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 When used as input for wildland fire behavior models, outdated fuel datasets can contribute to misleading forecasts, which have implications for operational firefighting, mitigation, and wildland fire...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
On-line přístup:	Získat plný text
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