Using MODIS FRP Values to Estimate Forest Fire PM 2.5 Emissions
Riley Start, Brian Lamb, Brian Potter, Alistair Smith
Forest and Rangeland Measurements Laboratory, College of Natural Resources, University of Idaho
Laboratory for Atmospheric Research, Department of Civil and Environmental Engineering, Washington State University
USDA Forest Service
MODIS Daily PM 2.5 Emissions (Mg)
Accurate estimates of PM 2.5 (particulate matter less than
2.5 micrometers in diameter) emissions from wild fires are
important for determining regional radiative budgets,
investigation of regional atmospheric chemistry, and
assessment of health risks. Several different modeling
systems are already in use to determine these emissions.
The BlueSky system was developed by the U.S. Forest
Service to estimate wildfire emissions for dispersion
modeling purposes. Recently, this system has been
upgraded with a module called SMARTFIRE to incorporate
both ground-based fire reports and MODIS (Moderate
Resolution Imaging Spectroradiometer) satellite data to
provide better fire location, fire size and fire progression
information.
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MODIS
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Bluesky/SMARTFIRE PM 2.5 Emissions (kg)
Tripod (y=3.108x)
Boundary Complex (y=.572x)
Red Mountain (y=.525x)
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Figure 4 shows MODIS and BlueSky/SMARTFIRE results vs.
time. This makes it easy to see the relationship between
the two methods. For the most part, when SMARTFIRE
results showed an increase in PM 2.5 emissions, MODIS did
as well.
Columbia Complex (y=2.144x)
Rattlesnake Complex (y=.574x)
South Fork Complex (y=.769x)
Uncertainties
Analysis of the MODIS FRP data relies on averages and
integrating over time. This becomes a problem considering
that the satellites cannot record data at all times,
sometimes going over a day without new FRP recordings.
In these cases, what in reality is a short burst of power
from the fire shows up as lasting a whole day in our results,
greatly increasing its value. A possible solution to this
would be using sine curves instead of straight lines to
extrapolate values. This would more accurately estimate
the behavior of wild fires.
Figure 5 is a plot of the best-fit lines for each fire we
analyzed. The two steep lines represent the Tripod and
Columbia Complex fires in Washington. The other four
fires occurred in central Idaho during the same summer of
2006. These latest best-fit lines show a close relationship,
perhaps because of the close proximity and timing of the
fire events.
FRP = (∑FRP /∑pixel area) x New Burn Area
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F5. Best-Fit lines for Analyzed Fires
To date, six fires have been analyzed using this approach.
Figure 3 and 4 are from the Tripod Complex fire which
burned in the summer of 2006. Figure 3 shows the data
found using MODIS compared to the data found with
BlueSky/SMARTFIRE, each orange triangle represents a
data point found using an emission factor of .04 kg/MJ.
Each corresponding line shows the relative uncertainty in
this emission factor and how it could affect the end result.
This plot shows a good correlation between the two
systems; for this fire, however, MODIS FRP emissions were
about 3 times those from BlueSky/SMARTFIRE.
5.
Future Work
Emission factors to be found at the University of Idaho may
later be used in this research.
F2. Columbia Complex FRP Recordings over
Time
Average FRP (MW/km^2)
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For this research, we employ satellite data in terms of time
of day, FRP, and area of each FRP detection. Figure 1 shows
a fire perimeter with MODIS pixels superimposed on it.
Pixels inside or within 1 km of the fire perimeter are
considered part of the fire. MODIS data are available
several times each day. The total FRP for a MODIS snapshot
was calculated as:
F1. Columbia
Complex Fire
shown in
ArcGIS
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Process
The FRP values between each recorded period were
assumed to follow a linear path between them. A plot of
these values is shown in Figure 2, FRP values are shown as
MW per km2. The area under this curve is called the Fire
Radiative Energy (FRE). FRE can be used with empirical
regression curves from several recent studies to estimate
PM 2.5 emissions for each fire period. Regression
relationships have been reported for several different fuel
types. For evergreen forests in the Northwest, PM2.5 mass
per FRE are in the range of 0.018 and 0.066 kg/MJ; for this
project, we take 0.04 kg/MJ as an average and multiply the
FRE for each day by this factor to produce daily PM 2.5
emissions in kg/day.
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Bluesky/SMARTFIRE Daily PM 2.5 Emission (Mg)
MODIS sensor data, collected onboard two satellites,
provide data that can be used to estimate FRP (Fire
Radiative Power) which is a measure of fire intensity. In
this project, MODIS FRP data are used with empirical
relationships between FRP and PM2.5 emissions to
estimate PM2.5 emissions for selected fires in the Pacific
Northwest. These estimates are compared to
BLUESKY/SMARTFIRE emissions. Thus the objective is to
employ FRP/PM2.5 relationships to provide an
independent evaluation of current wildfire emission
estimates and to assess the feasibility of the FRP approach
for providing emissions for regional air quality modeling.
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Daily PM 2.5 Emissions (kg)
Introduction
F4. 2006 Tripod Complex
MODIS PM 2.5 Emissions (kg)
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F3. SMARTFIRE vs. MODIS Emission
Comparison
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Acknowledgements
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Special thanks to the Forest Service AirFire team and
Sonoma Technologies, Inc. for their emissions data and the
Joint Fire Science Program for their support for this
research: JFS 07-2-1-60. Thanks to the USFS RSAC and
NASA for the MODIS fire data, and the Laboratory for
Atmospheric Research and Department of Civil and
Environmental Engineering for funding this research.
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