1. Fire Detection using Geostationary and Polar Orbiting Satellites Dr. Bernadette Connell CIRA/CSU/RAMMT Dr. Vilma Castro UCR/RMTC March 2005

2. Objectives Background Environmental and weather conditions conducive to fires Satellite fire detection techniques for hot spots Examples Lab exercise

3. Monitoring Fire Activity Why? To detect and monitor wildfires in real-time for response and mitigation. –Are the fires posing danger to population centers or economic resources? To determine trends in fire activity from year to year. –Are they the result of agriculture burning and deforestation? –Are they the result of a buildup of fuels? –Are they affected by drought? To determine the extent of smoke transport To determine the effect of burning on the environment.

4. United States - Fire Weather Activities Various FIRE DANGER RATING systems have been developed to express fire hazard. They incorporate some of these basic questions: Are the “fuels” dry enough to burn? Is the current or forecast weather conducive to starting fires and sustaining them? –Is it dry, windy? –Is the atmosphere stable or unstable? –Will there be lightning with very little rain?

5. United States - Fire Weather Activities To address the condition of fuels: –Long term monitoring for drought (satellite) –Monitoring of vegetation health and accumulation of dead vegetation (fuels) (satellite and ground) To address weather conditions: –Outlooks for precipitation and temperature (climatology/model prognosis) Information Sources: –Climate Prediction Center (CPC) –USDA Forest Service –NOAA/NESDIS/ORA

6. Real-time NWS Fire Weather Services Storm Prediction Center – issues 1 and 2 day fire outlooks http://www.spc.noaa.gov/products/fire_wx –maps –text discussion –hazard categores: critical areas – outlines extremely critical – hatched dry thunderstorm risk - scalloped

7. Real-time NWS Fire Weather Services Weather Forecast Offices – issues fire weather forecasts/watches, smoke forecasts, red flag warnings, spot forecasts IMET – Incident METeorological information for fire behavior forecasts, spot forecasts, nowcasts

8. Real-time (non-routine) Products Fire Weather Watch; valid 24-48 hr –1-min sustained winds at 20 ft. > 15-25 kts –Relative humidity < threshold (see following slide – varies by region) –Temperature >65-75°F –Vegetation moisture <8-12% Red Flag Warning: valid 0-24 hr –Same criteria as Fire Weather Watch (above) “Spot” Forecasts –Forecasts for prescribed burns, rescues, wildfires in progress

9. Threshold Relative Humidities for Red Flag Watches/Warnings

10. Haines Index This index is correlated with fire growth in plume dominated fires Composed of two parts: –stability: temperature difference between two atmospheric layers near the surface –moisture: temperature/dew point difference for that layer The index is adaptable for varying elevation regimes Index value estimates rate of spread: 2-3: Very Low Potential (Moist Stable Lower Atmosphere) 4: Low Potential 5: Moderate Potential 6: High Potential ( Dry Unstable Lower Atmosphere)

11. Calculating Haines Index LOW ELEVATION <2,000 FT Stability Term (T950- T850) 1… 3 C or less 2… 4 to 7 C 3… >= 8 C Moisture Term (T850-Td 850) 1… 5 C or less 2… 6 to 9 C 3… >= 10 C MID ELEVATION 2,000-6,000 FT Stability Term (T850- T700) 1… 5 C or less 2… 6 to 10 C 3… >= 11 C Moisture Term (T850-Td 850) 1… 5 C or less 2… 6 to 12 C 3… >= 13 C HIGH ELEVATION >6,000 FT Stability Term (T700- T500) 1… 17 C or less 2… 18 to 21 C 3… >= 22 C Moisture Term (T700-Td 700) 1… 14 C or less 2… 15 to 20 C 3… >= 21 C Sum of two terms = Haines Index GOES Fire Detection - VISITview

12. 2-very low 3-very low 4-low 5-moderate 6-high water

13. U.S. Drought Monitor – Severity Classification CategoryDescriptionFire Risk Palmer Drought Index CPC Soil Moisure (percentiles) Weekly Streamflow (percentiles) % of Normal Precip Standardized Precipitation Index Satellite Vegetation Health Index D0 Abnormally Dry Above average -1.0 to -1.9 21-30 <75% for 3 months -0.5 to -0.736-45 D1 Moderate Drought High -2.0 to -2.9 11-20 <70% for 3 months -0.8 to -1.226-35 D2 Severe Drought Very high -3.0 to -3.9 6-10 <65% for 6 months -1.3 to -1.516-25 D3 Extreme Drought Extreme -4.0 to -4.9 3.53-5 <60% for 6 months -1.6 to -1.96-15 D4 Exceptional Drought Exceptional and Widespread < -5.00-2 <65% for 12 months < -2.01-5 GOES Fire Detection - VISITview

15. Vegetation Health Showing vegetation health for this year compared with last year. Fire becomes a concern when the vegetation is stressed (values less than 50) and when drought and other weather is of concern.

16. Loop of plume dominated fire VIS 03246 IR2 03246 Washington Oregon Idaho Montana British Columbia Alberta

17. Loop of wind driven fire VIS Mexico California IR2 IR2 24hr

18. Satellite Monitoring of FIRES Geostationary or Polar Orbiting? Monitoring from both types of satellites utilize visible, shortwave, and longwave infrared channel observations. Geostationary Satellites (GOES) –Coarser resolution (~4km) –Good temporal resolution (every 15-30 min.) which provides information on the diurnal timing and spatial distribution of fires. –Saturation brightness temperature: 338K (for GOES-8 and 12) Polar Orbiting Satellites (AVHRR) –Finer resolution (~1km) –Only 2 passes per day –Saturation brightness temperature: 320 K

19. “Quick” RAMSDIS Products for fire detection These products are made with images from channels 3.9 and 10.7 µm NIGHT: Fog-Stratus Product DAY: Reflectivity Product

20. Characteristics of 3.9 micrometer channel that make it suitable for “hot” spot detection Radiance is not linear with temperature A small change in radiance at 300 K at 3.9 um creates a larger change in temperature than at 10.7 um (note the different scales: 3.9 um from 0-4 10.7 um from 0-200

21. Sub pixel response R λ = R λ cloud * % area cloud + R λ ground * % area ground Similarly for fires: R λ = R λ fire * % area fire + R λ ground * % area ground GOES 3.9 um Channel Tutorial

22. NIGHT: Fog-Stratus Product Subtract temperature, pixel by pixel, of: 10.7  m - 3.9  m images The result is a negative number As temperature is warmer at 3.9  m

23. NIGHT: Fog-Stratus Product The result is normalized by adding 150 to each pixel’s value Values correspond to a scale of 0.1 K per brightness unit In a black and white color table, pixels with fire appear darker than the background

24. NIGHT: Fog-Stratus Product Pixels with fire are 80 brightness units darker than the background

25. Observations: 1 brightness unit = 0.1 Kelvin 80 brightness units = 8 K Temperature difference among pixels without fire: among pixels without fire: 3 K 4- 6 K Difference among pixels: fire cannot be detected with certainty

26. DAY: Reflectivity Product Channels involved: 3.9 and 10.7 microns Reflective component is subtracted from the 3.9 micron signal. The temperature at 10.7 microns is used to estimate the reflective component at 3.9 microns Fires appear as white spots Do not need to set thresholds Limited to daytime use

27. Reflectivity Product

28. Observations Products allow the identification of fires smaller than a pixel Weaver et al. show that it is possible to detect: – 500K fires against a 300K background –covering only 5 % of a 4 x 4 km pixel Weaver, J.F., Purdom, J.F.W, and Schneider, T.L. 1995. Observing forest fires with the GOES-8, 3.9 µm imaging channel. Weather and Forecasting, 10, 803-808

29. Observations Can the visible channel be used to detect fire? Yes. The smoke plume can be seen in the visible. However: Fire must be well developed to create a plume that can be detected in the visible.

30. Types of Fire Detection Algorithms Fixed threshold techniques –Rely on pre-set thresholds and consider a single pixel at a time. Spatial analysis or contextual techniques –Compute relative thresholds based on statistics calculated from neighboring pixels. Real-time products for Central America: http://www.cira.colostate.edu/ramm/sica/main.html

31. Example of Fixed Threshold Algorithm by Arino et al. (1993) 1.BT 3.9 > 320 K (to identify probable fires) 2.BT 3.9 – BT 10.7 > 15 K 3.BT 10.7 > 245 K (to prevent false alarms due to reflective clouds)

32. GOES-8 3.9 micron GOES-8 3.9 micrometer

33. Blue areas represent pixels: T3.9 >320K

34. GOES-8 Product: T3.9 – T10.7 Blue regions represent pixels with: T3.9 – T10.7 > 15 K

35. Resulting Fire Threshold Product Blue represents fire pixels

36. Problems Very warm, dry ground is detected as fire. Will not pick up night fires that are cooler than 320 K

37. Example of Contextual Algorithm by Justice et al. (1996) 1.BT 3.9 > 316 K (to identify probable fires) 2.Estimate a background temperature with surrounding ‘valid’ pixels: A valid pixel* Is not a cloud * Is not a fire pixel 3.The window starts as a 3X3 pixel area and expands to a 21X21 pixel grid until at least 25% of the background pixels (or at least 3) are valid. 4.Let DT=MAX(2 std dev of BT 3.9 -BT 10.7, 5 K) FIRE pixel: if BT 3.9 -BT 10.7 > mean BT 3.9 -BT 10.7 + DT and BT 10.7 > mean BT 10.7 – std dev of BT 10.7

38. Fire Justice Product Blue pixels represent detected fires

39. Problems Does not adequately detect fire pixels in regions of very warm and dry ground. May also need to implement a correction for (horizontal) temperature changes in mountainous regions. Will not pick up night fires that are cooler than 316 K

40. GOES- 8 Reflectivity Product

41. Shot-noise filter applied to Reflectivity Product Red pixels denote potential fires.

42. Experimental ABBA Automated Biomass Burning Algorithm Contextual Algorithm Developed at the Cooperative Institute for Meteorological Satellite Studies (CIMSS) at the University of Wisconsin in Madison. Initially ‘calibrated’ to Brazil Fires http://cimss.ssec.wisc.edu/goes/burn/wfabba.html

44. Polar Orbiting Satellites The same detection algorithms presented here can be applied to imagery from polar orbiting satellites. For AVHRR, the 3.9 um sensor saturates at ~ 323 K (GOES-8 saturates at 338 K) We will view an example of GOES vs. AVHRR imagery in the lab.

45. References/links GOES Fire Detection – VISITview session http://www.cira.colostate.edu/ramm/visit/detection.html see reference/links at the bottom of their page Fire Products for Central America http://www.cira.colostate.edu/ramm/sica/main.html Wildfire ABBA http://cimss.ssec.wisc.edu/goes/burn/wfabba.html CIRA GOES 3.9 um Channel Tutorial http://www.cira.colostate.edu/ramm/goes39/cover.htm Storm Prediction Center – 1 and 2 day fire outlooks http://www.spc.noaa.gov/products/fire_wx Drought Monitor - long term drought indicators for the US: Drought Index, Crop Moisture Index, Standardized Precipitation Index, Percent of Normal Rainfall, Daily Streamflow, Snowpack, Soil Moisture, Vegetation Health http://drought.unl.edu/dm