validation and comparison of terra/modis active fire detections from inpe and umd/nasa algorithms...
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Validation and comparison of Terra/MODIS active fire detections from INPE and UMd/NASA algorithms
Validation and comparison of Terra/MODIS active fire detections from INPE and UMd/NASA algorithms
LBA EcologyLand Cover – 23
Jeffrey T. Morisette1, Ivan Csiszar2, Louis Giglio2 Wilfrid Schroeder3, Doug Morton2, João Pereira3, Chris Justice2,
1National Aeronautics and Space Administration, Greenbelt, Maryland, USA2Universityof Maryland, College Park, Maryland, USA 3Instituto Brasileiro do Meio Ambiente e dos Recursos Naturais Renováveis, Brazilia, Brazil
AcknowledgementsAcknowledgements
…special thanks to- Darrel Williams and Peter Griffith and the
LBA-Eco project office- Diane Wickland for including this work into
LBA-Ecology project- Heloisa Miranda and Alexandre Santos for
collaboration on the Thermocouple data- Alberto Setzer for collaboration on
implementing the INPE algorithm- Ruth Defries for continued collaboration on
MODIS-related research- Mike Abrams and Leon Maldonado for
assistance in acquiring ASTER imagery
BackgroundBackground
• IBAMA/PROARCO is charged with monitoring Brazilian fires
• IBAMA posts “Hot Spot” detections from several satellites and algorithms.
• MODIS provides “state of the art” fire detection, but needs to be validated
• Two algorithms on the same sensor’s data and a high resolution sensor on the same satellite create and unique opportunity for this validation study
GoalsGoals
• Assess the accuracy of the 1 km fire product from the Moderate Resolution Imaging Spectroradiometer (MODIS) over the LBA-Study area
• Compare the INPE and UMd algorithm as they relate to the ASTER fire detection
MODIS Instrument (1/2)MODIS Instrument (1/2)
• “Moderate Resolution Imaging Spectroradiometer”
• On board AM-1 (“Terra”) and PM-1 (“Aqua”) polar orbiters– Terra 10:30 & 22:30 local overpass– Aqua 01:30 & 13:30 local overpass
MODIS Instrument (2/2)MODIS Instrument (2/2)
• 36 spectral bands covering 0.4 to 14.4 micrometers– Two 250 m bands– Five 500 m bands– Twenty nine 1 km bands
• Enable comprehensive daily evaluation of land, ocean, and atmosphere
Daily Global BrowseDaily Global Browse
ASTER imageryASTER imagery
ASTER CharacteristicsASTER Characteristics
• “Advanced Spaceborne Thermal Emission and Reflection Radiometer”
• 14 channels– 4 visible and near-IR @ 15 m resolution, 8 bits– 6 SWIR @ 30 m resolution, 8 bits– 5 LWIR @ 90 m resolution,12 bits
• 60 km swath width
Roraima: prescribed burn, 19 JanRoraima: prescribed burn, 19 Jan
ASTER fire mask band 3 and 8 240, 30m pixelsred = band 3, ~22 ha green & blue = band 8
Fire pixels shown in ASTER band3/band8 space
ASTER fire detectionASTER fire detection
Mask water pixels. If 8 < 0.04, a pixel is flagged
as water and excluded from further processing. Identify obvious fire pixels. Pixels for which r > 2 and > 0.2 are considered to be obvious fire pixels and are flagged as such. Identify candidate fire pixels. Pixels for which r > 1 and > 0.1 are considered to be candidate fire pixels. - contextual tests
3
8
r
38
3rz 38 z
5.0 Brr
5.088 B
Omission and Commission errorOmission and Commission error
INPE
(no UMDfires detection)
ASTER/MODIS scatter plotASTER/MODIS scatter plot
ASTER fire counts
Mo
ran
's I
0 50 100 150 200 250 300
0.0
0.2
0.4
0.6
0.8
1.0
LBA Scene 5 INPE
ASTER fire counts
Mo
ran
's I
0 50 100 150 200 250 300
0.0
0.2
0.4
0.6
0.8
1.0
LBA Scene 5 UMd
UMd Omission errorUMd Omission error
INPE
UMD
ASTER/MODIS scatter plotASTER/MODIS scatter plot
ASTER fire counts
Mo
ran
's I
0 50 100 150 200 250 300
0.0
0.2
0.4
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1.0
LBA Scene 12 INPE
ASTER fire counts
Mo
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's I
0 50 100 150 200 250 300
0.0
0.2
0.4
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1.0
LBA Scene 12 UMd
ASTER fire counts
Pro
ba
blity
MO
DIS
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tectio
n
0 50 100 150
0.0
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1.0
Estimated Probabilities for UMd Algorithm
0 50 100 150
0.0
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1.0
ASTER fire counts
Pro
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blity
MO
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tectio
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0 50 100 150
0.0
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Estimated Probabilities for INPE Algorithm
0 50 100 150
0.0
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1.0
UMd
INPE
ASTER fire counts
Pro
ba
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y M
OD
IS d
ete
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0 50 100 150
0.0
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Estimated Probabilities for both Algorithms
0 50 100 150
0.0
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1.0
Logistic RegressionLogistic Regression
Results from 22 ASTER scenesResults from 22 ASTER scenes
0 100 200 300 400 500 600
counts
-0.1
0.1
0.3
0.5
0.7
0.9
Mo
ran Larger circles are
MODIS fires
Red = high confidenceBlue = lower confidence
ASTER fire counts
“Adj
acen
cy”
inde
x
Matriz de ErrorMatriz de Error
ASTER fire counts
Pro
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blity
MO
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tectio
n
0 50 100 150
0.0
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Estimated Probabilities for INPE Algorithm
0 50 100 150
0.0
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MODIS - INPENo Fire Fire
ASTER No Fire A BFire Fire C D
A
B
C
D
Error matrixError matrixASTER threshold = 1
MODIS - INPENo Fire Fire sum P(error)
ASTER No Fire 81000 115 81115 0.0014Fire Fire 359 55 414 0.8671
sum 81359 170 81529Overall Accuracy:
P(error) 0.0044 0.6765 0.994
ASTER threshold = 1MODIS - UMdNo Fire Fire sum P(error)
ASTER No Fire 81106 9 81115 0.0001Fire Fire 332 82 414 0.8019
sum 81438 91 81529Overall Accuracy:
P(error) 0.0041 0.0989 0.9958
for any ASTER fire detection
Error matrixError matrixASTER threshold = 1
MODIS - INPENo Fire Fire sum P(error)
ASTER No Fire 81000 115 81115 0.0014Fire Fire 359 55 414 0.8671
sum 81359 170 81529Overall Accuracy:
P(error) 0.0044 0.6765 0.994
For variable fire size
ASTER threshold = 100MODIS - INPENo Fire Fire sum P(error)
ASTER No Fire 81358 149 81507 0.002Fire Fire 1 21 22 0.045
sum 81359 170 81529Overall Accuracy:
P(error) 0.0000 0.8765 0.998
ASTER threshold = 50MODIS - INPENo Fire Fire sum P(error)
ASTER No Fire 81335 130 81465 0.002Fire Fire 24 40 64 0.375
sum 81359 170 81529Overall Accuracy:
P(error) 0.0003 0.7647 0.998
Fires > .0009 km2
Fires > .045 km2
Fires > .090 km2
Error Matrix figuresError Matrix figures…as a function of fire size
P(MODIS "no fire" | ASTER "fire") Chance of Missing a fire or "Ommission Error"
0
0.1
0.2
0.3
0.4
0.5
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0.9
1
1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89 93 97 101
105
109
113
117
121
ASTER threshold or "Fire Size"
Pro
bab
ility
INPE P(MNF|AF)
UMD P(MNF|AF)
ConclusionsConclusions
• ASTER fire detection algorithm is now established
• Comparison of ASTER with MODIS fire products and possible and enlightening
• Both UMd and INPE algorithm do a good job at detecting large fires– INPE has less error for large fires– UMd has less error for small fire & less likely to have
false positives
Questions?Questions?
MODIS: UMd AlgorithmMODIS: UMd Algorithm
• Bands used for fire algorithm“T4”=
– Channel 22: 3.96 µm, ≈ 330 K saturation(lower noise, lower quantization error, but lower saturation)
- or -
– Channel 21: 3.96 µm, ≈ 500 K saturation(used when channel 22 saturates)
“T11” =
– Channel 31: 11.0 µm, ≈ 400 K saturation
MODIS: UMd AlgorithmMODIS: UMd Algorithm
• T4 > 360 Kor• {T4 > mean(T4) + 3xStandardDeviation
or T4 > 330 K}and
• {T4–T11>median(T4-T11) + 3xStandardDeviation(T4-T11) or T4-T11 > 25}
• Then rejected if red and near-infrared channels have reflectance > 30% (to avoid false positives)
From: “The MODIS fire products”, C.O. Justice, L. Giglio et al., Remote Sensing of Environment 83(2002) 244-262.
MODIS: INPE AlgorithmMODIS: INPE Algorithm
channel 20 > 3000andchannel 09 < 3300
Error Matrix figuresError Matrix figures…as a function of fire size
P(MODIS "fire" | ASTER "no fire") = False positive or "commission error"
0
0.0002
0.0004
0.0006
0.0008
0.001
0.0012
0.0014
0.0016
0.0018
0.002
1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 88
ASTER threshold(ASTER fire counts below this threshold are considered non-fires)
Pro
bab
ility
INPE P(MF|ANF)
UMD P(MF|ANF)