1 Title By Jiarui Dong Department of Geography, Boston University, 675 Commonwealth Av., Boston, Ma...

1

TitleBy Jiarui Dong

Department of Geography, Boston University,

675 Commonwealth Av., Boston, Ma 02215, USA

Dynamics of Carbon Storage in the Woody Biomass of Northern Forests

Ranga B. Myneni

Robert K. Kaufmann

Compton J. Tucker

Guido D. Salvucci

Yuri Knyazikhin

Boston Universitywork funded by nasa earth science enterprise

2

workloadworks at BU

wor

ks a

t BU

1. 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)

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

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workloadworks at BU

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ks a

t BU

4. 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).

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

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Abstractabstract

abst

ract

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.

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contents

cont

ents

Motivation

Introduction

Definitions

Data

Methods

Results

Discussion

Concluding Remarks

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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

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.

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Global carbon estimates

introduction (1 of 3)

glob

al c

arbo

n bu

dget

1980s (Gt C/yr) 1990s (Gt C/yr)

Industrial emissions 5.40.3 6.3 0.4

Land-use change 1.7 (0.6 to 2.5) Assume 1.6 0.8

Ocean-atmosphere flux -1.9 0.5 -1.7 0.5

Land-atmosphere flux -0.2 0.7 -1.4 0.7

Atmospheric increase 3.3 0.1 3.2 0.1

Global Carbon Budget for the 1980s and 1990s1

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

A recent IPCC assessment updated the global carbon budgets.

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8

Heimann estimatesintroduction (2 of 3)

glob

al c

arbo

n bu

dget

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

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.

land

carbon

pools

introduction (3 of 3)

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

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

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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.

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.

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-7.5

-2.5

0

5

10

15

20

25

11

Woody biomass Woody biomass consists 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

definitions (2 of 2)

woo

dy b

iom

ass

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

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Data data (1 of 4)

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 datadata: satellite (2 of 4)

sate

llit

e da

ta

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

The key processing features included:

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.

- cloud screening- calibration- El Chichon & Mt. Pinatubo corrections- data quality assessed1,2

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Satellite datadata: satellite (3 of 4)

sate

llit

e da

ta 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.

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-7.5

-2.5

0

5

10

15

20

25

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

15

Inventory datadata: inventory (4 of 4)

forest

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.

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.

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GIS-1methods: GIS (1 of 5)

matching

inventory

and

ndvi

data

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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).

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

Administrative map of Sweden

17


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.

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This map is at a spatial resolution of 1x1 km.1

Forests are defined as the 6 remote sensing land covers.

evergreen needle forestsevergreen broadleaf forestsdeciduous needle forestsdeciduous broadleaf forestsmixed forestswoody savannassavannasclosed shrub landsopen shrub landsgrasslandscroplandsbarren

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

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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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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)

Estimates of forest and land area

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

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

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matching

inventory

and

ndvi

data

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

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

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GIS-5 large

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Regression modelmethods: regression model (1 of 3)

ndvi

biomass

relation

The relation between woody biomass and seasonal greenness is estimated with the following specification,

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

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

prior to inventory dateLatitude: average of latitudes over forest pixels in each province, and : regression coefficients ( = -0.0377; = 3809.65;

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Regression testsmethods: regression model (2 of 3)

statistical

tests

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

Countries 1 t statistic DF**

Sweden (1982-86) 2836 2.93 (p<0.004) 18

Sweden (1993-97) 2743 2.89 (p<0.004) 18

Norway 9858 4.84 (p<0.0001) 14

Finland 2793 1.82 (p<0.07) 5

Canada 1631 4.27 (p<0.0001) 8

Russia 8315 3.12 (p<0.002) 54

USA 747 1.33 (p<0.19) 29

USA* 1371 2.60 (p<0.01) 28

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

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Regression testsmethods: regression model (3 of 3)

statistical

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.

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estimatesresults: spatial patterns (1 of 4)

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.

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Pool patternresults: spatial patterns (2 of 4)

spatial

pattern

of

pools

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).

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0 10 20 30 40 50 60

Carbon Pool (tons C/ha)

1995 to 1999

27

Pool pattern

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0 10 20 30 40 50 60

Carbon Pool (tons C/ha)

1995 to 1999

28

Pool patternresults: spatial patterns (3 of 4)

spatial

pattern

of

pools

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).

Country Average Pool (tons/ha)

Carbon Pool (Gt C)

Carbon Sink (Gt C/yr)

Forest Area (Mha)

Canada 44.09 10.56 0.07312 239.5

USA 57.91 12.48 0.14153 215.5

Russia 37.98 24.39 0.28359 642.2

Scandinavia 37.80 1.915 0.02220 50.66

A.F.G. 68.81 2.022 0.02482 29.38

Europe 54.07 8.71 0.13559 161.1

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

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Sink patternresults: spatial patterns (4 of 4)

spatial

pattern

of pool

changes

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

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

-0.3 0 0.3 0.6 0.9

Sources Sinks

30

Uncertainty results: uncertainties (1 of 3)

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.

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uncertainties

in

remote

sensing

estimates

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

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Uncertainty

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Uncertainty

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uncertainties

in

remote

sensing

estimates

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)

x x

x

i

N

i

N

1 21

21

Null Hypothesis t statistic (pool) t statistic (sink)

=0 0.83 0.05

=1 0.40 1.28

=0; =1 0.35 1.07

Inventory = + Remote Sensing +

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Countrywise

NH

estimate

results: estimates (1 of 1)

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 0.680.34 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.

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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 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.

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Canadian estimates

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USA estimates

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Russia estmates

russian

estimates

results: country estimates (3a of 6)

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

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Russia estmates

russian

estimates

results: country estimates (3b of 6)

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

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

When expressed on per ha forest area basis,

41

annex 1

countries:

sinks


RussiaUSA

Canada

SwedenGermany

JapanItaly

FranceRomania

SpainPolandTurkey

FinlandUK

BulgariaAustriaBelarus

CzechNorwayGreece

PortugalLatvia

UkraineSwitzerland

LithuaniaEstonia

HungaryBelgium

NetherlandsDenmark

anne

x 1

coun

trie

s#

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

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annex 1

countries:

sinks to

emissions

ratio


SwedenLatvia*

Russia*CanadaFinlandNorway

Lithuania*Austria

PortugalEstonia*BulgariaRomania

TurkeyBelarus*

GreeceSpain

SwitzerlandUSAItaly

Czech*FrancePoland

HungaryGermany

JapanUK

Ukraine*DenmarkBelgium

Netherlands

anne

x 1

coun

trie

s#

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.


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

Sinks are a function of forest area.

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annex 1

countries:

sinks to

emissions

per capita


Latvia*Estonia*SwedenNorwayFinland

Lithuania*

AustriaBulgariaPortugalCanada

Belarus*Switzerland

GreeceCzech*

RomaniaHungaryRussia*

SpainTurkey

ItalyPoland

DenmarkFrance

BelgiumGermany

UKUSA

JapanNetherlands

Ukraine*

anne

x 1

coun

trie

s#

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

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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.

The spatial patterns, however, offer some clues.

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

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

pool

changes

in

canada

o Increased incidence of fires and infestations in Canada.


-0.3 0 0.3 0.6 0.9

Sources Sinks

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

pool

changes

in

usa


-0.3 0 0.3 0.6 0.9

Sources Sinks

o Fire suppression and forest regrowth in the USA.

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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

Sources Sinks

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

pool

changes

in

europe


-0.3 0 0.3 0.6 0.9

Sources Sinks

o Improved silviculture in the Nordic countries.

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discussion: reasons (6 of 6)

pool

changes

in china

japan

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


-0.3 0 0.3 0.6 0.9

Sources Sinks

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.

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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 coordinate because of definition issues.

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

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Contributions co

ntr

ibu

tion

s

discussion: contributions (1 of 1)

o 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.

o The inversion studies cannot partition the sink between vegetation, soil and other pools. Estimates of vegetation pool changes would complement inversion results.

o 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.

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

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future fu

ture

discussion: future (1 of 1)

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.

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thanks th

ank

s

discussion: thanks

Thank you for your attention.

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