Download - Christelle Michel (1,2) Jean-Marie Grégoire (3) , Kevin Tansey (3) , Catherine Liousse (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
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