Applications of Geostationary Data for Operational Forest Fire Monitoring in Brazil

Global Geostationary Fire Monitoring Applications Workshop EUMETSAT Darmstadt, Germany March 23-25

Wilfrid Schroeder 1 João Antônio Raposo Pereira 1 Alberto Setzer 2 1 PROARCO/IBAMA 2 CPTEC/INPE [email protected]

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 GOES Fire Detection Algorithm Cloud Masking

Tb 4 >= 2ºC

Potential Fires

Night: Tb 2 > 17ºC Day: Tb 2 > 41ºC

Statistics

1 4 6 2 X 7 3 5 8

Day: (B i -B x )/B x >=0.25) Night: (B i -B x )/B x >=0.10) 6 out of 8 Sunglint Model (S o ZA-S a ZA >15 o ) +/- 5 o lat Persistence For visualization only

April 2003: Transition to Win2000 – GOES-12

Pros  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

Output Sample File Lat 13.97

13.95

13.25

12.87

12.57

12.53

12.22

12.19

Lon -90.41

-89.15

-87.41

-87.13

-87.11

-70.01

-71.8

-86.39

SZA 43.73

42.91

41.28

40.8

40.56

32.49

32.73

39.82

50 51 47 50 CH4 43 49 50 49 29 30 23 31 CH2 27 31 31 28 D D D D Day/Night CH4_thre CH2_thre Perc_dif Num_pix D D D D 86 86 86 86 32 32 32 32 0.25

0.25

0.25

0.25

6 6 6 6 86 86 86 86 32 32 32 32 0.25

0.25

0.25

0.25

6 6 6 6

Automatic Fire Detection – Case Study Roraima 28 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 0 – 3% Tb (Ch2) 3.9

 m Tb (Ch4) 10.7  m Tb2-Tb4 > 308.15K (35 o C) > 263.15K (-10 o C) > 16K (16 o C) 3 – 12% 12 – 24% > 318.15K (45 o C) > 263.15K (-10 o C) < 308.15K (35 o C) > 22K (22 o C) > 323.15K (50 o C) > 263.15K (-10 o C) < 303.15K (30 o C) > 25K (25 o C)

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 > 45 o C (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 1 Lat 0.95

Lon -62.7167

LatDMS N 0 57 0.00

LongDMS O 62 43 0.00

Date Time 20040207 84500 Sat Mun GOES-12 Barcelos State Country AM Brasil Veg Suscept Prec DWR Risk Persist 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 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 MEDIA 0.9 10 0 10 0.8

0.9

0 0 6 -7.983

-40.3167

S 7 58 60.0 O 40 19 0.00

20040207 114500 GOES-12 Ouricuri 7 -0.016

-62.6167

S 0 1 0.00

O 62 37 0.00

20040207 144500 GOES-12 Barcelos 8 -0.016

-62.6333

S 0 1 0.00

O 62 37 60.00 20040207 144500 GOES-12 Barcelos 9 0 -62.6333

S 0 0 0.00 O 62 37 60.00 20040207 144500 GOES-12 Barcelos 10 0.05

-62.6167

N 0 3 0.00 O 62 37 0.00 20040207 144500 GOES-12 Barcelos PE Brasil AM Brasil AM Brasil AM Brasil AM Brasil NaoFloresta NaoFloresta Contato Contato NaoFloresta MEDIA BAIXA BAIXA BAIXA 27.5 0 27.5 0 BAIXA 5 5 0 10 9 9 0 0.9

0.4

0 0.4

0 0 0 0 0

Automatic Fire Detection – Case Study

Noaa_12 Noaa_16 MODIS GOES-12

Barcelos Amazonas 2004 Total area burned:18000ha

Automatic Fire Detection – Case Study Fire in Barcelos Jan-Feb 2004 40 35 30 25 20 15 10 5 0 2 0 0 4 0 1 2 6 2 0 0 4 0 1 2 7 2 0 0 4 0 1 2 8 2 0 0 4 0 1 2 9 2 0 0 4 0 1 3 0 2 0 0 4 0 1 3 1 2 0 0 4 0 2 0 1 2 0 0 4 0 2 0 2 2 0 0 4 0 2 0 3 2 0 0 4 0 2 0 4 2 0 0 4 0 2 0 5 2 0 0 4 0 2 0 6 2 0 0 4 0 2 0 7 2 0 0 4 0 2 0 8 2 0 0 4 0 2 0 9 2 0 0 4 0 2 1 0 T o ta l M is si n g Noaa_16 Noaa_12 MODIS GOES

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)