Background 

Since 1928, the USDA Forest Service has 

measured the Nation's forest resources, supplying 

information about their nature and condition. In the 

past, forest inventories focused on volume 

estimation: cubic feet for growing stock and board 

feet for sawtimber (these and other terms are 

defined in Appendix F). Volume statistics have 

proven useful to resource planners and managers 

for quantifying the timber supply and for tracking 

changes in the timber resource over time. But 

conventional methods for measuring timber volume 

do not adequately define conditions of the entire 

forest and, therefore, have limited applicability to the 

kinds of assessments required of today's resource 

planners, managers, and researchers. 

We now recognize that our forests are not only a 

source of wood for traditional timber products but 

also of wood fiber for new industrial products and 

nonindustrial products such as fuelwood. In 

addition, the nation's forest resource plays an 

important part with respect to wildlife habitat, plant 

diversity, nutrient recycling, and biochemical 

interchanges so essential to our survival that are 

affected by climatic change and manmade pollution. 

Over the years, the complete-tree concept has 

become more accepted even though methods for 

measuring the content of the whole tree have not 

always kept pace. Many methods for measuring 

biomass have been explored during the past couple 

of decades, but there is little consistency among 

them. The relationships that have been developed 

between common tree measurements and the 

weight of individual tree components, determined 

largely by regression equations, include many datagathering and analytical techniques. The resulting 

concern over their applicability is compounded by 

the fact that many of the regression equations now 

being used are designed only for common species. 

The question then arises: which equations should be 

used for species for which a specific regression 

equation is not available? 

Since It appears likely that research on 

environmental interactions, sometimes on a global 

scale, will not only continue but increase in 

complexity, and because social and economic 

factors and their interaction have increased pressure 

on our forest resources, it is necessary to evaluate 

the accumulated procedures for estimating biomass 

so that "complete-forest" assessments of the 

resource can be made. Keays (l97la, b, c, d, e) 

published the earliest synthesis of literature available 

on biomass estimation. Additional reviews of the 

North American literature were made by Young 

(1 976) and Hitchcock and McDonnell (1 979). Pelz 

(1 987) reviewed European literature, and there have 

been regional reviews for the southern (Baldwin 

1987) and northeastern (Tritton and Hornbeck 1982) 

United States. We are interested in these syntheses 

of the literature as they relate to the analysis and 

application of biomass regression techniques, 

particularly in the Northeastern United States. 

An evaluation of available biomass-estimation 

techniques and their potential use as a base for 

regional biomass analyses of important tree species 

seems appropriate. The first factor to be considered 

is which unit of measure should be used-green 

weight or dry weight? 

Biomass Units of Measure 

Many factors contribute to variation of weight in 

trees; certainly moisture content is a critical one. In 

the past, green weight was commonly used by the 

USDA Forest Service to measure biomass supplies, 

at least in the Northeast, simply because green 

weight most closely approximates the condition of 

standing trees. And because it reflects the 

economic cost of removing wood from the forest, 

this measure is preferred by the timber-using 

industry. To the industry, however, 

merchantable-stem biomass may be the only greenweight statistic of value. Comparisons to dry weight 

can be made if needed, and yields of industrial 

forest products harvested and resulting residual 

weights can be developed from them. 

By contrast, dry weight is more consistent over time, 

avoiding seasonal variations that occur when trees 

are measured in terms of green weight. The 

dry-weight measure also provides a more common 

base, yielding an easier solution to environmental 

questions such as those related to the carbon 

content of forests. Scientists prefer dry weight when 

accessing different biomass components because it 

allows analysis between trees and other vegetation. 
Merchantable-stem biomass is best reported in both 

green weight and dry weight. Most of the other 

components of forest biomass-branches, foliage, 

stumps and roots, cull trees, seedlings and saplings, 

shrubs, etc.-are best reported in dry weight since 

biomass studies today tend to be directed more to 

vegetation analysis and biodiversity studies, that 

generally require biomass to be reported in dry 

weight. 

Procedures for Estimating Green Weight 

There are many regression equations that predict 

the green weight of trees and their components. We 

are interested in how well these equations predict 

the green weight of trees in areas for which 

equations have not been developed. Tritton and 

Hornbeck (1982) indicated that while most biomass 

equations for northeastern tree species Rive been 

developed locally and their use has been relatively 

limited, some may be useful for predicting weights of 

trees over extensive geographic regions. But 

Hocker and Early (1983) argued against using 

equations to predict weights of trees over large 

regions. A study by Jacobs and Monteith (1981) 

has shown that the weights of identical northeastern 

tree species are similar in different regions of the 

Northeast. And results from a more indepth study 

(Barrett and Jastrembski 1990) based on an 

analysis of indicator variables showed that 

differences in tree green weight between locations 

may not be important enough to preclude using 

regional models on a wide geographic scale. 

Sufficient research has been conducted to assure us 

of the validity of using regional biomass equations. 

Regional Regression Equations 

We used regional regression equations developed 

by Barrett and Jastremski (1990) from sources in 

New Hampshire, New York, Ohio, West Virginia, 

and Michigan. 

The form of the regional regression equation is: 

where the dependent variable Y is total tree-stem 

biomass (in green pounds), and the independent 

variables D and H are, respectively, diameter at 

breast height (in inches) and total tree height (in 

feet). Regression coefficients for 14 species were 

developed (Table 1). Species with no specific 

regression coefficients were grouped with those that 

did based on similar green-weight densities 

(Appendix A). Species for which there is a 

regression equation are called "key species" in this 

report. 

The Forest Inventory and Analysis research unit of 

the USDA Forest Service equates the merchantable 

stem to growing stock, that is, the tree bole between 

a 1 .O-foot stump height and a specified top. The 

growing-stock top is at a minimum 4.0-inch diameter 

outside the bark on the central stem, or the point at 

which the central stem breaks into branches if this 

occurs before the minimum diameter is reached. 

Table 1. Total stem-weight regression 

coefficients for selected species 

Y = green weight (in pounds) 

D = diameter at breast height (in inches) 

H = total tree height (in feet) 

Coefficients 

Species 

b 0 "1 

Hemlock 

Spruce 

Pine 

Fir 

Oak 

Maple 

Hickory 

Basswood 

Aspen 

Cherry 

Birch 

Yellow-poplar 

Ash 

Beech 
Since the regional model predicts total-stem 

biomass, adjustments had to be made to quantify 

merchantable-stem biomass (growing-stock 

biomass). First, conversions were applied that 

relate total-stem biomass (the parameter predicted 

by the regional model) to aboveground tree biomass 

on a green-weight basis from regression equations 

developed by Monteith (1 979). Then, conversions 

from the same study were applied that relate 

aboveground tree biomass to merchantable-stem 

biomass (the parameter of interest). The 

relationship of total-stem and merchantable-stem to 

aboveground tree biomass were averaged by 

evergreen and deciduous species, and by standard 

Forest Service stand-size classes (Table 2). 

In the regional model, total tree height is used as an 

independent variable to predict biomass. This is a 

limitation because trees often are measured only to 

a certain merchantable height, as is done by Forest 

Inventory and Analysis. The regional equations 

described are unusable unless total tree height can 

be measured or predicted. 

Total Tree Height 

Measuring the total height of sample trees has been 

considered frequently in statewide inventory 

designs. Given the high cost of obtaining tree 

information as currently,collected, we have found the 

cost of measuring total tree height to be prohibitive. 

Yet the value of total tree height cannot be 

overstated, especially considering its use as a 

parameter in many biomass regression equations. 

Most equations that predict total tree biomass 

incorporate total tree height. And in some 

instances, it can prove to be a significant predictor of 

biomass. Because of great differences in height 

over the range of some species, the measure of 

total tree height often is seen as essential. 

Cunia and others (1984) developed a procedure for 

predicting total tree height from diameter and 

merchantable-height measurements on forest 

inventory field plots. Developed to simulate 

populations of trees in order to test sample designs, 

this procedure can be applied to actual tree 

dimensions. 

The form of the model is: 

y = bo + bl (D) + b2(H) 

where the dependent variable Y is total tree height 

(in feet); the independent variables D and H are, 

respectively, diameter at breast height (in inches) 

and merchantable height (in feet). Consequently, 

we did not have to measure total tree height but 

could estimate it from measurements we already 

had. 

Regression coefficients for 17 species were used 

(Table 3). Species with no specific regression 

coefficients were grouped with those that did based 

on average height and bole and crown 

characteristics that most closely approximated a 

species for which there was an equation 

(Appendix D). 

Generally, evergreen species of similar heights were 

grouped together. These groupings occurred mostly 

within the same genus, e.g., red pine was used for 

pines. Deciduous species were more difficult to 

group because of their many different bole and 

crown characteristics. 

Table 2. Total-stem biomass and merchantable-stem biomass as a percentage of 

aboveground tree biomass, green-weight basis 

Total stem Merchantable stem 

Stand-size class 

Evergreen Deciduous Evergreen Deciduous - -- -- - -- 

Poletimber 0.761 7 0.801 0 0.6503 0.7269 

Small sawtimber 0.8032 0.7990 0.7695 0.7737 

Large sawtimber < 21 .Ow d.b.h. 18.81 94 0.7979 0.8053 0.7852 

Large sawtimber 2 21 .Om d.b.h. 0.8235 0.7975 0.81 36 0.7884 
Table 3. Total tree-height regression 

coefficients for selected species 

Y = total tree height (in feet) 

D = diameter at breast height (in inches) 

H = merchantable height (in feet) 

- 

Coefficients 

Species 

0 1 "2 

Red maple 

Sugar maple 

Yellow birch 

Beech 

White ash 

Black cherry 

Basswood 

Yellow-poplar 

White oak 

Scarlet oak 

Chestnut oak 

Northern red oak 

Black oak 

Trembling aspen 

Balsam fir 

Red pine 

White spruce 

Among deciduous trees with well-formed boles and 

open, irregular crowns, white ash, sugar maple, and 

yellow birch were designated as key species. 

Yellow birch was used for small trees, sugar maple 

for medium trees, and white ash for tall trees 

exhibiting these growth characteristics. Among 

deciduous trees with straight, clear boles and small, 

oblong or narrow crowns, yellow-poplar, basswood, 

and black cherry were designated as key species. 

Black cherry was used for small trees, basswood for 

medium trees, and yellow-poplar for tall trees with 

these characteristics. 

As a group, oaks exhibit many bole and crown 

characteristics, but white oak and chestnut oak tend 

to have shorter boles and wide, spreading crowns. 

So chestnut oak was used for smaller oaks and 

white oak was used for taller oaks with these 

characteristics. Similarly, black oak and northern 

red oak were used for smaller and larger oak trees, 

respectively, but for trees with straight boles and 

relatively smaller crowns. Finally, scarlet oak was 

used for oaks with relatively straight boles but more 

irregularly shaped crowns. 

Two species remained for which we had found 

equations-beech and red maple. Beech was used 

for deciduous trees with large tree boles and large, 

spreading crowns. Red maple was used for small 

deciduous trees with irregular crowns-the majority of 

the trees found in the Northeast. 

This technique of grouping species with those that 

have regression equations for predicting total tree 

height based on observable growth characteristics 

should be used only if no other means are available. 

Other possibilities are to develop similar groupings 

based on local average tree heights, height and 

diameter relationships, periodic and mean annual 

height-growth curves, and cross-sectional stem 

analysis. We assumed that simple growth 

characteristics are sufficient to place species into 

appropriate groupings for predicting total tree height. 

Procedures for Estimating Dry Weight 

For extensive forest surveys, the most important 

parts of total forest biomass to report (Fig. 1) are: 

*The woody biomass in the merchantable stem of 

growing-stock trees (also called growing stock), 

which can be further apportioned into poletimber, 

sawtimber, and the upper stem portion above 

sawtimber. 

*Additional sources of biomass in growing-stock 

trees-branches, foliage, and the stump-root 

system-and cull trees which together constitute 

timber. 

.The biomass in nontimber trees, e.g., salvable 

dead trees, seedlings, and saplings. 

*Other kinds of nontree-related forest biomass, for 

example, shrubs. 

.The biomass on land that is not capable of 

producing commercial crops of timber. 
Forest Biomass 

Biomass of Timber 

-I\\\ \' CULL TREES 

GROWING-STOCK 

TREES 

Upper stem 

Sawtimber 

Foliage 

Unmerchantable 

stem and branches 

Stump and roots 

Figure 1. Relationship of total forest biomass components 
Biomass of Growing Stock 

Growing-stock trees are live, predominantly sound 

trees from which it is possible to harvest commercia~ 

timber products. When measuring the volume of 

growing-stock trees, it is customary to deduct the 

volume of rotten cull when reporting board-foot 

volume, and to also deduct the volume of sound cull 

(grade deductions that are still solid wood) when 

reporting cubic-foot volume. Rotten cull affects tree 

weight, but sound cull does not, so only the 

percentage of rotten cull in a tree is deducted when 

reporting tree quantities in terms of weight. 

Three sets of regression equations were chosen 

from the literature that covered the greatest range of 

species encountered in the Northeast: Young and 

others (1980) developed models for species in 

Maine, Wiant and others (1977) developed models. 

for species in West Virginia, and Monteith (1979) 

developed models for species in New York. 

Equations developed in Maine were the source for 

all evergreen species found: balsam fir, eastern 

white pine, red pine, spruce, hemlock, northern 

white-cedar, and larch; and for some deciduous 

species: red maple, yellow birch, and aspen. 

Equations developed in West Virginia were the 

source primarily for Appalachian deciduous species: 

hickory, yellow-poplar, black cherry, white oak, 

scarlet oak, chestnut oak, northern red oak, and 

black oak. Equations developed in New York were 

the source for sugar maple, American beech, and 

white ash. 

The form of the models to predict aboveground 

woody biomass for those developed in Maine (I), 

West Virginia (2), and New York (3) are: 

LnY = bo + bl Ln(D) (1 ) 

where Ln is the natural logarithm, the dependent 

variable Y is dry weight (in pounds), and the 

independent variable D is diameter at breast height 

(in inches); and 

where the dependent variable Y is dry weight (in 

kilograms), and the independent variables D and H 

are, respectively, diameter at breast height (in 

6 

millimeters) and merchantable tree height (in 

meters). Regression coefficients for 21 species 

were developed (Table 4). Species with no specific 

regression coefficients were grouped with species 

that did based on similar dry-weight densities 

(Appendix B). 

Biomass of Timber 

The biomass of growing stock can be extended to 

include portions of the entire growing-stock treebranches, foliage, stumps, and roots. Also included 

were cull trees, which along with the biomass of 

individual components of growing-stock trees 

comprise the biomass of timber. 

Table 4. Aboveground tree-weight regression 

coefficients for selected species 

LnY = bo + bl Ln(D) (1 

Y = bO + bl (D) + b2(H) + b3(~2~) (3) 

Y = dry weight (in pounds or kilograms) 

D = d.b.h. (in inches or millimeters) 

H = merchantable height (in meters) 

Coefficients Equation 

Species 

bo bl b2 number 

Balsam fir 

Eastern white pine 

Red pine 

Spruce 

Hemlock 

N. white-cedar 

Larch 

Red maple 

Sugar maple 

Yellow birch 

Hickory 

American beech 

White ash 

Yellow-poplar 

Black cherry 

Aspen 

White oak 

Scarlet oak 

Chestnut oak 

Northern red oak 

Black gum 
Bark also could be included but little research has 

been conducted on the amount of bark biomass 

contained in standing trees, either on a green- or 

dry-weight basis. For those interested in estimates 

of bark biomass, one technique is to divide 

estimates of whole-tree wood biomass by estimates 

of whole-tree wood and bark biomass. Comparative 

regression equations useful for this purpose have 

been synthesized from the literature and presented 

in tabular format by the USDA Forest Service 

(1 984). However, they were developed mostly for 

southeastern tree species. 

Branch biomas+We derived biomass in branches 

from regression equations developed by Young 

(1980). By dividing the weight of branches by total 

aboveground tree biomass, the proportion of branch 

biomass can be determined using averages by 

evergreen and deciduous species and by stand-size 

class (Table 5). Resulting branch percentages are 

applied to estimates of aboveground biomass 

provided by the dry-weight procedures outlined. 

Foliage biomass-This component of forest biomass 

has taken on more importance recently. It has been 

used as a data input for determining hydrocarbon 

emissions and is a critical component of research on 

carbon sinks and sequestering. Foliage biomass 

can be estimated through the same technique as 

outlined for branches. Average proportions are 

presented in Table 5. 

Siump/root biomass-Information on stumps and 

roots seems of little value currently, but it may be of 

more value in the future. This component can be 

estimated by the same technique as described for 

branches. Average proportions are presented in 

Table 5. 

Cull trees-These are trees with more than 67 

percent of their volume deducted due to rot or poor 

form. As with other timber trees, only rotten cull is 

deducted to determine net weight. Cull trees pose 

no special problem other than that the regression 

equations may not fit trees of poor form. Some of 

these trees may be wolf trees whose biomass could 

be underestimated because regression equations 

usually are developed for dominant and codominant 

trees. Until regression equations are developed for 

rough, poorly formed, and similar kinds of trees, the 

biomass equations now available will have to suffice. 

Forest Biomass 

When other kinds of nontimber such as salvable 

dead trees, small trees, and shrubs are added to the 

biomass of timber, we approach total forest 

biomass. Biomass on unproductive forest land also 

is a necessary component if researchers are to have 

a complete picture of the total biomass on our 

forests. Even grasses, forbs, and other types of 

nontimber vegetation have been measured in parts 

of the country (Mead and others 1987; Yarie and 

Mead 1988,1989), other than the Northeast. 

Salvable dead trees-These are dead trees with 

intact bark. Excluded from this particular 

classification of nontimber are snags, which have 

lost their bark and for the most part contain no 

sound wood. Salvable dead trees are assumed to 

contain sound wood, and are treated in the same 

way as cull trees. 

Table 5. Branch biomass and foliage biomass as a percentage of aboveground tree biomass, and 

stump-root biomass as a percent of complete tree biomass, dry-weight basis 

Branches Foliage Stump/root 

Stand-size class 

Evergreen Deciduous Evergreen Deciduous Evergreen Deciduous 

Poletimber 0.1 126 0.0348 0.1402 0.1093 0.2033 0.21 09 

Small sawtirnber 0.0960 0.0268 0.1 41 0 0.0991 0.2030 0.201 3 

Large sawtimber < 21 .Ot'dd.b.h. 0.0860 0.0224 0.1427 0.0932 0.2032 0.1943 

Large sawtimber 2 21 .OM d.b.h. 0.0768 0.01 75 0.1457 0.0860 0.2040 0.1 844 
Seedlings and sapling+Seedlings are trees less 

than 1.0 inch d.b.h., and saplings are trees at least 

1.0 inch d.b.h. but less than 5.0 inches d.b.h. A 

single regression equation may be sufficient to 

estimate their aboveground biomass. 

in a study by Tritton and Hornbeck (1982), graphical 

analysis of biomass regression equations for a 

single species-red maple-showed little differences 

in the smaller diameter classes. The regressed lines 

diverged between a d.b.h. of 15 and 20 cm (6 and 8 

inches). For trees between 1 and 5 inches d.b.h., 

the regression equations were virtually identical. 

Additional graphical analysis that we conducted 

showed that regression equations for many different 

species did not appear to differ for small-diameter 

trees. 

A single biomass regression equation chosen from 

the literature to predict the weight of small trees 

should be selected on the basis of the following 

criteria: 

0D.b.h. must be the sole independent variable 

used to predict tree weight since total tree height 

usually is not measured for saplings. Because 

saplings have no merchantable height, the 

regression equation to predict total tree height from 

diameter and merchantable height cannot be used. 

~Aboveground tree weight must be predicted on a 

dry-weight basis. 

.Tree weight must be predicted for as many of the 

most common northeastern species as possible. 

A biomass regression equation developed by 

Wartluft (1977) met all of these criteria. In his study 

conducted in West Virginia, 17 randomly selected, 

naturally occurring Appalachian deciduous species 

(Table 6) were weighed and a single biomass 

equation developed using d.b.h. as the sole 

predictor of aboveground tree biomass. 

Equations to predict both green and dry weight were 

developed. The dry-weight equation is: 

LnY = 1.54934 + 2.39376 Ln (D) 

where Ln is the natural logarithm, the dependent 

variable Y is total aboveground tree biomass (in dry 

pounds), and the independent variable D is d.b.h. (in 

inches). 

Table 6. Species and number of observations 

used to develop a biomass regression equation 

for small Appalachian deciduous species 

Species 

n 

Common name Scientific name 

Soft maple 

Hard maple 

Sweet birch 

Hickory 

Dogwood 

Beech 

Yellow-poplar 

Cucumber 

Mountain magnolia 

Blackgum 

Sou wood 

Aspen 

Black cherry 

White oak 

Chestnut oak 

Red oak 

Sassafras 

Acer rubrum 

A. saccharum 

Betula lenta 

Carya sp. 

Cornus florida 

Fagus grandifolia 

Liriodendron tul@ifera 

Magnolia acuminata 

M. fraseri 

Nyssa sylvatica 

Oxydendrum arboreum 

Populus sp. 

Prunus serotina 

Quercus alba 

Q. prinus 

Q. sp. 

Sassafras albidum 

Shrubs-interest in shrub biomass has continued to 

increase because of biodiversity studies and 

research on global climate change, carbon 

sequestering, wildlife habitats, and forest fuel. A 

recent national biomass report (Cost and others 

1990) included this component of forest biomass 

that is not usually assessed. At that time, shrub 

biomass statistics were available only for the NorthCentral United States and Alaska. 

As with the biomass of timber, shrub biomass can 

be determined by regression equations extracted 

from the literature. Most equations that predict 

shrub biomass include some measure of diameter, 

either at the base or at some point above that. 

Unfortunately, diameter measurements are not 

taken for the shrubs encountered on our field plots, 

only stem counts. As a result, median stem 

diameters had to be substituted for diameters in the 

shrub biomass equations. These were obtained 

from the source of the equations themselves. 
Since the North Central Forest Experiment Station 

has led in the development of shrub-biomass 

statistics, the shrub-biomass equations were 

obtained from Smith and Brand (1983). They 

synthesized equations from various sources along 

with ranges of diameters which could be used to 

determine average stem diameter. Occasionally, 

several equations were available for a particular 

species, so decisions had to be made concerning 

the best equation to use. 

The first selection criterion is that equations must 

predict both total aboveground biomass and foliage 

biomass. Those that did not predict foliage biomass 

or predicted only woody aboveground biomass were 

excluded. The second criterion is geographic 

source-equations that were developed closest to the 

Northeast were selected over those developed 

elsewhere. Finally, equations that had higher 

correlation coefficients (r2) were selected. These 

three criteria were sufficient to judge the equations 

suitable for each species and resulted in regression 

equations for 30 species (Table 7). 

The form of the equation is: 

where the dependent variable Y is total aboveground 

biomass (in oven-dry grams), and the independent 

variable D is the median stem diameter (in 

centimeters). 

In the shrub-biomass equations, two diameter 

measurements were used. Most included basal 

stem diameter, but some included diameter 15 cm 

aboveground. This did not influence the results 

because median stem diameters (based on the 

range of published diameters) are used to determine 

average biomass per stem, which are then applied 

to stem counts. 

An equation for a single vine species-vine 

honeysuckle (Lonicera hirsuta) also was found. At 

this time, vines (and dwarf shrubs) are excluded 

from assessments of shrub biomass. However, 

vines may be included in future assessments. In 

anticipation of this, the coefficients for vine 

honeysuckle are included in Table 7. 

Unproductive forest land-By USDA Forest Service 

definition, unproductive forest land is forest land 

from which no commercial crops of timber can be 

harvested. This definition is biased toward timber 

harvesting but in no way reflects on the capability of 

such an area to provide other goods and services. 

Biomass from this land class is a necessary 

component of the biological potential of an area and 

sRould be included in any resource assessment of 

the total forest resource. 

However, because of the very nature of the resource 

surveys conducted by the Forest Service, it is 

difficult to quantify biomass on forest land other than 

timberland. The forest surveys are designed 

primarily to measure the volume of growing timber, 

so few plots fall on unproductive forest land, urban 

forest land, or reserved forest land. However, area 

estimates of reserved forest land are available from 

public agencies. The simplest way to quantify 

biomass on reserved forest land, and the one we 

have adopted, is to obtain average biomass per acre 

on timberland, and apply those ratios to known land 

areas classified as reserved. Since reliable 

information is not available for unproductive land or 

urban land, we must rely on the data provided by the 

plots encountered (even though sampling errors will 

be high). 

Species Groups 

Most biomass regression equations are for a limited 

number of species. Species for which no regression 

equation is available must be grouped with other, 

related species for which an equation is available. 

In the past, density and tree form were the primary 

criterion for segregating tree species. Brenneman 

and others (1978) used excurrent and deliquescent 

determinations as a measure of tree form. 

Excurrent branching is descriptive of a tree whose 

main axis or trunk extends to the top of the crown, 

for example, pine, spruce, and fir; deliquescent 

branching is descriptive of a tree lacking a main 

axis-elm is an extreme example (Harlow and 

Harrar 4969). 

These classifications add little to distinguish 

branching habit over much simpler classifications 

such as evergreen and deciduous species. Most 

evergreens are excurrent and most deciduous 

species are delequiscent. A more detailed method 

for grouping species is required. 
Table 7. Shrub-biomass regression coefficients for selected species 

Y = dry weight (in grams) 

D = median stem diameter (in centimeters) 

Aboveground Foliage Median 

biomass biomass stem 

coefficients coefficients Source b 

diameter 

Alnus sp. 

Berberis repens 

Ceanothus velutinus 

Chamaedaphne calyculata 

Comptonia peregrina 

Cornus rugosa 

C. stolonifera 

Corylus cornuta 

Gaylussacia baccata 

Hamamelis virginiana 

llex verticillata 

Juniperus communk 

Kalmia augustifolia 

Ledum groenlandicum 

Lonicera canadensis 

L. hirsu taC 

Menziesia ferruginea 

Myrica pensylvanica 

Nemopanthus mucronatus 

Physocarpus malvaceus 

Rhamnus alnifoliad 

Rhododendron canadense 

Ribes sp. 

Rosa sp. 

Rhubus idaeus 

Shepherdia candensie 

Spirea sp. 

Symphoricarpos alba 

Vaccinium sp. 

Viburnum latinoides 

Minnesota (b) 

IdahoIMontana 

IdahoIMontana 

Canada 

Canada 

Minnesota (b) 

Minnesota (a) 

Minnesota (b) 

Canada 

Canada 

Canada 

IdahoIMontana 

Canada 

Canada 

Canada 

Minnesota (c) 

Ida ho1Montana 

Canada 

Canada 

IdaholMontana 

Minnesota (c) 

Canada 

IdahoIMontana 

Idaho/Montana 

IdahoIMontana 

IdahoIMontana 

Canada 

IdahoIMontana 

Canada 

Canada 

aSpecies presented are common shrub species of the North-Central United States. Scientific names are given 

to relate these species to shrub species found in the Northeastern United States. 

b~ources are: Canada, Maritime Provinces (Telfer 1969); IdahoIMontana, northern Idaho and western Montana 

(Brown 1976); Minnesota (a), northern Minnesota (Connally 1981); Minnesota (b), northern Minnesota 

(Roussopoulos and Loomis 1979); and Minnesota (c), northeastern Minnesota (Grigal and Ohmann 1977). 

cYine honeysuckle, a vine species currently excluded from the assessment. 

d~oody aboveground biomass is used as the dependent variable in the equation to predict aboveground 

biomass because an equation for total aboveground biomass is unavailable. 
Smith (1962) recognized a natural tendency of tree 

species to regulate branchiness depending on the 

degree of light tolerance. Relatively intolerant 

species are most likely to lose their lower branches 

through natural pruning. Species such as Douglas 

fir, yellow-poplar, and ash are examples. 

Shade-tolerant species such as hemlock, fir, and 

beech, frequently are branchy. A measurable tree 

parameter relating to branching habit, such as crown 

ratio, could be used. Crown ratio relates to the 

percentage of total tree height that supports a full, 

live crown, and can be used as an indicator of light 

tolerance. 

Assessments relating tree species to crown ratio 

have not been made to date, but should be 

considered for the future. In the meantime, density 

alone will have to suffice when grouping the species 

to which the tree-biomass regression equations will 

be applied. Tables of density with associated 

species groupings on a green- and dry-weight basis 

(Appendix A and B) have been developed entirely 

from the literature. The primary source of 

information was Markwardt (1930), who published 

densities on both green and dry basis, and specific 

gravities for 75 northeastern tree species. Additional 

dry-weight densities were from Peattie (1958). 

Species were segregated into evergreen and 

deciduous species. All tree species for which there 

is no biomass regression equation were matched 

with the closest species that did on the basis of both 

green and dry weight. Shrub species with no 

specific regression coefficients were grouped with 

those that did based on similar form (Appendix C). 

Judgments were made as to which density should 

be used when equations were unavailable for a 

particular species. These judgments were related to 

the morphology, taxonomy, and utilization 

characteristics of the species in question, and were 

based on information in Harlow and Harrar (1969), 

Johnson (1973), Peattie (1 958), and Shaw (1 914). 

If a judgment could not be made on the basis of 

these criteria, averages were assigned. Averages 

were developed for evergreen, light deciduous, and 

heavy deciduous species. The distinction between 

light and heavy deciduous species has been 

commonly accepted to be a specific gravity of 0.50. 

Species greater than OK equal to 0.50 were 

considered heavy deciduous, and those less than 

0.50 were considered light deciduous species. This 

classification has been checked for consistency with 

species segregated into soft and hard deciduous 

species by the Forest Service. There is a difference 

only for winged elm. The Forest Service considers 

this species a soft deciduous species, but we have 

treated it similar to rock elm, that is, a heavy 

deciduous species. 

The densities we used were oven dry, based on 

volume when green (Markwardt 1930). Species 

densities were averaged over 226 evergreen trees, 

251 light deciduous trees and 425;424 heavy 

deciduous trees that Markwardt reportedly tested. 

The tables of species density in this report are 

footnoted to clarify where judgments were made. 

Application to Extensive Surveys 

The Forest Service conducts periodic forest 

inventories of all states in the Nation. Authority to 

conduct these inventories is provided by the 

McSweeny-McNary Forest Research Act of I928 

and subsequent acts, including the Renewable 

Resources Planning Act of 1974 and the Renewable 

Resources Research Act of 1978. 

The Forest Inventory and Analysis unit of the 

Northeastern Forest Experiment Station is 

responsible for conducting periodic forest inventories 

in 14 northeastern states. A recent inventory of 

Ohio provided an opportunity to show how 

procedures for estimating biomass can be applied to 

an extensive forest area. Ohio is one of the first 

Northeastern states for which biomass statistics 

were published (Dennis 1983). Since that time our 

estimation techniques have been refined. Data for 

the most recent inventory, the fourth of its kind for 

Ohio, were collected during 1990 and 1991 (Griffith 

and others 1992). 

Methods and Specifications 

The inventory design employs a multistage sampling 

procedure of aerial photo points and a subsample of 

ground plots. The basic design entails sampling 

with partial replacement, using previous inventory 

data. Remeasurement is based on selected ground 

plots from inventories conducted for 1952, 1968, and 

1979. These inventories were based on sampling 

designs that featured a combination of photo and 

ground plot techniques. Field crews observed a 

large sample of 1 -acre plots on aerial photographs, 

then permanently located a subsample of 1R-acre 

and 10-point prism ground plots, tallying tree and 

area data on these plots. 
The current sample is based on interpretation of 

points on new aerial photographs for stratification 

into land use and cubic-foot volume classes. A 

sample of these points stratified as forest land and 

nonforest land was selected for ground observation; 

a subsample of 4,807 ground plots was measured. 

Means and variances for each photo stratum were 

calculated from the ground sample. Of these 

ground plots, all were remeasured plots, though 38 

new 10-point prism plots were installed on National 

Forest lands. 

The current inventory of Ohio is designed to make 

efficient use of previous data while capitalizing on 

the additional stratification possible from the new 

photography. Since a sample of previous plots was 

remeasured, the sampling scheme is called 

Sampling with Partial Replacement. However, since 

the remeasurement portion was so great, no 

updating of unremeasured plots was undertaken. 

The primary building blocks for reporting results are 

counties. One of the prerequisites for developing 

county-level statistics is that a county must have at 

least 60,000 acres of timberland. Counties that do 

not meet this criterion have too few plots to allow 

reliable estimates. Such counties are grouped with 

a neighboring county or counties to form a sampling 

base large enough to provide reliable estimates 

(Fig. 2). These are called supercounties. Cuyahoga 

County was excluded because all the forest land 

within that county was classified as urban, and 

therefore, not timberland. 

It is to this sampling base that biomass-estimation 

procedures described have been applied. The 

tables that result provide a guide to the kinds of 

information required by data users. Judgments were 

based on a decade of supplying biomass-related 

information to forest-land managers, policymakers, 

and others operating within the forest community. 

Structure of Biomass Tables 

The tables that follow are divided into four broad 

groups. The first three relate volume, green weight, 

and dry weight within the merchantable-stem portion 

of growing-stock trees. The tables that follow these 

depict an increasingly broader range of biomass 

information-first, all timber, and then all forest 

biomass. The final table shows the broadest 

classification of biomass on total forest land. Some 

of the data in the final table is sketchy. The forest 

survey was designed to primarily determine timber 

production, so predominantly urban areas and areas 

in Christmas tree production were excluded. 

Cuyahoga county was excluded from the survey 

because all forest land in the county was classified 

as urban, and therefore, not timberland. Since it 

was excluded from previous tables, for consistency, 

it was also excluded from the final table. However, 

the area and biomass are footnoted. Likewise, the 

area in Christmas tree production has been 

excluded. Christmas trees are assumed never to 

reach timber size, so no measurements were taken 

that would have enabled estimation of biomass. The 

area, however, has been footnoted. 

Except for the first two tables, all tables report 

biomass on a dry-weight basis in English units. 

Conversions and metric equivalents have been 

provided for data users requiring green-weight 

biomass statistics,'for calculating energy values, or 

for converting the data to metric units. 

In most of the tables, biomass is reported by 

evergreen and deciduous species. Classical 

distinctions of softwoods and hardwoods seem 

inappropriate for some components. The biomass 

of shrubs is one example. More descriptive terms 

were required to identify species groups even 

though nearly every classification is somewhat 

misleading. For example, some evergreen species 

loose their needles annually, and some deciduous 

trees have persistent leaves. Evergreenldeciduous 

was preferred to both the softwoodlhardwood or the 

coniferous/broad-leaved classifications. 

It should be noted that cell estimates may not sum 

to row and column totals. Because of the way in 

which sample data has been expanded to develop 

the statistics, rounding errors will occur. Sometimes, 

an estimate of zero occurs in the cells. This may 

not mean that a true null quantity exists but that no 

sample trees may have been encountered for that 

cell. This also happens when trees may have been 

sampled but the resulting biomass was fewer than 

500 tons; consequently, the total is rounded to zero 

when reported in millions of tons 
Northwestern Northeastern 

WILLIAMS I FULTON ' 

WOOD 1 ------ k - - - _ _ _ _ _ 

/ 

ASHTABUM 

TRUMBULL 

DEFIANCE ' HENRY 

ALLEN 

-1 I 

8- - - 1 

---- 

MERCER <AUGM'2E 

' PORTAGE 

------ I SENECA 

I 

MANDOT , CRAWFORD COLUMBlANA 

Southwestern 

South-Central 

Figure 2. Ohio counties and supercounties, by geographic unit 
Reliability of the Estimates 

The data in this report were based on a carefully 

designed sample of forest conditions throughout 

Ohio. But since field crews did not measure every 

tree or every acre in the state, the data are 

estimates. The reliability of the estimates can be 

judged by two important statistical measures: 

accuracy and precision. Accuracy refers to the 

success of estimating the true value; precision refers 

to the clustering of sample values about their own 

averages-the variation among repeated samples. 

We are interested primarily in the accuracy of the 

inventory, but in most cases we can only measure 

precision. However, accuracy can be increased by 

keeping sources of procedural error to a minimum 

through careful training of personnel, frequent 

inspection of work, and application of the most 

reliable inventory methods. 

The precision of an estimate is described by 

sampling error. In this report, It is expressed as a 

percentage of the estimate corresponding to one 

standard deviation, calculated as the square root of 

the variance, divided by the estimate, and multiplied 

by 100. 

The sampling errors reported are for row and 

column totals only. However, Alegria and Scott 

(1 991) have developed a procedure to approximate 

individual cell sampling errors from row and column 

totals. An indication of the magnitude of the cell 

sampling errors is provided. Estimates that have 

associated sampling errors greater than 25 percent 

but less than 50 percent are denoted by (t). These 

estimates are suspect and should be used with 

caution. Cell estimates that have associated 

sampling errors greater than or equal to 50 percent 

are denoted by ($). These cell estimates are not 

significantly different from zero, and are unreliable. 

The sampling errors are used as follows: the 

estimate of total biomass of timber on Ohio 

timberlands is 459.3 million dry tons. The estimate 

has an associated sampling error of 1.8 percent, or 

8.3 million dry tons. This means that if the inventory 

were repeated, the odds are 2 to 1 (66 percent 

probability) that the resulting estimate would be 

between 451.0 and 467.6 million dry tons. The odds 

are 19 to 1 (95 percent probability) that the resulting 

estimate would be between 442.7 and 475.9 million 

dry tons. 

State estimates have the smallest sampling errors 

and, therefore, are the most precise. County 

estimates are less reliable. For example, the 

sampling error for total biomass of timber on Ohio 

timberlands is 1.8 percent. The similar estimate for 

Adams County is 8.7 percent. In general, as the 

size of the estimate decreases in relation to the 

total, the sampling error increases. 

To improve the reliability of the estimates, cell 

values can be combined to reduce the sampling 

error. Data users may require combinations of 

counties, components, etc. that are different from 

those we might select. 

As a general rule, the sampling error of a combined 

estimate will be approximately equal to or less than 

the smallest sampling error of the two estimates that 

are being combined. However, because individual 

cell values that have resulted from the inventory are 

independent, the calculated sampling errors of 

combined cells will be approximations. They will 

underestimate the true sampling error, but will be 

sufficient to make judgments of acceptability. 

Combining cell values will not always reduce 

sampling errors to acceptable levels, so sampling 

errors of combined estimates must be calculated to 

determine if the sampling errors have indeed been 

reduced to acceptable levels. The technique to 

approximate the sampling error of a combined 

estimate is not difficult and entails using the 

following formulas: 

where; 

Xl = first cell-value estimate 

X2 = second cell-value estimate 

Xt = combined cell-value estimate 

SI = sampling error of first cell value estimate 

$2 = sampling error of second cell value estimate 

v(Xt) = variance of combined cell value estimate 

s(Xt) = sampling error of combined cell value 

estimate 
Index to Ohio Biomass Tables 

Table 

Volume of growing stock on timberland, by county and component, cubic foot basis, Ohio, 19 

Biomass of growing stock on timberland, by county and component, green weight basis, Ohio, 1991 

Biomass of growing stock on timberland, by county and component, dry weight basis, Ohio, 1991 

Biomass of evergreen timber on timberland, by county, class of timber, and component, 

dry weight basis, Ohio, 1991 

Biomass of deciduous timber on timberland, by county, class of timber, and component, 

dry weight basis Ohio, 1991 

Biomass of all timber on timberland, by county, class of timber, and component, 

dry weight basis, Ohio, 1991 

Biomass of evergreen trees and shrubs on timberland, by county and class of material, 

dry weight basis, Ohio, 1991 

Biomass of deciduous trees and shrubs on timberland, by county and class of material, 

dry weight basis, Ohio, 1991 

Biomass of all trees and shrubs on timberland, by county and class of material, 

dry weight basis, Ohio, 1991 

Area and biomass of trees and shrubs on forest land, by county and land class, 

dry weight basis, Ohio, 1991 

Conversions and Metric Equivalents 

1 cubic foot of softwoods = 0.027 green tons 

1 cubic foot of hardwoods = 0.033 green tons 

1 green ton of softwoods = 9.0 million b.t.u. 

1 green ton of hardwoods = 8.6 million b.t.u. 

1 green ton = 2.1 dry tons 

1 inch = 2.54 centimeters 

1 cubic foot = 0.02832 cubic meters 

1 ton = 2,000 pounds 

1 ton = 907.1 848 kilograms 

I acre = 0.4047 hectares 

Page 

16 

18 

20 

22 
Volume of growing stock ow timberland. by county and component. Ohio. 1991 

County and 

geographic unit 

. Sawtimber Total Sampling Poletimber Sawlog Upper stem growing stock error -. .. 

......................................... thousand cubic feet ......................................... 

Adams ............................................ 92, 806 38, 355 170, 098 301, 259 

Brown ............................................ 39, 343 18, 369 86, 196 143, 908 

Clermont .......................................... 46, 093 14, 056 61, 270 121, 418 

Gallia ............................................. 91, 614 18, 853 81, 579 192, 046 

Highland .......................................... 35, 663 14, 712 67, 012 1 17, 387 

Jackson ................................. ........ 64, 553 20, 462 96, 240 181, 255 

Lawrence ......................................... 88, 900 40, 996 184, 911 31 4, 807 

Pike .............................................. 56, 257 25, 745 1 14, 323 1 96, 325 

Ross .............................................. 54, 973 27, 029 123, 971 205, 973 

Scioto ............................................ 1 04, 793 56, 432 253, 132 41 4, 356 

percent 

TOTAL. SOUTH CENTRAL UNlT 674. 994 275. 009 1.238. 731 2.188. 734 3.7 . . p~ . p~ 

Athens .............. ... .... .................... 81, 641 36, 433 160. 970 279. 044 8.8 

Hocking/Perry ...................................... 158. 138 55. 293 258. 033 471. 464 7.5 

Meigs ............................................. 75. 952 28. 964 122. 761 227. 677 9.5 

MorganNashington ............................... 167. 479 63. 302 298. 011 528. 792 8.5 

Vinton ............................................ 97. 824 40. 406 1 80. 556 31 8. 787 6.6 

TOTAL. SOUTHEASTERN UNIT 581. 034 224. 399 1.020. 331 1.825. 764 3.8 

Belmont ........................................... 77. 079 26. 376 1 17. 780 221. 235 11.1 

Carroll ............................................ 43. 412 16. 559 76. 476 136. 447 17.2 

Coshocton ........................................ 83. 216 28. 576 125. 947 237. 739 10.1 

Guernsey ......................................... 68. 857 26. 295 121. 109 21 6. 261 13.2 

Harrison .......................................... 58. 761 16. 028 70. 000 1 44. 789 14.4 

Holmes ........................................... 43. 641 18. 204 85. 304 147. 149 19.1 

Jefferson ............ .... ......................... 65. 108 19. 681 80. 515 165. 304 11.0 

Monroe ........................................... 102. 975 52'21 5 238. 962 394. 151 8.2 

Muskingum ....................................... 72. 495 25. 230 109. 568 207. 294 8.5 

Noble ............................................. 69. 318 19. 008 83. 922 172. 249 14.0 

Tuscarawas ....................................... 71. 915 20. 748 90. 757 183. 419 15.0 

TOTAL. EAST CENTRAL UNIT 756. 777 268. 919 1.200. 339 2.226. 035 3.7 

(continued) 
County and 

geographic unit Poletimber Sawtimber Total Sampling 

Sawlog Upper stem growing stock error 

......................................... thousand cubic feet ......................................... 

AshlandIRichland ................... .... ........ 77, 907 37, 996 1 74, 553 290, 456 

Ashtabula ......................................... 143, 614 25, 790 104, 714 274, 118 

Columbiana ....................................... 58, 255 19, 218 83, 193 160, 666 

ErieIHuron ........................................ 46, 952t 17, 51 gt 79, 848+ 144, 319 

GeaugaILake ..................................... 90, 643 29, 658 1 29, 695 249, 996 

Lorain/Medina ..................................... 64, 412 26, 202 1 16, 807 207, 421 

Mahoning ......................................... 28, 521 7, 908~ 35, 382t 71, 811 

Portage/Summit ................................... 76, 158 22, 691 98, 728 1 97, 577 

StarkWayne ...................................... 55, 506 1 9, 957 94, 738 170, 201 

Trumbull .......................................... 72, 487 19, 891 87, 022~ 179, 400 

percent 

TOTAL. NORTHEASTERN UNIT 71 4. 456 226. 830 1 .004. 678 1.945. 963 5.4 

TOTAL. SOUTHWESTERN UNIT 1 84. 634 94. 546 444. 188 723. 368 7.2 

Allen/Auglaize/Mercer/Putnam/\/an Wert ........... Champaign/Logan/SheIby/Union .................. Crawford/Hardin/Wyandot .......... .' .............. Defiance/Fulton/Henry/Lucas/Paulding/Williams .... Delaware/Marion/Morrow .......................... Hancock/Ottowa/Sandusky/Seneca/Wood ......... 

Knox .............................................. . 

TOTAL. NORTHWESTERN UNlT 

TOTAL. ALL COUNTIES 3.277. 152 1.235. 457 5.583. 595 10.096. 205 1.9 

Sampling error (percent) 2.1 2.2 2.4 1.9 

NOTE: Cell estimates with sampling errors greater than 25 percent but less than 50 percent-denoted by (t) -are suspect and should be used with caution . Those with sampling errors 

greater than or equal to 50 percent-denoted by ($)-are not significantly different from zero and are unreliable . -4 

County and 

geographic unit Poletimber Sawtimber Total Sampling 

Sawlog Upper stem growing stock error 

......................................... thousand qreen tons ......................................... percent 

AshlandIRichland ................................. Ashtabula ......................................... Columbiana ....................................... ErieIHuron ........................................ GeaugaILake ..................................... LorainIMedina ..................................... Mahoning ......................................... PortageISummit ................................... StarkWayne ...................................... Trumbull .......................................... 

TOTAL. NORTHEASTERN UNIT 31. 529 45. 161 10. 115 86. 805 5.2 

TOTAL. SOUTHWESTERN UNlT - 

AlIen/Auglaize/Mercer/Putnam/\/an Wert ........... Champaign/Logan/Shelby/Union .................. CrawfordIHardinlWyandot ......................... Defiance/Fulton/Henry/Lucas/Paulding~Williams .... 

Delaware/Marion/Morrow .......................... Hancock/Ottowa/Sandusky/Seneca/Wood ......... 

Knox .............................................. 

TOTAL. NORTHWESTERN UNIT 17. 164 31. 840 6. 829 55. 833 5.3 

TOTAL. ALL COUNTIES 148. 331 253. 250 55. 726 457. 308 1.8 

Sampling error (percent) 2.1 2.3 2.2 1.8 

I- NOTE: Cell estimates with sampling errors greater than 25 percent but less than 50 percent-denoted by (t) -are suspect and should be used with caution . Those with sampling errors Lo 

greater than or equal to 50 percent-denoted by ($)-are not significantly different from zero and are unreliable . 
N 

0 

Biomass of growing stock on timberland. by county and component. dry-weight basis. Ohio. 1991 

County and 

geographic unit Poletimber Sawtimber Total Sampling 

Sawlog Upper stem growing stock error 

Adams ............................................ Brown ..................... ... .................. Clermont .......................................... Gallia ............................................. Highland .......................................... Jackson ................................ .. ....... Lawrence ......................................... Pike .............................................. Ross .............................................. Scioto ............................................ 

.......................................... thousand dry tons .......................................... percent 

TOTAL. SOUTH CENTRAL UNIT 1 9. 903 39. 039 8. 525 67. 466 3.8 

Athens ............................................ 2. 335 4. 887 1. 078 8. 299 8.7 

HockingIPerry ..................................... 3. 930 6. 852 1. 466 1 2. 249 6.9 

Meigs ............................................. 2. 125 3. 691 853 6. 670 9.6 

MorganlWashington ............................... 4. 499 8. 887 1. 862 15. 248 8.5 

Vinton ............................................ 2. 645 5. 123 1. 136 8. 905 6.9 

TOTAL. SOUTHEASTERN UNIT 1 5. 535 29. 440 6. 395 51. 370 3.8 

Belmont ........................................... Carroll ............................................ Coshocton ................ ...................... Guernsey ......................................... 

Harrison .......................................... 

Holmes ........................................... Jefferson .......................................... 

Monroe ........................................... 

Muskingum ....................................... 

Noble ............................................. 

Tuscarawas ....................................... 

TOTAL. EAST CENTRAL UNIT 20. 926 34. 314 7. 588 62. 827 3.7 

(continued) 
County and 

geographic unit Poletimber Sawtimber Total Sampling 

Sawlog Upper stem growing stock error 

.......................................... thousand dry tons .......................................... 

AshlandIRichland ................................. 2, 249 4, 880 1, 054 8, 182 

Ashtabula ......................................... 3, 849 3, 170 768 7, 787 

Columbiana ....................................... 1, 711 2, 951 661 5, 323 

ErieIHuron ........................................ 1 , 245t 2, 539" 539" 4, 323 

GeaugaILake ..................................... 2, 492 3, 829 864 7, 185 

LorainIMedina ..................................... 1, 576 3, 129 688 5, 392 

Mahoning ......................................... 850t 1, 175~ 263" 2, 288 

PortageISummit ................................... 2, 238 3, 194 707 6, 139 

StarkWayne ...................................... 1, 502 3, 108~ 633" 5, 242 

Trumbull .......................................... 2, 010 2, 943 655 5, 609 

percent 

14.9 

12.0 

15.7 

27.2 

13.0 

20.3 

24.2 

12.1 

20.2 

18.6 

TOTAL. NORTHEASTERN UNIT 1 9. 721 30. 91 9 6. 830 57. 469 5.4 

ButlerIHamilton ................................... 778 1. 478 323 2. 579 19.5 

ClarkIDarkelMiami ................................. 653 2. 083 41 9 3. 155 21.3 

CIinton/Greene/Montgomey/Preble/Warren ........ 1. 506 3. 742 799 6. 047 15.5 

FairfieIdlLicking ................................... 2. 172 5. 665 1. 178 9. 015 11.2 

Fayette/Franklin/Madison/Pickaway ................ 553" 1. 61 5t 331 2. 499 32.1 

TOTAL. SOUTHWESTERN UNlT 

Allen/Auglaize/Mercer/Putnam/Van Wert ........... ChampaignlLoganIShelbylUnion .................. CrawfordIHardinWyandot ......................... Defiance/Fulton/Henry/Lucas/Paulding/Williams .... Delaware/Marion/Morrow .......................... 

HancocklOttawalSanduskyEeneca/Wood ......... Knox .............................................. 

TOTAL. NORTHWESTERN UNIT 10. 415 21. 252 4. 490 36. 157 5.5 

TOTAL. ALL COUNTIES 92. 162 1 69. 547 36. 878 298. 586 1.9 

Sampling error (percent) 2.0 2.4 2.2 1.9 

N NOTE: Cell estimates with sampling errors greater than 25 percent but less than 50 percent-denoted by (t) -are suspect and should be used with caution . Those with sampling errors +' 

greater than or equal to 50 percent-denoted by ($)-are not significantly different from zero and are unreliable . 

County and 

geographic unit 

Growing-stock trees 

Stump Cull trees Total timber Sampling 

Growing Branches Foliage stock and roots error 

........................................... thousand dry tons ........................................... 

Ashland/Richland ................................. 83* 1 6* 12* 28* 1 8* 157 

Ashtabula ......................................... 1 04* ; $ 1 4* 

35* 1 * 1 74 

Columbiana ....................................... 8gt 1 2t 3ot 0 147 

Erie/Huron ........................................ 0 0 0 0 0 0 

GeaugaILake ..................................... 44* 8* 6* 1 5* 0 73 

LorainIMedina ..................................... 28* 5* 4* 9* 0 47 

Mahoning ......................................... 1 32* 25* 1 7* 44* 0 21 8 

Portage/Summit ................................... 0 0 0 0 0 0 

StarklWayne ...................................... 92: 1 7* 13% 31 * 6* 160 

Trumbull .......................................... 70 1 3* 1 O* 24* 0 116 

percent 

67.6 

56.6 

45.4 

0.0 

60.7 

100.0 

71.6 

0.0 

72.4 

91.5 

TOTAL. NORTHEASTERN UNIT 643t 1 2ot 87t 21 7t 26* 1. 092 25.7 

Butler/Hamilton ................................... 3* 1 * 0* 1 * 6* 11 60.8 

Clark/Darke/Miami ................................. 0 0 0 0 0 0 0.0 

Clinton/Greene/Montgomery/Preble/Warren ........ 22* 4* 3* 8* 0 37 63.9 

Fairfield/Licking ................................... 345* 64* 44* 115* 65* 633 52.9 

Fayette/Franklin/Madison/Pickaway ................ 0 0 0 0 0 0 0.0 

TOTAL. SOUTHWESTERN UNIT 370t 6gt 48t 1 24t 71 * 682 49.3 

Allen/Auglaize/Mercer/Putnam/van Wert ........... 0 

Champaign/Logan/Shelby/Union .................. 39* 

Crawford/Hardin/Wyandot ......................... 0 0 

Defiance/Fulton/Henry/Lucas/Paulding/Williams .... 135* 25* 

Delaware/Marion/Morrow .......................... 18* 3* 

Hancock/Ottowa/Sandusky/Seneca/Wood ......... 0 0 

Knox .............................................. 258* 48* 

TOTAL. ' NORTHWESTERN UNIT 450t 84t 

TOTAL. ALL COUNTIES 

Sampling error (percent) 

NOTE: Cell estimates with sampling errors greater than 25 percent but less than 50 percent-denoted by (t) -are suspect and should be used with caution . Those with sampling errors 

greater than or equal to 50 percent-denoted by ($)-are not significantly different from zero and are unreliable . 

County and 

geographic unit 

Growinq-stock trees 

Growing Stump Cull trees Total timber Sampling 

stock Branches Foliage and roots error 

AshlandIRichland ................................. Ashtabula ......................................... Columbiana ....................................... ErielHuron ........................................ GeaugaILake ..................................... LorainIMedina ..................................... Mahoning ......................................... PortageISummit ................................... StarkMay ne ...................................... Trumbull .......................................... 

percent 

TOTAL. NORTHEASTERN UNIT 56. 826 6. 437 1. 752 16. 267 6. 084 87. 366 5.3 

TOTAL. SOUTHWESTERN UNlT . 

Allen/Auglaize/Mercer/Putnam/van Wert ........... ChampaignILoganlShelbylUnion .................. 

Crawford/Hardin/Wyandot ......................... Defiance/Fulton/Henry/Lucas/Paulding/Williams .... 

DelawareIMarionlMorrow .......................... Hancock/Ottowa/Sandusky/Seneca/Wood ......... 

Knox .............................................. 

TOTAL. NORTHWESTERN UNlT 35. 707 3. 974 1. 054 1 0. 087 4. 883 55. 705 5.4 

TOTAL. ALL COUNTIES 292. 149 32. 889 8. 867 83. 274 31. 186 448. 365 1.9 

Sampling error (percent) 2.0 1.9 1.8 1.9 5.2 1.9 

NOTE: Cell estimates with sampling errors greater than 25 percent but less than 50 percent-denoted by (t) -are suspect and should be used with caution . Those with sampling errors 

greater than or equal to 50 percent-denoted by ($)-are not significantly different from zero and are unreliable . ln 

County and 

geographic unit 

grow in^-stock trees 

Stump Cull trees Total timber Sampling 

Growing Branches Foliage stock and roots error 

........................................... thousand dry tons ........................................... 

Ashland/Richland ................................. 8,182 921 253 2,326 721 12,404 

Ashtabula ......................................... 7,787 925 275 2,300 878t 12,166 

Columbiana ....................................... 5,323 608 1 72 1,526 556 8,186 

ErieIHuron ........................................ 4,323t 481 1 28t 1,221 t 624t 6,776 

GeaugaILake ..................................... 7,185 821 229 2,066 4~7~ 10,768 

LorainIMedina ..................................... 5,392 606 165 1,533 406~ 8,102 

Mahoning ......................................... 2,288 271 85t 666 1 06: 3,416 

Portage/Summit ................................... 6,139 695 189 1,754 1,450 10,227 

StarkiWayne ...................................... 5,242 584 161 1,473 528t 7,988 

Trumbull .......................................... 5,609 645 1 84 1,618 372 8,426 

percent 

14.3 

11.8 

14.4 

24.8 

13.0 

20.1 

23.5 

14.7 

18.4 

18.3 

TOTAL, NORTHEASTERN UNIT 57,469 6,557 1,840 16,483 6,110 88,459 5.2 

ButlerIHamilton ................................... 2,579 288 77 731 624t 4,300 18.8 

Clark/Darke/Miami ................................ 3,155 341 86 872 ~64~ 5,119 19.9 

Clinton/Greene/Montgomery/Preble/Warren ........ 6,047 670 178 1,701 1,121 9,717 14.0 

FairfieldILicking ................................... 9,015 1,022 295 2,553 1,446 14,332 11.5 

Fayette/Franklin/Madison/Pickaway ................ 2,49gt 271' 69+ 693t 476% 4,008 29.6 

TOTAL, SOUTHWESTERN UNIT 23,296 2,593 706 6,550 4,331 37,477 7.4 

Allen/Auylaize/Mercer/Putnam/Van Wert ........... 5,409 591 152 1,507 402~ 8,060 12.8 

Champaign/Logan/Shelby/Union .................. 4,456 503 139 1,268 808~ 7,175 16.8 

C rawford/Hardin/Wyandot ......................... 5,743 61 9 156 1,585 258' 8,361 15.3 

Defiance/Fulton/Henry/Lucas/Paulding/VVilliams .... 5,962 684 197 1,711 501 9,055 13.0 

Delaware/Marion/Morrow .......................... 4,696 533 146 1,344 1,208~ 7,927 14.8 

Hancock/Ottowa/Sandusky/Seneca/Wood .. i ...... 5,248 588 157 1,489 1 ,297t 8,780 12.4 

Knox .............................................. 4,644 540 166 1,333 409 7,091 14.2 

TOTAL, NORTHWESTERN UNIT 36,157 4,058 1,113 10,238 4,883 56,449 5.3 

TOTAL, ALL COUNTIES 298,586 34,085 9,723 85,437 31,430 459,261 1.8 

Sampling error (percent) 1.9 1.8 1.8 1.9 5.2 1.8 

NOTE: Cell estimates with sampling errors greater than 25 percent but less than 50 percent-denoted by (t) -are suspect and should be used with caution. Those with sampling errors P3 

,I areater than or eaual to 50 ~ercent-denoted bv (t) -are not sianificantly different from zero and are unreliable. 
0 

t 

m 

a, 

7 

i-t 

a0 

C\] 

t 

w 

a 

Ln_ 

C\] 

W 

y county and cla 

0 

County and 

geographic unit 

Nontimber 

Timber Total trees Sampling 

Sa'vab'e Saplings Seedlings dead trees Shrubs and shrubs error 

........................................... thousand dry tans ........................................ percent 

Adarns ............................................ 13, 601 121 1, 081 322 140 15, 265 8.5 

rown ............................................ 7, 842 73 582 89 33t 8, 622 15.4 

Clermont .......................................... 6, 096 73t 495 115 43 6, 822 14.3 

Gallia ...................................... ...... 7, 597 9et 909 208 74 8, 883 9.4 

Highland .......................................... 5, 368 78* 444t 89 23t 6, 001 13.3 

Jackson ......................... ............... 7, 435 8gt I, 003 1 74 141t 8, 842 10.4 

rence ......................................... 13, 288 1 44t 1, 430 240 143 -1 5, 246 8.5 

e .............................................. 9, 094 1 3Zt 1, 084 226 81t 10, 616 8.0 

Ross .............................................. 10, 474 107~ 862 1 70 65 11, 678 11.9 

Scioto ...................... ... .. ............ma.. 18, 591 87t 1, 492 364 165 20, 699 8.6 

TOTAL, SOUTH CENTRAL UNIT 99, 387 6, 004 9, 381 1, 995 908 1 1 2, 674 3.3 

Athens ............................................ 12. 247 320t 1. 258 1 73 147 14. 145 7.5 

HockingIPerry ..................................... 1 7. 864 1 1 gt 2. 092 31 7 184 20. 576 5.9 

Meigs ............................................. 9. 814 1 22: 991 21 0 1 52 1 1. 289 9.3 

Morgan~Washington ............................... 22. 746 281 2. 243 462 286 26. 018 7.5 

Vinton ...................... .................... 12. 682 57t 1. 266 251 68 1 4. 323 6.4 

TOTAL. SOUTHEASTERN UNIT 75. 352 899 7. 850 I. 413 837 86. 350 3.3 

Belmont ........................................... Carroll ............................................ Coshocton ........................................ Guernsey ......................................... Hardson .......................................... Holmes ........................................... Jefferson .......................................... Monroe ........................................... Muskingum ....................................... Noble ............................................. Tuscarawas ....................................... 

TOTAL. EAST CENTRAL UNIT 93. 760 1. 161 10. 092 1. 510 1. 284 107. 807 3.4 

(continued) 
County and 

geographic unit 

Nontimber 

Timber Total trees Sampling 

Saplings Seedlings dead trees Shrubs and shrubs error 

.......................................... thousand dry tons .......................................... percent 

AshlandIRichland ................................. 12. 248 673 131t 13. 191 14.1 

Ashtabula ......................................... 1 1. 991 6st 1. 907 309 1 96t 14. 468 9.9 

Columbiana ....................................... 8. 039 33$ 894t 1 38 78t 9. 181 12.2 

ErieIHuron ........................................ 6. 776 77t 440 47t 32t 7. 372 23.6 

GeaugaILake ..................................... 1 0. 695 117~ 1. 050 1 24 87t 12. 073 12.7 

LorainIMedina ..................................... 8. 056 357t 952 78 166 9. 609 18.2 

Mahoning ......................................... 3. 198 14* 481' 62t 34* 3. 790 21.4 

PortageISummit ................................... 10. 227 7st 948 235: 21 gt 1 1. 703 13.5 

StarkWayne ...................................... 7. 828 5gt 630 108 98t 8. 723 17.5 

Trumbull .......................................... 8. 310 33* 801 1 25 171t 9. 439 16.8 

TOTAL. NORTHEASTERN UNIT 87. 366 91 5 8. 777 1. 356 1. 135 99. 550 4.8 . 

ButlerIHamilton ................................... 4, 289 st 379 64+ 23t 4. 809 18.0 

ClarkIDarkelMiami ................................. 5. 119 59 466t 82t 2ot 5. 745 18.5 

Clinton/Greene/MontgomerylPreble/Warren ........ 9. 680 118~ 709 170 10. 747 13.4 

71 t FairfieldILicking ................................... 13. 699 1 47t 1 . 3Gt 180 113 15. 482 12.0 

Fayette/Franklin/Madison/Pickaway ................ 4. 008~ 1 2* 284 mt 26t 4. 374 28.3 

TOTAL. SOUTHWESTERN UNIT 36. 795 389 3. 182 538 254 41. 157 7.3 . - . - 

Allen/Auglaize/Mercer/P~nam/Van Wert ........... 8, 060 551 87 45* 8, 785 12.3 

Champaign/Logan/Shelby/Union .................. 7, 110 787 1 24 107 8, 184 15.8 

Crawford/Hardin/Wyandot ......................... 8, 361 130* 424 57t 5* 8. 977 15.2 

Defiance/Fulton/Henry/Lucas/Paulding/Williams .... 8. 833 72t 754 148 9. 885 13.3 ;$ DelawarelMarionIMorrow .......................... 7. 898 228 853 88 9. 138 13.6 

Hancock/Ottawa/Sandusky/Seneca/Wood ......... 8. 780 1llt 888 132 81 9. 991 11.6 

Knox .............................................. 6. 664 SO* 692 6zt 1 OO* 7. 577 13.9 

TOTAL. NORTHWESTERN U 55. 705 699 4. 950 697 486 62. !536 5.2 

TOTAL. ALL COUNTIES 448. 365 5. 066 44. 231 7. 508 4. 904 51 0. 074 1.7 

Sampling error (percent) 1.9 ,9.8 2.3 2.5 4.4 1.7 

NOTE: Cell estimates with sampling errors greater than 25 percent but less than 50 percent-denoted by (t) -are suspect and should be used with caution . Those with sampling errorsgreater than or equal to 50 percent-denoted by ($) -are not significantly different from zero and are unreliable . 
w Biomass of all trees and s rubs on timberland. by county and class of 

N 

County and 

geographic unit 

Nontimber Total trees Sampling Timber Sa'vab'e Saplings Seedlings Shrubs and shrubs error dead trees 

........................................... thousand dry tons ......................................... 

Adams ............................................ Brown ................................. .. ........ Clermont .......................................... Gallia ............................................. Highland .......................................... Jackson ............................... .......... Lawrence ............................ ........... Pike .............................................. Ross .............................................. Scioto .............................. ... .......... 

percent 

TOTAL. SOUTH CENTRAL UNIT 102. 771 1. 096 10. 117 2. 012 908 1 16. 9e 3.2 

Athens ............................................ 12. 352 320t 1. 267 1 73 147 14. 260 7.5 

HockingfPerry ..................................... 18. 697 1 33t 2. 155 31 8 184 21. 487 5.7 

Meigs ............................................. 10. 189 126~ 1. 008 21 4 1 52 11. 689 8.8 

Morgan/Washington ............................... 23. 31 9 296t 2. 317 464 286 26. 683 7.3 

Vinton ........................................... 13. 221 57t 1. 345 252 68 1 4. 943 5.6 

TOTAL. SOUTHEASTERN UNIT 77. 778 931 8. 093 1. 420 837 89. 060 3.2 

Belmont ........................................... Carroll ............................................ Coshocton ........................................ Guernsey ......................................... Harrison .......................................... Holmes ........................................... Jefferson .......................................... Monroe ........................................... Muskingum ....................................... Noble ............................................. Tuscarawas ....................................... 

TOTAL. EAST CENTRAL UNIT 96. 329 1. 18gt 10. 279 1. 510 1. 284 1 10. 591 3.2 

(continued) 
County and 

geographic unit 

Nontimber 

Timber Total trees Sampling 

Sa'vable Saplings Seedlings dead trees Shrubs and shrubs error 

AshlandIRichland ................................. Ashtabula ......................................... Columbiana ....................................... ErieIHuron ........................................ GeaugaILake ..................................... LorainlMedina ..................................... Mahoning ......................................... PortagelSummit ................................... Stark~Wayne ...................................... Trumbull .......................................... 

.......................................... thousand dry tons ........................................... percent 

TOTAL. NORTHEASTERN UNIT 88. 459 945 8. 884 1. 357 1. 135 100. 780 4.8 

ButlerIHamilton ................................... 4. 300 5d 3mt mt 23t 4. 824 18.0 

Clark/Darke/Miami ................................. 5. 119 57* 466t 82t 2ot 5. 745 18.5 

Clinton/Greene/Montgomery/Preble~Warren ........ 9. 717 118; 729 1 70 71 1 0. 804 13.2 

FairfieldILicking ................................... 14. 332 147 1. 364 181 113~ 16. 138 11.2 

Fayette/Franklin/Madison/Pickaway ................ 4. 008~ 1 2* 285t 43t 26t 4. 375 28.3 

TOTAL. SOUTHWESTERN UNIT 37. 477 389 3. 226 539 254 41. 885 7.0 

Allen/Auglaize/Mercer/Putnam/van Wert ........... 8. 060 42* 551 87 45* 8. 785 12.3 

Champaign/Logan/Shelby/Union .................. 7. 175 55* 800 1 24 107 8. 261 15.7 

CrawfordlHardinlWyandot ......................... 8. 361 130* 424 57t 5* 8. 977 15.2 

Defiance/Fulton/Henry/Lucas/Paulding~Williams .... 9. 055 72t 760 148 78t 10. 113 12.9 

Delaware/Marion/Morro\n~ .......................... 7. 927 228 853 88 7ot 9. 167 13.5 

Hancock/Ottawa/Sandusky/Seneca/Wood' ......... 8. 780 lilt 888 132 81 9. 991 11.6 

Knox .............................................. 7. 091 61 * 692 62' 1 OO* 8. 005 12.7 

TOTAL. NORTHWESTERN UNIT 56. 449 700 4. 969 697 486 63. 300 5.1 

TOTAL. ALL COUNTIES 

Sampling error (percent) 

NOTE: Cell estimates with sampling errors greater than 25 percent but less than 50 percent-denoted by (t) -are suspect and should be used with caution . Those with sampling errors 

greater than or equal to 50 percent-denoted by ($)-are not significantly different from zero and are unreliable . 
W 

Area and biomass of all trees and shrubs on forest land. by county and land class. Ohio. 1991 

County and 

geographic unit 

Timberland Other forest land a Total forest land 

Area Biomass Area Biomass Area Biomass 

thousand thousand thousand 

acres dry tons acres dry tons acres dry tons 

Adams ............................................ 236. 315 16. 343 456 32 236. 771 16. 375 

Brown ............................................ 107. 845 8. 688 10 1 107. 855 8. 689 

Clermont .......................................... 107. 833 7. 006 8. 787 672 1 1 6. 620 7. 678 

Gallia .............................................. 184. 553 9. 825 0 0 184. 553 9. 825 

Highland .......................................... 85. 308 6. 017 2. 611 184 87. 919 6. 201 

Jackson .......................................... 166. 029 9. 850 1. 653 98 167. 682 9. 948 

Lawrence ......................................... 225. 693 15. 581 44 3 225. 737 15. 584 

Pike ................................. ...... 178. 515 1 0. 867 702 43 179. 217 10. 91 0 

Ross .............................................. 164. 014 11. 810 3. 993 288 168. 007 1098 2. 

Scioto ....................................... . 280. 937 20. 918 477 36 281. 414 20. 954 

TOTAL. SOUTH CENTRAL UNIT 1.737. 041 1 16. 904 18. 733 1. 356 1.755. 774 1 18. 260 

Athens ............................................ 203. 255 14. 260 2. 912 204 206. 167 14. 464 

HockingIPerry ..................................... 326. 711 21. 487 4. 659 88 331. 370 21. 575 

Meigs ............................................. 194. 712 1 1. 689 1. 345 1 86 196. 057 1875 1. 

MorganlWashington ............................... 394. 714 26. 683 5. 968 31 5 400. 682 26. 998 

Vinton ............................................ 205. 076 1 4. 943 3. 095 435 208. 171 15. 378 

TOTAL. SOUTHEASTERN UNIT 1.324. 468 89. 060 17. 979 1. 228 1.342. 447 90. 288 

Belmont ........................................... Carroll ............................................ Coshocton ........................................ Guernsey ......................................... Harrison .......................................... Holmes ........................................... Jefferson ... 

11. 145 3. 262 206 182. 331 1351 1. 

7. 129 65 4 1 12. 094 7. 133 

1 1. 292 100 7 169. 255 1299 1. 

11. 046 1. 235 75 183. 760 11. 121 

7. 544 75 4 156. 227 7. 548 

7. 276 1. 400 1 26 82. 399 7. 402 

8. 867 940 56 151. 087 8. 923 

Monroe ........................................... 204. 005 17. 062 0 0 204. 005 1062 7. 

Muskingum ....................................... 210. 764 1 1. 724 3. 240 1 80 21 4. 004 1904 1. 

Noble ............................................. 134. 289 8. 241 1. 130 69 135. 41 9 8. 310 

Tuscarawas ....................................... 1 57. 522 9. 266 2. 712 20 160. 234 9. 286 

TOTAL. EAST CENTRAL UNIT 1.736. 654 11 0. 591 14. 158 746 1.750. 812 111. 337 

(continued) 
County and 

geographic unit 

Timberland Other forest land a 

Area Biomass Area Biomass 

Total forest land 

Area Biomass 

AshlandIRichland ................................. Ashtabula ......................................... Columbiana ....................................... ErieIHuron ........................................ GeaugaILake ..................................... LorainIMedina ..................................... Mahoning ......................................... PortageISummit ................................... Stark~Wayne ...................................... Trumbull .......................................... 

acres 

thousand 

dry tons acres 

thousand 

dry tons acres 

151. 223 

229. 306 

1 47. 024 

71. 512 

185. 125 

126. 262 

63. 325 

21 4. 254 

1 20. 442 

139. 516 

thousand 

dry tons 

TOTAL. NORTHEASTERN UNIT 1.362. 509 100. 780 85. 479 6. 503 1.447. 988 107. 283 

TOTAL. SOUTHWESTERN UNIT 609. 295 41. 885 40. 664 2. 582 649. 959 44. 467 

Allen/Auglaize/Mercer/Putnam/Van Wert ........... 95. 743 8. 785 14. 194 597 6 09. 937 9. 382 

Champaign/Logan/Shelby/Union .................. 151. 272 8. 261 2. 624 1 43 153. 896 8. 404 

CrawfordIHardin~Wyandot ......................... 71. 080 8. 977 327 41 71. 407 9. 018 

Defiance/FuIton/Henry/Lucas/PauIding/WiIliams .... 120. 562 10. 113 9. 157 867 129. 719 1980 0. 

Delaware/Marion/Morrow .......................... 126. 352 9. 167 4. 122 298 130. 474 9. 466 

HancockIOttawalSanduskylSeneca~Wood ......... 134. 602 9. 991 25. 674 1. 906 1 60. 276 1897 1. 

Knox .............................................. 97. 841 8. 005 35 3 97. 876 8. 008 

TOTAL. NORTHEASTERN UNIT 797. 452 63. 300 56. 133 3. 856 853. 585 67. 156 

TOTAL. ALL COUNTDES 7.567. 419 522. 520 233. 146 16. 271 7.800. 565 538. 791 

%oes not include 4.865. 267 dry tons on 48. 577 acres of urban forest land and 41 1. 642 dry tons on 5. 346 acres of productive-reserved forest land in Cuyahoga County; or biomass on 

9. 626 acres in Christmas tree production . 
Improving Accuracy of Biomass 

Estimates 

As mentioned previously, the validity of estimates is 

judged by their accuracy and precision. Precision 

relates to how well the population is sampled to 

reduce variation of sample means. This is reflected 

by sampling error. Precision of an estimate can be 

improved primarily by increasing sample size. 

To increase accuracy, procedural techniques must 

be improved that provide estimates closer to the true 

mean of the population. We have presented 

techniques that we use to develop our biomass 

estimates. We can say with confidence that these 

procedures give us as accurate an estimate as 

possible using the tools available and operating 

under current restrictions. 

One important source of bias that affects nearly all 

biomass estimates is the kinds of trees upon which 

prediction equations are based. In most cases, 

these equations are developed from dominant and 

codominant trees. Very few suppressed and cull 

trees in the forest understory are sampled when 

researchers develop prediction equations for 

biomass. Consequently, when applied over 

extensive areas, most biomass statistics probably 

overestimate the resource. 

During our annual statewide surveys, Forest 

Inventory and Analysis field crews collect 

information on individual-tree crown class. This 

information could be used to apply form-specific 

prediction equations or factors that adjust 

predicative estimates, if they existed. Presently, no 

such procedures are available. 

The accuracy of biomass estimates can be 

improved by implementing the following: 

*Develop regional biomass equations that predict 

dry weight similar to those developed for green 

weight. 

*Group species with those for which a biomass 

prediction equation exists based on branching habit, 

such as light tolerance and crown ratio. 

*Estimate bark weight by developing more widely 

applicable relationships of wood biomass to wood 

and bark biomass. 

*Obtain actual stem diameters of shrub species. 

*Group species with those for which there is a 

prediction equation for total tree height based on 

average tree heights, height diameter relationships, 

cross-sectional stem analysis, etc. 

Of course, the need for prediction equations for total 

tree height could be alleviated by developing a wider 

range of equations that incorporate total tree height 

as a dependent variable. But there are problems 

even with this approach. Total tree height, 

especially for hardwoods, always will be difficult to 

measure with existing techniques. Even highly 

evolved mensurational equipment may depend on 

visually identifying exactly where the tip of the tree 

is. 

Finally, we are still coming to grips with exactly what 

kind of information to publish. With new avenues for 

research opening up, we are faced with determining 

what is most valuable to researchers interested in 

more nontraditional forest-related issues and 

environmental concerns. New and more highly 

evolved estimation procedures may be needed for 

important issues like determining leaf-surface mass 

that relate to emissions, or for identifying the carbon 

sequestering potential of forests. Possibly, 

measuring the forest in terms of weight will be too 

confining in much the same way that measuring 

trees in terms of volume has come to be viewed as 

too "timber oriented." All are issues that need to be 

faced in the future. 

Literature Cited 

Alegria, J.; Scott, C.T. 1991 . Generalized variance 

function applications in forestry. Res. Note 

NE-345. Radnor, PA: U.S. Department of 

Agriculture, Forest Service, Northeastern Forest 

Experiment Station. 4 p. 

Baldwin, V.C., Jr. 1987. Regression functions 

available for estimating biomass of planted 

southern pine. In: Wharton, E. H.; Cunia, T., 

eds. Estimating tree biomass regressions and 

their error; 1986 May 26-30; Syracuse, NY. Gen. 

Tech. Rep. NE-117. Broomall, PA: U.S. 

Department of Agriculture, Forest Service, 

Northeastern Forest Experiment Station: 

157-1 71. 

Barrett, J.; Jastrembski, J. 1990. Regional green 

weight biomass equations for the Northeast. 

Northern Journal of Applied Forestry. 7(4): 

187-1 89. 
Brenneman, 6.6.; Frederick, D.J.; Gardner, W.E.; 

Schoenhofen, L.H.; Marsh, P.L. 1978. Biomass 

of species and stands of West Virginia 

hardwoods. In: Central hardwoods forest 

conference, proceedings of the second meeting; 

1978 November 14-16; West Lafayette, IN: 

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Brown, J.K. 1976. Estimating shrub biomass from 

basal stem diameters. Canadian Journal of 

Forest Research. 6(2): 153-1 58. 

Connally, B.J. 1981. Shrub biomass-soil 

relationships in Minnesota wetlands. St. Paul. 

MN: University of Minnesota, Department of Soil 

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equations for some shrubs from Northeastern 

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leaf area equations for northern forestry species. 

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27 p. 

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developing regional weight tables. Journal of 

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Johnson, H., ed. 1973. The international book of 

trees. New York: Simon and Shuster, Inc. 

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analysis of the literature-part I: unmerchantable 

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Keays, J.L. 1971 b. Complete tree utilization, an 

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VP-X-70. Vancouver, BC: Canadian Forestry 

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94 p. 

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Wartluft, J.L. 1977. Weights of small appalachian 

hardwood trees and components. Res. Pap. 

NE-366. Upper Darby, PA: U.S. Department of 

Agriculture, Forest Service, Northeastern Forest 

Experiment Station. 4 p. 

Wiant, H.V., Jr.; Sheetz, C.E.; Colaninno, A,; 

DeMoss, J.C.; Castaneda, F. 1977. Tables and 

procedures for estimating weights of some 

appalachian hardwoods. Bull. 659T. 

Morgantown, WV: West Virginia University, 

Agricultural and Forestry Experiment Station, 

College of Agriculture and Forestry. 36 p. 

Yarie J.; Mead, B.R. 1988. Twig and foliar biomass 

equations for major plant species in the Tanana 

River Basin of interior Alaska. Res. Pap. 

PNW-401. Portland, OR: U.S. Department of 

Agriculture, Forest Service, Pacific Northwest 

Forest and Range Experiment Station. 20 p. 

Yarie, J.; Mead, B.R. 1989. Biomass regression 

equations for determination of vertical structure of 

major understory species of southeast Alaska. 

Northwest Science. 63(5): 221 -231. 

Young, H.E. 1976. A summary and analysis of 

weight table studies. In: Oslo biomass studies, 

XVlth international congress of IUFRO; 1976 

June 22; Oslo, Norway. Orono, ME: University of 

Maine: 251 -282. 

Young, H.E.; Ribe, J.H.; Wainwright, M. 1980. 

Weight tables for tree and shrub species in 

Maine. Misc. Rep. 230. Orono, ME: University 

of Maine, Life Sciences and Agriculture 

Experiment Station. 84 p. 
Appendix A 

Species key for green weight regression equations of northeastern tree species 

Tree species 

2 Density' Tree species 

2 

Key species (I b/ff) Key species (I b/f?) 

Evergreen Trees 

Atlantic white-cedar 

northern white-cedar 

black spruce 

red spruce 

blue spruce3 

Norway spruce3 

white spruce 

eastern white pine 

eastern redcedar 

Douglas fir4 

red pine 

Scotch pine5 

Austrian pine6 

misc. evergreen trees7 

balsam fir 

tamarack 

larch3 

pond pine 

jack pine 

pitch pine 

baldcypress 

eastern hemlock 

shortleaf pine 

Table Mountain pine8 

Virginia pine8 

loblolly pine 

Deciduous Trees 

pin cherry 

striped maple 

boxelderg 

mountain maplei0 

ailanthus 

yellow-poplar 

balsam poplar 

cherries and plumsl1 

northern catalpa 

~aulownial~ 

American basswood 

white basswood3 

bigtooth aspen 

quaking aspen 

sassafrass 

silver maple 

blackgum 

swamp tupelo13 

aspens and cottonwoodsi1 

gray birch 

pumpkin ash 

white spruce 

white spruce 

white spruce 

white spruce 

white spruce 

white spruce 

key species 

key species 

redhvhite pine 

redhvhite pine 

key species 

redhvhite pine 

redlwhite pine 

redhvhite pine 

key species 

balsam fir 

balsam fir 

eastern hemlock 

eastern hemlock 

eastern hemlock 

eastern hemlock 

key species 

eastern hemlock 

eastern hemlock 

eastern hemlock 

eastern hemlock 

yellow-poplar 

yellow-poplar 

yellow-poplar 

yellow-poplar 

yellow-poplar 

key species 

basswood 

basswood 

basswood 

basswood 

key species 

basswood 

quaking aspen 

key species 

quaking aspen 

black cherry 

black cherry 

black cherry 

black cherry 

black cherry 

black cherry 

blue ash 

butternut 

black cherry 

pawpaw14 

eastern redbudi4 

Kentucky coffeetree15 

sugarberry 

white ash 

ashes1 l 

yellow buckeye 

Ohio buckeye3 

buckeyes\horsechestnutsl 

green ash 

cucumbertree 

sweetbay3 

magnolia3 

eastern cottonwood 

swamp cottonwood3 

red maple 

paper birch 

river birchi6 

hackberry 

sweetgum 

black willow 

willows1 

maples11 

sycamore 

American hornbeam 

black ash 

sourwood 

black maple 

American beech 

winged elmi7 

American elm 

rock elm 

birches1' 

elms11 

American chestnut 

apple 

misc. deciduous treesi1 

sugar maple 

water tupelo 

slippery elm 

yellow birch 

sweet birch 

American holly 

black locust 

yellowwood~8 

black walnut 

black cherry 

black cherry 

key species 

black cherry 

black cherry 

black cherry 

white ash 

key species 

white ash 

white ash 

white ash 

white ash 

white ash 

white ash 

white ash 

white ash 

white ash 

white ash 

key species 

redlsugar maple 

redlsugar maple 

redlsugar maple 

redlsugar maple 

redlsugar maple 

redtsugar maple 

redlsugar maple 

American beech 

American beech 

American beech 

American beech 

American beech 

key species 

American beech 

American beech 

American beech 

American beech 

American beech 

yellow birch 

yellow birch 

yellow birch 

yellow birch 

yellow birch 

yellow birch 

key species 

yellow birch 

yellow birch 

yellow birch 

yellow birch 

yellow birch 
Density' Tree species (~b/fP) 

2 Key species Density' Tree species (lblf?) 

2 Key species 

60 nutmeg hickory 

eastern hophornbeam 

white mulberryig 

red mulberryig 

mu~berries'~ 

chokecherry20 

60 American mountain-ash2' 

European mountain-ash2' 

61 serviceberry 

pecan 

honeylocust 

chestnut oak 

62 Osage-orange 

white oak 

scarlet oak 

southern red oak 

bur oak 

63 overcup oak22 

chinkapin oak22 

water oak 

pin oak 

northern red oak 

oaks 

oaks 

oaks 

oaks 

oaks 

oaks 

oaks 

oaks 

oaks 

oaks 

oaks 

key species 

oaks 

key species 

key species 

oaks 

oaks 

oaks 

oaks 

oaks 

oaks 

key species 

63 post oak 

black oak 

bitternut hickory 

shellbark hickory23 

hawthorn 

persimmon 

hickories1 l 124 

64 pignut hickory 

shagbark hickory 

mockernut hickory 

flowering dogwood 

northern in oak22 

bear oak f5 

shingle oak25 

blackjack oak25 

Shumard oak25 

65 laurel oak 

swamp chestnut oak 

67 willow oak 

68 water hickory 

cherrybark oak 

69 swamp white oak 

oaks 

key species 

key species 

key species 

hickories 

hickories 

key species 

key species 

key species 

key species 

hickories 

oaks 

oaks 

oaks 

oaks 

oaks 

oaks 

oaks 

oaks 

key species 

oaks 

oaks 

'where no density was available, averages have been assigned. Evergreen trees averaged 42 pounds per cubic foot, 

light deciduous trees averaged 47 pounds per cubic foot, and heavy deciduous trees averaged 60 pounds per cubic foot, 

green-weight basis. 

2~ey species are those for which there is a regression equation . The coefficients for key species were used to 

approximate other species with similar characteristics, primarily wood density. 

3~o green-weight density is available, but densities were available from within the same genus. 

4~verage of coast type, inland empire type, and Rocky Mountain type (Markwardt 1930). 

5~haw (1914) places Scotch pine in the subgenus Diploxylon, subsection Pinaster, and group Lariciones. Red pine also 

is placed in this taxonomic classification. 

6~uperficially, Austrian pine is similar to red pine (Harlow and Harrar 1969). 

7~o green-weight density is available. The average density for evergreen trees-42 pounds per cubic foot-has been used , 

(refer to footnote 1). 

8~oth Table Mountain pine and Virginia pine are small to medium-sized trees. In the Northeast, Table Mountain pine is 

found sparingly on dry, often rocky slopes (Harlow and Harrar 1969). Both species are similar to shortleaf pine in superficial 

characteristics and range. 

g~oxelder, in terms of dry weight, most closely approximates silver maple and striped maple. Boxelder averages 27 

pounds per cubic foot; the latter two maples average 33 pounds per cubic foot (Peattie 1958). Lacking a justifiable greenwei ht density, boxelder is placed with striped maple, the least dense maple in terms of green weight. 

QOMountain maple is similar to striped maple, both superficially and by density; both species average 33 pounds per cubic 

foot, dry-weig ht basis (Peattie 1958). 

I1~verage pounds per cubic foot are used for those species where only the genus is known. Cherries and plums 

average 40; aspens and cottonwoods average 45; ashes average 48; willows average 50; maples average 51 ; birches and 

elms average 54; hickories average 63; and miscellaneous or unknown evergreen trees and deciduous trees average, 

respectively, 42 and 55 pounds per cubic foot, green-weight basis. In the case of buckeyes and horsechestnuts, only one 

density is available: 49 pounds per cubic foot for yellow buckeye (Markwardt 1930). 

12~aulownia issimilar to catalpa in many ways, even though they are in different botanical families, Bignoniaceae and 

Scro hulariaceae respectively (Johnson 1973). ! Harlow and Harrar (1 969) consider swamp tupelo a variety of blackgum, Nyssa sylvatica var. biflora. I 

'"0 green-weight density is available. The average density for light deciduous trees47 pounds per cubic foot-has ,' been used (refer to footnote 1). 
I5~entucky coffeetree is botanically in the same subfamily as honeylocust and eastern redbud, Caesalpiniodeae 

(Hariow and Harrar 1969). In terms of wood density, it is more likely related to eastern redbud; the Forest Service identifies 

Kentucky coffeetree as a soft hardwood. 

%he desity of river birch, 40 pounds per cubic foot, most closely resembles paper birch, 39 pounds per cubic foot, 

dry-weig ht basis (Peattie 1958). 

17~arlow and Harrar (1 969) indicate that winged elm has been harvested as a substitute for rock elm. 

18~ellowwood is of the subfamily Papilionoideae, of the Leguminosea family (Harlow and Harrar 1969). The only other 

northeastern species in this taxonomic classification is black locust. 

Ig~ulberry is similar to Osage-orange; both are in the family Moraceae. While the wood of mulberry probably is not as 

heavy as that of Osage-orange, it is considered to have wood of medium weight (Peattie 1958). This, coupled with the fact 

that the Forest Service lists red mulberry as a hard deciduous tree, indicates that both red and white mulberry may be at or 

slightly over the limit for heavy deciduous trees. The average density for a heavy deciduous tree-60 pounds per cubic foothas been used (refer to footnote 1). 

20~eattie (1958) indicates that the wood of chokecherry is heavy and hard. The average density for a heavy deciduous 

trees-60 pounds per cubic foot-has been used (refer to footnote 1). 

21~he wood of the rowan-tree, as these two species have been called, generally is characterized as medium-light 

(Peattie 1958). However, its dry-weight density of 34 pounds per cubic foot indicates that it may be a heavy deciduous tree; 

medium-light deciduous trees such as eastern redbud and mulberry are classed as heavy deciduous trees. The average 

density for a heavy deciduous trees-60 pounds per cubic foot-has been used (refer to footnote 1). 

22~hispecies falls in the subgenera Leucobalanus, the white oaks (Harlow and Harrar 1969), which average 63 pounds 

per cubic foot, green-weight basis (Markwardt 1930). 

23~hellbark hickory falls into the subgenera Eucarya, the true hickories (Harlow and Harrar 1969), which average 63 

pounds per cubic foot, green-weight basis (Markwardt 1930). 

240ak or hickory could be used to predict the weight of trees whose densities are 63 pound per cubic foot or greater. 

Hickory was selected to predict the weight of trees in its own genus and other heavy deciduous trees; oak was selected to 

predict the weight of all oaks greater than 63 pounds per cubic foot. 

25~his species falls in the subgenera Erythrobalanus, the red or black oaks (Harlow and Harrar 1969), which average 64 

pounds per cubic foot, green-weight basis (Markwardt 1930). 
Appendix B 

Species key for dry weight regression equations of northeastern tree species 

Density' Tree species 

2 Density' Tree species 2 

(I blf?) Key species glwrt') Key species 

Evergreen Trees 

northern white-cedar 

Atlantic white-cedar 

eastern white pine 

balsam fir 

white spruce 

black spruce 

red spruce 

blue spruce3 

Norway spruce3 

eastern hemlock 

jack pine 

Table Mountain pine4 

misc. evergreen trees5 

baldcypress 

eastern redcedar 

Virginia pine4 

Douglas fir6 

red pine 

pitch pine 

Scotch pine7 

Austrian pine8 

tamarack 

larch3 

shortleaf pine 

pond pine 

loblolly pine 

Deciduous Trees 

23 balsam poplar 

24 ailanthus 

25 yellow buckeye 

pawpaw9 

swamp cottonwoodg 

buckeyes/horsechestnuts10 

26 black willow 

American basswood 

white basswoodll 

willows 

aspens and cottonwoods 

27 boxelderg 

butternut 

bigtooth aspen 

quaking aspen 

28 Ohio buckeyeg 

yellow-poplar 

eastern cottonwood 

pin cherry 

29 northern catalpa 

~aulownial~ 

key species 

n. white cedar 

key species 

key species 

red spruce 

red spruce 

key species 

red spruce 

red spruce 

key species 

eastern hemlock 

eastern hemlock 

eastern hemlock 

red pine 

red pine 

red pine 

red pine 

key species 

red pine 

red pine 

red pine 

larch 

key species 

larch 

larch 

larch 

quaking aspen 

quaking aspen 

quaking aspen 

quaking aspen 

quaking aspen 

quaking aspen 

quaking aspen 

quaking aspen 

quaking aspen 

quaking aspen 

quaking aspen 

quaking 'aspen 

quaking aspen 

quaking aspen 

key species 

yellow-poplar 

key species 

yellow-poplar 

yellow-poplar 

yellow-poplar 

yellow-poplar 

American chestnut 

striped maple 

sassafrass 

cherries and plums1° 

silver maple 

mountain mapleg 

black ash 

sweetgum 

cucumbertree 

sweetbayl l 

American mountain-ashg 

European mountain-ashi1 

gray birch 

water tupelo 

blackgum 

swamp tupelo13 

sycamore 

black cherry 

sugarberry 

pumpkin ash 

American elm 

hackberry 

red mulberryg 

white mulberry1 l 

slippery elm 

red maple 

soutwood 

ashes 

39 paper birch 

eastern redbudg 

yellowwood9 

black walnut 

birches 

elms10 

40 black maple 

river birchg 

green ash 

blue ash 

American holly 

maples10 

41 Kentucky coffeetreeg 

southern red oak 

42 nutmeg hickory 

white ash 

hardwoodst0 

43 yellow birch 

water hickory 

black oak 

44 sugar maple 

honeylocust 

yellow-poplar 

black cherry 

black cherry 

black cherry 

black cherry 

black cherry 

black cherry 

black cherry 

black cherry 

black cherry 

black cherry 

black cherry 

black cherry 

black cherry 

black cherry 

black cherry 

black cherry 

key species 

red maple 

red maple 

red maple 

red maple 

red maple 

red maple 

red maple 

key species 

red maple 

red maple 

red maple 

red maple 

American beech 

red maple 

red maple 

red maple 

red maple 

red maple 

red maple 

red maple 

red maple 

red maple 

white ash 

white ash 

white ash 

key species 

white ash 

key species 

black oak 

key species 

key species 

sugar maple 
Density' Tree species MP) 

2 Key species Density' Tree species wf?) Key speciesz 

44 northern pin oak 

bear oak14 

laurel oak 

water oak 

pin oak 

northern red oak 

Shumard oakt4 

rock elm 

45 American beech 

bur oak 

46 sweet birch 

bitternut hickory 

blackjack oak9 

chestnut oak 

47 pecan 

apple 

scarlet oak 

shingle oak9 

swamp chestnut oak 

post oak 

hawthorn 

northern red oak 

northern red oak 

norrthern red oak 

northern red oak 

northern red oak 

key species 

northern red oak 

northern red oak 

key species 

American beech 

chestnut oak 

chestnut oak 

chestnut oak 

key species 

scarlet oak 

scarlet oak 

key species 

scarlet oak 

scarlet oak 

scarlet oak 

white oak 

water hickory 

winged elm9 

American hornbeam 

black locust 

osage-orangels 

chokecherry15 

white oak 

cherrybark oak 

willow oak 

eastern hophornbeam 

shellbark hickoryg 

swamp white oak 

hickories1° 

shagbark hickory 

mockernut hickory 

flowering dogwood 

overcup oak9 

serviceberry 

persimmon 

chinkapin oak9 

pignut hickory 

scarlet oak 

scarlet oak 

white oak 

white oak 

white oak 

white oak 

key species 

white oak 

white oak 

hickory 

hickory 

hickory 

hickory 

hickory 

hickory 

hickory 

hickory 

hickory 

hickory 

hickory 

hickory 

'where no density was available, averages have been assigned. Evergreen trees and light deciduous irees averaged 31 

pounds per cubic foot, and heavy deciduous trees averaged 48 pounds per cubic foot, dry-weight basis. 

2~ey species are those for which there is a regression equation. The coefficients for key species were used to 

approximate other species with similar characteristics, primarily wood density. 

3~o dry-weight density is available, but densities were available from within the same genus. 

4~ry-weight ratio from Peattie (1958). 

5~o dry-weight density is available. The average density for evergreen trees-31 pounds per cubic foot-has been used 

(refer to footnote 1). 

6~verage of coast type, inland empire type, and Rocky Mountain type (Markwardt 1930). 

 haw (1914) places Scotch pine in the subgenus Diploxylon, subsection Pinaster, and group Lariciones. Red pine also 

is in this taxonomic classification. 

8~uperficially, Austrian pine is similar to red pine (Harlow and Harrar 1969). 

g~ry-weight ratio from Peattie (1 958). 

1°~verages are used for those species where only the genus is known. Cherries and plums average 32, elms average 

39, maples average 40, deciduous trees average 42, and hickories average 50 pounds per cubic foot, dry-weight basis. In 

the case of buckeyes and horsechestnuts, only one density is available: 25 pounds per cubic foot for yellow buckeye 

(Markwardt 1930). 

dry-weight density is available, but densities were available from within the same genus. 

12~aulownia is similar to catalpa in many ways, even thcugh they are in different botanical families, Bignoniaceae and 

Scro hulariaceae, respectively (Johnson 1973). ! Harlow and Harrar (1 969) consider swamp tupelo a variety of blackgum, classifying it as Nyssa sylvatica var. biflora. 

14~his species falls into the subgenera Erythrobalanus, the red or black oaks (Harlow and Harrar 1969), which average 

44 pounds per cubic foot, dry-weight basis (Markwardt 1930). 

15No dry-weight density is available. The average density for heavy deciduous trees-48 pounds per cubic foot-has been 

used (refer to footnote 1). 
Appendix C 

Species key for dry weight regression'equations of northeastern shrub species 

Shrub species 1 Key species Shrub species Key species1 

common juniper 

Canada yew 

alder 

Hercules club 

bog rosemary 

chokeberry 

azalea 

barberry 

New Jersey tea 

leatherleaf 

fringetree 

sweetfern 

alternate-leaved dogwood 

silky dogwood 

round-leaved dogwood 

gray-stemmed dogwood 

red-osier dogwood 

American hazelnut 

bleaked hazelnut 

leatherwood 

autumn olive 

huckleberry 

witch-hazel 

large-leaf holly 

winterberry holly 

sheep laurel 

mountain laurel 

Labrador tea 

common spicebush 

honeysuckle 

Allegheny menziesia 

key species 

common juniper2 

key species 

European red raspberry2 

Labrador tea3 

alder-leaved, iyckthorn3 

rhododendron3 

key species 

key species 

key species 

sheep laurel3 

key species 

red-osier dogwood 

red-osier dogwood 

key species 

round-leaved dogwood 

key species 

bleaked hazelnut 

key species 

red-osier dogwood3 

mountain-holly3 

black huckieberry4 

key species 

winterberry holly 

key species 

key species 

sheep laurel 

key species 

red-osier dogwood3 

American-fly honeysuckle4 

rust menziesia4 

bayberry 

mountain-holly 

ninebark 

buckthorn 

rhododendron 

winged sumac 

smooth sumac 

staghorn sumac 

poison sumac 

current, gooseberry 

rose 

brier, bramble, raspberry 

American elderberry 

red-berried elderberry 

buffalo-berry, soapberry 

spirea 

snowberry 

sweetleaf 

American bladdernut 

blueberry 

viburnum 

maple-leaved viburnum 

hobblebush viburnum 

wild raisin 

arrowwood 

nannyberry 

blackhaw 

highbush cranberry 

common prickly-ash 

unknown deciduous shrub 

unknown evergreen shrub 

key species 

key species 

mallow ninebark4 

alder-leaved buckthorn4 

rhodora4 

red-osier dogwood2 

red-osier dogwood2 

red-osier dogwood2 

red-osier dogwood2 

key species 

key species 

European red raspberry4 

red-osier dogwood2 

red-osier dogwood2 

key species 

key species 

key species 

mountain-holly3 

red-osier dogwood3 

key species 

American wayfaring tree4 

American wayfaring tree4 

American wayfaring tree4 

American wayfaring tree4 

American wayfaring tree4 

American wayfaring tree4 

American wayfaring tree4 

American wayfaring tree4 

red-osier dogwood2 

European red raspberry5 

sheep laurel6 

'~ey species are those for which there is a regression equation. The coefficients for key species were used to 

approximate other species with similar characteristics. Unless otherwise noted, key species were from within the same 

genus. 

2~ased on Symonds and Merwin (1 963). Where an equation did not exist, common juniper, Juniperus communis, was 

used for evergreen, needled species; sheep laurel, Kaimia augustifolia, was used for evergreen, broad-leaved species; and 

red-osier dogwood, Cornus sfoionifera, was used for high deciduous shrubs. 

3~ased on personal communication with Robert T. Brooks, USDA Forest Service, Northeastern Forest Experiment 

Station, Forest Inventory and Analysis, Amherst, MA, 30 May 1991. 

4~ey species that are not northeastern species, and so do not appear in Appendix E, northeastern trees and shrubs. 

They are: black huckleberry, Gaylussacia baccata; American-fly honeysuckle, Lonicera canadensis; rust menziesia, 

Menziesia ferruginea; mallow ninebark, Physocarpus malvaceus; alder-leaved buckthorn, Rhamnus alnifolia; rhodora, 

Rhododendron canadense; European red raspberry, Rubus idaeus; American wayfaring tree, Viburnum latanoides. 

5~nknown deciduous shrubs were assumed to be low shrubs, probably brambles or briers. 

%-iknown evergreen shrubs were assumed to be broad leaved, probably closest in form and habitat to laurel. 
Appendix D 

Species key for total tree height regression equations, northeastern tree species 

Species Height Bole and crown characteristics 1 

(feet) Key speciesL 

vergreen Trees 

balsam fir 

Atlantic white-cedar 

eastern redcedar 

larch 

tamarack 

Norway spruce 

white spruce 

black spruce 

blue spruce 

red spruce 

jack pine 

shortleaf pine 

Table Mountain pine 

red pine 

pitch pine 

pond pine 

eastern white pine 

Scotch pine 

loblolly pine 

Virginia pine 

Austrian pine 

Douglas fir 

baidcypress 

northern white-cedar 

eastern hemlock 

Deciduous Trees 

boxelder 

black maple 

striped maple 

red maple 

silver maple 

sugar maple 

mountain maple 

Ohio buckeye 

yellow buckeye 

ailanthus 

serviceberry 

pawpaw 

yellow birch 

sweet birch 

river birch 

paper birch 

gray birch 

American hornbeam 

water hickory 

bitternut hickory 

40-60 

80-85 

40-50 

140-1 80 

40-80 

tall 

60-70 

30-40 

100-1 20 

60-70 

70-80 

80-1 00 

medium 

50-80 

50-60 

medium 

80-1 00 

3 

90-1 10 

40 

50-80 

180-250 

100-1 20 

40-50 

60-70 

75 

60-80 

small 

50-70 

60-80 

60-80 

small 

60-90 

60-90 

3 

small 

-3 

60-70 

50-60 

70-80 

50-70 

20-30 

small 

110-140 

50-60 

moderately tapering bole-dense narrow pyramidal crown 

long, cylindrical, clear bole-small, narrow, conical crown 

tapering bole-dense, narrow, pyrimidal crown 

long, clear bole-short, open, pyramidal crown 

long, clear, cylindrical bole-open, pyramidal crown 

3 

3 

long, straight, tapering bole-irregular, cylindrical crown 

3 

long, cylindrical bole-pagoda-shaped crown 

irregular, rounded crown 

well-formed, clear bole-small, narrow, pyramidal crown 3 

well-formed, long, cylindrical bole-symmetric, oval crown 

mostly tall, columnar bole-open, small crown 

southern variety of pitch pine 

tall, clear, cylindrical bole-plume-like crown 

-3 

long, cylindrical bole-open, yet dense crown 

small bole-unkempt crown 

similar to red pine 

long, cylindrical bole-rounded or irregular, flat-topped crown 

tapering bole-irregular, flattened crown 

tapering bole-irregular, oblong crown 

tapering bole-pyramidal crown; branches may touch ground 

irregular bole-bushy, spreading crown 

similar to sugar maple 

shrub-like 

long bole-irregular crown 

short bole, dividing low into branches 

straight, full bole 

shrub-like 

3 

-3 

shrub-like 

-3 

long, well-formed bole-round, irregular crown 

long, clear bole 

dividing bole, several branches 

long, clear bole-open, irregular, pyramidal crown 

poo-rly shaped bole, limby-open, irregular, pyramidal crown 

poorly formed, twisted bole 

similar to pecan 

-3 

key species 

balsam fir 

balsam fir 

red pine 

red pine 

white spruce 

key species 

white spruce 

white spruce 

white spruce 

red pine 

red pine 

red pine 

key species 

red pine 

red pine 

red pine 

red pine 

red pine 

red pine 

red pine 

white spruce 

white spruce 

balsam fir 

balsam fir 

red maple 

sugar maple 

red maple 

key species 

red maple 

key species 

red maple 

white ash 

white ash 

red maple 

red maple 

red maple 

key species 

yellow birch 

yellow birch 

yellow birch 

red maple 

red maple 

yellow-poplar 

yellow-poplar 
Species Height Bole and crown characteristics 1 

(feet) 

2 Key species 

pignut hickory 

pecan 

shellbark hickory 

nutmeg hickory 

shagbark hickory 

mockernut hickory 

American chestnut 

Northern catalpa 

sugarberry 

hackberry 

eastern redbud 

yellowwood 

flowering dogwood 

hawthorn 

persimmon 

American beech 

white ash 

black ash 

green ash 

pumpkin ash 

blue ash 

honeylocust 

Kentucky coffeetree 

American holly 

butternut 

black walnut 

sweetgum 

yellow-poplar 

Osage-o range 

cucumbertree 

sweetbay 

apple 

white mulberry 

red mulberry 

water tupelo 

blackgum 

swamp tupelo 

eastern hophornbeam 

sourwood 

Paulownia 

Sycamore 

balsam poplar 

eastern cottonwood 

bigtooth aspen 

swamp cottonwood 

quaking aspen 

pin cherry 

black cherry 

chokecherry 

white oak 

swamp white oak 

scarlet oak 

northern pin oak 

southern pin oak 

50-60 

110-140 

70-80 

80-1 00 

70-80 

40-60 

small 

120 

60-80 

30-40 

small 

3 

40 

small 

30-50 

70-80 

70-80 

40-50 

30-50 

-3 

medium 

70-80 

medium 

40-50 

40-60 

70-90 

80-1 20 

100-1 20 

small 

80-90 

60-70 

3 

small 

small 

80-90 

50-60 

50-60 

small 

small 

-3 

80-1 00 

60-80 

80-1 00 

60-70 

medium 

50-60 

small 

50-60 

small 

80-1 00 

60-70 

70-80 

70-80 

60-80 

similar to shagbark hickory 

-3 

straight, cylindrical bole-oblong crown 

-3 

blight-affected 

well-formed bole (may be crooked) 

-3 

-3 

-3 

-3 

short bole, branchy-low, dense crown 

shrub-like 

short bole-round-topped crown 

straight, clear, massive bole-spreading crown 

long, straight, cylindrical bole-crown open 

poorly formed bole-small, open crown 

short, poorly formed bole-broad, irregular crown 

3 

short bole-open, spreading crown - 3 

straight, short bole 

short bole, stout limbs-broad, open crown 

tall, well-formed bole-small, open crown 

long, straight bole-small, oblong or pyramidal crown 

tall, clear, arrow-straight bole-small, open, oblong crown 

shrub-like 

straight, clear bole-pyramidal crown 

similar to cucumbertree 

-3 

shrub-like 

shrub-like 

long, clear bole-crown narrow and open 

-3 

similar to blackgum 

similar to hornbeam 

-3 

-3 

open, spreading crown 

long, cylindrical bole-narrow, pyramidal crown 

ong, cylindrical bole-somewhat small crown 

similar to quaking aspen 

similar to other cottonwoods 

long, clear bole-small, rounded crown 

shrub-like 

long, straight, clear, cylindrical bole-narrow, oblong crown 

shrub-like 

short, stocky bole-wide-spreading crown 

poorly pruned bole-irregular crown 

3 

similar to pin oak 

similar to southern red oak 

black cherry 

yellow-poplar 

basswood 

basswood 

basswood 

black cherry 

red maple 

yellow-poplar 

sugar maple 

yellow birch 

red maple 

red maple 

red maple 

red maple 

red maple 

key species 

key species 

yellow birch 

yellow birch 

yellow birch 

yellow birch 

red maple 

red maple 

red maple 

beech 

basswood 

basswood 

key species 

red maple 

basswood 

basswood 

red maple 

red maple 

red maple 

basswood 

black cherry 

black cherry 

red maple 

red maple 

red maple 

beech 

quaking aspen 

quaking aspen 

quaking aspen 

quaking aspen 

key species 

red maple 

key species 

red maple 

key species 

scarlet oak 

key species 

white oak 

northern red oak 
Species Height Bole and crown characteristics 1 

(feet) 

-- 

Key specie$ 

cherrybark oak 

bear oak 

shingle oak 

laurel oak 

overcup oak 

bur oak 

blackjack oak 

swamp chestnut oak 

chinkapin oak 

water oak 

pin oak 

willow oak 

chestnut oak 

northern red oak 

Shumard oak 

post oak 

black oak 

black locust 

black willow 

sassafrass 

American mountain-ash 

Europeon mountain-ash 

American basswood 

white basswood 

.winged elm 

American elm 

slippery elm 

rock elm 

unknown species 

100-1 30 

3 

50-60 

60-70 

70-80 

70-80 

small 

60-80 

60-80 

60-70 

70-80 

80-1 00 

50-60 

60-80 

100-1 25 

40-50 

50-60 

40-60 

30-60 

60-80 

small 

small 

70-80 

60-80 

small 

80-1 00 

60-70 

60-80 

clear bole 

3 

3 

3 

poorly-formed, short, twisted bole-large, open crown 

massive bole, stout branches-broad crown 

poorly formed bole 

well-formed, straight, massive bole-narrow crown 

-3 

tall, slender bole-symmetrical, rounded crown 

tough-studded-branchlet bole-broad, pyramid crown 

full rounded crown, slender lower branches 

-3 

tall, straight, columnar bole-small rounded bole 

Long, clear, symmetrical bole-open, wide-spreading crown 

gnarled, twisted bole-often shrubby crown 

tapering, limby bole-irregular, rounded crown 

poorly formed bole-spreading crown 

broad, irregular crown 

3 

shrub-like 

shrub-like 

long, cylindrical bole 

similar to American basswood 

3 

columnar trunk, branching at 50-60 feet-small, arching crown 

great clear bole 

tall, straight, unbranched bole-narrow, oblong crown 

northern red oak 

chestnut oak 

chestnut oak 

scarlet oak 

white oak 

white oak 

chestnut oak 

northern red oak 

northern red oak 

northern red oak 

white oak 

white oak 

key species 

key species 

scarlet oak 

chestnut oak 

black oak 

red maple 

red maple 

red maple 

red maple 

red maple 

key species 

basswood 

red maple 

yellow-poplar 

yellow-poplar 

yellow-poplar 

red maple 

l~ole and crown characteristics from Harlow and Harrar (1969). 

*~ey species are those for which there is a regression equation. The coefficients for key species are then used to 

approximate other species with similar characteristics. Refer to the appropriate table of regression coefficients. 

31nformation is not available. 
Appendix E 

Northeastern tree and shrub species 

Common name Genus and species Common name Genus and species 

Evergreen Trees 

fir 

balsam fir 

cedar 

Atlantic white-cedar 

eastern redcedar 

larch 

tamarack 

Norway spruce 

white spruce 

black spruce 

blue spruce 

red spruce 

jack pine 

shortleaf pine 

Table Mountain pine 

red pine 

pitch pine 

pond pine 

eastern white pine 

Scotch pine 

loblolly pine 

Virginia pine 

Austrian pine 

Douglas fir 

baldcypress 

northern white-cedar 

eastern hemlock 

Deciduous Trees 

maple 

boxelder 

black maple 

striped maple 

red maple 

silver maple 

sugar maple 

mountain maple 

buckeye, horsechestnut 

Ohio buckeye 

yellow buckeye 

ailanthus 

serviceberry 

pawpaw 

birches 

yellow birch 

sweet birch 

river birch 

paper birch 

gray birch 

American hornbeam 

Abies sp. 

A. balsarnea 

Charnaecyparis sp. 

C. thyoides 

Juniperus virginiana 

Larix sp. 

L. laricina 

Picea abies 

P. glauca 

P. rnariana 

P. pungens 

P. rubens 

Pinus banksiana 

P. echinata 

P, pungens 

P. resinosa 

P. rigida 

P. serotina 

P. strobus 

P. sylvestris 

P. taeda 

P. virginiana 

P. nigra 

Pseudotsuga menziesii 

Taxodiurn distichurn 

Thuja occidentalis 

Tsuga canadensis 

Acer sp. 

A. negundo 

A. nigrurn 

A. pensylvanicurn 

A. rubrurn 

A. saccharinurn 

A. saccharurn 

A. spicaturn 

Aesculus sp. 

A. glabra 

A. octandra 

Ailanthus altissima 

Arnelarnchier sp. 

Asirnina triloba 

Betula sp. 

B. alleghaniensis 

5. lenta 

5. nigra 

5. papyrifera 

9. populifolia 

Carpinus caroliniana 

hickory 

water hickory 

bitternut hickory 

pignut hickory 

pecan 

shellbark hickory 

shagbark hickory 

mockernut hickory 

American chestnut 

catalpa 

Northern catalpa 

sugarberry 

hackberry , 

eastern redbud 

yellowwood 

flowering dogwood 

hawthorn 

persimmon 

American beech 

ash 

white ash 

black ash 

green ash 

pumpkin ash 

blue ash 

honeylocust 

Kentucky coffeetree 

American holly 

butternut 

black walnut 

sweetgum 

yellow-poplar 

Osage-orange 

magnolia 

cucumbertree 

sweetbay 

apple 

mulberry 

white mulberry 

red mulberry 

tupelo 

water tupelo 

blackgum 

swamp tupelo 

eastern hophornbeam 

sourwood 

Paulownia 

American sycamore 

poplar, cottonwood 

balsam poplar 

eastern cottonwood 

bigtooth aspen 

Carya sp. 

Carya aquatica 

C. cordiforrnis 

C. glabra 

C. illinoensis 

C. laciniosa 

C. ovata 

C, tomentosa 

Castanea dentata 

Catalpa sp. 

C. speciosa 

Celtis laevigata 

C. occidentalis 

Cercis canadensis 

Cladrastis lutea 

Cornus florida 

Crataegus sp. 

Diospyros virginiana 

Fagus grandifolia 

Fraxinus sp. 

F. americana 

F. nigra 

F. pennsylvanica 

F. profunda 

F. quadrangulata 

Gleditsia triacanthos 

Gyrnnocladus dioicus 

llex opaca 

Juglans cinerea 

J, nigra 

Liquidambar styraciflua 

Liriodendron tulipifera 

Maclura pornifera 

Magnolia sp. 

M, acuminata 

M, virginiana 

Malus sp. 

Morus sp. 

M. alba 

M. rubra 

Nyssa sp. 

N. aquatica 

N. sylvatica 

N. biflora 

Ostrya virginiana 

Oxydendrum arboreurn 

Paulownia tomentosa 

Platanus occidentalis 

Populus sp. 

P. balsamifera 

P. deltoides 

P. grandidentata 
Common name Genus and species 

swamp cottonwood 

quaking aspen 

cherry, plum 

pin cherry 

black cherry 

chokecherry 

oak 

white oak 

swamp white oak 

scarlet oak 

northern pin oak 

southern pin oak 

cherrybark oak 

bear oak 

shingle oak 

laurel oak 

overcup oak 

bur oak 

blackjack oak 

swamp chestnut oak 

chinkapin oak 

water oak 

pin oak 

willow oak 

chestnut oak 

northern red oak 

Shumard oak 

post oak 

black oak 

black locust 

willow 

black willow 

sassafrass 

American mountain-ash 

Europeon mountain-ash 

basswood 

American basswood 

white basswood 

elm 

winged elm 

American elm 

slippery elm 

rock elm 

Evergreen Shrubs 

common juniper 

Canada yew 

bog rosemary 

sheep laurel 

mountain laurel 

Labrador tea 

rhododendron 

sweetleaf 

Populus heterophylla 

P. tremuloides 

Prunus sp. 

P. pensylvanica 

P. serotina 

P. virginiana 

Quercus sp. 

Q. alba 

Q. bicolor 

Q. coccinea 

Q. ellipsoidalis 

Q. falcata var. falcata 

Q. falcata var. pagodaefolia 

Q. ilicifolia 

Q. imbricaria 

Q. laurifolia 

Q, lyrata 

Q. macrocarpa 

Q, marilandica 

Q. michauxii 

Q. muehlenbergii 

Q. nigra 

Q. palustris 

Q. ph ellos 

Q. prinus 

Q. rubra 

Q. shumardii 

Q, stellata 

Q. velutina 

Robinia psuedoacacia 

Salix sp. 

S, nigra 

Sassafras albidum 

Sorbus americana 

S, aucuparia 

Tilia sp. 

T. americana 

T. heterophylla 

Ulmus sp. 

U. alata 

U. americana 

U. rubra 

U, thomasii 

JunFerus communis 

Taxus canadensis 

Andromeda glaucophylla 

Kalmia augustifolia 

K. latifolia 

Ledum groenlandicum 

Rhododendron sp. 

Symplocos tinctoria 

Common name Genus and species 

Deciduous Shrubs 

aider 

Hercules club 

chokeberry 

azalea 

barberry 

New Jersey tea 

leatherleaf 

fringetree 

sweetfern 

alternate-leaved dogwood 

silky dogwood 

round-leaved dogwood 

gray-stemmed dogwood 

red-osier dogwood 

American hazelnut 

bleaked hazeknut 

leatherwood 

autumn olive 

huckleberry 

witch-hazel 

large-leaf holly 

winterberry holly 

common spicebush 

honeysuckle 

Allegheny menziesia 

bayberry 

mountain-holly 

ninebark 

buckthorn 

winged sumac 

smooth sumac 

staghorn sumac 

poison sumac 

current, gooseberry 

rose 

brier, bramble, raspberry 

American elderberry 

red-berried elderberry 

buffalo-berry, soapberry 

spirea 

snowberry 

American bladdernut 

blueberry 

viburnum 

maple-leaved viburnum 

hobblebush viburnum 

wild raisin 

arrowwood 

nannyberry 

blackhaw 

highbush cranberry 

common prickly-ash 

Alnus sp. 

Aralia spinosa 

Aronia sp. 

Azalea sp. 

Berberis sp. 

Ceanothus americanus 

Chamaedaphne calyculata 

Chionan thus virginicus 

Comptonia peregrina 

Cornus alternifolia 

C. amomum (obliqua) 

C. rugosa (circinata) 

C. racemosa (paniculata) 

C. stolonifera 

Corylus americana 

C. cornuta 

Dirca palustris 

Elaeagnus augustifolia 

Gaylussacia sp. 

Hamamelis virginiana 

llex montana (monticola) 

I. verticillata 

Lindera benzoin 

Lonicera sp. 

Menziesia pilosa 

Myrica pensylvanica 

Nemopanthus mucronatus 

Physocarpus opulifolius 

Rhamnus sp. 

Rhus copallina 

R. glabra 

R. typhina 

R. vernix 

Ribes sp. 

Rosa sp. 

Rubus sp. 

Sambucus canadensis 

S. racemosa 

Shepherdia canadensis 

Spirea sp. 

Symph oricarpos albus 

Staphylea trifolia 

Vaccinium sp. 

Viburnum sp. 

V. acerifolium 

V. alnifolium 

V. cassinoides 

V. dentatum 

V. lentago 

V. prunifolium 

V. trilobum 

Zanthoxylum americanum 
Definitions 

The following definitions are based on the USDA Forest Service's "Forest Survey Handbook", except for those 

dealing with shrubs, which are based on definitions from the Society of Range Management (1 974). 

Board foot-A unit of lumber measurement 1 foot 

long, 1 foot wide, and 1 inch thick, or its equivalent. 

Biomass-The quantity of material in a living 

organism, measured in weight. 

Branches-All wood and bark outside of the 

minimum top diameter of the merchantable stem. 

Includes the unmerchantable stem, branches, and 

twigs; excludes foliage. 

Cull tree-A rough cull tree or rotten cull tree. 

Dead tree-A tree whose cambium is no longer 

living, ordinarily indicated by permanent loss of all 

foliage. The tree may be standing, fallen, 

windthrown, knocked down, or broken off. 

Deciduous-Dicotyledonous trees and shrubs, 

having broad leaves that drop seasonally, though 

some are persistent. Deciduous trees are 

commonly called hardwoods even though some 

have soft wood. 

Density-Weight per unit volume, usually expressed 

in lb/ft3 or gm/cm3. Basic wood density is based on 

oven-dry weight and green volume. 

Diameter at breast height (d.b.h.)-The diameter 

taken outside the bark of standing trees measured 

at 4% feet above the ground. 

Diameter outside bark (d.o.b.kThe diameter 

taken outside the bark at some specified point on 

the main stem of a standing or down tree. 

Dry weight-A unit of measure, when applied to 

trees, is the oven-dry weight of wood and bark, 

expressed in pounds or tons. 

Evergreen-Trees and shrubs having needles or 

scale-like leaves, usually cone-bearing. Evergreen 

trees are commonly called softwoods even though 

some have hard wood. 

Fofiage-The leaves and fruits in trees and shrubs. 

Forest biomass-The weight of wood, bark, and 

foliage in all living woody plants. Includes the 

biomass of growing stock, above and below ground 

biomass of timber, and the aboveground biomass of 

salvable dead trees, seedlings, saplings, and 

shrubs. Does not include the biomass of grasses, 

forbs, or other similar herbaceous plants. 

Forest lancCLand areas 1 acre in size at least 

10-percent stocked with trees of any size, or that 

formerly had such tree cover and are not currently 

developed for a nonforest use. 

Geographic unit-A county or a group of counties 

within a state large enough to provide an adequate 

sample base and have common physiographic 

features. 

Green weight-A unit of measure, when applied to 

trees and shrubs, is the weight of wood and bark 

while they are alive, freshly cut, or still containing 

most of the moisture present at the time of cutting. 

Expressed in pounds or tons. 

Growing stock-The main stem of growing-stock 

trees between a 1 -foot stump height and the 

growing-stock top. Also called the merchantable 

stem. 

Growing-stock topThe point on the main stem of 

a growing-stock tree above which industrial products 

cannot be produced. The minimum growing-stock 

top is 4.0 inches d.o.b., or the point at which the 

main stem breaks into branches if this occurs before 

it reaches the top diameter. 

Growing-stock tree-A live tree of commercial 

species 5.0 inches d.b.h. or larger, that contains at 

least one 12-foot sawlog or two 8-foot 

noncontiguous sawlogs, and meets regional 

specifications for freedom from defect. Excludes 

rough cull trees and rotten cull trees. 

Growing stock biomass-The weight of wood and 

bark in growing stock. Includes sawlog biomass and 

upper stem biomass. 
Merchantable stem-The main stem of 

growing-stock trees between a l -foot stump height 

and the growing-stock top. Also called growing 

stock. 

Moisture content-In wood, the amount of water 

present, generally expressed as a percentage of its 

oven-dry weight. 

Nontimber-Trees that are not timber, such as 

salvable dead trees, seelings, and saplings. Does 

not include shrubs, which are not trees. 

Other forest land-Forest land other than 

timberland; includes reserved, unproductive, and 

urban forest land. 

Oven-dry-The process whereby green wood has 

had moisture removed in a kiln through the use of 

artificial heat, in the range of 102"o 105%. 

Poletimber-Growing-stock trees at least 5.0 inches 

d.b.h., but smaller than sawtimber trees. 

Reserved forest land-Forest land withdrawn from 

timber utilization by administrative designation, such 

as parks or exclusive use for Christmas trees. 

Roots-The entire below ground portion of trees. 

Rotten cull tree-A live tree of a commercial 

species, 5.0 inches d.b.h. or larger, that does not 

meet regional specifications for freedom from defect 

primarily because more than 50 percent of the tree 

is rotten. 

Rough cull tree-A live tree of commercial species 

5.0 inches d.b.h. or larger (or a live tree of a 

noncommercial species), that does not meet 

regional specifications for freedom from defect 

primarily because of poor form. 

Salvable dead tre+A dead tree 5.0 inches d.b.h. or 

larger that has died recently. Many branches remain 

and the bark is still intact (it is generally very tight 

and difficult to remove from the tree). 

Sapling-A live tree less than 5.0 inches d.b.h., but 

larger than 1.0 inch. 

Sawlog-The main stem of a sawtimber tree 

between the stump height and the sawlog top. 

Sawlog topThe point on the main stem of a 

sawtimber tree above which a sawlog cannot be 

produced. The minimum sawlog top is 7.0 inches 

d.0.b. for softwoods and 9.0 inches d.0.b. for 

hardwoods. 

Sawtimber-Growing-stock trees at least 9.0 inches 

d.b.h. for sofiwoods or 11.0 inches for hardwoods, 

containing at least one 12-foot sawlog or two 

noncontiguous 8-foot sawlogs, and meeting regional 

specifications for freedom from defect. Small 

sawtimber is less that 15.0 inches d.b.h.; large 

sawtimber is greater than or equal to 15.0 inches 

d.b.h. 

Seedling-A live tree less than 1.0 inch d.b.h., and 

that is free to grow. 

Shrub-A live plant that has a persistent, woody 

stem and relatively low growth habit, and that 

generally produces several basal shoots. Differs 

from a tree because of its low stature and 

nonarborescent form. 

SnapA dead tree 5.0 inches d.b.h. or larger that 

has been dead for some time. Most of the bark may 

be missing, or the tree may be covered with bark 

that is very loose (it can be removed with little 

effort). 

Sound cull-Defect that is free from decay. The 

defect may lower the timber's utility or commercial 

value, relegating it to a lower grade, but does not 

cause its rejection. It may be a blemish or 

imperfection such as stain that may be acceptable 

by regional standards, or cull such as crook or 

sweep which affects only the board-foot volume of a 

log. 

Specific gravity-As applied to wood, the ratio of 

the oven-dried weight of a wood sample to the 

weight of a volume of water equal to the volume of 

the sample at a specified moisture content (green, 

air-dried, oven-dried). 

StumpThe main stem of living trees 5.0 inches 

d.b.h. and larger, between the ground and a 1-foot 

height. 

Timber-All living trees 5.0 inches d.b.h. and larger, 

including growing-stock trees and cull trees. 
Timber biomass-The weight of wood, bark, and 

foliage in growing-stock trees and cull trees. 

Includes the biomass of all tree components above 

and below the ground such as the merchantable 

stem, unmerchantable stem, branches, twigs, 

stump, and roots. 

Timberland-Forest land producing or capable of 

producing annual crops of industrial forest products 

(at least 20 cubic feet per acre per year). Does not 

include productive-resewed land. Currently 

inaccessible and inoperable areas are included 

except when the areas involved are small and 

unlikely to become suitable for production of 

industrial wood. 

Tree-A woody plant that has a well-developed stem 

and is usually more than 12 feet high at maturity. 

Unmerchantable stem-The main stem of growingstock trees above the growing-stock top. 

Unproductive forest land-Forest land that is 

incapable of producing 20 cubic feet per acre per 

year of industrial wood under natural conditions, 

because of adverse site conditions (also called 

woodland). 

Upper stem-The main stem of sawtimber trees 

between the sawlog top and growing-stock top. 

Urban forest land-Forest land that is surrounded 

by urban development (not parks) whether 

commercial, industrial, or residential. 

QU.S. GOVERNMENT PRINTING OFFICE: 1993-551-163180901 

- .- - - - - - - - 
! l%'har[o;a, H.; Griffjth, C [as hi. "99. Methos $0 estgm&~ ] 

: tr%al~~restbiamass4ar B surveys: app2icetisns I2 1 the noflheasterw 3.S, Res. , Wadwar, %A: L.S. , 

Noflheastern Foras' j 

1 

I 

1 

i Freser!ts methcds fo- systhesizing ic-.%oa~ati~n from existing i ti 

; tineraw re when making biomass assessments over extel-she 

i 1 areas, SSU& as for a stsfe er rsgicn. Described a:e genera! applicalkxs j 

1 to Ibe noflheaslerc Uwiisd States, an@ specific a~pticstians to Ohio. 

1 Tabies oi appropriak regression equations ar,d the tree arid shrub I 1 s~eciies to which 5eee aq~alicns san be appkd ssa presented. 
shire