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Active Fire Detection using Geostationary Satellites

Active Fire Detection using Geostationary Satellites. L. Giglio SSAI/University of Maryland GOFC Global Geostationary Fire Monitoring Applications Workshop 23-25 March 2004. Overview. Satellite-based fire detection algorithms Generic issues related to multi-satellite fire monitoring

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Active Fire Detection using Geostationary Satellites

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  1. Active Fire Detection using Geostationary Satellites L. Giglio SSAI/University of Maryland GOFC Global Geostationary Fire Monitoring Applications Workshop 23-25 March 2004

  2. Overview • Satellite-based fire detection algorithms • Generic issues related to multi-satellite fire monitoring • Polar vs. geostationary satellite suite comparison • Issues • Biases

  3. 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

  4. 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

  5. GOES-8 1995-2003 GOES-10 1998 onward GOES-12 2003 onward MSG-1 2003 onward MTSAT Late 2004 Geostationary Satellite Suite

  6. International Global Geostationary Active Fire Monitoring:Geographical Coverage 322 80 120 160 -160 -120 -80 -40 0 40 80 GOES-W GOES-E MSG MTSAT 60 40 Satellite View Angle 80° 65° 20 0 -20 -40 -60 -80

  7. 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?

  8. 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

  9. Theoretical Detection Envelope • MODIS • Temperate deciduous rainforest • Night • 0° scan angle • Summer • No background fires

  10. 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

  11. Potential Gaps in Spatial Coverage

  12. 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

  13. Size of footprint relative to footprint size at sub-satellite point.

  14. 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

  15. High resolution sensors can provide much-needed fire size distributions. ASTER Scene 2.4 µm R 1.6 µm G 0.5 µm B

  16. Size Distribution of Active Fires

  17. Southern Africa, 2000 Morisette et al., in press.

  18. 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?

  19. TRMM VIRS Diurnal Fire Cycle Borneo 1999-2001

  20. GOES Local Sampling Time: Function of Longitude

  21. 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

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