Estimates of Active Fire Properties
using AVIRIS, ASTER and MODIS
D.A. Roberts
Department of Geography
U.C. Santa Barbara
Photograph of the Station Fire behind the Jet Propulsion Laboratory
from Philipp Schneider
Estimates of Active Fire Properties
•
Introduction
– Remote Sensing of Wildfire
– Why Fire Temperature and Area
•
Multiple Endmember Spectral Mixture Analysis
and Fire
– AVIRIS Examples from Simi
– MODIS
– ASTER
•
•
•
Early Detection using AVIRIS
Summary
Major Contributors
– Phil Dennison and Ted Eckmann
•
Agency Support
–
–
–
–
–
NASA Solid Earth and Natural Hazards
NASA Regional Earth Science and Applications
NASA EO-1 Science Validation Team
Joint Fire Science Program
NASA Earth System Science Program
MODIS: 9-5-2009
Remote Sensing of Wildfire
• Pre-fire Conditions
– Fuel Types (Fuel models)
– Fuel Condition (live to dead fuels)
– Live Fuel Moisture
• Fire Danger (current conditions)
– Incorporates measures of fuel properties and weather
– Fire Danger Indices
• Active fire
– Fire temperature, fire area and perimeters
– Fire spread modeling
– Emission modeling and suppression
• Post-fire conditions
– Burned area
– Fire effects
– Post-fire recovery
Why Active Fire Properties?
• Improved measures of fire temperature and
area may aid in fire suppression efforts
• Fire temperature and burned area are critical
for an improved understanding of emissions
• Fire intensity (temperature) impacts post-fire
recovery, soil properties
• While the TIR is widely considered the
preferred part of the spectrum, the VNIRSWIR also has considerable potential
Hot Object Emission
AVIRIS
•
5.000E+04
1600K
Radiance (W m-2 μ m -1 sr -1 )
4.500E+04
4.000E+04
•
3.500E+04 Solar*50
3.000E+04
2.500E+04
1400K
2.000E+04
1.500E+04
•
1200K
1.000E+04
1000K
5.000E+03
0.000E+00
400
1400
2400
3400
4400
5400
Hot objects emit
strongly in the VNIRSWIR
As objects cool, peak
emission shifts to the
TIR and the area
under the curve
declines
The spectral shape
provides temperature,
area under the curve
size
6400
Wavelength (nm)
ASTER (6 bands)
MODIS
(3 SWIR, 1 MID-IR, 3TIR)
Fire Temperature and Area
1.200E+04
1200K
1600K (0.1)
8.000E+03
-2
-1
-1
Radiance (Wm μm sr )
1.000E+04
6.000E+03
1000K
4.000E+03
1000K (0.5)
2.000E+03
800K
0.000E+00
400
1400
2400
3400
4400
5400
6400
Wavelength (nm)
•
•
Spectral shape is unique to a specific temperature
The area under the curve varies with area and temperature
– Single band estimates of fire properties are under-determined
Multiple Endmember Spectral Mixture
Analysis (MESMA)
• Extension of a Simple Mixture Model
N
–
Riλ'
=
∑
fki * P kλ
k =1
N
– RMS =
+ εiλ
∑ ε iλ
2
k=1
N -1
• Number and Types of Endmembers Vary Per Pixel
• For fires mixed radiance is a product of modeled
radiance of hot objects and a background
– The endmember selected provides temperature
– The fraction of hot and cold endmembers provides area
AVIRIS Fire Temperature and Area
(Dennison et al., 2006)
• Multiple Endmember Spectral Mixture Analysis
(MESMA) was used to model each pixel in the AVIRIS
image
• Tested on the 2003 Simi Fire
• Each pixel was modeled as a combination of:
– 1 emitted thermal radiance endmember
– 1 reflected solar radiance endmember
– Shade (zero radiance)
• Emitted thermal radiance endmembers were modeled
using MODTRAN
– Ranged from 400-1500 K (260°-2240°F) at increments of 10 K
• Reflected solar radiance endmembers were selected from
the image using Endmember Average RMSE (EAR)
– Six possible endmembers: riparian, dense chaparral, sparse
chaparral/sagescrub, grass, soil and ash
Reflected Solar Radiance Endmembers
Radiance (Wm2nm-1sr-1)
0.12
Dense Chap
Sparse Chap
Riparian
Grass
Soil
Ash
0.1
0.08
0.06
0.04
0.02
0
400
900
1400
1900
Wavelength (nm)
Dennison et al., 2006
2400
Radiance (Wm-2nm-1sr-1)
Subset of Emitted Thermal Radiance
Endmembers
2.5
2
1.5
1
1000 K
900 K
800 K
700 K
600 K
0.5
0
400
900
1400
1900
Wavelength (nm)
Dennison et al., 2006
2400
Example: Mixed Radiance
Radiance (Wm-2nm-1sr-1)
0.14
0.12
0.1
Soil
1000K
Combined Radiance
0.08
0.06
0.04
0.02
0
400
900
1400
Wavelength (nm)
Dennison et al., 2006
1900
2400
Retrieved Temperature Endmembers
Dennisonetetal.,
al.,2006
2006
Dennison
Retrieved Fire Fraction
Dennison et al., 2006
Land Cover
Dennison et al., 2006
Ash
Soil
Riparian
Sparse Chap.
Dense Chap.
Grass
MODIS Fire Temperature and Area
(Eckmann et al., 2008)
• MODIS provides far
greater spatial and
temporal coverage
• Applied Basic Principles to
MODIS data
– Utilized a subset of MODIS
bands
• Applied to a daytime fire
from Ukraine
• “Validated” using ASTER
Pixel Counts (band 9 > 2
Wm-1μm-1sr-1)
MODIS Endmembers
*Modtran Hot EMs
500-1500K @ 10K
*Convolved to MODIS
bands
* 7 MODIS bands used
in models
Eckmann et al., 2008
MODIS Endmembers
• Also required background spectra
– Systematically sampled from MODIS, screened for fires
• Also required “shade” endmember
– The equivalent of atmospheric emission
– Generated using Modtran with a cold (10K) background
Eckmann et al., 2008
MODIS Fire Pixels
(Eckmann et al., 2008)
19 pixels identified with 0 to 2.55% fire
Fire temperatures tended to be bimodal, most likely due to solar
contamination. This impacts fire area.
MESMA Compared to FRP
Fire Radiative Power (FRP) is estimated from the MODIS 4 μm band
FRP = 4.34x10-19Wm-2K-8*[(T4)8-(T4b)8]
ASTER fire counts match MESMA area better, although neither is
perfect
Eckmann et al., 2008
ASTER Night Time Image
Sawmill Fire (15 Sept 2006)
•
•
ASTER has a finite
field of view
– Thus ASTER fire
counts represent
mixtures
MESMA was applied to
ASTER imagery to
estimate fire
temperature and area
Eckmann et al., in press
ASTER Hot Endmembers
• Generated using Modtran
4.3
• Parameterized for
–
–
–
–
–
–
Sept 15, 2006 5:54:21 UTC
Mid-latitude Summer
5 km visibility
0.503 cm water vapor
385 ppm CO2
37.77 N, 118.39 W 1985 m
Eckmann et al., in press
Fire Temperature and
Area from ASTER
• Fire temperatures ranged 500
to 1500K, mostly around 1000K
• Fire sizes were generally small,
less than 5%
Eckmann et al., in press
Modeled vs Measured Radiance
• Modeled and
measured radiance
match well
• Examples are given
for 740, 910 and 1330
K with areas of
0.826, 0.313 and
0.0075%
Eckmann et al., in press
Early Fire Detection
(Dennison and Roberts, 2009)
j
• MESMA works but is
computationally intensive
• A Normalized Difference
i
Index (NDI) was evaluated as
a way of detecting fire emitted
radiance
• Optimal bands were identified
by developing band
combinations that produced
the highest accuracy
compared to MESMA
• Optimal bands were all
Kappa for fire detections
located in the SWIR
HDFI= (Li-Lj)/(Li+Lj)
Wavelength Dependence of the HFDI
• Emission sources can be
mapped by additional
radiance in strong
atmospheric absorption
bands
– HFDI= (L2429L2061)/(L2429+L2061)
• By rapid location of
emission sources,
MESMA can be
implemented only where
needed
Dennison and Roberts, 2009
Early Detection: Simi
• All combinations of
AVIRIS bands were tested
as an “NDVI”
• A combination of 2429 and
2061 nm produced the
highest detection rates
(highest kappa)
Dennison and Roberts, 2009
Early Detection: Indian Fire
•
HFDI was compared to three
other approaches
– CO2 Index (Dennison, 2006)
– K Emission Index (767, 770
nm)(Vodacek et al., 2002)
– ASTER Fire Detection
•
•
•
CO2 Index produced
numerous false positives
K Emission Index severely
impacted by smoke
HFDI and ASTER Fire count
very similar
Dennison and Roberts, 2009
Summary
• MESMA has shown considerable promise applied to
AVIRIS, ASTER and MODIS for estimating fire area and
temperature
• New approaches can be developed to improve MESMA
performance
– HFDI, followed by MESMA applied to targeted pixels
– Duel hot temperatures?
• Future potential is good
– MESMA applied to Savanna fires from MODIS
• Shows temporal trends in fire behavior consistent with changes in
emissions (Eckmann, in prep)
– Joint AVIRIS/MASTER data sets
• HyspIRI precursor science
• But there are issues
– Lack of validation data sets is a serious problem
Questions?
Questions?