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.

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

Testing VIIRS Existing fire detection systems –SERVIR –RSAC Giglio, et al. / Remote Sensing of Environment 87 (2003) ‘Contextual Fire Detection Algorithm for MODIS’ Absolute Threshold Test T 4 > 360 K (320 K at night) Brightness threshold MODIS 4um (T 4 ) (Bands 21 and 22 [1km]) –No VIIRS exact replacement Brightness threshold MODIS 11um (T 11 ) (Band 31 [1km]) –Two VIIRS bands (M15 [742m], I-5 [371m])

t 0.8 *

Monthly Rainfall Totals for July 2003

Weather Station Locations Evaporation Predictions

Inland Evaporation Coastal Evaporation 0 0 Evaporation Regression Models Accepted: Southeastern Geographer

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

Fairhope, AL Cumulative P-E (40 years average) and 1995 estimates Cumulative summaries - starting date January 1 st each year JanuaryDecember Precipitation – Evaporation (P-E t ) Cumulative CoastalE t

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

Road Density and Fire Ignition

Gravity vs. Road Density Gravity and Road Density Annual Critical Annual p-value Winter Critical Winter p-value Summer Critical Summer p-value Very Low Gravity and Very Low Road Density 3.51* * *0.007 Low Gravity and Low Road Density *0.05 Medium Gravity and Medium Road Density 3.09* * *0.007 High Gravity and High Road Density Very High Gravity and Very High Road Density Conclusions: Gravity models yield improved estimates of risk at very low levels Road density yields improved estimates of risk at medium levels

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

Physiographic RegionPearson Correlation (NDVI and Average Acre Burned) 0.01 level0.05 level Black Prairie-0.842√ Coastal Zone Delta Loess Hills-0.817√ North Central Hills-0.709√ Pine Belt-0.696√ South Central Hills-0.581√ Jackson Prairie Tombigbee Hills  NDVI and fire data averaged by month for each physiographic region  N = 12 Correlation Results – NDVI and Average Acre Burned

Physiographic RegionPearson Correlation (NDVI and Average Acre Burned) 0.01 level Black Prairie-.390√ Coastal Zone-.006 Delta-.119 Loess Hills-.293√ North Central Hills-.383√ Pine Belt-.212√ South Central Hills-288√ Jackson Prairie-.129 Tombigbee Hills-.090 Correlation Results – NDVI and Average Acre Burned  NDVI and fire data averaged by year and month for each physiographic region  N = 177

NDVI Departure from Average

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

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

Comparison of MODIS & VIIRS Bands Band # Band IDBand # Band ID I Ğ 3.790M I Ğ 3.930I Ğ M Ğ 4.128M M Ğ I Ğ Ğ 2275M M M M M Ğ 8.700M M Ğ M M I Ğ M M M MODIS Bands 1& 2 are 250 m at nadir MODIS Bands 3-7 are 500 m at nadir MODIS Bands 8-36 are 1,000 m at nadir VIIRS Bands I-1 & I-2 are 371 m at nadir VIIRS Band I-3 is 371 m at nadir VIIRS Bands I-4 & I-5 are 371 m at nadir MODISVIIRS MODISVIIRS

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

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