VALIDATION OF THE NPP VIIRS ACTIVE FIRE PRODUCT

VALIDATION OF THE NPP VIIRS ACTIVE FIRE PRODUCT

Ivan Csiszar1, Wilfrid Schroeder2, Louis Giglio2, Evan Ellicott2, Christopher O.

Justice2

1NOAA/NESDIS Center for Satellite Applications and

Research, Camp Springs, MD

2University of Maryland, College Park, MD

• VIIRS active fire product overview

• Active fire validation approach

• Reference datasets

• NPP VIIRS validation plan and recent results

• ConclusionI. Csiszar (NOAA), W. Schroeder, L. Giglio, E. Ellicott and C. Justice (UMD)

Outline

http://www.ipo.noaa.gov/I. Csiszar (NOAA), W. Schroeder, L. Giglio, E. Ellicott and C. Justice (UMD)

Prim

ary

fire

band

sVIIRS: Visible Infrared Imager Radiometer Suite

• VIIRS will provide radiometric measurements that offer useful information for the detection and characterization of active fires– principal bands: M13 (MIR) and M15 (TIR)

• Fire Mask Application Related Product (ARP) Baseline algorithm: moderate resolution M13 and M15– aggregated native resolution pixels– MODIS heritage algorithm

• Fire detection capability is driven by fire fraction – no direct continuity with any heritage sensor

• Goal is continuation of (AVHRR-) MODIS heritage– Real-time applications– Long-term monitoring (GCOS Essential Climate Variable)

I. Csiszar (NOAA), W. Schroeder, L. Giglio, E. Ellicott and C. Justice (UMD)

VIIRS fire product overview

VIIRS (aggregated)MODIS

7 Aug 2004 1405 UTC ~11.7o S 56.6o W

(Brazil)I. Csiszar (NOAA), W. Schroeder, L. Giglio, E. Ellicott and C. Justice (UMD)

(based on modeling using ASTER fire masks)

Example of expected VIIRS detection

VIIRS (aggregated)MODIS

7 Aug 2004 1405 UTC ~11.7o S 56.6o W

(Brazil)

(based on modeling using ASTER fire masks)


Example of expected VIIRS detection

750 m

90% probability of detection; boreal forest; nadir view

L. GiglioI. Csiszar (NOAA), W. Schroeder, L. Giglio, E. Ellicott and C. Justice (UMD)

1000 m

MODIS vs. VIIRS – simulation results

Primary Validation: “The process of assessing by independent means the quality of the data products derived from the system outputs” [Justice et al., 2000]– Use of independent data to directly estimate product uncertainty– Use of in-situ, airborne or spaceborne reference data sets

• Scaling-up methods can benefit from more than one coincident reference data set

Satellite Active Fire Product Validation Approach

Ground (point data) Airborne (<10m) Spaceborne (10<>100m)

Satellite Product (~1-4km)Secondary Validation

– Can be used to verify product consistency (e.g., spatial and temporal distribution of fires)• Example of application includes assessment of product performance immediately after launch when reference

data aren’t available• Ideally, the secondary data set must be validated especially when using similar algorithms/methods

– May complement primary validation

Layers Normally Used When Scaling-up Fire Data


Reference Data Requirements: spatial

MODIS/Terra FRP (MW)

A = Nominal pixel area

B = Effective pixel area

Adjusted Values (PSF):

Left pixel = 69.81 MW

Right pixel = 63.03 MW

PSF MODIS

PSF GOES

True Positive (MOD14) False Positive (MOD14)

Credit: Schroeder et al, 2010

Reference data must provide good spatial coverage to include effective pixel area plus background


Maximum separation between satellite fire and independent reference data must be limited to ~15min

Credit: Csiszar and Schroeder, 2008

Credit: Giglio, 2007

Credit: Schroeder et al, 2008

Reference Data Requirements: temporal


Reference Data Status : Ground

Collaboration with US Forest Service being established Leverage existing field campaigns

o Prescribed fire intensive monitoring site in the Florida Panhandle (Rx-CADRE/Eglin Air Force Base) being supported by the Joint Fire Science Program

Fire intensity (radiative power), temperature and size measurements Smoke characterization (particulate matter, trace gases)

o Precribed fires monitoring in National Parks Fire temperature and size

Collaboration with Kings College London (M. Wooster – through GOFC-GOLD) being established

o Prescribed fires in UK Fire intensity (radiative power), temperature and size measurements Smoke characterization (particulate matter, trace gases)

Potential for collaboration with researchers in the Amazon (links already established through LBA)

o Pending approval of regional projects

Other collaboration with GOFC-GOLD partners (e.g. Australian partners) being pursuedI. Csiszar (NOAA), W. Schroeder, L. Giglio, E. Ellicott and C. Justice (UMD)

Reference Data Status : Airborne

Collaboration with US Forest Service and NASA already establishedo Wildfires and prescribed burns in Alaska –NASA/HQ (PI: Charles Ichoku/NASA)

Airborne (UAV) mapping of surface fires Airborne (UAV) mapping of smoke plumes Complemented by field survey data

o Wildfires in Western US (Southern California) (NASA/Ames) Airborne (BeechCraft Kingair) mapping of surface fires

o Wildfires in Western US (Southern California) (USFS/Pacific Southwest Research Station)

Airborne (Beechcraft Kingair) mapping of surface fires (emergency response)

o Wildfires in Wedstern US (Southern California) –Joint Fire Science Program (PI: Phil Riggan/USFS)

Airborne (Beechcraft Kingair) mapping of surface fires (includes validation component of spaceborne fire retrievals)


Reference Data Status : SpaceborneLandsat-class data (incl. ASTER) have been used for MODIS, GOES and GOES-R ABI fire detection validation

Primary reference data sets include USGS LDCM (Dec 2012), ESA Sentinel 2 (2013) Seeking other complementary assets (national/international)

Early afternoon orbit of NPP/VIIRS prevent use of Landsat-class data acquired ~10am Lack of reference data may impair regional-global assessment of VIIRS active fire data Airborne data to fill in gap (limited sampling)

Collaboration with DLR being establishedo FireBIRD mission composed of TET-1 (May/2011) and BIROS (Dec/2012) sensors

building on previous DLR fire science small satellite (BIRD)o Targeted data acquisition @360m resolution (178km swath)o Fire-dedicated bands provide quality data for use in support of VIIRS and ABI active

fire product development and validationo BIROS orbit configuration still open

BIROS technical team demonstrated interest in supporting validation efforts Ongoing discussion to increase overlap/coincident acquisition with VIIRS

Collaboration with CONABIO (Mexico) already established (RedLaTIF)o MIROS (2015) mission being proposed based on BIROS technical specso Could augment targeted sampling capacity (increase data volume)


MODIS-BIRD Coincident Acquisition : Lake Baikal (Russia)

a) MODIS middle-infrared (fire) band

b) BIRD middlfe-infrared (fire) band

c) MODIS fire detection pixels

d) BIRD fire detection pixels

Credit: Zhukov et al., 2006

High quality reference data (in addition to primary mission objective of stand-alone monitoring)

Further improvement is expected with HyspIRI


Heritage: MODIS Global Fire Product Validation

Near-nadir pixels(using ~2,500 coincident ASTER scenes)

Off-nadir pixels(using ~3,700 near-coincident TM scenes)

12K MOD14 pixels sampled

270K MODIS pixels with 1+ TM fire pixel

17K MOD14 pixels sampled

120K MODIS pixels with 1+ ASTER fire pixel


Global Validation Data Sets (MOD14)Near-nadir pixels

(using ~2,500 coincident ASTER scenes)Off-nadir pixels

(using ~3,700 near-coincident TM scenes)

MODIS/ASTER 19 Jan 2006 0852UTC (near nadir) MODIS/TM 04 Aug 2007 1533UTC (52o scan angle)

ASTER 2001-2006SWIR detector problem > May 2007

Landsat5 TM 2001-2010Fire-related artifacts – saturation/bleeding


Global Validation Data Sets (MOD14)Near-nadir pixels

(using ~2,500 coincident ASTER scenes)Off-nadir pixels

(using ~3,700 near-coincident TM scenes)

MODIS/ASTER 19 Jan 2006 0852UTC (near nadir) MODIS/TM 04 Aug 2007 1533UTC (52o scan angle)

ASTER 2001-2006SWIR detector problem > May 2007

Landsat5 TM 2001-2010Fire-related artifacts – saturation/bleeding


JPSS program


MODIS Fire Pixels Detected per Sample VIIRS Detector Aggregation Scheme

Pixel size effect

Diurnal cycle

MODIS probability of detection (off nadir) MODIS commission error (off nadir)

View angle effects

Airborne Data for Validation of Fire Detection and CharacterizationNASA/Ames AMS image of California fire in 2007


• Cal/val rehearsal – July 18-22, 2011– Establish data access– Ingest and display of proxy VIIRS Active Fire EDR– Comparison with Aqua/MODIS detections– Reporting findings through Cal/val Findings Tool– Monitoring SDR cal/val results

• Proposed post-launch algorithm updates– Full fire mask– Fire Radiative Power– Compatibility with MODIS Collection 6

• Explore potential of alternative and additional VIIRS bands

• Coordinated efforts with NASA NPP Science Team– MODIS – VIIRS continuity


Current status and plans

Conclusion

NPP VIIRS fire product validation builds on the validation of MODIS/Terra binary active fire detection data– Analyses benefited from the availability of coincident data (MODIS-ASTER pairing) – Open data policy (USGS Landsat) making large amounts of reference data available

Use of Landsat-class data has and will continue to provide valuable active fire reference data

Currently local afternoon hours still poorly sampled– Missing a good assessment of peak fire activity– Problem likely to persist over the next years– Fire data retrieval will be pursued for upcoming missions (LDCM, Sentinel-2, BIROS, HysPIRI)

Tropical regions are more difficult to sample for off-nadir validation analysis– Will use secondary validation based on product inter-comparison (MODIS)

Development of more robust methods to simulate and test fire detection and characterization algorithms are being pursued

– Lessons learned from previous MODIS/Terra validation studies to be incorporated• Account for pixel spatial response• Use of more realistic surface conditions (temperature fields as well as morphology)• Additional tests for small-scale heterogeneities (e.g. Deforestation) etc.

Fine resolution (airborne) data must be used to constrain simulations and allow product verification on a case-study basis

– Sample size is not sufficient to resolve all the product dependencies although it can provide vital tie-points for product verification (reality check)

– Costs can be prohibitive although by combining efforts with other groups the science output could pay off


Documents

VALIDATION OF THE NPP VIIRS ACTIVE FIRE PRODUCT