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

Context and Objectives

Objectives:

To perform an inventory of gases and aerosols emitted by vegetation fires in Asia during the ACE-ASIA experiment: March 1st - May, 15th 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)

SPOT-VEGETATION imagery

Helicopter view

active fires

smoke

burnt areas

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

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

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 Modulespatio-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 ModuleForest-non forest masking

Algorithm: Ershov et al., 2001

Test of several processing algorithms

Selection of Ershov et al., 2001

GIS (Geographic Information System) analysis

* Assumption: 1 pixel burnt = 1 km2

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 / … / …

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

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]

Vegetation Class Biomass Density (g/m²) Burning efficiency EF(BC) EF(OC) EF(CO)

evergreen needleleaf forest 36700 0.25 0.6 6 107

evergreen broadleaf forest 23350 0.25 0.7 6.4 104

deciduous needleleaf forest 18900 0.25 0.6 6 107

deciduous broadleaf forest 20000 0.25 0.6 6 107

mixed forest 22250 0.25 0.6 6 107

woodland 10000 0.35 0.61 5 86

wooded grassland 3300 0.4 0.62 4 65

closed shrubland 7200 0.5 0.61 5 86

open shrubland 1600 0.85 0.62 4 65

grassland 1250 0.95 0.62 4 65

cropland 5100 0.6 0.725 2.1 92

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]

Comparison between 2000-2001 ABBI : Black Carbon emissions

Differences in spatial and temporal distribution

Strong inter-annual variability

0.00E+00

5.00E+03

1.00E+04

1.50E+04

2.00E+04

2.50E+04

3.00E+04

3.50E+04

4.00E+04

4.50E+04

BC

em

issi

on

s (t

on

nes

)

1-15 March 16-31 March 1-15 April 16-30 April 1-15 May

period

2000 India 2001 India 2000 China 2001 China 2000 Kazakhstan 2001 Kazakhstan

ABBI

Comparison ABBI [Michel et al., 2005] – ACESS [Streets et al., 2003]:

BC temporal distribution

0.0E+00

1.0E+04

2.0E+04

3.0E+04

4.0E+04

5.0E+04

6.0E+04

7.0E+04

8.0E+04

BC

em

issi

on

s (t

on

nes

)

March 1-10 March 11-20 March 21-31 April 1-10 April 11-20 April 21-30 May 1-10

period

ACESS ABBI

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

ABBI: Asian Biomass Burning Inventory

ACESS: Ace-Asia and Trace-P Modelling and Emission Support System

Mars 1-10: ABBI

Mars 1-10: ACESS

Mars 11-20: ABBI

Mars 11-20: ACESS

Mars 21-31: ABBI

Mars 21-31: ACESS

Avril 1-10: ACESS

Avril 1-10: ABBIAvril 11-20: ABBI

Avril 11-20: ACESS

Avril 21-30: ABBI

Avril 21-30: ACESSMai 1-10: ACESSMai 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

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