Journal cover Journal topic
Natural Hazards and Earth System Sciences An interactive open-access journal of the European Geosciences Union
Nat. Hazards Earth Syst. Sci., 18, 1535-1554, 2018
https://doi.org/10.5194/nhess-18-1535-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 3.0 License.
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 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.
Citation: Chaivaranont, W., Evans, J. P., Liu, Y. Y., and Sharples, J. J.: Estimating grassland curing with remotely sensed data, Nat. Hazards Earth Syst. Sci., 18, 1535-1554, https://doi.org/10.5194/nhess-18-1535-2018, 2018.
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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...
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