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Fire Detection using Geostationary and Polar Orbiting Satellites Dr. Bernadette Connell CIRA/CSU/RAMMT Dr. Vilma Castro UCR/RMTC March 2005 Objectives Background Environmental and weather conditions conducive to fires Satellite fire detection techniques for hot spots Examples

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fire detection using geostationary and polar orbiting satellites
Fire Detection using Geostationary and Polar Orbiting Satellites

Dr. Bernadette Connell


Dr. Vilma Castro


March 2005

  • Background
  • Environmental and weather conditions conducive to fires
  • Satellite fire detection techniques for hot spots
  • Examples
  • Lab exercise
monitoring fire activity
Monitoring Fire Activity


  • 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.
united states fire weather activities
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?
united states fire weather activities5
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
real time nws fire weather services
Real-time NWS Fire Weather Services
  • Storm Prediction Center – issues 1 and 2 day fire outlooks

    • maps
    • text discussion
    • hazard categores:
      • critical areas – outlines
      • extremely critical – hatched
      • dry thunderstorm risk - scalloped
real time nws fire weather services7
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
real time non routine products
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
haines index
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)

calculating haines index
Calculating Haines Index

Sum of two terms = Haines Index

GOES Fire Detection - VISITview





2-very low

3-very low


vegetation health
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.
loop of plume dominated fire
Loop of plume dominated fire

VIS 03246

IR2 03246

British Columbia






loop of wind driven fire
Loop of wind driven fire




IR2 24hr


satellite monitoring of fires geostationary or polar orbiting
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
quick ramsdis products for fire detection
“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

characteristics of 3 9 micrometer channel that make it suitable for hot spot detection
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

sub pixel response
Sub pixel response
  • Rλ = Rλ cloud * % area cloud + Rλ ground * % area ground
  • Similarly for fires:

Rλ = Rλ fire * % area fire + Rλ ground * % area ground

GOES 3.9 um Channel Tutorial

night fog stratus product
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

night fog stratus product23
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

night fog stratus product24
NIGHT: Fog-Stratus Product

Pixels with fire are 80 brightness units darker than the background


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

day reflectivity product
DAY: Reflectivity Product
  • Channels involved: 3.9 and 10.7 microns
  • Reflective component is subtracted from the 3.9 micron signal.

The temperature at10.7 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
  • 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

  • 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.

types of fire detection algorithms
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:

example of fixed threshold algorithm by arino et al 1993
Example of Fixed Threshold Algorithm by Arino et al. (1993)
  • BT3.9 > 320 K (to identify probable fires)
  • BT3.9 – BT10.7 > 15 K
  • BT10.7 > 245 K (to prevent false alarms due to reflective clouds)

GOES-8 3.9 micrometer

GOES-8 3.9 micron


GOES-8 3.9 micrometer

Blue areas represent pixels:

T3.9 >320K


GOES-8 Product: T3.9 – T10.7

Blue regions represent pixels with:

T3.9 – T10.7 > 15 K


Resulting Fire Threshold Product

Blue represents fire pixels

  • Very warm, dry ground is detected as fire.
  • Will not pick up night fires that are cooler than 320 K
example of contextual algorithm by justice et al 1996
Example of Contextual Algorithm by Justice et al. (1996)
  • BT3.9 > 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 DT=MAX(2 std dev of BT3.9-BT10.7, 5 K)

FIRE pixel:

if BT3.9-BT10.7 > mean BT3.9-BT10.7 + DT

and BT10.7 > mean BT10.7 – std dev of BT10.7


Fire Justice Product

Blue pixels represent detected fires

  • 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

Shot-noise filter applied to Reflectivity Product

Red pixels denote potential fires.

experimental abba
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

polar orbiting satellites
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.
references links

GOES Fire Detection – VISITview session

see reference/links at the bottom of their page

Fire Products for Central America

Wildfire ABBA

CIRA GOES 3.9 um Channel Tutorial

Storm Prediction Center – 1 and 2 day fire outlooks

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