Active Fire Detection using Geostationary

SatellitesL. Giglio

SSAI/University of MarylandGOFC Global Geostationary Fire

Monitoring Applications Workshop23-25 March 2004

Overview

• Satellite-based fire detection algorithms

• Generic issues related to multi-satellite fire monitoring

• Polar vs. geostationary satellite suite comparison– Issues– Biases

Introduction • Multiple systems currently providing active

fire data and new systems are being planned • Different systems offer different capabilities

– Different detection capabilities (spatial/temporal) – Different fire monitoring groups using different

methods and different algorithms

• Accuracy of the different systems not well quantified– Systematic validation activities being initiated

• User community is starting to combine data from these multiple systems – complementary data sets

Satellite-Based Fire Detection Algorithms

• Virtually all exploit tremendous radiative energy emitted at ≈4 µm, usually in conjunction with a longer wavelength ≈10 µm band – Exception is DMSP-OLS

• ABBA/WF-ABBA (Prins et al.) are the premier detection algorithms for geostationary satellite instruments– GOES VAS, GOES Imager

• Detection principals are well-described elsewhere

Geostationary Satellite Suite

• GOES-8– 1995-2003

• GOES-10– 1998 onward

• GOES-12– 2003 onward

• MSG-1– 2003 onward

• MTSAT– Late 2004

GOES-EGOES-W MSG MTSAT

0-40-80-120-160 40 80 120 16080

60

40

20

0

-20

-40

-60

-80

Satellite View Angle

80° 65°

Satellite Spectral Bands

Resolution IGFOV (km)

SSR (km)

Full Disk Coverage

4 m Saturation Temperature (K)

Minimum Fire Size at Equator (at 750 K)

GOES-E 1 visible 4 IR

1.0 4.0 (8)

0.57 2.3

3 hours 335 K 0.15

GOES-W 1 visible 4 IR

1.0 4.0 (8)

0.57 2.3

3 hours ???? 0.15

MSG SEVIRI (2003)

3 visible 1 near-IR 8 IR

1.6 (4.8) 4.8 4.8

1.0 (3.0) 3.0 3.0

15 minutes > 335 0.22

MTSAT-1R JAMI (2004)

1 visible 4 IR

0.5 2.0

18 minutes ~320 0.03

322

International Global Geostationary Active Fire Monitoring:Geographical Coverage

Multi-Satellite Fire Monitoring:

Generic Issues• Systems have

– Different spatial resolutions– Different radiometric characteristics– Different temporal sampling

• How do we combine observations from multiple instruments in a consistent, meaningful manner?

Polar Fire Monitoring:Strengths and Weaknesses

• Strengths– Global coverage

• Frequency of global coverage depends on scan width – Higher spatial resolution

• Moderate resolution – AVHRR, MODIS (1 km) • High resolution – Landsat, ASTER (30 m)

• Weaknesses– Fewer opportunities for cloud-free observations

• MODIS Terra/Aqua give four observations per 24 hrs– Greater variance in envelope of detectable fires

(off nadir vs. nadir) – Temporal sampling issues related to diurnal

fire cycle

Theoretical Detection Envelope

• MODIS• Temperate

deciduous rainforest

• Night• 0° scan angle• Summer• No background

fires

Geostationary Fire Monitoring Suite:

Strengths and Weaknesses• Current Strengths

– Hemispheric fire monitoring– Near-real time data for fire management – Few/no temporal sampling issues related

to diurnal fire cycle– Broad Direct Broadcast capability

• Current Weaknesses– Gaps in global spatial coverage– Spatial biases in envelope of detectable

fires

Potential Gaps in Spatial Coverage

Spatial Biases in Envelopeof Detectable Fires (1 of 2)

• For instruments on board geostationary satellites, pixel size varies as a function of distance from the sub-satellite point– Introduces spatial gradient in the

envelope of detectable fires

Size of footprint Size of footprint relative to footprint relative to footprint size at sub-satellite size at sub-satellite point.point.

Spatial Biases in Envelope of Detectable Fires (2 of 2)

• Complicates comparison of fire activity in different regions, even using a single satellite

• Not an issue for near-real time fire monitoring

• Will need to be addressed in production of a global data set

ASTER Scene2.4 µm R1.6 µm G0.5 µm B

High resolution sensors can

provide much-needed fire size

distributions.

Size Distribution of Active Fires

Morisette et al., in press.Morisette et al., in press.

Southern Southern Africa, Africa, 20002000

GOES Diurnal Cycle Research Issue

• How to merge different sampling of diurnal fire cycle?– Temporal sampling exhibits a spatial

dependence since local time varies with longitude

– What impact does this have on the number of fires detected when combined with the spatial variation in detection envelope?

TRMM VIRS Diurnal Fire CycleBorneo 1999-

2001

GOES Local Sampling Time: Function of Longitude

Summary

• Geostationary satellite suite will provide a major contribution to global fire monitoring capability

• Ultimately envision merging both polar-orbiting and geostationary fire data sets to exploit strengths of each

• Interesting research opportunities in addressing potential issues