INTRODUCTION

Forest biomass size and regional distribution arefactors controlling the global carbon budget, butare also the basis for prediction of future climatechange (Sedjo 1992; Dixon et al. 1994). An accu-rate estimation of forest biomass density and rateof change over time is a prerequisite to resolvinga long-standing controversy about the role of forestvegetation in the carbon cycle (e.g. Sedjo 1992;Fan et al. 1998; Brown et al. 1999). This informa-tion is also required to assist in meeting the green-house gas emission targets and commitmentperiods established by the Kyoto Protocol (UnitedNations Environment Program 1998). However,

accurate information on forest biomass and distri-bution is generally lacking (Schroeder et al. 1997).It is believed that a large portion of the imbalancein the global carbon budget may be accounted forby a proper estimation of biomass accumulation interrestrial ecosystems, especially in forest ecosys-tems (Kauppi et al. 1992; Sedjo 1992; Dixon et al.1994; Brown et al. 1999; Fang et al. 2001).Although some studies (e.g. Dixon et al. 1994;Walker et al. 1999) have improved on earlier eval-uations of the role of forest ecosystems in theglobal carbon budget, we suspect that global forestbiomass is still being overestimated (especially insome regions of the middle and high latitudes of the Northern Hemisphere). In this note andcomment, we introduce a method for estimatingforest biomass based on the relationship betweenstand biomass and stand volume, and the use ofregional and national forest timber inventory data. We also review forest biomass estimates ofmajor regions of the Northern Hemisphere, withspecific reference to forest biomass estimates fromChina.

Ecological Research (2001) 16, 587–592

NOTE AND COMMENT

Forest biomass estimation at regional and global levels, withspecial reference to China’s forest biomass

Jing-Yun Fang1* and Zhang Ming Wang2

1Department of Urban & Environmental Sciences, Peking University, Beijing 100871, China and2Department of Biology, McGill University, Montreal, Canada

Accurate estimation of forest biomass size and regional distribution is a prerequisite in answeringa long-standing debate on the role of forest vegetation in the regional and global carbon cycle.Appropriate biomass estimation methods and available forest data sources are two key factors for this purpose. Among the estimation methods, the continuous Biomass Expansion Factor (BEF;defined as the ratio of all stand biomass to stem volume or biomass) method is considered to be thebest. We applied the continuous BEF to forest inventory data of China and estimated a biomasscarbon of 4.6 PgC and a biomass carbon density of 38.4 Mg ha–1. A review of recent literature shows that forest carbon density in major temperate and boreal forest regions in the Northern Hemi-sphere has a narrow variance ranging from 29 Mg ha-1 to 50 Mg ha-1, with a global mean of 36.9 Mg ha-1. This suggests that the forest biomass density in China is closely coincident with theglobal mean.

Key words: biomass estimation method; biomass expansion factor (BEF); boreal forest, China; forestinventory; temperate forest.

*Author to whom correspondence should beaddressed. Email: jyfang@urban.pku.edu.cn

Received 19 April 2000.Accepted 14 February 2001.

BIOMASS ESTIMATION METHODSBASED ON FOREST INVENTORY

In earlier studies, the mean biomass densitymethod (i.e. multiplying the mean biomassdensity calculated from direct field measurementsby the forested area) was generally used to estimateregional-, national- and global-scale forest biomass(e.g. Whittaker & Likens 1973; Kira 1976; Brown& Lugo 1982). Some of these estimates have beenused as a basis for current global climate changeresearch (e.g. Prentice & Fung 1990; Walker et al.1999). However, direct measurements tend to becarried out in forests that have a greater biomassthan is the average for a region or a country; there-fore, the mean biomass density method resultsusually in an overestimation of biomass (Brown &Lugo 1984; Busing et al. 1992; Dixon et al. 1994).Accordingly, regional or national forest invento-ries, collected widely by many countries anddesigned to be statistically valid, have been recog-nized as the more appropriate databases for the calculation of forest biomass on a large scale (e.g.Brown & Lugo 1984; Kauppi et al. 1992; Turneret al. 1995; Schroeder et al. 1997; Fang et al. 1998;Brown et al. 1999; Fang et al. 2001).

Most forest inventories record detailed informa-tion regarding forest area and sellable timber(stem) volume (or biomass) by each age class andforest type. To use stem data to estimate the forestbiomass, it is necessary to calculate a biomassexpansion factor (BEF) that converts stem volumeto mass, and accounts for non-commercial compo-nents such as branches, roots, leaves and saplings.In this note and comment, we define BEF to be theratio of total stand biomass to stem volume (orbiomass), and the biomass estimation method is,therefore, the BEF method.

Sharp et al. (1975) first estimated biomass on aregional scale for forests of North Carolina in theUSA, based on forest inventory data and using aconstant BEF of 2.0 Mg m-3. Based on a literaturereview, Johnson & Sharpe (1983) analyzed BEFvariation for major forest types across the USA andCanada, and found that BEF values fluctuatedmarkedly by forest type and size classes within aforest type (ranging from 2.1 to 5.0). Brown &Lugo (1984) applied two different BEF values, 1.6and 3.0, to an estimation of biomass for closedtropical forests and open tropical forests, respec-

588 J.-Y. Fang and Z. M. Wang

tively. Kauppi et al. (1992) estimated the biomassand carbon budget of European forests by using aBEF range of 0.6–0.8. In the USA, Turner et al.(1995) calculated biomass for major forest types byapplying a constant BEF (ratio of bole carbon towhole-tree carbon) across age classes within a forest type.

Although the BEF method has provided a betterbiomass estimate than the mean biomass densitymethod, we believe that it can be improved furtherbecause the forest biomass of younger and less pro-ductive stands is underestimated, while the forestbiomass of older and more productive stands isoverestimated, when a constant BEF is appliedacross all age classes and site classes within a foresttype or forest type group (Turner et al. 1995; Fanget al. 1996, 1998; Schroeder et al. 1997; Brown et al. 1999). For example, biomass estimates forclosed tropical forests were 28–47% higher whenthree different conversion factors (instead of oneconstant ratio) were applied to the same datasource (Brown & Lugo 1984; Brown et al. 1989).Similarly, in an estimation of Russian forestbiomass conducted by Isaev et al. (1995) and Alexeyev et al. (1995), who estimated forestbiomass in Russia as 35 PgC and 28 PgC, respec-tively, (see Table 1) used the same forest inventorydatabase but different BEF values.

To solve this problem, a consistent BEF method,which could also reflect stand characteristics (age class, site class etc.) within each forest type or forest type group, was proposed separately byBrown’s group (e.g. Brown & Lugo 1992;Schroeder et al. 1997) and Fang’s group (‘biomass-volume relationship method’; Fang et al. 1993,1996, 1998).

In the case of Brown’s group, BEF is expressedas a power function of stem volume or biomass (x),namely,

(1)

where a and b are positive constants. By applyingthis method, Brown’s group estimated biomassand geographic distribution for tropical and northern American forests (Brown & Lugo 1992;Schroeder et al. 1997; Brown et al. 1999).

In the case of Fang’s group, BEF is expressed asa simple reciprocal equation of stem volume orbiomass (x) for a specific forest type or a forest typegroup:

BEF ax b= -

(2)

where a and b are constants for a specific forest typeor forest group. Figure 1 illustrates the change ofBEF with stand stem volume or biomass for fourforest types from tropical, temperate and borealforests or plantations, and suggests that the samerelationship holds true for all forest types in anygeographical region.

Using data from direct field measurements, theconstants in Equations 1 and 2 can be determinedeasily by regression analysis. Therefore, BEF canbe calculated for any given x (stand stem volumeor biomass). By multiplying each calculated BEFby x recorded forest inventory, forest biomass canbe obtained easily without any additional fieldmeasurements.

BEF a b x= + FOREST BIOMASS ESTIMATES INCHINA AND OTHER REGIONS IN THENORTHERN HEMISPHERE

Because China is a big Asian country, a forestbiomass estimate is one of the basic parameters for inclusion in a national and global carbon cycle.By applying the continuous BEF method shown in Equation 2, we obtained an estimate of theforest biomass of China as 4.6 PgC for totalbiomass carbon and 38.4 Mg ha-1 for biomasscarbon density (Fang et al. 1998). In comparison,considerably higher estimates (7.1 PgC for totalbiomass carbon and 60.1 Mg ha-1 for biomasscarbon density) were obtained using the meanbiomass density method, and lower estimates (4.0PgC for total biomass carbon and 34.1 Mg ha-1 for

Regional forest biomass estimation 589

Table 1 Forest biomass carbon pools in selected regions of the middle and high latitudes of the Northern Hemi-sphere. All estimates included in the table are based on forest inventory data. Only biomass of living vegetation(both above- and belowground, excluding soil carbon) is shown. Biomass is converted to carbon content by using afactor of 0.5

Forested Total Carbonarea (106 ha) carbon (Pg) density (Mg ha-1) Method used9 Reference10

Alaska 55.0 2.0 39.0 MBEFM Birdsey (1992)*Canada1 440.8 13.8 34.0 MBEFM Kurz et al. (1992)*Canada2 440.8 12.9 29.0 CBEFM Penner et al. (1997)

High productivity 222.8 10.2 45.4Low productivity 218.0 2.7 12.3

China 118.5 4.6 38.4 CBEFM Fang et al. (1998)Conterminous USA3 243.0 12.1 49.8 CBEFM Turner et al. (1995)Europe 324.0 9.0 32.0 MBEFM Kauppi et al. (1992)*Japan4 24.5 0.9 34.7 MBEFM Iwaki (1983)Russia5 771.1 28.0 36.2 CBEFM Alexeyev et al. (1995)Russia6 771.1 35.1 45.5 MBEFM Isaev et al. (1995)Total7 1976.9 70.4 36.98

1Only aboveground biomass was given in the original reference, thus 15% of the aboveground biomass was added for totalcarbon. The 15% proportion followed Kurz et al. (1992); 2total forest area, total biomass and biomass density were estimatedonly for high productivity forest types in the original paper. For low productivity forest types, forest area (218.0 ¥ 106 ha-1) wasestiamted from Kurz et al. (1992), and mean carbon density (12.3 Mg ha-1) was calculated based on 38 forest types (Penner et al. 1997); 3total area of forest (200.7 ¥ 106 ha-1) and woodland (42.3 ¥ 106 ha-1); 4only aboveground biomass was given in theoriginal paper, thus a mean ratio (3.5 Mg/Mg) of total biomass to belowground biomass was applied for total carbon (Kira 1976);5total biomass and biomass density were calculated on the basis of EBF at five growth stages (regeneration, pole-timber, middle-aged, maturing and mature) for each tree species for each ecoregion; 6total biomass and biomass density were calculated on thebasis of BEF at four growth stages (young, middle-age, premature and mature/overmature) for each tree species; 7estimates offorested area, carbon pool and carbon density for Canada and for Russia are based on those from Kurz et al. (1992) and Alexeyevet al. (1995), respectively; 8area-weighted mean carbon density; 9MBEFM, mean BEF method; CBEFM, continuous BEF method.The method using a few BEF within a forest type is considered to be CBEFM; 10references with asterisks were also cited in Dixonet al. (1994).

biomass carbon density) were obtained using theconstant BEF method within a forest type (Fang etal. 1998). We believe that the intermediate esti-mates from the continuous BEF method are themost accurate.

Dixon et al. (1994) applied Xu’s estiamtes (Xu1992) of carbon pools in China as 17 PgC and 16 PgC, and carbon densities as 114 Mg ha-1 and136 Mg ha-1, respectively, for forest biomasscarbon and soil carbon, to a discussion of the globaland regional carbon budget. However, Xu’s esti-mates are considerably higher than our estimatesbecause he used few field measurement records andan inappropriate method. For example, Xu’s valueis 3.7 times higher than our estimate; however, hisestimate of soil carbon is an acceptable value, so

590 J.-Y. Fang and Z. M. Wang

using our forest biomass carbon estimate and Xu’ssoil carbon estimate, the ratio of soil carbon to veg-etation carbon is 16/4.6 = 3.5. Many studies haveindicated that the world average ratio is approxi-mately 3.0 (Schlesinger 1984; Prentice & Fung1990), and within a range of 2.0–6.4 in temper-ate and boreal forest ecosystems (Kurz et al. 1992;Alexeyev et al. 1995; Turner et al. 1995).

Many studies have suggested that forests in themiddle and high latitudes of the Northern Hemi-sphere play a key role in accounting for the imbal-ance in the global carbon budget (Tans et al. 1990;Kauppi et al. 1992; Kurz et al. 1992; Sedjo 1992;Dixon et al. 1994; Turner et al. 1995; Fan et al.1998). Table 1 lists the most recent estimates offorest vegetation carbon pools in major regions or

Fig. 1. Changes in the biomass expansion factor (BEF) with stem size (biomass or volume) in different forest types.(a) Larch (Larix) forests in China that grow in different regions, ages and sites (Fang et al. 1996). (b) Tropical forestsfrom three geographically different regions: Sri Lanka dry to moist zone (�), Malaysia moist zone (�) and FrenchGuiana moist zone (�) (Brown et al. 1999). (c) Natural spruce–birch forests in European Russia, with four foresttypes, all of which were 30–100 years old (Chibisov 1995). (d) Slash pine (Pinus elliottii) plantations in Florida, USA,with seven age classes and three replications for each age class (Gholz & Fisher 1982). Only aboveground biomasswas used in computation of the BEF in (b) and (c). Solid lines were fitted by Equation 2. t, tonnes.

countries in the Northern Hemisphere. All ofthese estimates were obtained based on forestinventory data sources and using either the con-tinuous BEF method (CBEFM) or the mean BEFmethod (MBEFM), and were comparable. Becauseestimates derived from a few different BEF withina forest type are approximately like those derivedfrom CBEFM, the method using a few BEF withina forest type is handled as CBEFM in Table 1. Asa result, the area-weighted mean biomass carbondensities in all listed regions are within a narrowrange of 29–50 Mg ha-1, with an area-weightedaverage of 36.9 Mg ha-1 (Table 1). Brown & Lugo(1984) reported an average biomass density of 53 Mg ha-1 for tropical forests, which is 1.4 timesgreater than that of the forests of the middle andhigh latitudes. The narrow biomass density rangeshown in Table 1 may suggest indirectly that abetter estimate can be achieved based on forestinventory data. In contrast, carbon densities inthese regions (given by Dixon et al. 1994) fluctu-ated from 28 Mg ha-1 to 114 Mg ha-1. Such a widerange is difficult to explain biologically, and sug-gests that an overestimate of forest carbon pools ofthe Northern Hemisphere has been reported byDixon et al. (1994).

ACKNOWLEDGEMENTS

We thank Dr G. G. Wang for his helpful discus-sion in preparing the manuscript. This study wassupported by the State Key Basic Research andDevelopment Plan of China (G2000046801) andby the National Natural Science Foundation ofChina (#40024101 and #39425003).

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