Assessment of Forest Fire Risk in Mazandaran Province Using Fuzzy AHP Model

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.