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 When used as input for wildland fire behavior models, outdated fuel datasets can contribute to misleading forecasts, which have implications for operational firefighting, mitigation, and wildland fire...

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Bibliographic Details
Published in:	Remote sensing (Basel, Switzerland) Vol. 14; no. 6; p. 1447
Main Authors:	DeCastro, Amy L., Juliano, Timothy W., Kosović, Branko, Ebrahimian, Hamed, Balch, Jennifer K.
Format:	Journal Article
Language:	English
Published:	Basel MDPI AG 01.03.2022
Subjects:	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
Online Access:	Get full text
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