Methods of detecting burnt area and estimating emissions Dr. Kevin Tansey

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1 Methods of detecting burnt area and estimating emissions Dr. Kevin Tansey

2 Why fire is important Emitter of GHG and aerosols into the atmosphere Stohl, A. et al., 2006, record high air pollution levels in the European Arctic due to agricultural fires, ACP, 7, , 2007 Page S.E. et al., 2002, Nature, 420, Consequence of land cover/use change Amazonia (trees and grasslands) Indonesian peatlands Climate change impacts and feedbacks More fire-affected regions?

3 Current EO state of the art Burned area MODIS, L3JRC, GlobCarbon + regional data No standards on validation/intercomparisons Flaming fire detection MODIS, WFA, EUMETSAT, TRMM Limited detection capability FRP MODIS FRP, SEVERI Emissions databases GFED (mainly makes use of MODIS data) Detecting fire is easy disturbance less so Accuracy is certainly dependent on resolution

4 Validation activities Effort being placed on validation of global product More an evaluation of existing products Geographically limited Reliance on secondary ground data Normally based on Landsat pairs (USGS) High-res = in situ in most cases The community agrees on the need for validation protocols

5 Fire disturbance to emissions Fuel loadings and burning efficiency Fuel type data needed Fuel load data collected Burn severity Relationships between LAI and dnbr (Boer M. et al., 2008, RSE, 112, ) Regional calculations Bottom-up and top down approaches Carbon flux estimates using daily climate data input in SPITFIRE module in LPJ-GUESS model (Lehsten et al. 2008, BGD, 5, ) SAFARI 2000, GFED

6 Burned area MODIS, L3JRC, GlobCarbon + regional data Multi-year burned areas detected from from SPOT-VGT satellite Tansey, K., et al. GRL, 35, L01401 doi: /2007gl031567

7 L3JRC Reporting Example Burned area (km 2 ), number of scars and % of each vegetation type per country burned (comma delimited). Country Needleleaf forest Broadleaf forest Woodlands & shrublands Grasslands & croplands Angola ,1290, ,21077, ,8459,20.6 Australia 345,249, ,1219, ,20303, ,9083,3.3 Italy 84,54,0.2 48,21, ,318, ,707,1.4 USA 5867,1344, ,115, ,6739, ,5496,0.4 Tansey, K., et al. (2004), J. Geophys. Res., doi: /2003jd

8 Validation activities Validation tools and standards are being planned under EC FP7 Geoland2 & NASA CEOS WGVC

9 Fire disturbance to emissions Biomass loss Fuel type and fuel load data are critical Burn severity can be directly derived from FRP Emissions databases Global Fire Emissions Database (GFED) Lehsten V. & Tansey, K. et al. Biogeosci. Disc., 5,

10 Intercomparison experiments

12 MODIS MCD45A1 burned area product BA Month MONTHLY BURNED AREA MAPS

13 The MODIS Burned Area Product Slides courtesy of Luigi Boschetti & David Roy

14 Global MODIS Burned Area Product Funded as part of NASA MODIS Fire Science Team (Justice et al.) to complement the well established (Collection 1,3,5) MODIS 1km active fire product" Global applications" " Green house gas & aerosol emissions estimation " Applied users (e.g., natural resource management)" LCLUC research (e.g., Fire Climate People)" Collection 5 processing now completed for " MODIS data sensed New version (5.1) scheduled for october-2009"

15 Algorithm Rolling bidirectional reflectance distribution function (BRDF) based expectation change detection Semi-Physically based; less dependent upon imprecise but noise tolerant classification techniques; very few thresholds Automated, without training data or human intervention Applied independently per pixel to daily gridded MODIS 500m land surface reflectance time series => globally map 500m location and approximate day of burning

16 The challenge: change detection of Burned Areas BRDF Effects gaps Slides courtesy of L. Boschetti and D. Roy Algorithm Background

17 What is bidirectional reflectance? bidirectional reflectance effect is evident when an object or image viewed or illuminated from different angles Bidirectional reflectance effect on a grass lawn observed under different angles (source University of Zurich, Department of Geography)

18 backscattering forward scattering (sun behind observer) (sun opposite observer) Photographs by Don Deering

19 The challenge: change detection of Burned Areas Persistence of the signal BRDF Effects gaps Day of burning Slides courtesy of L. Boschetti and D. Roy Algorithm Background

20 Conceptual Scheme (one pixel, time series) observed ρ!me Slides courtesy of L. Boschetti and D. Roy Algorithm Background

21 Conceptual Scheme observed ρ t- 1!me Slides courtesy of L. Boschetti and D. Roy Algorithm Background

22 Conceptual Scheme BRDF Inversion window observed ρ t- 1!me Slides courtesy of L. Boschetti and D. Roy Algorithm Background

23 Conceptual Scheme BRDF Inversion window observed predicted ρ ρ (t t- 1) > t- 1!me Slides courtesy of L. Boschetti and D. Roy Algorithm Background

24 Conceptual Scheme BRDF Inversion window observed predicted ρ ρ (t t- 1) > ρ (t t- 1) t- 1!me Slides courtesy of L. Boschetti and D. Roy Algorithm Background

25 Conceptual Scheme BRDF Inversion window observed predicted ρ ρ (t+1 t) > ρ (t+1 t) t!me Slides courtesy of L. Boschetti and D. Roy Algorithm Background

Animation: 5 Months of burning, Okavango Delta, Botswana, 2002.

26 Animation: 5 Months of burning, Okavango Delta, Botswana, Produced using multitemporal rolling BRDF-based change detection approach, Roy et al. 2005

27 Burned Area algorithm run globally for first Dme in MODIS C5 - purposefully running to map burned areas conservadvely

28 500m burned areas 5 months 2002 Zambia/Zimbabwe 650*500km

29 1km active fires 5 months 2002 Zambia/Zimbabwe 650*500km

30 Slides courtesy of L. Boschetti and D. Roy Australia 500m burned areas 1 month 2002

31 Slides courtesy of L. Boschetti and D. Roy Australia 1km active fires 1 month 2002

32 Brazil, Southern Para, 500m burned areas 1 month 2002

33 Brazil, Southern Para, 1km active fires 1 month 2002

34 Example refinement C5 monthly burned area (MCD45) product Greece August 2007 BoscheJ, Roy, Barbosa, et al, 2008

35 Active Fire Information Slides from Martin Wooster, King s College London (KCL)

36 Terrestrial Fire Remote Sensing Products Burned Area Maps" Identifies the location of burned ground, after fire event." Active Fire Detections ( Hotspots )" Identifies the location of fires that are burning at the time of the satellite observation" Fire Radiative Power (FRP)" A measurement of the rate of thermal radiative energy release at the detected active fire pixels."

37 Terrestrial Fire Remote Sensing Products Burned Area Maps" Identifies the location of burned ground, after fire event." Active Fire Detections ( Hotspots )" Identifies the location of fires that are burning at the time of the satellite observation." Fire Radiative Power (FRP)" A measurement of the rate of thermal radiative energy release at the detected active fire pixels."

" Lower spatial resolution (~ 3 to 5 km)" Low Earth Orbit (~ Near Polar)

38 Observing Satellites " Geostationary Near continuous view of Earth, Meteosat provides data of Africa every 15 minutes." Lower spatial resolution (~ 3 to 5 km)" Low Earth Orbit (~ Near Polar) Temporal resolution few hrs to few days" Moderate to High spatial resolution" "(usually around ~ 1 km)"

39 Active Fire Detections ( hotspots ) The location of fires that are burning at the time of the satellite observation"

40 Active Fire Detections Theory true colour composite smoke Fires have very high temperatures (> 600 K) compared to their ambient surroundings.

41 Active Fire Detections Theory true colour composite smoke The high temperatures result in very intense radiant energy emissions at IR wavelengths, particularly in the middle IR (3-5 µm) spectral region.

42 Active Fire Detections Theory true colour composite infrared composite smoke The high temperatures result in very intense radiant energy emissions at IR wavelengths, particularly in the middle IR (3-5 µm) spectral region.

43 Veg Only (300 K) Sub-Pixel Fire Detection 1% Veg + 1% Fire Zhukov et al. (2006)

44 Veg Only (300 K) Sub-Pixel Fire Detection 1% Veg + 1% Fire x100 Possible to detect active fires covering < 1000 th of pixel!"

45 Sub-Pixel Fire Detection Spatial Resolutions GOES ( 2 km x 4 km) MODIS (1 km x 1 km) BIRD (370 m x 370 m) Wooster et al (2005) JGR

46 How Small a Fire Can we Detect? Assuming MODIS pixels = 1 km x 1 km pixel size MODIS pixel area = 1 km² = 1 x 10 6 m² Assuming fire size = 100 m long x 5 m wide Fire area = 500 m² Assume Fire temp = 850 K (background = 300 K) Proportion of pixel as fire (p) = 500 / 1x10 6 p = or 0.05%

47 TIR 10.8 µm TIR AVHRR Data of African Fires"

48 MIR 11 µm 3.7 µm MIR AVHRR Data of African Fires"

49 MIR 11 µm TIR 3.7 Brightness µm Temperature Difference MIR TIR Using MIR-TIR BT difference helps reduce influences due to ambient effects and highlights those due to fire"

Example LEO Fire Data ATSR (World Fire Atlas) http://dup.esrin.esa.

MODIS Active Fires http://rapidfire.sci.gsfc.nasa.

Jan Dec TRMM Global Fires ftp://ftp-tsdis.gsfc.nasa.

50 Example LEO Fire Data ATSR (World Fire Atlas) Long-term (since 95) but only night. MODIS Active Fires Every 6 hrs global since Jan Dec TRMM Global Fires ftp://ftp-tsdis.gsfc.nasa.gov/pub/yji/daily// Observatory/Datasets/fires.trmm.html ~ Monthly diurnal sampling, but only tropics

51 (GSE/GEOG-741-S01) Fire Location & Seasonality Global MODIS Active Fire Dataset

52 Intecomparison & Synergy: Active Fires & Burned Area Over Africa MODIS Burned Area (Roy et al) Metetosat Active Fire (Roberts & Wooster)

53 Fire Radiative Power The rate of thermal radiative energy release from an actively burning fire"

54 Fire Radiative Power vs. Rate of Fuel Combustion Fire Radiative Energy vs. Total Fuel Combustion Open points grassy fuels Solid points woody fuels Wooster et al (2005) JGR

55 Fire Radiative Power MSG SEVIRI Large emissions variability

SEVIRI Fire Radiative Power (FRP) Product (http:// landsaf.meteo.

product generated for four regions: Euro (Europa): Red NAfr (Northern Africa):

Product Simulated Global product generated from FRP pixel derived for different

56 SEVIRI Fire Radiative Power (FRP) Product ( landsaf.meteo.pt/) Spatial Resolution : SEVIRI Pixel Temporal Resolution : 15 Minutes FRP Pixel product generated for four regions: Euro (Europa): Red NAfr (Northern Africa): Magenta SAfr (Southern Africa): Blue SAme (Southern America): Brown SEVIRI FRP Pixel Product Simulated Global product generated from FRP pixel derived for different dates only (as a visual example; normally relatively few fires are burning in North and South Africa on the same date)

57 Southern Africa FRP, 3-8 September 2003 Biomass Combusted = 3.2 million tonnes ( million tonnes adj. for cloud cover) Roberts et al (2005) JGR Wooster et al (2005) JGR Integrate FRP [MW] over time..(can assume 15 mins [900 secs] x-axis interval) Biomass Burned [kg] = x FRE [MJ] and biomass is ~ 47% Carbon

58 Fire Seasonality and Location Temporal Emissions Variation [Very strong seasonal cycle] NH Africa Tg SH Africa Tg

59 Summary Active Fire Detections Can be Near Real Time Provide good data on fire timing and location Good for confirming or seeding burned area mapping methods Can be used to give rough estimate of burned area but Usefulness may depend on time of observation with respect to the fire diurnal cycle. Fire Radiative Power All the points at left AND Provide direct information on fuel consumption rate Can temporally integrate to produce total C emissions Independent of burned area/ fuel load approaches. but Missing small fires & cloud cover mean these estimates are likely to be minimums if adjustments not made.

60 Greenhouse gas emissions from wildfires in Africa Dr Bob Scholes, Sally Archibald. CSIR, Natural Resources and the Environment South Africa

61 The basic wildfire emissions model Emission = Area * Fuel * Completeness * Emission Factor*10-3 tons ha tons/ha % g kg -1 Can be applied to whole ecoregions, or on a pixel-by-pixel basis

62 To summarise: Tier 1: Countries should stratify by IPCC vegetation categories and early-season or late-season burning. Default values are provided for combustion factors (Table 2.6 ), emission factors (Table 2.5), and aboveground biomass (Table 6.4). Tier 2: Countries should develop their own stratification of vegetation and use country-specific combustion and emission factors. Tier 3: Countries should develop algorithms to estimate the area burnt, validating the products obtained with data from field observation

63 Combustion completeness: Early-season burn

64 Combustion completeness: late-season burn Hely et al (2003) J Arid Environments

65 Combustion completeness Fuel burned/fuel exposed IPCC guidelines Table

66 Emission factors:

67 The carbon neutral assumption It is assumed that for vegetation that burns regularly and regrows to its original state after burning, the CO 2 emissions during the fire are balance by CO 2 uptake during recovery This is only true if the fire frequency and fuel load are constant over time Not true if land is being cleared for agriculture If fires are becoming more frequent or intense, the carbon store on land will decline, ie there are net CO 2 emissions It is not true for non CO 2 emissions.

68 Changing fire regimes to accumulate carbon: ANNUAL BURN NO BURN IN 50 YEARS

69 Emission factors: Compound X g compound/ SD kg Dry Fuel burned Carbon dioxide CO Methane CH Nitrous oxide N 2 O Nitrogen oxide NO * x CO * * not a greenhouse gas, but a precursor to O 3, which is. Estimation not required by non Annex-1 countries

70 Emission factors:

71 Total Carbon Emissions from Burned Areas (via Active Fire Data) Global Fire Emissions Database (van der Werf et al.)

72 For more information: Andreae, MO 1997 Emissions of trace gases and aerosols from southern African savanna fires. In: van Wilgen, BW, MO Andreae, JG Goldammer, JA Lindesay (eds) Fire in southern African savannas. Witwatersrand University Press, Johannesburg. Pp Cachier, H., Liousse, C., Pertusiot, M., Gaudichet, A., Echalar, F. and Lacaux, J. (1996). African fire Particulate emissions and atmospheric influence, in Biomass Burning and Global Change: Volume 1. Remote Sensing, Modeling and Inventory Development, and Biomass Burning in Africa, J. Levine, Editor. MIT Press: Cambridge. p Cachier, H., Ducret, J., Brémont, M. P., Gaudichet, A., Yoboue, V., Lacaux, J. P., and Baudet, J., 1991, Characterization of biomass burning aerosols in a savanna region of the Ivory Coast, in J. S. Levine (ed.),global Biomass Burning: Atmospheric, Climatic and Biospheric Implications, MIT Press, Cambridge, MA, pp Lacaux, J., Cachier, H. and Delmas, R. (1993). Biomass burning in Africa: an overview of its impact on atmospheric chemistry, in Fire in the Environment: The Ecological, Atmospheric, and Climatic Importance of Vegetation Fires, P. Crutzen and J. Goldammer, Editors. John Wiley & Sons: Chichester. p Scholes, MC and MO Andreae 2000 Biogenic and pyrogenic emissions from Africa and their impact on the global atmosphere. Ambio 29, Scholes, RJ, D Ward and CO Justice 1996 Emissions of trace gases and aerosol particles due to vegetation burning in southern-hemishere Africa. JGR 101, Ward, D. E., W. M. Hao, R. A. Susott, R. E. Babbitt, R. W. Shea, J. B. Kauffman, and C. O. Justice (1996), Effect of fuel composition on combustion efficiency and emission factors for African savanna ecosystems, J. Geophys. Res., 101(D19), 23,569 23,576. Roy, D.P., Lewis, P.E. and Justice, C.O., 2002, Burned area mapping using multi-temporal moderate spatial resolution data a bi-directional reflectance model-based expectation approach. Remote Sensing of Environment, 83, p Roy, D.P., Jin, Y., Lewis, P.E. and Justice, C.O., 2005, Prototyping a global algorithm for systematic fire-affected area mapping using MODIS time series data. Remote Sensing of Environment, 97, pp Giglio, L., Loboda, T., Roy, D.P., Quayle, B. and Justice, C.O., 2009, An active-fire based burned area mapping algorithm for the MODIS sensor. Remote Sensing of Environment, 113, pp

A temporal active fire detection algorithm applied to geostationary satellite observations

8 th 10 th June LSA-SAF Workshop 2015 A temporal active fire detection algorithm applied to geostationary satellite observations Gareth Roberts (University of Southampton) Outline Active fire detection