1. Operational Forest Fire Monitoring in Brazil Wilfrid Schroeder, M.Sc. PROARCO - Fire Monitoring System Brazilian Institute for the Environment and Natural Renewable Resources – IBAMA swilfrid@sede.ibama.gov.br

2.  IBAMA is the Major Environmental Agency in Brazil About us...  Responsible for Forestry, Animal life, fishery, etc..  One of the Primary Goal is to Manage and Protect the Brazilian Legal Amazon

3. The Challenge:  Large land area requiring wide scale monitoring system  Little or no access from surface: observations need to be made from above  Illegal logging activities going on over remote areas  New land areas being created using fire as a tool for clearing fields  Large number of vegetation fires

4. The Brazilian Amazon Total Area: 5.2 Million km 2 Number of States Covered: 9

5. The Arc of Deforestation Total Area: 1.6 Million km 2 Number of States Covered: 7

7. The Beginning  Late 80’s – Start using AVHRR’s afternoon pass –Technical cooperation with INPE  Problems with detection algorithm –High number of spurious fires detected  Limited field inspection –Few satellite hot spot coordinates visited

8. The Need  January-March 1998 – The Great Roraima Forest Fire –Little operational capacity at that moment prevented early detection and combat  Operational Fire Monitoring Facility made necessary –Pressure from the international community  July 1998 – The PROARCO system was established

9. The Concept  Intensive use of Remote Sensing and GIS technologies for fire monitoring  Use of meteorological data for fire risk assessment  Quick access to reports/bulletins - providing near real time data through internet, fax, and vehicles with satellite communication capability  Increase law enforcement activities  Have the local communities involved

10. Remote Sensing Fires  System based on previous AVHRR use experience  Detection algorithm experiencing constant improvement  Use of evening overpass (NOAA-12) –to avoid saturation from bright surfaces  Satellite hot spot data being used for field inspections based on different alert levels (green and yellow) –Hot spot location and persistence criteria

11.  Fixed threshold method –Adjusting limits through histogram analyses –Trial and error  AVHRR Channel 3 –Separating all potential fires through saturation  AVHRR Channel 1, 2 and 4: –Eliminating bright targets (clouds, water bodies, bare soils,…) Algorithm Basics

12. Overall Performance  Number of spurious fires greatly reduced

13. Overall Performance  Poor image navigation are still noticed occasionally

14.  Day to day variation as a limiting factor Overall Performance

15.  Image Acquisition Problems Courtesy of INPE

16. Overall Performance  Resulting Spurious Fires

17. Overall Performance  Similar Effects Affecting the NDVI CPTEC/INPE Zoomed area showing a large number of false green pixels

18. Overall Performance Worth Mentioning – NOAA/AVHRR  Detection algorithm performing well  Image navigation still requires operator’s hands-on  Pixel distortion towards the edge of the image reduces detection capacity and affects hot spot statistics  Image acquisition characteristics affect the quality of derived products

19. Need for improvement July 2000 – Implementation of CIRA’s RAMSDIS system based on GOES-8 data Cloud Masking Potential Fires Tb 4 > 2ºC Day: Tb 2 > 17ºC 123 4X5 678 Night: Tb 2 > 41ºC Statistics Sunglint Model Persistence GOES Fire Detection Algorithm

20.  Improved monitoring capability (every 30min)  Reduced Response Time Need for Improvement

21.  Satellite data quality assessment facilitated Need for Improvement

22.  Great results from visual image interpretation Need for Improvement Northern Sectors Southern Sector

23. Poor results from automated fire detection algorithm Need for Improvement

24.  Persistence Check

25. Need for Improvement Worth Mentioning – GOES  Hot spot location errors are found to be in the 2km range  Visual image interpretation has been able to detect 100% of the major fires in National Parks all over Brazil  Response time is averaging 2 hours at most cases  Coincident meteorological analyses helps planning fire combat management in near real time  Constant image acquisition geometry  Coarse spatial resolution introduces high number of spurious fires  Automated detection is still of limited use

26. A New Era  July 2001 – MODIS hot spot data via ftp access

27.  September 2001 – visual in-flight inspection of MODIS hot spot coordinates showing great results A New Era

28.  Rapid Response System images used as a confirmation A New Era Where you see smoke there will be a fire!! Courtesy of NASA

29. A New Era  Day to day variation also observed

30. A New Era Worth Mentioning – MODIS  Hot spot location errors are found to be in the 250m range  Coincident high resolution visible images favors fire confirmation during day time  Pixel distortion creates similar problems observed with NOAA/AVHRR – what is made worst by non- overlapping images near the equator

31. Keep on Moving  January 2002 – DMSP OLS data made available through NGDC / C. Elvidge et al.

32.  Good image navigation Keep on Moving Cities State Boundaries

33.  Noise from the South Atlantic Magnetic Anomaly Keep on Moving Courtesy of NASA Multi-angle Imaging SpectroRadiometer (MISR) Instrument aboard NASA's Terra Spacecraft

34. Keep on Moving Spurious Fires Detected

35.  Fire detection requiring operator’s hands on  Stable lights file outdate as a limiting factor Keep on Moving

36. Worth Mentioning – DMSP  Good correlation with NOAA/AVHRR  Sources of contamination limits detection capacity to larger fires (increasing omission error by the use of more restrictive thresholds)  Image acquisition time does not match fire peak activity hours  Stable lights file must be updated on a regular basis

37. Data QA  Helicopters and small aircrafts are used to field inspect the hot spot coordinates, feeding back the monitoring system with valuable information for fine tuning the satellite fire detection algorithms and methods  Airborne sensors are used during specific satellite data validation campaigns  Satellite data inter-comparison helps identifying commission/omission errors and assessing image navigation problems

38.  Airborne satellite data validation campaigns Data QA

39. Prescribed Burn at IBGE Reserve in Brasília – September 2000 Data QA Visible Band Forest Mapper Instrument IR (8.55  m) Fire Mapper Instrument

40. Data QA Agricultural Burning in a Cerrado Area (savana) in the State of Tocantins - September 2000 Visible Band Forest Mapper Instrument IR 8.55  m Fire Mapper Instrument

41.  IR sensors to be used onboard orbital platforms Data QA Infrared Spectral Imaging Radiometer (ISIR) Image over Namib Desert Acquired from Space Shuttle Discovery on 7 August 1997 Airborne System: -Pair of Kodak MegaPlus digital cameras (Forest Mapper) -IR Sensor (Fire Mapper) Courtesy of NASA

42. SIVAM Aircrafts Data QA

43.  GIS system for satellite data ingestion

44. Final Considerations  Increasing spatial resolution (visible channels) allowed for visual confirmation of fires in the images (smoke plumes)  Improved navigation parameters reducing processing time (no GCP collection needed) and making field inspection easier  Increasing spectral resolution / mid-IR channel saturation facilitating fire/non fire discrimination Latest Improvements

45. Final Considerations  Varying pixel size through image cross section imposes some significant limitations to hot spot data applicability (specially with polar orbiting spacecrafts)  Full global cover every 12 hours is imperative. Tropical areas are affected by little image overlapping between consecutive orbits  Geostationary automated hot spot detection suffers from low confidence problems caused by spatial resolution limitations Remaining Points

46. Acknowledgements INPE – CPTEC United States Forest Service – USFS CIRA – Colorado State University NASA Goddard Space Flight Center University of Maryland National Geophysical Data Center World Bank

47. http://www.ibama.gov.br