Hassan Emami; Hassan Shahriyari
Abstract
Extended Abstract Introduction Forests play numerous critical roles in nature. They stabilize and fertilize soil, purify water and air, store carbon, and nurture environments abundant in biodiversity. Moreover, forests offer numerous job opportunities and hidden wealth toany economy. Unfortunately, wildfires ...
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Extended Abstract Introduction Forests play numerous critical roles in nature. They stabilize and fertilize soil, purify water and air, store carbon, and nurture environments abundant in biodiversity. Moreover, forests offer numerous job opportunities and hidden wealth toany economy. Unfortunately, wildfires have turned into a serious natural risk nowadays. Wildfires are a natural disaster threatening forests and ecosystem, from local to global level. Evaluating the risk of wildfires is an important factor in fire management. This can be performed at different spatial and temporal scales: global and local; short term, and long-term. At global scales, it can contribute to the establishment of general guidelines for fire management at continental level, while at local scales,it is more suitable for resources focusing on preventing specific fires in small regions. Long-term estimation addresses general, more permanent planning of firefighting resources, which is related to the more structural factors affectingwildfires or their spread, such as topography or terrain characteristics, vegetation structure, human activities or weather patterns. Materials & Methods Wildfire risk has become a major concern in recent years, particularly in areas where human settlements are in close proximity to forests. Wildfire origin canbe determined largely by environmental factors. However, fire related data is either unavailable, or mostly incomplete. Thus, reaching an overall annual estimate of wildfires is difficult. Some common methods are used toestimate the risk ofwildfires, including qualitative methods, quantitative methods based on specialized knowledge (multi-criteria evaluation techniques), regression techniques (linear regression and logical regression), and artificial neural networks. Wildfire initiation and spread depend on several important factors, including precipitation, presence of ignition elements, factors like topography, temperature, thunder, spreading of fuel, relative humidity, wind speed, and etc. The present study integrates data produced by remote sensing with data received from geographic information system. It also takes advantage of LDCM satellite imagery, and digital elevation model, along with natural/human factors such as wind speed and direction, vegetation, land surface temperature, slope, proximity to roads and residential areas. The present study seeks to quantify environmental and human elements effective in occurrence and spread of wildfires in the protected jungles of Arasbaran. To this end, a risk zone map was produced for the area, along with a map for areas with 50% risk. In the present study, the final map of risk zone was produced using the Fire Risk Index (FRI) and spatial statistics method. Results & Discussion In the present study, factors such as land cover type, slope, distance from residential area, distance from the road, and elevation were taken into account. During the process, different indices were assigned to each class of these factorsbased on their sensitivity to fire or their flammability. Land cover was one of the most important factors affecting the occurrence of wildfires. Slope was another important factor with a significant influence on the spread of fire. This natural factor affects fire spread and fire intensity. Proximity of human settlements to jungles is another important factor which sometimes threatsjungles. Therefore, forests in proximity of human settlements face a higher risk of wildfires. Elevation is another important topographical factorclosely related to wind behaviour, with a significant role in fire spreading. In Arasbaran forest, northern, eastern, and north-easternareas are more elevatedand thus, more prone to wildfires. In this study, a combination of environmental and human factors was applied to produce fire hazard maps along with a map for areas with 50% risk of wildfire. Conclusion Occurrence and spread of wildfires depends on many factors, some of which are more important and play a more significant role in these fires. A risk zone map was produced for wildfiresusing an integrated method consisting ofremote sensing and GIS methods. Risk zone was divided into 5 areas, i.e. very low, low, average, high, very high.Results indicate that the methodology presented based on a combination of RS and GIS techniquesin this study, is a reliable approach and tool for the prevention and mitigation of forest fires. They are also useful for all active institutes working in crisis management and emergency services, while helping jungle protectingorganizations to prevent fires or manage them. In addition, quantitative results indicate that vegetation index with a correlation of 58.36%, and slope with a correlation of 38.38 are the most affective factors, and other parameters are in the next ranks.Moreover, land cover, land surface temperature, direction, and slope with 29.20%, 29.11%, 21.93% and 19.75% normalized correlation coefficient respectively, have the highest correlation with the map of fire risk zone. In addition, results of evaluating 50% risk zone map indicate that around 17% of the study area have a high fire risk and more than 50% of the area is located in a high fire risk zone. In addition to environmental elements, results indicate that proximity to the road was the most affective factor in the occurrence of fire. Quantitative results showed that roads and residential areas were at least 32% and at most 68% correlated with fire risk in the study area.
Tahereh Ghaemi rad; Mohammad Karimi
Abstract
Forest fire is one of the most common ecological hazards whoseproper prediction of spreading is a vital issue in minimizing its destructive effects.This phenomenon depends on factors such as topography, vegetation and climate. Among the existing models, the definite empirical models presented in the ...
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Forest fire is one of the most common ecological hazards whoseproper prediction of spreading is a vital issue in minimizing its destructive effects.This phenomenon depends on factors such as topography, vegetation and climate. Among the existing models, the definite empirical models presented in the form of raster including cellular automata are more populardue to their modeling simplicityand the ability to model complex systems. Different simulation systems have been developed to simulate and predict the spread of fire using cellular automata. The quality of the results obtained from these systems, in addition to the complexity of the model, depends on the accuracy and reliability of the input parameters, most of which have a degree of uncertainty. One of the constructive suggestions to overcome the uncertainty problem is the use of a two-stage simulation approach. In this approach, all of theexisting parameters in the model are first optimized by comparing the results derived from the simulation with the reality, then,the related simulation model will performthe simulation of the next step fire spread by considering the optimal values obtained for the parameters. One of the most important points in designing this system is the use ofdesirable optimization method. In this research, two optimization methods namely Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) have been used to overcome the uncertainty problem and enhance the accuracy of forest fire spread modeling and implementation of two-stage simulation approach for a part of the forests of Gilan province. The results show that the Artificial Bee Colony (ABC) algorithm optimization method has abettercapability than the Particle Swarm Optimization (PSO) to produce optimal parameters of the desired model.
