Extended Abstract Background: Human activities, climate variability, and environmental stress have strongly affected forest ecosystems worldwide. Forest fires are among the major factors of global ecosystem destruction. Fires in the forest, whether of human or natural origin, have been raised as a serious crisis in recent years. Hence, fire risk assessment plays an important role in forest fire management because knowing where the highest risk is essential to minimize threats to resources, lives, and property. Integration of spatial information from different sources using statistical analysis in the GIS environment is a suitable tool for managing and spreading forest fires, which is one of the main natural hazards in northern Iran. Therefore, it is necessary to prepare a fire risk assessment map for the planning and protection of forests. Methods: The current practical research concerning its nature is a combination of documentary, descriptive, and quantitative model-based methods regarding the research method. In this study, fuzzy and hierarchical (AHP) logic models were combined to investigate the risk of forest fire in Mazandaran province in five classes, very high, high, medium, low, and very low, respectively, using four main criteria and nine sub-criteria, namely topography (height, slope, direction, and rivers), climatic factors (peak temperature and precipitation), human factors (residential areas and the network of communication roads), and biological factors (vegetation). To obtain the net vegetation cover, the Normalized Difference Vegetation Index (NDVI) was applied to the Sentinel-2 satellite image set in a 5-year period (2017-2022) in the GEE web system. The height, slope, and slope direction maps of the study area were prepared from the digital elevation model (DEM) of 12.5 m from the ALOS AVNIR-2 dataset. The distance from rivers, residential areas, and the road network was calculated using the Euclidean distance tool in ArcMAP software. The geographic location of meteorological synoptic stations was obtained from the Meteorological Organization, and its information was used as meteorological input data. In the ArcMap environment, a map of average annual precipitation and maximum temperature was prepared from synoptic stations through interpolation for the period from 2007 to 2021. Based on this modeling method, experts' opinions were used for the relative importance and priority of criteria and sub-criteria in the risk of forest fire in the study area to obtain the fuzzy weight of criteria and sub-criteria. Based on the weighting coefficients applied in the present plan, the final weights of the criteria and sub-criteria affecting forest fire from the highest to the lowest weights belong to the topographical, biological, climatic, and human criteria. Among the sub-criteria, the highest and lowest weights belong to vegetation and slope, respectively. The consistency rate (CR) for the matrices of the affecting factors is equal to 6.25%, which is less than 10%, actually indicating that the weight of the criteria is proportionate and reliable. The highest weights were obtained for the vegetation cover and the slope direction, and the lowest weights belonged to the distance from the river and the slope. Finally, the fire risk assessment map was prepared by combining the fuzzy maps of the sub-criteria in GIS. Results: Overall, medium to very high fire risk potential was found in 72% of the studied area. From a total area of about 2373189 hectares, very low (8.4%), low (18.3%), medium (23.66%), high (25.62), and very high (24%) vulnerability rates were identified in Mazandaran province. Higher fire potential was detected in the East and Southeast parts than in other parts of the study area. The aforementioned fuzzy layers clearly show that the height, slope, and amount of precipitation are low and the density of residential areas and the network of communication roads are high in these parts, with high temperatures. In fact, these factors have increased the risk of fire in these areas. In the present study, the highest fire potential was observed at low altitudes, which could have resulted from the concentration of human activities at low altitudes. Moreover, most fires occurred on low slopes in the studied area. The distance layer from waterways also plays a dual role in the occurrence of fire. The results of the model show an inverse correlation between the distance from roads and fire potential. Based on the results of the fuzzy AHP model, the probability of fire increased with the decrease in precipitation and the increase in annual temperature. A decrease in the amount of precipitation causes a decrease in soil moisture and vegetation, elevating the possibility of fire. On the other hand, the increase in temperature causes the drying of vegetation and reduces humidity, thereby increasing the possibility of fire. Conclusion: It can be concluded that preparing a fire risk assessment map can help managers and planners in identifying areas with high potential and in crisis management in vulnerable areas. The obtained fire risk assessment map can be used as a decision-making support system to predict future fires in the study area.

A study was conducted to evaluate fire risk using environmental- and human-induced factors in District Three of Neka-Zalemroud forests. For this purpose, a range of effective sub-criteria including physiographic, biological, climatic and human variables were applied. The historical fire map in study area was additionally used. It was overlaid on the spatial map of each sub-criterion to explore the correlation of the weighted high-risk classes with the historical fire occurrences. Fire risk potential map was provided based on weighted overlay of all effective sub-criteria in five classes. The historical fire map was consequently overlaid on fire risk potential map. Results showed that vegetation type and density, distance from river and the averaged relative annual humidity were associated with the highest effects in fire occurrence. Results also demonstrated the agreement of high-risk areas in the potential fire risk map with the historical fires, which supports the high validity of the applied method to assess the fire risk across the study area.

Fire in natural resources is one of the crises that causes irreparable damage to ecosystems and the environment every year. The purpose of this research is to attempt to study areas of risk aversion and to prepare a map of forest fire hazard area by integrating topographic data and other additional information from a GIS system for Golestan province. In order to carry out this research, firstly, with the removal of the recorded data related to the situation of fires occurred in 2009 and 2010, the domain of all natural resources of Golestan province was carried out. In order to identify areas with high fire potential, static parameters were used to control the burning of forest forests (elevation, slope, slope direction, land use / land cover, evaporation rate). Each of the static parameters is divided into different classes And to each class, using bachelor's knowledge and review of research, ground data and the results of the above studies are weighted from one to ten. In the following, by using overlap of these layers with different weights, areas with high fire potential were identified for the forests of Golestan province. Finally, all weights were summed up, the final weight was obtained and a fire hazard map was prepared. The Arctic GIS9. 2 software has been used to generate a fire hazard map. Also, The fire risk index (FRSI), the Normalized Difference Vegetation Index(NDVI), and the zoning map, have a fire hazard in the risk category (very low to high) ). The results showed that most of the fires occurred in hardy and covered with forested areas, as well as in the forested areas with a crown and an intermediate cover, and in the next stage, in the woods and shrubland areas. In calculating the calculation of fire density in altitudes, the results showed that approximately 90 percent of fires occurred in average altitudes between 700 and 1500 meters. Overall, the findings showed that 90 percent of burns occurred continuously in areas With fire hazard, 30% in hazardous areas and 60% in extreme areas, so that its Galikesh, Minoodasht, , Azadshahr has high risk of high fire.

Forest fires in Iran and particularly in the northern forests had destructive effects on the physiognomy of these forests. Recognition, prevention and controlling the Socio-economic destroys caused by natural hazard are the main objectives of administrative and educational organizations. One of the methods for prevention of forest fires is mapping the probability risk zones. In this study, map of fire probability risk for Golestan national park was prepared using regression logistic method and GIS. The effective factors on fires including climate, topography, vegetation and human factors were prepared in the GIS environment by different methods and sources. The occurred forest fires map was gathered and generated as a Boolean map. The logistic regression modelling was done using effective factors as independent variables and the occurred forest fire map as dependent variable. The obtained Pseudo R2= 0.3121 and ROC= 0.9132 from model indicate that regression logistic could modeled forest fire probabilities on the study area. The probability fire map was classified to four low, medium, high and sever dangerous classes. The obtained forest fire probability map was assessed using the some unused occurred fire points. The assessment results showed that more of occurred forest fire points were in the medium and high dangerous classes.

Fires, in the forests and parks become causative to fall away to natural resources largeness of locale sector Kohgiluyeh and Boyer-Ahmad forests. The purpose of this research is, Forest fire risk zone mapping using utilization from of topography criteria and sub criteria (slope, distance from the river, height, direction of inclination), Physical (type of vegetation, vegetation density, soil moisture), human (distance from roads, distance from the village), and climate (average annual temperature, precipitation, relative humidity, and windiness direction), that criterions connection became distinctive relationship between criteria Fuzzy DEMATEL technique. Network analysis process, was used to weighting all parameters in Super Decision software. by fuzzy logic method, maps is fuzzed, and in the GIS environment getting Forest fire risk zone final mapping. results of this study expressing that among of the criteria, was the topographic criteria (0.423) and the between sub-criteria's t slope map is high weight and human criteria, biological and climatic getting from right to left value of%0.257, %0.194 and%0.124. the percentage area classification mapping forest fire potential by Boolean operator for is in series, PRODUCT PROCESSOR value of 5%, AND 35.68%, Gamma 34.82%, and SUM 34.84%. Too model FIRE RISK using images Landsat 8, digital elevation model, slope and windiness direction provide for the region. Fire classification map was prepared using fire point data for 4 years (93-96).Comparing its results with the results of the FUZZY ANP model and FIRE RISK indicates a high degree of compliance in areas with high fire risk.

Forest areas are among the most important natural and ecological resources on the Earth and are considered as one of the main pillars of sustainable development in any country. Fires ruins almost 5500 hectares of Iran‘ s forests yearly. In this research, firstly, the fire points were identified using the fire data of Forest Organization in combination with MODIS sensor data between 2012 and 2017. Due to the fact that more than 75% of fires were happened in the hot season of the year (June, July, and August), the data of the three months was used for modeling. Then, the effective parameters in fire occurring were evaluated and the dependent parameters were removed. Accordingly, two methods, including multiple linear regression and multivariate adaptive regression spline were studied to predict the fire risk. Some important parameters including the root-mean-square error (RMSE), R2, the correct estimation percentage of fire and non-fire points, and error distribution were used to evaluate. After modeling, it was found that the multivariate adaptive regression spline has better performance— where its RMSE of test data was 0. 1628, its R2 of test data was 0. 893, and its correct estimation percentage of test fire points and test non-fire points was near 94% and 88% respectively, as well as its error distribution was better than the other method...

Fires, in the forests and parks become causative to fall away to natural resources largeness of locale sector Kohgiluyeh and Boyer-Ahmad forests. The purpose of this research is, Forest fire risk zone mapping using utilization from of topography criteria and sub criteria (slope, distance from the river, height, direction of inclination), Physical (type of vegetation, vegetation density, soil moisture), human (distance from roads, distance from the village), and climate (average annual temperature, precipitation, relative humidity, and windiness direction), that criterions connection became distinctive relationship between criteria Fuzzy DEMATEL technique. Network analysis process, was used to weighting all parameters in Super Decision software. by fuzzy logic method, maps is fuzzed, and in the GIS environment getting Forest fire risk zone final mapping. results of this study expressing that among of the criteria, was the topographic criteria (0.423) and the between sub-criteria's t slope map is high weight and human criteria, biological and climatic getting from right to left value of%0.257, %0.194 and%0.124. the percentage area classification mapping forest fire potential by Boolean operator for is in series, PRODUCT PROCESSOR value of 5%, AND 35.68%, Gamma 34.82%, and SUM 34.84%. Too model FIRE RISK using images Landsat 8, digital elevation model, slope and windiness direction provide for the region. Fire classification map was prepared using fire point data for 4 years (93-96).Comparing its results with the results of the FUZZY ANP model and FIRE RISK indicates a high degree of compliance in areas with high fire risk.

Forests as the lung of land part of our natural resources have an important role to play in the physical and mental health of living beings. Forest fires in Iran and especially in boreal forests had been devastating effects on the appearance of these areas. One method of prevention and risk management established within the field of forest fires is a determination of hazard distance. The purpose of this study was to determine areas vulnerable to fires in Noshahr forests. After identifying the factors contributing to the fire (slope, aspect, elevation, land cover, temperature, precipitation, distance from settlements, distance from roads), weights each of criteria and subcriteria calculated using multi-criteria decision-making techniques and fire map were prepared in the environment ARC GIS software. The results showed that 17. 32 percent of the area which is 311. 8 Km² in classes risk high and very high and 17. 32 percent of the area which is 311. 8 Km² are located in classes risk low and very low. Furthermore, evaluate result's using of kappa statistics coefficient shows that the methods used with coefficient 0. 64 are of a good accuracy zoning the forest fire risk.

Forest fires, whether of human or natural origin, are one of the most important challenges in recent years as a crisis. The aim of this study is to prepare a fire potential map using the geographic information system in the forests of rural areas of Asalem city in order to manage these areas for the executive departments. In this paper, zoning of fire hazard was investigated by spatial analysis method and using fuzzy hierarchical process and GIS. Using digital-elevation model, slope, geographical directions, altitude maps were prepared and then type of vegetation, roads, rural settlements and farmlands were mapped. In order to rank and weighting the effective criteria in the occurrence of fire through the method of fuzzy AHP, first 30 questionnaires were distributed among fire experts in the study areas. And they were weighed in the AHP fuzzy. Then the weighted layers were evaluated according to the Raster calculator order in GIS and using fuzzy membership functions. Forest fire risk zone mapping were classified into five classes of areas with very high potential to very low potential. From the total of the study area, the area of very high fire risk is equal to 7170 hectares and the area of very low fire risk is 6908 hectares. The results showed that after the fieldwork, the areas with fire history in previous years were completely matched with the final potential map. 48% of the fires occurred were in areas with high and very high potential.