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. (micc@areo.obs-mip.fr) (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