Fire regime analysis in the Province of Mpumalanga, South Africa, based on MODIS and Landsat satellite data

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Wildfire Risk Mapping for the Insurance
Industry Susanne Haas, University of
Cologne Ulrich Ebel, Swiss Re
( Reuters/ Mike Stone)
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Content
Content
Image www.guardian.co.uk
( Reuters/ Mike Stone)
I Monetary losses caused by wildfires II
Measuring the susceptibility towards
particularly large wildfires the Fire
Radiative Power as an indicator
Comparison of FRP measurements
amongst regions III Comparison of the
different fire hazard maps IV PROs CONs V
Cat net the number of wildfires above 600MW
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Monetary losses caused by wildfires

Source Insurance Services Office
Source Insurance Matters
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The Fire Radiative Power as an indicator for
particularly large wildfires
Fireline Intensity (kW/m)
Fire Radiative Power (MW/pixel)
MODIS Active Fire Data (MCD14ML)
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Comparison of FRP measurements amongst regions

Source Wikipedia
Source Holmes et. al 2007
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Comparison of FRP measurements amongst regions

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Comparison of FRP measurements amongst regions

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Comparison of FRP measurements amongst regions

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Validation Comparison of the results
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PROs CONs
Pareto alpha estimation Maximum FRP Fires above 600mW
proven statistical model the potential for ultra large fires can be displayed the frequency of large events is accounted for
threshold selection fit are critical the frequency of large fire events is neglected empirical threshold selection peak events are neglected


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Cat Net the number of wildfires with a FRP
above 600 MW
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any questions? Susanne_Haas_at_gmx.de
( Reuters/ Mike Stone)