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

NOAA’S OPERATIONAL FIRE AND SMOKE DETECTION PROGRAM
M. Ruminski 1 , P. Davidson 2 , R. Draxler 2 , S. Kondragunta 2 ,
J. Simko 2 , J. Zeng 3 , P. Li 4
1 Satellite Analysis Branch, NOAA/NESDIS, Camp Springs, USA, mark.ruminski@noaa.gov
2 NOAA, Silver Spring, USA, paula.davidson@noaa.gov; roland.draxler@noaa.gov;
shobha.kondragunta@noaa.gov; john.simko@noaa.gov
3 Earth Resources Technology, Camp Springs, USA, Jian.zeng@noaa.gov
4 Perot Systems, Camp Springs, USA, po.li@noaa.gov
Abstract: Biomass fire occurs in nearly every ecosystem and geographic
region of the world. It can be managed, as with prescribed or agricultural
burns, or rage out of control in the case of wildfires. Accompanying the
fires are emissions which can be limited and localized or which can literally
span the globe and impact regional atmospheric conditions. Knowledge
of the number, location, duration and size of the fires and estimates of
their emissions and subsequent trajectories has become increasingly important
for public health, property loss, transportation, etc. This paper briefly
describes NOAA’s operational fire and smoke analysis product and smoke
forecasting system.
1 - Fire Analysis Methodology
NOAA utilizes 7 satellites with multispectral imagery for optimal operational
fire detection. The WildFire Automated Biomass Burning Algorithm
(WFABBA) is employed at 30 minute intervals using GOES-East and GOES-
West imagery. Details on the algorithm can be found in Prins and Menzel,
1992. NOAA polar orbiting data from NOAA-15/17/18 is currently used in
the Fire Identification, Mapping and Monitoring Algorithm (FIMMA) based
on the scheme described in Li et al. (2000) and subsequently developed
and updated at NESDIS. The algorithm for MODIS Terra/Aqua imagery is
described in Justice et al. (2002) and Giglio et al. (2003). All of the algorithms
generate fire detection locations which are synthesized into an
interactive visualization system - the Hazard Mapping System (HMS)
(Ruminski et al., 2008) - which integrates algorithm output with all of the
underlying satellite imagery as well as numerous ancillary data layers. Data
layers include previously identified locations of persistent thermal anomalies,
power plant locations, land types, stable light regions, etc. which aid
the analyst in identifying possible false detects. Analysts quality control
the automated fire detections by deleting those detections that are deemed
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