USDA United States

Department of Proceed,ngs of the First Agriculture

Se,:rvice orest Annual Forest Inventory

N

Research orthCentral and Analysis Symposiu

Station

San Antonio, Texas

General Technical November 2-3, 1999

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North Central Research Station

U.S. Department of Agriculture--Forest Service

1992 Folwell Avenue

St. Paul, Minnesota 55108

Manuscript approved for publication June 12, 2000

2000
Proceedings of the 1st Annual

Forest Inventory and Analysis

Symposium

November 2-3, 1999

San Antonio Airport Hilton

San Antonio, Texas

Edited By:

Ronald E. MeRoberts

Gregory A. Reams

Paul C. Van Deusen

4
PREFACE

The national Forest Inventory and Analysis (FIA) program of the USDA Forest

Service officially initiated annual forest inventories following the mandate of

the Agricultural Research, Extension, and Education Reform Act of 1998

(Farm Bill). This Act represents a milestone in a decade of dramatic change

for the FIA program. The early part of the decade witnessed two annual

inventory pilot studies with similar objectives but somewhat different approaches. In the early 1990s, the Annual Forest Inventory System (AFIS) was

initiated in the North Central region to establish the capability of producing

standard statewide inventory estimates on an annual basis. In the mid1990s, the Southern Annual Forest Inventory System (SAFIS) was initiated

in the Southern region with extensive state support. With passage of the

Farm Bill, a national annual inventory system with a common plot layout, a

common sampling design, and common core estimates has emerged. Following the 2000 field season, all FIA regions are expected to have implemented

the new system in at least one state.

Throughout the 1990s, research on sampling, remote sensing, estimation,

and database methods to support and enhance annual forest inventories

was conducted by a variety of Forest Service, university, and forestry-related

scientists. The results of these research efforts were reported at conferences

and were published in a variety of conference proceedings and journals.

However, no single conference or publication documented the approaches to

annual forest inventories or the corresponding new methods that had been

proposed and developed. Thus, the primary objective of the 1st annual FIA

symposium and these proceedings was to document progress by the research stations of the USDA Forest Service and their partners in developing

and implementing a national annual forest inventory system. The presentations at the symposium and the papers submitted for publication in these

proceedings have achieved that objective. We thank the presenters for documenting their contributions and convey special thanks to those who documented them in writing and submitted them for publication.

Ronald E. McRoberts

Gregory A. Reams

Paul C. Van Deusen

St. Paul, Minnesota
TABLE OF CONTENTS

Page

Background for AFIS, the Annual Forest Inventory System

Ronald E. McRoberts .......................... ............................................................................................ 1

Moving to an annual inventory in the Pacific Northwest

David Azuma .................................................................................................................................. 5

The hexagon/panel system for selecting FIA plots under an annual inventory

Gary J. Brand, Mark D. Nelson, Daniel G. Wendt, and Kevin K. Nimerfro ...................................... 8

Pros and cons of the interpenetrating panel design

PauI C. Van Deusen ...................................................................................................................... 14

Using classified Landsat Thematic Mapper data for stratification in

a statewide forest inventory

Mark H. Hansen and Daniel G. Wendt ......................................................................................... 20

Forest/non-forest stratification in Georgia with Landsat Thematic Mapper data

William H. Cooke .......................................................................................................................... 28

Comparison of three annual inventory designs, a periodic design,

and a midcycle update design

Stanford L. Arner ......................................................................................................................... 31

SAFIS area estimation techniques

Gregory A. Reams ........................................................................................................................ 32

Diameter growth models using FIA data from the Northeastern,

Southern, and North Central Research Stations

Veronica C. Lessard, Ronald E. McRoberts, and Margaret R. Holdaway .................................... 37

Evaluating imputation and modeling in the North Central region

Ronald E. McRoberts ................................................................................................................... 43

Annual forest inventory: an industry perspective

Roger Lord .................................................................................................................................... 49

Maine's annual inventory: state perspectives

Kenneth M. Laustsen ................................................................................................................... 55
BACKGROUND FOR AFIS,

THE ANNUAL FOREST INVENTORY SYSTEM

Ronald E. McRoberts

ABSTRACT.raThe Annual Forest Inventory System, AFIS, was jointly

proposed and developed in the early 1990s by the Forest Inventory

and Analysis programs of the North Central and Rocky Mountain

Research Stations of the USDA Forest Service and the Forestry

Division of the Minnesota Department of Natural Resources. The

objective of AFIS was to establish the capability of producing standard statewide inventory estimates on an annual basis. The context

in which AFIS was proposed was defined by two crucial constraints:

the average annual cost could be no greater than that of periodic

inventories and the precision of estimates could be no less than that

•! obtained from periodic inventories. The system designed to achieve

|

] the objective while simultaneously satisfying the constraints included

an annual sample of measured field plots, satellite-based remote

sensing for area estimation and vegetation change detection, growth

and mortality models for updating plots measured in previous years,

and computerized databases of field plot information.

INTRODUCTION mates is compromised by the necessity of conducting inventoriesin heavilyforested states

The Renewable Forest and Rangeland Re- over multiple years; (3) the estimates are difficult

sources Planning Act of 1978 requires that the to integrate across state boundaries because

USDA Forest Service conduct inventories of they may be conducted at dates differing by as

forest land in the United States to determine its many as I0 years; and (4) immediate estimates

extent and condition and the volume of timber, of the effects of catastrophic events such as ice

timber growth, and timber removals. Under the storms, hurricanes, fire, and insect infestations

auspices of the agency's national Forest Inven- are usually impossible.

tory and Analysis (FIA) program, five regional

research stations conduct periodic, statewide In addition, the consequences of management

forest inventories and publish summary re- alternatives are often difficult to determine.

ports for individual states. The data and re- Changes in the management of public lands

ports resulting from these inventories have have resulted in substantial reductions in timber

been the primary source of information on the harvest on National Forest lands in some regions

status, trends, and use of public and privately (USDA-FS 1998) with the anticipated response

owned forest lands in the Eastern United being a sharp increase in timber harvest in other

States. This information has been crucial to regions (USDA-FS 1995). As a result, many

estimating current forest resource information segments of the populace are expressing conand monitoring forest ecosystems, cerns regarding the ecological and economic

sustainability of U.S. forests: some segments are

The timeliness, precision, and spatial attributes concerned with maintaining biological diversity;

of products resulting from the Forest Service's some are concerned with satisfying future

periodic approach to forest inventories have economic and societal needs; while some are

been issues of concern to users of inventory simply concerned that forest management

data. FIA clients have noted a variety of deft- practices conform to their personal value sysciencies associated with these periodic invento- terns. These concerns demand integrated assessries: (1) the precision of estimates decreases ments based on current and accurate data to

over time due to factors such as changes in identify trends, relate trends to causes, and

land use and tree growth, mortality, and evaluate the consequences of alternative manremovals; (2) the point-in-time nature of esti- agement strategies.
FIA clients have recognized these deficiencies THE AFIS APPROACH

and concerns and have proposed solutions

such as increasing the sampling intensity, To a large degree, the first two constraints

reducing the period between inventories, and dictated much of the form that AFIS would

conducting midcycle updates. While these take. The 1990 periodic inventory of Minnesota

solutions might resolve some of the deficien- (Miles et al. 1995) reported approximately 16.7

cies, they are expensive to implement and million acres of forest land and featured esticonstitute a piecemeal approach to dealing with mates based on data for 13,618 plots. Of these

the problems inherent in periodic inventories, plots, 10,212 plots had been measured between 1986 and 1991, and 3,406 plots had

BACKGROUND been updated to 1990 using the STEMS growth

and mortality models {Belcher eta/. 1982).

In 199 I, FIA scientists at the North Central Funding for this inventory came from the

Research Station (NCRS), USDA Forest Service, Forest Service and the State of Minnesota with

proposed research to develop procedures for the latter contributing funds to measure twice

conducting annual statewide forest inventories, as many additional plots as were funded for

At about the same time, the Resource Assess- measurement by the Forest Service. The 3,404

ment Unit, Division of Forestry, Minnesota plots whose measurement was funded by the

Department of Natural Resources (MN DNR) Forest Service were described as a single

initiated investigations into the use of remote intensity inventory, while the total of 10,212

sensing technology to support continuously plots (I/3 of 10,212 plots) was described as a

updated inventory on a production basis, triple intensity inventory. Thus, the cost conRecognizing their mutual interest, the two straint for a single intensity inventory was

units contacted the FIA unit at the Rocky quantified as 262 plots per year, the quotient of

Mountain Research Station (RMRS), which had 3,404 plots and the 13 years since the previous

national responsibilities for FIA techniques inventory.

research. The three-way collaboration resulted

in a vision for a comprehensive forest inventory The precision of the triple intensity inventory

system incorporating an annual sample of resulted from data for 13,618 plots distributed

measured field plots, satellite-based remote over approximately 16.7 million acres. Using

sensing, a computerized database of plot the previous 1:2 Federal to State funding ratio,

measurement data, and a set of growth and 4,539 of these plots were allocated to the

mortality models to update information for Forest Service's single intensity inventory.

plots measured in previous years. This system, Thus, the precision constraint for a single

the Annual Forest Inventory System (AFIS), had intensity inventory was quantified as no more

as its overarching objective the development of than 3,675 acres per plot, the quotient of 16.7

a set of procedures for forest inventory that million acres and 4,539 plots. Note the apparwould be capable of producing standard state- ent incompatibility between the precision

wide forest inventory estimates on an annual constraint of 3,675 acres per plot and the

basis. Development of AFIS was guided by the approximately 63,600 acres per plots resulting

following set of constraints: from the cost constraint of 262 plots per year.

1. Cost: the cost of implementing and The AFIS solution for simultaneously satisfying

maintaining AFIS in a state would not the annual cost and precision constraints was

exceed the average annual cost of to use models to update to the current year

conducting periodic inventories, information for a large proportion of plots

2. Precision: the precision of the annual measured in previous years. In particular, of

AFIS inventory estimates would be as the 4,539 plots for which data would be regreat or greater than those obtained quired annually, 262 plots would be measured

from periodic inventories, while current information for the remaining

3. Plots: AFIS would maintain the existing 4,277 plots would be obtained by using models

network of FIA plots as much as pos- to update previous measurements. Based on

sible, more than a decade of experience, FIA scien4. Design: AFIS would modify but not tists at NCRS were confident that the models

radically redesign FIA procedures, would produce adequate results, on average

over large areas, for plots that had not had

substantial vegetation loss due to mortality,

2
harvest, or other factors. However, the ad- year would include a random 25 percent

equacy of results for disturbed plots would be sample of those predicted to have experienced

questionable. Thus, the AFIS approach to substantial vegetation loss based on the most

annual inventories required identification of recent remote sensing analyses. The number of

disturbed plots so that data for them could be these disturbed plots selected each year was

obtained from field measurements rather than expected to be approximately 91 plots. The

from model updating. The proportion of forest remaining plots to be measured in a year,

area in Minnesota losing substantial vegetation expected to be approximately 171 plots, would

each year was estimated at 2 percent or less. be randomly selected from among the undisTherefore, of the 262 plots to be measured turbed plots with probability of annual selecannually, approximately 91 plots (2 percent of tion inversely related to the time since last

4,539 plots) would be selected because they measurement; i.e., plots that had been recently

had experienced substantial disturbance, while measured would have a low probability of

the remaining 171 would be selected from selection, while plots whose remeasurement

among undisturbed plots, interval was approaching 20 years would be

selected for remeasurement with a probability

At the rate of 262 plots per year, slightly more approaching one.

than 17 years would be required to complete a

single measurement of all the 4,539 plots FUNDING AND SUPPORT

necessary to satisfy the single intensity precision constraint. However, some plots measured Funding to support the AFIS research effort

early in a 17-year cycle would be expected to was contributed primarily by the USDA Forest

experience vegetation disturbance due to Service and the State of Minnesota and secondharvest or other removals later in the cycle and arily by states and forest industry groups in

therefore would require a second measurement the Lakes States. NCRS and RMRS contributed

before the end of the cycle. Thus, the time to the salaries and operating expenses for the

complete at least one measurement of all plots equivalent of four full-time scientists whose

was extended to 20 years, efforts included construction of a computer

database, data editing, development of the

MN DNR proposed using remote sensing tech- sampling design, initial work on construction of

niques with satellite data obtained from the diameter growth models and analysis of uncerLandsat-5 Thematic Mapper as a means of tainty, and preliminary data analyses. MN DNR

predicting the disturbance status of plots. The contributed the salaries and operating exbasis of the predictions would be differences penses for scientists conducting remote sensobserved between two sets of TM data for the ing research on techniques for using satellite

same area, separated by one or more years, but data to distinguish between forest and

for the same time of year. Initially, a new set of nonforest lands and to detect forest areas that

imagery and a set from 4 years in the past were had lost substantial vegetation. In addition, MN

obtained, indices of vegetation change were DNR funded the measurement of approximately

calculated for each pixel, and a disturbance 750 plots per year from 1992 to 1999. This

status was predicted for each plot. All plots latter funding was crucial to initiating AFIS

predicted to have experienced substantial while simultaneously proceeding with periodic

vegetation loss would be selected for measure- inventories in the other states for which NCRS

ment within the succeeding 4 years, the has inventory responsibilities. Without this

planned interval between acquisitions of new commitment by MN DNR, AFIS would not have

imagery and predictions of vegetation change, been financially feasible. Additional funding to

support the modeling research was obtained

In summary, current information for 4,539 from the States of Wisconsin and Michigan and

plots would be required annually to satisfy the forest companies and corporations including

precision constraint. Of these 4,539 plots, ABT, Blandin Paper, Champion International,

information for approximately 4,255 plots Colonial Craft, Louisiana Pacific, Mead, Potwould be based on measurements in previous latch, Tenneco, and Weyerhaeuser. The latter

years that had been updated to the current additional funding was held and disbursed by

year using models, while field measurements the Great Lakes Forest Alliance.

would be obtained for the remaining approximately 262 plots. The 262 plots measured in a
EPILOGUE LITERATURE CITED

In the mid-1990s, the Southern Research Belcher, D.W.; Holdaway, M.R.; Brand, G.J.

Station (SRS) implemented an annual inventory 1982. A description of STEMS - the stand

system that was both similar and dissimilar in and tree evaluation and modeling syskey aspects to the NCRS system. Although the tern. Gen. Tech. Rep. NC-79. St. Paul, MN:

NCRS effort was initiated before the SRS effort, U.S. Department of Agriculture, Forest

the political and industrial support generated Service, North Central Research Station.

!i by SRS was primarily responsible for placing

annual forest inventories on the national FIA Miles, P.D.; Chen, C.M." Leatherberry, E.C.

agenda. With passage of the 1998 Farm Bill, 1995. M|nnesota forest statistics, 1990,

formally known as the Agricultural Research, revised. Resour. Bull. NC-158. St. Paul,

Extension, and Education Reform Act of 1998, MN: U.S. Department of Agriculture, Forest

Congress required the USDA Forest Service to Service, North Central Research Station.

conduct annual forest inventories in all states.

Two features of national annual forest invento- USDA Forest Service (USDA-FS). 1995. The

ries emerged as a result of the Farm Bill: the 1995 RPA timber assessment update.

federally funded base sample would feature one Gen. Tech. Rep. RM-259. Fort Collins, CO:

_: FIA field plot per approximately 6,000 acres U.S. Department of Agriculture, Forest

, and 20 percent of these plots would be mea- Service, Rocky Mountain Research Station.

i sured each year. Based on 16.7 million acres of

! forest land in Minnesota, the Federal base USDA Forest Service (USDA-FS). 1998. Report

sample would require approximately 2,800 of the Forest Service: fiscal year 1997.

plots of which approximately 560 would be Washington, DC.

measured each year. These two requirements of

the Farm Bill, one plot per 6,000 acres and ABOUT THE AUTHOR

measurement of 560 plots per year, greatly

relaxed the original AFIS constraints of one plot Ron McRoberts is a Mathematical Statistician

per 3,675 acres and measurement of 262 plots and Group Leader with the FIA program at the

per year. Relaxation of these constraints had North Central Research Station, USDA Forest

an important impact on annual inventory Service, St. Paul, MN.

requirements in the North Central region.

Because trees in this region generally grow

relatively little in the 5 years between measurements, not updating information for plots

measured in previous years is expected to have

little detrimental impact on annual estimates.

Thus, the roles of models for updating purposes and remote sensing for disturbance

detection are non-essential for annual inventories under the Farm Bill, whereas they were

absolutely essential under the AFIS constraints. As a result, the AFIS concept of

i disturbance-based sampling has been abandoned in favor of a systematic distribuUon of

plots across all ownerships and forest cover

types, and inventory estimates based on 5-year

moving averages have been accepted as defaults, but with provision for optional enhancements using updating techniques based on

growth models, imputation, or other methods.
MOVING TO AN ANNUAL INVENTORY

IN THE PACIFIC NORTHWEST

David Azuma

ABSTRACT.--The process of moving toward an annual inventory in

the Pacific Coast states began with educating the individual states as

to what might be involved in the annual system. The states and some

industry groups voiced concerns about inventorying unproductive or

reserved lands on an annual basis. The states in particular were

concerned about the ability to estimate periodic change with an

annual system. The discussion presents these concerns and other

possible problems that the Pacific Northwest may face when moving

to an annual inventory system.

INTRODUCTION Pacific Northwest (the Mt. St. Helens eruption,

and the Columbus Day storm in the 1960s).

The process of implementing the 1998 Farm

Bill in the Pacific Northwest started with expos- Concern that we need a more frequent evaluaing our states (Alaska, California, Oregon, and tion of change is offset by our inventories

Washington) and cooperators to the possible showing little change. Bolsinger et al. (1997)

changes in the inventory. We explained the five estimated a less than 1 percent loss in softpanel design that was put forth in the Farm wood growing-stock volume between 1980 and

Bill and the reasons that the South and North 1991 in Washington. McKay et ol. (1998)

were moving to that design. In general, our estimated a 3-percent increase in softwood

states are more interested in the change be- growing-stock volume and less than a 1-

tween our periodic inventories than the actual percent increase In timberland area between

point estimate. At that time all three states 1986 and 1994 in western Oregon. Waddell

were happy with the present 10-year periodic and Bassett (1994 and 1997 a,b,c,d, five

cycle. In fact they would rather we used the California reports) found a decrease of approxiadditional funds for the annual inventories on mately 3 percent in primary forest area but an

other projects within their states. It was appar- increase of about 7 percent in growing-stock

ent from our original meetings that the states volume between 1984 and 1994 in California

would not be interested in funding an annual (table 1).

inventory on a 5-year cycle.

There are also concerns from industry and

With the exception of Washington in 1988, our state groups as to why we would inventory the

states do not generally give us funds to inca- National Parks on an annual cycle. The Nasure plots. The cooperation with our states is tional Parks also have some reservations about

usually for extra analysis or additional vari- us visiting their land on a yearly basis, and a

ables. The State of Washington was interested similar concern was expressed by the Native

in an intensification of the number of plots in American community. There are approximately

1988, but has no interest in spending dollars 4.1 million acres of reserved forest lands in

to increase the number of plots in our upcom- California, Oregon, and Washington and a

ing inventory. The State of Oregon cites data combined Native American acreage of 1.6

from our 1994 inventory (McKay 1998) con- million acres. There is also a general concern

cerning the small amount of change being about an annual inventory of 6.2 million acres

measured and doesn't see the reason to go to of juniper/pinyon lands in Oregon and Califorannual inventories. The states also do not see a nia where little change occurs and growth is

need to be able to capture catastrophic events minimal. A similar concern exists in the intewith an annual inventory, since there have only rior of Alaska, where there are 62 million acres

been two such events in the last 40 years in the of black spruce. Coastal Alaska also has 4.6

5
Table I .--Changes in area and volume in California, western Oregon, and Washington

in the latest inventories on non-Federal timberland

Growing-stock Timberland

State Year" volume area

Million cubic Thousand

feet acres

California 1984 24,390 8,247

1994 26,135 7,971

Western 1986 19,456 6,729

Oregon 1994 19,824 6,758

Washington 1980 39,331 11,939

1991 39,122 11,452

aThe year value is the ending year of the inventory.

million acres of reserved land in parks and

wilderness. The questionable lands in Califor- LITERATURE CITED

nia, Oregon, and Washington (parks, Native

American, and juniper) together represent Bolsinger, C.L., McKay, N.; Gedney, D.R.;

approximately 12 million acres of possible Alerich, C. 1997. Washington's public and

problems within the annual panels, private forests. Resour. Bull. PNW-218.

Portland, OR: U.S. Department of AgriculIn August 1999, we held a client meeting to ture, Forest Service, Pacific Northwest

discuss how we would move to annual invento- Research Station. 144 p.

ties. We presented several possible ways of

implementing an annual system, including McKay, N.; Bolsinger, C.L.; Lettman, G.L.; Mei,

panel, and annualized periodic. We did not get M.A.; Azuma, D.L. 1998. Timber resource

an overwhelming positive response: the State of trends on non-fedezal timberland in

Washington would like more data on its own western Oregon between 1984-86 and

lands; Oregon didn't see an advantage to 1994. Salem OR: Oregon Department of

moving to an annual system; and California Forestry. 126 p.

didn't see a problem with going annual. The

key was that no one was going to come forward WaddeU, K.L.; Bassett, P.M. 1994. Timber

with money to buy the cycle down from 10 to a resource statistics for the North Coast

smaller number of years. Most of the data Resource Area of California. Resour. Bull.

users present either were not sure how it would PNW-214. Portland, OR: U.S. Department

affect their use of the data or thought it would of Agriculture, Forest Service, Pacific Northnot affect them. west Research Station. 50 p.

We plan to complete the State of Washington on WaddeU, K.L., Bassett, P.M. 1997a. Timber

a periodic design in the next 3 years. This will resource statistics for the Sacramento

give us an updated starting point to move into Resource Area of California. Resour. Bull.

an annual inventory. We will start a 10-percent PNW-220. Portland, OR: U.S. Department

panel in Oregon in the summer of 2000 and of Agriculture, Forest Service, Pacific Northmove onto the 6,000 acre per plot hexagon grid west Research Station. 50 p.

in Oregon. If funding continues to progress, we

will start an annual system in southeast Alaska Waddell, K.L.; Bassett, P.M. 1997b. Timber

in 2002, and in Washington and California in resource statistics for the Central Coast

2003.
Resource Area of California. Resour. Bull. WaddeU, K.L.; Bassett, P.M. 1997d. Ttmber

PNW-221. Portland, OR: U.S. Department resource statistics for the San Joaquin

of Agriculture, Forest Service, Pacific North- and Southern Resource Areas of Califorwest Research Station. 45 p. nia. Resour. Bull. PNW-224. Portland, OR:

U.S. Department of Agriculture, Forest

Waddell, K.L.; Bassett, P.M. 1997c. Timber Service, Pacific Northwest Research Station.

resource statistics for the North Interior 51 p.

Resource Area of California. Resour. Bull.

PNW-222. Portland, OR: U.S. Department ABOUT THE AUTHOR

of Agriculture, Forest Service, Pacific Northwest Research Station. 49 p. David L. Azuma is a Research Forester with the

Foresty Inventory and Analysis program at the

Pacific Northwest Station, Forestry Sciences

Laboratory, USDA Forest Service, Portland, OR.
THE HEXAGON/PANEL SYSTEM FOR SELECTING FIA PLOTS

UNDER AN ANNUAL INVENTORY

Gary J. Brand, Mark D. Nelson, Daniel G. Wendt, and Kevin K. Nimerfro

ABSRACT.--Forest Inventory and Analysis (FIA) is changing to an

annual nationwide forest inventory. This paper describes the sampling grid used to distribute FIA plots across the landscape and to

allocate them to a particular measurement year. We also describe the

integration of the FIA and Forest Health Monitoring (FHM) plot

networks.

of events that occur over large areas, such as

INTRODUCTION hurricanes, ice storms, and windstorms. To do

so requires a spatially regular distribution of

In 1998, Federal legislation (Agricultural plots across the landscape measured each year.

Research, Extension, and Education Reform The FHM program has addressed this same

Act of 1998 - PL 105-185) was passed that need for regularly distributed plots by using a

requires major changes in the way Forest lattice of hexagonal cells as a sampling frameInventory and Analysis (FIA) conducts invento- work (Scott et al. 1993). A base hexagon posiries of the nation's forest resources. This tioned over the conterminous United States

legislation resulted from concerns expressed by was subdivided into approximately 28,000

FIA clients that changes were needed in exist- hexagons whose centers are about 16.9 mi (27

ing FIA methods (Van Deusen et a/. 1999, km) apart (White et a/. 1992). One field plot was

Gillespie 1999). selected for each hexagon, usually the existing

FIA plot closest to the center of the hexagon.

A fundamental change that the legislation Each of the hexagons was assigned to one of

requires is an annual inventory of each state, " four panels; a panel corresponds to a given

with 20 percent of the plots within a state measurement year of the cycle. After the fourth

measured each year. In contrast, FIA invento- panel is measured, the cycle is repeated. One of

ries have historically been conducted within a the advantages of this framework is that it is

single state over 1 to 3 years; each state has unlikely to be aligned with regularly spaced

been re-inventoried every 6 to 8 years in the landscape features.

South and every 11 to 18 years in the rest of

the country (Gillespie 1999). Because of these desirable features, we explored the possibility of using the FHM frameIn addition to FIA, the Forest Health Monitoring work as the basis for the FIA annual inventory

(FHM) program also collects data on our sampling framework. To meet its mandated

nation's forests. FHM data are collected annu- maximum sampling errors, the FIA program

ally on a 4-year cycle. Given the overlap in the requires a sampling intensity of one plot per

FIA and FHM programs, we have an opportu- approximately 6,000 acres (M. H. Hansen

nity to increase the efficiency of data collection 1998, pers. comm.). By creating a new lattice of

by merging the two programs (GiUespie 1999). hexagonal cells where each hexagon is 1/27

The remainder of this paper describes the the size of an FHM hexagon, the desired samsample design for implementing the annual piing intensity is achieved (A. R. Olsen 1998,

inventory and how it has been modified to pers. comm.). The size of each FIA hexagon is

accommodate the integration of the FIA and 5,937.2 acres.

FHM programs.

Staff of the Western Ecology Division of the

CONSTRUCTING THE HEXAGON SAMPLING U.S. Environmental Protection Agency National

FRAMEWORK Health and Environmental Effects Research

Laboratory in Corvallis, OR, performed a 27-

One advantage of an annual inventory is the factor enhancement of the FHM hexagons,

increased ability to quickly measure the effects resulting in more than 360,000 FIA hexagons

8
within and adjacent to the border of the conter- These two panel arrangements are shown in

minous United States. To minimize distortion figure 3.

of the area associated with each hexagon, the

Lambert azimuthal equal-area projection was

used when creating the FHM and FIA hexagons. Figure 1 shows the spatial arrangement

of an FHM hexagon and the FIA hexagons. _i

Attributes included for each hexagon were a "¢

unique 8-digit hexagon ID, a hierarchical ID ,,, 3

that can be used to decipher how the hexagon _'

was generated, and another ID that can be 3

used to determine the U.S. Geological Survey

7.5 minute quadrangle containing the center of

the hexagon. We also determined the state and

county where each FHM and FIA hexagon

center was located based on 1:100,000 U.S.

Bureau of the Census TIGER/Line files.

FHM hexagon

FIA hexagon Figure 2.--Asskjnment of hexagons to one of

five panels (shown by number).

3.27 mi SELECTING PLOTS

There are many ways to select a ground plot for

Figure 1.raThe FIA hexagon lattice. Each black each FIA hexagon. We followed two guiding

dot is at the center of an FHM hexagon, principles for determining plot selection procedures. The plot selected for an FIA hexagon:

By assigning one plot to each FIA hexagon, we 1. must be located in that hexagon and

create a regular spatial distribution of plots 2. should be an FIA ground plot, if one

across the landscape. The 1998 legislation exists, thereby retaining as much

requires that 20 percent of the plots be mea- historical information as possible.

sured each year. To distribute the hexagons

temporally, each is assigned to one of five However, to satisfy the first principle, the

panels. The arrangement shown in the next geographic location of existing plots must be

column (fig. 2) distributes the hexagons among known. Therefore, the first step in plot selecthe five panels in such a way that no adjacent tion was to establish the latitude and longitude

hexagons belong to the same panel. The plots for all existing plots. This was most often done

in hexagons from panel one were measured by transferring marked plot locations on aerial

from the fall of 1998 through the summer of photos to geo-referenced satellite imagery. The

1999 in Indiana, Iowa, Minnesota, and Mis- next step was to spatially overlay the plot

souri. Each of the remaining panels is assigned locations and the FIA hexagons in a GIS applito succeeding years; panel one will be mea- cation. The distance from the plot to the center

sured again from the fall of 2003 through the of its hexagon was also computed and resummer of 2004. As annual inventories begin corded. A database management procedure

in new states, we start with the same panel then assigned one plot to each hexagon based

that is being measured that year in states on the following criteria:

already under an annual inventory. Although

the intent in the Eastern U.S. is to operate on a 1. if the hexagon contains an FHM plot,

5-year cycle, funding or ecological conditions select it;

may require other cycles. In particular, 7-year 2. if not, then select the FIA plot within the

(as an eastern option) and 10-year (as a west- hexagon that is closest to the center of

ern standard) cycles have been proposed, the hexagon; 9
(2

<

Figure 3.,--Seven-panel (left) and ten-panel (right) arrangements.

3. if there are no FHM or FIA plots in the INTEGRATING FIA AND FHM SAMPLING

hexagon, select the center of the hexa- FRAMEWORK

gon as the location for a new plot (some

regions may choose a location near the Although FHM is a national program, FHM

center), plots have not yet been established in all

states. For example, in the North Central

In some of our states we had to adjust the region, FHM plots have not been established in

probability of selection because of unequal five (Iowa, Kansas, Nebraska, North Dakota,

sampling intensities in the previous inventory, and South Dakota) of our 11 states. However,

For example, in Wisconsin reserved areas were in states where there are existing FHM plots, it

sampled more intensively than other areas, is important to retain these plots, not only to

Figure 4 illustrates the selection of plots for keep them as plots selected for their respective

various situations encountered. FIA hexagons, but also to measure them in

their same temporal order.

As noted earlier, the FHM plots were measured

on a 4-year cycle, whereas the legislation

mandating annual inventories specified a 5-

year cycle. In addition to a 4-year cycle, one-

+ FHM plot third of the FHM plots were also measured in

[] FIA plot two consecutive years (overlap plots). To maintain the existing temporal intensity of FHM plot

O Selected plot measurements over a 5-year cycle, the FHM

Q Hexagon center program elected to increase the number of

plots established in most states by 67 percent.

Half of the additional plots are needed to make

up for the overlap plots (years one to four). The

other half are needed for the fifth year. The only

exceptions were in Maryland and Minnesota

where state funding permitted an original

sampling intensity three times greater than in

Figure 4.--Example results of plot selec- other states.

tion criteria.

I0
We obtained a grid of both new and original 2. if an existing FHM plot is not associated

locations and panel assignments for FHM plots with the FHM grid point, then choose

from the Forest Health Monitoring Program, the nearest FIA hexagon of the same

USDA Forest Service, Research Triangle Park, panel and in the same state as the FHM

NC (William D. Smith 1999, pers. comm.). The grid point to be the FIA/FHM hexagon

grid point designates the desired approximate (fig. 6),

location for an FHM plot, but not necessarily 3. if none of the nearby FIA hexagons

the ultimate location of the plot. In states (nearer than 8,500 m) are of the same

where FHM plots had not been selected, this panel and state as the grid point, then

grid was regularly spaced across the state and choose the one containing the FHM grid

equally distributed among the five panels. In point as the FIA/FHM hexagon; change

states where FHM plots had been selected, its panel to match the panel of the FHM

additional grid points were systematically grid point (fig. 7). This condition occurs

interspersed among the original grid points, along state borders and coastlines.

Old locations kept their original FHM panel

assignment. Approximately 50 perecent of the Rules 1 and 3 change the panels assigned to

new grid points were assigned to a new fifth FIA hexagons and therefore disrupt the original

panel, and the rest were spread evenly among FIA pattern. However, because there are only

the other four panels. In Maryland and Minne- about 1/16 as many FHM grid points as FIA

sota, no new grid points were needed and the hexagons, and not all of the FHM grid points

panel assignment was based on the FIA panel will cause the panel of the FIA hexagon to

assigned to that location. That is, the existing change, this disruption was considered acceptFHM plots were simply distributed among five able.

panels.

CONCLUSIONS

We chose an FIA hexagon for every FHM grid

point within the conterminous U.S. based on The hexagon/panel system is one way to

the following rules: distribute FIA plots systematically across the

conterminous United States and through time.

I. if there is an existing FHM plot associ- One plot is selected for each FIA hexagon.

ated with an FHM grid point, then Existing FHM and FIA plots are selected whenchoose the FIA hexagon containing the ever possible. To maintain the existing tempoFHM plot as the FIA/FHM hexagon; ral order of FHM plots, some perturbation of

change its panel to match the panel of the FIA panels was accepted and incorporated

the FHM grid point (fig. 5), into the system. The expected results will be a

a) l_ b)

Pane ; ;HMpglr_dPoint

• _ Panel 2

Figure 5.--Examp/es where rule I changes the original panel of the FIA hexagon a) to a different

panel b). The shaded hexagons have different panels.
a) b)

Figure 6.--Implemenmtion of rule 2. The panel of the FHM grid point in a) results in the shaded

hexagon b) becoming the FHM/FIA hexagon.

b) a)

I Q FHM grid point I

el=l

Figure 7.--Before a) and after b) implementing rule 3. The panel of the sho_ded hexagon has been

changed.

consistent inventory of all forested lands that hexagon/panel system; and William D. Smith

preserves historic data. This system will incor- of the FHM program in Research Triangle Park,

porate the FIA and FHM forest inventory ef- NC, for the original and new FHM grid locaforts, comply with legislative mandate, and tions.

provide a framework for future forest inventories. LITERATURE CITED

ACKNOWLEGMENTS Gillespie, A.J.R. 1999. Rationale for a national annual forest inventory program.

We thank Anthony R. Olsen, Suzanne Pierson, Journal of Forestry. 97: 16-20.

and Barb Rosenbaum of the Environmental

Protection Agency in Corvallis, OR, for provid- Scott, C.T.; CasseU, D.L.; Hazard, J.W. 1993.

ing the FIA hexagons. We also thank the follow- Sampling design of the U.S. National

ing people from the USDA Forest Service: Forest Health Monitoring Program. In:

Ronald McRoberts and Mark Hansen of the Proceedings of the Ilvessalo symposium on

North Central Research Station in St. Paul, national forest inventories; 1992 August

MN, and Andrew Gillespie of the Resource 17-21; Helsinki, Finland. Res. Pap. 444,

Valuation and Use Staff in Washington, DC, for Finnish Forest Research Institute: 150-157.

their suggestions and input in designing the

12
Van Deusen, P.C.; Prisley, S.P.; Lucier, A.A. ABOUT THE AUTHORS

1999. Adopting an annual inventory

system: user perspectives. Journal of The authors are employees of the North Central

Forestry. 97:11-14. Research Station, U.S. Department of Agriculture, Forest Service, St. Paul, MN.

White, D.; Kimerling, A.J.; Overton, W.S. 1992.

Cartographic and geometric components

of a global sampling design for environmental monitoring. Cartography and

Geographic Information Systems. 19: 5-22.

13
PROS AND CONS OF THE INTERPENETRATING PANEL DESIGN

Paul C. Van Deusen

ABSTRACT.--The interpenetrating sample design has been selected

for the USDA Forest Service's Annual Forest Inventory System. The

advantages and disadvantages of this design are discussed by considering alternatives such as the formerly used periodic design, a concentrated grid design, and disturbance based sampling. Factors

considered for each design include fulfilling 1998 Farm Bill requirements, relative cost, ease of implementation, and analysis options.

Each design alternative has positive and negative attributes, but the

interpenetrating design most clearly facilitates implementation of the

new annual inventory system.

INTRODUCTION SAMPLE DESIGN ALTERNATIVES

A survey designer has an array of choices to Each of four design alternatives will be disconfront when deciding how samples will be cussed, followed by a section that suggests

allocated in the field. Sample allocation also minor modifications to improve the interpenaffects the options for analyzing the data. etrating (INT) design. The old FIA periodic (PER)

Survey design involves both of these choices, design, which is being phased out, is discussed

but the emphasis here will be on the sample first. The INT and PER designs weight all plots

allocation aspect, equally, and both lend themselves to simple

analysis options. The concentrated grid (CON)

Sample allocation decisions for USDA Forest • design can be viewed as a hybrid of INT and

Service Forest Inventory and Analysis (FIA) PER. A CON design would divide each state

surveys must take into account the public-use into five regions, and one region would be

nature of FIA data. First and foremost, the data measured each year. The fourth design being

should be amenable to standard analyses, considered is disturbance based sampling

Sample allocation should lead to robust data in (DIS), which results in an annual system where

the sense that the data are not optimized only disturbed plots are sampled with higher probfor a limited purpose. For example, the data ability than undisturbed plots. As such, the

should not be collected in such a way that they DIS design is the only one that attempts to

are optimal for estimating forest growth but optimize for certain characteristics.

inadequate for estimating current volume by

species. Unfortunately, it is inevitable that a Periodic Design

design to optimize for variable A leads to suboptimality for variable B. FIA has employed the periodic design since the

program began in the 1930s. Ideally, under the

With the above factors in mind, FIA selected PER design, all plots in a state are measured in

the interpenetrating design for the annual the same year. In practice, it may take 3 or 4

forest inventory system. This design allocates years to complete large, heavily forested states

field plots to five panels that each provide like Georgia or Maine. This design puts all of

systematic coverage for a state or any other FIA's attention on the current few states where

region of interest. The systematic coverage field work is underway. It provides a snapshot

implies that no variable is favored at the ex- of the state that has maximum accuracy

pense of another, and a number of analysis immediately following the field work and then

methods are valid, deteriorates over the years until the next

14
measurements are available. Estimates are The same plots used for the PER design, laid

derived under the PER design by averaging over out on the national FIA grid, are used for the

all the data, which implicitly assumes that all INT design with minor exceptions. The annual

plots were measured at the same time. The panels for the INT design consist of roughly

periodic system worked well for the first 50 equal numbers of plots that are systematically

years of FIA's existence, but began drawing distributed over the FIA grid for each panel.

criticism around 1990 primarily because the Therefore, data from an annual panel could be

survey cycle was too long. analyzed using methods used for the PER

design. However, the precision of the estimates

The first Blue Ribbon Panel report (American obtained from a single INT panel would be less

Forest Council 1992) (BRP I) did not call for than that obtained from the full PER sample.

dismantling the PER design, but requested Alternative estimation procedures (Reams and

reduction of the survey cycle from 10 years to 5 Van Deusen 1999) that use data from previyears. Some progress was made on this, but ously measured panels can significantly imfiat budgets and increasing demands on FIA prove INT design precision. Multiple imputation

ultimately caused the cycle to become longer (Rubin 1987, Van Deusen 1997), which inthan ever. volves updating unmeasured plots with models

or database matching, is one viable option. A

Some of the positive aspects of this design are: moving average estimator can also incorporate

• The design can be funded on a 5-year measurements from all panels without complicycle, cations due to updating.

• This design maintains the status quo so

no other changes are required. The second Blue Ribbon Panel report (Ameri-

• Simple analysis options are available, can Forest and Paper Association 1998) (BRP

II) concluded that FIA should move to an

Some negative aspects of this design are: annual INT design that would measure 20

• Attempts to improve timeliness under percent of the plots in a state annually. Subsethis design following BRP I failed, quently, the 1998 Farm Bill mandated that FIA

• Budgets and activities within a state adopt the INT design and produce a strategic

fluctuate wildly over time. plan (USDA Forest Service 1999) for implemen-

• Cross-state analysis is difficult because tation.

adjacent states are measured in different years. Some of the positive aspects of this design

relativetothePERdesignare:

! Interpenetrating Design * It meets the 1998 Farm Bill requirements.

I The INT design was originally developed for the * Cross-state analyses are temporally

Southern Annual Forest Inventory System consistent.

i (SAFIS) pilot study, which began in 1995. The * Budgets don't fluctuate annually by

INT design is similar to the National Forest state.

Health Monitoring design and calls for annual * The data can be analyzed by a number

measurement of panels that consist of plots of approaches.

that systematically cover the region of interest. * The States are move involved.

This design appealed to many southern state * New computer programs will be develforesters who consequently supported SAFIS. oped for data management and analyThe INT design made SAFIS somewhat compat- sis.

ible with the original Annual Forest Inventory

System (AFIS) pilot study that began in the Some negative aspects of this design relative to

Lake States in 1992. The use of different the PER design are:

designs for AFIS and SAFIS gave FIA the oppor- • Longer travel time between plots is

tunity to study two alternative ways of "going required.

annual." AFIS used the DIS design discussed * The precision is lower in any given year.

below. • Newsoftware for data management and

analysis is required

• Requirement of more state involvement

i couldbeproblematic.

i 15

,]
Disturbance Design Some of the positive aspects of this design

relative to the INT design are:

The disturbance sampling design (DIS) was • It can be very economical.

developed for the AFIS pilot study in the Lake • Sampling is optimized for disturbed

States. This design allocates sampling effort to areas.

plots with probability proportional to distur- • It uses remote sensing to improve

bance. The design called for measuring all sampling efficiency.

disturbed plots each year and then taking a

random or systematic sample of undisturbed Some negative aspects of this design relative to

plots. Disturbance would be detected via the INT design are:

remote sensing. This design would be very good • Statistical analysis is difficult.

for determining the amount and impact of • It is optimal for disturbance, but subdisturbance, but it leads to more complicated optimal for growth.

analysis options than either the INT or PER • It depends on remote sensing to detect

designs. Any analysis would have to differenti- disturbance.

ate between plots that were measured because * It depends on models.

they were disturbed versus the randomly

chosen undisturbed plots. Proponents feel that Concentrated Grid Design

the DIS design could be more economical to

implement than a rigid INT design where 20 The concentrated grid (CON) design has been 

percent of the plots are measured each year. proposed as a compromise between the INT

and PER designs. A CON design calls for

The DIS design depends strongly on remote measuring an equal portion of the plots each 

sensing to detect disturbance. This capability is year by dividing each state into five concenavailable for Minnesota courtesy of the state trated zones. In this way, annual measureDepartment of Natural Resources, but not ments would be taking place in each state, but

necessarily for other states. The DIS design each within-state zone would be under a

also depends on models to make predictions for periodic survey. The CON design is very similar

unmeasured, undisturbed plots. However, it is to the PER design, which divided states into

statistically problematic to incorporate modeled survey units that were usually measured one at

plots that are selected with a different probabil- a time. Some would argue that it also meets the

ity than the measured disturbed plots. One Farm Bill requirements, even though it circummust account for the fact that models predict vents the spirit of the Farm Bill. The CON

expected plot means rather than individual plot design might also allow for reduced travel costs

values. Therefore, treating modeled predictions relative to the INT design. The CON design

like actual measurements leads to understating would make it difficult to produce state-level

the true variance. Multiple imputation (Rubin reports because plots in different parts of the

1987) is one way to incorporate variance into state are measured in different years.

the process and to use models in a valid

manor. This approach requires making several Some of the positive aspects of this design

predictions for each plot and incorporating relative to the INT design are:

variability into the predictions. However, the * It may meet the Farm Bill requirements.

systematically different handling of disturbed * Travel costs could be lower.

and undisturbed plots under the DIS design * Precision for sub-regions is higher for a

complicates the use of multiple imputation. In given year.

effect, the DIS design creates two strata: a * It is similar to the PER design and

disturbance stratum and a non-disturbance therefore involves less change.

stratum. The complications arise, in part,

because these strata change each year (Van Some negative aspects of this design relative to

Deusen 1993). Resulting change estimates will the INT design are:

involve plots that were measured with prob- * It may not meet the spirit of the Farm

abilities and stratum that change over time. Bill.

Incorporating modeled estimates under the INT * It makes cross-region analyses difficult.

design is much easier, specifically because of * It is more periodic than annual in

the equal probability (systematic) plot selection nature.

process.

16
MODIFICATIONS TO THE Consider the possibility of creating small

INTERPENETRATING PANEL DESIGN clusters of adjacent plots. Each cluster would

contain one plot for each panel being mainThe INT panel design has been chosen for the tained under the INT design. For example, the

new annual forest inventory system being basic design that meets Farm Bill requirements

implemented by FIA. Alternative designs are of has five panels, so each cluster would have five

academic interest, but FIA has already made plots (fig. I). Panel assignments could be

substantial commitment to the INT design, rotated within clusters at periodic intervals as

While the INT design has many desirable a simple way to obtain design unbiasedness.

attributes, it has some aspects that can be Thus, the plots change panel membership on a

legitimately criticized. The purpose of this periodic basis. This ensures that a mix of

section is to suggest minor modifications to the growth intervals is always being measured.

standard INT design to rectify limitations that it

can impose on analysis and logistics options. The second problem with the basic INT design

can be alleviated by creating "extra" panels,

Strict adherence to the INT design would preferably in increments of five (fig. 2). If there

eventually lead to having only 5-year growth were 10 panels, for example, it would still be

intervals in the database. However, estimates possible to measure 20 percent of the plots

will be made annually, which implies that the each year by measuring two panels. Measuring

INT design is not design unbiased. In other 3 of 15 or 4 of 20 panels would also work.

words, estimates from a rigid INT design for Extra panels become advantageous when the

intervals other than 5 years would depend on need arises to deviate from annually measuring

models/assumptions. Fluctuating budgets and 20 percent of the plots. If the budget decreases

special surveys may create additional problems under a 15-panel system, one can drop back to

with a rigid INT design. For example, it would measuring either one or two panels rather than

be difficult to measure 20 percent of the plots the usual three per year. Alternatively, the

annually in a year when the budget is reduced, number of panels can be increased in a good

It would be equally problematic to measure budget year.

more than 20 percent of the plots in a budget

increase year without deviating from the basic The extra panel approach adds flexibility to the

design. However, a simple alteration to the basic INT design, so that fluctuating budgets or

basic INT design can alleviate these problems, special surveys can be seamlessly accommodated. Rotation of within cluster plot-to-panel

v

x4 x2 x3 x4

xs,, x421Xl x,2 5x

Figure I .--A five-panel design showing four clusters of five plots each. The plot location is represented by an x. The panel assignment is given by the number next to the plot.

17
Xl x3 x5 4x

x4 x2 \ x6 x 1 8x

x5 x x2 9x

xlO x7

Figure 2.--A l O-panel design showing four clusters of 10 plots each.

assignments allows for a range of measurement

intervals to be present in the database. This Analysis options for either the PER, INT, or

adds the desirable feature of design unbiased- CON designs have much overlap because all

ness to the resulting estimates and can work . use systematic, equal-probability sampling.

within the context of extra panels. These ideas The DIS design selects disturbed and undisare discussed in somewhat more detail in Van turbed plots with different probabilities, and

Deusen (2000). depends on remote sensing and models to be

effective. The remote sensing and modeling

SUMMARY capabilities required for the DIS design are not

available at this time in each state, which

Four sampling designs that could be used by precludes the use of this design at the national

FIA have been briefly discussed, with emphasis level. However, the advantages that can accrue

on the INT design that has already been se- from modeling and remote sensing can also be

lected for the new annual forest inventory realized under the INT design. The INT design

system. All designs could operate with plots does not require models or remote sensing to

laid out on the national FIA grid using tradi- be effective, but they can be used if available.

tional field procedures. Recent changes made The INT design can use models within the

to FIA plot configuration and measurements context of a procedure like multiple imputation

were not required for the annual inventory to improve the precision of estimates and

system. For example, the decision to change obtain valid confidence intervals. Therefore, the

from variable radius plots to fkxed area plots choice of the INT design by FIA is a prudent

with mapping (Scott and Bechtold 1995) was one.

made prior to the 1998 Farm Bill. Current

plans also call for fitting FIA field plot locations LITERATURE CITED

to a triangular grid (Roesch and Reams 1999).

This will result in equal plot intensity nation- American Forest Council. 1992. The report of

ally and will facilitate formation of five panels the first blue ribbon panel on forest

for the INT design, inventory and analysis. Washington, DC.

18
American Forest and Paper Association. 1998. Van Deusen, P.C. 1993. Sampling moving

The report of the second blue ribbon strata on two occasions. Canadian Jourpanel on forest inventory and analysis, nal of Forest Research. 23: 96-100.

Washington, DC.

Van Deusen, P.C. 1997. Annual forest invenReams, G.A.; Van Deusen, P.C. 1999. The tory statistical concepts with emphasis

southern annual forest inventory sys- on multiple imputation. Canadian dourtem. Journal of Agricultural Biological and nal of Forest Research. 27: 379-384.

Environmental Statistics. 4(4): 346-360.

Van Deusen, P.C. 2000. Alternative sampling

Roesch, F.A.; Reams, G.A. 1999. Analytical designs and estimators for annual suralternatives for an annual inventory veys. In: Hansen, M.H.; Burk, T.E., eds.

system. Journal of Forestry. 12: 33-37. Integrated tools for natural resource Inventories in the 21 st century: proceedings of

Rubin, D.B. 1987. Multiple imputation for the IUFRO conference: 1998 August 16-20;

nonresponse in surveys. New York, NY: Boise, ID. Gen. Tech. Rep. NC-212. St.

Wiley. Paul, MN: U.S. Department of Agriculture,

Forest Service, North Central Research

Scott, C.T.; Bechtold, W.A. 1995. Techniques Station.

and computations for mapping plot

clusters that straddle stand boundaries. ABOUT THE AUTHOR

Forest Science Monograph. 31: 46-6 I.

Paul C. Van Deusen is a Principal Research

USDA Forest Service. 1999. A strategic plan Scientist for the National Council of Air and

for forest inventory and monitoring. Stream Improvement (NCASI) at Tufts UniverWashington, DC. sity in Medford, MA:

19
USING CLASSIFIED LANDSAT THEMATIC MAPPER DATA

FOR STRATIFICATION IN A STATEWIDE FOREST INVENTORY

Mark H. Hansen and Daniel G. Wendt

ABSTRACT.--The 1998 Indiana/Illinois forest inventory (USDA Forest

Service, Forest Inventory and Analysis (FIA)) used Landsat Thematic

Mapper (TM) data for stratification. Classified images made by the

National Gap Analysis Program (GAP) stratified FIA plots into four

classes (nonforest, nonforest/forest, forest/nonforest, and forest)

based on a two pixel forest edge buffer zone. Estimates based on twophase sampling for stratification were made at the county level. This

procedure differed from methods used in previous inventories where

stratification was based on the stereoscopic examination of aerial

photo plots. Changes in plot design, sampling intensity, and population parameters between 1986 and 1999 make it impossible to

attribute differences in sampling errors entirely to this change in

methods. The stratified sample estimates based on TM data provided

good estimates and greatly reduced costs by eliminating the need for

thousands of aerial photos and manual interpretation of several

hundred thousand photo plots.

INTRODUCTION estimate the size (area) of each stratum and

phase two is a sub-sample of the phase one

FIA statewide inventories provide estimates of plots that estimates the mean within each

forest resource parameters such as forest area stratum. Population level estimates (means and

and timber volume, growth, removals, and totals) are weighted sums of within stratum

mortality estimates at the state, unit, and estimates. The variance of the estimate is

county level. FIA has used two-phase sampling reduced by selecting strata that have low

for stratification (also called double-sampling) within stratum variances and by increasing

for a number of years. Cochran (1977) presents sampling. Increasing phase one intensity

a good general description of double-sampling decreases the variance of the estimated strafor stratification, and Loetsch and Hailer (1964) tum size estimates, and increasing the phase

present it in a forest inventory context, two intensity decreases the variance of the

estimated within stratum estimates.

This paper describes how the FIA program at

the North Central Research Station (NCFIA) In two-phase sampling for stratification, it is

used Landsat TM imagery to replace aerial very important that phase one determines

photos for phase one estimates in two state- strata for all plots (both phase one and phase

wide inventories. Using digital data to replace two plots). It is also important that this assignmanual interpretation of aerial photos greatly ment to a stratum does not change in phase

reduced the work required to complete these two when the plot is measured and that the

inventories. First, we describe the methods procedures used to determine this classificaused in the past and then the methods used in tion be identical for all phase one plots regard1998. Finally, results from these two invento- less if they are also phase two plots. Often in

ries are presented to show differences related forest inventory applications, the strata have

to changes in phase one procedures, names similar or identical to ground classifications that are of interest in the final estimate.

BACKGROUND For example, the class of sawtimber may be

one stratum. In the phase one sample, a plot is

Two-Phase SRmpling for Stratification assigned to the sawtimber class based on aerial

photo classification. This plot could be a phase

NCFIA uses two-phase sampling for stratifica- two plot and thus sent to the field where it may

tion. Phase one is a large sample used to or may not be ground classified as sawtimber.

20
Regardless of its ground classification, in the field visited and permanently marked by a

estimation procedure it must be included in the stake in the ground. In strata 10 and 11 there

sawtimber stratum, were very few field visits and the aerial photo

classification was considered the correct obser1986 Inventory Procedures vation. This eliminated visiting thousands of

nonforest plots that were obviously nonforest

In 1986, NHAP photos (I :40,000) were as- from the photo. It assumes no error in the

sembled into township mosaics, and a system- location and classification of these plots and no

atic grid (one plot per 190.4 acres) was overlaid conversion to forest between the dates of

on each township mosaic. These phase one photography and inventory. These assumptions

photo plots were classified by land use, forest were fairly safe due to a stable agricultural

type, and stand-size density. A total of about economy, but the lack of a field check of these

250,000 photo plots formed the basis for the two strata probably resulted in a small under1986 stratification. The photo classifications estimation of forest land in 1986. Plots in these

were collapsed into the 11 strata in table 1 for two strata were field visited only in a few

the final estimation, counties where the photos were fairly old or

where it was thought there could be significant

changes from nonforest to forest.

Table 1.--Aerial photo (size-density) classes

used for stratification in 1986 If nonforest plots are not visited, the only

permanent record of the location in two strata

1 Sawtimber high 6 Seedling/sapling high (10 & 11) is a pin prick. A stake in the ground

2 Sawtimber low 7 Seedling/sapling low is the permanent record of the location in

3 Poletimber high 8 Questionable strata 1-9. With remeasurement, errors in the

4 Poletimber med 9 Nonforest with trees transfer of a pin prick from an old photo to a

5 Poletimber low 10 Nonforest without trees new photo can result in an observation of

11 Water nonforest to forest change but not a forest to

nonforest change because old forest plots have

stakes marking their location. Transferring

A systematic sample (every 17th) of photo plots locations from one photo to another is never

was selected as a phase two sample and fur- perfect, especially with 1:40,000 scale photos

ther examined to measure the parameters of from different years that are not ortho-corinterest. The plot design used in 1986 was a rected. With remeasurement these errors would

cluster of ten 37.5 basal area factor (BAF) bias the sample towards forest land.

sample points distributed over a 1-acre area.

The plot was arranged with all 10 sample A second problem observed with the 1986

points in a single land use (forest or nonforest) procedures is an inconsistency in the photo

as determined by plot center. Under this plot classification of phase two plots. Double samdesign, each plot represents a binary observa- piing for stratification assumes phase two is a

tion for all area estimates, random sub-sample of phase one. Ideally, the

photo interpreters would first classify all the

Problems With 1986 Inventory phase one plots (without knowing which are

Procedures phase two plots) and then select the phase two

plots. A selection system transparent to the

Two procedural problems were identified that interpreters was not implemented because of

could produce bias in the estimates. One the difficulties involved. The systematic nature

problem relates to methods used on phase two of the sample and the need to pin prick and

plots that were obviously nonforest on the collect additional information from the photo

photos. The other problem was an apparent on phase two plots made it very inefficient to

inconsistency in the classification of phase two keep the identity of the phase two plots secret.

plots. The lack of independence became apparent in

the questionable stratum. A plot should be

In 1986 phase two plot locations were defined classed questionable if the interpreter cannot

by a pin prick on the photo; however, plots accurately make a forest or nonforest determiclassified in stratum 10 (nonforest without nation. If classifications were done without

trees) and 11 (water) were treated somewhat prior knowledge, the expected ratio of phase

differently. All strata 1-9 phase two plots were two plots to phase one plots is I" 17 in every

21
stratum. The observed ratio in Illinois was Currently it appears to be the most promising

1:8.11, about half the expected value in the available data source for use by FIA. The

questionable stratum. A chi-squared test for combinations of low cost, multi-spectral capalack of independence was significant at the bilities, long-term availability, and appropriate

10 _° level. It appears that the interpreters used pixel size are among the reasons it has been

this stratum more frequently on phase two the primary data source for most large-scale

photo plots than on non-phase two photo plots, classification efforts investigated by FIA. The

In training and supervising the interpreters, we minimum size of a forest area defined by FIA is

stressed the importance of consistency. The 1 acre, thus sensors with a pixel size larger

problem appears to be a tendency to do a than an acre can easily miss small areas of

"better" job at interpreting plots that wili be forest land, especially where forest is not the

sent to the field over those that will not. Inter- primary land cover.

preters know someone will be visiting the site

they are classifying and must look at it just a The classification of TM data for an entire state

little bit more closely, is a very large project and should lead to a

product that can be used in many applications.

Ensuring complete independence in the selec- For NCFIA to obtain imagery and classify it for

tion of ground plots in a remeasurement the sole purpose of stratification of the phase

sample would greatly increase the phase one two plots would probably exceed the total cost

effort. It would require two people to work on of our current phase one procedures. As methevery township mosaic. Much of the work ods are improved and the cost of remote senswould be a duplication of effort. One person ing coverage decreases this will probably

would transfer the phase two plot locations (pin change. NCFIA has typically borrowed photos,

pricks) from the old photo to the new photo and or in some states new photos were taken by a

then locate and label the other phase one plots cooperating state agency with FIA and other

(those that are not phase two plots) on the new uses in mind. Following this approach of using

photo. The second person would then classify the best available photos for stratification we

each of these pin pricks. This would involve began looking for available classified imagery

placing 17 times more pin pricks and labels on for stratification as we planned the 1998

each photo. NCFIA uses photos that are bor- inventories. Aerial photo plot sampling would

rowed from other agencies; however, if we were be the fall back should the data not provide the

to pin prick and mark the photos every 191 level of accuracy needed.

acres rather than every 3,250 acres, we would

most likely be forced to purchase photography, METHODS AND DATA

greatly increasing our costs.

1998 Stratification Using GAP Classified

Classified Digital Imagery for Stratification Landsat TM Data

Computer aided classification of digital imagery NCFIA obtained two digital maps derived from

can efficiently map large areas independent of TM data. These were made by the National GAP

the phase two plot selection. Numerous plat- Analysis Program (GAP) (Scott et a/. 1993).

forms, sensors and spectral bands, pixel sizes, More information about GAP can be found at

and classification algorithms are available and www.gap.uidaho.edu/gap/and www.epa.gov/

much research is ongoing. It is beyond the mrlc/.

scope of this paper to review the work that has

been done in this area as it relates to forest The base data sets in Illinois were obtained

inventory applications. NCFIA has cooperated over approximately 5 years (1991 to 1995) and

in and/or supported research efforts in remote classified into 20 land cover categories by the

sensing with various groups including MN Illinois Natural History Survey. Four of these

DNR, Univ. of MN, IL Natural History Survey, categories describe woodland and forest land;

IN Univ., WI DNR, Rand Corp., EPA, other FIA the rest are nonforested ranging from water,

projects, and other USDA programs. NCFIA has marsh, and grasses to agricultural and urban.

also acquired the equipment and personnel The classification loosely followed an Anderson

necessary to process digital image data and has level 2 (Anderson eta/. 1976) scheme, and the

been using these capabilities on special minimum mapping unit for classification was a

projects in Nebraska and South Dakota. TM single 28.5 x 28.5 m pixel. The data sets in

data have been used in most of these projects. Indiana were taken between 1988 and 1994

22
and classified into 18 land cover categories by 1998 Phase Two Field Procedures

Indiana University. Five of these categories

describe woodland and forestland; the rest are Between 1986 and 1998, field procedures for

similar to those for Illinois. The classification NCFIA plots changed. The 1998 plot design

scheme followed the UNESCO system (UNESCO consists of four 1/24 acre fixed area subplots

1973) with a minimum mapping unit of i ha distributed over an acre. Another major change

(2.47 acres). A sample portion of this image is is that plots can now sample more than one

shown in figure I with the 18 classes repre- land use. A plot that straddles two land uses is

sented by different gray tones, considered a partial observation of each. Under

this system, area estimation is not a binary

The classifications for both states were grouped procedure; instead observations can fall anyinto binary forest/nonforest images (fig. 2). where in the range 0-1. For example, a plot

Furthermore, since FIA defines forestland as that lands 60 percent on forest land and 40

being at least 1 acre in size, the Illinois forest percent on nonforest land becomes an observaclasses were clumped and sieved to a one-acre tion of .6 in the estimation of forest area.

minimum unit. The Indiana data were left at a

one-ha minimum unit. This difference in Aerial photos were used in this inventory, but

minimum mapping units was of concern to us; only to assist in plot location and observation.

however, the raw classified Indiana data were It was necessary only to obtain photos for the

not available to us and in the final analysis we actual phase two plots rather than complete

did not detect any problems as a result of the photo coverage. This greatly reduced the num1-ha mapping unit in Indiana. To improve the ber of photos needed and enabled us to puridentification of plots that were likely to be chase these photos rather than borrow them.

misclassified or straddle a forest/nonforest The first step in field plot work was to transfer

edge, two new classes were created. Pixels near every phase two plot location from the old

the boundary of a forest-nonforest edge were photos to new photos. Plots were examined to

identified. Any forest pixel within two pixels of determine if a field visit was necessary. Again,

a nonforest pixel was placed in the forest/ plots that were obviously nonforested were not

nonforest class, and any nonforest pixel within sent out for field check, but any plot that

two pixels of a forest pixel was placed in the appeared to possibly have trees on it was

nonforest/forest class. This provided the four remeasured by a field crew. This nonforest

classes used for stratification in the estimation interpretation from the photo should not be

process. Figure 3 shows the same area with the confused with the nonforest classification in

final four classes delineated, the TM data. Because of the change in plot

1 I _ e_j !_

11"

mr tr

Figure I .--A part of the Figure 2.--The same part of Figure 3.--The same part of

classified Landsat TM the image with classes 8- the image with the final 4

image produced by Indi- 15 combined into one strata. Nonforest pixels

ana State University for class (forest - gray) and within 2 pixels of forest

GAP. This classification the other classes com- (nonforest/forest - black)

contains the original 18 bined into a second class and forest pixels within 2

classes identified by GAP. (nonforest- white), pixels of nonforest (forest/

nonforest- light gray).

23
design, pin pricked plot locations that were forest land. In counties within a National

obviously nonforest without trees (not visited Forest, lands owned by the Forest Service were

under the old design) but that fell within 200 treated as a population separate from those

feet of a forest area required a field visit, that were not. This differed from the procedure

used in 1986, where estimation was done by

Linking the phase two plots to the classified treating each forest inventory unit excluding

Landsat TM data requires accurate location Forest Service lands as populations (there are

information for every phase two plot. A GPS three forest inventory units in Illinois and four

unit was used to obtain this information on in Indiana) with National Forest lands making

every plot that was field visited. Obtaining a final population in each state. In both invenlocation information on nonforest plots without tories, sampling errors were computed for

trees that were not field visited involved a individual populations using two-phase samdigitizing procedure where the plot locations piing for stratification estimators described in

from the aerial photos were transferred to a Cochran (1977). In the 1998 estimates, the

georeferenced image file. The UTM coordinate numbers of phase one plots (pixels) are so large

obtained through digitizing or the GPS unit was that stratum areas can be considered known

used to link each plot to a specific pixel making without error and the estimates are equal to

it possible to assign each plot to a stratum, stratified sampling estimates. State total samErrors in this UTM coordinate as well as errors piing errors for estimates of forest area, growassociated with the georeferenced image file ing-stock volume and growth and sawtimber

contribute to the overall estimates of sampling volume and growth from both the 1986 and

error. In the inventory we used the best meth- 1998 inventories are summarized in table 2,

ods we could to reduce this source of error. In along with the number of phase one and phase

this report we do not attempt to examine the two plots.

contribution of this single source of error to the

overall sampling error of the final estimates. The differences in sampling errors cannot be

entirely attributed to the change in stratificaRP__I/L'PS tion procedures. Changes in plot design, sampiing intensity, and population parameters

The described procedures produced estimates between 1986 and 1999 also have major efwith sampling errors at or below the national - fects. The new plot design samples trees 5

accuracy standard for FIA inventories. Estima- inches dbh and larger on a fixed area plot

Lion was done on a county basis; however, in (equal probability) rather than a variable radius

some predominantly nonforest parts of each plot (probability proportional to basal area) so

state, several counties were grouped to create larger trees were sampled with a lower probpopulations containing at least 30,000 acres of ability in 1998. This can have a major effect on

Table 2.mSelected sampling errors and number of plots (phase one and two)for the 1986 and 1998

Illinois and Indiana inventories. Sampling errors were computed using double sampling for

stratification equations (Cochran 1977).

Illinoi_ Indi_nil

Sampling errors 198(_ 1998 1986 1998

Forest area 0.94% 1.49% 1.00% 1.52%

Growing-stock volume 1.99% 2.28% 1.57% 2.18%

Sawtimber volume 2.50% 2.57% 1.86% 2.47%

Growing-stock growth 3.36% 2.09% 3.42% 2.04%

Sawtimber growth 5.27% 2.47% 5.47% 2.39%

Plots 1986 1998 1986 1998

Number of phase one plots 194,815 179,674,504 126,629 104,057,965

Number of phase two plots (total) 10,847 11,521 11,440 6,326

Number of phase two (ground visits) 1,342 2,114 2,430 1,847

Percent ground visits 12.37% 18.35% 21.24% 29.20%

24
the variance of the observed volume per acre are probably not quite as high as stratum 4 in

measurements in stands with large diameter figures 5 and 7. If, in 1986, as few as 1 in

trees. The change to a plot design with one or 1,000 nonforest without trees phase one plots

more conditions had an impact on the observed were misclassified and were actually forest on

variance in area estimates. Also, in 1986, the the ground, the sampling errors on forest area

State of Indiana provided additional resources estimates in 1986 would exceed those of the

to NCFIA to increase the intensity of phase two. 1998 shown in table 2. Under stratified samThis additional funding was not available in pling, a small error in a very large stratum can

1998 and the phase two sampling intensity was have as much impact as a large error within a

substantially reduced, very small stratum.

Figures 4-7 show the average observed mean The 1998 strata 2 and 3 were designed to

and standard deviation of forest area and contain the plots near forest edges and pergrowing-stock volume estimates by stratum for formed well. Over 60 percent of the ground

both the 1986 and 1998 inventories. These plots with both a forest and nonforest condition

figures show that the classified TM data pro- were in one of these strata.

vided reasonable stratification. The TM classification was not prefect in the identification of The change from considering each unit a

nonforest resulting in nonzero observations of population to considering counties or county

forest land in stratum 4. This stratum groups as populations had significant effects

(nonforest) is by far the largest stratum in both on our county-level estimates. In 1986, all

states, and the impact of a good, but less than estimates were developed at the unit level and

perfect, stratification of nonforest lands in prorated back to the county on the basis of

phase one has a large impact on the variance of phase one data. This procedure used all phase

any item that is zero on nonforest land. Under two ground plots from the entire unit to estithe 1986 system, where most nonforest phase mate within stratum means for every county

two plots were not field checked and assumed and masked real differences between counties

to be a nonforest observation, the true mean and underestimated the true sampling errors of

and standard deviation within strata 10 and i I the county level estimates. The direct developshown in figures 4 and 6 are probably underes- ment of county-level data for 1998 estimates

timates. The true mean and standard deviation provides estimates of true differences between

I

11L Water [] Stdev I _ [] Stdev

10 _-r=Nontor. without trees [] Mean J 4 Nonforest [] Mean

9 __ Nonfor. with trees

8 _ Questionable

7 _'_'" E ...... _"'\'_'_"_ [Seed-sap lowden. _ ! Nonforest/forest

"_ JPolelowden. __,_.'_,_X_._ Forest]

5 -_._'_'\""'_"_'_"\\ "_" " 2

4 "L_\\\\-..,,........ \\ \-..,,,,,,\,,,_\\\\\,_.I_POle med. den. 1 / I nonforest i

3 "L'\\\\\\\-.-.\\\-.-,\-.-_.\\_ !Pole high den. Forest

2 tSaw low den. , 1

0 0.5 1

0 0.5 1

Portion forest Portion forest

i

Figure 4.N1986 forest area data by the 11 Figure 5.N1998 forest area data by the 4

aerial photo strata used in 1986. Landsat TM strata used in 1998.

25
1 Water ] ]

/ B Stdev _ [] Stdev

1 Nonfor.withoutrees 4 J Nonforest

I !"1Mean [] Mean

Nonfor. with trees

[

Questionable 1

1 __'_ Nonforest/forest Seed-saplowden. 3

 o,ow'

i 0n 2

POlereed.en. nonforest

olehighden.

Sawlowden.

Sawhighden,

F J ........ [

0 500 1000 1500 0 500 1000 1500

Volume (cubic feet per acre) Volume (cubic feet per acre)

Figure 6.--I 986 growing-stock volume data by Figure 7.--1998 growing-stock volume data by

the 11 aerial photo strata used in 1986. the 4 Landsat TM strata used in 1998.

counties and the sampling errors reported are be quantified. Thls can only be done if the image

not based on assumptions that may not be data are referenced and accurate plot location

valid. This level of estJmatlon was possible data are available for phase two plots. Errors in

because of the relatively high resolution (num- referencing and/or plot locations will be an adber of phase one sample plots per county) of ditional source of error and contribute to

the TM data compared to the photo plot sam- misclassification. Further, if a classified image is

piing used in 1986. going to be used for stratification, then the ground

plot data cannot be used as an aid in the classiCONCLUSIONS AND RECOMMENDATIONS flcation, such as training sets in supervised classification.

Consistency in classification across large areas

is most important in an application such as Although the TM data classified by GAP was a

this. FIA provides estimates at many levels good source of phase one data, we need to keep

(county, unit, state, regional, and national). A looking at other sources. Since FIA is an ongosystem that does a good job of classifying forest ing project, new data for stratification are

land in one part of the state but not another or needed on a periodic basis, especially in areas

classifies one forest type but not another may where the forest landscape is changing. GAP

significantly bias estimates for some users. The does not currently have the long-term commitapproach we used here, to identify only a few ment that FIA needs. The classified data being

strata (four) and produce estimates for rela- produced by EPA's Landscape Characterization

tively small areas (counties or groups of coun- in the Environmental Monitoring and Assessties), was selected to reduce any inconsisten- ment Program (EMAP) are one possible source

cies in classification that could exist in the data of classified data that should be considered. We

due to scene differences across the state, need to continue to search for cooperators who

are interested in land cover classification at or

One major advantage with this approach to strati- above the state level on a continuous basis. FIA

fication is that it enabled us to conduct a strati- needs to continue to develop its own capabiliflcation completely blind of the ground plot loca- ties to classify remote sensing data. Because it

tions, removing a source of bias that could not is such a large effort to gather the data and do

26
this kind of classification cooperators will Loetsch, F.; Hailer, K.E. 1964. Forest invenalways be needed in these efforts, tory, volume I: statistics of forest inventory and information from aerial photoAs inventories are repeated, the identification graphs. Munchen: BLV Verlagsgesellschaft,

of changes in classification will provide addi- 436 p.

tional strata and improve our estimates of

change. If strata for forest to nonforest and Scott, J.M.; Davis, F.; Csuti, B.; Noss, R.;

nonforest to forest change over time can be Buterfield, B.; Groves, C.; Anderson, H.;

created, then estimates of land use change over Calcco, S.; D'Erchia, F.; Edwards, T.C., Jr.;

time will benefit. Other strata that help in the Ulliman, J.; Wright, R.G. 1993. Gap analyestimation of growth, removals, and mortality sis: a geographic approach to protection

may also be possible, of biological diversity. Wildlife Monograph. 123: 1-41.

LITERATURE CITED

UNESCO. 1973. International classification

Anderson, J.R.; Hardy, E.; Roach, d.; Witmer, and mapping of vegetation. Paris, France:

R. 1976. A land use and land cover elassi- UNESCO. 35 p.

flcation system for use with remote

sensor data. Prof. Pap. 964. Washington, ABOUT THE AUTHORS

DC: U.S. Geological Survey. 9.8 p.

Mark H. Hansen is a Research Forester and

Cochran, W.G. 1977. Sampling techniques. Daniel G. Wendt is a Supervisory Computer

New York: John Wiley. 413 p. Specialist at North Central Research Station,

USDA Forest Service, St. Paul, MN.

27
FOREST/NON-FOREST STRATIFICATION IN GEORGIA WITH

LANDSAT THEMATIC MAPPER DATA

William H. Cooke

ABSTRACT.--Geographically accurate Forest Inventory and Analysis

(FIA) data may be useful for training, classification, and accuracy

assessment of Landsat Thematic Mapper (TM) data. Minimum expectation for maps derived from Landsat data is accurate discrimination

of several land cover classes. Landsat TM costs have decreased

dramatically, but acquiring cloud-free scenes at optimum seasons for

vegetation discrimination is still problematic. FIA plot locations

determined from hand-held GPS units can vary + 5-20 m. Landsat

pixels can also vary + 25 m. These spatial inaccuracies restrict the

use of pixels on feature edges and decrease the usefulness of plots

that have split conditions. Current research at the USDA Forest

Service's, Southern Research Station involves aggregating forest

types in the lab based on field plot measurements of dominant, codominant, and intermediate trees. We believe this methodology is

most appropriate for tying FIA field plot data to the satellite imagery.

We are testing methodological approaches for image processing that

can satisfy the dual goals of repeatability and timeliness.

INTRODUCTION stratification. Remote sensing also plays an

important role as a tool for providing timely

Typically, remote sensing efforts at the South- information on natural disasters and for getting

ern Research Station (SRS) of the USDA Forest information about forest conditions in inaccesService have focused on large area estimates of sible areas. These applications of remote

forested and non-forested lands. Proportions of sensing should not be overlooked for funding.

forested and non-forested lands within pixels of

Advanced Very High Resolution Radiometer BACKGROUND

Data (AVHRR) have been predicted using high

resolution Landsat Thematic Mapper (TM) data Achieving the goal of providing the land cover

in a multiple regression scenario (Zhu and classes of interest requires classification of

Evans 1994). To date, Forest Inventory and large amounts of TM data with FIA field plots

Analysis (FIA) managers have asked remote serving as the basis for classification and

sensing analysts What can you do for me? with verification of those classes. Acquisition of large

respect to rapid large area analysis for simplis- amounts of TM data is much less costly now

tic land cover conditions. I believe that our that Landsat 7 has been successfully deployed.

partners and cooperators in the Southern Full-scene (185 km x 185 km) costs have

Annual Forest Inventory System (SAFIS) want decreased from $4,000 to $600 per scene.

more than delineation of forested from non- However, acquiring cloud-free scenes at optiforested lands and attendant acreage calcula- mum seasons for vegetation discrimination is

tions. At a bare minimum, we should be able to still problematic. Also problematic are the

discriminate among broad land cover classes radiometric differences between adjacent

including pine, hardwood, scrub, grass, culti- scenes. Figure 1 illustrates the radiometric

vated, and inert. As a remote sensing analyst, differences that exist among four full TM

my question to FIA is What can you do for me? scenes in the Piedmont of Georgia.

Or, how can plots taken under an annual

inventory system be used to train and validate Investigation by Zhiliang Zhu (U.S. Geological

remotely sensed data to produce useful maps? Survey, EROS Data Center, pers. comm.)

Remote sensing efforts that benefit FIA should indicates that normalizing the radiometric

extend well beyond Phase I estimates for components of adjacent TM scenes before

28
k ain

/ Plot

,/

228_ _ Subplot

,(_ /// 255.8fl \

P

93.5fl (1 Landsat TM pixel)

I

Figure 1._Radiometric differences for four Figure 2._Comparison of the FIA plot design

winter scenes in Georgia. with _at TM data.

classification can result in up to a 50 percent ment require a model more like the one shown

loss in reflectance characteristics important for in figure 3. Each subplot falls close to a pixel

automated classification efforts. At SRS, classi- center, but GPS coordinates are accurate to + 5

fication is done before scenes are mosaicked m to + 20 m depending on averaging techtogether. Although this approach maximizes niques, tlme of acquisition, number of satellites

classification accuracy on a scene-by-scene acquired, and overhead "line-of-sight" (Rockwell

basis, it can result in a discontinuity of classifi- Avionics 1996). Each TM pixel's registration to

cation results between adjacent images. Higher its "real-world" location is assumed to be about

per scene accuracy results from this methodol- + 1 pixel (28.5 m) in relatively fiat terrain,

ogy, but a more visually pleasing map product possibly greater in steeply dissected terrain.

results from pre-classification scene normaliza- ' Misregistration errors can be additive in a

tion and mosaicking. Ultimately, there is a worst-case scenario (20 m + 28.5 m = 48.5 m).

trade off between higher map accuracy versus The reality is that the main plot falls somemore aesthetically pleasing map products, where within a 25-pixel window. The full

Managers and data users should be educated

about this aesthetic problem if maximum

information content is the desired outcome.

Scientists at SRS are currently examining the

usefulness of FIA plot variables for training and \

for verifying Landsat TM imagery. SAFIS inven- O i (f^,_/

tories employ Global Positioning System (GPS) /v,..y_

receivers to acquire geographic coordinates for "

the center plot of the four-plot cluster design |

(Rockwell Avionics 1996). Figure 2 illustrates l

how this four-plot cluster compares to a ninepixel window of Landsat TM imagery.

On the surface, the correspondence between 1 = pixel mismgistration

the FIA plot design and Landsat TM data 2 = maximum GPS misr_gistration

appears conveniently located within this ninepixel window. But misregistration of the imagery and sources of error in the GPS measure- Figure 3.--Pixel and GPS misregistration problems.

29
cluster of four plots falls within a 7 x 7 pixel RECOMMENDATIONS

window or greater. This translates into roughly

an 1 l-ac (4.4 ha) ground area. This inherent Work is needed to determine which plot vari-

"slop" in location makes training of automated ables are appropriate for training and verifying

classifiers and accuracy assessment proce- TM classifications. Current efforts involve

dures more difficult. These spatial registration aggregating from individual tree data for domiproblems will likely restrict the use of pixels on nant and co-dominant species. We believe that

feature edges and limit the potential usefulness the satellite *view-from-above" makes these

of plots that have split land cover conditions, crown classes most likely to provide useful

The possibility of deriving an edge class is information for modeling land cover. Plot-level

being investigated by SRS scientists. Classiflca- variables like forest type are subject to fieldtion techniques being used for wall-to-wall TM level interpretation. A forest type designation

efforts in Georgia are variations on methods calculated in the lab from the dominant and

used by Coppin and Bauer (1994) and Cooke co-dominant members of a stand is more likely

(1991). to be representative of crown reflectance in

TAM data and is easily reproducible in the lab.

Classification Techniques

LITERATURE CITED

1. Stratify TM scenes by physiographic/

ecological condition. Cooke, W.H. 1991. Use of temporal Landsat

2. Use National Wetlands Inventory data to data for discrimination of selected forest

mask wetlands, species composition group. Mississippi

3. Use Census data to mask high-density State University. M.S. thesis.

urban areas.

4. ALlow low-density urban areas to be classi- Coppin, P.R.; Bauer, M.E. 1994. Processing of

fled. multitemporal Landsat TM imagery to

5. Use differential highway masks, optimize extraction of forest cover

6. Use edge detection spatial filtering algo- change features. IEEE Transactions on

rithms to locate and eliminate some edge Geoscience and Remote Sensing. 32(4):

pixels before classification. 918-927.

7. Classify the data using these TM channels:

a. Raw data channels 3, 4, and 5 Rockwell Avionics. 1996. Precision Lightb. Ist Principal Component weight GPS Receiver PLGR+96 and PLGR

c. Brightness and Greenness components FEDERAL Precise Positioning Service

of the Kauth-Thomas transformation 0PPS). Operations and Maintenance

d. Ratio of channels 3 and 4 (NDVI). Manual.

8. Classify 75 classes using unsupervised

techniques to reduce class variance. Zhu, Z.; Evans, D.L. 1994. U,S. forest types

9. Aggregate in classes (Pine, HW, Brush, and predicted percent forest cover from

Inert, at a minimum), then iteratlvely re- AVHRR data. Photogrammetric Engineerclassify if necessary, ing and Remote Sensing. 5:525-53 I.

10. Aggregate classes by following methods

developed by Llnda Garnett for her Master's ABOUT THE AUTHOR

Thesis.

I I. Use a 5 x 5 majority scan to filter out "salt William Cooke is a Research Forester, Southern

and pepper" pixels. Research Station, USDA Forest Service, Forest

12. Assess accuracy/refine classifications for Inventory and Analysis program, StarkviUe,

areas > 25 pixels using FIA plots. MS.

13. Assess supervised classifications for accuracy with FIA plots for cross validation.

3O
COMPARISON OF THREE ANNUAL INVENTORY DESIGNS, A

PERIODIC DESIGN, AND A MIDCYCLE UPDATE DESIGN

Stanford L. Arner

ABSTRACT.--Three annual inventory designs, a periodic design, and

a periodic measurement with midcycle update design are compared

using a population created from 14,754 remeasured Forest Inventory

and Analysis plots. Two of the annual designs and the midcycle

update design allow updating of plots using sampling with partial

-_ replacement procedures. Individual year and moving average estlmates are determined. The moving average estimates are compared

to both the population means of the most recent year used in the

average, and to population averages covering the same period as the

estimate. Comparisons for net cubic-foot volume per acre and annual

change in volume are based on root mean square error (RMSE) and

estimator bias. Among annual designs, the rotating panel design

produced the smallest RMSE for volume. For multiple year comparisons, the rotating panel and periodic designs resulted in the smallest

RMSE's, while for single year comparisons, the periodic design resulted in the smallest RMSE. For annual change, the smallest

RMSE's were produced by the periodic design, while among annual

designs, the rotating panel design resulted in the smallest RMSE's for

multiple year comparisons, and the rotating panel and balanced

annual partial remeasurement designs resulted in the smallest

RMSE's for single year comparisons.

ABOUT THE AUTHOR

Stanford L. Arner is a Supervisory Forester at

the Northeastern Research Station, USDA

Forest Service, Newtown Square, PA.

31
SAFIS AREA ESTIMATION TECHNIQUES

Gregory A. Reams

ABSTRACT.--The Southern Annual Forest Inventory System (SAFIS)

is in various stages of implementation in 8 of the 13 southern states

served by the Southern Research Station of the USDA Forest Service.

Compared to periodic inventories, SAFIS requires more rapid generaLion of land use and land cover maps. The current photo system for

phase one area estimation has changed little over the last four decades and provides area estimates within the precision requirements

of the FIA program. A stated goal of the national FIA program is to

eventually replace photo Interpretation with digital satellite classification because the photo system cannot produce maps of forest and

nonforest area, and it takes an enormous amount of time to photo

interpret the phase one photo plots. Using automated classification

procedures for TM satellite data, we anticipate that the time to complete phase one will decline and wall-to-wall maps will be available.

In the interim period of switching to satellite data, the photo system

must be modified to provide current estimates of inventory. A method

being used by Southern FIA is documented.

INTRODUCTION Second, it takes a considerable amount of time

(up to a year per state) to photo interpret an

Historically, the Southern FIA program has entire state. A stated goal of SAFIS and the

produced forest area estimates using a varia- national FIA program is to eventually replace

tion of double sampling. The process consists photo interpretation with digital satellite classiof interpreting a large number of sample plots fication to address the two shortcomings of the

on aerial photographs and subsampling a photo system. In the interim, the following

proportion of the plots on the ground. The photo-based procedures are being used to

aerial photo sampling is referred to as phase estimate forest area under the annual invenone and is used to estimate the percent of the tory system in the Southern FIA program.

total area occurring in forest and nonforest

subpopulations. The phase two samples are the USING DOUBLE SAMPLING FOR AREA

FIA ground plots that provide the basic ESTIMATION

mensurational data used to further stratify

forest area and estimate timber volume, Currently the Southern FIA is using a double

growth, mortality, and removals, sample to estimate forest area. Frayer and

Furnival (1999) provide a chronology of how

The southern Annual Forest Inventory System double sampling for stratification has been

(SAFIS) requires rapid generation of land use implemented nationwide by FIA. Cochran

and land cover maps for the southern United (1977) and De Vries (1986) provide statistical

States. The current photo system for phase one references for estimation when employing a

area estimation has changed little over the last double sample. Estimates of timberland area

four decades and provides area estimates are based on forest and nonforest interpretawithin the precision requirements of the FIA tion of a large number of plots on aerial photos

program. The photo method does have two and a smaller sample of ground plots. There is

shortcomings for the annual Inventory pro- approximately 1 photo plot per 230 acres

gram. First, although the photo method can across the South. The current definition of

provide estimates of forestland down to the forest in FIA is land I acre in size, 120 feet

county level, the method cannot produce maps wide, and at least 10 percent stocked by forest

of forest and nonforest area and distribution, trees of any size, or formerly having had such

32
tree cover and not currently developed for C, = no. of plotsphotointerpretedasnonforestbutare forest

nonforest use. total number of plots photo interpreted as nonforest

The photo interpretation points are arranged in

a 5 x 5 grid where one of the photo plots is Assuming the following confusion matrix (table

spatially coincident with a phase two ground 1),

plot. The current phase two ground plots are

conceptually distributed on a 3 x 3 mile grid. In

addition to the double sample that occurs with Table 1.raTio-way contingency table where the

each FIA ground plot, there are intensification diagonal elements represent correct c!assiflplots arranged on a 3 x 6 mile grid. Intensifica- cations as compared to ground truth, and

tion plots are used to increase sample size for the off-diogonal elements represent plots

forest and nonforest ground truth type calls, with incorrect photo classification

An intensification plot is simply a spatially

coincident photo plot and field truth location. Grounds Photo Photo Total

This results in 173.6 ground plots and 86.8 plots interpreted interpreted

intensification plots (used to correct area forest nonforest

estimates) per million acres. Combined, there

are 260.4 (173.6 + 86.8) ground reference Forest 108 2 110

samples per million acres and 4,347.8 photo Nonforest 3 81 84

interpretation points per million acres. Thus, Total 111 83 194

an approximate 6 percent field sample of the

photo plots is used to form the double sample

estimate of forest area.

the proportion of forest area ( p' ) is then

A quick review of how forest area is determined estimated as,

J

using a periodic survey will naturally lead into

modifications necessary to operate under the (no. points forest xCI) + (no. points nonforest x C,)

new annual panel system. For detailed refer- _ =

ence information on the new annual panel total no. points photo interpreted

system, see Reams and Van Deusen (1999) and

Roesch and Reams (1999). To estimate forest

area, several types of information are needed. Assuming that 1,962 photo points were interFirst, the total area estimate and census water preted as forest and 1,288 photo points interestimate by county are obtained from the U.S. preted as nonforest for a total of 3,250 interCensus Bureau. Census land is computed as preted points, the proportion of forest area

censuAs total area minus census water. Forest would be estimated as,

area fAr) is then computed as:

Aj = Pj" x A, (11 pj = (1962 x .973).024)325 + (12880 x =.5969

where Proportion of nonforest is simply 1- jD;.

_'. = (P[ x Cf ) + (P, x C,) Forest area {equation 1)is thus equal to 0.5969

x A t. The variance of forest area is determined

and, At is census land area, as follows,

A

Pf is th_ proportion of phase one photo plots in cr2 (_,.) (/3i.)(/3,) (Cf_C,)2 + forest. Pn is the proportion of phase one photo - '

plots in nonforest, and n

(c,)(1-

number of plots correctly photo interpreted forest m_ m 2

CI = totalnumber of plots photo interpreted forest

where, /_y = proportion forest, _ = proportion

and, nonforest, Cf = proportion correctly classified

forest, C n = proportion incorrectly classified

33
nonforest, m, = number of forest field locations, Because new photography is available on about

rn 2= number of nonforest field locations, and n a 5-year cycle, all photo plots will be inter-

= number of photo points interpreted. Inserting preted as available, and will be completed in

values for each variable results in, one calendar year. There will be 52 ground

truths per million acres available per year, and

this will be too small a sample size for reliable

[ ] estimation at the county level because the

or"(/37)= (.6037)(.3963) (.973 -.024)" + ('6037)2 (.973)(1 -.973) +

3250 110 average size of counties in most states in the

(.3963)-"(.0241)(1-.0241)q 84 J The Southreason rangesforfromstating200,000 that theto 350,000 sample size acres.for

a panel {year) is too small for reliable estimation at the county level, is because the offdiagonal elements of the misclassification

Completing the above mathematical operations, matrix will often be zero.

o2(/3 • ) -- 0.00019729 and the s.c. of the estimatel is 0.014. One remedy for this situation is to estimate

forest area at the multi-county level based on

MODIFICATION OF CORRF, CTION FACTORS 100 percent measurement of phase one photo

FOR _AIL SYSTIgM interpretations but only 20 percent of the

phase two ground plots (truths). Counties must

Because the Southern FIA program is depen- be aggregated such that the misclassiflcation

dent on the National Aerial Photography Pro- matrix for the multi-county area has at least

gram (NAPP) for acquisition of 1:40,000 scale 200 ground truths per year. This number of

photography that is flown on an approximate ground truths usually ensures that the off5-year cycle, new photography will not be diagonals are not zero.

available on an annual basis. For the Southern

annual survey, only one-fifth of the present If one were to develop a correction factor on one

number of phase two ground plots are panel (20 percent of the ground plots) of data, a

remeasured In any one year. If relying solely on reasonable recommendation to group counties

the FIA ground plots for the double sampling either by immediate adjacency, percent forest,

estimate of forest area, this results in approxi- or percent change in forest area. Because

mately 35 ground truths per million acres. The misclassificatlon errors are more often related

Southern FIA program has additional ground to percent forest area, this option is being

truths arranged on a 3 x 6 mile grid, and if we developed and Is favored by Southern Station

assume one-fifth of these plots are visited every analysts. Using western (survey unit 1) Tennesyear, this equates to approximately 17 ground see as an example, aggregation of counties

truths per million acres. Thus, in any given totaling at least 2.5 million acres is.recomyear, 52 ground truths per million acres are mended (fig. I). The resulting misclassification

available for forest area estimation, matrix and correction factors are then applied

,_19 ;_26 .4!

Figure l.--Examp/e of how to group counties in survey unit I, Tennessee for development of correction factors based on one panel of data. Shaded counties are aggregated for a common correction

factor.

34
individually to each county. Thus, a set of spatial resolution compared to AVHRR (Wynne

counties will have the same set of correction et al. 2000).

factors applied to each county's unique estimate of percent forest area from the phase one To estimate map class area totals and varisample. In a recent application, counties were ances, we use two-phase or double sampling,

aggregated by survey unit and percent forest where the less accurate data are the map

area class with class 1 (0-35 percent), class 2 whose accuracy is in question and the more

(36-55 percent), class 3 (56-65 percent), class 4 accurate but costly data are the FIA ground

(66-75 percent), and class 5 (76-100 percent), plot. The less accurate data are complete in

Forest area by county is determined then as that each map pixel has been classified.

(percent forest land x census land) as given

previously in equation (i). A sampling scheme designed to evaluate and

correct for map area misclassification is as

If estimating area based on multiple panels follows: A sample of n points/pixels is located

(years) of ground truths, there is the need to on the map, and the true and map categories

either assume that previous panel ground are determined for each point. The n points are

truths are correct, or revisit those plots or a allocated as a simple random sample. This

subset of the previous panel of plots, since the results in a two-way contingency table where ng

forest/nonforest classification may have is the number of points in the sample whose

changed. Because of cost factors, the assump- true category is i and whose map category isj.

tion made is that previous year panels or a

subset of the previous year panel plots will not There is an important difference between using

be visited. Assuming the same amount of field satellite-derived maps and aerial photos for this

effort every year, the number of counties or the process. The satellite-derived thematic map

land area that must be aggregated each year allows us to know the actual map marginal

for calculation of correction factors is cut in probabilities, which can be used as additional

half in year two, a third in year three, and so constraints in a maximum likelihood estirnaon. By the time all five panels have been mea- tion process (Card 1982, Van Deusen 1996,

sured, we are back at the intensity of ground Reams and Van Deusen 1999). This reduces

truths analysts are accustomed to under the the variance of estimates of true map category

periodic system. As many analysts well know, proportions. Formulas for estimating the true

even with I00 percent (all five panels) measure- "probabilities of interest are given in Card

ment of ground plots, small counties are often (1982), along with variance estimates. Methods

combined because of small sample sizes. The for estimating change in category proportions

need to combine counties for correction factors and variances between two times are given in

is thus a one-time adjustment with implemen- Van Deusen (1994). The estimators for the true

tation of the five panel system, map proportions are the same for simple

random sampling or stratified sampling of map

USING SATELLITE DATA FOR AREA pixels. However, variance estimates are differF__T_TION ent under the two sampling strategies.

As stated earlier, the goal of SAFIS and the In a pilot study in central Georgia, a winter

national FIA program is to eventually replace cloud-free TM scene was classified and statistiphoto interpretation with digital satellite classi- cal estimates of forest and nonforest area were

fication to address the two shortcomings of the derived using the methods of Card (1982).

photo system. An area estimation method that Estimates of percent forest and nonforest from

replaces photo interpretation with digital the classified TM scene compare quite favorably

satellite data follows, to FIA survey cycle six estimates of forest area

based on the photo method. The FIA cycle six

The three most commonly used satellite sen- survey for central Georgia indicates that 68.7

sors are the Landsat Thematic Mapper (TM), percent of survey unit 3 is in forest fThompson

the French Systeme Probatoire pour 1989), and the estimate based on TM data

l'Observation de la Terre (SPOT), and Advanced indicates that 69.4 percent of the scene is

Very High Resolution Radiometer (AVHRR). We forest (Reams and McCollum, in review). Varihave concentrated on the use of TM classifica- ance estimates are comparable to those derived

tions because TM has greater spectral resolution relative to SPOT and better spectral and

35

J:

.-._
m

from the photo-based method that uses stan- Roesch, F.A.; Reams, G.A. 1999. Analytical

dard double sampling for stratification statisti- alternatives for an annual inventory

cal estimates. With the wall-to-wall TM classifi- system. Journal of Forestry. 97(12): 33-37.

cation, it is important to use the known map

marginals to reduce variance of the final esti- Thompson, M.T. 1989. Forest statistics for

mates (Card 1982, Reams and Van Deusen Central Georgia, 1989. Resour. Bull. SE1999). 105. Asheville, NC: U.S. Department of

Agriculture, Forest Service, Southeastern

LITERATURE CITED Forest Experiment Station. 51 p.

Card, D.H. 1982. Using known map category Van Deusen, P.C. 1994. Correcting bias in

marginal frequencies to improve esti- change estimates from thematic maps.

mates of thematic map accuracy. Photo- Remote Sensing of the Environment. 50:

grammetric Engineering and Remote Sens- 67-73.

ing. 48(3): 431-439.

Van Deusen, P.C. 1996. Unbiased estimates of

Cochran, W.G, 1977. Sampling techniques, class proportions from thematic maps.

New York: Wiley. 413 p. Photogrammetric ]Engineering and Remote

Sensing. 62(4): 409-412.

De Vries, P.G. 1986. Sampling theory for

forest inventory, New York: Springer- Wynne, R.H.; Oderwald, R.G.; Reams, G.A."

Verlag. 399 p. Scrivani, J.A. 2000. Optical remote sensing for forest area estimation. Journal of

Frayer, W.E.; Furnlval, G.M. 1999. Forest Forestry. 98{5): 31-36.

survey sampling designs: a history.

Journal of Forestry. 97(12): 4-10. ABOUT THE AUTHOR

Reams, G.A.; Van Deusen, P.C. 1999. The Gregory A. Reams is a Supervisory Mathematisouthern annual forest inventory sys- cal Statistician, Southern Research Station,

tern. Journal of Agricultural, Biological, Forest Inventory and Analysis Program, USDA

and Environmental Statistics. 4(4): 346- Forest Service.

360.

The author thanks Bill Bechtold, Ray Sheffield,

Reams, G.A.; McCollum, J.M. (In review), and Mike Thompson for review and suggestions

Estimates of forest area using thematic that improved the manuscript.

map data. Asheville, NC: U.S. Department

of Agr/culture, Forest Service, Southern

Research Station.

36
DIAMETER GROWTH MODELS USING FIA DATA FROM

THE NORTHEASTERN, SOUTI-IRRN, AND

NORTH CENTRAL RESEARCH STATIONS

Veronica C. Lessard, Ronald E. McRoberts, and Margaret R. Holdaway

ABSTRACT.--Nonlinear, individual-tree, distance-independent annual diameter growth models are presented for species in two

ecoregions defined by R.G. Bailey in the northern Lake States and in

parts of the central and southern regions of the U.S. The models were

calibrated using Forest Inventory and Analysis (FIA) data from undisturbed plots on land classified as timberland across all ownership

categories. The data were generally from stands of mixed age and

mixed species. The dependent variable is average annual diameter

growth and the independent variables include crown ratio, crown

class, stand basal area, stand basal area larger than the subject tree,

physiographic class, and latitude and longitude of plot locations. The

models have minimal bias and may be recalibrated easily to include

new data sets.

INTRODUCTION

Province 212

In response to the 1998 Farm Bill, formally __---_ _....... _/" :_

known as the Agricultural Research, Extension, _)____ fl_ l_

and Education Reform Act, the North Central I!_ _-___ _!_ .,_l_;_

Research Station (NCRS) of the USDA Forest ,_,__ ,_<j_ __

Service, has developed an annual inventory __--_, -_P_'_/ ,_-w'7_ ,_,_

system featuring a hexagonal grid system of "__,t_,_//6_

FIA plots to be measured in 5-year inventory Province 222"

cycles, with 20 percent of the plots to be measured each year (Brand et o.1. 2000). Diameter

growth models for individual trees provide a Figure I .--Ecoregions of North America (Bailey

method to update the information on FIA plots et al. 1994).

not measured in the current year.

A study was conducted to develop individual- U.S. side of the U.S./Canadian border falls

tree, distance-independent, diameter growth within the northern half of the Lake States.

models using FIA data collected in previous Province 212 is a subdivision of the Warm

inventories. The criteria for the models, cali- Continental Division and is characterized by

brated for major species groups within two snowy, cold winters and warm summers. Most

ecoregions of the central part of the United precipitation occurs in the summer but is

States, were that they would produce minimal plentiful throughout the year. Province 212 is

bias in their estimates and be of a form that a transition zone between the boreal forest and

can be recalibrated easily with inventory data the broadleaf deciduous forest zones, and its

collected under the annual system, habitant species include members of both

zones.

ECOREGION PROVINCES

The Eastern Broadleaf Forest Province is a

The diameter growth models were calibrated for subdivision of the Hot Continental Division.

two ecoregions: the 212-Laurentian Mixed Most precipitation in Province 222 occurs

Forest Province and the 222-Eastern Broadleaf during the growing season and generally

Forest (Continental) Province defined by Bailey decreases in quantity and adequacy as dis-

(I 995) (fig. I). The western contiguous portion tance from the Atlantic Ocean increases. This

of the Laurentian Mixed Forest Province on the province favors drought-resistant oak-hickory

37
associations. Province 222 lles to the east of The data were split into two databases used to

the prairie regions, south and west of Province calibrate and validate the models. Every fourth

212 in the northern areas, and west of the plot was systematically assigned to the validaAppalachian Mountains in the southern re- tion database. The remaining 75 percent of the

gions: It extends from the Minnesota/Canadian plots were used for calibration of the models.

border in the north to Missouri and Tennessee Associated plot information was retained with

in the south, each tree record, and tree records were sorted

into species groups.

DATA

Diameter at breast height (DBH) is used as the

The diameter growth models were calibrated major predictor variable in the growth model

using FIA data from undisturbed plots on land for predicting change in DBH. The average

classified as timberland over all ownership observed change in DBH, calculated as the

categories. Timberland is defined as non- ratio of the difference in DBH measurements at

reserved forestland that is producing or is the two inventories and the number of years

capable of producing 20 cubic feet of industrial between inventories, is the dependent variable.

wood per year. The FIA survey design and data Both individual tree and plot variables are

collection are described by Hansen et al. considered for use as growth predictors. Indivi-

(1992). ArcView GIS was used to overlay dual tree variables include initital crown ratio

Bailey's ecoregion map on the FIA plot loca- (CR) and initial crown class (CC). Crown ratio is

tions to select plots within each of the prov- the percentage of total tree height that is crown

inces. Growth models for Province 212 were and is assigned in FIA data to one of nine catecalibrated using FIA data from the following gories, where each of the category values 1-8

states (the numbers in parentheses refer to the represents 10 percent interval widths and the

year of the inventory}: Michigan (1966, 1980, final category represents 81-100 percent.

1993}, Wisconsin (1968, 1983, 1996), and Crown class is recorded in FIA data in five

Minnesota (1977, 1990, 1993). Models for categories ranging from nonsuppressed to

Province 222 were calibrated using data from suppressed.

parts of Michigan (1966, 1980, 1993), Wisconsin (1968, 1983, 1996), Minnesota (1977, 1990, Variables related to the plot information

1993), Illinois (1962, 1985, 1998), Indiana include BA, BAL, physiographic class (PC),

(1967, 1986, 1998), Iowa (1974, 1990), Ohio latitude (LAT), and longitude (LNG). Informa-

(1978, 1990), Missouri (1972, 1989), Kentucky tion about competition within the stand is

(1974, 1987), and Tennessee (1989, 1996). given by BAL and BA, while physiographic

class gives information related to site soil and

Trees included in the calculation of total plot water conditions that affect site productivity.

basal area per acre (BA} and total plot basal Latitude and longitude are surrogates for

area per acre greater than the subject tree climatic conditions in the regional models.

(BAL) for time 1 were restricted to all living

trees recorded on the plot in the first inventory. MODELING METHODOLOGY

Trees included in the calculation of BA and

BAL at time 2 were restricted to the remea- Mathematical Form of the Diameter

sured trees that were alive and included in the Growth Model

plot at time I and still alive at time 2. Plot

variables associated with individual trees were The form of the diameter growth models is the

maintained with each tree record. The data product of two components, an average DBH

were sorted by species and the diameter growth growth model and a modifier. The average

models were fit to the data. Only remeasured model is based on a two-parameter gamma

trees that were alive during both inventories probability density function using DBH as the

were used to calibrate the individual-tree independent variable to predict diameter

diameter growth models. For states with data growth rates. The modifier is a product of

from three inventories, data were extracted exponentials, each of which incorporates a

separately for growth periods from the first to single additional independent variable. The

the second inventory and from the second to modifier gives greater accuracy and precision to

the third inventory, and the two growth periods the growth model by allowing the predicted

were treated as separate observations, growth values to increase or decrease from

38
those given by the average model. The form of function to a power function or an exponential

the diameter growth model is function, respectively. Of the two functions, the

power function generally provided the better fit,

A DBH = AVE(DBH) * MOD(X ,,..., X 7 ) , (la) based on the mean square error (MSE) of the

resulting fits of the models to the data.

where

In addition to the independent variables used

AVG(DBH) = fl_exp(-fl2DBH ) DBH _', (lb) in the diameter growth models (DBH, CR, CC,

BA, BAL, PC, LAT. and LNG), a number of other

and independent variables were explored for inclu7 sion in the diameter growth models by

MOD(X,, ..., X7) = I-I ef,¢x_ , (ic) Holdaway (2000). These included the number

_=_ of trees per acre, average stand diameter, and

the ratio of DBH to average stand diameter.

The X_s and the f_s are defined in table 1. The Our goal was to build diameter growth models

functions incorporate the difference of the that would explain the most variability while

observed and average ecosystem values of the using the smallest number of variables (and

variables. The greater the deviation of the associated parameters) to achieve a parsimoniobserved variable's value from the average ous model. No variable was included in the

ecosystem value, the greater the impact that model if the asympto,tic 95 percent confidence

variable has on the model prediction. The interval for the parameter estimate associated

values, -90 and 46, in the functions 6 and 7 with the variable included zero. Additionally, a

are the average values of longitude and latitude comparison criterion, given by Linhart and

in Province 212 data. The average longitude Zucchini (1986), accounting for both an estiand latitude values are -88 and 40, respec- mate of the MSE and the number of estimated

tively, for Province 222. parameters was used to determine the variables for inclusion in the models.

The parameterization of the gamma probability

density function used in the average compo- Weighted Regression

nent (lb) of the model is a simplified form of

that given by Johnson and Kotz (1970). In the Heterogeneity in the variation of the residuals

formulation, 132serves as the scale parameter "was adequately addressed using linear regresthat defines the spread of the distribution, and sion to find a functional form relating standard

the shape parameter, Ba' establishes the deviation of the residuals to predicted growth

peakedness of the curve. The parameter, _, is as (McRoberts et al. 2000):

a multiplier to better adjust the model fit to the

data either upward or downward in conjunc- E[In(6)] = o_ + a21n(ADl3H) , (2)

tion with the rates of average annual growth.

The parameter estimation routine failed to where E(.) represents the statistical expectaconver_e for some species. For those species, tion, AD_H is the average predicted annual

either ]32or _a was set to 0 in separate calibra- diameter growth for predicted diameter growth

tions of the model, thus changing the gamma classes, _ is the standard deviation of the

Table I .raThe functional forms for variables used in the modifier (the exponential of the sum of these

functions) are presented

Variables i Functionalform

f,(X)

Crown ratio 1 134* (CR- 4)

Plot basal area larger than the subject tree 2 exp (13s * (BAL- 50)) - 1

Plot basal area 3 138* (BA- 100)

Crown class 4 137* (exp (CC/3)- 2.718)

Physiographic class 5 13e* (PC- 5) + 139* (PC- 5)2

Longitude 6 p,0*(LNG+90)

Latitude 7 13.*(LAT- 46) , _

39
residuals for predicted diameter growth RESULTS

classes, and the as are parameters to be estimated. Parameter Estimates

Bias Assessments Model parameter estimates for l 0 of the most

frequently occurring tree species in the calibraThe fit of the models was verified using the tion data sets for Province 212 and Province

calibration data set, while the data from the 25 222 are given in tables 2 and 3, respectively.

percent of all plots initially set aside were used Variables in the modifier component of the

to validate the models. Verification and valida- model not found to be important for a particutlon were carried out by applying the models to lar species were assigned parameter values of

the data. Predicted average annnual growth 0, which is analogous to multiplying the model

was compared with actual average annual by 1.

growth over the interval between measurements. The resulting residuals (observed minus Model Verification and Validation

predicted average annual growth for the interval between measurements) from the calibra- The results of the analysis of the residuals,

tlon and validation data were analyzed sepa- calculated as the difference of the observed and

rately, predicted annual diameter growth values for

the observations in the calibration and validaAnnualized residual medians, median ratios, tion data sets, are given in tables 4 and 5,

standard deviations, and r 2 values were calcu- respectively. Negative values of the median

fated to examine the prediction bias of the residuals and relative bias indicate overestimamodels. Relative bias presents the median bias tion of the models.

as a percent of the median observed growth

and gives perspective to the importance of bias CONCLUSIONS

from a biological viewpoint. Relative bias was

calculated as 100 percent times the ratio of the For most species, observed average annual

median annualized growth residual to the change in DBH ranges from 0.05 to 0.15 inches

median annualized observed growth. The r 2 per year, and DBH is measured to the nearest

values are calculated as the square of the 0.1 inch. The values of the median residuals for

coefficient of correlation between the annual- the models applied to the calibration and

ized predicted and observed values of diameter validation data for Province 212 and Province

growth. 222 show the models are relatively unbiased,

with median overestimation generally near or

less than 0.01 inch per year. The median bias

Table 2.--Parameter estimates for the anmta! diameter growth models Ha-c) are given for the i0 most

.frequently occurring speAci_s groups in the 212-Laurentian Mixed Forest Province. Variables to

_ which the parameters, fl_-flzz, correspond are given in .the column headings.

Species ^ ^ A A ^ ^ ^ ^ A A

DBH DBH DBH CR BAL BA CC PC PC2 LNG LAT

Softwtxxls

Blackspruce 0.0432 0.0321 0.2194 0.1952 0 0 -0.0447 -0.1514 0 0 0

Balsamfir 0.0497 0.0829 0.6517 0.1402 0 -0.0012 -0.0589 -0.1125 0 -0.0354 -0.1074

Tamarack 0.0333 0 0.4205 0.1570 0 0 -0.1069 -0.1262 0 0 0

N. white-cedar 0.0326 0.0202 0.4353 0.1575 0 -0.0005 -0.0667 -0.0895 0 -0.0113 -0.0586

Hardwoods

N. redoak 0.0724 0 0.2237 0.0349 -0.0029 0 -0.1447 0 0 -0.0246 -0.1090

Hardmaple 0.0293 0.0566 0.8377 0.0959 0 -0.0010 -0.1955 0 0 -0.0215 -0.1595

Softmaple 0.0509 0.0167 0.4651 0.1281 0 -0.0014 -0.1274 0 -0.0307 -0.0544 -0.1363

Blackash 0.0535 0 0.2278 0.1323 0 -0.0006 -0.1042 -0.0727 0 -0.0295 -0.1598

Quakingaspen 0.0902 0.0333 0.3930 0.1144 -0.0023 0 -0.1084 0 -0.0280 -0.0261 -0.0940

--_ paperbirch 0.0639 0 0.1284 0.1330 -0.0021 0 0 0 -0.0616 -0.0565 -0.2244 , ,,,,,,

4O
Table 3.mParameter estimates for the annual diameter growth models (1 a-c) are given for the I0

most frequently occurring species groups in the 222-Eastern Broadleaf Forest (Continental)

Province. Variables to which the parameters, _,-_,, correspond are given in the column headings.

A A A A A A A A A

Species it, it2 133 _, _s _, It, _,o i3,,

DBH DBH DBH CR BAt CC PC LNG LAT

Hardwoods

Select white oak 0.0658 0.0105 0.3150 0.0372 -0.0031 -0.1959 0.1441 0.0264 -0.0200

Other white oak 0.0813 0 0.1103 0.0575 -0.0025 -0.2107 0.1509 0.0823 0

Select red oak 0.0910 0.0163 0.3374 0.0422 -0.0022 -0.1773 0.1458 0.0242 -0.0087

Other red oak 0.0871 0.0290 0.3832 0.0685 -0.0036 -0.1378 0.1145 0.0485 -0.0082

Select hickory 0.0630 0 0.1656 0.0523 -0.0018 -0.2359 0 0.0197 0

Other hickory 0.0522 0.0253 0.4692 0.0809 -0.0016 -0.2380 0.3244 0.0246 0.0683

Hard maple 0.0643 0.0547 0.5735 0.1021 -0.0035 -0.1963 0.1204 0.0157 0

Soft maple 0.0841 0.0313 0.5007 0.0793 -0.0026 -0.1842 0.1006 0 -0.0365

White and

green ash 0.0621 0.0340 0.5834 0.0972 -0.0013 -0.1673 0 0.0282 0

Elm 0.0428 0.0535 0.8250 0.1166 -0.0015 -0.0541 0.1033 0.0522 0.0829

Table 4.--Residual analysis of the diameter growth models fit to FIA calibration (Cal) and validation

(Val) data from the 212-Laurentian Mixed Forest Province

Species Observations Median Standard Relative r_

residual deviation bias

Cal Val Cal Val Cal Val Cal Val Cal Val

Number In / yr In/ yr Percent

Softwoods

Black spruce 5,921 2,184 -0.006 -0.00.5 0.036 0.034 -13.2 -13.3 0.297 0.266

Balsam fir 5,916 1,974 -0.008 -0.005 0.051 0.051 -11.0 -6.8 0.383 0.413

Tamarack 2,572 986 -0.011 -0.009 0.050 0.045 -20.0 -15.5 0.294 0.287

Northern

white-cedar 7,496 2,497 -0.006 -0.005 0.040 0.041 -10.0 -8.9 0.280 0.288

Hardwoods

Northern red oak 2,900 991 -0.009 -0.010 0.060 0.055 -7.0 -7.8 0.299 0.388

Hardmaple 7,214 2,551 -0.006 -0.005 0.054 0.056 -7.2 -6.0 0.354 0.339

Soft maple 6,487 2,075 -0.008 -0.006 0.061 0.065 -7.3 -6.3 0.328 0.303

Blackash 3,591 1,311 -0.006 -0.003 0.041 0.040 -10.5 -5.7 0.207 0.202

Quaking aspen 12,264 4,033 -0.008 -0.007 0.066 0.066 -5.7 -5.0 0.239 0.228

Paper birch 5,981 1,763 -0.007 -0.006 0.046 0.043 -9.8 -9.3 0.173 0.185

is generally within 15 percent of the median investigate incorporation of climatic data such

observed growth rate, as given by the relative as temperature and precipitation. This inforbias (tables 4 and 5). Some larger percentages mation should contribute to better fitting

are due to the small median observed growth models by explaining more of the uncertainty.

rates in the denominators of the relative bias

ratios for slower growing species. AC]C.qO_FL]SI)GM]I_I¢I"S

The low values of the squared coefficients of We want to thank Doug Grifflth of the Northcorrelation, r 2, indicate that much unexplained eastern Research Station and Joseph Glover of

variation remains. This is not surprising when the Southern Research Station for kindly

one considers that the data, used in both the providing FIA data from Kentucky and Ohio

calibration and validation of the models, were and from Tennessee, respectively. We would

generally collected on uneven-aged, mixed- also like to thank Mark Nelson and Dan Wendt

species stands that range over an entire eco- for their suggestions and assistance in applying

system. Future work on these models will Arc-lnfo to this research. 41
Table 5.--Residual analysis of the diameter growth models fit to FIA calibration (Cal) and validation

(Val) data from the 222-Eastern Broadleaf Forest (Continental) Province

,, ,, , ,,, , ,,

Species Observations Median Standard Relative r2

residual deviation bias

Cal Val Cal ,, Val Cal Val .... Cal Val Cal Val

- Number In/yr In/yr Percent

Hardwoods

Select white oak 5,726 1,784 -0.007 -0.010 0.061 0.061 -5.7 -8.4 0.326 0.349

Otherwhite oak 1,708 475 -0.005 -0.003 0.052 0.051 -6.3 -3.6 0.291 0.315

Select red oak 2,185 717 -0.013 -0:010 0.082 0.086 -8.7 -6.0 0.215 0.223

Other red oak 1,474 1,628 -0.009 -0.015 0.073 0.074 -6.4 -10.7 0.239 0.258

Select hickory 1,400 396 -0.008 -0.004 0.049 0.058 -10.0 -4.9 0.238 0.266

Other hickory 1,616 535 -0.006 -0.008 0.053 0.054 -7.8 -9.4 0.358 0.362

Hard maple 2,317 764 -0.007 -0.007 0.069 0.067 -8.8 -7.6 0.365 0.333

Soft maple 1,602 466 -0.013 -0.018 0.103 0.110 -8.8 -13.8 0.304 0.260

White and

green ash 1,685 451 -0.010 -0.008 0.087 0.082 -6.8 -5.6 0.270 0.289

Elm 1,256 401 -0.011 -0.010 0.088 0.098 -12.2 -10.0 0.372 0.360

LITERATURE CITED tories in Minnesota. In: Hansen, M.H."

Burk, T.E., eds. Integrated tools for natural

Bailey, R.G. 1995. Description of the resources inventories in the 21st century:

ecoreglons of the United States. 2d ed. proceedings of the IUFRO conference; 1998

Revised and expanded. Misc. Publ. 1391. August 16-20; Boise, ID. Gen. Tech. Rep.

Washington, DC: U.S. Department of NC-212. St. Paul, MN: U.S. Department of

Agriculture, Forest Service. With separate Agriculture, Forest Service, North Central

map at 1:7,500,000. Research Station: 507-514.

Bailey, R.G.; Avers, P.E.: King, T.; McNabb, Johnson, N.L.; Kotz, S. 1970. Continuous

W.H., eds. 1994. Ecoregions and subre- unlvariate distributions. New York:

gions of the United States (map). Wash- Houghton Mifflin.

ington, DC: U.S. Geological Survey. Scale

1:7,500,000, colored. Prepared for the Linhart, H.; Zucchini, W. 1986. Model selecUSDA Forest Service. tion. New York: John Wiley.

Brand, G.J.; Nelson, M.D.; Wendt, D.G.; McRoberts, R.E.; Holdaway, M.R.; Lessard, V.C.

Nimerfro, K.K. 2000. Technical aspects of 2000. Comparing the STEMS and AFIS

the hexagon/panel system. In: growth models with respect to the uncerMcRoberts, R.E.; Reams, G.A.; Van Deusen, tainty of predictions. In: Hansen, M.H.;

P.C., eds. Proceedings of the Ist annual FIA Burk, T.E., eds. Integrated tools for natural

symposium; 1999 November 2-3; San resources inventories in the 21st century:

Antonio, T3L Gen. Tech. Rep. NC-213. St. proceedings of the IUFRO conference; 1998

Paul, MN: U.S. Department of Agriculture, August 16-20; Boise, ID. Gen. Tech. Rep.

Forest Service, North Central Research NC-212. St. Paul, MN: U.S. Department of

Station: 8-13. Agriculture, Forest Service, North Central

Research Station: 539-548.

Hansen, M.H.; Frieswuk, T.; Glover, J.F.; Kelly,

J.F. 1992. The Eastwide forest inventory ABOUT THE AUTHORS

data base: users manual. Gen. Tech. Rep.

NC-151. St. Paul, MN: U.S. Department of Veronica C. Lessard is a Research Forester and

Agriculture, Forest Service, North Central Ronald E. McRoberts and Margaret R.

Research Station. Holdaway are Mathematical Statisticians at the

North Central Research Station, USDA Forest

Holdaway, M.I_ 2000. The AFIS tree growth Service, St. Paul, MN.

model for updating annual forest inven42
EVALUATING IMPUTATION AND MODELING

IN THE NORTH CENTRAL REGION

Ronald E. McRoberts

ABSTRACT.--The objectives of the North Central Research Station,

USDA Forest Service, in developing procedures for annual forest

inventories include establishing the capability of producing annual

estimates of timber volume and related variables. The inventory

system developed to accomplish these objectives features an annual

sample of measured field plots and techniques for updating data for

plots measured in previous years. This paper describes and evaluates

the feasibility of updating techniques and compares the bias and

precision of the annual estimates they produce. The analyses indicated that simple, plot-level imputation and modeling techniques

produced adequately unbiased and precise estimates of basal area

per acre for large area estimates.

INTRODUCTION have been considered. The simplest approach

is to use the data from the 20-percent panel of

The Renewable Forest and Rangeland Re- plots measured in the current year. Although

sources Planning Act of 1978 requires that the these estimates reflect current conditions, their

USDA Forest Service conduct inventories of precision may be unacceptable for some variforest land in the United States to determine its ables due to the small annual sample size. An

extent and condition and the volume of stand- alternative is to use the data for all plots

ing timber, timber growth, and timber remov- obtained from the five most recent panels of

als. Passage of the Agricultural Research, measurements and employ a moving average

Extension, and Education Reform Act of 1998 "estimator. This alternative increases precision

further requires that the Forest Service con- because data for all plots are used for estimaduct annual forest inventories in all states with tion; the disadvantage is that the estimates do

20 percent of plots to be measured in each not reflect current conditions but rather a

state each year. moving average of conditions over the past 5

years. A third approach is to update to the

Forest Inventory and Analysis (FIA) precision current year data for plots measured in previstandards (USDA-FS 1970) require a sampling ous years and then base estimates on the data

intensity of one plot for approximately every for all plots. If the updating procedures are

6,000 acres in the North Central region. To unbiased and sufficiently precise, this alternasatisfy this requirement, the geographical tive provides nearly the same precision as the

sampling hexagons established for the Forest average of all plots but without the adverse

Health Monitoring Program (White eta/. 1992) effects of using out-of-date information. Two

were divided into 27 smaller FIA hexagons, categories of updating techniques, imputation

each containing approximately 5,937 acres. An (Rubin 1987) and modeling, are of general

equal probability grid of field plots, designated interest and were evaluated using a specially

the Federal base sample, was constructed by created annual database of tree information.

establishing a plot in each FIA hexagon. The

Federal base sample was systematically divided ANNUAL DATABASE

into five interpenetrating, non-overlapping

panels. Each year the plots in a single panel ObServations of the same 101,398 trees on

are selected for measurement with panels 5,086 FIA plots for both the 1977 (Spencer

selected on a 5-year, rotating basis. 1982) and 1990 (Miles et al. 1995) Minnesota

inventories were used to evaluate the updating

At least three approaches to calculating annual techniques. These plots represent approxiFIA estimates from the Federal base sample mately 14.7 million acres of timberland. (In an

43
FLA context, timberland is defined as forest growth obtained from individual tree diameter

land that is capable of producing in excess of growth models (McRoberts and Lessard 2000).

20 ft 3 per acre per year of industrial wood crops Although these procedures create greater

under natural conditions and that is not uniformity in annual DBH growth than would

as_iated with urban or rural development be observed, the effects of differences between

(Miles et o3. 1995).) Plots included in the 1977 actual and calculated growth are expected to

inventory were measured between 1974 and have minimal impact on evaluations of the

1978; plots included in the 1990 inventory updating techniques. Alternatives would rewere measured between 1986 and 1991. These quire either annual measurement or destrucplots are termed variable radius plots due to tive sampling of all trees. The former alternathe use of point sampling techniques that tive would be prohibitively expensive and would

select trees with probability proportional to risk the masking of actual DBH growth by DBH

cross-sectional area rather than proportional tO instrument measurement error; the latter

the frequency of occurrence in the population alternative would be prohibited by landowners

(Myers and Beers 197 I). Thus, the number of ff not also ecologically disastrous.

trees in the population represented by a sample

tree, termed the tree factor, varies by tree and Evaluations of the updating techniques were

is calculated as a scaling constant divided by based on plot basal area per acre (BA), a varithe square of the tree diameter. Tree factors are able representing the sum, scaled to a per acre

used to expand the measurements of sample basis using tree factors, of the cross-sectional

trees to per unit area estimates, areas of live tree boles at breast height. _Calculation of unbiased estimates of change in

Based on observations of the individual trees, basal area per acre (ABA) is difficult using data

an I l-year database of annual diameters at from variable radius plots (Van Deusen et aL

breast height (DBH) (4.5 ft) and annual status 1986). One technique fixes tree factors at the

with respect to survival, mortality, and harvest time of the first measurement and bases estifor each tree was created. Construction of the mates of ABA on the increase in the crossdatabase required distributing total growth sectional areas of surviving trees and losses in

between inventories over varying numbers of BA due to mortality. This technique excludes

years for individual trees in each of three contributions to &BA of new trees entering the

categories: (I) trees alive at both inventories; (2) sample. A second technique recalculates tree

trees that died between inventories due to factors at every measurement, thus allowing

causes other than harvest; and (3) trees that new trees entering the sample to contribute to

were harvested between inventories. For trees the ABA estimates. However, recalculation of

alive in both inventories, average annual DBH tree factors excludes contributions to ABA of

growth was calculated by dividing the total the growth of surviving trees, because the

growth in DBH over the measurement interval product of their cross-sectional areas and tree

by the number of years between measure- factors remains constant. A consequence of

ments. Measured DBH for the 1977 inventory both techniques is that ABA is underestimated.

was assigned to year 0, and DBHs for the 10

subsequent years were calculated by adding Although complex approaches to unbiased

the average annual growth to the previous estimation of ABA using variable radius plots

year's DBH. For trees that died due to causes have been proposed, they were not considered

other than harvest, a year of mortality between for this study because evaluation of the updat1 and I0 was randomly selected and assigned ing techniques did not require absolutely

to the tree Independently of years of mortality precise _BA values. For this study, the conassigned for other trees on the same plot. For stant tree factor technique was selected beharvested trees, a year of harvest between I cause it incorporates the growth of surviving

and 10 was randomly selected and assigned to trees, a primary interest in the construction of

the tree but with the provision that all trees these updating techniques. Therefore, using

harvested on the same plot were harvested in

the same year. For both mortality and harvested trees, measured DBH for the 1977

inventory was assigned to year 0, and DBHs for _ Unless otherwise noted, all future references

subsequent years up to the year of mortality or to basal area (BA) and annual change in basal

harvest were calculated by adding previous area (ABA) are understood to be on a per acre

year's DBH and predictions of annual diameter basis.

44
the database of annual tree diameter values related to both previous year's BA and to the

and tree factors corresponding to the year 0 current survival, mortality, or harvest status of

DBHs, BA was calculated each year for each trees on the plot. Thus, based on the annual

plot, and ABA was calculated each year for status of trees, aU plots were placed into one of

each plot as the difference between BA for the three categories: (1) survival (no mortality or

current and previous years, harvest); (2) mortality (at least one mortality

tree); and (3) harvest (at least one harvested

UPDATING TECHNIQUES tree). There were no plots in the 1990 inventory

data that had experienced both mortality and

Both imputation and plot-level models were harvest since the 1977 inventory. For each

investigated as a means of updating data for category of plots, a simple model of the relaplots measured in previous years. Imputation tionship between ABA and previous year's BA

for this application was a three-step process: was selected, and its parameters were esti-

(1) plots measured in the current year were mated using weighted regression techniques

placed into similarity groups; (2) plots mea- (table 1). In practice, the annual survival,

sured in previous years were matched to a mortality, and harvest status of plots will not

group of similar plots measured in the current be known. Thus, models for predicting the

year; and (3) values from the group of similar status of plots were also developed. First, all

plots measured in the current year were se- plots in the annual database were ordered with

lected to replace missing values for plots respect to previous year's BA and then placed

measured in previous years. For this applica- into groups of 250 consecutive plots beginning

t_ion, plots were grouped on the basis of simi- with the plot with the lowest previous year's

larity in previous year's BA. The groups were BA. For each group, the proportions of plots in

created by first ordering all plots measured in the survival, mortality, and harvest categories

the current year with respect to previous year's were calculated. Simple models of the probBA and then creating groups of 20 consecutive abilities of survival, mortality, and harvest were

plots beginning with the plot with lowest then selected, and their parameters were

previous year's BA. Plots measured in previous estimated using maximum likelihood proceyears were then matched to a group of plots dures (table 1). With this technique, hereafter

measured in the current year on the basis of referred to as PREDICT, the survival, mortality,

previous year's BA, whether it was obtained as and harvest status of each plot measured in a

a measurement or as an updated estimate. For "previous year was predicted using random

each plot measured in a previous year, a plot numbers and the status models. Then, given

was randomly selected with replacement from the predicted status, ABA for the plot was

the group of 20 similar plots measured in the predicted using the ABA models.

current year, and the latter plot's average

annual ABA since last measurement was Although model predictions of survival, mortalimputed to the former plot; this technique is ity, and harvest status were expected to be

hereafter referred to as IMPUTE. unbiased, the combined effects of their uncertainties and those of the ABA predictions risked

Two model-based updating techniques were increasing the variability of the annual mean

also investigated. For both modeling tech- estimates around the means of the annual

niques, ABA for a plot was assumed to be database values, the standard errors of these

Table 1.--The mode/s

Prediction Category Modelform

Change in annual basal area, hBA Survival E(z_BA)=I3111-exp(I32BA])

Change in annual basal area, ABA Mortality E(ABA)=I31+I32BA

Change in annual basal area, ABA Harvest E(ABA)=I_l+I32BA

Change in annual basal area, _BA Disturbed E(ABA)=I_1+I32BA

Annual probability, Psu_, Survival E(P.,,_,)=exp(_IBA_2)

Annual probability, Pr.o. Mortality E(Pm_)=l-exp(131BA_2)

Annual probability, Ph._, Harvest E(P,._,)=I-E(Ps_,)-E(Pmo,)

45
means, or both. Thus, a second model updat- timberland acres in Minnesota. For each of 250

ing technique, based on the assumption that simulations, 2,476 plots from among the 5,086

satellite-based remote sensing techniques can timberland plots were randomly selected to

be used to accurately detect plots that have mimic the annual inventory intensity of 5,937

experienced substantial disturbance, was acres per plot. Each simulation was initiated

developed (Befort 2000). Disturbance for this with a simulated complete inventory of the

technique may be due to either mortality or 2,476 timberland plots by beginning with the

harvest; no distinction is made. Disturbance annual database year 0 values. On a rotating

using remote sensing techniques can be confi- basis, 20 percent of these plots were selected

dently detected for plots satisfying two criteria: for measurement each year. Simulated meaprevious year's BA.>.30 ft2/acre, and (ABA/BA)< surement of a plot consisted of replacing its

-0.3 (Befort, pers. comm.2). Using the annual estimated BA value with the value for the

database values, a simple model of the relation- appropriate year in the annual database. For

ship between ABA and previous year's BA for the remaining 80 percent of plots for which

plots satisfying these criteria was selected, and measurement was not simulated in the current

its parameters were estimated using weighted year, data were updated using each of the three

regression techniques (table 1). With this inventory techniques. Each year, the mean BA

technique, hereafter referred to as REMOTE, across all plots and the standard error of the

updating again involves prediction of both mean were calculated for each updating techstatus and ABA. First, plots measured in nique and for annual database of values; the

previous years that satisfied the remote sensing latter estimates were designated TRUE. Followdisturbance detection criteria were identified, ing the simulations, the median values of the

and their ABA was predicted using the model distributions of the annual means and the

constructed for this technique. For the remain- standard errors of the means were determined

ing plots measured in previous years, survival, for each technique.

mortality, and harvest status and _BA

were predicted in the same manner as for the RF._ULI_

PREDICT technique. However, considerably

fewer plots required status prediction with the Evaluations of the updating techniques enREMOTE technique, tailed comparing the median values for the 250

simulations of the estimated means of annual

For both modeling techniques, the uncertainty BA across all plots and the standard errors of

due to the residual variation around the esti- the means obtained using the three updating

mated ABA curves was incorporated into the techniques to the corresponding annual means

ABA predictions. For each estimated curve, and standard errors obtained from the annual

distributions of the residuals for narrow cat- database values. A comparison of the TRUE

egories of predicted ABA were estimated. In and IMPUTE annual means revealed that the

application, whenever a value of ABA was imputation technique produced estimates that

predicted, a corresponding residual from the exhibited negligible bias with respect to the

appropriate distribution was randomly gener- TRUE values (Table 2). In addition, the similarated and added to the prediction. Thus, the ity between the TRUE and IMPUTE standard

estimates of standard errors of mean BA estl- errors indicated that the IMPUTE technique

mates obtained using the model updating quite accurately estimated the uncertainty in

techniques include the uncertainty of the the TRUE means. A comparison of the median

model predictions due to residual variation, values of the TRUE, PREDICT, and REMOTE

annual means revealed that neither modeling

SIMIJI_TII_G THE _l_VrollT technique exhibited conspicuous bias (table 2).

A comparison of the median standard errors of

The feasibility of the updating techniques and the means indicated that both modeling techthe bias and precision of their annual BA niques adequately estimated the TRUE stanestimates were evaluated by using the annual dard errors. As expected, the variability of the

database as the basis for simulating the pro- PREDICT annual means around the TRUE

ceas of annually inventorying the 14.7 million means was greater than for the REMOTE

means.

2William Befort, Division of Forestry, Minne- A further comparison of the updating techsota DNR, November 9, 1999. niques was made by calculating the root mean

46
Table 2.--Median values of annual means and standard errors of means for 250 simu!o_tions

Year TRUE IMPUTE PREDICT REMOTE

Mean SE Mean SE Mean SE Mean SE

1 56.24 0.74 56.12 0.76 56.38 0.73 56.09 0.74

2 55.53 0.73 55.23 0.75 55.39 0.73 55.22 0.73

3 54.07 0.73 54.28 0.75 54.34 0.72 53.88 0.73

4 53.14 0.72 53.41 0.74 53.30 0.72 52,93 0.72

5 51.64 0.72 52.14 0.73 52.16 0.72 51.64 0.72

6 50.80 0.72 51.31 0.72 51.31 0.71 50.75 0.72

7 49.72 0.72 49.66 0.72 50.05 0.71 49.55 0.72

8 48.48 0.72 48.73 0.72 49.12 0.71 48.47 0.71

9 45.97 0.72 47.28 0.72 47.75 0.71 46.59 0.71

10 45.24 0.72 45.91 0.73 46.54 0.71 45.52 0.71

square error of the squared deviations of the REMOTE technique produced somewhat better

updated annual means from the corresponding results, the quality of the means obtained with

TRUE annual means for years 5-10 (table 3). the modeling and imlsuting techniques relative

The first 4 years were excluded in this compari- to the TRUE means was similar. Third, because

son, because annual means for these years 5-year _BA is usually small compared to BA 5

retained a component of the year 0 complete years in the past and because the uncertainty

inventory. The resulting 5th percentile, median, in Z_BApredictions is small compared to the

and 95th percentile values for distributions of natural variation in BA among plots, _BA

root mean square errors indicated that the appears to be an appropriate quantity to use as

differences between the IMPUTE and REMOTE the basis for updating. Fourth, but less conclumeans were small with respect to root mean sively, a combination of disturbance detection

square deviation, although the REMOTE re- using remote sensing procedures and model

suits were somewhat better than the IMPUTE predictions of survival, mortality, and harvest

results. The similarity of results for these •status appeared to be a better alternative than

updating techniques may be partially attrib- using only model predictions of status. Finally,

uted to the large area represented by the additional testing is appropriate to determine if

aggregation of data over this large number of these large area results hold for smaller numplots; it is yet to be determined if these results bers of plots representing smaller timberland

hold for smaller areas, areas.

CONCLUSIONS LITERATURE CITED

Several conclusions emerged from these analy- Befort, W. 2000. Change detection, stJmtiflcases. First, the simple, plot-level updating tion, and mapping for continuous forest

techniques were not only feasible, but they inventory. 1999 proceedings of the Statisproduced acceptable estimates of both annual tics and the Environment Section; annual

BA means and standard errors of the means for meeting of the American Statistical Associalarge area estimates. Second, although the tion; 1999 August 8-12; Baltimore, MD.

Table 3.--Root mean square deviatiorL" updated versus TRUE annual means

Statistic TRUE IMPUTE PREDICT REMOTE

5th percentile 0.00 0.58 0.66 0.29

Median 0.00 0.68 1.01 0.33

95th percentile 0.00 0.77 1.34 0.54

47
Alexandria, VA: American Statistical Asso- projections of t_mber supply. Resour.

ciation. Bull. NC-57. St. Paul, MN: U.S. Department

of Agriculture, Forest Service, North Central

McRoberts, I_E.; Lessard, V.C. 2000. Estimat- Research Station.

ing uncertainty in annual forest inventory estimates. 1999 proceedings of the USDA Forest Service (USDA-FS). i970. OperaStatistics and the Environment Section; tional procedures. For. Serv. Handb.

annual meeting of the American Statistical 4809. I I, Chapter I0: I I. I -i--I I. I-3.

Association; 1999 August 8-12; Baltimore, Washington, DC: U.S. Department of

MD. Alexandria, VA: American Statistical Agriculture, Forest Service.

Association.

Van Deusen, P.C.; Dell, T.R., Thomas, C.E.

Miles, P.D.; Chen, C.M.; Leatherberry, E.C. 1986. Voblme estimation from perma1995. Minnesota forest statistics, 1990, nent horizontal points. Forest Science.

revised. Resour. Bull. NC-158. St. Paul, 32(2): 415-422.

MN: U.S. Department of Agriculture, Forest

Service, North Central Research Station. White, D.; Kimerling, J.; Overton, S.W. 1992.

Cartographic and geometric components

Myers, C.C.; Beers, T.W. 1971. Point sampling of a global sampling design for environand plot sampling compared for forest mental monitoring. Cartography and

inventory. Res. Bull. 877. August, 1971. Geographic Information Systems. 19: 5-22.

West Lafayette, IN: Purdue University,

Agriculture Experiment Station. ABOUT THE AUTHOR

Rubtn, D.B. 1987. Multiple Imputation for Ron McRoberts is a Mathematical Statistician

nonr_ponse in surveys. New York: Wiley. and Group Leader with the Forest Inventory

and Analysis program of the North Central

Spencer, J.S., Jr. 1982. The fourth Minnesota Research Station, USDA Forest Service, St.

forest inventory: timber volumes and Paul, MN.

48
ANNUAL FOREST INVENTORY: AN INDUSTRY PERSPECTIVE

Roger Lord

ABSTRACT.raThe Forest Inventory and Analysis Program serves

important public interests by providing credible data for informed

public forest policy debates as well as feedback to the forest-based

economic market. This feedback, which affects timber price expectations, helps ensure resource sustainability by promoting better

investment decisionmaking within the forest products sector.

Industry's use of FIA data is illustrated by the types of analysis

performed by Boise Cascade Corporation. Key needs include more

timely and consistent data across all forestlands, improved spatial

resolution, and integration of socioeconomic variables that affect

timber availability. Concerns about the implementation of the annual

inventory program required by the 1998 Farm Bill, particularly in the

western states, are discussed.

IMPORTANCE OF FOREST INVENTORY South, it is the impact of chip mills and clearINFORMATION cutting of hardwood stands as well as wetland

protection that has recently drawn the most

After two Blue Ribbon Panel reports and count- attention. Wetlands, endangered species, and

less other discussions, it should by now be forest sustainability are important issues in the

unnecessary to point out the value of the Lake States, while forest fragmentation, acid

Forest Inventory and Analysis program in rain, and loss of wildlife habitat are important

monitoring the status and health of the in the Northeast. Finally, on a national scale,

Nation's forests. The program represents the the U.S. must address the forest sustaJnability

only continuous inventory system that quanti- .issue through the internationally accepted

ties the condition of forest ecosystems across forest sustainability criteria and indicators.

the United States. The critical information

provided by the program can promote informed Objective forest resource data from the FIA

discussion of public forest policy issues and program are essential to developing a factcan serve as the basis for sound business based, intelligent discussion of these issues.

decisions within forest industry. Yet, FIA data are useful only when they are

current, consistent, and reliable. When they

Public Forestry Issues are not, public policy debate quickly degenerates into emotions, perceptions, and opinions.

The FIA program provides the information base

upon which we can intelligently address impor- Sound Business Decisions

tant public issues such as forest sustainability,

ecosystem health, land use, and timber policy. Business decisions within the forest products

sector are often strongly influenced by expectaIntense public debate rages in virtually every tions of future timber prices. The decision to

region of the country over forestland use and add new manufacturing capacity, or reconmanagement. In the coastal Pacific Northwest, figure or close existing facilities, for example, is

the spotted owl controversy, old growth protec- essentially an analysis of the expected cost

tion, and threatened and endangered species competitiveness of the facility and return on

continue to focus attention on the management investment. Fiber costs, which can be up to 80

_ of both public and private timberlands. In the percent of the variable costs of production of

_ Inland Northwest, forest health and sustain- some wood products, are key to this analysis.

ability, roadless areas, and endangered species As another example, determining the appropri-

=,i::i issues dominate. In the Rocky Mountain ate level of investment in productivity-enhanc-

: :!i i States, forest health issues such as the decline ing forest management practices also hinges on

i;:_)_: of aspen stands in Colorado are debated. In the

_ 49
expectations of future timber prices. Whether a Northwest, Lake States, and Southern U.S. on

particular silvicultural practice earns an a regular basis. In the Northwest and Lake

adequate f'mancial return is highly dependent States, we have developed a projection system

on the expected increase in value at harvest, called Dynamic Forest Simulator TM to project FIA

Price expectations provide the industry with tl_e plot data for up to 20 years using a combinamarket feedback signals it needs to make tion of stand table projection and individual

adjustments to demand (e.g., via changes in tree diameter growth and mortality modeling.

mill capacity or development of technology to The current model covers California, Oregon,

decrease fiber use) and supply (e.g., through Washington, Idaho, and Montana as well as

silvicultural investments, genetic research, or North Dakota, Minnesota, Wisconsin, and

development of new supply sources) to main- Michigan. Users can select sample plots based

tain competitive raw material costs, on geographic location and plot characteristics,

update the pl0t data to the current year, and

Price expectations depend on changes in the project into the future by simulating historical

available supply of fiber relative to demand, and projected harvest levels. Harvest can be

Accurate projections of future changes in specified by owner, species, and diameter class,

timber prices and availability, in turn, are or it can default to historic harvest patterns.

dependent on accurate, current resource data. Using a different approach in the U.S. South,

To the extent that data are out-of-date and we have looked at the impact of increased

misleading, price expectations will be inaccu- silvicultural intensity, urbanization and forest

rate and inappropriate business decisions may fragmentation, and wetland and coastal zone

be made as a result, regulations on future available supply versus

biological inventory.

There are ob_ious public benefits to good

business decisions within the industry. Ques- In addition to regional studies, we also undertionable business investments, such as adding take more detailed modeling in the woodnew manufacturing capacity in an already baskets where we operate. For example, we

supply-constrained woodbasket, may have a have developed linear program-based resource

negative impact not only on the individual allocation models for our southern woodcompany involved, but also on resource baskets in which FIA data are used as the

sustainability. Thus, it is in the public interest basis for the supply side of the model. The

as well as the industry's interest to maintain demand side is based on an individual mill

current resource inventories, database of the primary wood processors

within the woodbasket and surrounding area.

HOW ]BOISE _E USES FIA DATA FIA data are used to predict supply to each mill

from within and outside of the designated

Current Uses woodbasket. Marginal wood cost curves (supply

curves] are also developed to predict delivered

To understand how FIA data are used by wood cost to each mill.

industry, it may be helpful to review the use of

that data within one company. Boise Cascade FIA data also support investment analysis

Corporation has timberlands and manufactur- efforts, including financial analysis of both

ing operations in Washington, Oregon, Idaho, manufacturing investments, and timberland

Minnesota, Louisiana, and Alabama. Thus, it and silvicultural investments. For example, we

has direct business interests in FIA data from use FIA data to analyze potential fiber supply

four of the five program regions. Current use of and develop delivered fiber cost estimates for

FIA data within the Timberland Resources proposed manufacturing facilities. FIA data

department at Boise Cascade can be broken also help set the timber price scenarios for our

down into four general categories: timberland planning models by which we

develop our silvicultural and harvesting plans.

Broad regional resource monitoring

t Detailed woodbasket modeling Another use of FIA data is in the assessment of

t Support of investment analysis the impact of regulatory changes on timber

Regulatory impact assessment supply. In the West, much attention has been

focused on the scenario analysis in which we

To better understand our general operating looked at the impacts of alternative degrees of

environment, Timberland Resources under- riparian and endangered species restrictions on

takes regional resource studies for the Pacific

5O
available timber supplies. In the South, we Vegetative Growth Stage (e.g., Medium Tree,

have examined the implications of wetland and Multi-Story) that describes stand structure.

coastal zone management regulations using The resulting acreage matrix describes the

FIAdata. habitat contribution of company lands and can

be projected over time using Boise Cascade's

Future Uses forest planning models. Company fee land,

however, is only one component of the ecologiThere are at least two areas of analysis in cal landscape. For example, in Idaho, Boise

which we would like to be able to use FIA data Cascade owns 200,000 acres of the 5.9 million

in the future. The first is more spatially explicit acre Southern Idaho Batholith ecoregion. To

timber supply analysis. The FIA sample plot fully describe the landscape and depict the

framework is only pseudo-spatial at best. contribution of company lands within the

Currently, we know only that a plot is repre- context of the entire landscape, we need similar

sentative of forested acres somewhere in the data across other ownerships within the ecocounty, but we don't know where in the county logical region. Aside from access issues, data in

the represented acreage is. Higher spatial the detail acquired on company lands would be

resolution to forest resource data is needed to prohibitively expensive to collect across all

better address issues of availability such as the acres. As an alternative, FIA data, particularly

impacts of urbanization and land use regula- augmented with more spatially explicit remote

t.ion. Better spatial resolution of the data would sensing data, could provide the information

also be more helpful in regions with large base for this type of l_irge landscape analysis.

counties and more scattered forest cover such

as the Inland Northwest. KEY DATA NEEDS

A second area of future need is data to support Current, Consistent, and

landscape analysis. Boise Cascade has com- Comprehensive Data

pleted ecosystem management projects in

Idaho, Washington, and Minnesota and has Current, consistent, comprehensive FIA data

successfully classified its fee-owned land base are required to meet the challenges of addressinto an ecological classification scheme known ing public forest policy and business issues.

as an ecosystem diversity matrix (Haufler et at Data older than 5 years are simply not credible

1996). This system classifies land into Habitat .with decisionmakers or the public. Table I

Type Class (e.g., Warm-Dry Douglas-fir) to shows the age of FIA data Boise Cascade

describe the potential vegetation type and regularly uses in analysis. On an acreageTable 1 .--Age of key FIA data used by Boise Cascade

Area oi

timberland Latest Previous Approximate

State (Thousand acres) survey survey midpoint

Alabama 21,932 1990 1982 1986

Louisiana 13783 1991 1984 1987.5

Mississippi 18 587 1994 1987 1990.5

Tennessee 13265 1989 1980 1984.5

Texas 11774 1992 1986 1989

Minnesota 14723 1990 1977 1983.5

Idaho 21427 1991 1981 1986

Oregon-Eastside 2 978 1992 1987 1989.5

Oregon-Westside 6,777 1986 1976 1981

Washington-Eastside 4,008 1992 1980 1986

Washin_lton-Westside 9,581 1991 1979 1985

Total 138,836

Weighted Avg Date 1990.8 1981.9 1986.4

Weighted Avg Age as of 1/1/2000 9.2 13.6

51
weighted basis, the average plot is over 9 years Techniques such as remote sensing and GIS

old and the average to the last period midpoint offer potential for both reducing program costs

(the relevant age for growth and removals and enhancing the spatial component of the

estimates) is nearly 14 years. Within the 13 data. More work is needed on growth and

southern states, average age to plot midpoint is change modeling so that FIA statistics can be

about 11 years. Since that time, we estimate updated through modeling and perhaps menthat the southern timber harvest has increased sured less frequently. The Farm Bill also

by 20 to 25 percent. Harvests in the Pacific requires that the FIA program develop 20-year

Northwest meanwhile have fallen by about 40 projections of forest conditions, yet it appears

percent across all ownerships, and harvesting that little work has been directed to this effort.

patterns have changed. Yet these changes are

not yet being fully reflected in FIA statistics Moving Beyond Biological Inventory

because of the long cycle times.

From an industrial perspective, I believe we

However, long cycle time is only part of the must get beyond the practice of using biological

problem. We can also no longer afford the inventory as a measure of timber supply. We

delays of sometimes up to 2 to 3 years in the can encourage this change by capturing sociorelease of FIA data and analysis once field economic aspects that relate to the availability

collection has been completed. The value of the of inventory for commercial use. The role of FIA

data decreases rapidly, perhaps exponentially, in this would be to integrate socioeconomic

with time. As has been demonstrated, particu- data into the plot database. For example,

larly by the Southern Station, technology linking plot data with measures of urbanizaallows significant improvements in data collec- tion, such as the Rural-Urban Continuum

tlon, editing, management, and release. These Codes developed by the USDA Economic Retechnologies need to be fully exploited. Further, search Service or population density statistics

allowing raw field data to sit on the shelf for 6 from the Bureau of Census, would allow users

months or more because of a lack of analysts is to take into account population and land use

difficult to understand given the cost of data pressures when analyzing resource data. It

collection and value of timely release. Stations may also be useful to develop a sub-classiflcashould be adequately staffed with analysts to tion of the timberland definition (e.g., suburban

efficiently process and release the data. timberland) to take into account factors affecting availability and to draw further attention to

Consistency within and between FIA admlnis- the availability issue.

trative and survey units is also essential, as the

Blue Ribbon Panel reports have pointed out. IMPLEMENTATION OF THE FARM BILL

Rarely in my industrial experience is FIA data

from only one survey unit or state used. We Inequity Between FIA ProgrRms in the

regularly merge data from two or more states in Eastern and Western U.S.

our analyses. Much time and frustration has

been spent uncovering the differences in data The 1998 Farm Bill addressed many of the

collection methods, definitions, and coding issues raised previously in this paper and sets

between the various FIA data sets. forth a framework for an improved FIA program. Boise Cascade, however, is concerned

Finally, it is very important that data provide a about progress to date in implementing aspects

wall-to-wall coverage across all ownerships, of the annual forest inventory program. To be

including National Forests, and all classes of sure, a great deal of progress is being made as

forested land. To understand the ecological we have seen in the papers presented at this

condition of our forest resource, we must workshop and I do not want to minimize the

necessarily have consistent data for all forests tremendous amount of work that has been

regardless of commercial availability for har- accomplished and change that has already

vest, taken place. However, Boise Cascade is particularly concerned about implementation in the

Increased Emphasis on New Techniques West and continued inadequate funding and

support from the Chiefs Office.

Several other papers in this workshop have

already covered this topic in some detail.

52
The FIA Program is currently administered by the Pacific Northwest and Pacific Southwest

five experiment stations including the South- than they are in the Southern U.S.

ern, Northeastern, North Central, Rocky Mountain, and Pacific Northwest Research Stations. Second and more importantly, tree growth is

The lion's share of funding for the FIA program only one component of change in forest ecosyshistorically has gone to the stations in the East tems and should not dictate inventory intenand particularly to the Southern and North sity. Other forest and landscape-scale change

Central Stations. Meanwhile, the FIA programs agents make the western forests potentially as

in the West (including the Pacific Northwest dynamic if not more dynamic than many

and Rocky Mountains) and the Northeast have eastern forests. These change agents include

languished for lack of funding and staff, changes in harvesting patterns, forest management systems, insect and disease outbreaks,

The Forest Service's current plan to implement fire, and urbanization and development.

the Farm Bill program only perpetuates this

inequity by adopting a base program that calls The base FIA program should be defined as

for measuring 15 percent of the sample plots that level of effort that ensures that the desired

annually in the East but only 10 percent in the national standard of statistical accuracy is

West. This arbitrary decision is more a product achieved in each region and state. The starting

of agency politics and culture than science. It point should be the annual sample size remakes little scientific or statistical sense from quired for a scientifically acceptable estimate at

the standpoint of providing the consistent and state level using only" that year's data. Science

timely base of forest resource information, should set the base program effort in each

state, not politics.

To support this decision, it has been argued

that trees grow slower in the West and there- I want to be very clear that I am not arguing for

fore change is less dynamic. This is fallacious a reaUocation of static funding from the Southreasoning on at least two counts. First, FIA ern and North Central Stations to the other

data refute this argument. Data on net annual Stations. The point of the Blue Ribbon Panel

growth and timberland acreage by region in reports as well as the Farm Bill legislation was

Powell et al. (1993) indicate that net annual that the FIA program was inadequate nationgrowth per acre of timberland is actually higher wide and needed to be raised to a higher stanin the West than the East (table 2). Per acre •dard of consistency, comprehensiveness and

growth rates on a cubic foot basis are higher in timeliness. This is true in every region. All FIA

units need to be brought up to a new, higher,

common level of performance. Thls is essentially a funding issue and there is little eviTable 2.--Growth per acre on timberland, by dence that the FIA program has received the

region called-for level of priority from the U.S. Forest

Service Chiefs Office. Despite its national

Net annual importance and broad support, the FIA proNet annual growth gram represents only about 1 percent of the

Region growth Timberland per acre

MM Ft_ MM Ac Ft_/A_r agency's budget.

Partner funding through the state forestry

Northeast 3,093 79.4 38.92 agencies is another area of concern to Boise

North Central 2,269 78.4 28.96 Cascade. Differences in agency focus and

Southeast 4,323 84.8 50.98 landownership patterns between the states in

South Central 5,509 114.5 48.10 the West and East bring into question the Great Plains 98 3.5 27.81

feasibility of reliance on state funding. For

East 15,292 360.6 42.40 example, given that 70 percent of the forest

land in Idaho is federally owned, is it reason- Intermountain 2,074 59.1 35.09 able to expect the state Department of Lands to

Alaska 270 15. I 17.90 partner with the FIA program at the same level

PNW 2,904 37.9 76.73 as another state where 95 percent or more of

PSW 1,087 16.9 64.30 the timberland is privately owned? Finally, we

West 6,334 128.9 49.13 believe that securing and maintaining consisb tent, continuous funding from 40 or more state

53
legislatures will greatly complicate the task of LITERATURE CITED

providing a stable and capable FIA program.

Haufler, J.B.; Mehl, C.A.; Roloff, G.J. 1996.

The annual FIA system holds promise to at last Using a coarse-filter approach with

bring.the program up to the level of quality species assessment for ecosystem manenvisioned by the two Blue Ribbon Panels, the agement. Wildlife Society Bulletin. 24(2):

National Association of State Foresters, and 200-208.

other stakeholders. Progress is being made but

the annual program has not yet been embraced Powell, D.S.; Faulkner, J.L.; Darr, D.R.; Zhu,

nationwide and there appear to be many Z.; MacCleary, D.W. 1993. Forest retechnical questions and funding inequities that sources of the United States, 1992. Gen.

have yet to be addressed. Industry will con- Tech. Rep. RM-234. Fort Collins, CO: U.S.

tinue to monitor closely how the Forest Service Department of Agriculture, Forest Service,

resolves these important issues and truly Rocky Mountain Research Station.

begins implementing a consistent nationwide

program. ABOUT THE AUTHOR

Roger Lord is Senior Forest Economist, Timberland Resources Planning and Development,

Boise Cascade Corporation, Boise, ID.

54
MAINE'S ANNUAL INVENTORY: STATE PERSPECTIVES

Kenneth M. Laustsen

ABSTRACT.--In 1999, Maine became the first northeastern state to

begin implementing the USDA Forest Service's annual inventory

system as directed by PL 105-185, the Agricultural Research, Exteni sion, and Education Reform Act of 1998. The Maine Forest Service,

i in collaboration with Forest Inventory and Analysis program of the

Northeastern Research Station of the USDA Forest Service is currently measuring Panel # 1, a 20-percent component of the annual

inventory design. This paper covers five major topics: the implementation plan, training and measurement progress, data analysis,

reporting goals, and conclusions.

IMPLEMENTATION PLAN and get the program jump-started in 1999. The

future expectation is for NERS to fund their 75-

The current implementation plan for Maine's percent proportion of the required 20-percent

annual inventory system, dated April 30, 1999, annual measurement expenses, based on the

grew from a meeting of the Advisory Committee U.S. Congress providing sufficient appropriaconvened by the Maine Forest Service (MFS) at tions for a 7-year annual inventory system (15

the University of Maine on November 24, 1998. percent) in the East. However, MFS does not

This meeting, attended by a wide array of intend to continue to fund the staff allocation

stakeholders, was held to outline the critical into the future.

needs for implementing the new inventory

system. Additional meetings and teleconfer- TRAINING AND MEASUREMENT PROGRESS

ences of USDA Forest Service's Northeastern

Research Station (NERS) and MFS specialists .In April 1999, two NERS sponsored and five

focused on the core variable listing and the MFS sponsored crews were trained and certidevelopment of a field guide. Then, in February fled over a 2-week period by David Alerich,

1999, the Advisory Committee reconvened to Jason Morrison, and other NERS personnel.

finalize the list of measurement variables and

: other procedural details. Following crew certification, field measurement

i of Panel # I plots began immediately In late

, This plan was fluid over the 6-month planning April. The graph on the next page displays both

!: horizon, changing as the new national mea- weekly and overall-to-date crew plot measuresurement protocols were proposed, other ment production in Maine's Panel # 1 (fig. 1).

information needs evaluated, and variable

_ tradeoffs discussed. With the initiation of Currently, 88.5 percent of plot measurements

training in April 1999, its content became have been completed for the year, and the

formalized into a study plan. measurement season is expected to conclude

around the end of November. More impo_anfly

The Advisory Committee met again in late May to date, crew production has averaged 3.3

1999 for an update on training, early measure- completed plots per week.

ment progress, potential changes, and a field

visit to a simulated measurement plot. This Panel # 1 had its fair share of start-up problems

committee is expected to become actively to overcome. In terms of crew efficiency, the

i} involv,__.d again early in the year 2000, for the greatest impact was the unavailability of the

discussion of analyses and reporting of the full complement of Panel # I plot tallysheets at

Panel # 1 measurements, the start of the measurement season. Plots

trickled in over a 2-month period, delaying

As part of the implementation plan, MFS preparatory work like landowner contacts and

:_ agreed to supply the personnel requirements increasing crew allocation inefficiencies. Also,

55
4.75 ................................

4.00 ........

3.75 ......

,a¢

Z

3.50 ........

¢.)

0 3.25 ..........

Q.

'10

• 3.O0 -

Q,

E

0

U

2.75 -

2.25 ......................................

--m--Avg. Plots/Crew/Week

2.00 - .

_Overall Avg.Plots/Crew/Week

1.75 ............. -- ...... "----_ '"' - .... '--"--'...... , " - ......

4/24 5/8 5/22 6/5 6/19 7/3 7/17 7/31 8/14 8/28 9/11 9/25 10/910/231116111201214

Measurement Week Ending Date

Figure l.--Plot measurement productivity.

data entry and analysis was handcuffed by the and its discovery at that time of the year in

unavailability of portable data recorders. When Maine caused a slight panic.

the recorders finally became available in August, 50 percent of the plots were already Two of the more controversial issues involving

measured, and MFS decided not to introduce the annual inventory system are the core plot

them at that time to maintain measurement design and the core variables. MFS supports

progress and data reliability, the goal of creating data collection procedures

for a single, consistent, and uniform framework

One additional problem cropped up late in the across all FIA units, in accordance with the

measurement season that could be described Blue Ribbon Panel recommendation (American

as a minor difficulty with major implications. Forest and Paper Association 1998). Beyond

On October 1, the NERS Quality Assurance/ that level, regions and states can each add

Quality Control Coordinator and the MFS Field other variables or data collection procedures to

Supervisor were verifying completed plots when incorporate special information needs. Maine,

they collectively realized that the real plot for example, has 1 additional mil-acre seedcount for Panel # 1 was 21 more than originally ling/sapling plot and is doing the full suite of

scheduled. This number constituted an average Forest Health Monitoring crown damage coding

week's production for the seven field crews, on all measured trees. Finally, MFS is adamant

56
that on the average, a two-person crew must I have a mandate to make a March 2000

complete a plot measurement in a 1-day visit, presentation to the Maine Legislature on Panel

Based on experiences in the first year, we are # l's progress and gross comparisons of populaalmost there, tion estimates to the 1995 periodic inventory.

No excuses will be accepted for not meeting

DATA ANALYSIS that mandate.

In September 1999, I requested a subset of CONCLUSIONS

validated plots, preferably a single FIA unit, to

begin data familiarization, graphic template In conclusion, MFS and I are confident that the

construction, and statistical testing/compari- spirit of collaboration and cooperation exson to the 1995 periodic inventory. That re- pressed and demonstrated to date will conquest mirrors a separate mandate for MFS, in tinue, and that both MFS and NERS will meet

that the State Legislature is requiring the their own unique, independent, and collective

publication of an annual inventory report. It needs in supplying new inventory information

was disappointing to learn that only very to the public.

limited data entry for completed plots had

begun by a point in the season when total LITERATURE CITED

panel measurements were 80 percent completed. Maine plot data, recorded on paper American Forest and.Paper Association. 1998.

tallysheets, were being used to beta test the The report of the second Blue Ribbon

portable data recorder programming for data Panel. Washington, DC. (Available at

entry and error checking, www.srsfla.usfs.msstate.edu/wo/brp2.htm)

One of the major issues that the Advisory USDA Forest Service, Forest Inventory and

Committee discussed in detail was the continu- Analysis. 1998. Strategic plan for forest

ing lack of remeasurement data for component inventory and monitoring. Washington,

of change analysis and trends. Only 50 percent DC. (Available at

of the plots measured in the 1995 periodic www.srsfia.usfs.msstate.edu/wo/

inventory are part of the new annual 5-year strategy setup.htm)

Panel sample. Furthermore, the change to the

core plot design meant that only the 1/24-acre USDA Forest Service, Forest Inventory and

concentric overlay of subplot # 1 in the new Analysis and Maine Forest Service. April

cluster would contain remeasured information 30, 1999. An implementation plan for

from the 1995 1/6-acre plot area. As a result, Maine's annual inventory system.

only 13 percent of the data are of remeasure- Radnor, PA. Unpublished document.

ment quality. The Advisory Committee was well

aware of this predicament and the inherent ABOUT THE AUTHOR

analytical weakness, but the alternative of

aggressively collecting additional remeasure- Kenneth M. Laustsen is a Biometrician with the

ment data on the 1995 1/6-acre plots would Maine Forest Service in Augusta, ME,

have increased measurement time beyond the

one plot/crew/day productivity threshold. All

parties agreed to recognize the lack and to

move onward with just the core plot design.

REPORTING GOALS

At this point in the inventory process of Panel

# 1, the remaining goals are pretty distinct, at

least from my viewpoint:

• Complete data collection

• USFS provides keypunched data, common

tables, analyses, and descriptive statistics

• Begin creation of templates on a state-level

analysis of inventory

• Reconcile mutual reporting responsibilities.

57
McRoberts, Ronald E.; Reams, Gregory A.; Van Deusen, Paul C.

2000. Proceedings of the first annual forest inventory and analysis symposium; 1999 November 2-3; San Antonio, TX. Gen.

Tech. Rep. NC-213. St. Paul, MN: U.S. Department of Agriculture,

Forest Service, North Central Research Station. 57 p.

Documents progress in developing the techniques required for

full implementation of the national Forest Inventory and Analysis

program's new annual forest inventory system.

KEY WORDS: Annual forest inventory, interpenetrating sample,

remote sensing, growth models, imputation, 1998 Farm Bill.