terrestrial biomass and the effects of deforestation on the global carbon cycle

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 glo-

bal 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 reduc- ing net terrestrial emissions of carbon dioxide on an international scale. Emissions may be reduced by curb- ing 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 bud- get; 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). Dur- ing 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 Intergov- ernmental 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. Un- certainties in the global carbon bud- get 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 and fluxes. Global ecosystem models are valuable tools in situations in which ground-based measurements of carbon pools are not adequate to

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 Aero- nautics and Space Administration- Carnegie 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 NASA- CASA 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 longi- tude. 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,

Christopher S. Potter (e-mail: cpotter@ mail.arc.nasa.gov) is a research scientist at NASA Ames Research Center, Ecosystem Science and Technology Branch, Moffett Field, CA 94035.

October 1999 769

University of California Pressis collaborating with JSTOR to digitize, preserve, and extend access to

BioSciencewww.jstor.org

®

at Tulane U

niversity on October 19, 2014

http://bioscience.oxfordjournals.org/D

ownloaded from

http://bioscience.oxfordjournals.org/

(a) Soil Moisture Balance (b) Ecosystem Production (c) Biogenic Trace Gas Flux and Plant Functional Types Nutrient Mineralization

NDVI - FPAR

PPT TEMP PE T ,PPT N- NPP

. PET

SOLAR

Mo Mo Mo:4? Soil Surface oi

. . Profile Leaf Litter

MM" M"

Layers Root Litter Heat & reeze/Thaw Microbes Water Flux M3 MS M oil Organic

Sf(Temp Matter f(WFPS)I f (Temp)

f(WFPS) f(Lit q) C02 -1CH4 N20

GNO

Grass Shrub Tree Mineral N

Figure 1. Structure of 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 accumulated heat flux. (b) Climate controls on NPP are defined by the equation NPP = Sr FPAR Emax T W, where Sr is solar irradiance (SOLAR), FPAR is fraction of absorbed photosynthetically 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-filled pore 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 terrestrial production 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 mod-

els, which link carbon metabolism with water, energy, and nutrient cycling in plants (e.g., Kindermann et al. 1996, Schimel et al. 1996b, Chur- kina 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 relation- ship between greenness reflectance properties and absorption of photosynthetically active radiation (PAR), assuming that net conversion effi- ciencies 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 for- mulation (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 ex-

770 BioScience Vol. 49 No. 10

at Tulane U



ownloaded from


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:

NPP = S FPAR max

TW The Smax term is set at 0.56 g/MJ (carbon/PAR), a value that derives from calibration of predicted annual NPP to previous field estimates of NPP (Potter et al. 1993). The T stress term is computed with reference to deri- vation of optimal temperatures (Topt) for plant production. The Topt setting varies with latitude and longi- tude, ranging from near 0 ?C in the Arctic to the mid-30s (?C) in low- latitude deserts. The W term is estimated from monthly water deficits, which are based on a comparison of moisture supply (precipitation and stored soil water) to evapotranspiration water potential demands using the method of Thornthwaite (1948).

Algorithms in the NASA-CASA model allow evapotranspiration to be connected to water content in the soil profile layers (Figure la; Potter 1997). The model design includes heat and moisture content computa- tions for three soil layers: surface organic matter (M1), topsoil (0.3 m; M2), and subsoil to rooting depth (1-10 m; M3). These layers can differ in soil texture, moisture-holding capacity, and carbon-nitrogen dynamics. Water balance for each layer of the soil is modeled as the difference between precipitation plus volumet- ric percolation inputs, on the one hand, and monthly estimates of potential evapotranspiration plus drain- age output, on the other. Inputs from rainfall can recharge the soil layers to field capacity. Excess water per-

colates through to lower layers and may eventually leave the system as seepage and runoff. In the NASA- CASA model, freeze-thaw dynamics with soil depth operate according to the empirical degree-day accumulation method (Jumikis 1966, as de- scribed by Bonan 1989).

Because it is based on plant production as the primary carbon and nitrogen cycling source, the NASA- CASA 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 the NASA-CASA model (Potter et al. 1996a, 1996b) are summarized in Figures lb and 1c. The soil model component of NASA-CASA uses a set of compartmental difference equations with a structure compa- rable to those of the CENTURY ecosystem model (Parton et al. 1992). First-order equations simulate ex- changes of decomposing plant resi- due (metabolic and structural fractions) at the soil surface. They also simulate surface soil organic matter fractions that presumably vary in age and chemical composition. Ac- tive (microbial biomass and labile substrates), slow (chemically protected), and passive (physically protected) fractions of the soil organic matter are represented in the model. Along with moisture availability and litter quality, estimated soil temperature in the M1 layer controls soil organic matter decomposition.

Interannual results of global net ecosystem production estimates from the NASA-CASA model (Potter and Klooster 1999, Potter et al. 1999) suggest that land plants have been absorbing more carbon dioxide in the Northern Hemisphere during the late 1980s than previously believed- almost one-third of the annual amount of carbon dioxide released from the burning of fossil fuels. When AHVRR satellite data are used in the NASA- CASA simulation model of net ecosystem production, the results show increasing carbon dioxide accumulation in vegetation over extensive

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 NASA- CASA model. For global simulations, the 1 grid resolution Soil Map of the World (FAO/UNESCO 1971, Zobler 1986) is classified in the NASA- CASA model according to three rela- tive 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 bright-

October 1999 771

at Tulane U



ownloaded from


Table 1. Allocation and residence time parameters for major plant functional types.a

Plant functional typeb tc leaf croot a wood Cd leaf x root u wood

Tundra 0.25 0.25 0.50 1.5 3.0 50

High-latitude forest 0.30 0.25 0.45 1.0 3.0 50 Boreal coniferous forest 0.25 0.25 0.50 2.5 3.0 50

Temperate grassland 0.45 0.55 NAe 1.5 5.0 NA Mixed coniferous forest 0.25 0.25 0.50 1.5 3.0 40

Temperate deciduous forest 0.30 0.25 0.45 1.0 3.0 40 Desert and bare ground 0.25 0.25 0.50 1.5 3.0 50 Semi-arid shrubland 0.25 0.25 0.50 1.5 3.0 50 Savanna and woody 0.30 0.25 0.45 1.0 5.0 25 grassland Tropical evergreen rain 0.25 0.25 0.50 1.5 2.0 25 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 cross- track 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 neces- sary 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 Cen- ter, 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 cross- ing 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 (1931- 1960) average values from Leemans and Cramer (1990) were used after being adjusted with monthly 1' climate anomalies for the period 1980- 1988 (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

Average Average Average Total area carbon in Total carbon carbon in Total carbon NPPd carbon Total NPP

Vegetation class (x 106 km2)b AGB (g/m2)c (Pg) in AGBc AGB (g/m2)b in AGB (Pg)b (g. M-2. yr-1)b carbon (Pg/yr)b

Broadleaf evergreen 15.4 8159 126.1 12622 195.0 1075.4 16.6 forest Coniferous evergreen 13.5 11381 153.5 6014 84.9 233.3 3.3 forest and woodland

High-latitude 5.9 11918 70.1 5603 34.0 244.5 1.5 deciduous forest and woodland Tundra 8.6 428 3.8 1878 17.7 72.6 0.7 Mixed coniferous 7.5 5321 40.2 7386 56.9 386.2 3.0 forest and woodland Wooded grassland 24.3 4540 111.2 8193 202.4 782.4 19.3 and shrubland

Temperate grassland 22.1 476 10.6 154 3.5 172.5 4.0 Desert 17.7 190 3.4 525 9.4 21.4 0.4 Cultivated 14.6 747 10.9 182 2.7 365.0 5.4 Broadleaf deciduous 3.6 7390 26.9 6715 24.7 397.4 1.5 forest and woodland Semi-arid shrubland 11.5 971 11.1 1721 20.0 69.0 0.8 Total 145.4 567.8 651.1 56.4

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.


at Tulane U



ownloaded from


generate ecosystem model results typical of the 1980s.

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 broadleaf evergreen forests-mostly those in tropical zones-at more than 12 kg/m2, with lower storage in mixed coniferous-deciduous forests of temperate zones. Predicted carbon stocks in wood biomass were 20-100 times greater than predicted carbon stocks in leaf biomass for most forest ecosystem areas (Figure 2).

Accuracy of the model predictions can be evaluated by comparison to available ground-based observations. For example, the NASA-CASA model value for global carbon in aboveground biomass, 651 Pg, is somewhat higher than Olson et al.'s (1983) estimate of 568 Pg, which was based on a selected data set of biomass observations (Table 2). Al- though there are few other estimates of standing biomass for comparison, maximum predicted aboveground biomass from the NASA-CASA model for tropical moist forests ex- ceeds 17 kg/m2 of carbon. This figure, unlike the lower Olson et al. (1983) estimates, is in close agreement with measured amounts of aboveground biomass from recent field studies (Kauffman et al. 1995, Carvalho et al. 1998). By contrast to the fairly similar estimates of global aboveground biomass, average predicted aboveground biomass from the NASA-CASA model for coniferous evergreen forests and high-latitude deciduous forests deviates no- tably from the Olson et al. (1983) estimates, mainly because of the relatively low estimated annual production in these vegetation classes based on the monthly NDVI drivers used in the model. Compared to the Olson et al. (1983) observations, the lower NASA-CASA model estimate of aboveground biomass for grasslands and cultivated areas is attributable

a

0 5000 10000 15000 20000 25000

b

0 250 500 750 1000

gCm-2

Figure 2. Results from the NASA-CASA model for aboveground biomass. (a) Predicted wood carbon. (b) Predicted leaf carbon. Global patterns are representa- tive of net primary production (NPP) computed using satellite and climate inputs to the NASA-CASA ecosystem model for the 1980s.

partially to the model ratio setting (leaf versus root tissue) for biomass allocation from NPP. The model ratio results in higher belowground than aboveground allocation of biomass at the expense of aboveground biomass in grasslands.

Overall, the two different methods for estimating aboveground biomass, as compared in Table 2, may represent the potential extremes and the variability of biomass storage in many terrestrial ecosystems. Unlike the extrapolated values from site measurements reported by Olson et al. (1983), the NASA-CASA model estimate is designed to reflect actual

land surface conditions observed by satellite remote sensing for a recent time period of less than 5 years, that is, the late 1980s. NASA-CASA predictions also include climate controls on plant production at 10 spatial resolution, effects of soil fertility on carbon allocation to different plant tissues, and AVHRR-based mapping of 1980s global land cover and use patterns (DeFries and Townshend 1994). Another advantage of the NASA-CASA model estimates for carbon in aboveground biomass is that they make it possible to separate leaf from standing wood and fine-root stocks, thereby ac-

October 1999 773

at Tulane U



ownloaded from


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 important where land use is changing rapidly.

Carbon losses from deforestation FAO (1997) provides worldwide statistics on the status of forest cover, circa 1995, and on recent (1990- 1995) rates of annual change in forest cover and condition. Changes in forest area or condition can be docu- mented as positive (e.g., through natural colonization of nonforest land or regrowth on degraded stands) or negative (e.g., through deforestation, forest fires, or unsustainable exploitation for wood). The estimates of forest cover change during 1990- 1995 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 deforestation in developing countries 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 (Fig- ure 2) with country-by-country rates 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 aban- donment of other land uses. To de- rive 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

FAO (1997) conversion rates for 1990-1995, suggests that the net terrestrial loss of carbon dioxide from changes in area of the world's forest ecosystems was 1.15 Pg/yr of 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 accumulated in the terrestrial biosphere through forest area regrowth and expansion. In this estimate, countries with the highest in- dividual losses 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 accumulation in 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 should be noted that although the age structure of forests can have a significant impact on estimated carbon pools from regrowth, it is not feasible to consider these types of age-dependent factors 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 temperate forest zones total nearly 0.06 Pg/yr for the period 1990-1995.

As an important footnote to this study, FAO (1997) mentions that a highly reliable estimate for change in 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. How- ever, 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 offi- cial fire statistics, a discrepancy with

major implications for global carbon dioxide emissions that has ap- parently not been fully considered in 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 projected net 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 area would make the area covered by the former USSR the highest national deforestation source of carbon dioxide in the world, contributing to global net carbon losses that would ex- ceed 1.7 Pg/yr from 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 dwarfed by 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 measur- able gains are currently offset by worldwide deforestation losses of carbon to the atmosphere at a ratio of approximately 5 to 1. At this time, there appears to be no practical means to alter the carbon balance equation except by mitigating deforestation 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, re- ducing 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 un- dertake measures to reach specific


at Tulane U



ownloaded from


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 reported in this article, the Kyoto Protocol's sector on Land Use Change and Forestry will continue to be one of the more difficult sectors to define and quantify out of the Kyoto nego- tiations 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 pas- tures-can continue to release their stored carbon for several decades after forest clearing. Satellite remote sensing alone cannot detect changes in these soil surface and belowground processes.

The unique advantage of ecosystem modeling, which cannot be achieved through a small set of soil sampling measurements alone, is underscored in this context. For in- stance, the global NASA-CASA model estimates that 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 more than 170 Pg of carbon stored in dead litter and soil carbon pools (those pools with mean residence time 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

7.

-150 -100 -50 0 50

gC m2yr-1 Figure 3. Global carbon flux resulting from annual changes in forest area. Predic- tions are based on FAO (1997) conversion rates for 1990-1995, combined with NASA-CASA model predictions of standing plant biomass. Negative flux values (-) indicate loss of carbon to the atmosphere from 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 proportion to the geographic distribution of standing biomass amount on a national level.

drivers have generated regional 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.

Several methods 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 systems for extrapolation across the landscape. These same field measurements can be used to calibrate ecosystem models.

A series of tower-based measurements of net ecosystem carbon exchange over several seasons can be

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 images of dominant forest cover types from Landsat or other relatively high resolution mapping sources, these eddy correlation tower flux measurements of net ecosystem carbon exchange may be extended with confi- dence to regional scales and compared to ecosystem model predictions. From regional to global scales, a network of gas sampling stations would provide records over time for atmospheric carbon dioxide concen- trations that could be used with models of global circulation to track terrestrial locations and fluxes of net ecosystem production.

A promising new method for char- acterizing forest structure is the Veg- etation Canopy Lidar (VCL), an ac-

October 1999 775

at Tulane U



ownloaded from


Table 3. Average estimated change in forest aboveground biomass (AGB) over the years 1990-1995 for countries with the highest carbon flux rates.

Annual rate of Predicted change AGB in forestsa change (% of in AGB (Pg/yr

Country (Pg carbon) forest area)b carbon)

Brazil 113.56 -0.45 -0.247 Zaire 31.50 -0.66 -0.099 Indonesia 21.74 -0.94 -0.098 Angola 12.86 -1.01 -0.063 Bolivia 11.22 -1.13 -0.061 Venezuela 10.65 -1.08 -0.055 Paraguay 4.54 -2.48 -0.054 Malaysia 4.66 -2.29 -0.051 Burma 7.16 -1.33 -0.046 Thailand 3.69 -2.48 -0.044 Mexico 10.15 -0.88 -0.043 Philippines 2.66 -3.24 -0.041 Colombia 11.37 -0.48 -0.026 Tanzania 5.58 -0.95 -0.025 Zambia 6.25 -0.81 -0.024 India 6.75 +0.01 +0.001 Poland 0.55 +0.14 +0.001 Italy 1.09 +0.09 +0.001 Portugal 0.47 +0.87 +0.004 Norway 1.33 +0.34 +0.005 United Kingdom 1.08 +0.56 +0.006 Australia 19.62 +0.04 +0.008 Greece 0.48 +2.43 +0.012 Ireland 0.62 +2.80 +0.017 Canada 39.43 +0.07 +0.028 France 3.20 +1.13 +0.036 Former USSR 98.35 +0.07 +0.067c United States 37.65 +0.28 +0.106

aFrom the NASA-CASA model. bNegative 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.

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 En- terprise (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 an- ticipated within the next few years, the VCL will sample extensively over the earth's closed-canopy forests, col- lecting 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 uncer-

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 con- tained 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 popu- lation change and density, initial forest cover, and ecological zone of the forest area under consideration. Fur- ther 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 pen- etrate 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 shed- ding and greater flammability. Sat- ellites 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 importance of deforestation and weather-related carbon emissions because of severe drought conditions.

The need to accurately inventory and monitor carbon storage over entire nations will likely gain eco- nomic 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, ground- based sampling, tower flux measurements, ecosystem modeling, statisti- cal analysis, and geographic information systems. Errors from each of the methods must be reduced to the greatest extent possible by


at Tulane U



ownloaded from


systematic comparisons of their in- dependent estimates.

Acknowledgments Thanks to Steven Klooster for con- tinued development of the NASA- CASA 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 Re- search Council (Code No. 274-52- 71-28).

References cited Aber JD, Melillo JM. 1991. Terrestrial Eco-

systems. Philadelphia: Saunders College Publications.

Bishop JKB, Rossow WB. 1991. Spatial and temporal variability of global surface solar irradiance. Journal of Geophysical Research 96: 16839-16858.

Bonan GB. 1989. A computer model of the solar radiation, soil moisture and soil thermal regimes in boreal forests. Ecological Modelling 45: 275-306.

Bouwman AF. 1990. Global distribution of major soils and land cover types. Pages 33-59 in Bouwman AF, ed. Soils and the Greenhouse Effect. New York: John Wiley & Sons.

Cannell MGR. 1982. World Forest Biomass and Primary Production Data. London: Academic Press.

Carvalho JAJr, Higuchi N, Araujo TM, Santos JC. 1998. Combustion completeness in a rainforest clearing experiment in Manaus, Brazil. Journal of Geophysical Research 103: 13195-13199.

Churkina G, Running S. 1998. Contrasting climatic controls on the estimated productivity of global terrestrial biomes. Eco- systems 1: 206-215.

Conard SG, Davidenko EP. 1996. Fire in Siberian boreal forests-Implications for global climate and air quality. Paper presented at the International Symposium on Air Pollution and Climate Change Effects on Forest Ecosystems; 5-9 February 1996; Riverside, CA.

Dai A, Fung IY. 1993. Can climate variability contribute to the missing CO2 sink? Glo- bal Biogeochemical Cycles 7: 599-609.

Davidson EA, Potter CS, Klooster SA, Schlesinger P. 1998. Model estimates of regional nitric oxide emissions from soils of the southeastern United States. Eco- logical Applications 8: 748-759.

DeFries R, Townshend J. 1994. NDVI-derived land cover classification at global scales. International Journal of Remote Sensing 15: 3567-3586.

DeFries R, et al. 1995. Mapping the land surface for global atmosphere-biosphere models: Toward continuous distributions of vegetation's functional properties. Jour- nal of Geophysical Research 100: 20867- 20882.

Denning AS. 1994. Investigations ofthe trans-

port, sources, and sinks of atmospheric CO2 using a general circulation model. PhD dissertation. Colorado State Univer- sity, Fort Collins, CO.

Dubayah R, Blair B, Bufton J, Clarke D, JiJai J, Knox R, Luthcke S, Prince S, Weishampel J. 1997. The Vegetation Canopy Lidar Mission. Land Satellite Information in the Next Decade. Vol. II. Washington (DC): American Society for Photogrammetry and Remote Sensing.

Esser G. 1990. Modelling global terrestrial sources and sinks of CO2 with special reference to soil organic matter. Pages 247-262 in Bouwman AF, ed. Soils and the Greenhouse Effect. New York: John Wiley & Sons.

[FAO] Food and Agriculture Organization of the United Nations. 1997. State of the World's Forests 1997. Rome: Food and Agriculture Organization- of the United Nations.

[FAO/UNESCO] Food and Agricultural Or- ganization/United Nations Educational, Scientific and Cultural Organization. 1971. Soil Map of the World. 1:5,000,000. Paris: United Nations Educational, Scien- tific and Cultural Organization.

Gleeson SK, Tilman D. 1990. Allocation and the transient dynamics of succession on poor soils. Ecology 71: 1144-1155.

Goetz SJ, Prince SD. 1998. Variability in light utilization and net primary production in boreal forest stands. Canadian Journal of Forest Research 28: 375-389.

Goulden MJ, Munger JW, Fan SM, Daube BC, Wofsy SC. 1996. Exchange of carbon dioxide by a deciduous forest: Response to interannual climate variability. Science 271: 1576-1577.

Holben BN. 1986. Characteristics of maximum-value composite images from temporal AVHRR data. International Jour- nal of Remote Sensing 7: 1417-1434.

Houghton RA. 1994. The worldwide extent of land-use change. BioScience 44: 305- 313.

[IPCC] Intergovernmental Panel on Climate Change. 1994. Climate Change 1994: Ra- diative Forcing of Climate Change and an Evaluation of the IPCC IS92 Emission Scenarios. Cambridge (UK): Cambridge University Press.

[IGBP] International Geosphere Biosphere Programme Terrestrial Carbon Working Group. 1998. The terrestrial carbon cycle: Implications for the Kyoto Protocol. Sci- ence 280: 1393-1394.

Jumikis AR. 1966. Thermal Soil Mechanics. New Brunswick (NJ): Rutgers University Press.

Kauffman JB, Cummings DL, Ward DE, Bab- bitt R. 1995. Fire in the Brazilian Ama- zon: Biomass, nutrient pools, and losses in slashed primary forests. Oecologia 104: 397-408.

Kindermann J, Wiirth G, Kohlmaier GH. 1996. Interannual variation of carbon exchange fluxes in terrestrial ecosystems. Global Biogeochemical Cycles 10: 737- 755.

Leemans R, Cramer WP. 1990. The IIASA database for mean monthly values of temperature, precipitation and cloudiness of a global terrestrial grid. Laxenberg (Aus- tria): International Institute for Applied

Systems Analysis. Report WP-41. Los SO, Justice CO, Tucker CJ. 1994. A

global lx1 NDVI data set for climate studies derived from the GIMMS conti- nental NDVI data. International Journal of Remote Sensing 15: 3493-3518.

Lusk CH, Contreras O, Figueroa J. 1997. Growth, biomass allocation and plant nitrogen concentration in Chilean temperate rainforest tree seedlings: Effects of nutrient availability. Oecologia 109: 49-58.

Maisongrande P, Ruimy A, Dedieu G, Saugier B. 1995. Monitoring seasonal and interannual variations of gross primary productivity, net primary productivity, and net ecosystem productivity using a diagnostic model and remotely sensed data. Tellus Series B Chemical and Physi- cal Meteorology 47: 178-190.

Malhi Y, Nobre AD, Grace J, Kruijt B, Pereira MGP, Culf A, Scott S. 1998. Carbon dioxide transfer over a central Amazonian rain forest. Journal of Geophysical Research 103: 31593-31612.

Malmstr6m CM, Thompson MV, Juday GP, Los SO, Randerson JT, Field CB. 1997. Interannual variation in global scale net primary production: Testing model estimates. Global Biogeochemical Cycles 11: 367-392.

Monteith JL. 1972. Solar radiation and productivity in tropical ecosystems. Journal of Applied Ecology 9: 747-766.

Nepstad DC, et al. 1999. Large-scale impoverishment of Amazonian forests through logging and fire. Nature 398: 505-508.

Olson JS, Watts JA, Allison LJ. 1983. Carbon in Live Vegetation of Major World Eco- systems. Oak Ridge (TN): Oak Ridge Na- tional Laboratory. Environmental Sciences Division Publication Number 1997. ORNL-5862.

Parton WJ, McKeown B, Kirchner V, Ojima D. 1992. CENTURY Users Manual. Fort Collins (CO): Natural Resource Ecology Laboratory, Colorado State University.

Potter CS. 1997. An ecosystem simulation model for methane production and emission from wetlands. Global Biogeochemi- cal Cycles 11: 495-506.

Potter CS, Klooster SA. 1997. Global model estimates of carbon and nitrogen storage in litter and soil pools: Response to change in vegetation quality and biomass allocation. Tellus Series B Chemical and Physi- cal Meteorology 49: 1-17.

_ . 1998. Interannual variability in soil trace gas (CO2, N20, NO) fluxes and analysis of controllers on regional to global scales. Global Biogeochemical Cycles 12: 621-637.

.1999. Detecting a terrestrial biosphere sink for carbon dioxide: Interannual ecosystem modeling for the mid-1980s. Cli- matic Change 42: 489-503.

Potter CS, Randerson J, Field CB, Matson PA, Vitousek PM, Mooney HA, Klooster SA. 1993. Terrestrial ecosystem production: A process model based on global satellite and surface data. Global Bio- geochemical Cycles 7: 811-841.

Potter CS, Davidson EA, Verchot L. 1996a. Estimation of global biogeochemical controls and seasonality in soil methane con- sumption. Chemosphere 32: 2219-2246.

Potter CS, Matson PA, Vitousek PM,

October 1.999 777

at Tulane U



ownloaded from


Setv rritle

a new course guide for high school

science teachers

:+ Meets the National Science Education Standards

SIncludes laboratory activities and tests stressing inquiry-based science, for each unit

.: Stresses interdisciplinary assignments that pro- mote hands-on learning, with an emphasis on

honing the student's research skills

Cultural Uses of Plants: A Guide to Learning about Ethnobotany

Gabriell DeBear Paye

ISBN 0-89327-422-4

October 1999

THE NEW YORK BOTANICAL GARDEN PRESS

200th Street and Kazimiroff Boulevard

Bronx, NY 10458-5126 Tel: (718) 817-8721 0 Fax: (718) 817-8842 E-mail: [email protected] * Web Site: www.nybg.org

Davidson EA. 1996b. Process modeling of controls on nitrogen trace gas emissions from soils world-wide. Journal of Geo- physical Research 101: 1361-1377.

Potter CS, Davidson EA, Klooster SA, Nepstad DC, de Negreiros GH, Brooks V. 1998. Regional application of an ecosystem production model for studies of biogeochem- istry in Brazilian Amazonia. Global Change Biology 4: 315-334.

Potter CS, Klooster SA, Brooks V. 1999. Interannual variability in terrestrial net primary production: Exploration of trends and controls on regional to global scales. Ecosystems 2: 36-48.

Redente EF, Friedlander JE, Mclendon T. 1992. Response of early and late semiarid seral species to nitrogen and phosphorus gradients. Plant and Soil 140: 127-135.

Running SW, Gower ST. 1991. FOREST- BGC, A general model of forest ecosystem processes for regional applications. II. Dynamic carbon allocation and nitrogen budgets. Tree Physiology 9: 147-160.

Running SW, Nemani RR. 1988. Relating seasonal patterns of the AVHRR vegetation index to simulated photosynthesis and transpiration of forests in different climates. Remote Sensing of Environment 24: 347-367.

Schimel DS, Alves D, Enting I, Heimann M, Joos F, Raynaud D, Wigley T. 1996a. CO2 and the Carbon Cycle. Pages 76-86 in Houghton JT, ed. Climate Change 1995. Cambridge (UK): Cambridge University Press.

Schimel DS, Braswell BH, McKeown R, Ojima DS, Parton WJ, Pulliam W. 1996b. Climate and nitrogen controls on the geography and timescales of terrestrial biogeochemical cycling. Global Biogeochemical Cycles 10: 677-692.

Sellers PJ, Tucker CJ, Collatz GJ, Los SO, Justice CO, Dazlich DA, Randall DA. 1994. A global 1x1 NDVI data set for climate studies. Part 2: The generation of global fields of terrestrial biophysical parameters from the NDVI. International Journal of Remote Sensing 15: 3519-3545.

Thornthwaite CW. 1948. An approach toward rational classification of climate. Geographical Review 38: 55-94.

Wilson SD, Tilman D. 1991. Components of plant competition along an experimental gradient of nitrogen availability. Ecology 72: 1050-1065.

Zobler L. 1986. A world soil file for global climate modeling. Washington (DC): Na- tional Aeronautics and Space Administra- tion. NASA Technical Memo 87802.


at Tulane U



ownloaded from


terrestrial biomass and the effects of deforestation on the global carbon cycle

Documents