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a large carbon sink in the woody biomass of northern forests. Title. Myneni, Dong Tucker Kauppi Liski Kaufmann Zhou Alexeyev Hughes. department of geography, boston university, 675 commonwealth av., boston, ma 02215, usa

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title

a large carbon sink in the woody biomass of

northern forests

Title

Myneni, Dong

Tucker

Kauppi

Liski

Kaufmann

Zhou

Alexeyev

Hughes

department 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

work funded by nasa earth science enterprise

boston university

abstract

abstract

Abstract
  • 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.

abstract

motivation

motivation

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

1. Bousquet, P., Peylin, P., Ciais, P., Qu\'er\'e, C.L., Friedlingstein, P. & Tans, P.P. (2000) Science290, 1342-1346.

land carbon pool

introduction

Land carbon pool
  • Carbon on land is contained in various pools such as,1

land

carbon

pools

- vegetation

- detritus - black carbon residue from fires

- soil - harvested products, etc.

  • 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.

forest

definitions (1 of 2)

Forest
  • We define forests as the following remote sensing land covers
  • - broad leaf forests - mixed forests
  • - needle leaf forests - woody savannas

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.

forests

0

10

20

30

40

50

60

forest fraction (% of pixel area)

woody biomass

definitions (2 of 2)

Woody biomass
  • Woody biomassconsists 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

woody biomass

- aging

- mortality - disease

- harvest - insect attacks

- fire - windthrow

slide7

data (1 of 9)

Data
  • 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.

remote

sensing

of biomass

satellite data

data: satellite (2 of 9)

Satellite data
  • normalized difference vegetation index (NDVI)
  • global
  • 15-day maximum value composite
  • 8 km resolution
  • july 1981 to december 1999

processing included

  • cloud screening
  • calibration
  • El Chichon & Mt. Pinatubo corrections
  • research quality assessed1

satellite data

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.

satellite data1

-7.5

-2.5

0

5

10

15

20

25

data: satellite (3 of 9)

Satellite data
  • 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.

climatological

seasonal

greenness

total

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

inventory data

data: inventory (4 of 9)

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.

forest

inventory

data

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.

forest area distribution

30

20

10

0

Frequency (%)

0 2 4 6 8 10 12 14 16 18 20

0 1 2 3 4 5

provincial forest area (million ha)

data: inventory (5 of 9)

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).

provincial

forest

area

distribution

forest area by genus

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%)

USA

0 50 100 150 200

forest area (million ha)

data: 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%).

forest

area

by

genus

  • The area of broad leaves (mostly oak) in the USA is comparable to that of needle leaves (pine, fir and spruce), about 40%.
slide13

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

0 20 40 60 80 100

Russia

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

forest area (million ha)

data: inventory (7 of 9)

forest

area

by

genus

age structure

data: inventory (8 of 9)

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.

forest

age

structure

100

80

60

40

20

0

All species

Pine

Oak

Birch

Fir

Spruce

Others

USA

Forest area (million ha)

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

slide15

data: inventory (9 of 9)

400

300

200

100

0

All species

Pine

Spruce

Larch

Fir

Birch

Russia

forest

age

structure

forest area (million ha)

Early Advanced Middle-aged Premature Mature

100

80

60

40

20

0

Canada

forest area (million ha)

Regeneration Immature Mature Overmature Unclassfied

gis 1

methods: GIS (1 of 5)

GIS-1
  • 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.

matching

inventory

and

ndvi

data

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

slide17

methods: GIS (2 of 5)

matching

inventory

and

ndvi

data

evergreen needleleaf forests

evergreen broadleaf forests

deciduous needle forests

deciduous broadleaf forests

mixed forests

woody savannas

savannas

closed shrublands

open shrublands

grasslands

croplands

barren

(1)

(2)

gis 2

methods: GIS (3 of 5)

GIS-2

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.
slide19

Forest and land area in each province

Remote sensing estimates

Inventory report (1982-86)

Inventory report (1993-97)

0 1 2 3 4 5

Area (million ha)

Forest

Area (million ha)

0 2 4 6 8 10

Land

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

methods: GIS (4 of 5)

matching

inventory

and

ndvi

data

gis 3

methods: GIS (5 of 5)

GIS-3
  • 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.

matching

inventory

and

ndvi

data

(1)

(2)

regression relation

methods: regression model (1 of 4)

Regression 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 = a + [(1/NDVI)/Latitude2] + g Latitude

ndvi

biomass

relation

Biomass: inventory estimate of biomass (tons/ha)

NDVI: cumulative growing season NDVI averaged over a five year period prior to inventory date

Latitude: the centriod of the area sampled by forest inventory in a province

,  and : regression coefficients

regression tests

methods: regression model (4 of 4)

Regression 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.

statistical

tests

slide25

results: spatial patterns (1 of 8)

  • 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.

spatial

pattern

of

pools

pool pattern

0 10 20 30 40 50 60

Carbon Pool (tons C/ha)

1995 to 1999

results: spatial patterns (2 of 8)

Pool pattern
  • 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).

spatial

pattern

of

pools

canada pool

0 10 20 30 40 50 60

Carbon Pool (tons C/ha)

1995 to 1999

results: spatial patterns (3 of 8)

Canada (pool)

pool

pattern

in

canada

area: 239.5 Mha

pool size: 10.56 Gt C

sink: 0.073Gt C/yr

usa pool

0 10 20 30 40 50 60

Carbon Pool (tons C/ha)

1995 to 1999

results: spatial patterns (4 of 8)

USA (pool)

pool

pattern

in the

usa

area: 215.5 Mha

pool size: 12.48 Gt C

sink: 0.142Gt C/yr

russia pool

0 10 20 30 40 50 60

Carbon Pool (tons C/ha)

1995 to 1999

results: spatial patterns (5 of 8)

Russia (pool)

pool

pattern

in

russia

area: 642.2 Mha

pool size: 24.39 Gt C

sink: 0.284Gt C/yr

europe pool

0 10 20 30 40 50 60

Carbon Pool (tons C/ha)

1995 to 1999

results: spatial patterns (6 of 8)

Europe (pool)

area: 161.1 Mha

pool size: 8.71 Gt C

sink: 0.136Gt C/yr

pool

pattern

in

europe

china japan pool

0 10 20 30 40 50 60

Carbon Pool (tons C/ha)

1995 to 1999

results: spatial patterns (7 of 8)

China & japan (pool)

pool

pattern

in china

japan

area: 161.6 Mha

pool size: 4.58 Gt C

sink: 0.05054Gt C/yr

pool changes

-0.3 0 0.3 0.6 0.9

Source Sinks

Changes in Carbon Pool (tons C/ha/yr)

1980s & 90s

results: spatial patterns (8 of 8)

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

spatial

pattern

of pool

changes

  • 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.
uncertainty

results: uncertainties (1 of 3)

Uncertainty
  • 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.

uncertainties

in

remote

sensing

estimates

slide34

results: uncertainties (2 of 3)

uncertainties

in

remote

sensing

estimates

slide35

results: uncertainties (3 of 3)

uncertainties

in

remote

sensing

estimates

countrywise pool

results: pool estimates (1 of 3)

Countrywise (pool)
  • 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

NH

pool

estimate

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

countrywise pool1

results: spatial pattern (12 of 12)

results: pool estimates (2 of 3)

Countrywise (pool)

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).
countrywise sink

results: sink estimates (1 of 2)

Countrywise (Sink)
  • Our estimate for the woody biomass sink during the 1980s and 90s is 0.680.34 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).

NH

sink

estimates

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.

north amer euriasia

results: sink estimates (2 of 2)

North amer. & euriasia
  • 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.

eurasia vs.

north

america

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

canadian estimates

results: country estimates (1 of 6)

Canadian estimates
  • 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

canadian

estimates

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

usa estimates

results: country estimates (2 of 6)

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).

usa

estimates

  • 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).
russia estmates

results: country estimates (3 of 6)

Russia estmates
  • 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

russian

estimates

  • 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,
  • The various pool estimates are comparable (38-43 tons C/ha).
  • The difference in sink estimates between remote sensing and TBFRA-2000 is smaller (0.44 vs. 0.53; in tons C/ha/yr).
slide44

results: country estimates (4 of 6)

sinks (Mt C/yr)

0 50 100 150 200 250 300

Russia*

USA

Canada

Sweden

Germany

Japan

Italy

France

Romania

Spain

Poland

Turkey

Finland

UK

Bulgaria

Austria

Belarus*

Czech*

Norway

Greece

Portugal

Latvia*

Ukraine*

Switzerland

Lithuania*

Estonia*

Hungary

Belgium

Netherlands

Denmark

annex 1

countries:

sinks

annex 1 countries#

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

0 2 4 6 8 10 12 14 16

slide45

results: country estimates (5 of 6)

sinks to emissions

0 0.2 0.4 0.6 0.8 1

annex 1

countries:

sinks to

emissions

ratio

Sweden

Latvia*

Russia*

Canada

Finland

Norway

Lithuania*

Austria

Portugal

Estonia*

Bulgaria

Romania

Turkey

Belarus*

Greece

Spain

Switzerland

USA

Italy

Czech*

France

Poland

Hungary

Germany

Japan

UK

Ukraine*

Denmark

Belgium

Netherlands

annex 1 countries#

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

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

slide46

results: country estimates (6 of 6)

sinks to emissions per capita (10-8 )

0 5 10 15 20 25 30 35 40

Latvia*

Estonia*

Sweden

Norway

Finland

Lithuania*

Austria

Bulgaria

Portugal

Canada

Belarus*

Switzerland

Greece

Czech*

Romania

Hungary

Russia*

Spain

Turkey

Italy

Poland

Denmark

France

Belgium

Germany

UK

USA

Japan

Netherlands

Ukraine*

annex 1

countries:

sinks to

emissions

per capita

annex 1 countries#

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

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

0 0.5 1 1.5 2 2.5 3 3.5

reasons

discussion: reasons (1 of 6)

Reasons
  • 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.

reasons

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

-0.3 0 0.3 0.6 0.9

Source Sinks

Changes in Carbon Pool (tons C/ha/yr)

1980s & 90s

discussion: reasons (2 of 6)

Canada (sink)
  • Increased incidence of fires and infestations in Canada.

pool

changes

in

canada

usa sink

-0.3 0 0.3 0.6 0.9

Source Sinks

Changes in Carbon Pool (tons C/ha/yr)

1980s & 90s

discussion: reasons (3 of 6)

USA (sink)
  • Fire suppression and forest regrowth in the USA.

pool

changes

in

usa

russia sink

-0.3 0 0.3 0.6 0.9

Source Sinks

Changes in Carbon Pool (tons C/ha/yr)

1980s & 90s

discussion: reasons (4 of 6)

Russia (sink)
  • Declining harvests in Russia.

pool

changes

in

russia

europe sink

-0.3 0 0.3 0.6 0.9

Source Sinks

Changes in Carbon Pool (tons C/ha/yr)

1980s & 90s

discussion: reasons (5 of 6)

Europe (sink)
  • Improved silviculture in the Nordic countries.

pool

changes

in

europe

slide52

-0.3 0 0.3 0.6 0.9

Source Sinks

Changes in Carbon Pool (tons C/ha/yr)

1980s & 90s

discussion: reasons (6 of 6)

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

pool

changes

in china

japan

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.

limitations

discussion: limitations (1 of 1)

Limitations
  • How robust are these results?
  • Residual atmospheric effects and calibration errors in satellite data cannot be ruled out.
  • Uncertainties in inventory data are country-specific and difficult to quantify.
  • Simple models are used to convert wood volume and greenness data to biomass.
  • The differences in forest area estimates between remote sensing and inventories are not easy to reconcile because of definition issues.

limitations

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

discussion: contributions (1 of 1)

Contributions
  • This work contributes to global carbon cycle research in four ways.
  • 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.
  • The NDVI data when used in inversion studies provide additional constraints to inferences of source/sink distribution from atmospheric CO2 and isotopic concentration data.
  • 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.
  • 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.

contributions

  • 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.