The aim of this research was to produce forest fire susceptibility maps based on evidential belief function (EBF) and binary logistic regression (BLR) models in the Minudasht Forests, Golestan Province, Iran. At first, 151 forest fire locations were identified from MODIS data, extensive field surveys, and some reports (collected in year 2010). Out these, 106 (70%) locations were randomly selected as training data and the remaining 45 (30%) cases were used for the validation goals. In the next step, fifteen effective factors such as slope degree, slope aspect, elevation, plan curvature, Topographic Position Index (TPI), Topographic Wetness Index (TWI), land use, NDVI, distance to villages, distance to roads, distance to rivers, wind effect, soil texture, annual temperature and rainfall were extracted from the spatial database. Subsequently, forest fire susceptibility maps were prepared using EBF and BLR models, and the results were plotted in ArcGIS. Finally, the receiver operating characteristic (ROC) curves and area under the curves (AUC) were constructed for verification purposes. The validation of results showed that the area under the curve for evidential belief function and binary logistic regression models are 0.8193 (81.93%) and 0.7430 (74.30%), respectively. In general, the mentioned results can be applied for Land use planning, management and prevention of future fire hazards.
کلید واژگان :Forest fire mapping, Evidential belief function, Binary logistic regression, Iran
ارزش ریالی : 600000 ریال
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