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Active Fire Detection using Geostationary
SatellitesL. Giglio
SSAI/University of MarylandGOFC Global Geostationary Fire
Monitoring Applications Workshop23-25 March 2004
Overview
• Satellite-based fire detection algorithms
• Generic issues related to multi-satellite fire monitoring
• Polar vs. geostationary satellite suite comparison– Issues– Biases
Introduction • Multiple systems currently providing active
fire data and new systems are being planned • Different systems offer different capabilities
– Different detection capabilities (spatial/temporal) – Different fire monitoring groups using different
methods and different algorithms
• Accuracy of the different systems not well quantified– Systematic validation activities being initiated
• User community is starting to combine data from these multiple systems – complementary data sets
Satellite-Based Fire Detection Algorithms
• Virtually all exploit tremendous radiative energy emitted at ≈4 µm, usually in conjunction with a longer wavelength ≈10 µm band – Exception is DMSP-OLS
• ABBA/WF-ABBA (Prins et al.) are the premier detection algorithms for geostationary satellite instruments– GOES VAS, GOES Imager
• Detection principals are well-described elsewhere
Geostationary Satellite Suite
• GOES-8– 1995-2003
• GOES-10– 1998 onward
• GOES-12– 2003 onward
• MSG-1– 2003 onward
• MTSAT– Late 2004
GOES-EGOES-W MSG MTSAT
0-40-80-120-160 40 80 120 16080
60
40
20
0
-20
-40
-60
-80
Satellite View Angle
80° 65°
Satellite Spectral Bands
Resolution IGFOV (km)
SSR (km)
Full Disk Coverage
4 m Saturation Temperature (K)
Minimum Fire Size at Equator (at 750 K)
GOES-E 1 visible 4 IR
1.0 4.0 (8)
0.57 2.3
3 hours 335 K 0.15
GOES-W 1 visible 4 IR
1.0 4.0 (8)
0.57 2.3
3 hours ???? 0.15
MSG SEVIRI (2003)
3 visible 1 near-IR 8 IR
1.6 (4.8) 4.8 4.8
1.0 (3.0) 3.0 3.0
15 minutes > 335 0.22
MTSAT-1R JAMI (2004)
1 visible 4 IR
0.5 2.0
18 minutes ~320 0.03
322
International Global Geostationary Active Fire Monitoring:Geographical Coverage
Multi-Satellite Fire Monitoring:
Generic Issues• Systems have
– Different spatial resolutions– Different radiometric characteristics– Different temporal sampling
• How do we combine observations from multiple instruments in a consistent, meaningful manner?
Polar Fire Monitoring:Strengths and Weaknesses
• Strengths– Global coverage
• Frequency of global coverage depends on scan width – Higher spatial resolution
• Moderate resolution – AVHRR, MODIS (1 km) • High resolution – Landsat, ASTER (30 m)
• Weaknesses– Fewer opportunities for cloud-free observations
• MODIS Terra/Aqua give four observations per 24 hrs– Greater variance in envelope of detectable fires
(off nadir vs. nadir) – Temporal sampling issues related to diurnal
fire cycle
Theoretical Detection Envelope
• MODIS• Temperate
deciduous rainforest
• Night• 0° scan angle• Summer• No background
fires
Geostationary Fire Monitoring Suite:
Strengths and Weaknesses• Current Strengths
– Hemispheric fire monitoring– Near-real time data for fire management – Few/no temporal sampling issues related
to diurnal fire cycle– Broad Direct Broadcast capability
• Current Weaknesses– Gaps in global spatial coverage– Spatial biases in envelope of detectable
fires
Potential Gaps in Spatial Coverage
Spatial Biases in Envelopeof Detectable Fires (1 of 2)
• For instruments on board geostationary satellites, pixel size varies as a function of distance from the sub-satellite point– Introduces spatial gradient in the
envelope of detectable fires
Size of footprint Size of footprint relative to footprint relative to footprint size at sub-satellite size at sub-satellite point.point.
Spatial Biases in Envelope of Detectable Fires (2 of 2)
• Complicates comparison of fire activity in different regions, even using a single satellite
• Not an issue for near-real time fire monitoring
• Will need to be addressed in production of a global data set
ASTER Scene2.4 µm R1.6 µm G0.5 µm B
High resolution sensors can
provide much-needed fire size
distributions.
Size Distribution of Active Fires
Morisette et al., in press.Morisette et al., in press.
Southern Southern Africa, Africa, 20002000
GOES Diurnal Cycle Research Issue
• How to merge different sampling of diurnal fire cycle?– Temporal sampling exhibits a spatial
dependence since local time varies with longitude
– What impact does this have on the number of fires detected when combined with the spatial variation in detection envelope?
TRMM VIRS Diurnal Fire CycleBorneo 1999-
2001
GOES Local Sampling Time: Function of Longitude
Summary
• Geostationary satellite suite will provide a major contribution to global fire monitoring capability
• Ultimately envision merging both polar-orbiting and geostationary fire data sets to exploit strengths of each
• Interesting research opportunities in addressing potential issues