Terrestrial
Biomass and
the
Effects of Deforestation
on
the
Global Carbon Cycle
Results from a model of primary production using
satellite observations
Christopher S. Potter
nvironmental policymakers,
particularly those who consider options for mitigating global warming, require accurate assessments of sources and sinks for atmospheric carbon dioxide. A case in point
is the Kyoto Protocol of 1997, the
first negotiated agreement for reducing net terrestrial emissions of carbon dioxide on an international scale.
Emissions may be reduced by curbing air pollution sources, slowing
"slash and burn" deforestation, and
promoting regrowth of forest areas
that have been logged. The goal of
the Kyoto Protocol is to restrict average annual carbon emissions to a
percentage of 1990 emissions.
Successful implementation of the
Kyoto Protocol depends on a reliable accounting of how much carbon is stored and released from the
world's forests. Several major uncertainties have hindered the development of a more accurate carbon budget; these uncertainties have to do
with global carbon cycling in terrestrial ecosystems, carbon storage in
standing plant biomass, and the net
effects of forest loss and secondary
regrowth (Schimel et al. 1996a). During the 1980s, terrestrial exchange
of carbon dioxide with the atmosphere may have accounted for carbon sources of 0.6-2.6 Pg/yr, mainly
from land-use change in the tropics
(IPCC 1994). The wide range in these
Uncertainties in
implementing
international agreements
for greenhouse gas
emissions can be
addressed with better
quantified values for
forest biomass and
regional variability in
terrestrial productivity
source estimates from the Intergovernmental Panel on Climate Change,
which exemplifies the difficulty in
implementing agreements like the
Kyoto Protocol, is due in large part
to poorly quantified values for forest
biomass and to variability in terrestrial productivity over scales that are
relatively small (100-200 km) compared to global forest coverage. Uncertainties in the global carbon budget would be reduced to more
acceptable levels with the development of improved techniques for estimating variability in carbon stocks
over vast forested areas (IGBP 1998).
One such technique is the use of
ecosystem
modeling of carbon pools
S.
Potter
(e-mail: cpotter@
Christopher
fluxes.
and
Global ecosystem modmail.arc.nasa.gov)is a researchscientistat
NASA Ames Research Center, Ecosystem els are valuable tools in situations in
Science and Technology Branch, Moffett which ground-based measurements
of carbon pools are not adequate to
Field, CA 94035.
October 1999
realistically capture regional variability. A computer model of this
type based on satellite measurements
has been developed to simulate ecosystem carbon cycling (Potter and
Klooster 1997, 1998). This model,
the NASA-CASA (National Aeronautics and Space AdministrationCarnegie Ames Stanford Approach)
model, is designed to estimate daily
and seasonal patterns in carbon fixation, plant biomass, nutrient allocation, litter fall, soil nutrient mineralization, and carbon dioxide exchange,
including carbon emissions from soils
worldwide. Direct input of satellite
"greenness" data from the Advanced
Very High Resolution Radiometer
(AVHRR) sensor into the NASACASA model can be used to accurately estimate global monthly net
primary production (NPP), biomass
accumulation, and litterfall inputs
to soil carbon pools at a geographic
resolution of 10 latitude and longitude. Soil fertility factors included in
the NASA-CASA model control the
allocation of new plant growth to
aboveground tissues (leaf and wood)
versus fine-root tissue allocation for
acquisition of soil nutrients.
In this article, I examine several
different methods for estimating
changes in terrestrial biomass sources
of atmospheric carbon dioxide using
a combination of global satellite
observations, ecosystem model (such
as NASA-CASA) predictions of
aboveground biomass for the late
1980s, and data on country-by-country changes in global forest cover for
the years 1990-1995 (FAO 1997).
When the NASA-CASA model is used,
769
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(b) EcosystemProduction
NutrientMineralization
(a) Soil MoistureBalance
andPlantFunctionalTypes
NDVI
PPT
PE
(c) Biogenic TraceGas Flux
FPAR
-
TEMP
T
,PPT
N-
.
NPP
PET
SOLAR
Soil Surface
Mo
Mo
Mo:4?
.
.
Heat &
Water
Flux
MM"
M3
M"
MS
oi
Leaf Litter
Root Litter
reeze/Thaw
Microbes
Profile
Layers
M
Sf(Temp
f
(Temp)
oil Organic
Matter
f(Lit q)
GNO
Grass
Shrub
f(WFPS)I
f(WFPS)
Tree
C02
-1CH4
N20
Mineral N
Figure 1. Structureof the NASA-CASA model. Predictions are based on plant functional types, such as deserts, grasses, and
overstory woody plants (shrubs and trees). Plant functional types determine net primary productivity (NPP) and nutrient
cycling rates, which in turn control trace gas fluxes. (a) Typical soil water balance is shown as the shaded depth level in soil
profile layers (M1-M3; see text for details). Soil water balance is a function of both precipitation (PPT) and potential
evapotranspiration(PET). Freeze-thaw of the soil profile is a function of the seasonally accumulatedheat flux. (b) Climate
controls on NPP are defined by the equation NPP = SrFPAR Emax T W, where Sr is solar irradiance(SOLAR),FPARis fraction
of absorbedphotosynthetically active radiation (derived from NDVI, the Normalized Difference Vegetation Index), Emax is a
model constant, T is air temperature(TEMP),and W is soil moisture balance (i.e., a function of PPT and PET). Controls on
litter and soil carbon decomposition are a function of soil temperature(Temp),of the water-filledpore space (WFPS)of mineral
soil, and of the ratio of litter nitrogen to lignin (Lit q). (c) Biogenic emission fluxes of soil trace gases include heterotrophic
respiration (CO2),methane (CH4),and nitrous (N20) and nitric (NO) oxides.
the analysis suggests that yearly net
terrestrial losses of carbon dioxide
from changes in the world's forest
ecosystems are 1.2-1.3 Pg of carbon
for the early 1990s. This estimate,
which accounts for forest area regrowth and expansion sinks in temperate and boreal forest zones, is
based on the most recent global maps
for observed climate, soils, plant
cover, and changes in forest areas
from natural and human forces.
Modeling terrestrialproduction
from remote sensing
Major carbon fluxes between the
atmosphere and terrestrial biosphere
are often expressed in terms of net
biomass accumulation from annual
NPP. Several methods exist for estimating terrestrial NPP over large
areas of the globe. One set of methods is based on ecophysiology mod770
els, which link carbon metabolism
with water, energy, and nutrient cycling in plants (e.g., Kindermann et
al. 1996, Schimel et al. 1996b, Churkina and Running 1998). Other
methods use remotely sensed data to
provide direct time-series data on
properties of the vegetation cover,
such as changes in surface "greenness," which is commonly expressed
as leaf area index or canopy light
absorption (e.g., Maisongrande et al.
1995, Potter et al. 1999) and which
represents the status of the leaf canopy
of any plant functional type (e.g.,
forest, savanna, grassland, tundra,
or desert). Global NPP of vegetation
can be predicted using the relationship between greenness reflectance
properties and absorption of photosynthetically active radiation (PAR),
assuming that net conversion efficiencies of PAR to plant carbon can
be approximated for different eco-
systems or are nearly constant across
all ecosystems (Goetz and Prince
1998).
A successful approach to estimating terrestrial plant production is
based on the concept of vegetation
greenness in the NASA-CASA formulation (Potter and Klooster 1999).
Canopy greenness is measured using
a Normalized Difference Vegetation
Index (NDVI). NDVI is a unitless
parameter (scaled from 0 to 1000)
that is computed from the ratio of
visible and near-infrared radiation
reflected from the canopy as detected
by the AVHRR satellite sensor. The
AVHRR NDVI of greenness has been
closely correlated with vegetation parameters such as the fraction of absorbed PAR (FPAR) and leaf area
index (Running and Nemani 1988,
Sellers et al. 1994, DeFries et al. 1995).
Terrestrial NPP fluxes from the
NASA-CASA model have been exBioScience Vol. 49 No. 10
tensively validated against both seasonal patterns of atmospheric carbon
dioxide measurements at sampling
stations around the world (Denning
1994) and multi-year estimates of
NPP from field stations and tree rings
(Malmstr6m et al. 1997). The monthly
fraction of annual NPP flux, defined
as net fixation of carbon dioxide by
vegetation, is computed, in the
NASA-CASA model, on the basis of
light-use efficiency (Monteith 1972).
Monthly production of plant biomass is estimated in the model as the
product of surface solar irradiance,
Sr(Bishop and Rossow 1991), FPAR
from the AVHRR NDVI, and a light
utilization efficiency term (Smax) that
is multiplied by air temperature (T)
and soil moisture balance (W) stress
scalars:
colates through to lower layers and
may eventually leave the system as
seepage and runoff. In the NASACASA model, freeze-thaw dynamics
with soil depth operate according to
the empirical degree-day accumulation method (Jumikis 1966, as described by Bonan 1989).
Because it is based on plant production as the primary carbon and
nitrogen cycling source, the NASACASA model is able to couple daily
and seasonal patterns in soil nutrient
mineralization plus soil heterotrophic
respiration (Rh) of carbon dioxide
with net nitrous oxide, nitric oxide,
and methane emissions from soils
worldwide. Net ecosystem production can be computed as NPP minus
Rh fluxes, excluding direct effects of
fire and other small-scale disturbances. The trace gas components of
NPP = S FPAR
TW
the NASA-CASA model (Potter et
max
The Smax term is set at 0.56 g/MJ (car- al. 1996a, 1996b) are summarized in
bon/PAR), a value that derives from Figures lb and 1c. The soil model
calibration of predicted annual NPP component of NASA-CASA uses a
to previous field estimates of NPP set of compartmental difference
(Potter et al. 1993). The T stress term equations with a structure compais computed with reference to deri- rable to those of the CENTURY ecovation of optimal temperatures
system model (Parton et al. 1992).
for plant production. The Topt(Topt)
set- First-order equations simulate exting varies with latitude and longi- changes of decomposing plant resitude, ranging from near 0 ?C in the due (metabolic and structural fracArctic to the mid-30s (?C) in low- tions) at the soil surface. They also
latitude deserts. The W term is esti- simulate surface soil organic matter
mated from monthly water deficits, fractions that presumably vary in
which are based on a comparison of age and chemical composition. Acmoisture supply (precipitation and tive (microbial biomass and labile
stored soil water) to evapotranspirasubstrates), slow (chemically protion water potential demands using tected), and passive (physically prothe method of Thornthwaite (1948).
tected) fractions of the soil organic
in
the
matter are represented in the model.
NASA-CASA
Algorithms
model allow evapotranspiration to Along with moisture availability and
be connected to water content in the litter quality, estimated soil temperasoil profile layers (Figure la; Potter ture in the M1 layer controls soil
1997). The model design includes organic matter decomposition.
heat and moisture content computaInterannual results of global net
tions for three soil layers: surface ecosystem production estimates from
organic matter (M1), topsoil (0.3 m; the NASA-CASA model (Potter and
M2), and subsoil to rooting depth Klooster 1999, Potter et al. 1999)
(1-10 m; M3). These layers can differ suggest that land plants have been
in soil texture, moisture-holding ca- absorbing more carbon dioxide in
pacity, and carbon-nitrogen dynam- the Northern Hemisphere during the
ics. Water balance for each layer of late 1980s than previously believedthe soil is modeled as the difference almost one-third of the annual amount
between precipitation plus volumet- of carbon dioxide released from the
ric percolation inputs, on the one burning of fossil fuels. When AHVRR
hand, and monthly estimates of po- satellite data are used in the NASAtential evapotranspiration plus drain- CASA simulation model of net ecoage output, on the other. Inputs from system production, the results show
rainfall can recharge the soil layers increasing carbon dioxide accumuto field capacity. Excess water per- lation in vegetation over extensive
October 1999
areas of Canada, Europe, and Russia
during 1985-1988. This simulation
modeling indicates precisely which
forest and tundra areas on Earth act
as temporary "sinks" for atmospheric
carbon dioxide in response to warmer
than average spring temperatures or
lower than average summer drought
stress. Many large-scale disturbances,
such as conversion of land to agriculture, may be detected in the
AHVRR data used to drive the model.
Soil fertility effects on biomass
allocation are included in the NASACASA model. For global simulations,
the 1 grid resolution Soil Map of the
World (FAO/UNESCO 1971, Zobler
1986) is classified in the NASACASA model according to three relative levels of soil fertility (low, medium, and high), following the
scheme proposed by Esser (1990)
and Bouwman (1990). For low-fertility soils, a -10% adjustment is
made that allocates increasing root
biomass from NPP for greater acquisition of soil nutrients (Wilson and
Tilman 1991). For medium- and
high-fertility soils, a +10% adjustment is made that allocates increasing stem and leaf biomass from NPP
to support greater light-harvesting
functions in the canopy (Gleeson and
Tilman 1990, Redente et al. 1992,
Lusk et al. 1997). These adjustments
represent conservative effects of fertility on root allocation for forests.
Carbon turnover resulting from
tree mortality is expressed, in the
NASA-CASA model, in terms of the
mean residence time (t, in years) of
carbon in the standing woody tissue
pool, depending on the plant functional type. Allocation ratios (a, as
percentage of NPP) and mean residence times for leaf and fine-root
biomass are expressed in a similar
manner, based on estimates from the
literature (Table 1). These empirical
values for t and a together determine the accumulation rates of plant
biomass in living plant and soil pools
across the model's 10 global grid.
Global data drivers
Complete AVHRR data sets for the
1980s have been produced from
National Atmospheric and Oceanic
Administration (NOAA) Global Area
Coverage Level 1B data. These data
consist of reflectances and bright771
Table 1. Allocation and residence time parameters for major plant functional types.a
Plant functional typeb
tc leaf
croot
a wood
Cdleaf
x root
uwood
Tundra
0.25
0.30
0.25
0.45
0.25
0.30
0.25
0.25
0.30
0.25
0.25
0.25
0.55
0.25
0.25
0.25
0.25
0.25
0.50
0.45
0.50
NAe
0.50
0.45
0.50
0.50
0.45
1.5
1.0
2.5
1.5
1.5
1.0
1.5
1.5
1.0
3.0
3.0
3.0
5.0
3.0
3.0
3.0
3.0
5.0
50
50
50
NA
40
40
50
50
25
0.25
0.25
0.50
1.5
2.0
25
High-latitude forest
Boreal coniferous forest
Temperate grassland
Mixed coniferous forest
Temperate deciduous forest
Desert and bare ground
Semi-arid shrubland
Savanna and woody
grassland
Tropical evergreen rain
forest
sSources for information on parameter settings include Cannell (1982), Aber and Melillo
(1991), Running and Gower (1991), Redente et al. (1992), and Lusk et al. (1997).
bGlobal cover classes defined by DeFries and Townshend (1994) using Advanced Very High
Resolution Radiometer (AVHRR) observations of seasonal plant greenness.
coa,the proportional allocation constant of plant tissue pools.
d,, the residence time (in years) of carbon in plant tissue pools.
eNA, not applicable.
ness temperatures for cloud cover
derived from the five-channel crosstrack scanning AVHRR aboard the
NOAA Polar Orbiter "afternoon"
satellites (NOAA-7, NOAA-9, and
NOAA-11).
Monthly composite
NDVI data sets remove much of the
contamination due to cloud cover
that is present in the daily AVHRR
data sets (Holben 1986).
Additional processing of the satellite imagery is nevertheless necessary to eliminate remaining artifacts.
As part of the Global Inventory
Monitoring and Modeling Studies
(GIMMS) program (Los et al. 1994)
of NASA Goddard Space Flight Center, Sellers et al. (1994) developed
Fourier algorithms and solar zenith
angle adjustments for interannual
AVHRR data sets to further correct
NDVI signals from global 10 data
sets (averaged from 8 km values) for
the 1980s. Fourier algorithm/solar
zenith (FAS) processing removes
many artifacts present in previous
NDVI data sets, including cloud
cover and aerosol interference. These
GIMMS NDVI data show minimal
correlations with equatorial crossing times of the NOAA satellites
(Malmstr6m et al. 1997), which suggests that corrections have been made
for orbital drifts and switches between satellites.
For surface temperature and precipitation drivers, long-term (19311960) average values from Leemans
and Cramer (1990) were used after
being adjusted with monthly 1' climate anomalies for the period 19801988 (Dai and Fung 1993). Average
monthly FAS-processed NDVI and
solar irradiance data (Bishop and
Rossow 1991) from the respective
1980s time series data sets (Potter
and Klooster 1998) were used to
Table 2. Ecosystem carbon estimates for vegetation classes from the NASA-CASA model and the field data analysis of Olson
et al. (1983).a
Vegetation class
Total area
(x 106 km2)b
Average
carbon in
AGB (g/m2)c
Total carbon
(Pg) in AGBc
Average
carbon in
AGB (g/m2)b
Total carbon
in AGB (Pg)b
Average
NPPd carbon
(g. M-2. yr-1)b
Total NPP
carbon (Pg/yr)b
Broadleaf evergreen
forest
Coniferous evergreen
forest and woodland
15.4
8159
126.1
12622
195.0
1075.4
16.6
13.5
11381
153.5
6014
84.9
233.3
3.3
High-latitude
deciduous forest
and woodland
Tundra
Mixed coniferous
forest and woodland
Wooded grassland
and shrubland
5.9
11918
70.1
5603
34.0
244.5
1.5
8.6
7.5
428
5321
3.8
40.2
1878
7386
17.7
56.9
72.6
386.2
0.7
3.0
24.3
4540
111.2
8193
202.4
782.4
19.3
22.1
17.7
14.6
3.6
476
190
747
7390
10.6
3.4
10.9
26.9
154
525
182
6715
3.5
9.4
2.7
24.7
172.5
21.4
365.0
397.4
4.0
0.4
5.4
1.5
11.5
145.4
971
11.1
567.8
1721
20.0
651.1
69.0
0.8
56.4
Temperate grassland
Desert
Cultivated
Broadleaf deciduous
forest and woodland
Semi-arid shrubland
Total
aAboveground biomass (AGB) was estimated from extrapolation of field data reported by Olson et al. (1983) and from the NASA-CASA
model. AGB is presented for both average values and totals by vegetation class. Average carbon (g/m2) is multiplied by area in each vegetation
class to estimate total carbon in AGB (Pg).
bFrom the NASA-CASA model.
cFrom Olson et al. (1983).
dNPP, net primary production.
772
BioScience Vol. 49 No. 10
generate ecosystem model results
typical of the 1980s.
a
Biomass and production
estimates for the 1980s
For the 1980s, predicted worldwide
NPP from the NASA-CASA model
was estimated at 56.4 Pg/yr of carbon (Table 2). Predicted worldwide
aboveground biomass (including leaf
and wood carbon) was estimated at
651 Pg of carbon (Figure 2). Average
carbon storage in aboveground biomass was predicted to be highest in
broadleafevergreenforests-mostly
thoseintropicalzones-at morethan
0
5000 10000 15000 20000 25000
12kg/m2,withlowerstorageinmixed
forestsof temconiferous-deciduous
carbonstocks
peratezones.Predicted
in woodbiomasswere20-100 times b
greaterthanpredictedcarbonstocks
in leaf biomassfor most forestecosystemareas(Figure2).
Accuracyof themodelpredictions
can be evaluatedby comparisonto
observations.
availableground-based
For example, the NASA-CASA
model value for global carbon in
abovegroundbiomass, 651 Pg, is
somewhathigherthanOlsonet al.'s
(1983) estimateof 568 Pg, which
was basedon a selecteddata set of
biomassobservations(Table2). Althoughtherearefew otherestimates
of standingbiomassforcomparison,
0
250
500
750
1000
maximumpredicted aboveground
biomass from the NASA-CASA
gCm-2
modelfor tropicalmoist forestsexceeds17 kg/m2of carbon.This figure, unlike the lower Olson et al. Figure 2. Results from the NASA-CASA model for aboveground biomass. (a)
(1983) estimates,is in close agree- Predictedwood carbon. (b) Predictedleaf carbon. Global patterns are representament with measured amounts of tive of net primaryproduction (NPP)computedusing satellite and climate inputs to
abovegroundbiomass from recent the NASA-CASA ecosystem model for the 1980s.
fieldstudies(Kauffmanet al. 1995,
Carvalhoet al. 1998).Bycontrastto partiallyto the model ratio setting landsurfaceconditionsobservedby
thefairlysimilarestimatesof global (leafversusroot tissue)for biomass satelliteremotesensingfor a recent
abovegroundbiomass,averagepre- allocationfromNPP.Themodelra- timeperiodof lessthan5 years,that
dicted aboveground biomass from tio results in higher belowground is, thelate 1980s.NASA-CASApreallocationof bio- dictions also include climate conthe NASA-CASAmodel for conifer- thanaboveground
ous evergreenforests and high-lati- massat the expenseof aboveground trols on plantproductionat 10 spatialresolution,effectsof soil fertility
tude deciduous forests deviates no- biomassin grasslands.
on carbon allocation to different
the
et
al.
the
two
different
methfrom
Olson
Overall,
(1983)
tably
bio- plant tissues, and AVHRR-based
estimates,mainlybecauseof the rela- ods forestimatingaboveground
tively low estimated annual produc- mass, as comparedin Table2, may mappingof 1980s globallandcover
tion in these vegetation classes based representthepotentialextremesand and use patterns (DeFries and
on the monthlyNDVI driversused in the variabilityof biomassstoragein Townshend1994). Anotheradvanthe model. Comparedto the Olson et manyterrestrialecosystems.Unlike tageof theNASA-CASAmodelestial. (1983) observations, the lower the extrapolatedvalues from site mates for carbon in aboveground
NASA-CASA model estimate of measurements
reportedby Olsonet biomassis thattheymakeit possible
abovegroundbiomass for grasslands al. (1983), the NASA-CASAmodel to separateleaf fromstandingwood
and cultivated areas is attributable estimateis designedto reflectactual and fine-root stocks, thereby acOctober1999
773
counting for effects of deforestation
on net carbon dioxide fluxes to the
atmosphere. By contrast, site measurements reported by Olson et al.
(1983) are for total standing biomass only and are not specific to a
recent time period of less than 5
years,which is importantwhere land
use is changing rapidly.
Carbonlossesfrom
deforestation
FAO (1997) providesworldwide statistics on the status of forest cover,
circa 1995, and on recent (19901995) rates of annual change in forest cover and condition. Changes in
forest area or condition can be documented as positive (e.g., through
natural colonization of nonforest
landor regrowthon degradedstands)
or negative (e.g., through deforestation, forest fires, or unsustainable
exploitationfor wood). Theestimates
of forest cover change during 19901995 for more than 170 countries
indicate a net loss of 56.3 million ha
of forests worldwide (FAO 1997).
This value represents a decrease of
65.1 million ha in developing countries that was partly offset by an
increase of 8.8 million ha in developed countries. Although the rate of
deforestationin developingcountries
is still considered high (13.7 million
ha/yr), the rate appears to have
slowed somewhat since the 1980s
(15.5 million ha/yr; FAO 1997).
By combining the 10 resolution
NASA-CASA model estimates for
standing biomass (i.e., aboveground
biomass) in forests worldwide (Figure2) with country-by-countryrates
of change in forest area compiled by
FAO (1997), detailed estimates can
be made for global carbon losses
from deforestation and gains from
expansion of planted forest area or
secondary regrowth following abandonment of other land uses. To derive rates of carbon flux to the atmosphere from forest cutting and
burning, a biomass combustion factor of 48% total aboveground biomass, which has been reported from
tropical field studies by Kauffman et
al. (1995), was used as a converter
for FAO annual deforestation rates
over the period 1990-1995.
The resulting estimate for global
carbon flux, which was based on
774
FAO (1997) conversion rates for
1990-1995, suggeststhat the net terrestrial loss of carbon dioxide from
changes in area of the world's forest
ecosystemswas 1.15 Pg/yrof carbon
(Figure 3). This estimate includes
1.44 Pg/yr of carbon lost to the atmosphere from deforestation
sources, offset in part by 0.29 Pg/yr
of carbon accumulatedin the terrestrial biosphere through forest area
regrowthand expansion. In this estimate, countries with the highest individuallosses of carbon from deforestation are Brazil, with 0.25 Pg/yr,
followed by Zaire and Indonesia,
with losses of just under 0.1 Pg/yr
each (Table 3). Estimated rates of
carbon accumulationin areas of forest regrowth are highest in Canada,
France, and the United States, each
with annual gains of more than 0.02
Pg/yr.It shouldbe notedthat although
the age structureof forestscan have a
significantimpact on estimated carbon pools from regrowth, it is not
feasible to consider these types of
age-dependentfactors in this kind of
global-scale analysis because of a
lack of data from satellite sources.
Compiled by global vegetation
classes, the highest net carbon losses
from deforestation are predicted to
occur in tropical evergreen forests,
where they reach 0.69 Pg/yr, and
tropical savanna woodlands, where
they reach 0.56 Pg/yr. Net carbon
gains from regrowth and expansion
of forest areas over boreal and other
high-latitude forest zones are predicted to be 0.09 Pg/yr,whereas predicted carbon gains in biomass over
all temperateforestzones total nearly
0.06 Pg/yrfor the period 1990-1995.
As an important footnote to this
study, FAO (1997) mentions that a
highly reliableestimatefor changein
forest area between 1990 and 1995
could not be made for the Russian
Federation. Reported rates of change
in forested areas of Russia are listed
in the 1997 FAO forest resource assessment at +0.07% annually. However, other recent reports estimate
that forest fires in Russia consume
7.3 million ha, or approximately
1.18%, of the Federation's boreal
forest area each year (Conard and
Davidenko 1996). This forest loss
estimate is an order of magnitude
greater than that indicated by official fire statistics, a discrepancy with
major implications for global carbon dioxide emissions that has apparentlynot been fully consideredin
other recent assessments of the carbon cycle (e.g., IGBP 1998).
If a modified (and conservative)
loss estimate of 0.05% for net annual change in forest area for Russia
over the period 1990-1995 is
adopted, the projectednet terrestrial
loss of carbon dioxide from changes
in the world's forest ecosystems
would be adjusted to 1.24 Pg/yr of
carbon for the early 1990s. A less
conservative loss estimate of 1.2%
for annual change in Russian forest
areawould make the areacoveredby
the formerUSSRthe highestnational
deforestationsource of carbon dioxide in the world, contributingto global net carbon losses that would exceed 1.7 Pg/yrfrom changes in forest
ecosystems worldwide.
Implications and conclusions
A major finding from the global
modeling analysis presented in this
article is that the potential for recent
carbon gains from forest area regrowth or expansion are dwarfedby
the continuing losses from deforestation and fires. Although temperate
and boreal forests may be increasing
somewhat in area and possibly in
biomass per unit area, this global
analysis suggests that the measurable gains are currently offset by
worldwide deforestation losses of
carbon to the atmosphere at a ratio
of approximately5 to 1. At this time,
there appears to be no practical
means to alter the carbon balance
equation except by mitigatingdeforestation and forest degradation in
the tropical countries and, possibly,
the Russian Federation. Mitigation
measures to limit the conversion of
the world's forest biomass to atmospheric carbon dioxide would involve
expanding the amount of forest area
under protection from logging, reducing the accidental spread of savanna and agricultural wildfires into
tropical forests, and improving harvesting practices to reduce waste and
prevent damage to remaining vegetation and to soils.
The Kyoto Protocol specifies that
developed countries and those with
economies in transition should undertake measures to reach specific
BioScience Vol. 49 No. 10
reduction targets for greenhouse
gases, restricting average annual
emissions to a percentage of 1990
emissions. These reduction targets
are based on the assumption that all
emission sources and sinks can be
accurately counted in assessing national compliance.However, despite
the improved information on biomass carbon losses from deforestation reportedin this article,the Kyoto
Protocol'ssectoron LandUse Change
and Forestrywill continue to be one
of the more difficult sectors to define
and quantify out of the Kyoto negotiations and beyond. For example,
soils and belowground biomass
stocks of carbon decompose more
slowly than the standing biomass
that is burned or removed during
deforestation. Some disturbed soils
with large stocks of dead wood-for
example, in converted cattle pastures-can continue to release their
stored carbon for several decades
after forest clearing. Satelliteremote
sensing alone cannot detect changes
in thesesoil surfaceand belowground
processes.
The unique advantage of ecosystem modeling, which cannot be
achieved through a small set of soil
sampling measurements alone, is
underscoredin this context. For instance, the global NASA-CASA
modelestimatesthat the total amount
of belowground (fine-root) biomass
in forested ecosystems worldwide
represents 50 Pg of carbon, which
could eventually make its way into
the atmosphere in the event that
standing biomass stocks in forests
are intensively cut or burned. This
root biomass total must be added to
the NASA-CASA model estimate of
morethan 170 Pg of carbonstoredin
deadlitterandsoil carbonpools (those
pools with meanresidencetime of less
than 25 years) in global forest ecosystems (Potter and Klooster 1997).
Higher-resolution applications of
the NASA-CASA ecosystem model
can be made to estimate forest biomass and production at the state or
national level, down to at least the 1
km2 grid size for AHVRR greenness
inputs. This spatial resolution can
capture many direct effects of deforestation on land cover and predicted
biomass carbon stocks. NASA-CASA
model simulations using 8 km resolution land cover and NDVI data
October 1999
7.
-150
-100
-50
0
50
gC m2yr-1
Figure 3. Global carbon flux resulting from annual changes in forest area. Predictions are based on FAO (1997) conversion rates for 1990-1995, combined with
NASA-CASAmodel predictionsof standingplant biomass. Negative flux values (-)
indicate loss of carbon to the atmospherefrom deforestation, whereas positive flux
values (+) indicate gains of carbon from the atmosphere resulting from forest
regrowth or expansion. Gridded 10 flux values are based on the assumption that
rates of forest carbon loss or gain occur in proportionto the geographicdistribution
of standing biomass amount on a national level.
made using the eddy correlation approach (Goulden et al. 1996, Malhi
et al. 1998), in which fast-response
measurements of the vertical component of wind speed and humidity
allow for calculation of forest moisture and carbon fluxes based on the
covariance of these meteorological
variables. Together with reliable
imagesof dominantforestcovertypes
from Landsator other relativelyhigh
resolution mapping sources, these
eddy correlationtower flux measurements of net ecosystem carbon exchange may be extended with confidenceto regionalscalesandcompared
to ecosystem model predictions.
From regional to global scales, a
network of gas sampling stations
would provide records over time for
atmosphericcarbon dioxide concentrationsthat could be used with models of global circulation to track terrestrial locations and fluxes of net
ecosystem production.
ecosystem models.
A promisingnew method for charA series of tower-based measurements of net ecosystem carbon ex- acterizingforest structureis the Vegchange over several seasons can be etation Canopy Lidar (VCL), an ac-
drivershave generatedregional budgets for NPP and trace gas fluxes
over the southeastern United States
(Davidson et al. 1998) and for the
entire nation of Brazil (Potter et al.
1998). A common limiting factor for
more extensive (e.g., pan-tropical)
application of the model at 1-8 km
resolution is the lack of compatible
high-quality maps for monthly climate and soil data inputs.
Severalmethods can complement
global ecosystem models driven by
satellite remotely sensed data to aid
in defining limits on carbon stocks
and narrowing the uncertainties in
net terrestrial sources of carbon in
the atmosphere. At the forest stand
level, intensive sampling of plant,
litter, and soil carbon pools can be
combined with geographic information systemsfor extrapolationacross
the landscape.These same field measurements can be used to calibrate
775
Table 3. Averageestimatedchange in forest abovegroundbiomass (AGB)over the
years 1990-1995 for countries with the highest carbon flux rates.
Annualrateof
AGB in forestsa
Country
Brazil
Zaire
Indonesia
Angola
Bolivia
Venezuela
Paraguay
Malaysia
Burma
Thailand
Mexico
Philippines
Colombia
Tanzania
Zambia
India
Poland
Italy
Portugal
Norway
United Kingdom
Australia
Greece
Ireland
Canada
France
Former USSR
United States
(Pg carbon)
change (% of
forest area)b
Predictedchange
in AGB (Pg/yr
carbon)
113.56
31.50
21.74
12.86
11.22
10.65
4.54
4.66
7.16
3.69
10.15
2.66
11.37
-0.45
-0.66
-0.94
-1.01
-1.13
-1.08
-2.48
-2.29
-1.33
-2.48
-0.88
-3.24
-0.48
5.58
-0.95
-0.025
6.25
6.75
0.55
1.09
0.47
1.33
1.08
19.62
0.48
0.62
39.43
3.20
98.35
37.65
-0.81
+0.01
+0.14
+0.09
+0.87
+0.34
+0.56
+0.04
+2.43
+2.80
+0.07
+1.13
+0.07
+0.28
-0.024
+0.001
+0.001
+0.001
+0.004
+0.005
+0.006
+0.008
+0.012
+0.017
+0.028
+0.036
+0.067c
+0.106
-0.247
-0.099
-0.098
-0.063
-0.061
-0.055
-0.054
-0.051
-0.046
-0.044
-0.043
-0.041
-0.026
aFrom the NASA-CASA model.
values indicate loss of carbon to the atmosphere from deforestation, whereas
positive values indicate gains of carbon from the atmosphere resulting from forest regrowth
or expansion. Data from FAO (1997).
cThis value for States of the Former USSR is based on annual rates of change of +0.07% for
forest areas in Russia (FAO 1997). By including reported rates of forest fire in Russia
(Conard and Davidenko 1996), this estimated change in forest carbon converts to a minimum
value of -0.024 Pg/yr of carbon for States of the Former USSR.
bNegative
tive remote sensing method using
lidar (light detection and ranging)
technology. VCL is the first selected
Earth System Science Pathfinder mission for NASA's Earth Science Enterprise (Dubayah et al. 1997). Lidar
systems have been used extensively
for small-scale observations taken
from airborne platforms. The Shuttle
Laser Altimeter provided the first
spaceborne vehicle for global-scale
lidar observations. With launch anticipated within the next few years,
the VCL will sample extensively over
the earth's closed-canopy forests, collecting elevation data for canopy tops
as well as for the underlying terrestrial topography. The sensor should
provide direct estimates of degraded
forest areas, areas of regrowth, and
areas of intact forest structure for
quantifying changes in biomass.
In the full assessment of human
impacts on carbon emissions, several noteworthy sources of uncer776
tainty remain in the type of global
modeling analysis presented in this
article for biomass and deforestation effects on terrestrial carbon
fluxes. For example, FAO's forest
resource assessments before 1997
have been questioned as giving somewhat conservative estimates of deforestation rates (Houghton 1994).
As an improvement, the forest cover
data for developing countries contained in FAO (1997) are based on
national-level assessments that were
prepared on different dates and that,
for reporting purposes, have been
adjusted to the standard reference
years of 1990 and 1995. This adjustment was made using a model function developed to correlate changes
in forest cover area over time with
ancillary variables, including population change and density, initial forest cover, and ecological zone of the
forest area under consideration. Further improvements are being added
with each successive FAO report.
For example, new national-level assessments were submitted by several
governments to FAO as updated inputs to the model for the calculation
of the forest cover area for 1995 and
the recalculation of the 1990 estimate. Specifically, forest inventory
information was revised for several countries, including Brazil,
Bolivia, Mexico, Cambodia, and
the Philippines.
Despite improved methods of deforestation rate assessment, certain
destructive land-use practices are
rarely reported and therefore not
included in assessments of tropical
forest inventories-namely, selective
logging and the accidental spread of
agricultural fires into forests. For
example, in the Amazon rain forest,
logging companies substantially reduce the living biomass of forests
through the harvest and associated
damage of trees (Nepstad et al. 1999).
This damage allows sunlight to penetrate to the forest floor, where it
dries out the organic debris created
by logging. The incidence of ground
fires can increase dramatically when
the combined effect of severe
droughts provoke forest leaf shedding and greater flammability. Satellites are generally unable to detect
these "hidden" processes of forest
impoverishment. Eventually, the effects of logging and drought can lead
to large-scale forest fires in Amazonia
and many other tropical countries,
particularly during El Nifio events,
which intensify the ecological imand
portance of deforestation
weather-related carbon emissions
because of severe drought conditions.
The need to accurately inventory
and monitor carbon storage over
entire nations will likely gain economic importance with the expected
establishment of an international
system for trading carbon credits.
Accounting for carbon pools and
fluxes on a global level will be possible only by bringing to bear and
improving all available methods of
ecosystem monitoring and inventory,
including remote sensing, groundbased sampling, tower flux measurements, ecosystem modeling, statistiand geographic
cal analysis,
information systems. Errors from
each of the methods must be reduced
to the greatest extent possible by
BioScience Vol. 49 No. 10
systematic comparisons of their independent estimates.
Acknowledgments
Thanks to Steven Klooster for continued development of the NASACASA model computer program.
Three anonymous reviewers provided helpful comments on a previous version of the manuscript. This
research was supported by the NASA
Ames Research Center Basic Research Council (Code No. 274-5271-28).
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