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Applications of Geostationary Data for Operational Forest Fire Monitoring in Brazil

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

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Applications of Geostationary Data for Operational Forest Fire Monitoring in Brazil

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  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 Schroeder1 João Antônio Raposo Pereira1 Alberto Setzer2 1PROARCO/IBAMA 2CPTEC/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 GOES Fire Detection Algorithm July 2000 – Implementation of CIRA’s RAMSDIS system based on GOES-8 data & McIDAS OS/2 Warp Cloud Masking Tb4 >= 2ºC Night: Tb2 > 17ºC Potential Fires Day: Tb2 > 41ºC Day: (Bi -Bx)/Bx >=0.25) Night: (Bi -Bx)/Bx >=0.10) 6 out of 8 Statistics Sunglint Model (SoZA-SaZA >15o) +/- 5o lat For visualization only Persistence

  5. April 2003: Transition to Win2000 – GOES-12

  6. 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

  7. 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

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

  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 > 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

  13. CPTEC/INPE Web Product

  14. CPTEC/INPE Web Product

  15. Output Sample File NrLatLon LatDMS LongDMSDateTimeSat MunStateCountryVegSusceptPrecDWRRiskPersist 10.95-62.7167N 0 57 0.00O 62 43 0.002004020784500GOES-12BarcelosAMBrasilOmbrofilaDensaBAIXA2400.10 21.1-62.7333N 1 6 0.00 O 62 43 60.002004020784500GOES-12BarcelosAMBrasilOmbrofilaDensaBAIXA2400.10 3-12.9167-38.6167S 12 55 0.00O 38 37 0.0020040207114500GOES-12ItaparicaBABrasilOmbrofilaDensaBAIXA23.6000 4-9.383-38.2333S 9 22 60.00O 38 13 60.0020040207114500GOES-12Paulo AfonsoBABrasilNaoFlorestaMEDIA0.9100.80 5-8.55-40.2S 8 33 0.00O 40 12 0.0020040207114500GOES-12Lagoa GrandePEBrasilNaoFlorestaMEDIA0100.90 6-7.983-40.3167S 7 58 60.0O 40 19 0.0020040207114500GOES-12OuricuriPEBrasilNaoFlorestaMEDIA0100.90 7-0.016-62.6167S 0 1 0.00 O 62 37 0.0020040207144500GOES-12BarcelosAMBrasilNaoFlorestaBAIXA590.40 8-0.016-62.6333S 0 1 0.00 O 62 37 60.0020040207144500GOES-12BarcelosAMBrasilContatoBAIXA27.5000 90-62.6333S 0 0 0.00 O 62 37 60.0020040207144500GOES-12BarcelosAMBrasilContatoBAIXA27.5000 100.05-62.6167N 0 3 0.00 O 62 37 0.0020040207144500GOES-12BarcelosAMBrasilNaoFlorestaBAIXA590.40

  16. Automatic Fire Detection – Case Study Noaa_12 Noaa_16 MODIS GOES-12 Barcelos Amazonas 2004 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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