Estimating grassland curing with remotely sensed data.

Wildfire can become a catastrophic natural hazard, especially during dry summer seasons in Australia. Severity is influenced by various meteorological, geographical, and fuel characteristics. Modified Mark 4 McArthur’s Grassland Fire Danger Index (GFDI) is a commonly used approach to determine the fire danger level in grassland ecosystems. The degree of curing (DOC, i.e. proportion of dead material) of the grass is one key ingredient in determining the fire danger. It is difficult to collect accurate DOC information in the field, and therefore ground-observed measurements are rather limited. In this study, we explore the possibility of whether adding satellite-observed data responding to vegetation water content (vegetation optical depth, VOD) will improve DOC prediction when compared with the existing satellite-observed data responding to DOC prediction models based on vegetation greenness (normalised difference vegetation index, NDVI). First, statistically significant relationships are established between selected ground-observed DOC and satellite-observed vegetation datasets (NDVI and 2VOD) with an r up to 0.67. DOC levels estimated using satellite observations were then evaluated using field measurements with an r 2 of 0.44 to 0.55. Results suggest that VOD-based DOC estimation can reasonably reproduce ground-based observations in space and time and is comparable to the existing NDVI-based DOC estimation models.

Figure 5. Example satellite-based and site-observed degree of curing (DOC) time series comparison at Silent Grove, WA (17.131◦ S, 125.374◦ E) (b), where the star (∗ ) indicate the location of the time series on a map of Australia (a). Satellite-based DOC across Australia during summer (December, January, February) for 2002–2003 (c) and 2010–2011 (d) are shown with forest areas masked out.