Workshop QUEST 27-28 October 2005

ABBI: Asian Biomass Burning Inventory

from burnt area data given by SPOT-VEGETATION system

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, (2)

Now at

14 avenue Edouard Belin 31400 Toulouse, France.

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.

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

 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) active fires smoke burnt areas Helicopter view SPOT-VEGETATION imagery

Zoom on India: comparison of the 2 acquisition methods

0 50 04/26/01 : SPOT-Vegetation zoom 20 – 29 April 2001 : nb. fire events (derived from AVHRR) 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) 04/22/01: Landsat TM  Strong uncertainty related to the active fire maps (derived from NOAA-AVHRR)

Consistency of the burnt area method

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

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) 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  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) 

Test of several processing algorithms

• • Start of the monsoon season at the end of the ACE-Asia period (desert to evergreen moist forest) 

Selection of Ershov et al., 2001

GIS (Geographic Information System) analysis

Burnt pixels map GIS Vegetation Map Administrative Map Latitudinal Strip 1x1° Grid burnt area* / country / vegetation burnt area / country / latitudinal strip burnt area / vegetation / 1°x1° grid burnt area / … / … * Assumption: 1 pixel burnt = 1 km 2

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 a x b – where: 

A

the burnt area   

B

a b the biomass density the fraction of aboveground biomass the burning efficiency available (SPOT-VGT) from literature “ “

Adaptation of the various factors to the vegetation classes

Vegetation Class evergreen needleleaf forest evergreen broadleaf forest deciduous needleleaf forest deciduous broadleaf forest mixed forest woodland wooded grassland closed shrubland open shrubland grassland cropland Biomass Density (g/m²) 36700 23350 18900 20000 22250 10000 3300 7200 1600 1250 5100 Burning efficiency 0.25

0.25

0.25

0.25

0.25

0.35

0.4

0.5

0.85

0.95

0.6

EF(BC) 0.6

0.7

0.6

0.6

0.6

0.61

0.62

0.61

0.62

0.62

0.725

EF(OC) 6 6.4

6 6 6 5 4 5 4 4 2.1

EF(CO) 107 104 107 107 107 86 65 86 65 65 92  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]

Results of the spatial and temporal distribution of the emissions (March – May 2001)



Daily distribution for 58 gases and BC and OC particulate species (1 March – 15 May 2001) : ABBI inventory [Michel et al., 2005]

BC emissions (1-10 may 2001)

Comparison between 2000-2001 ABBI : Black Carbon emissions

ABBI  Differences in spatial and temporal distribution  Strong inter-annual variability

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

Comparison ABBI [

Michel et al., 2005

] – ACESS [

Streets et al., 2003

]:

BC spatial distribution

ABBI

: Asian Biomass Burning Inventory

ACESS

: Ace-Asia and Trace-P Modelling and Emission Support System !! ACESS doesn’t take into account FSU countries and Kazakhstan

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