Observations of Fire

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Observations of Fire Smoke from Space

• Nikisa Jordan(1), Charles Ichoku (2), and Raymond
  Hoff(1)
• (1) CREST, Joint Center for Earth Systems
  Technology, University of Maryland Baltimore
  County, 5523 Research Park Drive, Suite 320,
  Baltimore, MD, 21250 email njordan1_at_umbc.edu
• (2) Earth System Science Interdisciplinary Center
  (ESSIC), University of Maryland, College Park,
  MD, 20742

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Biomass Burning its Effects

• Biomass Burning..
• combustion of organic matter (live/dead fuel)
  from natural or man-made activities
• releases trace gases and various particulates
  (mostly black organic carbon) into the atmosphere
• Effects1
• climate weather
• visibility
• animal, plant, and human health

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Problem

• Smoke emission estimates from biomass combustion
  often contain significant errors
• Global and regional emissions of many compounds
  from different vegetation species are still
  poorly constrained
• Correct estimates of regional and inter-annual
  variations are necessary before conclusive
  evaluations are made of effects on climate and
  environment

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Study Area
Shown is a recent MODIS land cover map of the
conterminous United States (Chandler and Zalisk
2002 ).
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Data Used

• MODIS Thermal Anomalies
• detects fires at a spatial resolution of 1 km2 at
  nadir
• measures Fire Radiative Power (FRP) or the actual
  strength of the fire
• method isolates fires in the MIR spectral region
  (4mm channel) where fires are most intense
• MODIS derived Aerosol Optical Depth (AOD)
• AOD is a measure of light attenuated by a
  vertical column of aerosol
• resolution 10 km x 10 km
• NCEP Wind Data
• Data was acquired and analyzed for the year of
  2004

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An example of an aerosol pixel with fires is
shown (central aerosol pixel). Aerosol pixels
surrounding the central pixel are used to
determine smoke emitted
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Methodology CONTD

• Deriving Ce
• linear regression of the daily rates of emitted
  smoke and FRE release rates
• lines were fitted through to the zero-intercept
• assumed that zero FRP yielded no smoke emission
• the intercept did not vary significantly from
  zero
• Slope of regression line is the FRE-based
  coefficient of smoke emission (Ce)
• Qx Ce Rfre (3)

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All aerosol pixels containing fire(s) observed by
TERRA and AQUA MODIS for 2004
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Smoke Emission Uncertainty

• Traditional Technique
• Mx -gt 50 uncertainty or greater
• Direct Approach Used Here
• Average uncertainty of FRE-based smoke emission
  coefficient (Ce)
• 0.049 0.024 (49 uncertainty)
• Range of uncertainty of smoke mass flux estimates
  (QPM)
• 49 gt QPM gt 62

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Conclusion

• For the first time, smoke emission estimates by
  way of MODIS FRE release rates have been
  presented for the U.S. Southern Great Plains
• Better to study small areas with minimum
  variability in fuel types rather than large
  regions based on geographical convenience
• Error related to predicting QPM is similar to the
  smoke emission uncertainty (?50) postulated by
  Andreae and Merlet (2001) for the indirect
  technique.
• Minimizing errors in transport wind speed and
  improving the AOD retrieval will be most
  effective in improving the reliability of the
  smoke mass flux approximations.

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Thank You!
Jordan, N.S., et al., Estimating smoke emissions
over the US Southern Great Plains using MODIS
fire radiative power and aerosol observations.
Atmospheric Environment (2008),
doi10.1016/j.atmosenv.2007.12.023
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References

• Andreae, M. O. and P. Merlet (2001), Emission of
  trace gases and aerosols from biomass burning,
  Global Biogeochemical Cycles, 15 955-966,
  2000GB001382
• Houghton et al., Eds. (2001), Climate Change
  2001 The Scientific Basis (Cambridge Univ.
  Press, Cambridge) (available at
  http//www.ipcc.ch)
• Seiler, W., and P. J. Crutzen (1980), Estimates
  of gross and net fluxes of carbon between the
  biosphere and the atmosphere from biomass
  burning, Clim. Change, 2, 207 247
• Chandler L. and B. Zalisk (2006). NASAs Earth
  Observatory (EO) NASAs Terra Satellite Refines
  Map of Global Land Cover, RELEASE NO 02-126
  http//earthobservatory.nasa.gov/Newsroom/LCC/
  (accessed 06/24/06)
• Ichoku, C. and Kaufman, Y.J., (2005), A method to
  derive smoke emission rates from MODIS fire
  radiative energy measurements, IEEE Transactions
  on Geosceince and Remote Sensing, 43, 11,
  2636-2649.
• Korontzi, S., Roy, D.P., Justice C.O., Ward D.E.
  2004. Modeling and sensitivity analysis of fire
  emissions in southern Africa during SAFARI
  2000.Remote Sensing of Environment,
  92(2)255-275.
• Wooster, M.J., Zhukov, B. and Oertel, D. (2003)
  Fire radiative energy for quantitative study of
  biomass burning Derivation from the BIRD
  experimental satellite and comparison to MODIS
  fire products, Remote Sensing of Environment, 86,
  83-107
• Wooster, M. J., G. Roberts, G. L. W. Perry, and
  Y. J. Kaufman (2005), Retrieval of biomass
  combustion rates and totals from fire radiative
  power observations FRP derivation and
  calibration relationships between biomass
  consumption and fire radiative energy release, J.
  Geophys. Res., 110, D24311, doi10.1029/2005JD0063
  18
• U.S. Environmental Protection Agency (2002)
  Current EPA Emissions Factors and Inventory
  Guidance and Resource Material . available on the
  Internet at http//www.epa.gov/ttn/chief/publicati
  ons.htmlreports.
• Zhang, X., S. Kondragunta, F. Kogan, Jerald D.
  Tarpley, and W. Guo. (2006) Satellite-Derived
  PM2.5 Emissions from Wildfires for air quality
  forecast. Presented at the 15th International
  Emission Inventory Conference-Reinventing
  Inventories - New Ideas in New Orleans. New
  Orleans, USA, May 16-18 2006a.
• Giglio, L., van der Werf, G. R., Randerson, J.
  T., Collatz, G. J., and Kasibhatla, P. S. Global
  estimation of burned area using MODIS active fire
  observations, Atmos. Chem. Phys., 6, 957974,
  2006
• Wolfe, R. E., Nishihama, M., Fleig, A. J.,
  Kuyper, J. A., Roy, D. P., Storey, J. C., Patt,
  F. S. (2002). Achieving sub-pixel geolocation
  accuracy in support of MODIS land science. Remote
  Sensing of Environment, 83, 31-49
• Kaufman, Y., and Justice, C. (1998). MODIS Fire
  Products, Algorithm Theoretical Basis Document,
  Version 2.2, MODIS Fire Team (EOS ID2741) (p.
  77).

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Back Up Slides
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Estimates from Spaceborne Sensors

• Indirect Method
• Mbiomass is estimated indirectly by
• combining field and satellite measures (derived
  burned area and fuel load) with an emissions
  model
• Direct Method
• Use the energy at which a fire radiates
• Fire Radiative Energy (FRE)
• Fire Radiative Power (FRP)

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Traditional Estimates of Smoke Emission

• Basic formula used to estimate emissions2
• Mx EFx Mbiomass (1)
• where Mx is the amount of compound released (g),
  EFx is the emission factor (g/kg) for the species
  of interest x and Mbiomass (kg) is the amount of
  dry fuel consumed.
• Emission factors or how much mass of pollution
  is discharged (g) per mass of fuel burned (kg)
  of biomass species can be adequately determined
  in contained experiments
• Harder to accurately determine Mbiomass

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Traditional Estimates of Smoke Emission CONTd

• Seiler and Crutzen 1980 approach to estimate
  Mbiomass and subsequently smoke emission have
  been widely accepted within the scientific
  community
• Mbiomass A x B x a x b (2)
• where Mbiomas biomass burned (kg), A total
  land area burned (m2), B biomass loading or
  fuel density (kg/m2), a fraction of the average
  above-ground biomass burned, and b burn
  efficiency.

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Objective

• Derive the FRE-based coefficient of smoke
  emission (Ce) for the Midwest-Central U.S. from
  MODIS FRP and AOD measurements
• Given the coefficient and satellite fire
  radiative power measurement one could determine
  the amount of smoke emitted from any fire in the
  area of interest since
• Qx Ce Rfre (3)

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Study Area

• U.S. Southern Great Plains
• Region chosen since extensive burning occurs
  annually

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Methodology

• Cluster Fires on a Daily Basis
• FRE release rates were summed
• Smoke Load ltcolumn SMDgt AT (1)
• where ltcolumn SMDgt is the average SMD and AT is
  the total area of all aerosol pixels containing
  fire
• (2)

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TERRA overpass 1030AM AQUA overpass 230PM
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Future Work

• Employ more stringent quality and control methods
  by
• utilizing improved parameters (available in 2007
  MODIS Collection 5 products), such as estimates
  of fire pixel confidence
• Integrate MODIS FRE measurements with data from a
  spaceborne instrument with better temporal
  resolution
• Incorporate CALIPSO(Cloud-Aerosol Lidar and
  Infrared Pathfinder Satellite Observation)
  observations to determine better assessments of
  smoke injection height

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Few Cases- Impact of Smoke on Local AQ
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• SPRING - April 6, 2004

• FALL - Sept 30, 2004

Images edited from source (UW MODIS Direct
IDEA)
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• SUMMER July 23, 2004

Images edited from source (HYSPLIT, GASP, and
IDEA)