Fire%20Detection%20using%20Geostationary%20and%20Polar%20Orbiting%20Satellites

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. gt 15-25 kts
• Relative humidity lt threshold (see following
  slide varies by region)
• Temperature gt65-75F
• Vegetation moisture lt8-12
• 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 lt2,000 FT Stability Term (T950-T850) 1 3 C or less 2 4 to 7 C 3 gt 8 C Moisture Term (T850-Td 850) 1 5 C or less 2 6 to 9 C 3 gt 10 C
MID ELEVATION 2,000-6,000 FT Stability Term (T850-T700) 1 5 C or less 2 6 to 10 C 3 gt 11 C Moisture Term (T850-Td 850) 1 5 C or less 2 6 to 12 C 3 gt 13 C
HIGH ELEVATION gt6,000 FT Stability Term (T700-T500) 1 17 C or less 2 18 to 21 C 3 gt 22 C Moisture Term (T700-Td 700) 1 14 C or less 2 15 to 20 C 3 gt 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
Category Description Fire 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 21-30 lt75 for 3 months -0.5 to -0.7 36-45
D1 Moderate Drought High -2.0 to -2.9 11-20 11-20 lt70 for 3 months -0.8 to -1.2 26-35
D2 Severe Drought Very high -3.0 to -3.9 6-10 6-10 lt65 for 6 months -1.3 to -1.5 16-25
D3 Extreme Drought Extreme -4.0 to -4.9 3.5 3-5 lt60 for 6 months -1.6 to -1.9 6-15
D4 Exceptional Drought Exceptional and Widespread lt -5.0 0-2 0-2 lt65 for 12 months lt -2.0 1-5
GOES Fire Detection - VISITview
14
(No Transcript)
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
British Columbia
Alberta
Montana
Idaho
Washington
Oregon
17
Loop of wind driven fire
California
VIS
IR2
IR2 24hr
Mexico
18
Satellite Monitoring of FIRESGeostationary 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
NIGHT Fog-Stratus Product DAY
Reflectivity Product

• These products are made with images from channels
  
• 3.9 and 10.7 µm

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? R? cloud area cloud R? ground area
  ground
• Similarly for fires
• R? R? fire area fire R? ground area
  ground

GOES 3.9 um Channel Tutorial
22
NIGHT Fog-Stratus Product

• Subtract temperature, pixel by pixel, of
  10.7mm - 3.9 mm images

As temperature is warmer at 3.9 mm
The result is a negative number
23
NIGHT Fog-Stratus Product

• The result is normalized by adding 150 to each
  pixels 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 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)

• BT3.9 gt 320 K (to identify probable fires)
• BT3.9 BT10.7 gt 15 K
• BT10.7 gt 245 K (to prevent false alarms due to
  reflective clouds)

32
GOES-8 3.9 micrometer
GOES-8 3.9 micron
33
GOES-8 3.9 micrometer
Blue areas represent pixels T3.9 gt320K
34
GOES-8 Product T3.9 T10.7
Blue regions represent pixels with T3.9 T10.7
gt 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)

• BT3.9 gt 316 K (to identify probable fires)
• Estimate a background temperature with
  surrounding valid pixels
• A valid pixel Is not a cloud
• Is not a fire pixel
• 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.
• Let DTMAX(2 std dev of BT3.9-BT10.7, 5 K)
• FIRE pixel
• if BT3.9-BT10.7 gt mean BT3.9-BT10.7 DT
• and BT10.7 gt mean BT10.7 std dev of BT10.7

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

43
(No Transcript)
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.ht
  m
• 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