EUMETSAT 2004, March 24 th Earth Observation Dep.t Automatic Fire Detection and Characterization by...

EUMETSATEUMETSAT20020044, March , March 2244thth Earth Observation Dep.tEarth Observation Dep.t

AutomaticFire Detection andCharacterizationby MSG/SEVIRI

A. Bartoloni, E. Cisbani, E. Zappitelli(Telespazio/Rome)

B. Greco(ESA/ESRIN)

Monitoring the summer of 2003 forest fires of Spain and Portugal, part I

EUMETSATEUMETSAT20020044, March , March 2244thth Earth Observation Dep.tEarth Observation Dep.tTOC / CreditTOC / Credit

Approach Assumptions Fire Equation

Performance on real data Validated Data Comparison Fire Evolution Characterization False Alarm / Efficiency

Implementation SEVIRI data Processor Flow Chart

Conclusions User Needs Prospect

Credit:

- ESA/CDMC: Prototype

processor implementation and

analysis of real SEVIRI data

(this presentation)

- ESA/FiresMed: Simulated data

analysis

- FIRES/RIT-NASA: Original

ideas (for GOES)

EUMETSATEUMETSAT20020044, March , March 2244thth Earth Observation Dep.tEarth Observation Dep.tRationaleRationale

Use of existing Satellite Sensors for fire detection and characterization

Need of high spatial sensitivity

Smart processing Use Polar sensors data

Geo-stationarysensors

Polarsensors

Need of frequent revisit Geo-stationary

Optimal Sensors(non existing)

EUMETSATEUMETSAT20020044, March , March 2244thth Earth Observation Dep.tEarth Observation Dep.tApproachApproach

Quasi-stationary parameters from polar sensors

Fast changing quantities from geostationary sensor/SEVIRI

Improved spatial sensitivity by multi band, sub-pixel exploitation

Contrast analysis for fire temporal changes enhancement

Procedure based on a simple Radiative Transfer Model

Looking for thermal ground changes

SWIR/MIR/TIR atmospheric windows

Ground emissivity

Background/Fire Temperatures/Size, Atmospheric water vapor content

Include atmospheric transmissivity, ground emissivity, solar term


(t+t)

- =

SolAtm)()()()( bckfire TBTBftRttRR

Effective PixelFraction Change

Fire temperature

Background temperature

Atmospheric and Solar changes neglected (except SWIR)

One equation for each atmospheric window in IR/TIR

(t)

Fire Background

Fire EquationFire Equation

The technique is sensible to fire variations


SEVIRI

MODIS

Overall Excellent Image Quality

Geolocation pretty good Frame coregistration second order

effect in detection

Saturation effects on very large fires (see images)

SWIR

ch 3

MIR ch 4

TIR chs 7,9,10

Input Data: SEVIRI RadiancesInput Data: SEVIRI Radiances

EUMETSATEUMETSAT20020044, March , March 2244thth Earth Observation Dep.tEarth Observation Dep.tImplementation: ProcessorImplementation: Processor

SEVIRIRadiances

Sea/Land and plain Cloud Masking

Estimate Bck. Temperature

Estimate TPW

Compute Acquisition Geometry

Evaluate Solar Terms

Minimize Fire System of Equations

GroundEmissivity

Detected Fire Characteristics

Cuts on: - Minimization Residue - Fire Temperature - Minimum Fire Size - Minimum Power

Processor: Pixel Based, fully automated

and parallelizzable

EUMETSATEUMETSAT20020044, March , March 2244thth Earth Observation Dep.tEarth Observation Dep.tData for Processor CharacterizationData for Processor Characterization

Heavy Cloudy

SEVIRI/MSG Data:

sparse temporal coverage

due to acquisition station

malfunctioning

Commissioning data!

Raw Validated Data:

MODIS: ch 21/22 + ch 31

BIRD: MIR 3.4-4.2 + TIR

8.5-9.3 m

Test Case:

Portugal Fires beginning

of August 2003

No Ground Truth

analyzed !

BIRD Calibrated/Geolocated Images from BIRD Team @ DLR


Visually identify hot spots on BIRD MIR image and corresponding ones on MODIS

Define background and fire ROIs

Validation FiresValidation Fires

04/Aug/03 12:03 BIRD/MIR

Red: BIRDYellow: MODIS (4 Aug)

FireBackground

Apply the traditional sub-pixel Dozier method (MIR/TIR) to the fire ROI

Assume as firing pixels those with temperature above 350-400 K (starting plateau)

Retrieve hot spot characteristics: fire / background temperature burning area fire power location

EUMETSATEUMETSAT20020044, March , March 2244thth Earth Observation Dep.tEarth Observation Dep.tValidated Data ConsistencyValidated Data Consistency

BIRD – MODIS comparison

EUMETSATEUMETSAT20020044, March , March 2244thth Earth Observation Dep.tEarth Observation Dep.tSEVIRI Detection on 04/Aug/03 SEVIRI Detection on 04/Aug/03

10:45

12:00

11:00

12:1511:30

12:45

09:15

11:15

12:30

09:00

11:45

10:15

14:30

T B

A

Available SEVIRI frames(15 minutes apart)on 4/Aug/2003

T: Terra MODISB: BirdA: Aqua MODIS

EUMETSATEUMETSAT20020044, March , March 2244thth Earth Observation Dep.tEarth Observation Dep.tSEVIRI DetectionSEVIRI Detection

SEVIRI 04/Aug/03 12:00 upsampled

Red: BIRDYellow: MODIS (4 Aug)Green: SEVIRI (4 Aug)

BIRD 12:0304/Aug/03

downsampled

EUMETSATEUMETSAT20020044, March , March 2244thth Earth Observation Dep.tEarth Observation Dep.tPerformancePerformance

SEVIRI 04/08/03 12:00 upsampled

BIRD MIR detail Box = SEVIRI fire detection (04/Aug/03)Box Area ~ Fire Power


NOTE:-several false alarms associated to cloud boundaries (next)

-a bunch of them due to SEVIRI saturation

158 Frame Processed = 898700 pixels

Processing Time: 20 min whole Iberian Peninsula (on Athlon 1.7 GHz)

40-60 False Hits

False Alarms: 6·10-5/pixel

EUMETSATEUMETSAT20020044, March , March 2244thth Earth Observation Dep.tEarth Observation Dep.tLong lasting hot spotsLong lasting hot spots

Detected on Aug 1st by SEVIRI

Size and Power variation (SEVIRI)

generally smaller than

corresponding MODIS/BIRD

quantities

EUMETSATEUMETSAT20020044, March , March 2244thth Earth Observation Dep.tEarth Observation Dep.tLarge, 1 day fireLarge, 1 day fire

EUMETSATEUMETSAT20020044, March , March 2244thth Earth Observation Dep.tEarth Observation Dep.tA different fireA different fire

EUMETSATEUMETSAT20020044, March , March 2244thth Earth Observation Dep.tEarth Observation Dep.tLong lasting, moving fireLong lasting, moving fire

Fire moves from SW to NE

MODIS/SEVIRI/BIRD geolocations

do not fully agree

EUMETSATEUMETSAT20020044, March , March 2244thth Earth Observation Dep.tEarth Observation Dep.tFire Parameters DistributionsFire Parameters Distributions

04/Aug/2003 Fires

EUMETSATEUMETSAT20020044, March , March 2244thth Earth Observation Dep.tEarth Observation Dep.tMovieMovie

NOTE:

several frames are mission

The detection is sensible to fire condition changes

Very High radiances in TIR have been not processed

MIR VIS08 SWIR

TIR 8.7 TIR 11

EUMETSATEUMETSAT20020044, March , March 2244thth Earth Observation Dep.tEarth Observation Dep.tNoise from cloudsNoise from clouds

MIR VIS08 SWIR

TIR 8.7 TIR 11


Search MODIS images for fire outbreaks between two consecutive acquisitions:

TERRA 10:45 AQUA 14:00

24 fires identified (1-5 Aug)

17 Detected by SEVIRI (histogram)

SEVIRI/MODIS Efficiency ~ 70% some SEVIRI frames are missing

SEVIRI-MODIS Detection EfficiencySEVIRI-MODIS Detection Efficiency


Up to 2 hour before MODIS

Processor False Alarms: better than 10-4/pixel

HRV channel can be usedto improve the location

~70% MODIS efficiency

User Needs and Detection Perf.User Needs and Detection Perf.


• First results of the prototyped, fully automated, geo-stationary fire detection processor on SEVIRI commissioning data show:– comparable efficiency with MODIS (visual analysis)– reasonable false alarms rate– fire characterization capabilities

• First results of the prototyped, fully automated, geo-stationary fire detection processor on SEVIRI commissioning data show:– comparable efficiency with MODIS (visual analysis)– reasonable false alarms rate– fire characterization capabilities

Next generation of SEVIRI sensors may fulfill the tight user requirements

Next generation of SEVIRI sensors may fulfill the tight user requirements

ConclusionsConclusions

• Margin of improvements (at least):– effective cloud masking (reduce false alarms)– better integration of the SWIR channel (replace MIR on

saturated pixel)– moving toward a contextual analysis– validation on ground truth data (fire reports)

• Margin of improvements (at least):– effective cloud masking (reduce false alarms)– better integration of the SWIR channel (replace MIR on

saturated pixel)– moving toward a contextual analysis– validation on ground truth data (fire reports)

EUMETSAT 2004, March 24 th Earth Observation Dep.t Automatic Fire Detection and Characterization by...

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