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Optimizing Fire Detection Models for Sustainable Management

Utilizing fire potential and detection models to reduce false positives, validate risk levels, and leverage MODIS data for more accurate predictions in under-developed regions. Testing VIIRS for enhanced outcomes based on climate, fuels, topography, and ignition sources.

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Optimizing Fire Detection Models for Sustainable Management

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  1. Project Idea • Fire potential models can help stratify and reduce the number of false positive fire ‘detections’ by assigning probability levels to the landscape based on climate, fuels, ignition and topography. • Fire detection models can serve as an independent validation source for fire potential models, particularly in under-developed regions. • Fire potential and fire detection models both depend on MODIS data.

  2. Existing eastern fire potential models Standardization issues AVHRR/MODIS/VIIRS Existing fire detection systems SERVIR RSAC Testing VIIRS FRANKE, Jonas & Gunter MENZ Remote Sensing Research Group (RSRG) Department of Geography, University of Bonn Bonn, Germany jonasfranke@freenet.de

  3. Existing fire detection systems SERVIR RSAC Giglio, et al. / Remote Sensing of Environment 87 (2003) 273-282 ‘Contextual Fire Detection Algorithm for MODIS’ Absolute Threshold Test T4 > 360 K (320 K at night) Brightness threshold MODIS 4um (T4) (Bands 21 and 22 [1km]) No VIIRS exact replacement Brightness threshold MODIS 11um (T11) (Band 31 [1km]) Two VIIRS bands (M15 [742m], I-5 [371m]) Testing VIIRS

  4. t * 0.8

  5. Monthly Rainfall Totals for July 2003

  6. Weather Station LocationsEvaporation Predictions

  7. Inland Evaporation 0 Coastal Evaporation 0 Evaporation Regression ModelsAccepted: Southeastern Geographer

  8. Precipitation – Evaporation (P-Et) Cumulative Inland January December Stoneville, MS Cumulative P-E (average over 40 years) and 2000 estimates Cumulative summaries - Starting date January 1st each year

  9. January December Fairhope, AL Cumulative P-E (40 years average) and 1995 estimates Cumulative summaries - starting date January 1st each year Precipitation – Evaporation (P-Et) Cumulative Coastal

  10. Fire Risk Very Low Low Med High Very High Road Density/Gravity and Fire Ignition

  11. Road Density and Fire Ignition

  12. Gravity vs. Road Density Conclusions: Gravity models yield improved estimates of risk at very low levels Road density yields improved estimates of risk at medium levels

  13. 18-year Historic AVHRR NDVI 7-day Composites Departure from average greeness

  14. Correlation Results – NDVI and Average Acre Burned • NDVI and fire data averaged by month for each physiographic region • N = 12

  15. Correlation Results – NDVI and Average Acre Burned • NDVI and fire data averaged by year and month for each physiographic region • N = 177

  16. NDVI Departure from Average

  17. June 1 Terra June 2 Aqua June 3 Terra June 4 Aqua June 6 Aqua June 7 Aqua June 5 Aqua June 8 Terra

  18. VIIRS Simulation • ITD and Chuck O’hara • Florida and Georgia 2007 for tests • Methods transferrable to Central America?

  19. MODIS VIIRS MODIS VIIRS l l l l Band # Band ID Band # Band ID 1 620 - 670 600 - 680 I-1 3.610 Ğ 3.790 M-12 20 3.660 - 3.840 2 841 - 876 845 - 885 I-2 3.550 Ğ 3.930 I-4 3 459 - 479 21 3.929 - 3.989 4 545 - 565 22 3.940 Ğ 4.001 5 1230 - 1250 1230 - 1250 M-8 23 4.020 - 4.080 3.973 Ğ 4.128 M-13 1580 - 1670 M-10 24 4.433 Ğ 4.498 6 1628 - 1652 1580 - 1610 I-3 25 4.482 Ğ 4.549 7 2105 - 2155 2225 Ğ 2275 M-11 26 1.360 - 1.390 M-9 8 405 - 420 402-422 M-1 27 6.535 - 6.895 9 438 - 448 436-454 M-2 28 7.175 - 7.475 10 483 - 493 478-498 M-3 29 8.400 - 8.700 8.400 Ğ 8.700 M-14 11 526 - 536 30 9.580 - 9.880 12 546 - 556 545-565 M-4 10.263 Ğ 11.263 M-15 31 10.780 - 11.280 13 662 - 672 662-682 M-5 10.050 - 12.400 I-5 14 673 - 683 32 11.770 - 12.270 11.538 Ğ 12.488 M-16 15 743 - 753 739-754 M-6 33 13.185 - 13.485 16 862 - 877 846-885 M-7 34 13.485 - 13.785 17 890 - 920 35 13.785 - 14.085 18 931 - 941 36 14.085 - 14.385 19 915 - 965 MODIS Bands 1& 2 are 250 m at nadir VIIRS Bands I-1 & I-2 are 371 m at nadir MODIS Bands 3-7 are 500 m at nadir VIIRS Band I-3 is 371 m at nadir MODIS Bands 8-36 are 1,000 m at nadir VIIRS Bands I-4 & I-5 are 371 m at nadir Comparison of MODIS & VIIRS Bands

  20. VIIRS Vis/NIR BandsFire detection, spatial resolution SNR values are as specified for un-aggregated pixel. At nadir SNR will be ~ better after aggregation. (Predicted are better still)

  21. VIIRS S/MW & LW IR BandsFire detection, spatial resolution SNR values are as specified for un-aggregated pixel. At nadir SNR will be ~ better after aggregation. (Predicted are better still)

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