جستجوی مقالات مرتبط با کلیدواژه « آتش سوزی جنگلی » در نشریات گروه « علوم انسانی »

Today, Iran is one of the most vulnerable countries to climate change due to its geographical and climatic features. One of the most important negative effects of climate change is the increase in temperature, which can increase the risk of wildfires. Wildfires are an environmental challenge and one of the threats to a large part of the forests in the world, including Iran. This study aims to investigate the effect of global warming on the increase in the risk of wildfires in Iran by using remote sensing technology and spatial analysis. 

The temperature data of the synoptic stations (Mehrabad, Yazd, Bam, Shiraz, Isfahan and Bushehr) in a 32-year period from 1988 to 2020 were first prepared from the Meteorological Organization. Then, Excel macros and XLSTAT plugin were used to check the temperature trend. To identify the vegetation cover, the Normalized Difference Vegetation Index (NDVI) was used from the Landsat 8 satellite with a spatial resolution of 27.84 meters, whose source of data was the Google Earth Engine. Then, the parameters of elevation, slope, aspect, and amount of solar radiation were used to calculate the topography. To identify the spatial distribution of fire occurrence, the kernel density estimation (KDN) was used by classification of areas into low-risk and high-risk areas based on NASA’s MODIS C6 satellite images (2000-2023).

The results of examining the temperature trend in all stations in the 32-year period showed an increasing and significant trend at 95 and 99% confidence intervals. This could be due the increase of greenhouse gases in urban centers, which is logical. The results of the NDVI showed that areas with high vegetation density were more susceptible to wildfires. Investigation of the solar radiation area showed that the northern highlands of Tehran, the banks of the Caspian Sea and northern areas of Iran, northwestern Iran (Ardebil and Tabriz cities), and central Iran (Kerman, Isfahan, Yazd and Shiraz cities) had a high pixel value, indicating that the amount of solar radiation was higher in these regions than in other regions of Iran. Finally, the spatial distribution map and the KDN showed that the provinces of Kohgiluyeh & Boyer-Ahmad and Khuzestan were more prone to a wildfires.

Iran has faced climate change caused by global warming. Considering the new climate of Iran, which will be hotter and dryer, it will be more prone to wildfires. Therefore, comprehensive planning to reduce the effect of climate change is needed in the field of crisis management in Iran, which can help reduce the vulnerability and increase resilience.

Forest fire is one of the most common ecological disaster which it''s accurate spread prediction is a very essential issue in minimizing it''s destructive effects. This phenomenon depends on factors such as topography، vegetation and climate. Among the available models، experimental deterministic models that are presented in the form of raster such as cellular automata are more popular due to their simplicity and ability in modeling complex systems. Many simulator systems have been developed in order to simulate and predict fire spread by cellular automata. The quality of their results depends on accuracy and reliability of input parameters as well as model complexity which most of these parameters have a degree of uncertainty. A constructive suggestion to overcome the problem of uncertainty is using a two phase simulation approach. In this approach، at first، model parameters will be optimized by comparing simulation results with reality، then simulation model will be used for simulating next step of fire spread considering optimal values which already has been obtained in the first step. One of the important points in designing this system is using optimal optimization method. In this study، two optimization methods named Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC) have been used to overcome the problem of uncertainty and enhance the precision of forest fire spread modeling and performe two phase simulation approach for part forests of Gilan provinces. Results show that Artificial Bee Colony has higher ability than Particle Swarm Optimization in producing optimal parameters of the model.