United States

Department

of Agriculture

Forest Service

Rocky Mountain

Research Station

General Technical

Report RMRS-GTR-181

October 2006

Forest Inventory and Analysis 

National Data Quality Assessment 

Report for 2000 to 2003

James E. Pollard

James A. Westfall

Paul L. Patterson

David L. Gartner

Mark Hansen

Olaf Kuegler
Pollard, James E.; Westfall, James A.; Patterson,Paul L.; Gartner, David L.; Hansen, Mark; Kuegler, Olaf. 2006. Forest Inventory 

and Analysis National Data Quality Assessment Report for 2000 to 2003. Gen. Tech. Rep. RMRS-GTR-181. Fort Collins, 

CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 43 p.

Abstract _________________________________________________________

The Forest Inventory and Analysis program (FIA) is the key USDA Forest Service (USFS) program that provides the information needed to assess the status and trends in the environmental quality of the Nation’s forests. The goal of the FIA Quality Assurance (QA) program is to provide a framework to assure the production of complete, accurate and unbiased forest 

information of known quality. Specific Measurement Quality Objectives (MQO) for precision are designed to provide a window 

of performance that we are striving to achieve for every field measurement. These data quality goals were developed from 

knowledge of measurement processes in forestry and forest ecology, as well as the program needs of FIA. This report is a 

national summary and compilation of MQO analyses by regional personnel and the National QA Advisor.

The efficacy of the MQO, as well as the measurement uncertainty associated with a given field measurement, can be tested 

by comparing data from blind check plots where, in addition to the field measurements of the standard FIA crew, a second 

QA measurement of the plot was taken by a crew without knowledge of the first crew’s results. These QA data were collected 

between 2000 and 2003 and analyzed for measurement precision between FIA crews.

The charge of this task team was to use the blind-check data to assess the FIA program’s ability to meet data quality goals 

as stated by the MQO. The results presented indicate that the repeatability was within project goals for a wide range of measurements across a variety of forest and nonforest environments. However, there were some variables that displayed noncompliance with MQO goals. In general, there were two types of noncompliance: the first is where all the regions were below the 

MQO standard, and the second is where a subset of the regions was below the MQO standards or was substantially different 

from the other remaining regions. Results for each regional analysis are presented in appendix tables. In the course of the 

study, the task team discovered that there were difficulties in analyzing seedling species and seedling count variables for MQO 

compliance, and recommends further study of the issue. Also the task team addresses the issue of trees missed or added and 

recommends additional study of this issue. Lastly, the team points out that traditional MQO analysis of the disturbance and 

treatment variables may not be adequate.

Some attributes where regional compliance rates are dissimilar suggest that regional characteristics (environmental variables 

such as forest type, physiographic class, and forest fragmentation) may have an impact on the ability to obtain consistent 

measurements. Additionally, differences in data collection protocols may cause differences in compliance rates. For example, 

a particular variable may be measured with a calibrated instrument in one region, while ocularly estimated in another region.

The Authors ______________________________________________________

James E. Pollard is FIA Quality Assurance Advisor, University of Nevada, Las Vegas.

James A. Westfall is Research Forester, USDA Forest Service, Northeast Research Station, Newtown Square, PA.

Paul L. Patterson is Mathematical Statistician, USDA Forest Service, Rocky Mountain Research Station, Interior 

West Research Station, Ogden, UT.

David L. Gartner is Mathematical Statistician, USDA Forest Service, Southern Research Station, Knoxville, TN.

Mark Hansen is Research Forester, USDA Forest Service, North Central Research Station, St. Paul, MN.

Olaf Kuegler is Mathematical Statistician, USDA Forest Service, Pacific Northwest Research Station, PortlandForestry 

Sciences Lab, Portland, OR.

You may order additional copies of this publication by sending your 

mailing information in label form through one of the following media. 

Please specify the publication title and number.

Telephone (970) 498-1392

FAX (970) 498-1122

E-mail rschneider@fs.fed.us

Web site http://www.fs.fed.us/rm

Mailing Address Publications Distribution

Rocky Mountain Research Station

240 West Prospect Road

Fort Collins, CO 80526
Contents________________________________________

Page

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

Methods ................................................................................................................... 2

Field Data Collection Methods .............................................................................. 2

Data Analysis Techniques ..................................................................................... 3

Data Matching Algorithms ..................................................................................... 7

Disturbance and Treatment Attributes................................................................... 8

Results ................................................................................................................... 9

Core Variable Results ........................................................................................... 9

Extra Trees............................................................................................................ 9

Extra Seedlings ........................................................................................................ 10

Extra Boundaries and Extra Conditions .............................................................. 10

Discussion ................................................................................................................ 12

Overall MQO Compliance ................................................................................... 12

Seedlings ............................................................................................................ 13

Plot Level Variables............................................................................................. 14

Subplot Level Variables....................................................................................... 14

Boundary Level Variables ................................................................................... 14

Condition-Level Variables ................................................................................... 14

Disturbances and Treatments ............................................................................. 15

Extra Trees.......................................................................................................... 15

Extra Boundaries and Extra Conditions .............................................................. 16

Effects of Having Previous Cycle Information Available...................................... 16

Conclusions.............................................................................................................. 16

References ............................................................................................................... 17

Appendix A: Results of Regional Analyses at 1 to 4 times the MQO 

 tolerance levels.............................................................................................. 18

Appendix B: Additional Analysis of Disturbance and Treatment............................... 35

Discussion of Results.......................................................................................... 37

Appendix C: Explanation and Examples of Missing/Extra Tree Analyses ................ 38

USDA Forest Service Gen. Tech. Rep. RMRS-GTR-181. 2006 

Introduction_______________________________________________________

The Forest Inventory and Analysis program (FIA) is the key USDA Forest Service 

(USFS) program that provides the information needed to assess the status and trends in 

environmental quality of the Nation’s forests. The FIA program reports on status and 

trends in forest area and location; the species, size, and health of trees; total tree growth, 

mortality, and removals by harvest; wood production and utilization rates by various 

products; and forest land ownership. Recent enhancements to the FIA program have 

added information relating to tree crown condition, lichen community composition, 

soils, ozone indicator plants, extent of forest damages, complete vegetation diversity, 

and down woody material. The major purpose of this program is to provide a scientifically sound census of the Nation’s forests that meets the policy and program management needs of the USFS and its partners and constituencies. This is accomplished with 

the implementation of a nationally consistent sampling design and plot configuration 

(Bechtold and Patterson 2005). The goal of the FIA quality assurance (QA) program is 

to provide a framework to assure the production of complete, accurate, and unbiased 

forest information of known quality.

The heart of the FIA QA program is extensive crew training and nationally consistent 

protocols and procedures used in the inventory. Quality control (QC) procedures include 

direct feedback to field staff to provide continual real time assessment and improvement 

of crew performance. In addition to extensive QC activities, data quality is assessed and 

documented using performance measurements and post survey assessments. These assessments are used to identify areas of the data collection process that need improvements or 

refinements to meet the quality objectives of the program. Specific measurement quality 

objectives (MQO) for precision are designed to provide a window of performance that 

is allowable for any given field measurement. These data quality goals were developed 

from knowledge of measurement processes in forestry and forest ecology. The MQO 

consist of two parts: a compliance standard and a measurement tolerance. A detailed 

description of the MQO is given in the Methods section. The efficacy of the MQO, as 

well as the measurement uncertainty associated with a given measurement can be tested 

using QA data from blind checks. The techniques used to analyze the blind-check data 

are described in the Methods section.

In some instances, the MQO were established as the “best guess” of what experienced field crews should be able to consistently achieve. However, these measurement 

quality goals have not been rigorously evaluated for meeting FIA program needs. The 

results of this report are intended to not only to assess whether the current MQO standards are being met, but more importantly to also provide data collection experts with 

the information necessary to develop recommendations for changes to the current data 

collection system. These recommendations must be attribute-specific and derive from 

Forest Inventory and Analysis National Data

Quality Assessment Report for 2000 to 2003

James E. Pollard

James A. Westfall

Paul L. Patterson

David L. Gartner

Mark Hansen

Olaf Kuegler
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-181. 2006

intimate knowledge of data collection procedures, extent of training, regional environmental variability, etc. As such, specific recommendations for improvement are beyond 

the scope of this report.

Analysis of blind-check data has been reported in annual quality assurance reports 

and FIA State reports (Pollard and Smith 1999, 2001; Frieswyk 2001) and has been 

summarized in national reports for the Forest Health Monitoring program (Conkling 

and others 2005). This report is a national summary of Phase 2 (P2) variables and MQO 

analyses from FIA blind check measurements prepared by regional personnel and the 

national QA advisor. Details regarding measurement protocols and MQO standards for 

national and regional attributes can be found in the field guides produced by each FIA 

region (USDA 2003; USDA 2004b-e). Quality of the Phase 3 (P3) indicator data will 

be addressed in future reports.

As mentioned above, the task team was not charged with making recommendations 

for specific variables; but in the process of analyzing the data several items arose 

that were outside the scope of traditional MQO analysis and the task team would 

be remiss to not address them and make recommendations. First, analysis using 

the current MQO for the variables seedling count and seedling species can lead to 

potentially misleading results; an explanation of the issue is in the Methods section 

with a further exploration in the Discussion section. The task team recommends 

that an extensive review of the seedling count and seedling species variables be 

done. Second, during the development of the process used for MQO analysis the 

issue of extra and missing trees, conditions, and boundaries arose; an explanation 

of the issue is in the Methods section with recommendations in the Discussion section. Additional analysis for extra trees is given in appendix C. Third, traditional 

MQO for disturbance and treatment variables do not provide a complete picture; 

therefore, additional analysis of the disturbance and treatment variables are given 

in appendix B.

The primary audience for this report is those intimately familiar with FIA data collection protocols and processes. Although the main audience is data collection experts, 

users of FIA data may also find the information useful to determine if the repeatability 

of certain attributes is sufficient to meet their analytical or research needs.

The remainder of the paper is organized into four sections and three appendices. 

The first section, Methods, starts with a discussion of the process used to assess MQO 

compliance. This is followed by a discussion of the need for a matching algorithm for 

the tree-, condition-, boundary-, and seedling-level data and an outline of the matching 

algorithm used. Lastly, the Methods section contains an explanation of the observations 

used to analyze the Disturbance and Treatment suites of variables. The Results section 

contains the observed MQO compliance rates for the P2 core variables and results of 

the analysis of extra trees, seedlings, boundaries, and conditions. The Discussion section 

starts with an overview of the observed MQO compliance rates followed by subsections 

devoted to specific suites of variables. The last section contains the conclusions of the 

task team. Appendix A contains more detailed results for each of the regions, and appendices B and C are devoted to more detailed analysis of the Disturbance variables 

and extra trees respectively.

Methods__________________________________________________________

Field Data Collection Methods

Data that are used to evaluate crew performance are generated by a second measurement of a field plot termed a blind check. This technique involves the re-installation of 

an inventory plot performed by a qualified crew without production crew data on hand. 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-181. 2006 

In this report, the first measurement of the plot is referred to as that of the field measurement (FM) crew and the second visit is referred to as that of the quality assessment (QA) 

crew. This type of QA measurement is considered a “blind” measurement because the 

FM crews do not know when or which of their plots will be remeasured by the QA crew, 

and cannot therefore alter their performance because of knowledge that the plot will be 

measured by a QA crew. In addition, the QA crew does not have knowledge of the FM 

crews’ original measurements because this knowledge might bias their measurements. 

This type of blind measurement provides a direct, unbiased observation of measurement 

precision from two independent crews. Blind-check plots are randomly selected to be

a representative sub-sample of all plots measured.

Blind-check plots can be measured at any time during the field season or panel 

completion, but are generally planned to be within a 2-week window of the FM crew 

measurement to avoid the confounding effects of seasonal changes on the plots. All 

plot measurements are dated and identified as regular or blind-check status so that the 

results can be interpreted with reference to length of time between FM crew measurement and the QA measurement. Blind-check plot data used for analysis in this report 

were generated in 2001-2002 for the Northeast, in 2000-2003 for the North Central, 

in 2001-2003 for the South, in 2001-2003 for the Interior West, and in 2001-2003 for 

the Pacific Northwest FIA units. The total number of blind-check plots included in the 

following analyses varied by region. For example, the Northeast, Interior West, and the 

Pacific Northwest measured a total of 77, 118, and 64 complete plot remeasurements 

respectively. The North Central and South regions measured 877 and 194 partial plot 

remeasurements, respectively, using the criteria of completed sub-plots with a minimum 

of 15 trees in the data sets. This generally resulted in a larger number of plots being 

visited in the North Central and South, although the number of trees observed was similar 

in all regions except in the North Central. Sample sizes for the number of observations 

for a given variable are included in the result tables (table 1, tables A1-A5).

Data Analysis Techniques

Evaluation of regional and national performance is accomplished by calculating 

the differences between FM crew and QA crew data. Results of these calculations are 

compared to pre-established measurement quality objectives, which are documented 

in the FIA National Field Manual (USDA 2004a). Computation and comparison of the 

MQO compliance rates of various data elements measured by FIA crews is the primary 

method of analysis used in this report.

Each data element collected by FIA has been assigned a tolerance or acceptable level 

of measurement error. These tolerances were selected by experts in FIA measurements 

based on their estimates of the ability for crews to make repeatable measurements or 

observations within the assigned tolerance. In the analysis of blind-check data, an observation is within tolerance when the difference between the FM crew and QA crew 

observations do not exceed the assigned tolerance for that data element. For many 

categorical elements, the tolerance is “no error,” thus only observations that are identical are within tolerance. For example, the tolerance for measurement of tree DBH is 

+/– 0.1 inch for each 20.0 inches of diameter of a live tree with the MQO for DBH 

set at 95 percent. The quality of the data is evaluated by comparing the desired rate of 

differences within tolerance (as a percent of observations) to the MQO. In the example 

above, the objective for DBH would be that 95 percent or more of the DBH observations are within +/– 0.1 inch for each 20.0 inches of diameter for all trees measured 

by both FM and QA crews. Results can be displayed as a simple percent of difference 

calculations that fell within the program tolerances. This percent will be referred to as 

the observed compliance rate.
 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-

181. 2006

Table 1—Percent compliance to Measurement Quality Objectives (MQO) tolerances of variables for the Northeast (NE), North Central 

(NC), Southern (SO), Interior West (IW), and Pacific Northwest (PNW) FIA Units. National average MQO achievement (NAT AVG) is computed as the weighted average percent MQO achievement based on total regional forest land. Compliance rates below the MQO standard are shown in bold.

Tree level MQO MQO NAT #OBS

variables Standard (%) Tolerance NE NC SO IW PNW AVG NE NC SO IW PNW

DBH 95.0 ±0.1 /20 inch 96 93 87 89 87 89 1,874 13,962 1,301 1,220 1,647

DRC 95.0 ±0.1 /20 inch – – – 64 45 55 0 0 0 1,114 87

Azimuth 90.0 ±10 degrees 99 98 97 97 96 97 1,871 13,963 1,301 2,334 1,735

Horizontal distance 90.0 ±0.2 /1.0 ft 95 97 96 83 86 91 1,871 13,963 1,301 2,334 1,735Species 95.0 No tolerance 95 96 94 97 95 95 1,874 13,963 1,301 2,334 1,735Tree genus 99.0 No tolerance 98 99 98 – 98 98 1,868 13,963 1,301 0 1,735

Tree status 95.0 No tolerance 99 99 100 100 100 100 1,424 13,963 1,301 2,334 1,735

Rotten/missing cull 90.0 ±10 % 93 98 98 94 98 96 1,250 9,600 1,283 1,960 1,735Total length 90.0 ±10 % 60 83 78 79 78 77 1,395 9,011 1,201 1,960 1,619Actual length 90.0 ±10 % 47 81 63 66 79 67 140 1,052 97 56 1,732Compacted crown ratio 90.0 ±10 % 65 88 81 65 67 73 1,664 12,549 1,301 2,076 1,619Uncompacted crown ratio 90.0 ±10 % 62 73 – 82 – 74 190 481 0 347 0Crown class 85.0 No tolerance 73 83 77 74 67 75 1,662 12,540 1,301 2,076 1,619Decay class 90.0 ±1 class 85 97 81 93 83 88 158 1,307 77 192 93

Cause of Death 80.0 No tolerance 90 93 94 84 – 90 157 1,307 77 63 0Mortality year 70.0 ±1 yr – 96 97 92 – 84 0 0 77 63 0Condition class 99.0 No tolerance – 98 100 100 – 100 0 13,963 1,283 2,334 0Seedling variables

Genus 90.0 No tolerance – 97 94 – – 95 0 2,866 457 0 0Species 85.0 No tolerance 94 91 70 97 – 84 377 2,866 457 153 0Seedling count 90.0 ± 20% 64 67 100 58 – 77 377 2,866 457 153 0
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-

181. 2006

Table 1 (Continued).

Plot level MQO MQO NAT #OBS

variables Standard (%) Tolerance NE NC SO IW PNW AVG NE NC SO IW PNW

Distance to road 90.0 No tolerance 72 81 77 47 52 65 50 877 139 109 64Water on plot 90.0 No tolerance 78 88 91 86 – 87 50 877 139 109 0P3 hex number 99.0 No tolerance 100 – – 100 – 100 5 0 0 17 0P3 plot number 99.0 No tolerance 100 – – 82 – 89 5 0 0 17 0Subplot level variables

Subplot status 99.0 No tolerance – – – 96 – 96 0 0 0 472 0Subplot center condition 99.0 No tolerance 96 97 98 97 93 96 308 3,497 580 472 276

Microplot center condition 99.0 No tolerance 96 97 98 97 94 97 308 3,497 580 472 276

Slope 90.0 ±10 % 98 98 85 93 94 92 183 3,497 481 392 260

Aspect 90.0 ±10 degrees 74 90 79 59 59 71 167 3,259 353 360 244

Snow/water deptha NA ±0.5 ft 96 66 99 99 99 94 224 3,497 481 392 258

Boundary variables

Boundary change 90.0 No tolerance 75 100 – – – 88 16 170 0 0 0Contrasting condition 90.0 No tolerance 94 93 92 100 80 92 16 170 51 8 86

Left azimuth 90.0 ±10 degrees 75 87 86 63 74 77 16 170 51 8 86

Corner azimuth 90.0 ±10 degrees 33 60 57 – 72 57 3 10 7 0 29

Corner distance 90.0 ±1 ft 33 50 57 – 66 44 3 10 7 0 29

Right azimuth 90.0 ±10 degrees 69 85 88 100 72 84 16 170 51 8 86

a No national MQO standard has been set for snow/water depth. Snow/water depth often changes in the time between the field and QA measurements 

due to weather conditions.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-

181. 2006

Table 1 (Continued).

Condition MQO MQO NAT #OBS

variables Standard (%) Tolerance NE NC SO IW PNW AVG NE NC SO IW PNW

Condition status 99.0 No tolerance 96 99 99 99 100 99 115 1,191 262 141 61Reserve status 99.0 No tolerance 100 99 99 99 100 99 63 1,191 164 141 48

Owner group 99.0 No tolerance 98 98 99 97 98 98 63 930 157 141 48Forest type (type) 99.0 No tolerance 48 83 87 93 92 84 62 930 164 111 48Forest type (group) 99.0 81 91 91 – 92 88 62 930 164 0 48

Stand size 99.0 No tolerance 76 87 80 86 67 79 63 930 164 111 48Regeneration status 99.0 No tolerance 98 99 98 100 92 97 63 930 164 111 48Tree density 99.0 No tolerance 100 97 98 100 96 98 63 930 164 111 48Owner class 99.0 No tolerance 94 95 97 92 90 94 63 930 158 141 48Owner status 99.0 No tolerance 94 99 99 92 82 93 50 930 164 26 17Regeneration species 99.0 98 99 98 – 100 99 62 930 164 0 11

Stand age 95.0 ±10 % 37 68 70 94 42 66 54 930 157 111 48Disturbance 1 99.0 No tolerance 97 96 95 87 79 88 63 921 157 111 48Disturbance Year 1 99.0 ±1 yr 100 85 100 71 67 85 3 40 7 7 3

Disturbance 2 99.0 No tolerance 100 95 100 84 100 99 5 73 14 19 12

Disturbance Year 2 99.0 – 67 – – – 67 0 3 0 0 0Disturbance 3 99.0 No tolerance – 100 – 100 – 100 0 6 0 3 0Disturbance Year 3 99.0 – – – – – 0 0 0 0 0 0Treatment 1 99.0 No tolerance 97 97 94 98 88 94 63 921 157 111 48Treatment Year 1 99.0 ±1 yr 75 92 92 100 100 93 8 63 13 2 2Treatment 2 99.0 No tolerance 100 96 96 75 43 88 10 89 22 4 7Treatment Year 2 99.0 – 100 100 – 100 100 0 13 3 0 1

Treatment 3 99.0 No tolerance – 94 100 100 75 92 0 17 4 1 4Treatment Year 3 99.0 – 100 – – – 100 0 5 0 0 0Physiographic class 80.0 No tolerance 81 78 91 54 50 73 63 930 160 111 48Present nonforest use 99.0 – 100 – – – 100 0 219 0 0 0
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-181. 2006

Data Matching Algorithms

FIA collects a myriad of data at a number of levels of detail. Data collection protocols must be examined at each of these levels and determinations made on appropriate 

methods of comparison. The most difficult areas to obtain valid comparisons are tree, 

condition, boundary, and seedling-level data. For these types of data, there is no unique 

identifier between FM and QA measurements. As such, data matching algorithms are 

implemented to help ensure that every paired measurement (FM and QA) is an observation of the same tree, condition, boundary or seedlings. Matching of FM crew and 

QA crew measurements for plot and subplot-level variables are easily accomplished, 

because there is a unique identifier between FM crew and QA crew measurements (e.g., 

plot number).

Tree Level Data—For tree level data, the matching algorithm uses weighted distance 

functions based on azimuth, horizontal distance from plot center, and tree diameter to 

find the tree from the FM data that most closely matches the QA data on a given subplot. 

The weights were used to standardize the contribution of each variable in relation to 

their assigned measurement tolerance. A two-pass process is used to create the list of 

matched trees. The first pass matches the trees with the smallest weighted distance. Strict 

decision rules for maximum allowable distance and species differences are implemented 

to remove any questionable matches; every FM tree is matched to one QA tree. The 

trees remaining after the first pass are subjected to a second pass, which uses a more 

complex distance function that helps to account for relatively large differences in any 

one of the three matching variables (azimuth, horizontal distance, and diameter). Decision rules regarding distance and species are used to prohibit matches that appear to be 

invalid. After the second pass, each FM tree and QA tree is on one of three lists: a list 

of matched trees, a list of unmatched FM trees, or a list of unmatched QA trees. The 

automated part of the matching program is designed to be conservative. As such, there 

likely exist valid matches in the remaining unmatched data after the algorithm has been 

run. Generally, a match may still exist when there is a very large difference in one of 

the matching function variables due to some type of data entry or measurement error. 

To avoid bias in the MQO analyses, it is imperative that the lists of unmatched items be 

scrutinized and matched manually where necessary. The program creates output to assist 

in identifying situations where a match may exist. The manual matching is done by QA 

personnel. There are three lists when the automated and manual process is completed: 

matched trees, extra FM trees, and extra QA trees. This MQO analysis was done using 

the matched tree list only.

Condition Level Data—The condition-level matching program logic is the same as 

the tree matching program. The execution is based on the condition class delineation 

variables, which are condition class number, condition status, reserved status, owner 

group, forest type, stand size class, regeneration status, and tree density. However, for 

nonforest conditions, condition class number and condition status are the only variables 

with recorded values; it is impossible to match forest and nonforest conditions in the 

automated process. After the automated process, the remaining unmatched conditions 

are inspected by QA personnel for possible matches. When the automated and manual 

process is completed, there are three lists: matched condition classes, extra FM condition classes, and extra QA condition classes. This MQO analysis was done using the 

matched condition class list.

Seedling Level Data—Seedling data are different from trees in that individual 

seedlings are not identified. Instead, seedlings are aggregated by species, with species 

and count having separate MQO. After discussion with several regional QA supervisors, it became apparent that there was no satisfactory way to match on seedlings as 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-181. 2006

opposed to match on records; therefore, the MQO analysis is based on matched records, 

as opposed to matched seedlings. It was decided to undertake three objectives in this 

document with regard to seedlings: (1) recommend that analysts, statisticians, and field 

representatives conduct an extensive review of the seedling data collection; (2) with the 

limitation, analyze the current data for MQO compliance; and (3) present the difficulties 

in analyzing the current seedling data.

The matching of seedling data is done at the subplot level. The only variables available are species and count records. Records with the same species for the FM crew and 

the QA crew are matched. Records having no species match are retained for manual 

matching by QA personnel. That person decides to either match a FM seedling record 

and QA seedling record with different species (indicating that the QA and FM crews 

disagreed on the species of a particular seedling or group of seedlings), or leave them 

in the unmatched records (indicating that the QA crew tallied a species that the FM 

crew didn’t while the FM crew tallied a species that the QA crew didn’t). Unmatched 

seedling records are not matched to seedling records that have already been matched 

to other records.

Boundary Level Data—Due to the fact that boundaries occur on a relatively small 

number of subplots (or microplots), boundary matching is currently being completed 

manually.

Disturbance and Treatment Attributes

As noted in the Condition Level Data description within the Data Matching Algorithms section, the MQO analysis of condition level variables is done using the matched 

condition classes list. As a reminder, a matched condition class is a FM condition class 

and a QA condition class that, through a combination of automated and manual matching, are deemed to represent the same condition on a plot. This subsection contains a 

discussion of the matched condition classes that were used to analyze the Disturbance 

and Treatment variables for MQO compliance. The definitions of the matched condition classes that were used in the analysis of Disturbance and Treatment variables are 

summarized at the end of this subsection.

The FIA national core field guide says that Disturbance 1 through 3 should be collected for all conditions where the crew records a Condition Class Status = 1 (i.e., 

Forested). So the natural set of matched condition classes to use for the MQO analysis 

is the set where both the FM crew and QA crew record Condition Class Status = 1. 

Using this set of matched condition classes to analyze Disturbance 2 and Disturbance 

3 yields artificially high MQO compliance percentages; if both crews record no observable disturbance for Disturbance 1, then clearly both crews will record no observable 

disturbance for Disturbance 2. The set of matched condition classes that should be used 

is the set where there is a possibility of there being an observable second disturbance, 

and this is the set of matched condition classes where at least one of the crews recorded 

an observable disturbance for Disturbance 1. Continuing with this reasoning, the set of 

matched condition classes that was used for Disturbance 3 were those where at least 

one of the crews recorded an observable Disturbance 2.

Similarly a restricted set of matched condition classes was used for analyzing 

Disturbance Year 1, Disturbance Year 2, and Disturbance Year 3. Both the FM crew 

and QA crew must record an observable disturbance for there to be any possibility of 

agreement on the year of the disturbance. So for Disturbance Year 1, the set of matched 

condition classes used was where both crews recorded an observable disturbance for 

Disturbance 1. Similar sets of matched condition classes were used for Disturbance 

Year 2 and Disturbance Year 3.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-181. 2006

To analyze the suite of Treatment variables (Treatment 1-3 and Treatment Year 1-3) 

a similar set of restricted sets of matched condition classes was used. For example, the 

MQO compliance for Treatment 2 was checked using the set of matched condition classes 

where at least one of the crews recorded an observable Treatment 1.

These restricted sets of matched condition classes allow one to analyze the conditional agreement between the crews (e.g., how often does either the FM crew or the 

QA crew record no observable disturbance while the other crew records an observable 

disturbance). This type of analysis is explored in appendix B.

Description Summary of the Restricted Set of Matched Condition Classes Used 

in Disturbance and Treatment MQO Analysis—The description is limited to Disturbance and Disturbance Year, and the explanation for Treatment and Treatment Year is 

identical.

Disturbance 1: Matched Condition classes where both the FM crew and the QA crew 

recorded Condition Status = 1.

Disturbance 2: Matched condition classes where at least one of the crews recorded 

an observable disturbance for Disturbance 1.

Disturbance 3: Matched condition classes where at least one of the crews recorded 

an observable disturbance for Disturbance 2.

Disturbance Year 1: Matched condition classes where both the FM crew and the QA 

crew recorded an observable disturbance for Disturbance 1.

Disturbance Year 2: Matched condition classes where both the FM crew and the QA 

crew recorded an observable disturbance for Disturbance 2.

Disturbance Year 3: Matched condition classes where both the FM crew and QA 

crew recorded an observable disturbance for Disturbance 3.

Results___________________________________________________________

Core Variable Results

Phase 2 variables collected in all FIA regions using common protocols (Core Variables) 

were included in this analysis. The use of nationally consistent protocols and standards 

allows for comparability among FIA regions. The results of MQO achievement for tree, 

seedling, subplot, plot, boundary, and condition variables are presented in table 1. For 

each FIA region, results are expressed as the percentage of values observed that fell 

within the MQO tolerances established for the program.

Regional add-on variables are also collected by each FIA unit, but these regional 

variables cannot be evaluated from a national perspective. However, the programs used 

to analyze core variables were also used to compute results for regional variables. These 

results have been included in appendix A, where each region’s analyses are tabled with 

MQO achievement percentages at one to four times the measurement tolerance levels. 

These results can be used to evaluate how wider tolerances would affect MQO compliance rates for these regional variables as well as for national core variables measured 

in each region.

Extra Trees

Following the careful matching of all trees tallied by both crews, any trees tallied 

by the QA crew and not by the FM crew were identified as extra QA trees, and any 

trees tallied by the FM crew and not the QA crew were identified as extra FM trees. 

(The analysis of extra trees did not include data from PNW due to issues with using 

the macroplot.) The number of extra trees for both crews is given in table 2. Besides 

the overall total number of extra trees by region, table 2 also contains subtotals of the 
10 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-181. 2006

Table 2—Tabulation of extra trees observed by FM and QA crews in each regiona.

Region

NE NC SO IW Total

Total matched trees (trees tallied by both crews)

Saplings 450 3161 266 374 4,251

5-inches + trees 1,424 10,802 1,053 1,962 15,241

Total 1,874 13,963 1,319 2,336 19,492

Extra QA trees (trees tallied by the QA crew but not by the FM crew)

Saplings 20 (4.3%) 72 (2.2%) 5 (1.8%) 21 (5.3%) 118 (2.7%)

5-inches + trees 15 (1.0%) 152 (1.4%) 5 (0.5%) 66 (3.3%) 238 (1.5%)

Total 35 (1.8%) 224 (1.6%) 10 (0.8%) 87 (3.6%) 356 (1.8%)

Extra FM trees (trees tallied by the FM crew but not by the QA crew)

Saplings 30 (6.3%) 40 (1.2%) 4 (1.5%) 21 (5.2%) 95 (2.2%)

5-inches + trees 13 (0.9%) 46 (0.4%) 9 (0.8%) 72 (3.5%) 140 (0.9%)

Total 43 (2.2%) 86 (0.6%) 13 (1.0%) 93 (3.8%) 235 (1.2%)

a PNW data not available for extra tree analysis.

number of extra saplings and 5-inch+ trees by region. Extra tree information can be 

used to look for bias in FM crew tallies of trees relative to QA crews using methods 

and assumptions described in Appendix C. These relative bias values are shown in 

fig. 1. The values are all small and none are significantly different from zero at the 

p = 0.05 level.

The percentages given in tables 2 through 5 are percents of the observations made by 

a crew that were not matched. The equation for the percent unmatched observation is:

Percent Unmatched Number of extras

Number of _ _ __ = _ _ _ *

( extras N+ umber of matches)





 



 100

The percent unmatched for the total number of observations for both crews is a weighted 

average of the percent unmatched for the two crews. However, the unweighted average 

will be accurate enough for most uses.

Extra Seedlings

As with the trees, after the FM and QA seedlings records were matched there were 

extra seedling records tallied by both the FM and QA crews. Table 3 contains the number 

of extra seedling records by region (except PNW). The rate of extra seedlings is greater 

than that for trees because of the inherit difficulty in matching the seedling records where 

the species and count are confounded. A detailed exploration of the analytical problems 

associated with this type of QA data is included in the Discussion section.

Extra Boundaries and Extra Conditions

Following the careful matching of all boundaries tallied by both crews, any 

boundaries tallied by the QA crew and not by the FM crew were identified as extra 

QA boundaries, and any boundary tallied by the FM crew and not the QA crew were 

identified as extra FM boundaries. The number of extra boundaries for both crews is 

given in table 4. Similarly, table 5 contains the number of extra conditions by region. 

The PNW boundary and condition data were not available for this analysis.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-181. 2006 11

Figure 1—Observed bias in the probability that an FM crew will tally an extra tree relative 

to the probability that a QA crew will tally an extra tree. None of these relative bias values 

are significantly different from zero at the p = 0.05 level.

-0.015

-0.010

-0.005

0.000

0.005

0.010

0.015

NE NC SO IW All

Region

Observed relative bias

Saplings

5” + Trees

Table 3—Tabulation of extra seedling records for FM and QA crews in 

each regiona. 

Region

NE NC SO IW Total

Matched records 403 2,866 457 153 3,879

QA extra records 50 431 80 15 576

(12.4%) (13.1%) (14.9%) (8.9%) (12.9%)

FM extra records 55 185 66 29 335

(13.6%) (5.8%) (12.5%) (16.6%) (7.7%)

a

 PNW data not available for extra seedling analysis.

Table 4—Boundaries: extra records for QA crews and FM Crewsa.

Region

NE NC SO IW Total

Matched records 16 170 39 8 233

QA extra records 10 43 2 5 60

(38.5%) (20.2%) (4.9%) (38.5%) (20.5%)

FM extra records 0 4 2 3 49

(0.0%) (20.6%) (4.9%) (27.3%) (17.4%)

a

 PNW data not available for extra boundary analysis.
12 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-181. 2006

Discussion________________________________________________________

The charge of this task team was to use the blind-check data to assess the FIA program’s 

ability to meet its stated MQO. The results of the analysis of the MQO compliance are 

in table 1, with the more detailed regional tables in appendix A. The basic results for 

extra trees, extra seedlings, extra boundaries and extra conditions are in tables 2 through 

5. While the discussion in this section will be limited to the above mentioned charge, 

additional analysis of the data is presented in appendices B and C. The extra analyses 

will be mentioned at the appropriate places in the discussion below.

Overall MQO Compliance

Two types of noncompliance will be discussed: the first is where all the regions were 

below the MQO standard, and the second where a subset of the regions was below the 

MQO compliance of the other remaining regions.

The results indicate repeatability within MQO goals for a wide range of measurements across a variety of forest and nonforest environments. In some cases, this range 

of environments appears to have little effect on measurement precision (i.e., the percent 

MQO compliance is similar across all regions). However, there are some attributes 

where regional compliance rates are quite dissimilar, suggesting that regional characteristics may have an impact on t he ability to obtain consistent measurements. These 

characteristics may include environmental variables such as forest type, physiographic 

class, and forest fragmentation. Additionally, differences in data collection protocols 

may also cause differences in compliance rates. For example, a particular variable may 

be measured with a calibrated instrument in one region, while ocularly estimated in 

another region.

Rates of repeatability for tree-level variables (table 1) illustrate the aforementioned 

circumstances. MQO compliance rates are consistently high across all regions for azimuth, species (and genus), tree status, rotten/missing cull, and cause of death. Decay 

class and DBH had fairly consistent results across all FIA regions, with most regions 

just below the MQO standard. There are a number of variables for which compliance 

rates notably vary by region. Horizontal distance compliance is consistent in the eastern U.S. (NE, NC, and SO), but is lower for the western regions (IW and PNW). This 

is due to the necessity of measuring woodland species in the western regions, where 

horizontal distance to multi-stemmed trees is based on geographic center. This makes 

the measurement less repeatable. As an illustration, timberland species in the IW region 

had a compliance rate for horizontal distance is 95 percent, while woodland species 

compliance was 68 percent (appendix A). The poor compliance rates for DRC are also 

attributable to woodland species issues.

Table 5—Conditions: extra records for QA crews and FM crewsa.

Region

NE NC SO IW Total

Matched records 115 1,191 262 141 1,709

QA extra records 4 32 2 8 46

(3.4%) (2.6%) (0.8%) (5.4%) (2.6%)

FM extra records 6 18 4 1 29

(5.0%) (1.5%) (1.5%) (0.7%) (1.7%)

a

 PNW data not available for extra condition analysis.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-181. 2006 13

Other attributes that have range of repeatability statistics are related to tree height. 

For tree heights, MQO compliance is poorest in the NE, somewhat better in the SO, IW, 

and PNW, and best in the NC. For the three tree-crown variables, all regions are below 

compliance, although there is some variation of the compliance rates among regions 

for a specific variable and variation of the compliance rates among the three variables 

for a specific region. The reason for these variations is not apparent; however, differing 

forest conditions or measurement methods may provide an explanation.

Seedlings

Under the limitation of matching on seedling records, the seedlings results from 

table 1 show that the Northeast, North Central and Interior West FIA units are making 

the MQO compliance standard for species but not for counts. On the other hand, the 

Southern unit is making the MQO compliance standard for counts but not for species. 

At first glance, these results suggest that the seedling quality issues for the Southern 

unit are fundamentally different from those of the Northeast, North Central, and Interior 

West. However, these apparent differences may be an artifact of matching on records.

If there are species with relatively few seedlings per microplot, then differences in 

species calls between the FM crew and the QA crew will lead to a low observed species compliance rate. Example 1 demonstrates this situation. If there is only one maple 

seedling and the FM crew calls it a red maple and the QA crew calls it a sugar maple, the 

two seedling records will be manually matched. This combination would be interpreted 

as one seedling record with a count agreement and a species call disagreement.

Example 1:

FM Crew QA Crew

1 red maple 1 sugar maple

However, if the species have more seedlings per acre, then differences in species 

call will lead to a low observed count compliance rate. Simply adding two red maple 

seedlings and two sugar maple seedlings to each crew count illustrates how this happens. The result will be Example 2, where the FM crew and the QA crew have agreed on 

the species of four of the five seedlings, two red maple seedlings and two sugar maple 

seedlings, but disagree on the species of the fifth seedling. The records with the same 

species will be matched. Therefore, this set of records will be interpreted as two records 

with species call agreements, but with counts disagreements.

Example 2:

FM Crew QA Crew

3 red maples 2 sugar maples

2 red maples 3 sugar maples

Therefore, the low observed count compliance rates for the Northeast, North Central, and Interior West units may have the same root causes as the low observed species 

compliance rate for the Southern unit.

In addition to the difficulty in interpreting the MQO results due to the fact that 

species call disagreements can affect observed compliance rates for both species and 

count, is the difficulty caused by the large percentages of unmatched seedling records 

(5.8 percent-16.6 percent). Because of these large percentages of unmatched records, 

additional caution should be used in interpreting the observed compliance rates. The 

FIA units are meeting one of the seedling MQO compliance standards in table 1 as 
14 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-181. 2006

percents of matched records. However, if the compliance standards were stated in terms 

of percent of total records instead of percent of matched records, only the Interior West 

would be meeting either of the seedling MQO compliance standards—the compliance 

standard for counts—and they would be just making this compliance standard with an 

observed compliance rate of 85 percent.

Plot Level Variables

There are few plot-level variables that lend themselves to meaningful MQO analyses. The distance to road attribute has both types of repeatability issues; all regions 

are below the MQO standard, and there is disparity between the level of repeatability 

for the eastern regions versus the western regions. Repeatability in the eastern regions 

was much greater than in the western regions. It is suspected that poorer compliance 

for western regions is due to the further distances to roads. Statistics for the water on 

plot variable are close to the compliance standard of 90 percent for all regions except 

the NE. One possible explanation is that temporary water is more difficult to identify 

in NE forests.

Subplot Level Variables

Most subplot-level measurements had high levels of repeatability that were near 

or above the stated compliance standards. Differences between crews for subplot and 

microplot center conditions are attributable to differences in condition delineation 

arising from the use of mapped plots. Subplot slope was above the compliance standard for all regions except SO. The most problematic subplot attribute was aspect, with 

eastern regions having better repeatability than the western regions. It is likely that the 

more variable topography in Western States presents additional difficulty. Snow and 

water depth have no MQO standard, but there is clearly a disparity between NC and the 

other regions, because the majority of the forest land in the NC region is in the northern 

portions of Michigan, Minnesota, and Wisconsin. FM crews measure plots year-round 

in these areas and snow is often present on these plots. In other regions snow is typically not present when measurements are taken, which would account for the disparity 

between NC and the other regions.

Boundary Level Variables

Boundary data are collected when more than one condition class occurs on a sample 

subplot. This occurs relatively infrequently, so the sample sizes for these analyses are 

somewhat small. For the NE and IW the numbers of observations were too small to 

make meaningful statistical comparisons. Variables whose national average exceeds the 

standard are boundary change and contrasting condition. Left azimuth and right azimuth 

compliance rates are similar and near the desired compliance level within NC, SO and 

PNW. Boundary corners are less repeatable than azimuths. Obtaining agreement on the 

existence and location of a corner point is difficult because the exact path of boundary 

line is often poorly defined. Note that if one crew observed a corner and the other crew 

did not, then this was considered an out-of-compliance situation.

Condition-Level Variables

The statistics for the condition-level variables indicate that repeatability can vary 

widely, depending on the attribute measured. Variables that were consistently close to 

or above the standard were condition status, reserve status, owner group, regeneration 

status, regeneration species, tree density, owner class, and owner status. The compliance 

rates for forest type and forest type group were below the specified standard, but fairly 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-181. 2006 15

consistent across regions with the exception of NE, which had poorer repeatability. 

The NE region extends from Maine to West Virginia, and westward to Ohio, resulting in diverse forests with a large number of forest types, many of which have similar 

species compositions. The difficulty in identifying particular forest types for the NE 

is depicted by comparing the increases in percent compliance between forest type and 

forest type group variables. The increase for the other regions ranges from 2-8 percent, 

but the increase is 32 percent for the NE, indicating that most of the disparity arises 

from disagreement among forest types within a forest type group.

Stand size exhibits both noncompliance for all regions and a wide variability across 

the regions. Of all variables analyzed, stand age exhibited the widest range of compliance statistics. The overall average was 66 percent, with a range of 37 percent for the 

NE to 94 percent for the IW. These differences are likely the result of differing forest 

conditions and measurement protocols. There are differences among regions in numbers of trees sampled to obtain stand age. Also, some regions monument the location of 

trees used to determine age, which allows QA crews to sample the same trees. In other 

regions, the FM crew and QA crew sample trees independently, which can contribute to 

poor compliance rates, especially in areas where uneven-aged forests are commonplace. 

Finally, large trees can be difficult to bore and obtain accurate ring counts, a situation 

that occurs most frequently in the PNW region. Physiographic class is another example 

of differences in eastern and western regions. Compliance rates are reasonably similar 

within the eastern and western regions, but differ notably between the regions. This may 

be attributed to the more variable landscape of the Western United States.

Disturbances and Treatments

For disturbances and treatments, there are three variables to indicate the type of 

activity and each has an associated variable to indicate the year of occurrence. The 

results show that for Disturbance and Treatment most regions are within compliance, 

but some regions are out of compliance for Disturbance Year and Treatment Year. Much 

of the compliance for Disturbance and Treatment comes from the crews agreeing that 

no Disturbance or Treatment has occurred, while for Disturbance Year and Treatment 

Year the compliance is determined over the smaller set where both crews have recorded 

a Disturbance or Treatment respectively.

As indicated in the Methods section, subsets of the matched condition classes were used 

for the analysis of the MQO compliance for Disturbance, Disturbance Year, Treatment, 

and Treatment Year. Using the these subsets, additional analysis can be done to address 

questions such as how repeatable is the measurement of an observable disturbance. Since 

this is not an analysis of MQO compliance, the results are documented in appendix B. 

The analysis in the appendix shows that the disturbance and treatment variables should 

be reviewed from perspectives other than MQO compliance.

Extra Trees

One aspect of tree-level measurement that is not specifically addressed by MQO 

standards is the numbers of trees sampled. Missed or extra trees can bias inventory 

estimates. Additional analyses found that these extra tally trees were usually those 

near plot edges or with tree diameters near the 5-inch threshold. Also, differing calls 

on forest or nonforest status resulted in extra tally trees. Additional analysis of the 

extra tree data was done to more fully explore the reasons why crews miss or find 

extra trees (appendix C). Given the assumptions and analysis methods in appendix C, 

the results of a nonparametric bootstrap test show that overall regional differences in 

numbers of trees sampled between QA and FM crews are not significant. The analysis 

performed by reason within each region indicated that there are small (<0.20 percent) 
16 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-181. 2006

but significant (p = 0.05) relative biases due to missed trees near plot edges and forest/

nonforest status in some regions. It is suggested that some type of quality assurance 

standard be implemented for sample tree counts in the future.

Extra Boundaries and Extra Conditions

Two points are worth noting. First, for both boundaries and conditions the rates of 

extra observations is quite different for the Southern region than for the other regions. 

While the NE, NC, and IW have similar overall rates of extra observations, the distribution between QA extra observations and Crew extra observations is different among the 

three regions. Second, for boundaries there is clearly a high rate of extra observations. 

A study should be conducted to determine the reasons for and solution to these high 

rates. The small sample size associated with three of the regions boundary data makes it 

difficult to interpret. It appears, however, that there are considerably more occurrences 

of extra boundary calls than there are extra condition calls.

Effects of Having Previous Cycle Information Available

The data analyzed in this report consist of a mixture of observations from new plots 

(plots installed at locations where there were no previous FIA plots), remeasured plots 

(plots that are a remeasurement of the national 4-subplot mapped design), and overlaid 

plots (plots where the national design is being installed at a location where a different 

plot design was previously measured). As the national annual inventory is fully implemented, most plots will be remeasurements of the national design, and most crews 

will have some of the data from the first cycle of mapped plots available to them. The 

actual data available to them will vary from unit to unit, affecting how the MQO data 

are interpreted.

The North Central unit’s current procedures and the Southern unit’s plan on how 

information from the previous cycle will be used for boundaries is presented as an 

example of how to deal with the above situation. If during the second cycle of mapped 

plots, the cruiser finds a condition boundary, the cruiser checks to see if the boundary 

existed during the first cycle of mapped plots. If the boundary existed during the first 

cycle, the cruiser checks to see if the boundary has changed. If the boundary has not 

changed, the cruiser accepts the boundary data from the first cycle. Therefore, the QA 

data become partly a test on the repeatability of the agreement with the data from the 

previous cycle and not just a test of the repeatability of the measurement and placement 

of the boundary. While there is a regional variable for boundary change (is it a real 

change, or a cruiser error, or no change), there will be similar variables (forest type, tree 

grade, and cubic foot cull) for which there is no regional change variable. The MQO 

analysis of the data coming from the second cycle of mapped plots will need to try to 

separate the repeatability of the measurements from the repeatability of agreeing with 

the previous cycle’s data.

Conclusions_______________________________________________________

The results presented above indicate that the P2 variables analyzed were generally 

repeatable within Measurement Quality Objectives for a wide range of measurements 

across a variety of forest and nonforest environments. However, there were some variables that displayed noncompliance with MQO goals. This was particularly evident 

with seedling variables. Because of the ambiguity inherent in having the two different 

levels of observation in the same record for seedlings, the decision on how to evaluate 

data quality for seedlings was not straightforward. There is a definite need to reestablish 

less ambiguous MQO standards for seedlings.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-181. 2006 17

In addition, it is recommended that data collection experts, as well as data users, 

review the results of this study to determine if the current level of data quality is adequate for the FIA program needs. These results need to be evaluated to determine if 

actions are needed to improve MQO compliance for some inventory variables, or if 

new measurement quality goals need to be formulated. If corrective action is needed 

to improve MQO compliance, this may include enhanced training methods, revamping 

data collection protocols, and respecification of MQO criteria to reflect more realistic 

repeatability standards.

In addition to some of the more obvious factors that affect MQO compliance rates, 

as noted above, there are also other more difficult to assess factors that may affect 

repeatability of crew performance. Examples might include calibration of measurement equipment, measurement protocols, crew experience, and seasonal variability of 

environmental and forest conditions. The results presented in this report form the basis 

for evaluation of the adequacy of the P2 FIA data.

References________________________________________________________

Bechtold, W.A.; Patterson, P.L., eds. 2005. The enhanced forest inventory and analysis program—national 

sampling design and estimation procedures. Gen. Tech. Rep. SRS-80. Asheville, NC: U.S. Department 

of Agriculture, Forest Service, Southern Research Station. 85 p.

Conkling, B.L.; Coulston, J.W.; Ambrose, M.J. 2005. Forest health monitoring 2001 national technical 

report. Gen. Tech. Rep. SRS-81. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern 

Research Station. 204 p.

Frieswyk, T.S. 2001. Forest statistics for Maryland: 1986 and 1999. Resour. Bull. NE-154. Newtown Square, 

PA: U.S. Department of Agriculture, Forest Service, Northeastern Research Station. 164 p.

Pollard, James E.; Smith, W.D. 1999. Forest health monitoring 1998 plot component quality assurance 

report, volume I. Research Triangle Park, NC: U.S. Department of Agriculture, Forest Service, Southern 

Research Station.

Pollard, James E.; Smith, W. D. 2001. Forest health monitoring 1999 plot component quality assurance 

report. Research Triangle Park, NC: U.S. Department of Agriculture, Forest Service, Southern Research 

Station.

U.S. Department of Agriculture. 2003. Forest inventory and analysis Southern Research Station field guide, 

volume 1: field data collection procedures for phase 2 plots, version 2.0. Ashville, NC: U.S. Department 

of Agriculture, Forest Service, Southern Research Station. Available: www.srs.fs.usda.gov/fia/manual, 

[2006, March 23].

U.S. Department of Agriculture. 2004a. Forest inventory and analysis national core field guide, volume I: 

field data collection procedures for phase 2 plots, version 2.0. Internal report. On file at: U.S. Department 

of Agriculture, Forest Service, Forest Inventory and Analysis, Washington, DC.

U.S. Department of Agriculture. 2004b. Forest inventory and analysis national core field guide, volume 1: 

field data collection procedures for phase 2 plots, version 2.0. Newtown Square, PA: U.S. Department 

of Agriculture, Forest Service, Northeastern Research Station. Available: http://www.fs.fed.us/ne/fia/

datacollection/manualver2_0/NEp2%202.0_0-8%20Ssections.pdf [2006 March 28].

U.S. Department of Agriculture. 2004c. Interior West forest inventory and analysis forest survey field 

procedures, version 2.0. Ogden, UT: U.S. Department of Agriculture, Forest Service, Rocky Mountain 

Research Station, Forestry Sciences Laboratory. Available: http://www.fs.fed.us/rm/ogden/manual/

pdf/2004_manual.pdf [2006, March 23].

U.S. Department of Agriculture. 2004d. Forest inventory and analysis national core field guide: Volume 

I: Field data collection procedures for phase 2 plots, version 2.0. St. Paul, MN: U.S. Department of 

Agriculture, Forest Service, North Central Research Station. 281 p. Available: http://www.ncrs.fs.fed.

us/4801/field-guides/Annual/NC-2004-field-guide.pdf [2006, March 28].

U.S. Department of Agriculture. 2004e. Field instructions for the annual inventory of Washington, Oregon, 

and California, version 2.0. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific 

Northwest Research Station, Forest Inventory and Analysis. Available: http://www.fs.fed.us/pnw/fia/local-resources/pdf/field_manuals/2004_annual_manual_final.pdf [2006, March 28].
18 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-181. 2006

Appendix A: Results of Regional Analyses at 1 to 4 times

the MQO tolerance levels____________________________________________ QA results for tree variables from 1,874 trees in the Northeast Percentage of data within tolerance Number of times data exceeded tolerance Variable Tolerance @1x @2x @3x @4x @1x @2x @3x @4x Records DBH ±0.1 /20 inch 95.5 98.3 98.9 99.0 84 32 21 18 1,874 DRC ±0.1 /20 inch . . . . . . . . – Azimuth ±10 degrees 98.6 99.5 99.6 99.7 26 10 7 6 1,871 Horizontal distance ±0.2 /1.0 ft 94.8 98.2 98.9 99.2 98 33 20 15 1,871 Species No tolerance 94.6 102 1,874 Tree genus No tolerance 98.4 29 1,868 Previous tree status No tolerance . . – Present tree status No tolerance 99.0 14 1,424 Standing dead No tolerance . . – Rotten/missing cull ±10 % 92.8 96.3 97.8 98.2 90 46 27 22 1,250 Total length ±10 % 59.9 87.5 96.3 98.9 559 174 51 15 1,395 Actual length ±10 % 47.1 84.3 97.1 99.3 74 22 4 1 140 Compacted crown ratio ±10 % 65.1 90.9 97.8 99.6 581 151 36 6 1,664 Uncompacted crown ratio ±10 % 61.6 87.9 94.7 97.4 73 23 10 5 190 Crown class No tolerance 72.5 446 1,622 Decay class ±1 class 84.8 94.4 100 24 1 0 158 Cause of death No tolerance 90.4 15 157 Regional variables Tree history No tolerance 98.8 3 241 Tree condition No tolerance 94.6 77 1,424 Sapling total height ±10 % 51.9 80.9 91.7 99.0 151 60 26 3 314 Bole height ±4 ft 56.0 83.8 94.9 97.9 547 201 64 26 1,244 Sawlog height ±4 ft 54.3 74.4 88.4 94.2 118 66 30 15 258 Tree grade No tolerance 71.7 80 283 Board-foot cull ±10 % 68.9 83.0 86.6 91.2 88 48 38 25 283 Board-foot sound ±1 class 84.8 87.3 88.3 89.8 43 36 33 29 283 Cubic-foot cull ±10 % 85.4 95.2 97.4 97.8 183 60 32 28 1,250 Cubic-foot sound ±1 class 89.3 90.9 92.2 93.2 134 114 97 85 1,250 Tree class No tolerance 90.3 133 1,376 Table A-1—Northeast data analysis for core and regional variables.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-

181. 2006

19

QA results for seedling-level variables from 132 microplots in the Northeast

Percentage of data within tolerance Number of times data exceeded tolerance

Variable Tolerance @1x @2x @3x @4x @1x @2x @3x @4x Records

Species No tolerance 93.8 21 337

Seedling count ±20 % 63.8 65.0 65.6 66.8 122 118 116 112 337

QA results for plot-level variables from 77 plots in the Northeast

Percentage of data within tolerance Number of times data exceeded tolerance

Variable Tolerance @1x @2x @3x @4x @1x @2x @3x @4x Records

Distance to road No tolerance 72.0 14 50

Water on plot No tolerance 78.0 11 50

P3 hex number No tolerance 100.0 0 5P3 plot number No tolerance 100.0 0 5

Regional variables

Terrain position No tolerance 76.6 18 77

Productivity class No tolerance 98.7 1 77

QA results for subplot-level variables from 308 subplots in the Northeast

Percentage of data within tolerance Number of times data exceeded tolerance

Variable Tolerance @1x @2x @3x @4x @1x @2x @3x @4x Records

Subplot center condition No tolerance 95.8 13 308

Microplot center condition No tolerance 95.8 13 308

Slope ±10 % 97.8 98.9 98.9 98.9 4 2 2 2 183

Aspect ±10 degrees 74.3 86.2 89.2 92.2 43 23 18 13 167

Snow/water depth ±0.5 ft 96.0 97.8 99.1 99.6 9 5 2 1 224

QA results for boundary-level variables from 16 boundaries in the Northeast

Percentage of data within tolerance Number of times data exceeded tolerance

Variable Tolerance @1x @2x @3x @4x @1x @2x @3x @4x Records

Boundary change No tolerance 75.0 4 16

Contrasting condition No tolerance 93.8 1 16

Left azimuth ±10 degrees 75.0 87.5 87.5 87.5 4 2 2 2 16

Corner azimuth ±10 degrees 33.3 33.3 33.3 33.3 2 2 2 2 3

Corner distance ±1 ft 33.3 66.7 66.7 66.7 2 1 1 1 3

Right azimuth ±10 degrees 68.8 75.0 81.3 81.3 5 4 3 3 16

Table A-1 (Continued)
20 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-

181. 2006

QA results for condition-level variables from 115 conditions in the Northeast

Percentage of data within tolerance Number of times data exceeded tolerance

Variable Tolerance @1x @2x @3x @4x @1x @2x @3x @4x Records

Condition status No tolerance 95.7 5 115

Reserve status No tolerance 100.0 0 63

Owner group No tolerance 98.4 1 63

Forest type (type) No tolerance 48.4 32 62

Forest type (group) No tolerance 80.6 12 62

Stand size No tolerance 76.2 15 63

Regeneration status No tolerance 98.4 1 63

Tree density No tolerance 100.0 0 63

Owner class No tolerance 93.7 4 63

Owner status No tolerance 94.0 3 50

Regeneration species No tolerance 98.4 1 62

Stand age ±10 % 37.0 64.8 83.3 88.9 34 19 9 6 54

Disturbance 1 No tolerance 96.8 2 63

Disturbance Year 1 ±1 yr 100.0 0 3

Disturbance 2 No tolerance 100.0 0 5

Disturbance Year 2 ±1 yr

Disturbance 3 No tolerance

Disturbance Year 3 ±1 yr

Treatment 1 No tolerance 96.8 2 63

Treatment Year 1 ±1 yr 75.0 100.0 2 0 8

Treatment 2 No tolerance 100.0 0 10

Treatment Year 2 ±1 yr

Treatment 3 No tolerance

Treatment Year 3 ±1 yr

Physiographic class No tolerance 81.0 12 63

Present nonforest use No tolerance

Regional variables

Land use No tolerance 88.5 13 113

Timber management class No tolerance 74.6 16 63

Stand history No tolerance 93.7 4 63

Stocking class No tolerance 81.0 12 63

Stand structure No tolerance 42.3 15 26

Table A-1 (Continued)
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-181. 2006 21 QA results for tree-level variables from 13,963 trees in the North Central Region Percentage of data within tolerance Number of times data exceeded tolerance Variable Tolerance @1x @2x @3x @4x @1x @2x @3x @4x Records DBH ±0.1 /20 inch 93.2 96.4 97.5 98.2 950 504 343 257 13,962 Azimuth ±10 degrees 98.3 99.0 99.2 99.3 239 137 113 95 13,963 Horizontal distance ±0.2 /1.0 ft 97.3 98.9 99.1 99.3 383 159 123 93 13,963 Species No tolerance 95.8 581 13,963 Tree genus No tolerance 99.0 136 13,963 Tree status No tolerance 99.2 113 13,963 Rotten/missing cull ±10 % 98.4 99.1 99.5 99.7 154 82 46 27 9,600 Total length ±10 % 82.9 95.3 98.5 99.4 1543 425 139 56 9,011 Actual length ±10 % 81.0 92.5 94.5 96.4 200 79 58 38 1,052 Compacted crown ratio ±10 % 88.4 96.8 99.0 99.7 1458 401 122 38 12,549 Uncompacted crown ratio (p3) ±10 % 72.8 80.0 83.8 85.9 131 96 78 68 481 Crown class No tolerance 83.4 2086 12,549 Decay class ±1 class 96.6 99.5 99.8 99.9 44 7 2 1 1,307 Cause of death No tolerance 93.3 88 1,307 Condition No tolerance 97.7 322 13,963 Crown–position No tolerance 74.0 104 400 Crown–light–exposure ±1 class 89.8 97.3 98.8 99.8 49 13 6 1 481 Sapling–crown–vigor–class No tolerance 68.3 26 82 Crown–density ±10 % 67.0 79.5 84.3 88.0 132 82 63 48 400 Crown–dieback ±10 % 96.8 99.3 99.8 99.8 13 3 1 1 400 Transparency ±10 % 79.3 90.0 95.3 97.8 83 40 19 9 400 Regional variables NC TREE CLASS No tolerance 92.6 1019 13,817 NC–damage–agent–1 No tolerance 92.0 1007 12,549 NC–damage–agent–2 No tolerance 85.9 283 2,005 Missouri damage code No tolerance 71.5 319 1,121 Utilization No tolerance 77.9 31 140 NC–tree–grade No tolerance 68.6 710 2,264 DBH of live and decay class 1-2 trees ±0.1 /20 inch 92.8 96.3 97.5 98.1 922 480 324 240 12,817 DBH of decay class 3–5 trees ±1 /20 inch 98.7 99.4 99.7 99.8 8 4 2 1 619 Total length trees >40 ft ±10 % 84.3 96.3 99.1 99.7 1173 275 65 21 7,489 Total length trees <40 ft ±10 % 75.7 90.1 95.1 97.7 370 150 74 35 1,522 Total length trees <5 inches DBH ±10 % 67.6 91.5 94.4 97.2 23 6 4 2 71 Table A-2—North Central data analysis for core and regional variables.
22 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-

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Table A-2 (Continued)

QA results for seedling-level variables from 1,404 microplots in the North CentralFIA

Percentage of data within tolerance Number of times data exceeded tolerance

Variable Tolerance @1x @2x @3x @4x @1x @2x @3x @4x Records

Species No tolerance 91.4 247 2,866

Genus No tolerance 97.2 79 2,866

Seedling count ±20 % 66.5 72.2 74.3 75.8 960 798 736 695 2,866

QA results for plot-level variables from 877 plots in the North Central FIA

Percentage of data within tolerance Number of times data exceeded tolerance

Variable Tolerance @1x @2x @3x @4x @1x @2x @3x @4x Records

Distance to road No tolerance 81.4 163 877

Water on plot No tolerance 88.3 103 877

Road code No tolerance 78.4 189 877

Use restrictions No tolerance 92.0 70 877

Recreational use 1 No tolerance 94.6 47 877Recreational use 2 No tolerance 100.0 0 27Recreational use 3 No tolerance 60.9 9 23

Road–access No tolerance 93.4 58 877

Regional variables

Elevation GPS ±50 ft 82.6 94.0 96.5 97.5 139 48 28 20 801

Latitude decimal degrees

±0.0001

degree 89.8 95.6 96.8 97.6 82 35 26 19 801

Longitude decimal degrees

±0.0001

degree 89.9 95.6 96.8 97.4 81 35 26 21 801

Latitude feet ±140 ft 97.5 98.8 99.0 99.0 20 10 8 8 801

Longitude feet ±140 ft 96.5 97.6 97.9 98.3 28 19 17 14 801
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23

Table A-2 (Continued)

QA results for subplot-level variables from 3,497 subplots in the North CentralFIA

Percentage of data within tolerance Number of times data exceeded tolerance

Variable Tolerance @1x @2x @3x @4x @1x @2x @3x @4x Records

Subplot center condition No tolerance 97.3 94 3,497

Microplot center condition No tolerance 96.9 107 3,497

Slope ±10 % 97.7 99.0 99.4 99.5 80 35 22 17 3,497

Aspect ±10 degrees 89.7 94.3 95.4 96.1 337 187 149 126 3,259

Snow/water depth ±0.5 ft 65.6 70.3 70.3 75.0 1203 1037 1037 876 3,497

QA results for boundary-level variables from 170 boundaries (both subplot and microplot boundaries in the NC Region)

Percentage of data within tolerance Number of times data exceeded tolerance

Variable Tolerance @1x @2x @3x @4x @1x @2x @3x @4x Records

Boundary change No tolerance 100.0 0 170

Contrasting condition No tolerance 92.9 12 170

Left azimuth ±10 degrees 86.5 93.5 94.7 95.3 23 11 9 8 170

Corner mapped No tolerance 98.8 2 170

Corner azimuth ±10 degrees 60.0 80.0 80.0 80.0 4 2 2 2 10

Corner distance ±1 ft 50.0 50.0 50.0 50.0 5 5 5 5 10

Right azimuth ±10 degrees 85.3 92.4 92.4 94.1 25 13 13 10 170
2

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QA results for condition-level variables from 1,191 conditions in the North Central FIA

Percentage of data within tolerance Number of times data exceeded tolerance

Variable Tolerance @1x @2x @3x @4x @1x @2x @3x @4x Records

Condition status No tolerance 99.2 10 1,191

Reserve status No tolerance 99.2 10 1,191

Owner group No tolerance 98.1 18 930

Forest type (type) No tolerance 83.1 157 930

Forest type (group) No tolerance 91.1 83 930

Stand size No tolerance 87.4 117 930

Regeneration status No tolerance 98.9 10 930

Tree density No tolerance 97.1 27 930

Owner class No tolerance 95.4 43 930

Owner status No tolerance 98.6 13 930

Regeneration species No tolerance 98.7 12 930

Stand age ±10 % 68.4 83.5 89.7 92.5 294 153 96 70 930

Disturbance 1 No tolerance 96.0 37 921

Disturbance Year 1 ±1 yr 85.0 85.0 85.0 85.0 6 6 6 6 40

Disturbance 2 No tolerance 94.5 4 73

Disturbance Year 2 ±1 yr 66.7 66.7 100.0 1 1 0 3

Disturbance 3 No tolerance 100.0 0 6

Disturbance Year 3

Treatment 1 No tolerance 97.2 26 921

Treatment Year 1 ±1 yr 92.1 95.2 100.0 5 3 0 63

Treatment 2 No tolerance 95.5 4 89

Treatment Year 2 ±1 yr 100.0 0 13

Treatment 3 No tolerance 94.1 1 17

Treatment Year 3 ±1 yr 100.0 0 5

Physiographic class No tolerance 78.1 204 930

Present nonforest use No tolerance 100.0 0 219

Regional variables

NC–land–use No tolerance 94.4 67 1,191

Table A-2 (Continued)
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Table A-3—South data analysis for core and regional variables.

QA results for tree variables from 1,301 trees in the South FIA

Percentage of data within tolerance Number of times data exceeded tolerance

Variable Tolerance @1x @2x @3x @4x @1x @2x @3x @4x Records

DBH ±0.1 /20 inch 87.3 94.1 96.3 97.4 165 77 48 34 1,301

Azimuth ±10 degrees 97.3 98.7 98.8 99.0 35 17 15 13 1,301

Horizontal distance ±0.2 /1.0 ft 96.4 99.2 99.5 99.5 47 11 6 6 1,301

Species No tolerance 94.2 76 1,301

Tree genus No tolerance 98.3 22 1,301

Tree status No tolerance 99.6 5 1,301

Rotten/missing cull ±10 % 98.2 99.3 99.5 99.6 23 9 7 5 1,283

Total length ±10 % 77.5 93.4 97.2 98.6 274 81 34 17 1,219

Actual length ±10 % 62.9 85.6 89.7 91.8 36 14 10 8 97

Compacted crown ratio ±10 % 81.2 93.8 98.4 99.4 244 81 21 8 1,301

Crown class No tolerance 77.1 298 1,301

Decay class ±1 class 88.8 98.7 100 15 1 0 78

Cause of death No tolerance 93.5 5 77

Mortality year ±1 yr 97.4 97.4 97.4 97.4 2 2 2 2 77

Condition No tolerance 99.7 4 1,283

Regional variables

Tree class No tolerance 88.9 145 1,301

Tree grade No tolerance 69.1 72 233

Board foot cull ±10 % 97.2 98.7 99.2 99.7 37 17 11 4 1,301

Fusiform rust/dieback incidence No tolerance 97.7 30 1,301

Horizontal distance nonwoodland ±0.2 /1.0 ft 96.4 99.2 99.5 99.5 47 11 6 6 1,301

QA results for seedling-level variables from 97 microplots in the South FIA

Percentage of data within tolerance Number of times data exceeded tolerance

Variable Tolerance @1x @2x @3x @4x @1x @2x @3x @4x Records

Species No tolerance 94.1 27 457

Seedling count ±20 % 69.6 71.8 72.4 73.3 139 129 126 122 457

Genus No tolerance 99.6 2 457
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Table A-3 (Continued)

QA results for plot-level variables from 139 plots in the South FIA

Percentage of data within tolerance Number of times data exceeded tolerance

Variable Tolerance @1x @2x @3x @4x @1x @2x @3x @4x Records

Distance road No tolerance 77.0 32 139

Water on plot No tolerance 91.4 12 139

Regional variables

Recreation use 1 No tolerance 89.2 15 139Recreation use 2 No tolerance 94.2 8 139Recreation use 3 No tolerance 99.3 1 139

Road access No tolerance 85.6 20 139

Trail/roads No tolerance 75.5 34 139

QA results for sub-plot-level variables from 580 subplots in the South FIA

Subplot center condition No tolerance 97.8 13 580

Microplot center condition No tolerance 97.6 14 580

Slope ±10 % 85.4 94.8 97.3 98.5 70 25 13 7 481

Aspect ±10 o 79.0 87.5 91.8 92.9 74 44 29 25 353

Snow/water depth ±0.5 ft 99.8 100.0 1 0 481

QA results for boundary-level variables from 51 boundaries in the South FIA

Boundary change

Contrasting condition No tolerance 92.2 4 51

Left azimuth ±10 degrees 86.3 92.2 92.2 92.2 7 4 4 4 51

Corner azimuth ±10 degrees 57.1 57.1 71.4 71.4 3 3 2 2 7

Corner distance ±1 ft 57.1 57.1 57.1 57.1 3 3 3 3 7

Right azimuth ±10 degrees 88.2 94.1 94.1 94.1 6 3 3 3 51
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QA results for condition-level variables from 265 conditions in the South FIA

Percentage of data within tolerance Number of times data exceeded tolerance

Variable Tolerance @1x @2x @3x @4x @1x @2x @3x @4x Records

Condition status No tolerance 98.9 3 262

Reserve status No tolerance 98.8 2 164

Owner group No tolerance 99.4 1 157

Forest type (type) No tolerance 86.6 22 164

Forest type (group) No tolerance 90.9 15 164

Stand size No tolerance 79.9 33 164

Regeneration status No tolerance 98.2 3 164

Tree density No tolerance 98.2 3 164Owner class No tolerance 96.8 5 158

Owner status No tolerance 98.8 2 164

Regeneration species No tolerance 98.2 3 164

Stand age ±10 % 70.0 84.9 92.4 96.2 47 24 12 6 157

Disturbance 1 No tolerance 94.9 8 157

Disturbance Year 1 ±1 yr 100.0 100.0 100.0 100.0 0 0 0 0 7

Disturbance 2 No tolerance 100.0 0 14

Disturbance Year 2Disturbance Year 3

Treatment 1 No tolerance 93.8 10 157

Treatment Year 1 ±1 yr 92.3 92.3 92.3 100.0 1 1 1 0 13

Treatment 2 No tolerance 95.5 1 22

Treatment Year 2 ±1 yr 100.0 0 3

Treatment 3 No tolerance 100.0 0 4

Treatment Year 3

Physiographic class No tolerance 91.3 13 157

Present nonforest use

Regional variables

Land use No tolerance 96.6 9 261

Table A-3 (Continued)
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QA results for tree variables from 2,334 trees in the Interior West FIA

Percentage of data within tolerance

Number of times data exceeded

tolerance

Variable Tolerance @1x @2x @3x @4x @1x @2x @3x @4x Records

DBH ±0.1 /20 inch 89.1 95.8 97.5 97.7 133 51 31 28 1,220

DRC ±0.1 /20 inch 63.9 76.5 83.1 86.8 402 262 188 147 1,114

Azimuth ±10 degrees 97.1 98.6 98.7 98.8 67 33 30 27 2,334

Horizontal distance ±0.2 /1.0 ft 82.5 91.3 94.9 96.5 408 204 120 81 2,334

Species No tolerance 97.1 67 2,334

Tree genus

Tree status No tolerance 99.6 9 2,334

Rotten/missing cull ±10 % 94.4 97.6 98.5 99.0 110 47 30 19 1,960

Total length ±10 % 79.2 93.2 98.2 99.0 408 134 36 20 1,960

Actual length ±10 % 66.1 85.7 94.6 98.2 19 8 3 1 56

Compacted crown ratio ±10 % 64.9 88.6 96.5 98.7 729 236 72 28 2,076

Uncompacted crown ratio (p3) ±10 % 82.4 95.4 98.0 99.7 61 16 7 1 347

Crown class No tolerance 74.0 540 2,076

Decay class ±1 class 93.2 99.0 100 13 2 0 192

Cause of Death No tolerance 84.1 10 63

Mortality year ±1 year 92.1 98.4 100 5 1 0 63

Condition class No tolerance 99.9 3 2,334

Regional variables

Lean angle No tolerance 98.8 27 2,334

Mistletoe ±1 class 98.7 99.2 99.6 100.0 27 17 8 1 2,076

Number of stems No tolerance 85.8 158 1,114

Height to crown ±10 % 77.4 93.2 97.2 98.9 470 142 59 22 2,076

Percent missing top ±10 % 98.7 99.1 99.4 99.6 25 17 11 7 1,960

Sound dead ±10 % 86.5 92.0 94.4 96.1 264 156 109 77 1,960

Form defect ±10 % 79.5 90.1 94.4 96.6 198 95 54 33 964

Current tree class No tolerance 97.3 64 2,334

Radial growth ±1/20 inch 54.0 72.2 82.0 87.3 253 153 99 70 550

Breast Height Tree age ±10 % 56.9 81.4 93.1 96.9 125 54 20 9 290

DRC using IW MQO

±0.2

inch/stem 91.1 95.1 96.8 97.7 99 55 36 26 1,114

Horizontal distance–timberland ±0.2 /1.0 ft 95.7 98.2 98.6 98.7 53 22 17 16 1,220

Horizontal distance–woodland ±0.2 /1.0 ft 68.1 83.7 90.8 94.2 355 182 103 65 1,114

Total length: saplings inches ±10 % 71.1 91.2 95.7 98.7 108 33 16 5 374

Actual length: saplings inches ±10 % 42.9 71.4 71.4 85.7 8 4 4 2 14

Table A-4—Interior West data analysis for core and regional variables.
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QA results for seedling-level variables from 121 microplots in the Interior West FIA

Percentage of data within tolerance

Number of times data exceeded

tolerance

Variable Tolerance @1x @2x @3x @4x @1x @2x @3x @4x Records

Species No tolerance 97.4 4 153

Seedling count ±20 % 57.5 68.6 73.2 77.8 65 48 41 34 153

Regional variables

Total tree age ±2 Years 28.8 52.5 57.5 77.5 57 38 34 18 80

QA results for plot-level variables from 118 plots in the Interior West FIA

Percentage of data within tolerance

Number of times data exceeded

tolerance

Variable Tolerance @1x @2x @3x @4x @1x @2x @3x @4x Records

Distance to road No tolerance 46.8 58 109

Water on plot No tolerance 85.3 16 109

P3 hex number No tolerance 100.0 0 17P3 plot number No tolerance 88.2 2 17

Regional variables

Current location status No tolerance 100.0 0 118

Condition class change

Range data No tolerance 100.0 0 118

Future forest No tolerance 96.6 4 118

Sample kind No tolerance 100.0 0 118

Manual version No tolerance 96.6 4 118

GPS elevation ±50 ft 82.0 92.8 97.3 98.2 20 8 3 2 111

Distance to road–IW tolerance ±1 class 100.0 0 109

Table A-4 (Continued)
3

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Table A-4 (Continued)

QA results for subplot-level variables from 472 subplots in the Interior West FIA

Percentage of data within tolerance

Number of times data exceeded

tolerance

Variable Tolerance @1x @2x @3x @4x @1x @2x @3x @4x Records

Subplot status No tolerance 96.2 18 472

Subplot center condition No tolerance 96.6 16 472

Microplot center condition No tolerance 96.6 16 472

Slope ±10 % 93.4 98.2 99.5 99.5 26 7 2 2 392

Aspect ±10 degrees 58.9 80.6 88.1 91.7 148 70 43 30 360

Snow/water depth ±0.5 ft 99.0 99.5 100.0 4 2 0 392

Regional variables

Subplot condition list 1 No tolerance 96.6 16 472Subplot condition list 2 No tolerance 100.0 0 7Subplot condition list 3Subplot condition list 4

Root disease severity ±1 class 79.7 89.8 98.3 100.0 12 6 1 0 59

QA results for boundary-level variables from 8 boundaries in the Interior West FIA

Percentage of data within tolerance

Number of times data exceeded

tolerance

Variable Tolerance @1x @2x @3x @4x @1x @2x @3x @4x Records

Boundary change No tolerance

Contrasting condition No tolerance 100.0 0 8

Left azimuth ±10 degrees 62.5 100.0 3 0 8

Corner azimuth ±10 degrees

Corner distance ±1 ft 0

Right azimuth ±10 degrees 100.0 8
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QA results for condition-level variables from 141 conditions in the Interior West FIA

Percentage of data within tolerance

Number of times data exceeded

tolerance

Variable Tolerance @1x @2x @3x @4x @1x @2x @3x @4x Records

Condition status No tolerance 99.3 1 141

Reserve status No tolerance 98.6 2 141

Owner group No tolerance 96.5 5 141

Forest type (type) No tolerance 92.8 8 111

Forest type (group) No tolerance

Stand size No tolerance 85.6 16 111

Regeneration status No tolerance 100.0 0 111

Tree density No tolerance 100.0 0 111Owner class No tolerance 92.2 11 141

Owner status No tolerance 92.3 2 26

Regeneration species No tolerance

Stand age ±10 % 93.7 93.7 93.7 94.6 7 7 7 6 111

Disturbance 1 No tolerance 86.5 15 111

Disturbance Year 1 ±1 yr 71.4 85.7 85.7 85.7 2 1 1 1 7

Disturbance 2 No tolerance 84.2 3 19

Disturbance Year 2 ±1 yr

Disturbance 3 No tolerance 100.0 0 3

Disturbance Year 3 ±1 yr

Treatment 1 No tolerance 98.2 2 111

Treatment Year 1 ±1 yr 100.0 0 2

Treatment 2 No tolerance 75.0 1 4

Treatment Year 2 ±1 yr

Treatment 3 No tolerance 100.0 0 1

Treatment Year 3 ±1 yr

Physiographic class No tolerance 54.1 51 111

Regional variables

Hazard status No tolerance 100.0 0 1

Nonstocked forest type No tolerance 50.0 1 2

Percent crown cover ±10 % 88.3 99.1 100.0 13 1 0 111Percent bare ground ±10 %. 75.7 90.1 94.6 97.3 27 11 6 3 111

Habitat type 1 No tolerance 52.3 53 111Habitat type 2 No tolerance 93.7 7 111

Condition class number No tolerance 100.0 0 141

Table A-4 (Continued)
3

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QA results for tree variables from 1,735 trees in California, Oregon and Washington

Percentage of data within tolerance

Number of times data exceeded

tolerance

Variable Tolerance @1x @2x @3x @4x @1x @2x @3x @4x Records

DBH ±0.1 /20 inch 85.9 92.9 94.2 95.1 233 117 95 80 1,647

DRC ±0.1 /20 inch 44.8 57.5 63.2 64.4 48 37 32 31 87

Azimuth ±10 degrees 95.5 98.2 98.7 99.1 78 32 22 16 1,735

Horizontal distance ±0.2 /1.0 ft 86.4 94.5 96.3 97.6 236 96 65 41 1,735

Species No tolerance 95.1 85 1,735

Tree genus No tolerance 97.9 37 1,735

Tree status No tolerance 99.9 2 1,735

Rotten/missing cull ±10 % 98.3 98.8 99.1 99.3 29 21 15 12 1,735

Total length ±10 % 78.1 92.2 96.0 97.8 355 127 64 35 1,619

Actual length ±10 % 78.5 92.6 96.1 97.9 373 128 67 37 1,732

Compacted crown ratio ±10 % 66.6 88.3 96.8 99.1 540 190 52 15 1,619

Crown class No tolerance 66.5 542 1,619

Decay class ±1 class 82.8 93.5 98.9 100.0 16 6 1 0 93

Cause of death No tolerance . . –

Mortality year ±1 yr . . . . . . . . –

Table A-5—West Coast data analysis for core and regional variables.
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Table A-5 (Continued).

QA results for plot-level variables from 69 plots in California, Oregon and Washington

Percentage of data within tolerance

Number of times data exceeded

tolerance

Variable Tolerance @1x @2x @3x @4x @1x @2x @3x @4x Records

Distance to road No tolerance 51.6 31 64

Water on plot No tolerance . . –

QA results for subplot-level variables from 276 sub-plots in California, Oregon andWashington

Percentage of data within tolerance

Number of times data exceeded

tolerance

Variable Tolerance @1x @2x @3x @4x @1x @2x @3x @4x Records

Subplot center condition No tolerance 93.5 18 276

Microplot center condition No tolerance 93.0 19 272

Slope ±10 % 93.5 98.1 98.5 99.2 17 5 4 2 260

Aspect ±10 degrees 59.0 77.5 86.1 89.8 100 55 34 25 244

Snow/water depth ±0.5 ft 99.2 99.2 99.2 99.2 2 2 2 2 258

QA results for boundary-level variables from 86 boundaries in California, Oregon and Washington

Percentage of data within tolerance

Number of times data exceeded

tolerance

Variable Tolerance @1x @2x @3x @4x @1x @2x @3x @4x Records

Boundary change No tolerance . . .

Contrasting condition No tolerance 80.2 17 86

Left azimuth ±10 degrees 74.4 94.2 94.2 95.3 22 5 5 4 86

Corner azimuth ±10 degrees 72.4 82.8 86.2 86.2 8 5 4 4 29

Corner distance ±1 ft 65.5 72.4 72.4 79.3 10 8 8 6 29

Right azimuth ±10 degrees 72.1 91.9 93.0 95.3 24 7 6 4 86
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QA results for condition-level variables from 61 conditions in California, Oregon and Washington

Percentage of data within tolerance

Number of times data exceeded

tolerance

Variable Tolerance @1x @2x @3x @4x @1x @2x @3x @4x Records

Condition status No tolerance 100.0 0 61

Condition nonsampled No tolerance . . .

Reserve status No tolerance 100.0 0 48

Owner group No tolerance 97.9 1 48

Forest type (type) No tolerance 91.7 4 48

Forest type (group) No tolerance 91.7 4 48

Stand size No tolerance 66.7 16 48

Regeneration status No tolerance 91.7 4 48

Tree density No tolerance 95.8 2 48

Owner class No tolerance 89.6 5 48

Owner status No tolerance 82.4 3 17

Regeneration species No tolerance 100.0 0 11

Stand age ±10 % 41.7 58.3 60.4 70.8 28 20 19 14 48

Disturbance 1 No tolerance 79.2 10 48

Disturbance Year 1 ±1 yr 66.7 66.7 66.7 66.7 1 1 1 1 3

Disturbance 2 No tolerance 100.0 0 12

Disturbance Year 2 ±1 yr . . . . . . . . .

Disturbance 3 No tolerance . . .

Disturbance Year 3 ±1 yr . . . . . . . . .

Treatment 1 No tolerance 87.5 6 48

Treatment Year 1 ±1 yr 100.0 0 2

Treatment 2 No tolerance 42.9 4 7

Treatment Year 2 ±1 yr 100.0 0 1

Treatment 3 No tolerance 75.0 1 4

Treatment Year 3 ±1 yr . . . . . . . . .

Physiographic class No tolerance 50.0 24 48

Present nonforest use No tolerance . . .

Table A-5 (Continued).
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-181. 2006 35

Appendix B: Additional Analysis of Disturbance and Treatment____________

The sets of matched condition classes used in analyzing the Disturbance, Disturbance 

Year, Treatment, and Treatment Year facilitate additional analysis of the Disturbance 

and Treatment variables. This will be illustrated by analyzing the Disturbance 1 and 

Treatment 1 variables. A short discussion section follows the analysis. The definition 

of the sets of matched condition classes used in analyzing Disturbance, Disturbance 

Year, Treatment, and Treatment Year are in the main body of the report (see Disturbance 

and Treatment Attributes in the Methods section); for ease of reference we repeat the 

definitions at the end of this appendix.

For easy reference, the pertinent sections of the number of matched condition classes 

from table 1 have been reproduced in table B-0.

The format of this analysis is to pose a question about the Disturbance 1 variable and 

then explain how to use the data in table 1 (or table B-0) to answer the question. The 

explanation of the analysis will be followed by examples of the calculations. Lastly the 

results of the analysis are presented in a table. The same questions can also be asked 

about the Treatment 1 variable; the results of the analysis for the Treatment 1 variable 

will be presented without any explanation.

Table B-0—The number of matched condition classes used in the analysis of 

MQO compliance. This is replication of entries from the Condition 

Variables section of Table 1, the columns labeled “# OBS.”

Region

NE NC SO IW PNW

Disturbance 1 63 921 157 111 48

Disturbance Year 1 3 40 7 7 3

Disturbance 2 5 73 14 19 12

Treatment 1 63 921 157 111 48

Treatment Year 1 8 63 13 2 2

Treatment 2 10 89 22 4 7

Question 1: For what percentage of matched condition classes do both crews record 

no observable disturbance? This can also be stated as: how much of the MQO compliance is due to both crews agreement that there was no observable disturbance?

Analysis 1: It is the ratio of the number of matched condition classes where both crews 

did not record observable disturbance divided by the number of matched condition classes 

where a Condition Status = 1 was recorded. The number of matched condition classes 

where both crews did not record an observable disturbance is the difference between 

the number of matched condition classes where both crews could have recorded an observable disturbance (i.e., both crews recorded a Condition Status = 1) and the number 

of matched condition classes where at least one of the crews recorded an observable 

disturbance (i.e., # Obs Disturbance 2); in equation form this difference is [(# Obs 

Disturbance 1) minus (# Obs Disturbance 2)]. Lastly we need to convert the ratio to 

a percentage. Summarizing,

100* [(# Obs Disturbance 1) minus (# Obs Disturbance 2)]/ (# Obs Disturbance 1)

Examples 1: Calculations based on entries from Table B-0.

Interior West: 100*(111–19)/111 = 83%

Northeast: 100(63–5)/63 = 92%
36 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-181. 2006

Question 2: What percentage of the matched condition classes where at least one crew 

recorded an observable disturbance did both crews record an observable disturbance?

Analysis 2: The matched condition classes where both crews recorded an observable 

disturbance is the set of matched condition classes used in the MQO analysis of Disturbance Year 1; while the matched condition classes where at least one crew recorded 

an observable disturbance is the set of matched condition classes used in the MQO 

analysis of Disturbance 2. So the percentage we want is the ratio of these two numbers 

converted to a percentage:

100 * (# Obs Disturbance Year 1)/ (# Obs Disturbance 2)

Examples 2: Calculations based on entries from table B-0.

Interior West: 100*(7/19) = 37%

Northeast: 100*(3/5) = 60%

Table B-1—Of the matched condition classes where both crews recorded a 

condition status 1, the percentage where both crews recorded no 

disturbance/treatment.

Region

NE NC SO IW PNW

Disturbance 1 92 92 91 83 75

Treatment 1 84 90 86 96 85

Table B-2—Of the matched condition classes where at least one crew 

recorded an observable disturbance/treatment, the percentage where both crews recorded an observable disturbance/treatment.

Region

NE NC SO IW PNW

Disturbance 1 60 55 50 37 25

Treatment 1 80 71 59 50 29

Question 3: For what percentage of the matched condition classes where both crews 

record an observable Disturbance 1 do the crews agree what the disturbance is?

Analysis 3: It is easier to calculate the percentage where they disagree and then subtract from 100. The number of matched condition classes where the crews disagreed 

on the value of Disturbance 1 is the 1X value in the regional tables (tables A-1 through 

A.5 in appendix A). The crews disagree on the value of Disturbance 1 for two reasons: 

first, one crew records an observable disturbance and the other crew does not record 

an observable disturbance; and second, both crews record an observable disturbance 

but they disagree on what the disturbance is. The number of matched condition classes 

where the first reason occurs is (# Obs Disturbance 2 – # Obs Disturbance Year 1); this 

number is subtracted from 1X to obtain the number of matched condition classes where 

both the crews recorded an observable disturbance but they disagreed as to what the 

disturbance was. So the answer of the percentage where they disagree is:

100 *[(# Obs for 1X) – {(# Obs Disturbance 2)- (# Obs Disturbance Year 1)}]/ (# 

Obs Disturbance Year 1)
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-181. 2006 37

The percentage where the crews agree is 100 minus the above number.

Examples 3: Calculations are based on entries from table B-0 and the cell determined 

by line Disturbance 1 and column 1X from appendix A.

Interior West: 100–100*(15–(19–7))/7 = 57%

South: 100–100*(8–(14–7))/7 = 86%

Table B-3—Of the matched condition classes where both crews recorded an 

observable disturbance/treatment, the percentage where both 

crews recorded the same disturbance/treatment.

Region

NE NC SO IW PNW

Disturbance 1 100 90 86 57 67

Treatment 1 100 100 92 100 50

Discussion of Results

In table 1, the MQO compliance for the Disturbance and Treatment variables is generally good. The above analysis shows that most of the MQO compliance is because of 

agreement that there is no disturbance/treatment (see table B-1). In four of the regions, 

if both crews agree that there was a disturbance/treatment, then there is good agreement 

between the crews as to the type of disturbance (see table B-3). There is poor agreement on whether an observable disturbance/treatment occurred when we restrict to the 

condition classes where at least one of crews called an observable disturbance/treatment 

(table B-2).

Summary of the Restricted Set of Matched Condition Classes Used in Disturbance and 

Treatment MQO Analysis—The description is limited to Disturbance and Disturbance 

Year, and the explanation for Treatment and Treatment Year is identical.

Disturbance 1: Matched Condition classes where both the FM crew and the QA crew 

recorded Condition Status = 1.

Disturbance 2: Matched condition classes where at least one of the crews recorded 

an observable disturbance for Disturbance 1.

Disturbance 3: Matched condition classes where at least one of the crews recorded 

an observable disturbance for Disturbance 2.

Disturbance Year 1: Matched condition classes where both the FM crew and the 

QA crew recorded an observable disturbance for Disturbance 1.

Disturbance Year 2: Matched condition classes where both the FM crew and the 

QA crew recorded an observable disturbance for Disturbance 2.

Disturbance Year 3: Matched condition classes where both the FM crew and QA crew 

recorded an observable disturbance for Disturbance 3.
38 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-181. 2006

Appendix C: Explanation and Examples of Missing/

Extra Tree Analyses_ _______________________________________________

The FIA plot is designed to measure all trees on a fixed area. If trees that are on the 

plot are not measured (missed trees), the sample underestimates the true population 

total. If trees that are not on the plot are included in the sample (extra trees), the sample 

overestimates the true population total. The inclusion of trees on a sample plot will 

never be perfect; however, missed and extra trees can be kept to a minimum with good 

field procedures, and estimates will be unbiased if the rate of missed trees is equal to 

that of extra trees.

The blind checks can be used to estimate the probability that a FM crew will miss 

a tree or tally an extra tree. Following the careful matching of all trees tallied by both 

crews, any trees tallied by the QA crew and not by the FM crew were identified as extra 

QA trees and any trees tallied by the FM crew and not the QA crew were identified as 

extra FM trees.

Estimates of the probability of a FM crew missing a tree or tallying an extra tree are 

based on the following assumptions:

1. All trees tallied by both crews are truly on the plot and should be tallied (the 

probability that both crews will tally a tree that is truly not on the plot is equal to 

zero).

2. All trees that are truly on the plot will be tallied by at least one of the two crews 

(the probability that both crew will miss the same tree is equal to zero).

3. Both crews are unbiased in their tally of trees; on average the number of trees they 

record that are truly outside the plot is equal to the number of trees they miss that 

are truly inside the plot.

The assumption is not that QA and FM crews miss trees or tally trees outside the plot 

area at the same rate, but rather that they are both unbiased.

These assumptions are illustrated in the figure C-1. In this hypothetical plot, both 

the QA crew and the FM crew tallied 33 trees; however, only 30 were matched. 

Matched tree (tallied by both crews)

Extra FM tree

Extra QA tree

True plot boundary

Not tallied by either crew

Figure C-1—Hypothetical plot with 30 matched trees, three extra FM trees, 

and three extra QA trees.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-181. 2006 39

The plot truly contains 33 trees. The QA crew missed one of these trees and tallied one 

tree that was outside the plot. The FM crew missed two of the trees that were on the 

plot and tallied two trees that were outside the plot. Both crews are unbiased in their 

tally; they both tallied the correct number of trees. There are three QA crew extra trees 

(two are truly on the plot and one was off the plot) and there are three FM crew extra 

trees (one is truly on the plot and two are off the plot).

Based on these assumptions, the ratio

miss p

extra QA trees

matched trees

extra QA =

+

_ _

_ _

2

 _ trees + extra _ FM _ trees



 



 



 



 2

provides an estimate of the rate at which FM crews are missing trees and the ratio

extra p

extra FM trees

matched trees

extra Q =

+

_ _

_ _

2

 A t _ rees + extra _ FM _ trees



 



 



 



 2

provides an estimate of the rate at which FM crews are recording extra trees. The difference between these two ratios (pextra – pmiss) is a statistic that, under the assumptions, 

has an expected value of zero, with negative values indicating the FM crew is missing 

trees that are truly on the plot more often than they are recording trees that are truly not 

on the plot or the QA crew is recording trees that are truly off the plot more often than 

they are missing trees that are truly on the plot.

In the hypothetical example illustrated in figure C-1, both ratios are equal to 1.5/33 

or 4.5 percent and their difference is zero because both crews tallied the same number 

of trees. In the analysis, the ratios are computed using all of the trees tallied on the blind 

checks for both the micro plot measurements (trees <5-inches diameter) and subplot 

measurements (trees >5-inches diameter). Table C-1 provides these estimates based 

on the blind check tree data but does not include data from the PNW FIA program due 

to problems related to use of the macro plot. These estimates of the bias in the FM 

crew tally relative to QA crew can be interpreted as observations of the FM crew bias 

when the QA crew is assumed to be unbiased. These differences are all quite small. A 

nonparametric bootstrap test found none of the estimated bias values in table C-1 to be 

significantly different from zero at the p = 0.05 level.

In addition to obtaining estimates of extra QA trees and extra FM trees, each extra 

tree was assigned a reason for being extra. These reasons were based on the tree’s location, size, and the condition land status call of the tree from the crew that tallied the tree. 

We attempted to identify all of the reasons a crew would have for not recording a tree 

that was truly on the plot or recording a tree that was truly off the plot. The reasons we 

identified and the criteria used to assign these reasons to extra trees are:

CODE REASON

50 Status—Extra trees due to differences in the condition status. Here one crew 

determined that the tree existed in a forest condition and should be tallied and 

the other crew determined that land status of the condition was nonforest and 

therefore the tree was not tallied.
40 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-181. 2006

60 Edge trees—Extra trees near the edge of the 6.8-ft microplot (6.5 ft or more) or 

the 24.0-ft subplot (23.5 ft or more)

70 Diameter calls—Extra trees near the diameter limit, (1.2 inch or smaller for 

microplot trees, 5.2 inches or smaller for subplot trees

80 Forked trees—Trees tallied as one tree by one crew and two trees by the other 

crew that resulted in one matched tree and one extra tree.

90 Clumps—Extra trees in a clump of trees (a clump is two or more trees of the 

same species within 15 degrees and 2 ft for trees >5 inches and 15 degrees and 

1 ft for trees 1 inch-4.9 inches diameter.

100 Other—Extra trees that do not meet any of the above criteria.

When multiple reasons applied to a tree, the lowest reason code that is applicable to 

the tree was assigned. Table C-2 and figure C-2 summarize the number of extra trees 

by reason for all FIA regions.

Again, as with overall number of number of extra trees, a nonparametric bootstrap 

test for bias was performed for each region-tree type-reason for a total of 48 tests. In 

most of these tests, the estimated bias values were not significantly different from zero 

at the p = 0.05 level; however, five resulted in bias values that were significantly different from zero, and in all five cases they showed that the QA crews were recording more 

trees than the FM crews. The five tests that showed significant bias were:

1. Reason = status, region = NC, trees = saplings

2. Reason = status, region = NC, trees = >5 inches

3. Reason = edge, region = NC, trees = saplings

4. Reason = edge, region = NC, trees = >5 inches

5. Reason = edge, region = IW, trees = >5 inches

Saplings

0

10

20

30

40

50

60

Status

Edge

Dia.

Fork

Clump

Other

Reason

Number of treesNumber of trees

QA

FM

Trees 5"+

0

20

40

60

80

100

120

140

Status

Edge

Dia.

Fork

Clump

Other

Reason

QA

FM

Figure C-2—Number of extra trees tallied by QA and FM crews by identified reason.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-181. 2006 41

Although we were not able to detect an overall bias between QA and FM crews, 

there may be a small bias in the measurement of edge trees and plots where the land 

status falls into question. Most of the significant bias was detected in the NC region, 

which had the most observations giving us the ability to detect very small bias values 

if they exist.

For both the micro plot and the subplot “Other” is the largest reason for extra trees, 

with approximately equal number of extra trees reported by both the FM and QA crews. 

In no case was there significant bias for this reason. This suggests that both crews appear 

to randomly miss trees at the same rate. We were not able to assign any obvious reason 

for these extra trees based on the data available. There are four reasons why a tree could 

be tallied by one crew but not another that we were not able to able to identify:

1. Failure of the crews to agree that the tree is alive and needs to be tallied

2. Failure of one crew member to communicate to the other crew member which 

trees are on the plot

3. Improper data recording or transfer techniques that result in loosing data for a 

tree after the data has been entered

4. Failure to see a tree

Although it was not possible to assign these reasons to extra tally trees, it appears 

that the rate at which trees are missed for these reasons is consistent between the 

QA and FM crews.

In the tally of saplings, disagreements related to diameter measurement and the 

location of trees near the edge of the plot were both major reasons for extra trees. This 

might suggest that an increased emphasis on the careful measurement of the diameter 

of trees near the 5 inches threshold and the location of trees near the 6.8 inches limiting 

distance could possibly improve the precision of the tally of saplings.

Edge was found to be a significant reason for bias in three cases (NC-saplings, 

NC-trees >5 inches and IW-trees >5 inches). There may be a small bias. FM crews may 

have a slight tendency to record these edge trees less often than the QA crews; however, 

the bias, if it exists, is so small that it was not significantly different from zero in all 

cases given the number of measurements we have. In the NC region where the majority 

of the observations were made, the estimated relative bias is –0.20 percent for saplings 

and –0.04 percent for trees >5 inches.

Status was found to be a significant reason for bias in both saplings and trees >5 inches 

in the NC region. Only the QA crews recorded any extra trees where status was identified as the reason for an extra tree. These are trees where the QA crew determined that 

the tree existed in a forest condition and should be tallied and the FM crew determined 

that land status of the condition was nonforest and therefore the tree was not tallied. In 

no cases did the FM crew tally a tree as existing in a forest condition and the QA crew 

determine that the condition was not forest and the tree should not be tallied. There 

were subplots and portions of subplots where the FM crew classified the area as forest 

and the QA crew classified the area as nonforest; however, in none of these areas did 

this result in an extra tree being tallied by the FM crew.

It is important to note that for most reasons, both the FM and QA crews had relatively 

equal numbers of trees. QA crews tend to have more experience and training than FM 

crews. If we are to focus our training to reduce bias caused by missed trees, it appears 

that we should focus more training on land status determination and the measurement of 

edge trees. Also, we need to improve the consistency of training among the regions.
42 USDA Forest Service Gen. Tech. Rep. RMRS-GTR-181. 2006

Region

NE NC SO IW Total

Total matched trees (trees tallied by both crews)

Saplings 450 3,161 266 374 4251

5-inches +

trees

1,424 10,802 1,053 1,962 15,241

Total 1,874 13,963 1,319 2,336 19,492

Extra QA trees (trees tallied by the QA crew but not by the FM crew)

Saplings 20 72 5 21 118

5-inches +

trees

15 152 5 66 238

Total 35 224 10 87 356

Extra FM trees (trees tallied by the FM crew but not by the QA crew)

Saplings 30 40 4 21 95

5-inches +

trees

13 46 9 72 140

Total 43 86 13 93 235

Estimate of the probability that an FM crew missed a treeb

Saplings 0.021 0.011 0.009 0.027 0.014

5-inches +

trees

0.005 0.007 0.002 0.016 0.008

Total 0.009 0.008 0.004 0.018 0.009

Estimate of the probability that an FM crews recorded an extra treec

Saplings 0.032 0.006 0.007 0.027 0.011

5-inches +

trees

0.005 0.002 0.004 0.018 0.005

Total 0.011 0.003 0.005 0.019 0.006

Estimate of the bias in the FM crew tally relative to QA crewd

Saplings 0.011 –0.005 –0.002 0.000 –0.003

5-inches +

trees

–0.001 –0.005 0.002 0.001 –0.003

Total 0.002 –0.005 0.001 0.001 –0.003 a Data from the PNW were not available for extra tree analysis due to problems related to use of the macro

plot.

Table C-1—Tabulation of extra trees observed by FM and QA crews in each regiona, the estimated rates at which FM crews are missing trees and 

recording exra trees, and the estimated bias in the FM crew tree tally 

relative to the QA crew.

b pmiss for example sapling in the NE region 0.021

⎟

⎟

⎟

⎟

⎟

⎠

⎞

⎜

⎜

⎜

⎜

⎜

⎝

⎛

⎥

⎦

⎤ ⎢

⎣

⎡ ⎟

⎠

⎞ ⎜

⎝

⎛ +

+

=

2

20 30 450

2

20

c pextra for example sapling in the NE region 0.032

⎟

⎟

⎟

⎟

⎟

⎠

⎞

⎜

⎜

⎜

⎜

⎜

⎝

⎛

⎥

⎦

⎤ ⎢

⎣

⎡ ⎟

⎠

⎞ ⎜

⎝

⎛ +

+

=

2

20 30 450

2

30

d

 (pextra – pmiss) for example sapling in the NE region 0.011 

( )

⎟

⎟

⎟

⎟

⎟

⎠

⎞

⎜

⎜

⎜

⎜

⎜

⎝

⎛

⎥

⎦

⎤ ⎢

⎣

⎡ ⎟

⎠

⎞ ⎜

⎝

⎛ +

+

−

=

2

20 30 450

2

30 20
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-181. 2006 43

Reason

Region-Tree

type

Crew

Type

Status Edge

tree

Diameter

call

Forked

tree

Clumps Other

All

reasons

NE - Sapling QA 0 2 8 0 4 6 20

NE - Sapling Crew 0 5 23 0 2 0 30

NE - Tree QA 3 4 2 1 0 5 15

NE - Tree Crew 0 2 5 0 0 6 13

NC - Sapling QA 6 18 11 0 5 32 72

NC - Sapling Crew 0 5 9 0 2 24 40

NC - Tree QA 50 20 9 3 5 65 1,84

NC - Tree Crew 0 11 10 0 1 24 71

SO - Sapling QA 0 1 3 0 0 1 5

SO - Sapling Crew 0 0 0 0 0 5 5

SO - Tree QA 0 0 2 0 1 4 7

SO - Tree Crew 0 3 2 1 0 6 12

IW - Sapling QA 0 2 7 0 0 12 21

IW - Sapling Crew 0 5 4 0 0 12 21

IW - Tree QA 0 11 7 0 0 48 66

IW - Tree Crew 0 3 2 0 0 67 72

Total - Sapling QA 6 23 29 0 9 51 1,18

Total - Sapling Crew 0 15 36 0 4 41 96

Total - Tree QA 53 35 20 4 6 122 2,72

Total - Tree Crew 0 19 19 1 1 103 1,68

Table C-2—Number of extra trees enumerated by either FM crews (Crew) or QA crews and reason.

Notes

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The Rocky Mountain Research Station develops scientific information and technology to improve management, protection, and use of 

the forests and rangelands. Research is designed to meet the needs 

of National Forest managers, Federal and State agencies, public and 

private organizations, academic institutions, industry, and individuals. 

Studies accelerate solutions to problems involving ecosystems, 

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Studies are conducted cooperatively, and applications may be found 

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Research Locations

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