Biomass burning emission inventory from a satellite based approach: the ACE-Asia case study...
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Transcript of Biomass burning emission inventory from a satellite based approach: the ACE-Asia case study...
Biomass burning emission inventory from a satellite based approach:
the ACE-Asia case studyChristelle 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 31400 Toulouse, France. ([email protected]) (2) Global Vegetation Monitoring Unit, Joint Research Centre, European Commission, TP.440, I-21020, Ispra (VA), Italy. (http://www.gvm.sai.jrc.it/fire/gba_2000_website/index.htm)
Context and ObjectivesContext 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, 15th 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 km2 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 & AnalysisData 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, 1st to May, 15th 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
1x1° Grid
Latitudinal Strip
Administrative Map
Vegetation Map
Burnt pixels map
GIS
burnt area / country / latitudinal strip
burnt area* / country / vegetation
burnt area / vegetation / 1x1° grid
burnt area / … / …
1x1° Grid
Latitudinal Strip
Administrative Map
Vegetation Map
1x1° Grid
Latitudinal Strip
Administrative Map
Vegetation Map
Burnt pixels map
GIS
burnt area / country / latitudinal strip
burnt area* / country / vegetation
burnt area / vegetation / 1x1° grid
burnt area / … / …
GIS (Geographic Information System) analysisGIS (Geographic Information System) analysis
* Assumption: 1 pixel burnt = 1 km2
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 High uncertainty associated with the active fire maps (as derived from NOAA-AVHRR data)maps (as derived from NOAA-AVHRR data)
050
20 – 29 April 2001 : nb. fire events (derived from AVHRR)
Helicopter view
SPOT-VEGETATION imagery
Active fires
Smoke
Burnt area
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 maskingAlgorithm: Ershov et al.,
2001
Building the emission inventoryBuilding 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 studyB the biomass density from literatureα the fraction of aboveground biomass “β the burning efficiency “
Temporal evolution of CO emissions from March to May Temporal evolution of CO emissions from March to May 20012001
PerspectivesPerspectives
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)
burnt areas (km2) per vegetation type
0
5000
10000
15000
20000
25000
30000
evergreenneedleleaf
forest
evergreenbroadleaf
forest
deciduousneedleleaf
forest
deciduousbroadleaf
forest
mixedforest
woodland woodedgrassland
closedshrubland
openshrubland
grassland cropland
vegetation type
burn
t are
as (k
m2 )
1-15 march 16-31 march 1-15 April 16-30 April 1-15 May
burnt areas (km2) computed over 14 latitudinal strips per 15 days
0 5000 10000 15000 20000 25000 30000 35000 40000 45000 50000
55N - 60N
50N - 55N
45N - 50N
40N - 45N
35N - 40N
30N - 35N
25N - 30N
20N - 25N
15N - 20N
10N - 15N
5N - 10N
0 - 5N
5S - 0
10S - 5S
latitudinal strips
burnt areas (km2)
1-15 march 16-31 march 1-15 April 16-30 April 1-15 May
Latitudinal distribution of burnt areasLatitudinal distribution of burnt areas Burnt areas per type of vegetation Burnt areas per type of vegetation cover cover
1-15 March 2001 16-31 March 2001
1-15 April 2001 15-30 April 2001
1-15 May 2001
BC emissions from March to May 2001BC 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
Vegetation
type
EF (g/kg)
forest grassland cropland
EF(CO) (1) 104 - 230 58 - 90 90
EF(BC) (2) 0.75 - 1.53 0.8 - 1.16 0.75
EF(BC) (1) 0.56 - 0.66 0.48 - 0.57 0.69
Scenario 2: EF(BC) from Liousse et al., 1996
BC = 393.4 Gg (March to 15th May 2001)
Scenario 1: EF(BC) from Andreae and Merlet, 2001
BC = 286.2 Gg (March to 15th May 2001)
Comparison with the ACE-Asia and TRACE-P reference (based on the inventories done for the year 2000; CGRER, 2002 (http://www.cgrer.uiowa.edu/EMISSION_DATA/index_16.htm))
CGRER, 2002: BC = 453.69 Gg/year This study: scenario 1: BC = 286.2 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