United States

Department of

Agriculture

Forest Service

Northeastern

Research Station

General Technical

Report NE-312

Rachel Riemann

Pilot Inventory of FIA Plots

Traditionally Called ‘Nonforest’
Published by: For additional copies:

USDA FOREST SERVICE USDA Forest Service

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November 2003 Fax: (740)368-0152

Visit our homepage at: http://www.fs.fed.us/ne

Abstract

Forest-inventory data were collected on plots defined as “nonforest” by the USDA Forest Service’s

Forest Inventory and Analysis (FIA) unit. Nonforest plots may have trees on them, but they do not fit

FIA’s definition of forest because the area covered by trees is too small, too sparsely populated by

trees, too narrow (e.g., trees between fields or in the middle of a divided highway), or has a disturbed

understory (e.g., mowing or grazing) such that natural regeneration of trees probably does not occur.

Recent inventories and associated photointerpretation work showed that 30 to 50 percent of these

nonforest plots contained trees and were located in urban, suburban, industrial, and rural areas. Data

were collected for trees on traditionally nonforest plots in a five-county area in Maryland that was 30

percent forested in 1999. Nonforest plots added at least 43 percent to the total-tree basal area

measured on forest plots. Species composition, tree size, damage, and number of exotics differed

between forest and nonforest plots. Costs were about one-third of those on a regular FIA plot. Field

collection methods, including field preparation, plot design, and variables collected are outlined, and

recommendations for future inventories of similar areas are presented.

The Author

RACHEL RIEMANN is a research forester/geographer with the Northeastern Research Station’s

Forest Inventory and Analysis Unit and is currently located in Troy, New York. She received a B.A.

degree in ecology from Middlebury College, an M.S. degree in forestry from the University of New

Hampshire, and an M.Phil degree in geography from the London School of Economics.

Acknowledgments

This pilot project, a group effort by the Northeastern FIA unit (NE-FIA), tested the integration of a

nonforest inventory with the regular FIA process and investigated the feasibility, effectiveness, and

amount of additional information provided by a nonforest inventory. By taking advantage of the current

NE-FIA process of hiring and training, data quality assurance, data processing, and statistical

protocols, we were able to ensure the quality, consistency, and repeatability of the data, and save time

and money in conducting the nonforest inventory. The following contributed significantly to this study:

Mike Whitehill and John Higham, FIA (field inventory crew)

Stan Arner and Jim Westfall, FIA, and Sara Duke, NE (statistical support)

Tom Frieswyk, FIA (data processing)

Mike Hoppus and Andy Lister, FIA (crew preperation support)

Dan Twardus, NA, and Barbara O’Connell, FIA (FHM variable training)

Anne Cumming and Dan Kincaid, NA (newspaper article and brochures)

Sean Lehman, FIA intern (data entry)

Mike Galvin, Maryland DNR

Dave Alerich and Bob Ilgenfritz, FIA (crew hiring and training)

Jen Jenkins, FIA/Global Change (NPP and carbon sequestration analysis)

Dave Nowak and Dan Crane, NE (city inventories)
Contents

Highlights.................................................................................................................................. 1

Introduction .............................................................................................................................. 2

Goals ......................................................................................................................................... 4

Methods .................................................................................................................................... 5

Plot Design .......................................................................................................................... 6

Mixed Conditions................................................................................................................ 6

Variables .............................................................................................................................. 6

Special Situations ................................................................................................................. 7

Owner Contact .................................................................................................................... 7

Number of Plots .................................................................................................................. 7

Results and Discussion ............................................................................................................... 8

Tree Cover in Nonforest Areas ............................................................................................. 8

Forest Carbon Stocks and Wood Production on Nonforest Land ......................................... 8

Differences Between Forest and Nonforest Plots ................................................................ 10

Other Characteristics of Nonforest Plots ............................................................................ 32

Distribution of Nonforest Plots by Land Use ..................................................................... 32

Effect of Nonforest Plot Data on Estimates of Tree Cover in Urban Areas ......................... 34

Population Density on Nonforest Plots .............................................................................. 34

Changes in Land Use ......................................................................................................... 34

Plot Variation in Nonforest Areas....................................................................................... 35

Costs.................................................................................................................................. 36

Conclusions ............................................................................................................................. 37

Lessons Learned ................................................................................................................. 37

Literature Cited ....................................................................................................................... 38

Appendix I ............................................................................................................................... 40

Variables Collected in the Nonforest Inventory.................................................................. 40

Appendix II ............................................................................................................................. 42

Variables Specific to This Inventory ................................................................................... 42

1

Highlights

• Seventy percent of the land area in the five-county study area in Maryland was nonforest in

1999. Thirty-six percent of that nonforest area contained trees. On average, the nonforest plots

in this pilot study contain 18.4 percent of the tree basal area that occurs on forested plots,

adding 43.2 percent to the total-tree basal area on forest plots. Calculations of total number of

stems also are affected significantly when data from nonforest plots are included. The contribution

of nonforest plots to basal area totals is even greater in specific strata such as census-designated

urban areas or residential land uses, where a higher proportion of the plots tend to be nonforest.

• Species composition, average tree size, and number of saplings differed between forest and

nonforest plots.

◊ On average, nonforest plots had fewer but larger trees (only 14 percent of the trees per acre

on forest plots, but an average basal area per tree of 0.99 versus 0.74).

◊ Nonforest areas typically had little natural reproduction in the understory, and only 11

percent of the sapling stems per acre.

◊ Nonforest plots also differed in both the species occurring in the top 10 list and their ranking

within it. This was true for both basal area and number of stems.

• Including data from nonforest plots can make a substantial difference in calculations of total

basal area, total biomass, total number of stems, carbon stocks, and net primary productivity.

• The subset of nonforest plots that contain trees occurred largely in residential areas even though

much of the suburban development in Maryland has occurred on previously agricultural land.

In areas where residential development has been substantial in previously forested areas, the

amount of tree basal area in residential land uses likely is even greater.

• It is interesting that nonforest areas contain fewer tree species. One might assume that interest

in importing exotic species would increase raw local species diversity.1

 However people apparently

reduce raw diversity in nonforest areas by their collective preference for only several popular

species.

• According to the native species list used in this study, some of these species are found more

frequently on nonforest than on forest plots. These include black locust, white pine, silver

maple, and sugar maple. This may reflect of the types of areas that have been converted primarily

to nonforest, species that are most tolerant of nonforest conditions and survive, and/or species

that are preferred and cultivated by owners of nonforest land.

• Conducting a nonforest inventory in conjunction with the regular forest inventory of a state

can save considerable time and costs due to increased efficiencies.

• The 0.10-acre plot design was easy to implement and limited the number of owner contacts per

plot. The relative accessibility of nonforest plots, a reduction in the time-consuming timberrelated variables collected, and access to ownership information prior to the fieldwork contributed

to the relatively low cost of these plots. On average, one crew was able to inventory three to four

plots per day and complete the entire inventory (170 plots) during one summer. Total costs

were much lower than our initial estimate.

1

No consideration for choosing only native species in the calculation.
2

Introduction

The USDA Forest Service’s Forest Inventory and

Analysis (FIA) units periodically assess the Nation’s

forest resources and also conduct forest inventories by

state that provide data on the amount, status, and

character of the forest resources across the country. This

information is summarized from general data collected

on all plots and detailed tree data collected on forested

plots. The latter are defined by FIA as areas at least 1 acre

in size, at least 120 feet wide, and at least 10 percent

stocked with trees. Also, forest plots must have an

understory that is undisturbed by another land use.2

 FIA

usually does not collect data on nonforest plots because

they cannot be used to describe “forest lands,” a national

definition used when summarizing information across

the United States.

However, classification of “nonforest” does not mean

that a plot is devoid of trees. The most recent inventory

cycle and the standard photointerpretation work by the

Northeastern FIA revealed many areas in the Northeast

that contain trees that do not fit FIA’s definition of forest

land. For example, more than 62 percent of the

“nonforest” conditions3

 in Connecticut and New

Hampshire contained trees, and 44 percent of the plots

defined as nonforest in Maryland have a land-use code

indicating some type of tree cover. The percentage of

nonforest plots with trees in nine Northeastern States

inventoried since 1995 is shown in Table 1. These plots

represent a portion of the tree resource for which

information on species, health, and biomass is not

currently collected. We are interested in determining the

amount of tree biomass in these areas. A substantial

amount of biomass could affect the accuracy of regional

models developed from FIA data, e.g., those for net

primary productivity (NPP) or carbon sequestration, as

well as FIA’s ability to accurately describe and monitor

tree cover both in urban areas and in more developed

counties and states. Although FIA does not claim that its

data capture more than forested areas, this information is

sometimes used by assumption to describe all of the trees

in a state because it is the only inventory that exists over

large areas. The accuracy of this assumption depends on

the amount of tree cover in areas classified as nonforest.

The Northeastern United States is heavily impacted by

the increasing spread of suburban and urban areas into 2

U.S. Department of Agriculture, Forest Service. 2000. Forest

inventory and analysis national core field guide, volume 1:

field data collection procedures for phase 2 plots, version 1.6.

Internal report on file at U.S. Department of Agriculture,

Forest Service, Forest Inventory and Analysis, Washington, DC.

3

A “condition” equals a plot or a portion of a plot if mixed

conditions occur (see Mixed Conditions).

Table 1.—Number of plots, nonforest (NF) plots, NF plots with trees, and primary use on NF land

in percent of total area, by state

State Number of Number of NF plots with trees Nonforest land usec

plots NF plotsa

Numberb Percentb Agriculture Urban Residential Natural

CT 451 181.22 112.12 62 10 8 13 2

DE 215 132.16 22.05 17 48 3 5 7

ME 646 62.00 25.59 41 5312

MD 1098 632.23 275.87 44 39 4 6 4

MA 798 298.65 178.19 60 6 7 14 4

NH 930 150.54 93.62 62 6332

NJ 791 432.13 202.21 47 20 5 18 4

RI 178 72.71 41.89 58 4 10 15 2

VT 926 209.55 76.07 36 17 2 1 2

a Plot fractions occur because FIA plots are now “mapped” and a single plot can contain portions of both forest and

nonforest.

b

From the land-use code (LU) used in the regular FIA inventory. Results from this study indicate that this is an

overestimation of the number of NF plots that actually contain trees within plot boundaries because the LU label is

determined by assessing tree cover in the general area rather than within a specific plot area when an NF plot is not

physically established on the ground.

c

Derived from National Land Cover Dataset (NLCD) using 1992 (approx.) imagery (Vogelmann and others 1998).
3

forest and agricultural land. Some areas previously

inventoried as forest land may have lost that status as

residential development has divided them into pieces

that are too small to fit FIA’s definition, or the degree of

understory disturbance has increased sufficiently to

require a different classification. In other areas,

residential development on previously open agricultural

areas may be old enough to contain large, planted tree

cover that today constitutes a substantial resource. As

such areas increase, so does the amount of land with tree

cover that is not captured by the regular FIA inventory.

In fact, the amount of tree cover that is missed in the

FIA inventory may be considerable and probably is

increasing.

Non-FIA inventories do exist, for example, city tree

inventories conducted periodically by Nowak et al.

(1996). However, these inventories are conducted almost

exclusively within city limits, and so trees in suburban,

rural-residential, and rural-agricultural areas that occur

outside those city limits are not captured. These areas

may contain trees in backyards, small woodlots in the

middle of developments, strips of trees along roads or

along highway medians, or patches of remnant

woodland between scattered, low-density housing,

riparian buffer strips, or between agricultural fields.

Thus, there is a potentially large gap in the information

available from current inventories. Examples of these

“nonforest” areas are shown in Figure 1. Trees in these

areas are rarely used for timber on a continuing basis,

but they do sequester carbon, produce oxygen, modify

the climate, create habitat for wildlife species, support

casual recreation, and generally contribute to the quality

of life. Such trees are an important resource that is

receiving increasing attention and questions with respect

to its health, characteristics, increase, or decline.

In 1999 in Maryland, nonforest plots were inventoried

in five counties: Anne Arundel, Baltimore, Carroll,

Harford, and Howard (Fig. 2). In 2000, this area

covered 2,237 square miles and had a combined

population of 2,512,431.4

 The five counties together

capture a gradient of population density, urbanization,

and land use and coincide with the Baltimore Urban

LTER (Long Term Ecological Research) site. The City of

Baltimore is entirely urban, four counties have large

areas of suburban, and one county extends into largely

rural-agricultural areas. Population density ranges from

336 per square mile in agricultural Carroll County to

1,261 per square mile in Baltimore County (excluding

the city), to 8,059 per square mile in the City of

Figure 1.—Aerial view of “nonforest” areas that contain trees.

4

Source: U.S. Census Bureau, Census 2000 Redistricting Data

(Public Law 94-171) Summary File, Matrices PL1, PL2, PL3,

and PL4.
4

Baltimore (Table 2). Heavily populated Baltimore

County is the most forested of the five counties. The

distribution of land use/land cover and human

population in this region is shown in Figures 3-4. The

pilot inventory was conducted simultaneously with the

regular FIA inventory of the area to maximize the

integration of the two inventories, and thus the

efficiency of data collection. For example, by using FIA

protocols, the nonforest inventory (NFI) crew could

effectively reduce the number of plots that the regular

FIA crews had to visit.

Goals

The pilot inventory of nonforest plots addressed the

following questions:

• How many trees are on nonforest plots in the fivecounty study area?

• Is the amount of nonforest tree biomass sufficient to

make a substantial difference in global and regional

modeled estimates of total biomass, NPP, and carbon

sequestration?

• Does the character of that resource differ

from that of the traditionally forested

resource in species composition, tree size,

total basal area, forest structure, and/or

health? And if certain types of forest

conditions or tree species are more prevalent

in nonforest areas, does this affect

interpretations of regular FIA data when

describing the entire tree resource?

• Can these data be used to extend FIA

summary statistics into areas considered as

nonforest or to generate statistics of “all tree

cover” in a particular area, e.g., number of

trees by species and diameter class, total basal

Figure 2.—The five-county study area and FIA plots. Any FIA plot with

nonforest at the center subplot was visited by the nonforest inventory (NFI)

crew; this included both nonforest and mixed nonforest plots.

Table 2.—Population density (2000 census), percent forest

(1999 inventory), and percent urban area (1990 census) in the

five-county study area (includes City of Baltimore)

County Forested Urban Population density

Persons/mi2

City of Baltimore 2.8 100 8059

Anne Arundel 24.7 46 1177

Baltimore 35.4 40 1261

Howard 30.6 35 983

Harford 33.6 22 496

Carroll 23.5 6 336
5

area, and number of saplings and shrubs by species

and stand-size class? How easy or difficult is it to

integrate data from the two inventories?

• Would this additional information add to our current

knowledge of the “urban forest” in a state?

• How well are we able to inventory these areas using

FIA plots? For example, are Forest Health Monitoring

(FHM) variables and indicators appropriate in these

areas? Is FIA sampling intensity and the 1/10th-acre

plot design sufficient to capture tree biomass in these

areas, and/or related data on forest characteristics?

What kinds of plot designs and inventory protocols

are necessary for data collection in these areas?

• What is the cost of an FIA nonforest inventory?

• What areas contain the most tree biomass that the FIA

inventory is missing, i.e., is there a way to capture

most of this tree biomass if funds are not available to

inventory the entire area?

Methods

The nonforest inventory was conducted from June 1 to

August 31, 1999. Leaf-on conditions allowed the

collection of data on crown condition and

characteristics. The regular FIA plot “grid” for Maryland

was used in the pilot study to ensure an unbiased

procedure. Plots were determined to be nonforest by the

previously mentioned FIA standard definition. A 0.10-

acre nonforest plot (37.24-foot radius circle) was

established if any nonforest condition occurred on the

center subplot (subplot 1) of the standard FIA plot (Fig.

5). The nonforest portion of that 0.10-acre plot was

inventoried by the NFI crew. Forest plots were visited by

regular FIA crews and standard data were collected. To

maximize efficiency, the nonforest inventory was

coordinated with the regular FIA inventory. To avoid

revisiting plots, the NFI crew also completed the

standard FIA plot sheet for all plots visited, and

conducted the regular FIA inventory on the forested

portions of mixed plots. This occurred on 24 of the 162

non-water plots inventoried by the NFI crew.

Figure 3.—General land use/land cover in the five-county

study area. Source: National Landscape Characterization

Dataset, derived from 1992 Landsat TM imagery

(NLCD’92) (Vogelmann and others 1998).

Figure 4.—Population density and census-designated

urban areas in the five-county study area (1990 census).
6

The same methods and protocols were used by the

regular FIA and NFI crews when possible.2,5 Differences

between the two inventories included the inventory

season, plot design, certain variables for which data were

collected, protocols for addressing unique circumstances

in nonforest areas, e.g., buildings on plots and planted

exotic species, and media publicity prior to the

inventories.

Plot Design

The 0.10-acre plot size was a compromise between being

small enough to avoid multiple ownerships and large

enough to capture sufficient tree data. This plot size also

has been used in citywide inventories of urban forest

(McPherson et al. 1994). With the higher spatial density

of ownerships expected in these areas, contacting

numerous owners on a single distributed cluster plot

could slow the inventory considerably.

Mixed Conditions

All FIA plots inventoried since 1995 can contain more

than one condition (nonforest or forest or both). On

such plots, all conditions are mapped and inventoried

separately. If a nonforest condition occurred on subplot

1, a 0.10-acre nonforest plot was established on plot

center. If nonforest conditions occurred on any of

subplots 2-4 but not on subplot 1, no nonforest plot was

established (Fig. 6). Although there should be no bias to

this procedure, it is possible that this sampling design

underestimates slightly the nonforest area because it

misses the nonforest portion of those plots where

nonforest occurred only on subplots 2-4. Once the

nonforest plot was established, no further breakdown of

conditions was mapped and the entire 0.10-acre plot (or

the nonforest portion of it) was treated as a single

nonforest condition.

Variables

Data collected on each plot consisted of a subset of

standard FIA variables plus additional variables designed

to better describe the health, biodiversity, and ground

cover of trees in nonforest areas. FIA variables considered

to be less important in nonforest areas, e.g., timberrelated cull and board-foot measures, were excluded

from the nonforest inventory. FIA variables describing

location, site, and tree characteristics were retained.

Additional variables for crown size, condition, and

damage used in the nonforest inventory were developed

for the FHM plots (USDA For. Serv. 1999). Ground

cover variables that were added were based on those from

city inventories (Nowak et al. 1996) and describe

characteristics such as the percentage of impervious

surfaces, vegetation, and tree canopy cover on the plot

(Nowak and Crane 2000).

Several plot-level variables specific to this inventory also

were included. First, to better distinguish the types of

areas with nonforest plots and large amounts of tree

basal area, three variables were added: a land-use class, an

owner class, and primary reason for nonforest status.

The variable for NFI land-use class, unlike the regular

FIA land-use variable, provided an additional breakdown

of residential land uses by density and industrial/

commercial land uses by type. The owner-class codes

were the same as those used in the regular inventory. The

variable for “primary reason for nonforest” consisted of

five codes: 1 = stocking less than 10 percent; 2 = forest

area less than 1 acre in size; 3 = forest area less than 120

feet wide; 4 = a disturbed understory due to nonforest

land use; 0 = no trees. A fourth variable, “obstruction at

plot center,” was included to identify plots for which

distances and azimuths were measured via photo rather

than on the ground because the plot center could not be

occupied. Finally, “number of forested subplots” was

included to determine the additional time that the NFI

crew was on the plots that was unrelated to the nonforest

inventory itself. This information was collected to gain a

better understanding of the types of areas missed by the

regular forest inventory and perhaps help to identify in

the future nonforest areas that contain the most tree

cover.

Figure 5.—Plot design for the nonforest and standard

FIA inventory plots.

5

Alerich, D. 1999. Field instructions for the fourth inventory

of Maryland. Unpublished report on file at USDA Forest

Service, Northeastern Research Station, Forest Inventory and

Analysis, Newtown Square, PA.
7

Plot, tree, and sapling variables and their sources are

listed in Appendix I. All trees (≥ 5 inches in d.b.h.) and

saplings (1 to 4.9 inches d.b.h.) were measured on the

entire 0.10-acre plot. The resulting data were used to

summarize species and forest type, biomass, NPP, carbon

storage and sequestration, and indicators of health

(crown condition, sustainability, species diversity) and

structure (tree size, density, d.b.h. distribution, ground

vegetation, ratio of height to crown).

Codes for all new variables (non-FIA and non-FHM) are

listed in Appendix II. Codes were added for exotic and

planted tree species on nonforest plots that are not listed

in the Eastwide Database (Hansen et al. 1992), the

Westwide Database (Woudenberg and Farrenkopf 1995)

or the FIA’s national core field guide.2

 Most of these

species were identified and recorded by species group

only, e.g., the willows and crabapples.

Special Situations

Plots that were on tops of buildings or in the middle of

streets were inventoried, but distances and azimuths to

trees were calculated from a photo when the plot center

could not be occupied. To accomplish this, the NFI crew

used 1:4000 prints of a digital orthophotoquad

(DOQQ) for each plot. The DOQQs were from photos

taken from 1986 to 1992. The 1:40,000 NAPP

(National Aerial Photography Program) photography

that crews use to assist in navigating to plots was too

small a scale for measuring portions of a plot. Because

trees could not be marked and a permanent stake could

not be placed at plot center on nonforest plots, plot

location was identified for future reference by recording

the distance and direction of plot center from known

objects.

Owner Contact

Owner information (names, addresses, and telephone

numbers) was obtained by permission from MdProperty

View, a digital property map and parcel database

maintained by the Maryland Department of Planning

(http://www.mdp.state.md.us/data/mdview.htm). Plots

were overlaid on the georeferenced pict file, read off the

file manually, and placed in a spreadsheet. (The files are

now available in a more user-friendly format.) This

information was not always accurate due to uncertain

plot locations, e.g., plots that fall near the edge of several

ownerships. Nevertheless, this method substantially

reduced the time typically required to locate current

plot-ownership information at the local tax office.

Because most owners were not available during the day,

contacting them entailed several steps: 1) distributing

information about the nonforest inventory at each

residence, 2) a follow-up phone call to the owner(s), and

3) upon receiving permission from an owner, returning

to the plot to conduct the inventory. Since nearly every

plot location was easily accessible by road, this process

did not consume an inordinate amount of time. And

because many owners actually resided on or near the

plot, there was far less uncertainty about who the owner

was or where he or she lived. The NFI crew also was

aided by an article about the inventory that was

published in a local newspaper prior to the pilot study.

Number of Plots

There are 243 FIA plots in the five-county study area.

Of those, 146 were nonforest (NF), 44 were forest (F),

and 53 were mixed (M) (25 completely forested and 28

containing some nonforest on subplot 1) (Fig. 2). The

Figure 6.—Nonforest plots were established if a nonforest condition occurred entirely (a)

or partially (b) on the center subplot of the standard FIA plot. A nonforest plot was not

established if the center subplot was entirely forested, i.e., even if nonforest conditions

occurred on another subplot.
8

NFI crew inventoried 162 plots (138 NF plots and 24

M plots). Thus, eight NF plots and four M plots were

missed by the NFI crew (Fig. 7). It is likely that these

plots were missed because they were entirely forested at

the photointerpretation stage (1996 photography in

Maryland) and nonforest when the crew visited the plot

(1999), or the original plot in that hex was dropped for

some reason (e.g., access denied), and these were

replacement plots chosen after the NFI crew had

completed the inventory. In either case, they are entirely

or partially nonforest and should be measured in future

inventories. Thus, for calculations of total forest and

nonforest area, the data from the regular FIA inventory

are required and all 243 plots are used (72.49 of which

are F plots). For calculations using variables inventoried

on NF plots, only 231.1 plots are used (160.14 NF and

70.96 F plots), as the NF plots or portions of plots for

which no data were collected were labeled as “missing

data” (Table 3).6

In this study, we used data on percent forest and

nonforest in the five-county area from the regular FIA

inventory. One also can calculate roughly the number of

nonforest plots containing trees using the current landuse variable collected on all FIA plots. The nonforest

inventory introduces additional information about trees

on NF plots, e.g., tree species, basal area, percent crown

cover, number of trees and saplings, ground cover, crown

conditions, and damage.

Although we used the regular FIA grid-based sample

design for Maryland to eliminate bias, this procedure

might have been compromised because the 12 plots that

were missed and subsequently dropped from the pilot

inventory were located primarily in one section of Anne

Arundel County and thus may have had other

characteristics in common.

Results and Discussion

Tree Cover in Nonforest Areas

Total basal area (ba) for all trees on F plots and portions

of plots was 1,324.2 ft2 on 72.49 1/6th acre plots. The

total ba for all trees on NF plots and portions of plots

was 323.805 ft2

 on 160.14 1/10th-acre plots. Calculating

population totals for the five-county area in Maryland

results in a total ba of 46,978,517 ft2

 for forested areas

and 20,312,288 ft2

 for nonforest areas. Including data

from NF plots adds 43.2 percent to the total ba in the

five-county area. Average ba/acre on F plots is 110 ft2

 vs.

20 ft2

 on NF plots. On average, NF plots contain 18.3

percent of the ba on F plots (Table 4).

On average there are 20.5 stems (3 1 inch d.b.h.) per acre

on NF plots vs. 148.2 on F plots. Calculating

population totals results in 63,324,410 stems on F plots

and 20,575,440 stems on NF plots. Including data from

NF plots adds 33.5 percent to the total number of stems

in the study area for trees and 26 percent to the total

number for saplings (Table 5).

For Tables 8-9, population estimates were calculated

using a plot/acre expansion factor and average area

expansion factors. The more complex calculation used in

Tables 17-25 is the same as that proposed for use

nationwide by FIA. With the latter approach, a weighted

mean is calculated based on stratum weightings, and a

single factor is used to expand the mean to the

population estimate (unpublished data).

Forest Carbon Stocks and Wood Production

on Nonforest Land

Preliminary estimates based on methods of Jenkins et al.

(2001a, 2003) indicate that tree-biomass stocks and

Figure 7.—All FIA plots and those missed by the

nonforest inventory crew.

6

The total number of NF plots used in the analyses = 138 +

22.14 = 160.14. The total number of F plots used for

estimates of population basal area and number of stems = 44 +

26.96 + 1.53 = 72.49, i.e., all F plots. The total F plots for

analyses comparing F and NF characteristics = 44 + 26.96 =

70.96, i.e., all F plots minus those mixed plots for which

nonforest data were not collected.
9

Table 3.—Number of plots by county (including City of Baltimore) and forest status

Item Anne Baltimore Carroll Harford Howard City of Total

Arundel Baltimore

Land area (mi2

) 416 598.6 449.2 440.4 252.2 80.8 2237.2

Completely NF plots (no.) 22 35 33 25 15 8 138

NF plots missed (no.) 5 1 1 1 0 0 8

F plots (no.) 6 17 6 9 6 0 44

F/mixed plots (no.) 6.18 7.42 5.01 5.79 2.31 0.25 26.96b

NF/mixed plots (no.) 4 4.9 5 2.79 4.45 1 22.14c

Mixed plots missed in 2 (.91)a 0 1 (.37) 0 1 (.25) 0 4 (1.53)

 NF inventory (no.)

Total plots minus missed 38.18 64.32 49.01 42.58 27.76 9.25 231.1

 NF plots (no.)

Total land plots 46 66 50 44 28 9 243

 inventoried by FIA (no.)

a

Proportion that is forested in parentheses.

b

Sum of forested condition percentages on all mixed plots (derived from entire 1/6th-acre plot). c

Sum of nonforest condition percentages on all mixed plots (derived from 0.10-acre plot).

Table 4.—Total basal area (ba) of forest (F) and nonforest (NF) plots in five-county study area (trees only

(dbh ≥ 5 inches))

Expansion factora

Total ba

Plot type Number Average Percent of Total ba No. plots/ Land area/ (population Percent of

of plots ba/acre F plots on plots acre no. plots estimate) F plots

ft2 ft2 ft2

F 72.49 110.0 1324.2 (6.02) (5893) 46,978,517

NF 160.1 20.2 18.4 323.8 (10) (6273) 20,312,288 43.2

a

Plot totals are multiplied by expansion factors to obtain population estimates. Expansion factors are a combination of a plots/acre

factor (10 for NF plots and 6.02 for F plots) and the total land area that each plot represents (based on the number of plots

inventoried).

Table 5.—Total number of trees and saplings on forest (F) and nonforest (NF) plots in the five-county study area

Expansion factora

Plot type Number Avg. no. Percent Total no. No. plots/ Land area/ Total no. stems Percent

of plots stems/acre of F plots stems on acre no. plots (population of F plots

plots estimate)

Trees

F 72.49 148.2 1785 (6.02) (5893) 63,324,410

NF 160.1 20.5 13.8 328 (10) (6273) 20,575,440 32.5

Saplings

F 72.49 294.9 286 (74.75) (5893) 125,983,501

NF 160.1 32.7 11 524 (10) (6273) 32,870,520 26.1

a

Plot totals are multiplied by expansion factors to obtain population estimates. Expansion factors are a combination of a plots/acre

factor (corresponding to the 1/10th-acre NF plot, the approximately 1/6th-acre F plot, and the approximately 1/75th-acre F sapling

plot) and the total land area that each plot represents (based on the number of plots inventoried).
10

wood production on nonforest land are, respectively, 25

and 22 percent of the totals computed for forest land in

Maryland. This result suggests that for the five-county

study area, where nonforest land is substantially more

common than forest land, including nonforest land

could add as much as 33 percent to current estimates of

forest carbon stocks and 29 percent to existing estimates

of annual wood production. Depending on the ratios of

nonforest to forest land and the percentage tree cover on

nonforest land in different regions, carbon sequestration

on nonforest land could be an important contributor to

regional and national carbon balances (Jenkins and

Riemann 2003).

Differences Between Forest and Nonforest Plots

What are the differences between forest and nonforest

plots, and does adding the nonforest data to the area

summary change our description of these five counties

from what we already knew? With data from the

nonforest inventory we can examine differences in

average number of stems/acre, average ba/acre, species

composition, percentage of exotic species found,

damage, stand-size, species, and diameter-class

distributions, and number and species of saplings

(regeneration).

Stems and basal area/acre. Nonforest plots generally

had fewer but larger trees. The average d.b.h. on F plots

was 11.6 inches (0.74 ft2

 of ba) vs. 13.5 inches (0.99 ft2)

on NF plots. Relative size did vary by species. On the

latter plots, the ba of chestnut oak was 21⁄2 times that on

F plots, while red maple were roughly the same size on F

and NF plots. The average number of trees/acre and ba/

acre was much lower on NF than on F plots (20.5 vs.

148 trees/acre, and 20.2 vs. 110 ft2

/acre). The average

was 58 trees and 57 ft2

/acre on NF plots with trees

(Table 6). The proportion of NF plots with trees does

correspond to land use, so calculating values separately

for NF plots with trees is useful when estimating the

impact of a nonforest inventory where advance

information on land-use composition of the area of

interest is available.

Tree-species composition. On NF plots, yellow-poplar

and chestnut oak were the top species by basal area,

accounting for 30.3 percent of total ba. On the basis of

number of stems, white pine and yellow-poplar were the

top species, accounting for 20.5 percent of total stems

on NF plots. On F plots, yellow-poplar and red maple

accounted for 33.3 percent of the ba and 27.2 percent of

total stems (Tables 7-10). When NF plots were included,

the top 10 species in the study area changed only in their

ranking in the list.7

Of the tree species inventoried, 26 were common to

both F and NF plots (88 percent native, 8 percent

exotic, and 4 percent unknown).8

 Twenty-eight different

species were identified on F plots only (79 percent native

and 21 percent exotic) and 16 species of trees were

identified on NF plots only (19 percent native and 63

percent exotic). Thus, NF plots have fewer species in

general and a larger percentage of exotics in the overall

species mix. The types of exotics on NF plots also

differed from those on F plots except for Norway maple

8

Based on a list of native species compiled in 1999 by Dan

Crane, USDA Forest Service, Syracuse, NY (Nowak et al.

2002), “Unknown” species are typically those identified and

recorded only at the genus level and for which some species are

native and others exotic. Thus, genus-level identification did

not allow determination of native or exotic status.

7

In all tables in which the characteristics of F and NF plots are

compared, only 70.49 F plots are included in the analysis, i.e.,

the four mixed plots in which the nonforest portion of the plot

was missed in the inventory are considered missing data. The

remaining 70.49 forest plots contained 1,749 stems and a total

basal area of 1288.2 ft2.

Table 6.—Average number of stems and basal area/acre on forest plots, all nonforest plots, and

nonforest plots with trees in the five-county study area (does not include 15 sapling-only plots)

Trees Saplings

Plot type Number of Average no. Average Average no. Average

plots stems/acre ba/acre stems/acre ba/acre

ft2 ft2

Forest 72.49 148 110 551 10

Nonforest 160.14 21 (7) a 20 (18) 33 (6) 1.4 (14)

Nonforest with trees 57 58 (39) 57 (52) 75 (14) 3.3 (33)

a

Percent of forest plots in parentheses.
11

and Ailanthus, which were found on both F and NF

plots. This difference probably corresponds closely to

species that have escaped, e.g., Paulownia found on F

plots, vs. those that have been planted, e.g., pear and

ornamental cherry found on NF plots (Cynthia

Huebner, USDA Forest Service, 2000, pers. commun.).

We also observed differences in the presence and

abundance of native species on F and NF plots. Black

locust and eastern white pine were much more prevalent

on NF plots (5:1), and silver maple and sugar maple

were found only on NF plots. Much more prevalent on

F plots were sweetgum (5:1), Virginia pine (42:1), and

northern red oak (3:1). These differences might be

attributed to areas that have been converted primarily to

nonforest, e.g., bottomlands in the southern United

States (Rudis 1995), areas that are left with trees (e.g.,

riparian), species that are most tolerant of and survive in

nonforest conditions, and/or species that are preferred

and cultivated by owners of nonforest land. For example,

white pine may be a frequently planted species while

black locust, a disturbance-related species, may be a

frequent volunteer in disturbed nonforest areas. Also,

silver maple may reflect long-linear riparian areas that are

not sampled by the regular FIA inventory, and/or an area

that has been preferentially converted to nonforest uses.

Additional research is needed to better understand such

differences.

Table 8.—Top 10 species on forest plots, by total

basal area

Species Basal area Percent of total

ft2

Yellow-poplar 303.8 23.6

Red maple 137.7 10.7

Scarlet oak 80.4 6.2

Chestnut oak 75.5 5.9

Black oak 73.4 5.7

Sweetgum a 72.5 5.6

Virginia pine a 71.9 5.6

White oak 62.2 4.8

Black cherry 58.3 4.5

Northern red oak a 57.2 4.4

a

 More prevalent on forest than nonforest plots.

Table 7.—Top 10 species on nonforest plots, by

total basal area

Species Basal area Percent of total

ft2

Yellow-poplar 64.6 20.0

Chestnut oak 33.4 10.3

White oak 22.0 6.8

Black locust a 18.9 5.8

Scarlet oak 17.7 5.5

Red maple 17.6 5.5

Black oak 16.9 5.2

White pine a 16.4 5.0

Black cherry 12.4 3.8

Silver maple a 10.7 3.3

a

More prevalent on nonforest than forest plots.

Table 9.—Top 10 species on nonforest plots by

number of trees

Species Number of trees Percent of total

White pine 35 10.7

Yellow- poplar 32 9.8

Red maple 28 8.5

Black locust 22 6.7

Black cherry 18 5.5

Chestnut oak 16 4.9

White oak 15 4.6

Silver maple 12 3.7

American beech 12 3.7

Scarlet oak 10 3.0

Table 10.—Top 10 species on forest plots by number

of trees

Species Number of trees Percent of total

Yellow-poplar 263 15.0

Red maple 213 12.2

Virginia pine 151 8.6

Sweetgum 135 7.7

Black cherry 130 7.4

Chestnut oak 89 5.1

Black oak 66 3.8

White oak 61 3.5

Scarlet oak 60 3.4

Blackgum 60 3.4
12

Exotics. The percentage of exotic tree species also

differed between F and NF plots (Table 11). On NF

plots, 83 percent of the trees were native, 13 percent

were exotic, and 5 percent were unknown.9

 Summarized

by basal area, exotic species made up 8 percent of the

total on NF plots. By contrast, exotics accounted for

only 1 percent of the trees and 2 percent of the basal area

on F plots. When all plots are included, exotics made up

4 percent of the trees and 3 percent of the basal area

present.

Damage. Damage to trees on F and NF plots can be

compared by tree condition and special damage.2

 All but

two trees in the nonforest inventory (99 percent) were

recorded as being live with an intact top, and 95 percent

of the trees on F plots were free of this damage. “Special

damage” is noticeable damage recorded by apparent

cause (“dieback” is recorded separately). On NF plots,

special damage was recorded for 38 percent of the trees;

20 percent of the trees on forest plots had special

damage. The types of special damage were similar on

both plot types except for damage by the white pine

weevil, which prefers feeding on terminal shoots of white

pine in full sunlight. This condition is more common

for trees on NF plots (Table 12).

Information on tree damage on NF plots also can be

derived from the FHM variables that record damage by

location, severity, and symptom rather than by cause. By

this method, 34 percent of all nonforest trees exhibited

some damage (Tables 13-14), though the accuracy of this

percentage is suspect because many FHM variables failed

to meet standards for quality assurance/quality control

(Dan Twardus, USDA Forest Service, 2000, pers.

commun.). How this compares with trees on forest plots

is unknown as this variable was not included in the

regular FIA inventory of Maryland in 1999. This

variable has since been added for FIA forest plots to

allow comparisons between F and NF plots in future

inventories.2

Crown dieback is another indicator of tree health. Of

328 trees on NF plots, 145 (44 percent) had no dieback,

149 had dieback of 1 to 9 percent, 29 had dieback of 10

to 25 percent, and 5 trees had > 25 percent dieback. It is

not known how this compares with trees on F plots as

crown dieback was recorded only on the relatively few

FHM plots.

Stand size. Stand size can be difficult to measure on NF

plots, particularly where trees are sparse and tree size

varies widely. Nevertheless, this measure is useful for

understanding the types of potential habitats in these

areas, and whether these habitats differ from those on F

plots.

In this study, smaller stand sizes were more prevalent on

NF plots. All F plots were classed as sawtimber (67

percent) or poletimber (33 percent) stands. Nonforest

plots on which trees or saplings were present were classed

Table 12.—Percentage of trees on forest and nonforest plots that incurred “special damage”

Trees damaged by:

Plot type Other bark beetle White pine weevil Other borers Dieback (20-50%)

Forest 11 — 3 1.6

Nonforest 23 6 6 3

9

Unknown includes six species groups that contain both exotic

and native species: redwood, chestnut, ash, cherry/plum,

willow, and unknown species. It is possible that many of these

trees were exotics.

Table 11.—Percentage of exotic, native, and unknown species on nonforest and forest plots, by

number of trees and total basal area

 Nonforest plots aaaa Forest plots

Variable Exotic Native Unknown Exotic Native Unknown

Number of trees 13 83 5 2 98 0

Basal area (ft2

) 8 88 4 1 98 0
13

Table 14.—Number of trees with damage on nonforest plots, by type and incidence of

damage

Type of damage FIA code Damage incidencea

First Second Third

Canker/gall 1 4 3 2

Conk/cavity/sign of decay 2 67 13 1

Open wound 3 19 7 0

Resinosis/gumnosis 4 1 0 0

Crack/seam > 5 feet 5 1 1 0

Broken bole/root < 3 feet on bole 11 0 0 0

Broom on root/bole 12 0 0 0

Broken/dead root > 3 feet 13 1 0 0

Vine in crown 20 7 2 2

Dead terminal 21 0 1 2

Broken/dead branch 22 9 7 4

Excessive branching/brooms 23 0 0 0

Damaged bud/foliage/shoot 24 0 0 0

Discolored foliage 25 0 0 0

Other 31 0 0 0

Trees with damageb 109 34 11

a

1, 2, and 3, are the first, second, and third damage recorded on a tree. When there are multiple types of

damage, damage lower in the tree is recorded first as this is usually considered to be more dangerous to the

overall health of the tree.

b

328 total trees on nonforest plots.

Table 13.—Number of trees with damage on nonforest plots, by location and

incidence of damage

Location on tree FIA codea Damage incidenceb

First Second Third

Exposed root/stump 1 23 0 0

Root/stump/lower bole 2 16 3 0

Lower bole 3 26 8 0

Lower/upper bole 4 6 2 2

Upper bole 5 8 6 1

Crownstem 6 16 8 2

Branch > 1 inch 7 14 7 5

Bud/shoot 8 0 0 1

Foliage 9 0 0 0

Trees with damagec 109 34 11

a

No damage = 0.

b

1, 2, and 3, are the first, second, and third damage recorded on a tree. When there are multiple

types of damage, damage lower in the tree is recorded first as this usually is considered more

hazardous to overall tree health.

c

328 total trees on nonforest plots.
14

as sawtimber (48 percent), poletimber (32 percent), and

sapling (20 percent) stands (Table 15).

Regeneration – saplings. Differences in the number

and species of saplings between F and NF plots may

indicate the degree to which regeneration occurs on NF

plots, and whether certain species are selected for or

against relative to their occurrence on F plots (Table 16).

Black cherry and black gum saplings were found on F

and NF plots in roughly equal proportions. Cherry,

eastern redcedar, and blue spruce were much more

abundant on NF plots, probably due to specific

planting. Red maple occurred less frequently on NF than

on F plots. Sapling-size yellow-poplar, sweetgum, white

ash, and chestnut oak were at least twice as common on

F as NF plots A study of owner attitudes would be

needed to determine whether these trees/species will be

allowed to grow to maturity or are preferred only as

sapling-size individuals, e.g., as hedgerow or small yard

trees.

Regeneration – seedlings. Data on seedlings were

collected differently on F and NF plots. The number

and species of seedlings also can reflect the amount and

type of tree regeneration. A direct comparison of

seedling data on F and NF plots was not possible

because individual seedlings were not measured on NF

plots. However, seedling ground cover was recorded on

only 28 of 162 plots, and, on average, seedling ground

cover accounted for less than 10 percent of the plot on

these 28 plots. Thus, seedling regeneration on NF plots

was substantially limited.

Table 16.—Number of saplings on forest and nonforest plots, by species

Species FIA code Saplings on forest plots a Saplings on nonforest plots a

Number Percent of total Number Percent of total

Beech 531 20 0.07 48 0.09

Black cherry 762 18 0.06 57 0.11

Blackgum 693 35 0.12 56 0.11

Blue spruce 96 0 0 15 0.03

Cherry 760 0 0 34 0.06

Dogwood 491 36 0.13 30 0.06

Eastern redcedar 68 0 0 21 0.04

Mockernut hickory 409 11 0.04 5 0.01

Red maple 316 56 0.20 34 0.06

Sassafras 931 7 0.02 38 0.07

Sweetgum 611 13 0.05 11 0.02

White ash 541 8 0.03 3 0.01

Yellow-poplar 621 23 0.08 16 0.03

Table 15.—Forest and nonforest plots by stand-size class

Size class Nonforest aaa Forest aaa

No. of plots Percent of totala No. of plots Percent of total

Sawtimber 34.0 22 (48) 48.5 67

Poletimber 22.4 14 (32) 24.0 33

Seedling/sapling 14.4 9 (20)

Nonstocked 89.2 56

a

Percent of plots with trees/saplings is in parentheses.
15

Species and diameter distributions. Three sets of

standard FIA tables were calculated: using F plots only,

NF plots only, and all plots (Tables 17-25). The

numbers in these tables are expanded to population

estimates. Including data from NF plots to Table 17

(number of live trees on forest plots) adds 22 new species

and increases the number of softwood and hardwood

trees by 50 and 16 percent, respectively. Including data

from NF plots to Table 20 (number of saplings on F

plots) adds 19 new species and increases the number of

softwood and hardwood sapling stems by 168 and 11

percent, respectively. Including data from NF plots to

Table 23 (basal area of live trees on F plots) adds 22 new

species and increases the total ba of softwood and

hardwood trees by 30 and 35 percent, respectively.

Among individual species, totals for ba changed the

most for eastern white pine (221 percent) followed by

black locust (159 percent), and chestnut oak (76

percent). Changes in ba totals by diameter class were

greatest for the 21 to 29 ft2

 class (75 percent), the 29+ ft2

class (52 percent), and the 13 to 15 ft2 class (44 percent).
16 Table 17.—Number of live trees on all forest land by species and diameter class

(thousands of trees)

Diameter class (inches at breast height)

Species Species 1.0- 3.0- 5.0- 7.0- 9.0- 11.0- 13.0- 15.0- 17.0- 19.0- 21.0- All classes %SE

code 2.9 4.9 6.9 8.9 10.9 12.9 14.9 16.9 18.9 20.9 28.9 29+

eastern redcedar 68 814 0 206 0 0 0 0 0 0 0 0 0 1020 81.1eastern white pine 129 0 0 113 92 174 83 144 165 0 0 0 0 771 35.8loblolly pine 131 0 0 109 61 31 92 0 78 78 0 78 0 527 37.2Virginia pine 132 0 610 3142 2273 1395 1045 147 382 0 0 0 0 8992 34.6eastern hemlock 261 0 0 0 0 0 0 0 0 0 0 0 22 22 100.0 All softwoods 814 610 3570 2426 1600 1219 290 625 78 0 78 22 11332 28.5boxelder 313 0 241 561 195 56 22 0 0 0 0 0 0 1074 37.1red maple 316 17988 5677 4135 2506 1415 1230 424 166 242 422 301 202 34708 23.1Norway maple 322 257 0 125 48 0 0 0 0 0 0 0 0 430 64.0ailanthus 341 571 0 154 123 0 62 0 0 0 0 0 0 909 66.1pawpaw 367 2346 0 0 0 0 0 0 0 0 0 0 0 2346 70.1sweet birch 372 0 0 106 591 162 373 211 106 0 211 56 0 1815 36.5river birch 373 0 0 166 0 0 135 0 0 0 0 0 0 301 64.3American hornbeam 391 2345 0 161 19 83 0 0 0 0 0 0 0 2608 44.0bitternut hickory 402 1286 0 105 0 49 56 0 0 0 0 0 0 1495 86.2pignut hickory 403 2071 0 485 493 307 403 185 19 83 106 0 0 4153 33.3shagbark hickory 407 0 0 83 0 83 83 0 0 0 0 0 0 248 57.7mockernut hickory 409 3013 1754 435 379 326 49 243 132 62 0 105 0 6496 29.4flowering dogwood 491 15611 1575 365 0 0 0 0 0 0 0 0 0 17551 33.7common persimmon 521 571 0 481 128 0 49 0 0 0 0 0 0 1228 57.2American beech 531 6789 1504 1349 724 261 316 267 0 45 67 101 0 11422 31.9white ash 541 2361 3074 1284 508 83 93 84 0 83 0 75 49 7694 36.0green ash 544 0 0 0 0 26 0 0 0 0 0 0 0 26 100.0pumpkin ash 545 0 0 19 0 0 0 0 0 0 0 0 0 19 100.0American holly 591 1875 938 207 97 62 0 0 0 0 0 0 0 3179 66.2butternut 601 0 0 0 0 0 0 26 0 0 0 0 0 26 100.0black walnut 602 0 1142 228 74 187 19 106 49 0 0 22 0 1826 64.0sweetgum 611 5452 3982 4247 1692 846 824 1056 572 78 31 196 0 18977 32.0yellow-poplar 621 8941 8704 3016 1297 1479 1219 1231 1397 1334 1065 1006 230 30921 23.7apple 660 938 469 0 0 0 0 0 0 0 0 0 0 1407 74.5mulberry 680 551 0 0 0 0 0 0 0 0 0 0 0 551 100.0white mulberry 681 0 0 62 0 0 82 0 0 0 0 0 0 143 71.4blackgum 693 14265 2615 1326 564 289 206 126 0 31 61 49 0 19531 28.2
17

eastern hophornbeam 701 257 0 56 0 0 0 0 0 0 0 0 0 313 84.0Paulownia 712 0 0 31 107 31 0 0 0 0 0 0 0 168 52.2sycamore 731 1040 469 198 82 179 0 82 0 83 49 0 103 2283 40.0bigtooth aspen 743 947 0 212 130 376 82 0 0 0 0 0 0 1747 57.5quaking aspen 746 0 0 0 0 123 0 0 0 0 0 0 0 123 100.0cherry, plum 760 0 0 0 0 139 138 0 0 0 0 0 0 277 50.8pin cherry 761 0 0 0 31 0 0 0 0 0 0 0 0 31 100.0black cherry 762 5480 4109 2968 1934 1257 588 362 164 319 0 0 0 17181 21.9white oak 802 327 2060 636 456 332 716 328 394 216 83 57 144 5749 24.9swamp white oak 804 0 0 0 0 0 0 0 0 0 22 0 0 22 100.0scarlet oak 806 814 0 385 168 423 555 407 320 470 158 488 62 4250 23.4northern pin oak 809 0 0 0 0 0 19 0 0 0 0 0 19 39 70.7southern red oak 812 762 0 205 240 61 31 31 31 31 0 0 0 1390 58.2shingle oak 817 0 0 22 0 0 0 0 0 0 0 22 0 43 70.7swamp chestnut oak 825 0 0 146 97 0 0 0 0 0 31 0 0 273 65.0pin oak 830 0 0 405 0 0 161 78 0 0 0 78 0 723 46.2willow oak 831 0 0 0 0 0 83 0 0 0 0 0 0 83 100.0chestnut oak 832 367 1402 659 606 592 390 452 83 533 57 167 0 5307 22.0northern red oak 833 241 0 300 210 157 281 119 219 144 106 249 31 2057 22.4black oak 837 1885 0 261 289 480 643 266 162 362 144 123 0 4615 38.7black locust 901 0 571 367 1002 459 104 82 0 0 56 0 0 2641 30.9willow 920 0 0 0 83 0 135 0 0 0 0 0 0 218 72.7sassafras 931 2830 429 1405 540 157 0 0 0 22 0 0 0 5383 31.1American elm 972 0 0 0 0 0 0 0 0 0 0 106 0 106 100.0slippery elm 975 0 0 127 65 43 106 0 0 0 0 0 0 341 49.8 All hardwoods 102181 40713 27478 15477 10522 9250 6165 3814 4137 2667 3200 840 226445 7.5All species 102995 41323 31048 17903 12122 10469 6456 4439 4215 2667 3278 862 237777 7.3

Table 17.—Continued.

%SE 14.0 18.7 13.8 14.1 14.5 13.2 15.4 18.6 19.2 25.9 18.8 31.1 7.3
18 Table 18.—Number of live trees on all nonforest land by species and diameter class

(thousands of trees)

Diameter class (inches at breast height)

Species Species 1.0- 3.0- 5.0- 7.0- 9.0- 11.0- 13.0- 15.0- 17.0- 19.0- 21.0- All classes %SE

code 2.9 4.9 6.9 8.9 10.9 12.9 14.9 16.9 18.9 20.9 28.9 29+

Atlantic white-cedar 43 60 121 60 0 0 0 0 0 0 0 0 0 242 61.2eastern redcedar 68 218 245 0 0 0 0 0 0 0 0 0 0 463 55.2Norway spruce 91 0 73 0 0 58 0 0 0 0 0 0 0 131 71.2blue spruce 96 57 257 343 0 0 0 0 0 0 0 0 0 657 57.9shortleaf pine 110 85 73 0 0 0 0 0 0 0 0 0 0 158 70.9eastern white pine 129 337 396 587 1080 569 142 76 151 0 0 0 0 3339 28.3Scotch pine 130 0 76 76 0 0 0 0 0 0 0 0 0 151 70.7loblolly pine 131 120 0 0 0 0 0 0 0 0 0 0 0 120 71.8Virginia pine 132 0 0 0 81 0 0 0 0 0 0 0 0 81 100.0redwood spp. 210 0 0 60 0 0 0 0 0 0 0 0 0 60 100.0

northern white-cedar 241 200 76 0 0 0 0 0 0 0 0 0 0 276 63.8 All softwoods 1077 1316 1126 1162 627 142 76 151 0 0 0 0 5678 19.3maple 310 270 169 0 0 0 0 0 0 0 0 0 0 439 49.5red maple 316 1014 672 600 481 256 199 409 85 134 0 0 0 3850 21.7silver maple 317 164 182 307 58 115 58 0 0 58 0 115 0 1058 33.2sugar maple 318 164 115 0 0 70 0 85 0 0 0 0 0 435 46.7Norway maple 322 98 133 125 85 85 0 0 76 0 0 0 76 676 33.9ailanthus 341 120 0 0 58 0 0 0 0 0 0 0 0 177 58.4mimosa 345 285 85 0 0 0 0 0 0 0 0 0 0 369 53.9pignut hickory 403 58 114 131 197 231 0 174 58 0 0 0 0 962 30.3mockernut hickory 409 196 57 57 58 142 58 85 0 0 0 0 0 652 32.2chestnut spp. 420 0 0 0 0 115 0 0 0 49 0 115 0 280 60.9Ozark chinkapin 423 0 0 0 0 73 73 0 0 0 0 0 0 147 70.7dogwood spp. 490 60 0 0 0 0 0 0 0 0 0 0 0 60 100.0flowering dogwood 491 1162 517 202 000000000 1881 31.5

American beech 531 681 364 330 225 244 58 0 0 0 0 116 0 2016 28.5ash 540 0 0 0 0 0 0 49 0 0 0 0 0 49 100.0white ash 541 165 85 200 58 0 85 0 0 0 58 85 0 734 34.6honeylocust 552 0 0 60 0 0 0 0 0 0 0 0 0 60 100.0American holly 591 70 70 0 0 0 0 0 0 0 0 0 0 141 70.7black walnut 602 0 0 0 0 115 0 0 0 0 115 0 0 231 70.7sweetgum 611 257 257 0 81 81 0 49 0 49 0 0 0 776 33.0yellow-poplar 621 521 247 258 0 174 58 424 282 300 197 892 85 3435 21.5
19

magnolia 650 73 0 0 0 0 0 70 0 0 0 0 0 144 70.7apple 660 200 212 0 58 0 0 0 0 0 0 0 0 470 51.0mulberry 680 209 320 630 0 76 0 187 0 0 0 0 0 1422 36.2white mulberry 681 200 115 0 0 0 0 0 0 0 0 0 0 316 58.3blackgum 693 597 399 171 0 245 116 200 0 0 0 0 0 1729 28.5sycamore 731 70 70 0 81 0 0 0 0 0 0 0 0 222 57.9cherry, plum 760 351 469 107 213 49 0 0 0 0 0 0 0 1188 31.6black cherry 762 1319 448 481 315 85 169 0 170 85 0 58 0 3129 25.0pear spp. 790 0 0 488 244 0 0 0 0 0 0 0 0 732 74.5white oak 802 143 0 73 58 147 204 281 0 0 76 175 58 1214 27.8scarlet oak 806 98 73 0 131 0 131 0 142 58 0 115 85 834 31.0southern red oak 812 0 0 0 0 0 0 0 81 49 0 0 0 130 72.9chestnut oak 832 115 0 0 151 151 57 86 161 0 73 458 57 1309 35.4northern red oak 833 149 73 114 0 289 116 0 0 0 116 0 0 858 39.3black oak 837 57 0 0 73 58 0 131 76 58 58 245 0 755 34.4black locust 901 49 139 115 115 498 312 437 184 0 0 57 0 1907 30.9sassafras 931 703 230 57 0 85 0 0 0 0 0 0 0 1076 42.9American basswood 951 60 0 0 0 0 0 0 0 0 0 0 0 60 100.0elm 970 0 0 49 0 0 0 0 0 0 0 0 86 135 73.3American elm 972 169 85 0 0 169 151 85 0 0 0 0 0 659 46.6unknown or not listed tree 999 0 0 0 0 85 0 0 0 0 0 0 0 85 100.0 All hardwoods 9851 5704 4555 2739 3637 1845 2749 1314 838 693 2431 445 36803 6.4All species 10928 7021 5681 3901 4264 1987 2825 1465 838 693 2431 445 42481 6.1

Table 18.—Continued.

%SE 11.8 13.6 19.7 24.2 17.8 23.3 24.4 25.2 30.8 36.7 27.3 41.4 6.1
20

Table 19.—Number of live trees on all forest and nonforest land by species and diameter class

(thousands of trees)

Diameter class (inches at breast height)

Species Species 1.0- 3.0- 5.0- 7.0- 9.0- 11.0- 13.0- 15.0- 17.0- 19.0- 21.0- All classes %SE

code 2.9 4.9 6.9 8.9 10.9 12.9 14.9 16.9 18.9 20.9 28.9 29+

Atlantic white-cedar 43 60 121 60 0 0 0 0 0 0 0 0 0 242 61.2eastern redcedar 68 1033 245 206 0 0 0000000 1483 58.4Norway spruce 91 0 73 0 0 58 0 0 0 0 0 0 0 131 71.2blue spruce 96 57 257 343 0 0 0 0 0 0 0 0 0 657 57.9shortleaf pine 110 85 73 0 0 0 0 0 0 0 0 0 0 158 70.9eastern white pine 129 337 396 700 1172 744 225 219 316 0000 4110 24.0Scotch pine 130 0 76 76 0 0 0 0 0 0 0 0 0 151 70.7loblolly pine 131 120 0 109 61 31 92 0 78 78 0 78 0 647 33.1Virginia pine 132 0 610 3142 2354 1395 1045 147 382 0000 9074 34.3redwood spp. 210 0 0 60 0 0 0 0 0 0 0 0 0 60 100.0

northern white-cedar 241 200 76 0 0 0 0 0 0 0 0 0 0 276 63.8eastern hemlock 261 0 0 0 0 0 0 0 0 0 0 0 22 22 100.0

 All softwoods 1892 1926 4696 3588 2227 1361 366 776 78 0 78 22 17010 25.6maple 310 270 169 0 0 0 0 0 0 0 0 0 0 439 49.5boxelder 313 0 241 561 195 56 22 000000 1074 37.1

red maple 316 19002 6350 4735 2986 1672 1429 832 251 376 422 301 202 38557 20.9silver maple 317 164 182 307 58 115 58 0 0 58 0 115 0 1058 33.2sugar maple 318 164 115 0 0 70 0 85 0 0 0 0 0 435 46.7Norway maple 322 355 133 249 132 85 0 0 76 0 0 0 76 1106 32.4ailanthus 341 690 0 154 181 0 62 000000 1086 56.1mimosa 345 285 85 0 0 0 0 0 0 0 0 0 0 369 53.9pawpaw 367 2346 00000000000 2346 70.1

sweet birch 372 0 0 106 591 162 373 211 106 0 211 56 0 1815 36.5river birch 373 0 0 166 0 0 135 0 0 0 0 0 0 301 64.3American hornbeam 391 2345 0 161 19 83 0000000 2608 44.0bitternut hickory 402 1286 0 105 0 49 56 000000 1495 86.2

pignut hickory 403 2129 114 616 690 538 403 359 77 83 106 0 0 5115 27.7shagbark hickory 407 0 0 83 0 83 83 0 0 0 0 0 0 248 57.7mockernut hickory 409 3209 1812 492 437 468 106 327 132 62 0 105 0 7149 26.8chestnut spp. 420 0 0 0 0 115 0 0 0 49 0 115 0 280 60.9Ozark chinkapin 423 0 0 0 0 73 73 0 0 0 0 0 0 147 70.7dogwood spp. 490 60 0 0 0 0 0 0 0 0 0 0 0 60 100.0

flowering dogwood 491 16773 2093 566 0 0 0000000 19432 30.6common persimmon 521 571 0 481 128 0 49 000000 1228 57.2American beech 531 7469 1868 1678 949 505 373 267 0 45 67 217 0 13439 27.4ash 540 0 0 0 0 0 0 49 0 0 0 0 0 49 100.0white ash 541 2526 3158 1484 566 83 178 84 0 83 58 159 49 8428 33.0green ash 544 0 0 0 0 26 0 0 0 0 0 0 0 26 100.0

pumpkin ash 545 0 0 19 0 0 0 0 0 0 0 0 0 19 100.0
21

honeylocust 552 0 0 60 0 0 0 0 0 0 0 0 0 60 100.0

American holly 591 1946 1008 207 97 62 0000000 3320 63.5butternut 601 0 0 0 0 0 0 26 0 0 0 0 0 26 100.0black walnut 602 0 1142 228 74 303 19 106 49 0 115 22 0 2057 57.4sweetgum 611 5710 4239 4247 1774 927 824 1105 572 127 31 196 0 19753 30.8yellow-poplar 621 9462 8951 3274 1297 1652 1276 1655 1679 1634 1262 1898 315 34356 21.4magnolia 650 73 0 0 0 0 0 70 0 0 0 0 0 144 70.7apple 660 1138 681 0 58 0 0000000 1876 57.3mulberry 680 760 320 630 0 76 0 187 00000 1973 38.2white mulberry 681 200 115 62 0 0 82 0 0 0 0 0 0 459 45.9blackgum 693 14863 3014 1497 564 534 322 327 0 31 61 49 0 21260 26.0eastern hophornbeam 701 257 0 56 0 0 0 0 0 0 0 0 0 313 84.0Paulownia 712 0 0 31 107 31 0 0 0 0 0 0 0 168 52.2sycamore 731 1110 539 198 163 179 0 82 0 83 49 0 103 2506 36.8bigtooth aspen 743 947 0 212 130 376 82 0 0 0 0 0 0 1747 57.5quaking aspen 746 0 0 0 0 123 0 0 0 0 0 0 0 123 100.0cherry, plum 760 351 469 107 213 188 138 000000 1465 27.4pin cherry 761 0 0 0 31 0 0 0 0 0 0 0 0 31 100.0

black cherry 762 6800 4557 3448 2249 1342 757 362 335 404 0 58 0 20311 19.0pear spp. 790 0 0 488 244 0 0 0 0 0 0 0 0 732 74.5white oak 802 470 2060 710 514 479 919 609 394 216 158 232 202 6963 21.1swamp white oak 804 0 0 0 0 0 0 0 0 0 22 0 0 22 100.0

scarlet oak 806 912 73 385 299 423 687 407 462 528 158 604 146 5084 20.2northern pin oak 809 0 0 0 0 0 19 0 0 0 0 0 19 39 70.7southern red oak 812 762 0 205 240 61 31 31 112 80 0 0 0 1520 53.5shingle oak 817 0 0 22 0 0 0 0 0 0 0 22 0 43 70.7swamp chestnut oak 825 0 0 146 97 0 0 0 0 0 31 0 0 273 65.0pin oak 830 0 0 405 0 0 161 78 0 0 0 78 0 723 46.2willow oak 831 0 0 0 0 0 83 0 0 0 0 0 0 83 100.0chestnut oak 832 482 1402 659 757 743 447 537 244 533 130 625 57 6616 19.0northern red oak 833 390 73 415 210 446 397 119 219 144 221 249 31 2914 19.6black oak 837 1943 0 261 362 538 643 397 238 420 202 368 0 5370 33.6black locust 901 49 710 482 1118 956 417 518 184 0 56 57 0 4548 22.1willow 920 0 0 0 83 0 135 0 0 0 0 0 0 218 72.7sassafras 931 3534 659 1463 540 241 0 0 0 22 0 0 0 6458 26.9American basswood 951 60 0 0 0 0 0 0 0 0 0 0 0 60 100.0

elm 970 0 0 49 0 0 0 0 0 0 0 0 86 135 73.3American elm 972 169 85 0 0 169 151 85 0 0 0 106 0 764 42.5slippery elm 975 0 0 127 65 43 106 0 0 0 0 0 0 341 49.8unknown or not listed tree 999 0 0 0 0 85 0 0 0 0 0 0 0 85 100.0

 All hardwoods 112032 46417 32033 18216 14160 11095 8915 5128 4975 3360 5631 1286 263248 6.5All species 113923 48344 36730 21804 16386 12456 9281 5905 5054 3360 5709 1307 280258 6.2

Table 19.—Continued.

%SE 12.7 16.1 12.1 12.3 11.7 11.7 13.0 15.3 16.8 21.9 15.9 24.9 6.2
22

Table 20.—Number of saplings on all forest land by species and stand-size class

(thousands of trees)

Stand-size class

Species Species Seedling and Pole- Saw- All classes %SE

code sapling timber timber

eastern redcedar 68 814 0 0 814 100.0

Virginia pine 132 0 610 0 610 100.0

 All softwoods 814 610 0 1424 71.4

boxelder 313 241 0 0 241 100.0

red maple 316 15833 7832 0 23665 26.5

Norway maple 322 257 0 0 257 100.0

ailanthus 341 0 0 571 571 100.0

pawpaw 367 2346 0 0 2346 70.1

American hornbeam 391 2345 0 0 2345 48.6

bitternut hickory 402 1286 0 0 1286 100.0

pignut hickory 403 1521 551 0 2071 51.2

mockernut hickory 409 4767 0 0 4767 49.6

flowering dogwood 491 13399 1097 2690 17186 33.0

common persimmon 521 0 0 571 571 100.0

American beech 531 4046 3477 770 8293 40.5

white ash 541 3722 0 1713 5435 57.8

American holly 591 0 2813 0 2813 100.0

black walnut 602 0 1142 0 1142 100.0

sweetgum 611 3142 6293 0 9434 57.8

yellow-poplar 621 16451 1194 0 17645 54.8

apple 660 0 1407 0 1407 100.0

mulberry 680 551 0 0 551 100.0

blackgum 693 15514 1366 0 16880 34.5

eastern hophornbeam 701 257 0 0 257 100.0

sycamore 731 938 0 571 1509 72.7

bigtooth aspen 743 947 0 0 947 100.0

black cherry 762 6998 1449 1142 9589 37.2

white oak 802 898 1489 0 2386 53.2

scarlet oak 806 0 814 0 814 100.0

southern red oak 812 762 0 0 762 100.0

chestnut oak 832 1402 0 367 1769 51.8

northern red oak 833 241 0 0 241 100.0

black oak 837 257 1629 0 1885 87.4

black locust 901 0 0 571 571 100.0

sassafras 931 2280 979 0 3259 42.1

 All hardwoods 100397 33531 8966 142894 12.1

All species 101211 34141 8966 144318 12.0

%SE 14.8 23.7 36.0 12.0
23

Table 21.—Number of saplings on all nonforest land by species and stand-size class

(thousands of trees)

Stand-size class

Species Species Seedling and Pole- Saw- All classes %SE

code sapling timber timber

Atlantic white-cedar 43 0 181 0 181 100.0

eastern redcedar 68 125 0 338 463 75.6

Norway spruce 91 0 0 73 73 100.0

blue spruce 96 85 229 0 314 63.7

shortleaf pine 110 158 0 0 158 70.9

eastern white pine 129 494 169 70 733 47.1

Scotch pine 130 76 0 0 76 100.0

loblolly pine 131 0 70 49 120 71.8

northern white-cedar 241 227 0 49 276 84.1

 All softwoods 1163 650 580 2394 26.4

maple 310 70 369 0 439 62.8

red maple 316 710 220 757 1687 38.4

silver maple 317 98 249 0 347 61.4

sugar maple 318 231 0 49 280 84.3

Norway maple 322 58 76 98 231 59.0

ailanthus 341 0 0 120 120 71.8

mimosa 345 0 320 49 369 62.8

pignut hickory 403 58 114 0 172 74.4

mockernut hickory 409 58 196 0 253 59.9

dogwood spp. 490 0 60 0 60 100.0

flowering dogwood 491 1287 392 0 1679 38.1

American beech 531 719 325 0 1045 50.5

white ash 541 116 0 134 249 60.8

American holly 591 0 141 0 141 100.0

sweetgum 611 0 163 352 515 59.7

yellow-poplar 621 652 0 116 768 57.2

magnolia 650 0 0 73 73 100.0

apple 660 0 314 98 412 58.9

mulberry 680 227 254 49 529 64.9

white mulberry 681 115 142 58 316 52.2

blackgum 693 858 139 0 997 49.7

sycamore 731 0 0 141 141 100.0

cherry, plum 760 231 468 121 819 48.3

black cherry 762 715 549 503 1767 36.1

white oak 802 86 57 0 143 72.1

scarlet oak 806 73 0 98 172 71.4

chestnut oak 832 58 57 0 115 70.7

northern red oak 833 147 76 0 223 74.3

black oak 837 0 57 0 57 100.0

black locust 901 58 81 49 188 59.1

sassafras 931 877 57 0 934 60.8

American basswood 951 0 0 60 60 100.0

American elm 972 254 0 0 254 100.0

 All hardwoods 7755 4875 2925 15555 11.6

All species 8919 5525 3505 17949 10.6

%SE 16.4 17.0 22.4 10.6

25

Table 22.—Number of saplings on all forest and nonforest land by species and stand-size class

(thousands of trees)

Stand-size class

Species Species Seedling and Pole- Saw- All classes %SE

code sapling timber timber

Atlantic white-cedar 43 0 181 0 181 100.0

eastern redcedar 68 939 0 338 1277 69.4

Norway spruce 91 0 0 73 73 100.0

blue spruce 96 85 229 0 314 63.7

shortleaf pine 110 158 0 0 158 70.9

eastern white pine 129 494 169 70 733 47.1

Scotch pine 130 76 0 0 76 100.0

loblolly pine 131 0 70 49 120 71.8

Virginia pine 132 0 610 0 610 100.0

northern white-cedar 241 227 0 49 276 84.1

 All softwoods 1978 1260 580 3818 54.2

maple 310 70 369 0 439 62.8

boxelder 313 241 0 0 241 100.0

red maple 316 16543 8052 757 25352 24.9

silver maple 317 98 249 0 347 61.4

sugar maple 318 231 0 49 280 84.3

Norway maple 322 314 76 98 488 59.6

ailanthus 341 0 0 690 690 83.6

mimosa 345 0 320 49 369 62.8

pawpaw 367 2346 0 0 2346 70.1

American hornbeam 391 2345 0 0 2345 48.6

bitternut hickory 402 1286 0 0 1286 100.0

pignut hickory 403 1578 665 0 2243 47.6

mockernut hickory 409 4825 196 0 5020 47.2

dogwood spp. 490 0 60 0 60 100.0

flowering dogwood 491 14686 1489 2690 18866 30.3

common persimmon 521 0 0 571 571 100.0

American beech 531 4765 3802 770 9337 36.4

white ash 541 3838 0 1846 5685 55.3

American holly 591 0 2954 0 2954 95.3

black walnut 602 0 1142 0 1142 100.0

sweetgum 611 3142 6455 352 9949 54.9

yellow-poplar 621 17103 1194 116 18413 52.6

magnolia 650 0 0 73 73 100.0

apple 660 0 1720 98 1818 78.5

mulberry 680 778 254 49 1080 60.1

white mulberry 681 115 142 58 316 52.2

blackgum 693 16372 1505 0 17877 32.7

eastern hophornbeam 701 257 0 0 257 100.0

sycamore 731 938 0 712 1650 67.1

bigtooth aspen 743 947 0 0 947 100.0

cherry, plum 760 231 468 121 819 48.3

black cherry 762 7714 1998 1645 11356 31.9

white oak 802 983 1546 0 2529 50.4

scarlet oak 806 73 814 98 986 83.5

southern red oak 812 762 0 0 762 100.0

chestnut oak 832 1460 57 367 1884 48.9

northern red oak 833 388 76 0 463 63.0

black oak 837 257 1686 0 1943 84.9

black locust 901 58 81 620 759 76.6

sassafras 931 3156 1037 0 4193 35.4

American basswood 951 0 0 60 60 100.0

American elm 972 254 0 0 254 100.0

 All hardwoods 108152 38407 11890 158449 11.0

All species 110130 39666 12470 162267 10.7

%SE 13.6 20.5 26.6 10.7
26 Table 23.—Basal area of live trees on all forest land by species and diameter class

(thousands of square feet)

Diameter class (inches at breast height)

Species Species 1.0- 3.0- 5.0- 7.0- 9.0- 11.0- 13.0- 15.0- 17.0- 19.0- 21.0- All classes %SE

code 2.9 4.9 6.9 8.9 10.9 12.9 14.9 16.9 18.9 20.9 28.9 29+

eastern redcedar 68 35 0 37 0 0 0 0 0 0 0 0 0 72 60.5eastern white pine 129 0 0 17 30 94 62 139 232 0 0 0 0 573 47.1loblolly pine 131 0 0 23 17 15 76 0 100 149 0 230 0 610 49.7Virginia pine 132 0 77 627 716 708 766 165 517 0000 3576 29.9eastern hemlock 261 0 0 0 0 0 0 0 0 0 0 0 161 161 100.0 All softwoods 35 77 704 762 816 904 305 849 149 0 230 161 4992 23.2boxelder 313 0 21 95 66 28 15 0 0 0 0 0 0 224 33.5red maple 316 245 445 761 821 726 959 436 245 412 849 923 1502 8325 20.0Norway maple 322 2 0 28 13 0 0 0 0 0 0 0 0 43 56.6ailanthus 341 9 0 25 35 0 44 0 0 0 0 0 0 113 53.8pawpaw 367 29 0 0 0 0 0 0 0 0 0 0 0 29 76.2sweet birch 372 0 0 24 194 84 302 201 133 0 438 155 0 1531 39.8river birch 373 0 0 28 0 0 117 0 0 0 0 0 0 145 82.0American hornbeam 391 28 0 33 7 38 0 0 0 0 0 0 0 106 43.8bitternut hickory 402 31 0 25 0 28 39 0 0 0 0 0 0 123 48.7pignut hickory 403 50 0 90 169 169 289 182 28 161 238 0 0 1377 22.9shagbark hickory 407 0 0 20 0 53 57 0 0 0 0 0 0 131 61.9mockernut hickory 409 56 149 86 135 167 33 277 167 97 0 288 0 1454 22.9flowering dogwood 491 312 111 54 0 0 0 0 0 0 0 0 0 477 29.4common persimmon 521 7 0 84 38 0 34 0 0 0 0 0 0 163 49.3American beech 531 107 133 278 241 142 246 268 0 89 141 313 0 1957 23.1white ash 541 63 264 236 167 38 72 96 0 143 0 219 271 1569 28.4green ash 544 0 0 0 0 13 0 0 0 0 0 0 0 13 100.0pumpkin ash 545 0 0 3 0 0 0 0 0 0 0 0 0 3 100.0American holly 591 19 73 44 30 28 0 0 0 0 0 0 0 194 47.5butternut 601 0 0 0 0 0 0 30 0 0 0 0 0 30 100.0black walnut 602 0 72 49 26 106 17 102 62 0 0 59 0 493 34.6sweetgum 611 88 355 802 548 473 611 1164 775 141 60 490 0 5507 19.8yellow-poplar 621 158 583 564 453 803 949 1295 1928 2326 2364 3255 1333 16010 16.0apple 660 36 54 0 0 0 0 0 0 0 0 0 0 90 72.1mulberry 680 13 0 0 0 0 0 0 0 0 0 0 0 13 100.0white mulberry 681 0 0 8 0 0 58 0 0 0 0 0 0 66 88.2
27

blackgum 693 225 188 246 201 157 148 140 0 55 133 138 0 1630 18.1eastern hophornbeam 701 5 0 11 0 0 0 0 0 0 0 0 0 16 75.8Paulownia 712 0 0 5 32 19 0 0 0 0 0 0 0 56 53.9sycamore 731 32 26 35 22 96 0 78 0 137 116 0 504 1045 43.9bigtooth aspen 743 43 0 48 35 205 71 0 0 0 0 0 0 403 43.7quaking aspen 746 0 0 0 0 60 0 0 0 0 0 0 0 60 100.0cherry, plum 760 0 0 0 0 72 94 0 0 0 0 0 0 167 50.9pin cherry 761 0 0 0 9 0 0 0 0 0 0 0 0 9 100.0black cherry 762 119 280 556 619 681 465 390 221 602 0 0 0 3932 17.6white oak 802 8 152 126 160 181 563 347 528 360 163 141 737 3466 21.8swamp white oak 804 0 0 0 0 0 0 0 0 0 49 0 0 49 100.0scarlet oak 806 6 0 70 56 227 436 421 461 861 325 1687 294 4845 21.4northern pin oak 809 0 0 0 0 0 13 0 0 0 0 0 158 171 92.4southern red oak 812 7 0 34 84 28 24 31 40 60 0 0 0 307 37.6shingle oak 817 0 0 5 0 0 0 0 0 0 0 95 0 100 94.8swamp chestnut oak 825 0 0 27 29 0 0 0 0 0 61 0 0 117 62.3pin oak 830 0 0 81 0 0 121 84 0 0 0 307 0 593 56.5willow oak 831 0 0 0 0 0 63 0 0 0 0 0 0 63 100.0chestnut oak 832 14 114 129 201 324 306 477 114 947 114 458 0 3199 22.1northern red oak 833 6 0 57 78 85 216 128 298 250 240 725 145 2228 25.5black oak 837 48 0 49 101 251 505 278 222 637 318 371 0 2779 22.5black locust 901 0 45 75 321 228 75 89 0 0 127 0 0 959 26.5willow 920 0 0 0 25 0 121 0 0 0 0 0 0 146 84.7sassafras 931 56 47 248 168 93 0 0 0 37 0 0 0 649 34.5American elm 972 0 0 0 0 0 0 0 0 0 0 279 0 279 100.0slippery elm 975 0 0 26 21 21 82 0 0 0 0 0 0 150 59.8 All hardwoods 1821 3110 5165 5103 5627 7145 6513 5223 7314 5736 9901 4944 67604 5.9Total 1856 3187 5870 5866 6444 8049 6817 6073 7464 5736 10131 5105 72596 5.7

Table 23.—Continued.

%SE 13.7 18.0 14.0 13.8 14.4 13.3 15.5 18.5 19.1 26.1 19.0 33.7 5.7
28 Table 24.—Basal area of live trees on all nonforest land by species and diameter class

(thousands of square feet)

Diameter class (inches at breast height)

Species Species 1.0- 3.0- 5.0- 7.0- 9.0- 11.0- 13.0- 15.0- 17.0- 19.0- 21.0-

code 2.9 4.9 6.9 8.9 10.9 12.9 14.9 16.9 18.9 20.9 28.9 29+ All classes %SE

Atlantic white-cedar 43 2 11 12 0 0 0 0 0 0 0 0 0 25 65.5eastern redcedar 68 6 20 0 0 0 0 0 0 0 0 0 0 26 63.9Norway spruce 91 0 5 0 0 28 0 0 0 0 0 0 0 33 86.5blue spruce 96 1 23 57 0 0 0 0 0 0 0 0 0 81 72.4shortleaf pine 110 1 8 0 0 0 0 0 0 0 0 0 0 9 89.4eastern white pine 129 7 36 131 373 334 98 84 201 0 0 0 0 1264 31.1Scotch pine 130 0 5 10 0 0 0 0 0 0 0 0 0 15 74.7loblolly pine 131 1 0 0 0 0 0 0 0 0 0 0 0 1 71.8Virginia pine 132 0 0 0 33 0 0 0 0 0 0 0 0 33 100.0redwood spp. 210 0 0 8 0 0 0 0 0 0 0 0 0 8 100.0northern white-cedar 241 4 5 0 0 0 0 0 0 0 0 0 0 9 67.4 All softwoods 22 114 218 405 362 98 84 201 0 0 0 0 1505 26.6maple 310 5 15 0 0 0 0 0 0 0 0 0 0 20 76.0red maple 316 27 54 100 145 150 143 422 118 249 0 0 0 1407 32.4silver maple 317 4 12 59 23 52 45 0 0 96 0 347 0 639 58.0sugar maple 318 4 10 0 0 42 0 92 0 0 0 0 0 148 68.5Norway maple 322 2 12 20 33 54 0 0 115 0 0 0 522 758 71.0ailanthus 341 1 0 0 21 0 0 0 0 0 0 0 0 23 93.6mimosa 345 7 9 0 0 0 0 0 0 0 0 0 0 16 63.7pignut hickory 403 1 10 23 70 127 0 186 78 0 0 0 0 495 36.8mockernut hickory 409 4 4 12 24 87 46 102 0 0 0 0 0 279 47.1chestnut spp. 420 0 0 0 0 63 0 0 0 89 0 356 0 507 73.3Ozark chinkapin 423 0 0 0 0 35 49 0 0 0 0 0 0 85 71.6dogwood spp. 490 1 0 0 0 0 0 0 0 0 0 0 0 1 100.0flowering dogwood 491 27 43 33 0 0 0 0 0 0 0 0 0 103 34.5American beech 531 16 30 60 80 124 42 0 0 0 0 464 0 816 59.6ash 540 0 0 0 0 0 0 45 0 0 0 0 0 45 100.0white ash 541 2 9 45 18 0 62 0 0 0 130 203 0 470 53.6honeylocust 552 0 0 10 0 0 0 0 0 0 0 0 0 10 100.0American holly 591 2 8 0 0 0 0 0 0 0 0 0 0 10 80.0black walnut 602 0 0 0 0 58 0 0 0 0 260 0 0 318 83.7sweetgum 611 7 21 0 31 36 0 58 0 91 0 0 0 244 48.8
29

yellow-poplar 621 10 22 43 0 95 51 479 408 522 456 2842 481 5409 28.8magnolia 650 1 0 0 0 0 0 66 0 0 0 0 0 67 98.6apple 660 4 18 0 16 0 0 0 0 0 0 0 0 38 58.3mulberry 680 6 28 121 0 42 0 175 0 0 0 0 0 373 52.4white mulberry 681 4 10 0 0 0 0 0 0 0 0 0 0 14 76.2blackgum 693 13 37 37 0 133 96 219 0 0 0 0 0 535 40.6sycamore 731 2 5 0 30 0 0 0 0 0 0 0 0 37 82.0cherry, plum 760 8 39 21 76 25 0 0 0 0 0 0 0 169 35.3black cherry 762 30 32 101 113 46 123 0 242 161 0 150 0 999 33.0pear spp. 790 0 0 99 69 0 0 0 0 0 0 0 0 168 71.8white oak 802 4 0 11 16 77 159 311 0 0 165 506 316 1565 36.5scarlet oak 806 2 5 0 47 0 97 0 190 98 0 376 396 1210 47.7southern red oak 812 0 0 0 0 0 0 0 111 81 0 0 0 192 71.5chestnut oak 832 1 0 0 62 82 44 85 212 0 146 1409 386 2429 50.3northern red oak 833 3 5 23 0 149 82 0 0 0 233 0 0 495 55.7black oak 837 2 0 0 30 34 0 147 108 91 116 705 0 1233 46.0black locust 901 1 15 23 39 274 260 472 245 0 0 196 0 1524 36.7sassafras 931 11 18 10 0 52 0 0 0 0 0 0 0 91 60.7American basswood 951 2 0 0 0 0 0 0 0 0 0 0 0 2 100.0elm 970 0 0 7 0 0 0 0 0 0 0 0 544 551 98.7American elm 972 4 6 0 0 87 122 85 0 0 0 0 0 304 56.8unknown or not listed tree 999 0 0 0 0 51 0 0 0 0 0 0 0 51 100.0 All hardwoods 220 478 856 943 1975 1421 2943 1826 1478 1506 7555 2646 23847 11.2All species 242 591 1075 1349 2337 1519 3027 2027 1478 1506 7555 2646 25352 10.6

Table 24.—Continued.

%SE 11.9 13.9 20.5 23.8 17.9 23.9 24.1 25.2 31.0 36.8 27.3 41.5 10.6
30Table 25.—Basal area of live trees on all forest and nonforest land by species and diameter class (thousands of square feet)

Diameter class (inches at breast height)

Species Species 1.0- 3.0- 5.0- 7.0- 9.0- 11.0- 13.0- 15.0- 17.0- 19.0- 21.0- All classes %SE

code 2.9 4.9 6.9 8.9 10.9 12.9 14.9 16.9 18.9 20.9 28.9 29+

Atlantic white-cedar 43 2 11 12 0 0 0 0 0 0 0 0 0 25 65.5eastern redcedar 68 40 20 37 0 0 0 0 0 0 0 0 0 98 47.7Norway spruce 91 0 5 0 0 28 0 0 0 0 0 0 0 33 86.5blue spruce 96 1 23 57 0 0 0 0 0 0 0 0 0 81 72.4shortleaf pine 110 1 8 0 0 0 0 0 0 0 0 0 0 9 89.4eastern white pine 129 7 36 148 403 427 159 224 433 0 0 0 0 1837 26.0Scotch pine 130 0 5 10 0 0 0 0 0 0 0 0 0 15 74.7loblolly pine 131 1 0 23 17 15 76 0 100 149 0 230 0 611 49.5Virginia pine 132 0 77 627 748 708 766 165 517 0 0 0 0 3609 29.6redwood spp. 210 0 0 8 0 0 0 0 0 0 0 0 0 8 100.0northern white-cedar 241 4 5 0 0 0 0 0 0 0 0 0 0 9 67.4eastern hemlock 261 0 0 0 0 0 0 0 0 0 0 0 161 161 100.0 All softwoods 57 190 923 1168 1179 1002 389 1050 149 0 230 161 6497 26.5maple 310 5 15 0 0 0 0 0 0 0 0 0 0 20 76.0boxelder 313 0 21 95 66 28 15 0 0 0 0 0 0 224 33.5red maple 316 272 499 860 967 876 1103 858 364 661 849 923 1502 9732 17.8silver maple 317 4 12 59 23 52 45 0 0 96 0 347 0 639 58.0sugar maple 318 4 10 0 0 42 0 92 0 0 0 0 0 148 68.5Norway maple 322 4 12 48 45 54 0 0 115 0 0 0 522 801 67.3ailanthus 341 10 0 25 56 0 44 0 0 0 0 0 0 136 47.5mimosa 345 7 9 0 0 0 0 0 0 0 0 0 0 16 63.7pawpaw 367 29 0 0 0 0 0 0 0 0 0 0 0 29 76.2sweet birch 372 0 0 24 194 84 302 201 133 0 438 155 0 1531 39.8river birch 373 0 0 28 0 0 117 0 0 0 0 0 0 145 82.0American hornbeam 391 28 0 33 7 38 0 0 0 0 0 0 0 106 43.8bitternut hickory 402 31 0 25 0 28 39 0 0 0 0 0 0 123 48.7pignut hickory 403 51 10 113 239 297 289 367 106 161 238 0 0 1872 19.4shagbark hickory 407 0 0 20 0 53 57 0 0 0 0 0 0 131 61.9mockernut hickory 409 59 152 98 159 254 79 379 167 97 0 288 0 1733 20.6chestnut spp. 420 0 0 0 0 63 0 0 0 89 0 356 0 507 73.3Ozark chinkapin 423 0 0 0 0 35 49 0 0 0 0 0 0 85 71.6dogwood spp. 490 1 0 0 0 0 0 0 0 0 0 0 0 1 100.0flowering dogwood 491 340 154 86 0 0 0 0 0 0 0 0 0 580 25.0common persimmon 521 7 0 84 38 0 34 0 0 0 0 0 0 163 49.3American beech 531 123 163 338 321 267 287 268 0 89 141 777 0 2773 23.9ash 540 0 0 0 0 0 0 45 0 0 0 0 0 45 100.0white ash 541 65 273 281 185 38 134 96 0 143 130 422 271 2038 25.1green ash 544 0 0 0 0 13 0 0 0 0 0 0 0 13 100.0pumpkin ash 545 0 0 3 0 0 0 0 0 0 0 0 0 3 100.0
31

honeylocust 552 0 0 10 0 0 0 0 0 0 0 0 0 10 100.0American holly 591 21 80 44 30 28 0 0 0 0 0 0 0 204 45.3butternut 601 0 0 0 0 0 0 30 0 0 0 0 0 30 100.0black walnut 602 0 72 49 26 165 17 102 62 0 260 59 0 811 39.0sweetgum 611 95 376 802 579 509 611 1221 775 233 60 490 0 5751 19.1yellow-poplar 621 168 605 607 453 899 1000 1774 2336 2848 2820 6097 1814 21419 14.0magnolia 650 1 0 0 0 0 0 66 0 0 0 0 0 67 98.6apple 660 40 72 0 16 0 0 0 0 0 0 0 0 128 53.7mulberry 680 19 28 121 0 42 0 175 0 0 0 0 0 386 50.7white mulberry 681 4 10 8 0 0 58 0 0 0 0 0 0 80 74.1blackgum 693 239 225 283 201 290 243 358 0 55 133 138 0 2166 16.9eastern hophornbeam 701 5 0 11 0 0 0 0 0 0 0 0 0 16 75.8Paulownia 712 0 0 5 32 19 0 0 0 0 0 0 0 56 53.9sycamore 731 34 31 35 52 96 0 78 0 137 116 0 504 1082 42.5bigtooth aspen 743 43 0 48 35 205 71 0 0 0 0 0 0 403 43.7quaking aspen 746 0 0 0 0 60 0 0 0 0 0 0 0 60 100.0cherry, plum 760 8 39 21 76 97 94 0 0 0 0 0 0 335 30.9pin cherry 761 0 0 0 9 0 0 0 0 0 0 0 0 9 100.0black cherry 762 149 312 657 732 727 588 390 464 763 0 150 0 4931 15.6pear spp. 790 0 0 99 69 0 0 0 0 0 0 0 0 168 71.8white oak 802 11 152 137 177 258 722 658 528 360 328 647 1053 5031 18.8swamp white oak 804 0 0 0 0 0 0 0 0 0 49 0 0 49 100.0scarlet oak 806 9 5 70 102 227 532 421 651 959 325 2063 690 6054 19.6northern pin oak 809 0 0 0 0 0 13 0 0 0 0 0 158 171 92.4southern red oak 812 7 0 34 84 28 24 31 150 141 0 0 0 499 35.9shingle oak 817 0 0 5 0 0 0 0 0 0 0 95 0 100 94.8swamp chestnut oak 825 0 0 27 29 0 0 0 0 0 61 0 0 117 62.3pin oak 830 0 0 81 0 0 121 84 0 0 0 307 0 593 56.5willow oak 831 0 0 0 0 0 63 0 0 0 0 0 0 63 100.0chestnut oak 832 15 114 129 264 406 350 563 326 947 260 1868 386 5628 25.1northern red oak 833 10 5 80 78 234 298 128 298 250 473 725 145 2723 23.2black oak 837 50 0 49 131 285 505 424 330 728 434 1075 0 4012 21.1black locust 901 1 60 97 360 501 335 560 245 0 127 196 0 2483 24.7willow 920 0 0 0 25 0 121 0 0 0 0 0 0 146 84.7sassafras 931 68 65 258 168 144 0 0 0 37 0 0 0 740 31.1American basswood 951 2 0 0 0 0 0 0 0 0 0 0 0 2 100.0elm 970 0 0 7 0 0 0 0 0 0 0 0 544 551 98.7American elm 972 4 6 0 0 87 122 85 0 0 0 279 0 582 56.3slippery elm 975 0 0 26 21 21 82 0 0 0 0 0 0 150 59.8unknown or not listed tree 999 0 0 0 0 51 0 0 0 0 0 0 0 51 100.0 All hardwoods 2041 3588 6022 6047 7602 8566 9456 7049 8792 7241 17456 7590 91451 5.2All species 2098 3778 6944 7214 8781 9568 9845 8099 8942 7241 17685 7751 97948 5.0

Table 25.—Continued.

%SE 12.2 15.4 12.2 12.0 11.6 11.8 13.1 15.3 16.7 22.0 16.0 26.3 5.0
32

Other Characteristics of Nonforest Plots

Nonforest designation. Sixty-six percent of the NF

plots were so designated because there were no trees on

them. Of those with trees, 75 percent were labeled as

nonforest because they had a disturbed understory

attributed to a land use other than forest management,

i.e., the primary eliminating factor was not low stocking,

small area size, or small width (Table 26).

Ground cover on nonforest plots. Crops and grass are

the dominant ground cover on NF plots, accounting for

30 and 25 percent of the cover, respectively. Impervious

surfaces (tar/blacktop/asphalt, buildings, cement, and

other impervious) account for 17 percent of the ground

cover (Table 27).

Distribution of Nonforest Plots by Land Use

Among all land uses, agricultural and residential areas

had the most NF plots. There were relatively few NF

plots in the open, transportation, commercial/industrial,

and urban open classes. Although this distribution

pattern corresponds closely to the relative proportions of

each land use in the five-county study area, only lowdensity residential and cropland likely contain sufficient

plots to allow characteristics such as tree size, health, and

percent canopy cover to be summarized by land use.

Plot- and tree-level variables are summarized by land-use

class in Tables 28-29.

Thirty-four percent of the NF plots fell in agricultural

areas (crops and pasture), but they accounted for only 5

Table 26.—Primary reason for designating plot as “nonforest”

Reason FIA code Number of Percent of Percent of nonforest

plots nonforest plots plots w/ trees

No trees 0 103.1 65 —

< 1 acre in size 1 5 3 9

< 120 feet in width 2 7 4 12

< 10% stocking 3 2 1 4

Disturbed understory due 4 43 27 75

 to nonforest land use

Table 27.—Ground cover on nonforest plots

Number of Percent of Average percent

Cover type plots nonforest of plot where 

with cover ground cover it occurs

Crops 55 29.9 88

Grass 89 24.6 45

Herb 61 8.1 21

Tar-blacktop-asphalt 34 7.7 37

Buildings 30 6.1 33

Duff 52 5.9 18

Shrubs 52 3.9 12

Soil 38 2.9 13

Cement 28 2.6 15

Wildgrass 14 2.3 26

Water 7 1.8 41

Other 7 1.6 36

Seedlings 28 1.5 9

Rock 10 0.5 9

Other impervious 7 0.4 9

Wood 6 0.2 7
33

Table 28.—Summary of plot data for all live trees, by land use

Land-use classa Avg. percent Avg. basal Avg. no. stems/acre a No. of Total no. stems

canopy cover area/acre aTrees Saplings nonforest plots Trees Saplings

Cropland (A) 2.3 6.0 1 3 50.3 6 14

Pasture (A) 3 0 0 46 5 0 18

Freshwater wetland (W) 27.5 118.4 65 30 2 13 6

Open land (O) 20.7 78.2 37 66 7 26 45

Multifamily residential (R) 14.3 17.8 23 14 7 16 10

High-density residential (R) 15.0 3.0 10 30 1 1 3

Medium-density residential (R) 22.1 29.4 20 39 12 24 47

Low-density residential (R) 34.3 78.0 49 67 43 210 289

Commercial (C/I) 3.0 5.9 2 50 5 1 14

Industrial (C/I) 0 0 0 0 5 0 0

Urban open (UO) 17.3 50.8 22 32 13 29 38

Transportation (T) 3.0 12.1 2 46 9.4 2 40

Water (W) 0 0 0 0 .4 0 0

TOTAL 160.1 328 524

a

Land-use group in parentheses.

Table 29.—Summary of tree data for all live trees, by land use

Total Average crown Nonforest plots with trees

Land-use classa Average d.b.h. basal area dieback aTrees Plots

Inches ft2 Percent Number

Cropland (A) 6.9 1.8 15 6 3

Pasture (A) 0 0 0 0 0

Freshwater wetland (W) 12.2 11.8 7 13 1

Open land (O) 11.3 23.5 4 26 3

Multifamily residential (R) 8.5 7.1 3 16 4

High-density residential (R) 7.4 0.3 0 1 1

Medium-density residential (R) 11.2 23.5 3 24 8

Low-density residential (R) 12.3 218.4 4 210 28

Commercial (C/I) 10.4 0.6 0 1 1

Industrial (C/I) 0 0 0 0 0

Urban open (UO) 13.2 35.5 4 29 7

Transportation (T) 10.5 1.2 3 2 1

Water (W) 0 0 0 0 0

TOTAL 323.8 328 57

a

Land-use group in parentheses.
34

percent of the NF plots with trees and 0.6 percent of tree

ba. By contrast, only 39 percent of the NF plots were in

residential areas, but these accounted for 72 percent of

the NF plots with trees and 67 percent of tree ba. Urban

open was the land use with the next highest proportion

of nonforest trees to area (only 8 percent of all NF plots

but 12 percent of the NF plots with trees. Using this

information in similar regions, one could better estimate

in advance the cost of a nonforest inventory based on the

relative amounts of residential and agricultural land use

and their relative percent tree cover.

Effect of Nonforest Plot Data on Estimates

of Tree Cover in Urban Areas

In the five-county study area, estimates of total basal area

and number of stems differed greatly when data from the

NF plots were added to the total. This difference was

particularly dramatic for census-designated urban areas.

Occupying 38 percent of the study area, these urban

areas were only 25 percent forest in 1999, with

subsequently only 19.8 regular FIA plots to describe the

tree resource in these areas. Adding data from 57 NF

plots increased total tree ba reported in these areas by 76

percent (Table 30). Since forest plots captured only 57

percent of the tree ba in urban areas, adding data from

NF plots may be important if urban-area summaries of

tree cover are desired.

Population Density on Nonforest Plots

The greatest amount of nonforest basal area was in the

population density class of 100 to 250 persons/mi2

, or

30 percent of the total. Population density between 100

and 500 accounted for 55 percent of total ba and 60

percent of the plots (Table 31). Land use provides a

more distinct division of nonforest areas where trees

predominate than population density, and thus is a strata

of greater interest to a nonforest inventory.

Changes in Land Use

Are nonforest areas increasing or decreasing? And do

these areas have trees? From 1986 to 1999, forest cover

in the five-county study area has decreased from

Table 30.—Number of nonforest and forest plots and total basal area, by urban/rural designation

Designation Nonforest plots a Nonforest plots w/ trees Forest plots aa Total basal area aa

Number Percent Number Percent Number Percent Nonforest Forest 

plots plotsa

ft2

Urban 58.4 36 29 51 19.8 28 9,472,613 12,446,435

Rural 101.7 64 28 49 51.1 72 10,839,668 35,258,687

a

Combined total for urban and rural forest plots differs from total in Table 4 because it is based on 70.96 rather than the 72.49

forest plots.

Table 31.—Number of nonforest and forest plots and percent of NF basal area, by population density

Nonforest plots Percent of total

Population density Nonforest plots Forest plots

 (persons/mi2

) w/ trees nonforest basal area

Number Number Number

0 2 1 42 2

1-100 18 2 2 4

100-250 62 16 10 30

250-500 35 14 7 25

500-1000 10 4 3 18

1,000-20,000 35 20 5 23

Total 162 57 69 100
35

approximately 35 to 31 percent. Only 76 of the 243

plots in the study area were remeasured plots that had

been inventoried in 1986. Summarizing this somewhat

limited information, two plots (2.6 percent of the land

area) shifted from forest to nonforest while 23 percent of

the F or NF plots in the previous inventory now are

classified as mixed. These latter shifts likely are due more

to a change in inventory protocol in which plots are no

longer rotated into a single condition but left where they

fall than to an actual change in land use, though the

latter could be hidden within that number. The

remaining 43 NF plots did not change in use, but there

was an increase in the number of plots designated as

“nonforest with trees.” Also, all three idle farm plots in

1986 converted to another land use by 1999. Thus, it

appears that the amount of nonforest land—and perhaps

the number and size of trees on this land—is continuing

to increase slightly in Maryland. As a primarily

agricultural state historically, it is not surprising that

much of the increase in residential development in

Maryland has been on formerly agricultural land. Other

areas of the country, e.g., northeastern Pennsylvania, are

experiencing more residential development on previously

forested land. In those areas, smaller increases in the

amount of residential development could result in

greater conversions of forest to nonforest land with trees.

Plot Variation in Nonforest Areas

How much do nonforest areas vary in tree species,

biomass, density, and size? Is the number of plots at the

FIA sampling intensity sufficient to summarize the data

at the county level, by population-density or land-use

class, or by urban/rural designation?

Using the typical FIA recommendation that any %SE

(percent sampling error) value greater than 25 suggests

questionable results, one can determine which values in

Tables 32-34 (and Tables 17-25) are useable, and thus

how finely the nonforest data can be divided for analysis

Table 32.—Basal area per acre (ba/acre) and percent sampling errors (SE) for nonforest, nonforest with trees,

and forest plots, by county (includes City of Baltimore)

County Total nonforest a Nonforest with trees a Forest a

Plots Ba/acrea SE Plots Ba/acre SE Plots Ba/acre SE

Number ft2 Percent Number ft2 Percent Number ft2 Percent

Anne Arundel 26 9.3 51.3 7 32.5 44.2 12.2 59.4 19.4

Baltimore County 39.9 32.4 27.9 17 77.9 19.9 24.4 85.2 14.0

Carroll 38 14.7 40.0 8 69.7 24.7 11.0 63.7 24.0

Harford 27.8 30.6 32.7 14 60.8 26.8 14.8 86.9 15.0

Howard 19.4 20.1 59.3 7 57 50.0 8.3 79.5 34.0

Baltimore City 9 7.6 46.3 4 16.9 28.9 0.3 6.2 – –

Total 160.1 21.3 16.8 57 60.1 12.7 71.0 75.7 8.7

a

Includes both trees and saplings.

Table 33.—Basal area per acre (ba/acre) and percent sampling errors (SE) for nonforest, nonforest with trees,

and forest plots, by urban/rural designation

Designation Total nonforest a Nonforest with trees a Forest a

Plots Ba/acrea SE Plots Ba/acre SE Plots Ba/acre SE

Number Ft2 Percent Number Ft2 Percent Number Ft2 Percent

Urban 58.4 26.9 24.7 29 54.3 20.7 19.8 68.2 17.7

Rural 101.7 18.2 23.5 28 66.1 17.1 51.1 78.7 10.5

Total 160.1 21.3 17.0 57 60.1 13.3 71.0 75.7 9.0

a

Includes both trees and saplings.
36

given the sampling intensity. It should be noted that the

“25 percent rule” is a function of how the information is

used and whether that amount of uncertainty associated

with the estimate is tolerable.

In this study, NF plots varied more than F plots in

average ba/acre. Few differences were dramatic, though

they usually resulted in a %SE exceeding 25 percent.

Comparing sampling errors for estimates on nonforest

vs. forest land, %SE values were about twice as high

when summarizing by county, 40 percent higher when

summarizing by census-designated urban (vs. rural)

areas, and only 16 to 44 percent higher when

summarizing by the land use classes of residential, urban,

and open, as identified by the 1992 National Land

Cover Dataset (NLCD’92) (Loveland and Shaw 1996)

(Tables 32-34). With respect to individual species, %SE

values were comparably high for both forest and

nonforest areas. With respect to ba, %SE values on forest

land generally were slightly lower for forest than

nonforest land, but the reverse was true for number of

live trees or saplings. Nonforest areas were more variable

than forest areas by diameter class. Including the

nonforest with forest areas in summary tables of “all

lands” always resulted in lower %SE values.

Importantly, %SE is lower when only NF plots with trees

are examined than when all NF plots are combined. Thus,

if we are able to accurately identify and separate nonforest

areas with and without trees into two strata, we will be able

to increase the appropriateness of our estimates and lower

associated sampling errors in these areas.

Costs

The cost of regular FIA plots is $800 per plot equivalent

(2000 dollars) estimated at a rate of approximately one

plot per day. This total includes salary, travel, vehicles,

equipment, and field offices. A cost of $1,300 per plot

equivalent includes plot setup, quality control,

processing, and analysis. A plot containing forest land

equals one plot equivalent. Entirely NF plots are

typically counted as one-third or one-half of a plot

equivalent in the regular inventory.

In contrast to the amount of time typically required to

inventory F plots, the NFI crew was able to inventory

slightly more than three plots per day, greatly reducing

the cost of NF plots compared to that of F plots, and

even compared to plot-equivalent estimates for NF plots

when few data are collected. The NF plots usually were

more accessible and had fewer trees. Also, fewer variables

were recorded on NF than on F plots. The NFI crew also

had advance information on ownership, which further

speeded the inventory. In the interest of overall efficiency

for the FIA inventory in Maryland, the NFI crew took

additional time to inventory any portions of the

nonforest plots they visited that were forested (13

percent). As a result, these plots did not have to be

visited twice by FIA crews. This protocol is

recommended when the nonforest inventory is

conducted simultaneously with the FIA inventory. At

about one-third the cost of an F plot, the field portion of

an NF plot cost $276 (2000 dollars).

Table 34.—Basal area per acre (ba/acre) and percent sampling errors (SE) for nonforest

and nonforest with trees, by land-use groups of MacConnell and others (1991)

Land-use group Total nonforest a Nonforest with trees

Plots Ba/acrea SE Plots Ba/acre SE

Number Ft2 Percent Number Ft2 Percent

Residential 63 41.9 17.6 41 64.2 15.1

Urban 32.4 12.4 46.3 9 43.8 38.0

Agriculture 55.3 0.5 612.4 4 7.2 28.4

Open 7 36.5 60.4 3 85.3 41.8

Water/wetland 2.4 40.7 101.6 1 122.2 – –

Total 160.1 21.3 15.6 57 60.1 12.8

a

Includes both trees and saplings.
37

Conclusions

Across the United States there is a gap in our knowledge

of tree resources. It is the gap between the national FIA

inventory and city inventories, and includes all trees that

are growing in areas not considered “forest” by FIA’s

definition. In this pilot study, we investigated the size of

this gap, its characteristics, and methods for collecting

data to fill in the gap. The summaries presented here

assume no city inventories, but in reality this diminishes

only slightly the magnitude of the numbers observed, as

the area within the city limits is a small portion of both

the area and the unmeasured tree resource.

There is a substantial amount of tree biomass in

nonforest areas, enough to make a substantial difference

in calculations of total basal area, total biomass, total

number of stems, carbon stocks, and NPP. And this tree

resource in nonforest areas does differ, sometimes

substantially, from forest areas in species composition,

average tree size, natural reproduction, species diversity,

and proportion of exotic species.

The information collected on NF plots in the fivecounty study area in Maryland adds considerably to our

knowledge of a resource about which we knew little, and

the study design and implementation provide a

foundation for the collection of similar information in

other areas.

Additional data could be collected on these plots

without revisiting them. This information would

increase our understanding of the nature of nonforest

trees and plots. Large-scale remotely sensed imagery

could be used to identify the land use context in which

the plot occurs, measure the patch or tract size, and, if

historical imagery is available, measure the land-use and

land-cover history of the plot. A knowledge of the spatial

and temporal context associated with an FIA plot is

invaluable for understanding current inventory data.

Regulations that would affect future use of the properties

on which plots occur, e.g., zoning codes, city ordinances,

and park rules, are another valuable source of information.

With such information we can increase our

understanding of the role of trees in nonforest areas with

respect to forest processes, human social systems, and

even climatic effects. The processes affecting trees in

nonforest areas are also many and varied, and need to be

better understood. What are these areas like and what are

the trees on them used for? What are the patch/tract sizes

and the spatial context that surrounds them? Is there a

functioning understory or evidence of natural

regeneration or natural forest processes at work? Are

these processes being disrupted? What other vegetation

grows in these areas? Where are these trees in their life

cycle and how long is it? How are they changing? What

is their health, and what stresses are they experiencing?

What is the aesthetic value of these trees? With an

effective inventory of these areas, we can better monitor

the health, magnitude, and character of this increasingly

significant resource and detect changes in its status on a

regionwide basis, including that portion of the urbanrural continuum that is not covered by traditional

inventories.

Lessons Learned

This pilot study generated several observations and

recommendations for future inventories of nonforest areas.

A nonforest inventory should be conducted in

conjunction with the regular forest inventory of a state.

A coordinated effort can save considerable time overall

due to increased efficiencies.

The 0.10-acre plot design was easy to implement and

limited the number of owner contacts per plot. The

relative accessibility of NF plots, a reduction in the time

needed to collect timber-related variables, and access to

ownership information prior to the fieldwork

contributed to the relatively low cost of these plots. With

this design, one crew was able to inventory an average of

three or four plots per day and complete the entire

inventory (170 plots) during one summer. Also, costs

were much lower than our initial estimate.

Contacting owners was not inordinately time-consuming

primarily because FIA was able to access Maryland’s

digital property database. This required additional work

prior to the inventory but eliminated the need to search

ownership files at local tax offices. Similar property

databases are not yet available to FIA crews in all states,

though there is a growing trend in this direction. It is

recommended that these databases be used if possible.

The two visits per plot required because owners typically

were not available during the day consumed little

additional time because nearly every plot was easily

accessible by road and there was far less uncertainty

about who the owner was or where she or he lived.

The number of refused plots was less than expected in

the nonforest inventory. It is not known whether this

surprising degree of owner cooperation reflected the

information given to owners about the inventory, the

additional media publicity, the “people skills” of this

particular NFI crew, or the types of owners who hold

nonforest land. The greatest concern of owners usually

was related to liability issues associated with injuries

incurred by crew members.
38

The following are additional recommendations based on

this pilot study.

• A better division of exotic and native species codes

would be useful. In this study, all species not typically

encountered on FIA plots (and thus having no

previous individual FIA code) were identified and

coded only by species group. However, if identifying

the presence and abundance of exotic species

continues to be an issue, mixed-species groups should

be placed into exotic and native classes. This might

require additional training in the identification of

exotic species for NFI crews.

• When the inventories are conducted simultaneously, a

strong link between regular and nonforest inventories

is essential, i.e., when plots are rechosen and the new

plot is an NF or partially NF plot, the revised

information must be conveyed to the NFI crews so

that the new plot can be visited.

• To bring the nonforest database into line with the

regular inventory for ease of integration, an additional

FIA “condition” variable must be recorded as there

may be more than one nonforest condition present (by

FIA definitions) on the plot; data on these plots would

be recorded by condition rather than by plot.

• The actual percent NF on the 0.10-acre plot should be

recorded at the plot rather than assuming that it is the

same as that on subplot 1 or mapping and calculating

it manually in the office from the plot sheet.

• A better method is needed to determine the

proportion of the plot that remains subject to natural

forest processes, for example, adding a “percent natural

ground cover” category. It was not possible to

summarize the percentage of natural ground cover

from the data that were collected.

• For most conditions, if a lower standard error is

desired, adding more plots probably would be more

effective than using a larger plot size, particularly in

developed areas where travel costs between plots are

relatively low.

• Many of the FHM variables failed to meet QA/QC

standards in this study. The crew received training in

these variables but overall accuracy and consistency

were not as high as expected and need to be improved.

As of 2000, FHM damage variables are being added to

the regular FIA inventory, so FIA experience and QA/

QC results with such variables should improve.

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40

Appendix I

Variables Collected in the Nonforest Inventory

Tree-level variables used in nonforest inventory (all trees ≥ 5 inches in d.b.h.)

Variablea Source and manual reference

Tree number (ID) FIA – 6.105

Species FIA – 6.110

Horizontal distance (from point) FIA – 6.120

Azimuth FIA – 6.130

D.b.h. FIA – 6.150

Tree condition FIA – 6.170

Total height FIA – 6.205

FHM crown ratio FHMa

Crown class FIA – 6.250

Crown dieback FHM

Crown transparency FHM

Crown density FHM

Crown width (N-S) FHM

Crown width (E-W) FHM

Damage/cause of death FIA – 6.270

Special damage 1 FIA – 6.280

Special damage 2 FIA – 6.280

FHM damage (for the top 3 damage symptoms found) FHM

Location

Damage

Severity

Notes

a

Not available on FIA forest plots for comparison.

Sapling-level variables used in nonforest (NF) inventory

(all trees ≥ 1 to 5 inches in d.b.h.)

Variablea Source and manual reference 

Species NF inventorya

1- to 2-inch size class NF inventory

 Stems (no.)

 Avg. height (inches)

2- to 3-inch size class NF inventory

Stems (no.)

Avg. height (inches)

3- to 4-inch size class NF inventory

Stems (no.)

Avg. height (inches)

4- to 5-inch size class NF inventory

Stems (no.)

Avg. height (inches)

a

Unique to nonforest inventory and not available on FIA forest plots

for comparison.
41

Plot-level variables used in nonforest (NF) inventory

Variable a Source and manual reference 

State FIA – 3.100

Unit FIA – 3.110

County FIA – 3.120

Plot number FIA – 3.130

Sample kind FIA – 3.140

Current photo class (PI) FIA – 3.150

Current month FIA – 3.160

Current year FIA – 3.170

Previous month FIA – 3.200

Previous year FIA – 3.210

Previous land use FIA – 3.190

Cruiser number FIA – 3.220

Tally person number FIA – 3.230

Terrain position FIA – 3.250

Why is it nonforest? NF inventorya

No. of forested subplots NF inventory

Obstruction at plot center NF inventory

% of plot covered in tree canopies NF inventory

Land-use class (MacConnell NF inventory

and others 1991)

Land-use class group (MacConnell NF inventory

and others 1991)

Stand size FIA – 4.340

Physiographic class FIA – 4.430

Owner class FIA – 3.270

Ground-cover data NF inventory

Buildings

Cement

Tar/blacktop/asphalt

Wood

Other impervious

Soil

Rock

Duff/mulch

Herbaceous (nongrass)

Grass

Wild (unmanaged grass)

Water (including pools)

Shrubs

Seedlings

Crops

Other 

Notes

a

Unique to nonforest inventory and not available on FIA forest plots for comparison.
42

Appendix II

Variables Specific to This Inventory

Reason Plot is Nonforest

0 no trees

1 < 1 acre in size

2 < 120 feet wide

3 < 10 percent stocking

4 Disturbed understory due to other land use

Number of Forested Subplots

0 no subplots with forested ecotypes

1 one subplot contains forest land

2 two subplots contain some forest land

3 three subplots contain some forest land

4 all four subplots contain some forest land

Obstruction at Plot Center

0 no obstruction

1 building or street at plot center; cannot be occupied

2 other obstruction

Percentage of Plot Covered by Tree Canopies

When looking upward from within the plot, one will see tree canopies or areas of open

sky between the canopies. The proportion of the sky that is obscured by tree crowns

within the plot ranges from 0 to 100 percent. This information is necessary only if treecover data for the area are lacking. Does not include saplings. Data are recorded in 5-

percent classes.

Land-Use Class (MacConnell)

Code Description

10 R0 Multifamily residential (apartments and tenements)

11 R1 High-density residential (≤ 1/4-acre house lots)

12 R2 Medium-density residential (< 1/2-acre house lots)

13 R3 Low-density residential (≥ 1/2-acre house lots)

15 UC Commercial (city buildings, shopping centers, “business parks,” etc.

16 UI Industrial (manufacturing facilities)

17 UO Urban open (schools, colleges, churches, cemeteries, city parks, etc.)

18 UT Transporation (airports, docks, railroads roadways > 220 feet wide)

05 M Mining (sand, gravel, etc.)

19 UW Waste disposal (landfills, junkyards, sewage plants)

01 AC Cropland (tilled and untilled fields, farm buildings)

02 AP Pasture

21 WP Woody perennials (orchards, Christmas trees, nurseries)

23 CB Cranberry bog

06 O Open land (abandoned fields and orchards, right-of-ways > 100 feet

wide, dunes, heath)

20 W Water (lakes and ponds ≥ 1 acre, rivers and streams ≥ 120 feet wide)

04 FW Inland water (flood plain, bog, swamp, meadow, marsh, beaver pond)

14 SW Salt wetland (salt marsh and meadow)

03 F All forest land (3 1 acre in size and 120 feet wide)

30 OC Ocean
43

More detailed descriptions:

UI Urban-industrial (land containing primary and secondary manufacturing facilities)

UC Urban-commercial (land used primarily for distributing and merchandising goods and

services to the public)

R0 Multifamily housing (apartments, town houses, row houses, etc.)

R1 High-density residential (single family houses on small lots, generally < 1⁄4 of an acre)

R2 Medium-density residential (single family houses on predominantly 1⁄4-acre lots)

R3 Low density residential (single family houses on lots of 1⁄2 to 1)

UT Transportation (land used for airports, commercial docks, rail yards, truck and bus

terminals, and divided highways with rights-of-way > 200 feet)

UO Urban-open (undeveloped land in the midst of urban areas, or land in agricultural or

forested areas that has been cleared for urban development; also, land with “green

spaces” such as schools, colleges, churches, hospitals, and cemeteries)

M Mining (land used for the extraction of sand, gravel, stone or minerals)

UW Waste disposal (land used for sewage, waste and refuse disposal, automobile junk yards)

AC Agriculture-crops (tilled or tillable land that is or has been farmed intensively)

AP Agriculture-pasture (land used to raise hay)

WP Agriculture-woody perennials (land used for fruit orchards, greenhouses, nurseries,

Christmas tree plantations)

CB Cranberry bog (productive cranberry bogs)

O Open land (abandoned fields with < 30 percent tree crown cover, abandoned orchards,

utility rights-of-way, heath, and open land)

Land-Use Class Group (MacConnell)

Code Description

1 R0, R1, R2, R3 (residential)

2 UC, UI (urban commercial or industrial

3 UO (urban open)

4 UT (transportation/roads)

5 M, UW (mining/waste)

6 AC, P (agriculture crops/pasture)

7 WP (woody perennials)

8 CB (cranberry bog)

9 O (open)
44

Ground Cover

The inventory crew should note the proportion of the ground area of the plot is covered by one

or more of the the following materials (the sum of these proportions should equal 100 percent;

recorded in 5-percent classes):

buildings herbaceous (excluding grass and shrubs)

cement grass

tar/blacktop/asphalt wild (unmaintained grass)

wood water (including pools)

other impervious shrubs

soil seedlings

rock crops

duff/mulch other

Owner Class

Owner categories are the same as those used in the regular FIA inventory except that code 99

for nonforest will not be used.

New Species Codes

 Ten species codes were added in order to account for exotics found in the nonforest inventory.

Except for mimosa, these also represent additions to the 1999 version of the national manual.

210 Sequoia spp. (redwood)

220 Taxodium spp. (cypress)

250 Torreya spp.

345 Albizzia julibrisson (mimosa)

420 Castanea spp. (chestnut)

490 Cornus spp. (dogwood)

550 Ginko spp.

610 Liquidambar spp.

620 Liriodendron spp.

790 Pyrus spp. (pear)
Printed on Recycled Paper

Riemann, Rachel. 2003. Pilot Inventory of FIA plots traditionally called ‘nonforest’.

Gen. Tech. Rep. NE-312. Newtown Square, PA: U.S. Department of Agriculture,

Forest Service, Northeastern Research Station. 44 p.

Forest-inventory data were collected on plots defined as “nonforest” by the USDA

Forest Service’s Forest Inventory and Analysis (FIA) unit. Nonforest plots may have

trees on them, but they do not fit FIA’s definition of forest because the area covered by

trees is too small, too sparsely populated by trees, too narrow (e.g., trees between

fields or in the middle of a divided highway), or has a disturbed understory (e.g.,

mowing or grazing) such that natural regeneration of trees probably does not occur.

Recent inventories and associated photointerpretation work showed that 30 to 50

percent of these nonforest plots contained trees and were located in urban, suburban,

industrial, and rural areas. Data were collected for trees on traditionally nonforest plots

in a five-county area in Maryland that was 30 percent forested in 1999. Nonforest plots

added at least 43 percent to the total-tree basal area measured on forest plots.

Species composition, tree size, damage, and number of exotics differed between forest

and nonforest plots. Costs were about one-third of those on a regular FIA plot. Field

collection methods, including field preparation, plot design, and variables collected are

outlined, and recommendations for future inventories of similar areas are presented.

Keywords: forest inventory; trees on nonforest lands; urban forestry; total tree cover;

total basal area
Headquarters of the Northeastern Research Station is in Newtown Square,

Pennsylvania. Field laboratories are maintained at:

Amherst, Massachusetts, in cooperation with the University of Massachusetts

Burlington, Vermont, in cooperation with the University of Vermont

Delaware, Ohio

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

Hamden, Connecticut, in cooperation with Yale University

Morgantown, West Virginia, in cooperation with West Virginia University

Parsons, West Virginia

Princeton, West Virginia

Syracuse, New York, in cooperation with the State University of New York,

College of Environmental Sciences and Forestry at Syracuse University

Warren, Pennsylvania

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