EGU21-10320, updated on 04 Mar 2021
https://doi.org/10.5194/egusphere-egu21-10320
EGU General Assembly 2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Developing a Wildfire danger index based on the satellite-derived parameters 

Suresh Babu1, Vernon Visser2, Glenn Moncrieff3, Jasper Slingsby4, and Res Altwegg5
Suresh Babu et al.
  • 1Postdoctoral Research Fellow, Centre for Statistics in Ecology, Environment and Conservation, Department of Statistical Sciences, University of Cape Town, Cape Town, South Africa (sureshbabu.iiith@gmail.com)
  • 2Research Fellow, Centre for Statistics in Ecology, Environment and Conservation, Department of Statistical Sciences, University of Cape Town, Cape Town, South Africa (vervis@gmail.com)
  • 3Data Scientist, Fynbos Node South African Environmental Observation Network (SAEON), Cape Town, South Africa (glenn@saeon.ac.za)
  • 4Scientist, Fynbos Node South African Environmental Observation Network (SAEON), Cape Town, South Africa (jasper@saeon.ac.za)
  • 5Associate Professor, Director centre for Statistics in Ecology, Environment and Conservation, Department of Statistical Sciences, University of Cape Town, Cape Town, South Africa (res.altwegg@gmail.com)

Wildfires are occurring throughout the world, causing immense damage to forest resources, flora, and fauna. The Fire Danger indices are used as a tool for the decision-makers to issue warnings to the public, based on the level of fire danger classes for implementing mitigation measures to control wildfires. In this study, a Wildfire danger index (WDI) is developed from static and dynamic factors, which are derived from satellite datasets. The Static fire danger Index (SFDI) is generated using MODIS Land cover type (MCD12Q1), Shuttle Radar Topography Mission (SRTM) DEM, and Open Street Map datasets. The Random Forest algorithm is used to generate SFDI from the parameters LULC map, slope map, aspect map, and elevation maps based on the historical MODIS active thermal anomaly product (MCD14). Dynamic Fire Danger Index (DFDI) is developed from the MODIS Terra datasets such as Land Surface Temperature (MOD11A2) and surface reflectance (MOD09A1) datasets.  The DFDI is developed from four parameters viz. Land surface temperature, Visible Atmospherically Resistant Index (VARI) and Normalized Multiband Drought Index (NMDI), and Normalized Difference Infrared Index – B6 (NDIIB6). Finally, the wildfire danger index is calculated by integrating SFDI and DFDI and found that the accuracy is more than 80% during the 2018-19 fire season. Therefore, the WDI can be useful for disseminating daily fire danger maps on near real-time basis using the MODIS TERRA Near Real-Time datasets so that the fire officials to take necessary actions to control the spread of wildfires.

 

 

 

How to cite: Babu, S., Visser, V., Moncrieff, G., Slingsby, J., and Altwegg, R.: Developing a Wildfire danger index based on the satellite-derived parameters , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10320, https://doi.org/10.5194/egusphere-egu21-10320, 2021.

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