Fire behavior modeling for operational decision-making

Simulation frameworks are necessary to facilitate decision-making to many fire agencies. An accurate estimation of fire behavior is required to analyze potential impact and risk. Applied research and technology together have improved the implementation of fire modeling, and decision-making in operational environments.Dr Cardil acknowledges the support of Technosylva USA and Wageningen University in his research stays in the USA and the Netherlands to develop this work. The authors of this paper acknowledges the support of the EUfunded PYROLIFE project (Reference: 860787; https://pyrolife.lessonsonfire.eu/), a project in which a new generation of experts will be trained in integrated wildfire management

Geovisualization and Analysis of Landscape-Level Wildfire Behavior Using Repeat Pass Airborne Thermal Infrared Imagery

Geovisualization tools can supplement the statistical analyses of landscape-level wildfire behavior by enabling the discovery of nuanced information regarding the relationships between fire spread, topography, fuels, and weather. The objectives of this study were to develop and evaluate the effectiveness of geovisualization tools for analyzing wildfire behavior and specifically to apply those tools to study portions of the Thomas and Detwiler wildfire events that occurred in California in 2017. Fire features such as active fire fronts and rate of spread (ROS) vectors derived from repetitive airborne thermal infrared (ATIR) imagery sequences were incorporated into geovisualization tools hosted in a web geographic information systems application. This geovisualization application included ATIR imagery, fire features derived from ATIR imagery (rate of spread vectors and fire front delineations), growth form maps derived from NAIP imagery, and enhanced topographic rasters for visualizing changes in local topography. These tools aided in visualizing and analyzing landscape-level wildfire behavior for study portions of the Thomas and Detwiler fires. The primary components or processes of fire behavior analyzed in this study were ROS, spotting, fire spread impedance, and fire spread over multidirectional slopes. Professionals and researchers specializing in wildfire-related topics provided feedback on the effectiveness and utility of the geovisualization tools. The geovisualization tools were generally effective for visualizing and analyzing (1) fire spread over multidirectional slopes; (2) differences in spread magnitudes within and between sequences over time; and (3) the relative contributions of fuels, slope, and weather at any given point within the sequences. Survey respondents found the tools to be moderately effective, with an average effectiveness score of 6.6 (n = 5) for the visualization tools on a scale of 1 (ineffective) to 10 (effective) for postfire spread analysis and visualizing fire spread over multidirectional slopes. The results of the descriptive analysis indicate that medium- and fine-scale topographic features, roads, and riparian fuels coincided with cases of fire spread impedance and exerted control over fire behavior. Major topographic features such as ridges and valleys slowed, or halted, fire spread consistently between study areas. The relationships between spotting, fuels, and topography were inconclusive

Examining Landscape-Scale Fuel and Terrain Controls of Wildfire Spread Rates Using Repetitive Airborne Thermal Infrared (ATIR) Imagery

The objectives of this study are to evaluate landscape-scale fuel and terrain controls on fire rate of spread (ROS) estimates derived from repetitive airborne thermal infrared (ATIR) imagery sequences collected during the 2017 Thomas and Detwiler extreme wildfire events in California. Environmental covariate data were derived from prefire National Agriculture Imagery Program (NAIP) orthoimagery and USGS digital elevation models (DEMs). Active fronts and spread vectors of the expanding fires were delineated from ATIR imagery. Then, statistical relationships between fire spread rates and landscape covariates were analyzed using bivariate and multivariate regression. Directional slope is found to be the most statistically significant covariate with ROS for the five fire imagery sequences that were analyzed and its relationship with ROS is best characterized as an exponential growth function (adj. R2 max = 0.548, min = 0.075). Imaged-derived fuel covariates alone are statistically weak predictors of ROS (adj. R2 max = 0.363, min = 0.002) but, when included in multivariate models, increased ROS predictability and variance explanation (+14%) compared to models with directional slope alone