1. Christelle Michel (1,2) Jean-Marie Grégoire (3), Kevin Tansey (3), Catherine Liousse (1) (1) Laboratoire d’Aérologie UMR 5560 CNRS/UPS, Observatoire Midi Pyrénées, 14 avenue Edouard Belin 31400 Toulouse, France. (2) Now at Service d’Aéronomie, IPSL, Université Paris 6, 4 Place Jussieu, 75005 Paris, France (3) Global Vegetation Monitoring Unit, Joint Research Centre European Commission, TP.440, I-21020, Ispra (VA), Italy. ABBI: Asian Biomass Burning Inventory from burnt area data given by SPOT-VEGETATION system Workshop QUEST 27-28 October 2005

2. Context and Objectives  Objectives: To perform an inventory of gases and aerosols emitted by vegetation fires in Asia during the ACE-ASIA experiment: March 1 st - May, 15 th 2001  Rationale for a satellite based approach: Quantitative and repetitive observations in space and time  Availability of long time series: past and future  Frequency of observations  Spatial and temporal consistency of data

3.  Mapping burnt area instead of detection of fire events To minimize the effect of temporal sampling (long lasting « signature » /instantaneous « signature ») A step towards a quantitative assessment of the burnt biomass (structural information, i.e. geographical area of burnt scar) SPOT-VEGETATION imagery Helicopter view active fires smoke burnt areas

4.  Strong uncertainty related to the active fire maps (derived from NOAA-AVHRR) zoom 04/22/01: Landsat TM 04/26/01 : SPOT-Vegetation 20 – 29 April 2001 : nb. fire events (derived from AVHRR) 0 50 The expected high fire activity on the East coast of India is not confirmed by the burnt areas (even on the high resolution TM images) Zoom on India: comparison of the 2 acquisition methods

5. 03/26/001 : SPOT-VGT 03/06/2001 : Landsat TM  The burn scars detected on the TM images are also visible on the SPOT- VEGETATION data despite the different spatial resolution Consistency of the burnt area method

6. Data processing & Analysis  Input data: Images SPOT-VEGETATION imagery (S1: daily,1 km, “ground reflectance”) Global Land Cover product of University of Maryland (Hansen et al., 2000)  Processing: GBA-2000 processor (Tansey et al., 2002)  Output: location (lat-long) of pixels classified as burnt and date of burning  A series of problems have been encountered Dense cloud cover Small and scattered fires (fire practices) Start of the monsoon season at the end of the ACE-Asia period Wide range of vegetation cover types & climatic conditions (desert to evergreen moist forest) Extraction Module spatio-temporal subset from the global archive: 1 Gb/day out of 6.6 Gb/day Pre-processing Module (masking of clouds, shadows, snow, SWIR saturation, extreme view angle, non-vegetated surf., temporal compositing) Processing Module Forest-non forest masking Algorithm: Ershov et al., 2001  Test of several processing algorithms  Selection of Ershov et al., 2001

7. GIS (Geographic Information System) analysis * Assumption: 1 pixel burnt = 1 km 2 1x1° Grid Latitudinal Strip Administrative Map Vegetation Map Burnt pixels map GIS burnt area / country / latitudinal strip burnt area* / country / vegetation burnt area / vegetation / 1°x1° grid burnt area / … / …

8. Building the emissions inventory ABBI  The emission flux for the species X ( Q ) may be calculated as following [Seiler and Crutzen, 1980] : Q = M x EF(X)  EF(X): the emission factor, defined as the ratio of the mass of the emitted species to the mass of dry vegetation consumed (g/kg dry plant).  M: the burnt biomass: M = A x B x  x  – where:  A the burnt areaavailable (SPOT-VGT)  B the biomass densityfrom literature   the fraction of aboveground biomass “   the burning efficiency “

9. Adaptation of the various factors to the vegetation classes The estimates of the biomass density and the burning efficiency are based on recent improvements in vegetation parameterization [from a review conducted by Palacio et al., 2002] For carbonaceous aerosols : emission factors have been specially selected for the vegetation classes present in Asia [from Liousse et al., 2004] [Michel et al., 2005] For gases : emission factors given by Andreae and Merlet [2001]

10. Results of the spatial and temporal distribution of the emissions (March – May 2001) BC emissions (1-10 may 2001)  Daily distribution for 58 gases and BC and OC particulate species (1 March – 15 May 2001) : ABBI inventory [Michel et al., 2005]

11. Comparison between 2000-2001 ABBI : Black Carbon emissions Differences in spatial and temporal distribution  Strong inter-annual variability ABBI

12. Comparison ABBI [ Michel et al., 2005 ] – ACESS [ Streets et al., 2003 ]: BC temporal distribution BC (ABBI) = 2.5E+5 tonnes (of which 1.39E+5 tonnes for FSU countries and Kazakhstan) BC (ACESS) = 1.83E+5 tonnes !! ACESS doesn’t take into account FSU countries and Kazakhstan

13. ABBI: Asian Biomass Burning Inventory ACESS: Ace-Asia and Trace-P Modelling and Emission Support System Mars 1-10: ABBIMars 1-10: ACESSMars 11-20: ABBI Mars 11-20: ACESSMars 21-31: ABBI Mars 21-31: ACESS Avril 1-10: ACESSAvril 1-10: ABBI Avril 11-20: ABBI Avril 11-20: ACESS Avril 21-30: ABBIAvril 21-30: ACESS Mai 1-10: ACESS Mai 1-10: ABBI !! ACESS doesn’t take into account FSU countries and Kazakhstan Comparison ABBI [ Michel et al., 2005 ] – ACESS [ Streets et al., 2003 ]: BC spatial distribution

14. Conclusion  Comparison ABBI-ACESS and years 2000 – 2001 :  multi-system approach hot spot products in dense tropical forest burnt area products in all the other types of vegetation cover + seasonal factors for vegetation parameterization (biomass density and burning efficiency) + accurate land cover maps