United States Department of Agriculture

Forest Service

Northeastern Research Station

General Technical Report NE-298 

# Forest Volume-to-Biomass Models and Estimates of Mass for Live and Standing Dead Trees of U.S. Forests

James E. Smith Linda S. Heath Jennifer C. JenkinsPublished by: For additional copies:
USDA FOREST SERVICE USDA Forest Service 11 CAMPUS BLVD SUITE 200 Publications Distribution NEWTOWN SQUARE PA 19073-3294 359 Main Road Delaware, OH 43015-8640 January 2003 Fax: (740)368-0152

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### Abstract

We present methods and equations for nationally consistent estimates of tree-mass density at the stand level (Mg/ha) as predicted by growing-stock volumes reported in USDA Forest Service surveys for forests of the conterminous United States. Developed for use in FORCARB, a carbon budget model for U.S. forests, the equations also are useful for converting stand-, plot-, and regional-level forest merchantable volumes to estimates of total mass. Tree biomass is about 50 percent carbon, so carbon estimates can be derived from estimates of biomass by multiplying by 0.5. We include separate equations for live and standing dead trees. Similarly, separate equations predict the components of aboveground only vs. full trees (including coarse roots) and hardwood vs. softwood species. Equations aredeveloped for broad forest types by region and are applicable to large-scale forestinventory data. Example estimates are provided for regional tree-mass totals using summary forest statistics for the United States.



### The Authors

JAMES E. SMITH, jsmith11@fs.fed.us, and LINDA S. HEATH, lheath@fs.fed.us, are research plant physiologist and research forester/project leader, respectively, with the Northeastern Research Station at Durham, New Hampshire; JENNIFER C. JENKINS, jjenkins@fs.fed.us, is a research forester with the Northeastern Research Station at Burlington, Vermont.

Manuscript received for publication 19 April 2002

## Forest Volume-to-Biomass Models and Estimates of Mass for Live and Standing Dead Trees of U.S. Forests

James E. Smith, Linda S. Heath, and Jennifer C. Jenkins



## Acknowledgments

We thank Richard Birdsey, Paul Van Deusen, Sarah Duke, Steve Prisley, and Harry Valentine for helpful comments on drafts of this manuscript. Eric Fiegenbaum provided the cover artwork. This work was partly supported by the USDA Forest Service’s Northern Global Change Program and the RPA Assessment Management Group.

1 megagram (Mg) or metric tonne = 1,000 kg or 1 x 10 6 g 1 metric tonne = 1.102 U.S. ton, or 2,205 lb 1 megatonne (Mt) = 1 x 10 6 tonne, or teragram (Tg) or 1 x 10 12 g 1 gigatonne (Gt) = 1 x 10 9 tonne, or petagram (Pg) or 1 x 10 15 g 1 hectare (ha) = 2.471 acres, or 10,000 m 2 1 cubic meter (m 3 ) = 35.31 ft 3
100 m 3 /ha = 1429 ft 3 /acre 100 Mg/ha = 44.6 U.S. tons/acre, or 89,200 lb/acre1 

## Introduction
The potential for U.S. forests to sequester carbon dioxide from the troposphere is well established. A large portion of assimilated carbon accumulates as tree biomass. The effect of this accumulation on atmospheric levels of greenhouse gasses and the role of forests in this process remain the subjects of national 1 and international research discussions (Watson and others 2000). Thus, the need for a nationwide carbon budget of U.S. forests extends beyond the current year’s carbon gains, losses, and net inventory. Information needed for policy development includes estimates of past trends and projected future scenarios. The mass estimators presented in this report are part of an effort to improve carbon budget estimates for U.S. forests. The value of biomass equations for this effortis based on the link between individual-tree and wholestand biomass estimates (Clutter and others 1983; Parresol 1999), coupled with the assumption that mass of wood is about 50 percent carbon (Birdsey 1992).

The few regional- to national-scale budgets of biomass or carbon mass developed for the United States are based largely on forest structure as described by previous versions of the USDA Forest Service’s Forest Inventory and Analysis Database (FIADB; Miles and others 2001) developed by the Forest Inventory and Analysis (FIA) program. Currently, the database contains only recent data (within about the last 10 years), though extensive statistically based continuous forest surveys date back to about 1950. These surveys are designed to estimate the amount of volume of growing stock, which is a phrase describing merchantable trees. Data from the surveys also have been used to estimate biomass or carbon. Cost and others (1990) summarized FIA data into national estimates of growing-stock biomass. Birdsey (1992) derived volume-to-biomass ratios by comparing the estimates of growing-stock biomass in Cost and others (1990) to equivalent growing-stock volumes in Powell and others (1993). Birdsey used these ratios to calculate forest carbon budgets that later served as the basis for FORCARB, a carbon budget model for U.S. forests (Plantinga and Birdsey 1993; Heath and Birdsey 1993; Birdsey and Heath 1995). Turner and others (1995) published carbon estimates largely based on FIA data and Birdsey’s (1992) values for carbon density. Schroeder and others (1997) and Brown and others (1999) improved on the volume-to-biomass relationship by recognizing that volume-to-biomass ratios vary by tree size or, on anaggregated scale, forest structure. They developed largescale biomass estimates for the Eastern United States based on FIA data and generalized biomass expansion factors for select eastern forest types. None of these previous studies provided estimates of biomass of standing dead trees, nor were the biomass estimates based on equations that reflect the species composition of U.S. forests.

Our objective was to develop equations for estimating the mass (Mg/ha) of live and standing dead trees as predicted by FIA growing-stock volume (m 3 /ha) for forests of the 48 conterminous States. Thus, values calculated by the FIA can readily serve as inputs to the regression-based estimates. Although these equations were developed for use with FIA volumes as applied in the Aggregated TimberLand Assessment System (ATLAS) model (Mills and Kincaid 1992), they also can be applied to statistics for large regions and broad classifications of forest types as presented in periodic national inventory compilations (see Smith and others 2001, Powell and others 1993, and Waddell and others 1989). Because the equations are based on current FIA datasets at a vegetation-type scale, they might be less precise for specific sites or for inventories with growing-stock definitions that differ from those of FIA. Similar cautions extend to applying regional-scale historical data or long-term projections. We used the equations to develop national-level estimates of tree mass and compare them with those produced following the methods of Birdsey (1992) and Brown and others (1999).

These equations are part of a larger project to develop estimates of forest carbon using FORCARB, which also accounts for carbon in forest products (Heath and others 1996; Skog and Nicholson 1998). An understanding of how the carbon budget numbers were obtained and how alternate scenarios or interpretations of data affect results is useful for policy development or negotiations. FORCARB was used to produce projections for the 2001 U.S. Submission to the United Nations Framework Convention on Climate Change on Land Use, Land Use Change, and Forestry (U.S. State Dep. 2000), and most recently to examine uncertainty in U.S. forest carbon budgets (Smith and Heath 2000, 2001; Heath and Smith 2000). With such intended applications, our models are fundamental, tractable, and transparent—with few inputs, widely applicable, and obvious relationships among the parts. 1 See U.S. Global Change Research Information Office Internet site: http://www.gcrio.org/index.shtml (accessed March 28, 2002).2 

## Methods
The estimates of tree-mass density presented here andincorporated in FORCARB are based on tree- and plotlevel data in the FIADB (Miles and others 2001) and the individual-tree biomass equations of Jenkins and others (in press). Estimates are formed in two steps: (1) summaries of tree mass are developed at the FIA plotlevel, and (2) regressions are developed to estimate plotlevel tree mass as functions of growing-stock volume. The individual-tree biomass equations are applied to determine mass for each tree recorded on an FIA inventory plot. Tree mass and merchantable volume of growing stock are summed for each plot and expressed as densities (Mg/ha and m 3 /ha for mass and volume, respectively). The paired mass and volume densities are then incorporated in regressions with growing-stock volume as the independent variable.

Plot summary pairs and corresponding regression equations are classified and sorted by various categories, a requirement for their subsequent inclusion in FORCARB. Region and forest type are the highest levels of classification. The 48 conterminous states are divided into 10 regions (Fig. 1), each of which includes six to eight forest types. Relationships between classifications used in ATLAS and FORCARB and those of the FIADB are described in Table 1. Additional classifications include live or standing dead trees; aboveground only or whole trees (including coarse roots); and live softwood or hardwood tree species.

A consequence of these classification schemes is a proliferation in the number of estimators of tree-mass density in FORCARB simulations. Use of the regression estimates was an important consideration in developing the procedures described in this report. We standardized inputs (independent variable) to a single summary value available for all FIADB plots, and limited the form of the regression models to one for live trees and another for standing dead trees.



### Forest-Inventory Design and Data Description Unlike the U.S. census, which uses complete enumeration (every individual is counted), the FIA inventory design relies on a sampling scheme to estimate growing-stock volumes at a designated level of precision. Sampling is conducted in different phases, allowing cost-efficient data collection. In the past, these surveys were conducted periodically by state, usually every 5 to 7 years in the South and every 10 to 15 years in other regions. The data used in this study are from the most recent summary for each state (Table 2). Although FIA has adopted an annualized inventory with three sampling phases, the current data are from inventories of two phases based on double-sampling for stratification (Schreuder and others 1993). In the first phase, sample points on aerial photographs are interpreted and classified by land use and type of vegetation or land cover on an area of known size. These areas are taken from U.S. Bureau of Census reports and other sources. Depending on the individual state, additional classifications might include productivity, estimated volume, or stand age. In the second phase, a sample of points from the first phase is chosen for crews to visit in the field. Until FIA recently adopted a national plot design, many designs were used in the second phase of past inventories. Detailed observations are made on forest plots, particularly those that meet a productivity standard and are labeled as timberland. The data from Southeast (SE)

South Central (SC) Northeast (NE) Northern Lakes States (NLS) Northern Prairie States (NPS)

Rocky Mountains, South (RMS) Rocky Mountains, North (RMN)

Pacific Southwest (PSW) Pacific Northwest, Westside (PWW)

Pacific Northwest, Eastside (PWE)

Figure 1.—Regions of the United States used in classifying forest types.3 Table 1.—Forest types classified for mass estimates of trees in this report (based on the FIADB forest-type groups)

Region a Forest type FIADB forest-type group
NE Aspen-Birch Aspen-Birch MBB/Other HW Oak-Gum-Cypress, Elm-Ash-Cottonwood, and Maple-Beech-Birch Oak-Hickory Oak-Hickory Oak-Pine Oak-Pine Other Pine Longleaf-Slash Pine, Loblolly-Shortleaf Pine, and pines other than White-Red-Jack Spruce-Fir Spruce-Fir and other non-pine conifers WRJ-Pine White-Red-Jack Pine Nonstocked Nonstocked
NLS Aspen-Birch Aspen-Birch Lowland HW Oak-Gum-Cypress and Elm-Ash-Cottonwood MBB Maple-Beech-Birch Oak-Hickory Oak-Hickory Pine All pine groups and Oak-Pine Spruce-Fir Spruce-Fir Nonstocked Nonstocked NPS Conifer All conifer groups Lowland HW Oak-Gum-Cypress, Elm-Ash-Cottonwood, and Aspen-Birch MBB Maple-Beech-Birch Oak-Hickory Oak-Hickory Oak-Pine Oak-Pine Nonstocked Nonstocked
SC, SE Bottomland HW Oak-Gum-Cypress, Elm-Ash-Cottonwood, and Aspen-Birch Natural Pine Longleaf-Slash Pine and Loblolly-Shortleaf Pine, naturally occurring Oak-Pine Oak-Pine Other Conifer Other conifer groups Planted Pine Longleaf-Slash Pine and Loblolly-Shortleaf Pine, planted Upland HW Oak-Hickory and Maple-Beech-Birch Nonstocked Nonstocked PSW Douglas-fir Douglas-fir and Hemlock-Sitka Spruce Fir-Spruce Fir-Spruce-Mountain Hemlock Hardwoods Hardwoods Other Conifer Ponderosa Pine, Lodgepole Pine, and other conifer groups Pinyon-Juniper Pinyon-Juniper Redwood Redwood Nonstocked Nonstocked PWE Douglas-fir Douglas-fir, W estern Larch, and Redwood Fir-Spruce Fir-Spruce-Mountain Hemlock and Hemlock-Sitka Spruce Hardwoods Hardwoods Lodgepole Pine Lodgepole Pine Ponderosa Pine Ponderosa Pine and Western White Pine Pinyon-Juniper Pinyon-Juniper

Continued4 2 U.S. Department of Agriculture, Forest Service. 2001. Forest inventory and analysis national core field guide, volume 1: field data collection procedures for phase 2 plots, version 1.5. Internal report on file at: U.S. Department of Agriculture, Forest Service, Forest Inventory and Analysis, 201 14th St., Washington, DC. Nonstocked Nonstocked PWW Douglas-fir Douglas-fir and Redwood Fir-Spruce Fir-Spruce-Mountain Hemlock Other Conifer Ponderosa Pine, Western White Pine, Lodgepole Pine, and other conifer groups Other Hardwoods Other hardwoods Red Alder Alder-Maple Western Hemlock Hemlock-Sitka Spruce Nonstocked Nonstocked RMN, RMS Douglas-fir Douglas-fir, Western White Pine, Hemlock-Sitka Spruce, Western Larch, and Redwood Fir-Spruce Fir-Spruce-Mountain Hemlock Hardwoods Hardwoods Lodgepole Pine Lodgepole Pine Other Conifer Other conifer groups Ponderosa Pine Ponderosa Pine Pinyon-Juniper Pinyon-Juniper Nonstocked Nonstocked

a NE=Northeast; NLS=Northern Lake States; NPS=Northern Prairie States; SC=South Central; SE=Southeast; PSW=Pacific Southwest; PWE=Pacific Northwest Eastside; PWW=Pacific Northwest Westside; RMN=Rocky Mountains North; RMS=Rocky Mountains South. Region a Forest type FIADB forest-type group Table 1.—continued.

both phases are used to determine the area that each ground plot represents. Allowable tolerances are specified for the measurements; for example, diameters are measured to the nearest 0.1 inch. The designated maximum allowable sample error for area is 3 percent per 1 million acres of timberland. For more information, see the documentation accompanying the FIADB (Miles and others 2001) and the “Forest Inventory and Analysis National Core Field Guide.” 2

In this section we describe how we used the FIADB to estimate plot-level tree-mass density based on generalized individual-tree biomass equations, and subsequently to develop regression-based estimates of mean tree-mass density. We first describe our interest in selected variables and our rationale for organizing the data into separate forest groups. Where useful, we provide specific variable names as found in the FIADB as of March 2002, for example, STDAGE (stand age). FIA data are collected in English units; we converted them to metric units.

For our purposes, the FIADB includes data at two levels of organization: FIA inventory plot and individual tree. Plot information includes location (state and county), landowner classification, current forest type, stand origin (plantation or natural regeneration), site productivityclassification, estimated stand age, current and past landuse classification, area (in acres) that each plot represents, and years between remeasurements. Individual-tree information — for all trees larger than 1 inch in diameter at breast height (d.b.h.) — includes species, diameter, status (live or dead), whether growing stock or cull, growing-stock volume if applicable, and number per acre represented by each individual. Plot volumes are calculated by summing individual-tree growing-stock volumes on the plot and expressed as volume per unit area (m 3 /ha).

We classify forests according to region and forest type with a goal to gain added flexibility in applying results. We wanted forest groupings consistent with: (1) classifications used in the FIADB, (2) timber units used in ATLAS (Mills and Kincaid 1992; Haynes and others5 1995), (3) forest-type groups listed in Forest Service statistical reports, for example, Smith and others (2001), and (4) other components of FORCARB. The variables for state ( STATECD ) and FIA inventory unit ( UNITCD ) are used to define the 10 regions (Fig. 1). Forest types ( FORTYPCD ) are grouped to reflect species composition and other aspects of stand structure that influence overall biomass. The principal goals in grouping forest types for this analysis were to maintain a small set of types to represent each region and conform to types used in ATLAS’ timber projections.

We considered additional forest groupings by ownership ( OWNCD or OWNGRPCD ) and productivity ( SITECLCD ). Decisions about whether to include such additional classifications were based on preliminary analyses of the data rather than required links to other models as with region and forest type. Preliminary analysis of covariance identified some forest types as showing an effect of ownership on the relationship between volume of growing stock and tree-mass density. These ownerships are classified as “public” or “private” lands and included in the classification scheme described in Table 1. Analyses also revealed slight interactions between productivity and the initial slope of this same biomass-to-volume relationship. However, this effect was inconsistent across forest types of the two areas where productivity is an important variable in simulation models—the South and the Pacific Northwest. Thus, no estimates in this report are classified by productivity.

Some older inventories identified and measured only live or merchantable dead (salvageable) trees on new plots; that is, all standing dead trees were not necessarily included in the initial survey for a plot. To avoid plots where standing dead trees might be underrepresented, we used only remeasured plots ( KINDCD =2) or recently completed surveys, which were more likely to include standing dead trees. Current surveys include the identification and measurement of all trees. We used all measured plots ( KINDCD =1 through 3) on surveys since 1999.



### Estimating Mass for Individual Trees Mass estimates are provided for individual trees ( DRYBIOT ) in the FIADB. However, we applied the nationally consistent set of individual-tree biomass estimates of Jenkins and others (in press) because FIA biomass estimates may differ considerably by FIA unit. We also wanted to extend mass estimates to standing dead trees and coarse tree roots. The 10 equations are designed to estimate all tree species in U.S. forests: five softwood species groups, four hardwood species groups, and one group for woodland species. These equations estimate Table 2.—Most recent statewide forest inventories included in FIADB and used in this analysis

State Date of inventory

Alabama 2000 Arizona 1999 Arkansas 1995 California 1994 Colorado 1983 Connecticut 1998 Delaware 1999 Florida 1995 Georgia 1997 Idaho 1991 Illinois 1998 Indiana 1998 Iowa 1999 Kansas 1994 Kentucky 1988 Louisiana 1991 Maine 1995 Maryland 1999 Massachusetts 1998 Michigan 1993 Minnesota 1990 Mississippi 1994 Missouri 1999 Montana 1989 Nebraska 1994 Nevada 1989 New Hampshire 1997 New Jersey 1999 New Mexico 1999 New York 1993 North Carolina 1990 North Dakota 1995 Ohio 1993 Oklahoma 1993 Oregon 1995 Pennsylvania 1989 Rhode Island 1998 South Carolina 1999 South Dakota 1995 Tennessee 1999 Texas 1992 Utah 1995 Vermont 1997 Virginia 1992 Washington 1991 West Virginia 1989 Wisconsin 1996 Wyoming 19846 total aboveground (that is, above the root collar) biomass for trees of 1 inch or larger in d.b.h. Additional equations are provided for estimating the ratio of components to total aboveground biomass. The component equations are for foliage, coarse roots, stem bark, and stem wood.

Live trees

Live trees are identified as STATUSCD =1. Once identified as a live tree larger than 1 inch d.b.h. in an FIA plot, the only variables needed to estimate individual-tree mass are diameter ( DIA ) and species ( SPCD ). The value of DIA usually is measured at breast height except for woodland species, which generally are measured at the root collar. The individual-tree biomass equations provide estimates of the mass (kg) of individual live trees.

Mass per unit area is then determined by multiplying by the number of trees per acre ( TPACURR ). Mass is summed across all trees per plot and the sum is converted to metric units. The mass density of live trees at the plot level is expressed as Mg/ha. Growing-stock volume as estimated per tree by FIA ( VOLCFNET where TREECLCD =2) also is summed for each plot and expressed as m 3 /ha. The paired values (volume, mass density) from each FIA plot were the source of the observations used in the regressions we developed. The same process of estimating volume-density and mass-density pairs per FIA plot can be repeated for both aboveground andtotal-tree estimates. Similarly, hardwood-only or softwoodonly estimates are developed with mass and volume pairs representing only the hardwood or softwood portion of the live trees on a plot.

Standing dead trees

Trees identified in the FIADB as STATUSCD =2 are standing dead trees. For the same diameter, these are likely to have less mass than live trees, which were the basis for the individual-tree biomass equations. We adjust tree mass to reflect an expected difference between live and dead trees of the same d.b.h. by reducing the mass of some parts of dead trees. We do not have specific information on mass of standing dead trees, which can encompass a wide range of structural damage and decay, so we use the component equations of Jenkins and others (in press) to reduce the mass of standing dead relative to live by the following amounts: 10 percent of stem wood and bark; 100 percent of leaves; 33 percent of branches; and 20 percent of coarse roots. Separate component equations are for hardwood and softwood species and are based on d.b.h. The net effect of the component reductions is illustrated in Figure 2 by the ratios of standing dead to live mass according to d.b.h. and species group (softwood or hardwood).

Adjusting for cull trees
The biomass of cull trees ( TREECLCD =3 or 4) is likely to differ from that of trees of similar diameter classified as growing stock ( TREECLCD =2). The biomass of live cull trees represents more than 10 percent of live-tree biomass in the East (estimated from FIADB). Cull status is assigned to a tree if it is a nonmerchantable species or if a significant portion of the bole of a merchantable species isunusable as timber. Cull status suggests that diameter-tobiomass relationships likely differ from those of the individual-tree biomass equations. We did not have specific estimates of biomass for cull trees, so we developed generalized adjustments to the individual-tree biomass equations by examining the apparent effect of cull classification on volume; that is, we developed ratios for estimating the woody mass of cull trees that were proportional to ratios of cull volumes to growing-stock volumes.

Biomass correction factors for cull trees are based on analysis of the Eastwide and Westwide inventory D.b.h. (cm) 0 50 100 150 200Standing Dead :Live Tree Mass

0.76 0.78 0.80 0.82 0.84 0.86 0.88 0.90

Hardwood Species

Softwood Species

Figure 2.—Ratios of standing dead mass to live tree mass for individual trees, by d.b.h. and classification as softwood or hardwood.7 databases (Hansen and others 1992; Woudenberg and Farrenkopf 1995), which provided the format for FIA inventory data prior to the FIADB. Cull trees aredistributed across similar diameter ranges as growingstock trees with proportionally more rough cull at smaller diameters. We focused on trees less than 40 cm d.b.h. because most trees are in this size range. The ratio of volume for cull to volume for growing stock changed slightly with diameter, but we used average ratios over the range of 25 to 40 cm. We plotted values for the net volume of wood in the central stem (the variable NETCFVL in the Eastwide and Westwide databases) as functions of d.b.h. for growing stock, rough cull, and rotten cull for the broad classifications of hardwood vs. softwood, and for the Eastern vs. Western United States. Volumes of cull trees are consistently less than those for growing stock.

The tree classification rough cull ( TREECLCD =3) can be based on form defect or identity as a noncommercial species. No adjustments were made in applying the individual-tree biomass equations to rough cull of noncommercial species. We did adjust mass for such trees where the classification was based on form defect. The adjustment was based on the assumption that defect may reduce the volume of rough cull proportionally more than biomass. The ratio of volume for rough cull to volume for growing stock obtained from FIA tree data was 0.74 and 0.64 for hardwoods and softwoods in the East, respectively. The ratio of volume for rough-cull trees to volume for growing stock was 0.64 and 0.42 for hardwoods and softwoods in the West. We assumed that 25 percent of the volume reduction of cull trees (that is, compared to volume for regular growing stock) reduced the biomass of the tree, and adjusted the estimated biomass for the cull trees by this factor. For example, in the East, volume was 26 percent lower for rough-cull hardwoods relative to growing-stock volume. We apply 25 percent of this reduction (0.26 x 0.25 = 0.06) to the mass of a cull tree by reducing the estimated mass of a noncull live tree by 6 percent. The net effect of these assumptions was reductions in bole mass of 6 and 9 percent for hardwoods and softwoods in the East, respectively, and of 9 and 14 percent for hardwoods and softwoods in the West.

The tree classification of rotten cull ( TREECLCD =4) is based on threshold levels of rot in bole wood. We adjusted mass for rotten cull by applying assumptions about the extent of rot to ratios of volume in rotten culls to volume in growing stock. This was similar to the way in which we adjusted mass for rough cull. The ratio of volume for rotten-cull trees to volume for growing stock obtained from FIA tree data was 0.42 and 0.30 for hardwoods and softwoods in the East, respectively, and 0.40 and 0.22 for hardwoods and softwoods in the West. We assumed that more than 50 percent of the volume reduction had some degree of rot. Of this volume reduction, we assumed 75 percent was rotten wood that was assumed to have lost 45 percent of its mass, or specific gravity, depending on the state of decay (Heath and Chojnacky 2001). Multiplying these factors produced an adjustment factor for the estimated biomass for rotten-cull trees. For example, volume was 58 percent lower for rotten-cull hardwoods in the East (relative to volume of growing stock). If 75 percent of this volume was missing 45 percent of its intact mass, overall bole-wood mass was reduced by 20 percent (0.58 x 0.75 x 0.45 = 0.20). Total-tree mass was adjusted by reducing wood mass by 20 percent. The net effect of these assumptions was reductions in bole-wood mass of 20 and 24 percent for hardwoods and softwoods in the East, respectively, and 20 and 26 percent for hardwoods and softwoods in the West.

To summarize, adjusting the tree mass estimates from the individual-tree biomass equations to account for cull-tree mass depends on our assumptions. The assumptions that likely had the greatest effect were that: 1) cull trees have a lower bole mass than a tree of the same diameter, and 2) differences in volume between rotten cull and growing stock were represented by woody mass that was 75 percent rotten. The low precision in values subtracted from bole mass reflects the level of information available. However, these differences in individual-tree mass have little impact on total-tree biomass at regional and national level summaries because they represent only several percent of density of the mass of all trees.



### Equations for Estimating Density of Forest-Tree Mass Applying the individual-tree biomass equations to FIA trees and summarizing to the plot produces paired values of growing-stock volume density (m 3 /ha) and tree-mass density (Mg/ha) on each plot. After sorting the plot-level summary data according to region and forest classification, we developed regression-based estimates of mass density as predicted by growing-stock volume. Stand age was considered as a candidate predictor variable for regression. However, the poor relationship shown in Figure 3 for some northeastern hardwoods is typical of many forest types. Thus, stand age was dropped from consideration.

As mentioned earlier, preliminary regression analyses were performed to help establish a classification scheme for forest types. Second-order polynomial regressions were useful in classifying forest types, particularly in identifying effects of ownership and productivity. The polynomial model worked initially because we were interested only in the initial slope of the relationship. We8 restricted analyses to points below the 75 th percentile of growing-stock volumes. Analyses of covariance with the second-order polynomial model and ownership or productivity as the class variable identified the importance of ownership for some forest types (Table 1). However, the sign and the magnitude of the quadratic effect coefficient often produced unrealistic estimates relative to other important assumptions about the volume and biomass relationship. Thus, this regression form was not useful for further development of stand-level estimates, so we adopted a different equation form for the analyses.

Live trees

Several candidate linear and nonlinear models were considered for the regression estimates of live-tree mass density. A form of the Chapman-Richards growth equation (Clutter and others 1983) was selected primarily because of its flexibility in the shape of the initial portion of the curve and the continuous decrease in slope at greater volumes. Although this relationship usually describes net growth (for example, of populations), it was suitable for our purpose. We added an intercept term because the usual form of the Chapman-Richards equation is forced through the origin, but tree biomass is expected to remain greater than zero as growing-stock volume approaches zero. The addition of the intercept meant that four coefficients were estimated. Nonlinear regression (Proc NLIN in SAS) was used to determine values for these coefficients. Estimates of regression coefficients showed that the coefficient determining the shape of the initial portion of the curve was unimportant. Thus, the regression was changed to essentially an exponential model with a non-zero intercept, and mean mass density of live trees is estimated by:

Live-tree mass density = F · (G + (1-e (-volume/H) ))

where volume is in m 3 /ha and coefficients F, G, and H areestimated using nonlinear regression. Because some fixedradius FIA plots are assigned to more than one condition class ( CONDID ), the number of trees per area represented by each tree can vary within a plot. Thus, the proportion of plot in each condition ( CONDPROP ) is used as a weighting variable in the regressions.

In addition to estimates of total (hardwood plus softwood) tree mass, we develop separate estimates for live-tree mass of hardwood and softwood species within each forest type based on their respective growing stock. We estimate absolute mass density of hardwoods and NE, MBB/Other HW

Stand Age (years) 0 50 100 150Live Tree Mass(Mg/ha)

0 100 200 300 400 500

Growing Stock Volume (m 3 /ha) 0 100 200 300 400Live Tree Mass(Mg/ha)

0 100 200 300 400 500 Mixed age or unknown

Figure 3.—Mean live-tree mass density per FIA plot as function of stand age (upper graph) and growing-stock volume (lower graph) for NE MBB/Other HW forests. The same set of FIA inventory plots contributed to each graph. However, 23 percent of points were classified as mixed or unknown age in the upper graph.9 softwoods rather than model hardwoods and softwoods as a percentage of total mass. We chose this method over modeling percentages to avoid regressions with skewed data, as would be expected with high or low percentages. A disadvantage of estimating components with separate independent and dependent variables is that individual predictions of hardwood and softwood mass may not sum to total mass, which is estimated separately.

Standing dead trees Mean mass density of standing dead trees is estimated by fitting nonlinear regressions to the FIA plot-level ratio of the mass of standing dead to predicted live-tree mass (Fig. 4). A three-parameter Weibull function is used to model this ratio, which generally decreases with increasing live growing stock-volume. Regression procedures and weighting are the same as for estimating live mass. The basic form of the equation is:

Dead-tree mass density = (Estimated live-tree mass density) · A · e (-((volume/B)^C))

where live-tree mass density is in Mg/ha, volume is in m 3 / ha, and coefficients A, B, and C are estimated using nonlinear regression.

Applying the estimates

Interest in biomass and carbon mass often focuses on specific subsets of the entire forest system (Birdsey 1992; Watson and others 2000). Hence, we developed estimates of specific subsets of total-tree mass. This approach was extended to provide estimates for both the entire tree — including coarse roots — and the aboveground portion only. Similarly, hardwood or softwood live-tree mass density can be estimated separately from hardwood or softwood growing-stock volume. Carbon mass or carbon dioxide equivalents often are the quantities of interest where tree-mass estimates extend beyond converting from merchantable-wood volumes. Carbon mass is about 50 percent of wood dry weight; more precise values for carbon content depend on the identity of the species and tissue or part of the tree.

Several units are used in reporting estimates of forest carbon, so the results can be confusing. The use of metric units internationally but English units in the United States has resulted in hybrid measures, for example, metric tons/acre. For clarity, values taken from the FIADB are in the original units, for example, inches for d.b.h. Our analysis was conducted in metric units so our results generally are expressed in those units. International discussions of greenhouse gas inventories (Watson and others 2000) in which the United States has participated for many years report carbon mass in tonnes (t) and megagrams (Mg), which are identical values (also defined as 10 3 kg and 10 6 g, respectively). Larger aggregate values of mass are reported as teragrams (10 12 g) and petagrams (10 15 g). Area is in hectares (10,000 m 2 ).



## Results and Discussion

Model Parameters Coefficients for estimating mean tree-mass densities are provided in Tables 3 through 10 (pages 12-31) by stand component, region, and forest type. In the Appendix, examples from Tables 3 and 4 are illustrated in Figures 5 through 62. The mass of live and dead trees can be estimated for the full tree (including coarse roots) or aboveground only. All the forest types listed in Table 1 are represented in Tables 3-10. However, some sets of coefficients are not based on type-specific regressions. Estimates for the Nonstocked and Pinyon/Juniper forest types are simply means from the FIA plots. Pinyon/ Juniper averages are based on all FIA plots of that type across the West. Type-specific regression estimates were not possible for several forest types. For example, nonlinear procedures failed to fit coefficients for hardwood tree mass in a publicly owned lodgepole pine forest in the Pacific Northwest Eastside (PWE) region. We substituted regression-based estimates of hardwood components in all softwood forests of PWE. See table footnotes for cases in which regional summary values were substituted for type-specific regression equations.

Estimates of some components of forest-tree mass are based on regressing over data points that tend to be grouped near the origin. For example, this occurs in hardwood species of western pine forests or softwoods in northern hardwood forests. In such cases, the regressions are applicable over a limited range of growing-stock volumes. For this reason, the tables of coefficients also provide an indication of the upper end of the range of growing stock volume that contributed to the coefficient estimates. We also provide the mean square error of the regression models and the number of FIA plot summaries that contributed to each regression.

The use of remeasured FIA plots and the substitution of estimates from other forest types when necessary can affect estimates of mass density. This effect is most likely for mass density of standing dead trees because fewer regressions for standing dead trees successfully estimated parameters without pooling forest types within a region. The effect of these assumptions in our model will be a major part of any difference between our estimates and the direct application of the individual-tree biomass equations of Jenkins and others (in press) to FIADB tree data. However, dead mass is a small part of overall tree10 mass, as illustrated in Figure 4 and Appendix Figures 5 through 62.



### Comparing Alternate Estimates of Total-Tree Biomass We maintained many separate and distinct forest types when developing our estimates. This is possible largely because of the set of individual-tree biomass equations. Both species composition and other characteristics of stand structure, such as tree size and stem density, can affect tree-mass density. The equations in Tables 3-10 are based on linked datasets and regression models, so they can be updated easily as the FIADB is updated. We structured our classifications to conform to commonly used forest types and regions such as are used in timber projection models. A key point was maintaining flexibility for application back to historical data and forward to forest projections.

We compared estimates of average biomass density among four relatively separate sets of estimates (Table 11, page 32). All forest types were classified as hardwood or softwood to facilitate comparison; Nonstocked and Pinyon/Juniper types were excluded from this summary. These values are described as average tree-mass density because total biomass was estimated for large areas by forest type and then divided by the total area. Estimates were developed from: 1) our analysis, 2) the biomass information included as part of the FIADB, 3) summaries developed by Birdsey (1992), and 4) the biomass expansion factors developed by Brown and others (1997, 1999).

Consistent sets of results were developed for making the comparisons in Table 11. All sets were based on the same dataset of plot and tree information extracted from the FIADB in March 2002. Estimates were for live trees, aboveground only; all live trees at least 1 inch d.b.h. were included. Estimates taken directly from the FIADB were based on the variable DRYBIOT . The estimates from Birdsey (1992) were derived by applying information in Tables 1.1 and 1.2 to plot-level summaries of hardwood and softwood growing-stock volume by forest type and region. Similarly, the estimates of Brown and others (1999) were by applying the three biomass expansion factors to plot-level summaries of growing-stock volume by forest type. Estimates of Birdsey (1992) and Brown and others (1999) were calibrated with some reference to Eastwide or Westwide data at different times. The same is true of preliminary analysis of our estimates.

There were similarities between our method and that of Schroeder and others (1997) and Brown and others (1999). Both included constraints on the regression line at greater volumes; that is, both featured asymptotic limits to increasing biomass at large growing-stock volumes. Additionally, both were based on a regression of estimated biomass on growing stock-volume. However, the regression models were slightly different. We estimate 0 100 200 300 400 500 600Standing Dead :Live Tree Mass

0 1 2 3 4

Growing Stock Volume (m 3 /ha)

0 100 200 300 400 500 600Standing Dead TreeMass (Mg/ha)

0 100 200 300

Figure 4.—Estimates of the ratio of the mass of standing dead tree to predicted live-tree mass (upper graph) and estimate of standing dead tree mass (lower graph) for RMS Fir-Spruce forests (individual points are plot-level density summaries).11 mass density directly from growing-stock volume. Schroeder and others (1997) and Brown and others (1999) included an additional step of calculating a biomass expansion factor; we believe that step is useful only when comparing ratios. The biomass expansion factors also were undefined at zero volume, while the direct relationship of volume to mass density provided an estimate of mass at zero volume. Finally, we developed equations for mass of live and dead trees for a number of forest types in the United States; previous literature focused on the live biomass of several eastern forest types.



### Suitability of Equations and Spatial Scale The estimates developed here are likely to be applied at a range of spatial scales that sometimes differ greatly from those of the FIA plots used as the bases for regressions. For example, biomass predictions based on the full set of FIA forest plots or plot-level RPA data (a component of the 1997 RPA forest dataset; Smith and others 2001) are at the same scale as the original regressions, so scaling is not a likely source of systematic error. By contrast, biomass predictions associated with the linked forest models ATLAS and FORCARB are applied to volume of growing stock aggregated over areas of tens to hundreds of thousand hectares—one to two orders of magnitude larger than the FIA plots used in developing the regressions.

Applying these equations at a different scale might result in systematic error (Rastetter and others 1992). Although both independent and dependent variables are expressed per unit area, the predictions are scale-dependent because volume and biomass densities can be averaged over different areas from one prediction to the next. For example, aggregating FIA plot summary values to county, unit, or state levels and then applying the equations may produce average volume-mass density paired values on the concave side of a regression fit to the FIA plot-scale values. This could result in lower estimates of biomass for many forest types. This form of bias is unlikely when these predictors are applied to ATLAS/FORCARB summary values because such aggregation in ATLAS is systematic rather than random. Forest volumes and areas are classified by age class prior to aggregation; thus, samples are effectively stratified. Specific information on aggregation of forest areas, for example, by ATLAS/ FORCARB, can offset this potential bias through: 1) quantification of possible systematic error, and 2) modification of regression to reflect specific levels of aggregation. In fact, preliminary analyses indicate that applying our equations at scales greater than plot level, for example, UNIT or COUNTY , produces estimates that are within 5 percent of the actual value. This effect is from essentially random aggregation—any bias associated with stratified aggregation is likely to be considerably less. Thus, for most purposes, these equations can be applied at more aggregated spatial scales with only negligible

error.


### Continuing ResearchThe most immediate application of the estimates of treemass density is shown in Table 12 (page 33), which includes mass totals obtained from applying estimates to 1997 RPA forest data (Smith and others 2001). Current research is focused on extending these for more general applicability and links to other models or forest assessments. Carbon estimators based on alternate forest classification schemes as well as estimates of uncertainty in these values will be available in subsequent publications. We also are considering alternate approaches to modeling mass for standing dead trees. Specifically, gaps in recording or expanding tree data for dead trees need to be addressed – this likely would reduce bias for under representing dead stems.12 Table 3.—Coefficients for estimating mass density of live trees (above- and belowground, Mg/ha) by type, region, and owner (as appropriate); F , G , and H are coefficients; n = number of FIA plots; mse = mean squared error of the prediction relative to individual plots; volume limit (m 3 /ha) = 99 th percentile of growing-stock volumes within each set of FIA plots (upper limit of independent variables in the regressions) a
Forest type F G H n mse Volume limit

NE Aspen-Birch 508.5 0.0361 397.4 264 510 250 MBB/Other HW (Priv.) 425.3 0.0476 254.7 2302 936 273 MBB/Other HW (Publ.) 558.4 0.0276 374.7 362 1140 342 Oak-Hickory 488.2 0.0509 312.8 3314 1262 306 Oak-Pine 369.0 0.0490 245.5 304 656 268 Other Pine 715.5 0.0348 697.2 295 595 304 Spruce-Fir 306.4 0.0419 156.3 236 524 237 WRJ-Pine 415.6 0.0349 276.1 398 1056 354 Nonstocked 5.8 0.0000 0.0 14 19 10 NLS Aspen-Birch 362.5 0.0524 270.1 8072 568 239 Lowland HW (Priv.) 505.2 0.0419 359.1 1322 858 223 Lowland HW (Publ.) 744.5 0.0229 555.0 662 570 281 MBB 350.0 0.0496 187.0 5871 1077 259 Oak-Hickory 364.8 0.0755 186.1 2049 1136 238 Pine 432.1 0.0346 373.2 2056 548 280 Spruce-Fir 391.9 0.0582 278.8 4926 647 217 Nonstocked 8.5 0.0000 0.0 83 466 119 NPS Conifer 583.1 0.0482 577.4 256 569 236 Lowland HW 1192.0 0.0284 988.5 1030 1378 303 MBB 465.9 0.0725 321.6 1682 1223 252 Oak-Hickory 1012.1 0.0393 727.6 2516 1437 243 Oak-Pine 255.9 0.1240 157.6 172 1011 200 Nonstocked 22.9 0.0000 0.0 41 903 71 SC Bottomland HW (Priv.) 314.5 0.1091 174.9 2092 1528 309 Bottomland HW (Publ.) 313.5 0.1022 147.9 295 1379 329 Natural Pine (Priv.) 358.3 0.0935 421.9 1620 811 321 Natural Pine (Publ.) 363.1 0.1133 480.0 448 838 373 Oak-Pine 282.3 0.0904 197.4 2000 922 265 Other Conifer 162.6 0.1869 85.5 72 792 208 Planted Pine 161.8 0.1398 116.1 1477 578 265 Upland HW (Priv.) 229.7 0.1533 112.2 3934 1399 204 Upland HW (Publ.) 285.9 0.0977 140.7 481 1094 252 Nonstocked 56.5 0.0000 0.0 19 2679 8 SE Bottomland HW (Priv.) 963.2 0.0261 800.4 2516 2176 453 Bottomland HW (Publ.) 429.2 0.0573 291.6 334 2268 427 Natural Pine (Priv.) 356.4 0.0429 340.9 2892 541 382 Natural Pine (Publ.) 1213.0 0.0140 1610.1 662 689 357 Oak-Pine 420.1 0.0353 309.8 2307 660 347

Continued13 Other Conifer 324.8 0.0233 201.1 145 1000 397 Planted Pine 226.4 0.0670 183.7 3865 413 267 Upland HW (Priv.) 417.4 0.0618 283.8 5071 1156 334 Upland HW (Publ.) 405.2 0.0915 260.0 779 1890 336 Nonstocked 56.5 0.0000 0.0 74 4515 214 PSW Douglas-fir 2094.3 0.0164 2177.5 93 3605 1090 Fir-Spruce 897.3 0.0108 773.5 51 1445 1020 Hardwoods 1463.7 0.0000 1119.4 695 6812 639 Other Conifer 1323.8 0.0372 1480.6 795 2864 687 Pinyon-Juniper 58.0 0.0000 0.0 6335 2165 48 Redwood 4409.2 0.0124 6562.8 108 3639 1691 Nonstocked 35.7 0.0000 0.0 18 601 119 PWE Douglas-fir (Priv.) 653.6 0.0168 572.1 226 719 488 Douglas-fir (Publ.) 3234.5 0.0000 3884.9 972 1564 636 Fir-Spruce (Priv.) 615.3 0.0129 561.6 95 870 496 Fir-Spruce (Publ.) 4480.5 0.0000 5880.2 1213 2512 761 Hardwoods 614.0 0.0534 660.4 46 2856 292 Lodgepole Pine (Priv.) 368.2 0.0195 390.1 65 289 325 Lodgepole Pine (Publ.) 692.3 0.0111 975.0 835 699 389 Ponderosa Pine (Priv.) 378.5 0.0178 330.0 262 425 336 Ponderosa Pine (Publ.) 1504.1 0.0058 2042.9 1939 331 402 Pinyon-Juniper 58.0 0.0000 0.0 6335 2165 48 Nonstocked 13.6 0.0000 0.0 49 357 293 PWW Douglas-fir (Priv.) 1191.2 0.0187 1251.2 1032 1781 913 Douglas-fir (Publ.) 5062.8 0.0052 6830.2 2106 3510 1626 Fir-Spruce (Priv.) 793.9 0.0165 754.4 123 1305 835 Fir-Spruce (Publ.) 1837.9 0.0122 2418.7 776 3399 1517 Other Conifer 6695.1 0.0043 10108.6 158 748 616 Other Hardwoods 16854.7 0.0021 20533.0 512 2867 858 Red Alder 3100.3 0.0101 4587.8 557 1208 719 Western Hemlock 2017.5 0.0196 2967.6 963 3894 1556 Nonstocked 27.1 0.0000 0.0 49 2025 304
RMN Douglas-fir 592.8 0.0415 503.8 3122 2389 526 Fir-Spruce 913.3 0.0225 960.9 1820 1684 578 Hardwoods 427.8 0.0526 374.6 243 642 243 Lodgepole Pine 422.1 0.0488 519.3 1381 967 491 Other Conifer 671.2 0.0314 544.4 273 1166 360 Ponderosa Pine 481.7 0.0263 484.2 889 483 401 Pinyon-Juniper 58.0 0.0000 0.0 6335 2165 48 Nonstocked 41.0 0.0000 0.0 78 1381 79 RMS Douglas-fir 835.8 0.0461 703.2 833 1658 491 Fir-Spruce 764.4 0.0342 658.6 1371 1656 574 Table 3.—continued.
Forest type F G H n mse Volume limit

Continued14 Hardwoods 669.7 0.0538 621.4 1712 1127 337 Lodgepole Pine 452.7 0.0581 567.6 533 907 422 Other Conifer 378.1 0.0326 234.1 263 1409 500 Ponderosa Pine (Priv.) 436.7 0.0556 399.8 585 658 289 Ponderosa Pine (Publ.) 353.0 0.0673 350.5 1192 624 321 Pinyon-Juniper 58.0 0.0000 0.0 6335 2165 48 Nonstocked 26.2 0.0000 0.0 620 1929 39

a Prediction of mass density of live trees based on the following equation: Live mass density (Mg/ha) = F*(G+(1-exp(- volume/H))). If coefficient H equals 0, then F is the predicted value, which is the mean for that forest type (units for F are then Mg/ha). Table 3.—continued.
Forest type F G H n mse Volume limit

Continued Table 4.—Coefficients for estimating mass density of standing dead trees (above- and belowground, Mg/ha) by type, region, and owner (as appropriate); A , B , and C are coefficients; n = number of FIA plots; mse = mean squared error of the prediction relative to individual plots; volume limit (m 3 /ha) = 99 th percentile of growing-stock volumes within each set of FIA plots (upper limit of independent variables in the regressions) a
Forest type A B C n mse Volume limit

NE Aspen-Birch b 0.0436 704.78 3.506 264 12 250 MBB/Other HW (Priv.) c 0.1189 240.36 2.391 2302 143 273 MBB/Other HW (Publ.) c 0.1189 240.36 2.391 362 151 342 Oak-Hickory 0.0610 459.83 1.617 3314 137 306 Oak-Pine 0.0605 342.95 2.044 304 83 268 Other Pine d 0.1334 228.25 1.368 295 73 304 Spruce-Fir d 0.1334 228.25 1.368 236 47 237 WRJ-Pine d 0.1334 228.25 1.368 398 94 354 Nonstocked 9.9137 0.00 0.000 14 98 10 NLS Aspen-Birch 0.4176 127.05 0.426 8072 342 239 Lowland HW (Priv.) 0.5147 97.24 0.642 1322 1894 223 Lowland HW (Publ.) 1.8111 1.00 0.149 662 3291 281 MBB c 0.1189 240.36 2.391 5871 524 259 Oak-Hickory 0.1888 329.05 0.432 2049 269 238 Pine 0.3818 7.48 0.156 2056 132 280 Spruce-Fir 0.1517 415.08 1.088 4926 221 217 Nonstocked 9.9137 0.00 0.000 83 418 119 NPS Conifer 0.0396 287.04 11.740 256 79 236 Lowland HW 0.1178 220.67 1.409 1030 276 303 MBB 0.1006 164.17 1.145 1682 151 252 Oak-Hickory 0.0589 339.57 1.892 2516 151 24315 Oak-Pine 0.0594 127.97 1.382 172 83 200 Nonstocked 14.7956 0.00 0.000 41 464 71 SC Bottomland HW (Priv.) 0.1493 145.42 0.484 2092 147 309 Bottomland HW (Publ.) 0.3291 30.12 0.305 295 242 329 Natural Pine (Priv.) 0.0550 424.29 1.901 1620 55 321 Natural Pine (Publ.) 0.0467 974.52 1.355 448 61 373 Oak-Pine 0.0622 835.98 0.892 2000 67 265 Other Conifer 0.0457 116.64 4.311 72 30 208 Planted Pine 0.0631 5137.75 0.136 1477 29 265 Upland HW (Priv.) 0.0666 315.70 1.314 3934 82 204 Upland HW (Publ.) 0.0616 313.09 2.438 481 91 252 Nonstocked 3.6926 0.00 0.000 19 29 8 SE Bottomland HW (Priv.) e 0.0774 347.43 1.104 2516 112 453 Bottomland HW (Publ.) e 0.0774 347.43 1.104 334 204 427 Natural Pine (Priv.) e 0.0510 826.84 1.353 2892 42 382 Natural Pine (Publ.) e 0.0510 826.84 1.353 662 31 357 Oak-Pine e 0.0510 826.84 1.353 2307 42 347 Other Conifer e 0.0510 826.84 1.353 145 44 397 Planted Pine e 0.0510 826.84 1.353 3865 18 267 Upland HW (Priv.) e 0.0774 347.43 1.104 5071 80 334 Upland HW (Publ.) e 0.0774 347.43 1.104 779 128 336 Nonstocked 3.6926 0.00 0.000 74 136 214 PSW Douglas-fir f 0.2840 848.73 0.379 93 1737 1090 Fir-Spruce f 0.2840 848.73 0.379 51 1106 1020 Hardwoods f 1.0478 2.67 0.230 695 287 639 Other Conifer f 0.2840 848.73 0.379 795 937 687 Pinyon-Juniper 5.1131 0.00 0.000 6335 134 48 Redwood f 0.2840 848.73 0.379 108 2273 1691 Nonstocked 2.4773 0.00 0.000 18 99 119 PWE Douglas-fir (Priv.) 0.1005 401.57 1.175 226 491 488 Douglas-fir (Publ.) 8.4407 1.00 0.313 972 1074 636 Fir-Spruce (Priv.) g 4.7263 1.00 0.205 95 3905 496 Fir-Spruce (Publ.) g 4.7263 1.00 0.205 1213 1904 761 Hardwoods 0.9233 1.00 0.585 46 362 292 Lodgepole Pine (Priv.) g 0.3081 422.52 0.868 65 912 325 Lodgepole Pine (Publ.) g 0.3081 422.52 0.868 835 655 389 Ponderosa Pine (Priv.) g 0.8898 3.11 0.178 262 2050 336 Ponderosa Pine (Publ.) g 0.8898 3.11 0.178 1939 365 402 Pinyon-Juniper 5.1131 0.00 0.000 6335 134 48 Nonstocked 27.1975 0.00 0.000 49 2162 293 PWW Douglas-fir (Priv.) f 0.2840 848.73 0.379 1032 1440 913 Douglas-fir (Publ.) f 0.2840 848.73 0.379 2106 4218 1626 Table 4.—continued. Forest type A B C n mse Volume limit

Continued16 Fir-Spruce (Priv.) f 0.2840 848.73 0.379 123 1825 835 Fir-Spruce (Publ.) f 0.2840 848.73 0.379 776 3413 1517 Other Conifer 0.6505 1.76 0.132 158 244 616 Other Hardwoods f 1.0478 2.67 0.230 512 1078 858 Red Alder 0.3107 1.00 0.128 557 1485 719 Western Hemlock f 0.2840 848.73 0.379 963 5392 1556 Nonstocked 2.4773 0.00 0.000 49 1768 304 RMN Douglas-fir h 1.9177 1.00 0.197 3122 1054 526 Fir-Spruce 2.1076 1.00 0.155 1820 2118 578 Hardwoods 0.1906 284.53 1.030 243 250 243 Lodgepole Pine 3.6764 1.00 0.235 1381 946 491 Other Conifer 0.7495 111.08 0.927 273 1742 360 Ponderosa Pine h 1.9177 1.00 0.197 889 388 401 Pinyon-Juniper 5.1131 0.00 0.000 6335 134 48 Nonstocked 15.8190 0.00 0.000 78 1622 79 RMS Douglas-fir 0.6134 1.00 0.100 833 813 491 Fir-Spruce 1.7428 2.70 0.166 1371 2464 574 Hardwoods 0.1441 811.36 1.448 1712 316 337 Lodgepole Pine h 1.9177 1.00 0.197 533 1004 422 Other Conifer 0.4705 128.54 0.324 263 1183 500 Ponderosa Pine (Priv.) 0.5260 1.00 0.186 585 144 289 Ponderosa Pine (Publ.) 0.1986 238.44 0.415 1192 328 321 Pinyon-Juniper 5.1131 0.00 0.000 6335 134 48 Nonstocked 10.0360 0.00 0.000 620 1327 39

a Prediction of mass density of standing dead trees based on the following equation: Standing dead mass density (Mg/ha) = (predicted live-tree mass density)*A*exp(-((volume/B) C )). If coefficient C equals 0, then A is the predicted value, which is the mean for that forest type (units for A are then Mg/ha).

b From pooled hardwood forests in NE.

c From pooled MBB/Other HW forests in NE and MBB forests in NLS.

d From pooled softwood forests in North (NE, NLS, and NPS).

e From pooled softwood or hardwood forests in South (SC and SE).

f From pooled softwood or hardwood forests in Pacific Northwest (PWW and PWE).

g From pooled private and public ownerships.

h From pooled softwood forests in Rocky Mountains (RMN and RMS). Table 4.—continued. Forest type A B C n mse Volume limit17 Table 5.—Coefficients for estimating mass density of live trees (aboveground only, Mg/ha) by type, region, and owner (as appropriate); F , G , and H are coefficients; n = number of FIA plots; mse = mean squared error of the prediction relative to individual plots; volume limit (m 3 /ha) = 99 th percentile of growing-stock volumes within each set of FIA plots (upper limit of independent variables in the regressions) a
Forest type F G H n mse Volume limit

NE Aspen-Birch 438.5 0.0347 410.7 264 351 250 MBB/Other HW (Priv.) 357.4 0.0470 255.1 2302 658 273 MBB/Other HW (Publ.) 473.3 0.0272 379.5 362 806 342 Oak-Hickory 412.5 0.0502 314.8 3314 890 306 Oak-Pine 310.1 0.0482 247.8 304 457 268 Other Pine 594.5 0.0342 699.3 295 408 304 Spruce-Fir 252.5 0.0413 155.6 236 354 237 WRJ-Pine 344.1 0.0345 274.9 398 736 354 Nonstocked 4.8 0.0000 0.0 14 13 10 NLS Aspen-Birch 304.5 0.0516 270.8 8072 397 239 Lowland HW (Priv.) 430.9 0.0411 366.8 1322 603 223 Lowland HW (Publ.) 645.4 0.0220 577.2 662 399 281 MBB 293.8 0.0491 187.2 5871 759 259 Oak-Hickory 307.5 0.0748 186.9 2049 806 238 Pine 358.7 0.0343 375.4 2056 381 280 Spruce-Fir 325.8 0.0569 280.8 4926 435 217 Nonstocked 7.1 0.0000 0.0 83 329 119 NPS Conifer 501.1 0.0463 601.4 256 395 236 Lowland HW 1016.1 0.0279 1002.5 1030 973 303 MBB 394.7 0.0715 324.5 1682 862 252 Oak-Hickory 864.5 0.0384 740.0 2516 1014 243 Oak-Pine 213.3 0.1234 157.6 172 707 200 Nonstocked 19.1 0.0000 0.0 41 635 71 SC Bottomland HW (Priv.) 263.0 0.1085 173.3 2092 1083 309 Bottomland HW (Publ.) 263.8 0.1011 147.9 295 977 329 Natural Pine (Priv.) 297.4 0.0926 422.4 1620 561 321 Natural Pine (Publ.) 301.3 0.1121 479.3 448 586 373 Oak-Pine 236.8 0.0893 198.8 2000 646 265 Other Conifer 136.8 0.1843 87.5 72 545 208 Planted Pine 134.4 0.1380 117.5 1477 393 265 Upland HW (Priv.) 193.5 0.1521 112.8 3934 988 204 Upland HW (Publ.) 241.0 0.0968 141.4 481 773 252 Nonstocked 47.4 0.0000 0.0 19 1887 8 SE Bottomland HW (Priv.) 808.6 0.0258 801.6 2516 1553 453 Bottomland HW (Publ.) 359.9 0.0567 291.6 334 1618 427 Natural Pine (Priv.) 296.9 0.0423 344.1 2892 374 382 Natural Pine (Publ.) 1044.8 0.0133 1680.6 662 480 357 Oak-Pine 352.9 0.0347 312.4 2307 462 347

Continued18 Other Conifer 268.5 0.0223 199.6 145 699 397 Planted Pine 187.3 0.0662 184.9 3865 281 267 Upland HW (Priv.) 352.6 0.0609 285.8 5071 816 334 Upland HW (Publ.) 342.3 0.0904 261.8 779 1337 336 Nonstocked 47.4 0.0000 0.0 74 3195 214 PSW Douglas-fir 1719.4 0.0164 2155.5 93 5861 1090 Fir-Spruce 741.8 0.0107 776.3 51 4177 1020 Hardwoods 1244.6 0.0000 1142.2 695 4813 639 Other Conifer 1127.0 0.0368 1536.5 795 3054 687 Pinyon-Juniper 47.9 0.0000 0.0 6335 1470 48 Redwood 3738.2 0.0122 6752.8 108 8123 1691 Nonstocked 34.7 0.0000 0.0 18 557 119 PWE Douglas-fir (Priv.) 540.6 0.0167 575.1 226 1603 488 Douglas-fir (Publ.) 2757.3 0.0000 4024.8 972 2556 636 Fir-Spruce (Priv.) 507.7 0.0127 562.3 95 1731 496 Fir-Spruce (Publ.) 3839.5 0.0000 6123.8 1213 4281 761 Hardwoods 557.1 0.0497 729.3 46 2015 292 Lodgepole Pine (Priv.) 303.4 0.0192 390.5 65 561 325 Lodgepole Pine (Publ.) 577.3 0.0108 989.6 835 1009 389 Ponderosa Pine (Priv.) 312.8 0.0176 331.2 262 727 336 Ponderosa Pine (Publ.) 1256.7 0.0057 2072.9 1939 729 402 Pinyon-Juniper 47.9 0.0000 0.0 6335 1470 48 Nonstocked 13.3 0.0000 0.0 49 332 293 PWW Douglas-fir (Priv.) 984.2 0.0185 1251.5 1032 3659 913 Douglas-fir (Publ.) 4190.5 0.0052 6848.0 2106 9529 1626 Fir-Spruce (Priv.) 658.8 0.0162 757.6 123 2811 835 Fir-Spruce (Publ.) 1523.8 0.0121 2432.5 776 8683 1517 Other Conifer 6139.8 0.0039 11258.9 158 1375 616 Other Hardwoods 10429.2 0.0028 15217.0 512 3891 858 Red Alder 2318.0 0.0111 4085.2 557 1643 719 Western Hemlock 1670.1 0.0194 2977.1 963 8663 1556 Nonstocked 26.3 0.0000 0.0 49 1895 304
RMN Douglas-fir 489.6 0.0413 505.6 3122 1622 526 Fir-Spruce 756.9 0.0223 967.6 1820 1142 578 Hardwoods 351.8 0.0530 366.2 243 450 243 Lodgepole Pine 348.2 0.0483 521.1 1381 649 491 Other Conifer 553.3 0.0313 545.5 273 789 360 Ponderosa Pine 398.4 0.0260 486.1 889 326 401 Pinyon-Juniper 47.9 0.0000 0.0 6335 1470 48 Nonstocked 34.2 0.0000 0.0 78 965 79 RMS Douglas-fir 694.2 0.0457 709.7 833 1128 491 Fir-Spruce 630.6 0.0341 659.0 1371 1125 574

Continued Forest type F G H n mse Volume limit Table 5.—continued.19 Hardwoods 556.1 0.0539 616.3 1712 783 337 Lodgepole Pine 373.1 0.0577 568.1 533 610 422 Other Conifer 311.6 0.0325 234.3 263 958 500 Ponderosa Pine (Priv.) 363.6 0.0552 405.0 585 450 289 Ponderosa Pine (Publ.) 291.4 0.0671 351.4 1192 424 321 Pinyon-Juniper 47.9 0.0000 0.0 6335 1470 48 Nonstocked 21.9 0.0000 0.0 620 1356 39

a Prediction of mass density of live trees based on the following equation: Live mass density (Mg/ha) = F*(G+(1- exp(-volume/H))). If coefficient H equals 0, then F is the predicted value, which is the mean for that forest type (units for F are then Mg/ha). Forest type F G H n mse Volume limit Table 5.—continued.

Table 6.—Coefficients for estimating mass density of standing dead trees (aboveground only, Mg/ha) by type, region, and owner (as appropriate); A , B , and C are coefficients; n = number of FIA plots;

mse = mean squared error of the prediction relative to individual plots; volume limit (m 3 /ha) = 99 th percentile of growing-stock volumes within each set of FIA plots (upper limit of independent variables in the regressions) a
Forest type A B C n mse Volume limit

NE Aspen-Birch b 0.0439 697.18 3.478 264 9 250 MBB/Other HW (Priv.) c 0.1194 240.22 2.383 2302 101 273 MBB/Other HW (Publ.) c 0.1194 240.22 2.383 362 106 342 Oak-Hickory 0.0615 459.11 1.609 3314 98 306 Oak-Pine 0.0610 340.62 2.023 304 59 268 Other Pine d 0.1340 228.56 1.348 295 50 304 Spruce-Fir d 0.1340 228.56 1.348 236 32 237 WRJ-Pine d 0.1340 228.56 1.348 398 65 354 Nonstocked 8.2496 0.00 0.000 14 68 10 NLS Aspen-Birch 0.4211 124.38 0.424 8072 240 239 Lowland HW (Priv.) 0.5168 97.30 0.641 1322 1362 223 Lowland HW (Publ.) 1.8157 1.00 0.149 662 2373 281 MBB c 0.1194 240.22 2.383 5871 373 259 Oak-Hickory 0.1879 329.99 0.441 2049 191 238 Pine 0.3847 7.12 0.155 2056 90 280 Spruce-Fir 0.1524 416.99 1.069 4926 151 217 Nonstocked 8.2496 0.00 0.000 83 291 119 NPS Conifer 0.0400 286.14 11.668 256 56 236 Lowland HW 0.1189 220.02 1.401 1030 198 303 MBB 0.1015 163.51 1.144 1682 109 252 Oak-Hickory 0.0593 338.68 1.887 2516 109 243 Oak-Pine 0.0605 125.62 1.387 172 60 200

Continued20 Nonstocked 12.4386 0.00 0.000 41 328 71 SC Bottomland HW (Priv.) 0.1501 144.76 0.483 2092 105 309 Bottomland HW (Publ.) 0.3253 31.65 0.309 295 172 329 Natural Pine (Priv.) 0.0549 425.16 1.880 1620 38 321 Natural Pine (Publ.) 0.0465 1003.34 1.337 448 42 373 Oak-Pine 0.0623 845.84 0.874 2000 46 265 Other Conifer 0.0460 114.35 4.398 72 21 208 Planted Pine 0.0632 3207.56 0.150 1477 19 265 Upland HW (Priv.) 0.0666 315.78 1.313 3934 58 204 Upland HW (Publ.) 0.0615 313.20 2.451 481 65 252 Nonstocked 3.1095 0.00 0.000 19 21 8 SE Bottomland HW (Priv.) e 0.0775 348.79 1.102 2516 80 453 Bottomland HW (Publ.) e 0.0775 348.79 1.102 334 146 427 Natural Pine (Priv.) e 0.0512 868.32 1.265 2892 29 382 Natural Pine (Publ.) e 0.0512 868.32 1.265 662 21 357 Oak-Pine e 0.0512 868.32 1.265 2307 30 347 Other Conifer e 0.0512 868.32 1.265 145 30 397 Planted Pine e 0.0512 868.32 1.265 3865 12 267 Upland HW (Priv.) e 0.0775 348.79 1.102 5071 57 334 Upland HW (Publ.) e 0.0775 348.79 1.102 779 91 336 Nonstocked 3.1095 0.00 0.000 74 97 214 PSW Douglas-fir f 0.2794 448.29 0.344 93 1040 1090 Fir-Spruce f 0.2794 448.29 0.344 51 792 1020 Hardwoods f 1.0857 1.90 0.224 695 172 639 Other Conifer f 0.2794 448.29 0.344 795 608 687 Pinyon-Juniper 4.2241 0.00 0.000 6335 92 48 Redwood f 0.2794 448.29 0.344 108 1264 1691 Nonstocked 2.0326 0.00 0.000 18 4 119 PWE Douglas-fir (Priv.) 0.0815 402.37 1.171 226 285 488 Douglas-fir (Publ.) 6.8161 1.00 0.312 972 719 636 Fir-Spruce (Priv.) g 3.8137 1.00 0.204 95 2585 496 Fir-Spruce (Publ.) g 3.8137 1.00 0.204 1213 1276 761 Hardwoods 0.9233 1.00 0.670 46 240 292 Lodgepole Pine (Priv.) g 0.2500 430.81 0.872 65 596 325 Lodgepole Pine (Publ.) g 0.2500 430.81 0.872 835 440 389 Ponderosa Pine (Priv.) g 0.6972 3.79 0.182 262 1340 336 Ponderosa Pine (Publ.) g 0.6972 3.79 0.182 1939 246 402 Pinyon-Juniper 4.2241 0.00 0.000 6335 92 48 Nonstocked 22.3113 0.00 0.000 49 1494 293 PWW Douglas-fir (Priv.) f 0.2794 448.29 0.344 1032 749 913 Douglas-fir (Publ.) f 0.2794 448.29 0.344 2106 2907 1626 Fir-Spruce (Priv.) f 0.2794 448.29 0.344 123 683 835

Continued Table 6.—continued.
Forest type A B C n mse Volume limit21 Fir-Spruce (Publ.) f 0.2794 448.29 0.344 776 2428 1517 Other Conifer 0.5689 1.15 0.127 158 164 616 Other Hardwoods f 1.0857 1.90 0.224 512 697 858 Red Alder 0.1675 1.00 0.059 557 911 719 Western Hemlock f 0.2794 448.29 0.344 963 3703 1556 Nonstocked 2.0326 0.00 0.000 49 75 304 RMN Douglas-fir h 1.9239 1.00 0.197 3122 719 526 Fir-Spruce 2.1217 1.00 0.155 1820 1447 578 Hardwoods 0.1911 284.22 1.025 243 173 243 Lodgepole Pine 3.7059 1.00 0.235 1381 643 491 Other Conifer 0.7522 110.91 0.929 273 1186 360 Ponderosa Pine h 1.9239 1.00 0.197 889 265 401 Pinyon-Juniper 4.2241 0.00 0.000 6335 92 48 Nonstocked 13.0819 0.00 0.000 78 1106 79 RMS Douglas-fir 0.6141 1.00 0.100 833 554 491 Fir-Spruce 1.7483 2.68 0.166 1371 1682 574 Hardwoods 0.1439 817.86 1.456 1712 219 337 Lodgepole Pine h 1.9239 1.00 0.197 533 681 422 Other Conifer 0.4729 126.66 0.325 263 807 500 Ponderosa Pine (Priv.) 0.5251 1.00 0.186 585 99 289 Ponderosa Pine (Publ.) 0.1996 235.82 0.414 1192 225 321 Pinyon-Juniper 4.2241 0.00 0.000 6335 92 48 Nonstocked 8.3065 0.00 0.000 620 904 39

a Prediction of mass density of standing dead trees based on the following equation: Standing dead mass density (Mg/ha) = (predicted live-tree mass density)*A*exp(-((volume/B) C )). If coefficient C equals 0, then A is the predicted value, which is the mean for that forest type (units for A are then Mg/ha).

b From pooled hardwood forests in NE.

c From pooled MBB/Other HW forests in NE and MBB forests in NLS.

d From pooled softwood forests in North (NE, NLS, and NPS).

e From pooled softwood or hardwood forests in South (SC and SE).

f From pooled softwood or hardwood forests in Pacific Northwest (PWW and PWE).

g From pooled private and public ownerships.

h From pooled softwood forests in Rocky Mountains (RMN and RMS). Table 6.—continued.
Forest type A B C n mse Volume limit22 Table 7.—Coefficients for estimating mass density of live softwood tree species (above- and belowground, Mg/ha) by type, region, and owner (as appropriate); F , G , and H are coefficients; n = number of FIA plots; mse = mean squared error of the prediction relative to individual plots; volume limit (m 3 /ha) = 99 th percentile of growing-stock volumes within each set of FIA plots (upper limit of independent variables in the regressions) a
Forest type F G H n mse Volume limit

NE Aspen-Birch 73.3 0.0183 38.3 264 58 41 MBB/Other HW (Priv.) 471.3 0.0020 386.6 2302 29 74 MBB/Other HW (Publ.) 175.9 0.0020 135.4 362 49 111 Oak-Hickory 405.3 0.0008 372.8 3314 13 47 Oak-Pine 460.0 0.0149 538.3 304 92 132 Other Pine 1209.8 0.0170 1517.9 295 344 256 Spruce-Fir 334.7 0.0351 220.6 236 356 227 WRJ-Pine 322.1 0.0259 261.2 398 476 279 Nonstocked 0.8 0.0000 0.0 14 7 2 NLS Aspen-Birch 181.8 0.0047 141.4 8072 44 84 Lowland HW (Priv.) 241.7 0.0018 169.8 1322 43 83 Lowland HW (Publ.) 193.5 0.0072 148.0 662 65 85 MBB 318.4 0.0009 230.8 5871 80 121 Oak-Hickory 118.8 0.0034 91.6 2049 18 60 Pine 428.7 0.0281 429.4 2056 341 268 Spruce-Fir 405.6 0.0517 320.0 4926 600 188 Nonstocked 2.5 0.0000 0.0 83 17 18 NPS Conifer 291.9 0.0674 313.1 256 276 182 Lowland HW 109.8 0.0011 96.7 1030 2 12 MBB 36.9 0.0047 22.5 1682 2 15 Oak-Hickory b 1438.0 0.0002 1420.2 2516 5 26 Oak-Pine 382.2 0.0321 459.7 172 92 145 Nonstocked 3.0 0.0000 0.0 41 84 56 SC Bottomland HW (Priv.) 342.8 0.0007 383.6 2092 75 184 Bottomland HW (Publ.) b 612.6 0.0012 793.0 295 56 85 Natural Pine (Priv.) 403.8 0.0561 655.7 1620 346 302 Natural Pine (Publ.) 396.0 0.0714 746.2 448 250 340 Oak-Pine 127.4 0.0748 177.4 2000 92 168 Other Conifer 105.4 0.1621 74.5 72 298 123 Planted Pine 132.6 0.1393 107.6 1477 404 253 Upland HW (Priv.) 84.4 0.0114 104.6 3934 13 59 Upland HW (Publ.) 57.7 0.0055 65.4 481 14 69 Nonstocked 5.0 0.0000 0.0 19 25 8 SE Bottomland HW (Priv.) 813.1 0.0006 973.8 2516 100 276 Bottomland HW (Publ.) 477.5 0.0036 556.1 334 276 268 Natural Pine (Priv.) 289.6 0.0397 354.2 2892 268 337 Natural Pine (Publ.) 617.7 0.0207 947.1 662 219 303 Oak-Pine 337.1 0.0146 456.4 2307 77 167

Continued23 Other Conifer 403.5 0.0254 423.4 145 487 386 Planted Pine 213.8 0.0621 200.2 3865 279 261 Upland HW (Priv.) 132.2 0.0042 160.9 5071 10 59 Upland HW (Publ.) 191.7 0.0035 224.6 779 16 71 Nonstocked 5.0 0.0000 0.0 74 121 79 PSW Douglas-fir 3537.8 0.0081 4159.6 93 2610 1008 Fir-Spruce 903.2 0.0110 784.0 51 1480 1020 Hardwoods 726.9 0.0075 735.0 695 356 383 Other Conifer 791.4 0.0115 736.1 795 1063 617 Pinyon-Juniper 56.6 0.0000 0.0 6335 2135 47 Redwood 1974.9 0.0115 2634.0 108 3183 1691 Nonstocked 29.4 0.0000 0.0 18 482 96 PWE Douglas-fir (Priv.) 645.3 0.0156 560.7 226 714 479 Douglas-fir (Publ.) 3364.5 0.0000 4050.1 972 1547 636 Fir-Spruce (Priv.) 709.1 0.0137 686.7 95 867 496 Fir-Spruce (Publ.) 4600.0 0.0000 6045.5 1213 2502 758 Hardwoods 237.4 0.0013 229.2 46 120 194 Lodgepole Pine (Priv.) 373.3 0.0199 405.4 65 318 305 Lodgepole Pine (Publ.) 689.2 0.0111 970.6 835 697 389 Ponderosa Pine (Priv.) 366.7 0.0194 325.7 262 330 327 Ponderosa Pine (Publ.) 1501.5 0.0057 2038.9 1939 312 402 Pinyon-Juniper 56.6 0.0000 0.0 6335 2135 47 Nonstocked 11.6 0.0000 0.0 49 262 291 PWW Douglas-fir (Priv.) 1244.6 0.0155 1319.9 1032 1434 913 Douglas-fir (Publ.) 5215.8 0.0046 7046.3 2106 3251 1626 Fir-Spruce (Priv.) 686.1 0.0164 657.5 123 1200 768 Fir-Spruce (Publ.) 1846.0 0.0120 2424.4 776 3312 1517 Other Conifer 5934.2 0.0045 9074.3 158 586 616 Other Hardwoods 7750.0 0.0000 8874.0 512 918 754 Red Alder 2253.7 0.0035 2922.4 557 414 475 Western Hemlock 2084.8 0.0176 3074.5 963 3835 1549 Nonstocked 22.4 0.0000 0.0 49 1324 191
RMN Douglas-fir 599.8 0.0397 510.9 3122 2374 526 Fir-Spruce 908.7 0.0221 954.4 1820 1681 578 Hardwoods 227.9 0.0045 169.1 243 65 118 Lodgepole Pine 422.1 0.0471 517.1 1381 966 491 Other Conifer 676.5 0.0306 549.0 273 1157 360 Ponderosa Pine 480.8 0.0261 483.5 889 463 401 Pinyon-Juniper 56.6 0.0000 0.0 6335 2135 47 Nonstocked 6.5 0.0000 0.0 78 189 79 RMS Douglas-fir 723.0 0.0441 581.5 833 1517 468 Fir-Spruce 810.5 0.0265 707.0 1371 1519 574

Continued Forest type F G H n mse Volume limit Table 7.—continued.24 Hardwoods 932.5 0.0038 844.5 1712 169 156 Lodgepole Pine 498.3 0.0476 640.5 533 874 422 Other Conifer 383.5 0.0295 240.1 263 1409 500 Ponderosa Pine (Priv.) 310.6 0.0453 251.7 585 431 280 Ponderosa Pine (Publ.) 350.8 0.0643 359.3 1192 538 312 Pinyon-Juniper 56.6 0.0000 0.0 6335 2135 47 Nonstocked 5.4 0.0000 0.0 620 206 39

a Prediction of mass density of live trees based on the following equation: Live mass density (Mg/ha) = F*(G+(1- exp(-volume/H))). Note that for this table, volume is growing-stock volume of softwood species only. If coefficient H equals 0, then F is the predicted value, which is the mean for that forest type (units for F are then Mg/ha).

b Coefficients from softwood tree mass in all hardwood forests across the region. Forest type F G H n mse Volume limit Table 7.—continued.

Table 8.—Coefficients for estimating mass density of live hardwood tree species (above- and belowground, Mg/ha) by type, region, and owner (as appropriate); F , G , and H are coefficients; n = number of FIA plots; mse = mean squared error of the prediction relative to individual plots; volume limit (m 3 /ha) = 99 th percentile of growing-stock volumes within each set of FIA plots (upper limit of independent variables in the regressions) a
Forest type F G H n mse Volume limit

NE Aspen-Birch 683.6 0.0245 566.9 264 395 250 MBB/Other HW (Priv.) 426.4 0.0479 261.0 2302 889 259 MBB/Other HW (Publ.) 737.5 0.0240 534.2 362 993 335 Oak-Hickory 456.3 0.0527 285.2 3314 1230 299 Oak-Pine 243.6 0.0695 139.3 304 511 166 Other Pine 160.5 0.0356 78.4 295 184 92 Spruce-Fir 261.1 0.0138 117.1 236 123 59 WRJ-Pine 235.4 0.0317 127.6 398 286 140 Nonstocked 4.9 0.0000 0.0 14 19 9 NLS Aspen-Birch 341.9 0.0534 258.2 8072 542 216 Lowland HW (Priv.) 504.1 0.0397 358.5 1322 795 213 Lowland HW (Publ.) 760.0 0.0204 561.8 662 479 257 MBB 328.1 0.0532 173.1 5871 990 244 Oak-Hickory 356.5 0.0760 176.8 2049 1088 236 Pine 5558.9 0.0000 3378.2 2056 177 87 Spruce-Fir 304.3 0.0084 182.0 4926 103 77 Nonstocked 6.0 0.0000 0.0 83 458 119 NPS Conifer 495.4 0.0156 321.2 256 186 159 Lowland HW 1170.7 0.0285 962.3 1030 1370 303 MBB 469.3 0.0711 323.5 1682 1213 252 Oak-Hickory 1038.5 0.0381 747.0 2516 1420 240

Continued25 Oak-Pine 289.3 0.0954 188.1 172 808 150 Nonstocked 19.8 0.0000 0.0 41 857 71 SC Bottomland HW (Priv.) 312.9 0.1041 166.5 2092 1387 256 Bottomland HW (Publ.) 302.3 0.1038 136.2 295 1271 329 Natural Pine (Priv.) 129.1 0.0908 54.0 1620 287 70 Natural Pine (Publ.) 149.2 0.0985 70.4 448 343 112 Oak-Pine 185.1 0.1132 82.0 2000 599 124 Other Conifer 177.2 0.0991 84.8 72 386 140 Planted Pine 98.6 0.0667 36.6 1477 133 40 Upland HW (Priv.) 218.9 0.1519 97.0 3934 1311 190 Upland HW (Publ.) 290.5 0.1068 145.3 481 1031 230 Nonstocked 51.4 0.0000 0.0 19 2264 5 SE Bottomland HW (Priv.) 1138.6 0.0204 893.0 2516 1776 370 Bottomland HW (Publ.) 470.7 0.0485 312.9 334 1910 359 Natural Pine (Priv.) 184.8 0.0430 97.0 2892 213 115 Natural Pine (Publ.) 238.0 0.0296 124.1 662 223 104 Oak-Pine 269.0 0.0582 154.5 2307 534 210 Other Conifer 306.0 0.0190 173.2 145 317 127 Planted Pine 156.2 0.0276 68.5 3865 116 40 Upland HW (Priv.) 413.4 0.0649 280.7 5071 1135 326 Upland HW (Publ.) 420.0 0.0926 277.3 779 1835 331 Nonstocked 51.4 0.0000 0.0 74 4420 214 PSW Douglas-fir 257.6 0.0224 166.1 93 849 247 Fir-Spruce 26.0 0.0134 6.9 51 6 28 Hardwoods 2851.0 0.0000 2199.2 695 5801 421 Other Conifer 1064.4 0.0126 681.3 795 1653 194 Pinyon-Juniper 1.4 0.0000 0.0 6335 35 0 Redwood b 1173.8 0.0102 832.7 108 832 141 Nonstocked 6.3 0.0000 0.0 18 190 41 PWE Douglas-fir (Priv.) 91.4 0.0023 66.8 226 9 36 Douglas-fir (Publ.) 134.7 0.0007 136.9 972 4 28 Fir-Spruce (Priv.) 153.0 0.0055 132.8 95 18 97 Fir-Spruce (Publ.) 555.4 0.0001 633.9 1213 3 14 Hardwoods 1883.5 0.0148 2026.8 46 2484 288 Lodgepole Pine (Priv.) 27.0 0.0088 1.4 65 3 57 Lodgepole Pine (Publ.) b 100.3 0.0006 67.3 835 1 3 Ponderosa Pine (Priv.) 81.3 0.0033 16.8 262 18 28 Ponderosa Pine (Publ.) b 100.3 0.0006 67.3 1939 9 3 Pinyon-Juniper 1.4 0.0000 0.0 6335 35 0 Nonstocked 2.0 0.0000 0.0 49 74 21 PWW Douglas-fir (Priv.) 174.9 0.0114 179.0 1032 131 178 Douglas-fir (Publ.) 253.1 0.0055 299.9 2106 81 134

Continued Forest type F G H n mse Volume limit Table 8.—continued.26 Fir-Spruce (Priv.) 825.2 0.0023 922.4 123 153 198 Fir-Spruce (Publ.) b 264.5 0.0039 309.2 776 39 73 Other Conifer 506.5 0.0052 573.2 158 113 90 Other Hardwoods 526.3 0.0506 548.3 512 1454 487 Red Alder 551.3 0.0306 755.8 557 594 488 Western Hemlock 362.8 0.0011 469.2 963 30 134 Nonstocked 4.7 0.0000 0.0 49 141 114 RMN Douglas-fir 147.8 0.0032 125.3 3122 15 35 Fir-Spruce 283.4 0.0007 252.6 1820 5 16 Hardwoods 309.1 0.0578 254.3 243 594 201 Lodgepole Pine 107.8 0.0011 71.4 1381 3 13 Other Conifer b 165.3 0.0018 139.9 273 9 0 Ponderosa Pine 27.2 0.0003 6.4 889 2 6 Pinyon-Juniper 1.4 0.0000 0.0 6335 35 0 Nonstocked 34.5 0.0000 0.0 78 1441 4 RMS Douglas-fir 238.8 0.0134 172.6 833 91 78 Fir-Spruce 273.6 0.0120 241.4 1371 121 106 Hardwoods 424.6 0.0737 379.2 1712 909 268 Lodgepole Pine 79.3 0.0253 42.5 533 41 23 Other Conifer 246.6 0.0047 154.8 263 26 28 Ponderosa Pine (Priv.) 132.3 0.0480 80.8 585 300 26 Ponderosa Pine (Publ.) 57.5 0.0409 23.9 1192 67 19 Pinyon-Juniper 1.4 0.0000 0.0 6335 35 0 Nonstocked 20.8 0.0000 0.0 620 1701 0

a Prediction of mass density of live trees based on the following equation: Live mass density (Mg/ha) = F*(G+(1- exp(-volume/H))). Note that for this table, volume is growing-stock volume of hardwood species only. If coefficient H equals 0, then F is the predicted value, which is the mean for that forest type (units for F are then Mg/ha).

b Coefficients from hardwood tree mass in all softwood forests across the region. Forest type F G H n mse Volume limit Table 8.—continued.27 Table 9.—Coefficients for estimating mass density of live softwood tree species (aboveground only, Mg/ha) by type, region, and owner (as appropriate); F , G , and H are coefficients; n = number of FIA plots; mse = mean squared error of the prediction relative to individual plots; volume limit (m 3 /ha) = 99 th percentile of growing-stock volumes within each set of FIA plots (upper limit of independent variables in the regressions) a
Forest type F G H n mse Volume limit

NE Aspen-Birch 60.3 0.0180 38.4 264 39 41 MBB/Other HW (Priv.) 387.3 0.0020 386.1 2302 20 74 MBB/Other HW (Publ.) 144.8 0.0020 135.5 362 33 111 Oak-Hickory 335.1 0.0008 374.6 3314 9 47 Oak-Pine 380.5 0.0147 540.6 304 62 132 Other Pine 1011.3 0.0167 1541.0 295 231 256 Spruce-Fir 276.7 0.0345 222.3 236 237 227 WRJ-Pine 265.1 0.0258 261.0 398 323 279 Nonstocked 0.7 0.0000 0.0 14 5 2 NLS Aspen-Birch 150.3 0.0046 142.5 8072 29 84 Lowland HW (Priv.) 199.6 0.0018 170.8 1322 29 83 Lowland HW (Publ.) 159.8 0.0071 148.8 662 44 85 MBB 262.7 0.0009 231.4 5871 54 121 Oak-Hickory 98.2 0.0033 92.2 2049 12 60 Pine 354.8 0.0278 432.4 2056 229 268 Spruce-Fir 336.0 0.0508 322.9 4926 400 188 Nonstocked 2.1 0.0000 0.0 83 12 18 NPS Conifer 238.8 0.0673 310.4 256 186 182 Lowland HW 90.8 0.0011 97.4 1030 1 12 MBB 30.4 0.0047 22.6 1682 2 15 Oak-Hickory b 1225.1 0.0002 1472.0 2516 3 26 Oak-Pine 317.6 0.0316 464.2 172 62 145 Nonstocked 2.5 0.0000 0.0 41 57 56 SC Bottomland HW (Priv.) 282.5 0.0007 383.7 2092 51 184 Bottomland HW (Publ.) b 505.0 0.0012 793.6 295 38 85 Natural Pine (Priv.) 333.0 0.0555 655.2 1620 232 302 Natural Pine (Publ.) 325.0 0.0709 740.6 448 168 340 Oak-Pine 105.6 0.0736 178.6 2000 62 168 Other Conifer 87.4 0.1597 75.5 72 199 123 Planted Pine 109.6 0.1376 108.4 1477 271 253 Upland HW (Priv.) 70.1 0.0112 105.6 3934 9 59 Upland HW (Publ.) 47.8 0.0054 66.0 481 9 69 Nonstocked 4.2 0.0000 0.0 19 17 8 SE Bottomland HW (Priv.) 670.4 0.0006 976.0 2516 67 276 Bottomland HW (Publ.) 390.1 0.0036 551.5 334 185 268 Natural Pine (Priv.) 239.3 0.0392 355.9 2892 179 337 Natural Pine (Publ.) 510.2 0.0205 949.6 662 147 303 Oak-Pine 279.4 0.0143 459.3 2307 52 167

Continued28 Other Conifer 332.8 0.0250 423.8 145 328 386 Planted Pine 176.6 0.0613 201.5 3865 186 261 Upland HW (Priv.) 109.6 0.0041 162.2 5071 6 59 Upland HW (Publ.) 160.1 0.0035 228.2 779 11 71 Nonstocked 4.2 0.0000 0.0 74 82 79 PSW Douglas-fir 2949.5 0.0080 4203.5 93 5302 1008 Fir-Spruce 746.9 0.0109 787.2 51 4198 1020 Hardwoods 602.8 0.0074 739.9 695 547 383 Other Conifer 654.6 0.0114 739.4 795 2055 617 Pinyon-Juniper 46.7 0.0000 0.0 6335 1449 47 Redwood 1631.3 0.0114 2637.1 108 7886 1691 Nonstocked 29.4 0.0000 0.0 18 482 96 PWE Douglas-fir (Priv.) 533.7 0.0155 563.8 226 1591 479 Douglas-fir (Publ.) 2860.8 0.0000 4185.0 972 2541 636 Fir-Spruce (Priv.) 586.7 0.0136 690.5 95 1723 496 Fir-Spruce (Publ.) 3939.7 0.0000 6292.4 1213 4274 758 Hardwoods 194.8 0.0013 228.1 46 183 194 Lodgepole Pine (Priv.) 307.0 0.0197 405.2 65 584 305 Lodgepole Pine (Publ.) 574.4 0.0109 984.8 835 1007 389 Ponderosa Pine (Priv.) 303.1 0.0192 327.2 262 662 327 Ponderosa Pine (Publ.) 1254.6 0.0056 2069.0 1939 716 402 Pinyon-Juniper 46.7 0.0000 0.0 6335 1449 47 Nonstocked 11.6 0.0000 0.0 49 262 291 PWW Douglas-fir (Priv.) 1031.2 0.0153 1327.3 1032 3353 913 Douglas-fir (Publ.) 4350.7 0.0045 7126.1 2106 9334 1626 Fir-Spruce (Priv.) 567.4 0.0162 660.1 123 2746 768 Fir-Spruce (Publ.) 1531.1 0.0118 2439.7 776 8598 1517 Other Conifer 5423.1 0.0040 10083.7 158 1276 616 Other Hardwoods 6636.5 0.0000 9217.1 512 2232 754 Red Alder 1877.9 0.0035 2953.7 557 911 475 Western Hemlock 1733.6 0.0173 3101.5 963 8611 1549 Nonstocked 22.4 0.0000 0.0 49 1324 191
RMN Douglas-fir 495.5 0.0395 512.9 3122 1612 526 Fir-Spruce 753.4 0.0219 961.4 1820 1141 578 Hardwoods 187.5 0.0045 169.0 243 44 118 Lodgepole Pine 348.3 0.0466 519.1 1381 649 491 Other Conifer 557.8 0.0305 550.1 273 783 360 Ponderosa Pine 397.6 0.0259 485.4 889 312 401 Pinyon-Juniper 46.7 0.0000 0.0 6335 1449 47 Nonstocked 5.3 0.0000 0.0 78 128 79 RMS Douglas-fir 597.7 0.0438 584.2 833 1029 468 Fir-Spruce 669.9 0.0263 710.1 1371 1030 574

Continued Forest type F G H n mse Volume limit Table 9.—continued.29 Hardwoods 778.3 0.0037 857.2 1712 114 156 Lodgepole Pine 411.2 0.0471 642.3 533 587 422 Other Conifer 316.2 0.0294 240.5 263 959 500 Ponderosa Pine (Priv.) 256.3 0.0451 252.4 585 291 280 Ponderosa Pine (Publ.) 289.5 0.0641 360.3 1192 364 312 Pinyon-Juniper 46.7 0.0000 0.0 6335 1449 47 Nonstocked 4.4 0.0000 0.0 620 140 39

a Prediction of mass density of live trees based on the following equation: Live mass density (Mg/ha) = F*(G+(1- exp(-volume/H))). Note that for this table, volume is growing-stock volume of softwood species only. If coefficient H equals 0, then F is the predicted value, which is the mean for that forest type (units for F are then Mg/ha).

b Coefficients from softwood tree mass in all hardwood forests across the region. Forest type F G H n mse Volume limit Table 9.—continued.

Table 10.—Coefficients for estimating mass density of live hardwood tree species (aboveground only, Mg/ha) by type, region, and owner (as appropriate); F , G , and H are coefficients; n = number of FIA plots; mse = mean squared error of the prediction relative to individual plots; volume limit (m 3 /ha) = 99 th percentile of growing-stock volumes within each set of FIA plots (upper limit of independent variables in the regressions) a
Forest type F G H n mse Volume limit

NE Aspen-Birch 581.3 0.0239 574.9 264 275 250 MBB/Other HW (Priv.) 360.4 0.0473 262.8 2302 626 259 MBB/Other HW (Publ.) 627.6 0.0236 541.8 362 702 335 Oak-Hickory 385.6 0.0519 286.9 3314 867 299 Oak-Pine 205.9 0.0685 140.5 304 359 166 Other Pine 135.3 0.0351 78.9 295 128 92 Spruce-Fir 221.1 0.0135 118.6 236 86 59 WRJ-Pine 198.9 0.0312 128.6 398 201 140 Nonstocked 4.1 0.0000 0.0 14 14 9 NLS Aspen-Birch 287.7 0.0525 258.3 8072 379 216 Lowland HW (Priv.) 428.2 0.0391 363.1 1322 562 213 Lowland HW (Publ.) 646.4 0.0199 569.3 662 338 257 MBB 276.4 0.0526 173.7 5871 699 244 Oak-Hickory 300.0 0.0756 177.2 2049 770 236 Pine 5420.6 0.0000 3931.4 2056 124 87 Spruce-Fir 258.4 0.0083 184.6 4926 72 77 Nonstocked 5.0 0.0000 0.0 83 324 119 NPS Conifer 427.2 0.0152 331.2 256 130 159 Lowland HW 996.9 0.0279 974.7 1030 967 303 MBB 397.4 0.0702 326.3 1682 855 252 Oak-Hickory 885.2 0.0374 757.8 2516 1002 240

Continued30 Oak-Pine 244.7 0.0943 189.7 172 568 150 Nonstocked 16.6 0.0000 0.0 41 604 71 SC Bottomland HW (Priv.) 263.9 0.1033 167.1 2092 980 256 Bottomland HW (Publ.) 254.7 0.1031 136.3 295 899 329 Natural Pine (Priv.) 108.8 0.0897 54.3 1620 201 70 Natural Pine (Publ.) 125.7 0.0975 70.7 448 241 112 Oak-Pine 156.1 0.1119 82.4 2000 421 124 Other Conifer 150.4 0.0979 86.0 72 272 140 Planted Pine 83.1 0.0655 36.9 1477 93 40 Upland HW (Priv.) 184.7 0.1508 97.6 3934 924 190 Upland HW (Publ.) 245.2 0.1058 146.2 481 727 230 Nonstocked 43.2 0.0000 0.0 19 1600 5 SE Bottomland HW (Priv.) 968.3 0.0200 904.5 2516 1254 370 Bottomland HW (Publ.) 397.5 0.0480 314.8 334 1350 359 Natural Pine (Priv.) 156.1 0.0423 97.9 2892 149 115 Natural Pine (Publ.) 200.6 0.0292 124.9 662 156 104 Oak-Pine 227.2 0.0574 155.7 2307 375 210 Other Conifer 260.0 0.0187 175.7 145 223 127 Planted Pine 132.6 0.0269 69.9 3865 81 40 Upland HW (Priv.) 349.1 0.0641 282.2 5071 801 326 Upland HW (Publ.) 355.3 0.0915 279.5 779 1297 331 Nonstocked 43.2 0.0000 0.0 74 3131 214 PSW Douglas-fir 217.2 0.0222 166.9 93 1006 247 Fir-Spruce 21.9 0.0134 7.0 51 7 28 Hardwoods 2431.0 0.0000 2232.3 695 6560 421 Other Conifer 897.1 0.0125 683.1 795 1811 194 Pinyon-Juniper 1.2 0.0000 0.0 6335 25 0 Redwood b 990.1 0.0101 835.7 108 624 141 Nonstocked 6.3 0.0000 0.0 18 190 41 PWE Douglas-fir (Priv.) 77.1 0.0023 67.2 226 11 36 Douglas-fir (Publ.) 114.1 0.0007 138.5 972 5 28 Fir-Spruce (Priv.) 127.6 0.0055 131.3 95 23 97 Fir-Spruce (Publ.) 482.1 0.0001 655.1 1213 4 14 Hardwoods 1620.9 0.0144 2079.2 46 2719 288 Lodgepole Pine (Priv.) 22.7 0.0087 1.6 65 4 57 Lodgepole Pine (Publ.) b 84.6 0.0006 67.7 835 1 3 Ponderosa Pine (Priv.) 68.5 0.0033 17.0 262 21 28 Ponderosa Pine (Publ.) b 84.6 0.0006 67.7 1939 10 3 Pinyon-Juniper 1.2 0.0000 0.0 6335 25 0 Nonstocked 2.0 0.0000 0.0 49 74 21 PWW Douglas-fir (Priv.) 147.4 0.0113 179.6 1032 159 178 Douglas-fir (Publ.) 214.5 0.0054 302.6 2106 96 134

Continued Forest type F G H n mse Volume limit Table 10.—continued.31 Fir-Spruce (Priv.) 703.3 0.0022 935.2 123 213 198 Fir-Spruce (Publ.) b 223.7 0.0039 311.4 776 30 73 Other Conifer 431.9 0.0051 582.8 158 121 90 Other Hardwoods 444.4 0.0501 550.7 512 1901 487 Red Alder 463.4 0.0303 754.3 557 1069 488 Western Hemlock 308.2 0.0011 474.5 963 43 134 Nonstocked 4.7 0.0000 0.0 49 141 114 RMN Douglas-fir 124.6 0.0032 125.9 3122 10 35 Fir-Spruce 241.9 0.0006 257.4 1820 3 16 Hardwoods 260.6 0.0571 255.1 243 418 201 Lodgepole Pine 90.9 0.0011 72.0 1381 2 13 Other Conifer b 139.7 0.0017 141.3 273 6 0 Ponderosa Pine 22.8 0.0003 6.4 889 1 6 Pinyon-Juniper 1.2 0.0000 0.0 6335 25 0 Nonstocked 28.8 0.0000 0.0 78 1007 4 RMS Douglas-fir 201.6 0.0132 173.9 833 63 78 Fir-Spruce 231.2 0.0119 243.1 1371 85 106 Hardwoods 357.3 0.0731 379.8 1712 636 268 Lodgepole Pine 66.8 0.0250 42.9 533 29 23 Other Conifer 208.9 0.0046 156.4 263 18 28 Ponderosa Pine (Priv.) 111.3 0.0475 81.0 585 210 26 Ponderosa Pine (Publ.) 48.5 0.0404 24.1 1192 47 19 Pinyon-Juniper 1.2 0.0000 0.0 6335 25 0 Nonstocked 17.5 0.0000 0.0 620 1201 0

a Prediction of mass density of live trees based on the following equation: Live mass density (Mg/ha) = F*(G+(1- exp(-volume/H))). Note that for this table, volume is growing-stock volume of hardwood species only. If coefficient H equals 0, then F is the predicted value, which is the mean for that forest type (units for F are then Mg/ha).

b Coefficients from hardwood tree mass in all softwood forests across the region. Forest type F G H n mse Volume limit Table 10.—continued.32 Table 11.—Estimated mass density of live trees (aboveground only, Mg/ha) by region and hardwood/ softwood types; to ensure consistent comparisons among estimates, all were applied to the same set of plot and tree records from the FIADB (nonstocked and woodland forest types were excluded)

Region Forest Estimates based FIADB Birdsey Brown and type on Table 3 ( DRYBIOT ) (1992) others (1999) Mg/ha

NE Hardwood 116.7 110.6 102.4 128.2 Softwood 84.0 79.3 60.2 82.8

NLS Hardwood 103.7 94.0 89.8 121.4 Softwood 81.1 67.0 58.3 87.8

NPS Hardwood 100.7 80.8 91.1 102.1 Softwood 69.1 51.5 54.0 62.8

SC Hardwood 104.6 112.1 90.5 104.0 Softwood 78.5 97.3 71.1 89.8

SE Hardwood 120.8 105.7 114.2 144.8 Softwood 67.0 66.0 59.8 89.8

PSW Hardwood 140.7 159.1 101.0 Softwood 178.3 174.1 133.1

PWE Hardwood 84.3 67.4 56.2 Softwood 108.3 98.4 96.4

PWW Hardwood 191.1 201.8 181.04 Softwood 264.0 264.5 268.5

RMN Hardwood 71.6 49.7 47.2 Softwood 122.3 111.7 119.8

RMS Hardwood 72.0 53.2 39.4 Softwood 113.0 92.7 91.033 Table 12.—Estimated total mass (Mt) of live and standing dead trees larger than 1 inch d.b.h. (aboveground and coarse roots) and area, by region and forest classification; values obtained by applying biomass estimates from Tables 3 and 4 to data from 1997 RPA database (Smith and others 2001)

Timberland a Reserved Other Region Live Dead Area Live Dead Area Live Dead Area

Thousand Thousand Thousand ha ha ha

NE 4846 349 31940 315 20 2009 79 6 647 NLS 2470 311 19906 19 6 775 11 3 402 NPS 1735 100 13937 27 2 408 17 3 340 North 9051 760 65783 362 28 3191 108 11 1388

SC 5722 311 47024 58 3 486 183 11 3254 SE 4230 206 34320 87 5 1092 22 1 469 South 9953 517 81344 146 8 1579 205 13 3723

PSW 1664 223 7265 714 82 2415 464 57 5920 PWE 1097 125 7989 185 28 1151 100 14 1559 PWW 2251 257 8671 275 33 1268 18 2 249 RMN 2104 201 14685 280 87 2893 57 11 701 RMS 1625 228 12796 232 126 4440 1335 140 21025 West 8740 1033 51407 1686 355 12167 1974 223 29454
All regions 27744 2311 198534 2193 391 16937 2286 248 34565

a Timberland is forest land classified as having a growth capacity of at least 20 cubic feet industrial wood per acre per year. Reserved forests are withdrawn by law from the production of wood products.34 

## Literature Cited
Birdsey, R.A. 1992. Carbon storage and accumulation in United States forest ecosystems. Gen. Tech. Rep. WO-59. Washington, DC: U.S. Department of Agriculture, Forest Service. 51 p.

Birdsey, R.A.; Heath, L.S. 1995. Carbon changes in U.S. forests. In: Joyce, L.A., ed. Productivity of America’s forests and climate change. Gen. Tech. Rep. RM-271. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Forest and Range Experiment Station: 56-70.

Brown, S.; Schroeder, P.; Birdsey, R. 1997. Aboveground biomass distribution of US eastern hardwood forests and the use of large trees as an indicator of forest development. Forest Ecology and Management. 96: 37-47.

Brown, S.L.; Schroeder, P.; Kern, J.S. 1999. Spatial distribution of biomass in forests of the eastern USA. Forest Ecology and Management. 123: 81-90.

Clutter, J.L.; Fortson, J.C.; Pienaar, L.V.; Brister, G.H.; Bailey, R.L. 1983. Timber management: a quantitative approach. New York: John Wiley & Sons. 333 p.

Cost, N.D.; Howard, J.O.; Mead, B.; McWilliams, W.H.; Smith, W.B.; Van Hooser, D.D.; Wharton, E.H. 1990. The forest biomass resource of the United States. Gen. Tech. Rep. WO-57. Washington, DC: U.S. Department of Agriculture, Forest Service. 21 p.

Hansen, M.H.; Frieswyk, T.; Glover, J.F.; Kelly, J.F. 1992. The Eastwide forest inventory data base: users manual. Gen. Tech. Rep. NC-151. St. Paul, MN: U.S. Department of Agriculture, Forest Service, North Central Forest Experiment Station. 48 p.

Haynes, R.W.; Adams, D.M.; Mills, J.R. 1995. The 1993 RPA timber assessment update. Gen. Tech. Rep. RM-259. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Forest and Range Experiment Station. 66 p.

Heath, L.S.; Birdsey, R.A. 1993. Carbon trends of productive temperate forests of the coterminous United States. Water, Air, and Soil Pollution. 70: 279- 293.

Heath, L.S.; Birdsey, R.A.; Row, C.; Plantinga, A.J. 1996. Carbon pools and flux in U.S. forest products. In: Apps, M.J.; Price, D.T., eds. Forest ecosystems, forest management, and the global carbon cycle. NATO ASI SER. Vol. I 40. Berlin: Springer-Verlag: 271-278.

Heath, L.S.; Chojnacky, D.C. 2001. Down dead wood statistics for Maine timberlands, 1995. Resour. Bull. NE-150. Newtown Square, PA: U.S. Department of Agriculture, Forest Service, Northeastern Research Station. 80 p.

Heath, L.S.; Smith, J.E. 2000. An assessment of uncertainty in forest carbon budget projections. Environmental Science and Policy. 3: 73-82.

Jenkins, J.; Chojnacky, D.; Heath, L.; Birdsey, R. National-scale biomass estimators for United States tree species. Forest Science. [in press].

Miles, P.D.; Brand, G.J.; Alerich, C.L.; Bednar, L.F.; Woudenberg, S.W.; Glover, J.F.; Ezell, E.N. 2001. The forest inventory and analysis database description and users manual version 1.0. Gen. Tech. Rep. NC218. St. Paul, MN: U.S. Department of Agriculture, Forest Service, North Central Research Station. 130 p.

Mills, J.R.; Kincaid, J.C. 1992. The aggregate timberland assessment system—ATLAS: a comprehensive timber projection model. Gen. Tech. Rep. PNW-GTR-281. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station. 16 p.

Parresol, B.R. 1999. Assessing tree and stand biomass: a review with examples and critical comparisons. Forest Science. 45: 573-593.

Plantinga, A.J.; Birdsey, R.A. 1993. Carbon fluxes resulting from US private timberland management. Climatic Change. 23: 37-53.

Powell, D.S.; Faulkner, J.L.; Darr, D.R.; Zhu, Z.; MacCleery, D.W. 1993. Forest resources of the United States. Gen. Tech. Rep. RM-234. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Forest and Range Experiment Station. 132 p.

Rastetter, E.B.; King, A.W.; Cosby, B.J.; Hornberger, G.M.; O’Neill, R.V.; Hobbie, J.E. 1992. Aggregatingfine-scale ecological knowledge to model coarserscale attributes of ecosystems. Ecological Applications. 2: 55-70.

Schreuder, H.T.; Gregoire, T.G.; Wood, G.B. 1993. S ampling methods for multiresource forest inventory. New York: John Wiley & Sons. 446 p.35 Schroeder, P.; Brown, S.; Mo, J.; Birdsey, R.; Cieszewski, C. 1997. Biomass estimation for temperate broadleaf forests of the United States using inventory data. Forest Science. 43: 424-434.

Skog, K.E.; Nicholson, G.A. 1998. Carbon cycling through wood products: the role of wood and paper products in carbon sequestration. Forest Products Journal. 48: 75-83.

Smith, J.E.; Heath, L.S. 2000. Considerations for interpreting probabilistic estimates of uncertainty of forest carbon. In: Joyce, L.; Birdsey, R., eds. The impact of climate change on America’s forests. Gen. Tech. Rep. RMRS-GTR-59. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station: 102-111.

Smith, J.E.; Heath, L.S. 2001. Identifying influences on model uncertainty: an application using a forest carbon budget model. Environmental Management. 27: 253-267.

Smith, W.B.; Vissage, J.S.; Darr, D.R.; Sheffield, R.M. 2001. Forest resources of the United States, 1997. Gen. Tech. Rep. NC-219. St. Paul, MN: U.S. Department of Agriculture, Forest Service, North Central Research Station. 190 p. Turner, D.P.; Koerper, G.J.; Harmon, M.E.; Lee, J.J. 1995. A carbon budget for forests of the conterminous United States. Ecological Applications. 5: 421-436.

U.S. State Department. 2000. U.S. submission to UNFCCC on land use, land-use change, and forestry. <http://www.state.gov/www/global/ global_issues/climate/climate_2000_submiss.html> (5 April 2002).

Waddell, K.L.; Oswald, D.D.; Powell, D.S. 1989. Forest statistics of the United States, 1987. Resour. Bull. PNW-RB-168. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station. 106 p.

Watson, R.T.; Noble, I.R.; Bolin, B.; Ravindranath, N.H.; Verardo, D.J.; Dokken, D.J., eds. 2000. Land use, land-use change, and forestry. Special report of the Intergovernmental Panel for Climate Change. Cambridge, UK: Cambridge University Press. 375 p.

Woudenberg, S.W.; Farrenkopf, T.O. 1995. The Westwide forest inventory data base: user’s manual. Gen. Tech. Rep. INT-317. Ogden, UT: U.S. Department of Agriculture, Forest Service, Intermountain Research Station. 30 p.36 

## Index to Figures
Figure 5. .................................................................. 42

Estimated mass density of live and standing dead treesin NE Aspen-Birch forests (individual points are plotlevel density summaries).
Figure 6. ................................................................... 42

Estimated mass density of live and standing dead trees in NE MBB/Other HW forests on privately owned land.
Figure 7. ................................................................... 42

Estimated mass density of live and standing dead trees in NE Oak-Hickory forests.
Figure 8. ................................................................... 43

Estimated mass density of live and standing dead trees in NE Oak-Pine forests.
Figure 9. ................................................................... 43

Estimated mass density of live and standing dead trees in NE Other Pine forests.
Figure 10. ................................................................. 43

Estimated mass density of live and standing dead trees in NE Spruce-Fir forests.
Figure 11. ................................................................. 44

Estimated mass density of live and standing dead trees in NE WRJ-Pine forests.
Figure 12. ................................................................. 44

Estimated mass density of live and standing dead trees in NLS Aspen-Birch forests.
Figure 13. ................................................................. 44

Estimated mass density of live and standing dead trees in NLS Lowland HW forests on privately owned land.
Figure 14. ................................................................. 45

Estimated mass density of live and standing dead trees in NLS MBB forests.
Figure 15. ................................................................. 45

Estimated mass density of live and standing dead trees in NLS Oak-Hickory forests.
Figure 16. ................................................................. 45

Estimated mass density of live and standing dead trees in NLS Pine forests.
Figure 17. ................................................................. 46

Estimated mass density of live and standing dead trees in NLS Spruce-Fir forests.
Figure 18. ................................................................. 46

Estimated mass density of live and standing dead trees in NPS Conifer forests. Figure 19. ................................................................. 46 Estimated mass density of live and standing dead trees in NPS Lowland HW forests.
Figure 20. ................................................................. 47

Estimated mass density of live and standing dead trees in NPS MBB forests.
Figure 21. ................................................................. 47

Estimated mass density of live and standing dead trees in NPS Oak-Hickory forests.
Figure 22. ................................................................. 47

Estimated mass density of live and standing dead trees in NPS Oak-Pine forests.
Figure 23. ................................................................. 48

Estimated mass density of live and standing dead trees in SC Bottomland HW forests on privately owned land.
Figure 24. ................................................................. 48

Estimated mass density of live and standing dead trees in SC Natural Pine forests on privately owned land.
Figure 25. ................................................................. 48

Estimated mass density of live and standing dead trees in SC Oak-Pine forests.
Figure 26. ................................................................. 49

Estimated mass density of live and standing dead trees in SC Other Conifer forests.
Figure 27. ................................................................. 49

Estimated mass density of live and standing dead trees in SC Planted Pine forests.
Figure 28. ................................................................. 49

Estimated mass density of live and standing dead trees in SC Upland HW forests on privately owned land.
Figure 29. ................................................................. 50

Estimated mass density of live and standing dead trees in SE Bottomland HW forests on privately owned land.
Figure 30. ................................................................. 50

Estimated mass density of live and standing dead trees in SE Natural Pine forests on privately owned land.
Figure 31. ................................................................. 50

Estimated mass density of live and standing dead trees in SE Oak-Pine forests.
Figure 32. ................................................................. 51

Estimated mass density of live and standing dead trees in SE Other Conifer forests.
Figure 33. ................................................................. 51

Estimated mass density of live and standing dead trees in SE Planted Pine forests. 

## Apendix37 Figure 34. ................................................................. 51

Estimated mass density of live and standing dead trees in SE Upland HW forests on privately owned land.
Figure 35. ................................................................. 52

Estimated mass density of live and standing dead trees in PSW Douglas-fir forests (estimates of standing dead tree mass for PSW were based on Pacific Northwest data, individual plot-level summaries are not available).
Figure 36. ................................................................. 52

Estimated mass density of live and standing dead trees in PSW Fir-Spruce forests.
Figure 37. ................................................................. 52

Estimated mass density of live and standing dead trees in PSW Hardwoods forests.
Figure 38. ................................................................. 53

Estimated mass density of live and standing dead trees in PSW Other Conifer forests.
Figure 39. ................................................................. 53

Estimated mass density of live and standing dead trees in PSW Redwood forests.
Figure 40. ................................................................. 53

Estimated mass density of live and standing dead trees in PWE Douglas-fir forests on publicly owned land.
Figure 41. ................................................................. 54

Estimated mass density of live and standing dead trees in PWE Fir-Spruce forests on publicly owned land.
Figure 42. ................................................................. 54

Estimated mass density of live and standing dead trees in PWE Hardwoods forests.
Figure 43. ................................................................. 54

Estimated mass density of live and standing dead trees in PWE Lodgepole Pine forests on publicly owned land.
Figure 44. ................................................................. 55

Estimated mass density of live and standing dead trees in PWE Ponderosa Pine forests on publicly owned land.
Figure 45. ................................................................. 55

Estimated mass density of live and standing dead trees in PWW Douglas-fir forests on publicly owned land.
Figure 46. ................................................................. 55

Estimated mass density of live and standing dead trees in PWW Fir-Spruce forests on publicly owned land.
Figure 47. ................................................................. 56

Estimated mass density of live and standing dead trees in PWW Other Conifer forests. Figure 48. ................................................................. 56 Estimated mass density of live and standing dead trees in PWW Other Hardwoods forests.
Figure 49. ................................................................. 56

Estimated mass density of live and standing dead trees in PWW Red Alder forests.
Figure 50. ................................................................. 57

Estimated mass density of live and standing dead trees in PWW Western Hemlock forests.
Figure 51. ................................................................. 57

Estimated mass density of live and standing dead trees in RMN Douglas-fir forests.
Figure 52. ................................................................. 57

Estimated mass density of live and standing dead trees in RMN Fir-Spruce forests.
Figure 53. ................................................................. 58

Estimated mass density of live and standing dead trees in RMN Hardwoods forests.
Figure 54. ................................................................. 58

Estimated mass density of live and standing dead trees in RMN Lodgepole Pine forests.
Figure 55. ................................................................. 58

Estimated mass density of live and standing dead trees in RMN Other Conifer forests.
Figure 56. ................................................................. 59

Estimated mass density of live and standing dead trees in RMN Ponderosa Pine forests.
Figure 57. ................................................................. 59

Estimated mass density of live and standing dead trees in RMS Douglas-fir forests.
Figure 58. ................................................................. 59

Estimated mass density of live and standing dead trees in RMS Fir-Spruce forests.
Figure 59. ................................................................. 60

Estimated mass density of live and standing dead trees in RMS Hardwoods forests.
Figure 60. ................................................................. 60

Estimated mass density of live and standing dead trees in RMS Lodgepole Pine forests.
Figure 61. ................................................................. 60

Estimated mass density of live and standing dead trees in RMS Other Conifer forests.
Figure 62. ................................................................. 61

Estimated mass density of live and standing dead trees in RMS Ponderosa Pine forests on publicly owned land.38 0 100 200 300LiveTreeMass(Mg/ha)

0 100 200 300

Growing Stock Volume (m 3 /ha) 0 100 200 300Standing Dead TreeMass (Mg/ha)

0 20 40
Figure 5.—Estimated mass density of live and standing dead trees inNE Aspen-Birch forests (individual points are plot-level density summaries).

0 100 200 300 400Live Tree Mass(Mg/ha)

0 200 400

Growing Stock Volume (m 3 /ha) 0 100 200 300 400Standing Dead TreeMass (Mg/ha)

0 50 100 150

Figure 6.—Estimated mass density of live and standing dead trees in NE MBB/Other HW forests on privately owned land.

0 100 200 300 400Live Tree Mass(Mg/ha)

0 100 200 300 400

Growing Stock Volume (m 3 /ha) 0 100 200 300 400Standing Dead TreeMass (Mg/ha)

0 50 100 150

Figure 7.—Estimated mass density of live and standing dead trees in NE Oak-Hickory forests.39 0 100 200 300Live Tree Mass(Mg/ha)

0 100 200 300 400

Growing Stock Volume (m 3 /ha) 0 100 200 300Standing Dead TreeMass (Mg/ha)

0 25 50 75

Figure 8.—Estimated mass density of live and standing dead trees in NE Oak-Pine forests.

0 100 200 300 400Live Tree Mass(Mg/ha)

0 100 200 300 400

Growing Stock Volume (m 3 /ha) 0 100 200 300 400Standing Dead TreeMass (Mg/ha)

0 25 50 75

Figure 9.—Estimated mass density of live and standing dead trees in NE Other Pine forests.

0 100 200 300Live Tree Mass(Mg/ha)

0 100 200 300

Growing Stock Volume (m 3 /ha) 0 100 200 300Standing Dead TreeMass (Mg/ha)

0 20 40

Figure 10.—Estimated mass density of live and standing dead trees in NE Spruce-Fir forests.40 0 100 200 300 400Live Tree Mass(Mg/ha)

0 100 200 300 400

Growing Stock Volume (m 3 /ha) 0 100 200 300 400Standing dead treemass (Mg/ha)

0 25 50 75

Figure 11.—Estimated mass density of live and standing dead trees in NE WRJ-Pine forests.

0 100 200 300Live Tree Mass(Mg/ha)

0 100 200 300 400

Growing Stock Volume (m 3 /ha) 0 100 200 300Standing Dead TreeMass (Mg/ha)

0 50 100 150

Figure 12.—Estimated mass density of live and standing dead trees in NLS Aspen-Birch forests.

0 100 200 300Live Tree Mass(Mg/ha)

0 100 200 300 400

Growing Stock Volume (m 3 /ha) 0 100 200 300Standing Dead TreeMass (Mg/ha)

0 50 100 150

Figure 13.—Estimated mass density of live and standing dead trees in NLS Lowland HW forests on privately owned land.41 0 100 200 300 400Live Tree Mass(Mg/ha)

0 100 200 300 400

Growing Stock Volume (m 3 /ha) 0 100 200 300 400Standing Dead TreeMass (Mg/ha)

0 50 100 150

Figure 14.—Estimated mass density of live and standing dead trees in NLS MBB forests.

0 100 200 300Live Tree Mass(Mg/ha)

0 100 200 300 400

Growing Stock Volume (m 3 /ha) 0 100 200 300Standing Dead TreeMass (Mg/ha)

0 50 100 150

Figure 15.—Estimated mass density of live and standing dead trees in NLS Oak-Hickory forests.

0 100 200 300 400Live Tree Mass(Mg/ha)

0 100 200 300 400

Growing Stock Volume (m 3 /ha) 0 100 200 300 400Standing Dead TreeMass (Mg/ha)

0 25 50 75 100

Figure 16.—Estimated mass density of live and standing dead trees in NLS Pine forests.42 0 100 200 300Live Tree Mass(Mg/ha)

0 100 200 300 400

Growing Stock Volume (m 3 /ha) 0 100 200 300Standing Dead TreeMass (Mg/ha)

0 25 50 75 100

Figure 17.—Estimated mass density of live and standing dead trees in NLS Spruce-Fir forests.

0 100 200 300Live Tree Mass(Mg/ha)

0 100 200 300

Growing Stock Volume (m 3 /ha) 0 100 200 300Standing Dead TreeMass (Mg/ha)

0 20 40

Figure 18.—Estimated mass density of live and standing dead trees in NPS Conifer forests.

0 100 200 300 400Live Tree Mass(Mg/ha)

0 200 400

Growing Stock Volume (m 3 /ha) 0 100 200 300 400Standing Dead TreeMass (Mg/ha)

0 50 100 150

Figure 19.—Estimated mass density of live and standing dead trees in NPS Lowland HW forests.43 0 100 200 300 400Live Tree Mass(Mg/ha)

0 100 200 300 400

Growing Stock Volume (m 3 /ha) 0 100 200 300 400Standing Dead TreeMass (Mg/ha)

0 50 100 150

Figure 20.—Estimated mass density of live and standing dead trees in NPS MBB forests.

0 100 200 300Live Tree Mass(Mg/ha)

0 100 200 300 400

Growing Stock Volume (m 3 /ha) 0 100 200 300Standing Dead TreeMass (Mg/ha)

0 50 100 150

Figure 21.—Estimated mass density of live and standing dead trees in NPS Oak-Hickory forests.

0 100 200 300Live Tree Mass(Mg/ha)

0 100 200 300

Growing Stock Volume (m 3 /ha) 0 100 200 300Standing Dead TreeMass (Mg/ha)

0 20 40

Figure 22.—Estimated mass density of live and standing dead trees in NPS Oak-Pine forests.44 0 100 200 300 400Live Tree Mass(Mg/ha)

0 200 400

Growing Stock Volume (m 3 /ha) 0 100 200 300 400Standing Dead TreeMass (Mg/ha)

0 50 100 150

Figure 23.—Estimated mass density of live and standing dead trees in SC Bottomland HW forests on privately owned land.

0 100 200 300 400 500LiveTreeMass(Mg/ha)

0 100 200 300 400

Growing Stock Volume (m 3 /ha) 0 100 200 300 400 500Standing Dead TreeMass (Mg/ha)

0 25 50 75

Figure 24.—Estimated mass density of live and standing dead trees in SC Natural Pine forests on privately owned land.

0 100 200 300 400Live Tree Mass(Mg/ha)

0 100 200 300 400

Growing Stock Volume (m 3 /ha) 0 100 200 300 400Standing Dead TreeMass (Mg/ha)

0 25 50 75 100

Figure 25.—Estimated mass density of live and standing dead trees in SC Oak-Pine forests.45 0 100 200 300LiveTreeMass(Mg/ha)

0 100 200 300

Growing Stock Volume (m 3 /ha) 0 100 200 300Standing Dead TreeMass (Mg/ha)

0 20 40

Figure 26.—Estimated mass density of live and standing dead trees in SC Other Conifer forests.

0 100 200 300 400LiveTreeMass(Mg/ha)

0 100 200 300

Growing Stock Volume (m 3 /ha) 0 100 200 300 400Standing Dead TreeMass (Mg/ha)

0 25 50 75

Figure 27.—Estimated mass density of live and standing dead trees in SC Planted Pine forests.

0 100 200 300Live Tree Mass(Mg/ha)

0 100 200 300 400

Growing Stock Volume (m 3 /ha) 0 100 200 300Standing Dead TreeMass (Mg/ha)

0 25 50 75 100

Figure 28.—Estimated mass density of live and standing dead trees in SC Upland HW forests on privately owned land.46 0 200 400 600Live Tree Mass(Mg/ha)

0 200 400 600 800

Growing Stock Volume (m 3 /ha) 0 200 400 600Standing Dead TreeMass (Mg/ha)

0 50 100 150

Figure 29.—Estimated mass density of live and standing dead trees in SE Bottomland HW forests on privately owned land.

0 200 400 600Live Tree Mass(Mg/ha)

0 100 200 300 400

Growing Stock Volume (m 3 /ha) 0 200 400 600Standing Dead TreeMass (Mg/ha)

0 25 50 75 100

Figure 30.—Estimated mass density of live and standing dead trees in SE Natural Pine forests on privately owned land.

0 100 200 300 400 500Live Tree Mass(Mg/ha)

0 100 200 300 400

Growing Stock Volume (m 3 /ha) 0 100 200 300 400 500Standing Dead TreeMass (Mg/ha)

0 25 50 75 100

Figure 31.—Estimated mass density of live and standing dead trees in SE Oak-Pine forests.47 0 100 200 300 400LiveTreeMass(Mg/ha)

0 100 200 300 400

Growing Stock Volume (m 3 /ha) 0 100 200 300 400Standing Dead TreeMass (Mg/ha)

0 20 40

Figure 32.—Estimated mass density of live and standing dead trees in SE Other Conifer forests.

0 100 200 300 400Live Tree Mass(Mg/ha)

0 100 200 300

Growing Stock Volume (m 3 /ha) 0 100 200 300 400Standing Dead TreeMass (Mg/ha)

0 25 50 75 100

Figure 33.—Estimated mass density of live and standing dead trees in SE Planted Pine forests.

0 100 200 300 400 500Live Tree Mass(Mg/ha)

0 200 400

Growing Stock Volume (m 3 /ha) 0 100 200 300 400 500Standing Dead TreeMass (Mg/ha)

0 25 50 75 100

Figure 34.—Estimated mass density of live and standing dead trees in SE Upland HW forests on privately owned land.48 0 300 600 900 1200Live Tree Mass(Mg/ha)

0 250 500 750 1000

Growing Stock Volume (m 3 /ha) 0 300 600 900 1200Standing Dead TreeMass (Mg/ha)

0 25 50 75 100 Figure 35.—Estimated mass density of live and standing dead trees in PSW Douglas-fir forests (estimates of standing dead tree mass for PSW were based on Pacific Northwest data, individual plot-level summaries are not available).

0 200 400 600Live Tree Mass(Mg/ha)

0 200 400 600

Growing Stock Volume (m 3 /ha) 0 200 400 600Standing Dead TreeMass (Mg/ha)

0 25 50 75 100

Figure 36.—Estimated mass density of live and standing dead trees in PSW Fir-Spruce forests.

0 100 200 300 400 500LiveTreeMass(Mg/ha)

0 200 400 600

Growing Stock Volume (m 3 /ha) 0 100 200 300 400 500Standing Dead TreeMass (Mg/ha)

0 25 50

Figure 37.—Estimated mass density of live and standing dead trees in PSW Hardwoods forests.49 0 100 200 300 400 500LiveTreeMass(Mg/ha)

0 200 400 600

Growing Stock Volume (m 3 /ha) 0 100 200 300 400 500Standing Dead TreeMass (Mg/ha)

0 25 50

Figure 38.—Estimated mass density of live and standing dead trees in PSW Other Conifer forests.

0 300 600 900 1200Live Tree Mass(Mg/ha)

0 250 500 750 1000

Growing Stock Volume (m 3 /ha) 0 300 600 900 1200Standing Dead TreeMass (Mg/ha)

0 50 100 150

Figure 39.—Estimated mass density of live and standing dead trees in PSW Redwood forests.

0 200 400 600 800 1000Live Tree Mass(Mg/ha)

0 250 500 750 1000

Growing Stock Volume (m 3 /ha) 0 200 400 600 800 1000Standing Dead TreeMass (Mg/ha)

0 50 100 150 200

Figure 40.—Estimated mass density of live and standing dead trees in PWE Douglas-fir forests on publicly owned land.50 0 200 400 600 800 1000Live Tree Mass(Mg/ha)

0 200 400 600 800

Growing Stock Volume (m 3 /ha) 0 200 400 600 800 1000Standing Dead TreeMass (Mg/ha)

0 100 200

Figure 41.—Estimated mass density of live and standing dead trees in PWE Fir-Spruce forests on publicly owned land.

0 100 200 300Live Tree Mass(Mg/ha)

0 250 500

Growing Stock Volume (m 3 /ha) 0 100 200 300Standing Dead TreeMass (Mg/ha)

0 25 50 75 100

Figure 42.—Estimated mass density of live and standing dead trees in PWE Hardwoods forests.

0 100 200 300 400 500Live Tree Mass(Mg/ha)

0 100 200 300 400

Growing Stock Volume (m 3 /ha) 0 100 200 300 400 500Standing Dead TreeMass (Mg/ha)

0 100 200

Figure 43.—Estimated mass density of live and standing dead trees in PWE Lodgepole Pine forests on publicly owned land.51 0 200 400 600Live Tree Mass(Mg/ha)

0 200 400

Growing Stock Volume (m 3 /ha) 0 200 400 600Standing Dead TreeMass (Mg/ha)

0 100 200

Figure 44.—Estimated mass density of live and standing dead trees in PWE Ponderosa Pine forests on publicly owned land.

0 500 1000 1500 2000Live Tree Mass(Mg/ha)

0 400 800 1200

Growing Stock Volume (m 3 /ha) 0 500 1000 1500 2000Standing Dead TreeMass (Mg/ha)

0 100 200 300

Figure 45.—Estimated mass density of live and standing dead trees in PWW Douglas-fir forests on publicly owned land.

0 500 1000 1500 2000Live Tree Mass(Mg/ha)

0 300 600 900 1200

Growing Stock Volume (m 3 /ha) 0 500 1000 1500 2000Standing Dead TreeMass (Mg/ha)

0 100 200 300

Figure 46.—Estimated mass density of live and standing dead trees in PWW Fir-Spruce forests on publicly owned land.52 0 200 400 600 800Live Tree Mass(Mg/ha)

0 200 400 600

Growing Stock Volume (m 3 /ha) 0 200 400 600 800Standing Dead TreeMass (Mg/ha)

0 50 100 150

Figure 47.—Estimated mass density of live and standing dead trees in PWW Other Conifer forests.

0 200 400 600 800 1000Live Tree Mass(Mg/ha)

0 250 500 750 1000

Growing Stock Volume (m 3 /ha) 0 200 400 600 800 1000Standing Dead TreeMass (Mg/ha)

0 50 100 150 200

Figure 48.—Estimated mass density of live and standing dead trees in PWW Other Hardwoods forests.

0 200 400 600 800Live Tree Mass(Mg/ha)

0 200 400 600

Growing Stock Volume (m 3 /ha) 0 200 400 600 800Standing Dead TreeMass (Mg/ha)

0 50 100 150 200

Figure 49.—Estimated mass density of live and standing dead trees in PWW Red Alder forests.53 0 500 1000 1500 2000Live Tree Mass(Mg/ha)

0 300 600 900 1200

Growing Stock Volume (m 3 /ha) 0 500 1000 1500 2000Standing Dead TreeMass (Mg/ha)

0 100 200 300

Figure 50.—Estimated mass density of live and standing dead trees in PWW Western Hemlock forests.

0 200 400 600 800Live Tree Mass(Mg/ha)

0 200 400 600

Growing Stock Volume (m 3 /ha) 0 200 400 600 800Standing Dead TreeMass (Mg/ha)

0 100 200

Figure 51.—Estimated mass density of live and standing dead trees in RMN Douglas-fir forests.

0 200 400 600 800Live Tree Mass(Mg/ha)

0 200 400 600

Growin g Stock Volume ( m 3 /ha ) 0 200 400 600 800Standing Dead TreeMass (Mg/ha)

0 100 200

Figure 52.—Estimated mass density of live and standing dead trees in RMN Fir-Spruce forests.54 0 100 200 300Live Tree Mass(Mg/ha)

0 100 200 300

Growing Stock Volume (m 3 /ha) 0 100 200 300Standing Dead TreeMass (Mg/ha)

0 25 50 75 100

Figure 53.—Estimated mass density of live and standing dead trees in RMN Hardwoods forests.

0 100 200 300 400 500 600Live Tree Mass(Mg/ha)

0 100 200 300 400

Growing Stock Volume (m 3 /ha) 0 100 200 300 400 500 600Standing Dead TreeMass (Mg/ha)

0 50 100 150

Figure 54.—Estimated mass density of live and standing dead trees in RMN Lodgepole Pine forests.

0 100 200 300 400Live Tree Mass(Mg/ha)

0 100 200 300 400

Growing Stock Volume (m 3 /ha) 0 100 200 300 400Standing Dead TreeMass (Mg/ha)

0 50 100 150

Figure 55.—Estimated mass density of live and standing dead trees in RMN Other Conifer forests.55 0 100 200 300 400 500Live Tree Mass(Mg/ha)

0 100 200 300 400

Growing Stock Volume (m 3 /ha) 0 100 200 300 400 500Standing Dead TreeMass (Mg/ha)

0 25 50 75 100

Figure 56.—Estimated mass density of live and standing dead trees in RMN Ponderosa Pine forests.

0 100 200 300 400 500 600Live Tree Mass(Mg/ha)

0 200 400 600

Growing Stock Volume (m 3 /ha) 0 100 200 300 400 500 600Standing Dead TreeMass (Mg/ha)

0 50 100 150 200

Figure 57.—Estimated mass density of live and standing dead trees in RMS Douglas-fir forests.

0 100 200 300 400 500 600Live Tree Mass(Mg/ha)

0 200 400

Growing Stock Volume (m 3 /ha) 0 100 200 300 400 500 600Standing Dead TreeMass (Mg/ha)

0 100 200 300

Figure 58.—Estimated mass density of live and standing dead trees in RMS Fir-Spruce forests.56 0 100 200 300 400 500Live Tree Mass(Mg/ha)

0 100 200 300 400

Growing Stock Volume (m 3 /ha) 0 100 200 300 400 500Standing Dead TreeMass (Mg/ha)

0 25 50 75 100

Figure 59.—Estimated mass density of live and standing dead trees in RMS Hardwoods forests.

0 100 200 300 400 500Live Tree Mass(Mg/ha)

0 100 200 300 400

Growing Stock Volume (m 3 /ha) 0 100 200 300 400 500Standing Dead TreeMass (Mg/ha)

0 50 100 150 200

Figure 60.—Estimated mass density of live and standing dead trees in RMS Lodgepole Pine forests.

0 100 200 300 400 500Live Tree Mass(Mg/ha)

0 100 200 300 400

Growing Stock Volume (m 3 /ha) 0 100 200 300 400 500Standing Dead TreeMass (Mg/ha)

0 50 100 150

Figure 61.—Estimated mass density of live and standing dead trees in RMS Other Conifer forests.57 0 100 200 300 400Live Tree Mass(Mg/ha)

0 100 200 300 400

Growing Stock Volume (m 3 /ha) 0 100 200 300 400Standing Dead TreeMass (Mg/ha)

0 25 50 75 100

Figure 62.—Estimated mass density of live and standing dead trees in RMS Ponderosa Pine forests on publicly owned land.Printed on Recycled Paper Smith, James E.; Heath, Linda S.; Jenkins, Jennifer C. 2003. Forest volume-tobiomass models and estimates of mass for live and standing dead trees of U.S. forests. Gen. Tech. Rep. NE-298. Newtown Square, PA: U.S. Department of Agriculture, Forest Service, Northeastern Research Station. 57 p.

Includes methods and equations for nationally consistent estimates of tree-mass density at the stand level (Mg/ha) as predicted by growing-stock volumes reported by the USDA Forest Service for forests of the conterminous United States. Developed for use in FORCARB, a carbon budget model for U.S. forests, the equations also are useful for converting plot-, stand- and regional-level forest merchantable volumes to estimates of total mass. Also includes separate equations for live, standing dead, aboveground only and full trees (including coarse roots), and for hardwood and softwood species. Example estimates are provided for regional tree-mass totals using summary forest statistics for the United States.

Keywords : biomass, carbon, carbon sequestration, forest, live and standing dead treesHeadquarters of the Northeastern Research Station is in Newtown Square, Pennsylvania. Field laboratories are maintained at:

Amherst, Massachusetts, in cooperation with the University of Massachusetts

Burlington, Vermont, in cooperation with the University of Vermont

Delaware, Ohio

Durham, New Hampshire, in cooperation with the University of New Hampshire

Hamden, Connecticut, in cooperation with Yale University

Morgantown, West Virginia, in cooperation with West Virginia University

Parsons, West Virginia

Princeton, West Virginia

Syracuse, New York, in cooperation with the State University of New York, College of Environmental Sciences and Forestry at Syracuse University

Warren, Pennsylvania

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