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Remote Sensing
Volume 14
Issue 6
10.3390/rs14061447
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Communication

A Computationally Efficient Method for Updating Fuel Inputs for Wildfire Behavior Models Using Sentinel Imagery and Random Forest Classification

by
Amy L. DeCastro
1,2,*,
Timothy W. Juliano
1,
Branko Kosović
1,
Hamed Ebrahimian
3 and
Jennifer K. Balch
2
1
National Center for Atmospheric Research, Boulder, CO 80307, USA
2
Department of Geography, University of Colorado Boulder, Boulder, CO 80309, USA
3
Department of Civil and Environmental Engineering, University of Nevada Reno, Reno, NV 89557, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2022, 14(6), 1447; https://doi.org/10.3390/rs14061447
Submission received: 1 February 2022 / Revised: 9 March 2022 / Accepted: 15 March 2022 / Published: 17 March 2022
(This article belongs to the Special Issue Advances in Forest Fire Behaviour Modelling Using Remote Sensing)
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Abstract

Disturbance events can happen at a temporal scale much faster than wildland fire fuel data updates. When used as input for wildland fire behavior models, outdated fuel datasets can contribute to misleading forecasts, which have implications for operational firefighting, mitigation, and wildland fire research. Remote sensing and machine learning methods can provide a solution for on-demand fuel estimation. Here, we show a proof of concept using C-band synthetic aperture radar and multispectral imagery, land cover classes, and tree mortality surveys to train a random forest classifier to estimate wildland fire fuel data in the East Troublesome Fire (Colorado) domain. The algorithm classified over 80% of the test dataset correctly, and the resulting wildland fire fuel data was used to simulate the East Troublesome Fire using the coupled atmosphere—wildland fire behavior model, WRF-Fire. The simulation using the modified fuel inputs, where 43% of original fuels are replaced with fuels representing dead trees, improved the burn area forecast by 38%. This study demonstrates the need for up-to-date fuel maps available in real time to provide accurate prediction of wildland fire spread, and outlines the methodology based on high-resolution satellite observations and machine learning that can accomplish this task.
Keywords: wildland fire behavior model; fuel model; random forest; Sentinel; WRF-Fire wildland fire behavior model; fuel model; random forest; Sentinel; WRF-Fire
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MDPI and ACS Style

DeCastro, A.L.; Juliano, T.W.; Kosović, B.; Ebrahimian, H.; Balch, J.K. A Computationally Efficient Method for Updating Fuel Inputs for Wildfire Behavior Models Using Sentinel Imagery and Random Forest Classification. Remote Sens. 2022, 14, 1447. https://doi.org/10.3390/rs14061447

AMA Style

DeCastro AL, Juliano TW, Kosović B, Ebrahimian H, Balch JK. A Computationally Efficient Method for Updating Fuel Inputs for Wildfire Behavior Models Using Sentinel Imagery and Random Forest Classification. Remote Sensing. 2022; 14(6):1447. https://doi.org/10.3390/rs14061447

Chicago/Turabian Style

DeCastro, Amy L., Timothy W. Juliano, Branko Kosović, Hamed Ebrahimian, and Jennifer K. Balch. 2022. "A Computationally Efficient Method for Updating Fuel Inputs for Wildfire Behavior Models Using Sentinel Imagery and Random Forest Classification" Remote Sensing 14, no. 6: 1447. https://doi.org/10.3390/rs14061447

APA Style

DeCastro, A. L., Juliano, T. W., Kosović, B., Ebrahimian, H., & Balch, J. K. (2022). A Computationally Efficient Method for Updating Fuel Inputs for Wildfire Behavior Models Using Sentinel Imagery and Random Forest Classification. Remote Sensing, 14(6), 1447. https://doi.org/10.3390/rs14061447

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