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Improved fuel moisture prediction in non-native tropical Megathyrsus maximus grasslands using Moderate Resolution Imaging Spectroradiometer (MODIS) derived vegetation indices
The synergistic impacts of nonnative grass invasion and frequent human-derived wildfire threaten endangered species, native ecosystems, and developed land throughout the tropics. Fire behavior 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 behavior. To address this limitation, we developed empirical models to predict fuel moisture in nonnative 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 nonnative grass.
WF16131 Accepted 08 March 2017
© CSIRO 2017