7th Workshop on Forest Fire Management - EARSeL, European ...

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III - FIRE DETECTION AND FIRE MONITORING
to be false detects and adding fires that the algorithms have not detected.
False detects can be due to a number of conditions including urban heat
islands, solar specular reflection off water surfaces, highly reflective clouds,
relatively brighter vegetated land embedded within darker areas, instrument
noise, etc. Fires not detected by the algorithms can be attributed to
partial obscuration by clouds, overhead vegetation canopy, detected temperature
that is not sufficiently hot or hotter than the surrounding area,
etc. Validation results (Schroeder et al., 2008) using 30m ASTER data found
that fires added by analysts reduced the omission error rate compared to
WFABBA and MODIS detections. However, the commission error rate was not
reduced by automated detections that were deleted, with actual fires being
erroneously removed. Validation using ground based reports from Florida,
Montana, Idaho and Manitoba supported the finding of reduced omission
errors in the HMS compared to the automated only product (WFABBA,
MODIS and FIMMA) with approximately twice as many detections, although
the overall detection rate was only 25-30%. The low detection rate is
ascribed to the small size (less than 2 ha) of many of the fires as well as
prohibitive cloud cover. HMS products may be accessed at
www.osdpd.noaa.gov/ml/land/hms.html.
2 - Smoke Detection and Model Transport
Smoke detection is exclusively performed with visible imagery, primarily
utilizing animated GOES data, although polar imagery is occasionally used.
Detection is optimized over the contiguous US with GOES-West in the morning
and GOES-East in the evening due to favorable solar zenith and satellite
viewing angles. Analysts graphically depict the smoke extent by manually
drawing polygons. An estimate of the vertically integrated smoke concentration
is assigned to each polygon. Three ranges of values (in µm/m 3 )
are available for the analyst to assign to the polygon. The automated GOES
Aerosol and Smoke Product (GASP) (Knapp et al., 2005) which generates
Aerosol Optical Depth (AOD) is a tool to aid analysts in this determination.
The smoke depicted may be attached to actively burning fires or may be
several days old and have drifted hundreds or thousands of km from the
source.
Many fires do not produce emissions that are detectable. Clouds obscure
some smoke emissions while other fires may have minimal emissions due to
the short duration of the fire, limited biomass or the engineering of the fire
in the case of agricultural/prescibe burns. For fires producing smoke that is
detected the analyst provides an estimate of the initiation and duration of
emissions and a coarse estimate of the fire size. The size is ideally estimated
using higher (1km) resolution polar data with the fire close to the
suborbital track. This is not always possible, especially in dynamic wildfire
situations in the mid and lower latitudes. In these cases the analyst pro-