Forest

Service

United States Department of Agriculture

General Technical

Report NRS-154

Northern

Research Station November 2015

The U.S. Forest Carbon 

Accounting Framework: Stocks 

and Stock Change, 1990-2016
Visit our homepage at: http://www.nrs.fs.fed.us/

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November 2015 Fax: 740-368-0152

Manuscript received for publication August 2015

As a signatory to the United Nations Framework Convention on Climate Change, the 

United States annually prepares an inventory of carbon that has been emitted and 

sequestered among sectors (e.g., energy, agriculture, and forests). For many years, the 

United States developed an inventory of forest carbon by comparing contemporary forest 

inventories to inventories that were collected using different techniques and definitions 

from more than 20 years ago. Recognizing the need to improve the U.S. forest carbon 

inventory budget, the United States is adopting the Forest Carbon Accounting Framework, 

a new approach that removes this older inventory information from the accounting 

procedures and enables the delineation of forest carbon accumulation by forest growth, 

land use change, and natural disturbances such as fire. By using the new accounting 

approach with consistent inventory information, it was found that net land use change 

is a substantial contributor to the United States forest carbon sink, with the entire forest 

sink offsetting approximately 15 percent of annual U.S. carbon dioxide emissions from 

the burning of fossil fuels. The new framework adheres to accounting guidelines set forth 

by the Intergovernmental Panel on Climate Change while charting a path forward for the 

incorporation of emerging research, data, and the needs of stakeholders (e.g., reporting 

at small scales and boreal forest carbon).

Cover Image: © Michael Halbert 2000

Abstract

Quality Assurance 

This publication conforms to the Northern Research Station’s Quality Assurance 

Implementation Plan which requires technical and policy review for all scientific 

publications produced or funded by the Station. The process included a blind technical 

review by at least two reviewers, who were selected by the Assistant Director for 

Research and unknown to the author. This review policy promotes the Forest Service 

guiding principles of using the best scientific knowledge, striving for quality and 

excellence, maintaining high ethical and professional standards, and being responsible 

and accountable for what we do. 
The U.S. Forest Carbon 

Accounting Framework: 

Stocks and Stock Change, 

1990-2016

Christopher W. Woodall, John W. Coulston, 

Grant M. Domke, Brian F. Walters, David N. Wear, 

James E. Smith, Hans-Erik Andersen, Brian J. Clough, 

Warren B. Cohen, Douglas M. Griffith, 

Stephen C. Hagen, Ian S. Hanou, Michael C. Nichols, 

Charles H. Perry, Matthew B. Russell, 

James A. Westfall, and Barry T. Wilson
About the Authors

Christopher W. Woodall is a research 

forester with the U.S. Forest Service, Northern 

Research Station, St. Paul, MN, 55108.

John W. Coulston is a research forester with 

the U.S. Forest Service, Southern Research Station, 

Blacksburg, VA, 24061.

Grant M. Domke is a research forester with the 

U.S. Forest Service, Northern Research Station, 

St. Paul, MN, 55108.

Brian F. Walters is a forester with the U.S. 

Forest Service, Northern Research Station, St. Paul, 

MN, 55108.

David N. Wear is a Project Leader with the U.S. 

Forest Service, Southern Research Station, Raleigh, 

NC, 27695.

James E. Smith is a research plant physiologist 

with the U.S. Forest Service, Northern Research 

Station, Durham, NH, 03824.

Hans-Erik Andersen is a research forester with 

the U.S. Forest Service, Pacific Northwest Research 

Station, Seattle, WA, 98103.

Brian J. Clough is a research associate with 

the University of Minnesota, Department of Forest 

Resources, St. Paul, MN, 55108.

Warren B. Cohen is a research forester with the 

U.S. Forest Service, Pacific Northwest Research 

Station, Corvallis, OR, 97331.

Douglas M. Griffith is a forester with the 

U.S. Forest Service, Northern Research Station, 

Newtown Square, PA, 19073.

Stephen C. Hagen is a senior scientist with 

Applied GeoSolutions, Newmarket, NH, 03857.

Ian S. Hanou is the Director, Plan-It Geo, LLC., 

Arvada, CO, 80002.

Michael C. Nichols is an information technology 

specialist with the U.S. Forest Service, Northern 

Research Station, Durham, NH, 03824.

Charles H. Perry is a research soil scientist with 

the U.S. Forest Service, Northern Research Station, 

St. Paul, MN, 55108.

Matthew B. Russell is an assistant professor at 

the University of Minnesota, Department of Forest 

Resources, St. Paul, MN, 55108.

James A. Westfall is a research forester with 

the U.S. Forest Service Northern Research Station, 

Newtown Square, PA, 19073.

Barry T. Wilson is a research forester with the 

U.S. Forest Service, Northern Research Station, St. 

Paul, MN, 55108.

Author Contributions

C.W. Woodall is the carbon accounting group leader 

for the Forest Inventory and Analysis (FIA) program 

and coordinated the development of this new 

accounting approach and creation of this GTR. 

C.W. Woodall, J.W. Coulston, G.M. Domke, 

B.J. Clough, M.C. Nichols, C.H. Perry, and M.B. 

Russell developed the initial framework for carbon 

accounting at a workshop in October 2014. 

J.W. Coulston and D.N. Wear developed the R 

code and techniques for projecting annual forest 

inventory, separating land use change from forests 

remaining forest and attribution to disturbance. 

G.M. Domke developed new techniques for 

estimating litter and soil organic carbon. 

B.F. Walters conducted the majority of the FIA 

database development for this project and created 

infographics and maps.

J.E. Smith contributed writing material. 

H.-E. Andersen contributed information on Alaska’s 

carbon accounting. 

B.J. Clough contributed the Bayesian uncertainty 

description. 

W.B. Cohen contributed the Landsat/LiDAR research 

description. 

D.M. Griffith contributed carbon pool maps. 

S.C. Hagen contributed the NASA-Carbon 

Monitoring System study description and results. 

I.S. Hanou contributed to the description and 

development of the phone and tablet carbon 

application. 

C.H. Perry contributed the U.S. forest carbon in 

context infographic.

M.B. Russell contributed to the pool estimation and 

phone application description. 

J.A. Westfall contributed the national volume/

biomass study description. 

B.T. Wilson contributed carbon maps and the 

disaggregation description. 
Preface

A Modeling System That Moves Annual 

Forest Inventory Data through Time and 

Attributes Changes to Disturbances and 

Land Use

The United States is currently in the process 

of adopting a new approach to forest carbon 

accounting. The Forest Carbon Accounting 

Framework (FCAF) will incorporate the most 

consistently measured annual forest inventories 

(including all the pools of forest carbon) while 

aligning with projections of forest resources. Such 

an approach will reduce inconsistencies, and thus 

uncertainty, associated with the U.S. forest carbon 

baseline while empowering policy makers in light 

of potential future greenhouse gas emission 

reduction targets. 

One role for the new framework is meeting the 

reporting commitments for the United States 

under the United Nations Framework Convention 

on Climate Change (UNFCCC). The forest 

ecosystem carbon estimates contained herein 

are initial estimates from the new FCAF that is 

in development for official release as part of the 

National Greenhouse Gas Inventory submission 

of the United States to the UNFCCC in 2016. 

The United Nations Intergovernmental Panel on 

Climate Change documents Good Practice Guidance 

(IPCC 2006) for several attributes that national 

carbon inventories should aspire to: transparency, 

consistency, comparability, completeness, and 

accuracy. In this context transparency means that 

the data sources, assumptions, and methodologies 

are clearly documented. Consistency assures the 

estimates for all reported inventory years account 

for changes in data or methods over time. Forest 

carbon inventories should also be comparable across 

Nations, an attribute that is empowered 

by clear documentation. Completeness refers 

to the identification of all appropriate carbon in 

forest ecosystems (i.e., pools). Accuracy is attained 

by removing bias and minimizing uncertainty 

as much as is practically possible. The new 

FCAF of the United States seeks to embrace all 

these tenets through the development of a novel 

accounting approach that addresses attribution (i.e., 

disturbances) and land use change and incorporates 

emerging science and diverse data sources, all while 

providing clear documentation such as we have done 

in this first publication.

Forest Inventory and Analysis field crew measuring a plot 

in southeast Alaska. Photo by U.S. Forest Service.
Contents

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

The U.S. Forest Carbon Cycle in Context . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Forest Carbon Inventories: A History of Improvements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

The Forest Carbon Accounting Framework Vision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

How do the Forest Carbon Estimates in this Report 

 Differ from Previous Greenhouse Gas Inventories? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

What is a Forest? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

How Much Forest in the United States is Managed? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

What is in a Forest Carbon Pool? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

Forest Carbon Indicators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

Forest Carbon Vital Signs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

The U.S. Forest Carbon Baseline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

The Carbon Baseline in Context . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

Forest Carbon Stocks and Pools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

Change in Forest Stocks by Pool . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

Carbon Stocks and Stock Change . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

Regional Forest Carbon Estimates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

National Forest Carbon Estimates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

Methods and Data Sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

FCAF Technical Specifications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

Carbon in Soil Organic Matter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

Estimation of Other Pools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

Attribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

Projections: Moving Annual Inventory Data Backward and Forward in Time . . . . . . . . . 22

Theoretical Age Transition Matrices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

Projections and Backcast for Pacific Coast, Rocky Mountains, West Texas, 

 and West Oklahoma . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

Projections and Backcast for North, South, East Texas, and East Oklahoma . . . . . . . . . 24

Total Uncertainty . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

Annual Data Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
Planned Improvements to the Forest Carbon Inventory . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

Uncertainty . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

More Data: Forests, Woodlands, and Urban Trees . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

Refined Land Use Assessment: The Image Change Estimation Program . . . . . . . . . . . . . . 29

Reconciling Improvements to Forest Accounting with All Land Uses . . . . . . . . . . . . . . . . . . 30

Pool Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

The Boreal Forests of Interior Alaska . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

Disaggregation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

Harvested Wood Products . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

Research to Improve and Disseminate the Forest Carbon Inventory . . . . . . . . . . . . . . . . . 35

National Volume/Biomass Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

Remote Sensing of Forest Carbon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

Inventorying Boreal Forest Carbon: The Tanana Experiment . . . . . . . . . . . . . . . . . . . . . . . . 36

Bayesian Hierarchical Models for Uncertainty . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

Surf Forest Carbon with your Thumb: The Forest Carbon Xplorer . . . . . . . . . . . . . . . . . . . . 38

Timely Attribution of Carbon Emissions by Disturbance . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

English Equivalents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

Introduction

Carbon dioxide is one of the most important 

greenhouse gases that contributes to the warming 

of our planet via the greenhouse gas effect. Carbon 

is emitted through the combustion of fossil fuels 

and through the decay and combustion of organic 

material such as wood. In contrast, carbon can 

be sequestered through the growth of trees. 

Recognizing the need to monitor the emission 

and sequestration of carbon on Earth, the United 

Nations Framework Convention on Climate Change 

(UNFCCC) called for the annual monitoring of 

carbon among signatory Nations. This annual 

monitoring mechanism is known as a National 

Greenhouse Gas Inventory (NGHGI) which contains 

accounting of carbon emissions and removals (i.e., 

sequestration). The United States is a signatory to 

the UNFCCC whereby annual inventory reports 

of carbon are developed. This document provides 

additional information on the context of the U.S. 

forest carbon cycle, a brief history of carbon 

accounting efforts, and a vision for the future of 

forest carbon accounting in the United States.

Of all the land uses across the United States 

(e.g., settlements, croplands, or wetlands), forests 

sequester the vast majority of carbon from the 

atmosphere, which partially offsets carbon emissions 

from sources such as fossil fuel combustion. Due 

to this important role of forests in the carbon 

budget, there is a continuous effort to improve 

the accounting of forest carbon across the diverse 

ecosystems and ownerships of the United States. 

In this document we introduce a new approach to 

forest carbon accounting in the United States, the 

Forest Carbon Accounting Framework (FCAF). 

Across this broader document you will gain access 

to preliminary estimates from the 2016 carbon 

inventory of U.S. forests using the FCAF along with 

contextual analysis of these estimates. In addition, 

because new data and scientific advances occur 

annually, planned inventory improvements and 

promising research directions will be outlined as a 

potential roadmap for the future.

The U.S. Forest Carbon Cycle in Context

Currently, annual net sequestration of carbon 

in managed U.S. forests offsets approximately 

15 percent of the annual emissions of carbon that 

result from the combustion of fossil fuels (Fig. 1). In 

addition, forests contain substantial stores of carbon 

which have accumulated in some pools (e.g., soils) 

for millennia. The amount of carbon stored in U.S. 

forests represents more than 67 years of fossil fuel 

emissions at today’s rate of combustion (see Forest 

Carbon Vital Signs on page 7). However, just as 

there are numerous sources of fossil fuel emissions, 

including electrical generation and transportation, 

the forest also has numerous carbon pools that 

emit carbon through decay and combustion. An 

important difference is that forest carbon pools also 

serve as active sinks of carbon, in strong contrast 

to fossil fuels which can only serve as an emission 

source. New biomass that arises from forest growth 

and expansion that is not emitted via decay or 

combustion can serve as a longer-term store of 

carbon as a wood product, by being assimilated into 

the forest floor or soils, or by remaining in their 

constituent pool (i.e., old growth forests). Without 

the continued expansion and growth of forests, their 

ability to serve as a substantial offset of U.S. fossil 

fuel emissions will be diminished.
Introduction

Figure 1.—The components of the U.S. forest carbon cycle (2016 estimates, pools, and flow) in the context of fossil fuel 

emissions (values for fuel emission estimates from USEPA 2015a). Diagram courtesy of Perry et al. (in press).

Forest Carbon Inventories: 

A History of Improvements

In the United States, monitoring forest carbon 

has been a process of continuous improvement 

as data (both field and remotely sensed) have 

accumulated in the face of emerging research (e.g., 

carbon pool science) (Fig. 2). In the 1990s, Birdsey 

(1992) converted a compendium of periodic forest 

inventories into carbon assessments using numerous 

assumptions about volume to carbon conversions. 

In the 2000s, Heath and Smith ushered in a new era 

of carbon reporting (Smith et al. 2010) that focused 

on comparing the nationally consistent annual forest 

inventories to past periodic inventories to estimate 

carbon change. In the 2010s, carbon monitoring 

(Woodall 2012) has focused on improving pool 

estimation using the full breadth of the national 

forest inventory system (e.g., including soil 

measurements) while developing approaches to 

incorporate emerging remotely sensed information 

(e.g., Landsat and LiDAR) and aligning with 

projection systems.

The Forest Carbon Accounting 

Framework Vision

In response to calls for improved terrestrial carbon 

accounting in the United States stemming from 

the Agricultural Act of 2014, also known as the 

2014 Farm Bill (Agricultural Act 2014), and 

the President’s 2013 Climate Action Plan (U.S. 

Executive Office of President 2013), a cohesive 

carbon accounting framework (the FCAF) was 

envisioned that linked the Forest Inventory and 

Analysis (FIA) annual inventory system of all forest 

carbon pools to modules that compile predictions of 

carbon stocks and stock changes back to the 1990 
Introduction 

Periodic and Inconsistent

Forest Inventory

Annual and Consistent

Forest Inventory

1990 2000 2010 2015

Birdsey’s

carbon storage

report1

FORCARB

model

developed1

Models used to

estimate non-live

tree C pools1

1998 Farm Bill authorizing

annual inventory2

Standing dead tree

sampling2

Downed dead

wood,soil, forest

floor, understory

sampling3

Smith’s carbon

calculation tool

1

Transition from FORCARB

modeled NGHGI estimatesto

NFI data1

Component ratio method for

volume/biomass adopted5

Transferred

woodlands,

incorporated

forest floor

New accounting

framework, incorporate

forest soils obs. 8

Standing dead tree

data incorporated6

Jenkins’

volume/biomass

equations4

Downed dead

data

incorporated7

Modeling

approachesto

belowground,

understory, foliage

Figure 2.—Timeline of the development of national greenhouse gas inventories for U.S. forests showing the phasing out 

and replacement of periodic forest inventories with annual forest inventories. References include: 1(Birdsey 1992, Birdsey 

and Heath 1995, Birdsey 1996, Heath et al. 1996, Smith et al. 2010); 2(U.S. Forest Service 2014a, U.S. Forest Service 

2015a); 3(Woodall et al. 2011a); 4(Jenkins et al. 2003); 5(Woodall et al. 2011b); 6(Woodall et al. 2012); 7(Domke et al. 

2013); 8(See this publication; Domke et al., in prep.). NGHGI = National Greenhouse Gas Inventory; NFI = National Forest 

Inventory.

baseline and forward to user-defined time horizons. 

Recognizing the host of obligatory forest carbon 

reporting needs (e.g., United Nations Framework 

Convention on Climate Change [UNFCCC 2015a], 

Global Forest Resources Assessments [UN FAO 

FRA 2015], United Nations Framework Convention 

on Climate Change Biennial Reports [UNFCCC 

2015b], Montréal Process Criteria and Indicators 

[Montréal Process Working Group, n.d.], and U.S. 

Forest Service Climate Change Scorecard [U.S. 

Forest Service 2015b]), the new framework had 

to be able to estimate the carbon implications of 

disturbances and management and disaggregate land 

use change into deforestation versus afforestation 

effects on carbon stocks and stock changes at 

national and regional scales (Fig. 3). Past approaches 

(Fig. 3a) often depended on reconciling differences 

between two different U.S. forest inventory systems, 

which had a higher probability of creating carbon 

flux estimates stemming from differences in sample 

design rather than changes in forest ecosystems. 

Furthermore, this previous accounting approach did 

not easily enable linkage to forecasting mechanisms 

(Fig. 3a). The new, data-driven accounting 
 Introduction

framework (Fig. 3b) has already been designed 

for implementation at the strategic scale using the 

national forest inventory of the United States, and 

with the appropriate data the framework can be used 

by partners interested in carbon accounting at other 

spatial scales of interest. Beyond the peer review and 

publication of all technical aspects of this vision, 

an outreach and extension component still needs 

to be developed to interpret the results of the new 

accounting system for policy makers, land managers, 

and citizens.

Figure 3.—Hypothetical carbon baselines that illustrate past issues with forest carbon accounting techniques, namely 

(a) misalignment between current baselines and future projections in addition to a lack of attribution to disturbance. It is 

expected that (b) future forest carbon baselines using the techniques described in this document will align baselines with 

projections while affording attribution to disturbance.

-1,000.00

-800.00

-600.00

-400.00

-200.00

0.00

1990 1995 2000 2005 2010 2015 2020

Annualnet flux (Tg C

O2)

Year

0

50

100

150

200

250

Gross e

missio

ns(Tg

C

02)

Harvest

Drought

Wind

Insects &

Disease

Fire

Where We’re Headed

Modeling Annual Data across Space/Time with LUC and Attribution

b

-1,000.00

-800.00

-600.00

-400.00

-200.00

0.00

1990 1995 2000 2005 2010 2015 2020

Annualnet flux (Tg CO2)

Year

Where We’ve Been

Periodic Data Artifacts and Unaligned Projections

a

NGHGI net 

annual flux

Projected 

annual net flux

Land use change 

(LUC) net transfer

Annual net flux (Tg C

O2 ) Annual net flux (Tg C

O2 )
Introduction 

How do the Forest Carbon 

Estimates in this Report Differ 

from Previous Greenhouse Gas 

 Inventories?

• Older, periodic inventories which did not 

cover all managed forest land and were 

inconsistent with current annual inventories 

were removed from the accounting system, 

thus removing inconsistencies and reducing 

carbon baseline uncertainty.

• A new modeling approach was used 

to generate estimates of forest carbon 

derived from the annual inventory system 

both backward (to 1990) and forward (to 

2016) through time to satisfy UNFCCC 

requirements. 

• Repeated measures of the hundreds of 

thousands of permanent plot locations 

were used to inform land use change and 

disturbance effects on carbon stocks.

• In the eastern United States, remeasured 

individual plots were used to estimate carbon 

stocks and change. For the western states, 

the FIA annual inventory panel design was 

divided into two distinct time periods, with 

the older and more recent periods representing 

two independent estimates of the forest carbon 

population. In the future, as annual plots are 

remeasured in the western states, this approach 

can be replaced with remeasured annual plots, 

per the technique adopted for eastern states.

• Harvested wood products were not included 

in this forest ecosystem carbon assessment but 

will be included in the upcoming UNFCCC 

submission.

• This analysis focuses solely on the forest 

sector, so carbon transfers to other land 

uses were subtracted from the forest carbon 

inventory but were not reconciled with 

estimates made independently for other land 

sectors as will be required for the United 

States submission under the UNFCCC. 

• A new approach for estimating soil organic 

carbon, the largest pool of carbon in U.S. 

forests, was included in this analysis (Domke 

et al., in prep.). The approach builds on the 

thousands of in situ observations from across 

U.S. forests in conjunction with modeling 

refinements developed in coordination with 

the International Soil Carbon Network (ISCN; 

http://iscn.fluxdata.org/Pages/default.aspx).

What is a Forest? 

The UNFCCC does not prescribe to a specific 

definition for forest, but instead each country 

determines its own classification. Starting with the 

2015 National Greenhouse Gas Inventory report 

(USEPA 2015a, b) and for this report, forest land is 

defined as: Land at least 120 feet (37 m) wide 

and at least 1 acre (0.5 ha) in size with at least 

10 percent cover (or equivalent stocking) by live 

trees, including land that formerly had such tree 

cover and that will be naturally or artificially 

regenerated. Trees are woody plants having a 

more or less erect perennial stem(s) capable of 

achieving at least 3 inches (7.6 cm) in diameter at 

breast height, or 5 inches (12.7 cm) diameter at root 

collar, and a height of 16.4 feet (5 m) at maturity in 

situ. The definition here includes all areas recently 

having such conditions and currently regenerating 

or capable of attaining such condition in the near 

future. Forest land also includes transition zones, 

such as areas between forest and nonforest lands, 

that have at least 10 percent cover (or equivalent 

stocking) with live trees and forest areas adjacent to 

urban and built-up lands. Unimproved roads, trails, 

streams, and clearings in forest areas are classified 

as forest if they are less than 120 feet (37 m) wide or 

an acre in size. Forest land does not include land that 

is predominantly under agricultural or urban land 

use (Oswalt et al. 2014: 31).

?

?

Forest and hayfield in southern Indiana. Photo by 

Christopher Woodall, U.S. Forest Service.
Introduction

How Much Forest in the 

United States is Managed?

Approximately 34 percent (310 million ha) of the 

U.S. land area is estimated to be forested (Oswalt 

et al. 2014). The most recent forest carbon 

inventories from each of the conterminous 

48 states (U.S. Forest Service 2014a, b) includes 

an estimated 264 million ha (Oswalt et al. 2014) of 

forest land that are considered managed (Ogle et 

al., in prep.) and are included in this inventory. An 

additional 6 million ha of southeast and south central 

Alaskan forest are inventoried and are included in 

this analysis. Annual forest inventory data are not 

yet available for Hawaii and interior Alaska, but 

estimates for these areas are included in Oswalt 

et al. (2014). 

? ?

Standing dead tree in Superior National Forest in 

Minnesota. Photo by Christopher Woodall, U.S. 

Forest Service.

What is in a Forest Carbon Pool? 

Carbon estimates obtained directly from 

an inventory or from inventory-based models are 

classified to account for all forest ecosystem organic 

carbon and fall into the following distinct, nonoverlapping, forest ecosystem carbon pools which 

can be arrayed in various combinations for domestic 

or international (e.g., UNFCCC) reporting:

Live tree biomass—biomass of all 

live trees with a diameter of 2.5 cm 

and greater measured 1.37 m above the 

forest floor (diameter at breast height; 

d.b.h.) including stem, stump, branches, bark, and 

belowground coarse living roots with a diameter 

greater than 2 mm.

Understory vegetation—all live 

biomass including woody shrubs 

and trees smaller than the above tree 

classification, specifically less than 

 2.5-cm d.b.h. 

Standing dead trees—entire 

aboveground and belowground portions 

of standing dead trees with a d.b.h. of 

12.7 cm and greater.

Downed dead wood—all nonliving 

woody material lying on the ground 

and having a diameter greater than 

7.5 cm at transect intersection. This 

pool also includes stumps (both aboveground and 

belowground portions).

Forest floor or litter—includes the 

duff, humus, and fine woody debris 

located above the mineral soil, and 

all nonliving woody biomass with a 

diameter less than 7.5 cm at transect intersection, 

lying on the ground.

Soil organic carbon (SOC)—all 

organic material in soil to a depth of 

1 m, including fine roots but excluding 

the coarse roots of the belowground 

pools.


FOREST CARBON INDICATORS

Forest Carbon Vital Signs

A number of key statistics regarding U.S. forest 

carbon using the new FCAF are presented in 

Figure 4. It is useful to policy makers to evaluate 

forest carbon rates relative to other sectors such 

as fossil fuel emissions. Please refer to the Carbon 

Stocks and Stock Change section on page 12 for 

complete national results.

From 1990 to the Present

AverageAnnual Net Forest

Sequestration

Net Change in LandUse Change as

Percentageof Forest Sink

GrossIncrease in Total Ecosystem

Carbon Stocks

Net Change in Managed Forest

LandArea

Forest Carbon Pool with Largest

Percentage Increase in Stocks

204.9 Tg C yr-1

+10,835,218 ha

5,338 Tg C

Live Trees

+3%

From 2005 to the Present

AverageAnnual Net Forest

Sequestration

Net Change in LandUse Change as

Percentageof Forest Sink

GrossIncrease in Total Ecosystem

Carbon Stocks

Net Change in Managed Forest

LandArea

Forest Carbon Pool with Largest

Percentage Increase in Stocks

214.7 C Tg C yr-1

+4,576,753 ha

2,377 Tg C

Live Trees

-1.3%

67 Years

Time needed for

cumulativeU.S. fossil

fuel emissions of

carbon (at current

rates) to equal the total

amount of carbon

stored in U.S. forests

15 %

Amount of annual

emission of carbon

fromcombustion of

fossil fuelsin the U.S.

currently offset by

growth and expansion

of U.S. forests

Figure 4.—Key statistics for annual U.S. forest carbon monitoring derived from the new Forest Carbon Accounting 

Framework introduced in this document.
 Forest Carbon Indicators

The U.S. Forest Carbon Baseline

Overall, the total net carbon stock change (i.e., 

net sequestration) provided by U.S. forests has 

been trending above 200 Tg C yr-1 since the Great 

Recession began in 2007 (Fig. 5a). There has been 

more fluctuation in net forest sequestration (i.e., net 

growth) due to the effects of variations in regional 

harvest patterns which have a stronger effect on 

sequestration than wildfire (Fig. 5b). Land use 

change has been a steady net positive contributor to 

the forest carbon sink, with a slight decrease in the 

positive rate of change in recent years (Fig. 5a). 

Figure 5.—The 1990-2016 forest carbon baseline (a) and 2011 attribution to disturbance (b), including delineation 

between forests remaining forest and land use change, obtained from the new Forest Carbon Accounting Framework 

in managed forests of the United States.

a

b

U.S. Carbon Dynamics
Forest Carbon Indicators

The Carbon Baseline in Context

Although trends in land use and net forest growth 

in relation to the forest carbon baseline have only 

marginally varied over the past couple of decades, 

more conclusions can be drawn when viewing these 

trends relative to the total forest sector and fossil fuel 

emissions (Fig. 6). As carbon from U.S. fossil fuel 

emissions has slightly diminished over the past few 

years, the offset from both land use change and net 

forest growth have increased, from lows of 5.3 and 

7.2 percent in the early 2000s, to recent highs of 6.4 

and 9.3 percent, respectively. Relative to the entire 

forest ecosystem sector, net forest growth since the 

Great Recession has represented a larger proportion 

of the forest ecosystem sink than carbon transfers 

resulting from net land use change to forests.

0.00

2.00

4.00

6.00

8.00

10.00

12.00

30.00

35.00

40.00

45.00

50.00

55.00

60.00

65.00

1990 1994 1998 2002 2006 2010

Percent of Annual Fossil Fuel Emissions

Percent of Total Forest Sector

Year

Forestsremaining

forest as percent of

forestsector

Land use change as

percent of forest

sector

Forestsremaining

forest as percent of

fossil fuel

emissions

Land use change as

percent of fossil

fuel emissions

Figure 6.—Net annual sequestration resulting from forests remaining forest (i.e., net forest growth) versus land use 

change (i.e., net carbon transferred into forests from other land uses) relative to the forest ecosystem carbon sink and 

carbon from fossil fuel emissions.

Forest Carbon Stocks and Pools

Carbon pools are the individual, nonoverlapping 

categories where carbon resides in a forest, such as 

soils or live trees. Stocks are the amount of carbon in 

each particular pool. Figure 7 displays the proportion 

of U.S. carbon stocks by pool and as a coal 

equivalent, and also shows the spatial delineation of 

each carbon pool across the United States. In some 

regions, such as the upper Great Lakes, the majority 

of forest carbon resides in the soils pool.
10 Forest Carbon Indicators

Figure 7.—Coal equivalents (a) and proportion of forest carbon stocks by pool (b) along with preponderance maps (c) by 

pool across the conterminous United States. Note: Pool preponderance maps from Wilson et al. (2013) will eventually be 

updated per emerging pool estimation techniques (e.g., Domke et al, in review).

Live Tree

Standing Dead

Understory

Down Wood

Litter

Soil Organic

Carbon

= 60 million rail cars

Live Tree

16,248 Tg C

Standing Dead

1,014 Tg C

Understory

735 Tg C

Down Wood

1,737 Tg C

Litter

6,156 Tg C

Soil Organic Carbon

64,876 Tg C

a b

c
Forest Carbon Indicators 11

Change in Forest Stocks by Pool

In the period from 1990 to 2016, the most dynamic 

forest carbon pools included those most closely 

associated with tree growth, removals, and mortality: 

live, standing dead, and downed dead trees. 

However, not all net carbon sequestration occurs in 

forests remaining forests across the reporting period 

as there is net accumulation of forest, and hence, the 

movement of carbon from other land uses and pools 

into forests. As a case study, Woodall et al. (2015) 

examined the movement of carbon from pools in 

other land uses to forests for the eastern United 

States over the period 2000 to 2012 (Fig. 8). For 

the soil organic carbon pool, more than 60 percent 

of carbon accumulation resulted from net positive 

carbon transfer into forests from other land uses 

(e.g., agriculture or developed). In contrast, for the 

aboveground live tree carbon pool only ~18 percent 

of carbon accumulation resulted from net land use 

transfers. 

Figure 8.—FCAF results for changes in forest carbon stocks by pool from 1990-2016 in managed U.S. forests (a), and a 

case study examination (b) of land use change and carbon accretion for the two largest stocks, soil organic carbon and 

aboveground live trees, in eastern U.S. forests, 2000-2012 (Woodall et al. 2015). 

0

2

4

6

8

10

12

14

16

Down dead Litter Soils Standing

dead

Understory Live tree

Change in Carbon Stocks (%)

Forest Carbon Pool

0

10

20

30

40

50

60

70

80

90

100

Soil organic carbon

Netsequestration (%)

Net land

use change

Net forests

remaining

forest

Aboveground live tree

Net land

use change

Net forests

remaining

forest

a

b
12

CARBON STOCKS AND STOCK CHANGE

Regional Forest Carbon Estimates

Forest carbon stocks increased across all regions of 

the United States (Fig. 9). In forests that remained 

forest, carbon accumulation from net forest growth 

resulted in a net annual accumulation in all regions. 

Trends in net forest growth were variable across the 

baseline period, with the North, South, and Pacific 

Coast regions demonstrating an increasing rate of 

net forest growth while the Rocky Mountain region 

had a decreasing (but still positive) rate of carbon 

accumulation. Due to the availability of remeasured 

annual plots in the eastern United States, there is 

greater resolution of the effects of land use change, 

with the effect of net transfers of land into forest 

representing a portion of the total sink nearly equal 

to that of net forest growth. In this documentation, 

general assumptions about rates of land use change 

in western states were adopted based on initial 

results from examining split annual inventory cycles 

(see Methods section on page 16). If resources 

permit continued remeasurement of annual plots in 

the western United States, then FCAF outputs can 

refine these regional land use baselines in the future 

as remeasured data become available.

Figure 9.—Regional disaggregation of forest carbon analysis, 1990-2016: (a) regional delineations, (b) U.S. forest carbon 

stocks, and (c) annual forest carbon flux delineated by forests remaining forest (net forest carbon accretion) and land use 

change (net carbon transfer into forest land use) by region.

b

a
Carbon Stocks and Stock Change 13

1990 1995 2000 2005 2010 2015

0

10

20

30

40

50

60

70

1990 1995 2000 2005 2010 2015

Tg C year-1

Year

Pacific Coast North

Forest remaining forest

Year

Land use transfer

0

10

20

30

40

50

60

70

1990 1995 2000 2005 2010 2015

Tg C year-1

1990 1995 2000 2005 2010 2015

Rocky Mountain South

Year Year

Figure 9 (continued).—Regional disaggregation of forest carbon analysis, 1990-2016: (a) regional delineations, (b) U.S. 

forest carbon stocks, and (c) annual forest carbon flux delineated by forests remaining forest (net forest carbon accretion) 

and land use change (net carbon transfer into forest land use) by region.

c
14 Carbon Stocks and Stock Change

National Forest Carbon Estimates

Since 1990, forests in the United States have 

contributed from 194.4 to 224.5 Tg C∙yr -1 toward 

the net sequestration of atmospheric carbon 

(Table 1). Net land use change (nonforest converted 

to forest after subtracting forest converted to 

nonforest) accounted for a substantial portion of net 

sequestration although forest growth (i.e., growth in 

forest remaining forest) continued to be the major 

component of net sequestration. The total U.S. forest 

carbon stock was tremendous at over 90,000 Tg C 

(Table 2).

Table 1.—Annual carbon flux (Tg C∙yr -1) by net land use change, forests remaining forest, and total 

net sequestration in managed U.S. forests, 1990-2016. Positive values indicate carbon removal from 

the atmosphere.

Year Net land use change Forests remaining forest Net sequestration

1990 75.2 119.2 194.4

1991 75.8 121.4 197.2

1992 76.4 123.2 199.6

1993 77.1 123.1 200.2

1994 77.7 123.1 200.9

1995 78.5 121.4 199.9

1996 79.2 120.1 199.3

1997 79.9 115.2 195.1

1998 80.7 114.2 194.9

1999 81.4 112.9 194.3

2000 82.1 114.5 196.6

2001 82.8 111.8 194.6

2002 83.5 111.1 194.6

2003 84.2 112.0 196.3

2004 85.0 113.2 198.2

2005 85.7 113.8 199.4

2006 86.4 117.5 203.9

2007 87.1 120.7 207.8

2008 87.8 123.3 211.1

2009 88.5 125.4 213.9

2010 89.2 127.2 216.4

2011 89.9 134.6 224.5

2012 90.2 132.1 222.3

2013 90.2 130.6 220.8

2014 90.2 130.3 220.5

2015 90.2 130.0 220.2

2016 90.2 125.7 216.0
Carbon Stocks and Stock Change 15

Table 2.—Forest carbon stocks (Tg C) by pool and year in managed U.S. forests, 1990-2016

 Downed Soil Total

 dead organic Standing Live ecosystem

Year wooda Litter carbon deada Understorya treesa stocks

1990 1,532.0 5,839.2 62,230.3 893.2 725.4 14,208.1 85,428.0

1991 1,538.9 5,852.2 62,323.5 895.9 725.3 14,289.4 85,625.2

1992 1,546.0 5,865.2 62,418.8 898.8 725.2 14,370.8 85,824.8

1993 1,553.2 5,878.2 62,514.5 901.7 725.2 14,452.2 86,025.0

1994 1,560.4 5,891.2 62,610.7 904.8 725.1 14,533.7 86,225.9

1995 1,567.7 5,904.2 62,707.3 907.8 725.1 14,613.7 86,425.8

1996 1,575.0 5,917.0 62,801.2 911.5 725.1 14,695.2 86,625.1

1997 1,582.7 5,928.6 62,897.9 917.5 725.5 14,768.1 86,820.2

1998 1,590.3 5,940.0 62,995.6 923.6 725.8 14,839.6 87,015.1

1999 1,598.0 5,951.5 63,094.0 929.8 726.3 14,909.9 87,209.4

2000 1,605.7 5,963.1 63,193.8 936.2 726.7 14,980.6 87,406.0

2001 1,612.9 5,973.5 63,300.2 942.3 727.6 15,044.1 87,600.6

2002 1,619.9 5,984.4 63,407.0 945.8 728.5 15,109.6 87,795.2

2003 1,627.1 5,995.4 63,514.3 949.3 729.4 15,176.0 87,991.5

2004 1,634.3 6,006.6 63,622.3 952.9 730.3 15,243.3 88,189.6

2005 1,641.6 6,017.9 63,730.8 956.1 731.2 15,311.5 88,389.1

2006 1,649.9 6,029.6 63,828.6 960.1 731.5 15,393.2 88,593.0

2007 1,658.1 6,041.2 63,929.7 964.9 731.9 15,474.8 88,800.8

2008 1,666.3 6,053.1 64,031.7 969.8 732.3 15,558.6 89,011.8

2009 1,674.7 6,065.1 64,134.5 974.7 732.7 15,644.0 89,225.7

2010 1,683.1 6,077.1 64,238.3 979.7 733.1 15,730.6 89,442.1

2011 1,692.1 6,090.7 64,345.9 984.9 733.4 15,819.6 89,666.6

2012 1,701.1 6,104.1 64,451.2 990.7 733.8 15,908.0 89,888.9

2013 1,710.1 6,117.3 64,556.6 996.7 734.1 15,994.8 90,109.7

2014 1,719.2 6,130.5 64,662.1 1,002.7 734.5 16,081.2 90,330.2

2015 1,728.4 6,143.6 64,767.6 1,008.7 734.9 16,167.3 90,550.4

2016 1,748.1 6,218.4 65,244.3 1,017.4 739.5 16,294.5 91,262.1

a Includes aboveground and belowground components. 
16

METHODS AND DATA SOURCES

FCAF Technical Specifications

Overview

The FCAF is fundamentally driven by the annual 

forest inventory system conducted by the Forest 

Inventory and Analysis program of the U.S. 

Forest Service (2014d, 2015a). The FIA program 

is considered to be the National Forest Inventory 

(NFI) for the United States, so these terms are used 

interchangeably. The FCAF system is comprised of 

a forest dynamics module and a land use dynamics 

module. The forest dynamics module assesses forest 

sequestration, forest aging, and disturbance effects 

(i.e., disturbances such as wind, fire, and floods 

identified by foresters on inventory plots). The land 

use dynamics module assesses carbon stock transfers 

associated with afforestation and deforestation. Both 

modules are developed from land use area statistics 

and carbon stock change or carbon stock transfer by 

age class. The required inputs are estimated from 

more than 625,000 forest and nonforest observations 

in the FIA national database (U.S. Forest Service 

2014a, b, c). Model predictions for before or after 

the annual inventory period are constructed from 

the FCAF system using the annual observations. 

This modeling framework includes opportunities 

for user-defined scenarios to evaluate the impacts 

of land use change and disturbance rates on future 

carbon stocks and stock changes. The accounting 

system is flexible and can incorporate emerging 

inventory data (e.g., remeasured western plots and 

Alaskan lichen biomass), future image-based change 

estimation information (see Planned Improvements 

section on page 28), data from trends in burn 

severity (Eidenshink et al. 2007), and process model 

output (i.e., inform future forest carbon densities or 

land use dynamics). The future accounting system 

will be transparent and verifiable through opensource, publicly available R software that links with 

the FIA database and associated distillations. The 

accounting system is scalable to allow other users to 

parameterize models at scales relevant to them, but 

inherently the framework is built for application at 

the strategic scale, using FIA data to parameterize 

the matrices. This introduction to the FCAF is just 

the first step in a process to engage the public and 

policy makers in interpreting forest carbon status and 

trends. Whether it is remeasuring western inventory 

plots, initiating an inventory of interior Alaska, 

or vetting emerging research, we expect years of 

refinements to the FCAF. 

Data

Data from the FIA program are the basis for the 

FCAF and the U.S. forest carbon inventory program 

(U.S. Forest Service 2014a). The FIA program relies 

on a rotating panel statistical design with a sampling 

intensity of one 674.5 m2 ground plot per 2,403 ha 

of land and water area. A multi-panel design, 

with a percentage of all field plots systematically 

distributed and measured each year within each 

panel, is used with a base federal measurement 

period of 7 years (i.e., 7 panels) in eastern states 

and 10 years (i.e., 10 panels) in western states. The 

interpenetrating hexagonal design across the U.S. 

landscape enables the sampling of plots at various 

intensities in a spatially and temporally unbiased 

manner. Typically, tree and site attributes are 

measured with a higher sample intensity while other 

ecosystem attributes such as downed dead wood are 

sampled during summer months at lower intensities. 

From the measurements taken on the field plots, 

carbon estimates are predicted for six pools: live 

trees (aboveground and belowground), understory 

vegetation, standing dead trees, downed dead wood, 

litter, and soil organic carbon. The techniques for 

estimating these pools are detailed in the recent 
Methods and Data Sources 17

National Greenhouse Gas Inventory annexes 

(USEPA 2015b). An important refinement of these 

techniques is the adoption of a new approach 

(Domke et al., in prep.) for estimating soil organic 

carbon, whose preliminary results are included in 

this assessment.

Carbon in Soil Organic Matter

Soil organic carbon (SOC) is the largest terrestrial 

carbon sink, and management of this pool is a 

critical component of efforts to mitigate atmospheric 

carbon concentrations. Soil organic carbon also 

affects essential biological, chemical, and physical 

soil functions such as nutrient cycling, water 

retention, and soil structure (Jandl et al. 2014). Much 

of the SOC on earth is found in forest ecosystems 

and is thought to be relatively stable. However, 

there is growing evidence that SOC is sensitive 

to global change effects, particularly land use 

histories, resource management, and climate. Given 

the cost and time required to measure SOC, and 

particularly changes in SOC, many of the UNFCCC 

signatory nations report estimates of SOC stocks 

and stock changes using default values from the 

Intergovernmental Panel on Climate Change or 

country-specific models (Keith et al. 2009, Kurz 

and Apps 2006). Country-specific models are 

often developed from soil inventories that are not 

representative of all land uses and vegetation types, 

resulting in unquantified uncertainties. In the United 

States, SOC in forests is monitored by the national 

forest inventory conducted by the FIA program 

(O’Neill et al. 2005). The FIA program currently 

uses SOC predictions based, in part, on a model 

using the Soil Survey Geographic (SSURGO) and 

the State Soil Geographic (STATSGO) databases 

compiled by the Natural Resources Conservation 

Service (NRCS) (Amichev and Glabraith 2004), 

hereafter referred to as the country-specific (CSsoc ) 

model. Most estimates of forest SOC found in 

the SSURGO and STATSGO databases are based 

primarily on expert opinion and lack systematic 

field observations, but these country-specific 

model predictions have been used in past UNFCCC 

reporting (USEPA 2015a, b). The FIA program 

has been consistently measuring soil attributes as 

part of the inventory since 2001 and has amassed 

an extensive inventory of SOC in forest land in 

the conterminous United States and coastal Alaska 

(O’Neill et al. 2005). More than 5,000 profile 

observations of SOC on forest land from FIA and 

the International Soil Carbon Monitoring Network 

were used to develop and implement a modeling 

framework used for UNFCCC reporting that 

includes site-, stand-, and climate-specific variables 

that yield predictions of SOC stocks and stock 

changes specific to forest land in the United States. 

This section provides a summary of the methodology 

used to predict SOC for this report. A complete 

description of the approach is given in Domke et al. 

(in prep.). 

The data used to develop the new modeling 

framework to predict SOC on forest land came from 

the FIA program and the ISCN. Since 2001, the FIA 

program has collected soil samples on every 16th 

base intensity plot distributed approximately every 

38,848 ha, where at least one forested condition 

exists (Woodall et al. 2011a). On fully forested plots, 

mineral and organic soils were sampled adjacent to 

subplots 2, 3, and 4 by taking a single core at each 

location from two layers: 0 to 10.16 cm and 10.16 

to 20.32 cm. The texture of each soil layer was 

estimated in the field, and physical and chemical 

properties were determined in the laboratory (U.S. 

Forest Service 2011). For this analysis, estimates 

of SOC from the FIA program were calculated 

following O’Neill et al. (2005):

∑ SOCFIA_TOTAL = Ci • BDi • ti • ucf (1)

Where 

∑ SOCFIA_TOTAL = total mass (Mg C ha-1 ) of the 

 mineral and organic soil carbon over all 

 ith layers, 

Ci = percent organic carbon in the ith layer, 

BDi = bulk density calculated as weight per unit 

 volume of soil (g∙cm-3) at the ith soil layer, 

t

i

 = thickness (cm) of the ith soil layer (either 

 0 to 10.16 cm or 10.16 to 20.32 cm), and 

ucf = unit conversion factor (100). 
18 Methods and Data Sources

The SOCFIA_TOTAL estimates from each plot were 

assigned and averaged by forest condition, resulting 

in 3,667 profiles with SOC layer observations at 

0 to 10.16 and 10.16 to 20.32 cm depths. Since 

the United States has historically reported SOC 

estimates to a depth of 100 cm (USEPA 2015a), 

ISCN data from forests in the United States were 

harmonized with the FIA soil layer observations to 

develop models of SOC by soil order to a depth of 

100 cm. All observations used from the ISCN were 

contributed by the Natural Resources Conservation 

Service. A total of 16,504 soil layers from 2,037 

profiles were used from ISCN land uses defined as 

deciduous, evergreen, or mixed forest. The FIAISCN harmonized dataset used for model selection 

and prediction included a total of 5,704 profiles with 

23,838 layer observations at depths ranging from 

0 to 1,148 cm. 

The modeling framework developed to predict 

SOC for this report was built around strategiclevel forest and soil inventory information and 

auxiliary variables available for all FIA plots in the 

United States. The first phase of the new estimation 

approach involved fitting models using the midpoint 

of each soil layer from the harmonized dataset and 

SOC estimates at those midpoints. Several linear 

and nonlinear models were evaluated, and a log-log 

function provided the optimal fit to the harmonized 

data:

log10 SOCi = I + log10 Depth (2)

Where

log10 SOCi = SOC density (Mg C ha-1 cm depth-1) 

 at the midpoint depth,

I = intercept, and

log10 Depth = profile midpoint depth (cm).

The model was validated by partitioning the 

complete harmonized dataset multiple times into 

training and testing groups and then repeating 

this step for each soil order to evaluate model 

performance by soil order. Extra sum of squares 

F tests were used to evaluate whether there were 

statistically significant differences between the 

model coefficients from the model fit to the 

complete harmonized dataset and models fit to 

subsets of the data by soil order. Model coefficients 

for each soil order were used to predict SOC for 

the 20.32 to 100 cm layer for all FIA plots with 

soil profile observations. Next, the SOC layer 

observations from the FIA and predictions over the 

100 cm profile for each FIA plot were summed:

SOC100 = SOCFIA_TOTAL + SOC20-100 (3)

Where

SOC100 = total estimated SOC density from 0-100 cm

 for each forest condition with a soil sample in 

 the FIA database,

SOCFIA_TOTAL = as defined in model (1), and

SOC20-100 = predicted SOC from 20.32 to 100 cm 

 from model (2).

Note that bias correction factors will be incorporated 

into the SOC20-100 predictions and evaluated in 

Domke et al. (in prep.) but were not included in the 

SOC100 estimates used in this analysis. 

In the second phase of the modeling framework, 

SOC100 estimates for FIA plots were used to predict 

SOC for plots lacking SOC100 estimates using 

Random Forests (RF), a machine learning tool 

that uses bootstrap aggregating (i.e., bagging) to 

develop models to improve prediction (Breiman 

2001). Random Forests also relies on random 

variable selection to develop a forest of uncorrelated 

regression trees. These trees recognize the 

relationship between a dependent variable, in 

this case SOC100 , and a set of predictor variables. 

All relevant predictor variables—those that may 

influence the formation, accumulation, and loss 

of SOC—from annual inventories collected on 

all base intensity plots and auxiliary climate, 

soil, and topographic variables obtained from the 

PRISM climate group (Northwest Alliance 2015), 

Natural Resources Conservation Service (NRCS 
Methods and Data Sources 19

2015), and U.S. Geological Survey (Danielson 

and Gesch 2011), respectively, were included in 

the RF analysis. Due to regional differences in 

sampling protocols, many of the predictor variables 

included in the RF variable selection process were 

not available for all base intensity plots. To avoid 

problems with data limitations, pruning was used to 

reduce the RF models to the minimum number of 

relevant predictors (including both continuous and 

categorical variables) without substantial loss in 

explanatory power or increase in root mean squared 

error (RMSE). The general form of the full RF 

models were:

P(SOC) = f (lat,lon,elev,fortypgrp,ppt,

 tmax,gmi,order,surfgeo) + u (4)

where lat = latitude, lon = longitude, elev = 

elevation, fortypgrp = forest type group, ppt = mean 

annual precipitation, tmax = average maximum 

temperature, gmi = the ratio of precipitation to 

potential evapotranspiration, order = soil order, 

surfgeo = surficial geological description, and u = 

the uncertainty in the prediction resulting from the 

sample-based estimates of the model parameters and 

observed residual variability around this prediction. 

For each replacement, u was independently and 

randomly generated from a N(0,σ) distribution, 

with σ incorporating the variability from both 

sources. This process of randomly selecting and 

incorporating u may be considered an imputation. 

Each model prediction was replaced independently 

m times, and m separate estimates were combined 

following Rubin (1987):

(5)

where is the estimate for the kth completion 

of the dataset. In this analysis, m = 1,000, which 

is markedly larger than the m recommended by 

Rubin (1987), but given the extremely high level of 

replacement in this study, it was deemed necessary 

(Bodner 2008).

The FIA dataset used to develop the full RF model 

was partitioned multiple times into training and 

testing groups, and the results were evaluated 

graphically and with a variety of statistical metrics, 

including RMSE and modeling efficiency (EF). 

This provided an index of model performance on 

a relative scale where 1 indicated a perfect fit, 0 

suggested the model was no better than the mean, 

and negative values indicated a poor model fit. 

The RMSE for the full model was 32.78 Mg C ha-1

and the EF was 0.36. The RF predictions from the 

full model were also evaluated against the SOC100

estimates using an equivalence testing framework 

(Wellek 2003). This method assumes the values 

are not equivalent unless the P(SOC) predictions 

and SOC100 estimates demonstrate they are similar 

to within a predefined tolerance. A region of 

indifference was defined as ±25 percent, meaning 

the absolute value of the mean of the difference is 

less than 25 percent of the standard deviation. The 

SOC100 estimates and P(SOC) predictions were 

statistically equivalent with a mean difference of 

0.95 (standard deviation = ±15.53) Mg C ha-1. 

The P(SOC) predictions represent an estimated 

40 percent (42.52 ± 46.80 Mg C ha-1) increase in 

carbon stocks per unit area relative to the countyspecific predictions which have been used in 

previous UNFCCC reports. As a final step in this 

analysis, the P(SOC) predictions were compared 

to SOC estimates published in the recent literature. 

The P(SOC) predictions ranged from 47.90 to 

374.57 Mg C ha-1 with a mean of 105.40 Mg C ha-1. 

These predictions are consistent with estimates 

from studies across forest types of the United 

States (Jobbagy and Jackson 2000, Lal 2005, Sun 

et al. 2004, Tan et al. 2004, Thompson and Kolka 

2005, Woldeselassie et al. 2012) and with estimates 

obtained from a large scale soil survey in European 

forests (De Vos et al. 2015). 

Estimation of Other Pools

Techniques for estimating the carbon stocks of all 

other pools (other than SOC) are identical to those 

used in the previous NGHGI (USEPA 2015b). 

Briefly, forest inventory data are converted to 

carbon units or augmented by other ecological 
20 Methods and Data Sources

data. Expansion factors are applied to the survey 

data at the scale of FIA inventory plots. The results 

are estimates of carbon density (Mg C ha-1) for 

the various forest pools. Carbon density for live 

trees, standing dead trees, understory vegetation, 

downed dead wood, litter, and soil organic matter 

are estimated. All nonsoil pools except litter can 

be separated into aboveground and belowground 

components. The live tree and understory 

carbon pools have been pooled as biomass in the 

greenhouse gas inventory. Similarly, standing dead 

trees and downed dead wood have been pooled as 

dead wood. Regardless of the pool delineations 

used in this initial report, they can be summarized in 

various ways for varying reporting formats.

• Live trees—Live tree carbon pools include 

aboveground and belowground (coarse root) 

biomass of live trees with diameter at breast 

height of at least 2.54 cm at 1.37 m above 

the forest floor. Separate estimates are made 

for aboveground and belowground biomass 

components. If inventory plots include data 

on individual trees, tree carbon is based on 

Woodall et al. (2011b) using the component 

ratio method (CRM), which is a function 

of volume, species, diameter, and, in some 

regions, tree height and site quality. The 

estimated sound volume (i.e., after rotten and 

missing tree volume have been deducted) 

provided in the tree table of the FIA database 

is the principal input to the CRM biomass 

calculation for each tree (Woodall et al. 

2011b). The estimated volumes of wood and 

bark are converted to biomass based on the 

density of each. Additional components of the 

trees, such as tops, branches, and coarse roots, 

are estimated according to adjusted component 

estimates from Jenkins et al. (2003). Live 

trees with a d.b.h. of less than 12.7 cm do 

not have estimates of sound volume in the 

FIA database, and CRM biomass estimates 

follow a separate process (see Woodall et al. 

2011b for details). An additional component 

of foliage, which is not explicitly included 

in Woodall et al. (2011b), was added to each 

tree following the same CRM method. Carbon 

was estimated by multiplying the estimated 

oven-dry biomass by a carbon constant of 0.5 

because carbon is 50 percent of dry weight 

(IPCC 2006). Further discussion and example 

calculations are provided in Woodall et al. 

(2011b) and Domke et al. (2012).

• Understory vegetation—Understory 

vegetation is a minor component of total forest 

ecosystem biomass. Understory vegetation is 

defined as all biomass of undergrowth plants 

in a forest, including woody shrubs and trees 

less than 2.5-cm d.b.h. Estimates of carbon 

density are based on Birdsey (1996), and 

calculation details are provided by the U.S. 

Environmental Protection Agency (2015b). 

The approach to quantifying carbon in this 

pool is expected to be revised in the near 

future.

• Standing dead trees—The standing dead tree 

estimates are primarily based on plot-level 

measurements (Domke et al. 2011, Woodall 

et al. 2012). This carbon pool includes 

aboveground and belowground (coarse root) 

mass and includes trees of at least 12.7-cm 

d.b.h. Calculations follow the basic CRM 

method applied to live trees (Woodall 

et al. 2011b) with additional modifications 

to account for decay and structural loss. 

In addition to the lack of foliage, two 

characteristics of standing dead trees that can 

substantially affect carbon mass are decay, 

which affects density and thus specific carbon 

content (Domke et al. 2011, Harmon et al. 

2011), and structural loss such as missing 

branches and bark (Domke et al. 2011). 

Dry weight is converted to carbon mass by 

multiplying by 0.5. 

• Downed dead wood—Downed dead wood, 

inclusive of logging residue, are sampled 

on a subset of FIA plots. Despite a reduced 

sampling intensity, empirical estimates of 

downed dead wood are used in combination 

with model predictions to compile a single 

downed woody material population estimate 

(Domke et al. 2013, Woodall et al. 2013) per 

state. Downed dead wood is defined as pieces 

of dead wood with a diameter greater than 

7.5 cm at transect intersection, that are not 

attached to live or standing dead trees. It also 

includes stumps and roots of harvested trees. 
Methods and Data Sources 21

Estimates for downed dead wood correspond 

to the region and forest type classifications 

described in Smith et al. (2003). An additional 

component of downed dead wood is a regional 

average estimate of logging residue based on 

Smith et al. (2006) applied at the plot level. 

These are based on a regional average carbon 

density at age zero and first order decay. These 

amounts are added to explicitly account for 

downed dead wood following harvest. The 

sum of these two components is then adjusted 

by the ratio of population totals; that is, the 

ratio of plot-based to modeled estimates 

(Domke et al. 2013). Details are provided 

in U.S. Environmental Protection Agency 

(2015b).

• Litter—Carbon in the litter layer is currently 

sampled on a subset of the FIA plots. Litter 

carbon is the pool of organic carbon (including 

material known as duff, humus, and fine 

woody debris) above the mineral soil and 

includes woody fragments with diameters of 

up to 7.5 cm. Because litter attributes are only 

collected on a subset of FIA plots, a model 

was developed to predict carbon density based 

on site-, stand-, and climate-specific variables 

from plots that lacked litter information 

(Domke et al., in review). Since the modeling 

framework used to obtain predictions of 

litter carbon is new this year, a more detailed 

overview of the methods is provided here, and 

a full description of the methods is described 

in Domke et al. (in review). 

The first step in model development was to evaluate 

all relevant variables—those that may influence 

the formation, accumulation, and decay of litter 

organic matter—from annual inventories collected 

on base intensity plots. This was done using RF 

variable selection and litter carbon estimates from 

FIA plots (n = 4,553) where litter attributes were 

sampled from 2001-2012. Random Forests was 

used to evaluate the importance of all relevant 

predictor variables available on base intensity 

plots in the FIA. Due to regional differences in 

sampling protocols, many of the predictor variables 

included in the RF variable selection process were 

not available for all base intensity plots. To avoid 

Attribution

Attribution is the separation of impacts of major 

disturbance from forest sequestration. In the 

eastern United States where remeasured data are 

available, this was done directly based on repeated 

observations (Coulston et al. 2015). In the western 

United States where only one measurement is 

available, a coarse-scale model was adopted 

(Coulston, in prep.). The coarse-scale model arises 

from a finer-scale model:

E(δCdp ) = E(Cdp )- E(C-dp ) (7)

Where

E( ) is the expected value,

δCdp is the change in carbon for plot p with 

 disturbance d, 

Cdp is the current carbon stock of plot p with 

 disturbance d, and

C-dp is the carbon stock for plot p without 

 disturbance.

problems with data limitations, pruning was used 

to reduce the RF models to the minimum number 

of relevant predictors (including both continuous 

and categorical variables) without substantial loss 

in explanatory power or increase in RMSE. The 

general form of the full RF models was:

P(Litter) = f (elev,lon,above,gmi,fortypgrp,

 ppt,tmax,lat) + u (6)

where P(Litter) = litter carbon (Mg C ha-1), above

= aboveground live tree carbon (trees ≥ 2.54-cm 

d.b.h.), and other variables are as defined for model 

(4). For each replacement, u was independently and 

randomly generated from a N(0,σ) distribution with 

σ incorporating the variability from the samplebased estimates of the model parameters and 

observed residual variability around the predictions. 

Each model prediction was replaced independently 

m times, and m separate estimates were combined 

following Rubin (1987) using model (5).The FIA 

dataset used to develop the full RF model was 

partitioned multiple times into training and testing 

groups, and the results were evaluated graphically 

and with a variety of statistical metrics. 
22 Methods and Data Sources

E(C-dp ) may be developed from a model such as:

E(C-dp ) = f(fortypgrp,Ctree,SD,SI...) (8)

Where 

Ctree = total live and standing dead tree carbon, 

SD = stand density, and 

SI = site index. 

In simple terms this approach models δCdp as the 

observed deviation from a carbon yield curve. 

However, the same general idea can be employed 

as a population model. Suppose that a disturbance 

or management activity occurs at random within a 

forest type (population). Under this assumption for 

each forest type:

E(ΔCd ) =Fd*(Dd - Du ) (9)

Where 

ΔCd = vector of population carbon change by pool 

 associated with the disturbance, 

Fd = area of the disturbance, 

Dd = vector of carbon density (stock per unit area) 

 by pool in disturbed areas, and 

Du = vector carbon density by pool in undisturbed 

 areas.

The coarse-scale model was implemented for the 

western United States including west Texas and west 

Oklahoma. For fire disturbance Dfire was compared 

to Du by forest type group. For cutting Dcut was 

compared to Du by forest type group where Du

was restricted to observations between ages 5 and 

105. This age restriction was based on a small set 

of remeasurements for the western United States 

where 90+ percent of all observed cutting occurred 

in stands between 5 and 105. Note that cutting 

includes a range of cutting types from timber stand 

improvement to clearcutting. 

Projections: Moving Annual Inventory Data 

Backward and Forward in Time 

Wear and Coulston (2015) and Coulston et al. (2015) 

provide the framework for the projection model. 

The overall objective is to estimate unmeasured 

historical changes and future changes in forest 

carbon consistent with annual forest inventory 

measurements. For most regions, forest conditions 

are observed at time t0 and at a subsequent time 

t

1

 = t0+s , where s is the time step (time measured in 

years) and is indexed by discrete (5 year) forest age 

classes. The inventory from t0 is then backcasted 

to the year 1990 (on average about 16 years) and 

projected from t1 to 2016 (about 5 years for the 

next inventory report). This backcasting/projection 

approach requires simulating changes in the age 

class distribution resulting from forest aging and 

disturbance events and then applying carbon density 

estimates for each age class. For the North, South 

(except for west Texas and west Oklahoma), and 

Rocky Mountains regions of the country, age class 

transition matrices are estimated from observed 

changes in age classes between t0 and t1 . In the 

remainder of the regions (Pacific Coast including 

Alaska, west Texas, and west Oklahoma), only one 

inventory was available (t0 ) so transition matrices 

were derived from theory but informed by the 

condition of the observed inventory to backcast 

from t0 to 1990 and project from t0 to 2016.

Theoretical Age Transition Matrices

Without any mortality-inducing disturbance, a 

projection of forest conditions would proceed by 

increasing all forest ages by the length of the time 

step until all forest resided in a terminal age class 

where the forest is retained indefinitely (this is 

by assumption, where forest carbon per unit area 

reaches a stable maximum). For the most basic case, 

disturbances (e.g., wildfire or timber harvesting) 

can reset some of the forest to the first age class. 

Disturbance can also alter the age class in more 

subtle ways. If a portion of trees in a multiple-age 

forest dies, the trees comprising the average age 

calculation change, thereby shifting the average age 

higher or lower (generally by one age class). 

With n age classes, the age transition matrix (T) 

is an n x n matrix, and each element (Tqr ) defines 

the proportion of forest area in class q transitioning 

to class r during the time step (s). The values of 

the elements of T depend on a number of factors, 
Methods and Data Sources 23

that shifts the age distribution of the forest F. The 

difference in forest area by age class between time 

t and t+s is FtT – Ft . This quantity is multiplied 

by carbon density by age class (Den) to estimate 

carbon stock change of forest remaining forest 

between t and t+s. Land use change is accounted 

for by the addition of Lt ∙ Den, where Lt identifies 

the age distribution of net land shifts into or out of 

forests. A query of the forest inventory databases 

provides estimates of F and Den, while inventory 

observations and modeling assumptions are used 

to estimate T. By expanding Den to a matrix of 

carbon contained in all the constituent pools of forest 

carbon, projections for all pools are generated.

Land use change is incorporated as a 1 x n vector 

L, with positive entries indicating increased forest 

area and negative entries indicating loss of forest 

area, which provides insights of net change only. 

Implementing a forest area change requires some 

information and assumptions about the distribution 

of the change across age classes (the n dimension 

of L). In the eastern states, projections are based on 

the projection of observed gross area changes by age 

class. In western states, total forest area changes are 

applied using rules. When net gains are positive, the 

area is added to the youngest forest age class; when 

negative, area is subtracted from all age classes in 

proportion to the area in each age class category.

Backcasting forest carbon inventories generally 

involves the same concepts as forecasting. An initial 

age class distribution is shifted at regular time steps 

backward through time, using a transition matrix 

(B): 

Ft–s = Ft

. B (13)

B is constructed based on similar logic used for 

creating T. The matrix cannot simply be derived 

as the inverse of T (Ft–s = Ft T–1 ) because of the 

accumulating final age class (i.e., T does not contain 

enough information to determine the proportion of 

the final age class derived from the n–1 age class and 

the proportion that is retained in age class n from the 

including forest disturbances such as harvests, fire, 

and storms, and the value of s, especially relative 

to the span of the age classes. For example, holding 

area fixed, allowing for no mortality, defining the 

time step s equivalent to the span of age classes, 

and defining five age classes results in:

T = (10)

where all forest area progresses to the next age class 

and forests within the terminal age class are retained 

forever. With this version of T, after five time 

steps all forests would be in the terminal age class. 

Relaxing these assumptions changes the structure of 

T. If all disturbances, including harvesting and fire, 

that result in stand regeneration are accounted for 

and stochastic elements in forest aging are allowed, 

T defines a traditional Lefkovitch matrix population 

model (e.g., Caswell 2001) and becomes:

T = (11)

Where

t

q

 is the proportion of forest of age class q

 transitioning to age class q + 1,

dq is the proportion of age class q that experiences 

 a stand-replacing disturbance, and 

(1 – t

q – dq ) is the proportion retained within 

 age class q (Tqr ). 

Projections and Backcast for Pacific Coast, 

Rocky Mountains, West Texas, and West 

Oklahoma 

Projections of forest carbon in the Pacific Coast 

region (including Alaska), Rocky Mountains, west 

Texas and west Oklahoma are based on a life stage 

model:

∆Ct = C(t+m) – Ct = (FtT – Ft ) ∙ Den + Lt ∙ Den (12)

In this framework T is an age transition matrix 
24 Methods and Data Sources

1 Simulation experiments show that a population that 

evolves as a function of T can be precisely backcast using 

T–1. However, applying the inverse to a population that is 

not consistent with the long run outcomes of the transition 

model can result in projections of negative areas within 

some stage classes. 

Forest area changes need to be accounted for in the 

backcasts as well:

Ft–s = Ft B – Lt (15)

where Lt is the forest area change between t1 and t0

as previously defined.

In the Rocky Mountains, age class transition 

matrices were empirically derived from observed 

changes in age classes between t0 and t1 . The 

frequency of transitions was constructed between 

age classes observed at t0 and t1 to define T and 

between age classes t1 and t0 to define B. In the 

Pacific Coast region, including Alaska, west Texas, 

and west Oklahoma, the theoretical life-stage models 

described by matrices 11 and 14 were applied. 

The disturbance factors (d) in both T and B are 

derived from the current inventory by assuming 

that the area of forest in age class 1 resulted from 

disturbance in the previous period, the area in age 

class 2 resulted from disturbance in the period before 

that, and so on. The source of disturbed forest was 

assumed to be proportional to the area of forest in 

each age class. For projections (T), the average of 

implied disturbance for the previous two periods 

was applied. For the backcast (B), we move the 

disturbance frequencies implied by the age class 

distribution for each time step. For areas with 

empirical transition matrices, change in forest area 

(Lt ) was backcasted/projected using the change in 

forest area observed for the period t0 to t1 . In the 

Pacific Coast region, including Alaska, west Texas, 

and west Oklahoma, it was assumed that total forest 

land area remained constant for the time period 

examined.

Projections and Backcast for North, 

South, east Texas, and east Oklahoma 

For the eastern United States a full set of remeasured 

plots were available. When remeasured data are 

available, the previously described approach is 

extended to estimate change more directly; in this 

case ΔCt = Ft ∙ δC where ∆C is net stock change 

by pool within the analysis area, F is as previously 

defined, and δC is an n x cp matrix of per unit 

area forest carbon stock change per year by pool 

(cp) arrayed by forest age class. Inter-period forest 

carbon dynamics are previously described, and 

the age transition matrix (T) is estimated from 

the observed data directly. Forest carbon change

at the end of the next period is defined as: 

ΔCt+s = Ft ∙ T ∙ δC. Land use change and 

disturbances such as cutting, fire, weather, insects, 

and diseases were incorporated by generalizing 

to account for the change vectors and undisturbed 

forest remaining as undisturbed forest: 

∆Ct+s = ∑ (Atd ∙ Td ∙ δCd ) (16)

Where 

Atd = area by age class of each mutually exclusive 

land category in L which includes d disturbances at 

time t. 

L = (FF, NFF, FNF, Fcut , Ffire , Fweather , Fid ) where 

FF = undisturbed forest remaining as undisturbed 

forest, NFF = nonforest to forest conversion, 

FNF = forest to nonforest conversion, Fcut = cut 

forest remaining as forest, Ffire = forest remaining 

as forest disturbed by fire, Fweather = forest remaining 

as forest disturbed by weather, and Fid = forest 

remaining as forest disturbed by insects and 

diseases. 

In the case of land transfers (FNF and NFF), Td is 

an n x n identity matrix and δCd is a carbon stock 

transfer rate by age. Paired measurements for all 

plots in the inventory provide direct estimates of all 

elements of δCd , Td , and Atd matrices. 

dЄL

previous time step).1 However, B can be constructed 

using observed changes from the inventory and 

assumptions about transition/accumulation including 

nonstationary elements of the transition model:

B = (14)

q dq
Methods and Data Sources 25

Projections are developed by specifying either Ft+s

or At+sd for either a future or a past state. To move 

the system forward, T is specified so that the age 

transition probabilities are set up as the probability 

between a time 0 and a time 1 transition. To move 

the system backward, T is replaced by B so that 

the age transition probabilities are for transitions 

from time 1 to time 0. Forecasts were developed 

by assuming the observed land use transitions 

and disturbance rates would continue for the next 

5 years. Backcasts were developed using a Markov 

Chain process for land use transitions, observed 

disturbance rates for fire, weather, and insects. 

Historical forest cutting was incorporated by using 

the relationship between the area of forest cutting 

estimated from the inventory plots and the volume 

of roundwood production from the Timber Products 

Output program (U.S. Forest Service 2014d). This 

relationship allowed for the modification of Fcut

such that it followed trends described by Oswalt 

et al. (2014). 

Total Uncertainty

There are many input variables (e.g., aboveground 

live biomass, forest land area) in the FCAF (Fig. 10) 

and each input variable has uncertainty. Techniques 

to account for the total uncertainty associated with 

the FCAF output (i.e., annual C flux in Table 1) are 

still under development. As a first approximation 

we intend to use a Monte Carlo-based framework. 

Uncertainty parameters (e.g., distribution functions 

and variance) that best reflect the potential errors 

associated with each input variable will be defined. 

For each input variable, many realizations (e.g., 

nmc = 10,000) will be randomly generated using the 

uncertainty parameters. The realizations for each 

input variable will then be used in the FCAF to 

produce a distribution of predictions, means, and 

standard deviations for the annual C flux.

Figure 10.—Conceptual flowchart of the FCAF outlining the first approximation that is intended to measure total 

uncertainty for annual C flux predictions in the United States. Sources of uncertainty (with references) represent input 

variables that will be included in the total uncertainty assessment.

Post stratified estimator for population estimates by age class and domain 

(Bechtold and Patterson 2005)

Current carbon stock change by age class and domain (Wear and Coulston 2015) 

Empirical age class transition matrix (Coulston et al. 2015)

Annual C flux by land use change, forests remaining forest, and total net sequestration 

in managed U.S. forests, 1990-2016 (Table 1)

Theoretical age class transition matrix (Wear and Coulston 2015)

Area by age class 

and domain 

(Wear and 

Coulston 2015)

Aboveground live (Woodall et al. 

2011a, USEPA 2015)

Belowground live (Woodall et al. 

2011a, USEPA 2015)

Deadwood (Domke et al. 

2011, 2012)

Litter (Domke et al., in review)

SOC (Domke et al., in prep.)

Area by age class 

and domain 

(Coulston et al. 

2015)

Aboveground live (Woodall et al. 

2011a, USEPA 2015)

Belowground live (Woodall et al. 

2011a, USEPA 2015)

Deadwood (Domke et al. 

2011, 2012)

Litter (Domke et al., in review)

SOC (Domke et al., in prep.)

Carbon stock change by age class and domain 

between t

1

 and t

2

 (Coulston et al. 2015)

Eastern United States Western United States

Carbon density by age class and domain 

at t

1

 and t

2

 (Wear and Coulston 2015)
26 Methods and Data Sources

Annual Data Description

Contrary to past greenhouse gas inventories, 

periodic data were not used in the FCAF to develop 

national carbon baselines, instead we relied solely on 

annual FIA data (Tables 3 and 4). Annual FIA plots 

are consistently located across the United States in 

a spatially balanced and unbiased manner (Fig. 11). 

The base intensity of FIA plots is approximately 

one plot for every 2,428 ha. Strategic partners, such 

as states, have the opportunity to increase the base 

intensity by adding plots through direct or in-kind 

contributions. Some of these added plots may be 

established and measured at one point in time but are 

then not subsequently remeasured. The FIA database 

is structured such that statistically valid samples 

of change can be derived owing to the hexagonal 

and panelized system across the United States (see 

Bechtold and Patterson 2005). The attribution and 

projection system of FCAF will be greatly improved 

once western annual plots are remeasured in the 

years ahead.

Remeasured annual plots

State

Time 1 

year range

Time 2 

year range

Alabama 2001 - 2009 2006 - 2014

Arkansas 2000, 2002, 

2004 - 2010

2009 - 2014

Connecticut 2004 - 2008 2009 - 2013

Delaware 2004 - 2008 2009 - 2013

Florida 2002 - 2004, 

2006 - 2007

2009 - 2013

Georgia 1998 - 2009 2005 - 2007, 

2009 - 2013

Illinois 2004 - 2009 2009 - 2014

Indiana 2004 - 2008 2009 - 2013

Iowa 2004 - 2009 2009 - 2014

Kansas 2004 - 2009 2009 - 2014

Kentucky 2000 - 2009 2005 - 2006, 

2008 - 2012

Louisiana 2001 - 2005, 

2008

2009 - 2013

Maine 2004 - 2008 2009 - 2013

Maryland 2004 - 2008 2009 - 2013

Massachusetts 2004 - 2008 2009 - 2013

Michigan 2004 - 2009 2009 - 2014

Minnesota 2005 - 2009 2010 - 2014

Mississippi 2006 2009 - 2014

Missouri 2004 - 2009 2009 - 2014

Table 3.—Inventory data used to develop forest carbon estimates presented in this report by state 

and time (time 1 and time 2) for states with remeasured annual plots. Valid population estimates were 

made using the two independent measurement cycles (otherwise known as evaluations, see U.S. 

Forest Service 2014a, b). 

Remeasured annual plots

State

Time 1 

year range

Time 2 

year range

Nebraska 2004 - 2008 2009 - 2013

New Hampshire 2003 - 2008 2009 - 2013

New Jersey 2004 - 2008 2009 - 2013

New York 2003 - 2008 2009 - 2013

North Carolina 2002 - 2007 2003, 

2005 - 2007, 

2009 - 2014

North Dakota 2004 - 2009 2009 - 2014

Ohio 2003 - 2008 2009 - 2013

Oklahoma (East) 2008 2010 - 2013

Pennsylvania 2004 - 2008 2009 - 2013

Rhode Island 2004 - 2008 2009 - 2013

South Carolina 2002 - 2010 2007 - 2013

South Dakota 2004 - 2009 2009 - 2014

Tennessee 2000 - 2009 2005 - 2012

Texas (East) 2002 - 2008 2005, 

2007 - 2012

Vermont 2004 - 2008 2009 - 2013

Virginia 2002 - 2003, 

2005 - 2010

2008 - 2013

West Virginia 2004 - 2008 2009 - 2013

Wisconsin 2004 - 2009 2009 - 2014
Methods and Data Sources 27

Table 4.—Inventory data used to develop forest carbon estimates presented in this report by state 

and time (time 1 and time 2) for states where an insufficient number of annual plots have been 

remeasured. A single cycle of annual plots was split into two statistically valid samples of the state’s 

forest carbon at the start and end of the annual inventory cycle. 

Split annual cycle plots

State

Time 1 

year range

Time 2 

year range

Alaska (Coastal) 2004 - 2008 2009 - 2013

Arizona 2004 - 2008 2009 - 2013

California 2003 - 2007 2008 - 2012

Colorado 2004 - 2008 2009 - 2013

Idaho 2004 - 2008 2009 - 2013

Montana 2004 - 2008 2009 - 2013

Nevada 2004 - 2008 2009 - 2013

Split annual cycle plots

State

Time 1 

year range

Time 2 

year yange

New Mexico 1999 2005 - 2013

Oklahoma (West) 2009 - 2010 2011 - 2013

Oregon 2003 - 2007 2008 - 2012

Texas (West) 2004 - 2007 2008 - 2012

Utah 2004 - 2008 2009 - 2013

Washington 2003 - 2007 2008 - 2012

Wyoming 2000 2011 - 2013

Figure 11.—Annual FIA plots (remeasured and not remeasured) across the United States including coastal Alaska through 

2014. Note: Due to the vast number of plots, data points appear spatially contiguous when displayed in small maps. 

Source: FIA data.
28

PLANNED IMPROVEMENTS 

TO THE FOREST CARBON INVENTORY

Uncertainty

Estimates of uncertainty are not provided in this 

report as techniques for calculating uncertainty are 

still being developed for application in the United 

States’ official submission to the UNFCCC, which 

requires such estimates. Good practice guidelines by 

UNFCCC (IPCC 2006) recognize multiple possible 

approaches to defining and quantifying uncertainty, 

and the documentation provides some guidance, 

particularly for lower tier estimates, but guidelines 

are not prescriptive. The primary role for uncertainty 

analysis is in model development and improving 

estimates by identifying the important sources of 

uncertainty in a country’s report. Overall uncertainty 

in the current estimate of forest carbon change is 

a 95 percent confidence interval (USEPA 2015a), 

which is combined with the emission/sequestration 

estimates of other sectors. This uncertainty estimate 

enables comparability across Nations as it is required 

input to UNFCCC from all signatory Nations.

Uncertainty associated with forest carbon baselines 

can be substantial compared to other sectors, such 

as fossil fuel combustion, due to the numerous 

sources of error that all contribute to forest carbon 

uncertainty. Sampling error results from using 

sample plots to estimate the entire forest population 

(Fig.12a). Estimates of forest carbon stores are also 

subject to error associated with the models used for 

calculating carbon (e.g., Melson et al. 2011), with 

model selection error, parameter estimation error, 

and residual error (Fig. 12b) being the primary 

sources. Finally, there is measurement error that 

arises in the field when collecting measurements 

such as tree height and diameter (Fig. 12c). Total 

uncertainty attempts to represent all of these sources 

of error simultaneously. Monte Carlo approaches 

have been used to simulate total uncertainty, and this 

approach for estimating uncertainty associated with 

the U.S. forest carbon inventory will be evaluated 

for application in the 2016 submission to the 

UNFCCC, as Bayesian approaches continue to be 

researched.

0

500

1000

1500

2000

2500

10 20 30 40 50 60 70 80

Biomass(kg)

Diameter (cm at d.b.h.)

Figure 12.—Sources of forest carbon inventory uncertainty: (a) sampling error, (b) model error, and (c) measurement 

error. Photo by Kimberly Rowe, U.S. Forest Service.

a b c
Planned Improvements to the Forest Carbon Inventory 29

More Data: Forests, 

Woodlands, and Urban Trees

The foundation of the forest carbon accounting 

system is the annual forest inventory. The ongoing 

annual surveys by FIA are expected to improve the 

accuracy and precision of forest carbon estimates 

as new state surveys become available (U.S. Forest 

Service 2014a), particularly in western states as the 

annual plot system is remeasured. Hawaii and U.S. 

territories will be included when appropriate forest 

carbon data are available (compiled Hawaii data 

from the annualized sampling design is anticipated 

in summer 2016) (Fig. 13). In addition, the more 

intensive sampling of fine woody debris, litter, and 

SOC on a subset of FIA plots continues and will 

Figure 13.—FIA forest land use plots indicating fully and 

partially forested plots in contrast to FIA measurement of 

other land uses across the entire plot network. Note: Due 

to the vast number of plots, data points appear spatially 

contiguous when mapped. Source: FIA data.

substantially improve resolution of carbon pools 

(Westfall et al. 2013). Increased sample intensity of 

some carbon pools and using annualized sampling 

data as it becomes available for those states that 

are not currently reporting on an annual basis 

are planned for future National Greenhouse Gas 

Inventories, which will greatly improve the FCAF. 

Beyond the monitoring of the forest land use, the 

FIA program is expanding its field measurements to 

other land uses of woodlands and settlements where 

tree resources are present. The U.S. Forest Service 

has been conducting inventories of urban tree carbon 

(Nowak et al. 2013) for a number of years, with a 

recent push to begin annual inventories in a number 

of cities. Carbon inventory-relevant information 

for these land uses will likely become increasingly 

available in the future.

Refined Land Use Assessment: 

The Image Change Estimation Program

The 2014 Farm Bill (Agricultural Act 2014; Subtitle 

D: SEC. 8301 (B) (9)) directs FIA to explore refined 

approaches to understanding and reporting on 

changes in land cover and use. In response to this, 

the FIA program has initiated the Image Change 

Estimation (ICE) program to interpret imagery 

from the USDA National Agricultural Imagery 

Program (NAIP) in a more rigorous manner. The 

FIA program already employs NAIP imagery to 

assist with identification of land use (e.g., forest or 

settlements) across the FIA plot network. However, 

ICE efforts will identify the land use on every FIA 

plot concurrent with the measurement cycle of NAIP 

imagery, which is often far shorter (2 to 3 years) 

than the typical FIA plot measurement cycle (5 to 

10 years). The measurement of land use is not an 

automated cover interpretation (although a tandem 

cover assessment will be recorded); rather each 

FIA plot is manually reviewed by an experienced 

aerial photogrammetry interpreter. In addition, if 

change is identified on a plot, the agent of change 

will be identified if possible (e.g., forest converted 

to development) (Fig. 14). This program has the 

potential to greatly refine the monitoring of land 

use change as a component of the FCAF, as it will 

operate on the same national plot network.
30 Planned Improvements to the Forest Carbon Inventory

Figure 14.—Measuring land use and land cover changes from time 1 to time 2 on an FIA plot using Image Change 

Estimation protocols.

Time 1 Time 2

Reconciling Improvements to 

Forest Accounting with All Land Uses

The refinement of forest carbon accounting to 

benefit U.S. reporting to UNFCCC certainly is 

not conducted in isolation. Forest land use is part 

of a larger terrestrial sector entitled “Agriculture, 

Forestry, and Other Land Uses” (AFOLU), as land 

use is constantly changing across the United States 

(Fig. 15). If there is perfect alignment between the 

carbon accounting of agriculture and forests then 

the soil carbon associated with a deforested hectare 

of forest would potentially transfer to a hectare of 

agriculture, thus balancing out its transfer among 

land uses. Because land use change can have a 

substantial effect on the AFOLU carbon accounting, 

accurately representing the transfer of carbon 

by pool across the matrix of land use change is 

paramount. Forests account for the vast majority 

of terrestrial carbon flux in the United States, and 

thus it is logical that the refinement of forest carbon 

accounting should occur first, with subsequent 

Figure 15.—Percent forest land use change in the eastern 

United States derived from FIA plot land use identification, 

2002-2012 (Woodall et al. 2015).
Planned Improvements to the Forest Carbon Inventory 31

cascading of carbon accounting improvements 

across the remaining land uses of AFOLU. It 

is envisioned that the initiation of a number of 

improvements, including the implementation of 

the Image Change Estimation program, refined soil 

carbon accounting and modeling across all land uses, 

refined integration between the U.S. Department 

of Agriculture Forest Inventory and Analysis 

forest monitoring and Natural Resources Inventory 

cropland monitoring programs, and expanded 

monitoring of urban trees and agroforestry systems, 

will greatly enable refined accounting across the 

breadth of AFOLU.

Pool Estimation

In an effort to reduce the uncertainty associated with 

the estimation of individual forest carbon pools, 

the empirical data and associated models for each 

pool are being evaluated for potential improvement 

(Woodall 2012). In the 1990 through 2010 UNFCCC 

submission, the approach to tree volume/biomass 

estimation was evaluated and refined (Domke et 

al. 2012). In the 1990 through 2011 UNFCCC 

submission, the standing dead tree carbon model was 

replaced with a nationwide inventory and associated 

empirical estimation techniques (Domke et al. 2011, 

Harmon et al. 2011, Woodall et al. 2012). In the 

1990 through 2012 Inventory report (2013 NGHGI), 

the downed dead tree carbon model was refined by 

incorporation of a national field inventory of downed 

dead wood (Domke et al. 2013, Woodall et al. 2013). 

In the current Inventory report (2015 NGHGI), the 

litter carbon density model was refined with a nearly 

nationwide field inventory (Domke et al., in review). 

In this report, a new approach to estimating soil 

organic carbon is included (Domke et al., in prep.). 

Components of other pools, such as carbon 

in belowground biomass (Russell et al. 2015) 

(Fig. 16), understory vegetation (Russell et al. 2014), 

and foliage (Clough et al., in review) are being 

explored for application in future submissions.

Figure 16.—Distribution of differences between live tree belowground C estimates from the current U.S. National 

Greenhouse Gas Inventory and refined estimates (Mg∙ha-1 ). Red colors indicate higher estimated C and purple colors 

indicate less C (Russell et al. 2015).
32 Planned Improvements to the Forest Carbon Inventory

The Boreal Forests of Interior Alaska

A national system of field inventory plots is the 

primary data source for the annual assessment of 

U.S. forest carbon stocks and stock change to meet 

reporting requirements under the UNFCCC. The 

only area of potentially managed forests included 

under UNFCCC reporting that is not sampled by the 

national plot network is the boreal forest of interior 

Alaska (Fig. 17). A preliminary analysis suggests 

that these forests may represent over a third of all 

forest carbon in the conterminous United States (Zhu 

and McGuire, in review), hence the assessment of 

their carbon balance is critical to the monitoring 

of the U.S. terrestrial carbon sink. Furthermore, it 

is the nonlive biomass carbon pools, namely the 

forest floor, lichen/moss mats, and soils, which may 

Figure 17.—Delineation of managed and unmanaged forest land in Alaska. Currently south central and southeast coastal 

Alaska are inventoried by FIA, but interior Alaska remains the only area not sampled by the national plot network.

A vast stretch of boreal forest reaches along the Tanana 

River in interior Alaska. Photo by Christopher Woodall, 

U.S. Forest Service.
Planned Improvements to the Forest Carbon Inventory 33

account for the majority of Alaska’s carbon stock 

that cannot be quantified using remote sensing 

products alone. A variety of field and research 

efforts are currently underway to ameliorate these 

knowledge gaps. In the interim, a managed land 

analysis has been completed for interior Alaska 

(Ogle et al., in prep.) which identifies tens of 

millions of acres of forest that can be considered 

influenced by humans, and hence deemed managed 

per UNFCCC good practice guidance. 

Disaggregation

The FCAF is designed to monitor carbon to satisfy 

UNFCCC reporting requirements while monitoring 

progress towards future commitments. Such a 

framework aligns well with the FIA inventory as 

it is intended to address strategic-level questions 

about forest resources across large geographic 

areas under a design-based mode of inference 

(Bechtold and Patterson 2005). Undoubtedly, 

there is increasing interest in using this information 

for smaller-scale reporting within the population. 

The FCAF is also applicable in this case, but 

the base FIA data may not be sufficiently dense 

to parameterize the system. By using auxiliary 

variables from data collected for all population 

units, such as those obtained from remote sensors, 

and shifting to a model-based mode of inference, 

dramatic gains in the precision of estimates can be 

achieved for disaggregated areas, though possibly 

at the expense of violating the unbiasedness 

assumption for the estimators (Gregoire 1998). 

Through the application of a model-based 

approach, Wilson et al. (2013) developed spatially 

extant estimates of forest carbon density for the 

conterminous United States using FIA and auxiliary 

data (e.g., multi-temporal satellite imagery from 

the National Aeronautics and Space Administration 

[NASA]) (Fig. 18). The relationship between 

the auxiliary and FIA data was defined using an 

Figure 18.—Forest carbon pool which constitute the plurality of forest carbon at each pixel across the conterminous U.S.: 

live biomass, soil organic carbon, or detritus (dead wood and forest floor) (Wilson et al. 2013).
34 Planned Improvements to the Forest Carbon Inventory

ecological ordination model enabled by a k nearest 

neighbor estimator (Eskelson et al. 2009). It 

provides an estimate for each unsampled unit in the 

population as a weighted average of the observed 

response variable for the k-nearest sample units in 

the feature space. Research is currently underway 

to incorporate finer spatial resolution imagery 

(e.g., Landsat time series) using a similar modeling 

approach. Although the new accounting framework 

is designed to meet national scale monitoring needs, 

it is hoped through research partnerships with 

stakeholders (e.g., national forests or states) that 

valid downscaling techniques can be developed 

to disaggregate national results to the entity level. 

These finer scale estimates of carbon density and 

land use may be used to parameterize the FCAF so 

that smaller areas can be monitored. 

Harvested Wood Products

Carbon stored in long-term harvested wood 

products (HWP) is an important driver of carbon 

sequestration within the forest sector and as such 

is included when estimating the contribution of the 

forest sector to greenhouse gas sources and sinks. 

For example, in 2013 the accumulation of carbon in 

HWP was 19.3 Tg∙yr -1 (USEPA 2015a), which was 

as large as accumulation rates in litter, standing dead 

trees, and understory pools combined. However, 

the HWP estimates through the Woodcarb II model 

(Skog 2008) are currently exogenous to the FCAF. 

Efforts to more directly incorporate HWP within 

FCAF will further increase the analytical capacity 

to assess carbon dynamics in the United States and 

provide for consistent scenario-based projections. 

The key link between FCAF inventory estimates 

and HWP estimates is domestic harvest. Domestic 

harvest simultaneously influences sequestration 

within the forest and material moving into 

durable wood products. The attribution of forest 

carbon dynamics allows for the development of 

relationships between harvest effects witnessed 

in the inventory and quantities of wood products 

observed from a census of mills. These two sources 

of information exhibit a strong relationship (ordinary 

linear regression; R2 = 0.98) when observed at an 

aggregate scale (Fig. 19). Further research is needed 

to determine the relationship among products, 

imports, and exports. As these relationships are 

quantified, the FCAF will also support a HWP 

module. 

Figure 19.—The relationship between live tree C stock change from cutting in the inventory and of roundwood production 

(m3 ) from wood using facilities. The figure is based on the eastern United States where state-level production is compared 

to state-level changes in live tree carbon from cutting.

Roundwood production (million m3 )

Live tree C stock change (Tg)
 35

Research to Improve and Disseminate 

the Forest Carbon Inventory

National Volume/Biomass Study

National, regional, state and county estimates for 

both bioenergy and greenhouse gas analyses are 

derived from FIA field data that rely on individual 

tree species volume/biomass models. Recently, 

there has been an increasing need for tree biomass 

estimates, including various portions of the 

tree (e.g., merchantable bole and stumps), that 

requires not only a re-evaluation of older volume/

biomass models but perhaps development of new 

modeling approaches. Unfortunately, there has 

been no consistent national source of individual 

tree volume/biomass measurements of all tree 

components. To ensure FIA estimates provide an 

accurate representation of forest resources and 

trends, comprehensive data on tree biomass are 

needed. These data would provide: (1) a means 

of assessing current FIA methods, and (2) a basis 

for nationally-consistent estimation of biomass/

carbon. Unfortunately, obtaining such data is both 

time consuming and expensive, as very detailed 

measurements on individual trees are required. 

To accomplish the considerable amount of work 

necessary, FIA has engaged a number of partners 

and stakeholders including: University of Maine, 

Potlatch Corporation, University of Montana, 

University of Georgia, Rayonier Inc., U.S. Forest 

Service Forest Management Service Center, 

Michigan State University, Oregon State University, 

National Council for Air and Stream Improvement 

Inc., Virginia Polytechnic Institute and State 

University, Weyerhaeuser NR Company, and U.S. 

Forest Service Forest Products Laboratory. The 

objective of this study is to collect data (Fig. 20) and 

develop methods for estimating the biomass/carbon 

content of individual trees (Weiskittel et al. 2015). 

Once developed, these methods will have wideranging application; however, the primary purpose 

is for application to trees measured by FIA. These 

methods will provide a nationally-consistent basis 

for assessments of current conditions and trends in 

forest resources. In addition to providing consistent 

methods and definitions nationwide, substantial 

increases in analytical flexibility will be realized.

Figure 20.—The national volume study has already gathered thousands of legacy individual tree observations, such 

as taper and biomass data collected from often overlooked data files spanning nearly a century (a) in addition to 

contemporary felled tree studies (b) to augment gaps in the legacy dataset. Photo by David MacFarlane, Michigan State 

University, used with permission.

a b
36 Research to Improve and Disseminate the Forest Carbon Inventory

Remote Sensing of Forest Carbon

Remote sensing combined with field inventory data 

can provide spatially and temporally consistent 

biomass estimates. Strong statistical relationships 

between LiDAR forest structure metrics and 

field-measured biomass enable accurate spatial 

predictions of biomass well beyond the plot 

locations (e.g., Pflugmacher et al. 2014) (Fig. 21). 

But LiDAR data remain expensive and are limited 

historically. Landsat data are available everywhere 

since 1972 and can be linked to the LiDAR biomass 

estimates to extend those over space and time. A 

single date of Landsat data does not correlate well 

with biomass over a wide range of biomass values; 

however, the strength of the Landsat data is in the 

time series. Because disturbance and recovery 

history are strong determinants of current biomass, 

history metrics (e.g., predisturbance reflectance 

trend, year of disturbance, causal agent and 

magnitude of disturbance, and recovery rate) derived 

from times series data transcend the weakness of 

the biomass to single-date Landsat relationship. The 

advantages are twofold: (1) high-quality current 

biomass maps can be derived for any locations 

where there are samples of LiDAR data (i.e., LiDAR 

strips) supported by field measurements; and (2) the 

annual statistical models can be walked back to an 

arbitrary date (e.g., 1990) using the history metrics 

prior to that date, thereby providing an annual time 

series of biomass maps using a consistent set of 

methods so that trend lines are devoid of artifacts 

associated with changes in sample design or 

approach. The basic methodology has been tested 

and published. A current NASA-Carbon Monitoring 

Systems research effort is extending the approach 

across six diverse forest areas (~22,500 km2 each) of 

the conterminous United States. 

Inventorying Boreal Forest Carbon: 

The Tanana Experiment

The interior Alaska boreal forest biome represents 

one-fifth of the forest land in the entire United 

States. Despite the importance of these natural 

resources to local Alaskan communities, the 

ecological significance of changes in these resources 

and their role in the global energy, water, and carbon 

cycles, interior Alaska is the only forest ecosystem 

in the country that is not currently being funded for 

monitoring as part of the national Forest Inventory 

and Analysis program. A full inventory of interior 

Alaska could answer fundamental and pressing 

questions about this important landscape, including: 

• What is the status and condition of forests 

in interior Alaska, and how can they be 

sustainably managed to provide woody 

biomass to support bioenergy production in 

remote communities? 

Figure 21.—Example of integrating Landsat and LiDAR 

information to create robust maps of aboveground live tree 

biomass that can reduce the lack of temporal sensitivity 

often associated with delays in remeasuring forest 

inventory plots following disturbances such as wildfire 

(Pflugmacher et al. 2014).

AGB (Lidar)

AGB (Landsat)

Landsat Tasseled Cap
Research to Improve and Disseminate the Forest Carbon Inventory 37

• How are changes in forest composition and 

condition affecting the quality of wildlife 

habitat (e.g. caribou) and associated effects on 

subsistence economies? 

• How much carbon is stored below ground in 

cold, moist soils? 

• How vulnerable is this carbon reservoir to 

potential changes in the wildfire regime, 

insects, and disease? 

• How can the FIA program draw from the local 

knowledge of natural resources, and in turn 

provide employment opportunities in these 

remote areas of the state?

The U.S. Forest Service initiated an inventory 

pilot in the Tanana Valley of interior Alaska in 

the summer of 2014 to evaluate a cost-effective 

inventory design utilizing a reduced sample of field 

plots and state-of-the-art airborne remote sensing 

which enables FIA to inventory the entire state at a 

cost level of $25 million over 10 years—less than 

one-fifth the cost of the traditional FIA sampling 

design. Field measurement protocols specific 

to boreal forest conditions are being examined 

including: (1) ground cover measurements to 

quantify biomass/carbon of lichens and mosses; (2) 

soil core sampling to quantify soil carbon content; 

(3) using two microplots (or one larger microplot) 

to increase sampling of small (2.54 to 12.7 cm) 

diameter trees; and (4) using high-precision global 

positioning system (GPS) to enable accurate 

registration of field plots to airborne remote sensing 

data. The U.S. Forest Service has built partnerships 

with a number of groups to leverage resources and 

expertise for this pilot work. NASA’s Goddard 

Space Flight Center augmented the FIA sample 

with detailed measurements acquired from a newlydeveloped LiDAR-hyperspectral-thermal airborne 

remote sensing instrument (G-LiHT) (Cook et al. 

2013) that provides detailed measurements of 3-D 

forest structure (tree heights), species composition, 

and condition (e.g., insect damage) (Fig. 22). The 

State of Alaska Division of Forestry (Tanana Valley 

State Forest) assisted with logistics planning and 

in-kind support. The University of Alaska, Fairbanks 

has been instrumental in logistics planning, protocol 

development, and ground-access field plot data 

collection. Finally, the U.S. Fish and Wildlife 

Service aided with protocol development and ground 

plot location in the Tetlin National Wildlife Refuge.

Figure 22.—Spatially explicit estimates of terrain aspect, tree canopy heights, and red edge (i.e., measure of canopy 

stress) derived from NASA Goddard’s G-LiHT and hyperspectral data, 300 m swath, interior Alaska, 2014.

Terrain Aspect Canopy Heights Red Edge
38 Research to Improve and Disseminate the Forest Carbon Inventory

Bayesian Hierarchical Models 

for Uncertainty

A key challenge of deriving national forest 

carbon stocks from the FIA database is capturing 

uncertainty that arises across different scales 

within the data (i.e., from tree to plot, from plot to 

Nation). Monte Carlo approaches to estimating total 

uncertainty provide initial estimates of uncertainty 

within FCAF (Fig. 10); however, other uncertainty 

procedures are being researched. For example, we 

are evaluating a hierarchical modeling framework, 

based on Bayesian statistical inference, to provide 

robust uncertainty estimation for national forest 

carbon stock estimates. This approach presents 

several key advantages when compared to other 

methods: (1) uncertainty is determined by observed 

data hence removing the need to make assumptions; 

(2) tree and plot scale errors are seamlessly 

aggregated, allowing for realistic reporting of 

uncertainty bounds around national forest carbon 

stocks; (3) the framework is easily expanded and 

updated as new data is collected; (4) a hierarchical 

structure integrates with satellite imagery and other 

remote sensing data which enables high resolution 

mapping of forest carbon; and (5) this approach 

accommodates prediction and uncertainty estimation 

across space and through time (Fig. 23).

Ongoing work is focused on foliage biomass 

(Clough et al., in review), which is a carbon pool 

that is difficult to quantify due to tremendous 

variability (see Pool Estimation section on page 31). 

Results show markedly higher uncertainty than 

has been previously reported, with important 

consequences for reporting stocks at the national 

level. Future work seeks to expand the framework 

to other biomass pools and will lead to the 

development of user-friendly tools to facilitate wider 

adoption of hierarchical models for quantifying 

forest carbon stocks and robust quantification of 

their associated uncertainty throughout the FCAF. 

Figure 23.—Bayesian approaches to estimating 

uncertainty enable the integration of numerous error 

sources into final estimates of uncertainty.

Surf Forest Carbon with your Thumb: 

The Forest Carbon Xplorer 

The FIA program conducts a systematic inventory 

of U.S. forests that serves as not only the primary 

source of national statistics but also supports 

informed forest management. This wealth of data 

has been analyzed to provide wall-to-wall spatial 

geographic information system (GIS) coverage of 

forest carbon estimates by pool across the lower 

48 states to support carbon monitoring. Through 
Research to Improve and Disseminate the Forest Carbon Inventory 39

a research agreement and collaboration with the 

University of Minnesota, an online, mobile/web 

browser map application (app) has been developed 

that provides a 21st century digital experience of 

the distribution of the forest carbon estimation in 

the FIA program (Fig. 24). This web and mobile 

software tool (forestcarbonx.umn.edu) enables the 

public to learn about carbon and how much is stored 

in their vicinity by using the location of the user’s 

phone or tablet. The data is presented interactively 

on a map along with charts, graphs, and links to 

related resources. The intended audience is anyone 

interested in exploring forest carbon information. 

The tool is a web browser map application for 

smartphones, tablets, and desktop use that primarily 

works online but that can also be used offline with 

simplified functionality. This provides any range 

of users with access to the carbon summaries in the 

field and at the desk. 

Timely Attribution of Carbon Emissions 

by Disturbance

Funded under NASA’s Carbon Monitoring System 

program, a team of researchers from Applied 

GeoSolutions, the Jet Propulsion Laboratory, 

Winrock International, the U.S. Forest Service, and 

NASA Ames Research Center has worked together 

to develop a methodology based on a combination 

of ground inventory and satellite observations 

to resolve the drivers of forest carbon emissions 

and sequestration at 1 ha spatial resolution in the 

continental United States for 2006 to 2010 (Fig. 25). 

Using this method, the team found that 75 percent of 

the carbon emissions committed during this period 

originated from anthropogenic sources, including 

harvest (69 percent) and land use change (6 percent). 

The remaining committed emissions were from 

natural sources, including fire (10 percent), wind 

Figure 24.—Example screenshots from Forest Carbon Xplorer (forestcarbonx.umn.edu), a smartphone application that 

allows users to explore gridded forest carbon data and equivalents using their phone’s location.
40 Research to Improve and Disseminate the Forest Carbon Inventory

Figure 25.—Average annual committed emissions attributed to the most likely source and estimated at the county scale 

except where reduced observations necessitated combining counties. Combining these sources of emissions results in 

estimates of committed gross forest carbon emissions from disturbance occurring between 2006 and 2010.
Research to Improve and Disseminate the Forest Carbon Inventory 41

(7 percent), insect outbreaks (7 percent), and 

droughts (<1 percent). This was accomplished 

by integrating the nationally consistent FIA 

database with a host of products derived from earth 

observation satellites, including fire from Monitoring 

Trends In Burn Severity (MTBS) (Eidenshink et. al. 

2007), forest cover change (Hansen et. al. 2013), and 

the National Land Cover Database (NLCD) (Homer 

et. al. 2015).

The framework and accompanying results from 

this research represent a step towards enabling 

the disaggregation of natural and anthropogenic 

causes of carbon fluxes in forest land of the United 

States. The results also allow U.S. policy makers 

and negotiators to better understand the drivers 

of forest carbon fluxes more completely so that 

they can participate more effectively in domestic 

policy discussions about forest management and 

monitoring as well as in the upcoming international 

negotiations. For example, the team found that 

timber harvesting, and not land use change or fire, 

was the largest source of gross emissions from U.S. 

forests between 2006 and 2010. Integration of results 

from this and other studies should further enable 

development of future U.S. carbon inventories 

that include disturbance attribution and full land 

use change accounting in expectation of post-2020 

commitment requirements.
42

CONCLUSIONS

The annual monitoring of forest carbon in the United 

States is critical to both domestic and international 

needs for forest managers and policy makers 

alike as forests annually offset almost 15 percent 

of carbon dioxide from the combustion of fossil 

fuels in the United States. In an effort to improve 

the forest carbon inventory that the United States 

submits annually to the United Nations Framework 

Convention on Climate Change, a new accounting 

framework was introduced that moves the nationally 

consistent forest inventory backward and forward 

in time to meet reporting standards back to the 1990 

baseline year while enabling forecasting of future 

forest carbon dynamics (per biennial reporting). 

Preliminary results from the new Forest Carbon 

Accounting Framework demonstrate the ability 

of the new framework to both backcast the annual 

inventory system while attributing changes in forest 

carbon to disturbances and delineating land use 

change from forests remaining forest. Numerous 

improvements are planned, such as refining the 

estimation of individual carbon pools and land use 

change identification, which can be incorporated 

into the framework with future iterations. The 

National Greenhouse Gas Inventory will continue 

to be refined as new data become available and 

numerous areas of emerging research are identified 

that someday may substantially improve our ability 

to monitor forest carbon.
 43

Acknowledgments

Special thanks to the hundreds of field crew and 

cooperators who collected data on hundreds of 

thousands of plots over the course of more than a 

decade, the information management staff in western 

states who re-stratified western states to enable 

change estimation, and USDA leadership that steered 

our course, namely Greg Reams and Linda Langner.

English Equivalents

When you know: Multiply by: To find:

Centimeters (cm) 0.394 Inches

Meters (m) 3.28 Feet

Hectares (ha) 2.47 Acres

Square meters (m2) 10.76 Square feet

Kilograms (kg) 0.0011 Tons

Kilograms per cubic meter (kg m-3 ) 0.0624 Pounds per cubic foot

Megagrams per hectare (Mg ha-1 ) 0.446 Tons per acre

Teragram (Tg) 1,102,311 Tons

Square meters per hectare (m2/ha) 4.37 Square feet per acre
44

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As a signatory to the United Nations Framework Convention on Climate Change, 

the United States annually prepares an inventory of carbon that has been emitted 

and sequestered among sectors (e.g., energy, agriculture, and forests). For many 

years, the United States developed an inventory of forest carbon by comparing 

contemporary forest inventories to inventories that were collected using different 

techniques and definitions from more than 20 years ago. Recognizing the need to 

improve the U.S. forest carbon inventory budget, the United States is adopting the 

Forest Carbon Accounting Framework, a new approach that removes this older 

inventory information from the accounting procedures and enables the delineation 

of forest carbon accumulation by forest growth, land use change, and natural 

disturbances such as fire. By using the new accounting approach with consistent 

inventory information, it was found that net land use change is a substantial 

contributor to the United States forest carbon sink, with the entire forest sink 

offsetting approximately 15 percent of annual U.S. carbon dioxide emissions from 

the burning of fossil fuels. The new framework adheres to accounting guidelines 

set forth by the Intergovernmental Panel on Climate Change while charting a 

path forward for the incorporation of emerging research, data, and the needs of 

stakeholders (e.g., reporting at small scales and boreal forest carbon).

Key words: forest, carbon accounting, baselines, attribution, 

 United Nations Framework Convention on Climate Change

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