United States 

Department of 

Agriculture

Forest 

Service

North Central

Research Station

General 

Technical Report

NC-251

The 2002 

RPA Plot Summary 

Database Users Manual

Patrick D. Miles, John S. Vissage, and W. Brad Smith
North Central

Research Station 

USDA Forest Service

1992 Folwell Avenue

Saint Paul, Minnesota,

55108

Published 2004

www.ncrs.fs.fed.us
Miles, Patrick D.; Vissage, John S.; Smith, W. Brad

2004. The 2002 RPA Plot Summary database users manual. Gen. Tech. Rep. NC-251. St.

Paul, MN: U.S. Department of Agriculture, Forest Service, North Central Research Station.

80 p.

Describes the structure of the RPA 2002 Plot Summary database and provides information on generating estimates of forest statistics from these data. The RPA 2002 Plot

Summary database provides a consistent framework for storing forest inventory data across

all ownerships across the entire United States. The data represents the best available data as

of October 2001.

––––––––––––––

KEY WORDS: RPA, inventory, forest statistics, information management, data processing

The U.S. Department of Agriculture (USDA) prohibits discrimination in all its programs and activities on the basis of

race, color, national origin, gender, religion, age, disability,

political beliefs, sexual orientation, and marital or family

status. (Not all prohibited bases apply to all programs.)

Persons with disabilities who require alternative means for

communication of program information (Braille, large

print, audiotape, etc.) should contact USDA’s TARGET

Center at (202) 720-2600 (voice and TDD).

To file a complaint of discrimination, write USDA, Director,

Office of Civil Rights, Room 326-W, Whitten Building, 14th

and Independence Avenue, SW, Washington, DC 20250-

9410, or call (202) 720-5964 (voice or TDD). USDA is an

equal opportunity provider and employer.

For further information contact:

Publications

North Central Research Station

1992 Folwell Avenue

St. Paul, MN 55108

or visit our web site:

www.ncrs.fs.fed.us
Forest Inventory and Analysis (FIA) is a continuing endeavor mandated by Congress in the Forest and

Rangeland Renewable Resources Planning Act (RPA) of 1974 and the McSweeney-McNary Forest

Research Act of 1928. FIA’s primary objective is to determine the extent, condition, volume, growth,

and depletions of timber on the Nation’s forest land. RPA reports are generated every 5 years. This manual describes the database used to produce the tables in the report entitled “Forest Resources of the

United States, 2002” (Smith et al.).

1

Foreword
Chapter 1—The RPA Database ..................................................................................... 5

Chapter 2—Database Structure .................................................................................... 7

Chapter 3—Algorithms for Summarizing Data ........................................................... 43

Literature Cited .......................................................................................................... 48

Appendix A—Sources of 2002 RPA Data ................................................................... 49

Appendix B—State, Survey Unit, and County Codes ................................................. 53

Appendix C—Species Group Names and Crosswalk to FIADB .................................. 79

2

Table of Contents
3

The 2002 

RPA Plot Summary 

Database Users Manual

Patrick D. Miles, John S. Vissage, and W. Brad Smith

About the Authors:

Patrick D. Miles and 

John S. Vissage are

Research Foresters with the

Forest Inventory and Analysis

Unit, North Central Research

Station, St. Paul, MN.

W. Brad Smith 

is a Research Forester with

the Science Policy, Planning,

Inventory, and Information

Staff, Washington, DC.
4
Chapter 1 – The RPA Database

5

The original RPA Plot Summary database format

was developed in 1987 to meet RPA reporting

requirements (Waddell et al. 1989). At that time

computer resources were at a premium and the

cost of data storage was prohibitive. For these

and other reasons, data were summarized and

stored at the plot level rather than the tree level

even though individual tree data existed for most

of the forest area of the United States.

Beginning in 1995 the FIA program began collecting tree information on all forest land across

the entire Nation. Once tree data are available for

all States, the Forest Inventory and Analysis

Database (FIADB) will be the primary database

for reporting RPA statistics. The FIADB (Miles et

al. 2001) contains all plot and tree measurements

collected by the FIA program -– not just data

summarized to the plot level. Although the RPA

Summary Database is able to report the volume

of softwoods, the FIADB, with its tree data, can

report the volume of softwoods by individual tree

characteristics such as species and diameter class.

Although not as flexible as the FIADB, the RPA

Plot Summary Database is much easier to work

with and is the only format available for exploring long-term trends in the forest resources of the

United States.

Data for the 2002 RPA were gathered from several different sources (appendix A). Tree data were

available for the 48 coterminous States and

southeast Alaska, but not for Hawaii or interior

Alaska. Data were collected primarily by FIA

units except for some of the lands administered

by the National Forest System in California,

Colorado, Idaho, Nevada, Oregon, Washington,

and Wyoming.

Data in RPA Plot Summary Database format are

available through the Internet at

http://www.fia.fs.fed.us. This is the Internet

address for the Forest Inventory and Analysis

national program and should remain unchanged

for the immediate future. On this page there is a

link to “Online databases.” Also available at this

Web site will be instructions for obtaining a CDROM containing the RPA Plot Summary Database

and a WindowsTM based program called the RPA

Data Wiz. The RPA Data Wiz program allows

users to easily generate reports and graphics on

the forest resources of the United States.

Chapter 2 provides detailed documentation of

the database. Chapter 3 presents algorithms on

how to compute estimates of area; current timber

volume; biomass; number of trees; average annual growing-stock growth; and mortality. Timber

removals estimates for the RPA are derived from

timber product output studies (Johnson 2001).
6
7

There are two tables in the RPA Database

(RPA_PLOT2002 and SPDBH_2002). The format

of these tables is presented in the following

pages. For each column or variable in a table,

there is a section that describes the unabbreviated

name and other details of the variable. Coded

items also include a list of the codes and their

meanings.

The RPA_PLOT2002 table provides information

about a condition. In 95 percent of cases a sample plot will have one condition. Sample plots

will have multiple conditions only when there is

a discrete change in one of the following landscape attributes: land use, reserved status, ownership group, forest type, stand-size class, stand

origin, and stand density. 

Data for the 1987 and 1992 RPA Assessments

were submitted in similar formats. Beginning

with the 1997 RPA Assessment, data were usually

converted to this format from tree-level databases

such as the Eastwide (Hansen et al. 1992),

Westwide (Woudenberg and Farrenkopf 1995),

or FIADB. For the 2002 RPA Assessment, all of

the data except for interior Alaska and Hawaii

were derived from data in FIADB format (see

appendix A). Appendix B contains information

on state, survey unit, and county codes. 

The SPDBH_2002 table contains tree species

group volume information summarized at the

State level by ownership and diameter classes.

This table is used solely to produce reports on

species group volumes by State, ownership class,

and diameter class. Appendix C contains a listing

of the species group names and how they were

derived from the FIADB.

Chapter 2 – Database Structure

Value or Key

Column Oracle unit of data

name data type measure item

1. PID NUMBER (8) Number

2. RECNUM NUMBER (13) PLOT X

3. YEAR NUMBER (4) Year (YYYY)

4. RSID NUMBER (2) Coded

5. INVSOURCE NUMBER(1) Coded

6. SRCDATE NUMBER (4) Year (YYYY)

7. STATE NUMBER (2) Coded

8. STATEABB VARCHAR (2) Coded

9. SUBREGION NUMBER (1) Coded

10. RPA_REGION NUMBER (1) Coded

11. RPA_SUBREGION NUMBER (1) Coded

12. ADMINFOR NUMBER (2) Coded

13. ADMINFORU NUMBER (4) Coded

Plot Table (Oracle table name is RPAPLOT_2002)

(table continued on next page)
8

Value or Key

Column Oracle unit of data

name data type measure item

14. LANDCC NUMBER (2) Coded

15. RESERCLASS NUMBER (1) Coded

16. SPCLASS NUMBER (2) Coded

17. FORCODE NUMBER (1) Coded

18. OWNGROUP NUMBER (2) Coded

19. OWNER NUMBER (2) Coded

20. AEF NUMBER (8) Acres

21. VEF NUMBER (7) Number

22. FORTYPE NUMBER (3) Coded

23. LOCALTYPE NUMBER (3) Coded

24. STANDORIGIN NUMBER (2) Coded

25. SSCLASS NUMBER (1) Coded

26. STDIAM NUMBER (5,2) Coded

27. AGECLASS NUMBER (3) Years

28. STOCKPC NUMBER (2) Coded

30. LOCALINFO1 NUMBER (1) Coded

31. LOCALINFO2 NUMBER (1) Coded

32. LOCALINFO3 NUMBER (1) Coded

33. LOCALINFO4 NUMBER (1) Coded

34. LOCALINFO5 NUMBER (1) Coded

35. LOCALINFO6 NUMBER (1) Coded

36. BDFTSW NUMBER (6) Number

37. BDFTHW NUMBER (6) Number

38. BDFT NUMBER (6) Number

39. BDFTSW__LOCAL NUMBER (6) Number

40. BDFTHW__LOCAL NUMBER (6) Number

41. BDFT__LOCAL NUMBER (6) Number

42. LOCAL_RULE NUMBER (2) Coded

43. CUBICSW NUMBER (5) Number

44. CUBICHW NUMBER (5) Number

45. CUBIC NUMBER (5) Number

46. CULLSW NUMBER (4) Number

47. CULLHW NUMBER (4) Name

48. CULL NUMBER (4) Number

49. DEADSW NUMBER (4) Number

Plot Table (Oracle table name is RPAPLOT_2002) continued

(table continued on next page)
9

Value or Key

Column Oracle unit of data

name data type measure item

50. DEADHW NUMBER (4) Number

51. DEAD NUMBER (4) Number

52. MORTSW NUMBER (4) Number

53. MORTHW NUMBER (4) Number

54. MORT NUMBER (4) Number

55. GROWTHSW NUMBER (4) Number

56. GROWTHHW NUMBER (4) Number

57. GROWTH NUMBER (4) Number

58. COUNTY NUMBER (3) Coded

59. CONDITION NUMBER (1) Coded

60. TSOURCE NUMBER (2) Coded

61. BIOBOLESW NUMBER (6) Number

62. BIOBOLEHW NUMBER (7) Number

63. BIOBOLE NUMBER (7) Number

64. BIOSAPSSW NUMBER (6) Number

65. BIOSAPSHW NUMBER (6) Number

66. BIOSAPS NUMBER (6) Number

67. BALIVE NUMBER (9,1) Number

68. LAT NUMBER (6,2) Degrees

69. LON NUMBER (6,2) Degrees

70. CONDPROP NUMBER (5,4) Number

71. FHMHEXID NUMBER (6) Coded

72. BALIVE_5 NUMBER (9,1) Number

73. TPA_5 NUMBER (9,3) Number

74. DWD NUMBER (9,3) Number

75. TPA_1 NUMBER (9,3) Number

76. STDIAM_1 NUMBER (5,2) Number

77. ECOSUBCD VARCHAR2 (6) Coded

78. CONGCD NUMBER (4) Coded

79. BIOSTUMPTOPSW NUMBER (6) Number

80. BIOSTUMPTOPHW NUMBER (7) Number

81. BIOSTUMPTOP NUMBER (7) Number

82. BIOSALVDEADSW NUMBER (6) Number

83. BIOSALVDEADHW NUMBER (6) Number

84. BIOSALVDEAD NUMBER (6) Number

Plot Table (Oracle table name is RPAPLOT_2002) continued
10

1. PID Plot ID. Only used in Hawaii. Used to tie plot back to original field plot data.

2. RECNUM Record number. Unique plot identifier. Record numbers for the 48 contiguous

States and southeast Alaska were generated by combining State, unit, county, plot,

and condition information to form a unique identifier. RECNUM=

(state*100000000000) + (unit*1000000000) + (county*1000000) + (plot*10) +

condition. 

3. YEAR RPA Year. This is set to 2002 for all records in the 2002 RPA plot summary

database.

4. RSID Region or station identification number. This coded value identifies which of the

16 locations submitted the record.

Code Region or Station Code Region or Station

5. INVSOURCE Source of the inventory data. A coded value identifying the source of the data.

Record sources include variations of original inventory data and inventory data

updated by bookkeeping or projection. Code 1 is used for all State inventories in

the 2002 RPA except for parts of Alaska and Hawaii.

Code Description

1 PLOT LEVEL - Original Eastwide/Westwide or FIADB format

2 PLOT LEVEL - Updated Eastwide/Westwide standard format

3 PLOT LEVEL - Original inventory data

4 PLOT LEVEL - Updated inventory data

5 STAND LEVEL - Original inventory data

6 STAND LEVEL - Updated inventory data

7 STRATUM LEVEL - Original inventory data

8 STRATUM LEVEL - Updated inventory data

1 Region 1

2 Region 2

3 Region 3

4 Region 4

5 Region 5

6 Region 6

7 Bureau of Land Management (Oregon)

8 Region 8

9 Region 9

10 Region 10

22 Rocky Mountain Research Station

23 North Central Research Station

24 Northeastern Research Station

26 Pacific Northwest Research Station

27 Alaska – PNW Research Station

33 Southern Research Station
6. SRCDATE Source date. The year the field plot was measured.

7. STATE State code. The State in which the plot is located.

Code Stateabb State Name Code Stateabb State Name

8. STATEABB State abbreviation. Two-character identifier for each State (see above).

9. SUBREGION Subregion code. The subregion code is always equal to 1 except in Alaska, Oregon,

South Dakota, and Washington where values of 2 (East) and 3 (West) are used.

10. RPA_REGION RPA Region. Grouping of States into four regions (Northern=1, Southern=2,

Rocky Mountain=3, and Pacific Coast=4) for reporting purposes (see definition

and map for RPA_SUBREGION below)

11

01 AL Alabama

02 AK Alaska

04 AZ Arizona

05 AR Arkansas

06 CA California

08 CO Colorado

09 CT Connecticut

10 DE Delaware

11 DC District of Columbia

12 FL Florida

13 GA Georgia

15 HI Hawaii

16 ID Idaho

17 IL Illinois

18 IN Indiana

19 IA Iowa

20 KS Kansas

21 KY Kentucky

22 LA Louisiana

23 ME Maine

24 MD Maryland

25 MA Massachusetts

26 MI Michigan

27 MN Minnesota

28 MS Mississippi

29 MO Missouri

30 MT Montana

31 NE Nebraska

32 NV Nevada

33 NH New Hampshire

34 NJ New Jersey

35 NM New Mexico

36 NY New York

37 NC North Carolina

38 ND North Dakota

39 OH Ohio

40 OK Oklahoma

41 OR Oregon

42 PA Pennsylvania

44 RI Rhode Island

45 SC South Carolina

46 SD South Dakota

47 TN Tennessee

48 TX Texas

49 UT Utah

50 VT Vermont

51 VA Virginia

53 WA Washington

54 WV West Virginia

55 WI Wisconsin

56 WY Wyoming
11. RPA_SUBREGION RPA Subregion. Grouping of States into nine subregions for reporting purposes.

Subregions 1 and 2 belong in the Northern Region, subregions 3 and 4 belong in

the Southern Region, subregions 5 and 6 belong in the Rocky Mountain Region,

and Regions 7, 8, and 9 belong in the Pacific Coast Region.

12
12. ADMINFOR Administrative forest. A two-digit code identifying the administrative forest within

a region where the inventoried land is located.

13

(table continued on next page)

Region Adminfor National Forest Name

0 00 Non-national forest

1 02 Beaverhead

1 03 Bitterroot

1 04 Idaho Panhandle

1 05 Clearwater

1 08 Custer

1 09 Deerlodge

1 10 Flathead

1 11 Gallatin

1 12 Helena

1 14 Kootenai

1 15 Lewis and Clark

1 16 Lolo

1 17 Nez Perce

1 20 Cedar River NGL 

1 21 Little Missouri NGL

1 22 Sheyenne NGL

1 24 Grand River NGL

1 99 Other R1

2 02 Bighorn

2 03 Black Hills

2 04 Grand MesaUncompahgre-Gunnison

2 06 Medicine Bow

2 07 Nebraska

2 09 Rio Grande

2 10 Arapaho-Roosevelt

2 11 Routt

2 12 Pike and San Isabel

2 13 San Juan

2 14 Shoshone

2 15 White River

2 17 Cimarron NGL

2 18 Commanche NGL

2 19 Pawnee NGL

2 20 Oglala NGL

2 21 Buffalo Gap NGL

2 22 Fort Pierre NGL

2 23 Thunder Basin NGL

2 99 Other R2

3 01 Apache-Sitgreaves

3 02 Carson

3 03 Cibola

3 04 Coconino

Region Adminfor National Forest Name

3 05 Coronado

3 06 Gila

3 07 Kaibab

3 08 Lincoln

3 09 Prescott

3 10 Santa Fe

3 12 Tonto

3 99 Other R3

4 01 Ashley

4 02 Boise

4 03 Bridger-Teton

4 05 Caribou

4 06 Challis

4 07 Dixie

4 08 Fishlake

4 09 Humboldt

4 10 Manti-La Sal

4 12 Payette

4 13 Salmon

4 14 Sawtooth

4 15 Targhee

4 17 Toiyabe

4 18 Uinta

4 19 Wasatch-Cache

4 20 Desert Range Exp St

4 99 Other R4

5 01 Angeles

5 02 Cleveland

5 03 Eldorado

5 04 Inyo

5 05 Klamath

5 06 Lassen

5 07 Los Padres

5 08 Mendocino

5 09 Modoc

5 10 Six Rivers

5 11 Plumas

5 12 San Bernadino

5 13 Sequoia

5 14 Shasta-Trinity

5 15 Sierra

5 16 Stanislaus

5 17 Tahoe

5 19 Lake Tahoe Basin
13. ADMINFORU Administrative Unit. A four-digit code identifying the National Forest Service

Region/Forest where the inventoried land is located. The first two digits are the

region code and the second two digits are the forest code (for codes see 

ADMINFOR above).

14

Region Adminfor National Forest Name

5 99 Other R5

6 01 Deschutes

6 02 Fremont

6 03 Gifford Pinchot

6 04 Malheur

6 05 Mt. Baker-Snoqualmie

6 06 Mt. Hood

6 07 Ochoco

6 08 Okanogan

6 09 Olympic

6 10 Rogue River

6 11 Siskiyou

6 12 Siuslaw

6 14 Umatilla

6 15 Umpqua

6 16 Wallowa-Whitman

6 17 Wenatchee

6 18 Willamette

6 20 Winema

6 21 Colville

6 99 Other R6

8 01 NFS in Alabama

8 02 Daniel Boone

8 03 Chattahoochee-Oconee

8 04 Cherokee

8 05 NFS in Florida

8 06 Kisatchie

8 07 NFS in Mississippi

8 08 George Washington

Region Adminfor National Forest Name

8 09 Ouachita

8 10 Ozark and St. Francis

8 11 NFS in North Carolina

8 12 Francis Marion-Sumter

8 13 NFS in Texas

8 14 Jefferson

8 16 Caribbean

8 99 Other R8 

9 02 Chequamegon

9 03 Chippewa

9 04 Huron-Manistee

9 05 Mark Twain

9 06 Nicolet

9 07 Ottawa

9 08 Shawnee

9 09 Superior

9 10 Hiawatha

9 11 Hoosier

9 18 Wayne

9 19 Allegheny

9 20 Green Mountain

9 21 Monongahela

9 22 White Mountain

9 99 Other R9 

10 02 Tongass-Stikine

10 03 Tongass-Chatham

10 04 Chugach

10 05 Tongass-Ketchikan

10 99 Other R10
14. LANDCC Land Use Class. Indicates the basic land cover.

Code Description

-1 Denied access/hazardous/not in sample.

20 Forest land: Forest land is defined as lands with at least a stocking of 10 (or at least 5 percent

cover in chaparral type) of live forest trees of any size, or formerly having such tree cover, and

not currently developed for nonforest use. The minimum area for classification of forest land

is usually 1 acre with a minimum width of 120 feet stem-to-stem. Forested strips must be at

least 120 feet wide for a continuous length of at least 363 feet to meet the acre threshold.

Unimproved roads and trails as well as clearings in forest areas are classified as forest if less

than 120 feet wide or smaller than 1 acre.

60 Nonforest land: Land that has never supported forests or land formerly forested but now

developed for uses such as agriculture (including Christmas tree plantations, orchards, nurseries, and agroforestry), residences, commerce, industry, city parks, or improved roads. If

located within forest areas, unimproved roads and nonforested strips must be more than 120

feet wide. Clearings and other openings in a forest area must be more than 1 acre to qualify

as nonforest land.

91 Census water: Streams, sloughs, estuaries, and canals more than 200 feet wide, and lakes,

reservoirs, and ponds more than 4.5 acres in size (1990 census definition).

92 Noncensus water: Streams, sloughs, estuaries, and canals between 30 and 200 feet, and

lakes, reservoirs, and ponds between 1 and 4.5 acres in area. This definition was used in the

1990 census and applied when the data became available. Earlier inventories defined noncensus water differently.

15. RESERCLASS Reserved class. Reserved land is land withdrawn by law(s) prohibiting the management of the land for wood products.

Code Description

1 Unreserved forest land: All private forest lands; and public forest lands where the harvest of

trees is not prohibited by statute or administrative regulation.

2 Non-National Forest System reserved forest land: Lands that have statutory or administrative

restrictions prohibiting the harvest of trees. Examples include forest land within national

parks, monuments, national wilderness preservation system areas outside the national

forests, State parks. 

3 National Forest System reserved forest land/non-wilderness: (Not used in 2002 RPA) In 1997,

this code was used to identify all reserved or withdrawn National Forest System forest lands

not within the national wilderness preservation system. Examples include primitive areas, scenic research areas, scenic areas, wild and scenic rivers, recreation areas, game refuges,

monument areas, and historic areas. 

4 National Forest System reserved forest land: National Forest System forest lands that have

statutory or administrative restrictions prohibiting the harvest of trees. Examples include land

within the national wilderness preservation system or State-designated wilderness areas.

15
16. SPCLASS Site productivity class. A classification of forest land in terms of inherent capacity

to grow crops of industrial wood. The class identifies the average potential growth

in cubic feet/acre/year and is based on the culmination of mean annual increment

of fully stocked natural stands.

Code Description

1 225-999 Cubic feet/acre/year 

2 165-224 Cubic feet/acre/year 

3 120-164 Cubic feet/acre/year 

4 85-119 Cubic feet/acre/year 

5 50- 84 Cubic feet/acre/year 

6 20- 49 Cubic feet/acre/year 

7 < 20 Cubic feet/acre/year 

8 Unproductive timberland

17. FORCODE Forest land code. Used to differentiate between productive/unproductive and

reserved/nonreserved forest land.

Code Description

0 Nonforest

1 Productive non-reserved forest land (Timberland)

2 Productive reserved forest land

3 Unproductive non-reserved forest land

4 Unproductive reserved forest land

18. OWNGROUP Ownership group. A broad grouping of ownership classes.

Code Recreation Use

1 National forest land: Federal lands designated by Executive order or statute as national

forests or purchase units, and other lands under the administration of the Forest Service

including experimental areas and Bankhead-Jones Title III lands. 

2 Other public land: Publicly owned lands other than national forest lands.

3 Forest industry land: Lands owned by companies or individuals operating wood-using plants

(includes Indian lands if they qualify as industry). 

4 Nonindustrial private land: All private lands except those owned by forest industry (includes

nonindustrial Indian lands). 

5 Unknown ownership: Owner group not recorded. 

16
19. OWNER Owner class code. Indicates the class in which the landowner (at the time of the

inventory) belongs.

Code Owner

11 National forest 

12 Bureau of Land Management. Federal lands administered by the Bureau of Land

Management, U.S. Department of the Interior.

14 Other Federal agencies: Federal lands other than lands administered by the Forest Service or

BLM.

15 State: Lands owned by State governments, or lands leased by State governmental units for

more than 50 years. 

16 County and municipal: Lands owned by county or municipal agencies, or lands leased by

these agencies for more than 50 years.

20 Forest industry: Lands owned by companies, tribes, or individuals operating wood-using

plants. 

60 Other private – corporate: Lands owned by private corporations other than forest industry.

70 Other private – individual: Lands owned by individuals, including farmers.

80 Other private-corporate–leased: Lands owned by corporations but leased to forest industry.

90 Other private-individual–leased: Lands owned by individuals but leased to forest industry.

99 Unknown: Ownership not recorded or unavailable. This code is legal for nonforest land and

water cover classes only.

20. AEF Area Expansion Factor. The number of acres the sample plot represents for 

making current estimates of area. The sum of AEF over all plot records for a 

particular State will closely match the area reported for that State by the 

Bureau of Census in 1990.

21. VEF Volume Expansion Factor. The number of acres the sample plot represents for

making current estimates of volume and biomass.

17
22. FORTYPE Forest type group. The forest cover type of the inventoried stand, based on the

tree species forming a plurality of the stocking within the stand. The first digit of

this three-digit code represents either eastern (1) or western (2) type groups. The

second and third digits are the historic RPA forest type codes.

23. LOCALTYPE Local forest type. Available for every State except Hawaii and interior Alaska.

18

Code Forest Type Group Name

100 White - red - jack pine 

110 Spruce - fir 

120 Longleaf - slash pine

130 Loblolly - shortleaf pine

140 Oak - pine

150 Oak - hickory

160 Oak - gum - cypress

170 Elm - ash - cottonwood

180 Maple - beech - birch

190 Aspen - birch

198 Other forest types 

199 Nonstocked

200 Douglas-fir

Code Forest Type Group Name

210 Ponderosa pine

220 Western white pine 

230 Fir - spruce

240 Hemlock - Sitka spruce

250 Larch

260 Lodgepole pine

270 Redwood

280 Other hardwoods 

290 Unclassified and other forest types 

293 Pinyon - juniper 

297 Chaparral 

299 Nonstocked

100 WHITE-RED-JACK PINE TYPE GROUP

101 Jack pine

102 Red pine

103 White pine

104 White pine - hemlock

105 Hemlock

211 Ponderosa pine

110 SPRUCE-FIR TYPE GROUP

111 Balsam fir

112 Black spruce

113 Red spruce - Balsam fir

114 Northern white-cedar

115 Tamarack

116 White spruce

120 LONGLEAF-SLASH PINE TYPE GROUP

121 Longleaf pine

122 Slash pine

130 LOBLOLLY-SHORTLEAF PINE TYPE GROUP

131 Loblolly pine

132 Shortleaf pine

133 Virginia pine

134 Sand pine

135 Eastern redcedar

136 Pond pine

137 Spruce pine

138 Pitch pine

139 Table-mountain pine

EASTERN FOREST TYPES

(table continued on next page)
19

140 OAK-PINE TYPE GROUP

141 White pine - northern red oak - white ash

142 Eastern redcedar - hardwood

143 Longleaf pine - scrub oak

144 Shortleaf pine - oak

145 Virginia pine - southern red oak

146 Loblolly pine - hardwood

147 Slash pine - hardwood

149 Other oak - pine

150 OAK-HICKORY TYPE GROUP

151 Post oak, black oak or bear oak

152 Chestnut oak

153 White oak - red oak - hickory

154 White oak

155 Northern red oak

156 Yellow poplar - white oak - northern red oak

157 Southern scrub oak

158 Sweetgum - yellow poplar

159 Mixed hardwoods

160 OAK-GUM-CYPRESS TYPE GROUP

161 Swamp chestnut oak - cherrybark oak

162 Sweetgum - Nuttall oak - willow oak

163 Sugarberry - American elm - green ash

165 Overcup oak - water hickory

166 Atlantic white cedar

167 Baldcypress - water tupelo

168 Sweetbay - swamp tupelo - red maple

169 Palm-mangrove-other tropical

170 ELM-ASH-COTTONWOOD TYPE GROUP

171 Black ash - American elm - Red maple

172 River birch - sycamore

173 Cottonwood

174 Willow

175 Sycamore - pecan - American elm

176 Red maple-lowland

179 Mixed lowland hardwoods

180 MAPLE-BEECH-BIRCH TYPE GROUP

181 Sugar maple - beech - yellow birch

182 Black cherry

183 Black walnut

184 Red maple-northern hardwoods

187 Red maple-upland

188 Northern hardwood-reverting field

189 Mixed northern hardwoods

190 ASPEN-BIRCH TYPE GROUP

191 Aspen

192 Paper birch

194 Balsam poplar

198 OTHER FOREST TYPES

199 NONSTOCKED

EASTERN FOREST TYPES (continued)

WESTERN FOREST TYPES 

200 DOUGLAS-FIR TYPE GROUP

201 Douglas-fir

202 Douglas-fir - Western hemlock

203 Port-Orford-cedar - Douglas-fir

210 PONDEROSA PINE TYPE GROUP

211 Ponderosa pine

212 Jeffrey pine

213 Ponderosa pine - sugar pine - fir

220 WESTERN WHITE PINE TYPE GROUP

221 Western white pine

230 FIR-SPRUCE TYPE GROUP

116 White spruce (in Alaska)

231 White fir and grand fir

232 Red fir

234 Pacific silver fir - hemlock

235 Engelmann spruce

236 Engelmann spruce - subalpine fir

(table continued on next page)
24. STANDORIGIN Stand origin code. Method of stand regeneration for the trees in the condition. An

artificially regenerated stand is established by planting or artificial seeding.

Code Stand origin

-1 Unknown

1 Natural stands

2 Clear evidence of artificial regeneration

20

240 HEMLOCK-SITKA SPRUCE TYPE GROUP

241 Western redcedar

242 Sitka spruce

247 Mountain hemlock - subalpine fir

248 Western hemlock

250 LARCH TYPE GROUP

255 Larch - Douglas-fir

256 Grand fir - larch - Douglas-fir

257 Ponderosa pine - larch - Douglas-fir

260 LODGEPOLE PINE TYPE GROUP

261 Lodgepole pine

270 REDWOOD TYPE GROUP

271 Redwood

280 OTHER HARDWOODS TYPE GROUP 

281 Red alder

282 Poplar - birch

283 Aspen

284 California black oak

285 Cottonwood - willow

286 Canyon live oak

287 Oak - Madrone

288 Other oaks

289 Ohia

192 Paper birch

290 OTHER FOREST TYPES 

(includes Arizona cypress-western juniper)

112 Black spruce (in Alaska)

291 Coulter pine

292 Digger pine - oak

294 Knobcone pine

295 Bristlecone pine

296 Whitebark pine

298 Limber pine

293 PINYON-JUNIPER

297 CHAPARRAL

299 NONSTOCKED

WESTERN FOREST TYPES (continued)
25. SSCLASS Stand-size class code. Classification of the predominant (based on stocking) diameter class of live trees within the condition. Large diameter trees are at least 11.0

inches diameter for hardwoods and at least 9.0 inches diameter for softwoods.

Medium diameter trees are at least 5.0 inches diameter but not as large as large

diameter trees. Small diameter trees are less than 5.0 inches diameter. Chaparral

communities, land consisting of chapparal with all live stocking less than 10 and

at least 5 percent, are considered nonstocked.

Code Stand-size class

-1 Unknown (only allowed for nontimberland plots)

1 Nonstocked: Forest land with all live stocking less than 10

2 Small diameter: Stands with an all live stocking value of at least 10 (base 100) on which at

least 50 percent of the stocking is in small diameter trees

3 Medium diameter: Stands with an all live stocking of at least 10 (base 100); with more than 50

percent of the stocking in medium and large diameter trees; and with the stocking of large

diameter trees less than the stocking of medium diameter trees

4 Large diameter: Stands with an all live stocking of at least 10 (base 100); with more than 50

percent of the stocking in medium and large diameter trees; and with the stocking of large

diameter trees equal to or greater than the stocking of medium diameter trees

26. STDIAM Mean stand diameter. Value of the mean stand diameter of the main stand (which

is composed of all trees >= 5" d.b.h.). Estimated by calculating the quadratic

mean diameter (except for Hawaii and interior Alaska).

27. AGECLASS Stand age. Average total age, to the nearest year, of the trees (plurality of all live

trees not overtopped) in the predominant stand-size class of the condition, determined using local procedures. Age is difficult to measure and therefore stand age

may have large measurement errors. Nonstocked stands are recorded as 0. Any

inventory dated 1995 or later will contain stand ages recorded to the nearest year.

For some older inventories, stand age was recorded in 10- or 20-year classes (CT,

DE, KY, MD, NH, PA, RI, VT, and WV) for stands less than 100 years old, 20-year

age classes for stands between 100 and 200 years, and 100-year age classes for

stands older than 200 years. The value recorded is the midpoint of the age class.

Mixed age classes were allowed in older inventories (AR, CT, KY, LA, ME, MA, MS,

NH, NY, OH, OK, PA, RI, TX, VT, and WV) and were assigned a value of –999.

21
28. STOCKPC Percent stocking class. A coded value indicating the percent stocking class for

growing stock in the stand; the 10-percent interval classes range from nonstocked

to 100 percent stocking, relative stocking basis. All 10 codes were used for

Hawaii and interior Alaska. Only codes 1, 3, 5, 7, 9, and –1 were used for the 48

contiguous States and southeast Alaska (the STOCKPC data for these States came

from the GSSTKCD variable in the FIADB, which has fewer classes than STOCKPC). For the 48 contiguous States and southeast Alaska, a code of 1 is equivalent

to Nonstocked, 3 Poorly stocked, 5 Medium stocked, 7 Fully stocked, 9

Overstocked, and –1 Data unavailable.

29. TREATOPP Treatment opportunity class code. Identifies the physical opportunity to improve

stand conditions by applying management practices. This variable is mandatory for

nonindustrial private timberland but may not be available for other ownerships.

No values available for AL, AZ, CO, HI, ID, MT, NM, NV, SC, TN, UT, and WY.

Code Treatment opportunity class

1 Regeneration without site preparation: The area is characterized by the absence of a manageable stand because of inadequate stocking of growing stock. Growth will be much below

the potential for the site if the area is left alone. Prospects are not good for natural regeneration. Artificial regeneration will require little or no site preparation.

2 Regeneration with site preparation: The area is characterized by the absence of a manageable stand because of inadequate stocking of growing stock. Growth will be much below the

potential for the site if the area is left alone. Either natural or artificial regeneration will

require site preparation.

3 Stand conversion: The area is characterized by stands of undesirable, chronically diseased, or

off-site (found where not normally expected) species. Growth and quality will be much below

the potential for the site if the area is left alone. The best prospect is to convert the area to a

different forest type or species.

4 Thinning seedlings and saplings: The stand is characterized by a dense stocking of growing

stock. Stagnation appears likely if left alone. Stocking must be reduced to help crop trees

attain dominance.

5 Thinning poletimber: The stand is characterized by a dense stocking of growing stock.

Stocking must be reduced to prevent stagnation or to confine growth to selected, high-quality

crop trees.

6 Other stocking control: The stand is characterized by an adequate stocking of seedlings,

saplings, and poletimber growing stock, mixed with competing vegetation either overtopping

or otherwise inhibiting the development of crop trees. The undesirable material must be

removed to release overtopped trees, to prevent stagnation, or to improve composition, form,

or growth of the residual stand.

7 Other intermediate treatments: The stand would benefit from other special treatments, such

as fertilization to improve the growth potential of the site, and pruning to improve the quality of

individual crop trees.

8 Clearcut harvest: The area is characterized by a mature or overmature sawtimber stand of

sufficient volume to justify a commercial harvest. The best prospect is to harvest the stand

and regenerate.

22
9 Partial cut harvest: The stand is characterized by poletimber- or sawtimber-size trees with

sufficient merchantable volume for a commercial harvest, which will meet intermediate stand

treatment needs or prepare the stand for natural regeneration. The stand is of a favored

species composition and may be even- or uneven-aged. Included are such treatments as

commercial thinning, seed tree, or shelterwood regeneration, and use of the selection system

to maintain an uneven-aged stand.

10 Salvage harvest: The stand is characterized by excessive damage to merchantable timber

because of fire, insects, disease, wind, ice, or other destructive agents. The best prospect is

to remove damaged or threatened material.

11 No treatment: No silvicultural treatment is needed.

-1 Not available/unclassified.

30. LOCALINFO1 Local info 1. Only used in Hawaii. Class codes of 0 and 7.

31. LOCALINFO2 Local info 2. Only used in Hawaii. Class codes of 0 and 9.

32. LOCALINFO3 Local info 3. Only used in Hawaii. Class codes of 0, 3, and 7.

33. LOCALINFO4 Local info 4. Only used in Hawaii. Class codes of 0, and 8.

34. LOCALINFO5 Local info 5. Not used.

35. LOCALINFO6 Local info 6. Not used.

36. BDFTSW Softwood board foot volume (International 1/4-inch rule). The net volume/acre

(board feet) of softwood growing stock. Trees must be >= 9 inches d.b.h. The

minimum saw log top is 7 inches diameter outside bark. Available only for timberland plots.

37. BDFTHW Hardwood board foot volume (International 1/4-inch rule). The net volume/acre

(board feet) of hardwood growing stock. Trees must be >= 11 inches d.b.h. The

minimum saw log top is 9 inches diameter outside bark. Available only for timberland plots.

23
38. BDFT Board foot volume (International 1/4-inch rule). The net volume/acre (board feet)

of softwood and hardwood growing stock. Available only for timberland plots.

39. BDFTSW_LOCAL Softwood board foot volume (local rule). The net volume/acre (board feet) of softwood growing stock. Not used in the 2002 RPA.

40. BDFTHW_LOCAL Hardwood board board foot volume (local rule). The net volume/acre (board feet)

of softwood growing stock. Not used in the 2002 RPA.

41. BDFT_LOCAL Board foot volume (local rule). The net volume/acre (board feet) of softwood and

hardwood growing stock. Not used in the 2002 RPA.

42. LOCAL_RULE Local Board foot rule. Identifies the rule used for the local volume estimation. Not

used in the 2002 RPA.

Code Local rule

1 Scribner - long log

2 Scribner - short log

3 Doyle

4 International 1/8"

5 local cubic 

43. CUBICSW Softwood cubic foot volume. Net volume/acre (cubic feet) of softwood growing

stock. Trees must be >=5 inches d.b.h., to a minimum 4-inch diameter outside

bark. Available for all forest land plots. Values are imputed where the value of the

variable TSOURCE is not equal to 1 (TSOURCE may not be equal to 1 for some

reserved and unproductive forest land).

44. CUBICHW Hardwood cubic foot volume. Net volume/acre (cubic feet) of hardwood growing

stock. Trees must be >=5 inches d.b.h., to a minimum 4-inch diameter outside

bark. Available for all forest land plots. Values are imputed where the value of the

variable TSOURCE is not equal to 1 (TSOURCE may not be equal to 1 for some

reserved and unproductive forest land).

24
45. CUBIC Cubic foot volume. Net volume/acre (cubic feet) of hardwood growing stock.

Trees must be >=5 inches d.b.h., to a minimum 4- inch diameter outside bark.

Available for all forest land plots. Values are imputed where the value of the variable TSOURCE is not equal to 1 (TSOURCE may not be equal to 1 for some

reserved and unproductive forest land).

46. CULLSW Softwood live cull volume. Net volume/acre (cubic feet) of live cull softwood

trees; trees must be >=5 inches d.b.h. Available for all forest land plots. Values are

imputed where the value of the variable TSOURCE is not equal to 1 (TSOURCE

may not be equal to 1 for some reserved and unproductive forest land).

47. CULLHW Hardwood live cull volume. Net volume/acre (cubic feet) of live cull hardwood

trees; trees must be >=5 inches d.b.h. Available for all forest land plots. Values are

imputed where the value of the variable TSOURCE is not equal to 1 (TSOURCE

may not be equal to 1 for some reserved and unproductive forest land).

48. CULL All live cull volume. Net volume/acre (cubic feet) of all live cull trees; trees must

be >=5 inches d.b.h. Available for all forest land plots. Values are imputed where

the value of the variable TSOURCE is not equal to 1 (TSOURCE may not be

equal to 1 for some reserved and unproductive forest land).

49. DEADSW Salvable dead softwood. Net volume/acre (cubic feet) of merchantable sound dead

softwood trees – merchantability determined by regional standards. Available for

all forest land plots. Values are imputed where the value of the variable TSOURCE

is not equal to 1 (TSOURCE may not be equal to 1 for some reserved and unproductive forest land). 

50. DEADHW Salvable dead hardwood. Net volume/acre (cubic feet) of merchantable sound

dead hardwood trees – merchantability determined by regional standards.

Available for all forest land plots. Values are imputed where the value of the variable TSOURCE is not equal to 1 (TSOURCE may not be equal to 1 for some

reserved and unproductive forest land).

51. DEAD Salvable dead. Net volume/acre (cubic feet) of merchantable sound dead trees –

merchantability determined by regional standards. Available for all forest land plots.

Values are imputed where the value of the variable TSOURCE is not equal to 1

(TSOURCE may not be equal to 1 for some reserved and unproductive forest land).

25
52. MORTSW Softwood mortality. Volume/acre (cubic feet) of annual mortality of softwood growing stock; trees must be >= 5 inches d.b.h. Available only for timberland plots.

53. MORTHW Hardwood mortality. Volume/acre (cubic feet) of annual mortality of hardwood

growing stock; trees must be >= 5 inches d.b.h. Available only for timberland plots.

54. MORT Mortality. Volume/acre (cubic feet) of annual mortality of growing-stock trees;

trees must be >= 5 inches d.b.h. Available only for timberland plots.

55. GROWTHSW Net annual softwood growth. Net annual growth/acre (cubic feet) of softwood

growing stock. Net growth is gross growth minus mortality minus negative cull

increment plus positive cull increment. Negative cull increment occurs when

growing-stock trees at time zero are reclassed as cull trees at time one. Positive

cull increment is when trees classified as cull at time zero are reclassified as growing stock at time one. Available only for timberland plots.

56. GROWTHHW Net annual hardwood growth. Net annual growth/acre (cubic feet) of hardwood

growing stock. Net growth is gross growth minus mortality minus negative cull

increment plus positive cull increment. Negative cull increment occurs when

growing-stock trees at time zero are reclassed as cull trees at time one. Positive

cull increment is when trees classified as cull at time zero are reclassified as growing stock at time one. Available only for timberland plots.

57. GROWTH Net annual growth. Net annual growth/acre (cubic feet) of softwood growing

stock. Net growth is gross growth minus mortality minus negative cull increment

plus positive cull increment. Negative cull increment occurs when growing-stock

trees at time zero are reclassed as cull trees at time one. Positive cull increment is

when trees classified as cull at time zero are reclassified as growing stock at time

one. Available only for timberland plots.

58. COUNTY County code. The identification number for a county, parish, watershed, borough,

or similar governmental unit in a State. FIPS codes from the Bureau of the

Census, 1990, are used if a single county is represented. See appendix B for specific codes. Note: A county code of 0 was allowed in Alaska, and all of Hawaii

was given a county code of 1.

26
59 CONDITION Condition number. Unique identifying number assigned to each condition on a

plot. Condition is defined by owner group class, reserved status, and land class.

Differences in broad forest type, stand size, stand origin, and stand density further

define condition for forest land. Mapped nonforest conditions are also assigned

numbers. The condition at the center of subplot one is given condition number 1.

Other conditions on the plot are assigned numbers sequentially. Once a number

has been assigned, the number is reused whenever that same condition is

encountered on the plot.

60. TSOURCE Source of volume data. Identifies whether data source for volume record (trees,

stands, etc.) is based on observed or modeled tree d.b.h.

Code Source of volume data

0 Nonforest land.

1 Observed from tree data (All timberland plots are based on observed tree data).

2 Used for unproductive forest land or reserved forest land plots where no tree data were available. Plots with a value of 2 for TSOURCE have imputed values for the following variables:

cubic, cubicsw, cubichw, cullsw, cullhw, cull, deadsw, deadhw, dead, biobolesw, biobolehw,

biobole, biosapssw, biosapshw, biosaps, balive. These variables were assigned the average

values from similar plots (unproductive plots were matched with unproductive plots and

reserved plots were matched with reserved plots where the plots had the same first two characters for the ECOSUBCD variable). A minimum of five plots having the same ECOSUBCD

string through two characters was required for the plot to be assigned a TSOURCE code of 2.

This plot failed the requirements to receive a TSOURCE code of 3.

3 Used for unproductive forest land or reserved forest land plots where no tree data were available. Plots with a value of 3 for TSOURCE have imputed values for the following variables:

cubic, cubicsw, cubichw, cullsw, cullhw, cull, deadsw, deadhw, dead, biobolesw, biobolehw,

biobole, biosapssw, biosapshw, biosaps, balive. These variables were assigned the average

values from similar plots. (Unproductive plots were matched with unproductive plots and

reserved plots were matched with reserved plots where the plots had the same first three

characters for the ECOSUBCD variable.) A minimum of five plots having the same ECOSUBCD

string through three characters was required for the plot to be assigned a TSOURCE code of

3. This plot failed the requirements to receive a TSOURCE code of 4.

4 Used for unproductive forest land or reserved forest land plots where no tree data were available. Plots with a value of 4 for TSOURCE have imputed values for the following variables:

cubic, cubicsw, cubichw, cullsw, cullhw, cull, deadsw, deadhw, dead, biobolesw, biobolehw,

biobole, biosapssw, biosapshw, biosaps, biostumptopsw, biostumptophw, biostumptop, balive.

These variables were assigned the averge values from similar plots. (Unproductive plots were

matched with unproductive plots and reserved plots were matched with reserved plots where

the plots had the same first four characters for the ECOSUBCD variable.) A minimum of five

plots having the same ECOSUBCD string through four characters was required for the plot to

be assigned a TSOURCE code of 4. This plot failed the requirements to receive a TSOURCE

code of 5.

5 Used for unproductive forest land or reserved forest land plots where no tree data were available. Plots with a value of 5 for TSOURCE have imputed values for the following variables:

cubic, cubicsw, cubichw, cullsw, cullhw, cull, deadsw, deadhw, dead, biobolesw, biobolehw,

biobole, biosapssw, biosapshw, biosaps, balive. These variables were assigned the average

27
values from similar plots. (Unproductive plots were matched with unproductive plots and

reserved plots were matched with reserved plots where the plots had the same first five characters for the ECOSUBCD variable.) A minimum of five plots having the same ECOSUBCD

string through five characters was required for the plot to be assigned a TSOURCE code of 5.

This plot failed the requirements to receive a TSOURCE code of 6.

6 Used for unproductive forest land or reserved forest land plots where no tree data were available. Plots with a value of 2 for TSOURCE have imputed values for the following variables:

cubic, cubicsw, cubichw, cullsw, cullhw, cull, deadsw, deadhw, dead, biobolesw, biobolehw,

biobole, biosapssw, biosapshw, biosaps, balive. These variables were assigned the average

values from similar plots. (Unproductive plots were matched with unproductive plots and

reserved plots were matched with reserved plots where the plots had the same first two characters for the ECOSUBCD variable.) A minimum of five plots having the same ECOSUBCD

string through six characters was required for the plot to be assigned a TSOURCE code of 6.

96 Hawaii. Values for variables biosapssw, biosapshw, and biosaps were imputed based on the

average ratio of biostumptop to biobole for the lower 48 States. Individual tree data were

unavailable for computing trees per acre and basal area variables.

97 Interior Alaska. Values for variables cubic, cubicsw, cubichw, cullsw, cullhw, cull, deadsw,

deadhw, dead, biobolesw, biobolehw, biobole, biosapssw, biosapshw, biosaps, and balive

were imputed from information contained on six plots measured in the Northwest Territories.

98 Productive reserved plots that did not meet requirements for codes 1 through 6. They received

the average values from all other productive reserved plots that were measured in this State

or neighboring States (a minimum of five plots was required to develop the average).

99 Unproductive plots that did not meet requirements for codes 1 through 6. They received the

average values from all other unproductive plots that were measured in this State or neighboring States (a minimum of five plots was required to develop the average). Exception for

Florida (332 plots). Information for these 332 plots was derived from publication by Neal Cost

(1982) on unproductive forest land.

61. BIOBOLESW Softwood biomass in the bole. Total gross biomass (including bark) in dry pounds

per acre of all live softwood trees 5 inches d.b.h. or larger from a 1-foot stump to

a minimum 4-inch top diameter outside bark of the central stem. Values are

imputed where the value of the variable TSOURCE is not equal to 1 (TSOURCE

may not be equal to 1 for some reserved and unproductive forestland). Available

for all forest land plots.

62. BIOBOLEHW Hardwood biomass in the bole. Total gross biomass (including bark) in dry

pounds per acre of all live hardwood trees 5 inches d.b.h. or larger from a 1-foot

stump to a minimum 4-inch top diameter outside bark of the central stem. Values

are imputed where the value of the variable TSOURCE is not equal to 1

(TSOURCE may not be equal to 1 for some reserved and unproductive forest

land). Available for all forest land plots.

28
63. BIOBOLE Biomass in the bole. Total gross biomass (including bark) in dry pounds per acre

of all live trees 5 inches d.b.h. or larger from a 1-foot stump to a minimum 4-inch

top diameter outside bark of the central stem. Values are imputed where the value

of the variable TSOURCE is not equal to 1 (TSOURCE may not be equal to 1 for

some reserved and unproductive forest land). Available for all forest land plots.

64. BIOSAPSSW Softwood sapling biomass. Total gross aboveground biomass (including bark) in

dry pounds per acre of all live softwood trees from 1 to 5 inches d.b.h., including

tops and limbs. Values are imputed where the value of the variable TSOURCE is

not equal to 1 (TSOURCE may not be equal to 1 for some reserved and unproductive forest land). Available for all forest land plots.

65. BIOSAPSHW Hardwood sapling biomass. Total gross aboveground biomass (including bark) in

dry pounds per acre of all live hardwood trees from 1 to 5 inches d.b.h., including tops and limbs. Values are imputed where the value of the variable TSOURCE

is not equal to 1 (TSOURCE may not be equal to 1 for some reserved and unproductive forest land). Available for all forest land plots.

66. BIOSAPS Sapling biomass. Total gross aboveground biomass (including bark) in dry

pounds per acre of all live trees from 1 to 5 inches d.b.h., including tops and

limbs. Values are imputed where the value of the variable TSOURCE is not equal

to 1 (TSOURCE may not be equal to 1 for some reserved and unproductive forest

land). Available for all forest land plots.

67. BALIVE Basal area (sq.ft./ac.) of all live trees 1 inch and larger in diameter. Only calculated

when TSOURCE=1. Diameters are usually measured at breast height except for

certain woodland species where diameters are measured at the root collar.

Available for forest land plots.

29
68. LAT Latitude NAD 83 datum (not available for public distribution due to privacy policy). Due to the Privacy Ammendment: H.R.3423 Department of the Interior and

Related Agencies Appropriations Act, 2000 (November 17, 1999) latitude and

longitude are zeroed out. For internal FIA users the approximate latitude of the

plot in decimal degrees to the nearest 100 seconds (0.028 degrees). The precision

of this item along the meridian is ± 1542 m at latitude 45 degrees north. A value

of -1 means that latitude was not recorded. However, in some cases the county

centroid may be entered when the actual location is not available. Actual plot

locations cannot be released.

69. LON Longitude NAD 83 datum (not available for public distribution due to privacy

policy). Due to the Privacy Ammendment: H.R.3423 Department of the Interior

and Related Agencies Appropriations Act, 2000 (November 17, 1999) latitude

and longitude are zeroed out. For internal FIA users the approximate longitude of

the plot in decimal degrees to the nearest 100 seconds (0.028 degrees). The precision of this item along the parallel is ± 1094 m at latitude 45 degrees. A value of -

1 means that longitude was not recorded. However, in some cases the county

centroid may be entered when the actual location is not available.

70. CONDPROP Condition proportion. Proportion of the plot in the condition. Values of 1 indicate

that the plot was not mapped. Values of less than 1 indicate that data for this

record came from a mapped portion of a plot.

71. FHMHEXID Forest Health Monitoring hexagon identifier. Number of the hexagon wherein the

plot resides. FHM hexagons are approximately 160,000 acres in size.

72. BALIVE_5 Basal area (sq.ft./ac.) of all live trees 5 inches and larger in diameter. Only calculated when TSOURCE=1. Diameters are usually measured at breast height except

for certain woodland species where diameters are measured at the root collar.

73. TPA_5 Number of live trees per acre 5 inches in diameter and larger. Only calculated

when TSOURCE=1. Diameters are usually measured at breast height except for

certain woodland species where diameters are measured at the root collar.

74. DWD Down woody debris. Not populated.

30
75. TPA_1 Number of live trees per acre 1 inch in diameter and larger. Only calculated when

TSOURCE=1. Diameters are usually measured at breast height except for certain

woodland species where diameters are measured at the root collar.

76. STDIAM_1 Quadratic mean stand diameter using all live trees over 1 inch in diameter. Only

calculated when TSOURCE=1.

77. ECOSUBCD Ecological subsection code. An area of similar surficial geology, lithology, geomorphic process, soil groups, subregional climate, and potential natural communities.

Subsection boundaries usually correspond with discrete changes in geomorphology. Subsection information is used for broad planning and assessment. Subsection

codes may consist of up to six characters and were developed by the Forest

Service as part of the National Hierarchical Framework of Ecological Units.

78. CONGCD Congressional District code for the 107th Congress (2001-2002). A territorial

division of a State from which a member of the U.S. House of Representatives is

elected. There are 435 congressional districts in the United States apportioned to

the States based on population; each State receives at least one congressional district. The Congressional District code assigned to a plot (regardless of when it was

measured) is for the most recent Congress. CONGCD is a four-digit number. The

first two digits are the State FIPS code and the last two digits are the congressional district number. If a State has only one congressional district, the congressional

district number is 00.

79. BIOSTUMPTOPSW Biomass in the stump and tops of live softwood trees 5 inches in diameter and

larger. Difference between total dry biomass and merchantable drybiomass on live

trees 5 inches in diameter and larger. Values are imputed where the value of the

variable TSOURCE is not equal to 1 (TSOURCE may not be equal to 1 for some

reserved and unproductive forestland). Available for all forest land plots.

80. BIOSTUMPTOPHW Biomass in the stump and tops of live hardwood trees 5 inches in diameter and

larger. Difference between total dry biomass and merchantable dry biomass on

live trees 5 inches in diameter and larger. Values are imputed where the value of

the variable TSOURCE is not equal to 1 (TSOURCE may not be equal to 1 for

some reserved and unproductive forest land). Available for all forest land plots.

31
81. BIOSTUMPTOP Biomass in the stump and tops of live trees 5 inches in diameter and larger.

Difference between total dry biomass and merchantable dry biomass on live trees

5 inches in diameter and larger. Values are imputed where the value of the variable TSOURCE is not equal to 1 (TSOURCE may not be equal to 1 for some

reserved and unproductive forest land). Available for all forest land plots.

82. BIOSALVDEADSW Total gross biomass ovendry weight for salvable dead softwood trees. The total

aboveground biomass of a sample tree 5.0 inches in diameter or larger, including

all tops and limbs (but excluding foliage). Values are imputed where the value of

the variable TSOURCE is not equal to 1 (TSOURCE may not be equal to 1 for

some reserved and unproductive forest land). Available for all forest land plots.

83. BIOSALVDEADHW Total gross biomass ovendry weight for salvable dead hardwood trees. The total

aboveground biomass of a sample tree 5.0 inches in diameter or larger, including

all tops and limbs (but excluding foliage). Values are imputed where the value of

the variable TSOURCE is not equal to 1 (TSOURCE may not be equal to 1 for

some reserved and unproductive forest land). Available for all forest land plots.

84. BIOSALVDEAD Total gross biomass ovendry weight for salvable dead trees. The total aboveground

biomass of a sample tree 5.0 inches in diameter or larger, including all tops and

limbs (but excluding foliage). Values are imputed where the value of the variable

TSOURCE is not equal to 1 (TSOURCE may not be equal to 1 for some reserved

and unproductive forest land). Available for all forest land plots.

32
Column Oracle Value or unit Key 

name data type of measure data item

1. SRID NUMBER

2. STATE NUMBER Coded X

3. STATEABB VARCHAR2(2)

4. RSID NUMBER Coded X

5. SUBREGION NUMBER

6. RPA_REGION NUMBER

7. RPA_SUBREGION NUMBER

8. YEAR NUMBER

9. OWNGROUP NUMBER Coded X

10. OWNN VARCHAR2(16)

11. DBHCODE NUMBER Coded X

12. DCN VARCHAR2(22)

13. NVGSESW NUMBER

14. NVGSLGSL NUMBER

15. NVGSLBSH NUMBER

16. NVGSOTYP NUMBER

17. NVGSEWRD NUMBER

18. NVGSJACK NUMBER

19. NVGSSPFR NUMBER

20. NVGSEHEM NUMBER

21. NVGSCYPR NUMBER

22. NVGSOTESW NUMBER

23. NVBFESW NUMBER

24. NVBFLGSL NUMBER

25. NVBFLBSH NUMBER

26. NVBFOTYP NUMBER

27. NVBFEWRD NUMBER

28. NVBFJACK NUMBER

29. NVBFSPFR NUMBER

30. NVBFEHEM NUMBER

31. NVBFCYPR NUMBER

32. NVBFOTESW NUMBER

33. NVGSEHW NUMBER

34. NVGSSWOK NUMBER

35. NVGSSROK NUMBER

36. NVGSOWOK NUMBER

37. NVGSOROK NUMBER

38. NVGSHICK NUMBER

39. NVGSYBIR NUMBER

33

Species Diameter Class Table (Oracle table name is SPDBH2002)

(table continued on next page)
Column Oracle Value or unit Key 

name data type of measure data item

40. NVGSHMAP NUMBER

41. NVGSSMAP NUMBER

42. NVGSBECH NUMBER

43. NVGSSGUM NUMBER

44. NVGSBGUM NUMBER

45. NVGSASH NUMBER

46. NVGSBASS NUMBER

47. NVGSYPOP NUMBER

48. NVGSECOAS NUMBER

49. NVGSBWAL NUMBER

50. NVGSBCHR NUMBER

51. NVGSOTEHW NUMBER

52. NVBFEHW NUMBER

53. NVBFSWOK NUMBER

54. NVBFSROK NUMBER

55. NVBFOWOK NUMBER

56. NVBFOROK NUMBER

57. NVBFHICK NUMBER

58. NVBFYBIR NUMBER

59. NVBFHMAP NUMBER

60. NVBFSMAP NUMBER

61. NVBFBECH NUMBER

62. NVBFSGUM NUMBER

63. NVBFBGUM NUMBER

64. NVBFASH NUMBER

65. NVBFBASS NUMBER

66. NVBFYPOP NUMBER

67. NVBFECOAS NUMBER

68. NVBFBWAL NUMBER

69. NVBFBCHR NUMBER

70. NVBFOTEHW NUMBER

71. NVGSWSW NUMBER

72. NVGSDOUG NUMBER

73. NVGSPDJEFF NUMBER

74. NVGSFIR NUMBER

75. NVGSWHEM NUMBER

76. NVGSSUGAR NUMBER

77. NVGSWWPN NUMBER

78. NVGSRDWD NUMBER

34

Species Diameter Class Table (Oracle table name is SPDBH2002) continued

(table continued on next page)
Column Oracle Value or unit Key 

name data type of measure data item

79. NVGSSITKA NUMBER

80. NVGSENGEL NUMBER

81. NVGSWLARCH NUMBER

82. NVGSINCEN NUMBER

83. NVGSLODGE NUMBER

84. NVGSWRCED NUMBER

85. NVGSOTWSW NUMBER

86. NVGSWHW NUMBER

87. NVGSWCOAS NUMBER

88. NVGSALDER NUMBER

89. NVGSWOK NUMBER

90. NVGSOTWHW NUMBER

91. NVBFWSW NUMBER

92. NVBFDOUG NUMBER

93. NVBFPDJEFF NUMBER

94. NVBFFIR NUMBER

95. NVBFWHEM NUMBER

96. NVBFSUGAR NUMBER

97. NVBFWWPN NUMBER

98. NVBFRDWD NUMBER

99. NVBFSITKA NUMBER

100. NVBFENGEL NUMBER

101. NVBFWLARCH NUMBER

102. NVBFINCEN NUMBER

103. NVBFLODGE NUMBER

104. NVBFWRCED NUMBER

105. NVBFOTWSW NUMBER

106. NVBFWHW NUMBER

107. NVBFWCOAS NUMBER

108. NVBFALDER NUMBER

109. NVBFWOK NUMBER

110. NVBFOTWHW NUMBER

1. SRID Station/Region ID number. Not used.

2. STATE State. The State in which the plot is located. Two-digit FIPS code.

35

Species Diameter Class Table (Oracle table name is SPDBH2002) continued
3. STATEABB State abbreviation. Two-character State abbreviation.

4. RSID Region or station identification number. This coded value uniquely identifies each

of the 16 locations contributing data to the RPA Database. This ID can be used as

a reference key to delete an entire set of data from the aggregated database, to

updata a particular RSID database, to retrieve a database, or to produce a summary report for individual regions or stations.

5. SUBREGION The subregion code indicates geographic location (east or west) within four States

(Alaska, Oregon, South Dakota, and Washington).

6. RPA_REGION RPA Region. Grouping of States into four regions for reporting purposes.

7. RPA_SUBREGION RPA Subregions. Grouping of States into nine subregions for reporting purposes.

Subregions 1 and 2 belong in the Northern Region, subregions 3 and 4 belong in

the Southern Region, subregions 5 and 6 belong in the Rocky Mountain Region,

and Regions 7, 8, and 9 belong in the Pacific Coast Region.

8. YEAR RPA Year. This is set to 2002 for all records in the 2002 RPA plot summary

database.

9. OWNGROUP Ownership group. A broad grouping of ownership classes.

Code Recreation Use

1 National forest land: Federal lands designated by Executive order or statute as national

forests or purchase units, and other lands under the administration of the Forest Service

including experimental areas and Bankhead-Jones Title III lands. 

2 Other public land: Publicly owned lands other than national forest lands.

3 Forest industry land: Lands owned by companies or individuals operating wood-using plants

(includes Indian lands if they qualify as industry). 

4 Nonindustrial private land: All private lands except those owned by forest industry (includes

nonindustrial Indian lands). 

10. OWNN Owner name. Not applicable. Value is set to “own_name” for all records.

11. DBHCODE Diameter class code.

36
DBHcode Diameter class name

1 5.0-6.9

2 7.0-8.9

3 9.0-10.9

4 11.0-12.9

5 13.0-14.9

6 15.0-16.9

7 17.0-18.9

8 19.0-20.9

9 21.0-28.9

10 29.0+

11 All d.b.h. classes

12. DCN Diameter class name.

13. NVGSESW Net volume growing-stock all eastern softwoods. Includes species groups:

NVGSLGSL, NVGSLBSH, NVGSOTYP, NVGSEWRD, NVGSJACK, NVGSSPFR,

NVGSEHEM, NVGSCYPR, NVGSOTESW

14. NVGSLGSL Net volume growing-stock longleaf and slash pines. 

15. NVGSLBSH Net volume growing-stock loblolly and shortleaf pines.

16. NVGSOTYP Net volume growing-stock other yellow pines.

17. NVGSEWRD Net volume growing-stock white and red pines.

18. NVGSJACK Net volume growing-stock jack pine.

19. NVGSSPFR Net volume growing-stock spruce and balsam fir.

20. NVGSEHEM Net volume growing-stock eastern hemlock.

21. NVGSCYPR Net volume growing-stock cypress.

22. NVGSOTESW Net volume growing-stock other eastern softwoods.

23. NVBFESW Net volume sawtimber all eastern softwoods.Includes species groups: NVBFLGSL,

NVBFLBSH, NVBFOTYP, NVBFEWRD, NVBFJACK, NVBFSPFR, NVBFEHEM,

NVBFCYPR, NVBFOTESW

37
24. NVBFLGSL Net volume sawtimber longleaf and slash pines.

25. NVBFLBSH Net volume sawtimber loblolly and shortleaf pines.

26. NVBFOTYP Net volume sawtimber other yellow pines.

27. NVBFEWRD Net volume sawtimber white and red pines. 

28. NVBFJACK Net volume sawtimber jack pine. 

29. NVBFSPFR Net volume sawtimber spruce and balsam fir. 

30. NVBFEHEM Net volume sawtimber eastern hemlock. 

31. NVBFCYPR Net volume sawtimber cypress. 

32. NVBFOTESW Net volume sawtimber other eastern softwoods. 

33. NVGSEHW Net volume growing-stock all eastern hardwoods. Includes species groups:

NVGSSWOK, NVGSSROK, NVGSOWOK, NVGSOROK, NVGSHICK, 

NVGSYBIR, NVGSHMAP, NVGSSMAP, NVGSBECH, NVGSSGUM, NVGSBGUM,

NVGSASH, NVGSBASS, NVGSYPOP, NVGSECOAS, NVGSBWAL, NVGSBCHR,

NVGSOTEHW

34. NVGSSWOK Net volume growing-stock select white oak.

35. NVGSSROK Net volume growing-stock select red oak.

36. NVGSOWOK Net volume growing-stock other white oak.

37. NVGSOROK Net volume growing-stock other red oak.

38. NVGSHICK Net volume growing-stock hickory.

39. NVGSYBIR Net volume growing-stock yellow birch.

40. NVGSHMAP Net volume growing-stock hard maple.

41. NVGSSMAP Net volume growing-stock soft maple.

38
42. NVGSBECH Net volume growing-stock beech.

43. NVGSSGUM Net volume growing-stock sweetgum.

44. NVGSBGUM Net volume growing-stock tupelo and black gum.

45. NVGSASH Net volume growing-stock ash.

46. NVGSBASS Net volume growing-stock basswood.

47. NVGSYPOP Net volume growing-stock yellow-poplar.

48. NVGSECOAS Net volume growing-stock cottonwood and aspen.

49. NVGSBWAL Net volume growing-stock black walnut.

50. NVGSBCHR Net volume growing-stock black cherry.

51. NVGSOTEHW Net volume growing-stock other eastern hardwoods.

52. NVBFEHW Net volume sawtimber all eastern hardwoods. Includes species groups: 

NVBFSWOK, NVBFSROK, NVBFOWOK, NVBFOROK, NVBFHICK, NVBFYBIR,

NVBFHMAP, NVBFSMAP, NVBFBECH, NVBFSGUM, NVBFBGUM, NVBFASH,

NVBFBASS, NVBFYPOP, NVBFECOAS, NVBFBWAL, NVBFBCHR, 

NVBFOTEHW

53. NVBFSWOK Net volume sawtimber select white oak. 

54. NVBFSROK Net volume sawtimber select red oak. 

55. NVBFOWOK Net volume sawtimber other white oak.

56. NVBFOROK Net volume sawtimber other red oak.

57. NVBFHICK Net volume sawtimber hickory.

58. NVBFYBIR Net volume sawtimber yellow birch. 

59. NVBFHMAP Net volume sawtimber hard maple. 

39
60. NVBFSMAP Net volume sawtimber soft maple. 

61. NVBFBECH Net volume sawtimber beech.

62. NVBFSGUM Net volume sawtimber sweetgum. 

63. NVBFBGUM Net volume sawtimber tupelo and black gum. 

64. NVBFASH Net volume sawtimber ash.

65. NVBFBASS Net volume sawtimber basswood. 

66. NVBFYPOP Net volume sawtimber yellow-poplar.

67. NVBFECOAS Net volume sawtimber cottonwood and aspen. 

68. NVBFBWAL Net volume sawtimber black walnut. 

69. NVBFBCHR Net volume sawtimber black cherry. 

70. NVBFOTEHW Net volume sawtimber other eastern hardwoods.

71. NVGSWSW Net volume growing-stock all western softwoods. Includes species groups: 

NVGSDOUG, NVGSPDJEFF, NVGSFIR, NVGSWHEM, NVGSSUGAR,

NVGSWWPN, NVGSRDWD, NVGSSITKA, NVGSENGEL, NVGSWLARCH,

NVGSINCEN, NVGSLODGE, NVGSWRCED, NVGSOTWSW

72. NVGSDOUG Net volume growing-stock Douglas fir.

73. NVGSPDJEFF Net volume growing-stock ponderosa and Jeffrey pine.

74. NVGSFIR Net volume growing-stock true fir.

75. NVGSWHEM Net volume growing-stock western hemlock.

76. NVGSSUGAR Net volume growing-stock sugar pine.

77. NVGSWWPN Net volume growing-stock western white pine.

40
78. NVGSRDWD Net volume growing-stock redwood.

79. NVGSSITKA Net volume growing-stock sitka spruce.

80. NVGSENGEL Net volume growing-stock Engelmann and other spruces.

81. NVGSWLARCH Net volume growing-stock western larch.

82. NVGSINCEN Net volume growing-stock incense cedar.

83. NVGSLODGE Net volume growing-stock lodgepole pine.

84. NVGSWRCED Net volume growing-stock western red cedar.

85. NVGSOTWSW Net volume growing-stock other western softwoods.

86. NVGSWHW Net volume growing-stock all western hardwoods. Includes species groups:

NVGSWCOAS, NVGSALDER, NVGSWOK, NVGSOTWHW

87. NVGSWCOAS Net volume growing-stock cottonwood and aspen.

88. NVGSALDER Net volume growing-stock red alder.

89. NVGSWOK Net volume growing-stock oak.

90. NVGSOTWHW Net volume growing-stock other western hardwoods.

91. NVBFWSW Net volume sawtimber all western softwoods. Includes species groups: 

NVBFDOUG, NVBFPDJEFF, NVBFFIR, NVBFWHEM, NVBFSUGAR, 

NVBFWWPN, NVBFRDWD, NVBFSITKA, NVBFENGEL, NVBFWLARCH,

NVBFINCEN, NVBFLODGE, NVBFWRCED, NVBFOTWSW 

92. NVBFDOUG Net volume sawtimber Douglas-fir. 

93. NVBFPDJEFF Net volume sawtimber ponderosa and Jeffrey pine.

94. NVBFFIR Net volume sawtimber true fir.

95. NVBFWHEM Net volume sawtimber western hemlock. 

41
96. NVBFSUGAR Net volume sawtimber sugar pine.

97. NVBFWWPN Net volume sawtimber western white pine.

98. NVBFRDWD Net volume sawtimber redwood. 

99. NVBFSITKA Net volume sawtimber Sitka spruce.

100. NVBFENGEL Net volume sawtimber Engelmann and other spruces. 

101. NVBFWLARCH Net volume sawtimber western larch. 

102. NVBFINCEN Net volume sawtimber incense cedar. 

103. NVBFLODGE Net volume sawtimber lodgepole pine.

104. NVBFWRCED Net volume sawtimber western red cedar. 

105. NVBFOTWSW Net volume sawtimber other western softwoods. 

106. NVBFWHW Net volume sawtimber all western hardwoods. Includes species groups: 

NVBFWCOAS, NVBFALDER, NVBFWOK, NVBFOTWHW

107. NVBFWCOAS Net volume sawtimber cottonwood and aspen. 

108. NVBFALDER Net volume sawtimber red alder.

109. NVBFWOK Net volume sawtimber oak. 

110. NVBFOTWHW Net volume sawtimber other western hardwoods. 

42
Chapter 3 – Algorithms for Summarizing Data

43

Data in the RPADB were designed for easy use with

most database management systems, statistical

packages, and other data summary software. Data

are typically provided as comma-delimited ASCII

files. Database management systems that support

hierarchical data structures, as well as those based

on the relational model, can easily process RPADB

files. Chapter 2 should give the user of almost any

software package the information needed to input

an RPADB file into a processing system.

The procedures or algorithms used to compute

population estimates are provided in tables 1

through 6. Those familiar with the relational data

model and the standard Structured Query

Language (SQL) database language available in

many database management systems will find it

easy to load RPADB files into one of these systems

and to retrieve information from a loaded database.

ALGORITHMS FOR POPULATION ESTIMATES

All the variables used in these algorithms are defined in Chapter 2.

To calculate the area of all land and noncensus water for the State of Alabama:

Select Sum(aef)

From rpa_plot2002 

Where state=1 and landcc in (20,60,92)

To calculate the area of forestland for the State of Alabama:

Select Sum(aef)

From rpa_plot2002 

Where state=1 and landcc in (20)

To calculate the area of timberland for the State of Alabama:

Select Sum(aef)

From rpa_plot2002 

Where state=1 and landcc in (20) and spclass in (1,2,3,4,5,6) and reserclass=1

Units Type Calculation Requirements

Acres Area of all land and noncensus water Sum(aef) landcc in (20,60,92)

Acres Area of forest land Sum(aef) landcc=20

Acres Area of timberland Sum(aef) forcode=1

Table 1. Algorithms for calculating area estimates. Use the RPA_PLOT2002 table.
or, you could also use the following retrieval to calculate the area of timberland

for Alabama:

Select Sum(aef)

From rpa_plot2002 

Where state=1 and forcode=1

To calculate the number of all live trees 5 inches d.b.h. and larger on timberland

in Minnesota:

Select Sum(vef*tpa_5)

From rpa_plot2002

Where state=27 and forcode=1

To calculate the number of all live trees 1 inch d.b.h. and larger on timberland in

Minnesota:

Select Sum(vef*tpa_1)

From rpa_plot2002

Where state=27 and forcode=1

44

Units Type Calculation Requirements

Trees Number of all live trees Sum(vef*tpa_5) Forcode=1

5 inches and larger on timberland Note: Not available for

Hawaii and interior Alaska.

Trees Number of all live trees Sum(vef*tpa_1) Forcode=1

1 inches and larger on timberland Note: Not available for

Hawaii and interior Alaska.

Table 2. Algorithms for calculating numbers of trees. Use the RPA_PLOT2002 table.

Units Type Calculation Requirements

Cuft Merchantable volume of Sum(vef*cubic) Forcode=1

growing-stock trees on timberland

Cuft Merchantable volume of Sum(vef*cull) Forcode=1

cull trees on timberland

Cuft Merchantable volume of Sum(vef*cubic) Landcc=20

growing-stock trees on forest land

Cuft Merchantable volume of Sum(vef*cull) Landcc=20

cull trees on forest land

Cuft All live volume on forest land Sum(vef*(cubic+cull) Landcc=20

Cuft Salvable dead volume Sum(vef*dead) Forcode=1

on timberland

Bdft Merchantable volume of Sum(vef*bdft) Forcode=1

sawtimber trees on timberland

Table 3. Algorithms for calculating volumes. Use the RPA_PLOT2002 table.
To calculate merchantable volume of growing-stock trees on timberland in

Minnesota:

Select Sum(vef*cubic)

From rpa_plot2002

Where state=27 and forcode=1

To calculate merchantable volume of cull trees on timberland in Minnesota:

Select Sum(vef*cull)

From rpa_plot2002

Where state=27 and forcode=1

To calculate merchantable volume of growing-stock trees on forest land in

Minnesota:

Select Sum(vef*cubic)

From rpa_plot2002

Where state=27 and landcc=20

To calculate merchantable volume of cull trees on forest land in Minnesota:

Select Sum(vef*cull)

From rpa_plot2002

Where state=27 and landcc=20

To calculate merchantable volume of sawtimber trees on timberland in Minnesota:

Select Sum(vef*bdft)

From rpa_plot2002

Where state=27 and forcode=1

To calculate net annual merchantable growth of growing-stock trees on timberland in Minnesota:

Select Sum(vef*growth)

From rpa_plot2002

Where state=27

45

Units Type Calculation Requirements

Cuft/year Net annual merchantable growth Sum(vef*growth)

of growing-stock trees on timberland

Cuft/year Annual merchantable mortality of Sum(vef*mort)

growing-stock trees on timberland

Table 4. Algorithms for estimating net average annual growth and average annual mortality. Use the RPA_PLOT2002 table.
To calculate annual merchantable mortality of growing-stock trees on timberland

in Minnesota:

Select Sum(vef*mort)

From rpa_plot2002

Where state=27

To calculate aboveground gross biomass of all live trees (excluding foliage) on

timberland in Minnesota:

Select Sum(vef*(biobole+biosaps+biostumptop))

From rpa_plot2002

Where state=27 and forcode=1

To calculate aboveground merchantable biomass of all live trees (excluding

foliage) on timberland in Minnesota:

Select Sum(vef*(biobole))

From rpa_plot2002

Where state=27 and forcode=1

To calculate aboveground gross biomass of all live trees (excluding foliage) on 

forest land in Minnesota:

Select Sum(vef*(biobole+biosaps+biostumptop))

From rpa_plot2002

Where state=27 and landcc=20

To calculate aboveground merchantable biomass of all live trees (excluding

foliage) on forest land in Minnesota:

Select Sum(vef*(biobole))

From rpa_plot2002

Where state=27 and landcc=20

46

Units Type Calculation Requirements

Ovendry lbs. Gross biomass of all live Sum(vef* Forcode=1

trees on timberland (biobole+biosaps+

biostumptop))

Ovendry lbs. Merchantable biomass Sum(vef*biobole) Forcode=1

of all live trees on timberland

Ovendry lbs. Gross biomass of all live trees Sum(vef* Landcc=20

on forest land (biobole+biosaps+

biostumptop))

Ovendry lbs. Merchantable biomass of Sum(vef*biobole) Landcc=20

all live trees on forest land

Table 5. Algorithms for estimating biomass. Use the RPA_PLOT2002 table.
To calculate merchantable volume of growing-stock trees on timberland in Minnesota:

Select Sum(NVGSESW+NVGSEHW+NVGSWSW+NVGSWHW)

From SPDBH2002

Where state=27 and Dbhcode<>11

To calculate merchantable volume of sawtimber trees on timberland in Minnesota:

Select Sum(NVBFESW+NVBFEHW+NVBFWSW+NVBFWHW)

From SPDBH2002

Where state=27 and Dbhcode<>11

47

Units Type Calculation Requirements

Cuft Merchantable volume of Sum( Dbhcode<>11

growing-stock trees on timberland NVGSESW+

NVGSEHW+

NVGSWSW+

NVGSWHW)

Bdft Merchantable volume of Sum( Dbhcode<>11

sawtimber trees on timberland NVBFESW+

NVBFEHW+

NVBFWSW+

NVBFWHW)

Cuft Merchantable volume of longleaf Sum Dbhcode=1 and 

and slash pine growing-stock trees (NVGSLGSL) Owngroup=1

from 5 to 6.9 inches d.b.h. on national 

forest timberland

Bdft Merchantable volume of longleaf Sum Dbhcode=4 and 

and slash pine sawtimber trees from (NVBFLGSL) Owngroup=1

11 to 12.9 inches d.b.h. on national 

forest timberland

Table 6. Algorithms for estimating growing-stock volumes on timberland by species group and diameter class. Uses the

SPDBH2002 Table.
48

Cost, Noel D.; McClure, Joe P. [1983].

Multiresource inventories: techniques for estimating biomass on a statewide basis, 1982. Res. Pap.

SE-228. Asheville, NC: U.S. Department of

Agriculture, Forest Service, Southeastern Research

Station. 31 p.

Hansen, Mark H.; Frieswyk, Thomas; Glover,

Joseph F.; Kelly, John F. 1992. The Eastwide forest

inventory data base: users manual. Gen. Tech.

Rep. NC-151. St. Paul, MN: U.S. Department of

Agriculture, Forest Service, North Central Forest

Experiment Station. 48 p.

Johnson, Tony G. 2001. United States timber

industry—an assessment of timber product output and use, 1996. Gen Tech. Rep. SRS-45.

Asheville, NC: U.S. Department of Agriculture,

Forest Service, Southern Research Station. 145 p.

Miles, Patrick D.; Brand, Gary J.; Alerich, Carol L.;

Bednar, Larry F.; Woudenberg, Sharon W.; Glover,

Joseph F.; Ezzell, Edward N. 2001. The forest

inventory and analysis database: database description and users manual version 1.0. Gen. Tech.

Rep. NC-218. St. Paul, MN: U.S. Department of

Agriculture, Forest Service, North Central

Research Station. 130 p.

Smith, W. Brad; Miles, Patrick D.; Vissage, John S.

2004. Forest resources of the United States, 2002.

Gen. Tech. Rep. NC-241. St. Paul, MN: U.S.

Department of Agriculture, Forest Service, North

Central Research Station. 137 p.

Waddell, K.L.; Oswald, D.D.; Powell, D.S. 1989.

Forest statistics of the United States, 1987.

Resour. Bull. PNW-168. Portland, OR: U.S.

Department of Agriculture, Forest Service, Pacific

Northwest Research Station. 106 p.

Woudenberg, Sharon W.; Farrenkopf, Thomas O.

1995. The Westwide forest inventory data base:

user’s manual. Gen. Tech. Rep. INT-GTR-317.

Ogden, UT: U.S. Department of Agriculture,

Forest Service, Intermountain Research Station.

67 p.

Literature Cited
Appendix A

SOURCES OF 2002 RPA DATA

State Year Format Submitting Unit

Alabama 2000 FIADB Southern Research Station

Alaska 1998 FIADB/RPADB FIADB 31 million acres of forest land in southeast Alaska

were measured by the Pacific Northwest FIA; RPA

Summary DB format 96 million acres of interior Alaska

Arizona 1999 FIADB Rocky Mountain Research Station

Arkansas 1995 FIADB Southern Research Station

California 1994 FIADB 8.1 million acres of forest land were measured by the

Pacific Northwest FIA; Region 5 measured 9.6 million

acres; and Region 6 measured 0.6 million acres.

Colorado 1983 FIADB 6.2 million acres of forest land were measured by the

Rocky Mountain FIA; Region 2 measured 5.5 million acres

Connecticut 1998 FIADB Northeastern Research Station

Delaware 1999 FIADB Northeastern Research Station

District of Columbia 2002 FIADB Southern Research Station

Florida 1995 FIADB Southern Research Station

Georgia 1997 FIADB Southern Research Station

Hawaii RPADB Pacific Northwest Research Station

Idaho 1991 FIADB Rocky Mountain Research Station

Illinois 1998 FIADB North Central Research Station

Indiana 1998 FIADB North Central Research Station

Iowa 1990 FIADB North Central Research Station

Kansas 1994 FIADB North Central Research Station

Kentucky 1988 FIADB Southern Research Station

Louisiana 1991 FIADB Southern Research Station

Maine 1995 FIADB Northeastern Research Station

Maryland 1999 FIADB Northeastern Research Station

Massachusetts 1998 FIADB Northeastern Research Station

Michigan 1993 FIADB North Central Research Station

Minnesota 1990 FIADB North Central Research Station

Mississippi 1994 FIADB Southern Research Station

Missouri 1989 FIADB North Central Research Station

Montana 1989 FIADB Rocky Mountain Research Station

Nebraska 1994 FIADB North Central Research Station

(table continued on next page)

49
State Year Format Submitting Unit

Nevada 1989 FIADB Rocky Mountain Research Station

New Hampshire 1997 FIADB Northeastern Research Station

New Jersey 1999 FIADB Northeastern Research Station

New Mexico 1999 FIADB Rocky Mountain Research Station

New York 1993 FIADB Northeastern Research Station

North Carolina 1990 FIADB Southern Research Station

North Dakota 1995 FIADB North Central Research Station

Ohio 1993 FIADB Northeastern Research Station

Oklahoma 1993 FIADB Southern Research Station

Oregon 1992 FIADB 9.8 million acres of forest land measured by the Pacific

Northwest FIA; 14.0 million were measured by Region 6

Pennsylvania 1989 FIADB Northeastern Research Station

Rhode Island 1998 FIADB Northeastern Research Station

South Carolina 2000 FIADB Southern Research Station

South Dakota 1995 FIADB 0.6 million acres of forest land were measured by the

North Central FIA (1995); 1.0 million acres by the Rocky

Mountain FIA

Tennessee 1999 FIADB Southern Research Station

Texas 1992 FIADB Southeast and northeast Texas were inventoried by the

Southern FIA; West Texas was estimated using remotely

sensed data (AVHRR)

Utah 1995 FIADB Rocky Mountain Research Station

Vermont 1997 FIADB Northeastern Research Station

Virginia 1992 FIADB Southern Research Station

Washington 1991 FIADB 11.4 million acres of forest land were measured by the

Pacific Northwest FIA; 5.9 million acres were measured

by Region 6

West Virginia 1989 FIADB Northeastern Research Station

Wisconsin 1996 FIADB North Central Research Station

Wyoming 1984 FIADB 5.1 million acres of forest land were measured by the

Rocky Mountain FIA; 0.6 million acres were measured by

Region 2

50
Addresses of USDA Forest Service Research Stations with responsibilities for

forest inventories in the United States and their area of responsibility

Address Area of responsibility

Northeastern Research Station Connecticut, Delaware, Maine, 

11 Campus Boulevard Maryland, Massachusetts, New Hampshire,

Newtown Square, PA 19073 New Jersey, New York, Ohio, Pennsylvania,

Rhode Island, Vermont, and West Virginia

North Central Research Station Illinois, Indiana, Iowa, Kansas, Michigan, 

1992 Folwell Avenue Minnesota, Missouri, Nebraska, North Dakota, 

St. Paul, MN 55108 South Dakota, and Wisconsin

Southern Research Station Alabama, Arkansas, Florida, Georgia, Kentucky,

4700 Old Kingston Pike. Louisiana, Mississippi, North Carolina,

Knoxville, TN 37919 Oklahoma, South Carolina, Tennessee,

Texas, Virginia, and Puerto Rico

Pacific Northwest Research Station Alaska, California, Hawaii, Oregon,

Forestry Sciences Laboratory and Washington

620 SW Main, Suite 400

Portland, OR 97205

Rocky Mountain Research Station Arizona, Colorado, Idaho, Montana, Nevada, 

Forestry Sciences Laboratory New Mexico, Utah, and Wyoming

507 25th Street

Ogden UT 84401

Addresses of National Forest System Regional Offices in the United States

Address Region Location of national forests

Forest Service, USDA Region 1 Montana, northern Idaho, North Dakota,

Northern Region and northwestern South Dakota

Federal Building 

200 E.Broadway

PO Box 7669

Missoula, MT 59807 

Forest Service, USDA Region 2 Colorado, Kansas, Nebraska, South Dakota,

Rocky Mountain Region and eastern Wyoming

740 Simms St 

P.O. Box 25127 

Lakewood, CO 80225-0127

Forest Service, USDA Region 3 Arizona and New Mexico

Southwestern Region

333 Broadway SE

Albuquerque, NM 87102

Forest Service, USDA Region 4 Southern Idaho, Nevada, Utah, and

Intermountain Region western Wyoming

Federal Building 

324 25th Street

Ogden, UT 84401 

51
Forest Service, USDA Region 5 California

Pacific Southwest Region

1323 Club Drive

Vallejo, CA 94592

Forest Service, USDA Region 6 Oregon and Washington

Pacific Northwest Region

333 S.W. 1st Avenue

P.O. Box 3623

Portland, OR 97208

Forest Service, USDA Region 8 Alabama, Arkansas, Florida, Georgia, Kentucky,

Southern Region Louisiana, Mississippi, North Carolina, Oklahoma,

1720 Peachtree Road, N.W. Tennessee, Texas, Virginia, West Virginia, and

Atlanta, GA 30309 Puerto Rico

Forest Service, USDA Region 9 Connecticut, Delaware, Illinois, Indiana, Iowa

Eastern Region Maine, Maryland, Massachusetts, Michigan, 

310 West Wisconsin Avenue, Minnesota, Missouri, New Hampshire, New Jersey

Suite 500 New York, Ohio, Pennsylvania, Rhode Island, 

Milwaukee, WI 53203 Vermont, West Virginia, and Wisconsin

Forest Service, USDA Region 10 Alaska

Alaska Region

P.O. Box 21628

Juneau, AK 99802-1628 

For additional information, contact the Internet sites for the regional offices through the USDA Forest

Service home page: http://www.fs.fed.us. The forest management staff in each regional office manages

timber inventories conducted by the region.

52
01 Alabama

01 Southwest-South

003 Baldwin

039 Covington

053 Escambia

097 Mobile

129 Washington

02 Southwest-North

023 Choctaw

025 Clarke

035 Conecuh

091 Marengo

099 Monroe

119 Sumter

131 Wilcox

03 Southeast

001 Autauga

005 Barbour

011 Bullock

013 Butler

017 Chambers

021 Chilton

031 Coffee

041 Crenshaw

045 Dale

047 Dallas

051 Elmore

061 Geneva

067 Henry

069 Houston

081 Lee

085 Lowndes

087 Macon

101 Montgomery

109 Pike

113 Russell

123 Tallapoosa

04 West Central

007 Bibb

057 Fayette

063 Greene

065 Hale

075 Lamar

093 Marion

105 Perry

107 Pickens

125 Tuscaloosa

05 North Central

009 Blount

015 Calhoun

019 Cherokee

027 Clay

029 Cleburne

037 Coosa

043 Cullman

055 Etowah

073 Jefferson

111 Randolph

115 St. Clair

117 Shelby

121 Talladega

127 Walker

133 Winston

06 North

033 Colbert

049 DeKalb

059 Franklin

071 Jackson

077 Lauderdale

079 Lawrence

083 Limestone

089 Madison

095 Marshall

103 Morgan

02 Alaska

01 Alaska

013 Aleutians East Borough

016 Aleutians West Census Area

020 Anchorage Borough

050 Bethel Census Area

060 Bristol Bay Borough

070 Dillingham Census Area

090 Fairbanks North Star Borough

100 Haines Borough

110 Juneau Borough

122 Kenai Peninsula Borough

130 Ketchikan Gateway Borough

150 Kodiak Island Borough

164 Lake and Peninsula Borough

170 Matanuska-Susitna Borough

180 Nome Census Area

185 North Slope Borough

188 Northwest Arctic Borough

201 Prince of Wales-Outer Ketchikan

Census Area

220 Sitka Borough

231 Skagway-Yakutat-Angoon

Census Area

240 Southeast Fairbanks Census

Area

261 Valdez-Cordova Census Area

270 Wade Hampton Census Area

280 Wrangell-Petersburg Census

Area

290 Yukon-Koyukuk Census Area

04 Arizona

01 Southern

003 Cochise

009 Graham

011 Greenlee

012 La Paz

013 Maricopa

019 Pima

021 Pinal

023 Santa Cruz

027 Yuma

02 Northern

001 Apache

005 Coconino

007 Gila

015 Mohave

017 Navajo

025 Yavapai

05 Arkansas

01 South Delta

001 Arkansas

017 Chicot

041 Desha

069 Jefferson

077 Lee

079 Lincoln

085 Lonoke

095 Monroe

107 Phillips

117 Prairie

53

Appendix B – State, Survey Unit, and County Codes
02 North Delta

021 Clay

031 Craighead

035 Crittenden

037 Cross

055 Greene

067 Jackson

075 Lawrence

093 Mississippi

111 Poinsett

123 St. Francis

147 Woodruff

03 Southwest

003 Ashley

011 Bradley

013 Calhoun

019 Clark

025 Cleveland

027 Columbia

039 Dallas

043 Drew

053 Grant

057 Hempstead

059 Hot Spring

061 Howard

073 Lafayette

081 Little River

091 Miller

099 Nevada

103 Ouachita

109 Pike

133 Sevier

139 Union

04 Ouachita

051 Garland

083 Logan

097 Montgomery

105 Perry

113 Polk

119 Pulaski

125 Saline

127 Scott

131 Sebastian

149 Yell

05 Ozark

005 Baxter

007 Benton

009 Boone

015 Carroll

023 Cleburne

029 Conway

033 Crawford

045 Faulkner

047 Franklin

049 Fulton

063 Independence

065 Izard

071 Johnson

087 Madison

089 Marion

101 Newton

115 Pope

121 Randolph

129 Searcy

135 Sharp

137 Stone

141 Van Buren

143 Washington

145 White

06 California

01 North Coast

015 Del Norte

023 Humboldt

045 Mendocino

097 Sonoma

02 North Interior

035 Lassen

049 Modoc

089 Shasta

093 Siskiyou

105 Trinity

03 Sacramento

007 Butte

011 Colusa

017 El Dorado

021 Glenn

033 Lake

055 Napa

057 Nevada

061 Placer

063 Plumas

067 Sacramento

091 Sierra

101 Sutter

103 Tehama

113 Yolo

115 Yuba

04 Central Coast

001 Alameda

013 Contra Costa

041 Marin

053 Monterey

069 San Benito

075 San Francisco

079 San Luis Obispo

081 San Mateo

083 Santa Barbara

085 Santa Clara

087 Santa Cruz

095 Solano

111 Ventura

05 San Joaquin

003 Alpine

005 Amador

009 Calaveras

019 Fresno

029 Kern

031 Kings

039 Madera

043 Mariposa

047 Merced

051 Mono

077 San Joaquin

099 Stanislaus

107 Tulare

109 Tuolumne

06 Southern

025 Imperial

027 Inyo

037 Los Angeles

059 Orange

065 Riverside

071 San Bernardino

073 San Diego

54
08 Colorado

01 Northern Front Range

013 Boulder

019 Clear Creek

035 Douglas

039 Elbert

041 El Paso

047 Gilpin

059 Jefferson

065 Lake

069 Larimer

093 Park

119 Teller

02 Southern Front Range

015 Chaffee

023 Costilla

027 Custer

043 Fremont

055 Huerfano

071 Las Animas

101 Pueblo

03 West Central

003 Alamosa

021 Conejos

037 Eagle

049 Grand

051 Gunnison

053 Hinsdale

057 Jackson

079 Mineral

097 Pitkin

105 Rio Grande

107 Routt

109 Saguache

111 San Juan

117 Summit

04 Western

007 Archuleta

029 Delta

033 Dolores

045 Garfield

067 La Plata

077 Mesa

081 Moffat

083 Montezuma

085 Montrose

091 Ouray

103 Rio Blanco

113 San Miguel

05 Eastern

001 Adams

005 Arapahoe

009 Baca

011 Bent

017 Cheyenne

025 Crowley

031 Denver

061 Kiowa

063 Kit Carson

073 Lincoln

075 Logan

087 Morgan

089 Otero

095 Phillips

099 Prowers

115 Sedgwick

121 Washington

123 Weld

125 Yuma

09 Connecticut

01 State

001 Fairfield

003 Hartford

005 Litchfield

007 Middlesex

009 New Haven

011 New London

013 Tolland

015 Windham

10 Delaware

01 State

001 Kent

003 New Castle

005 Sussex

11 District of Columbia

12 Florida

01 Northeastern

001 Alachua

003 Baker

007 Bradford

019 Clay

023 Columbia

029 Dixie

031 Duval

035 Flagler

041 Gilchrist

047 Hamilton

067 Lafayette

075 Levy

079 Madison

083 Marion

089 Nassau

107 Putnam

109 St. Johns

121 Suwannee

123 Taylor

125 Union

127 Volusia

02 Northwestern

005 Bay

013 Calhoun

033 Escambia

037 Franklin

039 Gadsden

045 Gulf

059 Holmes

063 Jackson

065 Jefferson

073 Leon

077 Liberty

091 Okaloosa

113 Santa Rosa

129 Wakulla

131 Walton

133 Washington

03 Central

009 Brevard

017 Citrus

027 DeSoto

049 Hardee

053 Hernando

055 Highlands

057 Hillsborough

55
061 Indian River

069 Lake

081 Manatee

093 Okeechobee

095 Orange

097 Osceola

101 Pasco

103 Pinellas

105 Polk

111 St. Lucie

115 Sarasota

117 Seminole

119 Sumter

04 Southern

011 Broward

015 Charlotte

021 Collier

025 Dade

043 Glades

051 Hendry

071 Lee

085 Martin

087 Monroe

099 Palm Beach

13 Georgia

01 Southeastern

001 Appling

003 Atkinson

005 Bacon

025 Brantley

029 Bryan

031 Bulloch

039 Camden

043 Candler

049 Charlton

051 Chatham

065 Clinch

069 Coffee

091 Dodge

101 Echols

103 Effingham

107 Emanuel

109 Evans

127 Glynn

161 Jeff Davis

165 Jenkins

167 Johnson

175 Laurens

179 Liberty

183 Long

191 McIntosh

209 Montgomery

229 Pierce

251 Screven

267 Tattnall

271 Telfair

279 Toombs

283 Treutlen

299 Ware

305 Wayne

309 Wheeler

02 Southwestern

007 Baker

017 Ben Hill

019 Berrien

027 Brooks

071 Colquitt

075 Cook

081 Crisp

087 Decatur

093 Dooly

099 Early

131 Grady

155 Irwin

173 Lanier

185 Lowndes

201 Miller

205 Mitchell

253 Seminole

275 Thomas

277 Tift

287 Turner

315 Wilcox

321 Worth

03 Central

009 Baldwin

021 Bibb

023 Bleckley

033 Burke

035 Butts

037 Calhoun

053 Chattahoochee

061 Clay

073 Columbia

079 Crawford

095 Dougherty

125 Glascock

133 Greene

141 Hancock

145 Harris

153 Houston

159 Jasper

163 Jefferson

169 Jones

171 Lamar

177 Lee

181 Lincoln

189 McDuffie

193 Macon

197 Marion

207 Monroe

211 Morgan

215 Muscogee

225 Peach

231 Pike

235 Pulaski

237 Putnam

239 Quitman

243 Randolph

245 Richmond

249 Schley

259 Stewart

261 Sumter

263 Talbot

265 Taliaferro

269 Taylor

273 Terrell

289 Twiggs

293 Upson

301 Warren

303 Washington

307 Webster

317 Wilkes

319 Wilkinson

04 North Central

011 Banks

013 Barrow

045 Carroll

059 Clarke

063 Clayton

067 Cobb

077 Coweta

089 DeKalb

097 Douglas

56
105 Elbert 113 Fayette

117 Forsyth

119 Franklin 121 Fulton

135 Gwinnett

139 Hall

143 Haralson

147 Hart 149 Heard

151 Henry

157 Jackson

195 Madison

199 Meriwether

217 Newton

219 Oconee

221 Oglethorpe

223 Paulding

233 Polk

247 Rockdale

255 Spalding

285 Troup

297Walton

05 Northern

015 Bartow

047 Catoosa

055 Chattooga

057 Cherokee

083 Dade

085 Dawson

111 Fannin

115 Floyd

123 Gilmer

129 Gordon

137 Habersham

187 Lumpkin

213 Murray

227 Pickens

241 Rabun

257 Stephens

281 Towns

291 Union

295

Walker

311 White

313 Whitfield

15 Hawaii 01 State

001 Hawaii

003 Honolulu 005 Kalawao

007 Kauai

009 Maui 16 Idaho

01 Northern

009 Benewah

017 Bonner

021 Boundary

035 Clearwater

049 Idaho

055 Kootenai

057 Latah

061 Lewis

069 Nez Perce

079 Shoshone

02 Southeastern

001 Ada

003 Adams

015 Boise

027 Canyon

039 Elmore

045 Gem

073 Owyhee

075 Payette

085 Valley

087 Washington

03 Southwestern

005 Bannock

007 Bear Lake

011 Bingham

013 Blaine

019 Bonneville

023 Butte

025 Camas

029 Caribou

031 Cassia

033 Clark

037 Custer

041 Franklin

043 Fremont

047 Gooding

051 Jefferson

053 Jerome 059 Lemhi

063 Lincoln

065 Madison 067 Minidoka

071 Oneida

077 Power

081 Teton

083 Twin Falls

17 Illinois

01 Southern

003 Alexander

055 Franklin

059 Gallatin

065 Hamilton

069 Hardin

077 Jackson

087 Johnson

127 Massac

145 Perry

151 Pope

153 Pulaski

157 Randolph

165 Saline

181 Union

193 White

199 Williamson

02 Claypan

005 Bond

013 Calhoun

023 Clark

025 Clay

027 Clinton

033 Crawford

035 Cumberland

047 Edwards

049 Effingham

051 Fayette

061 Greene

079 Jasper

081 Jefferson

083 Jersey

101 Lawrence

117 Macoupin

119 Madison

121 Marion

133 Monroe

57
135 Montgomery

159 Richland

163 St. Clair

173 Shelby

185 Wabash

189 Washington

191 Wayne

03 Prairie

001 Adams

007 Boone

009 Brown

011 Bureau

015 Carroll

017 Cass

019 Champaign

021 Christian

029 Coles

031 Cook

037 DeKalb

039 De Witt

041 Douglas

043 DuPage

045 Edgar

053 Ford

057 Fulton

063 Grundy

067 Hancock

071 Henderson

073 Henry

075 Iroquois

085 Jo Daviess

089 Kane

091 Kankakee

093 Kendall

095 Knox

097 Lake

099 La Salle

103 Lee

105 Livingston

107 Logan

109 McDonough

111 McHenry

113 McLean

115 Macon

123 Marshall

125 Mason

129 Menard

131 Mercer

137 Morgan

139 Moultrie

141 Ogle

143 Peoria

147 Piatt

149 Pike

155 Putnam

161 Rock Island

167 Sangamon

169 Schuyler

171 Scott

175 Stark

177 Stephenson

179 Tazewell

183 Vermilion

187 Warren

195 Whiteside

197 Will

201 Winnebago

203 Woodford

18 Indiana

01 Lower Wabash

021 Clay

027 Daviess

051 Gibson

055 Greene

083 Knox

101 Martin

121 Parke

125 Pike

129 Posey

133 Putnam

153 Sullivan

163 Vanderburgh

165 Vermillion

167 Vigo

02 Knobs

013 Brown

019 Clark

025 Crawford

037 Dubois

043 Floyd

061 Harrison

071 Jackson

093 Lawrence

105 Monroe

109 Morgan

117 Orange

119 Owen

123 Perry

143 Scott

147 Spencer

173 Warrick

175 Washington

03 Upland Flats

029 Dearborn

041 Fayette

047 Franklin

077 Jefferson

079 Jennings

115 Ohio

137 Ripley

155 Switzerland

161 Union

04 Northern

001 Adams

003 Allen

005 Bartholomew

007 Benton

009 Blackford

011 Boone

015 Carroll

017 Cass

023 Clinton

031 Decatur

033 De Kalb

035 Delaware

039 Elkhart

045 Fountain

049 Fulton

053 Grant

057 Hamilton

059 Hancock

063 Hendricks

065 Henry

067 Howard

069 Huntington

073 Jasper

075 Jay

081 Johnson

085 Kosciusko

087 Lagrange

089 Lake

091 La Porte

095 Madison

097 Marion

58
099 Marshall 103 Miami

107 Montgomery

111 Newton 113 Noble

127 Porter

131 Pulaski

135 Randolph

139 Rush 141 St. Joseph

145 Shelby

149 Starke

151 Steuben

157 Tippecanoe

159 Tipton

169 Wabash

171 Warren

177 Wayne

179 Wells

181 White

183 Whitley

19 Iowa

01 Northeastern

005 Allamakee

011 Benton

013 Black Hawk

017 Bremer

019 Buchanan

023 Butler

031 Cedar

037 Chickasaw

043 Clayton

045 Clinton

055 Delaware

061 Dubuque

065 Fayette

067 Floyd

075 Grundy

089 Howard

097 Jackson

103 Johnson

105 Jones

113 Linn

131 Mitchell

163 Scott

171 Tama

191 Winneshiek

02 Southeastern 007 Appanoose

015 Boone

039 Clarke 049 Dallas

051 Davis

053 Decatur

057 Des Moines

077 Guthrie 079 Hamilton

083 Hardin

087 Henry

095 Iowa

099 Jasper

101 Jefferson

107 Keokuk

111 Lee

115 Louisa

117 Lucas

121 Madison

123 Mahaska

125 Marion

127 Marshall

135 Monroe

139 Muscatine

153 Polk

157 Poweshiek

169 Story

177 Van Buren

179 Wapello

181 Warren

183 Washington

185 Wayne

187

Webster

03 Southwestern

001 Adair

003 Adams

009 Audubon

027 Carroll

029 Cass

047 Crawford

071 Fremont

073 Greene

085 Harrison

129 Mills

133 Monona

137 Montgomery

145 Page

155 Pottawattamie

159 Ringgold 165 Shelby

173 Taylor

175 Union 193 Woodbury

04 Northwestern

021 Buena Vista

025 Calhoun 033 Cerro Gordo

035 Cherokee

041 Clay

059 Dickinson

063 Emmet

069 Franklin

081 Hancock

091 Humboldt

093 Ida

109 Kossuth

119Lyon

141 O’Brien

143 Osceola

147 Palo Alto

149 Plymouth

151 Pocahontas

161 Sac

167 Sioux

189 Winnebago

195 Worth

197 Wright

20 Kansas

01 Northeastern

005 Atchison

013 Brown

027 Clay

041 Dickinson

043 Doniphan

045 Douglas

059 Franklin

061 Geary

085 Jackson

087 Jefferson

091 Johnson

103 Leavenworth

117 Marshall

121 Miami

131 Nemaha

139 Osage

59
149 Pottawatomie

161 Riley

177 Shawnee

197 Wabaunsee

201 Washington

209 Wyandotte

02 Southeastern

001 Allen

003 Anderson

011 Bourbon

015 Butler

017 Chase

019 Chautauqua

021 Cherokee

031 Coffey

035 Cowley

037 Crawford

049 Elk

073 Greenwood

099 Labette

107 Linn

111 Lyon

115 Marion

125 Montgomery

127 Morris

133 Neosho

205 Wilson

207 Woodson

03 Western

007 Barber

009 Barton

023 Cheyenne

025 Clark

029 Cloud

033 Comanche

039 Decatur

047 Edwards

051 Ellis

053 Ellsworth

055 Finney

057 Ford

063 Gove

065 Graham

067 Grant

069 Gray

071 Greeley

075 Hamilton

077 Harper

079 Harvey

081 Haskell

083 Hodgeman

089 Jewell

093 Kearny

095 Kingman

097 Kiowa

101 Lane

105 Lincoln

109 Logan

113 McPherson

119 Meade

123 Mitchell

129 Morton

135 Ness

137 Norton

141 Osborne

143 Ottawa

145 Pawnee

147 Phillips

151 Pratt

153 Rawlins

155 Reno

157 Republic

159 Rice

163 Rooks

165 Rush

167 Russell

169 Saline

171 Scott

173 Sedgwick

175 Seward

179 Sheridan

181 Sherman

183 Smith

185 Stafford

187 Stanton

189 Stevens

191 Sumner

193 Thomas

195 Trego

199 Wallace

203 Wichita

21 Kentucky

01 Eastern

071 Floyd

095 Harlan

119 Knott

131 Leslie

133 Letcher

159 Martin

193 Perry

195 Pike

02 Northern Cumberland

019 Boyd

043 Carter

063 Elliott

089 Greenup

115 Johnson

127 Lawrence

135 Lewis

153 Magoffin

165 Menifee

175 Morgan

197 Powell

205 Rowan

237 Wolfe

03 Southern Cumberland

013 Bell

025 Breathitt

051 Clay

065 Estill

109 Jackson

121 Knox

125 Laurel

129 Lee

147 McCreary

189 Owsley

203 Rockcastle

235 Whitley

04 Bluegrass

005 Anderson

011 Bath

015 Boone

017 Bourbon

021 Boyle

023 Bracken

037 Campbell

041 Carroll

049 Clark

067 Fayette

069 Fleming

073 Franklin

077 Gallatin

079 Garrard

60
081 Grant

097 Harrison

103 Henry

111 Jefferson

113 Jessamine

117 Kenton

137 Lincoln

151 Madison

161 Mason

167 Mercer

173 Montgomery

181 Nicholas

185 Oldham

187 Owen

191 Pendleton

201 Robertson

209 Scott

211 Shelby

215 Spencer

223 Trimble

229 Washington

239 Woodford

05 Pennyroyal

001 Adair

027 Breckinridge

029 Bullitt

045 Casey

053 Clinton

057 Cumberland

085 Grayson

087 Green

091 Hancock

093 Hardin

099 Hart

123 Larue

155 Marion

163 Meade

169 Metcalfe

179 Nelson

199 Pulaski

207 Russell

217 Taylor

231 Wayne

06 Western Coalfield

003 Allen

009 Barren

031 Butler

033 Caldwell

047 Christian

055 Crittenden

059 Daviess

061 Edmonson

101 Henderson

107 Hopkins

141 Logan

149 McLean

171 Monroe

177 Muhlenberg

183 Ohio

213 Simpson

219 Todd

225 Union

227 Warren

233 Webster

07 Western

007 Ballard

035 Calloway

039 Carlisle

075 Fulton

083 Graves

105 Hickman

139 Livingston

143 Lyon

145 McCracken

157 Marshall

221 Trigg

22 Louisiana

01 North Delta

025 Catahoula

029 Concordia

035 East Carroll

041 Franklin

065 Madison

067 Morehouse

083 Richland

107 Tensas

123 West Carroll

02 South Delta

001 Acadia

005 Ascension

007 Assumption

009 Avoyelles

045 Iberia

047 Iberville

055 Lafayette

057 Lafourche

077 Pointe Coupee

089 St. Charles

093 St. James

095 St. John the Baptist

097 St. Landry

099 St. Martin

101 St. Mary

109 Terrebonne

113 Vermilion

121 West Baton Rouge

125 West Feliciana

03 Southwest

003 Allen

011 Beauregard

019 Calcasieu

039 Evangeline

043 Grant

053 Jefferson Davis

059 La Salle

069 Natchitoches

079 Rapides

085 Sabine

115 Vernon

04 Southeast

033 East Baton Rouge

037 East Feliciana

063 Livingston

091 St. Helena

103 St. Tammany

105 Tangipahoa

117 Washington

05 Northwest

013 Bienville

015 Bossier

017 Caddo

021 Caldwell

027 Claiborne

031 De Soto

049 Jackson

061 Lincoln

073 Ouachita

081 Red River

111 Union

119 Webster

127 Winn

61
Unsampled parishes

023 Cameron

051 Jefferson

071 Orleans

075 Plaquemines

087 St. Bernard

23 Maine

01 Washington

029 Washington

02 Aroostook

003 Aroostook

03 Penobscot

019 Penobscot

04 Hancock

009 Hancock

05 Piscataquis

021 Piscataquis

06 Capitol Region

011 Kennebec

013 Knox

015 Lincoln

027 Waldo

07 Somerset

025 Somerset

08 Casco Bay

001 Androscoggin

005 Cumberland

023 Sagadahoc

031 York

09 Western Maine

007 Franklin

017 Oxford

24 Maryland

02 North Central

003 Anne Arundel

005 Baltimore

011 Caroline

013 Carroll

015 Cecil

021 Frederick

025 Harford

027 Howard

029 Kent

031 Montgomery

033 Prince George’s

035 Queen Anne’s

041 Talbot

043 Washington

510 Baltimore city

03 Southern

009 Calvert

017 Charles

037 St. Mary’s

04 Lower Eastern Shore

019 Dorchester

039 Somerset

045 Wicomico

047 Worcester

05 Western

001 Allegany

023 Garrett

25 Massachusetts

01 State

001 Barnstable

003 Berkshire

005 Bristol

007 Dukes

009 Essex

011 Franklin

013 Hampden

015 Hampshire

017 Middlesex

019 Nantucket

021 Norfolk

023 Plymouth

025 Suffolk

027 Worcester

26 Michigan

01 Eastern Upper Peninsula

003 Alger

033 Chippewa

041 Delta

095 Luce

097 Mackinac

109 Menominee

153 Schoolcraft

02 Western Upper Peninsula

013 Baraga

043 Dickinson

053 Gogebic

061 Houghton

071 Iron

083 Keweenaw

103 Marquette

131 Ontonagon

03 Northern Lower Peninsula

001 Alcona

007 Alpena

009 Antrim

011 Arenac

017 Bay

019 Benzie

029 Charlevoix

031 Cheboygan

035 Clare

039 Crawford

047 Emmet

051 Gladwin

055 Grand Traverse

069 Iosco

073 Isabella

079 Kalkaska

085 Lake

089 Leelanau

101 Manistee

105 Mason

107 Mecosta

111 Midland

113 Missaukee

119 Montmorency

123 Newaygo

127 Oceana

129 Ogemaw

133 Osceola

135 Oscoda

137 Otsego

141 Presque Isle

143 Roscommon

165 Wexford

62
04 Southern Lower Peninsula

005 Allegan

015 Barry

021 Berrien

023 Branch

025 Calhoun

027 Cass

037 Clinton

045 Eaton

049 Genesee

057 Gratiot

059 Hillsdale

063 Huron

065 Ingham

067 Ionia

075 Jackson

077 Kalamazoo

081 Kent

087 Lapeer

091 Lenawee

093 Livingston

099 Macomb

115 Monroe

117 Montcalm

121 Muskegon

125 Oakland

139 Ottawa

145 Saginaw

147 St. Clair

149 St. Joseph

151 Sanilac

155 Shiawassee

157 Tuscola

159 Van Buren

161 Washtenaw

163 Wayne

27 Minnesota

01 Aspen-Birch

017 Carlton

031 Cook

071 Koochiching

075 Lake

137 St. Louis

02 Northern Pine

001 Aitkin

005 Becker

007 Beltrami

021 Cass

029 Clearwater

035 Crow Wing

057 Hubbard

061 Itasca

077 Lake of the Woods

087 Mahnomen

135 Roseau

159 Wadena

03 Central Hardwood

003 Anoka

009 Benton

019 Carver

025 Chisago

037 Dakota

041 Douglas

045 Fillmore

049 Goodhue

053 Hennepin

055 Houston

059 Isanti

065 Kanabec

079 Le Sueur

095 Mille Lacs

097 Morrison

109 Olmsted

111 Otter Tail

115 Pine

123 Ramsey

131 Rice

139 Scott

141 Sherburne

145 Stearns

153 Todd

157 Wabasha

163 Washington

169 Winona

171 Wright

04 Prairie

011 Big Stone

013 Blue Earth

015 Brown

023 Chippewa

027 Clay

033 Cottonwood

039 Dodge

043 Faribault

047 Freeborn

051 Grant

063 Jackson

067 Kandiyohi

069 Kittson

073 Lac qui Parle

081 Lincoln

083 Lyon

085 McLeod

089 Marshall

091 Martin

093 Meeker

099 Mower

101 Murray

103 Nicollet

105 Nobles

107 Norman

113 Pennington

117 Pipestone

119 Polk

121 Pope

125 Red Lake

127 Redwood

129 Renville

133 Rock

143 Sibley

147 Steele

149 Stevens

151 Swift

155 Traverse

161 Waseca

165 Watonwan

167 Wilkin

173 Yellow Medicine

28 Mississippi

01 Delta

011 Bolivar

027 Coahoma

051 Holmes

053 Humphreys

055 Issaquena

083 Leflore

119 Quitman

125 Sharkey

133 Sunflower

135 Tallahatchie

143 Tunica

149 Warren

151 Washington

163 Yazoo

63
02 North

003 Alcorn

009 Benton

013 Calhoun

015 Carroll

017 Chickasaw

019 Choctaw

025 Clay

033 DeSoto

043 Grenada

057 Itawamba

071 Lafayette

081 Lee

087 Lowndes

093 Marshall

095 Monroe

097 Montgomery

105 Oktibbeha

107 Panola

115 Pontotoc

117 Prentiss

137 Tate

139 Tippah

141 Tishomingo

145 Union

155 Webster

161 Yalobusha

03 Central

007 Attala

023 Clarke

061 Jasper

069 Kemper

075 Lauderdale

079 Leake

099 Neshoba

101 Newton

103 Noxubee

121 Rankin

123 Scott

127 Simpson

129 Smith

159 Winston

04 South

031 Covington

035 Forrest

039 George

041 Greene

045 Hancock

047 Harrison

059 Jackson

065 Jefferson Davis

067 Jones

073 Lamar

077 Lawrence

091 Marion

109 Pearl River

111 Perry

131 Stone

147 Walthall

153 Wayne

05 Southwest

001 Adams

005 Amite

021 Claiborne

029 Copiah

037 Franklin

049 Hinds

063 Jefferson

085 Lincoln

089 Madison

113 Pike

157 Wilkinson

29 Missouri

01 Eastern Ozarks

017 Bollinger

023 Butler

035 Carter

055 Crawford

065 Dent

093 Iron

123 Madison

149 Oregon

179 Reynolds

181 Ripley

187 St. Francois

203 Shannon

221 Washington

223 Wayne

02 Southwestern Ozarks

009 Barry

043 Christian

067 Douglas

091 Howell

119 McDonald

145 Newton

153 Ozark

209 Stone

213 Taney

215 Texas

225 Webster

229 Wright

03 Northwestern Ozarks

015 Benton

029 Camden

039 Cedar

059 Dallas

085 Hickory

105 Laclede

125 Maries

131 Miller

141 Morgan

161 Phelps

167 Polk

169 Pulaski

185 St. Clair

04 Prairie

001 Adair

003 Andrew

005 Atchison

007 Audrain

011 Barton

013 Bates

021 Buchanan

025 Caldwell

033 Carroll

037 Cass

041 Chariton

045 Clark

047 Clay

049 Clinton

053 Cooper

057 Dade

061 Daviess

063 DeKalb

075 Gentry

077 Greene

079 Grundy

081 Harrison

083 Henry

087 Holt

64
095 Jackson

097 Jasper

101 Johnson

103 Knox

107 Lafayette

109 Lawrence

111 Lewis

113 Lincoln

115 Linn

117 Livingston

121 Macon

127 Marion

129 Mercer

137 Monroe

147 Nodaway

159 Pettis

163 Pike

165 Platte

171 Putnam

173 Ralls

175 Randolph

177 Ray

195 Saline

197 Schuyler

199 Scotland

205 Shelby

211 Sullivan

217 Vernon

227 Worth

05 Riverborder

019 Boone

027 Callaway

031 Cape Girardeau

051 Cole

069 Dunklin

071 Franklin

073 Gasconade

089 Howard

099 Jefferson

133 Mississippi

135 Moniteau

139 Montgomery

143 New Madrid

151 Osage

155 Pemiscot

157 Perry

183 St. Charles

186 Ste. Genevieve

189 St. Louis

201 Scott

207 Stoddard

219 Warren

510 St. Louis city

30 Montana

01 Northwestern

029 Flathead

047 Lake

053 Lincoln

089 Sanders

02 Eastern

003 Big Horn

005 Blaine

009 Carbon

011 Carter

015 Chouteau

017 Custer

019 Daniels

021 Dawson

025 Fallon

027 Fergus

033 Garfield

035 Glacier

037 Golden Valley

041 Hill

051 Liberty

055 McCone

065 Musselshell

069 Petroleum

071 Phillips

073 Pondera

075 Powder River

079 Prairie

083 Richland

085 Roosevelt

087 Rosebud

091 Sheridan

095 Stillwater

097 Sweet Grass

099 Teton

101 Toole

103 Treasure

105 Valley

109 Wibaux

111 Yellowstone

113 Yellowstone National Park

03 Western

039 Granite

061 Mineral

063 Missoula

081 Ravalli

04 West Central

007 Broadwater

013 Cascade

043 Jefferson

045 Judith Basin

049 Lewis and Clark

059 Meagher

077 Powell

107 Wheatland

05 Southwestern

001 Beaverhead

023 Deer Lodge

031 Gallatin

057 Madison

067 Park

093 Silver Bow

31 Nebraska

01 Eastern

001 Adams

011 Boone

019 Buffalo

021 Burt

023 Butler

025 Cass

027 Cedar

035 Clay

037 Colfax

039 Cuming

041 Custer

043 Dakota

047 Dawson

051 Dixon

053 Dodge

055 Douglas

059 Fillmore

061 Franklin

063 Frontier

065 Furnas

067 Gage

073 Gosper

077 Greeley

65
079 Hall

081 Hamilton

083 Harlan

087 Hitchcock

093 Howard

095 Jefferson

097 Johnson

099 Kearney

109 Lancaster

119 Madison

121 Merrick

125 Nance

127 Nemaha

129 Nuckolls

131 Otoe

133 Pawnee

137 Phelps

139 Pierce

141 Platte

143 Polk

145 Red Willow

147 Richardson

151 Saline

153 Sarpy

155 Saunders

159 Seward

163 Sherman

167 Stanton

169 Thayer

173 Thurston

175 Valley

177 Washington

179 Wayne

181 Webster

185 York

02 Western

003 Antelope

005 Arthur

007 Banner

009 Blaine

013 Box Butte

015 Boyd

017 Brown

029 Chase

031 Cherry

033 Cheyenne

045 Dawes

049 Deuel

057 Dundy

069 Garden

071 Garfield

075 Grant

085 Hayes

089 Holt

091 Hooker

101 Keith

103 Keya Paha

105 Kimball

107 Knox

111 Lincoln

113 Logan

115 Loup

117 McPherson

123 Morrill

135 Perkins

149 Rock

157 Scotts Bluff

161 Sheridan

165 Sioux

171 Thomas

183 Wheeler

32 Nevada

01 Nevada

001 Churchill

003 Clark

005 Douglas

007 Elko

009 Esmeralda

011 Eureka

013 Humboldt

015 Lander

017 Lincoln

019 Lyon

021 Mineral

023 Nye

027 Pershing

029 Storey

031 Washoe

033 White Pine

510 Carson City

33 New Hampshire

02 Northern

003 Carroll

007 Coos

009 Grafton

03 Southern

001 Belknap

005 Cheshire

011 Hillsborough

013 Merrimack

015 Rockingham

017 Strafford

019 Sullivan

34 New Jersey

01 State

001 Atlantic

003 Bergen

005 Burlington

007 Camden

009 Cape May

011 Cumberland

013 Essex

015 Gloucester

017 Hudson

019 Hunterdon

021 Mercer

023 Middlesex

025 Monmouth

027 Morris

029 Ocean

031 Passaic

033 Salem

035 Somerset

037 Sussex

039 Union

041 Warren

35 New Mexico

01 Northwestern

001 Bernalillo

006 Cibola

028 Los Alamos

031 McKinley

039 Rio Arriba

043 Sandoval

045 San Juan

049 Santa Fe

055 Taos

061 Valencia

66
02 Northeastern

007 Colfax

019 Guadalupe

021 Harding

033 Mora

037 Quay

047 San Miguel

057 Torrance

059 Union

03 Southwestern

003 Catron

013 Dona Ana

017 Grant

023 Hidalgo

029 Luna

051 Sierra

053 Socorro

04 Southeastern

005 Chaves

009 Curry

011 DeBaca

015 Eddy

025 Lea

027 Lincoln

035 Otero

041 Roosevelt

36 New York

01 Adirondack

019 Clinton

033 Franklin

045 Jefferson

089 St. Lawrence

02 Lake Plain

011 Cayuga

029 Erie

037 Genesee

051 Livingston

053 Madison

055 Monroe

063 Niagara

067 Onondaga

069 Ontario

073 Orleans

075 Oswego

099 Seneca

117 Wayne

121 Wyoming

123 Yates

03 Western Adirondack

035 Fulton

043 Herkimer

049 Lewis

065 Oneida

04 Eastern Adirondack

031 Essex

041 Hamilton

113 Warren

05 Southwest Highlands

003 Allegany

009 Cattaraugus

013 Chautauqua

101 Steuben

06 South-Central Highlands

007 Broome

015 Chemung

017 Chenango

023 Cortland

025 Delaware

077 Otsego

097 Schuyler

107 Tioga

109 Tompkins

07 Capitol District

001 Albany

021 Columbia

057 Montgomery

083 Rensselaer

091 Saratoga

093 Schenectady

115 Washington

08 Catskill-Lower Hudson

005 Bronx

027 Dutchess

039 Greene

047 Kings

059 Nassau

061 New York

071 Orange

079 Putnam

081 Queens

085 Richmond

087 Rockland

095 Schoharie

103 Suffolk

105 Sullivan

111 Ulster

119 Westchester

37 North Carolina

01 Southern Coastal Plain

017 Bladen

019 Brunswick

047 Columbus

051 Cumberland

061 Duplin

079 Greene

085 Harnett

093 Hoke

101 Johnston

103 Jones

105 Lee

107 Lenoir

125 Moore

129 New Hanover

133 Onslow

141 Pender

153 Richmond

155 Robeson

163 Sampson

165 Scotland

191 Wayne

02 Northern Coastal Plain

013 Beaufort

015 Bertie

029 Camden

031 Carteret

041 Chowan

049 Craven

053 Currituck

055 Dare

065 Edgecombe

073 Gates

083 Halifax

091 Hertford

095 Hyde

117 Martin

127 Nash

67
131 Northampton 137 Pamlico

139 Pasquotank

143 Perquimans 147 Pitt

177 Tyrrell

187 Washington

195 Wilson 03 Piedmont

001 Alamance

003 Alexander

007 Anson

025 Cabarrus

033 Caswell

035 Catawba

037 Chatham

045 Cleveland

057 Davidson

059 Davie

063 Durham

067 Forsyth

069 Franklin

071 Gaston

077 Granville

081 Guilford

097 Iredell

109 Lincoln

119 Mecklenburg

123 Montgomery

135 Orange

145 Person

149 Polk

151 Randolph

157 Rockingham

159 Rowan

161 Rutherford

167 Stanly

169 Stokes

171 Surry

179 Union

181 Vance

183

Wake

185

Warren

197 Yadkin

04 Mountains

005 Alleghany

009 Ashe

011

Avery

021 Buncombe 023 Burke

027 Caldwell

039 Cherokee 043 Clay

075 Graham

087 Haywood

089 Henderson

099 Jackson 111 McDowell

113 Macon

115 Madison

121 Mitchell

173 Swain

175 Transylvania

189 Watauga

193 Wilkes

199 Yancey

38 North Dakota

01 Eastern

001 Adams

003 Barnes

005 Benson

007 Billings

009 Bottineau

011 Bowman

013 Burke

015 Burleigh

017 Cass

019 Cavalier

021 Dickey

023 Divide

025 Dunn

027 Eddy

029 Emmons

031 Foster

033 Golden Valley

035 Grand Forks

037 Grant

039 Griggs

041 Hettinger

043 Kidder

045 LaMoure

047 Logan

049 McHenry

051 McIntosh

053 McKenzie

055 McLean

057 Mercer 059 Morton

061 Mountrail

063 Nelson 065 Oliver

067 Pembina

069 Pierce

071 Ramsey

073 Ransom 075 Renville

077 Richland

079 Rolette

081 Sargent

083 Sheridan

085 Sioux

087 Slope

089 Stark

091 Steele

093 Stutsman

095Towner

097 Traill

099 Walsh

101Ward

103 Wells

105 Williams

39 Ohio

01 South-Central

001 Adams

015 Brown

025 Clermont

053 Gallia

071 Highland

079 Jackson

087 Lawrence

131 Pike

141 Ross

145 Scioto

02 Southeastern

009 Athens

073 Hocking

105 Meigs

115 Morgan

127 Perry

163 Vinton

167 Washington

68
03 East-Central 013 Belmont

019 Carroll

031 Coshocton 059 Guernsey

067 Harrison

075 Holmes

081 Jefferson

111 Monroe 119 Muskingum

121 Noble

157 Tuscarawas

04 Northeastern

005 Ashland

007 Ashtabula

029 Columbiana

035 Cuyahoga

043 Erie

055 Geauga

077 Huron

085 Lake

093 Lorain

099 Mahoning

103 Medina

133 Portage

139 Richland

151 Stark

153 Summit

155 Trumbull

169 Wayne

05 Southwestern

017 Butler

023 Clark

027 Clinton

037 Darke

045 Fairfield

047 Fayette

049 Franklin

057 Greene

061 Hamilton

089 Licking

097 Madison

109 Miami

113 Montgomery

129 Pickaway

135 Preble

165 Warren

06 Northwestern 003 Allen

011 Auglaize

021 Champaign 033 Crawford

039 Defiance

041 Delaware

051 Fulton

063 Hancock 065 Hardin

069 Henry

083 Knox

091 Logan

095 Lucas

101 Marion

107 Mercer

117 Morrow

123 Ottawa

125 Paulding

137 Putnam

143 Sandusky

147 Seneca

149 Shelby

159 Union

161 Van Wert

171 Williams

173Wood

175 Wyandot

40 Oklahoma

01 Southeast

005 Atoka

013 Bryan

023 Choctaw

029 Coal

061 Haskell

077 Latimer

079 Le Flore

089 McCurtain

121 Pittsburg

127 Pushmataha

02 Northeast

001 Adair

021 Cherokee

041 Delaware

091 McIntosh

097 Mayes

101 Muskogee

115 Ottawa 135 Sequoyah

Unsampled counties

003 Alfalfa

007 Beaver

009 Beckham

011 Blaine

015 Caddo 017 Canadian

019 Carter

025 Cimarron

027 Cleveland

031 Comanche

033 Cotton

035 Craig

037 Creek

039 Custer

043 Dewey

045 Ellis

047 Garfield

049 Garvin

051 Grady

053 Grant

055 Greer

057 Harmon

059 Harper

063 Hughes

065 Jackson

067 Jefferson

069 Johnston

071 Kay

073 Kingfisher

075 Kiowa

081 Lincoln

083 Logan

085 Love

087 McClain

093 Major

095 Marshall

099 Murray

103 Noble

105 Nowata

107 Okfuskee

109 Oklahoma

111 Okmulgee

113 Osage

117 Pawnee

119 Payne

123 Pontotoc

69
125 Pottawatomie

129 Roger Mills

131 Rogers

133 Seminole

137 Stephens

139 Texas

141 Tillman

143 Tulsa

145 Wagoner

147 Washington

149 Washita

151 Woods

153 Woodward

41 Oregon

00 Northwest

005 Clackamas

007 Clatsop

009 Columbia

027 Hood River

047 Marion

051 Multnomah

053 Polk

057 Tillamook

067 Washington

071 Yamhill

01 West Central

003 Benton

039 Lane

041 Lincoln

043 Linn

02 Southwest

011 Coos

015 Curry

019 Douglas

029 Jackson

033 Josephine

03 Central

013 Crook

017 Deschutes

021 Gilliam

031 Jefferson

035 Klamath

037 Lake

055 Sherman

065 Wasco

069 Wheeler

04 Blue Mountains

001 Baker

023 Grant

025 Harney

045 Malheur

049 Morrow

059 Umatilla

061 Union

063 Wallowa

42 Pennsylvania

00 South Central

043 Dauphin

055 Franklin

057 Fulton

061 Huntingdon

067 Juniata

087 Mifflin

099 Perry

109 Snyder

119 Union

05 Western

003 Allegheny

005 Armstrong

007 Beaver

019 Butler

039 Crawford

049 Erie

059 Greene

063 Indiana

073 Lawrence

085 Mercer

125 Washington

129 Westmoreland

06 North Central/Allegheny

023 Cameron

027 Centre

031 Clarion

033 Clearfield

035 Clinton

047 Elk

053 Forest

065 Jefferson

081 Lycoming

083 Mc Kean

105 Potter

113 Sullivan

117 Tioga

121 Venango

123 Warren

07 Southwestern

009 Bedford

013 Blair

021 Cambria

051 Fayette

111 Somerset

08 Northeastern/Pocono

015 Bradford

025 Carbon

037 Columbia

069 Lackawanna

079 Luzerne

089 Monroe

093 Montour

097 Northumberland

103 Pike

107 Schuylkill

115 Susquehanna

127 Wayne

131 Wyoming

09 Southeastern

001 Adams

011 Berks

017 Bucks

029 Chester

041 Cumberland

045 Delaware

071 Lancaster

075 Lebanon

077 Lehigh

091 Montgomery

095 Northampton

101 Philadelphia

133 York

44 Rhode Island

01 State

001 Bristol

003 Kent

005 Newport

007 Providence

009 Washington

70
45 South Carolina 01 Southern Coastal Plain

003 Aiken

005 Allendale 009 Bamberg

011 Barnwell

013 Beaufort

017 Calhoun

029 Colleton 035 Dorchester

049 Hampton

053 Jasper

063 Lexington

075 Orangeburg

02 Northern Coastal Plain

015 Berkeley

019 Charleston

025 Chesterfield

027 Clarendon

031 Darlington

033 Dillon

041 Florence

043 Georgetown

051 Horry

055 Kershaw

061 Lee

067 Marion

069 Marlboro

079 Richland

085 Sumter

089 Williamsburg

03 Piedmont

001 Abbeville

007 Anderson

021 Cherokee

023 Chester

037 Edgefield

039 Fairfield

045 Greenville

047 Greenwood

057 Lancaster

059 Laurens

065 McCormick

071 Newberry

073 Oconee

077 Pickens

081 Saluda

083 Spartanburg

087 Union 091 York 46 South Dakota

01 Eastern

003 Aurora

005 Beadle

007 Bennett 009 Bon Homme

011 Brookings

013 Brown

015 Brule

017 Buffalo

021 Campbell

023 Charles Mix

025 Clark

027 Clay

029 Codington

031 Corson

035 Davison

037 Day

039 Deuel

041 Dewey

043 Douglas

045 Edmunds

049 Faulk

051 Grant

053 Gregory

055 Haakon

057 Hamlin

059 Hand

061 Hanson

065 Hughes

067 Hutchinson

069 Hyde

071 Jackson

073 Jerauld

075 Jones

077 Kingsbury

079 Lake

083 Lincoln

085

Lyman

087 McCook

089 McPherson

091 Marshall

095 Mellette

097 Miner

099 Minnehaha

101 Moody

105 Perkins 107 Potter

109 Roberts

111 Sanborn 115 Spink

117 Stanley

119 Sully

121 Todd

123 Tripp 125 Turner

127 Union

129 Walworth

135 Yankton

137 Ziebach

02 Western

019 Butte

033 Custer

047 Fall River

063 Harding

081 Lawrence

093 Meade

103 Pennington

113 Shannon

47Tennessee

01 West

017 Carroll

023 Chester

033 Crockett

045 Dyer

047 Fayette

053 Gibson

069 Hardeman

075 Haywood

077 Henderson

079 Henry

095 Lake

097 Lauderdale

109 McNairy

113 Madison

131 Obion

157 Shelby

167 Tipton

183 Weakley

02 West Central

005 Benton

039 Decatur

71
071 Hardin

081 Hickman

083 Houston

085 Humphreys

099 Lawrence

101 Lewis

135 Perry

161 Stewart

181 Wayne

03 Central

003 Bedford

015 Cannon

021 Cheatham

027 Clay

031 Coffee

037 Davidson

041 DeKalb

043 Dickson

055 Giles

087 Jackson

103 Lincoln

111 Macon

117 Marshall

119 Maury

125 Montgomery

127 Moore

147 Robertson

149 Rutherford

159 Smith

165 Sumner

169 Trousdale

187 Williamson

189 Wilson

04 Plateau

007 Bledsoe

013 Campbell

035 Cumberland

049 Fentress

051 Franklin

061 Grundy

115 Marion

129 Morgan

133 Overton

137 Pickett

141 Putnam

151 Scott

153 Sequatchie

175 Van Buren

177 Warren

185 White

05 East

001 Anderson

009 Blount

011 Bradley

019 Carter

025 Claiborne

029 Cocke

057 Grainger

059 Greene

063 Hamblen

065 Hamilton

067 Hancock

073 Hawkins

089 Jefferson

091 Johnson

093 Knox

105 Loudon

107 McMinn

121 Meigs

123 Monroe

139 Polk

143 Rhea

145 Roane

155 Sevier

163 Sullivan

171 Unicoi

173 Union

179 Washington

48 Texas

01 Southeast

005 Angelina

071 Chambers

185 Grimes

199 Hardin

201 Harris

225 Houston

241 Jasper

245 Jefferson

289 Leon

291 Liberty

313 Madison

339 Montgomery

351 Newton

361 Orange

373 Polk

403 Sabine

405 San Augustine

407 San Jacinto

455 Trinity

457 Tyler

471 Walker

473 Waller

02 Northeast

001 Anderson

037 Bowie

063 Camp

067 Cass

073 Cherokee

159 Franklin

183 Gregg

203 Harrison

213 Henderson

315 Marion

343 Morris

347 Nacogdoches

365 Panola

387 Red River

401 Rusk

419 Shelby

423 Smith

449 Titus

459 Upshur

467 Van Zandt

499 Wood

Unsampled counties

003 Andrews

007 Aransas

009 Archer

011 Armstrong

013 Atascosa

015 Austin

017 Bailey

019 Bandera

021 Bastrop

023 Baylor

025 Bee

027 Bell

029 Bexar

031 Blanco

033 Borden

035 Bosque

039 Brazoria

72
041 Brazos 043 Brewster

045 Briscoe

047 Brooks 049 Brown

051 Burleson

053 Burnet

055 Caldwell

057 Calhoun 059 Callahan

061 Cameron

065 Carson

069 Castro

075 Childress

077 Clay

079 Cochran

081 Coke

083 Coleman

085 Collin

087 Collingsworth

089 Colorado

091 Comal

093 Comanche

095 Concho

097 Cooke

099 Coryell

101 Cottle

103 Crane

105 Crockett

107 Crosby

109 Culberson

111 Dallam

113 Dallas

115 Dawson

117 Deaf Smith

119 Delta

121 Denton

123 DeWitt

125 Dickens

127 Dimmit

129 Donley

131 Duval

133 Eastland

135 Ector

137 Edwards

139 Ellis

141 El Paso

143 Erath

145 Falls

147 Fannin

149 Fayette 151 Fisher

153 Floyd

155 Foard 157 Fort Bend

161 Freestone

163 Frio

165 Gaines

167 Galveston 169 Garza

171 Gillespie

173 Glasscock

175 Goliad

177 Gonzales

179 Gray

181 Grayson

187 Guadalupe

189 Hale

191 Hall

193 Hamilton

195 Hansford

197 Hardeman

205 Hartley

207 Haskell

209 Hays

211 Hemphill

215 Hidalgo

217 Hill

219 Hockley

221 Hood

223 Hopkins

227 Howard

229 Hudspeth

231 Hunt

233 Hutchinson

235 Irion

237 Jack

239 Jackson

243 Jeff Davis

247 Jim Hogg

249 Jim Wells

251 Johnson

253 Jones

255 Karnes

257 Kaufman

259 Kendall

261 Kenedy

263 Kent

265 Kerr

267 Kimble

269 King 271 Kinney

273 Kleberg

275 Knox 277 Lamar

279 Lamb

281 Lampasas

283 La Salle

285 Lavaca 287 Lee

293 Limestone

295 Lipscomb

297 Live Oak

299 Llano

301 Loving

303 Lubbock

305 Lynn

307 McCulloch

309 McLennan

311 McMullen

317 Martin

319 Mason

321 Matagorda

323 Maverick

325 Medina

327 Menard

329 Midland

331 Milam

333 Mills

335 Mitchell

337 Montague

341 Moore

345 Motley

349 Navarro

353 Nolan

355 Nueces

357 Ochiltree

359 Oldham

363 Palo Pinto

367 Parker

369 Parmer

371 Pecos

375 Potter

377 Presidio

379 Rains

381 Randall

383 Reagan

385 Real

389 Reeves

391 Refugio

73
393 Roberts

395 Robertson

397 Rockwall

399 Runnels

409 San Patricio

411 San Saba

413 Schleicher

415 Scurry

417 Shackelford

421 Sherman

425 Somervell

427 Starr

429 Stephens

431 Sterling

433 Stonewall

435 Sutton

437 Swisher

439 Tarrant

441 Taylor

443 Terrell

445 Terry

447 Throckmorton

451 Tom Green

453 Travis

461 Upton

463 Uvalde

465 Val Verde

469 Victoria

475 Ward

477 Washington

479 Webb

481 Wharton

483 Wheeler

485 Wichita

487 Wilbarger

489 Willacy

491 Williamson

493 Wilson

495 Winkler

497 Wise

501 Yoakum

503 Young

505 Zapata

507 Zavala

49 Utah

01 Northern

003 Box Elder

005 Cache

011 Davis

029 Morgan

033 Rich

035 Salt Lake

043 Summit

045 Tooele

049 Utah

051 Wasatch

057 Weber

02 Uinta

009 Daggett

013 Duchesne

047 Uintah

03 Central

023 Juab

027 Millard

031 Piute

039 Sanpete

041 Sevier

055 Wayne

04 Eastern

007 Carbon

015 Emery

019 Grand

037 San Juan

05 Southwestern

001 Beaver

017 Garfield

021 Iron

025 Kane

053 Washington

50 Vermont

02 Northern

005 Caledonia

009 Essex

011 Franklin

013 Grand Isle

015 Lamoille

017 Orange

019 Orleans

023 Washington

003 Southern

001 Addison

003 Bennington

007 Chittenden

021 Rutland

025 Windham

027 Windsor

51 Virginia

01 Coastal Plain

001 Accomack

025 Brunswick

033 Caroline

036 Charles City

041 Chesterfield

053 Dinwiddie

057 Essex

073 Gloucester

081 Greensville

085 Hanover

087 Henrico

093 Isle of Wight

095 James City

097 King and Queen

099 King George

101 King William

103 Lancaster

115 Mathews

119 Middlesex

127 New Kent

131 Northampton

133 Northumberland

149 Prince George

159 Richmond

175 Southampton

181 Surry

183 Sussex

193 Westmoreland

199 York

550 Chesapeake city

650 Hampton city

700 Newport News city

800 Suffolk city

810 Virginia Beach city

02 Southern Piedmont

007 Amelia

011 Appomattox

019 Bedford

029 Buckingham

031 Campbell

037 Charlotte

74
049 Cumberland

067 Franklin

083 Halifax

089 Henry

111 Lunenburg

117 Mecklenburg

135 Nottoway

141 Patrick

143 Pittsylvania

145 Powhatan

147 Prince Edward

03 Northern Piedmont

003 Albemarle

009 Amherst

013 Arlington

047 Culpeper

059 Fairfax

061 Fauquier

065 Fluvanna

075 Goochland

079 Greene

107 Loudoun

109 Louisa

113 Madison

125 Nelson

137 Orange

153 Prince William

157 Rappahannock

177 Spotsylvania

179 Stafford

04 Northern Mountains

005 Alleghany

015 Augusta

017 Bath

023 Botetourt

043 Clarke

045 Craig

069 Frederick

091 Highland

139 Page

161 Roanoke

163 Rockbridge

165 Rockingham

171 Shenandoah

187 Warren

05 Southern Mountains

021 Bland

027 Buchanan

035 Carroll

051 Dickenson

063 Floyd

071 Giles

077 Grayson

105 Lee

121 Montgomery

155 Pulaski

167 Russell

169 Scott

173 Smyth

185 Tazewell

191 Washington

195 Wise

197 Wythe

Unsampled cities

510 Alexandria city

515 Bedford city

520 Bristol city

530 Buena Vista city

540 Charlottesville city

560 Clifton Forge city

570 Colonial Heights city

580 Covington city

590 Danville city

595 Emporia city

600 Fairfax city

610 Falls Church city

620 Franklin city

630 Fredericksburg city

640 Galax city

660 Harrisonburg city

670 Hopewell city

678 Lexington city

680 Lynchburg city

683 Manassas city

685 Manassas Park city

690 Martinsville city

710 Norfolk city

720 Norton city

730 Petersburg city

735 Poquoson city

740 Portsmouth city

750 Radford city

760 Richmond city

770 Roanoke city

775 Salem city

780 South Boston city

790 Staunton city

820 Waynesboro city

830 Williamsburg city

840 Winchester city

53 Washington

05 Puget Sound

029 Island

033 King

035 Kitsap

053 Pierce

055 San Juan

057 Skagit

061 Snohomish

073 Whatcom

06 Olympic Peninsula

009 Clallam

027 Grays Harbor

031 Jefferson

045 Mason

067 Thurston

07 Southwest

011 Clark

015 Cowlitz

041 Lewis

049 Pacific

059 Skamania

069 Wahkiakum

08 Central

001 Adams

003 Asotin

005 Benton

013 Columbia

019 Ferry

021 Franklin

023 Garfield

025 Grant

043 Lincoln

051 Pend Oreille

063 Spokane

065 Stevens

071 Walla Walla

075 Whitman

09 Inland Empire

007 Chelan

75
017 Douglas

037 Kittitas

039 Klickitat

047 Okanogan

077 Yakima

54 West Virginia

02 Northeastern

001 Barbour

003 Berkeley

007 Braxton

023 Grant

027 Hampshire

031 Hardy

033 Harrison

037 Jefferson

041 Lewis

057 Mineral

065 Morgan

071 Pendleton

075 Pocahontas

077 Preston

083 Randolph

091 Taylor

093 Tucker

097 Upshur

101 Webster

03 Southern

005 Boone

015 Clay

019 Fayette

025 Greenbrier

039 Kanawha

045 Logan

047 McDowell

055 Mercer

059 Mingo

063 Monroe

067 Nicholas

081 Raleigh

089 Summers

109 Wyoming

04 Northwestern

009 Brooke

011 Cabell

013 Calhoun

017 Doddridge

021 Gilmer

029 Hancock

035 Jackson

043 Lincoln

049 Marion

051 Marshall

053 Mason

061 Monongalia

069 Ohio

073 Pleasants

079 Putnam

085 Ritchie

087 Roane

095 Tyler

099 Wayne

103 Wetzel

105 Wirt

107 Wood

55 Wisconsin

01 Northeastern

037 Florence

041 Forest

067 Langlade

069 Lincoln

075 Marinette

078 Menominee

083 Oconto

085 Oneida

115 Shawano

125 Vilas

02 Northwestern

003 Ashland

005 Barron

007 Bayfield

013 Burnett

031 Douglas

051 Iron

095 Polk

099 Price

107 Rusk

113 Sawyer

119 Taylor

129 Washburn

03 Central

001 Adams

017 Chippewa

019 Clark

035 Eau Claire

053 Jackson

057 Juneau

073 Marathon

077 Marquette

081 Monroe

097 Portage

135 Waupaca

137 Waushara

141 Wood

04 Southwestern

011 Buffalo

023 Crawford

033 Dunn

043 Grant

049 Iowa

063 La Crosse

065 Lafayette

091 Pepin

093 Pierce

103 Richland

109 St. Croix

111 Sauk

121 Trempealeau

123 Vernon

05 Southeastern

009 Brown

015 Calumet

021 Columbia

025 Dane

027 Dodge

029 Door

039 Fond du Lac

045 Green

047 Green Lake

055 Jefferson

059 Kenosha

061 Kewaunee

071 Manitowoc

079 Milwaukee

087 Outagamie

089 Ozaukee

101 Racine

105 Rock

76
117 Sheboygan

127 Walworth

131 Washington

133 Waukesha

139 Winnebago

56 Wyoming

01 Western

013 Fremont

017 Hot Springs

023 Lincoln

029 Park

035 Sublette

037 Sweetwater

039 Teton

041 Uinta

02 Central and Southeastern

001 Albany

003 Big Horn

007 Carbon

009 Converse

015 Goshen

019 Johnson

021 Laramie

025 Natrona

027 Niobrara

031 Platte

033 Sheridan

043 Washakie

03 Northeastern

005 Campbell

011 Crook

045 Weston

72 Puerto Rico

01 Puerto Rico

001 Adjuntas

003 Aguada

005 Aguadilla

007 Aguas Buenas

009 Aibonito

011 Anasco

013 Arecibo

015 Arroyo

017 Barceloneta

019 Barranquitas

021 Bayamon

023 Cabo Rojo

025 Caguas

027 Camuy

029 Canovanas

031 Carolina

033 Catano

035 Cayey

037 Ceiba

039 Ciales

041 Cidra

043 Coamo

045 Comerio

047 Corozal

049 Culebra

051 Dorado

053 Fajardo

054 Florida

055 Guanica

057 Guayama

059 Guayanilla

061 Guaynabo

063 Gurabo

065 Hatillo

067 Hormigueros

069 Humacao

071 Isabela

073 Jayuya

075 Juana Diaz

077 Juncos

079 Lajas

081 Lares

083 Las Marias

085 Las Piedras

087 Loiza

089 Luquillo

091 Manati

093 Maricao

095 Maunabo

097 Mayaguez

099 Moca

101 Morovis

103 Naguabo

105 Naranjito

107 Orocovis

109 Patillas

111 Penuelas

113 Ponce

115 Quebradillas

117 Rincon

119 Rio Grande

121 Sabana Grande

123 Salinas

125 San German

127 San Juan

129 San Lorenzo

131 San Sebastian

133 Santa Isabel

135 Toa Alta

137 Toa Baja

139 Trujillo Alto

141 Utuado

143 Vega Alta

145 Vega Baja

147 Vieques

149 Villalba

151 Yabucoa

153 Yauco

77
78
Crosswalk from FIADB to 

Species group name SPDBH2002 table Variable suffix

Longleaf and slash pines spgrpcd=1 lgsl 

Loblolly and shortleaf pines spgrpcd=2 lbsh 

Other yellow pines spgrpcd=3 otyp 

Eastern white and red pines spgrpcd=4 ewrd 

Jack pine spgrpcd=5 jack 

Spruce and balsam fir spgrpcd=6 spfr 

Eastern hemlock spgrpcd=7 ehem 

Cypress spgrpcd=8 cypr 

Other eastern softwoods spgrpcd=9 otesw 

Douglas-fir spgrpcd=10 doug 

Ponderosa and Jeffrey pines spgrpcd=11 pdjeff 

True fir spgrpcd=12 fir 

Western hemlock spgrpcd=13 whem 

Sugar pine spgrpcd=14 sugar 

Western white pine spgrpcd=15 wwpn 

Redwood spgrpcd=16 rdwd 

Sitka spruce spgrpcd=17 sitka 

Engelmann and other spruces spgrpcd=18 engel 

Western larch spgrpcd=19 wlarch 

Incense-cedar spgrpcd=20 incen 

Lodgepole pine spgrpcd=21 lodge 

Western redcedar spgrpcd=22 wrced 

Western woodland softwoods spgrpcd=23 N/A

Other western softwoods spgrpcd=24 otwsw 

Select white oaks spgrpcd=25 swok 

Select red oaks spgrpcd=26 srok 

Other white oaks spgrpcd=27 owok 

Other red oaks spgrpcd=28 orok 

Hickory spgrpcd=29 hick 

Yellow birch spgrpcd=30 ybir 

Hard maple spgrpcd=31 hmap 

Soft maple spgrpcd=32 smap 

Beech spgrpcd=33 bech 

Sweetgum spgrpcd=34 sgum 

Tupelo and blackgum spgrpcd=35 bgum 

Ash spgrpcd=36 ash 

79

Appendix C – 

Species group names and crosswalk to FIADB.

(table continued on next page)
Crosswalk from FIADB to 

Species group name SPDBH2002 table Variable suffix

Cottonwood and aspen spgrpcd=37 ecoas 

Basswood spgrpcd=38 bass 

Yellow-poplar spgrpcd=39 ypop 

Black walnut spgrpcd=40 bwal 

Black cherry spgrpcd=41 and spcd=762 bchr

Other eastern hardwoods spgrpcd=41,42,43 but not spcd=762 otehw 

Cottonwood and aspen spgrpcd=44 wcoas 

Red alder spgrpcd=45 alder 

Oak spgrpcd=46 wok 

Other western hardwoods spgrpcd=47,48 otwhw 

80
MISSION STATEMENT

We believe the good life has its roots in clean air, sparkling water, rich soil, healthy economies and 

a diverse living landscape. Maintaining the good life for generations to come begins with everyday

choices about natural resources. The North Central Research Station provides the knowledge and

the tools to help people make informed choices. That’s how the science we do enhances the 

quality of people’s lives.

For further information contact:

Or visit our web site:

www.ncrs.fs.fed.us

North Central 

Research Station

USDA Forest Service

1992 Folwell Ave.

St. Paul, MN 55108