MODELING POTENTIAL WILDFIRE BEHAVIOR CHARACTERISTICS USING MULTI-SOURCE REMOTELY SENSED DATA: TOWARDS WILDFIRE HAZARD ASSESSMENT

Wildfire spread is affected by various factors including weather, fuel, topography, and human intervention. Previous studies have focused on wildfire probability modeling for purposes of wildfire management, with less attention paid to potential wildfire behavior characteristics such as wildfire speed and intensity. Remote sensing technology has excellent advantages in deriving the characteristics and fuel variables. This study aimed to model these characteristics for wildfire hazard assessment in the Yunnan Province of China. The random forest (RF) model and the extreme gradient boosting (XGBoost) model, were selected to establish the potential wildfire behavior characteristics (PWBC) models based on explanatory variables. The results verified that elevation, fuel moisture content, and infrastructure variables played a more significant role in the models. The RF-based models performed better than the XGBoost-based ones with higher overall accuracy (>= 0.83) and kappa coefficient (>= 0.79), indicating the effectiveness of predicting potential wildfire behavior characteristics to assess wildfire hazards.