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Remote Measurements of
Alistair Smith (and many many others …)Alistair Smith (and many many others …)
Remote Measurements of
Active Fire Behavior and Post-Fire Effects
Remote Sensing: A very Brief OverviewRemote Sensing: A very Brief Overview
~ 5 million years B.C. : Humans Begin to Understand their Environment
Wildlife Management
Hazard Assessment
The Remote Sensing ProcessThe Remote Sensing Process
We aim to: Physically relate surface process to remotely derived measures
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What’s Happening?What’s Happening?
Fire-Remote Sensing Essentials: ReflectanceFire-Remote Sensing Essentials: Reflectance
100%
5%
5%
8%
40% 10%
More than just redMore than just red
What does the Reflectance Look Like?
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Fire-Remote Sensing Essentials: EmittanceFire-Remote Sensing Essentials: Emittance
Energy emitted (q λ) at a given wavelength and temperature is given by the Stefan-Boltzmann law:
q λ = εσ T4 [σ = 5.67 x 10-8 watts/m2/K4]
ε = emissivity, 0 <= ε <= 1, and is the efficiency that surface emits energywhen compared to a black body
Fires follow the curveFires follow the curve
Wooster et al 2005
Fire-Remote Sensing Fire Remote Sensing Terminology
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Fire Intensity, Fire Severity, and Burn Severity…
From Jain T, Pilliod D, Graham R (2004) Tongue-tied. Wildfire. 4, 22-26. [After: DeBano LF, Neary DG,Ffolliott PF (1998) ‘Fire’s effects on ecosystems.’ (John Wiley and Sons: New York) 333 pp.
Source of Confusion: The Terms Fire Severity and Burn Severityare used inconsistently in the Remote Sensing literature
Value Laden Term
• Negative Connotations: severity = bad
• Public & Policy Miscommunication
• Multiple Definitions in the Literature
The Severity Concern
p
* Fire duration and heat transfer
* Vegetation consumption
* White ash production
* Change in surface reflectance
* Alteration in soil properties
* Changes in the litter and duff layers
* Long-term vegetation mortality and recovery
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The Need for ClarificationThe Need for Clarification
Simplifying the Fire Disturbance Continuum:
• Limit use of the Terms Fire Severity & Burn Severity
• Describe and Quantify the Actual Processes Being Assessed
• Make sure that satellites CAN also measure these processes
Active Fire Characteristics
Post Fire EffectsPre-Fire Condition
During Combustion
Following Combustion
What Can Remote Sensing do for What Can Remote Sensing do for Fire Science?
Vegetation Mortality & Ecosystem Recovery
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Need immediate Active or Post-fire measures that:
• Relate to active fire characteristics (i.e. intensity) &
• Predict post-fire effects (i.e. severity)
How about the Post-Fire Measures?
From Unburned to Burned Surfaces
DECREASE in Visible-NIR
(in General)
From Unburned to Burned Surfaces
INCREASE in SWIR reflectance
Trigg and Flasse (2000)
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From Unburned to Burned Surfaces
INCREASE in surface temp
Trigg and Flasse (2000); Smith and Wooster (2005)
Several Remote Methods Use These ChangesSeveral Remote Methods Use These Changes
NIR SWIR
NIR – REDNIR + REDNDVI =
NIR – SWIRNIR + SWIRNBR =
dNBR = NBRprefire - NBRpostfire
Produce Maps of Area Burned and Maps of Vegetation Mortality and Recovery
The Severity ConcernThe Severity Concern
Subjective & Value Laden Term
∆NBR: non-linear asymptotic relationship with CBI that varies with sensor spatial resolution and environment
Van Wagtendonk et al (2004); Epting et al (2005) Lentile et al (2006)
Highlights need to evaluate alternative methods
∆NBR
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Multiple Agency’s use the dNBR method
dNBR is a good Measure of Current Canopy Condition
The Burn Severity Map Concern
dNBR BAER
Remember our Aim:Remember our Aim:
To physically relate surface process to remotely derived measures
The rapid-response measurement of active fire behavior and immediate post-fire effects is very difficult and immediate post-fire effects is very difficult
Need easy measure that can be related to the fire behavior.
From fire behavior we can predict or model the longer-term ecosystem condition.
Use Cover Fractions Within a PixelUse Cover Fractions Within a Pixel
• Direct Measure via Remote Sensing
• Comparable Measure via Field Methods
• Similar to traditional %Green, %Brown, % Black
Source: Hudak, Morgan, Hardy et al
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Use Cover Fractions Within a PixelUse Cover Fractions Within a Pixel
• Inherently Scalable
• Use Existing 30m Immediate Post-fire Landsat Data
• Physically Related to Carbon and Water Processes
Source: Smith et al in prep
Measuring Cover Fractions in a PixelMeasuring Cover Fractions in a Pixel
Standard and Simple Method: Linear Spectral Unmixing
Pixel: Green (Leaf) + Brown (dry grass) + Black (char)
We can break a pixel down into component fractions
Mapping Burned Areas: Woodland SavannahMapping Burned Areas: Woodland Savannah
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Setting a Char Fraction ThresholdSetting a Char Fraction ThresholdSmith et al. RSE (2005)
Smith et al. IJRS (in press)
Landsat ETM (4:3:2)>50% Charcoal
Mapping Burned Area: ComparisonMapping Burned Area: Comparison
Landsat (30m) IKONOS (4m)
Char Fraction >50% Supervised Classification
Smith et al. IJRS (in press)
Using Char Fractions to Predict Post-Fire Effects?Using Char Fractions to Predict Post-Fire Effects?
Jasper Fire, South Dakota started 24th Aug 2000
~33,800 ha Burned in 9 days
Ponderosa Pine Forest
Lentile et al (2005,2006b):Lentile et al (2005,2006b):
1-Yr post-fire Measures in 80 sites
Landsat Image: 14th Sept 2000
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Compare both ∆NBR and Char Fraction Cover to
1-yr Post-Fire Field Canopy and Sub-Canopy Measures
Also Measure Immediate Post-Fire ∆NBR
Landsat 7:5:4 ∆NBR Char Fraction
R2=0 55
Immediate ∆NBR
R2=0 56
Char Fraction
Canopy Variables: 1-Yr Post FireCanopy Variables: 1-Yr Post Fire Smith, Lentile et al. IJRS (in review)
R 0.55R 0.56
Percentage Live Tree
Immediate ∆NBRChar Fraction
Canopy Variables: 1-Yr Post FireCanopy Variables: 1-Yr Post Fire Lentile, Smith et al. (in review)
R2=0.49R2=0.52
Total Crown Effects (Crown Scorch + Crown Consumption)
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Retrospective Measurement of the Fire IntensityRetrospective Measurement of the Fire Intensity
R2=0.69
Char Fraction
Bole Scorch (surrogate of flame length Intensity)
What’s Potentially Happening with Bole Scorch?What’s Potentially Happening with Bole Scorch?
Char Fraction = 50%Char Fraction = 75%Char Fraction = 100%
Prediction of Sub-Canopy Ecosystem ConditionPrediction of Sub-Canopy Ecosystem Condition
R2=0.55
Char Fraction
Litter Organic Weight (g/m2)
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R2=0.29
Immediate ∆NBR
R2=0.47
Char Fraction
Sub-Canopy VariablesSub-Canopy Variables Lentile, Smith et al. (in prep)
Average Bark Thickness
What Can Remote Sensing do for What Can Remote Sensing do for Fire Science?
Fire Behavior and Active Fire Effects
Fire Line Intensity: I = HWR
How Much Fuel (Carbon) is Combusted?
H is known
Need Measurement of:
W – Fuel Combusted
R – Rate of Spread
In Crown Fires W can be ‘very Difficult’
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Also Many Large Fires Occur in Remote AreasAlso Many Large Fires Occur in Remote Areas
NASA 2000
Andrews and Rothermel 1982 – Heat Per Unit Area:Andrews and Rothermel 1982 – Heat Per Unit Area:
Energy= εσT4
HPA= Energy from Combusting Fuel
- Energy Absorbed by Background
HPA = εσ(T4fire - T4
background)
This Equation can be Applied to Satellite DataThis Equation can be Applied to Satellite Data
Wooster, M.J., et al. (2005) Retrieval of biomass combustion rates and totals from fire radiative power observations: FRP derivation and calibration relationships between biomass consumption and fire radiative energy release, JGR, 110, D24311, doi:10.1029/2005JD006318,
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Accurate MeasuresAccurate Measures
Wooster, M.J., et al. (2005) Retrieval of biomass combustion rates and totals from fire radiative power observations: FRP derivation and calibration relationships between biomass consumption and fire radiative energy release, JGR, 110, D24311, doi:10.1029/2005JD006318,
MODIS BIRD3.9 μm channel images
MODIS andBIRD FRP data in
Boreal Forest
FRP dataMODIS BIRD‘false alarms’
Zhukov, B., et al. (2005) Spaceborne detection and characterization of fires during the Bi-spectral Infrared Detection (BIRD) experimental small satellite mission (2001-2004) Remote Sensing of Environment, 100, 29-51
Regional W Estimates
MIR channel TIR channelMSG SEVIRI
MIR-TIR Fire Map
15 mins imaging frequency
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0 3 6 9 11Day of Burn
A Week of W: Southern AfricaA Week of W: Southern Africa
Roberts, G., et al. (2005) Retrieval of biomass combustion rates and totals from fire radiative power observations: Application to southern Africa using geostationary SEVIRI Imagery, JGR, 110, D21111, doi: 10.1029/2005JD006018
A Week of W: Southern AfricaA Week of W: Southern Africa
BiomassCombusted
= 3.2 million tonnes (1.5 Mtonnes C)(4.3-5.1 million tonnes adj. for cloud)
ect
Roberts, G., et al. (2005) Retrieval of biomass combustion rates and totals from fire radiative power observations: Application to southern Africa using geostationary SEVIRI Imagery, JGR, 110, D21111, doi: 10.1029/2005JD006018
Clo
ud e
ffe
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From Energy to Fire BehaviorFrom Energy to Fire Behavior
Head and Backing Grassland Fires
Smith AMS, Wooster MJ (2005) Remote classification of head and backfire types from MODIS fire radiative power observations. International Journal of Wildland Fire 14, 249-254.
From Energy to Fire BehaviorFrom Energy to Fire Behavior
Field
Image
From Energy to Fire Behavior
Crown and Surface Boreal Forest Fires
Wooster M.J, Zhang YH (2004) Boreal forest fires burn less intensely in Russia than in North America. Geophysical Research Letters 31, L20505. doi:10.1029/2004GL020805
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Fire Behavior Model Emissions (CONSUME/EPM)Fire Behavior Model Emissions (CONSUME/EPM)
Directly Relate Energy to PM Emissions
Linking Energy to Emissions and Air QualityLinking Energy to Emissions and Air Quality
Future: Linking Active Fire Measures to Post-fire EffectsFuture: Linking Active Fire Measures to Post-fire Effects
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“As a young man, my fondest dream was to become a geographer. However, while working in the Patents Office, I thought deeply about the matter and concluded that it was far too difficult a subject. With some reluctance, I then turned to physics as an alternative.”
- Albert Einstein