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David Peterson National Research Council – Monterey, CA

Using the Autonomous Modular Sensor (AMS) to Validate Satellite-Retrieved Sub-Pixel Fire Area: Radiative Flux of Wildfires and Fire Weather. David Peterson National Research Council – Monterey, CA Edward Hyer , Naval Research Laboratory – Monterey

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David Peterson National Research Council – Monterey, CA

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  1. Using the Autonomous Modular Sensor (AMS) to Validate Satellite-Retrieved Sub-Pixel Fire Area: Radiative Flux of Wildfiresand Fire Weather David Peterson National Research Council – Monterey, CA Edward Hyer, Naval Research Laboratory – Monterey Jun Wang, University of Nebraska – Lincoln Charles Ichoku, NASA Goddard Space Flight Center Vincent Ambrosia, NASA Ames Research Center Autonomous Modular Sensor Airborne Science Applications Use Workshop, 04/18/2013

  2. General Goal: Improve the Prediction of Smoke Emissions NRL’s FLAMBE Reid et al. (2009) Smoke Transport Modeling • Highlights of this Talk… • Sub-pixel-based calculation of fire intensity • AMS validation: general • AMS validation: background temperature • Short-term predictor of satellite fire activity http://alg.umbc.edu/usaq/images/

  3. Current MODIS Fire Radiative Power (FRPp) Advantages of FRPp over Standard Fire Counts • Quantitative indicator of fire intensity (Ichoku et al, 2008) • Proportional to amount of biomass consumed (Wooster et al., 2005) • Proportional to amount of smoke released (Ichoku and Kaufman, 2005) • Related to the smoke plume height (Val Martin et al., 2010) MODIS pixel-level FRPp(Kaufman et al., 1998) Current FRP Limitation (collection 5) FRP is currently derived over the pixel area These pixels have equal FRP? MODIS Pixel #1 MODIS Pixel #2 High fire temp. Small fire area Cooler fire temp. Large fire area Fit-line to many theoretical fire scenarios! Ap Ap We need FRP per fire area!

  4. Improved Sub-Pixel-Based Fire Radiative Power (FRPf) Based on retrieved fire area (Af) & temperature (Tf): Units: MW The flux of FRPf can also be calculated: Retrieval details are provided in: Peterson et al. & Peterson and Wang (2013), Remote Sens. Env. Units: Wm-2 FRP and Initial plume buoyancy, Kahn et al. (2007) & Val Martin et al. (2010) Sapkota et al. (2005) Af Tb Tf Is the smoke contained within the boundary layer? We need high-resolution validation data for fire area, temp., and FRP!

  5. NASA’s Ikhana • 4000 to 9000 AMS data points • per MODIS fire pixel! • AMS Pixel Resolution • Varies from 3 - 50 meters • Scan-to-scan differences • Topography • Flight Altitude varies • Limitations… • 4 µm channel saturates at ~510 K! • Can’t use for FRP validation! AMS fire area assessment algorithm developed by Peterson et al. (2013), Remote Sens. Env.

  6. Non-Fire Background Warmer than the MODIS Fire Pixel (11 µm)? MODIS Tb window: 8-21 valid pixels (Giglio et al., 2003) White = Tb error

  7. BackgroundTemperature Errors All 3 fire pixels with Tb > Tfire contain diffuse or pixel-edge hot spots! Cooler AMS fire temps… Peterson and Wang (2013), Remote Sens. Env.

  8. Background Temperature Investigation 2011 Texas Wildfires Day Error: 151 (26%) Night Error: 6 (2%)

  9. The In-Pixel Background Temperature MODIS vs. AMS Background Temperature (California, 2007) Error bars show the variability within the background region of a fire pixel Variation: 1-5 K Variation: 5-10 K Peterson and Wang (2013), Remote Sens. Env.

  10. Retrieval’s Sensitivity to Background Temperature Retrieved Fire Area (4 and 11 µm) Simulate potential errors in background temperature ΔTb = ± 5.0 K ΔTb = ± 1.0 K Small sensitivity to a large Tb error Large sensitivity to a small Tb error Incomplete error bars indicate Tb > Tpixel

  11. Fire Pixel Clustering Alleviates Random Error 2011 Texas Wildfires

  12. Fire Weather Application Choosing FRPf flux over fire counts? Ongoing fire growth/intensity inflow/circulation Do fire observations contain information to identify potential for high injection/blowups? How can we use weather information to make automated short-term forecasts of emissions for AQ models? How can we use weather information to improve smoke emission estimates in near-real-time and retrospectively? NARR Domain (~32 km)

  13. Toward Developing a Short-Term Predictor of Fire Activity MODIS Alaska Observed (Day 2/Day 1) Growth Decay Peterson et al. (2013) Atmospheric Environment Small symbols: < 10 fire counts on day 1

  14. Toward Developing a Short-Term Predictor of Fire Activity RMSE statistics for the fire count prediction model compared to persistence… • Highlights • Fire prediction model is an improvement over persistence. • Best with cases of decay/extinction! • Must overcome scan-to-scan variations! • Can also be applied to geostationary data. a Observed persistence is bounded by ±10 fire counts for MODIS. We need multiple AMS observations for the same fire event!

  15. Potential VIIRS Day-Night Band (DNB) Applications High Park Fire Fort Collins Greeley ~23 km x 23 km ~37 km x 37 km Loveland ASTER 8.3 µm image VIIRS DNB image Boulder Denver ~20 km VIIRS DNB image of the Denver / Front Range area T Images by: Tom Polivka, UNL ~37 km x 37 km ~37 km x 37 km VIIRS 4 µm image VIIRS 11 µm image

  16. Summary and Conclusions Value of AMS Data Collocated with a Satellite Overpass • Valuable validation tool for retrieved fire area, background temp., etc. • Non-saturated 4 µm data are required for fire temp and FRP validations! • Repeat looks are very useful for both applications and validation! Background Temperature • The AMS can identify reasons for errors in the MODIS background temp. • Important component of the sub-pixel retrieval's sensitivity analysis! Fire Weather and Changes in Smoke Emissions • A short-term predictor of fire counts has been developed, may also use FRPf • We need daily AMS observations from the same fire event! • Can we use the AMS before and after a significant change in meteorology? Future Goals • Retrieve FRPf flux using the next generation satellite sensors • Investigate potential applications using the VIIRS DNB • We need AMS collocations with VIIRS, especially at night! VIIRS

  17. david.peterson.ctr@nrlmry.navy.mil • Acknowledgements and Related Publications • National Research Council Postdoctoral Fellowship • NASA Earth and Space Science Fellowship • Naval Research Enterprise Intern Program • NASA Nebraska Space Grant Thank You! Peterson, D., Wang, J., Ichoku, C., Hyer, E., & Ambrosia, V.: A sub-pixel-based calculation of fire radiative power from MODIS observations: 1. Algorithm development and initial assessment, Remote Sensing of Environment, 129, 262-279, 2013. Peterson, D., & Wang, J.: A sub-pixel-based calculation of fire radiative power from MODIS observations: 2. Sensitivity analysis and potential fire weather application, Remote Sensing of Environment,129, 231-249, 2013. Peterson, D.,Hyer, E., & Wang. J.: A short-term predictor of satellite-observed fire activity in the North American boreal forest: toward improving the prediction of smoke emissions, Atmospheric Environment, 71, 304-310, 2013.

  18. Tf& P Modified from Dozier (1981) Calculations per MODIS pixel: Pixel radiance = fire + background Bi-Spectral Retrieval: L4= τ4PB(λ4,Tf) + (1-P)L4b • L11= τ11PB(λ11,Tf) + (1-P)L11b • Where: • Tf = fire (kinetic) temperature • Lb= background radiance • P = fire area fraction • B(λ,Tf) = IR Planck Function • Τ = atmospheric transmittance • L = pixel radiance • Radiance (L) or Brightness Temperature? Lb 1 km2

  19. MODIS Sub-Pixel Retrieval Inputs • Geolocation data (solar/sensor zenith, azimuth) • Level 1B pixel radiances • Fire product background temperatures (4 and 11 µm) MODIS Pixel Overlap Correction and sub-pixel calculations (iterations) • Predefined Lookup Tables: • (4 and 11 µm) • SBDART Model • Atmospheric effects • Geometry • Surface temp. variations Clustering-Level Retrievals: Single Retrieval via Averages: One retrieval for all fire pixels corresponding to the cluster General Summation Method: All pixel-level fire area retrievals are summed Pixel-Level Retrievals: One output per pixel • Output: • Fire area fraction and retrieved fire area (Af, in km2) • Surface kinetic fire temperature (Tf) Calculation of Sub-Pixel-Based FRPf

  20. Autonomous Modular Sensor (AMS) Flight Path: 8/16/2007 • Instrument Details • Ambrosia & Wegener (2009) • Range: ~ 4000 miles • Flight altitude can vary • 12 spectral channels • Fires detected at 4 (3.75) and 11 (10.76) µm • Flight domain: western United States NASA’s Ikhana Zaca Fire Example

  21. Creating an AMS Fire Mask for Each MODIS pixel • Goals • Obtain actively burning fires • Remaining data are disregarded • Obtain background temperature • Challenges • Saturation at 4 µm (not at 11 µm) • Scan-to-scan variations • Diurnal effects • Approach • Calculate minimum thresholds • Two fire thresholds (4 and 11 µm) • Search for regions of low density within the histograms • Fire thresholds vary per MODIS pixel • Day/night algorithm • Consider variation of AMS and MODIS pixel size • Calculate AMS fire fraction AMS Data Within Several MODIS Pixels 4 µm AMS Data Within Several MODIS Pixels Fire Hot Spots Background Background 11 µm Smoldering or cooling Smoldering or cooling Saturation Problem Fire Hot Spots No Saturation

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