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

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 When used as input for wildland fire behavior models, outdated fuel datasets can contribute to misleading forecasts, which have implications for operational firefighting, mitigation, and wildland fire...

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Vydáno v:	Remote sensing (Basel, Switzerland) Ročník 14; číslo 6; s. 1447
Hlavní autoři:	DeCastro, Amy L., Juliano, Timothy W., Kosović, Branko, Ebrahimian, Hamed, Balch, Jennifer K.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Basel MDPI AG 01.03.2022
Témata:	Algorithms Atmospheric models C band Case studies Colorado data collection Datasets Dead wood Endangered & extinct species Epidemics Fire fighting fire spread Forest & brush fires fuel model Fuels Land cover Learning algorithms Machine learning Mathematical models multispectral imagery Observational learning Polygons prediction Radar imaging random forest Remote sensing Role models Satellite observation Satellites Sentinel Synthetic aperture radar temporal variation tree mortality Vegetation Wildfires wildland fire behavior model WRF-Fire United States > US Colorado
ISSN:	2072-4292, 2072-4292
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Popis
Shrnutí:	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 When used as input for wildland fire behavior models, outdated fuel datasets can contribute to misleading forecasts, which have implications for operational firefighting, mitigation, and wildland fire research.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.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	2072-4292 2072-4292
DOI:	10.3390/rs14061447