WF_ABBA Version 6.5: An overview of the improvements and trend
analyses of fires from 1995 to present over the western Hemisphere
Jason C. Brunner1, Christopher C. Schmidt1, Elaine M. Prins2, Joleen M. Feltz1, Jay P. Hoffman1, and
Scott S. Lindstrom1
1Cooperative
Institute for Meteorological Satellite Studies (CIMSS)/Space Science Engineering Center (SSEC), University of Wisconsin-Madison
2Consultant in environmental remote sensing applications, Grass Valley, CA
http://cimss.ssec.wisc.edu/goes/burn/wfabba.html jason.brunner@ssec.wisc.edu
The WF_ABBA and LBA
The WildFire Automated Biomass Burning Algorithm
Year-to-Year Variations in Fire Detections
The current Geostationary Operational Environmental Satellite (GOES)
series has had the ability to detect and characterize biomass burning
primarily since the launch of GOES-8 in 1995. However, fires were
detected with the previous GOES VAS series as well, but fire detection was
hindered due to poor spatial resolution (16 km at sub-satellite point for 3.9
micron band).
The Wildfire Automated Biomass Burning Algorithm
(WF_ABBA) provides fire detections and fire characteristics (instantaneous
fire size, instantaneous fire temperature, and fire radiative power). The
WF_ABBA has recently been upgraded to include improved metadata that
allows for tracking when a specific place was observed by the satellite and
why individual pixels were not labeled as fires, such as opaque clouds,
block-out zones for sunglint, and disallowed surface types. This data
allows for correction of the diurnal cycle of fire detections for places and
times where fires could not be detected, which will enable the construction
of a high quality climatology of fires in the western hemisphere since
1995. The WF_ABBA has been applied to all GOES-8 data from 19951999, producing the first version of this climatology. This effort is made
possible through funding from the NASA Large Scale BiosphereAtmosphere Experiment in Amazonia (LBA).
The WF_ABBA uses inputs consisting of geostationary satellite data, total precipitable water from numerical
forecast models, and an ecosystem map, which are used to detect and characterize fires in near real-time,
providing users such as the National Oceanic and Atmospheric Administration and the hazards community with
high temporal and spatial resolution fire data. Today the WF_ABBA processes all data generated by GOES-10/11/-12, Meteosat-9, and MTSAT-1R, detecting fires within a satellite zenith angle of 80° (covering the better part of
the visible hemisphere). The WF_ABBA algorithm requires a minimum 3.9 μm and 11 μm bands that meet certain
performance requirements. However, for the new WF_ABBA version (Version 6.5), the algorithm does better if it
has access to visible and 12 μm bands as well for the cloud mask.
Some variation year to year is to be expected. Of particular note
however are the relatively high numbers of saturated and to a lesser
extent processed fires in 1995, corresponding to the noise that was
prevalent in 1995 and 1996 data. This data has not been corrected
for coverage rate, cloud cover, and so on, a process that is possible
by processing the mask output by the v65 WF_ABBA.
Analysis of the fire data immediately revealed problems with noise in the
GOES-8 data in 1995 and 1996 and to a much lesser extent in 1997. 1996
has been excluded from this poster while 1995 has been retained to
illustrate the magnitude of the noise issue. The noise filter associated with
the WF_ABBA has performed exceptionally since 2001, but the noise in the
early GOES-8 was greater and of a different character than any
experienced since 2000 when the WF_ABBA first began processing
realtime data. The noise filter is being upgraded and once that is done the
1995 data will improve dramatically.
Number of Fires in South America By Year and Category
A Brief Primer on Satellite Fire Detection
1000000
900000
800000
Number of Fires
Infrared fire detection from satellites takes advantage of the fact that as target temperature increases radiance
increases faster at the shortwave end of the spectrum as opposed to the longwave end. By using two windows
such as the 3.9 μm and 11 μm fire locations and characteristics can be determined. Algorithms can determine the
radiance due to the fire itself, within limits determined by viewing conditions and satellite characteristics. That fire
radiance can be split into instantaneous size and temperature via the Dozier bispectral method or converted into
fire radiated power (FRP). The key to successfully estimating any of those quantities is to have good radiance
information. The key to successfully using fire characteristics derived from satellite data is understanding their
limitations. Fires are almost always subpixel entities, and because the optical response of the sensor is not flat
within the field of view, characteristics derived for individual fires are inherently likely to be biased low or high.
However, when considering a large number of fires, the affect of the optical response averages out. Pixel radiance
data from some platforms such as Meteosat-9, GOES-10 (unlike the other GOES), and MTSAT-1R are remapped
before being sent to the users, a process which further alters the quality of the derived fire characteristics.
700000
v65 Metadata
WF_ABBA v65 output includes
pixel codes that can be used to
determine the coverage rate of
the satellite (which varies widely
over South America), the reasons
why specific pixels were not
processed for possible fires such
as opaque clouds or the
presence of water, and the fire
category for detected fires. Mask
codes are shown in the table to
the right.
Mask Flag
0
1
2
3
4
5
6
50
60
100
120
125
130
150
155
160
170
180
182
185
186
187
188
200
205
210
215
220
225
230
235
240
245
Definition
Non-processed region of input/output image
Processed fire pixel
Saturated fire pixel
Cloud contaminated fire pixel
High probability fire pixel
Medium probability fire pixel
Low probability fire pixel
Satellite zenith angle block-out zone
Reflectance angle or solar zenith angle block-out zone
Processed region of image
Bad input data (4 or 11 micron)
Invalid reflectivity product input. Can be indicative of localized
spikes in the reflectivity product/bad data
GOES-9/-10 test for saturated pixels due to noise
Invalid ecosystem type
GOES-9/-10 test for saturation associated with certain ecosystem
types
Invalid emissivity value
No background value could be computed, although the observed 4
micron temperature is indicative of a possible fire
Error in converting between temperature and radiance
Error in converting adjusted temperatures to radiance
Values used for bisection technique to hone in on solutions for
Dozier technique are invalid.
Invalid radiances computed for Newton’s method for solving
Dozier equations
Errors in Newton’s method processing
Error in computing pixel area for Dozier technique
11 micron threshold cloud test
4 minus 11 micron negative difference threshold cloud test
4 minus 11 micron positive difference threshold cloud test
Albedo threshold cloud test (daytime only)
12 micron threshold cloud test (only used when data available)
11 minus 12 micron negative difference threshold cloud test
11 minus 12 micron positive difference threshold cloud test
Additional 4 micron minus 11 micron difference cloud test applied
under certain conditions.
Along scan reflectivity product test to identify and screen for cloud
edge used in conjunction with 4 micron threshold
Along scan reflectivity product test to identify and screen for cloud
edge used in conjunction with albedo threshold
1995
600000
1997
500000
1998
400000
1999
300000
*** Note that low possibility fires are often indicative of false alarms
200000
100000
0
Proces sed
Saturated
Cloudy
High
Medium
Low
Detection Category
*** Note that low possibility fires are often indicative of false alarms
2 Detailed Regions for Fire Study
*** Arc of Deforestation – Amazon – South America
*** Central/Southern Plains – United States
For both regions monthly fire summary statistics and composites were
created for the fire categories from WF_ABBA
Image to the right denoting Arc of Deforestation is from Ecoregions in Amazonia
(Source: Philip M. Fearnside)
Temporally filtered WF_ABBA data was used to make the composites
below and to the right. Temporal filtering keeps a fire if there was another
detection within the previous 12 hours within 0.1 degrees of its location.
Noisy lines pass through the filter when they occur in large numbers due
to geomagnetic storms, ground antenna issues, or satellite related issues.
All fire categories but low possibility are included in the composites. The
“low possibility” category is often indicative of false alarms in North
America and along cloud edges and at high viewing angles at sunrise and
sunset, but should be monitored over time.
SUMMARY OF RESULTS – Central/Southern Plains
• May/June most active for 1998
• July/August most active for 1995 (if one disregards the large
number of low possibility fires in August 1998)
• September 1998 very active compared to other Septembers
• October/November most active for 1995 and 1999
• Fires seem to occur at different times for each year, no main peak
at same time of year for all the years
SUMMARY OF RESULTS – Arc of Deforestation
CONCLUSIONS/FUTURE WORK
• Peak of fires occurs in August/September and most active for 1995 especially for total and processed fires
• There are interesting inter- and intra-annual trends of fires over
• Decreasing trend in total and processed number of fires as go from 1995 to 1999 for peak of fire season (see September 1995-1999
images to left for example)
• May/June/July (early part of fire season) more active for 1998 and 1999
Arc of Deforestation and Central/Southern Plains for 1995-1999
• Version 6.5 of WF_ABBA will be re-run on 1995 and 1996 once
problem with bad data lines is fixed
• October/November (later part of fire season) more active for 1997
• Version 6.5 of WF_ABBA will be run on 2000-current to obtain a
complete 14 year record of fires over western Hemisphere
• Fires seem to start in southernmost region of Arc of Deforestation in May, then number of fires increases dramatically and develops
along entire Arc of Deforestation by September, then a significant decrease in number of fires by November along with the majority of
fires located over northeastern region of Arc of Deforestation near coast of Atlantic Ocean (see May-Nov 1997 images above for example)
• Inter- and intra-annual trend analyses of fires for 1995-2008 will be
investigated from Version 6.5 WF_ABBA data