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Active Fire Detection using Geostationary Satellites

This overview covers satellite-based fire detection algorithms, multi-satellite monitoring, comparisons between polar and geostationary satellites, and challenges in combining data from different systems. It explores strengths and weaknesses of different monitoring methods and discusses potential gaps in spatial coverage. Research issues include merging diurnal fire cycle sampling and addressing biases in detectable fires.

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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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