Extensive fires in the forests are examples of natural crises (Hosseinali، 2005) and forest fire events are increasing in the conditions of changing climate and global warming; (Azizi and Yousefi، 2005). This phenomenon is considered one of the most destructive factors for forest ecosystems، causing irreparable damages (Marozas et al، 2007). Some of these damages include land use changes، emissions of greenhouse gases، disruption of forest structure and nutrients losses in ecosystems resulting from burning of vegetation and forest floor layers (Vakalis et al،2007; Alexandridis et al، 2008; Bakhshandehsavad rudbari et al، 2011). According to FAO (1995) the annual average of fires in forested areas around the world is estimated to be two million hectares. In many parts of the world، fires in forests and ranges is one of the most important issues and concerns، not only from an environmental perspective، but also from economic، social and security viewpoints (Silvia Merino and Gonzale، 2010). One of the research fields for controlling forest fire is to identify critical points in terms of fire in forested areas، because the lack of knowledge about these areas will cause the occurrence and spreading of forest fire، delay in fire suppression، and damage to forest plant and animal life (Jaiswal et al، 2002). Therefore، preparing the potential map of fire (fire critical areas) plays an important role in reducing the number of fires and preventing the destruction of forests (Dong et al، 2005) and helps the forest managers to prevent fires with special care in critical areas before they occur. According to a study by Atrak Chali (2000) the average area in forest areas that is burned due to fires is 7،000 ha and return intervals of forest fire in Golestan province varies from 5 to 7 years; therefore preparing fire hazard maps with high-precision is necessary for planning and crisis management. When، preparing the maps of fire risk، GIS is used as a basic tool for spatial data management. In many studies، GIS have been used to identify critical points and potential techniques have been applied. Mohammadi et al (2009) attempted to map the fire susceptible areas in Paveh forests fires by conducting field operations and by considering the measures of vegetation، physiography، climate، human and distance from roads and streams as well as by applying the analytic hierarchy process (AHP) and they showed that the resulting map was highly corresponding with the real locations of fire. In a study in Spain، Nieto et al (2012) evaluated the occurrence of fire caused by lightning during a 3-year period from 2004 to 2002 using logistic regression. In this study some parameters such as lightning، topography، vegetation and climatic parameters were considered. The results showed that the lightning was most important factor causing fire in the area fires during mentioned period and a risk model with ROC of 0. 7 obtained، indicating the good performance of the model. Golestan National Park is First Park that was considered as the national park in Iran and one-eighth of plant species، one-third of bird species and more than 50% of the mammal species live in this park (Hassanzade Kiyabi et al، 1993). This park is susceptible to fire due to its characteristics vegetation as well as occurring at a region with winds formed by the clash between wet and dry weather fronts (Shokri et al، 2002). This study models and identifies areas susceptible to fire in Golestan National Park using logistic regression، taking into account parameters influencing on the fire in the region. Study Area: Golestan National Park is located at the Northeastern part of Iran، East of Golestan province، Northwest of Khorasan province، and North of Semnan province between 37º 17´ 43´´ to 37º 31´ 35´´ N، and 55º 43´ 25´´ to 56º 17´ 48´´ E. The park area is 91،895 ha with a perimeter of 198 km. A transit road، known as Asian road that connect the northern and central Iran to the north-eastern Iran passes through the park. There are several villages around the park including Tangeh rah، Terjenli، Ghoch Cheshmeh، Zav، Tomak، Kondosku and Dasht Shad in West of the park، Dasht، CheshmehKhan and Armadlu in South of the park، Robat Ghahrahbil in East of the park and Lohondor، Yelcheshmeh and Behkade in North of the park.
In this study we first determined parameters and their influences on fire event، then information related to these parameters was collected from the related organizations as well as by inventory in the study area. Affecting parameters were prepared in the ArcGIS and IDRISI software packages. Based on topographic map، Elevation ranges from 467 to 2342 m above sea level in the study area. slope from 0 to 72 degrees and slope aspect maps with 9 classes were extracted from a Digital Elevation Model (DEM). Normalized Difference Vegetation Index (NDVI) map was extracted from MODIS satellite images of 2011. Humidity and temperature isopleths maps were obtained from interpolation of meteorological data 2001-2011 years of climatology and synoptic stations around the park based on Inverse Distance Weighting (IDW). Considering the effect of the presence of hunters and ranchers on fire events in the park، the distance from arresting positions of offenders in 2007-2012 as was mapped the higher probable presences in the park. Also a buffer zone was established from camps as tourist sites، villages around the park، transit road passing through the park. In the study area، 9 vegetation classes were identified including dense-canopy forest، low-canopy forest، average-canopy forest، very low-canopy forest، good pasture، medium-vegetated pasture، woodlands، bushy vegetation، and agricultural land. The occurred fires during the last 30 years was determined based on records of Central Office of Park، information provided by the guards as well as signs of the fire using GPS، then the Boolean fire maps was drown in Arc GIS software. After selection the parameters، fire hazard map prepared using logistic regression method in the IDRISI software. Logistic regression is run as computational binomial regression in which the dependent variables of nature are entered binary. The uses model accessory was assist with Relative operating Characteristic (ROC) and Pseudo-R2 statistics. Finally، sensitivity of variables was examined by removing them using ROC and Pseudo-R2.
The regression equation resulting from the model was calculated as follows: Logit (fire) = 38. 792 + 0. 05475*Aspect + 0. 79038*Tem - 0. 000666*Camping + 0. 0014*Elevation - 0. 00037*Shepherds + 0. 00036*Hunter + 0. 0081*Landuse -0. 7874*Moisture + 0. 0011*NDVI + 0. 0004*Road - 0. 051747*Slope - 0. 00006*VillageIn this model، variables such as aspect، elevation، mean temperature (tem)، distance from road، and presence of hunter، land use and NDVI are positively correlated with fire risk while parameters of slope، camping، presence of ranchers، moisture and distance from village are negatively correlated with fire risk. The ROC، Pseudo-R2 and Chi-square were calculated 0. 9267، 0. 3133 and 102227. 3 in the model، respectively. The fire risk map was prepared using logistic regression. The results of this study both from the obtained equation and from measuring the sensitivity of variables indicate that temperature and humidity are affecting on the occurrence of regional fire. Since the ROC statistic of 0. 7 indicates low accuracy، 0. 7-0. 9 value indicate the practicability and a value more than 0. 9 represents the high accuracy of model and Pesudo-R2 values greater than 0. 2 can be considered as a relatively good fitness، in this study ROC Pesudo-R2 values were found 0. 9264 and 0. 3133، respectively، indicating the high accuracy and good fitness for the fire risk model. Therefore logistic regression can be introduced as an appropriate method to classify the fire risk. Understanding the behavior of forest fire and factors causing the appropriate environment for fire occurrence and influencing on fire behavior are of high importance for fire forest management، thus this model can be useful for designing of fire management and taking preventive measures in high fire risk areas.
Considering the location of area، high temperatures and low humidity that provides favorable conditions for fires، and also given the high traffic of travelers، tourists، ranchers and hunters allowed in the park، the results showed that fire risk is in this park. Due to the high accuracy obtained from the logistic regression model in this study، one can claim that this method is appropriate for the evaluation and modeling of fire risk thus the resulting model can be used for preventive measures.
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