Satellite Active Fire Product Development and Validation: Generating Science Quality Data from MODIS...
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Satellite Active Fire Product Development and Validation: Generating Science Quality Data from MODIS, VIIRS and GOES-R Instruments. Wilfrid Schroeder 1 , Ivan Csiszar 2 , Louis Giglio 3 , Evan Ellicott 3 , Christopher Justice 3 , Christopher Schmidt 4 1 ESSIC/CICS, UMD 2 STAR NOAA/NESDIS

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Wilfrid schroeder 1 ivan csiszar 2 louis giglio 3

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, UMD

2 STAR NOAA/NESDIS

3 Dept of Geography, UMD

4 CIMMS, UW-Madison


Team background

Team Background

Ongoing 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)


Wilfrid schroeder 1 ivan csiszar 2 louis giglio 3

Progress in the Last Three Decades

Major Data Sets**

1980

Adv Very High Res Radiometer (AVHRR)

1kmx12h within antenna range

A few dozen images

Simple Threshold (single or multi-band)

GOES VAS

13.8kmx30min Western Hemisphere

1990

Contextual methods (x,y) (dynamically adjusted)

GOES East Imager

4kmx30min Western Hemisphere

Tropical Rainfall Monitoring Mission (TRMM)

2.4kmx12h ±38º

Contextual methods (x,y,t) (dynamically adjusted)

2000

Mod Res Imaging Spectroradiometer

(MODIS/Terra)

(MODIS/Aqua)

1kmx12-24h global

Reprocessed in 2009

NOAA-12

EOS/Terra

EOS/Aqua

2010

400K+ images from GOES only

** Excluding nighttime sensors such as ATSR, DMSP


Essentials in active fire monitoring

Essentials in Active Fire Monitoring

Fires are highly dynamic events

Fires may/not leave detectable scars behind


Active fire reference data derived from aster and etm imagery

ASTER 10:30am

ASTER

ETM+

ETM+ 10am

Active Fire Reference Data Derived from ASTER and ETM+ Imagery

ASTER bands 3 and 8 and ETM+ bands 4 and 7


Wilfrid schroeder 1 ivan csiszar 2 louis giglio 3

MODIS/Terra Active Fire ValidationC3-C4 Algorithm Version

  • 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: 131 ASTER scenes

  • Region: Northern Eurasia

  • Development of active fire validation protocol

  • Csiszar et al. 2006

  • Sample Size: 100 ASTER scenes

  • Region: Global

  • Development of robust active fire detection algorithm for ASTER

  • Giglio et al. 2008


Modis terra active fire validation c3 c4 algorithm version

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

  • 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 algorithm stage 3 fire validation

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


Wilfrid schroeder 1 ivan csiszar 2 louis giglio 3

Temporal Consistency of MOD14 Detection Performance

  • 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)


Wilfrid schroeder 1 ivan csiszar 2 louis giglio 3

Overall Probability of Detection

Summary curve using all data points

(125K MODIS pixels with >0 ASTER fire pixels including16K MOD14 fire pixels)


Wilfrid schroeder 1 ivan csiszar 2 louis giglio 3

Daytime Probability of Detection as a Function of Percentage Tree Cover**

** average value calculated using a 20x20km window centered on the target pixel


Wilfrid schroeder 1 ivan csiszar 2 louis giglio 3

ASTER (RGB 8-3-1) 21 June 2003 17:38:35UTC

Manitoba, Canada


Wilfrid schroeder 1 ivan csiszar 2 louis giglio 3

ASTER (30m Fire Mask) 21 June 2003 17:38:35UTC

Manitoba, Canada


Wilfrid schroeder 1 ivan csiszar 2 louis giglio 3

Results – Commission Errors

Commission errors

Recently 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.

Schroeder et al. (in preparation)


Wilfrid schroeder 1 ivan csiszar 2 louis giglio 3

Results – Commission Errors

21 Aug 2003 1407UTC

20 Jul 2003 1407UTC

Typical false alarm in MOD14 data

Commission errors can occur multiple times at the same location

MODIS/Terra was found to detect twice as many false positives as MODIS/Aqua


Wilfrid schroeder 1 ivan csiszar 2 louis giglio 3

Early Assessment of NPP/VIIRS Active Fire Data

MIR – Initial Tests:

Deriving MODIS L1B TOA Radiances using

ASTER 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 Data

07 Aug 2004 1405 UTC

11.7o S 56.6o W


Wilfrid schroeder 1 ivan csiszar 2 louis giglio 3

Early Assessment of NPP/VIIRS Active Fire Data

MIR – Initial Tests:

Deriving MODIS L1B TOA Radiances using

ASTER Surface Kinetic Temperature data + Radiation Transfer Model


Initial results

Initial Results

MODIS/Terra (1kmx1km)

VIIRS (750m x 750m)

VIIRS (250m x 750m)

Results being used to support VIIRS hardware and software configuration to allow optimum fire detection capabilities

Defining TIR Saturation Levels


Wilfrid schroeder 1 ivan csiszar 2 louis giglio 3

Early Assessment of GOES-R/ABI Active Fire Data

Selection of Coincident MODIS and ASTER L1B Data

Proxy ABI

(derived from MODIS L1B)

Fire Mask


Initial results1

Initial Results

ABI Active Fire Product Validated using Reference ASTER Data

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

Probability of Detection (omission) Defined as a Function of ASTER Fire Statistics

GOES

ABI

MODIS


Wilfrid schroeder 1 ivan csiszar 2 louis giglio 3

0

100%

Supporting Science Quality Data Development Regionally

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

Fraction of observations obscured by clouds (JAS)


Wilfrid schroeder 1 ivan csiszar 2 louis giglio 3

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

Supporting Biomass Burning Emissions Products


Wilfrid schroeder 1 ivan csiszar 2 louis giglio 3

UW-Madison CIMSS

Supporting Science Quality Data Development Globally

Global Geostationary Fire Monitoring Network


Final remarks

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

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: “MODISCollection 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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