1 Title department of geography, boston university, 675 commonwealth av., boston, ma 02215, usa nasa...

1

Titledepartment of geography, boston university, 675 commonwealth av., boston, ma 02215, usa

nasa goddard space flight center, code 923, greenbelt, md 20771, usa

department of limnology and environmental protection, university of helsinki, fin-00014 helsinki, finland

european forest institute, and department of forest ecology, university of helsinki, fin-00014 helsinki, finland

department of geography, boston university, 675 commonwealth av., boston, ma 02215, usa

department of geography, boston university, 675 commonwealth av., boston, ma 02215, usa

the saint-petersburg forest ecological center, 21 institutskii av. st.petersburg, 194021 russia

laboratory of tree-ring research, university of arizona, tucson, az 85721, usa

a large carbon sink in the woody biomass ofnorthern forests

Myneni, Dong

Tucker

Kauppi

Liski

Kaufmann

Zhou

Alexeyev

Hughes

boston universitywork funded by nasa earth science enterprise

2

Abstractabstract

abst

ract

The terrestrial carbon sink, as of yet unidentified, represents 15 to 30% of annual global emissions of carbon from fossil fuels and industrial activities. Some of the missing carbon is sequestered in vegetation biomass and, under the Kyoto Protocol of the UNFCCC, industrialized nations can use certain forest biomass sinks to meet their greenhouse gas emissions reduction commitments.

Therefore, we analyzed 19 years of data from remote-sensing spacecraft and forest inventories to identify the size and location of such sinks.

The results, which cover the years 1981-99, reveal a picture of biomass carbon gains in Eurasian boreal and North American temperate forests and losses in some Canadian boreal forests.

For the 1.42 billion hectares of northern forests, roughly above the 30th parallel, we estimate the biomass sink to be 0.68 0.34 billion tons carbon per year, of which nearly 70% is in Eurasia, in proportion to its forest area and in disproportion to its biomass carbon pool.

The relatively high spatial resolution of these estimates permits direct validation with ground data and contributes to a monitoring program of forest biomass sinks under the Kyoto protocol.

3

Motivation About 1 to 2 giga (109) tons of carbon (gt c) a year are

suggested to be somehow sequestered in pools on land in the temperate and boreal regions.1

Debate is currently underway regarding which of the forest biomass sinks can be used by the Annex 1 parties, the industrialized nations, to meet their greenhouse gas emissions reduction commitments under the kyoto protocol of the united nations framework convention on climate change.

Thus, characterizing the location and mechanism of carbon sinks is of scientific and political importance.

motivation

mot

ivat

ion

1. Bousquet, P., Peylin, P., Ciais, P., Qu\'er\'e, C.L., Friedlingstein, P. & Tans, P.P. (2000) Science 290, 1342-1346.

4

Land carbon pool

This study is limited to analysis of the carbon pool in the woody biomass of temperate and boreal forests of the northern hemisphere, which cover an area of about 1.4 to 1.5 billion hectares.2

1. Schulze, E.-D., Wirth, C. and Heimann, M. (2000), Science, 289:2058-2059. 2. Liski, J. and Kauppi, P. (2000), in Forest Resources of Europe, CIS, North America, Australia, Japan and New Zealand

(industrialized temperate/boreal countries), UN-ECE/FAO contributions to the Global Forest Resources Assessment 2000, (United Nations, New York), pp. 155-171.

land

carbon

pools

introduction

Carbon on land is contained in various pools such as,1

- vegetation - detritus - black carbon residue from fires - soil - harvested products, etc.

5

Forest We define forests as the following remote sensing land covers

- broad leaf forests - mixed forests - needle leaf forests - woody savannas

fore

sts

definitions (1 of 2)

this land cover definition is broadly consistent with land use definitions of a forest but not of forest and other wooded land used by the FAO.

0

10

20

30

40

50

60

forest fraction (% of pixel area)

6

Woody biomass Woody biomass consists of - wood - twigs

- bark - stumps - branches - roots

of live trees, shrubs and bushes.

The vegetation pool gains carbon from productivity investment in these components loses carbon due to

definitions (2 of 2)

woo

dy b

iom

ass

- aging - mortality - disease- harvest - insect attacks- fire - windthrow

7

Data data (1 of 9)

remote

sensing

of biomass

Forest biomass cannot be directly measured from space yet.

Year-to-year changes in biomass are quite small, about two orders of magnitude smaller than the biomass pool. At decadal and longer time scales, the biomass changes can be considerable due to accrual of differences between gains and losses.

Potentially, these can be observed as low frequency variations in climatological greenness, in much the same way as greenness changes at century and longer time scales are suggestive of successional changes.

8

Satellite datadata: satellite (2 of 9)

sate

llit

e da

ta normalized difference vegetation index (NDVI)

- global- 15-day maximum value composite- 8 km resolution- july 1981 to december 1999

- cloud screening- calibration- El Chichon & Mt. Pinatubo

corrections- research quality assessed1

processing included

0.0

0.2

0.4

0.6

0.8

average greenness (81-99)

1. Zhou et al. (2001), J. Geophys. Res., 106(D17): 20069-20083.

9

Satellite datadata: satellite (3 of 9)

climatological

seasonal

greenness

total

In regions of distinct seasonality, as in the north, the cumulative growing season NDVI succinctly captures both the average seasonal level of greenness and the growing season duration, and is therefore an ideal measure of seasonal vegetation greenness.

We assume that climatological seasonal greenness total to be a surrogate for biomass in these northern forests.

change in NDVI total per year (80s &90s)

-7.5

-2.5

0

5

10

15

20

25

10

Inventory datadata: inventory (4 of 9)

forest

inventory

data

We analyzed 1980s and 90s inventory data of stem wood volume from 171 provinces in six countries (Canada, Finland, Norway, Russia, Sweden and USA) covering over one billion hectares (ha) of northern temperate and boreal forests.

The inventory data were converted to above-stump and total biomass using country specific coefficients.1

These data represent a wide variety of inventory practices, provincial forest area, ecosystem types, age structures, fire and insect dynamics, management practices and time periods.

1. Liski, J. & Kauppi, P. (2000) in Forest Resources of Europe, CIS, North America, Australia, Japan and New Zealand (industrialized temperate/boreal countries), UN-ECE/FAO contributions to the Global Forest Resources Assessment 2000 , (United Nations, New York), pp. 155-171.

11

Forest area distributiondata: inventory (5 of 9)

provincial

forest

area

distribution

About 54% of the provinces had forested area less than 1.5 million ha (37% less than 625 thousand ha). About 27% had area greater than 5 million ha (12% greater than 20 million ha).

0 2 4 6 8 10 12 14 16 18 20

Fre

quen

cy (

%)

30

20

10

00 1 2 3 4 5

provincial forest area (million ha)

12

Forest area by genusdata: inventory (6 of 9)

forest

area

by

genus

The dominant forest type is needle leaf (spruce, pine, fir and other conifers in Canada, >60%; larch, pine, spruce in Russia, > 70%; spruce and pine in Finland, Norway and Sweden, 70-90%).

The area of broad leaves (mostly oak) in the USA is comparable to that of needle leaves (pine, fir and spruce), about 40%.

Not stocked (1.56%)

Others (5.55%0

Larch (0.53%)

Oak (37.1%)

Fir (11.3%)

Birch (8.02%)

Spruce (8.93%)

pine (27%)0 50 100 150 200

forest area (million ha)

USA

13

data: inventory (7 of 9)

forest

area

by

genus

forest area (million ha)

Others (11.6%) Aspen (2.4%) Oak (1.3%) Fir (2.2%) Birch (12.9%)

Larch (37.1%) Spruce (10.8%) Pine (21.7%)

0 50 100 150 200 250 300 350

Russia

0 20 40 60 80 100

Unclassified (15.3%) Unspecified broadleaves (5.08%) Other broadleaves (2.1%)Maple (2.66%) Birch (3.87%) Polar (7.85%) Unspecified conifers (6.06%)Other conifers (1.49 %)Larch 0.38%)Hemlock (1.99%) Fir (8.5%) Spruce (26.3%) Pine (16.3%)

Canada

14

Age structuredata: inventory (8 of 9)

forest

age

structure

About 40% of the forest area in Canada and 55% in Russia is under mature and over mature forests.The area under immature forests in Canada is about 30% (23% middle-aged forests in Russia). The forest area under regression is less than 10% in Canada (20% in Russia).

Thus, in a broad sense, the Canadian and Russian forest age structure are comparable.

In the USA, fully three fourths of the forest area is under forests younger than 85 years.

0-20 20-50 50-85 85-120 >120 years

100

80

60

40

20

0

For

est

area

(m

illio

n ha

)

All species

Pine

Oak

Birch

Fir

Spruce

Others

USA

15

data: inventory (9 of 9)

forest

age

structure

Regeneration Immature Mature Overmature Unclassfied

fore

st a

rea

(m

illio

n ha

)

Canada

400

300

200

100

0fo

rest

are

a (

mill

ion

ha)

Early Advanced Middle-aged Premature Mature

RussiaAll speciesPineSpruceLarchFirBirch

100

80

60

40

20

0

16

GIS-1methods: GIS (1 of 5)

matching

inventory

and

ndvi

data

The methodology is illustrated here, using Sweden as an example.

Sweden spans a latitude range of about 55N to 70N, with 24 provinces for which the inventory data are available (Fig. 1). The data reported are stem wood volume (106 m3) and forest area (103 ha) for various tree types and trunk size classes.

A remote sensing land cover map is required to match these provincial inventory estimates to NDVI data (Fig. 2). This map is at a spatial resolution of 1x1 km.1 Forests are defined as the following remote sensing land covers: broad leaf and needle leaf forests, mixed forests and woody savannas.

1. Hansen, M.C., DeFries, R.S., Townshend, J.R.G. and Sohlberg, R.(2000), Int. J. Remote Sens., 21, 1331-1364.

17

methods: GIS (2 of 5)

matching

inventory

and

ndvi

data

evergreen needleleaf forestsevergreen broadleaf forestsdeciduous needle forestsdeciduous broadleaf forestsmixed forestswoody savannassavannasclosed shrublandsopen shrublandsgrasslandscroplandsbarren

(1) (2)

18


matching

inventory

and

ndvi

data

For each province, in a geographical information system, we evaluate the cumulative growing season greenness from NDVI data layers, by averaging over forest pixels, as identified from the land cover map. This assures that the resulting provincial cumulative growing season greenness is assembled from NDVI data of forested regions only.

The degree to which total forest area estimates from inventory and remote sensing match, provides some confidence in both inventory and remote sensing data.

19

methods: GIS (4 of 5)

matching

inventory

and

ndvi

data

0

1

2

3

4

5A

rea

(mil

lion

ha)

Are

a (m

illi

on h

a)

0

2

4

6

8

10

Remote sensing estimatesInventory report (1982-86)Inventory report (1993-97)

Forest and land area in each province

Nbtn Vbtn Jmtl Vnrl Gavl Kopp Vrml Oreb Vstm Upps Sthm Sadm Ostg Skbg Alvs Jkpg Kron Kalm Gotl Gtbg Hall Blek Skan

Forest

Land

20


matching

inventory

and

ndvi

data

The inventory stem wood volume data are converted to total and above stump biomass, and then plotted against the provincial growing season cumulative NDVI, as in Fig. 1. A similar plot for Russia is shown in Fig. 2.

(1)

(2)

21

Regression relationmethods: regression model (1 of 4)

ndvi

biomass

relation

Satellite and inventory data shown below (without outliers) were transformed to estimate a statistically significant relation between biomass and seasonal greenness totals.

1/Biomass = + [(1/NDVI)/Latitude2] +Latitude

Biomass: inventory estimate of biomass (tons/ha)NDVI: cumulative growing season NDVI averaged over a five

year period prior to inventory dateLatitude: the centriod of the area sampled by forest

inventory in a province, and : regression coefficients

22

methods: regression model (2 of 4)

ndvi

biomass

relation

23

methods: regression model (3 of 4)

ndvi

biomass

relation

24

Regression testsmethods: regression model (4 of 4)

statistical

tests

There is a statistically meaningful relation between NDVI and biomass in nearly every nation and sample period.

There is statistically meaningful relation between biomass and NDVI for values of NDVI that exceed 80.

The relation between NDVI and biomass for values of NDVI above 80 is not statistically different from the relation between NDVI and biomass for values of NDVI equal to or less than 80.

There is a relation between NDVI and biomass for nearly all values for NDVI.

There is a statistically meaningful relation between NDVI and biomass, regardless of latitude.

The spatial relation between biomass and NDVI in Sweden is not statistically different from the temporal relation between biomass and NDVI in Sweden.

Tests indicate that it is highly unlikely that the size the carbon sink reported in the text is a statistical artifact of uncertainty regarding the relation between biomass and NDVI.

25

results: spatial patterns (1 of 8)

spatial

pattern

of

pools

Because of their high spatial resolution, relative to provincial inventory measurements, biomass estimates from satellite data provide spatial detail of the carbon pool and where changes in the pool have occurred.

To document these regional features, we evaluated forest woody biomass carbon pools during the early 1980s (1982-86) and late 1990s (1995-99) with the regression model and pixel-level NDVI data.

Changes in the carbon pool were then evaluated as simply the difference between these two pool estimates, pixel-by-pixel, and quoted on a per year basis.

26

Pool patternresults: spatial patterns (2 of 8)

spatial

pattern

of

pools

0 10 20 30 40 50 60

Carbon Pool (tons C/ha)

1995 to 1999

The biomass map indicates larger average pools, in tons C/ha, in North America compared to Eurasia (51vs. 39).

The average pool size in Europe and the USA is larger than in Canada and Russia (54-58 vs. 38-44).

Amongst the European countries, Austria, France and Germany have notably large average pools (60, 67 and 73, respectively).

The estimates for Finland, Norway and Sweden are comparable to Russia (35-40 vs. 38).

27

Canada (pool)results: spatial patterns (3 of 8)

pool

pattern

in

canada

0 10 20 30 40 50 60


1995 to 1999

area: 239.5 Mha

pool size: 10.56 Gt C

sink: 0.073 Gt C/yr

28

USA (pool)results: spatial patterns (4 of 8)

pool

pattern

in the

usa

0 10 20 30 40 50 60


1995 to 1999

area: 215.5 Mha


sink: 0.142 Gt C/yr

29

Russia (pool)results: spatial patterns (5 of 8)

pool

pattern

in

russia

0 10 20 30 40 50 60


1995 to 1999

area: 642.2 Mha


sink: 0.284 Gt C/yr

30

Europe (pool)results: spatial patterns (6 of 8)

pool

pattern

in

europe

0 10 20 30 40 50 60


1995 to 1999

area: 161.1 Mha


sink: 0.136 Gt C/yr

31

China & japan (pool)results: spatial patterns (7 of 8)

pool

pattern

in china

japan

0 10 20 30 40 50 60


1995 to 1999

area: 161.6 Mha


sink: 0.05054 Gt C/yr

32

Pool changesresults: spatial patterns (8 of 8)

spatial

pattern

of pool

changes

-0.3 0 0.3 0.6 0.9

Source Sinks

Changes in Carbon Pool (tons C/ha/yr)1980s & 90s

Carbon gains, in excess of 0.3 tons C/ha/yr, are seen in Eurasian boreal and North American temperate forests.

The gains are observed in Eurasia over a large, broad, nearly contiguous swath of land, from Sweden, through Finland, European Russia, central Siberia to trans-Baikalia

In North America, similarly large gains are seen in the eastern temperate forests of the USA and in southern Ontario and Quebec below the 50th parallel.

Carbon losses, greater than 0.1 tons C/ha/yr, are seen in Canadian boreal forests, from Newfoundland to Northwest territories, except in smaller fragments in northern Saskatchewan and Alberta, where gains are observed.

33

Uncertainty results: uncertainties (1 of 3)

uncertainties

in

remote

sensing

estimates

The average absolute difference between remote sensing and inventory estimates is (next two slides)

10.4 tons C/ha for above-stump biomass (27%) 16.1 tons C/ha for total biomass (33%) 0.33 tons C/ha/yr for changes in pool size (50%)

There is no bias in the estimation of biomass pools and changes to the pools.

The national inventory sink estimates were derived from wood volume increment and loss data (natural and fellings), unlike remote sensing estimates which are biomass differences between two time periods. The comparability of the two estimates is thus noteworthy.

34

results: uncertainties (2 of 3)

uncertainties

in

remote

sensing

estimates

35

results: uncertainties (3 of 3)

uncertainties

in

remote

sensing

estimates

36

Countrywise (pool)

NH

pool

estimate

1. IPCC, 2000, Land use, Land-use change, and Forestry, Cambridge University Press, Cambridge, United Kingdom.

results: pool estimates (1 of 3)

We estimate the carbon pool in the woody biomass of 1420 million hectares (Mha) of temperate and boreal forests in the northern hemisphere to be 61 20 Gt C during the late 1990s.

This is comparable to the TBFRA-2000 which reports a carbon pool of 80 Gt C, but on 2477 Mha of forests and other wooded land.

Both these estimates are considerably lower than the estimate, 147 Gt C on 2410 Mha of forests and other wooded land, quoted by the IPCC.1

Earlier studies may have overestimated carbon pools possibly because of unrepresentative samples, which tend to bias towards sites with larger than average pools. If this has occurred for tropical forests and savannas, the current estimate of global vegetation carbon pool, 466 Gt C, may also be too large.1

37

Countrywise (pool)results: spatial pattern (12 of 12)results: pool estimates (2 of 3)

country

pool

estimates

The average pool size in Europe and the USA is larger than in Canada and Russia (54-58 vs. 38-44). Amongst the European countries, Austria, France and Germany have notably large pools (60, 67 and 73, respectively).

The estimates for Finland, Norway and Sweden are comparable to Russia (35-40 vs. 38).

38

results: pool estimates (3 of 3)

country

pool

estimates

39

Countrywise (Sink)

NH

sink

estimates

results: sink estimates (1 of 2)

Our estimate for the woody biomass sink during the 1980s and 90s is 0.680.34 Gt C/yr.

This is in the mid-range of estimates by Sedjo1 for mid-1980s (0.36 Gt C/yr) and TBFRA-20002 for early and mid-1990s (0.81 Gt C/yr).

1. Sedjo, R.A., 1992, Ambio, 21: 274-277.

2. Liski, J. & Kauppi, P., 2000, in Forest Resources of Europe, CIS, North America, Australia, Japan and New Zealand (industrialized temperate/boreal countries), UN-ECE/FAO contributions to the Global Forest Resources Assessment 2000, United Nations, New York, pp. 155-

171.

40

North amer. & euriasia

eurasia vs.

north

america

It is instructive to compare the sink estimates of North America and Eurasia in view of a large terrestrial North American sink and weak Eurasian sink reported for the 1988-92 time period.1

The average sequestration rate, in tons C/ha/yr, is highest in Europe (0.84) and the USA (0.66), and least in Canada and China (0.27-0.31), with values for Russia in between (0.44).

Consequently, the average sequestration rate is comparable between North America and Eurasia, (0.47-0.49), unlike the average pool sizes (51 vs. 39 tons C/ha).

Thus, nearly 70% of the biomass sink is in Eurasia (0.47 Gt C/yr), in proportion to its forest area and in disproportion to its pool size.

1. Fan et al., 1998, Science, 282: 442-446.

results: sink estimates (2 of 2)

41

Canadian estimates

canadian

estimates

results: country estimates (1 of 6)

1. Canadian Forest Service, The State of Canada's Forests 1993, Nat. Resour. Can., Ottawa, Ontario, Canada.

The estimates of the three large countries, Canada, Russia and the USA, are crucial to overall accuracy because they account for 78% of the pool, 73% of the sink and 77% of the forest area.

The losses observed in some Canadian boreal forests are consistent with reports of disturbances from fires and insects during the 1980s and 90s.

For Canada, we estimate a sink of about 0.073 Gt C/yr, which is comparable to an inventory estimate by the Canadian Forest Service (0.091 Gt C/yr). 1

42

USA estimates

usa

estimates


Our pool, sink and forest area estimates for the USA are comparable to TBFRA-2000 estimates.

Our sink estimate for the USA (0.142 Gt C/yr) is comparable to most estimates for the 1980s (0.02-0.15 Gt C/yr).

Our pool (10.6 Gt C) and forest area (239 Mha) estimates for the late 1990s are also comparable to the TBFRA-2000 estimates (11.9 Gt C and 244 Mha).

43

Russia estmates

russian

estimates


Estimates for Russia differ. The remote sensing estimate of forest area,

642 Mha, is about 130-180 Mha lower, possibly due to the resolution of satellite data, which may be too coarse for detecting

o The various pool estimates are comparable (38-43 tons C/ha).

o The difference in sink estimates between remote sensing and TBFRA-2000 is smaller (0.44 vs. 0.53; in tons C/ha/yr).

tree stands in the forest-tundra of Russia, where small lots of sparse stands with extremely low growing stock are distributed between the vast peatlands.

When expressed on per ha forest area basis,

44

annex 1

countries:

sinks


Russia*USA

Canada

SwedenGermany

JapanItaly

FranceRomania

SpainPolandTurkeyFinland

UKBulgariaAustriaBelarus*Czech*NorwayGreece

PortugalLatvia*

Ukraine*SwitzerlandLithuania*Estonia*HungaryBelgium

NetherlandsDenmark

ann

ex 1

cou

ntr

ies#

0 50 100 150 200 250 300

sinks (Mt C/yr)

#Australia, Iceland, Ireland, Luxembourg, New Zealand are not included.

0 2 4 6 8 10 12 14 16

45

annex 1

countries:

sinks to

emissions

ratio


SwedenLatvia*Russia*CanadaFinlandNorway

Lithuania*Austria

PortugalEstonia*BulgariaRomaniaTurkey

Belarus*GreeceSpain

SwitzerlandUSAItaly

Czech*FrancePoland

HungaryGermany

JapanUK

Ukraine*DenmarkBelgium

Netherlands

ann

ex 1

cou

ntr

ies#

0 0.2 0.4 0.6 0.8 1

sinks to emissions

*Annual mean emissions are from 1992 to 1998. The others are from 1982 to 1998.


46

annex 1

countries:

sinks to

emissions

per capita


Latvia*Estonia*SwedenNorwayFinland

Lithuania*

AustriaBulgariaPortugalCanadaBelarus*

SwitzerlandGreeceCzech*

RomaniaHungaryRussia*SpainTurkey

ItalyPoland

DenmarkFrance

BelgiumGermany

UKUSA

JapanNetherlands

Ukraine*

ann

ex 1

cou

ntr

ies#

sinks to emissions per capita (10-

8 )

*Annual mean emissions are from 1992 to 1998. The others are from 1982 to 1998.


0 5 10 15 20 25 30 35 40

0 0.5 1 1.5 2 2.5 3 3.5

47

Reasons

reas

ons

discussion: reasons (1 of 6)

The reasons for the observed changes in the forest woody biomass pool are not known.

This implies uncertainty regarding the future of biomass sinks and therefore the need for monitoring.

But, the spatial patterns offer some clues.

o Woody encroachment and longer growing seasons from warming in the northern latitudes possibly explain some of the changes, and

48

Canada (sink)discussion: reasons (2 of 6)

pool

changes

in

canada

-0.3 0 0.3 0.6 0.9

Source Sinks


o Increased incidence of fires and infestations in Canada.

49

USA (sink)discussion: reasons (3 of 6)

pool

changes

in

usa

o Fire suppression and forest regrowth in the USA.

-0.3 0 0.3 0.6 0.9

Source Sinks


50

Russia (sink)discussion: reasons (4 of 6)

pool

changes

in

russia

o Declining harvests in Russia.

-0.3 0 0.3 0.6 0.9

Source Sinks


51

Europe (sink)discussion: reasons (5 of 6)

pool

changes

in

europe

-0.3 0 0.3 0.6 0.9

Source Sinks


o Improved silviculture in the Nordic countries.

52

discussion: reasons (6 of 6)

pool

changes

in china

japan

o Forest expansion (afforestation and reforestation) and regrowth.1

1. Fang, J., Chen, A., Peng, C., Zhao, S., and Ci, L. (2001), Changes in forest biomass carbon storage in China between 1949 and 1998, Science, 292:2320-2322.

-0.3 0 0.3 0.6 0.9

Source Sinks


53

Limitations lim

itat

ion

s

discussion: limitations (1 of 1)

How robust are these results?

o Residual atmospheric effects and calibration errors in satellite data cannot be ruled out.

o Uncertainties in inventory data are country-specific and difficult to quantify.

o Simple models are used to convert wood volume and greenness data to biomass.

o The differences in forest area estimates between remote sensing and inventories are not easy to reconcile because of definition issues.

All of this suggests a cautionary reading of the results and need for further research.

54

Contributions co

ntr

ibu

tion

s

discussion: contributions (1 of 1)

o It provides spatial detail of the biomass carbon pool and where changes in this pool have occurred at a resolution that permits direct validation with ground data.

o The NDVI data when used in inversion studies provide additional constraints to inferences of source/sink distribution from atmospheric CO2 and isotopic concentration data.

o The inversion studies cannot partition the inferred sink between vegetation, soil and other pools. For example, if the vegetation is a sink and the soil is a source, estimates of vegetation pool changes would complement inversion results.

o Debate is currently underway regarding which of the forest biomass sinks can be used by the Annex 1 parties, the industrialized nations, to meet their greenhouse gas emissions reduction commitments under the Kyoto Protocol of the United Nations Framework Convention on Climate Change. Satellite estimates of biomass changes can be an important component of carbon accounting for verification of compliance, if the uncertainty of these estimates can be further reduced.

Improved observations of greenness levels from a new generation of spacecraft sensors such as the moderate resolution imaging spectroradiometer and multiangle imaging spectroradiometer, and possibly direct biomass measurements with lidars, offer promise for the future.

This work contributes to global carbon cycle research in four ways.

1 Title department of geography, boston university, 675 commonwealth av., boston, ma 02215, usa nasa...

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