Biomass burning emission inventory from a satellite based approach: the ACE-Asia case study Christelle Michel (1) Jean-Marie Grégoire (2), Kevin Tansey (2), Ilaria Marengo (2), Steffen Fritz (2), Luigi Boschetti (2), Catherine Liousse (1) (1) Laboratoire d’Aérologie UMR 5560 CNRS/UPS, Observatoire Midi Pyrénées, 14 avenue Edouard Belin Toulouse, France. (2) Global Vegetation Monitoring Unit, Joint Research Centre, European Commission, TP.440, I-21020, Ispra (VA), Italy. ( Context and Objectives To perform an inventory of aerosols and gases emitted by vegetation fires in Asia during the ACE-ASIA experiment (also available for Trace-P campaign): March - May, 15 th 2001  Rationale for a satellite based approach The main uncertainty in deriving biomass burning inventory is linked to the estimate of burnt areas  Quantitative improvements made using a satellite based approach (Barbosa et al., 1999, Liousse et al., 2002) Quantitative and repetitive observations in space and time  Availability of long time series: past and future  Frequency of observation: daily with SPOT-Vegetation  Spatial and temporal consistency of data Low cost (compared to ground observations) Drawbacks ‼ 1 km 2 pixel classified as burnt = 50 to 100 ha burnt Small burn scars (mainly agricultural fires) not detected  Despite this uncertainty, this method is still an improvement of burnt area estimation for global inventories.  Advantages of mapping of burnt areas –The effect of temporal sampling (long lasting “signature”) is minimized –A more reliable assessment of the burnt biomass becomes possible Data processing & Analysis  Input SPOT-Vegetation imagery (S1: daily, 1 km, “ground reflectance”) Global land cover product Uni. Maryland (Hansen et al., 2000)  Processing using: GBA-2000 processor (Tansey et al., 2002) on 2001 data set  Output: location (lat-long) of pixels classified as burnt and date of burning latitude: from 60 ° N to 10 ° S longitude: from 60 ° E to 150 ° E monitoring period: from March, 1 st to May, 15 th 2001  A series of difficulties have been encountered over the Asia area –Dense cloud cover –Small and scattered fires (fire practices) –Wide range of vegetation cover type & condition (desert to evergreen moist forest) –Start of the monsoon season at the end of the experimental period GIS (Geographic Information System) analysis * Assumption: 1 pixel burnt = 1 km 2 The expected high fire activity on the East coast of India (as shown by the active fire map) is not confirmed through burnt areas maps (even on the high resolution TM images) However the burn scars detected on the TM images are also visible on the SPOT-VGT data despite the different spatial resolution High uncertainty associated with the active fire maps (as derived from NOAA-AVHRR data) – 29 April 2001 : nb. fire events (derived from AVHRR) Helicopter view SPOT-VEGETATION imagery Active fires Smoke Burnt area 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 Building the emission inventory Source emissions for compound X (Q) may be calculated as follows: Q = M x EF(X)  EF(X), the emission factor, defined as the ratio of the mass of the emitted X to the mass of dry vegetation consumed (g/kg dry plant).  M is the burnt biomass : M = A x B x α x β Where:  A the burnt area determined from this study  B the biomass densityfrom literature  α the fraction of aboveground biomass “  β the burning efficiency “ Temporal evolution of CO emissions from March to May 2001 Perspectives  To compute the emissions for the other main chemical compounds (gases and aerosols)  To introduce these emissions in MESO-NH-C, a regional model (Tulet et al., 2002) with other emissions: fossil fuel, agricultural and domestic fires, natural emissions etc.  To study transport modelling and radiative impact of the aerosol mixture The approach based on the active fires provides a good overview of the temporal (seasonal and inter-annual) dynamics of fire activity, but should not be applied for a quantitative assessment of the biomass burnt. 26/03/2001 : SPOT-VGT 06/03/2001 : Landsat TM Range of the emission factors used in this study (1): Andreae and Merlet (2001), (2): Liousse et al., (1996) Latitudinal distribution of burnt areas Burnt areas per type of vegetation cover 1-15 March March April April May 2001 BC emissions from March to May 2001 At the beginning of the ACE-Asia campaign (March 2001), fires are located between 15 and 45°N. In the north, snow is still present. In April, burning is observed between 45 and 60°N just a few days after the snow has melted. Compared to further north, the extent of burning in India and continental South-East Asia is much lower. In this region, March to May is considered as late season burning. In insular South-East Asia, there is no detection of burnt areas, most probably because the burning season starts in June and finishes in November. 55N – 60N 10S – 5S This approach allows us to characterize in a very precise way, the distribution of sources both in time and space. The current spatial (1ºx1º) and temporal (15 days) resolution can be improved up to 0.25ºx0.25º and 5 days. Moreover, the use of burnt areas instead of the distribution of fire events allows us to improve the estimate of the biomass burnt and, therefore, the emissions. Global land cover product Uni. Maryland (Hansen et al., 2000) used to compiled the emissions. Vegetation classes Scenario 2: EF(BC) from Liousse et al., 1996 BC = Gg (March to 15 th May 2001) Scenario 1: EF(BC) from Andreae and Merlet, 2001 BC = Gg (March to 15 th May 2001)  Comparison with the ACE-Asia and TRACE-P reference (based on the inventories done for the year 2000; CGRER, 2002 ( ) CGRER, 2002:BC = Gg/year This study:scenario 1:BC = Gg/2.5 months (= 63.1% of CGRER annual estimates) CO (Gg) BC (Mg)  Discussion: what could be the reasons for such a large difference? The region considered in this study is larger (including South of Russia and Kazakhstan). The burnt biomass estimation by direct observation of the burnt area is more representative than indirect methods. Nevertheless, a good agreement may be observed in the range of the values. Further analysis will have to be done to assess the regional and temporal differences. A difference of 107 Gg is obtained over all of the region during the period of the ACE-Asia campaign just by changing BC emission factors. This shows a high sensitivity of the total emissions to the selection of emission factors. zoom 26/04/01 : SPOT-Vegetation 22/04/01: Landsat TM