Document Type : Complete scientific research article
Abstract
Forest fires in the Iran and particularly in the northern forests had destructive effects on the physiognomy of these forests. Recognition, prevention and controlling the Scio-economic destroys caused by natural hazard is one of the main objectives of applied and educational organizations. One of the methods for prevention on 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 PseudoR²= 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.
(2015). Forest fire risk zone mapping in the Golestan national park using regression logistic method. Journal of Wood and Forest Science and Technology, 22(1), 1-16.
MLA
. "Forest fire risk zone mapping in the Golestan national park using regression logistic method". Journal of Wood and Forest Science and Technology, 22, 1, 2015, 1-16.
HARVARD
(2015). 'Forest fire risk zone mapping in the Golestan national park using regression logistic method', Journal of Wood and Forest Science and Technology, 22(1), pp. 1-16.
VANCOUVER
Forest fire risk zone mapping in the Golestan national park using regression logistic method. Journal of Wood and Forest Science and Technology, 2015; 22(1): 1-16.
)