United States

Department of 

Agriculture

Forest Service

Northern 

Research Station

General Technical

Report NRS-13

Revised

U.S. Forest Carbon 

Calculation Tool:

Forest-Land Carbon Stocks 

and Net Annual Stock Change

James E. Smith

Linda S. Heath

Michael C. Nichols
Visit our homepage at: http://www.nrs.fs.fed.us/

Published by: For additional copies:

U.S. FOREST SERVICE U.S. Forest Service 

11 CAMPUS BLVD SUITE 200 Publications Distribution

NEWTOWN SQUARE PA 19073-3294 359 Main Road

August 2007 Delaware, OH 43015-8640

Revised March 2010 Fax: (740)368-0152

Revised Manuscript received for publication 8 January 2010

Abstract

The Carbon Calculation Tool 4.0, CCTv40.exe, is a computer application that reads 

publicly available forest inventory data collected by the U.S. Forest Service’s Forest 

Inventory and Analysis Program (FIA) and generates state-level annualized estimates 

of carbon stocks on forest land based on FORCARB2 estimators. Estimates can be 

recalculated as new inventory data become available. The input set of FIA data fi les 

available on the Internet (as well as some older inventory fi les used to fi ll in gaps) are 

summarized by the application, converted to carbon stocks, and saved as part of a state 

or substate level “survey summary” fi le. This is used to produce state-level and national 

tables with annualized carbon stocks and fl ux (or net stock change) beginning with the 

year 1990. This user’s guide includes instructions for use, example data sets, and a 

discussion of methods and assumptions.

The Authors

JAMES E. SMITH (Jsmith11@fs.fed.us), LINDA S. HEATH (Lheath@fs.fed.us), 

and MICHAEL C. NICHOLS (Mnichols@fs.fed.us) are, respectively, research plant 

physiologist, research forester, and programmer analyst with the U.S. Forest Service’s 

Northern Research Station at Durham, New Hampshire.

The computer program described in this publication was created using funding from 

the U.S. Environmental Protection Agency (EPA) and the USDA Forest Service for the 

purpose of estimating annual carbon stock change estimates for U.S. forests beginning in 

1990 through current year for the EPA’s National Greenhouse Gas Inventory. This program 

is made available with the understanding that the U.S. Department of Agriculture cannot 

assure its accuracy, completeness, reliability, or suitability for any other purpose than that 

reported. The recipient may not assert any proprietary rights thereto nor represent it to 

anyone as other than a Government-produced computer program.

The use of trade, fi rm, or computer names in this user’s guide is for the information and 

convenience of the reader. Such use does not constitute an offi cial endorsement by the 

USDA, Forest Service, or EPA of any product or service to the exclusion of others that 

may be suitable.
1

QUICK START GUIDE

Th e Carbon Calculation Tool 4.0, CCTv40.exe, is a computer application that reads 

publicly available forest inventory data collected by the U.S. Forest Service’s Forest 

Inventory and Analysis Program (FIA) and generates state-level annualized estimates 

of carbon stocks on forest land based on FORCARB2 estimators. Estimates can be 

recalculated as new inventory data become available. Th e input set of FIA data fi les 

available on the Internet (as well as some older inventory fi les used to fi ll in gaps) are 

summarized by the application, converted to carbon stocks, and saved as part of a state 

or substate level “survey summary” fi le. Th is is used to produce state-level and national 

tables with annualized carbon stocks and fl ux (or net stock change) beginning with the 

year 1990.

Th e entire application is oriented around the survey summary fi le. Operations are 

designed for: 1) updating the survey summary fi le based on new FIA inventory data; or 

2) generating annualized estimates from the summaries. On startup of CCTv40.exe, a 

window with three buttons appears. Th e choices are to: obtain data from FIA, update the 

survey summary, or produce annualized estimates (Fig. 1). Note that CCT modifi es the 

original survey summary fi le or a more recent version of the same fi le format.

Obtain Data from FIA

Th is button opens a web browser to the FIA site for downloading the FIADB 4.0 

inventory data fi les, which are organized and identifi ed by state. Th e four fi les needed for 

each state are POP_EVAL_GRP, PLOTSNAP, COND, and TREE, which are available 

in .zip (or compressed) or .csv (comma separated) formats. All downloaded and unzipped 

inventory data must be saved together in a single location, for example, all sets are placed 

together in the “DataFiles” folder.

Update Survey Summary File

Th is button opens a window for modifying the current survey summary fi le (initially, 

survey_summary_04Dec09.csv in the “SurveySummaryFiles” folder). Specify the current 

survey summary fi le and the directory containing the inventory data fi les, and then click 

the “Start” button. Th e current survey summary list appears in the lower right box, and 

a list of potential survey updates appears in the lower left box. List items are selected 

or unselected by clicking and highlighting. Surveys are moved from left to right by the 

two update buttons if an update is needed (as evaluated by CCT) or forced (overwrite). 

Execution of the update buttons may take some time. “Save + Clear” opens a save 

dialogue box for saving the modifi ed survey summary fi le.

Produce Annualized Estimates

Th is button opens a window to produce tabular annualized stock and fl ux estimates. 

Specify the survey summary fi le, the output directory, the summary sets of carbon pools, 

and the method for developing the annualized values. Th en select the desired tables 

and click “Generate the Selected Tables” to produce them. File names are based on the 

survey summary.
2

Changes in the Carbon Calculator Tool

Th e Carbon Calculation Tool has been revised to make it compatible with the current 

format of FIA inventory data—FIADB 4.0 (USDA For. Serv. 2009c, b). Th e resulting 

changes principally aff ect three general aspects of the calculator: 1) downloading 

inventory data; 2) processing and summarizing surveys; and 3) determination of 

annualized stock and change values. Th e Internet download page has a diff erent 

appearance, and the organization of data tables has changed. Surveys from all years are 

combined into single tables. Four data tables are required for each state, these are: POP_

EVAL_GRP, PLOTSNAP, COND, and TREE.

Within each state, inventory data are associated with evaluation groups and expanded 

to population totals according to methods described in the FIADB 4.0 User’s Guide 

(USDA For. Serv. 2009a). Each evaluation group is associated with a nominal reporting 

year. Th e expansion methods represent a slight change in data processing. Th e older 

non-FIADB data were also reprocessed to be consistent with these methods. As in 

the previous version of CCT, all state-level population summaries match separately 

published FIA data summaries.

An important change associated with the “Produce Annualized Estimates” window 

aff ects the development of the annualized stock and stock-change values for 1990 to 

present. Th e interpretation and use of the FIA annual inventory data has the potential to 

aff ect trends in annualized estimates for some states. CCT 4.0 now off ers our preferred 

default method as well as alternate methods for compiling these annual data. Details are 

provided in the text.
3

INTRODUCTION

Th e Carbon Calculation Tool 4.0, CCT, is an interactive executable program that reads 

publicly available forest inventory data collected by the U.S. Forest Service’s Forest 

Inventory and Analysis Program (FIA) and generates state-level annualized estimates of 

forest carbon stocks based on FORCARB2 estimators (Heath and others 2003, Smith 

and others 2004a). Specifi cally, these inventory data are in the Forest Inventory and 

Analysis Database (FIADB) available on the Internet (USDA For. Serv. 2009b). Values 

for average net annual carbon stock change on forest lands1 are based on annualized 

diff erences between successive stocks. Th ese calculated stock changes also are referred to 

as fl ux hereafter, which is consistent with the use of the term in reporting forest carbon 

(for example, U.S. EPA 2009). Th ese inventory-based methods for developing estimates 

of forest carbon for the United States are consistent with standard methodologies such 

as those in “Good Practice Guidance for Land Use, Land Use Change, and Forestry” 

(Penman and others 2003) developed by the Intergovernmental Panel on Climate 

Change (IPCC). Calculations replicate the process that we use to develop estimates 

of forest carbon supplied to the U.S. Environmental Protection Agency (EPA) for the 

annual “Inventory of U.S. Greenhouse Gas Emissions and Sinks” (U.S. EPA 2009).

Th e calculation tool:

• Estimates state-level forest ecosystem carbon stocks and provides annualized 

stock and fl ux estimates for 1990 to the present

• Provides a means of updating estimates to refl ect changes in or additions to 

forest inventory data as new data become available on the Internet2

• Produces tabular summaries by state or national total (49 states)3 for forest 

ecosystem pools identifi ed as “good practice” by the IPCC (Penman and others 

2003) and reported in EPA’s annual inventory of greenhouse gas emissions and 

sinks (for example, see U.S. EPA 2009)

Th e use of CCT can be described as two sequential, separate, steps. First, FIA data are 

used to develop estimates of forest carbon stocks at state or substate levels. Th ese carbon 

stocks are defi ned for each state according to reporting year, or evaluation group, in 

the FIADB. In the second step, the stock-change method is applied to all or a subset of 

reporting years to produce annualized stock and fl ux. “Annualized” values are essentially 

1Forest land in the United States includes land that is at least 10-percent stocked with trees of any 

size. Timberland is defi ned as unreserved productive forest land producing or capable of producing 

crops of industrial wood. Productivity is at a minimum rate of 20 cubic feet of industrial wood per 

acre per year. Th e remaining portion of forest land is classifi ed as “reserved forest land,” which is forest 

withdrawn from timber use by statute or regulation, or “other forest land,” which includes forests that 

are incapable of growing timber at a rate of less than 20 or more cubic feet per acre per year (Smith 

and others 2009).

2CCT is released with a survey summary fi le that refl ects the full set of FIA inventory data available 

at a particular date. Th e current version includes data posted to the Internet on December 4, 2009, as 

refl ected in the fi lename: survey_summary_04Dec09.csv. Th us, the set of annualized estimates generated 

from this survey summary are based on the FIA datasets included on the CD. Any changes in the 

annualized estimates depend on postings of new or revised FIA data after the aforementioned date.

3Hawaii and portions of Alaska are omitted because inventory data are currently unavailable.
4

the interpolated values (between successive reporting years) that correspond to the 

integer years 1990 through the current year. A survey summary fi le, which is modifi ed by 

information obtained in the fi rst step and is the starting point of the second step, is the 

core data fi le underlying CCT.

Factors used to convert FIA inventory variables to carbon are consistent among 

inventories and states. However, in the past, the timing, coverage, and survey methods 

of forest inventories were sometimes inconsistent—over time or among states. Because 

the stock-change method requires consistent sequential sets of inventory data, some 

state-specifi c methods for adapting and augmenting FIA data are applied by CCT. In 

some cases, inventories are subdivided to substate classifi cations; in other cases, older FIA 

inventory data4 are used to supplement those currently available on the Internet. Th ese 

modifi cations are refl ected in the organization of the survey summary fi le.

Annualized outputs of stock and fl ux are organized according to year (1990 to present), 

state, and carbon pool. Th e forest ecosystem carbon pools identifi ed for EPA and IPCC 

reporting are above- and belowground biomass, dead wood, litter, and soil organic 

carbon. However, CCT output allows for allocating biomass to live trees or understory 

vegetation. Similarly, dead wood can be allocated to standing dead trees or down dead 

wood (also known as coarse woody debris). Operational and then methodological details 

follow, in that order.

CCT PARTS AND BASIC OPERATION

Th e application is structured around a fundamental list of carbon stocks associated with 

each available inventory. Th is list, called the survey summary fi le (the initial version is 

survey_summary_04Dec09.csv), is located in the ”SurveySummaryFiles” folder. Th us, 

CCT focuses on: 1) updating the survey summary fi le from FIA data; or 2) using the 

survey summary fi le to generate annualized stock and fl ux estimates. Note that CCT uses 

only one survey summary fi le at any one time, but multiple versions of survey summary 

fi les are also possible because the survey summary represents a composite of a specifi c 

set of FIA inventory data. Alternate survey summary fi les can represent subsets of these 

data, such as data available for download as of a specifi ed date, for example. When the 

data change, it is possible to save a new summary under a diff erent name. Once CCT is 

started (by double clicking on the shortcut to CCTv40.exe), a window with three buttons 

appears. Th e choices are obtaining data from FIA, updating the survey summary fi le, or 

producing annualized estimates (Fig. 1). Each button opens a new window. Th e fi rsttime use of CCT and use of windows opened by the three buttons are described in the 

sections that follow.

First-Time Use

Th e CCT package contains three folders: “CCT”, “ArchiveRPAData” and “FIA_zipped.” 

Th e CCT folder includes the program executable and all necessary program fi les. Th e 

two remaining folders contain data used to develop the current survey summary fi le; 

4Older FIA data also are publicly available but not necessarily for download from the Internet. 

See Appendix B or methods discussed in the text.
5

Figure 1.—Carbon Calculation Tool 4.0 interface 

windows: (A) Startup window, (B) Obtain Data from 

FIA window, (C) Update Survey Summary File window, 

and (D) Produce Annualized Estimates window.

Figure 1A.

Figure 1B.

Figure 1C.
6

Figure 1D.

these may be useful only as background or example data. See Appendix A for details on 

the contents of the CCT electronic media. CCT cannot be run directly from the CD/

DVD because the tool needs to write fi les to a drive. Th e fi rst step is to copy the CCT 

folder to any location on your hard drive. Th en reset the properties from read-only (this 

is often the case when copied from a CD/DVD) by right-clicking on the newly copied 

version of the CCT folder, selecting “Properties” from the list, unchecking the read-only 

attribute, and applying changes to the folder, subfolder, and fi les.
7

Within the CCT folder, the “CCTFiles” folder includes the executable (CCTv40.exe) 

and other necessary fi les for execution (for example, .dll’s). We suggest making a shortcut 

to CCTv40.exe to start CCT and leave the contents of CCTFiles undisturbed once 

they are copied onto your PC. To make the shortcut, open the CCTFiles folder, rightclick on CCTv40.exe, and select “Create Shortcut.” Drag the newly created shortcut 

to a location outside of the CCTFiles folder and use it to launch the application. 

“UserGuideandExamples” includes the CCT user’s guide, example tables in spreadsheet 

format, and text fi les with additional information on the current version of CCT. Th e 

“SurveySummaryFiles” folder includes our initial and recommended version of the 

comma-delimited survey summary fi le (survey_summary_04Dec09.csv).

Th e three folders—“DataFiles,”5 “SurveySummaryFiles”, and “OutputFiles”—are 

provided as locations for input FIA data, intermediate results, and output tables, 

respectively. You can rename or relocate these folders as desired because names and 

locations are specifi ed from within CCT when accessing or saving data. Th ese can 

be relocated to any directory. Th e DataFiles folder is the only one likely to require 

signifi cant storage space because it contains the potentially large FIA inventory data fi les.

Start the application by double-clicking on the shortcut to CCTv40.exe.

Obtain Data from FIA

Th is button facilitates downloading inventory data directly from the FIA Internet site. 

Clicking on the button opens a dialog box in which a web address can be specifi ed. 

Clicking on “Go to Download Page” opens a web browser to the indicated web address. 

It is initially set to http://199.128.173.17/fi adb4-downloads/datamart.html,6 which is 

currently the address for the FIADB DataMart, the site for downloading FIA data fi les 

(USDA For. Serv. 2009b). Th e application currently processes version 4.0 fi les; when FIA 

changes the format of the fi les, the current version of the application may not be able to 

process them correctly. Th us, we expect to update this program as needed and will post 

these updates at the Northern Research Station website (http://nrs.fs.fed.us/carbon/tools).

FIADB fi les are available for download in .zip (compressed) format. Only 4 tables 

are used by CCT: POP_EVAL_GRP, PLOTSNAP, COND, and TREE. Th e link to 

“Recent load history” near the top of the page gives the dates when each of the states’ 

inventories was loaded. It is necessary to download only fi les that have been loaded since 

the last comprehensive download. Th ere are multiple methods of obtaining the fi les 

needed by CCT. You may click on a state on the leftmost U.S. map near the top of the 

web page. Th is will enable you to download one .zip fi le per state which contains all 

5Th e CD includes several example sets of FIADB 4.0 fi les in the DataFiles folder.

6Should this link fail, try again later as the web server might be down or there may be network 

problems, or FIA may have changed the web address. To locate the new address, open a web browser 

and navigate to the FIA home page: http://www.fi a.fs.fed.us/, then, follow links to “Data” until a link 

appears for download of “FIADB 4.0” fi les. Once the download page is located, copy the new web 

address (highlight and Ctrl+C) and paste (Ctrl+V) into the text box on the “Download Data from 

FIA” window, replacing the former address. Upon exiting normally from CCT, the new web address 

is saved for future use.
8

of that state’s data. Repeat these steps for additional states you want. Another method 

would be to scroll down the web page until you fi nd the state(s) you want. Here, you 

can either download a state’s complete fi le (as above), or you can download only the four 

individual fi les needed by CCT. Each state’s .zip fi le contains up to 47 fi les in .csv, or 

comma separated, format. After downloading, the fi les need to be extracted (unzipped). 

All downloaded and unzipped fi les must be saved together in a single location. For 

example, all sets of four fi les can be placed together in the DataFiles folder.

Update the Survey Summary File

Th is button opens a window used for modifying and renaming the survey summary fi le 

(initially, survey_summary_04Dec09.csv in the SurveySummaryFiles folder). Th e upper 

portion of the window allows you to specify the directory and name of the current survey 

summary fi le and the directory containing the input FIA data fi les. Fill in the boxes 

by typing, pasting, or browsing. File and directory names are retained and become the 

default values in these boxes the next time the window is opened. Once selections are 

made, click the “Start” button.

After fi les and directories are selected successfully, lists of text should appear in the two 

large lower boxes. Information about records in the current survey summary fi le is listed 

in the lower right box. A list of names identifying FIA inventory fi les appears in the 

lower left box. Th e list includes only FIA inventories that are in the designated input 

directory, contain a complete set of fi les, and represent potential updates to the survey 

summary list on the right. To choose list items for processing, click on each item to 

highlight (Ctrl+Click for multiple selections and Shift+Click to select a block of items).

Information from the survey summary fi le displayed in the box to the right identifi es the 

survey (fi rst two columns: inventory and forest) and specifi es two dates associated 

with the data (the two remaining columns: updated and avgyear). Th e fi rst value 

(inventory) generally is taken from FIA fi lenames and includes the state, and reporting 

year of the survey.7 Th is label distinguishes the current downloadable FIADB fi les 

from the older FIA data sources (see the methods section and Appendix B). Th e second 

column (forest) identifi es specifi c substate divisions used by CCT. For example, four 

states (Oklahoma, Oregon, Texas and Washington) are subdivided into east and west, 

and many states have summaries for National Forests, reserved lands, or woodlands 

separate from all other forest land within the state. Th ese fi rst two columns (inventory

and forest) uniquely identify each survey and thus each record in the survey summary 

fi le. Annualized stock and fl ux estimates are based on successive sets of surveys uniquely 

identifi ed by state and FOREST (see methods section). Th e third column (updated) 

displays the date of the most recent update of that set of inventory data. Th is column is 

set to zero for records based on non-FIADB surveys. Th e last column (avgyear) is the 

average year of fi eld-data collection for the entire set of data associated with each survey. 

See Table 1 for an explanation of the variables included in the survey summary fi le.

7For non-FIADB inventory formats, this reporting year is inventory year, which is a year selected by 

FIA that best represents the inventory, presumably when the majority of data were collected. For RPA 

datasets, the year given is the year associated with the published assessment.
9

Table 1.—Variables in the output tables or the survey summary fi le

Variable Value represented

INVENTORY Identifi es the source of the inventory data as FIADBLT (FIADB 4.0 – Lite), FIADBEW 

(Eastwide), FIADBWW (Westwide), FIADBSO (Southern FIA), or RPA data. The 

variable also includes information to identify the state and reporting year (that is, 

nominal year associated with an inventory) of each inventory.

FOREST A classifi cation for subdividing surveys in some states. If no subdivisions are made in a 

state, this is listed as “All forest”. When National Forests are treated as a group, they 

are identifi ed as “NatlFor” with remaining forest being identifi ed as “nonNatlFor.” When 

National Forests are considered individually, they are identifi ed by name, with remaining 

National Forests in that state labeled as “Other NatlFor.” Reserved lands are identifi ed 

as “Resrv” with remaining land being “Nonresrv.” Woodlands are identifi ed as “Wdlnd” 

with other forest in that state identifi ed as “Nonwdlnd.” Substate portions of Oklahoma, 

Oregon, Texas, and Washington are also identifi ed by “East,” “West,” or “Central.”

AVGYEAR The average year for fi eld collection of the plot-level inventory data assigned to the survey. 

For example, the midpoint of 1992 is represented as 1992.5.

UPDATED The date of last update or change in the fi les used to develop the summary, in the form 

YYYYMMDD (FIADB 4.0 only).

STATECD A 2-digit code (FIPS) to identify the state (an FIA variable).

AVGCYCLE The average inventory cycle number for that state (FIADB 4.0 only).Records based on 

data other than FIADB 4.0 are set to zero.

AREA_kha Area of forest land in 1,000 hectares (kilohectares).

TMLDAREA_kha Area of timberland in 1,000 hectares (kilohectares).

TREE_AG_Tg Total carbon stock in above-ground portions of live trees (teragrams). Units in fl ux tables 

are teragrams per year (TREE_AG_Tgpy).

TREE_BG_Tg Total carbon stock in below-ground (coarse root) portions of live trees (teragrams). Units in 

fl ux tables are teragrams per year (TREE_BG_Tgpy).

UNDERSTORY_Tg Total carbon stock in understory (teragrams). Units in fl ux tables are teragrams per year 

(UNDERSTORY_Tgpy).

LIVE_AG_Tg Total carbon stock in live above-ground biomass (teragrams). Units in fl ux tables are 

teragrams per year (LIVE_AG_Tgpy). This value is not in the survey summary fi le but is 

calculated from output for live tree and understory.

LIVE_BG_Tg Total carbon stock in live below-ground biomass (teragrams). Units in fl ux tables are 

teragrams per year (LIVE_BG_Tgpy). This value is not in the survey summary fi le but is 

calculated from output for live tree and understory.

STANDINGDEAD_Tg Total carbon stock in standing dead trees (teragrams). Units in fl ux tables are teragrams 

per year (SD_Tgpy).

DOWNDEAD_Tg Total carbon stock in down dead wood (also known as coarse woody debris; teragrams). 

Units in fl ux tables are teragrams per year (DD_Tgpy).

DW_Tg Total carbon stock in dead wood (teragrams). Units in fl ux tables are teragrams per year 

(DW_Tgpy). This value is not in the survey summary fi le but is calculated from output for 

standing dead tree and down dead wood.

FF_Tg Total carbon stock in litter (also known as forest fl oor; teragrams). Units in fl ux tables are 

teragrams per year (FF_Tgpy).

SOC_Tg Total stock of soil organic carbon (teragrams). Units in fl ux tables are teragrams per year 

(SOC_Tgpy).

TMLDGSVOL_Mm3 Total of live growing-stock volume on timberlands in million cubic meters (Mm3).

STATE The state name is included as a convenience for users who want to construct tables from 

the survey summary fi le or output datasets. 
10

Updates to the specifi ed survey summary fi le are achieved by creating new summaries 

from the selected data on the left (input FIA data fi les) and adding them to the right. 

Depending on the data, the newly calculated summaries can replace existing records 

on the survey summary list or add new records. Buttons allow some fl exibility when 

specifying updates to the survey summary fi le. An “Update if Needed” button allows 

you to add new fi les (takes fi les listed on the left and adds to the right box) if the data 

in those fi les represent an update. Th e need for the updates is determined by CCT 

according to classifi cation (inventory and forest) and date of last modifi cation 

(updated). Alternatively, selecting one or more inventories on the left and clicking the 

“Update Forced” button allows you to force the application to accept the input data to 

overwrite existing summary data, regardless of the most recent date of the inventory data. 

Th e “Remove Inventory” button deletes the selected records from the survey summary 

list (on the right).

Once all modifi cations or updates are complete, you can save the revised survey summary 

under the same or diff erent name by clicking the “Save + Clear” button, which opens 

a save dialogue box. To leave the window without saving changes, click on the “Close” 

button.

A “log” fi le is created to document changes to the survey summary fi le, which correspond 

to changes in the summary information appearing in the lower right box. Th e log is 

located in the same directory as the survey summary fi le, and its name is based on 

the survey summary fi le. Th us, as the survey summary fi le is modifi ed and saved in 

CCT these changes are documented in a readily viewed text fi le. Existing log fi les are 

appended as the corresponding survey summary fi le is changed; if the fi le is renamed, a 

new log is created. Th is log fi le is created for the convenience of users interested in this 

documentation.

All FIADB inventories placed in the specifi ed DataFiles folder that include the set of 

four necessary fi les can be processed by CCT. However, some FIADB inventory data 

are excluded from updating the surveys listed in the lower right box. Th is includes some 

older datasets that do not cover all forest land within a state. Th e list in the lower left box 

will include these inventories but only apply the usable portion to the survey summary 

list in the lower right box. CCT processing detects and excludes these data from the 

survey summary. For example, the 1985 data for Arizona are complete only for nonNational Forest land. Th erefore, the 1985 inventory for Arizona is listed in the lower 

left box, but only the non-National Forest portion of the data are processed and listed in 

the lower right box. Surveys included and excluded by CCT are listed in Tables 2 and 3, 

respectively; see the methods section for further explanation.

Th e execution time required after clicking an update button varies according to the 

number and size of the inventories selected.8 Execution time of calculations for a single 

8Ninety percent of the individual-state calculations took less than 2 minutes each on a PC with a 

Pentium 4, 3 Ghz processor (1.048 GB RAM). Th e maximum time was just over 14 minutes (based 

on the September 8, 2006 datasets).
11

survey usually is less than a minute; recalculation of all FIADB fi les currently posted to 

the Internet may exceed two hours. Th is variable length of time required after clicking 

either update button is the only discernable time required for any CCT operation. 

Should you wish to halt execution during an update, type Ctrl+Alt+Delete and select the 

Task List; next, select Carbon Calculation Tool on the Applications tab of the Windows 

Task Manager. Finally, click “End Task” and then exit the Task Manager. Updates 

for each survey are added to the list as they are calculated so that halting a long list of 

updates may result in a survey summary list that is diffi cult to evaluate. If it is necessary 

to halt an update, the simplest path to recovery may be to start over with the last good 

version of the survey summary fi le (a good reason to periodically change the name or 

save older versions).

Produce Tables of Annualized Estimates

Th is button opens a window used to produce arrays of annualized stock and fl ux 

estimates that are generated from a specifi ed survey summary fi le and then saved as 

output fi les. If necessary, fi ll in the location and name of a survey summary fi le and 

an output directory. Filenames for the output tables are constructed automatically by 

appending a specifi c table identifi er to the name of the survey summary fi le. All output 

tables are comma-delimited text fi les.

Th e annualized estimates are based on the survey summary fi le, which resolves each 

state’s inventory (or survey) to a population total carbon stock for all forestland at a 

specifi c time (i.e., a precise carbon stock at a precise time). Linear interpolation between 

successive inventory summaries provides annualized carbon stocks for the intervening 

years, and extrapolation beyond the most recent inventory provide annualized stocks up 

to the current year. Specifi cally, annualized values are the interpolated or extrapolated 

carbon stocks corresponding to integer years. Typically, the average years (avgyear) 

of the summaries are not whole numbers. Annualized stock change is based on the 

diff erence between successive year’s annualized stocks.

Annualized totals for stock and stock change can be subdivided according to state 

or carbon pool classifi cations. Two suites of carbon pools are possible: 1) Reporting: 

above- and below-ground biomass, dead wood, litter, and soil organic carbon; or 2) 

Comprehensive: above- and belowground live trees, understory, standing dead trees, 

down dead wood, forest fl oor, and soil organic carbon. Th e “reporting” set of pools are 

the forest ecosystem pools identifi ed as “good practice” by the IPCC (Penman and others 

2003) and reported in the annual inventory of greenhouse gas emissions and sinks (U.S. 

EPA 2009). Th e “comprehensive” set of pools corresponds to the set of inventory-tocarbon conversion factors. Th e set of ecosystem carbon pools included in the available 

output tables depends on the selected setting. If you change the set of pools selected, the 

list of available output tables also changes.

Th e full set of forest inventories available for making the carbon stock-change 

calculations are compiled and summarized in the survey summary fi le. Th e net list is 

also the result of some fi ltering of incomplete summaries from the FIADB and the 
12

Table 2.—Summary of average year, area, and volume for the separate surveys identifi ed for use by CCT from 

currently available FIADB 4.0 and other datasets

Continued

State Inventory Foresta Average year Average 

cycle

Forest area Timberland 

Area

thousand ha thousand ha 

Alabama SOFIA, 1982 All forest 1981.85 -- 8,774 8,765

Alabama FIADB 4.0, 1990 All forest 1990.16 6 8,889 8,876

Alabama FIADB 4.0, 2000 All forest 1999.20 7 9,303 9,277

Alabama FIADB 4.0, 2005 All forest 2003.40 8 9,184 9,138

Alabama FIADB 4.0, 2006 All forest 2004.07 8.14 9,132 9,089

Alabama FIADB 4.0, 2007 All forest 2004.79 8.30 9,153 9,118

Alabama FIADB 4.0, 2008 All forest 2005.56 8.44 9,219 9,186

Alaska FIADB 4.0, 2008 Nonresrv Southcentral 2006.69 2 1,075 762

Alaska FIADB 4.0, 2008 Nonresrv Southeast 2006.56 2 2,916 1,736

Alaska FIADB 4.0, 2008 Resrv Southcentral 2006.21 2 679 0

Alaska FIADB 4.0, 2008 Resrv Southeast 2005.74 2 1,521 0

Arizona RPA, 1987 NatlFor nonwdlnd 1984.90 -- 1,504 1,000

Arizona FIADB 4.0, 1999 NatlFor nonwdlnd 1996.20 2 1,322 973

Arizona FIADB 4.0, 2008 NatlFor nonwdlnd 2005.01 3 1,220 969

Arizona RPA, 1987 NatlFor wdlnd 1983.99 -- 2,001 0

Arizona FIADB 4.0, 1999 NatlFor wdlnd 1996.42 2 2,005 0

Arizona FIADB 4.0, 2008 NatlFor wdlnd 2004.86 3 1,875 0

Arizona FIADB 4.0, 1985 NonNatlFor nonwdlnd 1985.77 1 701 502

Arizona FIADB 4.0, 1999 NonNatlFor nonwdlnd 1995.84 2 723 454

Arizona FIADB 4.0, 2008 NonNatlFor nonwdlnd 2005.90 3 916 401

Arizona FIADB 4.0, 1985 NonNatlFor wdlnd 1987.05 1 3,552 0

Arizona FIADB 4.0, 1999 NonNatlFor wdlnd 1989.53 2 3,811 0

Arizona FIADB 4.0, 2008 NonNatlFor wdlnd 2005.19 3 3,547 0

Arkansas Eastwide, 1988 All forest 1987.88 -- 7,158 6,979

Arkansas FIADB 4.0, 1995 All forest 1995.62 7 7,604 7,443

Arkansas FIADB 4.0, 2005 All forest 2003.31 8 7,396 7,265

Arkansas FIADB 4.0, 2006 All forest 2004.13 8.21 7,448 7,317

Arkansas FIADB 4.0, 2007 All forest 2004.90 8.41 7,495 7,367

California RPA, 1997 NatlFor 1991.06 -- 5,748 4,082

California FIADB 4.0, 2007 NatlFor 2004.94 5 6,443 3,963

California Westwide, 1994 NonNatlFor 1993.78 -- 7,023 3,183

California FIADB 4.0, 2007 NonNatlFor 2004.71 5 7,068 3,862

Colorado RPA, 1997 NatlFor nonwdlnd 1981.21 -- 4,020 2,786

Colorado FIADB 4.0, 2008 NatlFor nonwdlnd 2005.57 2 4,081 3,166

Colorado RPA, 1997 NatlFor wdlnd 1975.18 -- 82 0

Colorado FIADB 4.0, 1984 NatlFor wdlnd 1997.37 1 174 0

Colorado FIADB 4.0, 2008 NatlFor wdlnd 2005.82 2 391 0

Colorado Westwide, 1983 NonNatlFor nonwdlnd 1980.48 -- 1,975 1,890

Colorado FIADB 4.0, 2008 NonNatlFor nonwdlnd 2005.68 2 1,933 1,446

Colorado Westwide, 1983 NonNatlFor wdlnd 1983.26 -- 2,351 0

Colorado FIADB 4.0, 2008 NonNatlFor wdlnd 2005.77 2 2,838 0

Connecticut FIADB 4.0, 1985 All forest 1984.58 3 754 735

Connecticut FIADB 4.0, 1998 All forest 1998.39 4 752 686

Connecticut FIADB 4.0, 2005 All forest 2004.52 5 726 707

Connecticut FIADB 4.0, 2006 All forest 2005.23 5 707 687

Delaware FIADB 4.0, 1986 All forest 1985.61 3 158 152

Delaware FIADB 4.0, 1999 All forest 1999.33 4 155 152

Delaware FIADB 4.0, 2004 All forest 2004.84 5 163 156

Delaware FIADB 4.0, 2005 All forest 2005.23 5 172 168

Delaware FIADB 4.0, 2006 All forest 2005.62 5 148 146

Florida FIADB 4.0, 1987 All forest 1987.31 6 6,697 6,064

Florida FIADB 4.0, 1995 All forest 1994.58 7 6,565 5,930

Florida FIADB 4.0, 2005 All forest 2003.66 8 6,534 6,294
13

Continued

Table 2.—continued.

State Inventory Foresta Average 

year

Average 

cycle

Forest area Timberland 

Area

thousand ha thousand 

Florida FIADB 4.0, 2007 All forest 2005.01 8 6,838 6,439

Georgia FIADB 4.0, 1989 All forest 1988.63 6 9,768 9,565

Georgia FIADB 4.0, 1997 All forest 1996.94 7 9,880 9,630

Georgia FIADB 4.0, 2004 All forest 2001.47 8 10,030 9,812

Georgia FIADB 4.0, 2005 All forest 2002.31 8.18 10,024 9,810

Georgia FIADB 4.0, 2006 All forest 2003.70 8.41 10,092 9,883

Georgia FIADB 4.0, 2007 All forest 2004.78 8.62 10,075 9,882

Georgia FIADB 4.0, 2008 All forest 2005.91 8.81 10,046 9,884

Idaho Westwide, 1991 Caribou-Targhee NatlFor 1992.22 -- 691 642

Idaho FIADB 4.0, 2008 Caribou-Targhee NatlFor 2006.71 2 746 664

Idaho RPA, 1987 Kootenai NatlFor 1987.50 -- 23 17

Idaho FIADB 4.0, 1991 Kootenai NatlFor 1994.62 1 20 20

Idaho FIADB 4.0, 2008 Kootenai NatlFor 2006.81 2 19 19

Idaho FIADB 4.0, 1991 NonNatlFor nonwdlnd 1990.35 1 2,038 1,900

Idaho FIADB 4.0, 2008 NonNatlFor nonwdlnd 2006.53 2 1,946 1,778

Idaho FIADB 4.0, 1991 NonNatlFor wdlnd 1981.58 1 153 0

Idaho FIADB 4.0, 2008 NonNatlFor wdlnd 2006.10 2 128 0

Idaho Westwide, 1991 Other NatlFor 1987.77 -- 2,978 2,800

Idaho FIADB 4.0, 1991 Other NatlFor 1999.94 1 3,396 2,707

Idaho FIADB 4.0, 2008 Other NatlFor 2006.55 2 3,343 2,700

Idaho RPA, 1987 Payette NatlFor 1981.66 -- 832 411

Idaho FIADB 4.0, 2008 Payette NatlFor 2006.56 2 747 489

Idaho RPA, 1987 Salmon-Challis NatlFor 1978.08 -- 1,225 502

Idaho FIADB 4.0, 2008 Salmon-Challis NatlFor 2006.61 2 1,250 699

Idaho Westwide, 1991 Sawtooth NatlFor 1982.72 -- 408 363

Idaho FIADB 4.0, 1991 Sawtooth NatlFor 1996.06 1 424 374

Idaho FIADB 4.0, 2008 Sawtooth NatlFor 2006.39 2 443 365

Illinois FIADB 4.0, 1985 All forest 1985.06 3 1,726 1,631

Illinois FIADB 4.0, 1998 All forest 1997.81 4 1,752 1,654

Illinois FIADB 4.0, 2003 All forest 2002.89 5 1,754 1,671

Illinois FIADB 4.0, 2004 All forest 2003.36 5 1,790 1,719

Illinois FIADB 4.0, 2005 All forest 2003.76 5 1,831 1,766

Illinois FIADB 4.0, 2006 All forest 2004.58 5.20 1,938 1,870

Illinois FIADB 4.0, 2007 All forest 2005.42 5.39 1,960 1,900

Indiana FIADB 4.0, 1986 All forest 1986.29 3 1,797 1,739

Indiana FIADB 4.0, 1998 All forest 1997.68 4 1,822 1,758

Indiana FIADB 4.0, 2003 All forest 2001.28 5 1,842 1,785

Indiana FIADB 4.0, 2004 All forest 2002.29 5.21 1,865 1,807

Indiana FIADB 4.0, 2005 All forest 2003.36 5.43 1,913 1,860

Indiana FIADB 4.0, 2006 All forest 2004.35 5.63 1,884 1,832

Indiana FIADB 4.0, 2007 All forest 2005.65 5.85 1,952 1,900

Indiana FIADB 4.0, 2008 All forest 2006.70 6 1,920 1,874

Iowa FIADB 4.0, 1990 All forest 1989.70 3 831 788

Iowa FIADB 4.0, 2003 All forest 2001.87 4 1,079 1,044

Iowa FIADB 4.0, 2004 All forest 2002.76 4.20 1,112 1,087

Iowa FIADB 4.0, 2005 All forest 2003.65 4.44 1,165 1,143

Iowa FIADB 4.0, 2006 All forest 2004.48 4.63 1,217 1,190

Iowa FIADB 4.0, 2007 All forest 2005.28 4.81 1,236 1,213

Iowa FIADB 4.0, 2008 All forest 2006.14 5 1,227 1,202

Kansas FIADB 4.0, 1981 All forest 1981.24 3 551 490

Kansas FIADB 4.0, 1994 All forest 1994.21 4 625 603

Kansas FIADB 4.0, 2003 All forest 2002.64 5 901 862

Kansas FIADB 4.0, 2004 All forest 2002.99 5 860 829

Kansas FIADB 4.0, 2005 All forest 2003.39 5 852 821
14

Continued

Table 2.—continued.

Kansas FIADB 4.0, 2006 All forest 2004.43 5.23 851 817

Kansas FIADB 4.0, 2007 All forest 2005.46 5.45 866 834

Kansas FIADB 4.0, 2008 All forest 2006.62 5.69 902 853

Kentucky FIADB 4.0, 1988 All forest 1987.27 4 5,129 4,997

Kentucky FIADB 4.0, 2004 All forest 2002.33 5 4,860 4,729

Kentucky FIADB 4.0, 2005 All forest 2003.04 5.20 4,891 4,764

Kentucky FIADB 4.0, 2006 All forest 2004.01 5.41 4,909 4,782

Louisiana SOFIA, 1984 All forest 1984.14 -- 5,614 5,614

Louisiana FIADB 4.0, 1991 All forest 1991.37 6 5,581 5,578

Louisiana FIADB 4.0, 2005 All forest 2003.54 7 5,722 5,680

Maine Eastwide, 1982 All forest 1982.50 -- 7,225 6,955

Maine FIADB 4.0, 1995 All forest 1995.34 4 7,163 6,860

Maine FIADB 4.0, 2003 All forest 2001.55 5 7,165 6,962

Maine FIADB 4.0, 2005 All forest 2003.54 5.40 7,152 6,946

Maine FIADB 4.0, 2006 All forest 2004.54 5.61 7,161 6,939

Maryland FIADB 4.0, 1986 All forest 1985.64 4 1,093 981

Maryland FIADB 4.0, 1999 All forest 1999.62 5 1,038 960

Maryland FIADB 4.0, 2004 All forest 2004.76 6 972 888

Maryland FIADB 4.0, 2005 All forest 2005.25 6 1,072 1,022

Maryland FIADB 4.0, 2006 All forest 2005.88 6 987 929

Massachusetts FIADB 4.0, 1985 All forest 1984.57 3 1,321 1,200

Massachusetts FIADB 4.0, 1998 All forest 1997.75 4 1,265 1,065

Massachusetts FIADB 4.0, 2005 All forest 2004.69 5 1,283 1,197

Massachusetts FIADB 4.0, 2006 All forest 2005.37 5 1,236 1,172

Michigan FIADB 4.0, 1980 All forest 1979.79 4 7,433 7,079

Michigan FIADB 4.0, 1993 All forest 1992.79 5 7,805 7,536

Michigan FIADB 4.0, 2003 All forest 2002.48 6 7,792 7,554

Michigan FIADB 4.0, 2004 All forest 2002.83 6 7,815 7,576

Michigan FIADB 4.0, 2005 All forest 2003.39 6.08 7,826 7,596

Michigan FIADB 4.0, 2006 All forest 2003.97 6.19 7,909 7,672

Michigan FIADB 4.0, 2007 All forest 2004.86 6.46 7,976 7,737

Minnesota FIADB 4.0, 1977 All forest 1977.05 4 6,692 5,518

Minnesota FIADB 4.0, 1990 All forest 1989.18 5 6,751 5,958

Minnesota FIADB 4.0, 2003 All forest 2001.41 12 6,568 5,963

Minnesota FIADB 4.0, 2004 All forest 2002.19 12.20 6,554 5,976

Minnesota FIADB 4.0, 2005 All forest 2003.20 12.40 6,597 6,048

Minnesota FIADB 4.0, 2006 All forest 2004.14 12.60 6,633 6,096

Minnesota FIADB 4.0, 2007 All forest 2005.19 12.80 6,768 6,215

Minnesota FIADB 4.0, 2008 All forest 2006.20 13 6,876 6,311

Mississippi Eastwide, 1987 All forest 1986.74 -- 6,876 6,872

Mississippi FIADB 4.0, 1994 All forest 1993.69 7 7,525 7,522

Mississippi FIADB 4.0, 2006 All forest 2006.77 8 7,941 7,906

Missouri FIADB 4.0, 1989 All forest 1988.21 4 5,666 5,412

Missouri FIADB 4.0, 2003 All forest 2001.74 5 5,899 5,692

Missouri FIADB 4.0, 2004 All forest 2002.66 5.19 5,933 5,734

Missouri FIADB 4.0, 2005 All forest 2003.57 5.37 5,927 5,749

Missouri FIADB 4.0, 2006 All forest 2004.42 5.56 6,102 5,935

Missouri FIADB 4.0, 2007 All forest 2005.29 5.76 6,189 6,032

Missouri FIADB 4.0, 2008 All forest 2006.27 6 6,231 6,090

Montana RPA, 1987 NatlFor 1987.50 -- 5,598 3,359

Montana FIADB 4.0, 1989 NatlFor 1996.02 1 5,910 4,707

Montana FIADB 4.0, 2008 NatlFor 2006.11 2 6,175 4,900

Montana FIADB 4.0, 1989 NonNatlFor nonresrv 1989.01 1 3,090 3,035

Montana FIADB 4.0, 2008 NonNatlFor nonresrv 2006.04 2 3,628 3,155

State Inventory Foresta Average 

year

Average 

cycle

Forest area Timberland 

Area

thousand ha thousand 
15

Continued

Table 2.—continued.

Montana RPA, 1997 NonNatlFor resrv 1989.50 -- 398 0

Montana FIADB 4.0, 2008 NonNatlFor resrv 2006.26 2 557 0

Nebraska FIADB 4.0, 1983 All forest 1982.81 2 291 218

Nebraska FIADB 4.0, 1994 All forest 1994.58 3 382 362

Nebraska FIADB 4.0, 2003 All forest 2002.67 4 516 471

Nebraska FIADB 4.0, 2004 All forest 2003.12 4 507 470

Nebraska FIADB 4.0, 2005 All forest 2003.51 4 504 475

Nebraska FIADB 4.0, 2006 All forest 2004.46 4.22 533 510

Nebraska FIADB 4.0, 2007 All forest 2005.54 4.45 535 513

Nevada RPA, 1987 NatlFor nonwdlnd 1973.60 -- 264 40

Nevada FIADB 4.0, 1989 NatlFor nonwdlnd 1996.58 1 250 95

Nevada FIADB 4.0, 2005 NatlFor nonwdlnd 2005.07 2 257 102

Nevada RPA, 1987 NatlFor wdlnd 1978.44 -- 701 0

Nevada FIADB 4.0, 1989 NatlFor wdlnd 1996.61 1 1,039 0

Nevada FIADB 4.0, 2005 NatlFor wdlnd 2005.14 2 1,101 0

Nevada RPA, 1997 NonNatlFor nonwdlnd 1985.38 -- 212 45

Nevada FIADB 4.0, 2005 NonNatlFor nonwdlnd 2005.17 2 373 66

Nevada FIADB 4.0, 1989 NonNatlFor wdlnd 1980.49 1 2,638 11

Nevada FIADB 4.0, 2005 NonNatlFor wdlnd 2005.20 2 2,757 0

New Hampshire FIADB 4.0, 1983 All forest 1983.22 4 1,970 1,948

New Hampshire FIADB 4.0, 1997 All forest 1997.10 5 1,952 1,825

New Hampshire FIADB 4.0, 2005 All forest 2004.12 6 1,963 1,898

New Hampshire FIADB 4.0, 2006 All forest 2004.71 6 1,914 1,837

New Jersey FIADB 4.0, 1987 All forest 1986.83 3 764 751

New Jersey FIADB 4.0, 1999 All forest 1998.80 4 863 759

New Jersey FIADB 4.0, 2004 All forest 2005.05 5 787 771

New Jersey FIADB 4.0, 2005 All forest 2005.53 5 879 841

New Jersey FIADB 4.0, 2006 All forest 2006.15 5 843 787

New Mexico RPA, 1987 NatlFor nonwdlnd 1985.79 -- 1,684 1,159

New Mexico FIADB 4.0, 1999 NatlFor nonwdlnd 1997.21 2 1,504 1,134

New Mexico RPA, 1987 NatlFor wdlnd 1985.80 -- 1,276 0

New Mexico FIADB 4.0, 1999 NatlFor wdlnd 1996.55 2 1,772 0

New Mexico FIADB 4.0, 1987 NonNatlFor nonwdlnd 1987.18 1 852 850

New Mexico FIADB 4.0, 1999 NonNatlFor nonwdlnd 1999.26 2 751 630

New Mexico FIADB 4.0, 1999 NonNatlFor wdlnd 1988.94 2 2,726 0

New York Eastwide, 1980 Nonresrv 1980.50 -- 6,362 6,299

New York FIADB 4.0, 1993 Nonresrv 1992.93 4 6,283 6,233

New York FIADB 4.0, 2005 Nonresrv 2004.02 5 6,465 6,404

New York FIADB 4.0, 2006 Nonresrv 2004.58 5 6,499 6,448

New York RPA, 1987 Resrv 1987.50 -- 1,108 0

New York FIADB 4.0, 2005 Resrv 2003.91 5 1,110 0

New York FIADB 4.0, 2006 Resrv 2004.59 5 1,146 0

North Carolina FIADB 4.0, 1984 All forest 1983.90 5 7,669 7,466

North Carolina FIADB 4.0, 1990 All forest 1990.04 6 7,801 7,572

North Carolina FIADB 4.0, 2002 All forest 2000.71 7 7,411 7,155

North Carolina FIADB 4.0, 2005 All forest 2004.65 8 7,518 7,300

North Carolina FIADB 4.0, 2006 All forest 2005.17 8 7,525 7,303

North Dakota FIADB 4.0, 1980 All forest 1979.25 2 230 140

North Dakota FIADB 4.0, 1995 All forest 1994.68 3 272 179

North Dakota FIADB 4.0, 2003 All forest 2002.47 4 301 213

North Dakota FIADB 4.0, 2004 All forest 2002.98 4 297 221

North Dakota FIADB 4.0, 2005 All forest 2003.38 4 293 216

North Dakota FIADB 4.0, 2006 All forest 2004.68 4.26 284 206

North Dakota FIADB 4.0, 2007 All forest 2005.56 4.43 283 202

State Inventory Foresta Average 

year

Average 

cycle

Forest area Timberland 

Area

thousand ha thousand 
16

Continued

Table 2.—continued.

North Dakota FIADB 4.0, 2008 All forest 2006.52 4.63 289 207

Ohio FIADB 4.0, 1991 All forest 1991.33 4 3,179 3,063

Ohio FIADB 4.0, 2004 All forest 2003.34 5 3,288 3,187

Ohio FIADB 4.0, 2005 All forest 2003.79 5 3,195 3,093

Ohio FIADB 4.0, 2006 All forest 2004.59 5 3,205 3,112

Oklahoma FIADB 4.0, 1989 Central & West 1988.88 51 909 540

Oklahoma SOFIA, 1986 East 1986.17 -- 1,946 1,919

Oklahoma FIADB 4.0, 1993 East 1992.81 6 2,193 1,981

Oregon Westwide, 1992 NatlFor East 1994.97 -- 3,558 2,979

Oregon FIADB 4.0, 2008 NatlFor East 2004.99 5 3,437 2,846

Oregon Westwide, 1992 NatlFor West 1995.42 -- 2,225 1,867

Oregon FIADB 4.0, 2008 NatlFor West 2005.04 5 2,235 1,836

Oregon Westwide, 1992 NonNatlFor East 1990.53 -- 2,352 1,212

Oregon FIADB 4.0, 2008 NonNatlFor East 2004.94 5 2,597 1,426

Oregon Westwide, 1992 NonNatlFor West 1988.50 -- 3,867 3,589

Oregon FIADB 4.0, 2008 NonNatlFor West 2005.10 5 3,908 3,775

Pennsylvania FIADB 4.0, 1989 All forest 1989.74 4 6,842 6,417

Pennsylvania FIADB 4.0, 2004 All forest 2002.52 5 6,707 6,490

Pennsylvania FIADB 4.0, 2005 All forest 2003.52 5.20 6,709 6,483

Pennsylvania FIADB 4.0, 2006 All forest 2004.53 5.40 6,718 6,509

Rhode Island FIADB 4.0, 1985 All forest 1984.53 3 165 144

Rhode Island FIADB 4.0, 1998 All forest 1998.58 4 159 138

Rhode Island FIADB 4.0, 2005 All forest 2004.55 5 144 142

Rhode Island FIADB 4.0, 2006 All forest 2005.17 5 148 146

South Carolina FIADB 4.0, 1986 All forest 1986.26 6 4,961 4,931

South Carolina FIADB 4.0, 1993 All forest 1992.73 7 5,116 5,040

South Carolina FIADB 4.0, 2001 All forest 2000.57 8 5,025 4,949

South Carolina FIADB 4.0, 2006 All forest 2004.64 9 5,218 5,180

South Carolina FIADB 4.0, 2007 All forest 2005.63 9.21 5,239 5,198

South Dakota RPA, 1997 NatlFor 1985.72 -- 402 380

South Dakota FIADB 4.0, 1995 NatlFor 1999.27 4 406 399

South Dakota FIADB 4.0, 2003 NatlFor 2002.55 5 421 405

South Dakota FIADB 4.0, 2004 NatlFor 2003.05 5 421 398

South Dakota FIADB 4.0, 2005 NatlFor 2003.57 5 418 398

South Dakota FIADB 4.0, 2006 NatlFor 2004.41 5.17 407 388

South Dakota FIADB 4.0, 2007 NatlFor 2005.41 5.37 406 390

South Dakota FIADB 4.0, 2008 NatlFor 2006.49 5.59 414 400

South Dakota RPA, 1987 NonNatlFor 1985.61 -- 290 216

South Dakota FIADB 4.0, 1995 NonNatlFor 1995.38 4 259 223

South Dakota FIADB 4.0, 2003 NonNatlFor 2002.43 5 240 190

South Dakota FIADB 4.0, 2004 NonNatlFor 2002.91 5 242 200

South Dakota FIADB 4.0, 2005 NonNatlFor 2003.59 5 263 226

South Dakota FIADB 4.0, 2006 NonNatlFor 2004.80 5.26 295 257

South Dakota FIADB 4.0, 2007 NonNatlFor 2005.96 5.50 318 283

South Dakota FIADB 4.0, 2008 NonNatlFor 2006.69 5.66 339 302

Tennessee FIADB 4.0, 1989 All forest 1988.98 5 5,495 5,368

Tennessee FIADB 4.0, 1999 All forest 1998.19 6 5,829 5,652

Tennessee FIADB 4.0, 2004 All forest 2002.69 7 5,578 5,356

Tennessee FIADB 4.0, 2005 All forest 2003.81 7.21 5,618 5,395

Tennessee FIADB 4.0, 2006 All forest 2004.86 7.41 5,646 5,417

Tennessee FIADB 4.0, 2007 All forest 2005.79 7.61 5,659 5,443

Texas FIADB 4.0, 2007 Central & West 2006.05 51 19,455 1,062

Texas SOFIA, 1986 East 1985.64 -- 4,683 4,617

Texas FIADB 4.0, 1992 East 1992.22 6 4,838 4,764

State Inventory Foresta Average 

year

Average 

cycle

Forest area Timberland 

Area

thousand ha thousand 
17

a Corresponds to the FOREST variable in the survey summary fi le; see Table 1 for description.

Table 2.—continued.

Texas FIADB 4.0, 2003 East 2002.66 7 4,909 4,810

Texas FIADB 4.0, 2006 East 2004.09 7.59 4,909 4,838

Texas FIADB 4.0, 2007 East 2005.07 7.81 4,896 4,831

Texas FIADB 4.0, 2008 East 2006.01 8 4,908 4,842

Utah FIADB 4.0, 1993 Nonwdlnd 1993.45 1 2,182 1,903

Utah FIADB 4.0, 2008 Nonwdlnd 2004.64 2 2,309 1,658

Utah FIADB 4.0, 1993 Wdlnd 1993.80 1 4,177 0

Utah FIADB 4.0, 2008 Wdlnd 2004.52 2 5,065 0

Vermont FIADB 4.0, 1983 All forest 1982.91 4 1,795 1,789

Vermont FIADB 4.0, 1997 All forest 1997.29 5 1,869 1,814

Vermont FIADB 4.0, 2005 All forest 2004.76 6 1,854 1,818

Vermont FIADB 4.0, 2006 All forest 2005.54 6 1,850 1,813

Virginia FIADB 4.0, 1984 All forest 1985.36 5 6,461 6,247

Virginia FIADB 4.0, 1992 All forest 1991.49 6 6,485 6,252

Virginia FIADB 4.0, 2001 All forest 1999.68 7 6,415 6,263

Virginia FIADB 4.0, 2005 All forest 2001.88 7.53 6,380 6,190

Virginia FIADB 4.0, 2006 All forest 2003.69 7.83 6,378 6,185

Virginia FIADB 4.0, 2007 All forest 2004.97 8 6,364 6,169

Virginia FIADB 4.0, 2008 All forest 2005.77 8.19 6,400 6,207

Washington Westwide, 1991 NatlFor East 1995.24 -- 1,831 1,391

Washington FIADB 4.0, 2008 NatlFor East 2005.63 5 2,022 1,428

Washington Westwide, 1991 NatlFor West 1995.43 -- 1,372 1,011

Washington FIADB 4.0, 2008 NatlFor West 2005.45 5 1,381 981

Washington Westwide, 1991 NonNatlFor East 1991.87 -- 2,026 1,623

Washington FIADB 4.0, 2008 NonNatlFor East 2005.62 5 2,119 1,995

Washington Westwide, 1991 NonNatlFor West 1991.15 -- 3,588 2,996

Washington FIADB 4.0, 2008 NonNatlFor West 2005.59 5 3,538 2,987

West Virginia FIADB 4.0, 1989 All forest 1988.20 4 4,900 4,816

West Virginia FIADB 4.0, 2000 All forest 2000.86 5 4,859 4,774

West Virginia FIADB 4.0, 2004 All forest 2005.05 6 4,746 4,666

West Virginia FIADB 4.0, 2005 All forest 2005.50 6 4,856 4,780

West Virginia FIADB 4.0, 2006 All forest 2006.13 6 4,857 4,747

Wisconsin FIADB 4.0, 1983 All forest 1982.44 4 6,213 5,975

Wisconsin FIADB 4.0, 1996 All forest 1995.11 5 6,460 6,354

Wisconsin FIADB 4.0, 2003 All forest 2001.97 6 6,428 6,324

Wisconsin FIADB 4.0, 2004 All forest 2002.38 6 6,490 6,376

Wisconsin FIADB 4.0, 2005 All forest 2003.24 6.18 6,523 6,426

Wisconsin FIADB 4.0, 2006 All forest 2004.11 6.37 6,586 6,489

Wisconsin FIADB 4.0, 2007 All forest 2005.06 6.56 6,640 6,545

Wisconsin FIADB 4.0, 2008 All forest 2006.19 6.80 6,757 6,653

Wyoming RPA, 1997 NatlFor 1981.52 -- 2,354 1,199

Wyoming FIADB 4.0, 2000 NatlFor 1999.95 2 2,430 1,562

Wyoming FIADB 4.0, 1984 NonNatlFor nonresrv 

nonwdlnd

1983.53 1 990 858

Wyoming FIADB 4.0, 2000 NonNatlFor nonresrv 

nonwdlnd

2001.82 2 1,104 867

Wyoming FIADB 4.0, 1984 NonNatlFor nonresrv 

wdlnd

1983.60 1 290 0

Wyoming FIADB 4.0, 2000 NonNatlFor nonresrv 

wdlnd

2002.05 2 352 0

Wyoming RPA, 1997 NonNatlFor resrv 1984.50 -- 793 0

Wyoming FIADB 4.0, 2000 NonNatlFor resrv 1999.90 2 747 0

State Inventory Foresta Average 

year

Average 

cycle

Forest area Timberland 

Area

thousand ha thousand 
18

addition of supplemental older FIA data. Th e CCT 4.0 user also has the option of 

editing the survey summary fi le to include only specifi c data of interest before developing 

the annualized estimates. Th is editing can be through directly modifying the comma 

delimited survey summary fi le or through CCT 4.0 by selecting a set of assumptions to 

extract a subset of survey summary records (as presented in the next couple paragraphs).

Th e interpretation and use of the FIA’s annual inventory data can aff ect trends in 

annualized stock and stock change for some states. In general, these annual inventory 

data are those collected since 1999 where a defi ned portion, or panel, of a state is 

surveyed each year so that once all panels are measured the process continues with the 

fi rst panel in the next inventory cycle (USDA For. Serv. 2009a). Th ese annual panels are 

typically 10 or 20 percent of the state; they are then combined in series from successive 

years to form whole-state inventory estimates in the FIADB (USDA For. Serv. 2009a). 

Th e sequence of whole-state estimates where a new panel replaces the oldest in the 

previous series are labeled “moving window” averages, which are identifi ed by reporting 

year or evaluation (Bechtold and Patterson 2005, USDA For. Serv. 2009c).

Th e moving window evaluations may include panels from a single survey cycle, but 

more often a moving window evaluation includes panels taken from two successive 

survey cycles. Successive moving windows may diff er by only one panel, or 20 percent 

of inventory plots. Th is potential for redundancy in data can aff ect the interpretation 

and use of the moving window averages when compiling the annualized stock-change 

estimates.9 Th e survey cycles included in an evaluation are indicated by the average cycle 

Table 3.—Summary of FIADB 4.0 datasets which were considered 

and not used because they were found to be incomplete.a

State Reporting Year Forestb

Arizona 1985 NatlFor nonwdlnd

Arizona 1985 NatlFor wdlnd

Colorado 1984 NatlFor nonwdlnd

Colorado 1984 NonNatlFor nonwdlnd

Colorado 1984 NonNatlFor wdlnd

Idaho 1991 Caribou-Targhee NatlFor

Idaho 1991 Payette NatlFor

Idaho 1991 Salmon-Challis NatlFor

Montana 1989 NonNatlFor resrv

Nevada 1989 NonNatlFor nonwdlnd

New Mexico 1987 NatlFor wdlnd

New Mexico 1987 NonNatlFor wdlnd

New York 1993 Resrv

South Dakota 1980 NonNatlFor

Wyoming 1984 NatlFor

Oregon 1999 NonNatlFor West

South Dakota 1980 NonNatlFor

Wyoming 1984 NatlFor

a As of December 4, 2009.

b Corresponds to the FOREST variable in the survey summary fi le; see Table 1 for 

description.

9Additional information on fi le as: Smith, J.E.; Heath, L.S. in prep. Approach for calculating forest 

carbon stock-change estimates in the United States using annualized inventory data.
19

variable (AVGCYCLE) in the survey summary fi le. Whole numbers indicate single-cycle 

evaluations, and decimal values indicate two-cycle evaluations such as 5.40 for the 2005 

evaluation for Maine (Table 4), which indicates that the evaluation is a combination 

of cycles 5 and 6, for example. Th e default CCT 4.0 selection of moving window 

evaluations is to include only the most recent version of each single-cycle evaluation as 

well as the most recent two-cycle evaluation if it is predominantly composed of panels 

from the newer cycle. Th e user has the option (by selecting buttons) of selecting one of 

two alternate options rather than the default subset of annual evaluations. Additional 

details of the three options are listed below with examples from two states as listed in 

Table 2; the example states are Kansas and Virginia with the reporting years for the 

earliest annual evaluation of 2003 and 2001, respectively.

Single-cycle plus majority new-cycle last evaluation (default). Includes only 

the most recent version of each single-cycle evaluation as well as the most recent 

two-cycle evaluation if it is predominantly composed of panels from the newer 

cycle. Th is approach is recommended because it makes the most use of available 

inventories while reducing the infl uence of redundant data. Example reporting 

years are 2005 and 2008 for Kansas, and 2001 and 2007 for Virginia.

All annual evaluations included in the FIADB. Includes all evaluations 

provided in the data. Th is approach is included because it makes unselected use 

of all evaluations provided in FIADB 4.0. Example reporting years are 2003-

2008 for Kansas, and 2001, 2005-2008 forVirginia.

Single-cycle evaluations. Includes only the most recent version of each singlecycle evaluation. Th is approach is included because it eliminates all redundancy 

in consecutive inventories used, eff ectively using the data as though they were 

Table 4.—An example of the surveys summarized as carbon stocks, using the five surveys 

for Maine (values are rounded)

Attribute EASTWIDE

1982

FIADB

1995

FIADB

2003

FIADB

2005

FIADB

2006

Average year 1982.50 1995.34 2001.55 2003.54 2004.53

Average cycle -- 4 5 5.40 5.61

Live above ground 

(Tg C)

407 398 401 401 404

Live below ground 

(Tg C)

83 80 81 81 82

Dead wood (Tg C) 80 81 81 81 81

Litter (Tg C) 197 178 166 167 169

Soil organic carbon 

(Tg C)

621 597 591 588 588

Forest area (kha) 7,225 7,163 7,165 7,152 7,161

Timberland area 

(kha)

6,955 6,860 6,962 6,946 6,939

Live growing-stock 

volume (Mm3)

609 592 634 634 638
20

from a periodic inventory. Example reporting years are 2005 for Kansas, and 

2001 and 2007 for Virginia.

Output tables are listed in two boxes in the lower portion of the window. Th e upper 

box contains available output tables for stock and fl ux for each of the carbon pools and 

estimates for forest area, timberland area, and timberland growing-stock volume. Th ese 

are given by state and year from 1990 to the current year. Th e fi rst stock table and the 

fi rst fl ux table in the list writes out all categories within one fi le. Th e lower box contains 

available national (49 states) stock and fl ux tables. Individual tables can be created by 

clicking on each item to highlight (Ctrl+Click for multiple selections and Shift+Click to 

select a block of items) and then clicking “Generate the Selected Tables.”

Annualized stocks estimated for each state are assumed to represent a value for January 

1 of each year; fl ux is the diff erence between two successive stocks. For example, fl ux for 

2003 is the diff erence between the annualized stocks for January 1, 2003 and January 

1, 2004, with the sign convention that negative fl ux represents net carbon sequestration 

by the forest, that is, net gain in ecosystem carbon. Annualized stocks are produced for 

each year from 1990 to the current year as determined by the system year on your PC. 

Annualized fl ux is calculated as the diff erence between estimated stock for each successive 

year between 1990 and the current year. Units in the output tables are the same as in the 

survey summary fi le: Tg (teragrams, which equals 1 million metric tons) for carbon mass, 

kha (thousand hectares) for area, and Mm3 (million cubic meters) for volume.

Additional Considerations for Use

Th e following are considerations for facilitating the use of this application.

• CCT is designed to update or modify existing survey summary fi les rather than 

to create a new survey summary fi le solely from FIA datasets. (However, an 

update can include replacing all existing summaries previously processed by 

CCT if you so designate.)

• Th e use of the download feature (from fi rst button of fi rst window) is optional. 

Any method of obtaining or downloading the FIADB 4.0 data is acceptable so 

long as the POP_EVAL_GRP, PLOTSNAP, COND, and TREE fi les for each 

survey are saved together in a single directory.

• Saving the modifi ed survey summary fi le under a new name is useful, particularly 

if errors are made in specifying changes; then, it is usually easiest to quit and start 

over with the last good survey summary.

• Execution time for a single state’s survey can be several minutes. Even 

determining if an update is necessary requires some time because each record of 

each fi le is read to verify dates. To minimize execution time, avoid running the 

application on FIADB fi les known not to have changed since the last update of 

the survey summary fi le.

• At least two survey summary points are necessary to calculate a net annualized 

fl ux. A single state by FOREST combination in the survey summary fi le results 

in a constant annualized stock and an annualized fl ux of zero.
21

• Th e survey summary fi le can be edited as text or within a spreadsheet. Th e most 

common reason for doing this would be to select a subset of the survey summary 

records before developing the annualized estimates.

• If the survey summary fi le is in an incorrect format, for example, missing 

variables in some records, CCT provides a message to that eff ect and then closes.

• CCT does not evaluate newly downloaded FIA fi les as to completeness or how 

representative they are for each state. However, you can compare survey summary 

values for total forest area, area of timberland, and growing-stock volume of live 

trees on timberland with published statistics such as those provided by Smith 

et al. (2009) based on FIA data. Note that comparisons with published values 

should be made with values provided in the survey summary fi le, not the areas 

and volumes in the output tables (since these are interpolated values).

METHODS AND ASSUMPTIONS PROGRAMMED INTO CCT

Th e carbon calculations are developed in three separate but related steps: 1) identifying 

appropriate forest inventories and classifi cation of forest land for each state; 2) 

estimating carbon on inventory plots and aggregating carbon stocks; and 3) estimating 

annualized carbon stock and fl ux from survey summaries. Th e fi rst two steps are parts 

of the process for updating the survey summary fi le from FIA data. Th e unique surveys, 

identifi ed by inventory and forest in the survey summary fi le, are separate state or 

substate inventories classifi ed to form consistent sequences of inventories for each state. 

Classifi cations are based on preliminary analysis of all available inventory data and are built 

in. Estimation of carbon on inventory plots and aggregation to the state or substate survey 

summary level is the most time-consuming part of CCT processing. Th e fi nal step uses 

information in the survey summary fi le to generate annualized stocks and fl uxes in tabular 

form for individual states or for national totals; this eliminates the need for extensive 

recalculation on all data with each use. Each step is discussed in greater detail in this section.

Inventory Data

Th e annualized stock and fl ux calculations are based on the stock-change method, which 

entails interpolation between, and can involve extrapolation beyond, discrete estimates 

of carbon stock. Th e reliability of this approach is strongly dependent on reasonably 

consistent sequences of inventories. Th us, if a discontinuity is apparent within a series of 

successive inventories for a state, we subdivide the data for purposes of determining stock 

and stock change (for example, see the discussion below on inventories of some National 

Forests). Discontinuities include a signifi cantly diff erent date of inventory for a portion 

of a state or the omission of an identifi ed part of a state’s forest lands. Th e inventory

and forest classifi cations are used to organize consistent sequences of inventories. In 

this user’s guide, and in the CCT application, the term survey refers to inventory data 

classifi cations appropriate for calculating stock change, that is, a defi ned portion of forest 

that was inventoried within an identifi ed interval of time. In this section we describe the 

sources and types of available forest inventory data, the assumptions and logic used in the 

survey classifi cations in CCT, and the methods in CCT for resolving data updates and 

survey dates.
22

Th e availability of forest inventory data ultimately determined the classifi cations used 

in the survey summary fi le. Data goals were inventories for all 48 conterminous states 

plus Alaska beginning with the latest pre-1990 data and all inventories in subsequent 

years. Th is is because these data were available through the principal source of inventory 

data, the USDA Forest Service FIADB 4.0 dataset (USDA For. Serv. 2009b). Th ese data 

include all inventories using the current annual inventory design as well as many of the 

older periodic inventories. Datasets are organized according to state (see USDA For. 

Serv. 2009a and Miles 2008 for details on the datasets and variables).

Some earlier FIA periodic inventories within the pre-1990-to-present interval have not 

been converted to the current downloadable database format. We identifi ed, obtained, 

and included those data to fi ll in gaps where possible. Earlier FIA inventory data formats 

with plot and individual-tree data, as in the FIADB 4.0 data, are the Eastwide (Hansen 

and others 1992), Westwide (Woudenberg and Farrenkopf 1995), and FIA-Southern 

Region (SOFIA).10 Where gaps remained, we considered plot-level inventory datasets 

that were produced as a part of forest-resource statistics developed for the Forest and 

Rangeland Renewable Resources Planning Act Assessment (RPA). (USDA For. Serv. 

2009c). RPA datasets of interest were those compiled for 1987, 1997, and 2002, which 

correspond to published summary statistics by Waddell and others (1989) and Smith 

and others (2001, 2004b).11 Many of these older inventories already were represented 

by FIADB 4.0 data, but others, if identifi ed as separate datasets, were considered when 

identifying appropriate surveys for calculations of stock change.

Classifi cation of surveys (for the survey summary fi le) was based primarily on resolving 

the dates of fi eld-data collection or comparing forest area within inventories to forest 

statistics from other sources. Th e current annual FIA inventory design includes 

sampling a portion of each state each year. Past periodic inventories were on an 

irregular schedule (about once every 5 to 14 years). CCT resolves each inventory to 

represent stock at a specifi c time. However, if identifi able portions of a state’s forest 

land were inventoried at intervals out of phase with other forest land, dividing the total 

inventory into a distinct series of surveys provides more accurate resolution of stock 

change. Examples of separate series of forest inventories within states include some 

National Forests, particularly in western states, which sometimes were inventoried at 

diff erent times from other ownerships in the state. Additional survey classifi cations 

resulted from identifying and accounting for all forest land within a state for each 

inventory. Occasionally, specifi c portions of a state’s forest land were absent from 

an inventory. For example, reserved forest land might be missing or signifi cantly 

underrepresented in an inventory. Th is resulted in surveys classifi ed to separately 

10Data used for CCT annualized estimates are included on the CD. Eastwide and Westwide data 

were downloaded from the Internet when they were available (date unknown). Th e Southern Region 

(SOFIA) data were obtained from Linda Heatherly, U.S. Forest Service, Southern Research Station, 

Forest Inventory and Analysis, Knoxville, TN.

11Data used for CCT annualized estimates are included on the CD. Th e unpublished national Forest 

and Rangeland Renewable Resources Planning Act Assessment (RPA) forest inventory databases for 

1987, 1997, and 2002 were provided by K.L. Waddell, W.B. Smith, and P.D. Miles, of the U.S. 

Forest Service, Forest Inventory and Analysis Program.
23

estimate the two parts, with fewer surveys in the state’s reserved forest land sequence 

than the nonreserved forest land, for example.

Most states were not subdivided into multiple surveys. Separate substate classifi cations 

were established for a number of states based on National Forests or reserved forest 

land. In Idaho, individual National Forests were treated as separate groups due 

to apparently distinct inventories. In the past, surveys for the eastern and western 

portions of Oregon and Washington (as determined by the crest of the Cascade 

Mountains) were conducted at diff erent times. Similarly, Oklahoma and Texas were 

divided according to area because only one survey of the central and western portions 

of those States is available. Additional survey classifi cations were based on forest type. 

For example, woodland12 forest types form a substate classifi cation where woodlands 

are a large part of forest land within a state (more than 5 percent of total forest area). 

Th is additional classifi cation was included so that CCT users can evaluate the infl uence 

of woodlands on estimates of total carbon. In California, chaparral communities have 

been omitted from the most recent survey but were included in earlier inventories. To 

remain consistent with current survey practice, chaparral was identifi ed and removed 

from the survey summary fi le representation of older inventory data.

Th e specifi c inventory data and survey classifi cations used by CCT are included in Table 

2. Surveys are identifi ed according to the State, Inventory, and Forest column headings, 

which identify spatial and temporal separation of the surveys. Th is same information 

is included in the survey summary fi le variables inventory, which includes state, and 

forest (Table 1). Final selection of these classifi cations was based on identifying separate 

inventory datasets and verifying complete coverage of forest land. Th is process included 

comparisons of date of fi eld-data collection, forest areas, and timber volumes among 

inventories and with published statistics, such as Smith and others (2009). Th e part of the 

FIADB 4.0 not compatible with the CCT forest classifi cation is described in Table 3. If 

multiple versions of surveys were available for a particular forest classifi cation, FIADB 

4.0 data were used preferentially because there was overall consistency with current data. 

Eastwide, Westwide, or SOFIA data were the secondary choice if needed because they also 

included individual-tree data rather than plot-level data. Finally, RPA datasets that were 

considered complete were used as needed.

All inventory data underlying the initial survey summary fi le provided with CCT are 

included in the CCT package in the FIA_zipped and ArchiveRPAData folders. Th e FIADB 

4.0, Eastwide, Westwide, and SOFIA data specifi ed in Table 2 are in the FIA_zipped 

folder. Th e RPA data are in the folder ArchiveRPAData as spreadsheets with the pertinent 

data extracted from the larger RPA datasets.

Two date identifi ers are important in defi ning and maintaining the survey summary fi le. 

One identifi es the date of most recent updates for the FIADB fi les. Each of the FIADB 

12Woodland currently refers to stands dominated by certain western U.S. species (particularly pinyon 

and juniper) for which stocking guides are not available. For a complete list of applicable forest type 

groups, see USDA For. Serv. (2009a).
24

4.0 fi les read by CCT includes two variables (created and modifi ed dates) in each record 

that are scanned to identify the last update to the datasets. From these values, the date of 

last data update is retained in the survey summary fi le as updated, which contains the 

date in the form YYYYMMDD. Th is variable (in the survey summary fi le) is read when 

an FIADB dataset with corresponding state and reporting year identifi ers is specifi ed 

as an input fi le. Th e date of the last update is compared with dates in the input FIADB 

data to determine whether the new fi le is more recent than the data summarized in 

the survey summary fi le. Annual inventories posted to the Internet are revised as each 

annual survey is added to the existing data; this provides an example of an update (to the 

survey summary) that might be necessary from a previously read set of FIADB fi les. Th e 

updated information is relevant only to the FIADB data.

A second date identifi er is avgyear, which is calculated from the dataset variables 

identifying the dates of plot data collection. Th ese dates are used to determine an 

average date for the overall survey. Th e variables generally are found in the plot fi le and 

specify the year, month, and day when the plot was surveyed. Assignment of year for a 

plot is defi ned as the date of fi eld data as a decimal fraction of the year. Th e fractional 

portion of the year ranges from 0.003 for January 1 to 0.997 for December 31. Th e 

determination depends on year, month, and day or which of these is available. For 

example, if year is available but month and day are missing, the fractional part is set to 

0.5. If year and month are available but day is missing, avgyear = year+(month-0.5)/12; 

for example, the fractional part specifi ed for March is 0.208. If all three variables are 

available, avgyear = year+((Julian date of day-0.5)/(Julian date of Dec 31)), where 

Julian date is a number between 1 and 366 inclusive. Subtracting 0.5 in the numerator 

of the calculations is to place the fractional value at the midpoint of the category. Plots 

missing such date information are not included in the calculation of avgyear. Note that 

this approach to establishing a fractional value for avgyear does not imply precision 

about the hour when the plot was measured, but it does provide a systematic approach 

for determining a midpoint of the available date information.

Estimating Carbon on Inventory Plots

Carbon estimates derived from FIA or RPA data are based on the current coeffi cients of 

the forest carbon simulation model FORCARB2 and applied at the plot level (Birdsey 

and Heath 1995, 2001; Heath and others 2003; Smith and others 2004a). Th e carbon 

conversion coeffi cients of the model are applied to the survey data at the scale of the 

FIA inventory plots. Th e results are estimates of carbon density (Mg C/ha, megagrams 

or metric tons carbon per hectare) for each carbon pool. Carbon density is converted to 

carbon mass based on expansion factors (USDA For. Serv. 2009a, Miles 2008) and then 

summed to determine total carbon stock for a survey.

Classifi cations of forest carbon in CCT output can be pooled according to: 1) plot-level 

FORCARB2 calculations (“Comprehensive” in CCT, Smith and others 2004a), or 2) 

IPCC specifi cations, which are used in the EPA greenhouse gas reporting (“Reporting” 

in CCT, Penman and others 2003, U.S. EPA 2009). FORCARB2 estimates carbon 

density for live trees, standing dead trees, understory vegetation, down dead wood, forest 

fl oor, and soil organic matter, which is the comprehensive set in CCT:
25

• Live trees, which includes all live woody vegetation at least 1 inch (2.54 cm) in 

diameter at breast height (d.b.h., 1.3 m). Separate estimates are made for both 

aboveground and whole-tree biomass, which includes all living biomass of coarse 

living roots more than 2 mm in diameter. Belowground live-tree carbon is based 

on the diff erence between whole trees and above ground only

• Understory, which includes all live herbaceous vegetation and woody vegetation 

up to 1 inch (2.54 cm) d.b.h.

• Standing dead trees, which are nonliving but otherwise follow the same defi nition 

as live trees, including coarse nonliving roots more than 2 mm in diameter

• Down dead wood, also known as coarse woody debris, includes all nonliving 

woody biomass with a diameter of at least 7.5 cm at transect intersection lying on 

the ground. Th is pool also includes stumps and coarse roots more than 2 mm in 

diameter. Nonliving vegetation that otherwise would fall under the defi nition of 

understory is included in this pool

• Forest fl oor, which includes the litter, fumic, and humic layers and all nonliving 

biomass with a diameter less than 7.5 cm at transect intersection lying on the 

ground above the mineral soil

• Soil organic carbon, including all organic material in soil to a depth of 1 m but 

excluding the coarse roots of the pools mentioned earlier

Th ese are the bases for the carbon-stock information summarized and saved in the survey 

summary fi le. However, carbon stocks also are reallocated to the following fi ve pools 

(Penman and others 2003) if required for formatting results, which is the reporting set in 

CCT:

• Aboveground biomass, which includes aboveground portions of live trees and 

understory. In CCT, we assume that 90 percent of the understory is above ground.

• Belowground biomass, which includes belowground portions of live trees and 

understory. In CCT, we assume that 10 percent of the understory is below 

ground.

• Dead wood, which includes standing dead trees and down dead wood.

• Litter, which is identical to the previous defi nition for forest fl oor.

• Soil organic carbon, which is identical to the previous defi nition.

Tree carbon is based on individual-tree data or the plot-level value calculated for 

merchantable volume of wood in live trees. Most inventory data included in the CCT 

estimates include measured values for individual trees; in such cases, tree biomass and 

thus carbon mass are calculated according to Jenkins and others (2003). Carbon mass 

of wood is approximately 50 percent of dry weight (IPCC 1997). However, the few 

estimates of tree carbon based on the RPA plot-level data are from modifi ed versions of 

the stand-level volume-to-biomass equations presented in Smith and others (2003). All 

estimates for carbon in standing dead trees also are based on modifi ed versions of the 

equations in Smith and others (2003) because individual-tree data for standing dead trees 

currently are inconsistent among the available datasets.13

13See U.S. EPA 2009, particularly Annex 3.12, for additional details as well as the specifi c volumebased equations and coeffi cients.
26

Estimates of understory and down dead wood are functions of region and forest type as 

well as plot-level estimates of live-tree carbon density. Understory carbon density is based 

on Birdsey (1996) and was estimated at the inventory plot level (U.S. EPA 2009). Here 

we assume that 10 percent of understory carbon mass is below ground. Th is general rootshoot ratio (0.11) is near the lower range of temperate forest values provided in Penman 

and others (2003) and was selected on the basis of two general assumptions: ratios likely 

will be lower for light-limited understory vegetation compared with larger trees, and a 

greater proportion of all root mass is less than 2 mm in diameter. Th e ratio of down dead 

wood to live-tree estimates were developed by FORCARB2 simulations and applied at 

the plot level (Smith and others 2004a, U.S. EPA 2009).

Estimates of forest-fl oor and soil organic carbon are not based on the carbon density of 

trees. Forest-fl oor carbon is based on region, forest type, and stand age and determined 

by equations of Smith and Heath (2002), which are applied at the plot level. Estimates 

of soil organic carbon (U.S. EPA 2009) are based on the national STATSGO spatial 

database (USDA Soil Conserv. Serv. 1991) and the general approach described by 

Amichev and Galbraith (2004). Th ey represent average soil organic carbon according to 

region and forest-type group in the current FIADB. Th ese values for soil carbon stock 

and the stock-change calculated fl ux do not include expected eff ects of past land use 

change.

Not all inventory data used by CCT are amenable for making the carbon estimates from 

plot-level data. An historical focus of the FIA program was to provide information on 

timber resources of the United States. For this reason, some forest land that was less 

productive or where harvesting was prohibited by law was surveyed less intensively. Th is 

generally means that forest type and area were identifi ed by aerial photography but trees 

were not measured in the fi eld. However, all annualized surveys initiated since 1998 

have followed a new national plot design for all forest land (USDA For. Serv. 2009a). 

Th e practical eff ect on estimating forest carbon stocks from 1990 through the present 

is that some older surveys of lands do not include the individual-tree or stand-level 

measurements, which are necessary inputs to FORCARB2 for predicting tree carbon. 

A preliminary analysis identifi ed the surveys with such data gaps on forest land; these 

were fi lled by assigning average carbon densities calculated from the more complete, later 

inventories from the respective states. Th ese forests are classifi ed as reserved (harvesting is 

prohibited by law) or other (low productivity and not timberland or reserved).

Annualized Estimates from Survey Summaries

Th e overall approach for estimating annualized forest-carbon stocks and fl ux (net 

annual stock change) is to fi rst interpolate stocks between successive survey summaries 

as defi ned by the survey summary fi le and then extrapolate up to the current calendar 

year. Carbon stocks, classifi ed according to forest, are interpolated or extrapolated to 

January 1 of each year. For example, interpolating between successive stock summaries 

in Table 4 produces the annualized stock estimates provided in Table 5. Th is process is 

repeated for each carbon pool and the area and volume totals. Net annual stock change, 

or fl ux, is estimated for each carbon pool in each state as the diff erence between the 
27

Table 5.—Annualized stock and fl ux based on fi ve surveys in Maine as summarized in Table 4; note that values are rounded and the convention for a net annual increase in ecosystem carbon stock is a negative fl ux

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

StockLive above ground (Tg C)

402 401 400 399 399 398 398 398 399 399 400 400 401 402 404 405 406 407 408 410

Live below ground (Tg C)

81 81 81 81 80 80 80 80 81 81 81 81 81 82 82 82 82 83 83 83

Dead wood (Tg C) 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 81 80Litter (Tg C) 186 185 183 182 180 179 177 175 173 171 169 167 166 167 168 169 170 171 172 172Soil organic carbon (Tg C)

607 605 603 602 600 598 597 596 595 593 592 591 590 590 589 588 587 586 586 585

FluxLive above ground (Tg C /yr)

0.75 0.75 0.75 0.75 0.75 -0.05 -0.46 -0.46 -0.46 -0.46 -0.46 -0.80 -1.22 -1.22 -1.22 -1.22 -1.22 -1.22 -1.22

Live below ground (Tg C /yr)

0.20 0.20 0.20 0.20 0.20 -0.04 -0.16 -0.16 -0.16 -0.16 -0.16 -0.22 -0.29 -0.29 -0.29 -0.29 -0.29 -0.29 -0.29

Dead wood (Tg C /yr)

-0.07 -0.07 -0.07 -0.07 -0.07 -0.07 -0.07 -0.07 -0.07 -0.07 -0.07 0.02 0.13 0.13 0.13 0.13 0.13 0.13 0.13

Litter (Tg C /yr) 1.48 1.48 1.48 1.48 1.48 1.80 1.97 1.97 1.97 1.97 1.97 0.69 -0.88 -0.88 -0.88 -0.88 -0.88 -0.88 -0.88Soil organic carbon (Tg C /yr)

1.83 1.83 1.83 1.83 1.83 1.34 1.08 1.08 1.08 1.08 1.08 0.95 0.80 0.80 0.80 0.80 0.80 0.80 0.80

Forest area (kha) 7,189 7,184 7,179 7,174 7,169 7,164 7,163 7,163 7,164 7,164 7,165 7,165 7,165 7,163 7,162 7,160 7,159 7,158 7,156 7,155Timberland area (kha)

6,900 6,892 6,885 6,878 6,870 6,863 6,871 6,888 6,904 6,920 6,937 6,953 6,959 6,951 6,943 6,935 6,927 6,919 6,911 6,903

Live growing-stock volume (Mm3)

599 598 596 595 593 592 596 603 610 617 623 630 635 636 637 639 640 642 643 645
28

succeeding year and the current year’s carbon stock, for example, fl ux for 2002 equals 

stock for 2002 minus stock for 2003. Note that this calculation of net annual fl ux sets 

the sign convention that net sequestration of carbon by forests is a negative fl ux. Th e 

moving window averages used to defi ne the annual inventory evaluations in the FIADB 

can aff ect the annualized estimates for some states. Th e means for selecting subsets 

of available evaluations are described above in the “Produce Tables of Annualized 

Estimates” section.

CONCLUSIONS

Th e basic results of the carbon calculator are the survey summary fi le and the tabular 

summaries of stocks and fl uxes. Carbon stock and fl ux estimates as processed by this 

revised version of CCT are the basis for the forest ecosystem carbon change values 

reported to the U.S. EPA by the U.S. Forest Service for inclusion in the annual 

inventory of greenhouse gas emissions and sinks.

Two infl uences on national-scale forest carbon budgets and overall carbon sequestration 

are not explicitly included in the CCT calculations: carbon in harvested wood products, 

and eff ects of land use change on soil. Long-lived wood products and sealed landfi lls 

represent signifi cant pools of sequestered forest carbon. Similarly, current carbon 

estimates do not include explicit calculation of eff ects of land use change. For example, 

estimates of soil organic carbon are broad regional averages according to forest type 

and do not include the expected long-term accumulation of soil carbon following 

aff orestation. In a broader sense, net annual carbon fl ux is the diff erence between total 

carbon on forest land at one time and total carbon fl ux on forest land the following 

year. Th is means that change in specifi c carbon density (Mg C/ha) or total forest land 

(hectares) can aff ect fl ux. A needed improvement, as an add-on or through further 

subdivision of the survey summary fi le, is the ability to distinguish net annual fl ux for 

the following three categories of forest land according to Penman and others (2003): 

forest remaining forest, forest becoming nonforest, and nonforest becoming forest.

ACKNOWLEDGMENTS

David Herman (U.S. Forest Service, retired) developed the initial interface and 

programming. Comments on early versions were provided by Richard Birdsey (U.S. 

Forest Service), Barbara Braatz (U.S. Environmental Protection Agency), and Tom 

Wirth (U.S. Environmental Protection Agency). We also acknowledge the invaluable 

assistance of U.S. Forest Service, Forest Inventory and Analysis Program employees with 

respect to the acquisition and interpretation of the inventory data. Th ey include Carol 

Alerich, David Azuma, Gary Brand, Brett Butler, Ali Conner, Larry DeBlander, Jeremy 

Fried, Andrew Gray, Mark Hansen, Linda Heatherly, Elizabeth LaPoint, Patrick Miles, 

Renee O’Brien, Ronald Piva, James Rosson, Larry Royer, the late Victor Rudis, Karen 

Waddell, Richard Widmann, and Sharon Woudenberg. Funding support was provided 

by the U.S. Environmental Protection Agency and the RPA program of the Forest 

Service.
29

LITERATURE CITED

Amichev, B.Y.; Galbraith, J.M. 2004. A revised methodology for estimation of forest 

soil carbon from spatial soils and forest inventory data sets. Environmental 

Management. 33(Suppl. 1): S74-S86.

Bechtold, W.A.; Patterson, P.L. 2005. Th e enhanced forest inventory and analysis 

program—national sampling design and estimation procedures. Gen. Tech. Rep. 

SRS-80. Asheville, NC: U.S. Department of Agriculture Forest Service, Southern 

Research Station. 85 p.

Birdsey, R.A. 1996. Carbon storage for major forest types and regions in the 

coterminous United States. In: Sampson, N.; Hair, D., eds. Forests and global 

change. Volume 2: forest management opportunities for mitigating carbon emissions. 

Washington, DC: American Forests: 1-25.

Birdsey, R.A.; Heath, L.S. 1995. Carbon changes in U.S. forests. In: Joyce, L.S., ed. 

Productivity of America’s forests and climate change. Gen. Tech. Rep. RM-GTR-271. 

Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain 

Research Station: 56-70.

Birdsey, R.; Heath, L.S. 2001. Forest inventory data, models, and assumptions for 

monitoring carbon fl ux. In: Lal, R., ed. Soil carbon sequestration and the greenhouse 

eff ect. SSSA Spec. Publ. 57. Madison, WI: Soil Science Society of America: 125-135.

Hansen, M.H.; Frieswyk, T.; Glover, J.F.; Kelly, J.F. 1992. Th e eastwide forest inventory 

data base: users manual. Gen. Tech. Rep. NC-151. St. Paul, MN: U.S. Department 

of Agriculture, Forest Service, North Central Research Station. 48 p.

Heath, L.S.; Smith, J.E.; Birdsey, R.A. 2003. Carbon trends in U. S. forest lands: a 

context for the role of soils in forest carbon sequestration. In: Kimble, J.M.; Heath, 

L.S.; Birdsey, R.A.; Lal, R., eds. Th e potential of US forest soils to sequester carbon 

and mitigate the greenhouse eff ect. Boca Raton, FL: CRC Press: 35-45.

Intergovernmental Panel on Climate Change. 1997. Revised 1996 IPCC Guidelines 

for national greenhouse gas inventories, volumes 1-3. Bracknell, UK: 

Intergovernmental Panel on Climate Change, WGI Technical Support Unit.

Jenkins, J.C.; Chojnacky, D.C.; Heath, L.S.; Birdsey, R.A. 2003. National-scale 

biomass estimators for United States tree species. Forest Science. 49: 12-35.

Miles, P. 2008. A simplifi ed Forest Inventory and Analysis Database: FIADB-Lite.

Gen. Tech. Rep. NRS-30. Newtown Square, PA: U.S. Department of Agriculture, 

Forest Service, Northern Research Station. 42 p.
30

Penman, J.; Gytarsky, M.; Hiraishi, T. [and others], eds. 2003. Good practice guidance 

for land use, land use change, and forestry. Hayama, Kanagawa, Japan: Institute 

for Global Environmental Strategies for the Intergovernmental Panel on Climate 

Change. 502 p. http://www.ipcc-nggip.iges.or.jp/public/gpglulucf/gpglulucf.html (15 

December 2009).

Smith, J.E.; Heath, L.S. 2002. A model of forest fl oor carbon mass for United 

States forest types. Res. Pap. NE-722. Newtown Square, PA: U.S. Department of 

Agriculture, Forest Service, Northeastern Research Station. 37 p.

Smith, J.E.; Heath, L.S. in prep. Approach for calculating forest carbon stock-change 

estimates in the United States using annualized inventory data.

Smith, J.E.; Heath, L.S.; Jenkins, J.C. 2003. Forest volume-to-biomass models and 

estimates of mass for live and standing dead trees of U.S. forests. Gen. Tech. Rep. 

NE-298. Newtown Square, PA: U.S. Department of Agriculture, Forest Service, 

Northeastern Research Station. 57 p.

Smith, J.E.; Heath, L.S.; Woodbury, P.B. 2004a. How to estimate forest carbon for 

large areas from inventory data. Journal of Forestry. 102: 25-31.

Smith, W.B.; Miles, P.D.; Perry, C.H.; Pugh, S.A. 2009. Forest resources of the United 

States, 2007. Gen. Tech. Rep. WO-78. Washington, DC: U.S. Department of 

Agriculture, Forest Service. 336 p.

Smith, W.B.; Miles, P.D.; Vissage, J.S.; Pugh, S.A. 2004b. 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.

Smith, W.B.; Vissage, J.S.; Darr, D.R.; Sheffi eld, R.M. 2001. Forest resources of the 

United States, 1997. Gen. Tech. Rep. NC-219. St. Paul, MN: U.S. Department of 

Agriculture, Forest Service, North Central Research Station. 190 p.

U.S. Department of Agriculture, Forest Service. 2009a. Th e forest inventory and 

analysis database: database description and users manual version 4.0 for phase 

2, revision 1. Washington, DC: U.S. Department of Agriculture, Forest Service, 

Washington Offi ce. [Online publication.] http://fi a.fs.fed.us/library/databasedocumentation/draft%20FIADB_user%20guide_v4-0_p2_05_27_09_pdf.pdf (15 

December 2009).

U.S. Department of Agriculture, Forest Service. 2009b. Forest inventory and analysis 

national program—data and tools - FIA data mart. Washington, DC: U.S. 

Department of Agriculture, Forest Service. http://199.128.173.17/fi adb4-downloads/

datamart.html (15 December 2009).
31

U.S. Department of Agriculture, Forest Service. 2009c. Forest inventory and analysis 

national program - user information. Washington, DC: U.S. Department of 

Agriculture, Forest Service. http://fi a.fs.fed.us/tools-data/docs/default.asp (15 

December 2009).

U.S. Department of Agriculture, Soil Conservation Service. 1991. State soil geographic 

(STATSGO) data base data use information. Misc. Publ. 1492, Fort Worth, TX: 

U.S. Department of Agriculture, Natural Resources Conservation Service, National 

Soil Survey Center.

U.S. Environmental Protection Agency. 2009. Inventory of U.S. greenhouse gas 

emissions and sinks: 1990-2007. USEPA #USEPA 430-R-09-004. Washington, DC: 

U.S. Environmental Protection Agency, Offi ce of Atmospheric Programs. 

http://epa.gov/climatechange/emissions/usinventoryreport09.html (10 March 2010).

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, Pacifi c Northwest Research Station. 106 p.

Woudenberg, S.W.; Farrenkopf, T.O. 1995. Th e westwide forest inventory data 

base: user’s manual. Gen. Tech. Rep. INT-317. Ogden, UT: U.S. Department of 

Agriculture, Forest Service, Intermountain Research Station. 67 p.
32

APPENDIX A

Contents of Electronic Media

Th ree main folders:

 CCT

 ArchiveRPAData

 FIA_zipped

CCT folder has fi ve subfolders:

 CCTFiles

 DataFiles

 OutputFiles

 SurveySummaryFiles

 UserGuideandExamples

CCTFiles folder:

CCT.chm - Th e compiled help fi le accessed in CCT by clicking on “Help.”

CCTv40.exe - Th e CCT executable; it is recommended that you create a shortcut to run 

this fi le.

CCT.ini - Th is text fi le is used as a storage area for the most recently used fi le and folder 

locations, as well as the current FIA download web address.

Copy of CCT.ini - Th is text fi le stores the original text for fi le and folder locations and the 

initial FIA download web address; if CCT.ini becomes corrupted, this fi le may be copied 

into its place.

GINO.CON and various .dll’s (dynamic link libraries) - Th ese fi les are needed by CCT 

to produce the graphical user interface. Do NOT delete, move, or edit these fi les in 

any way! For CCT to function properly, these fi les must be in the same directory as 

CCTv40.exe. If they are accidentally deleted or corrupted, the initial set should be 

retrieved from the electronic media.

DataFiles folder:

Th is folder can be used to place data fi les for use by CCT, though it is not necessary, 

that is, you may specify any folder location in CCT. It initially contains several example 

unzipped data (FIADB 4.0) fi les. Note that four fi les are included for each inventory. 

When the FIADB state fi les are unzipped, numerous fi les are extracted. However, CCT 

only requires these four; the others can be deleted or retained at your discretion. All fi les 

resulting from unzipping of the EASTWIDE, WESTWIDE, and SOFIA fi les are needed 

by CCT.
33

OutputFiles folder:

Th is folder is initially empty. It can be used as the location for output fi les from CCT; 

however, you may specify any folder location in CCT. Output fi les from CCT are 

named as follows: (survey summary fi le name)_(carbon pool abbreviation)_(table 

name).csv; for example, the initial output fi le containing below-ground biomass stocks 

(one of the reporting pools) would have the fi lename survey_summary_04Dec09_Rpt_

BGBiomassStock.csv.

SurveySummaryFiles folder:

survey_summary_04Dec09.csv - Th is is the initial survey summary fi le for use in this 

version of CCT. Th e date in the fi lename indicates the date the FIADB fi les were last 

updated from the FIA download website. Additional survey summary fi les can be created 

manually or by saving to a new fi le name within CCT. Th e fi les can be placed in this 

folder location but any location can be specifi ed in CCT.

UserGuideandExamples folder:

A copy of this user’s guide.

Stock&Flux_ReportingPools_04Dec09.xls - A spreadsheet version of the CCT output 

tables for the reporting set of carbon pools generated by using the initial survey summary 

fi le (survey_summary_04Dec09.csv). Th e fi rst worksheet in the fi le lists the tables with the 

names of their corresponding source fi les.

Stock&Flux_ComprehensivePools_04Dec09.xls - A spreadsheet version of the CCT output 

tables for the comprehensive set of carbon pools generated by using the initial survey 

summary fi le (survey_summary_04Dec09.csv). Th e fi rst worksheet in the fi le lists the 

tables with the names of their corresponding source fi les.

ReadMe fi les (if any) - Text fi les with information on updates or use of CCT on specifi c 

operating systems; this is information not included in this user’s guide.

ArchiveRPAData folder:

RPAinputs2CCT_22DEC09.zip - Th is is a .zip fi le containing a single .xls fi le by the 

same name. Th is spreadsheet contains the RPA data which were used to fi ll gaps which 

could not be covered through the use of available FIA inventory data. Th e fi le includes 

plot-level data from the 1987, 1997 and 2002 RPA assessments with corresponding 

carbon values based on FORCARB2 estimators.

FIA_zipped folder:

Th is folder contains all FIA inventory data used in creating the initial survey summary 

fi le (survey_summary_04Dec09.csv) and thereby used to generate the initial set of CCT 

output tables. Th e bulk of the folder consists of zipped fi les containing the FIADB 4.0 

data fi les used by CCT. Th ese represent the most recent set of available FIA inventories 

as of December 4, 2009. In addition, there are zipped fi les for each of the older-format 

inventories which were used to cover gaps in the data record (see Appendix B). Th ese 

are identifi ed by the prefi xes EASTWIDE, WESTWIDE, and SOFIA. Data fi les must 

be extracted from these zipped fi les before they can be processed by CCT (see DataFiles 

folder for additional information).
34

APPENDIX B

Alternative Inventory File Formats

Th e primary data source for CCT is the FIA database (FIADB 4.0). Future changes to 

the FIADB format will require updates to CCT; these updates will be posted at http://

nrs.fs.fed.us/carbon/tools or http://www.nrs.fs.fed.us/pubs/2394. As discussed in the 

text, data gaps were fi lled through the use of other inventories available in earlier formats. 

Th ese included inventories in Eastwide format (Hansen and others 1992), Westwide 

format (Woudenberg and Farrenkopf 1995), and an unpublished format available from 

the FIA’s Southern Region (SOFIA). Th us, CCT can process fi les in these formats. Th is 

section provides some additional information regarding these fi les.

During CCT development, numerous datasets were obtained in the three formats, but 

only 13 inventories were retained to supplement the FIADB data for the 49 states over 

the pre-1990-to-present interval. Th ese included four Eastwide, fi ve Westwide, and four 

SOFIA (Table 2). Zipped fi les representing each of these 13 inventories are included 

in the “FIA_zipped” folder. Th e fi le prefi xes are EASTWIDE, WESTWIDE, and 

SOFIA, respectively. Each zipped fi le contains the fi les CCT needs to process these older 

inventories. Upon unzipping the fi les, the extracted fi les are immediately available for 

use in CCT. Each of the fi le formats includes several comma-delimited input fi les which 

are read by CCT. Th ey are county(cty), plot(plt) and tree(tre) fi les for the Eastwide and 

Westwide data. Th e SOFIA data fi les have the designations STOCK, DESN, and TR. 

CCT identifi es the database format and fi le type according to the fi lenames.

Th e data structure of these earlier inventories diff ered from the FIADB 4.0 structure. 

Many of the currently recorded fi elds were absent or present in a distinctly diff erent 

format. Where possible, data were “translated” from the older format to the current; 

where this was not possible, the data were considered “missing.” For example, the 

FIADB 4.0 database structure has the plot-level fi eld RESERVCD, which indicates 

whether a plot is reserved forest. Th is fi eld is not present in the Eastwide database 

structure; however, it can be gleaned from the fi eld GLUCUR (current land use class). 

Likewise, the FIADB 4.0 fi eld FORTYPCD (or forest type) was derived from the 

Westwide fi eld TYPCUR; the Westwide codes were matched as closely as possible 

to the current codes. Th is “translation” procedure is done by the CCT application; 

should additional fi les be obtained in these earlier formats, preprocessing would not be 

necessary other than to ensure that the fi lenames and database structure follow the same 

conventions as those included in the CCT package.

An additional adjustment was made to these early inventory fi les by the authors; to 

conform to current federal privacy regulations, all location and detailed ownership 

information were removed from the fi les prior to writing to the available electronic 

media.
Printed on Recycled Paper

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

the basis of race, color, national origin, age, disability, and where applicable, sex, marital status, familial 

status, parental status, religion, sexual orientation, genetic information, political beliefs, reprisal, or because 

all or part of an individual’s income is derived from any public assistance program. (Not all prohibited 

bases apply to all programs.) Persons with disabilities who require alternate 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 fi le a complaint of discrimination, write to USDA, Director, Offi ce 

of Civil Rights, 1400 Independence Avenue, S.W., Washington, DC 20250-9410, or call (800)795-3272 

(voice) or (202)720-6382 (TDD). USDA is an equal opportunity provider and employer. 

Smith, James E.; Heath, Linda S.; Nichols, Michael C. 2007. U.S. forest carbon 

calculation tool: forest-land carbon stocks and net annual stock change. 

Revised. Gen. Tech. Rep. NRS-13. Newtown Square, PA: U.S. Department of 

Agriculture, Forest Service, Northern Research Station. 34 p. [DVD-ROM].

The Carbon Calculation Tool 4.0, CCTv40.exe, is a computer application that 

reads publicly available forest inventory data collected by the U.S. Forest Service’s 

Forest Inventory and Analysis Program (FIA) and generates state-level annualized 

estimates of carbon stocks on forest land based on FORCARB2 estimators. 

Estimates can be recalculated as new inventory data become available. The input 

set of FIA data fi les available on the Internet (as well as some older inventory fi les 

used to fi ll in gaps) are summarized by the application, converted to carbon stocks, 

and saved as part of a state or substate level “survey summary” fi le. This is used to 

produce state-level and national tables with annualized carbon stocks and fl ux (or net 

stock change) beginning with the year 1990. This user’s guide includes instructions 

for use, example data sets, and a discussion of methods and assumptions.

KEY WORDS: greenhouse gas inventory, carbon sequestration, forest inventory, FIA
www.nrs.fs.fed.us