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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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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 Generating Science Quality Data from MODIS, VIIRS and GOES-R Instruments

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)


Progress in the Last Three Decades Generating Science Quality Data from MODIS, VIIRS and GOES-R Instruments

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 Generating Science Quality Data from MODIS, VIIRS and GOES-R Instruments

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 Generating Science Quality Data from MODIS, VIIRS and GOES-R Instruments

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


MODIS/Terra Active Fire Validation Generating Science Quality Data from MODIS, VIIRS and GOES-R InstrumentsC3-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 Validation Generating Science Quality Data from MODIS, VIIRS and GOES-R InstrumentsC3-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 Algorithm Generating Science Quality Data from MODIS, VIIRS and GOES-R InstrumentsStage 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 Generating Science Quality Data from MODIS, VIIRS and GOES-R Instruments

  • 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 Generating Science Quality Data from MODIS, VIIRS and GOES-R Instruments

Summary curve using all data points

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


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

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


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

Manitoba, Canada


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

Manitoba, Canada


Results – Commission Errors Tree Cover**

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)


Results – Commission Errors Tree Cover**

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


Early Assessment of NPP/VIIRS Active Fire Data Tree Cover**

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


Early Assessment of NPP/VIIRS Active Fire Data Tree Cover**

MIR – Initial Tests:

Deriving MODIS L1B TOA Radiances using

ASTER Surface Kinetic Temperature data + Radiation Transfer Model


Initial results
Initial Results Tree Cover**

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


Early Assessment of GOES-R/ABI Active Fire Data Tree Cover**

Selection of Coincident MODIS and ASTER L1B Data

Proxy ABI

(derived from MODIS L1B)

Fire Mask


Initial results1
Initial Results Tree Cover**

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


0 Tree Cover**

100%

Supporting Science Quality Data Development Regionally

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

Fraction of observations obscured by clouds (JAS)


1998 Tree Cover**

1999

2000

2001

2002

2003

2004

2005

2006

2007

Supporting Biomass Burning Emissions Products


UW-Madison CIMSS Tree Cover**

Supporting Science Quality Data Development Globally

Global Geostationary Fire Monitoring Network


Final remarks
Final Remarks Tree Cover**

  • 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 Tree Cover**

  • 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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