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,GolestanProvince, northernIran.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.Keywords: custom fuel model; FlamMap; replantation; vegetation type; wildfire behavior.

Aim of study: Wildfires are one of the most significant natural hazards affecting ecosystems worldwide. They not only cause severe ecological damage but also pose substantial threats to human life and property. Therefore, modeling the spatial distribution of fire likelihood and potential fire behavior through Burn Probability (BP) and wildfire risk models is essential for effective planning, mitigation, and climate adaptation strategies. In recent decades, the Hyrcanian temperate forest region in northern Iran, particularly its protected areas, has been significantly affected by wildfires. This research aims to produce high-resolution and accurate BP maps, along with fire risk assessments for six protected areas in Golestan Province, northeastern Iran. Methodology: The Minimum Travel Time (MTT) fire growth algorithm was implemented in FlamMap to estimate spatial BPs, utilizing customized fuel models based on the study area and considering climatic, physiographic, vegetation, and anthropogenic variables. Support Vector Regression (SVR), employing the ε-SVM approach, was used to predict fire risk rates. The accuracy of the classification maps was evaluated using historical fire ignition data. The fire risk prediction maps were categorized into four risk classes. The study analyzed the impact of various factors on BP rates across the different sites. Results and disscussion: Results indicated significant variations in BP under different conditions and across different protected areas, ranging from near zero in high-elevation zones at all sites to a maximum of 0.096 in the Golestan National Park (GNP). The highest BPs were associated with lower elevations (< 500 m) and fine fuel models (FM1, FM3, and FM4). Notably, regions such as GNP, Loveh, and both Upper-Zav and Lower-Zav showed a higher concentration of fire occurrences in high and extreme-risk zones. The models achieved high overall accuracies, ranging from 88–95%, with areas classified as high and very-high risk accounting for 54–72% of the observed fire ignitions across the study regions. The findings suggest that support vector regression (SVR) methods, when combined with region-specific calibration and spatial analysis, offer a powerful and transferable approach for predicting wildfire risk.

Due to their increasing frequency wildfires and the area burned in natural areas, wildfires are becoming increasingly challenging to control

Wildfire risk assessment is essential for managing and mitigating the effects of wildfires, especially in regions frequently affected by severe fires This study focuses on optimizing the National Fire Danger Rating System (NFDRS) by creating 11 custom fuel models tailored to the climatic and vegetation conditions of Golestan province, NE Iran. These models were developed by sampling 125 homogeneous zones. The number of custom fuel models was determined by K-means analysis with relative squared Euclidean distances and the silhouette method using data collected from the homogeneous zones. The NFDRS outputs include the Spread Component (SC), Energy Release Component (ERC), and Burning Index (BI), with fire danger classes identified using the Static Fire Danger Index (SFDI). The models were evaluated using Overall Accuracy, Kappa, Sorensen, True Positive Rate (TPR), False Positive Rate (FPR), and Area under the Curve (AUC). The results revealed significant variability in fuel properties between vegetation types, affecting SC and ERC values. The custom fuel models outperformed the standard NFDRS models in accuracy, with Overall Accuracy (0.85), Kappa (0.78), and AUC (0.92) compared to the NFDRS's 0.70, 0.65, and 0.80, respectively. This study provides a clear illustration of the improved predictive performance of the custom models in the study area, which can help fire managers make more informed decisions, leading to better preparation and more efficient allocation of resources. these models can help mitigate wildfires' economic and environmental impacts. In addition, this research highlights the need for region-specific, customized fuel models to improve the accuracy of fire risk