United States

Department of

Agriculture

Forest Service

Pacific Northwest

Research Station

General Technical

Report

PNW-GTR-750

March 2008

Forest Inventory-Based

Estimation of Carbon Stocks

and Flux in California Forests

in 1990

Jeremy S. Fried and Xiaoping Zhou
The Forest Service of the U.S. Department of Agriculture is dedicated to the

principle of multiple use management of the Nation’s forest resources for sustained yields of wood, water, forage, wildlife, and recreation. Through forestry

research, cooperation with the States and private forest owners, and management of the national forests and national grasslands, it strives—as directed by

Congress—to provide increasingly greater service to a growing Nation.

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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.

Authors

Jeremy S. Fried is a research forester and Xiaoping Zhou is a forester, Forestry

Sciences Laboratory, 620 SW Main St., Suite 400, Portland, OR 97205.
Abstract

Fried, Jeremy S.; Zhou, Xiaoping. 2008. Forest inventory-based estimation of

carbon stocks and flux in California forests in 1990. Gen. Tech. Rep. PNWGTR-750. Portland, OR: U.S. Department of Agriculture, Forest Service,

Pacific Northwest Research Station. 25 p.

Estimates of forest carbon stores and flux for California circa 1990 were modeled

from forest inventory data in support of California’s legislatively mandated greenhouse gas inventory. Reliable estimates of live-tree carbon stores and flux on

timberlands outside of national forest could be calculated from periodic inventory

data collected in the 1980s and 1990s; however, estimation of circa 1990 flux on

national forests and forests other than timberland was problematic owing to a combination of changing inventory protocols and definitions and the lack of remeasurement data on those land categories. We estimate annual carbon flux on the 7.97

million acres of timberlands outside of national forests (which account for 24 percent of California’s forest area and 28 percent of its live tree aboveground biomass)

at 2.9 terragrams per year.

Keywords: Forest carbon flux assessment, biomass, carbon stocks, carbon

dioxide, Forest Inventory and Analysis.
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Forest Inventory-Based Estimation of Carbon Stocks and Flux in California Forests in 1990

Introduction

The U.S. Department of Agriculture, Forest Service (USFS), Pacific Northwest

Research Station, Forest Inventory and Analysis Program (PNW-FIA) was asked

on October 15, 2007, by the USFS Pacific Southwest (Region 5) and Pacific

Southwest Research Station to prepare an estimate of circa 1990 carbon stocks

and stock change (often referenced hereafter as flux1) in California forests by

November 1, 2007. This was to be done in support of greenhouse gas inventory

efforts by the state of California pursuant to Assembly Bill 32 (“AB 32”), also

known as the “Global Warming Solutions Act of 2006, a state law that mandates

a return to 1990 greenhouse gas emissions by 2020, with further reductions in

emissions thereafter. The California Air Resources Board has been engaged in

creating a greenhouse gas inventory for every sector of the state’s economy. As

perhaps the only sector with the potential for negative net emissions (i.e., sequestration of carbon in standing trees, long-lived forest products, and as biomassgenerated energy that substitutes for fossil fuel-generated energy), forestry is a

particularly important sector in this accounting effort.

In addition to the sector-wide and owner-class-specific tracking of sequestration of carbon in forest biomass, key questions concerning greenhouse gases and

climate change will depend on the georeferenced network of over 6,800 forested

PNW-FIA plots in California. The occurrence of fire, insect, and disease events is

dependent on both land ownership and location with respect to stressors. In addition to changing the carbon flux, these events can have substantial impacts on other

greenhouse gas emissions such as methane, nitrous oxide, biogenic hydrocarbons,

and the precursors to tropospheric ozone. Most of the current data on these relationships are forest-type-specific, and therefore cannot be used for statewide

accounting without spatially accurate products (e.g., Cahill et al. 2006). In addition, spatially accurate information on forest cover will be crucial in tracking

albedo impacts of changes such as the extent of the pinyon-juniper coverage in arid

parts of the state. Although albedo is not directly related to greenhouse gas emission, reductions in albedo from the replacement of grasslands with forests or

woodlands can have major impacts on the rate of radiative forcing, the driving

force of climate change (Solomon et al. 2007).

1

 We us the term “flux” synonymously with “stock change” such that positive values of

flux indicate sequestration of atmospheric carbon by the forest pool and negative values

suggest emissions from the forest pool into the atmosphere. This is opposite of how some

of the carbon literature defines flux, where positive numbers indicate emissions into the

atmosphere, so when comparing flux estimates in this report with such literature, it is

important to remember to reverse the sign.
2

GENERAL TECHNICAL REPORT PNW-GTR-750

Earlier, model-based efforts to characterize carbon stocks and fluxes in the

five major forest pools (live aboveground, live belowground, dead [standing and

down], litter, and soil organic carbon) did not successfully develop plausible estimates, and concerns about the estimates were registered with the California Air

Resources Board by various state and federal agencies. The PNW-FIA Program

was asked to complete a scientifically grounded analysis that will ensure valid

estimates, or at least the best possible estimates producible on this highly compressed timeline. Given the state of the data available, there is no one correct

answer or approach; scientists at PNW-FIA have undertaken a convergence-ofevidence approach—in essence, following multiple pathways to generate the

requisite estimates, and documenting the logic, attendant uncertainties, caveats, and

issues that must be considered when interpreting these estimates. In addition to the

development of a statistically supportable baseline for the state and for different

ownerships, the spatially georeferenced, plot-based approach will allow for future

integration of data from new plot-based information, forest-type-specific releases

of other greenhouse gases, as well as changes in forest cover. This report presents

the estimates and supporting logic.

Data Sources

Because this application requires estimation of change in carbon stocks as a proxy

for carbon flux (the primary attribute of interest), it is essential to perform calculations on comparable inventories, and ideally, on a remeasurement inventory in

which the same plots and trees are measured with essentially the same protocols

several years apart. Regrettably, for the year of interest (1990), consistent comparable inventories are lacking for most classes of forest land ownerships, productivity, and reserve status. A genuine, remeasurement inventory exists only for unreserved timberlands (as defined in the 1994 periodic inventory) outside of national

forests (ONF). This land base, which was sampled in 1981–1984 (nominal 1984)

and again in 1991-1994 (nominal 1994), comprises 24 percent of California’s

forest area and 28 percent of its live tree aboveground biomass. Because the remeasurement interval spans 1990, it is possible to estimate both carbon stocks and

(average) flux for this base year for this land base. Unfortunately, this land base is

not reflective of the other forest owner class/productivity class/reserved class

combinations, so findings from the remeasurement analysis of ONF timberland

cannot be extrapolated.

Comparing inventories with different designs and plot footprints is very challenging. Sampling errors are inherently higher, such that identifying significant
3

Forest Inventory-Based Estimation of Carbon Stocks and Flux in California Forests in 1990

differences becomes far more problematic (the differences must be much greater

before they can be interpreted as statistically significant, and not just random

artifacts of sampling error). The equation for the sampling error of the difference

between two inventories is:

1,2

2

2

2 SE = σ 1 +σ − 2CoVar (1)

where

=2 σ1 variance of the total (carbon, biomass, or any other inventory attribute)

from inventory 1,

=2 σ 2 variance of the total (carbon, biomass, or any other inventory attribute)

from inventory 2, and

CoVar1,2 = covariance between the two inventories.

For a complete remeasure (e.g., same footprint and trees), the covariance term

can be quite large, such that the sampling error is greatly reduced below the square

root of the sum of the variances of each inventory. When the inventories are completely independent (i.e., there is no connection between the samples for inventory

1 and inventory 2), the covariance term is zero and sampling error is maximized.

An even more daunting challenge results from the fact that the available inventories sample different forest owner class/productivity class/reserved class combinations, and have numerous instances of differences in definitions of, for example,

forest land, timberland, ownership class, and reserve class, among others. We chose

to use only inventory data structured as a systematic sample of California’s forest

with approximately uniform sampling intensity. A strata-based inventory was conducted on unreserved National Forest System (NFS) timberlands in the 1980s,

although a tree-level data set derived from this inventory is not, to our knowledge,

publicly available. Moreover, we believe that it would be difficult, if not impossible, to arrive at valid estimates of flux when comparing a strata-based inventory2

and a (systematic) grid inventory such as those undertaken post-1990. This is

especially true given that it is not even clear that the stratification layers used in this

pre-1990 inventory exist today.

2

 An inventory in which the total forest area is subdivided into strata, each believed to be

relatively homogeneous and delineated in a georeferenced database, and sample plots are

allocated to each stratum. Area expansion factors are developed as the quotient of stratum

area and plot count, although plot density (and thus also area expansion factors) may differ

greatly among strata.

An even more

daunting challenge

results from the fact

that the available

inventories sample

different forest

owner class/productivity class/reserved

class combinations,

and have numerous

instances of differences in definitions

of, for example,

forest land, timberland, ownership

class, and reserve

class, among others.
4

GENERAL TECHNICAL REPORT PNW-GTR-750

The earliest available grid inventories for NFS lands and ONF “other forest”

(forests that do not qualify as timberland owing to lower productivity) is post1990, so estimation of 1990 stocks and flux becomes enormously problematic and

necessitates what some might justifiably regard as “heroic” assumptions. The

available inventories that meet these most minimal criteria (systematic grid inventory) and some relevant attributes are listed in table 1.

The National Information Management System (NIMS) refers to the annual

FIA inventory of all forest lands (table 1). Each of these databases is a tree-level

database, meaning that live-tree carbon can be calculated at the individual tree

level, then expanded to account for the sample’s representation in the landscape.

The plots are divided into 10 interpenetrating (systematically spread out across the

entire state) “panels” of approximately equal size, and in California, all the plots in

one panel (10 percent of the total plots) are typically visited and assessed in the

field in a given year.

The Integrated Database (IDB) is the first comprehensive database that brought

together all the available forest inventories across ownerships (NFS and ONF) for

California, Oregon, and Washington. It attempted to provide consistency in the data

definitions, units of measure, expansion factors, and other inventory attributes to

the extent possible so that analyses could be conducted across ownerships. However, there are fundamental differences among the inventories combined into this

database that have inescapable implications for analysis. For example, the IDB

database contains data collected in special studies on ONF, unreserved lands that are

Table 1—Available inventories and their attributes for California

Remeas- Dates of NFS Other Reserved

Database urement collection NFSa reserved forest areas (ONF)b Timberland

NIMSc No 2001–2006 Yes Yes Yes Yes Yes

IDBd No 1991–1994 ONF

1993–2000 NFS Yes Yes Partial No Yes

94_CA_Changee Yes 1981–1983 and

1991–1994 No No Partial No Yes

a

NFS = National Forest System.

b ONF = outside of national forest.

c

NIMS = National Information Management System; data are available online (USDA Forest Service 2007).

d

IDB = Integrated Database assembled by the PNW-FIA; data are available on CD (Waddell and Hiserote 2005).

e

94_CA_Change refers to the 1984-1994 change database.
5

Forest Inventory-Based Estimation of Carbon Stocks and Flux in California Forests in 1990

not timberland (e.g., a sparse sample of oak woodland), but not all other forest

(e.g., pinyon-juniper was not sampled). Inventory dates range from 1991 to 1994

for ONF plots, but data were collected one survey unit3 at a time over that period

(i.e., not a sample spread across the whole state each year as is done today under

annual inventory). It also contains data collected on the national forests of Region 5

between 1993 and 2000, with national forests being sampled one at a time, moving

mainly from north to south. However, the underlying designs of the ONF and NFS

inventories differ, as do some of the key definitions, and both differ from the

design and definitions of the annual inventory, NIMS. For example, some of the

differences between periodic (IDB) and annual data pertinent to this analysis are:

• The annual inventory uses a different plot design (fixed plot with four

subplots) than that used by the periodic inventories (variable-radius plot

with five subplots), and only subplot one is co-located.

• The annual inventory samples all lands, whereas some of the periodic

inventories did not sample certain lands such as state and national parks or

unproductive forest land (other forest). Although this land area was

accounted for in the periodic inventory, the volume on these unsampled

lands was always unknown and implicitly characterized in the database as

zero (i.e., the IDB has “proxy plot” records in its plot table to account for

forested area within unsampled areas such as national parks, but no

corresponding tree records in the tree table from which volume could be

calculated).

• Plot stockability factors and stockable proportions were applied to different

sets of plots in the periodic and annual inventories. Because stockability

influences the level of productivity of a plot and whether or not it is

classified as timberland, this may account for some differences in timberland area and volume between the two inventories.

• Area that was classified and sampled as oak woodlands (by virtue of the

species present) during the periodic inventory represented in the IDB was,

in some cases, classified as timberland in the annual inventory.

3

 California is partitioned into six, multicounty survey units for reporting inventory results:

North Coast, Northern Interior, Sacramento, San Joaquin, Southern, and Central Coast.

Prior to initiating annual inventories in 2001, inventory data collection was completed in

one survey unit before moving on to the next such that collection dates for a given survey

unit typically spanned no more than a year.
6

GENERAL TECHNICAL REPORT PNW-GTR-750

• To standardize the annual inventory across all lands nationally, there were

differences (from the data contained in IDB) in definitions and protocols

for what is considered a tree, forest land, reserved land, and timberland.

Each of these databases covers a different land base and presents different

challenges with respect to data readiness, timing of data collection, and consistency

with other databases with respect to definitions.

Approach

Given the issues inherent in the available data (described above), we attacked the

estimation problem via stratification—in essence, subdividing the big problem of

generating statewide estimates of stocks and flux into several subproblems, each

addressing one or more of the following eight forest strata: NFS timberland, NFS

other forest, NFS reserved, other public timberland, other public other forest, other

public reserved, private timberland, and private other forest, or aggregations

thereof. Note that in the lexicon of FIA, ONF includes both other public and

private (which in turn includes both industry and other private). Note also that this

use of the term “forest strata” (above) has nothing to do with the strata-based

inventory described in the “Data Sources” section. We are still relying on systematic, grid-based inventories, but are analyzing owner/productivity/reserve-classbased strata within those systematic inventories separately.

Although it is not strictly necessary to analyze each stratum separately, doing so

allows for more critical analysis, review, and reasonableness checking. It cannot be

overemphasized that, although our analysis generates estimates for these strata, in

many cases, the standard errors are quite large (particularly for the relatively small

and heterogeneous strata, such as other public, and for nearly all of the calculated

fluxes), and the single stratum estimates should not be relied upon. The objective

of the analysis is statewide estimates, and it was our hope that these would be more

robust than nearly any of the individual stratum estimates, except those for ONF

timberland.

Methods

Biomass estimates were made for the five major forest carbon pools: live aboveground, live belowground, standing and down dead wood, litter, and soil organic

carbon. Where equations yielded biomass, estimates were converted to estimates

of the associated carbon pool via application of the conversion factor 0.5 (U.S.

Environmental Protection Agency 2007).

Biomass estimates

were made for the

five major forest

carbon pools: live

above-ground, live

belowground, standing and down dead

wood, litter, and soil

organic carbon.
7

Forest Inventory-Based Estimation of Carbon Stocks and Flux in California Forests in 1990

Calculation of Live Tree Aboveground Carbon

We subdivided the live aboveground pool into live tree and understory vegetation

because the live tree pool is amenable to direct and comparatively precise estimation based on detailed inventory measurements, as compared to the understory

component, which is derived from coarse models. For live-tree aboveground

biomass, we used both local volume equations used by PNW-FIA coupled with

species-specific parameters for specific gravity and the national-level equation

system developed by Jenkins and others (2003, 2004) to highlight the effect of

model selection on estimates of stocks and fluxes. Although we believe the PNW

volume equations to biomass calculation to carbon pathway better reflects true

carbon stocks and fluxes for California, others are routinely using the Jenkins

equations for state-level analysis, in part because they are embedded in analystfriendly accounting systems such as the Carbon Calculation Tool (Smith et al.

2007). The major difference between these two calculation pathways is that the

local or regional equations are tree species-specific, whereas the national model is

very general and groups about 400 tree species nationwide into 4 hardwood species

groups, 5 softwood species groups, and 1 woodland species group. In essence,

under the Jenkins approach, a single live-tree aboveground biomass equation will

be applied to several tree species classified in the same species group for the

national model. Another difference is that the PNW equations are functions of

both diameter and tree height, whereas the Jenkins equations depend only on

tree diameter. Biomass in both cases was converted to carbon via the factor 0.5.

Calculation of Other Carbon Pools

All other carbon pools were calculated using methods developed by the USDA

Forest Service (U.S. Environmental Protection Agency 2007, Smith and Heath

2002, Smith et al. 2007). The equations are developed by broad forest type groups.

Understory vegetation carbon and down dead wood carbon are estimated as a

proportion of live tree carbon (including above and belowground), standing dead

wood carbon as a function of growing-stock volume, and forest floor (litter)

carbon as a function of stand age.

Estimation of Carbon Stocks and Fluxes by Stratum

Outside of national forest timberland—

The ONF timberland (forest land owned privately or by government agencies other

than the Forest Service, capable of producing at least 20 ft3 ·ac-1·yr-1, and not within

For live-tree

aboveground biomass, we used both

local volume equations used by PNWFIA coupled with

species-specific

parameters for

specific gravity and

the national-level

equation system...to

highlight the effect

of model selection

on estimates of

stocks and fluxes.
8

GENERAL TECHNICAL REPORT PNW-GTR-750

areas formally withdrawn [reserved] from timber management as would be the case

for parks and wilderness) has been regularly assessed by PNW-FIA for decades.

The assessments pertinent to the calculation of 1990 carbon are the 1984 and 1994

California periodic inventories conducted by the PNW-FIA Program. We relied on

the 94_CA_Change database, derived from these inventories, which contains measurements of trees inventoried in both the 1984 and 1994 inventories on ONF

timberland, including, for example, diameters and heights, which can be used with

species-specific volume equations to estimate total stem volume and somewhat less

species-specific (i.e., where equations were lacking, an equation from a similar or

related species was used) equations to estimate branch and bark biomass. For each

tree, stem biomass was calculated from stem volume and the specific gravity of the

wood of that species. Live-tree biomass was expanded to a per-acre basis using tree

size-appropriate expansion factors (trees had been selected for measurement via

variable-radius sampling so tree expansion factors differed), and then expanded

again with plot expansion factors and condition proportions to account for the

sampled trees’ biomass representation in the larger landscape. There are a total of

4,824 plots in the 1994 change database; 1,444 of these plots contain tree-level

information, and 963 plots are classified as ONF timberland representing 7.97

million acres—7.54 million acres of private timberland and 0.43 million acres

owned by other public entities (e.g., state agencies and federal agencies other than

the national forests).

Survey dates (decimal years) for 1984 and 1994 on ONF timberland are shown

in the following tabulation.

Number Average Average

Survey84 Survey94 of plots survey84 survey94

81 91 70 81.53 91.80

81 92 215 81.67 92.60

81 93 103 81.67 93.52

81 94 2 81.67 94.50

82 91 200 82.45 91.67

82 92 147 82.55 92.58

82 93 185 82.67 93.66

82 94 4 82.46 94.46

83 91 3 83.75 91.53

83 92 3 83.69 92.69

83 93 23 83.58 93.54

84 91 2 84.50 91.63

84 92 3 84.53 92.61

84 93 1 84.50 93.67

84 94 2 84.71 94.75
9

Forest Inventory-Based Estimation of Carbon Stocks and Flux in California Forests in 1990

Plot-level, live-tree biomass (stem + bark + branches) and biomass of other

pools were calculated for each inventory date, and annual rates of biomass change

calculated as the biomass difference divided by the plot-specific remeasurement

interval (generally 10 years, ± 1 year). These were then converted to carbon stocks

as survey1984 and survey1994 and annual carbon flux over this period for each

plot. Plot-level annual flux was used to interpolate to arrive at an estimate of 1990

carbon stocks on ONF timberlands using the plot-specific information on interval

length between survey date in the (nominal) 1994 inventory and year 1990. Total

1990 carbon stocks on ONF timberland was then summarized by summing

representationally weighted 1990 carbon stocks for all plots.

Live-Tree, Aboveground Carbon on All Other Strata

Carbon stocks on all other strata had to be estimated from post-1990 inventory

data. Extensive and laborious attempts were made to estimate carbon flux for these

other strata, but none were fully successful.

For reasons discussed above, direct comparison between population and subpopulation total estimates from the IDB and NIMS is not feasible (differences are

more an artifact of the accounting strategies attempted than genuine signal). To

work around some of these issues for this analysis, a “paired plot” database was

developed that includes only periodic plots that were visited again during the

annual inventory; however, the sample design was different between these inventories (i.e., the plot footprint) so few of the same trees are remeasured, and such

remeasurement was not accounted for in this analysis. Because the plot footprints

do overlap partially, the covariance in equation 1 is not zero, but also not readily

determinable. For this analysis, we make the conservative assumption that there is

no footprint overlap or linkage between plots from inventories taken at different

times, so assign the covariance term as zero (thus likely overstating the sampling

error). Because of differences in area estimates among inventories, we calculated

biomass or carbon density (i.e., biomass or carbon per acre) and, except for ONF

timberland (analysis of which was done entirely with the 1994) change database

described earlier, relied on the area estimates from NIMS for each stratum to

expand these densities into carbon quantities.

Given that (1) between the periodic and annual inventory, only six plots converted out of forest (four from oak woodland to urban, one from oak woodland to

vineyard, and one from timberland to ski area); (2) these represent conversions of

only about 20,000 acres per year out of 33 million forested acres; (3) most of these

Plot-level, live-tree

biomass (stem +

bark + branches)

and biomass of

other pools were

calculated for each

inventory date, and

annual rates of

biomass change

calculated as the

biomass difference

divided by the plotspecific remeasurement interval

(generally 10 years,

± 1 year).
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GENERAL TECHNICAL REPORT PNW-GTR-750

conversions are from relatively low carbon systems (oak woodland); and (4) in

most of these cases, there is a strong possibility that some vegetation is retained, it

seems reasonable to assume that conversion of forest land to date has had a negligible impact on carbon stocks and flux in California, which supports the use of

NIMS area estimates (Bechtold and Patterson 2005) in all analyses.

Although we generated approximate annualized carbon flux statistics this way

for all NFS forest combined (timberland, other forest, and reserved) and for the

portions of unreserved other forest that were represented by data in the IDB, the

approximated sampling error of these fluxes was large and always greater than

annual flux (such that even a 66 percent confidence interval would include zero

flux). Moreover, because the IDB contains no tree data for reserved lands outside

of national forests, this analysis could not estimate fluxes for that stratum.

We also calculated flux from the annual inventory data by splitting that data set

into two, 3-year periods (a 2001–2003 block and a 2004–2006 block), referenced

hereafter as a NIMS 2-block analysis. By calculating the carbon totals for each

period, by stratum, and dividing the carbon stock difference between these two

periods by 3 years, we obtained estimates of annual flux.4

 Again, approximated

sampling errors of the block carbon estimates were large, and those of the flux,

even larger, and in every case, approximated sampling errors were larger than the

annualized flux. Sign and magnitude of the fluxes were consistent for private and

other public other forest between this analysis and the IDB paired plot estimation,

but signs were reversed on NFS flux (this analysis showed net sequestration,

whereas the paired plot analysis showed net emissions). However, in no case

could flux be established as significantly (α = 0.05) different from zero.

We believe that the best, most complete and most reliable estimate of post1990 carbon stocks can be found in the 6 years of annual inventory data for

California in NIMS (analyzed as a complete inventory, not divided into blocks).

Accordingly, we estimated stocks for every carbon pool type for every stratum.

Against our better judgment, we also attempted to “move” the NIMS carbon estimates backward in time to 1990 by applying the fluxes calculated in the NIMS

2-block analysis. This sometimes produced absurdly low values of carbon, and for

4

 Note that this approach is mathematically equivalent (assuming the panels contain the

same number of plots) as (1) computing differences between panels 2001 and 2004, panels

2002 and 2005, and panels 2003 and 2006; (2) averaging these three 3-year differences;

and (3) dividing by 3 to get an annualized estimate. This approach uses all the data once

and estimates change over the maximum period permissible. It also compares two clearly

independent data sets, so the covariance term in equation (1) can be disregarded.

Although we generated approximate

annualized carbon

flux statistics this

way for all NFS

forest combined

(timberland, other

forest, and reserved) and for the

portions of unreserved other forest

that were represented by data in

the IDB, the approximated sampling

error of these fluxes

was large and always greater than

annual flux (such

that even a 66 percent confidence

interval would include zero flux).
11

Forest Inventory-Based Estimation of Carbon Stocks and Flux in California Forests in 1990

one land type (other public reserved), negative carbon stocks as of 1990. Partly

because of such outcomes, we deemed this line of attack unsuccessful. It is likely

that the circa 2003 estimate represented by the annual inventory is a better representation of carbon stocks circa 1990 than any plausibly defensible manipulation

would achieve.

Results

Outside of National Forest Stock and Flux

The results in which we have the greatest confidence are the live tree, aboveground

carbon stocks (296 Tg carbon) and flux (2.9 Tg carbon/year) on ONF (i.e., private

and other public), unreserved timberland, shown in table 2. These results are the

only ones derived from consistent remeasurement of the same plots and trees.

Interestingly, the Jenkins equations (which are comparatively coarse in that they

are not species specific, and rely on diameter as the only tree size metric) not only

produce higher estimates of carbon (at both inventory occasions) relative to the

volume to biomass calculation pathway used at PNW, they also produce estimates

of live tree aboveground carbon flux that are 16 percent lower. This is a timely

reminder of the tremendous influence that model selection has in calculation of

Table 2—Forest carbon (C) stocks and flux on ONF (outside national forest) timberland in 1990 in California

using PNW 1994 change database (total 7.97 million acres of non-NFS timberland)

Aboveground Aboveground Below- Underlive tree live tree ground story Dead Soil

Year (PNW)a (Jenkins)b biomass vegetation wood organic Litter Totalc

Survey 1984

(TgC) 274.48 288.74 59.45 10.08 59.72 134.43 94.10 632.26

1990 Estimates

(TgC) 296.47 307.20 63.18 10.76 61.86 133.72 93.00 658.00

Survey 1994

(TgC) 303.95 313.38 64.43 11.09 62.79 134.00 93.24 669.50

Flux (TgC/yr) 2.879 2.432 0.492 0.086 0.287 -0.094 -0.136 3.514

a

 The live tree aboveground biomass is calculated based on the equations developed by Pacific Northwest Research Station, Forest

Inventory and Analysis Program.

b

 The live tree aboveground biomass is calculated based on the Jenkins equations.

c

 The total carbon density or density change does not include the column using Jenkins equations (aboveground live tree [Jenkins]).
12

GENERAL TECHNICAL REPORT PNW-GTR-750

Table 3—Average carbon density by ownership and carbon pool in California

Aboveground Below- Underlive tree ground story Dead Soil

Ownership Forest area biomassa biomass vegetation wood organic Litter Total

Million acres – – – – – – – – – – Metric tons of carbon per acre – – – – – – – – – –

National forest:

Timberland 9.275 38.247 9.595 1.499 9.652 16.799 10.414 86.207

Other unreserved 2.265 9.100 2.417 13.546 1.402 7.481 5.464 39.410

Other reserved 3.366 34.248 8.877 3.284 9.049 15.748 10.796 82.003

Other public:

Timberlandb 0.428 37.198 7.927 1.350 7.762 16.778 11.669 82.684

Other unreserved 1.795 6.952 1.523 5.668 0.880 7.252 4.761 27.035

Other reserved 2.485 45.549 9.809 10.237 8.957 15.266 11.501 101.319

Private:

Timberlandb 7.542 37.198 7.927 1.350 7.762 16.778 11.669 82.684

Other unreserved 5.660 12.107 3.010 2.040 1.823 10.124 7.064 36.169

Subtotals

Timberland 17.245 36.099 8.532 1.506 7.895 16.341 10.703 81.076

Other unreserved 9.720 10.379 2.591 5.975 1.545 8.881 6.218 35.589

Other reserved 5.851 39.047 9.273 6.237 9.010 15.544 11.096 90.206

Total 32.816 30.494 7.248 3.415 6.580 14.420 9.704 71.861

a

 The live tree aboveground biomass is calculated based on the equations developed by Pacific Northwest Research Station Forest

Inventory and Analysis Program (PNW-FIA).

b

 Timberland area and carbon density for other public and private (outside national forest) use 1994 change database data from

PNW-FIA.

carbon budgets; for more on this topic, see Melson (2004). Also of note is that,

although there are other carbon pools that in combination rival live-tree aboveground in size (e.g., dead wood, soil organic, and litter), the flux contributed by

these other pools (as modeled) is comparatively slight.

Carbon Densities and Stocks by Stratum and Carbon Pool

Tables 3 and 4 report carbon density and stocks data for all forest lands in

California. All columns for private and other public timberland are carried forward from the analysis completed for table 2 (described above) and are assessments
13

Forest Inventory-Based Estimation of Carbon Stocks and Flux in California Forests in 1990

(based on plot-by-plot interpolations between the 1980s and 1990s field visit dates)

for the year 1990. All other strata are derived from the annual inventory (NIMS)

for 2001–2006, a comprehensive inventory that samples all forested land in

California at the same intensity, including (for the first time) parks and other

reserved areas. The average inventory year for the NIMS data is 2004. With 60

percent of the annual inventory plots in California already collected, we are likely

at a point where stratum totals for the larger strata will not vary so much from year

to year. These are the first inventory results in California to characterize carbon

Table 4—Estimated total carbon on forest land by ownership and carbon pool in California

Aboveground Below- Underlive tree ground story Dead Soil

Ownership Forest area biomassa biomass vegetation wood organic Litter Total

Million acres – – – – – – – – – – – – – Teragrams carbon – – – – – – – – – – – – –

National forest:

Timberland 9.275 354.73 88.99 13.91 89.52 155.81 96.59 799.55

Other unreserved 2.265 20.61 5.48 30.69 3.18 16.95 12.38 89.28

Other reserved 3.366 115.29 29.88 11.06 30.46 53.01 36.34 276.04

Other public:

Timberlandb 0.428 15.92 3.39 0.58 3.32 7.18 4.99 35.39

Other unreserved 1.795 12.48 2.73 10.17 1.58 13.02 8.55 48.53

Other reserved 2.485 113.17 24.37 25.43 22.25 37.93 28.58 251.73

Private:

Timberlandb 7.542 280.55 59.79 10.18 58.54 126.54 88.01 623.60

Other unreserved 5.660 68.53 17.04 11.55 10.32 57.30 39.98 204.72

Subtotals

Timberland 17.245 651.20 152.17 24.67 151.39 289.53 189.59 1,458.54

Other unreserved 9.720 101.62 25.25 52.41 15.07 87.27 60.91 342.52

Other reserved 5.851 228.45 54.25 36.49 52.72 90.94 64.92 527.77

Total 32.816 981.28 231.66 113.56 219.17 467.74 315.42 2,328.83

Total CO2

equivalentc 3,601.28 850.21 416.78 804.37 1,716.59 1,157.58 8,546.82

a

 The live tree aboveground biomass is calculated based on the equations developed by Pacific Northwest Research Station Forest

Inventory and Analysis Program (PNW-FIA).

b Timberland area and carbon density for other public and private (outside national forest) use 1994 change database data from

PNW-FIA.

c

Total carbon dioxide (CO2) equivalent is calculated, in terragrams, as 3.67 times Tg carbon.
14

GENERAL TECHNICAL REPORT PNW-GTR-750

stocks on all forested land (forested by FIA definition, that is). As annual inventory

rolls forward and we remeasure these plots, we will be well-positioned to track

carbon flux—probably as a rolling 5-year average (i.e., using five panels, or 50

percent of the plots), beginning in 2016.

Carbon density on timberlands in ONF unreserved timberland in private and

other public is about 10 percent less in the NIMS data than in the 94 change table

estimate (carbon density estimates derived from the 94 change table replaces the

NIMS values in tables 3 and 4 for these strata). This is likely due to the additional

1.6 million acres of timberland as defined in NIMS, most of which was categorized

as oak woodland in the periodic inventory (and in the IDB database). That forest

type generally has a lower carbon density, so adding acres of it would tend to

reduce average carbon density on timberland somewhat. Because the estimates in

the 94 change tables relate only to the area classified as timberland in the periodic

inventory, the periodic timberland area is used in lieu of the NIMS timberland area

in these tables, and the excess timberland acres are recategorized to other forest.

A remarkable 23 percent of the state’s live-tree carbon is estimated to occur on

reserved lands (which are 18 percent of the state’s forest area), about half of this in

NFS wilderness and the other half in state and national parks. And carbon stocks

on all NFS strata combined represent more than half the statewide total.

Carbon Fluxes Calculated From Annual Inventory Data

Comparing the PNW and Jenkins live-tree aboveground carbon densities in table

5a, we see that the Jenkins estimates are generally (though not always) higher,

sometimes substantially. Although the discrepancy between the PNW-derived and

Jenkins estimates on timberland are relatively low (about 10 percent, comparable

with the discrepancies observed for the 1994 change tables), the discrepancies are

much greater for some of the strata, such as NFS reserved. If the literature equations on which the Jenkins equations are based were derived primarily for trees on

timberland, this could explain the higher estimates in reserved areas, where in

general, site quality is lower, so trees of a given diameter are likely to be shorter.

Because the PNW calculation methods account for height and the Jenkins equations

do not, use of Jenkin’s equations outside of timberland may be problematic in

terms of upward bias, not just in California but wherever large areas of lower site

class forest land exist.

A remarkable 23

percent of the

state’s live-tree

carbon is estimated

to occur on reserved lands (which

are 18 percent of

the state’s forest

area), about half of

this in NFS wilderness and the other

half in state and

national parks. And

carbon stocks on all

NFS strata combined

represent more than

half the statewide

total.
15

Forest Inventory-Based Estimation of Carbon Stocks and Flux in California Forests in 1990

Table 5a—Carbon density by ownership and carbon pool in California from NIMS database 2001–2003 and 2004–2006

Aboveground Aboveground Below- Underlive tree live tree ground story Dead Soil

Ownership Forest land (PNW)a (Jenkins)b biomass vegetation wood organic Litter Totalc

– – – – – – – – – – – Megagrams (metric tons) of carbon per acre – – – – – – – – – – –

Block 2001–2003

National forest Timberland 38.15 45.65 9.57 1.36 9.85 16.95 10.58 86.46

Other unreserved 8.94 11.84 2.41 15.88 1.39 7.19 5.60 41.40Other reserved 33.70 41.78 8.76 3.49 9.14 16.12 11.06 82.27

Other public Timberland 36.33 39.21 7.95 3.52 5.71 13.72 10.41 77.64

Other unreserved 7.29 8.60 1.68 5.78 0.90 7.24 6.02 28.92Other reserved 39.52 43.46 9.01 10.18 8.66 15.85 12.32 95.54

Private Timberland 34.38 37.23 7.64 1.25 6.72 16.95 11.71 78.64

Other unreserved 11.69 15.41 2.95 1.80 1.78 9.88 8.87 36.97

All owners Timberland 36.34 41.54 8.62 1.39 8.25 16.83 11.10 82.53

Other unreserved 10.23 13.34 2.60 6.40 1.53 8.71 7.50 36.97Other reserved 35.96 42.43 8.86 6.09 8.95 16.01 11.55 87.43

All forest 29.76 34.66 7.16 3.46 6.70 14.66 10.28 72.02

Block 2004–2006

National forest Timberland 38.26 46.02 9.61 1.74 9.31 16.58 10.13 85.63

Other unreserved 10.19 13.29 2.69 11.43 1.51 8.30 5.49 39.61Other reserved 35.54 43.86 9.19 3.40 9.05 15.38 10.46 83.01

Other public Timberland 32.02 34.91 7.15 3.33 4.63 12.29 8.94 68.37

Other unreserved 6.36 6.29 1.24 4.91 0.77 6.66 3.16 23.10Other reserved 49.30 49.54 10.31 10.18 9.07 14.68 10.59 104.12

Private Timberland 32.42 35.08 7.18 1.38 6.00 15.77 10.37 73.13

Other unreserved 12.30 15.64 2.99 2.34 1.85 10.33 4.49 34.31

All owners Timberland 35.13 40.18 8.31 1.66 7.47 15.96 10.18 78.71

Other unreserved 10.54 13.15 2.57 5.49 1.54 9.03 4.53 33.70Other reserved 41.78 46.43 9.70 6.48 9.06 15.06 10.52 92.59

All forest 30.82 35.25 7.28 3.35 6.43 14.26 8.98 71.11

NIMS stands for National Information Management System. a The live tree aboveground biomass is calculated based on the equations developed by Pacific Northwest Research Station, Forest Inventory and Analysis Program. b The live tree aboveground biomass is calculated based on the Jenkins equations.c The total carbon density or density change does not include the column using Jenkins equations (aboveground live tree).
16

GENERAL TECHNICAL REPORT PNW-GTR-750 Table 5b—Carbon density flux by stratum and carbon pool in California between 2001–2003 and 2004–2006 Aboveground Aboveground Below- Under- live tree live tree ground story Dead Soil

Ownership Forest land (PNW)a (Jenkins)b biomass vegetation wood organic Litter Totalc

– – – – – – – – – – – – – – Metric tons of carbon per acre per year – – – – – – – – – – – – –

National forest Timberland 0.038 0.121 0.014 0.128 -0.178 -0.125 -0.150 -0.274

Other unreserved 0.415 0.483 0.095 -1.483 0.038 0.372 -0.035 -0.599Other reserved 0.612 0.693 0.142 -0.030 -0.029 -0.247 -0.200 0.249

Other public Timberland -1.436 -1.432 -0.267 -0.062 -0.360 -0.475 -0.491 -3.090

Other unreserved -0.309 -0.769 -0.148 -0.289 -0.045 -0.195 -0.953 -1.938Other reserved 3.260 2.025 0.432 0.000 0.134 -0.389 -0.578 2.859

Private Timberland -0.651 -0.714 -0.152 0.045 -0.240 -0.391 -0.450 -1.838

Other unreserved 0.204 0.076 0.013 0.180 0.022 0.150 -1.457 -0.887

All owners Timberland -0.406 -0.454 -0.102 0.089 -0.261 -0.289 -0.305 -1.275

Other unreserved 0.103 -0.064 -0.011 -0.304 0.002 0.107 -0.990 -1.093Other reserved 1.938 1.333 0.279 0.128 0.035 -0.317 -0.344 1.719

All forest 0.352 0.200 0.039 -0.038 -0.090 -0.132 -0.432 -0.301

a The live tree aboveground biomass is calculated based on the equations developed by Pacific Northwest Research Station Forest Inventory and Analysis Program.b The live tree aboveground biomass is calculated based on the Jenkins equations.c The total carbon density or density change do not include the column using Jenkins equations (aboveground live tree)
17

Forest Inventory-Based Estimation of Carbon Stocks and Flux in California Forests in 1990

Note also how difficult it is to discern any meaningful pattern in how the estimates in the two blocks of annual panels vary; this can be attributed to the small

sample size when only three panels are considered. Thus, the calculated fluxes also

bounce around, in some cases almost certainly spuriously (e.g., on comparatively

small strata such as other public other forest). Regrettably, reliable flux data are not

yet available for ONF other forest and reserved lands or for any stratum within

national forest, and the flux data that are available for ONF timberland cover only

the 1980s to 1990s period (no current flux is available).

The carbon density fluxes in table 5b can be converted to carbon fluxes by

multiplying by the corresponding areas for each stratum; however, because these

fluxes are not significantly different from zero (α = 0.05), the resulting estimates

are not statistically defensible. Table 6 shows the annual density fluxes and their

standard errors for live tree, aboveground carbon derived from the 2-block NIMS

data set (2001–2003 vs. 2004–2006). For no stratum are these differences significantly different from zero at the 95 percent confidence level (2 standard errors).

At the 66 percent confidence level (1 standard error), a few of the strata and their

aggregates (other public reserved, reserved any owner, and ONF timberland) are

significant, and all forest land comes close. This (66 percent) is a highly unusual

significance level on which to base analysis (e.g., it means that in cases where there

is truly no difference, one would expect that in one out of three tests, you would

[erroneously] report differences as significant); however, Heath (2007b) reports

that standards of evidence in national greenhouse gas (GHG) inventories are different from those used by the FIA Program, so this information may be of interest

to some readers. Although the NIMS 2-block flux calculated for the strata covered

by the 1994 change tables (ONF timberland) is much less than the 1990 value of

2.9 Tg/ac (in fact, it is negative), it is not significant at the 95 or even the 66

percent significance level. If one wanted to make interpretations based on the 66

percent significance level, it is striking that the all-forest-land flux (almost significant at the 66 percent level) appears to be much lower than the timberland flux in

1990, and is probably as high as it is only because of the apparently high flux on

reserved lands. It is possible that when more data have been collected (e.g., such

that the annual data can be split into two blocks of five panels) and the difference

covers a longer period, sampling errors may be reduced to the point that confidence

intervals will not include zero for at least some strata.
18

GENERAL TECHNICAL REPORT PNW-GTR-750

Bottom Line

Table 7 shows estimates of stock change in live trees (aboveground part only)

computed two different ways: via the NIMS 2-block approach and the IDB to

NIMS paired plots approach. Estimated carbon stocks are also carried forward

from table 4 to highlight how small the estimated changes are relative to stocks

(on the order of 1 percent or less, and far less than the sampling error typical of

forest volume, biomass, or carbon at the state scale). Except for the stock change

on private and other public timberland shown in the NIMS 2-block table (these

estimates are actually from the 1994 change database), none of the cells in these

tables are particularly meaningful owing to lack of statistical significance. In the

case of the IDB to NIMS analysis (1990s to early 2000s), the changes in inventory

definitions made it impossible to report timberland and other forest separately, so it

is not even possible to use the 1994 change table estimates for ONF timberland in

that table. For that reason, we would place slightly greater confidence in the NIMS

2-block stock change table, bearing in mind that even for this table, most of the

cells are not significantly different from zero. It must also be remembered that

other than ONF timberland, the fluxes for NIMS 2-block are actually calculated for

the early 2000s, and simply assigned as our best estimate of flux for 1990.

Table 6—Mean carbon density flux and sampling errora by stratum for the live tree, aboveground carbon pool

in California between 2001–2003 and 2004–2006 b

Forest-land groups

Nonreserved,

All forest land Timberland excl. timberland Reserved

Stratum Mean SE Mean SE Mean SE Mean SE

– – – – – – – – Megagrams (metric tons) of carbon per acre per year – – – – – – – –

National forest 0.309 0.445 0.059 0.579 0.420 0.536 0.655 1.120

Other public and private

Other public 1.488 1.751 -1.443 2.684 -0.305 0.459 3.274 3.088

Private 0.065 0.502 -0.656 0.658 0.203 0.387

All other public

and private 0.491 0.596 -0.719 0.644 -0.018 0.316 3.274 3.088

All forest land 0.369 0.384 -0.391 0.433 0.104 0.273 1.966 1.500

a

 Sampling error calculations were consistent with Bechtold and Patterson 2005.

b

 Carbon density estimates differ slightly from those in table 5b because the estimates in table 5b calculate density using the stratum

area estimates from the full 2001–2006 NIMS database, whereas the differences in this table are calculated from densities based on

the 2001–2003 and 2004–2006 2-block NIMS stratum area estimates.
19

Forest Inventory-Based Estimation of Carbon Stocks and Flux in California Forests in 1990

The past 15 years have seen relative stability in the forces that could otherwise

make carbon flux highly dynamic. For example, this period was not marked by a

high incidence of large forest fires or widespread pest outbreaks, and NFS timber

harvest declined rapidly through 1992 to a level much lower than in past decades.

There were no large-scale changes in landowner class, or conversion to nonforest

land uses. This is fortunate, because it supports the option of using more contemporary observations that are consistent and assigning them to 1990 as the best available estimate we can make today. There is no way to ensure that any manipulation

of 2000s flux in forest carbon, undertaken to try to get a “1990 number,” will not

Table 7—Alternative calculations of annual change in carbon stocks in live trees

(aboveground) in California and estimated stocks in live trees

All forest Unreserved Unreserved

Owner group land Timberlanda other forest subtotal Reserved

Teragrams of carbon per year

Estimated annual carbon stock change (NIMS 2-block)

National forest 3.352 0.352 0.940 1.292 2.060

Other public 7.701 0.155 -0.555 -0.400 8.101

Private 3.879 2.724 1.155 3.879 0

Total 14.933 3.231 1.540 4.771 10.161

Estimated annual carbon stock change (IDB to NIMS)

National forest -3.325 Not avail. Not avail. -0.631 -2.694

Other public 7.295 Not avail. Not avail. -0.806 8.101b

Private -6.600 Not avail. Not avail. -6.600 0

Total -2.630 Not avail. Not avail. -8.037 5.407

Stocksc

Teragrams of carbon

National forest 490.630 354.730 20.610 375.340 115.290

Other public 141.570 15.920 12.480 28.400 113.170

Private 349.080 280.550 68.530 349.080 0

Total 981.280 651.200 101.620 752.820 228.460

Note: Estimates are for 1990, but contain a mix of data collected before and after 1990; fluxes on

approximately 75 percent of the forested lands are derived from inventories post-1990. Estimates of

stock change in these tables are, for the most part, not statistically significant (α = 0.5). a

 Other public and private derived from 1984–1994 change database.

b

 Other public reserved is from NIMS 2-block analysis because there are no data on this stratum

from IDB.

c

 This table is derived entirely from the aboveground live tree biomass column in table 4.

The past 15 years

have seen relative

stability in the

forces that could

otherwise make

carbon flux highly

dynamic. For example, this period

was not marked by a

high incidence of

large forest fires or

widespread pest

outbreaks, and NFS

timber harvest

declined rapidly

through 1992 to a

level much lower

than in past decades.
20

GENERAL TECHNICAL REPORT PNW-GTR-750

result in an estimate that is even less descriptive of 1990 emissions (e.g., adjusting

carbon stocks on other public land backwards in time using the NIMS estimates for

annual flux generates negative live tree carbon for one stratum). These tables are

included in this report only because the request that motivated this analysis specifically mandated 1990 estimates of stock and flux. The sizable discrepancies between

these tables (and between the NIMS and 1994 change database estimates for flux

on ONF timberlands) should be ample evidence to discourage any temptation to

rely on differencing inventories as a basis for estimating carbon flux. The state of

the data is such that the best estimate of aboveground, live tree carbon flux at the

statewide level is 2.879 Tg/yr on ONF timberland plus zero elsewhere, based on the

fact that given the data in hand and the time available to analyze it, there is no

significant difference for NFS and ONF other and reserved forest and the fact that

alternative calculation pathways result in post-1990 fluxes of different signs (e.g.,

NIMS 2-block versus IDB to NIMS).

The principal difficulty with attempting to discern flux via stock change, when

the estimates of stocks at two points in time are independent (or mostly independent, as in the case of IDB to NIMS), can be illustrated with a hypothetical example. Suppose the true stock at both time 1 and time 2 is 1,000 units of carbon

(i.e., there is no real change over the interval), and the inventory at time 1 generates an estimate of 975 (with a sampling error of 5 percent), whereas the inventory

at time 2 generates an estimate of 1,025 (again with a sampling error of 5 percent).

In real inventories such as FIA, volume estimates rarely have a sampling error

much less than 5 percent (and often it is larger). Were you to calculate flux as the

difference between the estimated total stocks generated by these inventories, you

would obtain 1,025 – 975 = 50 units or a slightly greater than 5 percent flux.

However, the 66 percent confidence intervals around the inventory estimates are for

1: 926 to 1,024 and for 2: 974 to 1,076, so were you to conduct a sensitivity

analysis even on these 66 percent confidence intervals, you would have to consider

the possibility that flux could range between -50 (974 to 1,024) and +150 (1,076 to

926), or from -5 percent to +15 percent. Remember that in this example, the true

flux is zero because the true stocks are identical at time 1 and time 2; even if it

were nonzero but small, say 1 percent, the estimated flux would be in error by an

enormous percentage over nearly all of this range. Were we to conduct sensitivity

analyses using 95 percent confidence intervals, the range of possible values for flux

would be even greater. So even though the individual inventory estimates are the

best available information about stocks at the respective times (actually within 2.5

percent of the true value in our hypothetical example) and it may well be tempting
21

Forest Inventory-Based Estimation of Carbon Stocks and Flux in California Forests in 1990

to attempt to estimate change as the difference in estimates of stocks (as has been

traditionally done for greenhouse gas inventories in the United States and elsewhere), unless change is very large, such estimates are as likely to be wildly

incorrect as to be close to accurate. The lesson here is that although flux may be

derived from stock change, it cannot be reliably derived from change in estimated

stocks. The strength of the analysis for ONF timberlands is that as remeasures of

tree attributes and accounting for mortality, removals, and ingrowth, it is an

estimate of stock change, versus the analysis for the other strata, which are changes

in estimated stocks.

In summary, this rapid response analysis has demonstrated that:

• Reliance on calculations of differences in estimated carbon stocks derived

from national inventory data collected at different times is unlikely to

produce meaningful results because:

o Different inventory dates cover different forest strata (and only the most

recent inventory covers all strata), protocols, and plot footprints, such that

this approach subtracts “apples from oranges.”

o Some of the nationally published/posted data have been adjusted/calibrated

to try to account for some of the discrepancies among dates, but such

adjustments are incomplete and may introduce other, unintended

consequences.

o Carbon stock change appears to be such a small fraction of stocks that it is

less than the sampling error of the total carbon estimates and thus

statistically insignificant in most cases.

• Where plots and trees are completely remeasured such that samples at two

points in time are not independent, the covariance term in the sampling

error (equation 1) grows large, and the sampling error drops much lower

than otherwise.

• As annual inventory progresses and plots are remeasured (with ingrowth,

harvest, and mortality accounted for, and with direct measurement of dead

wood), FIA is well-positioned to provide monitoring data on carbon flux

into the future, as well as the basis for understanding the dynamics of

interpool transfers (e.g., from live trees to wood products, bioenergy, or

atmospheric emissions via fire).

Reliance on calculations of differences

in estimated carbon

stocks derived from

national inventory

data collected at

different times is

unlikely to produce

meaningful results.
22

GENERAL TECHNICAL REPORT PNW-GTR-750

Next Steps

The results reported here for strata other than ONF timberland should be considered preliminary, with the possibility of improved estimates in the future

contingent upon additional analysis that is beyond the scope of what could be

accomplished within the time available for this analysis.

So what are the options for obtaining more reliable information on carbon

flux (other than waiting for the annual inventory remeasurements to roll in)?

One option is to assess flux on paired plots, looking only at the remeasured trees

on the one subplot on which overlap was most complete. This approach was used

in the growth, removals, and mortality analysis for the California 5-year report

(Christensen et al 2008); however, in that case, it was restricted to conifers on

timberland. Extending to other forest lands and considering hardwoods adds complications and would require additional modeling (as hardwoods are generally not

bored for increment). There is a wealth of increment data that might be used in this

analysis, but considerable analytic time would be required to model this successfully—most likely requiring several months of biometrician effort.

Another potentially productive avenue of inquiry would be to conduct analysis

on the two panels of remeasured NFS annual plots that have been collected to date,

and which will be loaded into NIMS in spring, 2008. Remeasurement analysis with

NIMS data has not yet been attempted, so this would be new territory and unforeseen challenges could well arise. Because only two panels are available, sampling

error will be very large, but at least the analysis would be based on remeasurements

of all the trees on the plots (not just a subset as in the IDB to NIMS paired plots).

Ultimately, if flux is to be determined by change in stocks, operationally on an

ongoing basis, there may well be a need for many additional sample plots to reduce

sampling error sufficiently for signal to be detectable over the short time horizons

under which information is critically needed, for example, to monitor progress in

achieving carbon sequestration.

Although tables 2 and 5b suggest that carbon flux in pools other than live trees

is small, all of those data are generated by coarse-scale models, not measurements

on FIA plots. Quite possibly, some of these fluxes are not small or will not be

small in the future (e.g., in dead wood, if widespread fuel treatment occurs or if

pest or disease outbreaks recruit large amounts of biomass into the dead wood

pool). However, the time available to conduct this analysis and data readiness issues

precluded using FIA field measurements of down wood and standing dead wood,
23

Forest Inventory-Based Estimation of Carbon Stocks and Flux in California Forests in 1990

for example, to estimate the dead wood pool. To have a system of accounting that

is sensitive to such events argues for reliance on field-observed data rather than

models for these pools. There is potential to generate accurate estimates of dead

tree flux once annual inventory plots have been remeasured (an early opportunity

to test this concept is presented by the remeasurements of annual inventory plots

on NFS lands in the Pacific Southwest [Region 5]). Whether or not we are able to

assess change in down wood will depend on whether we continue to measure that

pool in the future, and by what protocol we measure it. At a statewide level, there

is certainly good potential to generate estimates of down wood carbon stocks, and

this information could be used to validate, check, and perhaps improve upon

whatever are the currently “accepted” equations for this pool. With additional

analytic support, it is not hard to envision a program of research that would use

FIA understory vegetation data to validate or at least compare against the understory pool equations; this might be particularly important in the chaparral type,

which can contain substantial amounts of woody biomass, but which releases

surprisingly large amounts of carbon at relatively frequent intervals (via wildfires).

Acknowledgments

We are indebted to Olaf Kuegler for his assistance in calculating sampling errors

for the perodic to NIMS flux calculations, and for the hundreds of FIA data

collection employees who conducted the California inventories over the past three

decades. This manuscript benefited from helpful reviews provided by Tim Max,

Linda Heath, Olaf Kuegler, and William Stewart.

Equivalents

When you know Multiply by: To get:

Acres 0.405 Hectares

Feet .3048 Meters

Inches 2.54 Centimeter

Tons .907 Metric tonnes

Megagrams 1,000,000 Terragrams
24

GENERAL TECHNICAL REPORT PNW-GTR-750

References

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Analysis Program—national sampling design and estimation procedures. Gen.

Tech. Rep. SRS-80. Asheville, NC: U.S. Department of Agriculture, Forest

Service, Southern Research Station. 85 p.

Cahill, T.M.; Seaman, V.Y.; Charles, M.J.; Holzinger, R.; Goldstein, A.H.

2006. Secondary organic aerosols formed from oxidization of biogenic VOCs

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