Elaine M. Prins

NOAA/NESDIS/ORA/ARAD

Advanced Satellite Products Team

Madison, Wisconsin

Joleen M. Feltz

Chris C. SchmidtCooperative Institute for Meteorological Satellite Studies

Madison, Wisconsin

Collection of Slides on Biomass Burning and Applications of Meteorological

Satellite Fire Products

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 (>20%) , POC (39%)

- Within the Framework Convention on Climate Change (FCCC) countries will need to report on greenhouse gas emissions including those from biomass burning.

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How are Meteorological Satellites Used to Monitor Fires?

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. > 315K)

Multi-channel thresholdse.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 algorithmse.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 characterizatione.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.

Examples of Regional Fire Monitoring

AVHRR Brazil INPE Fire ProductJune- November 2000

UW-Madison CIMSS GOES ABBA Fire ProductJune - October 1995 and 1997

NGDC DMSP Operational Linescan System (OLS) Fire Product, July - December 1997

Canada Centre for Remote Sensing (CCRS) Fire M3 Product1999 fire season

GSFC TRMM Visible and Infrared Scanner (VIRS) Fire Product, July 2001

Examples of Global Fire Monitoring

ESA ERS Along Track Scanning Radiometer (ATSR) Fire Product, August 1999

Joint Research Centre World Fire WebAugust 23 - September1, 2000

GSFC MODIS Fire Product November 1, 2000

Shenandoah National Park, VA

Examples of Satellite Fire Monitoring in the United States

NOAA AVHRR FIMMA Fire Product, August 23, 2001

UW-Madison CIMSS WFABBA July 11, 2001

Thirty Mile Fire, WA

Valley Complex, Bitteroot National Forest, MTJuly 31 – October 3, 2000 292,070 acres

National Interagency Fire Center, Boise, Idaho

Sula Complex, Sula, MontanaAugust 6, 2000

John McColgan, BLMAlaska Fire Service

NIFC

MODIS, August 23, 2000, NASA GSFC

GOES Composite for August 2000, UW-Madison/CIMSS

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

Remote Sensing Fire Detection Validation Studyfor 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, Quebec’s forest fire detection and prevention agency.

Active Fire Detection in the Western HemisphereUsing the GOES Series

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Overview of Fires, Opaque Clouds, and Smoke/Aerosol Coverage in South AmericaDerived from the GOES-8 ABBA and MACADA: 1995 - 1999

FIRES SMOKE/AEROSOLOPAQUE CLOUDS

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Fires Opaque Clouds Smoke/Aerosol

Interannual Differences in Fires, Opaque Clouds, and Smoke/Aerosol: Each Fire Season (June - October) is Compared to the 1995 Benchmark Season

GOES-8 ABBA/MACADA South American Trend Analysis

Comparison of GOES-8 and AVHRR Fire Products for South America

Difference Plots of GOES-8 ABBA minus AVHRR INPE Fire Counts for South AmericaFire Seasons: 1995 through 2000

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

Chico Mendes Extractive Reserve

MOPITT CO composite is courtesy of the MOPITT team:

John Gille (NCAR),James Drummond (University of Toronto), David Edwards (NCAR)

EOS MOPITT identifies elevated carbon monoxide associated with

biomass burning detected with the GOES ABBA

GOES-8 South American ABBA Composite Fire Product

September 7, 2000

Comparison of GOES ABBA Fire Observations and the EOS MOPITT CO Product

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.

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GOES-8 Derived AOT During Study Period: 8/15/95 - 9/7/95 at 1445 UTC Using Single Scattering Albedos .83, .90, and .95

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GOES-8 Derived AOT for North Atlantic BasinStudy Period: July 11-31, 1996 at

1345, 1445, 1545, 1645, 1745, 1845 UTC

Examples of the GOES Wildfire ABBA Monitoring System in the Western Hemisphere

http://cimss.ssec.wisc.edu/goes/burn/wfabba.html

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

30-70°N: 6% 10-30°N: 10%70°S-10°N: 84%

Wildfires

Agricultural Burning

Crops and Pasture

Recent Road Construction

Desert/Grassland Border

Deforestation/maintenance

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.

GOES-8 Wildfire ABBA fire product for the Pacific Northwest

Date: August 17, 2001Time: 2200 UTC

NAAPS Model Aerosol Analysisfor the continental U.S.Date: August 18, 2001

Time: 1200 UTC

Real-Time Aerosol Transport Model Assimilation of the Wildfire ABBA Fire Product

FIRES

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

GOES-EGOES-W MSG MTSAT

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International Global Geostationary Active Fire Monitoring:Geographical Coverage

Satellite Spectral Bands

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Full Disk Coverage

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Minimum Fire Size at Equator (at 750 K)

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GOES-W 1 visible 4 IR

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MSG SEVIRI (2002)

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MTSAT-1R JAMI (2003)

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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