wilfrid schroeder 1 , ivan csiszar 2 , louis giglio 3 ,
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Satellite Active Fire Product Development and Validation: Generating Science
Quality Data from MODIS, VIIRS and GOES-R Instruments
Wilfrid Schroeder1, Ivan Csiszar2, Louis Giglio3, Evan Ellicott3, Christopher Justice3, Christopher Schmidt4
1 ESSIC/CICS, UMD2 STAR NOAA/NESDIS
3 Dept of Geography, UMD4 CIMMS, UW-Madison
Team BackgroundOngoing CICS Projects:
• GOES-R: • “Validation and Refinement of GOES-R ABI Fire Detection Capabilities” (GOES-R AWG)
• MODIS & VIIRS: • “Active Fire Product Evaluation and Development from MODIS and VIIRS” (NASA)• “Development of an Enhanced Active Fire Product from VIIRS” (IPO – includes NPP active fire
product validation program activities also)
Linkages and collaborations:
• Christopher Schmidt (UW-Madison) – GOES Imager/ GOES-R ABI Fire Product PI (GOES-R AWG)
• Christopher Justice and Louis Giglio (UMD/Geography) – MODIS Active Fire Product PIs (NASA)
• Ivan Csiszar (NESDIS/STAR) and Christopher Justice (UMD/Geography) – NPP/VIIRS Active Fire Product PIs (NASA, IPO)
• Wilfrid Schroeder, Christopher Schmidt, Ivan Csiszar, Elaine Prins, Christopher Justice – fire product evaluation in the Amazon and long-term fire data record (NASA LBA-ECO – recently concluded)
Progress in the Last Three Decades
Major Data Sets**
Adv Very High Res Radiometer (AVHRR) 1kmx12h within antenna range
1980
1990
2000
2010
GOES East Imager 4kmx30min Western Hemisphere
Tropical Rainfall Monitoring Mission (TRMM)2.4kmx12h ±38º
Mod Res Imaging Spectroradiometer (MODIS/Terra)(MODIS/Aqua)
1kmx12-24h global
GOES VAS13.8kmx30min Western Hemisphere
** Excluding nighttime sensors such as ATSR, DMSP
Simple Threshold (single or multi-band)
Contextual methods (x,y) (dynamically adjusted)
Contextual methods (x,y,t) (dynamically adjusted)
A few dozen images
400K+ images from GOES only
Essentials in Active Fire Monitoring
Fires are highly dynamic events
Fires may/not leave detectable scars behind
ETM+ 10am
ASTER 10:30am
0
5
10
15
20
25
30
35
40
45
0.00 0.25 0.50 0.75 1.00 1.25 1.50 1.75 2.00 2.25 2.50 2.75 3.00
Rad
ianc
e x
10 -6
(W
m-3
str-1
)
Fire x 10-2
700ºC
Sun x 10-6
6000ºC
ASTER Channel 3N
ASTER Channel 8
(m)
ETM+Channel 4
ETM+Channel 7
Active Fire Reference Data Derived from ASTER and ETM+ Imagery
ASTER
ETM+
ASTER bands 3 and 8 and ETM+ bands 4 and 7
0
10
20
30
40
50
60
70
1 2 3 4 5 6 7 8 9 10Fire Clusters per MODIS pixel
Fre
qu
en
cy
(%
)
ASTER
ETM+
Sample Size: 18 ASTER scenes
Region: South Africa
Proof of concept using fixed threshold method applied to ASTER band 9 to derive 30m resolution active fire masks
Morisette et al. 2005
Sample Size: 100 ASTER scenes
Region: Global
Development of robust active fire detection algorithm for ASTER
Giglio et al. 2008
Sample Size: 131 ASTER scenes
Region: Northern Eurasia
Development of active fire validation protocol
Csiszar et al. 2006
MODIS/Terra Active Fire ValidationC3-C4 Algorithm Version
Sample Size: 115 ASTER scenes
Region: CONUS
Validation of NOAA/NESDIS operational fire monitoring system including analyst data
Schroeder et al. 2008
Sample Size: 167 ASTER + 123 Landsat ETM+ scenes
Region : Brazilian Amazonia
Generalization of moderate-coarse resolution fire data validation (MODIS + GOES) using higher resolution imagery
Schroeder et al. 2008
MODIS/Terra Active Fire ValidationC3-C4 Algorithm Version
Sample Size: 24 ASTER + 8 Landsat ETM+ scenes
Region : Brazilian Amazonia
Assessment of short-term variation in fire behavior – implications to active fire validation
Csiszar and Schroeder 2008
MODIS/Terra C5 AlgorithmStage 3 Fire Validation
Sample Size: ~2500 ASTER scenes
Region : Global
Stage III validation of MOD14
Schroeder et al. (in preparation)
• Daytime & nighttime data
• Data equally distributed across the globe
• Multi-year analysis (2001-2006)
• ASTER SWIR anomaly May ‘07
• Omission/commission errors derived as a function of percent tree cover
Temporal Consistency of MOD14 Detection Performance
0
20
40
60
80
100
01<>20 41<>60 81<>100 121<>140 161<>180 >200
Fire Cluster Size (number of 30m ASTER fire pixels)
Pro
bab
ilit
y o
f D
etec
tio
n (
%)
2001-2002
2003-2004
2005-2006
Using a subset of points covering the range of 20-40% tree cover
No statistically significant difference over time (i.e., Dt = 0; p < 0.01)
Overall Probability of Detection
Summary curve using all data points
(125K MODIS pixels with >0 ASTER fire pixels including16K MOD14 fire pixels)
0
10
20
30
40
50
60
70
80
90
100
0 100 200 300 400 500 600 700 800 900
Fire Cluster Size (number of 30m ASTER fire pixels)
Pro
babi
lity
of D
etec
tion
(%)
Day
Night
Daytime Probability of Detection as a Function of Percentage Tree Cover**
** average value calculated using a 20x20km window centered on the target pixel
0
20
40
60
80
100
0 50 100 150 200 250 300 350 400 450 500
Fire Cluster Size (number of 30m ASTER fire pixels)
Pro
babi
lity
of D
etec
tion
(%)
vcf <20 vcf 20<>40 vcf 40<>60 vcf >60 All
ASTER (RGB 8-3-1) 21 June 2003 17:38:35UTC
Manitoba, Canada
ASTER (30m Fire Mask) 21 June 2003 17:38:35UTC
Manitoba, Canada
Commission errorsRecently burned pixels with discernable scars constitute a large fraction of the false detections. Overall fire-unrelated commission error ~2%
Nighttime commission error rate is zero.
Results – Commission ErrorsResults – Commission Errors
Schroeder et al. (in preparation)
Results – Commission ErrorsResults – Commission Errors
Typical false alarm in MOD14 data
20 Jul 2003 1407UTC21 Aug 2003 1407UTC
Commission errors can occur multiple times at the same location
MODIS/Terra was found to detect twice as many false positives as MODIS/Aqua
MIR – Initial Tests: Deriving MODIS L1B TOA Radiances usingASTER Surface Kinetic Temperature data + Radiation Transfer Model
MODIS L1B Ch21 07 Aug 2004 1405 UTC
11.7o S 56.6o W
UMD MODIS Ch21 Proxy Data07 Aug 2004 1405 UTC
11.7o S 56.6o W
Early Assessment of NPP/VIIRS Active Fire Data
Early Assessment of NPP/VIIRS Active Fire Data
MIR – Initial Tests: Deriving MODIS L1B TOA Radiances usingASTER Surface Kinetic Temperature data + Radiation Transfer Model
Initial Results
MODIS/Terra (1kmx1km) VIIRS (750m x 750m) VIIRS (250m x 750m)
Defining TIR Saturation Levels
Results being used to support VIIRS hardware and software configuration to allow optimum fire detection capabilities
Early Assessment of GOES-R/ABI Active Fire Data
Fire MaskProxy ABI (derived from MODIS L1B)
Selection of Coincident MODIS and ASTER L1B Data
0
10
20
30
40
50
60
70
80
90
100
0 50 100 150 200
Number of 30m Active Fire Pixels
Pro
ba
bili
ty o
f O
mis
sio
n (
%)
ABI_AMZ_CONUS
MOD14_Global
MOD14_AMZ
WFABBA_AMZ
Initial Results
ABI Active Fire Product Validated using Reference
ASTER Data
Probability of Detection (omission) Defined as a Function of ASTER
Fire Statistics
Results being used to assess and refine pre-flight fire detection algorithm performance and to define routine fire validation strategy for implementation during the post-launch phase
ABI
GOES
MODIS
Supporting Science Quality Data Development Regionally
1998 1999 2000 2001 2002
2003 2004 2005 2006 2007
0 100% Fraction of observations obscured by clouds (JAS)
1998 1999 2000 2001 2002
2003 2004 2005 2006 2007
Supporting Biomass Burning Emissions Products
UW
-Mad
iso
n
CIM
SS
Supporting Science Quality Data Development Globally
Global Geostationary Fire Monitoring Network
Final Remarks
• Development of MODIS active fire product continues after 10years – new versions incorporating refinements to account for problems identified during the validation analyses
• NPP/VIIRS pre-flight fire data analyses providing valuable information
• Thermal infrared band (M15) saturation issues being assessed
• Impact of pixel aggregation (M15) scheme on fire detection capabilities being quantified – results being used to support modification of platform configuration
• Results indicate that active fire product could perform better than originally thought
• GOES-R/ABI pre-flight active fire data assessment setting the stage for routine post-launch product validation
• Use of fine resolution data building on MODIS experience
• Science quality data being generated in support of regional and global fire monitoring systems
• Validation of fire characterization data (size, temp, fire radiative power) – moving beyond the binary (yes-no) fire detection information
Pending Support and Future Research
• ROSES 2010 Remote Sensing Theory: “Derivation of biomass burning properties based on the synergistic use of MODIS and ASTER global data” (PI: W. Schroeder)
• ROSES 2010 The Science of Terra and Aqua: “MODIS Collection 6 Active Fire Maintenance and Validation” (PI: L. Giglio)
• ROSES 2010 NPP Science Team for Climate Data Records: “The active fire data record from NPP VIIRS” (PI: I. Csiszar)
• GOESR3 : “Development of a blended active fire detection and characterization product from geostationary and polar orbiter satellite data” (Csiszar, Schroeder, Justice)
• Developement and support of fine resolution active fire products derived from Landsat TM, LDCM (2012), ESA Sentinel 2 (2012-2013), and HyspIRI (2017) instruments (Giglio, Csiszar, Schroeder)
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