EPA/6Q0/A-95/106 1
Estimates of Biomass Density for Tropical Forests
Sandra Brown and Greg Gaston
US Environmental Protection Agency
200 W 35th St
Corvallis, OR 97333, USA
The information in this document has been funded in part by the US Environmental Protection
Agency. It has been subjected to the Agency's administrative review, and it has been approved
for publication as an EPA document. Mention of trade names or commercial products does not
constitute endorsement of recommendation for use.
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Estimates of Biomass Density for Tropical Forests
Sandra Brown and Greg Gaston
US Environmental Protection Agency
200 W 35th St
Corvallis, OR 97333, USA
Abstract
An accurate estimation of the biomass density in forests is a necessary step in understanding the
global carbon cycle and production of other atmospheric trace gases from biomass burning. In
this paper we summarize the various approaches that we and colleagues have developed for
estimating aboveground biomass density of tropical forests relying for the most part on forest
inventory data and modeling in a geographic information system (GIS). Biomass density
estimates from forest inventory data range from about 50 to >550 Mg ha"1 in tropical Asia and
America and from about 25 to 380 Mg ha'1 in tropical Africa. This range of values for all
regions reflects differences in climate and intensity of human disturbances. To capture the
spatial distribution of biomass density, we have developed a geographic information system
(GIS) model of the biophysical parameters that influence the distribution of biomass density.
This model was combined with forest inventory and human population density data to produce a
spatially explicit estimation of biomass density both under natural conditions and with the
influence of human activity. These estimates are more representative of the landscape as a whole
and are better suited to regional or global analysis. To date, this approach has been applied to
the tropical regions of Africa and Asia.
Introduction
The role of tropical forests in global biogeochemical cycles, especially the carbon cycle and its
relation to climate change, has heightened interest in estimating the biomass density of tropical
forests. Forest biomass density provides estimates of the carbon pools in forest vegetation
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because about 50% of biomass is carbon. This pool is the potential amount of carbon, as carbon
dioxide, that can be added to the atmosphere when the forest is cleared and/or burned. Attempts
to estimate the biomass density of tropical forests have been made by the scientific community
for use in models that assess the contribution of tropical deforestation and biomass burning to
the increase in atmospheric carbon dioxide and other trace gases (Brown et al,, 1989; Crutzen
and Andreae, 1990; Hall and Uhlig, 1991; Houghton et al., 1987).
Estimates of the biomass density for many of the world's forests have been made. For
example, a detailed summary of biomass density studies in tropical forests, from lowland to
montane and from wet to very dry zones, was made by Brown and Lugo (1982). A later study by
Olson et al. (1983) produced a global map of the biomass density of all ecosystem types,
including disturbed and undisturbed forests, at a 0.5° x 0.5° grid-scale of resolution. These
summaries of biomass density were based on ecological studies creating several problems with
their use for global-scale analyses. Ecological studies are generally designed to characterize
local forest structure and the study sites are usually not truly randomly located nor represent
the population of interest (Brown and Lugo, 1992). These type of studies are suitable for
studying local forests but not for making inferences about larger populations (Brown et al.,
1989). Furthermore, the total area covered by these studies is a very small fraction of the
total forest area (e.g., less than 0.00001% for tropical forests; Brown and Lugo, 1984).
A further problem with using biomass data from ecological studies for national to global
analyses is the inherent bias of ecologists to adjust placement of plots based on the notion of
what a mature forest should look like, i.e., one with many large diameter trees (Brown and
Lugo, 1992), The effect of adjusting plot placement to include large diameter trees is to
overestimate biomass density of the forests because biomass per tree increases geometrically
with increasing diameter. The result of this bias is to yield high biomass density estimates for
forests (Brown et al., 1989), Thus data from ecological studies must be used with caution as
they may not represent the biomass density of the forest over large areas.
Biomass density estimates for tropical forests have also been made by the Food and
Agriculture Organization (FAO, 1993) based on the FAO FORIS data base (Forest Resources
Information System- a computerized data base) of volume over bark (VOB, commercial
volume to a minimum tree diameter of 10 cm) often measured in forest inventories. On the
positive side, VOB data from forest inventories are based on a large number of plots, generally
collected from large sample areas using a planned sampling design from the population of
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interest. However, very few national or subnational inventories that report VOB have been done
in the tropics. The compilation of the VOB data base by the FAO required much educated
guesswork to produce estimates on a tropic-wide country-level basis. This approach is,
therefore, of unknown reliability and any errors in VOB estimates were compounded during the
conversion of these data to biomass density values. Clearly, new efforts to estimate biomass
density more directly from forest inventory data are needed to provide more reliable data for
national to global assessments of the quantity of forest resources.
The purpose of this paper is to summarize the various approaches that we and colleagues
have developed over the past decade or so for estimating biomass density of tropical forests,
relying for the most part on forest inventory data and modeling in a geographic information
system (GIS). Estimates of biomass density for a variety of tropical forests from different
parts of the tropics are presented in tabular form and spatially distributed. We also discuss the
factors that affect biomass density and show that it is not a static parameter but rather a moving
target.
Definition of Biomass
A complete estimation of forest biomass density requires that the biomass of all forest
components be estimated, including the above and below ground living mass of trees, shrubs,
palms, saplings, other understory components, vines, epiphytes, etc. and the dead mass of fine
and coarse litter. In this paper we consider only the total amount of aboveground organic matter
present in trees including leaves, twigs, branches, main bole, and bark, expressed as oven-dry
tons per hectare (referred to as biomass density). For most forests or tree formations,
biomass density estimates are based only the biomass in trees with diameters greater than or
equal to 10 cm, the usual minimum diameter measured in most inventories of closed forests.
However, for forests or trees of smaller stature, such as those in the arid tropical zones,
degraded forests, or secondary forests, the minimum diameter could be as small as 2.5 cm.
Most efforts on biomass estimation to date have generally focused on the aboveground
tree component because it accounts for the greatest fraction of total biomass density and the
methods are straightforward and generally do not pose too many logistical problems. However, a
few estimates of these other components of tropical forests do exist, but they must be used with
caution as the data base on which they are built is limited.
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The amount of biomass in small diameter trees, understory shrubs, vines, and
herbaceous plants can be variable but generally about 3-5% or less of the aboveground biomass
of more mature forests (Jordan and Uhl, 1978; Tanner, 1980; Hegarty, 1989; Lugo, 1992).
However, in secondary forests or disturbed forest, this fraction could be higher (e.g., up to
30%; Brown and Lugo, 1990; Lugo 1992) depending on age of the secondary forest and openness
of canopy. Palms are common in many tropical moist forests are they are also often ignored in
forest inventories. Their contribution to total biomass density can be very variable, from
almost a 100 percent in almost pure palm forests to less than a few percent where they are a
minor component of the forest (Brown and Lugo, 1992).
The biomass of roots in tropical forests varies considerably among tropical forests
depending mainly upon climate and soil characteristics (Brown and L'ugo, 1982; Sanford and
Cuevas, 1995). Root biomass is often expressed in relation to aboveground biomass, such as a
root-to-shoot ratio (R/S ratio). From a recent review of the literature, R/S ratios for lowland
to montane forests range from 0.04 to 0.85 (Sanford and Cuevas, 1995). These estimates are
based on only a few studies (about 30) and not all of them are consistent with respect to depth of
sampling and whether all coarse roots were included.
The amount of dead plant material in a forest , or detritus, is composed of fine litter on
the forest floor, (leaves, fruits, flowers, twigs, bark fragments, branches less than 10 cm
diameter, etc.), standing dead trees and snags, and lying dead wood greater than 10 cm diameter;
the last two components are referred to as coarse woody debris (CWD). The biomass density of
fine litter ranges from about 2 to 16 t/ha (average of 6 t/ha or less than 5% of aboveground
biomass), with higher values generally in moist environments although no clear trend is
apparent in the data base (Brown and Lugo, 1982). The amount of fine litter on the forest floor
represents the balance between inputs from litterfall and outputs from decomposition, both of
which vary widely across the tropics.
The amount of CWD in tropical forests is poorly quantified but extremely variable. It is
potentially a large pool of organic carbon, perhaps accounting for an amount equivalent to 10 to
more than 40 percent of the aboveground biomass of a forest (Saldarriaga et al., 1986; Uhl et
al., 1988; Uhl and Kauffman, 1990). Lack of data on this significant forest component
obviously can lead to underestimates of the total amount of biomass in a forest.
- It is clear from the above discussion that ignoring these other forest components can
seriously underestimate the total biomass of a forest by an amount equivalent to about 70% or
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more of aboveground biomass. It is apparent that logistically and economically feasible methods
and approaches must be developed to estimate this significant quantity of biomass, especially for
improving estimates of terrestrial sources and sinks of carbon and other greenhouse gases.
Estimating Biomass Density from Inventory Data
Use of forest inventory data overcomes many of the problems present in ecological
studies as discussed above. Data from forest inventories are generally more abundant and are
collected from large sample areas (subnational to national level) using a planned sampling
method designed to represent the population of interest. However, inventories are not without
their problems (Brown and Iverson, 1992). Typical problems include:
• Inventories tend to be conducted in forests viewed as having commercial value, i.e., closed
forests, with little regard to the open, drier forests or woodlands.
• The minimum diameter of trees included in inventories is often greater than 10 cm, thus
excluding smaller trees which can account for more than 30% of the biomass (Gillespie et
al., 1992).
• The maximum diameter class in stand tables is generally open ended, with trees greater than
80 to 90 cm in diameter often lumped into one class; the actual diameter distribution of these
large trees significantly affects aboveground biomass density (Brown and Lugo, 1992;
Brown, 1 995).
• Not ail tree species are included.
• Many of the inventories are old 1960s to 1970s or earlier and he forests often no longer exist
or at least are not the same now as they were at the time of the inventory.
Despite the above problems, many inventories are very useful for estimating biomass
density of forests. During the last decade or so two main approaches for estimating the biomass
density of forests based on existing forest inventory data have been developed. One uses existing
volume estimates (VOB per ha), converted to biomass density (Mg/ha) using a variety of
"tools" (Brown et al., 1989; Brown and Lugo, 1992; Gillespie et al., 1992). A second
approach directly estimates biomass density from the application of an appropriate allometric
regression equation (biomass per tree as a function diameter) selected on the basis of climate
regime (dry, moist, or wet) to stand tables (number of trees /ha in a given diameter class)
often reported in forest inventories. The advantage of this second method is that it produces
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biomass estimates without having to make volume estimates and then to apply various expansion
factors to account for non-commercial tree components. The disadvantage is that a fewer
number of inventories report stand tables to small diameter classes for all species, thus not all
countries in the tropics are covered by these estimates.^
Biomass density estimates
The above approaches have been used with inventories from many tropical Asian (9) and
American (10) countries encompassing about 30 million ha. The resulting estimates of
aboveground biomass density for moist forests range from less than 50 Mg ha"1 to more than 550
Mg ha"1 (Fig. 1) with an arithmetic mean of 230 Mg ha"1 for both tropical regions. In the wet
zone of tropical America (mostly Panama), biomass density estimates range from less than 50 to
about 300 Mg ha*1, with an average of 150 Mg ha"1. Forests in the wet zone tended to have lower
biomass densities for a given basal area as has been shown before (Brown and Lugo, 1982).
The range of biomass density estimates for moist tropical American forests is
practically identical to that for moist tropical Asian forests (Fig. 1). As was the case for the
topical Asian forests (cf. Brown et al., 1991), many of the tropical American forests were
identified as being disturbed (e.g., commercial harvesting, harvesting by indigenous
communities, young to late secondary, shifting cultivation; Brown 1995).
Biomass density estimates for tropical moist forests of central and west Africa
(Cameroon, Gabon, Cote d'lvoire and Ghana) based on inventories range between 187 to 378 Mg
ha"1 (ongoing research by Brown and Gaston). In the drier zones of west and east Africa where
open forests or savanna woodlands dominate, biomass densities range from 22 to 196 Mg ha"1.
No inventory data for African moist forests available to date has produced biomass density
estimates as high as those for tropical Asia or America, even though estimates from ecological
studies show a similar range of values for all three tropical regions.
Estimating Biomass Density by Modeling in a GIS
Brown et al. (1993) and Iverson et al. (1994) developed a modeling approach using a
GIS to produce spatial distributions of biomass densities for tropical forests. The method was
developed to extend the few reliable, inventory-based biomass density estimates to regional
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scales in tropical Asia. The overall approach to making aboveground biomass density estimates
was based on the assumption that the present day distribution of biomass is a result of a
combination of the potential biomass density, based on prevailing climatic, edaphic and
geomorphologic conditions, and the cumulative impacts of human activities which reduce
biomass. We have used this approach to generate biomass density estimates for forests and
woodlands of tropical Africa (Brown and Gaston, 1995, and ongoing research)
The modeling approach (described in detail in Iverson et al., 1994) first estimates
potential biomass density by using a weighted overlay of input layers: precipitation, a climatic
index, elevation and slope, and soil texture. Weighting factors were adjusted through an
iterative process by comparing results to known localities (see Brown et al. 1993 and Iverson
et al. for more details). The final iteration produced a raster grid with each pixel (5 km x 5
km) containing a potential biomass density (PBD) index ranging in value from about 40 to 100.
To calibrate the PBD indices into biomass density values required the assignment of
biomass density estimates across the range of index values. The most critical values were those
that identified the upper and lower biomass limits. A very limited set of ecological studies that
gave biomass estimates for mature forests, woodlands, and wooded savannas were used to
establish the upper and lower limits of biomass density (Brown and Gaston, 1995). The
process of establishing the linkage of PBD index values to biomass density was iterative that
relied heavily on prior field experience, experts in the area, and published information.
A variety of natural and anthropogenic factors reduce biomass in any system from its
potential. Long-term human use has a dramatic effect on the density of biomass in forest
ecosystems. Fuel-wood gathering, sanctioned and unsanctioned logging (Callister, 1992),
grazing, shifting cultivation, and anthropogenic burning all reduce the amount and density of
biomass present. As these practices are continuing and ongoing as population pressure
increases, the biomass density of forests becomes a "moving targef. Past research has shown
that population density is a good empirical indicator to quantify the long-term human impact on
biomass density (cf. Brown et al., 1993). Using the methods described above, we estimated
actual forest biomass density from the available forest inventories. The amount of biomass
reduction as measured by the degradation index was calculated as the ratio of biomass density
estimated from forest inventories to the modeled potential biomass density for the inventory
location at the scale of a sub-national unit or administrative unit such as a state. We then
paired this degradation index to the population density of the subnational unit for the decade of
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the inventory and stratified the data base into two forest types: closed forest and open
forest/woodland (Brown and Gaston, 1995). We were able to identify only eight inventories for
the whole of Africa for this step, four in the closed forest zone and four in the open
forest/woodland zone.
We have shown that we can combine the African data base with a similar one for tropical
Asia and develope statistically significant regression equations of degradation ratio versus
population density for the closed forest and open forest/woodland zones (Brown and Gaston,
1995 and ongoing research). We used these two regression equations with the population
density map to produce a map of degradation ratios. The spatial distribution of "actual" biomass
was produced as the product of the potential biomass density map and the degradation ratio map.
The estimates of actual biomass density were calculated on a pixel by pixel basis.
The spatial distribution of the actual biomass density for tropical African forests
generally follows expected trends (Fig. 2). As so few forest inventory data are available in the
region, we were forced to use most of them to develop the degradation model. Only two
inventories were not used and these were used for one step in the validation process. Results
from a national forest inventory for the West African country of Guinea gave a weighted average
biomass density estimate 135 Mg ha"1. The weighted mean for this country from the modeling
approach is 140 Mg ha"1, almost equal to the measured estimate (Brown and Gaston, 1995).
Similarly for the wooded part of Mali, the inventory gave a range of 55 to 65 Mg ha"1 and the
model gave a somewhat lower weighted mean of 45 Mg ha"1. Furthermore, we used the process
described in Brown et al. (1993) as a further check for our results. We used a reclassified
map of the ecofloristic zones of Africa (something akin to a life zone map). Results of this step
confirmed expected patterns. For example, actual biomass density decreased from about 300
Mg ha"1 in the lowland moist zone to 140, 60, and 20 Mg ha"1 in the lowland seasonal, lowland
dry and lowland very dry zones, respectively.
Highest estimates (>300 Mg ha1) are for dense humid forests located in parts of the west
African countries of Liberia and Cote d'lvoire and the central African countries of Congo,
Equatorial Guinea, and Gabon (Fig. 2). Biomass densities decreased with increasing distance
from these wetter areas to a low of <50 Mg ha"1 in the dry open woodlands of countries in the
Sahel and East Africa. Area weighted, country -level estimates of actual biomass density were
also produced (Table 1). Low coefficients of variation (CV) were obtained for those countries
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with the highest biomass density estimates suggesting a relatively homogenous environment and
lower population pressure (Brown and Gaston 1995, and ongoing research).
Conclusions
The biomass density of tropical forests is one of the most important variables that
influences the magnitude of the terrestrial carbon flux and other trace gas fluxes. We have
shown that a variety of tools are available to estimate biomass density at country to regional
scales, yet still capturing the heterogeneity of the environment. We have also suggested that
estimates produced by these approaches are more suitable for regional-scale models because
they are more representative of the larger landscape and attempt to encompass the human
component. Finally, the GIS modeling approach has the advantage of producing biomass density
maps that can be matched to similar ones produced by high resolution satellite imagery that
show the actual forest areas undergoing change.
Acknowledgments
Research reported on here was partially supported by a grant from the US Department of
Energy (DOE DEFG02-90ER61081) to the University of Illinois (S. Brown P.I.).
References
Brown, S., Tropical forests and the global carbon cycle: estimating state and change in biomass
density, in The role of forest ecosystems and forest management in the global carbon
cycle, NATO Series, edited by M. Apps and D. Price, Springer Verlag, NY, 1995 (in
press).
Brown, S. and G. Gaston, Use of forest inventories and geographic information systems to
estimate biomass density of tropical forests: Application to tropical Africa, Journal of
Environmental Monitoring and Assessment.
Brown, S. and A. J. R. Gillespie, and A. E. Lugo, Biomass estimation methods for tropical forests
with applications to forest inventory data, Forest Science, 35, 881-902, 1989.
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11
Brown, S., A .J. R. Gillespie, and A. E. Lugo, Biomass of tropical forests of South and Southeast
Asia, Canadian Journal of Forest Research 21, 111-117, 1991.
Brown, S. and L. R. Iverson, Biomass estimates for tropical forests, World Resources Review 4,
366-384, 1992.
Brown, S., L. R. Iverson, A. Prasad, and D. Liu, Geographic distribution of carbon in biomass arid
soils of tropical Asian forests, Geocarto International, 8(4), 45-59, 1993.
Brown, S. L„ Iverson, and A. E. Lugo, Land use and biomass changes of forests in Peninsular
Malaysia during 1972-82: use of GIS analysis, in Effects of land use change on
atmospheric CO2 concentrations: Southeast Asia as a case study, edited by V. H. Dale, Ch.
4, Springer Verlag, NY, 1994.
Brown, S. and A. E. Lugo, The storage and production of organic matter in tropical forests and
their role in the global carbon cycle, Biotropica, 14, 161-187, 1982.
Brown, S. and A. E. Lugo, Biomass of tropical forests: a new estimate based on volumes, Science,
/
223,1290-1293, 1984.
Brown, S. and A. E. Lugo, Tropical secondary forests, Journal of Tropical Ecology, 6, 1-32,
1 990
Brown, S. and A. E. Lugo, Aboveground biomass estimates for tropical moist forests of the
Brazilian Amazon, Interciencia ,17, 8-18, 1992.
Callister, D. J., Illegal tropical timber trade: Asia-Pacific, TRAFFIC International, Cambridge,
UK, 1992.
Crutzen, P. J. and M. O. Andreae, Biomass burning in the tropics: impacts on atmospheric
chemistry and biogeochemical cycles Science, 250, 1669-1678, 1990.
Gillespie, A. J. R, S. Brown, and A. E. Lugo, Tropical forest biomass estimation from truncated
stand tables, Forest Ecology and Management, 48, 69-88, 1992.
Food and Agriculture Organization, Forest resources assessment 1990 tropical countries., FAO
Forestry Paper 112, Rome, Italy, 1993.
Hall, C. A. S. and J. Uhlig, Refining estimates of carbon released from tropical land-use change.
Canadian Journal of Forest Research ,21, 118-131, 1991.
Hegarty, E. E., The climbers - lianas and vines, in Tropical rain forest ecosystems, Ecosystems
of the World 14B, edited by H. Lieth and M. J. A. Werger, pp. 339-354, Elsevier, NY,
1989.
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Houghton, R. A., R. D. Boone, J. R. Frucci, J. E. Hobbie, J. M. Melillo, C. A. Palm, B. J. Peterson,
G. R. Shaver, G. M. Woodwell, B. Moore, D. L. Skole, and N. Myers, The flux of carbon
from terrestrial ecosystems to the atmosphere in 1980 due to changes in land use:
geographic distribution of the global flux, Tellus, 39B, 122-139, 1987.
Iverson, L. R., S. Brown, A. Prasad, H. Mitasova, A. J. R. Gillespie, and A. E. Lugo, Use of GIS for
estimating potential and actual forest biomass for continental South and Southeast Asia,
in Effects of land use change on atmospheric CO£ concentrations: Southeast Asia as a case
study, edited by V. H. Dale, Ch. 3, Springer Verlag, NY, 1994.
Jordan, C. F. and C. Uhl, Biomass of a "tierra firme" forest of the Amazon Basin, Oecologia
Piantarum, 13, 387-400, 1978.
Lugo, A. E., Comparison of tropical tree plantations with secondary forests of similar age,
Ecological Monographs , 62, 1-41, 1992.
Olson, J. S., J. A. Watts, and L. J. Allison, Carbon in live vegetation of major world ecosystems,
DOE/NBB-0037, National Technical Information Service, US Department of Commerce,
Springfield, VA 22161, USA, 1983
Saldarriage, J. G., D. C. West, and M. L. Thorp, Forest succession in the Upper Rio Negro of
Colombia and Venezuela, Environmental Sciences Division Publication No. 2694,
ORNUTM9712, Oak Ridge National laboratory, Oak Ridge, TN, 1986.
Sanford, R. L. and E. Cuevas, Root growth and rhizosphere interactions in tropical forests, in
Tropical forest plant physiology, edited by S. S. Mulkey, R. L. Chazdon, and A. P. Smith,
Chapman Hall, New York, 1995 (in press).
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Jamaica, Journal of Ecology, 68, 573-588, 1980.
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fire in eastern Amazon, Ecology, 71, 437-449, 1990.
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Figure 1. Aboveground biomass density estimates for forests of tropical America and Asia (from
Brown 1995). The estimates are plotted against basal area as a way of showing the range of
values; a high correlation is expected (see text).
Figure 2. Spatial distribution of actual aboveground biomass density for forests and woodlands
of tropical Africa for about 1980 (from Brown and Gaston, 1995). This map is available as a
ARC/INFO data base; a color version with ten biomass density classes is available from authors.
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Aboveground biomass (Mg/ha)
Aboveground biomass (Mg/ha)
-------
LJ< 60 Mg/ha
~ 50 - 100 Mg/ha
¦ lOO - 200 Mg/ha
0200 - 300 Mg/ha
¦300 - 400 Mg/ha
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Table 1. Mean area weighted actual biomass density (Mg ha"1) and coefficient of variation (CV)
for forests of tropical Africa by country (from Brown and Gaston, 1995).
Country
Actual
CV(%)
Angola
73.3
81
Benin
58.0
53
Botswana
13.2
55
Burkino Faso
34.4
70
Burundi
42.7
49
Cameroon
217.4
54
GAR
199.6
44
Chad
42.8
58
Congo
343.6
29
Cote d'lvoire
164.7
44
Equatorial Guinea
317.9
1 0
Ethiopia
51.5 '
1 00
Gabon
338.5
1 9
Gambia
29.2
55
Ghana
82.7
57
Guinea
139.6
58
Guinea Bissau
84.6
47
Kenya
33.0
80
Liberia
304.8
22
Madagascar
195.8
37
Malawi
47.1
65
Mali
44.9
57
Mozambique
57.3
75
Niger
8.6
50
Nigeria
49.0
88
Rwanda
33.7
40
Senegal
31.5
75
Sierra Leone
199.0
26
Somalia
12.5
40
Sudan
63.8
95
Tanzania
45.3
69
Togo
71.9
58
Uganda
102.2
43
Zaire
206.3
47
Zambia
46.8
85
Zimbabwe
13.6
7 1
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Estimates of Biomass Density for Tropical Forests
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S.Brown, G. Gaston
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1995 Biomass Burning & Global Change, ed
Williamsburg, VA
. Joel Levine
, Mar 13-17,
16. ABSTRACT
An accurate estimation of the biomass density in forests is a necessary step in
understanding the global carbon cycle and production of other atmospheric trace
gases from biomass burning. In this paper the authors summarize the various
approaches that have developed for estimating aboveground biomass density of
tropical forests relying for the most part on forest inventory data and modeling
in a geographic information system (GIS). Biomass density estimates from forest
inventory data range from about 50 to >550 Mg ha1 in tropical Asia and America
and from about 25 to 380 Mg ha1 in tropical Africa. This range of values for all
regions reflects differences in climate and intensity of human disturbances. To
capture the spatial distribution of biomass density, we have developed a GIS
model of the biophysical parameters that influence the distribution of biomass
density. This model was combined with forest inventory and human population
density data to produce a spatially explicit estimation of biomass density both
under natural conditions and with the influence of human activity. These
estimates are more representative of the landscape as a whole and are better
suited to regional or global analysis. To date, this approach has been applied
to the tropical regions of Africa and Asia.
17.
KEY WORDS ANO DOCUMENT ANALYSIS
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b. IDENTIF1ERS/OPEN ENDED TERMS
c. COSATI Field/Group
Tropical Forests, Biomass
Estimation, Biomass Burning
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type or otherwise subordinate it to main title. When a report is prepared in more than one volume, repeat the primary title, add volume
number and include subtitle for the specific title.
5. REPORT DATE
Each report shall carry a date indicating at least month and year, indicate the hasis on which it «as selected (e.g.. date of issue, Jatv of
approval, date of preparation, etc.).
6. PERFORMING ORGANIZATION CODE
Leave blank.
7. AUTHOR1S)
Give name(s) m conventional order (John R. Doe, J, Robert Doe, etc.). List author's affiliation if il differs from the performing organi-
zation.
8. PERFORMING ORGANIZATION REPORT NUMBER
Insert if performing organization wishes to assign this number.
9. PERFORMING ORGANIZATION NAME ANO ADDRESS
Give name, street, city, state, and ZIP code. List no more than two levels of an organizational hirearthy.
tO. PROGRAM ELEMENT NUMBER
Use the program element number under which the report was prepared. Subordinate numbers may be included m parentheses.
11. CONTRACT/GRANT NUMBER
Insert contract or grant number under which report was prepared.
12. SPONSORING AGENCY NAME ANO ADDRESS
Include ZIP code.
13. TYPE OF REPORT AND PERIOD COVERED
Indicate interim final, etc., and if applicable, dates covered.
14. SPONSORING AGENCY COOE
Insert appropriate code.
15. SUPPLEMENTARY NOTES
Enter information not included elsewhere but useful, such as: Prepared in cooperation with. Translation of, Presented al conference «f.
To be published in, Supersedes, Supplements, etc.
16. ABSTRACT
Include a brief (200 words or less) factual summary of the most significant information contained in the report. If (he report contains a
significant bibliography or literature survey, mention it here.
17. KEY WORDS AND DOCUMENT ANALYSIS
(a) DESCRIPTORS • Select from the Thesaurus of Engineering and Scientific Terms the proper authorized terms that identify the major
concept of the research and are sufficiently specific and precise to be used as index entries for cataloging.
(b) IDENTIFIERS AND OPEN-ENDED TERMS - Use identifiers for project names, code names, equipment designators, etc. Use open-
ended terms written in descriptor form for those subjects for which no descriptor exists.
(c) COSATI FIELD GROUP - Field and group assignments are to be taken from the 1965 COSATI Subject Category List. Since the ma-
jority of documents are multidisciplinary in nature, the Primary Field/Group assignments) will be specific discipline, area of human
endeavor, or type of physical object. The application(s) will be cross-referenced with secondary I ield/Group assignments that will follow
the primary posting(s).
18. DISTRIBUTION STATEMENT
Denote releasability to the public or limitation for reasons other than security for example "Release Unlimited." Cite any availability to
the public, with address and price.
19. 8t 20. SECURITY CLASSIFICATION
DO NOT submit classified reports to the National Technical Information service.
21. NUMBER OF PAGES
Insert the total number of pages, including this one and unnumbered pages, but exclude distribution list, il any.
22. PRICE
Insert the price set by the National Technical Information Service or the Government Printing Office, if known.
EPA Form 2220-1
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