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Using the Autonomous Modular Sensor (AMS) to Validate Satellite-Retrieved Sub-Pixel Fire Area: Radiative Flux of Wildfires and Fire Weather David Peterson.

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Presentation on theme: "Using the Autonomous Modular Sensor (AMS) to Validate Satellite-Retrieved Sub-Pixel Fire Area: Radiative Flux of Wildfires and Fire Weather David Peterson."— Presentation transcript:

1 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 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 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 NRL’s FLAMBE General Goal: Improve the Prediction of Smoke Emissions Reid et al. (2009) Smoke Transport Modeling http://alg.umbc.edu/usaq/images/ Highlights of this Talk… 1.Sub-pixel-based calculation of fire intensity 2.AMS validation: general 3.AMS validation: background temperature 4.Short-term predictor of satellite fire activity

3 Advantages of FRP p 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) High fire temp. Small fire area Cooler fire temp. Large fire area MODIS Pixel #1MODIS Pixel #2 These pixels have equal FRP? We need FRP per fire area! Current MODIS Fire Radiative Power (FRP p ) MODIS pixel-level FRP p (Kaufman et al., 1998) Fit-line to many theoretical fire scenarios! Current FRP Limitation (collection 5) FRP is currently derived over the pixel area ApAp ApAp

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

5 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! 4000 to 9000 AMS data points per MODIS fire pixel! AMS fire area assessment algorithm developed by Peterson et al. (2013), Remote Sens. Env. NASA’s Ikhana

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

7 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. Background Temperature Errors

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

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

10 Retrieval’s Sensitivity to Background Temperature ΔT b = ± 5.0 KΔT b = ± 1.0 K Simulate potential errors in background temperature Retrieved Fire Area (4 and 11 µm) Large sensitivity to a small T b error Incomplete error bars indicate T b > T pixel Small sensitivity to a large T b error

11 2011 Texas Wildfires Fire Pixel Clustering Alleviates Random Error

12 1.Do fire observations contain information to identify potential for high injection/blowups? 2.How can we use weather information to make automated short-term forecasts of emissions for AQ models? 3.How can we use weather information to improve smoke emission estimates in near-real-time and retrospectively? Fire Weather Application NARR Domain (~32 km) Choosing FRP f flux over fire counts? Ongoing fire growth/intensity inflow/circulation

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

14 Toward Developing a Short-Term Predictor of Fire Activity PersistenceAll Inputs ObservationNRMSE % Change 2004 Development Test (MODIS) Overall319218.315.9 -13.1 Growth/Ignition43534.632.9-5.0 Persistence a 23284.04.411.2 Decay/Extinction42934.626.2-24.3 2005 Independent Test (MODIS) Overall130221.220.1 -5.2 Growth/Ignition22833.534.01.5 Persistence a 8574.1 0.5 Decay/Extinction21738.133.8-11.4 a Observed persistence is bounded by ±10 fire counts for MODIS. RMSE statistics for the fire count prediction model compared to persistence… We need multiple AMS observations for the same fire event! 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.

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

16 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 FRP f 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 FRP f flux using the next generation satellite sensors Investigate potential applications using the VIIRS DNB We need AMS collocations with VIIRS, especially at night! VIIRS Summary and Conclusions

17 Thank You! 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 Acknowledgements and Related Publications National Research Council Postdoctoral Fellowship NASA Earth and Space Science Fellowship Naval Research Enterprise Intern Program NASA Nebraska Space Grant 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.

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19 T f & P LbLb 1 km 2 Modified from Dozier (1981) Calculations per MODIS pixel: Pixel radiance = fire + background Bi-Spectral Retrieval: L 4 = τ 4 PB(λ 4,T f ) + (1-P)L 4b L 11 = τ 11 PB(λ 11,T f ) + (1-P)L 11b Where: T f = fire (kinetic) temperature L b = background radiance P = fire area fraction B(λ,T f ) = IR Planck Function Τ = atmospheric transmittance L= pixel radiance Radiance (L) or Brightness Temperature?

20 Predefined Lookup Tables: (4 and 11 µm) SBDART Model Atmospheric effects Geometry Surface temp. variations MODIS Sub-Pixel Retrieval Inputs 1.Geolocation data (solar/sensor zenith, azimuth) 2.Level 1B pixel radiances 3.Fire product background temperatures (4 and 11 µm) MODIS Pixel Overlap Correction and sub-pixel calculations (iterations) Pixel-Level Retrievals: One output per pixel Single Retrieval via Averages: One retrieval for all fire pixels corresponding to the cluster Output: Fire area fraction and retrieved fire area (A f, in km 2 ) Surface kinetic fire temperature (T f ) Calculation of Sub-Pixel-Based FRP f General Summation Method: All pixel-level fire area retrievals are summed Clustering-Level Retrievals:

21 Zaca Fire Example 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 Autonomous Modular Sensor (AMS) Flight Path: 8/16/2007 NASA’s Ikhana

22 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 Fire Hot Spots Background Saturation Problem Smoldering or cooling AMS Data Within Several MODIS Pixels 4 µm 11 µm AMS Data Within Several MODIS Pixels Fire Hot Spots Background Smoldering or cooling No Saturation


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