scholarly journals Remote Sensing of Live Fuel Moisture

2011 ◽  
pp. 321-338
Keyword(s):  
Remote Sensing ◽  
Fuel Moisture ◽  
Live Fuel Moisture

scholarly journals Monitoring Live Fuel Moisture Using Soil Moisture and Remote Sensing Proxies

Fire Ecology ◽  
2012 ◽  
Vol 8 (3) ◽  
pp. 71-87 ◽  
Author(s):  
Yi Qi ◽  
Philip E. Dennison ◽  
Jessica Spencer ◽  
David Riaño
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Fuel Moisture ◽  
Live Fuel Moisture

A global review of remote sensing of live fuel moisture content for fire danger assessment: Moving towards operational products

2013 ◽  
Vol 136 ◽  
pp. 455-468 ◽  
Author(s):  
Marta Yebra ◽  
Philip E. Dennison ◽  
Emilio Chuvieco ◽  
David Riaño ◽  
Philip Zylstra ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Moisture Content ◽  
Fire Danger ◽  
Fuel Moisture ◽  
Danger Assessment ◽  
Live Fuel Moisture ◽  
Fuel Moisture Content

Improved fuel moisture prediction in non-native tropical Megathyrsus maximus grasslands using Moderate-Resolution Imaging Spectroradiometer (MODIS)-derived vegetation indices

2017 ◽  
Vol 26 (5) ◽  
pp. 384
Author(s):  
L. M. Ellsworth ◽  
A. P. Dale ◽  
C. M. Litton ◽  
T. Miura
Keyword(s):  
Vegetation Index ◽  
Vegetation Indices ◽  
Fuel Moisture ◽  
Fire Behaviour ◽  
Native Grass ◽  
Tropical Ecosystems ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Live Fuel Moisture ◽  
Moderate Resolution Imaging Spectroradiometer

The synergistic impacts of non-native grass invasion and frequent human-derived wildfires threaten endangered species, native ecosystems and developed land throughout the tropics. Fire behaviour models assist in fire prevention and management, but current models do not accurately predict fire in tropical ecosystems. Specifically, current models poorly predict fuel moisture, a key driver of fire behaviour. To address this limitation, we developed empirical models to predict fuel moisture in non-native tropical grasslands dominated by Megathyrsus maximus in Hawaii from Terra Moderate-Resolution Imaging Spectroradiometer (MODIS)-based vegetation indices. Best-performing MODIS-based predictive models for live fuel moisture included the two-band Enhanced Vegetation Index (EVI2) and Normalized Difference Vegetation Index (NDVI). Live fuel moisture models had modest (R2=0.46) predictive relationships, and outperformed the commonly used National Fire Danger Rating System (R2=0.37) and the Keetch–Byram Drought Index (R2=0.06). Dead fuel moisture was also best predicted by a model including EVI2 and NDVI, but predictive capacity was low (R2=0.19). Site-specific models improved model fit for live fuel moisture (R2=0.61), but limited extrapolation. Better predictions of fuel moisture will improve fire management in tropical ecosystems dominated by this widespread and problematic non-native grass.

scholarly journals SAR-enhanced mapping of live fuel moisture content

2020 ◽  
Vol 245 ◽  
pp. 111797 ◽  
Author(s):  
Krishna Rao ◽  
A. Park Williams ◽  
Jacqueline Fortin Flefil ◽  
Alexandra G. Konings
Keyword(s):  
Moisture Content ◽  
Fuel Moisture ◽  
Live Fuel Moisture ◽  
Fuel Moisture Content
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