Forest Fire Risk Zoning In An Administrative Division Of Cachoeiro De Itapemirim (Brazil/ES)

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.

The risk of fire in urban forests is high due to housing and living infrastructure adjacent to the forest, resulting in large human and property damage and high frequency of occurrence. It is very important to identify the forest fire environment and potential risk factors for each city in order to manage the forest fire risk in the cities adjacent to the forest. In this study, urban forest fire management measures were suggested by conducting a survey on the causes of forest fires and the entire adjacent forests in Nowon-gu, which has the most urban-forest adjacent areas in terms of administrative districts among Seoul, which has the highest population density in Korea. The forest fire investigation has been conducted for the last 5 years, and in addition to analyzing the forest, topography, and meteorological environment, analysis of fire-vulnerable areas in the event of a forest fire in downtown Seoul was conducted. Eight action plans were presented by dividing the actionable forest fire management into prevention, preparation, and response stages for areas adjacent to forests and areas vulnerable to forest fire risk. First, in prevention, fuel management that can reduce the risk of forest fires, map production and risk reduction management for temples in the forest, and selection of appropriate locations to install forest fire long-distance around monitoring cameras and drone autonomous monitoring stations were suggested. Second, in the preparation stage, a fuel blocking method was presented to establish a safe zone for hiking trails, prepare a mountaineering safety map for forest fire evacuation, and prevent the spread of residential fires in forest interface area. Third, in the response stage, the installation of a multi-purpose outdoor fire hydrant for suppression of forest fires and house fires adjacent to the forest, the activities of forest fire extinguishing vehicles and marking of the entry area, and the establishment of an urban forest disaster integrated control system were suggested.

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.

Flooded forests are very important ecosystems that are rich in terms of their diverse flora and fauna. However, they are mostly degraded in many parts of the world, and the remaining fragmented areas are in a critical condition. Forest fires are one of the major environmental disasters that cause serious damage to forest ecosystems, and negatively affect the sustainability of forest resources. In order to minimize the potential effects of fires on forest ecosystems, forest fire risk maps should be generated, and thereby the necessary precautionary measures can be taken in these areas, according to fire risk levels. Geographical information system (GIS) techniques, integrated with multi-criteria decision analysis (MCDA) methods, can be effectively used to develop risk maps for natural hazards, such as forest fires, winter storms, floods, etc. In this study, GIS techniques integrated with an AHP (analytic hierarchy process) method were used to generate a forest fire risk map. The study was implemented in the Karacabey flooded forest, located in the city of Bursa in Turkey. In the solution process, the forest fire risk was evaluated considering two major risk factors, including stand structures (tree species, crown closure, and tree stage) and topographic factors (slope and aspect). The vegetation factor under climate control was considered, instead of directly using data of climatic elements such as temperature and humidity. The results indicated that 25.28% of the forest area was of high fire risk, while 53.17% and 21.55% was of medium and low fire risk, respectively. It was found that the most effective criterion was tree species, followed by tree stage. This aspect had the least effective criterion on forest fire risk. It was revealed that GIS techniques integrated with MCDA methods can be used effectively to estimate forest fire risk zones.

Forest fire hazard and risk mapping is an essential tool for planning and decision making regarding the prevention and suppression of forest fires,as well as fire management in general, as it allows the spatial visualization of areas with higher and lower ignition probability. This study aimed to develop a forest fire risk zoning map for the Vila Velha State Park and its surroundings (Ponta Grossa, Paraná State, Brazil), for the period of higher incidence of forest fires (from April to September) and for the period of lower incidence (from October to March). The following risk and hazard variables were identified: human presence, usage zones, topographical features, soil coverage and land use and meteorological conditions. Coefficients (0 to 5) reflecting the fire risk or hazard degree were allocated to each variable in order to construct the maps. The integration of these maps, through a weighting model, resulted in the final risk mapping. The very high and extreme risk classes represented about 38% of the area for both periods. The forest fire risk mapping spatially represented the levels of fire risk in the area, allowing the managers to identify the priority sectors for preventive actions in both fire seasons.

Abstract. The forested areas along the coastal zone of the Mediterranean region in Turkey are classified as first-degree fire sensitive areas. Forest fires are major environmental disaster that affects the sustainability of forest ecosystems. Besides, forest fires result in important economic losses and even threaten human lives. Thus, it is critical to determine the forested areas with fire risks and thereby minimize the damages on forest resources by taking necessary precaution measures in these areas. The risk of forest fire can be assessed based on various factors such as forest vegetation structures (tree species, crown closure, tree stage), topographic features (slope and aspect), and climatic parameters (temperature, wind). In this study, GIS-based Multi-Criteria Decision Analysis (MCDA) method was used to generate forest fire risk map. The study was implemented in the forested areas within Yayla Forest Enterprise Chiefs at Dursunbey Forest Enterprise Directorate which is classified as first degree fire sensitive area. In the solution process, "extAhp 2.0" plug-in running Analytic Hierarchy Process (AHP) method in ArcGIS 10.4.1 was used to categorize study area under five fire risk classes: extreme risk, high risk, moderate risk, and low risk. The results indicated that 23.81 % of the area was of extreme risk, while 25.81 % was of high risk. The result indicated that the most effective criterion was tree species, followed by tree stages. The aspect had the least effective criterion on forest fire risk. It was revealed that GIS techniques integrated with MCDA methods are effective tools to quickly estimate forest fire risk at low cost. The integration of these factors into GIS can be very useful to determine forested areas with high fire risk and also to plan forestry management after fire.

Planning the analyses of the spatial distribution and driving factors of forest fires and regionalizing fire risks is an important part of forest fire management. Based on the Landsat-8 active fire dataset of the Liangshan Yi Autonomous Prefecture from 2014 to 2021, this paper proposes an optimal parameter logistic regression (OPLR) model, conducts forest fire risk zoning research under the optimal spatial analysis scale and model parameters, and establishes a forest fire risk prediction model. The results showed that the spatial unit of the optimal spatial analysis scale in the study area was 5 km and that the prediction accuracy of the OPLR was about 81%. The climate was the main driving factor of forest fires, while temperature had the greatest influence on the probability of forest fires. According to the forest fire prediction model, mapping the fire risk zoning, in which the medium- and high-risk area was 6021.13 km2, accounted for 9.99% of the study area. The results contribute to a better understanding of forest fire management based on the local environmental characteristics of the Liangshan Yi Autonomous Prefecture and provide a reference for related forest fire prevention and control management.

Forest and wildland fires are a natural part of ecosystems worldwide, but large fires in particular can cause societal, economic and ecological disruption. Fires are an important source of greenhouse gases and black carbon that can further amplify and accelerate climate change. In recent years, large forest fires in Sweden demonstrate that the issue should also be considered in other parts of Fennoscandia. This final report of the project “Forest fires in Fennoscandia under changing climate and forest cover (IBA ForestFires)” funded by the Ministry for Foreign Affairs of Finland, synthesises current knowledge of the occurrence, monitoring, modelling and suppression of forest fires in Fennoscandia. The report also focuses on elaborating the role of forest fires as a source of black carbon (BC) emissions over the Arctic and discussing the importance of international collaboration in tackling forest fires. The report explains the factors regulating fire ignition, spread and intensity in Fennoscandian conditions. It highlights that the climate in Fennoscandia is characterised by large inter-annual variability, which is reflected in forest fire risk. Here, the majority of forest fires are caused by human activities such as careless handling of fire and ignitions related to forest harvesting. In addition to weather and climate, fuel characteristics in forests influence fire ignition, intensity and spread. In the report, long-term fire statistics are presented for Finland, Sweden and the Republic of Karelia. The statistics indicate that the amount of annually burnt forest has decreased in Fennoscandia. However, with the exception of recent large fires in Sweden, during the past 25 years the annually burnt area and number of fires have been fairly stable, which is mainly due to effective fire mitigation. Land surface models were used to investigate how climate change and forest management can influence forest fires in the future. The simulations were conducted using different regional climate models and greenhouse gas emission scenarios. Simulations, extending to 2100, indicate that forest fire risk is likely to increase over the coming decades. The report also highlights that globally, forest fires are a significant source of BC in the Arctic, having adverse health effects and further amplifying climate warming. However, simulations made using an atmospheric dispersion model indicate that the impact of forest fires in Fennoscandia on the environment and air quality is relatively minor and highly seasonal. Efficient forest fire mitigation requires the development of forest fire detection tools including satellites and drones, high spatial resolution modelling of fire risk and fire spreading that account for detailed terrain and weather information. Moreover, increasing the general preparedness and operational efficiency of firefighting is highly important. Forest fires are a large challenge requiring multidisciplinary research and close cooperation between the various administrative operators, e.g. rescue services, weather services, forest organisations and forest owners is required at both the national and international level.

Forest fire risk mapping is an essential measure for forest fire management. Quickly and precisely assessing forest fire risks, rationally planning fire risk zones, and scientifically allocating firefighting resources are of great significance for mitigating the increasingly severe threat of forest fires. This study utilized the random forest (RF) algorithm and the Fuzzy Analytic Network Process (FANP) to conduct a forest fire risk-zoning study in the protection and development belt of Wuyishan National Park. The findings revealed that some areas in the western and southern parts of this region have relatively high fire risk levels. Particularly, forest fire prevention and control in the western area need to be strengthened to prevent potential hazards to Wuyishan National Park. The accuracy of the FANP model was as high as 88.5%; areas with fire risk levels of grade 3 and above could control 98.44% of forest fires, and the proportion of areas with fire risk levels of grade 4 and above was 33.41%, which could control 65.63% of forest fires. This finding indicates that the FANP has preferable applicability in small-scale forest fire risk zoning and can offer more reliable decision-making support and reference basis for regional forest fire management.

Global Climate change (CC) is featured by long-term changes in the mean values of climatic parameters (predominantly mean temperature) and in the profile of extreme weather events (e.g., increase in frequency, intensity, lengthening, and persistence). These climatic changes are supposed to have a deterioration impact on forest fire and flood disasters. Greece, an east Mediterranean country, is featured by a wide variety of micro-climates due to its unique geographical diversity, including hot and dry summers in the eastern part of the country (where a large amount of precipitation falls in the form of showers and thunderstorms) and wet winters in the western part. The combination of certain climatic zones with unfavorable land use and land cover changing patterns has resulted in several regions being prone to flooding and forest fires. The authors, based on relevant records, consider central and south Greece as flood and forest fire hotspots and attempt to: (a) present scientific estimations of local climate changes; (b) outline recent trends in the number of respective disasters and the amount of losses in these regions; (c) address recent changes in local climatic factors that might have influenced flood and forest fire hazard and risk in these regions; and (d) study the perceptions of the lay public and management authorities regarding the accountability of CC for flood and forest fire risk and hazard changes. The results show the variability of climate changes between neighboring areas, which directly affect the risk of forest fires and floods. Especially since the beginning of the 21st century, central Greece has been experiencing dramatic increases in both risks, while in south Greece the latter remain relatively stable. With regard to the perceptions of citizens and management authorities, the mental connection of local CC with forest fires and floods is still weak if not totally missing. Since knowledge and perceptions of the local “history” of forest fires and floods and the interconnections with CC by region is very important for the local communities to take appropriate mitigation and adaptation measures, this paper outlines a methodological path for similar studies to be conducted also in other regions of the Mediterranean basin and beyond.

Forest fires are characterized by a rapid and devastating nature, underscoring the practical significance of forest fire risk monitoring. Currently, forest fire risk assessments inadequately account for non-meteorological hazard factors, lack the hazard-formative environment and contextual disaster knowledge for fire occurrence mechanisms. In response, based on MODIS products, we augmented the FFDI (forest fire danger index) with the RDST (regional disaster system theory) and selected various fire risk indicators, including lightning. MOD14 was used for the correlation analysis of fire and its indicators. Through the amalgamation of the analytic hierarchy process (AHP), the entropy method, and the minimal relative entropy theory, we formulated the CFFRI (composite forest fire risk index) and assessed forest fire risks spanning from 2010 to 2019 in Southwest China, which were validated with historical disaster data and MCD64. The findings revealed that the CFFRI yields consistently higher overall fire risk values, with 89% falling within the high-risk category and 11% within the moderate-risk category. In contrast, the FFDI designated 56% of cases as fourth-tier fire risks and 44% as third-tier fire risks. Notably, the CFFRI achieved an accuracy of 85% in its calculated results, while the FFDI attained 76%. These outcomes robustly demonstrate a superior applicability of the CFFRI compared with the traditional FFDI.

The main purpose of this study is to develop a statistical model to prepare forest fire risk map using GIS. In this study eight important factors were used to determining the forest fire risk such as land use/land cover type, slope, aspect, altitude, settlement, road, temperature and precipitation. The analytic hierarchy process (AHP) was used to evaluate the factors. Precipitation and temperature were the most important factors to determining the forest fire risk. The study area has approximately 10.72% low fire risk, 28.21% moderate fire risk, 43.50% high fire risk, 14.65% very high fire risk, and 2.92% extreme forest fire risk. 61.07% of the study area has a high, very high and extreme forest fire risk. In order to prevent forest fires, land cover/land use should be planned in a way that does not damage forests. Especially vehicle roads, expressways, etc. which are located near the forests, have a high fire risk. Therefore, these areas should be planned in a way that will not damage the forests. The climatic characteristics of the study area should be examined, the urban texture should not be in a way to prevent microclimatic factors such as wind and precipitation.

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.

This study aimed at developing a forest fire risk model using a combination of GIS and Remote sensing techniques, which helped to identify the level of forest fire vulnerability in Virunga Massif, located at the edge of central and eastern Africa. The Analytic Hierarchical Process (AHP) approach was employed to rank and weigh the key variables and combine them into different fire risk input factors which were later integrated into the main forest fire risk model. The main input datasets, which were linked with a potential source of a forest fire, include the land cover (specifically vegetation type data generated through the Landsat 8 image classification); topographic variables such as slope, elevation and aspect retrieved from the existing Digital Elevation Model (DEM) of Rwanda; the concentration of illegal activities and proximity to beehives sites; as well as visibility from the road and human settlements. Input factor maps were generated, assigned weights and combined in a GIS environment to produce a Virunga massif fire risk model map, which was validated using the existing burnt areas map, and ground truth points recorded using GPS. The study found that the ignition factors are the most forest fire triggering factors in Virunga massif, followed by topographic factors which play a major role in the fire spreading across the ecosystem. The high forest fire risk areas were found in steep slope location around the peaks of the volcanoes, whereas areas with the lowest risk of forest fire were found inside the forest at gentle slopes. The model was validated at 75% accuracy using ground truth data. The study proposes measure to halt the ignition factors through prevention of illegal activities in the Virunga massif for the successful prevention of the forest fire risk in the ecosystem, with much effort invested during the dry season, along with the relocation of beehives to a farther distance from the ecosystem’s edge.
 Keywords: Forest Fire Risk Modelling, Biodiversity, Illegal Activities, Ignition Factors, Topographic Factors, Analytic Hierarchy Process