g. roberts, m. j. wooster and g. perry department of geography, king’s college london fire...

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G. Roberts, M. J. Wooster and G. Perry G. Roberts, M. J. Wooster and G. Perry Department of Geography, King’s College Department of Geography, King’s College London London Fire Radiative Energy: Fire Radiative Energy: Ground and Satellite Observations Ground and Satellite Observations stationary Fire Monitoring Applications Workshop ch 23-25, 2004, EUMETSAT

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G. Roberts, M. J. Wooster and G. PerryG. Roberts, M. J. Wooster and G. PerryDepartment of Geography, King’s College LondonDepartment of Geography, King’s College London

Fire Radiative Energy:Fire Radiative Energy:Ground and Satellite ObservationsGround and Satellite Observations

Geostationary Fire Monitoring Applications WorkshopMarch 23-25, 2004, EUMETSAT

Remote Sensing Fire Radiative Energy (FRE)Remote Sensing Fire Radiative Energy (FRE)

• Interested in FRE from SEVERI :

• Can be used to estimate rates and total amounts of biomass combusted using observations of emitted thermal energy released during vegetation fires

• This then acts as the basis for carbon and trace gas/aerosol emissions inventories

UK Fieldwork Spectrometer

MIR camera

video

Digital Scales

Fuel Bed

Mini Met Station

12 m tower

•FRE inter-comparison: MIR camera vs spectroradiometer

Spectroradiometer

MIR camera

Rate of FRE Release vs Rate of Mass Loss

Increasing Time

Fire Radiative Energy vs. Mass CombustedFire Radiative Energy vs. Mass Combusted

• Very good relationship – FRE well related to mass combusted

• BUT only ~ 2000 KJ radiated per kg burnt

• Net heat yield quoted at ~ 16,000 KJ/kg • 15 ± 7 % of theoretically released energy appears to be actually radiated

R2 = 0.964

FRE Derivation in the MIRFRE Derivation in the MIR

hMIRMIR

samplMIR L

a

AFRE ,.

..

•FRE derived as a function of MIR spectral radiance:

Advantages :• Linear

• computationally efficient• alterations can be applied later

• e.g. atmospheric correction • One spectral channel

• not sensor specific

Algorithm :• active fire detection and background characterisation• FRE derived per pixel and per fire

LMIR,h = ‘fire’ pixel MIR spectral radiance

MIR = ‘fire’ pixel MIR emissivity

a = constant from Planck fn approx.Asampl = ground-pixel area (m²)

SEVERI and MODIS

SEVERI (12:57 – Sept 1st 2003) MODIS (12:20 – Sept 1st 2003)

Green : MIR channelYellow : Detected active fires

SEVERI and MODIS FRE

R2 = 0.74

Total emitted energy (MW) = 500115 (9.7 MW/sec)

Total Biomass Combusted (Kg) = 250145 (4.9 Kg/sec)

6am9pm12:30pm

SEVERI MIR saturationSaturation point

Initial detection

Daytime: Fires detectable down to ~ 0.5 to 1.0 hectares (assume 800 K)Nighttime: Somewhat smaller (maybe to ½ this size)

6am9pm12:30pm

BUT SOME QUESTIONS REMAIN……….

• Do ground-based and spaceborne FRE agree ?

• Do very large fires have similar % of energy released as radiation?

• Cloud cover problem• coupling FRE & burned area products ?• fit a model to available samples or interpolation ?

• Active fire detection• Couple temporal and spatial domains

• Background characterisation• Fire detection

Acknowledgements

Thanks to :

• Rothamsted Agricultural Research• Botswana Wildlife Service• DLR• EUMETSAT• NASA• Staff and students at Kings/UCL

Current Approaches to Emission Current Approaches to Emission Inventory• Based on estimates of total biomass combusted (M)

– converted into emissions estimate via ‘emissions factors’

Biomass = Burnt * Biomass * Burning Burnt (M) Area Density Efficiency

• Difficulty reliably estimating biomass density & burning efficiency – uncertainty propagates through to estimates of M

• Andreae and Merlet (2001) demonstrate order-of-magnitude difference between fire frequency and EO-approaches and suggest a new route maybe needed to enhance the existing methodologies.