6/1/2023 0 Comments Exponential scatter plot![]() The simulation of forest fire spread is a key problem for the management of fire, and Cellular Automata (CA) has been used to simulate the complex mechanism of the fire spread for a long time. Providing a state-of-the-art survey, it is a useful reference for scientists, researchers, and graduate students interested in wildland fire behavior from a broad range of fields. Mathematical and dynamical principles are presented, and the complex phenomena that arise in wildland fire are discussed. This book provides an overview of the developments in modeling wildland fire dynamics and the key dynamical processes involved. This quantitative analysis of fire as a fluid dynamic phenomenon embedded in a highly turbulent flow is beginning to reveal the combined interactions of the vegetative structure, combustion-driven convective effects, and atmospheric boundary layer processes. Fire behavior models are commonly used to predict the direction and rate of spread of wildland fires based on fire history, fuel, and environmental conditions however, more sophisticated computational fluid dynamic models are now being developed. Wildland fires are among the most complicated environmental phenomena to model. 1999) and the generation of forward-shooting fingers of flame (Radke et al. Though many of the latter observations were gathered from fires of opportunity in uncontrolled conditions, the adaptation of image flow analysis techniques to the analysis of IR observations allowed documentation of modelpredicted phenomena such as the formation and role of vertically-inclined vortices in fire line dynamics (Clark et al. Other approaches to investigate this interplay estimated winds within flaming combustion zones, fire progression, and, in some cases, accompanying fire heat fluxes, from analysis of high-speed infrared (IR) imager data on the ground (Clark et al. Currently, high-resolution observations of wind and fuel within the three-dimensional combustion zone at the flame-scale are being more widely collected outside of the laboratory as existing meteorological instrumentation and analysis methods are applied to wildland fire field experiments (Charland and Clements 2013 Clements and Seto 2015 Liu et al. Directional slope is by far the most strongly associated covariate of ROS for the imaging sequences analyzed and the size of LSUs has little influence on any of the covariate relationships. GWR and ESF regressions reveal that relationships between covariates and ROS estimates are substantially non-stationary and suggest that the global association of fire spread controls are locally differentiated on landscape scales. Statistical relationships between fire spread rates and landscape covariates were analyzed using (1) bivariate regression, (2) multiple stepwise regression, (3) geographically weighted regression (GWR), (4) eigenvector spatial filtering (ESF) regression, (5) regression trees (RT), and (6) and random forest (RF) regression. Three separate landscape sampling unit (LSU) sizes were used to extract remotely sensed environmental covariates known to influence fire behavior. Wildfire progression maps and ROS estimates were derived from repetitive ATIR image sequences collected during the 2017 Thomas and Detwiler wildfire events in California. The objectives of this study were to evaluate spatial sampling and statistical aspects of landscape-level wildfire rate of spread (ROS) estimates derived from airborne thermal infrared imagery (ATIR).
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