International Journal of Wildland Fire International Journal of Wildland Fire Society
Journal of the International Association of Wildland Fire
RESEARCH ARTICLE

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

L. M. Ellsworth A B D , A. P. Dale C , C. M. Litton B and T. Miura B
+ Author Affiliations
- Author Affiliations

A Oregon State University, Department of Fisheries and Wildlife, 104 Nash Hall, Corvallis, OR 97331, USA.

B University of Hawaii at Manoa, Department of Natural Resources and Environmental Management, 1910 East–West Road, Honolulu, HI 96822, USA.

C GISPacific, LLC, 575 Ulumalu Street, Kailua, HI 96734, USA.

D Corresponding author. Email: lisa.ellsworth@oregonstate.edu

International Journal of Wildland Fire 26(5) 384-392 https://doi.org/10.1071/WF16131
Submitted: 20 July 2016  Accepted: 8 March 2017   Published: 27 April 2017

Abstract

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.

Additional keywords: fire model, guinea grass, Hawaii, invasive grass, Keetch–Byram Drought Index, live fuel moisture, National Fire Danger Rating System, remote sensing.


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