Applications of Geostationary Data for Operational Forest Fire Monitoring in Brazil

Global Geostationary Fire Monitoring Applications Workshop

EUMETSAT Darmstadt, Germany

March 23-25

Wilfrid Schroeder1

João Antônio Raposo Pereira1

Alberto Setzer2 1PROARCO/IBAMA

2CPTEC/INPE wilfrid.schroeder@ibama.gov.br

Current Status of Fire Monitoring in Brazil

• INPE is currently running fire detection for AVHRR (NOAA-12; NOAA-16), MODIS (Terra; Aqua), GOES-12

• IBAMA runs GOES-12 and DMSP fire products• On going agreement towards “the more the better”

as many real cases suggest that• Integration of different data sets using GIS tools

Geostationary Data Use in Brazil

• IBAMA is running CIRA’s RAMSDIS system since July 2000– fire monitoring nearly 100% based on visual analysis of

imagery (reflectivity product: ch2,ch4)– fire data from automatic processing still of limited use

• CPTEC/INPE is running own algorithm since August 2002– fire monitoring mostly based on data from automatic

processing– limited visual analyses of imagery (except during

algorithm tune up)

IBAMA’s July 2000 – Implementation of CIRA’s RAMSDIS system

based on GOES-8 data & McIDAS OS/2 Warp

Cloud Masking

Potential Fires

Tb4 >= 2ºC

Night: Tb2 > 17ºC

1 2 3

4 X 5

6 7 8

Day: Tb2 > 41ºC

Statistics

Sunglint Model

Persistence

GOES Fire Detection Algorithm

(SoZA-SaZA >15o) +/- 5o lat

Day: (Bi -Bx)/Bx >=0.25)

Night: (Bi -Bx)/Bx >=0.10)

6 out of 8

For visualization only

April 2003: Transition to Win2000 – GOES-12

Great results from visual image interpretation (reflectivity product)

Major fire events are 100% detectable System provides fast response in many different cases

Northern Sectors Southern Sector

Pros

Output Sample File

Lat Lon SZA CH4 CH2 Day/Night CH4_thre CH2_thre Perc_dif Num_pix

13.97 -90.41 43.73 43 27 D 86 32 0.25 6

13.95 -89.15 42.91 49 31 D 86 32 0.25 6

13.25 -87.41 41.28 50 31 D 86 32 0.25 6

12.87 -87.13 40.8 49 28 D 86 32 0.25 6

12.57 -87.11 40.56 50 29 D 86 32 0.25 6

12.53 -70.01 32.49 51 30 D 86 32 0.25 6

12.22 -71.8 32.73 47 23 D 86 32 0.25 6

12.19 -86.39 39.82 50 31 D 86 32 0.25 6

Automatic Fire Detection – Case Study

Roraima12:53h UTC

~400m of fire

18:20h UTC

smoldering28 Jan 2003

Automatic Fire Detection – Regional Scale

28 Jan 2003

Automatic Fire Detection – Continental Scale

28 Jan 2003

CPTEC/INPE Approach – Fire (by A. Setzer)

Albedo (Ch1)

0.65 m

Tb (Ch2)

3.9m

Tb (Ch4)

10.7 mTb2-Tb4

0 – 3% > 308.15K (35oC) > 263.15K (-10oC) > 16K (16oC)

3 – 12% > 318.15K (45oC)> 263.15K (-10oC) < 308.15K (35oC)

> 22K (22oC)

12 – 24% > 323.15K (50oC)> 263.15K (-10oC) < 303.15K (30oC)

> 25K (25oC)

CPTEC/INPE Approach – Non-fire (by A. Setzer)

Surface Characteristics:(i) Reflectivity (albedo) > 24%(ii) Water: 21x21 matrix having at least one pixel over 80%(iii) Water: 21x21 matrix having at least one pixel over 60% and Tb4 > 15K(iv) Reflective soils: 9x9 matrix having 25% of pixels with Tb2 > 45oC(v) Clouds: 3x3 matrix having 75% of pixels with albedo > 24%

Image Characteristics:(i) Night detection having over 300 hot spots(ii) 50 hot spot night time increase from latest synoptic hour(iii) Over 2000 hot spots during day time images (10:45h-23:45UTC)

Bad lines:(i) Any line having 10+ hot spots over ocean waters(ii) 50 neighbour pixels processed as fire(iii) 300 hot spots along the same line(iv) 97% of Vis Channel pixels having DN=0

CPTEC/INPE Web Product

CPTEC/INPE Web Product

Output Sample File

Nr Lat Lon LatDMS LongDMS Date Time Sat Mun State Country Veg Suscept Prec DWR Risk Persist

1 0.95 -62.7167 N 0 57 0.00 O 62 43 0.00 20040207 84500 GOES-12 Barcelos AM Brasil OmbrofilaDensa BAIXA 24 0 0.1 0

2 1.1 -62.7333 N 1 6 0.00 O 62 43 60.00 20040207 84500 GOES-12 Barcelos AM Brasil OmbrofilaDensa BAIXA 24 0 0.1 0

3 -12.9167 -38.6167 S 12 55 0.00 O 38 37 0.00 20040207 114500 GOES-12 Itaparica BA Brasil OmbrofilaDensa BAIXA 23.6 0 0 0

4 -9.383 -38.2333 S 9 22 60.00 O 38 13 60.00 20040207 114500 GOES-12 Paulo Afonso BA Brasil NaoFloresta MEDIA 0.9 10 0.8 0

5 -8.55 -40.2 S 8 33 0.00 O 40 12 0.00 20040207 114500 GOES-12 Lagoa Grande PE Brasil NaoFloresta MEDIA 0 10 0.9 0

6 -7.983 -40.3167 S 7 58 60.0 O 40 19 0.00 20040207 114500 GOES-12 Ouricuri PE Brasil NaoFloresta MEDIA 0 10 0.9 0

7 -0.016 -62.6167 S 0 1 0.00 O 62 37 0.00 20040207 144500 GOES-12 Barcelos AM Brasil NaoFloresta BAIXA 5 9 0.4 0

8 -0.016 -62.6333 S 0 1 0.00 O 62 37 60.00 20040207 144500 GOES-12 Barcelos AM Brasil Contato BAIXA 27.5 0 0 0

9 0 -62.6333 S 0 0 0.00 O 62 37 60.00 20040207 144500 GOES-12 Barcelos AM Brasil Contato BAIXA 27.5 0 0 0

10 0.05 -62.6167 N 0 3 0.00 O 62 37 0.00 20040207 144500 GOES-12 Barcelos AM Brasil NaoFloresta BAIXA 5 9 0.4 0

Automatic Fire Detection – Case Study

Barcelos

Amazonas

2004

Noaa_12

Noaa_16

MODIS

GOES-12

Total area burned:18000ha

Fire in Barcelos Jan-Feb 2004

0

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of H

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Noaa_16

Noaa_12

MODIS

GOES

Automatic Fire Detection – Case Study

Automatic Fire Detection – Continental Scale

Conclusions

• Image usefulness for visual identification of fires is outstanding and proves to be essential to any operational fire monitoring system

• Overall performance of automatic detection is still questionable• Balancing “conservative” x “liberal” algorithms/thresholds

would be desirable – is it attainable?• Field validation should be reinforced and aimed by different

groups – let’s optimize efforts and resources• If we are to consider realistic numbers of active fires being

detected, we must continue (and improve) use of geostationary imagery integrating their fire products to other systems (polar orbiting)