NDLI: Improving WRF-Fire Wildfire Simulation Accuracy Using SAR and Time Series of Satellite-Based Vegetation Indices

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Improving WRF-Fire Wildfire Simulation Accuracy Using SAR and Time Series of Satellite-Based Vegetation Indices

Content Provider	MDPI
Author	Michael, Yaron Kozokaro, Gilad Brenner, Steve Lensky, Itamar M.
Copyright Year	2022
Description	Wildfire simulations depend on fuel representation. Present fuel models are mainly based on the density and properties of different vegetation types. This study aims to improve the accuracy of WRF-Fire wildfire simulations, by using synthetic-aperture radar (SAR) data to estimate the fuel load and the trend of vegetation index to estimate the dryness of woody vegetation. We updated the chaparral and timber standard woody fuel classes in the WRF-Fire fuel settings. We used the ESA global above-ground biomass (AGB) based on SAR data to estimate the fuel load, and the Landsat normalized difference vegetation index (NDVI) trends of woody vegetation to estimate the fuel moisture content. These fuel sub-parameters represent the dynamic changes and spatial variability of woody fuel. We simulated two wildfires in Israel while using three different fuel models: the original 13 Anderson Fire Behavior fuel model, and two modified fuel models introducing AGB alone, and AGB and dryness. The updated fuel model (the basic fuel model plus the AGB and dryness) improved the simulation results significantly, i.e., the Jaccard similarity coefficient increased by 283% on average. Our results demonstrate the potential of combining satellite SAR data and Landsat NDVI trends to improve WRF-Fire wildfire simulations.
Starting Page	2941
e-ISSN	20724292
DOI	10.3390/rs14122941
Journal	Remote Sensing
Issue Number	12
Volume Number	14
Language	English
Publisher	MDPI
Publisher Date	2022-06-20
Access Restriction	Open
Subject Keyword	Remote Sensing Wildfire Wrf-fire Landsat Sar Mediterranean Fuel Model Agb
Content Type	Text
Resource Type	Article

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