Document


Title

Machine learning estimation of fire arrival time from Level-2 Active Fires satellite data
Document Type: Journal Article
Author(s): Angel Farguell; Jan Mandel; James Haley; Derek V. Mallia; Adam K. Kochanski; Kyle Hilburn
Publication Year: 2021

Cataloging Information

Keyword(s):
  • active fires
  • fire growth
  • fire monitoring
  • machine learning
  • satellite data
  • support vector machine
Region(s):
Record Maintained By:
Record Last Modified: June 10, 2021
FRAMES Record Number: 63795

Description

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.

Online Link(s):
Citation:
Farguell, Angel; Mandel, Jan; Haley, James; Mallia, Derek V.; Kochanski, Adam; Hilburn, Kyle. 2021. Machine learning estimation of fire arrival time from Level-2 Active Fires satellite data. Remote Sensing 13(11):2203.