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Natural Hazards and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 18, issue 6 | Copyright
Nat. Hazards Earth Syst. Sci., 18, 1535-1554, 2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 04 Jun 2018

Research article | 04 Jun 2018

Estimating grassland curing with remotely sensed data

Wasin Chaivaranont1, Jason P. Evans1, Yi Y. Liu1,2, and Jason J. Sharples3 Wasin Chaivaranont et al.
  • 1ARC Centre of Excellence for Climate Systems Science and Climate Change Research Centre, UNSW, Sydney, NSW 2052, Australia
  • 2School of Geographical Sciences, Nanjing University of Information Science and Technology, Nanjing, 210044, China
  • 3School of Physical, Environmental and Mathematical Sciences, UNSW, Canberra, ACT 2600, Australia

Abstract. 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 VOD) with an r2 up to 0.67. DOC levels estimated using satellite observations were then evaluated using field measurements with an r2 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.

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Short summary
This study explore the feasibility of using a combination of recent and traditional satellite products to estimate the grassland fire fuel availability across space and time over Australia. We found a significant relationship between both recent and traditional satellite products and observed grassland fuel availability and develop an estimation model. We hope our estimation model will provide a more balanced alternative to the currently available grass fuel availability estimation models.
This study explore the feasibility of using a combination of recent and traditional satellite...