Assessing forest fire behavior simulation using FlamMap software and remote sensing techniques in Western Black Sea Region, Turkey

Abstract

Aim of study: Forest fuels are very critical for fire behavior models and hazard maps. Relationship among wind speed, fuel moisture content, slope, and fuel type directs us to predict fire behavior of a given region. For this study, we evaluated fire behavior parameters such as fireline intensity and rate of fire spread using the fuel moisture content, slope, fuel load, and wind speed for the Bayam Forest District with the help of remote sensing techniques and FlamMap software. Area of study: The study area is located in Bayam Forest District in the city of Taskopru, Kastamonu, a Western Black Sea region of Turkey. Material and Methods: In order to estimate and map forest fuel load of the study area, fuel models were developed using the parameters of the average vegetation height, 1-hr, 10-hr, and 100-hr fuel load, foliage, total fuel load, litter load and litter depth. Three basic fire descriptors (fireline intensity, rate of fire spread, and flame length) were calculated using FlamMap software with the parameters fuel load, wind speed, fuel moisture, and slope. Using the descriptors above, the historical fire data was overlaid with the fireline intensity maps to determine fire potential areas within the remote sensing and GIS framework. Main results: The results of this study showed that 20.0% of the region had low (<2 m min-1 ), 43.2% had moderate (2- 15 m min-1 ), 12.0% had high (15-30 m min-1 ), and 24.8% had very high (>30 m min-1 ) rate of fire spread, respectively. The fireline intensity map showed that 60.7% of the area was in low (0-350 kW m-1 ), 24.9% was in moderate (350-1700 kW m1 ), 1.3% was in high (1700-3500 kW m-1 ), and 13.0% was in very high (>3500 kW m-1 ) fireline intensity. Highlights: The spatial extent of fuel types was observed and three of the potential fire behavior predictors (fire intensity, rate of fire spread and flame length) were estimated using remote sensing and GIS techniques. The overlaid historical fire data showed that the most fire-prone areas are in the mixed young Anatolian black pine - Scots pine tree stands that have 40-70% canopy cover and that are in the young Anatolian black pine tree stands that have more than 70% canopy cover