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Applications of Geostationary Data for Operational Forest Fire Monitoring in Brazil Global Geostationary Fire Monitoring Applications Workshop EUMETSAT.

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Presentation on theme: "Applications of Geostationary Data for Operational Forest Fire Monitoring in Brazil Global Geostationary Fire Monitoring Applications Workshop EUMETSAT."— Presentation transcript:

1 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 wilfrid.schroeder@ibama.gov.br

2 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

3 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)

4 IBAMA’s July 2000 – Implementation of CIRA’s RAMSDIS system based on GOES-8 data & McIDAS OS/2 Warp Cloud Masking Potential Fires Tb 4 >= 2ºC Night: Tb 2 > 17ºC 123 4X5 678 Day: Tb 2 > 41ºC Statistics Sunglint Model Persistence GOES Fire Detection Algorithm (S o ZA-S a ZA >15 o ) +/- 5 o lat Day: (B i -B x )/B x >=0.25) Night: (B i -B x )/B x >=0.10) 6 out of 8 For visualization only

5 April 2003: Transition to Win2000 – GOES-12

6  Great results from visual image interpretation (reflectivity product)  Major fire events are 100% detectable  System provides fast response in many different cases Northern SectorsSouthern Sector Pros

7 Output Sample File LatLonSZACH4CH2Day/NightCH4_threCH2_threPerc_difNum_pix 13.97-90.4143.734327D86320.256 13.95-89.1542.914931D86320.256 13.25-87.4141.285031D86320.256 12.87-87.1340.84928D86320.256 12.57-87.1140.565029D86320.256 12.53-70.0132.495130D86320.256 12.22-71.832.734723D86320.256 12.19-86.3939.825031D86320.256

8 Automatic Fire Detection – Case Study Roraima 12:53h UTC ~400m of fire 18:20h UTC smoldering 28 Jan 2003

9 Automatic Fire Detection – Regional Scale 28 Jan 2003

10 Automatic Fire Detection – Continental Scale 28 Jan 2003

11 CPTEC/INPE Approach – Fire (by A. Setzer) Albedo (Ch1) 0.65  m Tb (Ch2) 3.9  m Tb (Ch4) 10.7  m Tb2-Tb4 0 – 3%> 308.15K (35 o C)> 263.15K (-10 o C)> 16K (16 o C) 3 – 12%> 318.15K (45 o C) > 263.15K (-10 o C) < 308.15K (35 o C) > 22K (22 o C) 12 – 24%> 323.15K (50 o C) > 263.15K (-10 o C) < 303.15K (30 o C) > 25K (25 o C)

12 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

13 CPTEC/INPE Web Product

14

15 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

16 Automatic Fire Detection – Case Study Barcelos Amazonas 2004 Noaa_12 Noaa_16 MODIS GOES-12 Total area burned:18000ha

17 Automatic Fire Detection – Case Study

18 Automatic Fire Detection – Continental Scale

19 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)


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