Database for Landscape-Scale 

Carbon Monitoring Sites

United States Department of Agriculture

Forest 

Service

General Technical

Report NRS-119

Northern 

Research Station

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September 2013
Visit our homepage at: http://www.nrs.fs.fed.us/

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Abstract

This report describes the database used to compile, store, and manage intensive 

ground-based biometric data collected at research sites in Colorado, Minnesota, New 

Hampshire, New Jersey, North Carolina, and Wyoming, supporting research activities 

of the U.S. North American Carbon Program (NACP). This report also provides details 

of each site, the sampling design and collection standards for biometric measurements, 

the database design, data summary examples, and the uses of intensive ground-based 

biometric data. Additional information on location descriptions, data, databases, and 

documentation may be accessed at http://www.nrs.fs.fed.us/data/lcms.

Manuscript received for publication 22 January 2013
1

Database for Landscape-scale Carbon Monitoring Sites

Jason A. Cole, Kristofer D. Johnson, Richard A. Birdsey, Yude Pan, Craig A. Wayson, 

Kevin McCullough, Coeli M. Hoover, David Y. Hollinger, John B. Bradford, Michael G. Ryan, 

Randall K. Kolka, Peter Weishampel, Kenneth L. Clark, Nicholas S. Skowronski, John Hom, 

Scott V. Ollinger, Steven G. McNulty, and Michael J. Gavazzi

The Authors

JASON A. COLE is a forester with the U.S. Forest Service’s 

Northern Research Station at Syracuse, New York.

Jason Cole is corresponding author; to contact, call 315-448-

3208 or email jasoncole@fs.fed.us

KRISTOFER D. JOHNSON is a research biologist with the 

U.S. Forest Service’s Northern Research Station at Newtown 

Square, Pennsylvania.

RICHARD A. BIRDSEY is a project leader with the U.S. 

Forest Service’s Northern Research Station at Newtown 

Square, Pennsylvania.

YUDE PAN is a senior research scientist with the U.S. Forest 

Service’s Northern Research Station at Newtown Square, 

Pennsylvania.

CRAIG A. WAYSON is a program coordinator with the U.S. 

Forest Service International Program Office in Lima, Peru.

KEVIN McCULLOUGH is a geographer with the U.S. Forest 

Service’s Northern Research Station in Madison, Wisconsin.

COELI M. HOOVER is a research ecologist with the U.S. 

Forest Service’s Northern Research Station in Durham, New 

Hampshire.

DAVID Y. HOLLINGER is a research plant physiologist 

with the U.S. Forest Service’s Northern Research Station in 

Durham, New Hampshire.

JOHN B. BRADFORD is a research ecologist with the U.S. 

Geological Survey, Southwest Biological Science Center in 

Flagstaff, Arizona.

MICHAEL G. RYAN is a research forest ecologist with the 

U.S. Forest Service’s Rocky Mountain Research Station at 

Fort Collins, Colorado.

RANDALL K. KOLKA is a team leader and research soil 

scientist with the U.S. Forest Service’s Northern Research 

Station at Grand Rapids, Minnesota.

PETER WEISHAMPEL is an assistant professor of biology 

and natural resources at Northland College Ashland, WI 

54806.

KENNETH L. CLARK is a research forester with the U.S. 

Forest Service’s Northern Research Station at the Silas Little 

Experimental Forest, New Lisbon, New Jersey.

NICHOLAS S. SKOWRONSKI is a research forester with 

the U.S. Forest Service’s Northern Research Station at 

Morgantown, West Virginia.

JOHN HOM is a biological scientist with the U.S. Forest 

Service’s Northern Research Station at Newtown Square, 

Pennsylvania.

SCOTT V. OLLINGER is a professor in the Department of 

Natural Resources and the Environment, University of New 

Hampshire.

STEVEN G. McNULTY is a team leader, ecologist with the 

U.S. Forest Service’s Southern Research Station, Eastern 

Forest Environmental Threat Assessment Center at Raleigh, 

North Carolina.

MICHAEL J. GAVAZZI is a biological scientist with the U.S. 

Forest Service’s Southern Research Station, Eastern Forest 

Environmental Threat Assessment Center at Raleigh, North 

Carolina.
2

INTRODUCTION

Th is report describes the methodology used to collect, 

compile, and manage multi-tier land monitoring data at a 

network of long-term forest monitoring sites supporting 

the U.S. North American Carbon Program (NACP). Th e 

NACP is a multidisciplinary research program developed 

to obtain a scientifi c understanding of North America’s 

carbon sources, sinks, and changes in carbon stocks 

needed to meet societal concerns. Th e program also 

aims to provide tools for decisionmakers. Th e two main 

goals of the NACP are to (1) develop the scientifi c basis 

in support of full carbon accounting on regional and 

continental scales; and (2) support long-term quantitative 

measurements of fl uxes, sources, and sinks of atmospheric 

CO2 and CH4, and develop forecasts for future trends.

Managing forests to sustain or increase carbon stocks 

and to off set emissions requires knowledge of how 

management practices and natural disturbances aff ect 

carbon pools over time, and cost-eff ective techniques 

for monitoring and reporting. Accurate landscape-scale 

estimates and maps of carbon dynamics based on remote 

sensing, inventories, and intensive measurements are 

relevant to land managers and climate change policy 

because of the need to estimate and report carbon stocks 

and changes in carbon stocks to state, regional, national, 

international, and private greenhouse gas registries. 

Intensively monitored landscapes serve as “benchmarks” 

or “reference sites” to validate more spatially extensive 

observations from space, predictions from ecosystem 

models, and estimates compiled from national forest 

inventories. Th e data can be used to improve decisionsupport for carbon management by documenting the 

expected eff ects of management decisions on the most 

important carbon pools, and “factoring out” changes in 

carbon stocks that are not due to direct human infl uence, 

such as natural disturbances and climate variability.

A national network of landscape-scale monitoring 

sites should be representative of the diversity of forest 

conditions and geographic context. Networks such as 

AmeriFlux1

 have limited representation of mountainous 

terrain and highly disturbed landscapes and do not 

consistently represent land that is managed or disturbed, 

and locations where it is diffi cult to install and operate 

intensive monitoring equipment (mountains, wetlands, 

etc.). Extensive monitoring by the U.S. Forest Service 

Forest Inventory and Analysis (FIA) program is 

statistically representative of U.S forests but lacks 

detailed ecosystem measurements needed to investigate 

the complex dynamics associated with a diversity of 

forest carbon pools and fl uxes (Birdsey 2004). A report 

on ecological indicators identifi ed critical gaps in 

monitoring data which include forest carbon indicators 

(Heinz Center 2002). Indicators needing improved data 

availability included major components of forest carbon 

accounting: biomass, soils, forest fl oor and down woody 

debris, and wood products. Of these, only biomass on 

timberland was reported; the other components were 

judged by the Heinz Center to be defi cient in data 

availability through ongoing monitoring programs. 

Although resource inventory programs, such as FIA’s, 

continue to make progress for reporting carbon statistics 

in most circumstances (Woodall 2012), data gaps and 

technical advances are best addressed through intensive 

site research studies and related modeling eff orts (e.g., 

Birdsey and Heath 1995, Heath et al. 2003).

Data reported here were collected at a network of 

landscape monitoring sites representing forests with 

diff erent management, disturbance histories, and 

vegetation to bridge the gap between fl ux towers and 

national inventory programs. Key information for 

each site includes (1) estimates of carbon stocks and 

quantifi ed impacts of management activity; (2) estimates 

of net ecosystem production (NEP) and changes in 

carbon pools; and (3) estimates of forest/atmosphere 

carbon fl uxes. Th e database described in this report was 

developed to provide detailed, well-documented, and 

consistent information from a network of long-term 

observation sites in the United States. Th e design of the 

sampling protocol and database provide examples for 

applications in other regions.

1

Th e AmeriFlux network provides continuous observations of 

ecosystem level exchanges of CO2, water, and energy, spanning 

diurnal, synoptic, seasonal, and interannual time scales and 

is currently composed of sites from North America, Central 

America, and South America. For more information, see 

http://public.ornl.gov/amerifl ux/
3

STATION DESCRIPTIONS

Th e landscape-scale carbon monitoring project consists 

of 10 intensively monitored sites at seven locations or 

“stations” (Table 1), representing a variety of forest 

types scattered across the United States (Figure 1). All 

stations collect biometric, meteorological, and eddy fl ux 

measurements except for Fraser Experimental Forest, 

which collects only biometric and meteorological data. A 

brief description of each station is provided in this report; 

for a complete description of each station including 

available data, refer to the supplemental information 

available on the project Website.2

Bartlett Experimental Forest 

Bartlett Experimental Forest (BEF) is located within the 

Saco Ranger District in the White Mountain National 

Forest of New Hampshire. Th e station consists of 

relatively even-age stands of red maple (Acer rubrum), 

paper birch (Betula papyrifera), aspen (Populus spp.), 

and American beech (Fagus grandifolia) that developed 

after logging in the late 19th century. Red spruce (Picea 

rubens) stands occur on the highest slopes with white 

pine (Pinus strobus) confi ned to the lower elevations. 

Summer high temperatures occasionally reach above 

32 °C and winter temperatures as low as -35 °C with a 

mean annual precipitation of 1,270 mm. A hurricane 

caused widespread damage in 1938 and an ice storm in 

1998 was the most recent widespread natural disturbance 

aff ecting mostly higher elevation stands and paper birch 

at lower elevations.

BEF has one intensively monitored site:

• BEF NACP Tier III (NACP)

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Figure 1—Carbon monitoring station locations overlaid on MODIS imagery. Two stations, TPT and SLEF include 

two and three intensively monitored sites, respectively. See Table 1 for explanation of station abbreviations. 
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Fraser Experimental Forest

Fraser Experimental Forest (FEF) is located in the near 

Fraser, CO, in the Arapaho/Roosevelt National Forest 

of the southern Rocky Mountains. Th e vegetation is 

characteristic of subalpine forests and subalpine wetlands 

where the dominant tree species are subalpine fi r (Abies 

lasiocarpa) and Engelmann spruce (Picea engelmannii) 

in higher elevations and lodgepole pine (Pinus contorta) 

in lower elevations. Th e mean annual temperature and 

precipitation are 0 °C and 737 mm respectively, with 

about two-thirds of the precipitation falling as snow. In 

the 1950s, half of the timber in the Fool Creek watershed 

in FEF was harvested as part of an experiment to examine 

the eff ects of timber removal on water yield. Th e harvest 

was executed in alternating strips of cut and unharvested 

forest, ranging from 20 to 110 m wide. Recently, 

widespread overstory tree mortality has been caused by 

the mountain pine beetle (Dendroctonus ponderosae).

FEF has one intensively monitored site:

• Fool Creek (FC)

Glacier Lakes Ecosystem Experiments 

Site

Glacier Lakes Ecosystem Experiments Site (GLEES) 

station is located in the Snowy Range of the Medicine 

Bow Mountain Range of the Rocky Mountains in 

Wyoming. Th e vegetation is characteristic of subalpine 

forests and subalpine wetlands where the dominant tree 

species are subalpine fi r and Engelmann spruce; many 

trees are more than 400 years old. Th e mean annual 

temperature and precipitation are -2 °C and 1,000 mm, 

respectively, and most of the precipitation falls as snow. 

Recent, widespread overstory mortality has been caused 

by the spruce beetle (Dendroctonus rufi pennis) and the 

western balsam bark beetle (Dryocoetes confusus).

GLEES has one intensively monitored site:

• Brooklyn Lake (BL)

Marcell Experimental Forest (MEF)

Marcell Experimental Forest (MEF) is located in the 

Chippewa National Forest in Minnesota. Th e vegetation 

includes mainly aspen (Populus spp.), birch (Betula spp.), 

and other northern hardwoods on upland sandy loam till 

soils; red and jack pine (Pinus resinosa, P. banksiana) in fi re 

origin stands or plantations; mixed stands of aspen, white 

birch (Betula papyrifera), balsam fi r (Abies balsamea) 

and white spruce (Picea glauca) on upland sandy outwash 

soils; and black spruce (Picea mariana) and tamarack 

(Larix laricina) in forested peatlands. Th e mean annual 

temperature and precipitation are -3.4 °C and 771 mm, 

respectively, with most precipitation falling in the snowfree period.

MEF has one intensively monitored site:

• Marcel Experimental Forest (MEF)

Table 1.—The landscape-scale carbon monitoring station and site codes with location

Station code Station Site code Site State Location

BEF Bartlett Experimental 

Forest

NACP BEF NACP Tier III New Hampshire White Mountain 

National Forest

FEF Fraser Experimental 

Forest

FC Fool Creek Colorado Arapaho/Roosevelt 

National Forest

GLEES Glacier Lakes Ecosystem 

Experiments Site

BL Brooklyn Lake Wyoming Medicine Bow 

Mountain Range

MEF Marcell Experimental 

Forest

MEF Marcell Experimental 

Forest

Minnesota Chippewa National 

Forest

NIWOT Niwot Ridge Long-term 

Ecological Reseach Site

NRAT Niwot Ridge Ameriflux 

Tower

Colorado Front Range of the 

Rocky Mountains

SLEF Silas Little Experimental 

Forest

CB

FD

SL

Cedar Bridge

Fort Dix

Silas Little

New Jersey Pinelands National 

Reserve

TPT The Parker Tract NCCC

NCLP

North Carolina Clearcut

North Carolina Loblolly

North Carolina Plymouth
5

Niwot Ridge Long-term Ecological 

Research Site

Niwot Ridge Long-term Ecological Research Site 

(NIWOT) station is located in the Front Range of 

the Rocky Mountains in Colorado. Th e vegetation is 

characteristic of subalpine forests where the dominant 

tree species are subalpine fi r and Engelmann spruce in 

higher elevations; and lodgepole pine in lower elevations. 

Th e mean annual temperature and precipitation are 1.5 

°C and 800 mm, respectively; most of the precipitation 

falls as snow. NIWOT was clearcut between 1900 and 

1910, and evidence suggests that the forest was about 200 

years old at the time.

NIWOT has one intensively monitored site:

• Niwot Ridge Amerifl ux Tower (NRAT)

Silas Little Experimental Forest

Silas Little Experimental Forest (SLEF) is located in the 

northern section of the New Jersey Pinelands National 

Reserve of New Jersey. Th e vegetation consists of oaks 

(Quercus spp.), shortleaf pine (Pinus echinata), and pitch 

pine (Pinus rigida) on upland sandy soils (52 percent 

of the area); pitch pine and low-grade oaks on upland 

highly infertile sands (22 percent of the area); pitch 

pine and mixed hardwood swamp forests on lowland 

poorly drained soils (14 percent of the area); and mixed 

hardwood swamp forests (12 percent of the area). 

July is the warmest month with temperatures ~25 °C 

and February is the coldest month with temperatures 

around -1 °C. Th e mean annual precipitation is ~1,140 

mm, with reduced rainfall in the spring and fall.

SLEF includes three intensively monitored sites: 

• Silas Little (SL) oak/pine forest

• Fort Dix (FD) pine/oak forest 

• Cedar Bridge (CB) pine/scrub oak forest

Th ese sites are within 13 km of each other. Prescribed 

burning is used regularly in the Pinelands to reduce 

fuel loads and to restore native vegetation. Recent 

disturbances include occasional wildfi res (1946, 1963, 

1995), wind damage (especially 1991), and a number of 

signifi cant gypsy moth defoliation events, with the most 

recent occurring from 2006-2008.

The Parker Tract

Th e Parker Tract (TPT) is located in the lower coastal 

plain near Plymouth, NC. Th e region is considered a 

maritime temperate climate zone with mean annual 

temperature and precipitation of 15.5 °C and 1,320 mm, 

respectively. TPT is an intensively managed loblolly pine 

(Pinus taeda) plantation.

TPT includes two intensively monitored sites:

• North Carolina Loblolly Pine (NCLP).

• North Carolina Clearcut (NCCC) - 

approximately 4 km from NCLP.

Th e sites are intensively managed wetland plantations 

ditched and bedded to improve seedling survival and 

growth rates. A typical silvicultural management practice 

includes site preparation, planting of loblolly pine 

seedlings, fertilization, thinning, and clearcut harvest 27 

to 35 years after planting. Th e NCLP site was planted in 

1992 and thinned in 2009 to remove approximately 50 

to 60 percent of the pine biomass. Loblolly pine seedlings 

were planted at the NCCC site in 2005 after clearcutting 

a mixed pine hardwood site.

Sampling Design and Data Collection 

Standards

Th e NACP approach to biophysical measurements 

involves hierarchical, multi-tier monitoring that 

integrates current, extensive inventory and monitoring 

programs (such as FIA) with intensive monitoring 

and process studies at long-term research sites. Th e 

intermediate monitoring tier at the landscape scale is 

designed to link extensive monitoring with intensive 

monitoring using medium-intensity biometric 

measurements from a few selected sites such as Longterm Ecological Research sites, AmeriFlux sites, and U.S. 

Forest Service network of Experimental Forests. Th ese 

measurements represent the land conditions selected by 

investigators to answer specifi c research questions and 

include clusters of measurement sites that represent a 

range of conditions over selected land areas. One goal of 

landscape monitoring is to include sites that are managed 

or disturbed by natural events, representing diff erent 

stages of succession following disturbance.
6

Workshops in summer 20033

 identifi ed both a desired 

list of variables and a sampling design, and started the 

process of developing a fi eld manual (Hoover 2008). A 

summary list of variables is shown in Table 2. Similar 

to intensive monitoring, important variables that defi ne 

the ecosystem “state”—vegetation type, foliage nitrogen 

concentration, and the ratio of soil carbon to nitrogen—

are measured along with automated measurements of 

key “driving variables” such as light, temperature, and 

precipitation that control the rate of ecosystem carbon 

uptake and loss. Th ese state and driving variables are 

measured at diff erent vegetation conditions within 

landscapes, allowing estimates of net primary production 

(NPP) and net ecosystem production (NEP) to be 

derived as closely as possible from fi eld measurements. 

Th e variables are supplemented by statistical models 

of ecosystem carbon components, such as biomass 

equations, that are parameterized for each landscape 

monitoring location.

Figure 2 illustrates the idealized sampling scheme for 

landscape monitoring sites. At the center is either a 

fl ux tower or other intensive ecosystem monitoring 

system if a fl ux tower is not available. Sample locations 

are arrayed in a 1 km2

 by 1 km2 grid surrounding 

the fl ux tower, an area that approximates the source 

area, or footprint, of air moving past the fl ux tower. 

Th e FIA protocol for regional sampling is the basis 

for vegetation measurements, with the supplemental 

ecosystem carbon measurements applied to these 

sample locations. By using the FIA standard protocol 

for regional inventories (U.S. Forest Service 2002), 

estimates from landscape-scale monitoring can be related 

to commonly measured variables and extrapolated 

to similar sites over a larger area using geostatistical 

techniques.

Field methods and techniques employed in landscapescale forest carbon monitoring are described in Hoover 

(2008). Th is handbook provides detailed descriptions 

Figure 2—A landscape-scale sampling design using 16 FIA 

inventory plots. The exact number and configuration of sample 

plot locations were determined by variability of the landscape 

and number of sampling strata. The X at the center represents a 

flux or meteorological tower.

3 Portsmouth, NH: more than 40 scientists representing 

government agencies, academia, and nonprofi t research 

organizations from Canada, United States, and Mexico. 

Table 2.—Selected land measurement variables and scale of measurement

Example variable Extensive 

monitoring

Landscape 

monitoring

Intensive 

monitoring

Land cover X X X

Leaf area X X X

Disturbance X X X

Live biomass X X X

C in soil, litter, and coarse woody debris X X

Litterfall X X

Soil CO2 flux X X

Methane flux X X

Dissolved organic C X X

Net ecosystem exchange of CO2 X
7

of the measurements needed to characterize the 

standing stocks of carbon in a forest, assess key fl uxes 

in forest carbon, and collect related data such as forest 

canopy nitrogen concentrations and meteorological 

measurements that are often needed to drive process 

models, develop predictive relationships, and link to 

remote sensing data. It is intended that the established 

sampling locations be remeasured at appropriate 

intervals to estimate changes in carbon stocks of diff erent 

ecosystem components, which can be aggregated to 

periodically estimate productivity and forest-atmosphere 

carbon fl uxes.

Database Description

Th e biometric database (Cole et al. 2013) for the 

landscape-scale carbon monitoring sites is a relational 

database initially implemented in Microsoft Access 

2007®. Th e database includes measured and estimated 

data with a series of lookup tables (LUT) to describe 

the data. Th e database is designed to house data from 

multiple providers where the variables, collection 

methods, and calculations vary. Storing data from 

multiple providers with varying collection and 

processing methods necessitated the incorporation of 

documentation in the database that directly ties the 

documentation to the variable it describes. Th is allows 

direct access to the exact methods used at each site and 

allows common collection methods and processes to be 

grouped for viewing or further processing. Th e LUT 

provide transparency, documenting every process and/or 

procedure, making them repeatable.

To reduce redundancy, data were broken out into 

subject-based tables. Th e base tables hold information 

repeated in all records in the corresponding tables. 

For example, the plot table (Plot) describes general 

information about the plot (e.g., state, county, 

ownership, etc.), the subplot table (Subplot) is a series of 

subsamples on the plot (e.g. ,slope, aspect, area, etc.), the 

tree date table (TreeD) defi nes the date and area of trees 

sampled on the subplot, the tree table (Tree) includes 

individual tree attributes (e.g., status, crown class, dbh, 

height, etc.), and biomass tables (Biom_TreeL and Biom_

TreeD ) contain tree live and dead component biomass, 

respectively, as shown in Figure 3.

Table relationships were established for all tables within 

the database. Table relationships require that the 

corresponding LUT are populated before data can be 

entered into a data table, forcing the documentation to 

be created before data can be entered. Th e same applies to 

data tables—data in a base table must be entered before 

data can be entered into subsequent dependent tables.

Th e biometric data are stored in multiple tables within 

the database. Th e tables are based on core variables, 

collection interval, and data type. Th is was done to limit 

null values and information repetition, which can lead 

to confusion and problems during analysis. Th e tables 

were separated by collection interval or collection period, 

as some variables may be collected more frequently 

than others (e.g., plot and subplot information may be 

collected every 15 years while tree data may be collected 

Figure 3—Relationship diagram illustrating the one-to-many relationships for plot, subplot, tree, and tree 

biomass tables.
8

every 5 years) or collected during diff erent site visits (e.g., 

tree data was collected during a separate a visit from 

soils data). Th e data were further broken down by data 

type, as the data collection and methods for determining 

biomass may vary between variable type.

Some data may be missing from the biometric database. 

Missing data appear in two forms: data fi elds with null 

values, or an entire sites’ data missing from a table. 

Columns with null data values are due to the lack of 

data collection, either by the variables missed in the 

fi eld or the variable not being collected by the site. 

Tables missing an entire sites’ data are due to the sites 

not collecting any of the table’s variables. Missing data 

from estimated variable tables, where the necessary 

fi eld data has been collected, are due to the lack of an 

acceptable estimating methodology. Th ese variables may 

be estimated later when the required procedures are 

available.

Th e biometric database includes both measured 

and estimated data. Measured data include general 

descriptive information and detailed measurements. 

General descriptive information defi nes the sample area 

at the station, site, plot, and subplot level. Detailed 

measurements include tree, shrub, nonwoody vegetation, 

down woody material, stump, litter, forest fl oor, 

agricultural crop, leaf area index, fi ne root, and 

soils. Tree data were separated into three size 

classes: seedling, sapling, and tree. Soil data 

include chemistry, characteristics, respiration, 

water content, and capacity.

Biomass is estimated by component for 

individual subjects including trees by size class, 

shrubs, down woody material, and stumps. 

Components are the subject’s section of 

interest (e.g., stem, branch, foliage, roots, etc.). 

Aboveground component biomass estimates 

are summarized by subject type to the subplot, 

plot, and site level in a separate summary 

database.

For a copy of the data including a detailed 

user’s guide describing the data, refer to Cole 

et al. (2013) or the project Website.2

Summary Data and Biomass Maps for 

Each Site 

Comparing the intensely monitored area at each site, the 

Rocky Mountain sites (BL, FC, and NRAT) generally 

had higher total aboveground carbon than eastern sites, 

mostly due to higher biomass in both the live and dead 

tree pools (Figure 4). Th e exception was NACP, which 

had the highest live tree and live sapling carbon of all 

the sites. Substantial amounts of carbon were stored in 

the dead pools at the western sites, in contrast to most 

eastern sites. For example, FC had about 31 percent of its 

carbon in dead pools while FD only had about 5 percent. 

Additionally, 55 percent of the carbon at NCCC was in 

the coarse woody debris pool, with the rest being roughly 

split into the live sapling and shrub pools, due to recent 

clearcutting.

LiDAR-based aboveground live carbon maps show the 

spatial distribution of the tree biomass carbon pool 

(Figures 5 and 6) (methods described in Sherrill et 

al. 2008, Skowronski 2011) and are compared with 

Jenkins et al. (2004) live tree biomass estimates (Table 

3). An incomplete sampling of the BL site resulted in 

a bias toward forested conditions within the intensely 

Figure 4.—Total aboveground carbon and proportions of available carbon 

pools (megagrams/km2

) for all 10 of the intensely monitored areas. The 

BL estimate was multiplied by 0.8 to adjust for under representation of 

nonforest area in the field data.

14000

12000

10000

8000

6000

4000

2000

0

Aboveground Carbon (Mg/km2

)

NACP

NCCC

NCLP

FC

BL

MEF

NRAT

CB

FD

SL

Live tree

Live sapling

Seedling

Shrub

Dead tree

Dead sapling

Fine woody debris

Coarse woody debris
9

monitored area. Th us, to compare with the other sites, 

a factor of 0.8 was applied (Figure 4 and Table 3). Th is 

refl ected the percentage of plots from the uniform grid 

in Figure 2 identifi ed as having trees determined from 

photo imagery.

CONCLUSION

Better understanding and monitoring of forests and 

the global carbon cycle are required to develop climate 

change mitigation strategies, including improved 

forest management and reduced impacts of tropical 

deforestation. Pan et al. (2011) compiled worldwide 

forest inventory data and concluded that improving 

sampling of soil, litter, and dead wood was critical to 

resolving data gaps in many parts of the world. National 

forest inventories, such as FIA’s, conduct frequent 

and extensive sampling of biomass, data for the other 

carbon pools comes from sites such as those described 

in this report. Th e literature is full of reports from such 

sites, however sampling may not be done on a frequent 

or recurring basis. Th e methods used are inconsistent 

and often poorly documented, which sometimes 

hinders eff ective site comparison or aggregation of data. 

Moreover, the data are often not easily accessible except 

in summary form in scientifi c or technical publications.

Data about carbon stocks and fl uxes from intensive 

monitoring sites are used for many purposes. Th e main 

products of this research include precise statistical 

estimates and maps of carbon stocks and productivity 

for a variety of forest landscape conditions. Data may be 

used to improve ecosystem process models at ecoregion 

and stand scales; to validate estimates from remotesensing driven models; and for decision-support tools 

for land managers interested in carbon management. As 

such, the data are integral to establishing monitoring, 

reporting, and verifi cation (MRV) systems for reporting 

the eff ects of land management and other disturbances 

on carbon stocks. Th e MRV systems are emerging as 

greenhouse gas markets and registries are becoming more 

common, raising the need for consistent estimation of the 

quantity of carbon sequestered and emissions reduced by 

diff erent forestry activities, as these estimates will be used 

to determine the value of the credits. Th e accounting 

rules and guidelines must be based on solid scientifi c 

and technical work to be credible, and must not impose 

an excessive burden on voluntary reporters. Enhancing 

observations at experimental forests have additional 

benefi ts such as facilitating use of these sites for carbon 

management research and demonstration projects, and 

providing the basis for an “early warning” capability to 

detect the initial impacts of climate change.

Th e data and examples described in this report and in the 

online database represent only part of the data available 

for each site. At most of the sites, sample plots have been 

measured more than once, allowing users to estimate 

rates of tree growth and mortality, changes in many 

of the ecosystems carbon pools, and factors that cause 

observed changes. Data from each site as well as the data 

and site descriptions will be updated periodically.

ACKNOWLEDGMENTS

Th e research was supported in part by a grant 

from the National Aeronautical and Space Agency 

(NNH08AH971).

Table 3.—Live tree carbon estimates (megagrams/km2) 

for all 10 intensely monitored areas from field data 

using Jenkins et al. (2004) biomass equations compared 

with LiDAR derived estimates (methods described in 

Sherrill et al. 2008, Skowronski 2011).

Site Number of Plots Live tree LiDAR

---------Mg C/km2

---------

NACP 12 10,141 9,332

NCCC 12 0 N.d.

NCLP 13 5,877 N.d.

FC 9 7,728 8,633

BLa 9 8,887 9,452

MEF 16 5,055 5,553

NRAT 9 7,584 5,892

CB 12 2,276 1,712

FD 12 2,499 2,454

SL 16 4,534 4,263

a BL estimate was multiplied by 0.8 to adjust for underrepresentation 

of nonforest area in field data. N.d. = no data.
10

Figure 5.—National Agriculture Imagery Program (NAIP) 2008 imagery on left with 

corresponding LiDAR derived biomass map (megagrams carbon/km2

) (methods 

described in Sherrill et al. 2008) on right for the intensely monitored area at sites: 

NACP, FC, BL and MEF.

NACP 

FC 

BL 

MEF 

NAIP Imagery Live Tree LiDAR Biomass 

24,600 

0 

Biomass 

(Mg C/km2)
11

Figure 6.—National Agriculture Imagery Program (NAIP) 2008 imagery on left with 

corresponding LiDAR derived biomass map (megagrams carbon/km2

) (methods 

described in Sherrill et al. 2008, Skowronski 2011) on right for the intensely 

monitored area at sites: NRAT, CB, FD and SL.

NAIP Imagery Live Tree LiDAR Biomass 

CB

NRAT 

FD

SL

24,600 

0 

Biomass 

(Mg C/km2)
12

LITERATURE CITED

Birdsey, R.A. 2004. Data gaps for monitoring 

forest carbon in the United States: an inventory 

perspective. In: Mickler, R.A., ed. Environmental 

Management. 33(Supplement 1): S1-S8.

Birdsey, R.A.; Heath, L.S. 1995. Carbon changes 

in U.S. forests. In: Joyce L.A., ed. Productivity of 

America’s forests and climate change. Gen. Tech. 

Rep. RM-271. Ft. Collins, CO: U.S. Department of 

Agriculture, Forest Service: 56-70.

Cole, J.; Johnson, K.; Birdsey, R.A.; Pan, Y.; Wayson, 

C.; McCullough, K.; Hoover, C.; Hollinger, D.; 

Bradford, J.; Ryan, M.; Kolka, R.; Weishampel, P.; 

Clark, K.; Skowronski, N.; Hom, J.; Ollinger, S.; 

McNulty, S.; Gavazzi, M. 2013. North American 

Carbon Program biometric database. Newtown 

Square, PA: U.S. Department of Agriculture, Forest 

Service, Northern Research Station. http://dx.doi.

org/10.2737/RDS-2013-0008.

Heath, L.S.; Smith, J.E.; Birdsey, R.A. 2003. Carbon 

trends in U.S. forestlands: a context for the role of 

soils in forest carbon sequestration. In: Kimble, J.; 

Heath, L.S.; Birdsey, R.A.; Lal, R. Th e potential of 

U.S. forest soil to sequester carbon and mitigate the 

greenhouse eff ect. Boca Raton, FL: CRC Press: 35-46 

p.

Heinz Center. 2002. Th e state of the Nation’s 

ecosystems. New York, NY: Cambridge University 

Press. 270 p.

Hoover, C.M., ed. 2008. Field measurements for forest 

carbon monitoring: A landscape-scale approach.

New York, NY: Springer. 242 p.

Jenkins, J.C.; Chojnacky, D.C.; Heath, L.S.; Birdsey, 

R.A. 2004. Comprehensive database of diameterbased biomass regressions for Northern American 

tree species. Gen. Tech. Rep. NE-319. Newtown 

Square, PA: U.S. Department of Agriculture, Forest 

Service, Northeastern Research Station. 45 p.

U.S. Forest Service. 2002. Forest Inventory and 

Analysis national core fi eld guide, volume I: fi eld 

data collection procedures for phase 3 plots, 

version 1.6. Washginton, DC: U.S. Department of 

Agriculture, Forest Service. Available at http://www.

fs.fed.us/ne/fi a/datacollection/manualver1_6/index.

html (Accessed April 2012).

Pan, Y.; Birdsey, R.A.; Fang, J.; Houghton, R.; Kauppi, 

P.E.; Kurz, W.A.; Phillips, O.L.; Shvidenko, A.; 

Lewis, S.L. 2011. A large and persistent carbon sink 

in the world’s forests. Science. 333(6045): 988-993.

Sherrill, K.R.; Lefsky, M.A.; Bradford, J.B.; Ryan, M.G. 

2008. Forest structure estimation and pattern 

exploration from discrete-return lidar in subalpine 

forests of the central Rockies. Canadian Journal of 

Forest Research. 38: 1-16.

Skowronski, N.S. 2011. Quantifying three-dimensional 

vegetation structure and its responses to 

disturbances using laser altimetry in the New Jersey 

Pinelands. New Brunswick, NJ: Rutgers University. 

303 p. Ph.D. dissertation.

Woodall, C.W. 2012. Where did the US forest 

biomass/carbon go? Journal of Forestry. 110(2): 113-

114.
Printed on Recycled Paper

Cole, Jason A.; Johnson, Kristopher D.; Birdsey, Richard A.; Pan, Yude; Wayson, 

Craig A.; McCullough, Kevin; Hoover, Coeli M.; Hollinger, David Y.; Bradford, 

John B.; Ryan, Michael G.; Kolka, Randall K.; Wieshampel, Peter; Clark, Kenneth 

L.; Skowronski, Nicholas S.; Hom, John; Ollinger, Scott V.; McNulty, Steven G.; 

Gavazzi, Michael J. 2013. Database for landscape-scale carbon monitoring 

sites. Gen. Tech. Rep. NRS-119. Newtown square, PA: U.S. Department of 

Agriculture, Forest Service, Northern Research Station. 12 p.

This report describes the database used to compile, store, and manage intensive 

ground-based biometric data collected at research sites in Colorado, Minnesota, New 

Hampshire, New Jersey, North Carolina, and Wyoming, supporting research activities 

of the U.S. North American Carbon Program (NACP). This report also provides 

details of each site, the sampling design and collection standards for biometric 

measurements, the database design, data summary examples, and the uses of 

intensive ground-based biometric data. Additional information on location descriptions, 

data, databases, and documentation may be accessed at http://www.nrs.fs.fed.us/

data/lcms.

KEY WORDS: database, biometric data, inventory data, biomass, landscape-scale, 

carbon stocks

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

the basis of race, color, national origin, age, disability, and where applicable, sex, marital status, familial 

status, parental status, religion, sexual orientation, genetic information, political beliefs, reprisal, or because 

all or part of an individual’s income is derived from any public assistance program. (Not all prohibited 

bases apply to all programs.) Persons with disabilities who require alternate means for communication 

of program information (Braille, large print, audiotape, etc.) should contact USDA’s TARGET Center at 

(202)720-2600 (voice and TDD). To file a complaint of discrimination, write to USDA, Director, Office of 

Civil Rights, 1400 Independence Avenue, S.W., Washington, DC 20250-9410, or call (800)795-3272 

(voice) or (202)720-6382 (TDD). USDA is an equal opportunity provider and employer.
Northern Research Station

www.nrs.fs.fed.us