Abstract
Producing high-resolution near-real-time forecasts of fire behavior and smoke impact that are useful for fire and air quality management requires accurate initialization of the fire location. One common representation of the fire progression is through the fire arrival time, which defines the time that the fire arrives at a given location. Estimating the fire arrival time is critical for initializing the fire location within coupled fire-atmosphere models. We present a new method that utilizes machine learning to estimate the fire arrival time from satellite data in the form of burning/not burning/no data rasters. The proposed method, based on a support vector machine (SVM), is tested on the 10 largest California wildfires of the 2020 fire season, and evaluated using independent observed data from airborne infrared (IR) fire perimeters. The SVM method results indicate a good agreement with airborne fire observations in terms of the fire growth and a spatial representation of the fire extent. A 12% burned area absolute percentage error, a 5% total burned area mean percentage error, a 0.21 False Alarm Ratio average, a 0.86 Probability of Detection average, and a 0.82 Sørensen’s coefficient average suggest that this method can be used to monitor wildfires in near-real-time and provide accurate fire arrival times for improving fire modeling even in the absence of IR fire perimeters.
Highlights
Wildfires burn millions of hectares of forest every year across the globe
The fire arrival time estimation using the machine learning method from satellite data described in Section 2 is applied to the top 10 largest wildfires in California this past fire season 2020 (Table 1)
L2 Active Fire (AF) satellite data assessment to identify where the spatial resolution and temporal frequency from satellite detection pixels could impact the support vector machine (SVM) estimation, and (2) the validation of fire arrival time estimation where the resulting fire arrival time is compared to IR observed fire perimeters considering two commonly used metrics: area burned and spatial discrepancy
Summary
Wildfires burn millions of hectares of forest every year across the globe. Global trends in wildfires are strongly linked to climate change, which has caused fires to become more frequent and destructive, especially across the western United States [1,2,3,4]. As fire activity continues to increase in the coming decades [9], so does the importance of developing modeling tools that can assist fire and air quality managers in forecasting fire growth, smoke production, and downwind smoke transport. The importance of the fire-atmosphere interactions has been recognized in fire-atmosphere simulations [10,11,12,13], observed during experimental fires [14,15,16], as well as observed during wildfire events [17,18]. Several coupled fire-atmosphere models have been developed [19,20,21,22,23], in an effort to better capture the dynamics of fireatmosphere interactions often observed in large wildfires. The continuing increase in computational capabilities over the years has made it feasible to run high-resolution
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