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Carbon Emissions and Sequestration Potential of Central African EcosystemsAuthor(s): Quanfa Zhang and Christopher O. JusticeSource: AMBIO: A Journal of the Human Environment, 30(6):351-355. 2001.Published By: Royal Swedish Academy of SciencesDOI: http://dx.doi.org/10.1579/0044-7447-30.6.351URL: http://www.bioone.org/doi/full/10.1579/0044-7447-30.6.351

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351Ambio Vol. 30 No. 6, Sept. 2001 © Royal Swedish Academy of Sciences 2001http://www.ambio.kva.se

INTRODUCTIONIn the past two decades, there has been increasing interest in es-timating the carbon budget of terrestrial ecosystems due to theirimportance in the global carbon cycle. Subsequent research hasrevealed significant complexities in making such estimates.There are 3 active carbon reservoirs in the natural global cycle:i) the oceans; ii) the atmosphere; and iii) the terrestrial ecosys-tem. Carbon deposition in the oceans (modeled), atmosphericCO2 (measured) and carbon emissions by fossil fuel combustion(measured) are fairly well known. However, the fluxes of glo-bal carbon for the 1980s cannot be balanced (1–3), and manyresearchers believe that terrestrial ecosystems, in particular tropi-cal ecosystems, are the key component to balancing the globalcarbon budget (1, 4, 5).

Geographically, temperate and boreal ecosystems as a wholeare considered to be a carbon sink or in a steady state of carbonbalance with the atmosphere (3, 6, 7). It is in the tropics wheredepletion of forest resources and land-cover transformation havebeen the primary source of carbon emissions from the terrestrialecosystem (7, 8). Confusion exists among researchers about themagnitude of carbon emissions from the tropics (7–11). Esti-mates of the carbon emissions from tropical deforestation andland-use transformation in 1980 range from 0.4 to 2.6 Pg or 10to 50% of the carbon released from fossil fuel combustion. Thesevariations result from varied estimates of rates of deforestationand degradation (1, 8, 12–14), carbon stocks in vegetation andsoil (1, 8), and allocation of carbon after clearing and burning(8).

Central Africa, composed of Cameroon, the Central AfricanRepublic (CAR), Congo, Equatorial Guinea, Gabon, and theDemocratic Republic of Congo (DRC), comprises 10% of theworld’s remaining tropical rain and moist forests (14). Estimatesof carbon emissions from deforestation in 1980 for central Af-rica range from 17.9 to 22.5 million tonnes (t), accounting forabout 20% of the total carbon emissions over the entire area oftropical Africa (10, 15). In 1991, estimated carbon emissionswere about 40 million t of carbon from DRC and Cameroon (16).

The demand for food production, fuelwood and building mate-rials by the growing population will likely accelerate the trans-formation of forests to crop and pasture lands (17, 18). Conse-quently, these transformations will cause continued release ofgreenhouse gases to the atmosphere.

In addition to determining carbon emissions, knowledge ofactual carbon stocks and the capacity of sequestering carbon inthe terrestrial ecosystem could help in understanding the poten-tial of mitigating the increase of CO2 in the atmosphere and itsimplications for global change. This knowledge can provide ascientifically sound background for policy formulation in rela-tion to global warming mitigation through forestry (e.g. forestconservation and reforestation) and agroforestry options (19, 20).A subsequent cost-benefit analysis could evaluate the feasibilitiesof offsetting the greenhouse gas emissions of industrialized coun-tries in developing countries, for example through Clean Devel-opment Mechanisms (CDM). This study summarizes current ap-proaches to estimating carbon stocks and carbon sequestrationpotential in the terrestrial ecosystem, and carbon emissions fromland-cover change and degradation using central African eco-systems as an example.

METHODS OF ESTIMATING CARBON STORAGE OFFORESTS

Forest InventoryForest inventory is designed primarily for commercial purposesto assess the present state of forest assets. Since the early 1980s,forest inventory data have been applied for estimating biomassof forests in the context of the global carbon budget (15, 21–23). Comparative analyses of successive inventories are used tocalculate changes in biomass of forests through time (23). Pro-cedures to estimate biomass of forests using forest inventory datarequire 3 parameters: i) areal extent; ii) biomass density; and iii)expansion factor.

Areal extent: Areal extent refers to the distribution of areaamong vegetation types as determined by biogeographical fac-tors (e.g. climate and soil) and anthropogenic activities (e.g.pastureland). The importance of determining areal extent is re-lated to the differences in biomass densities for various vegeta-tion types. Changes in the areal extent over time, for examplethrough deforestation, regrowth and reforestation, could changethe carbon stock of a land area.

The estimation of the areal extent for tropical forests in cen-tral Africa has been hindered by an inability to collect reliabledata due to inaccessibility, the size of the forests, and the lowlevel of resources available for national forest inventories (24,25). Currently, the most comprehensive source of areal extentacross the region is provided by FAO, based on questionnaires,national forest statistics, figures, maps, and visual interpretationof existing imagery (13, 14). However, problems exist with thesemethods due mainly to the accuracy of the varied data sourcesand as a result, errors in the estimated value of areal extent aredifficult to quantify (23). Other attempts have been made to es-timate the areal extent of the central African forests through in-terpretation of remotely sensed images (26–28).

Biomass density: Biomass density refers to the mass per unitarea of a forest. The value of biomass density is dependentlargely on the influence of human activities and the determinant

Article Quanfa Zhang and Christopher O. Justice

Carbon Emissions and Sequestration Potentialof Central African Ecosystems

Joint Implementation under the Climate Change Con-vention and Clean Development Mechanism of the KyotoProtocol require a scientific understanding of current car-bon stocks, fluxes, and sequestration potential, especiallyin tropical ecosystems where there are large carbonreservoirs, significant carbon emissions, and large landareas available for reforestation. Central Africa contains10% of the world’s remaining tropical moist forests and hasreceived little attention in carbon studies. In 1980, above-ground carbon stocks in the central African ecosystemwere 28.92 Pg and were reduced to 24.79 Pg by 1990.Improved forest management aimed at increasing biomassdensity could sequester 18.32 Pg of carbon, and over500 000 km2 formerly forested land will be available by2050 for reforestation with a capacity to offset 10 Pgcarbon. Understanding the spatial distribution of biomasscarbon and sequestration potential will be essential forcarbon trading initiatives through Joint Implementation andClean Development Mechanism.

352 © Royal Swedish Academy of Sciences 2001 Ambio Vol. 30 No. 6, Sept. 2001http://www.ambio.kva.se

of biotic and abiotic factors such as climate and soil (24, 29).Historically, sample measurements from undisturbed forests havebeen used to estimate biomass density. As a result, applying theseestimates directly to disturbed forest tracts or in an effort to ex-trapolate data on a regional scale has the potential to overesti-mate biomass density (15). For instance, a biomass density of184.5 t carbon ha–1 was obtained by destructive samplings (4,30), compared to a density of 140 t carbon ha–1 derived fromforest inventory data in the same forest in Cameroon (15). Onelimitation using forest inventory data to derive abovegroundbiomass is that the estimates do not include all abovegroundcomponents such as saplings, shrubs, other understory plants,fine litter, and coarse wood debris and belowground biomass isexcluded due to the difficulty of accurate and precise estimates(24, 31).

Expansion factor: The expansion factor is a ratio used to con-vert merchantable weight (M), derived from forest inventorydata, to total forest biomass (T) (T/M ratio) (9, 21). The methodof determining and applying expansion factors to estimate car-bon stocks in forests has been refined over time (9). The majoruncertainties are due to the fact that the T/M ratio varies by veg-etation type, life zone, forest size class, and degradation (15, 21,32). A difference between 10 to 50%, depending on vegetationtypes, might exist for total biomass estimates from the valuesof expansion factors (4, 10, 15).

Remote SensingThe remote-sensing approaches to estimating areal extent andforest biomass consist of data acquisition, interpretation, post-processing validation, and data comparison among multiple im-age sources, e.g. SPOT, Landsat TM, AVHRR, and SAR (26).

The success of these approaches depends heavily upon dataavailability. Potentially, remote sensing can be used to directlyestimate total aboveground biomass (33). Yet, other studies havereported only a weak correlation between spectral reflectance andtotal biomass in densely forested areas (34).

Remote-sensing techniques have been used primarily to mapforest, estimate areal extent and determine the rate of deforesta-tion (26–28, 35). Although there is very good agreement on theareal estimates at the regional level, different methodologies re-sult in a difference of up to 15% at the country level and over100% over- or underestimation at the state level within a coun-try (28). In addition, cloud cover, spectral overlap betweenclasses, misinterpreting, and misclassifying mixed pixel datacause errors when interpreting data with various spatial resolu-tions (26).

The major advantage of using remote-sensing methods to es-timate total forest biomass is the ability to analyze large spatialexpanses of land in a timely fashion. This is valuable in areassuch as central Africa where the inaccessibility of forests posesa problem for ground sampling (25). High resolution image clas-sification can provide sufficient spatial and temporal coveragefor assessing areal extent and studying the forces driving regionalland-cover change.

ABOVEGROUND CARBON STORAGE IN CENTRALAFRICA

Carbon Stocks in 1980The aboveground carbon storage in the 1980s closed forests ofcentral Africa was estimated using the areal extent in the FAOreport (13) and the biomass densities derived from forest inven-

Table 1. Biomass densities (t C ha –1) used to estimate the carbon stocks in 1980 and 1990 and carbon sequestrationpotential of central African ecosystems.

1980 1 1990 2 Potential biomass density 3

Closed forests ForestsOpen

Country NHCf1uv NHCf1uc NHCf2 NHCf1m forests Fallow Shrub Others Forests Others Data 1 Data 2 Others

Cameroon 164.8 172.6 84.1 172.6 31.0 25.0 7.5 2.27 72.5 8.3 209.5 153.5 10.0CAR 164.8 185.4 42.1 185.4 31.0 25.0 7.5 2.27 62.5 8.3 206.5 121.5 10.0Congo Rep. 203.1 142.5 84.1 142.5 31.0 25.0 7.5 2.27 137.5 8.3 197.9 187.0 10.0Dem. Congo 147.2 140.6 109.3 140.6 31.0 25.0 7.5 2.27 126.0 8.3 202.4 148.5 10.0Eq. Guinea 129.5 108.7 162.3 108.7 31.0 25.0 7.5 2.27 131.5 8.3 209.5 221.0 10.0Gabon 147.2 140.6 168.2 140.6 31.0 25.0 7.5 2.27 140.5 8.3 203.7 187.5 10.0

1. Biomass densities for closed forests and open forests were taken from Hall and Uhlig (10) and Detwiler and Hall (1), respectively.Biomass densities for fallow, shrub and other land cover types were taken from Milltington et al. (37).

2. Forest biomass densities in 1990 were taken from the FAO report (14). Biomass density used in other cover types was the average of biomass densities for nonforest cover types (including fallow, shrub, and others) in 1980.3. Potential biomass densities of forests were derived from Borry (24) (Data 1) and Brown and Gaston (39) (Data 2).

Table 2. 1980s aboveground carbon stocks of central African ecosystems.

Forests 1 Nonforest 2 Total

Area Carbon Area Carbon Area Carbon % of theCountry km2 1015 g km2 1015 g km2 1015 g region

Cameroon 256 200 3.19 209 200 0.24 465 400 3.43 11.86CAR 358 900 1.59 264 080 0.24 622 980 1.83 6.32Congo Rep. 213 400 3.22 128 100 0.06 341 500 3.28 11.35Dem.Congo 1 775 900 16.81 491 700 0.35 2 267 600 17.16 59.34Eq. Guinea 12 950 0.17 15 100 0.03 28 050 0.20 0.69Gabon 205 750 2.97 51 920 0.05 257 670 3.02 10.44Regional Total 2 823 100 27.95 1 160 100 0.97 3 983 200 28.92 100.00

1. Forests included both closed and open forest classes in the FAO report (13).2. Including cover types of fallow, shrubland, and other nonforest land uses in the FAO reports (13).

353Ambio Vol. 30 No. 6, Sept. 2001 © Royal Swedish Academy of Sciences 2001http://www.ambio.kva.se

tories (Table 1) (10). A biomass density of 31 t ha–1 of carbon(1) was used to estimate aboveground biomass in open forests(13). A wide range of biomass density for fallow, depending onage, had been reported (30, 36). Because FAO (13) included sev-eral complexes of woody vegetation resulting from clearing byshifting cultivation, such as young secondary forests in fallow,we used a carbon density of 25 t ha–1 to estimate the carbon stockin fallow. Biomass densities of 7.5 and 2.27 t ha–1 of carbon wereutilized to calculate the carbon storage in shrublands andgrasslands, respectively (37). Estimated carbon in the centralAfrican terrestrial ecosystem in 1980 was 28.92 Pg (Table 2).Forest formations comprised 97% (27.95 Pg) of the total car-bon stock. The carbon stock in the DRC alone accounted for 59%(17.16 Pg) of the regional total.

Carbon Stocks in 1990FAO (14) reported the total biomass in forests of tropical coun-tries in 1990. These total biomass values were converted to unitsof carbon by multiplying by 0.5 (4). The FAO report did notquantify the areal extent of other land covers such as grasslandsand shrublands. The area-weighted carbon densities for thesecover types in 1980 (fallow, shrub, and others in Table 1) wereused to estimate carbon pools for cover types not specified inthe 1990 FAO report (14).

In 1990, the total carbon stock in central Africa was 24.79 Pg(Table 3). Using the same carbon density of forests provided byFAO (14) and areal extents given by the TREES project (28)and IUCN (38), the carbon stock in the central African regionwas estimated to be 24.23 and 24.60 Pg, respectively. These as-sessments are in close agreement. The change in abovegroundcarbon stock was -4.13 Pg in this 10-year period, i.e. 1980–1990

(Tables 2 and 3). This represents an average of 0.41 Pg yr–1 ofaboveground carbon released due to deforestation, land-use trans-formation, and degradation in the region. The DRC accountedfor a loss of 1.96 Pg and Cameroon a loss of 1.74 Pg of carbonduring this 10-year time period.

Carbon Sequestration PotentialPotential carbon content in ecosystems is determined primarilyby various biotic and abiotic factors, such as species composi-tion, temperature, precipitation, elevation, and soil. Borry (24)has used those variables and determined the potential biomassdensities from a diameter of 10 cm upwards in the central Afri-can moist forests. An expansion factor of 1.4 (15) was used tomultiply the estimates to account for all aboveground biomass(Table 1). Potential carbon pools were estimated as the poten-tial biomass densities multiplied by the areal extents of wet andmoist zones provided in the FAO report (14). For those areaswhere abiotic factors could not support wet and moist forests(listed as dry, very dry, and hill and montane zones in the FAOreport) (14), an average carbon density of 10 t ha–1 was used tocalculate the carbon pools (Table 1). This estimate does not ac-count for the actual availability of land. It only represents themaximum potential for sequestering carbon in the central Afri-can ecosystems (aboveground). Another set of potential biomassdensities, derived from an overlay of spatial data including pre-cipitation, climate, topography, and soil texture in a geographicinformation system (GIS) (39), was also used to estimate thepotential carbon pools for comparison (Table 1).

The results indicate that the central African region has a po-tential to sequester 73.39 Pg of carbon in aboveground biomasswithout considering the land availability (Table 4). Using the

Table 3. 1990s aboveground carbon stocks of central African ecosystems.

Forests 1 Nonforest 2 Total

Area Carbon Area Carbon Area Carbon % of theCountry km2 1015 g km2 1015 g km2 1015 g region

Cameroon 203 500 1.48 261 900 0.22 465 400 1.69 7.02CAR 305 620 1.90 317 360 0.26 622 980 2.17 8.98Congo Rep. 198 650 2.73 142 850 0.12 341 500 2.85 11.82Dem. Congo 1 132 750 14.26 1 134 850 0.94 2 267 600 15.20 63.01Eq. Guinea 18 260 0.24 9790 0.01 28 050 0.25 1.03Gabon 182 350 2.56 75 320 0.06 257 670 2.62 10.86Regional Total 2 041 130 23.17 1 942 070 1.61 3 983 200 24.79 100.00

1. The area and biomass of forests were taken from the FAO report (14). Total biomass was converted to unit of carbon by multiplying by 0.5.2. The carbon density used to estimate carbon stocks of nonforest cover types was 8.3 t ha–1 which was the average of carbon densities for nonforest cover types in 1980.

Table 4. Potential aboveground carbon pool of central African ecosystems.

Wet and Moist Forests1 Nonforest 2 Total 3 Total 4

Area Carbon Area Carbon Area Carbon % of the Carbon % of theCountry km2 1015 g km2 1015 g km2 1015 g region 1015 g region

Cameroon 360 160 7.55 105 240 0.11 465 400 7.65 10.42 7.14 11.86CAR 551 950 11.40 71 030 0.07 622 980 11.47 15.63 7.57 12.57Congo 341 500 6.76 0 0.00 341 500 6.76 9.21 6.39 10.60Dem. Congo 2 048 500 41.46 219 100 0.22 2 267 600 41.68 56.79 33.67 55.92Eq. Guinea 27 570 0.58 480 0.00 28 050 0.58 0.79 0.62 1.03Gabon 257 670 5.25 0 0.00 257 670 5.25 7.15 4.83 8.02Regional Total 3 587 350 72.99 395 850 0.40 3 983 200 73.39 100.00 60.22 100.00

1. The area was taken from the FAO report (14).2. The area where abiotic factors could not support wet and moist forests.3. Borry (24), FAO (14).4. Brown and Gaston (39).

354 © Royal Swedish Academy of Sciences 2001 Ambio Vol. 30 No. 6, Sept. 2001http://www.ambio.kva.se

biomass density estimates from Brown and Gaston (39), thevalue is 60.22 Pg of carbon. Considering the coefficient of vari-ation (19% to 54%) in potential biomass densities (39), these twoestimates are comparable. Of the 73.39 Pg carbon offsetting po-tential, 72.99 Pg (98.64% of the total) could be sequestrated inforest formations. The DRC alone has the potential to sequester41.68 Pg of carbon, which amounts to 57% of the regional car-bon potential.

Comparing the carbon sequestration potential and the actualcarbon stocks in 1990 (Tables 3 and 4), central Africa could se-quester an additional 48.51 Pg of carbon in aboveground biomass(Table 5). Improved forest management aimed at increasingbiomass density could potentially sequester 18.34 Pg of carbon,and 30.18 Pg of carbon could be sequestrated potentially inaboveground stocks by reforestation (without considering landavailability). With consideration of the increased demand forcropland resulting from population growth, there will be poten-tially c. 500␣ 000 km2 of formerly forested land area available by2025 for reforestation in central Africa (17), providing 10 Pg ofsequestrated carbon.

PROSPECTS

Methodological AssessmentPhysiologically, the balance between the photosynthetic flux(gross primary production) and the respiratory flux (autotrophicand heterotrophic respiration) determines the carbon sink-sourcestrength in forests. Because tropical forests have been subjectto various natural and anthropogenic disturbances, e.g. agricul-tural practices and deforestation, various stages of forest devel-opment exist in tropical forested regions. Secondary forests re-covering from disturbances assimilate carbon in tree tissues andsoils by attaining a positive net balance between photosynthesisand respiration and are capable of rapidly transporting carbonfrom the atmosphere to the biosphere (5, 34, 36). The time pe-riod of recovery and distribution of biomass, either potential oractual, across a landscape may differ from site to site becauseof various disturbance regimes (e.g. intensity, type, etc.) and theheterogeneity of ecological factors that control the recovery proc-ess (34).

Forest inventories typically include species-specific data andare accurate at the scale of a stand to characterize local foreststatus (e.g. biomass density, carbon uptake through secondarygrowth, etc.). Yet this approach produces large variations whenextrapolating to a regional level and has limitations especiallyin tropical regions like central Africa where cost and accessi-bility hinder a complete forest survey (25). Additionally, forestinventories are not sensitive enough to reflect policy implica-tions with respect to carbon stocks and carbon fluxes of forestsdue to the long interval between successive inventories. In con-trast, the remote-sensing approach is advantageous for consist-

Table 5. Carbon sequestration potential through reforestation and forest management in central Africa.

Reforestation Forest Management Total

Area 1 Carbon Area 2 Carbon Area Carbon % of theCountry km2 1015 g km2 1015 g km2 1015 g region

Cameroon 156 660 3.15 203 500 2.79 360 160 5.94 12.24CAR 246 330 4.88 305 620 4.40 551 950 9.28 19.14Congo Rep. 142 850 2.71 198 650 1.20 341 500 3.91 8.06Dem. Congo 915 750 17.77 1 132 750 8.65 2 048 500 26.43 54.48Eq. Guinea 9310 0.19 18 260 0.14 27 570 0.33 0.68Gabon 75 320 1.47 182 350 1.15 257 670 2.62 5.41Regional Total 1 546 220 30.18 2 041 130 18.34 3 587 350 48.51 100.00

1. The differences between the potential area of forest formations and the area in natural forests and plantations in 1990 (14).2. The area of natural forests and plantations in 1990 (14).

ently estimating areal extent at a spatial resolution of 1 km orfiner and is highly sensitive at a temporal scale for deriving therate of deforestation for large land areas (35). However, remote-sensing methods may fail to distinguish secondary forests fromprimary forests (34). In central Africa, remote sensing will be amore practical approach where other forms of systematic dataare unavailable. The challenge remains to develop a method tointegrate limited forest inventory information and remote sens-ing to estimate carbon stocks and carbon fluxes in the region(24).

The Global Carbon BudgetThe calculated carbon stocks in the central African region for1980 and 1990 showed a change of 4.13 Pg of carbon duringthis 10-year period or a reduction of 0.41 Pg yr–1 (Tables 2 and3). An approach using spatially explicit estimates of biomassdensities for the same time period resulted in a loss of 4.43 Pg(0.44 Pg yr–1) of carbon (29). These estimates obviously exceedthe lower limit of carbon emissions of 0.58 ± 0.06 Pg for alltropical land-use changes in the 1980s (10), and imply that car-bon emissions from tropical deforestation and degradation maybe in the upper limit of 1.6–2.4 Pg annually (10), unless the as-sumption about the steady-state condition of the carbon budgetof mature tropical forests is greatly challenged (5). Other stud-ies have shown large variations in the carbon emissions fromthe central African ecosystem (3, 10, 16). Differences are prob-ably due to the variations inherent in the different methodolo-gies such as using rates of deforestation or comparative analy-sis between successive forest inventories. Given the close agree-ment on 1990s carbon stocks among various assessments (14,28, 38), areal extents in the FAO report (13) and biomass den-sities derived from forest inventories (10) for the 1980s, forestsof central Africa could be the primary cause of such discrepan-cies.

ImplicationsForestry including forest conservation, reforestation, and sustain-able management of secondary forests could be an effective wayto mitigate increasing atmospheric CO2 without obvious politi-cal impediments related to the modification of energy consump-tion patterns (20, 40). Current understanding provides the no-tion that more carbon may be offset with the same effort andcost in developing tropical countries where there are large car-bon reservoirs, significant biogenic carbon emissions, and largeland areas available for reforestation (14, 19, 40, 41). In 1990,50% of the central African region was forested and slightly over5% of the land area is protected compared to 6.29% of the landarea protected worldwide (42). Protecting an additional 1% ofcurrent forested land area in the central African region (Table3) would preserve 230 million t of carbon. In addition, a forestedland area could offset additional carbon through forest manage-

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Quanfa Zhang is a postdoc research associate at the GlobalEnvironmental Change Program (GECP), Department ofEnvironmental Sciences, University of Virginia. His mainareas of interest are forest ecology, climate change, andremote sensing/GIS application for the environment. Hisaddress: Global Environmental Change Program (GECP),Department of Environmental Sciences, Clark Hall,University of Virginia Charlottesville, VA 22903, USA.Email: quanfazhang@yahoo.ca

Christopher O. Justice is a research professor in remotesensing at the Global Environmental Change Program(GECP), Department of Environmental Sciences, Universityof Virginia. He is also the Land Discipline Group leader ofthe NASA’s MODIS project and Project Scientist of theNASA’s Land Cover Land Use Change (LCLUC). Hisresearch interests include tropical forest monitoring,biomass burning in the tropics, and land use and land coverchange. His address: Department of Geography, Universityof Maryland, 2181 LeFrak Hall, College Park, MD 20742,USA.Email: justice@hermes.geog.umd.edu

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43. First submitted 29 Feb. 2000. Accepted for publication after revision 7 Dec. 2000.

ment aimed at increasing biomass density and add to the biomasssequestration potential (Table 5). Assuming a tradable value ofUSD 2 t–1, protecting 1% of the current forest in central Africais worth more than USD 500 million. There are also extensiveformerly forested land areas available for reforestation by 2050(17) with a capacity of offsetting additional 10 Pg of carbon.Thus, carbon trading initiatives in central Africa could providea carbon sink for the increasing atmospheric carbon and revenuefor the regions economic development. Knowledge of spatial dis-tribution of biomass carbon and sequestration potential will beessential for carbon trading initiatives.

Recently, a spatially explicit approach has been adapted to-

ward producing geographically referenced estimates of poten-tial biomass densities of forests (24, 39). This approach is tomodel potential biomass densities by formulating an index in-dicating the suitability of the environment, e.g. precipitation, cli-mate, elevation and slope, and soil texture, for forest biomassproduction. Comparing the modeled potential biomass densitieswith the actual biomass densities either derived from forest in-ventory data (24) or estimated using population density as a re-duction factor (39), one would be able to pinpoint locationswhere there are great carbon mitigation potentials for carbontrading initiatives through the Clean Development Mechanismand Joint Implementation.