Improved forest fire spread mapping by developing custom fire fuel models in replanted forests in Hyrcanian forests, Iran

Abstract

Aim of the study: Forest fuel classification and characterization is a critical factor in wildfire management. The main purpose of this study was to develop custom fuel models for accurately mapping wildfire spread compared to standard models. Area of study: The study was conducted at a replanted forest dominated by coniferous species, in the Arabdagh region, Golestan Province, northern Iran. Material and methods: Six custom fuel models were developed to characterize the main vegetation types in the study area. Fuel samples were collected from 49 randomly selected plots. In each plot, the fuel load of 1-hr, 10-hr, 100-hr, 1000-hr, live herbs, live woody plants, surface area volume ratio, and fuel depth were estimated using the Fuel Load (FL) sampling method along three transects. Canopy fuel load was calculated for each fuel model. The performance of the custom fuel models versus standard fuel models on wildfire behavior simulations was compared using the FlamMap MTT simulator. Main results: The results showed that, despite the similarity in the burned area between observed and modeled fires, the custom fuel models produced an increase in simulation accuracy. Compared to the observed fire, simulation results did not give realistic results to the crown fire. The simulation using standard fuel models did not result in crown fire, while the simulation using custom fuel models showed a moderate rate of crown fire with a Kappa coefficient of 0.54. Research highlights: The results demonstrated the importance of developing custom fuel models to simulate wildfire maps with higher accuracy for wildfire risk management.