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Spatially explicit predictions of fuel moisture content are crucial for quantifying fire danger indices and as inputs to fire behaviour models. Remotely sensed predictions of fuel moisture have typically focused on live fuels; but regional estimates of dead fuel moisture have been less common. Here we develop and test the spatial application of a recently developed dead fuel moisture model, which is based on the exponential decline of fine fuel moisture with increasing vapour pressure deficit (D). We first compare the performance of two existing approaches to predict D from satellite observations. We then use remotely sensed D, as well as D estimated from gridded daily weather observations, to predict dead fuel moisture. We calibrate and test the model at a woodland site in South East Australia, and then test the model at a range of sites in South East Australia and Southern California that vary in vegetation type, mean annual precipitation (129-1404 mm year− 1) and leaf area index (0.1-5.7). We found that D modelled from remotely sensed land surface temperature performed slightly better than a model which also included total precipitable water (MAE < 1.16 kPa and 1.62 kPa respectively). D calculated with observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) on the Terra satellite was under-predicted in areas with low leaf area index. Both D from remotely sensed data and gridded weather station data were good predictors of the moisture content of dead suspended fuels at validation sites, with mean absolute errors less than 3.9% and 6.0% respectively. The occurrence of data gaps in remotely sensed time series presents an obstacle to this approach, and assimilated or extrapolated meteorological observations may offer better continuity.
We would like to thank R. Gibson, M. Chick, D. Spencer, S. Khanal, A. Boer-Cueva, L. Serrano-Grijalva, D. Bridgman, and C. Beattie for their in-valuable assistance in collecting fuel moisture data. This project was funded by Victorian Department of Environment, Land, Water and Plan-ning via the Bushfire Cooperative Research Centre, grants from the US Department of Energy and the National Aeronautics and Space Administration to MLG, the Education Investment Fund, the Hawkesbury Institute for the Environment and a Ramón y Cajal Fellowship to VRD (RYC-2012-10970). MODIS data products are courtesy of the online Data Pool at the NASA Land Processed Distributed Archive Centre (LP
DAAC), USGS/Earth Resources Observation and Science (EROS) Center, Sioux Falls, South Dakota |
