Elaine M. Prins

1
Collection of Slides on Biomass Burning and
Applications of Meteorological Satellite Fire
Products
Elaine M. Prins NOAA/NESDIS/ORA/ARAD Advanced
Satellite Products Team Madison,
Wisconsin Joleen M. Feltz Chris C.
Schmidt Cooperative Institute for Meteorological
Satellite Studies Madison, Wisconsin
2

• Applications of Meteorological Satellite Fire
  Products
• Hazards Detection and Monitoring Each year
  millions of acres of forest and grassland are
  consumed by wildfire resulting in loss of
  life and property with significant economic costs
  and
  environmental implications.- Although the
  capabilities of current operational
  meteorological satellites are limited, they can
  provide valuable regional and global fire
  products in near real-time, and are critical for
  fire detection and monitoring in remote locations
  and developing countries.
• Global Change Monitoring Biomass
  burning is a distinct biogeochemical process that
  plays an important role in
  terrestrial ecosystem processes and global
  climate change - Land use and land cover
  change monitoring Fire is used in the process
  of deforestation and agricultural management.
  Approximately 85 of all fires occur
  in the equatorial and subtropical regions and are
  not adequately documented.- Estimates of
  atmospheric emissions Biomass burning is a
  major source of trace gases and an abundant
  source of aerosols NO, CO2 (40), CO
  (32), O3(38), NOX, N2O, NH3, SOX, CH4(10),
  NMHC (gt20) , POC (39)- Within
  the Framework Convention on Climate Change (FCCC)
  countries will need to report on greenhouse gas
  emissions including those from biomass burning.

3
How are Meteorological Satellites Used to Monitor
Fires?
Pixel
p
1-p
4

• Examples of Automated Fire Detection Algorithms
• Single channel thresholds e.g. AVHRR
  Instituto Nacional De Pesquisas Espaciais (INPE)
  fire product, European Space
  Agency ERS Along Track Scanning Radiometer (ATSR)
  fire product - Saturation in the 4 micron
  band - Elevated brightness temperature
  in the 4 micron band (I.e. gt 315K)
• Multi-channel thresholds e.g. Canada Centre for
  Remote Sensing (CCRS) Fire M3, CSU CIRA
  Fog/Reflectivity Product - 3 steps
  Use 4 micron band fixed thresholds to identify
  possible fires Use 11 micron band fixed
  thresholds to eliminate clouds Use 4
  minus 11 micron band differences to distinguish
  fires from warm background
• Contextual algorithms e.g. AVHRR Joint Research
  Centre of the European Commission (JRC) World
  Fire Web, Tropical Rainfall
  Mapping Mission (TRMM) Visible and Infrared
  Scanner (VIRS) GSFC fire product,
  AVHRR NOAA Fire Identification, Mapping and
  Monitoring Algorithm (FIMMA) fire product
  TERRA MODIS Fire Product -
  Implement multi-channel variable thresholds based
  on the heterogeneity of the background
• Contextual identification and sub-pixel
  characterization e.g. UW-Madison GOES Automated
  Biomass Burning Algorithm(ABBA) - Implement
  contextual algorithms and determine estimates of
  sub-pixel fire size and temperature. Include
  offsets for emissivity and atmospheric
  attenuation.

5
Examples of Regional Fire Monitoring
AVHRR Brazil INPE Fire Product June- November 2000
Canada Centre for Remote Sensing (CCRS) Fire M3
Product 1999 fire season
UW-Madison CIMSS GOES ABBA Fire Product June -
October 1995 and 1997
NGDC DMSP Operational Linescan System (OLS) Fire
Product, July - December 1997
6
Examples of Global Fire Monitoring
GSFC TRMM Visible and Infrared Scanner (VIRS)
Fire Product, July 2001
Joint Research Centre World Fire Web August 23 -
September1, 2000
ESA ERS Along Track Scanning Radiometer (ATSR)
Fire Product, August 1999
7
Examples of Satellite Fire Monitoring in the
United States
UW-Madison CIMSS WFABBA July 11, 2001 Thirty
Mile Fire, WA
GSFC MODIS Fire Product November 1,
2000 Shenandoah National Park, VA
NOAA AVHRR FIMMA Fire Product, August 23, 2001
8
MODIS, August 23, 2000, NASA GSFC
NIFC
Valley Complex, Bitteroot National Forest,
MT July 31 October 3, 2000 292,070
acres National Interagency Fire Center, Boise,
Idaho
Sula Complex, Sula, Montana August 6, 2000 John
McColgan, BLM Alaska Fire Service
2000 Fire Season in the U.S. (NIFC) of
fires 122,827 10-year average
106,343 Acres burned 8,422,237 10-year
average 3,786,411 Estimated Cost of
Fire Suppression 1.3 billion
GOES Composite for August 2000, UW-Madison/CIMSS
9
Remote Sensing Fire Detection Validation
Study for the 2000 Fire Season in Quebec
When considering fires that burned more than 10
ha, the GOES and AVHRR were the first to detect
many of the fires in the restricted protection
zone of Quebec. Approximately 16 of the fires
detected by the GOES were in remote locations and
were not detected by the SOPFEU, Quebecs forest
fire detection and prevention agency.
10
Active Fire Detection in the Western
Hemisphere Using the GOES Series
11
Overview of Fires, Opaque Clouds, and
Smoke/Aerosol Coverage in South AmericaDerived
from the GOES-8 ABBA and MACADA 1995 - 1999
FIRES
SMOKE/AEROSOL
OPAQUE CLOUDS
0
0
A
D
C
-10
-10
B
-20
-20
1995
Latitude S
Latitude S
-30
-30
-40
-40
-50
-50
0
-10
-20
1996
Latitude S
-30
Arc of Deforestation
-40
-50
0
0
-10
-10
-20
-20
Latitude S
1997
Latitude S
-30
-30
-40
-40
-50
0
-50
-10
-20
1998
Latitude S
-30
-40
-50
0
0
-10
-10
-20
-20
1999
Latitude S
Latitude S
-30
-30
-40
-40
-50
-50
70 60 50 40 30 20 10
70 60 50 40 30 20 10
70 60 50 40 30 20 10
Longitude W
Longitude W
Longitude W
12
Interannual Differences in Fires, Opaque Clouds,
and Smoke/Aerosol Each Fire Season (June -
October) is Compared to the 1995 Benchmark Season
Opaque Clouds
Smoke/Aerosol
Fires
0
C
E
D
A
-10
B
-20
Latitude S
-30
-40
G
-50
0
0
F
L
H
-10
-10
I
-20
-20
Latitude S
Latitude S
-30
-30
-40
-40
-50
-50
0
M
-10
J
-20
Latitude S
-30
-40
-50
0
0
-10
-10
-20
-20
N
K
Latitude S
Latitude S
-30
-30
-40
-40
-50
-50
70 60 50 40 30 20 10
70 60 50 40 30 20 10
70 60 50 40 30 20 10
Longitude W
Longitude W
Longitude W
13
GOES-8 ABBA/MACADA South American Trend Analysis
14
Comparison of GOES-8 and AVHRR Fire Products for
South America
15
Difference Plots of GOES-8 ABBA minus AVHRR INPE
Fire Counts for South America Fire Seasons 1995
through 2000
16
GOES South American ABBA Fire Products Used in
Land Use/Land Cover Change and Fire Dynamics
Research
Universities, research institutes, and government
planning agencies are using the GOES ABBA fire
product as an indicator of land-use and
land-cover change and carbon dynamics. GOES fire
products also are being used to study the impact
of road paving in South America on fire regime
feedbacks and the future of the Amazon forests.
Foster Brown, et al., 2001
17
Comparison of GOES ABBA Fire Observations and the
EOS MOPITT CO Product
EOS MOPITT identifies elevated carbon monoxide
associated with biomass burning detected with the
GOES ABBA
MOPITT CO composite is courtesy of the MOPITT
team John Gille (NCAR), James Drummond
(University of Toronto), David Edwards (NCAR)
GOES-8 South American ABBA Composite Fire
Product September 7, 2000
18
GOES-8 Experimental AOT Analysis 26-August-1995
at 1445 UTC
Panel a shows smoke as observed in GOES-8 visible
imagery at 1445 UTC on 26 August 1995. The
GOES-8 MACADA is applied to multi-spectral GOES
imagery to automatically distinguish between
clear-sky, cloud type, and smoke using both
textural and spectral techniques (panels b and
c). The MACADA textural/spectral analysis is
then used to create a 14-day clear-sky background
reflectance map shown as solar zenith weighted
albedo (panel d). The AOT product (panel f) is
determined by calculating the difference between
the observed and clear-sky reflectance and
relating this to AOT utilizing a look-up table.
If no clear-sky reflectance is available for a
given location, an ecosystem based reflectance
map (panel e) is used.
a.
b.
c.
d.
e.
f.
19
GOES-8 Derived AOT During Study Period 8/15/95 -
9/7/95 at 1445 UTC Using
Single Scattering Albedos .83, .90, and .95
Single Scattering Albedo 0.83
Single Scattering Albedo 0.90
Single Scattering Albedo 0.95
r 0.93
r 0.74
r 0.89
20
GOES-8 Derived AOT for North Atlantic Basin Study
Period July 11-31, 1996 at 1345, 1445, 1545,
1645, 1745, 1845 UTC
21
GOES Wildfire ABBA Fire Composite Web Distribution
Animations of Wildfire ABBA composite image
products are being provided via anonymous ftp and
the web every half-hour at http//cimss.ssec.wisc
.edu/goes/burn/wfabba.html Displays include
three overviews and 35 regional views providing
coverage of the entire Western Hemisphere.
Examples of Regional View Sectors
22
Examples of the GOES Wildfire ABBA Monitoring
System in the Western Hemisphere
http//cimss.ssec.wisc.edu/goes/burn/wfabba.html
23
GOES-8 Wildfire ABBA Summary Composite of
Half-Hourly Temporally Filtered Fire Observations
for the Western Hemisphere Time Period
September 1, 2000 to August 31, 2001
The composite shows the much higher incidence
of burning in Central and South America,
primarily associated with deforestation and
agricultural management. Fire
Pixel Distribution 30-70N 6
10-30N 10 70S-10N 84
Wildfires Agricultural Burning Crops and
Pasture Recent Road Construction Desert/Grasslan
d Border Deforestation/maintenance
24
Model Data Assimilation Activities
- At the Naval Research Laboratory
(NRL-Monterey) GOES ABBA fire product information
is being assimilated into the Navy Aerosol
Analysis and Prediction System (NAAPS) to analyze
and predict aerosol loading and transport as part
of the NASA-ESE Fire Locating And Mapping of
Burning Emissions (FLAMBE) project.
- Model output is being compared to GOES
satellite derived aerosol products and TOMS
products. Initial studies show the model output
and aerosol products are in close agreement.
25
Real-Time Aerosol Transport Model Assimilation of
the Wildfire ABBA Fire Product
GOES-8 Wildfire ABBA fire product for the
Pacific Northwest Date August 17, 2001 Time
2200 UTC
FIRES
NAAPS Model Aerosol Analysis for the continental
U.S. Date August 18, 2001 Time 1200 UTC
26

• Future Environmental Satellite Fire Monitoring
  Capabilities
• Global Geostationary Fire Monitoring System -
  GOES-E/W Imager - METEOSAT Second
  Generation (MSG) (2002) Spinning
  Enhanced Visible and InfraRed Imager (SEVIRI) -
  Multi-functional Transport Satellite (MTSAT-1R)
  (2003) Japanese Advanced Meteorological
  Imager (JAMI)
• NOAA Operational Systems - NPOESS Preparatory
  Project Visible/Infrared Imager Radiometer Suite
  (VIIRS) (2006) - Advanced Baseline Imager (ABI)
  (2010)
• International Platforms Designed for Fire
  Detection - German Aerospace Center (DLR)
  Bi-spectral Infrared Detection (BIRD) - German
  Aerospace Center (DLR) Intelligent Infrared
  Sensor System (FOCUS) (ISS) - Consortium of DLR
  and European space industries are designing
  the Forest Fire Earth Watch (FFEW-FUEGO)
  satellite mission

27
International Global Geostationary Active Fire
MonitoringGeographical Coverage
80
120
160
0
-40
-80
-120
-160
40
80
GOES-E
GOES-W
MSG
MTSAT
60
40
Satellite View Angle 80 65
20
0
-20
-40
-60
-80
28
Minimum Detectable Fire Size Estimates for GOES,
MSG, and MTSAT
29
GOES-R and GOES-I/M Simulations of Viejas Fire
Using MODIS Data January 3, 2001 at 1900 UTC
Simulated GOES-R 3.9 micron
Simulated GOES-I/M 3.9 micron
30
GOES-R and GOES-I/M Simulations of Viejas Fire
Smoke PlumeUsing MODIS Data
Simulated GOES-R visible .64 micron January 3,
2001 1900 UTC
Simulated GOES-I/M visible .64 micron January
3, 2001 1900 UTC