Multi-criteria decision modelling for forest fire risk mapping in protected areas of Mayurbhanj District, Odisha: A Case study in a geomorphologically diverse touristic landscape

Forest fire is one of the key drivers of forest degradation in Nepal. Most of the forest fires are human-induced and occur during the dry season, with ~89% occurring in March, April and May. The inaccessible mountainous terrain and narrow time window of occurrence complicate suppression efforts. In this paper, forest fire patterns are analysed based on historical fire incidence data to explore the spatial and temporal patterns of forest fires in Nepal. Three main factors are involved in the ignition and spread of forest fires, namely fuel availability, temperature and ignition potential. Using these factors a spatially distributed fire risk index was calculated for Nepal based on a linear model using weights and ratings. The input parameters for the risk assessment model were generated using remote sensing based land cover, temperature and active fire data, and topographic data. A relative risk ranking was also calculated for districts and village development committees (VDCs). In total, 18 out of 75 districts were found with high risk of forest fires. The district and VDC level fire risk ranking could be utilised by the Department of Forest for prioritisation, preparedness and resource allocation for fire control and mitigation.

Forest fires pose a critical problem for natural environments and human settlements, necessitating effective risk management approaches. This study focuses on forest fire risk (FFR) mapping in the Antalya Forest, southern Turkey, by analyzing different criteria. Extensive literature research identifies nearly twenty criteria, which we re-evaluate based on expert opinions and study area characteristics, leading to the selection of four main criteria and fourteen sub-criteria. We process the data using Geographic Information System (GIS) software and calculate weights using the Analytical Hierarchy Process (AHP) and Ordered Weighted Average (OWA) techniques. The main criteria are topographic, meteorological, land use, and forest structure. In the AHP sub-criteria, precipitation, tree species, distance to settlement areas, and elevation receive high values. We classify the resultant FFR maps into five risk classes using both the Jenks Natural Breaks method and equal interval classification. We evaluate the accuracy of our maps using Receiver Operating Characteristic (ROC) analysis and Area Under Curve (AUC) values, based on historical forest fire data. The equal interval classification shows decreased alignment with historical fire data, especially for the AHP method, which performs significantly worse. Both OWA and AHP methods show better performance with Jenks classification compared to equal interval classification, indicating that Jenks Natural Breaks more effectively captures natural groupings in the data, making it a more suitable choice for risk mapping. Applying AHP and OWA in FFR mapping benefits regional forest management and highlights the universal applicability of these methodologies for broader environmental hazard assessments under changing climates.

Fire prevention policies during different periods may lead to changes in the drivers of forest fires. Here, we use historical fire data and apply the boosted regression tree (BRT) model to analyze the spatial patterns and drivers of forest fires in the boreal forests of China from 1981 to 2020 (40 years). We divided the fire data into four periods using the old and new Chinese Forest Fire Regulations as a dividing line. Our objectives here were: to explore the influence of key historical events on the drivers of forest fires in northern China, establish a probability model of forest fire occurrence, and draw a probability map of forest fire occurrence and a fire risk zone map, so as to interpret the differences in the drivers of forest fires and fire risk changes over different periods. The results show that: (1) The model results from 1981 to 2020 (all years) did not improve between 2009 and 2020 (the most recent period), indicating the importance of choosing the appropriate modeling time series length and incorporating key historical events in future forest fire modeling; (2) Climate factors are a dominant factor affecting the occurrence of forest fires during different periods. In contrast with previous research, we found that here, it is particularly important to pay attention to the relevant indicators of the autumn fire prevention period (average surface temperature, sunshine hours) in the year before the fire occurrence. In addition, the altitude and the location of watchtowers were considered to have a significant effect on the occurrence of forest fires in the study area. (3) The medium and high fire risk areas in our three chosen time periods (1981–14 March 1988; 15 March 1988–2008; 2009–2020) have changed significantly. Fire risks were higher in the east and southeast areas of the study area in all periods. The northern primeval forest area had fewer medium-risk areas before the new and old regulations were formulated, but the medium-risk areas increased significantly after the old regulations were revised. Our study will help understand the drivers and fire risk distribution of forest fires in the boreal forests of China under the influence of history and will help decision-makers optimize fire management strategies to reduce potential fire risks.

Forest fires pose significant environmental and socioeconomic threats, particularly in regions such as Central India, where forest ecosystems are vital for biodiversity and local livelihoods. Understanding forest fire dynamics and identifying fire risk zones are crucial for effective mitigation. The current study explores the spatiotemporal dynamics of forest fires in the Khandwa and North Betul forest divisions in the Central Indian region over 22years using Mann-Kendall and Sen's slope tests on MODIS (Moderate Resolution Imaging Spectroradiometer) fire point data. We found a nonsignificant increase in forest fires in both divisions. Khandwa showed a nonsignificant slope rise of more than three events per year, while North Betul revealed an increase of around one event per year. The lack of statistical significance suggests that upward trends of forest fire events may result from random fluctuations rather than consistent patterns. Spatial autocorrelation analysis revealed significant clustering of fire incidents in both regions. Khandwa confirmed moderate clustering (Moran's I = 0.043), whereas North Betul showed robust clustering (Moran's I = 0.096). Kernel density estimation further identified high-risk clusters in both divisions, necessitating zonal-wise targeted fire management strategies. Fire risk zonation was developed using the analytic hierarchy process (AHP), combining 10 environmental and socioeconomic factors. The AHP model, validated using MODIS fire data, showed reliable accuracy. The results revealed many of both divisions in the high- to very high-risk categories. Approximately, 45% of the area of the Khandwa and nearly 50% of the area of North Betul fall under high to very high fire risk zones. Khandwa's high-risk areas mainly lie in the northern and southeastern parts, while North Betul lies in the northwestern and north-eastern regions. The identified fire-prone areas indicate the pressing need for local or region-specific fire prevention and mitigation strategies. Thus, the findings of this study provide valuable insights into forest fire risk management and contribute to more focused research and methodological developments.

The presented research was performed in order to model the fire risk in a part of Hyrcanian forests of Iran. The fuzzy sets integrated with analytic hierarchy process (AHP) in a decision-making algorithm using geographic information system (GIS) was used to model the fire risk in the study area. The used factors included four major criteria (topographic, biologic, climatic, and human factors) and their 17 sub-criteria. Fuzzy AHP method was used for estimating the importance (weight) of the effective factors in forest fire. Based on this modeling method, the expert ideas were used to express the relative importance and priority of the major criteria and sub-criteria in forest fire risk in the study area. The expert ideas mean was analyzed based on fuzzy extent analysis. Then, the fuzzy weights of criteria and sub-criteria were obtained. The major criteria models and fire risk model were presented based on these fuzzy weights. On the other hand, the spatial data of 17 sub-criteria were provided and organized in GIS to obtain the sub-criteria maps. Each sub-criterion map was converted to raster format and it was reclassified based on risk of its classes to fire occurrence. Then, all sub-criteria maps were converted to fuzzy format using fuzzy membership function in GIS. The fuzzy map of each major criterion (topographic, biologic, climatic, and human criteria) was obtained by weighted overlay of its sub-criteria fuzzy maps considering to major criterion model in GIS. Finally, the fuzzy map of fire risk was obtained by weighted overlay of major criteria fuzzy maps considering to fire risk model in GIS. The actual fire map was used for validation of fire risk model and map. The results showed that the fuzzy estimated weights of human, biologic, climatic, and topographic criteria in fire risk were 0.301, 0.2595, 0.2315, and 0.208, respectively. The results obtained from the fire risk map showed that 38.74% of the study area has very high and high risk for fire occurrence. Results of validation of the fire risk map showed that 80% of the actual fires were located in the very high and high risk areas in fire risk map. It can show the acceptable accuracy of the fire risk model and map obtained from fuzzy AHP in this study. The obtained fire risk map can be used as a decision support system for predicting of the future fires in the study area.

Bandwidth determination for kernel density analysis of wildfire events at forest sub-district scale

Abstract. Climate change has exacerbated the intensity as well as frequency of forest fire events in the Indian state of Uttarakhand. The present study focusses on undertaking forest fire risk mapping across the state by utilizing geospatial technology along with Google Earth Engine. Ten parameters were identified that have a strong influence in determining fire prone areas. The Analytic Hierarchy Process (AHP) was then implemented for the development of the risk map in which criteria weights were assigned to the parameters based on their ability to influence a forest fire event. The analysis revealed that out of the total forest area, 24.22% is under ‘very high’ risk zone, 29.24% is under ‘high’ risk zone, 18.23% is under ‘moderate’ risk zone, 7.69% is under ‘low’ risk zone and 20.62% is under ‘very low’ risk zone of forest fire. Further study was carried out to determine fire risk levels in populated regions and in some of the most critical nature reserves having high ecological importance which reveals that ‘very high’ and ‘high’ risk zones have greater population density indicating the influence of anthropogenic activities on forest fire occurrence. The results additionally indicate that four national parks and wildlife sanctuaries are particularly vulnerable to forest fires at present which is a source of concern and requires intervention from the stakeholders.

In recent decades, major growth is observed in wildfire incidents across the globe. These ecological disasters, triggered by natural and/or anthropogenic factors, can have long-lasting effects on the environment, ecosystems, and biodiversity. Advancements in remote sensing technology provided an impetus to forest fires research, enabling precise determination of the geographical locations susceptible to fire and assess fire risk. This study focuses on India, whose total forest cover (71.22 Mha) is about 21.67% of the country’s total geographical area. Around 90% of the forest fires in India are attributable to anthropogenic factors. Therefore, the generation of a forest fire risk map (FRM) is essential for devising strategies to mitigate/manage forest fires and avert their disastrous impacts. An attempt is made to develop FRM for the Budhabalanga river basin, which contains the Similipal national park, a part of the UNESCO World Network of Biosphere Reserves. For this purpose, covariates affecting forest fire are identified viz. fuel (vegetation), topographic features (elevation, aspect, and slope), and human activities. The covariates are assigned weights, depicting their relative importance in influencing the fire, byusing Analytical Hierarchical Process (AHP). The AHP is a widely used technique in multi-criteria decision-making models. The Similipal national park has experienced a prolonged dry spell and below-average monsoon in 2020. The consequent dry conditions led to a significant forest fire event in the last week of February 2021, which lasted nearly three weeks. Fine (30m) resolution satellite data (Landsat-8) are used to calculate the Normalized Difference Vegetation Index (NDVI) corresponding to the study area to assess vegetation conditions before the fire event. Furthermore, Cartosat-1 Digital Elevation Model (24m resolution) is used to extract topographic-related information. The FRM generated for the basin using the assigned weights was 80% accurate when validated with 375m resolution NASA’s VIIRS (Visible Infrared Imaging Radiometer Suite) fire point data for the analyzed fire event. Hence, the methodology considered for developing FRM appears promising. It can be extended to other river basins for identifying fire risk zones and devise timely strategies to mitigate fire risk.

Forest fires are the major cause of degradation of forest. Forest fires have caused substantial damage in the state of Karnataka in terms of economic, social, environmental impacts on humans and also loss of biodiversity. Fire risk indices are important tools for the management of forest fires. They are developed based on static and/or dynamic factors influencing the occurrence of fire and propagation of fire. The objective of the present study was to develop a new static fire risk index based on parameters influencing forest fire such as fuel type, elevation, slope, aspect, terrain ruggedness, proximity to a road, proximity to water bodies and proximity to settlements. MODIS Land cover type yearly L3 global 500m SIN grid(MCD12Q1) was used to compute fuel type index based on historical fire data, SRTM DEM was used to compute slope index, aspect index, elevation index, and terrain ruggedness index. Road index, settlement index, and water body index were developed from the proximity maps generated. A geographic information system (GIS) was utilized adequately to join diverse forest fire causing factors for demarcating static fire risk index. The evaluated exactness was around 87%, i.e., the developed GIS-based static fire risk index of the examination zone was observed to be in solid concurrence with actual fire affected regions. The study area exhibited 32.38% prone to fire risk.

Human-caused wildfire risk rating for prevention planning in Spain

This paper couples a dynamic model of meteorological risk of forest fires with historical fire data in a stochastic model in order to predict forest fire risk maps. Daily Severity Rating (DSR), a meteorological risk of forest fire index, from the Canadian Forest Fire Weather Index System (CFFWIS), results from the transformation of daily weather observations into relatively simple indices that can be used to predict fire occurrence, behaviour and impact. CFFWIS uses the daily weather observations or forecasts to calculate moisture of several fuel types and size classes, and combines them into indices of fire danger related to fire potential rate of spread, heat release, and fireline intensity. The DSR index depends only on daily measurements of air temperature (∘C), relative humidity (%), 10 m open wind speed (km/h) and 24 h accumulated precipitation (mm). DSR is extremely important for forest fire risk assessment but it is restricted to climatic factors. DSR itself is an incomplete measure of seasonal fire activity because the latter is also dependent on the ignition pattern and the available control resources. Durão proposed one Bayesian approach to calculate the local conditional probabilities of a forest fire occurring at any location x, given the class R(x) of predicted DSR for same location x. Suppose an indicator variable I(x) that takes the value 1 if a fire occurred in x, otherwise I(x) = 0. Let us call R(x) as the classes of DSR predicted for control points and inferred by simulation for any location x. In this paper, we calculate the probability of a forest fire occurring in x, given R(x) and the historical data of fires occurrence in x, D(x): $$\mathit{Prob}\ \{I({\bf X})\vert \ R({\bf X}),D({\bf X})\}$$ Both conditional probabilities Prob {I(x) | R(x)} and Prob {I(x) | D(. )} can be inferred at any location x. Hence conditional probability can be calculated with the method of Journel called tau model. Risk maps of forest fires can be driven from these conditional probabilities. A study was conducted for the period 2000–2005, but in this case study only the results for the 2 year period 2003–2004 of the Portuguese fire seasons are presented and discussed.KeywordsForest FireBurnt AreaFire RiskFire OccurrenceFire DangerThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Forest fires are a multidimensional phenomenon that affects many parts of the world, including the Zagros region of Iran. They are often caused by various factors that can have natural-, anthropogenic-, or combined origins. Considering the significant environmental and socio-economic impacts of forest fires, it is essential to take necessary measures to identify the areas that are prone to forest fires and develop plans and policies for crisis management and risk mitigation accordingly. In this study, we applied an integrated geoinformation (remote sensing and GIScience) approach to analyze and map forest fire risk in Gachsaran, Iran, which is highly prone to forest fires. For the forest fire risk mapping (FFRM), we employed a GIS-based multi-criteria decision analysis method in combination with fuzzy and analytical network process (ANP) methods to identify the forest areas with a high fire risk. To distinguish the vulnerable sites, we employed 13 independent variables encompassing geomorphological factors, land surface characteristics, climatological factors, and anthropological factors. To develop initial criteria maps, we determined the criteria weights using the ANP and used the fuzzy technique for standardization. Finally, the forest fire risk map was produced using the multi-layer perceptron artificial neural network. Our results were also validated against the historical forest fire data using the operating characteristics. Our results showed that 18.417% of the province is subject to a very high forest fire risk. These are areas that should be prioritized when designing precautionary and protective measures. Among the criteria examined in this study, the land surface temperature, soil moisture, and distance from historical forest fire sites received the highest scores in the ANP. The results of this study can be used to identify vulnerable areas, take appropriate planning measures to deal with forest fire risk, and make informed decisions regarding the allocation of facilities in high-risk areas.

In Turkey, forest areas located along the coastline of the Marmara, the Aegean and the Mediterranean regions are very sensitive to fire. As a result of forest fires, about 10000 hectares of forest area is damaged annually. One of the key elements in firefighting is early detection and quick intervention. In order to achieve this goal, first of all, the forest areas with fire risk should be determined especially for fire sensitive forest areas. The forest fire risk can be evaluated considering various risk factors such as stand structures, topographic factors, proximity to some features (roads, settlements, and water resources), and climatic factors. In this study, GIS techniques and Analytic Hierarchy Process (AHP) method was used to produce forest fire risk map for the first degree fire sensitive forest land located in Bodrum province of Muğla in Turkey. The results indicated that 11.83% and 21.98% of the forest area was categorized as very high and high fire risk, respectively, while 22.28% and 25.93% was moderate and low fire risk, respectively. The fire risk was found to be very low at the rest of the study area (17.98%). To compare the fire risk map with actual forest fire occurrences in the study area, it was overlapped with the fire map indicating forest components where previous forest fires (>1.0 ha) occurred in the study area in last five years. It was found that 38.32% of the areas damaged by the previous fires were categorized as high and very high fire risks zones in fire risk map, while 28.44% was moderate fire risk zones. The result showed that tree species was the most effective risk factor, followed by tree stages and proximity to water resources. This study revealed that the combination of GIS techniques and AHP method is very advantageous approach to map forest areas with fire risk in short time.

Forest fires constitute a foremost environmental calamity that distresses the sustainability of the forest. The main source of degradation of Jharkhand forests are forest fires conquered by forest species of Sal and Bamboo. Palamau Tiger Reserve in Jharkhand state, India, is becoming more susceptible to forest fire due to anthropogenic disturbance coupled with speedy upsurge in population. In this study, forest fire risk in PTR was evaluated based on various fire inducing factors, viz., forest fuel, settlements, roads, bare soil index, elevation slope and aspect. Geoinformatics based multi-criteria decision analysis (MCDA) through method of AHP (analytic hierarchy process) used to extract forest fire risk map in five classes: Very low risk, low risk, moderate risk, high risk and very high risk. The results obtained showed that about 180 km2 (14.85%) falls under very low fire risk zone, 234 km2 (19.30%) falls in low fire risk zone, 269.73 km2 (22.16%) falls under moderate fire risk zone, 299.36 km2 (24.59%) falls under high fire risk zone and 232.56 km2 (19.10%) falls in very high fire risk zone. Forest fire risk map was validated from historical fire incidents observed through field data, MODIS and SNPP-VIIRS satellite products. The results showed that the geoinformatics based forest fire risk zones delineated through MCDA-AHP method are in good agreement with historical forest fire occurrences, henceforth may be utilised for fire planning for mitigation in forest areas.

Forest fires are recurrent natural hazards threatening ecosystems, biodiversity, and nearby communities. The Chure Tarai Madhesh Landscape (CTML) is a biodiversity hotspot harboring endangered species of flora and fauna. The increasing severity of forest fires in this region has raised immediate concerns, yet research remains limited. This study explores synergistic approaches for forest fire risk mapping using a knowledge-based model (Fuzzy Analytical Hierarchy Process (FAHP)) and data-driven models (Random Forest (RF) and Boosted Regression Tree (BRT)). This study utilized eleven conditioning factors and assessed model accuracy using the ROC curve and multiclass error matrix. The results demonstrate low multicollinearity among factors and a robust FAHP consistency ratio of 0.03. The RF model outperformed with an AUC of 0.95 and an overall accuracy of 0.91. The study revealed an increasing seasonal trend in fire incidents, with the western region showing heightened vulnerability. The RF, BRT, and FAHP models classified landscape forest areas as highly susceptible to fires; 47.85%, 33.25%, and 50%, respectively, with fourteen out of thirty-six districts of CTML were at heightened risk of wildfires. This holistic approach to fire risk assessment aids in creating more impactful fire risk management plans and provides a foundation for automated fire risk assessment.