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Dynamics of Carbon Storage in the Woody Biomass of Northern Forests. Title. By Jiarui Dong Department of Geography, Boston University, 675 Commonwealth Av., Boston, Ma 02215, USA. Ranga B. Myneni Robert K. Kaufmann Compton J. Tucker Guido D. Salvucci Yuri Knyazikhin. Boston University.

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title

Dynamics of Carbon Storage in the Woody Biomass of Northern Forests

Title

By Jiarui Dong

Department of Geography, Boston University,

675 Commonwealth Av., Boston, Ma 02215, USA

Ranga B. Myneni

Robert K. Kaufmann

Compton J. Tucker

Guido D. Salvucci

Yuri Knyazikhin

Boston University

work funded by nasa earth science enterprise

workload

works at BU

workload
  • Improving the precision of simulated hydrologic fluxes in land surface model.
  • Dong, J., Salvucci, G.D., and Myneni, R.B. (2001), JGR., 106(D13):14357.
  • 2. Development and analysis of vegetation data sets from NOAA global data.
  • 3. Three field campaigns for the validation of the MODIS LAI/FPAR algorithm.
  • Buermann, W., Dong, J., Zeng, X., Myneni, R.B., and Dickinson, R.E. (2001), J. Climate, 14(17):3536.
  • Buermann, W., Wang, Y., Dong, J., Zhou, L., Zeng, X., Dickinson, R.E., Potter, C.S., and Myneni, R.B., JGR (accepted Dec. 2001)
  • Shabanov, N.V., Wang, Y., Buermann, W., Dong, J., Hoffman, S., Smith, G.R., Knyazikhin, Y., Gower, S.T., and Myneni, R.B., Validation of the radiative transfer principles of the MODIS LAI/FPAR algorithm with data from the Harvard Forest, RSE, (in review)

works at BU

JGR: Journal of Geophysics Research; PNAS: Proceedings of the National Academy of Sciences; RSE: Remote Sensing of Environment.

1a of 45

workload1

works at BU

workload
  • Remote sensing estimates of northern boreal and temperate forest woody biomass: carbon pools, sources, and sinks.
  • Myneni, R.B., Dong, J., Tucker, C.J., Kaufmann, R.K., et al. (2001), PNAS, 98(26):14784.
  • Dong, J., Kaufmann, R.B., Myneni, R.B., Tucker, C.J., et al., RSE, (accepted Feb. 2002).

works at BU

JGR: Journal of Geophysics Research; PNAS: Proceedings of the National Academy of Sciences; RSE: Remote Sensing of Environment.

1b of 45

abstract

abstract

Abstract
  • The relation between forest woody biomass and satellite greenness was estimated with data from 167 provinces in six countries and 19 years of remote sensing data.
  • Regression analyses indicated that the regression model can be used to represent the relation between forest woody biomass and NDVI across the spatial, temporal, and ecological scales.
  • For about 1.5 billion ha of the northern boreal and temperate forests, the estimates of carbon pools, sources, and sinks are provided at a relatively high spatial resolution.
  • This research may contribute to a monitoring program for the industrialized nations to meet their greenhouse gas reduction commitments under Kyoto Protocol.

abstract

2 of 45

slide5

contents

  • Motivation
  • Introduction
  • Definitions
  • Data
  • Methods
  • Results
  • Discussion
  • Concluding Remarks

contents

3 of 45

motivation

motivation

Motivation
  • About 1 to 2 giga (109) tons of carbon (Gt C) a year are suggested to be sequestered in pools on northern land.1
  • Debate is currently underway regarding which of the forest biomass sinks can be used by the industrialized nations to meet their commitments under the Kyoto Protocol.
  • 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.

4 of 45

global carbon estimates

introduction (1 of 3)

Global carbon estimates

Global Carbon Budget for the 1980s and 1990s1

global carbon budget

A recent IPCC assessment updated the global carbon budgets.

1. Schimel et al. (2001), Nature, 414:169-172.

5 of 45

heimann estimates

introduction (2 of 3)

Heimann estimates

global carbon budget

This figure, quoted in IPCC 2001, represents our current understanding, that is, about 1-2 billion tons of carbon are sequestered in sinks on northern land. Elsewhere, land is neutral.

Heimann, M. (2001), Max-Plank Institute for Biogeochemie, Technical Report 2. The results, for the 1980s (plain bars) and for 1990-96 (hatched bars), were deduced from eight inverse models. Positive numbers are fluxes to the atmosphere.

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land carbon pool

introduction (3 of 3)

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 northern temperate and boreal forests, 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.

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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

-7.5

-2.5

0

5

10

15

20

25

Forest Fraction (% of pixel area)

defined as the fraction of each quarter degree pixel occupied by these land covers, according to Hansen et al., 1 km satellite based land cover map.

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

8 of 45

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 photosynthetic investment in these organs.
  • loses carbon due to

woody biomass

- aging

- mortality - disease

- harvest - insect attacks

- fire - windthrow

9 of 45

slide12

data (1 of 4)

Data

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 the differences between gains and losses.
  • Potentially, these can be observed as low frequency variations in climatological greenness.

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satellite data

data: satellite (2 of 4)

Satellite data
  • normalized difference vegetation index (NDVI)
  • global
  • 15-day maximum value composites
  • 8 km resolution
  • July 1981 to December 1999

satellite data

  • The key processing features included:
  • cloud screening
  • calibration
  • El Chichon & Mt. Pinatubo corrections
  • data quality assessed1,2

1. Kaufmann et al. (2000), IEEE Trans. Geosci. Remote Sens., 38:2584-2597. 2. Zhou et al. (2001), J. Geophys. Res., 106(D17): 20069-20083.

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satellite data1

data: satellite (3 of 4)

Satellite data
  • Growing season NDVI total, the area under seasonal NDVI curve and above a threshold, can capture both the average seasonal level of greenness and growing season duration, and therefore is an ideal measure of seasonal vegetation greenness.

satellite data

-7.5

-2.5

0

5

10

15

20

25

Change in NDVI Total per year (80s &90s)

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inventory data

data: inventory (4 of 4)

Inventory data
  • The inventory data, in the form of stem wood volume, are from 167 provinces in six countries (can, fin, nor, rus, swe and usa).
  • The stem wood volume 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, 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.

13 of 45

gis 1

methods: GIS (1 of 5)

GIS-1
  • The methodology of matching pixel level NDVI data and provincial inventory data is illustrated here, using Sweden as an example.

matching

inventory

and

ndvi

data

  • Sweden spans a latitude range from 55oN to 70oN;
  • The inventory data are available for 24 provinces;
  • The reported data are stem wood volume (106 m3) and forest area (103 ha).

Administrative map of Sweden

14 of 45

gis 2

methods: GIS (2 of 5)

GIS-2
  • A remote sensing land cover map is required to match the provincial inventory estimates to pixel satellite data.

matching

inventory

and

ndvi

data

  • This map is at a spatial resolution of 1x1 km.1
  • Forests are defined as the 6 remote sensing land covers.

evergreen needle forests

evergreen broadleaf forests

deciduous needle forests

deciduous broadleaf forests

mixed forests

woody savannas

savannas

closed shrub lands

open shrub lands

grasslands

croplands

barren

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

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gis 3

methods: GIS (3 of 5)

GIS-3

matching

inventory

and

ndvi

data

  • For each province, the cumulative growing season greenness is estimated from NDVI data layers, by averaging over forest pixels, as identified from the land cover map.
  • This assures that the growing season greenness is assembled from the forested regions only.

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gis 4

Estimates of forest and land area

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)

GIS-4

matching

inventory

and

ndvi

data

Both inventory and remote sensing estimates match well. These provide some confidence in both data sets.

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gis 5

methods: GIS (5 of 5)

GIS-5
  • The inventory stem wood volume data are converted to total biomass, and plotted against the growing season NDVI total for each province.

matching

inventory

and

ndvi

data

1. British Columbia; 2. Washington, Oregon, and (north) California.

18a of 45

gis 5 large
GIS-5 large

18b of 45

regression model

methods: regression model (1 of 3)

Regression model
  • The relation between woody biomass and seasonal greenness is estimated with the following specification,
  • 1/Biomass = a + [(1/NDVI)/Latitude2] + g Latitude

ndvi

biomass

relation

Biomass: inventory estimate (tons/ha)

NDVI: cumulative growing season NDVI averaged over five years prior to inventory date

Latitude: average of latitudes over forest pixels in each province

,  and : regression coefficients

(a = -0.0377;  = 3809.65; g = 0.0006)

19 of 45

regression tests

methods: regression model (2 of 3)

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

statistical

tests

* Results for U.S. when one outlier is removed. ** DF: Degree of Freedom.

20 of 45

regression tests1

methods: regression model (3 of 3)

Regression tests
  • Regression analyses indicate that there is a statistically meaningful relation between biomass and NDVI, regardless of latitude.
  • The spatial relation between biomass and NDVI is not statistically different from the temporal relation.

statistical

tests

21 of 45

estimates

results: spatial patterns (1 of 4)

estimates

spatial

pattern

of

pools

  • Biomass estimates from satellite data can provide spatial detail of the carbon pool and pool changes at relatively high resolution.
  • To document these regional features, the forest woody biomass carbon pools were evaluated for two periods, the early 1980s (1982-86) and late 1990s (1995-99).
  • Pool changes were then evaluated as the difference between these two pool estimates, pixel-by-pixel, and quoted on a per year basis.

22 of 45

pool pattern

0 10 20 30 40 50 60

Carbon Pool (tons C/ha)

1995 to 1999

results: spatial patterns (2 of 4)

Pool pattern
  • Spatial patterns of pool size in the northern temperate and boreal forests during late 1990s.
  • The biomass map indicates larger average pools in North America compared to Eurasia (51 vs. 39 tons C/ha).

spatial

pattern

of

pools

23a of 45

pool pattern1

0 10 20 30 40 50 60

Carbon Pool (tons C/ha)

1995 to 1999

Pool pattern

23b of 45

pool pattern2

results: spatial patterns (3 of 4)

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

Scandinavia: Sweden, Finland and Norway; A.F.G.: Austria, France and Germany.

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sink pattern

-0.3 0 0.3 0.6 0.9

Sources Sinks

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

1980s & 90s

results: spatial patterns (4 of 4)

Sink pattern
  • Carbon sinks are seen in Eurasian boreal and North American temperate forests.

spatial

pattern

of pool

changes

25 of 45

uncertainty

results: uncertainties (1 of 3)

Uncertainty

uncertainties

in

remote

sensing

estimates

  • The inventory estimates were derived from wood volume increment and loss data.
  • Remote sensing estimates are from biomass differences between two time periods.
  • Thus, the comparison of the two estimates is valuable.

26 of 45

uncertainty1

results: uncertainties (2 of 3)

Uncertainty

A comparison of remote sensing and inventory estimates of (a) the biomass carbon pool, and (b) the pool changes.

uncertainties

in

remote

sensing

estimates

27a of 45

uncertainty2
Uncertainty

27b of 45

uncertainty3
Uncertainty

27c of 45

uncertainty4

results: uncertainties (3 of 3)

Uncertainty
  • Regression analysis shows that there is no bias in the estimation of biomass pools and pool changes .
  • The relative difference between remote sensing (x1) and inventory (x2) estimates is

27% for above-stump biomass (10.4 tons C/ha)

  • 33% for total biomass (16.1 tons C/ha)
  • 50% for changes in pool size (0.33 tons C/ha/yr)

uncertainties

in

remote

sensing

estimates

Inventory =  +  Remote Sensing + 

28 of 45

countrywise

results: estimates (1 of 1)

Countrywise
  • The carbon pool in the woody biomass of northern forests (1.5 billion ha) is estimated to be 61  20 Gt C during the late 1990s.
  • This is comparable to the TBFRA-2000 reports (80 Gt C), but on 2.5 billion ha of forests and other wooded land.
  • Our sink estimate for the woody biomass 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

estimate

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.

29 of 45

canadian estimates

results: country estimates (1 of 6)

Canadian estimates
  • The estimates of the three large countries, Canada, Russia and the USA, are crucial because they account for 78% of the pool, 73% of the sink and 77% of the forest area.
  • For Canada, we estimate a sink of about 73 Mt C/yr, which is comparable to an inventory estimate by the Canadian Forest Service,1 about 85 Mt C/yr.

canadian

estimates

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

30a of 45

russia estmates

results: country estimates (3a of 6)

Russia estmates
  • Estimates for Russia differ, because of differences in definitions of forest cover types.

russian

estimates

32a of 45

russia estmates1

results: country estimates (3b of 6)

Russia estmates
  • When expressed on per ha forest area basis,

russian

estimates

  • 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).

32b of 45

slide41

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

33 of 45

slide42

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

Emissions indicate the degree of industrialization, efficiency of the industries and the population.

Sinks are a function of forest area.

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.

34 of 45

slide43

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

35 of 45

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.
  • The spatial patterns, however, offer some clues.

reasons

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

36 of 45

canada sink

-0.3 0 0.3 0.6 0.9

Sources 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

37 of 45

usa sink

-0.3 0 0.3 0.6 0.9

Sources 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

38 of 45

russia sink

-0.3 0 0.3 0.6 0.9

Sources 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

39 of 45

europe sink

-0.3 0 0.3 0.6 0.9

Sources 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

40 of 45

slide49

-0.3 0 0.3 0.6 0.9

Sources Sinks

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

1980s & 90s

discussion: reasons (6 of 6)

  • Forest expansion (afforestation and reforestation) and regrowth in China.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.

41 of 45

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 coordinate because of definition issues.

limitations

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

42 of 45

contributions

discussion: contributions (1 of 1)

Contributions
  • This work contributes to global carbon cycle research in three ways.
  • It provides spatial detail of the biomass carbon pool and pool changes at a relatively high spatial resolution that permits direct validation with ground data.
  • The inversion studies cannot partition the sink between vegetation, soil and other pools. Estimates of vegetation pool changes would complement inversion results.
  • Debate is currently underway regarding which of the forest biomass sinks can be used for their commitments under the Kyoto Protocol. Satellite estimates of biomass changes can be an important component of carbon accounting for verification of compliance.

contributions

43 of 45

future

discussion: future (1 of 1)

future

future

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

44 of 45

thanks

discussion: thanks

thanks

thanks

Thank you for your attention.

45 of 45