1. Public messages about fire are generally negative. Positive or balanced messages are much rarer ( Courtesy to Peter Frost and Joaquim Macucua 5 th SAFNet meeting-Malawi, 2003. Public messages about fire are generally negative. Positive or balanced messages are much rarer ( Courtesy to Peter Frost and Joaquim Macucua 5 th SAFNet meeting-Malawi, 2003. Malawi Forestry Department Time magazineShenandoah National Park, USA Pauline Dube, University of Botswana, Southern Africa fire Network (SAFNet

2. Fire is not always bad 1. Our Ecosystems have evolved with Fire - Fire is part of the savanna landscape 2.Communities need to use fire to survive – Our Land use systems have evolved with fire. 3.To continue to be useful it must be controlled i.e. managed

3. The FOOD, AGRICULTURE AND NATURAL RESOURCES (FANR) DIRECTORATE ( FANR) list Causes of Food Insecurity as: Insufficient investment in agriculture Poor access to agricultural inputs and markets Low technology development and dissemination and Insufficient preparedness to disasters such as droughts and floods. **We need to add FIRE to this list!

4. Satellite Remote Sensing and fire monitoring & management Large areas of Southern Africa are subjected to frequent wildfires Fires burn in: –Managed areas - Communal areas & or private farms –Protected areas - National Parks, game reserves –Some of these fires are trans-boundary.

5. Fire has a long history as a land use tool in Africa Crop production – clearing fields for cultivation (Photos by B. Nduna & S. Sento – Okavango delta 2001) Pastoralism – Controling ticks & regeneration of pasture

6. Preparing land for fishing just before the floods arrive e.g. Okavango Delta (Photos by B. Nduna & S. Sento - 2001)

7. Agricultural use of fire - Senegal Land clearing Crop residues removal Slash and burn agriculture: Break the forest, more grass, more fires (fragmentation) Better peanut fields = clear lands - Residues gathered around the trees to accelerate their death. - Fertilisation, multitude little fires just before the rainy season Slide by Cheikh Mbow, 5 th SAFNet meeting 2004

8. Fire Impacts: Destruction of regeneration, grass, hinder flowering, reduce seed bank, disturb soil microbiology, exacerbating erosion and impacts of drought Perennial regrowth in pasture lands How much is lost and how much is gained from regrowth promotion? Slide by C. Mbow- Senegal, 2003

9. Tanzania - Forest & Beekeeping Dept. – Mini. of National Resources and Tourism identified 10 different sources of fire for the period 1990/91 – 1999/2000: Preparation of Farms-36% Hunting-27% Beekeeping -5.5% Arson-14% Livestock keeping- 5% Lightening- 4% Controlled Burning-3.5% Masonry- 2% Cigarette smoking-1.6% Others-1.4% TOTAL=100% From UHAI NGO in Tanzania – Prepared for the 4 th SAFNet workshop, Kruger National Park – South Africa, 2003

10. Tanzania: Fire in Plantation Forests: 1990/91 to 1999/2000 YearFire IncidencesAffected areas Ha 1990/91221002 1991/9228964 1992/93171934 1993/94196735 1994/95122614 1995/96233042 1996/97 452076 1997/9817632 1998/99404637 1999/00333501 TOTAL25626,237 *Department of Forestry and Beekeeping (FBD), 2001 UHAI NGO, Tanzania – Prepared for the 4 th SAFNet workshop, Kruger national Park, South Africa, 2003

11. Poverty and low technology development results in high reliance on fire for various livelihood activies Most catastrophic fire events – disasters originate from use of fire in land use activities But widespread poverty and low technology means that we do not have resources to handle large fire outbreaks and to monitor and manage fire in general

12. Complex interactions between Socio-economic changes and biophysical processes have resulted in change in fire regimes over the years - for e.g. changes in timing & frequency of fire M.B.M. Sekhwela – AIACC Limpopo Climate Change project- Botswana Some of the land use activities that result in fire outbreaks

13. A burning fire releases greenhouse gases & aerosols at a regional scale this affects climate

14. Deforestation Damage to plantations Loss of wildlife Air pollution Wild fires as threats to human life and property Damage to, or destruction of, vegetation (but not considered a priority) Deforestation Uncontrolled burning, especially in savanna, forest and mountainous areas; air quality (pollution) Damage to woodlands; loss of dry season grazing Deforestation; damage to plantations; reduced regeneration; threat to ecosystem functioning and biodiversity Deforestation; loss of grazing; damage to natural resources Key Policy Concerns (Slide: Peter Frost)

15. Different satellite products are available to assist in fire early warning, fire control and post fire management Satellite fire Management Information fall into these categories: -Fire Danger /Susceptibility/fire risk - Planning Measure to reduce fire risk - Fire Detection - timely location & tracking of a burning fire - Post Fire Assessment-fire damage -Post Fire Recovery -long term impacts and fire history

16. Information needs for fire danger analysis Satellite images - Live fuel moisture content (Live FMC) Meteorological danger indexes- Fire danger Dead fuel moisture content (Dead FMC ) Adjusted from:: E. Chuvieco U. Alcala – Spain Relative humidity, Air temperature, wind speed & direction Land use and population density - sources of ignition -Fuel types (biomass loads, density, flammability) –Fuel moisture content)

17. Air Temperature – 0 C Fire Intensity kJ/s/m Fire behavior and key meteorological factors W. Trollope, 4 rd SAFNet workshop, Kruger National Park, South Africa, 2003

18. Fire is a spatial process that involves land cover - Satellite data has the capability to detect fires Courtesy of Philip Frost – South Africa SAFNet Contact Point Different satellite products are available to assist in fire early warning, fire control and post fire Assessments

19. Global Geostationary Active Fire Monitoring: Geographical Coverage 80° satellite viewing angle65° satellite viewing angle Source: Elaine Prins/NOAA

21. Thermal infrared energy emitted by earth features is in the range of 3 - 14um.

22. Chobe National Park Wooster, Smith & Drake (2001) There is need to understand the characteristics of fires on the Ground to be able to effectively use Satellite data to address fire issues

23. Rainfall levels are a strong signal for pasture availability - Fire risks But also - pests e.g. ticks A Cloud Duration image for 1-10 November, 2001-SADC FANR Regional Remote Sensing Unit - An estimate of distribution of rainfall

24. March, Dekad 1, 2005 NDVI: Normalized Difference Vegetation Index -an estimate of vigor and density of vegetation Together with Early warning is the need for Continuous Monitoring The time series of NDVI data (from 1982-present)

25. The vegetation status image is used to show the condition of the biomass with respect to moisture stress. Botswana: Vegetation status data in May 1998 based on NOAA AVHRR NDVI Data T. Ntabeni, 2000

26. Botswana: Vegetation greeness (moisture) status in August 1998 - Towards the peak fire season T. Ntabeni, 2000

27. Botswana: Vegetation biomass 1997/8 growing season – based on NOAA AVHRR NDVI data T. Ntabeni, 2000

28. Proximity to fire ignition sources

29. Measures to reduce risk of fire outbreaks W. Trollope, 4 th SAFNet workshop, Kruger National Park, South Africa, 2003

30. - Fire Detection - timely location & tracking of a burning fire

31. Australia, Amhem land: WRS 104-069, 02/09/86. The image size is 185 km by 185 km, or ~34,225 km2. The centre of this scene is 13° 2' South, 133° 29' East Landsat MSS data

33. 300K – typical land surface 600K – typical smoldering 1000K – typical flaming

34. Detecting active fire: Sensor Saturation Fires require high sensor saturation levels (>500K) Traditionally middle infrared bands (3-4 microns) e.g. on NOAA AVHRR data - originally not designed for fire but for ocean & cloud Temp. measurements have been used for fire AVHRR saturation point is ~ 321-327K - well below the fire temperature: A problem for arid & semi-arid environments e.g. Botswana - non burning hot surfaces have been confused with fire =causing false fire Alarms

35. MAY JUNEJULY AUGUSTSEPTEMBER OCTOBERNOVEMBER APRIL Seasonal progression of fires across Angola, Zambia, Zimbabwe, Malawi and Mozambique, April- November 1993, as derived from analysis of fire hot spots from NOAA AVHRR imagery (Arino and Melinotte, 1997). Areas and times of extensive burning are clearly identifiable Seasonal progression of fires across Angola, Zambia, Zimbabwe, Malawi and Mozambique, April- November 1993, as derived from analysis of fire hot spots from NOAA AVHRR imagery (Arino and Melinotte, 1997). Areas and times of extensive burning are clearly identifiable Nation/region wide fire monitoring Slide by Peter Frost

36. (JRC-Ispra, 2000)

37. MODIS sensor on board the TERRA - has improved active fire detection and mapping: has special fire channels: - 4 and 11 micrometer channels brightness temperature - designed to saturate at ~ 500K and 400K, respectively - a spatial resolution of 250m, 500m and 1km

38. MODIS Fire Detections Quebec, Canada 07/06/02

39. NASA MODIS Fire Rapid Response Data System http://rapidfire.sci.gsfc.nasa.gov Global fire detections and RGB imagery via the Internet using Daily global coverage - 2~4 hour delay

40. Zimbabwe July 2004 Zambia – August 2004 http://rapidfire.sci.gsfc.nasa.gov MODIS Fire Rapid Response

41. MODIS Monthly Fire detections – October 2001 Fire Detection - timely location & tracking of a burning fire Moderate Resolution Spectro-radiometer (MODIS)

42. Philip Frost – CSIR/SAC Active fires 1-3 Sept. 2003 MODIS RRS

43. 1998 ATSR-2 World Fire Atlas (ESA) Source: Arino/ESA

44. Defense Meteorological Satellite Program (DMSP) – Night time lights

45. SAFNet members had the opportunity to participated in the validation of the Advanced Fire Information System(AFIS) –(A collaboration among CSIR, UMD and ESKOM Philip Frost: Proceedings of the 5 th SAFNet Workshop, Malawi 2004

46. Assessment of fire damage – burnt scars: Mostly visual based and therefore subject to inconsistencies Courtesy – Jomo Mafoko Agricutural Resource Board Botswana

47. Figure 4. Chobe National Park - September 2001. On the left is the 1000 m spatial resolution ATSR image covering Chobe National Park, of approximate size 200 x 200 km. A large fire scar is seen as a dark area at upper middle. The right hand image shows the result of a basic burned area mapping technique applied to these data, which has highlighted the burned area in white, though there is also other areas highlighted that are not burned area but some other landcover type. Data from the field campaign will be used to improve these burned area mapping algorithms. A photo of a typical fire scar on the ground is shown in Figure5, indicating why such areas appear dark on the remote sensed images. (Wooster, Smith & Drake, 2001) Remote Sensing can provide information Area burnt. Example ASTER image of Chobe National Park - Botswana

48. Area burnt - Mapping Burnt Scars Fire scars: These can be detected on the bases of 3 main changes on surface properties following a fire: – Vegetation removal – Deposited combustion residue –Temperature differences: At daytime burnt areas will be hotter than vegetated areas – best contrast is at mid-day. Other useful characteristics of fire can be derived from scars: Fire intensity and fire severity – Degree of vegetation removal can be inferred from date & pattern of burnt area –Homogeneous burn likely to occur in the dry season & indicate high fire intensity –Patchy fires are likely to occur early in the season – indicate lower fire intensity

49. Mapping Burnt Scars: Visual methods Fire scars are usually better detected visually e.g on screen manual digitizing –Superior ability of the human mind to detect patterns, texture in addition to spectral information. Digital methods rely only on spectral differences between burnt & non-burnt areas. Patchiness of burnt areas make manual digitizing tedious and subjective.

50. Mapping burnt scars: Computer based methods Methods sensitive to vegetation removal Most common are Vegetation indices (VIs)= simple algebraic combination of more than one band Basis for use of reflectance based VIs is that VI Values tend to decrease sharply after burning Such VIs are useful where primary photosynthesis veg. burns e.g. evergreen In savannas burning occurs over the dry season when veg. is senescence making it less easy to detect scars Where there is an area where veg. has been removed e.g clearing a field VIs may confuse this with fire scars Deposited combustion residue Recent scars covered by char combustion residues are usually more darker than unburnt areas especially in NIR band. - This is quickly blown away by wind

51. Computer based methods Temperature differences Methods that detect burnt areas as hot surface use thermal bands: these are generally robust but it is not always possible to get good results. In some cases the surface Temp. exceeds the saturation point of the sensor e.g AVHRR –AVHRR is for surface Temp are below 51 0 C - the highest measurable Temp. measurable for this sensor. In arid to semi-arid zones un-shaded surface Temp bt August & Oct. may exceed this: MODIS offers some solution here. Utility of night time images is limited: there is poor contrast between burnt & unburnt earth surfaces Smoke plumes can conceal burnt areas

52. Computer based methods : Spectral combinations Digital methods have the advantage of making use of various spectral combinations and dates Multispectral image classification – supervised and unsupervised classification algorithms – you can use as many bands as possible Spectral indices designed specifically to detect fire scars: e.g. the Normalised Difference Fire Scar Index (NDFSI) which utilises the Middle and Far Infrared ETM+ bands and is given as: NDFSI= (MIR-FIR) / (MIR + FIR) Methods are based on single image: assume all scars can be detected from one date – but this is rarely so

53. Computer based methods : Spectral combinations Multi-date methods These assume that a scar will be spectrally different when compared to an image taken before the burning e.g. others have used the difference between two date NDFSI images to produce Fire Scar Ratio Index (FSRI) – methods that look for fire changes bt. dates have proved more accurate. To use multiple dates - images must be geo-referenced correctly – co-registration is a problem for low spatial resol. Data eg. NOAA The same bands must be used for different dates Atmospheric effects should be reduced. Other constraints: Cloud cover especially towards end of dry season. Smoke-relatively opaque in visible bands, has smaller impact on NIR At MIR even thick smoke smokes plumes are transparent.

54. Fire scars in Hainaveld

55. To summarize - how can satellite data assist in wildfire Control Satellite can provide information on: The risk of fire - amount & moisture condition of fuel-load prior to a fire – NOAA NDVI data has been widely used for this purpose. Prior knowledge on fire risk assist in fire prevent measures – e.g. whether to build fire breaks, need to step up fire campaigns etc

56. To summarise Value of satellite data in wildfire management Locate fires & track a burning fire - help in putting out fires - e.g. NOAA and MODIS Products Assess level of damage - area burnt Help in deciding what measures to take to assist land users that are affected Recovery rate/status of fire scars - assist to manage burnt areas i.e. Post fire recovery-Determine when to use the area burnt – an important management issue Assess Fire history –long term changes in vegetation Amount of smoke, green house gases and aerosols released during burning – estimated form landcover burnt-assist in climate change studies

57. A number of national, regional & international efforts exist to address fire issues In Botswana: Agricultural Resource Board Regionally e.g.: -The Southern African Fire Network (SAFNet) -The SubSahara Wildfire Regional Network Internationally: - Global Observation of Forest Cover (GOFC) Fire Project -World Fire Web Network -MODIS Rapid Response System

58. Southern Africa Fire Network (SAFNet) A collaborative effort for developing capacity for operational fire monitoring & management Systems in Southern Africa.

59. In SAFNet we search for a balance:- Fire as a land use management tool & an ecological process visas fire as hazard & factor in climate change Goal: To achieve more effective and appropriate fire management policies & practices in southern Africa - through the use of remote sensing, GIS and other geospatial information technology.

60. An open network of southern Africa fire scientists, managers and communicators that has functioned over the past five years. In 2005 SAFNet comprises over 40 members drawn from 10 southern African countries. Country Contact Points in 2003 (Slide: David Roy)

61. SAFNet Burned Area validation sites,defined by Landsat ETM+ path/rows distributed from dry savanna to wet miombo woodland 1999 2000 sites superimposed on annual precipitation derived from TRMM 1º data (blue 1500mm) D. Roy UMD

62. Example burned area interpretation: Matabeleland Central Forest District, Zimbabwe Burned areas (blue vectors) mapped between Landsat acquisitions 8/27/00 and 9/28/00 superimposed on 9/28/00 Landsat false color image Produced by Kolethi Gumbo (Zimbabwe SAFNet Contact Point) Fragmented and small burned areas on communal land Slide: D. Roy UMd

63. SAFNet participated at the Sydney 2003 Fire Summit. SAFNet Statement to the International Fire Summit was circulated to representatives from over 60 countries Efforts are ongoing to disseminate Agreements made at the Fire Summit among its members

64. Since 2004 SAFNet has been working with the UNEP Division of Early Warning Assessment (DEWA) to include fire issues in the - Africa Environmental Outlook II (AEO-II)Process The A SAFNet strategy for Networking helps guide the UNEP/DEWA Africa Environment Information Network (AEIN) Proceedings of the 5 th 2004 SAFNet meeting are being published by UNEP to help with the AEO-II and also Global Environment Outlook (GEO) SAFNet in associated with UNEP/DEWA AEO-II is producing a Fire Case Study Report:Botswana, Namibia, Zimbabwe, Tanzania & Senegal

65. An Example of some of one of the studies reported in the proceedings of 5 th 2004 SAFNet Malawi meeting: Fire history in the Okavango by Conservation International, 96 satellite images (Landsat TM5, Landsat TM7, ALI) study area

66. 0/15 1/15 2/15 3/15 4/15 5/15 6/15 7/15 8/15 9/15 10/15 fire frequency CI - Fire frequency Years Conservation International

67. Johan le Roux Min. of Environment & Tourism Namibia Fire regions in Namibia. There is need for different fire policies for different parts of the country. Highlights of the SAFNet UNEP/DEWA Namibia Fire Case study

68. Thank You PULA! Courtesy – Peter Frost, Zimbawe

69. FOUR CORNERS TRANSBOUNDARY NATURAL RESOURCES MANAGEMENT AREA