Stochastic Behaviour of Directional Fire Spread: A Segmentation-Based Analysis of Experimental Burns

Understanding the dynamics of fire propagation is essential for improving predictive models and developing effective fire management strategies. This study examines the variability in temporal and directional rates of spread (ROS) under controlled environmental conditions and investigates the influence of terrain slope using experimental fire videos. To enable precise, frame-by-frame tracking of fire perimeters, we employed the Segment Anything Model (SAM) for semantic segmentation and object tracking, allowing us to quantify fire spread. Our study highlight that ROS exhibited substantial variability across and within videos that underscore the stochastic nature of fire behavior and raise concerns about the limitations of deterministic fire spread models. Analysis of the slope spread factor revealed discrepancies between model predictions and observed fire behavior. Estimated slope parameters deviated from values reported in existing literature, suggesting that fire dynamics are highly context-dependent and sensitive to local conditions. Our work highlights the need for probabilistic modeling approaches that explicitly account for inherent uncertainty and emergent dynamics in fire spread. Future research should focus on integrating directional drivers, refining slope-response formulations, and incorporating stochastic processes such as spotting. These improvements are essential for building more robust and generalizable fire behavior models capable of supporting operational forecasting and management decisions.