\\u003eFive conditioning factors for forest fire risk were selected based on their established influence in published fire risk mapping studies in Nepal and comparable Himalayan environments (Parajuli et al., \\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e; Joshi et al., \\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e; Matin et al., \\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e). All raster layers were projected to WGS 84 / UTM Zone 44N (EPSG:32644) and processed in QGIS (version 4.0.1). Following derivation and reclassification, all five layers were aligned to a common 30 m spatial resolution and identical extent using the QGIS Align Rasters tool with nearest neighbour resampling, using the slope raster as the reference layer.\\u003c/p\\u003e \\u003cdiv id=\\\"Sec7\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003e3.3.1 Slope\\u003c/h2\\u003e \\u003cp\\u003eTwo SRTM 1 Arc Second GeoTIFF tiles (N27E084 and N28E084) were downloaded from USGS EarthExplorer, merged using the QGIS Raster Merge tool, and clipped to the Lamjung district boundary. Slope was derived in degrees from the merged DEM using the QGIS Slope tool (Raster \\u0026rarr; Analysis \\u0026rarr; Slope). Slope was reclassified into five fire risk classes (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e) following Joshi et al. (\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e) and Parajuli et al. (\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e). Steeper slopes accelerate fire spread rate (Rothermel, \\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e1972\\u003c/span\\u003e) and impede suppression access, justifying their assignment of higher risk values.\\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\\u003eReclassification scheme for slope.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"3\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSlope (degrees)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eRisk Class\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eRisk Value\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e0\\u0026ndash;10\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eVery Low\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e10\\u0026ndash;20\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eLow\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e20\\u0026ndash;30\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eModerate\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e30\\u0026ndash;40\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eHigh\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e4\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u0026gt;\\u0026thinsp;40\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eVery High\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e5\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec8\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003e3.3.2 Aspect\\u003c/h2\\u003e \\u003cp\\u003eAspect was derived from the merged DEM using the QGIS Aspect tool (Raster \\u0026rarr; Analysis \\u0026rarr; Aspect), producing a raster with values in degrees (0-360\\u0026deg;). Aspect was reclassified based on directional solar exposure, with south facing slopes assigned the highest risk values due to greater insolation, lower relative humidity, and drier fuel conditions (Parajuli et al., \\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e). Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e shows the reclassification scheme applied.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab3\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 3\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eReclassification scheme for aspect.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"3\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAspect (degrees)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eDirection\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eRisk Value\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e315\\u0026ndash;360 or 0\\u0026ndash;45\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNorth\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e45\\u0026ndash;90 or 270\\u0026ndash;315\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNE / NW\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e90\\u0026ndash;135 or 225\\u0026ndash;270\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eEast / West\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e135\\u0026ndash;180 or 180\\u0026ndash;225\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eSE / SW\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e4\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e~\\u0026thinsp;180 (South-facing)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eSouth\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e5\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec9\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003e3.3.3 Land Cover\\u003c/h2\\u003e \\u003cp\\u003eLand cover data were obtained from the ESA WorldCover 2021 product (version 200), a 10 m resolution global land cover map derived from Sentinel-1 and Sentinel-2 satellite imagery (ESA, \\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). The dataset was clipped to the Lamjung district boundary and reclassified from 11 original ESA classes into five fire risk classes based on vegetation flammability and fuel load characteristics (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e). Coniferous and pine dominated forests, which are particularly abundant on south facing mid elevation slopes in Lamjung due to the presence of Chir pine (Pinus roxburghii), were assigned the highest risk value due to the flammability of dry needle litter accumulation (Matin et al., \\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab4\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 4\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eReclassification scheme for land cover.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"4\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eESA WorldCover Class\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eESA Code\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eRisk Value\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eJustification\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTree cover (conifer/pine)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e10\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eHighest fuel load, Chir pine litter\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eShrubland\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e20\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eModerate dry biomass\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eGrassland\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e30\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eModerate dry fuel\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCropland\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e40\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eLow fuel load\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBuilt-up\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e50\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eIgnition source, low fuel\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBare / sparse vegetation\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e60\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eNegligible fuel\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSnow and ice\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e70\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eCannot burn\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePermanent water bodies\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e80\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eCannot burn\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHerbaceous wetland\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e90\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eVery low fuel\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMoss and lichen\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e100\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eLow fuel load\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec10\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003e3.3.4 Distance from Roads\\u003c/h2\\u003e \\u003cp\\u003eRoad network data were obtained from OpenStreetMap (OSM) via the Geofabrik Nepal download server and clipped to the Lamjung district boundary. The road shapefile was reprojected to EPSG:32644, rasterised to 30 m resolution using the QGIS Rasterize (Vector to Raster) tool, and Euclidean distance from each pixel to the nearest road was calculated using the QGIS Proximity (Raster Distance) tool. Distance to road was reclassified inversely pixels closer to roads were assigned higher risk values (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e) reflecting the established role of road networks as conduits for human ignition in Nepal, where over 40% of fires have been recorded within 1 km of roads (Matin et al., \\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e). Table\\u0026nbsp;\\u003cspan refid=\\\"Tab5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e shows the reclassification scheme applied.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab5\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 5\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eReclassification scheme for distance from roads.\\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=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDistance from Road (m)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eRisk Value\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e0\\u0026ndash;499\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e5\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e500\\u0026ndash;999\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e4\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e1,000\\u0026ndash;1,999\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e3\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e2,000\\u0026ndash;2,999\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u0026ge;\\u0026thinsp;3,000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec11\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003e3.3.5 Distance from Settlements\\u003c/h2\\u003e \\u003cp\\u003eOpenStreetMap building footprint data (gis_osm_buildings_a_free_1) were used as a proxy for human settlement distribution, providing denser and more spatially comprehensive coverage than point based settlement data in Lamjung. The building layer was clipped to the district boundary, reprojected to EPSG:32644, rasterised to 30 m resolution, and distance rasters were produced using the Proximity tool, following the same procedure as for roads. Inverse distance weighting was applied (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003e), with areas closer to buildings assigned higher risk values, reflecting the dominant role of human activities including deliberate burning for pasture renewal and agricultural clearance as the primary ignition sources in Nepal's mid hills (Matin et al., \\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e). Table\\u0026nbsp;\\u003cspan refid=\\\"Tab6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003e. Reclassification scheme for distance from settlements. Table\\u0026nbsp;\\u003cspan refid=\\\"Tab6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003e shows the reclassification scheme.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab6\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 6\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eReclassification scheme for distance from settlements.\\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=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDistance from Settlement (m)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eRisk Value\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e0\\u0026ndash;499\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e5\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e500\\u0026ndash;999\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e4\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e1,000\\u0026ndash;2,999\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e3\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e3,000\\u0026ndash;4,999\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u0026ge;\\u0026thinsp;5,000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec12\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.4 Weighted Overlay Analysis and AHP Weight Determination\\u003c/h2\\u003e \\u003cp\\u003eThe five reclassified risk factor layers were combined into a single composite fire risk map using the Weighted Overlay method implemented in the QGIS Raster Calculator. Relative weights were determined using the Analytic Hierarchy Process (AHP) (Saaty, \\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e1980\\u003c/span\\u003e), a multi criteria decision making technique widely used in GIS based natural hazard mapping. Weights were assigned based on the relative importance of each factor as established in published fire risk studies in Nepal and comparable Himalayan settings (Parajuli et al., \\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e; Joshi et al., \\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e), as shown in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab7\\\" class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003e. Table\\u0026nbsp;\\u003cspan refid=\\\"Tab7\\\" class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003e presents the final weights applied.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab7\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 7\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eAHP derived weights for weighted overlay analysis.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"3\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eRisk Factor\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eWeight (%)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eJustification\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLand Cover\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e30\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003ePrimary determinant of fuel availability and flammability\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDistance from Settlements\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e25\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eDominant ignition source in Nepal's mid hills\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDistance from Roads\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e20\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eSecondary human access and ignition factor\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSlope\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e15\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eDirectly controls fire spread rate\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAspect\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e10\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eIndirectly controls fuel moisture and dryness\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eTotal\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e100\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eThe composite risk score for each pixel was computed using the following Raster Calculator expression in QGIS:\\u003c/p\\u003e \\u003cp\\u003eFire_Risk = (LandCover_Reclass \\u0026times; 0.30) +\\u003c/p\\u003e \\u003cp\\u003e(Settlement_Dist_Reclass \\u0026times; 0.25) +\\u003c/p\\u003e \\u003cp\\u003e(Road_Dist_Reclass \\u0026times; 0.20) +\\u003c/p\\u003e \\u003cp\\u003e(Slope_Reclass \\u0026times; 0.15) +\\u003c/p\\u003e \\u003cp\\u003e(Aspect_Reclass \\u0026times; 0.10)\\u003c/p\\u003e \\u003cp\\u003eThe resulting continuous raster with values ranging from 1.0 to 5.0 was reclassified into five discrete fire risk zones using equal interval thresholds (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab8\\\" class=\\\"InternalRef\\\"\\u003e8\\u003c/span\\u003e) to produce the final fire risk map.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab8\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 8\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eFinal fire risk zone classification.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"2\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eRaw Score\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eRisk Zone\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e0\\u0026ndash;1.79\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eVery Low\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e1.80\\u0026ndash;2.59\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eLow\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e2.60\\u0026ndash;3.39\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eModerate\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e3.40\\u0026ndash;4.19\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eHigh\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e4.20\\u0026ndash;5.00\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eVery High\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec13\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.5 Temporal Fire Trend Analysis\\u003c/h2\\u003e \\u003cp\\u003eAnnual and monthly fire frequency distributions were analysed using the MODIS fire point dataset (2012\\u0026ndash;2024), as MODIS provides a consistent, uninterrupted sensor record suitable for temporal trend analysis. The Mann Kendall non parametric trend test was applied to annual MODIS fire counts using the Kendall package in R (version 4.5.1) to determine whether fire frequency exhibited a statistically significant increasing or decreasing trend over the study period. The Mann Kendall tau statistic and associated p-value were reported. A p value\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05 was considered statistically significant.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec14\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.6 Model Validation\\u003c/h2\\u003e \\u003cp\\u003eTwo complementary validation approaches were employed to assess the accuracy of the fire risk map. Fire detections falling outside valid risk zone pixels (n\\u0026thinsp;=\\u0026thinsp;6,469, 66.6%) were excluded from percentage-based validation calculations but are noted in the results.\\u003c/p\\u003e \\u003cp\\u003e \\u003cstrong\\u003eMethod 1 - Spatial Overlay Validation\\u003c/strong\\u003e \\u003cp\\u003eThe VIIRS S-NPP fire point dataset (9,720 points, 2012\\u0026ndash;2024) was selected for spatial validation due to its higher spatial resolution (375 m) compared to MODIS (1 km). Each VIIRS fire point was assigned the underlying risk zone value from the final fire risk raster using the QGIS Sample Raster Values tool. The percentage of fire points falling within each risk zone was calculated. A model was considered satisfactory if\\u0026thinsp;\\u0026ge;\\u0026thinsp;40% of fire points fell within the Moderate, High, or Very High risk zones, consistent with benchmarks established in comparable Nepal studies (Parajuli et al., \\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e; Joshi et al., \\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e).\\u003c/p\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eMethod 2 - ROC / AUC Analysis\\u003c/b\\u003e: The Area Under the Receiver Operating Characteristic Curve (AUC) was calculated using the pROC package in R. Background absence points (n\\u0026thinsp;=\\u0026thinsp;500) were generated within the Lamjung district boundary, weighted according to the actual spatial distribution of risk zone proportions derived from the final risk map. AUC values were interpreted as: \\u0026lt; 0.6\\u0026thinsp;=\\u0026thinsp;poor; 0.6\\u0026ndash;0.7\\u0026thinsp;=\\u0026thinsp;acceptable; 0.7\\u0026ndash;0.8\\u0026thinsp;=\\u0026thinsp;good; \\u0026gt; 0.8\\u0026thinsp;=\\u0026thinsp;excellent (Hosmer et al., \\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"4. Results\",\"content\":\"\\u003cdiv id=\\\"Sec16\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e4.1 Spatiotemporal Patterns of Fire Occurrence (2012\\u0026ndash;2024)\\u003c/h2\\u003e \\u003cp\\u003eA total of 10,651 quality assured fire detections were recorded in Lamjung District between 2012 and 2024.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eOn the comprising of 931 MODIS and 9,720 VIIRS S-NPP detections. The VIIRS dataset produced approximately 10.4 times more detections than MODIS for the same study period, reflecting its superior spatial resolution (375 m vs. 1 km) and greater sensitivity to smaller fire events.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eAnnual MODIS fire counts showed considerable inter annual variability over the study period (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). The Mann Kendall trend test revealed no statistically significant trend in annual fire frequency over 2012\\u0026ndash;2024 (tau\\u0026thinsp;=\\u0026thinsp;0.156, p\\u0026thinsp;=\\u0026thinsp;0.501). Although a slight positive direction was observed, the result indicates that fire frequency fluctuated without a consistent directional change during the 13 year study period.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eMonthly fire distribution (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e) revealed a pronounced seasonal pattern, with fire activity strongly concentrated in the pre monsoon period. This aligns with the national pattern identified by Mishra et al. (\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e), where more than 78% of burned area was recorded between March and May. Fire activity was near absent during the monsoon season (June-September) and reached its minimum in the winter months (December-January), reflecting the role of monsoon precipitation in suppressing fire conditions.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eAnalysis of the temporal distribution of fire detections by time of day (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e) revealed that 50.2% of detections were recorded during daytime and 49.8% at night time, indicating that the majority of fires in Lamjung are associated with daytime human activity, consistent with anthropogenic ignition patterns documented elsewhere in Nepal's mid hills (Kunwar and Khaling, \\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e2006\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eComparison of MODIS and VIIRS annual fire counts (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e) demonstrated consistently higher detection rates by VIIRS across all years of the study period, confirming the value of using both sensors for comprehensive fire monitoring.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec17\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e4.2 Fire Risk Map\\u003c/h2\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eThe weighted overlay analysis produced a continuous fire risk surface that was classified into five discrete risk zones across the mapped area of Lamjung District (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003e). Table\\u0026nbsp;\\u003cspan refid=\\\"Tab9\\\" class=\\\"InternalRef\\\"\\u003e9\\u003c/span\\u003e presents the area statistics and fire point distribution for each risk zone.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab9\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 9\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eArea distribution of fire risk zones and VIIRS fire point distribution in Lamjung District.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"6\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eRisk Zone\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePixel Count\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eArea (km\\u0026sup2;)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e% of Total Area\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eFire Points (VIIRS)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e% of Fire Points\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eVery Low\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e147,391\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e124.04\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e7.45\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1,090\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e33.5\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLow\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e239,246\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e201.34\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e12.09\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e293\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e9.0\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eModerate\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e315,166\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e265.23\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e15.92\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e244\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e7.5\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eHigh\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e706,178\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e594.29\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e35.67\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e701\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e21.6\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eVery High\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e571,661\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e481.09\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e28.88\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e923\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e28.4\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eTotal\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e1,979,642\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e1,665.99\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e100.0\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e3,251\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e100.0\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003e\\u0026sup1; Mapped area\\u0026thinsp;=\\u0026thinsp;1,665.99 km\\u0026sup2; (98.46% of total district area of 1,692 km\\u0026sup2;). Remaining area consists of NoData pixels along district boundary margins and high-altitude zones where one or more input layers lacked valid coverage.\\u003c/p\\u003e \\u003cp\\u003e\\u0026sup2; Fire points validated\\u0026thinsp;=\\u0026thinsp;3,251 of 9,720 total VIIRS detections falling within valid mapped pixels.\\u003c/p\\u003e \\u003cp\\u003eThe combined High and Very High risk zones covered 64.55% of the total mapped area (1,075.38 km\\u0026sup2;). The Very High risk zone alone accounted for 28.88% of the mapped area (481.09 km\\u0026sup2;), predominantly concentrated in the mid elevation forest belt of the central and southern portions of the district. The Very Low risk zone (7.45%, 124.04 km\\u0026sup2;) was confined to the northern high altitude areas covered by permanent snow, ice, and bare rock, as well as major river channels in valley floors.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec18\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e4.3 Model Validation Results\\u003c/h2\\u003e \\u003cp\\u003e \\u003cb\\u003eSpatial Overlay Validation\\u003c/b\\u003e: Spatial Overlay Validation: Of the 3,251 VIIRS fire detections that could be matched to valid risk zone pixels,\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003e47.7% fell within the High and Very High risk zones (High: 21.6%, Very High: 28.4%), and 55.2% fell within the Moderate to Very High risk zones combined (Moderate: 7.5%, High: 21.6%, Very High: 28.4%). A total of 6,469 VIIRS points (66.6%) fell outside the valid mapped extent and were excluded from percentage calculations.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cstrong\\u003eROC / AUC Validation\\u003c/strong\\u003e \\u003cp\\u003eThe ROC curve analysis (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig8\\\" class=\\\"InternalRef\\\"\\u003e8\\u003c/span\\u003e) yielded an AUC value of 0.687, indicating acceptable discriminatory ability of the fire risk model. This result is consistent with the AUC of 0.787 reported by Joshi et al. (\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e) for a comparable weighted overlay fire risk model in Doti District, Nepal's Mid hills.\\u003c/p\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"5. Discussion\",\"content\":\"\\u003cdiv id=\\\"Sec20\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e5.1 Fire Risk Patterns and Their Drivers\\u003c/h2\\u003e \\u003cp\\u003eThe fire risk map produced in this study reveals that 64.55% of Lamjung District falls within High and Very High fire risk zones, with the greatest concentration in the mid elevation Chir pine and broadleaf forest belt. This finding is consistent with national level assessments indicating that Nepal's Middle Mountains zone has experienced a significant increase in fire incidents, driven by the combined effects of climate warming, fuel accumulation, and anthropogenic pressures (Mishra et al., \\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eThe dominance of land cover as the highest weighted risk factor (30%) reflects the critical role of vegetation type in determining fire susceptibility in Lamjung. The prevalence of Chir pine (Pinus roxburghii) on south facing mid elevation slopes creates concentrated zones of highly flammable dry needle litter, particularly during the pre monsoon season (Matin et al., \\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e). This finding aligns with comparable mid hill studies, where pine dominated forests were identified as the most significant contributors to fire activity (Joshi et al., \\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eThe proximity of high-risk zones to roads and settlements confirms the predominantly anthropogenic nature of fire ignition in Lamjung. Studies conducted across Nepal's Mid-hills have consistently documented that the majority of fires are initiated by deliberate human actions, including burning for pasture renewal, agricultural residue clearance, and hunting, and that fire incidence rates are significantly elevated within 1 km of roads and settlements (Matin et al., \\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e; Kunwar and Khaling, \\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e2006\\u003c/span\\u003e). The outmigration of younger populations from Lamjung's villages a well documented trend in Nepal's mid hills has reduced labour available for traditional fire management practices, further increasing vulnerability to uncontrolled fire spread (Nepal Economic Forum, \\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). The weights assigned to distance from settlements (25%) and roads (20%) collectively represent 45% of the final risk model, reflecting this anthropogenic dominance.\\u003c/p\\u003e \\u003cp\\u003eTopographic factors (slope and aspect), while assigned lower individual weights in the AHP scheme, play a critical amplifying role. South facing slopes in Lamjung's mid elevation zones tend to be both drier (due to greater solar radiation) and dominated by Chir pine creating a multiplicative interaction between aspect, vegetation, and fire risk that is well captured by the weighted overlay approach.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec21\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e5.2 Interpretation of Validation Results:\\u003c/h2\\u003e \\u003cp\\u003eThe AUC of 0.687 indicates acceptable model performance consistent with the complexity of fire occurrence patterns in Lamjung's diverse landscape. A notable proportion of fire detections (33.5%) were recorded within Very Low risk zones, which may be attributed to several factors. First, the VIIRS dataset captures all heat sources including agricultural burning, cooking fires, and industrial heat in addition to forest fires non forest fire events tend to occur in valley areas near settlements classified as low risk by vegetation-based factors. Second, the OSM building footprint data used as a settlement proxy, while more comprehensive than point-based data, may not fully capture the spatial extent of human activity in remote rural areas of Lamjung. Third, minor positional uncertainty in VIIRS fire detections (\\u0026plusmn;\\u0026thinsp;375 m) may result in some points being assigned to adjacent risk zones. These limitations are consistent with those reported by Parajuli et al. (\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e) and Joshi et al. (\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e) in comparable Nepal studies.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec22\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e5.3 Temporal Trends and Climate Implications\\u003c/h2\\u003e \\u003cp\\u003eThe Mann Kendall trend test revealed no statistically significant trend in annual fire frequency over the 2012\\u0026ndash;2024 study period (tau\\u0026thinsp;=\\u0026thinsp;0.156, p\\u0026thinsp;=\\u0026thinsp;0.501). This result should be interpreted carefully for several reasons. The 13 year VIIRS record, while valuable, may be insufficient to resolve decadal scale trends in the presence of high inter annual climate variability. The slight positive tau value (0.156) does suggest a weak increasing direction, consistent with national level observations, but this did not reach statistical significance at the district scale. This finding contrasts with national level studies reporting significant increasing trends in burned area across Nepal (Mishra et al., \\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e), suggesting that district level dynamics may be influenced by local factors including community forest management effectiveness and inter annual monsoon variability. Extension of the time series to include MODIS data from 2000 onwards is recommended to better characterise decadal fire trends in Lamjung.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec23\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e5.4 Comparison with Previous Studies\\u003c/h2\\u003e \\u003cp\\u003eThis study represents the first dedicated fire risk mapping exercise for Lamjung District and contributes to a growing body of district level fire risk assessments in Nepal's under-studied Mid-hills. The methodology applied here is most directly comparable to Joshi et al. (\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e), who produced a fire risk map for Doti District another mid hill district using a similar weighted overlay framework and reported an AUC of 0.787 and that 42.9% and 46.03% of the total forested area falling within High and Moderate risk zones respectively. The present study extends this approach to the Gandaki Province mid hills, where no equivalent study existed, and incorporates the higher resolution ESA WorldCover 10 m land cover product not used in previous Nepal district level studies.\\u003c/p\\u003e \\u003cp\\u003eThe present study's AUC of 0.687 is somewhat lower, potentially reflecting the greater landscape complexity of Lamjung's five zone altitudinal gradient compared to Doti's more uniform terrain. At the national scale, Parajuli et al. (\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e) confirmed that the majority of fire detections in Nepal fall within Moderate to High risk zones a pattern partially replicated in the present study, where 55.2% of matched fire detections fell within Moderate to Very High risk zones. Compared to Matin et al. (\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e), who conducted a national scale analysis across all physiographic zones, the present study provides substantially finer spatial detail at the district level, enabling identification of specific fire prone localities within Lamjung suitable for targeted management intervention.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec24\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e5.5 Limitations\\u003c/h2\\u003e \\u003cp\\u003eSeveral limitations of this study should be acknowledged. First, the MODIS fire detection dataset has a spatial resolution of 1 km, which may underrepresent small fires and introduce positional uncertainty in validation analyses. Although the higher resolution VIIRS dataset (375 m) was used for validation to mitigate this concern, sub pixel fire events remain undetectable. Second, the weighted overlay approach assumes linear relationships between risk factors and fire probability and does not capture complex non linear interactions that machine learning approaches may better represent (Mishra et al., \\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). Third, no field based validation was conducted due to logistical constraints, and the results should be interpreted with this caveat. Fourth, climatic variables including temperature, precipitation, and wind speed were not incorporated as explicit risk factors in the present study, representing an important avenue for future refinement. Fifth, the AHP weights, retain an element of subjectivity, and sensitivity analysis of alternative weight combinations is recommended in future work.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec25\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e5.5 Implications for Forest Fire Management\\u003c/h2\\u003e \\u003cp\\u003eThe fire risk map produced in this study has direct practical applications for forest fire management in Lamjung District. High and Very High risk zones should be prioritised for pre monsoon fire surveillance and early warning system coverage by the Lamjung Division Forest Office. Community Forest User Groups operating in high risk zones should receive targeted training and equipment for fire prevention and suppression. Road corridor management including roadside vegetation clearing and public awareness campaigns should be emphasised in road proximate high risk areas. The seasonal analysis, confirming peak fire occurrence in the pre monsoon period, provides a scientific basis for determining the optimal timing of seasonal fire management interventions.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"6. Conclusion\",\"content\":\"\\u003cp\\u003eThis study produced the first GIS based forest fire risk map for Lamjung District, Nepal, using a weighted overlay analysis approach combining five terrain, land cover, and anthropogenic proximity factors derived from freely available satellite and open access spatial datasets. A total of 10,651 quality filtered NASA FIRMS fire detections (MODIS: 931; VIIRS: 9,720) spanning 2012\\u0026ndash;2024 were used for temporal analysis and model validation. The combined High and Very High risk zones covered 64.55% of the total mapped area (1,075.38 km\\u0026sup2;), with the highest risk areas concentrated in the mid elevation Chir pine and broadleaf forest belt in proximity to roads and settlements. The fire risk model demonstrated acceptable performance with an AUC of 0.687, and 47.7% of matched VIIRS fire detections fell within High and Very High risk zones, with 55.2% in Moderate to Very High zones (n\\u0026thinsp;=\\u0026thinsp;3,251 matched points). The Mann Kendall trend test revealed no statistically significant trend in annual fire frequency over the study period (tau\\u0026thinsp;=\\u0026thinsp;0.156, p\\u0026thinsp;=\\u0026thinsp;0.501), though a slight positive direction was observed. This study fills a critical research gap in Nepal's Mid hills fire risk literature, provides a reproducible open data framework applicable to comparable under studied districts, and offers actionable spatial outputs directly relevant to forest fire management planning, community forest governance, and disaster risk reduction in Lamjung District and the broader Gandaki Province mid-hills. Future research should incorporate climatic variables and field based validation to further improve model accuracy.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003ch2\\u003eAcknowledgements\\u003c/h2\\u003e \\u003cp\\u003eThe authors acknowledge NASA's Fire Information for Resource Management System (FIRMS) for providing open access fire detection data, the European Space Agency (ESA) for the WorldCover 2021 land cover product, the United States Geological Survey (USGS) for SRTM DEM data, and OpenStreetMap contributors via Geofabrik for road and settlement data. All spatial analysis was conducted using QGIS (open source) and R (open source).\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eAbatzoglou JT, Williams AP, Barbero R (2019) Global emergence of anthropogenic climate change in fire weather indices. Geophys Res Lett 46(1):326\\u0026ndash;336. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1029/2018GL080959\\u003c/span\\u003e\\u003cspan address=\\\"10.1029/2018GL080959\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eChuvieco E, Congalton RG (1989) Application of remote sensing and geographic information systems to forest fire hazard mapping. Remote Sens Environ 29(2):147\\u0026ndash;159. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/0034-4257(89)90023-0\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/0034-4257(89)90023-0\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eESA (2021) WorldCover 2021 Product User Manual v2.0. European Space Agency. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://esa-worldcover.org\\u003c/span\\u003e\\u003cspan address=\\\"https://esa-worldcover.org\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eGeofabrik (2024) \\u003cem\\u003eOpenStreetMap Data Extracts: Nepal\\u003c/em\\u003e. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://download.geofabrik.de/asia/nepal.html\\u003c/span\\u003e\\u003cspan address=\\\"https://download.geofabrik.de/asia/nepal.html\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eHosmer DW, Lemeshow S, Sturdivant RX (2013) \\u003cem\\u003eApplied Logistic Regression\\u003c/em\\u003e (3rd ed.). Wiley. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1002/9781118548387\\u003c/span\\u003e\\u003cspan address=\\\"10.1002/9781118548387\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eJaiswal RK, Mukherjee S, Raju KD, Saxena R (2002) Forest fire risk zone mapping from satellite imagery and GIS. Int J Appl Earth Obs Geoinf 4(1):1\\u0026ndash;10. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/S0303-2434(02)00006-5\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/S0303-2434(02)00006-5\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eJones MW et al (2022) Global and regional trends and drivers of fire under climate change. Rev Geophys 60. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1029/2020RG000726\\u003c/span\\u003e\\u003cspan address=\\\"10.1029/2020RG000726\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e. e2020RG000726\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eJoshi KP, Parajuli A, Ayer K, Bhatt R, Gautam AP (2023) Forest fire dynamics in Nepal's Mid-hills: Insights from spatial analysis and risk mapping of Doti District. Forestry: J Inst Forestry Nepal 20(1):61\\u0026ndash;78. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.3126/forestry.v20i1.54338\\u003c/span\\u003e\\u003cspan address=\\\"10.3126/forestry.v20i1.54338\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eKunwar RM, Khaling S (2006) Wildfire management in the Himalayas: Learning from local and traditional practices. \\u003cem\\u003eInternational Forest Fire News\\u003c/em\\u003e, 34, 46\\u0026ndash;54. \\u003cem\\u003e(No DOI \\u0026mdash; grey literature)\\u003c/em\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eMatin MA, Chitale VS, Murthy MSR, Uddin K, Bajracharya B, Pradhan S (2017) Understanding forest fire patterns and risk in Nepal using remote sensing, geographic information system and historical fire data. Int J Wildland Fire 26(4):276\\u0026ndash;286. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1071/WF16056\\u003c/span\\u003e\\u003cspan address=\\\"10.1071/WF16056\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eMinistry of Forest and Environment (2023) Nepal's Forest Fire Management Strategy 2023. Government of Nepal, Kathmandu. (No DOI \\u0026mdash; government document)\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eMishra B, Panthi S, Poudel S, Ghimire BR (2023) Forest fire pattern and vulnerability mapping using deep learning in Nepal. Fire Ecol 19(1):3. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1186/s42408-022-00162-3\\u003c/span\\u003e\\u003cspan address=\\\"10.1186/s42408-022-00162-3\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eNASA FIRMS (2024) \\u003cem\\u003eFire Information for Resource Management System\\u003c/em\\u003e. NASA/EOSDIS. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://firms.modaps.eosdis.nasa.gov/\\u003c/span\\u003e\\u003cspan address=\\\"https://firms.modaps.eosdis.nasa.gov/\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eNDRRMA (2023) \\u003cem\\u003eNational Disaster Risk Reduction and Management Authority Annual Report 2023\\u003c/em\\u003e. Government of Nepal, Kathmandu\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eNepal Economic Forum (2023) What is flaring Nepal's issue of forest fires? \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://nepaleconomicforum.org\\u003c/span\\u003e\\u003cspan address=\\\"https://nepaleconomicforum.org\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eNepali Times (2023) More pre-monsoon forest fires in Nepal. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://nepalitimes.com\\u003c/span\\u003e\\u003cspan address=\\\"https://nepalitimes.com\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eParajuli A, Gautam AP, Sharma SP, Bhujel KB, Sharma G, Thapa PB, Bist BS, Poudel S (2020) Forest fire risk mapping using GIS and remote sensing in two major landscapes of Nepal. Geomatics Nat Hazards Risk 11(1):2569\\u0026ndash;2586. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1080/19475705.2020.1853251\\u003c/span\\u003e\\u003cspan address=\\\"10.1080/19475705.2020.1853251\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003ePokharel B et al (2023) Forest fire dynamics in Nepal's Mid-hills: Regional trends and socio-ecological drivers. \\u003cem\\u003eEnvironmental Development\\u003c/em\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eRothermel RC (1972) A Mathematical Model for Predicting Fire Spread in Wildland Fuels. USDA Forest Service Research Paper INT-115\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eSaaty TL (1980) The Analytic Hierarchy Process. McGraw-Hill, New York\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eUSGS (2000) Shuttle Radar Topography Mission (SRTM) 1 Arc-Second Global. U.S. Geological Survey. \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://earthexplorer.usgs.gov\\u003c/span\\u003e\\u003cspan address=\\\"https://earthexplorer.usgs.gov\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":true,\"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\":\"info@researchsquare.com\",\"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 risk mapping, GIS, weighted overlay, Lamjung, Nepal, MODIS, VIIRS, Analytic Hierarchy Process, Mid-hills, Mann-Kendall\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-9525250/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-9525250/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eForest fires represent one of the most severe natural disturbances threatening Nepal's mid hill ecosystems, yet district level fire risk mapping remains critically absent for the majority of the country's Middle Mountain physiographic zone. This study presents the first comprehensive GIS based forest fire risk map for Lamjung District, Gandaki Province, Nepal, using a weighted overlay analysis approach integrated with multi source remote sensing and open access spatial data. Five conditioning factors slope, aspect, land cover, distance from roads, and distance from settlements were derived from freely available datasets including the SRTM Digital Elevation Model (30 m), ESA WorldCover 2021 (10 m), and OpenStreetMap vector layers. Each factor was reclassified to a standardised risk scale of 1 (very low) to 5 (very high) and combined using Analytic Hierarchy Process (AHP) derived weights within QGIS 3.x. A total of 10,651 quality filtered active fire detections from NASA FIRMS (MODIS Collection 6.1: n\\u0026thinsp;=\\u0026thinsp;931; VIIRS S-NPP 375 m: n\\u0026thinsp;=\\u0026thinsp;9,720) spanning 2012\\u0026ndash;2024 were used for temporal trend analysis and spatial model validation. Results show that 64.55% of the total district area falls within the High (35.67%, 594.29 km\\u0026sup2;) and Very High (28.88%, 481.09 km\\u0026sup2;) fire risk zones, predominantly concentrated in mid elevation broadleaf and Chir pine forest belts in proximity to roads and settlements. Spatial validation confirmed that 47.7% of matched VIIRS fire detections (n\\u0026thinsp;=\\u0026thinsp;3,251) fell within High and Very High risk zones, with an Area Under the ROC Curve (AUC) of 0.687, indicating acceptable model performance. The Mann Kendall trend test revealed no statistically significant trend in annual fire frequency over the study period (tau\\u0026thinsp;=\\u0026thinsp;0.156, p\\u0026thinsp;=\\u0026thinsp;0.501), suggesting high inter annual variability. This study fills a critical research gap in Nepal's Mid hills fire risk literature and provides a reproducible, open-data framework applicable to comparable under studied districts across the Gandaki and other mid hill provinces.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e\",\"manuscriptTitle\":\"Forest Fire Risk Mapping Using GIS Based Weighted Overlay Analysis in Lamjung District, Nepal\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2026-04-28 19:58:47\",\"doi\":\"10.21203/rs.3.rs-9525250/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"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\":\"f3ff90d6-e805-43b3-b1c3-5e1e97e5bf8b\",\"owner\":[],\"postedDate\":\"April 28th, 2026\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[{\"id\":66997447,\"name\":\"Forestry\"}],\"tags\":[],\"updatedAt\":\"2026-04-28T19:58:47+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2026-04-28 19:58:47\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-9525250\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-9525250\",\"identity\":\"rs-9525250\",\"version\":[\"v1\"]},\"buildId\":\"XKTyCvWXoU3ODBz1xrDgd\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}