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

Yaron Michael; Gilad Kozokaro; Steve Brenner; Itamar M. Lensky

doi:10.3390/rs14122941

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

Yaron Michael, Gilad Kozokaro, Steve Brenner, Itamar M. Lensky

Department of Environment, Planning and Sustainability

Abiotix Environmental Services

Research output: Contribution to journal › Article › peer-review

5 Scopus citations

Abstract

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.

Original language	English
Article number	2941
Journal	Remote Sensing
Volume	14
Issue number	12
DOIs	https://doi.org/10.3390/rs14122941
State	Published - 1 Jun 2022

Bibliographical note

Publisher Copyright:
© 2022 by the authors. Licensee MDPI, Basel, Switzerland.

Funding

Acknowledgments: This work was conducted as part of Y.M.’s Ph.D. dissertation. Y.M. acknowledges the support provided by the Ministry of Science and Technology of Israel through the Eshkol Scholarship (grant 3-14504). The authors also thank the Jewish National Fund (KKL–JNF) for the wildfire perimeter database. Funding: This research was funded by the Israeli Ministry of Science, Technology and Space, Eshkol Scholarship (grant 3-14504).

Funders	Funder number
Eshkol Scholarship	3-14504
Ministry of Science, Technology and Space
Ministry of science and technology, Israel

Keywords

AGB
Landsat
Mediterranean
SAR
WRF-Fire
fuel model
wildfire

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10.3390/rs14122941

Cite this

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

abstract = "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.",

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

Abstract

Bibliographical note

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Keywords

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