Status and Trends of Late-Successional 

and Old-Growth Forests

United States Department of Agriculture

Forest 

Service

Pacific Northwest

Research Station General Technical Report

PNW-GTR-911

NORTHWEST

FOREST PLAN

The First 20 Years (1994–2013)

December

2015

Raymond J. Davis, Janet L. Ohmann, Robert E. Kennedy, Warren B. Cohen, 

Matthew J. Gregory, Zhiqiang Yang, Heather M. Roberts, Andrew N. Gray, and 

Thomas A. Spies
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Authors

Raymond J. Davis is the monitoring leader for old forests and northern spotted 

owls for the Northwest Forest Plan Interagency Monitoring Program, U.S. Department of Agriculture, Forest Service, Pacific Northwest Region, 3200 SW Jefferson 

Way, Corvallis, OR 97331; Janet L. Ohmann and Zhiqiang Yang are research 

associates, Department of Forest Ecosystems and Society, Richardson Hall, Oregon 

State University, Corvallis, OR 97331; Thomas A. Spies, Warren B. Cohen, and 

Andrew N. Gray are research foresters, U.S. Department of Agriculture, Forest 

Service, Pacific Northwest Research Station, Forestry Sciences Laboratory, 3200 

SW Jefferson Way, Corvallis, OR 97331; Robert E. Kennedy is an assistant professor in the College of Earth, Ocean and Atmospheric Science, 114 Wilkinson Hall, 

Oregon State University, Corvallis, OR 97331; Matthew J. Gregory and Heather 

M. Roberts are senior faculty research assistants, Department of Forest Ecosystems and Society, Richardson Hall, Oregon State University, Corvallis, OR 97331.

Cover: left photo by Raymond Davis, right photo by Corey Davis.
i

Abstract

Davis, Raymond J.; Ohmann, Janet L.; Kennedy, Robert E.; Cohen, Warren 

B.; Gregory, Matthew J.; Yang, Zhiqiang; Roberts, Heather M.; Gray, 

Andrew N.; Spies, Thomas A. 2015. Northwest Forest Plan–the first 20 years 

(1994-2013): status and trends of late-successional and old-growth forests. Gen. 

Tech. Rep. PNW-GTR-911. Portland, OR: U.S. Department of Agriculture, Forest 

Service, Pacific Northwest Research Station. 112 p.

This is the third in a series of periodic monitoring reports on LSOG or latesuccessional and old-growth (older) forest status trends on federally administered 

lands since implementation of the Northwest Forest Plan (NWFP or the Plan) in 

1994. The objective of this monitoring is to determine if the NWFP is providing for 

conservation and management of older forests as anticipated. This report focused on 

the amount, distribution, and spatial arrangement of older forests across the NWFP 

landscape, and how these have changed as a result of disturbance and ingrowth 

starting with the year of the NWFP analyses in 1993. 

We developed maps of older forest using three definitions. The first definition 

was the same as used to define LSOG for the prior monitoring reports (10- and 

15-year reports). The other two definitions were based on 80- and 200-year 

thresholds using an “old-growth structure index” (OGSI) developed in this report 

to represent the continuum of forest succession. The 80-year threshold (OGSI-80) 

represented forests that had achieved structure commonly associated with mature, 

late-successional, and old-growth forests in this region. The OGSI-200 threshold 

represented forests that had progressed past maturation and had achieved structure 

found in the later stages of succession commonly associated with old growth in 

this region. The OGSI threshold estimates bracketed the LSOG estimates using the 

previous definition, and changes in forest structure were better interpreted when 

using them. Thus, we terminated the use of the previous LSOG definition for use of 

the newer OGSI-based definitions.

We developed older forest maps for the beginning and ending periods of our 

analyses (1993 and 2012) and called these “bookend” maps. From these bookend 

maps, we assessed changes in the amount and distribution of older forests over 

time. We also used an annual time series of forest disturbance maps to characterize 

the agents of change (harvest, wildfire, and insects/disease) associated with areas 

mapped as older forest loss. To corroborate the mapped information, we estimated 

older forest area from Forest Inventory and Analysis plots, and older forest change 

from two successive forest inventories from which such data were available (Forest 

Service and Oregon Bureau of Land Management lands).
ii

The maps showed net changes in amount of older forests on federal lands 

managed under the NWFP have been small (a 2.8 to 2.9 percent net decrease). 

This occurred despite gross losses from wildfire (4.2 to 5.4 percent), timber 

harvest (1.2 to 1.3 percent), and from insects or other causes (0.7 to 0.9 percent), 

suggesting that processes of forest succession have compensated for some of the 

losses resulting from disturbance. The Plan anticipated a continued decline in 

older forests for the first few decades until the rate of forest succession exceeds the 

rate of gross losses. Decadal gross losses of about 5 percent per decade as a result 

of timber harvesting and wildfire were expected. Observed losses from wildfire 

were about what was expected, but losses from timber harvesting were about 

one quarter of what was anticipated. Results were consistent with expectations 

for older forest abundance, diversity, and connectivity outcomes for this period 

of time. Nothing in the findings suggests that attainment of desired outcomes 

over the next few decades is not feasible; however, we noted some portions of the 

NWFP federal landscape that had been set back from those outcomes, particularly 

resulting from large wildfires in the fire-prone portions of the NWFP area.

Keywords: Northwest Forest Plan, effectiveness monitoring, late-successional 

and old-growth forests, Gradient Nearest Neighbor imputation, LandTrendr change 

detection, Forest Service, Bureau of Land Management, late-successional reserves, 

physiographic provinces. 

Preface

Late-successional and old-growth (LSOG) forest monitoring of the Northwest Forest Plan (NWFP or the Plan) area was approved by an Intergovernmental Advisory 

Committee and is consistent with the framework for effectiveness monitoring 

described in “The Strategy and Design of the Effectiveness Monitoring Program for 

the Northwest Forest Plan” published in 1999. It follows protocols and guidance in 

the “Late-Successional and Old-Growth Forest Effectiveness Monitoring Plan for 

the Northwest Forest Plan” published in 1998. An interagency effectiveness monitoring framework was implemented to meet requirements for tracking the status 

and trends of LSOG forests, populations and habitats of northern spotted owls (Strix 

occidentalis caurina) and marbled murrelets (Brachyramphus marmoratus), watershed conditions, social and economic conditions, and tribal relationships. Monitoring is conducted and reported in 1- to 5-year intervals. Monitoring results for 

the first 10 and 15 years were documented in a series of general technical reports 

available online at http://www.fs.fed.us/pnw/publications/gtrs.shtml. This report, 

and the others in the current series, covers the first 20 years of the Plan.
iii
iv

Contents

1 Introduction

3 History of Mapping Old-Growth Forests in the Pacific Northwest

5 Defining Late-Successional/Old-Growth Forests

7 The Plan’s Expectations

9 Data Sources and Methods

9 Physiographic Provinces

9 Forest Vegetation Zones

11 Land Use Allocations

13 Forest Inventory Plots

16 Old-Growth Structure Index

18 Forest-Capable Area

19 LandTrendr Maps

22 Gradient Nearest Neighbor Maps

24 Bookend Analysis

25 Outcome Analysis

26 Results

26 Bookend Map Analysis

38 Outcome Analysis

43 Maps Versus FIA Annual Plots

44 Maps Versus Remeasured CVS Plots

47 Discussion

51 Uncertainty in Mapping and Estimating Losses, Gains, and Net Change

53 Conclusions

55 Acknowledgments

55 Metric Equivalents

55 References

64 Appendix A: 

Forest Inventory Plot Summaries of Old-Growth Structure Elements

80 Appendix B: 

Old-Growth Structure Index Element Curves

93 Appendix C: 

Old-Growth Structure Index Mapping and Analysis Thresholds

98 Appendix D: 

Attributing and Accuracy of LandTrendr Disturbance Maps

102 Appendix E: 

Gradient Nearest Neighbor (GNN) Older Forest Map Accuracy Report

106 Appendix F: 

Older Forest Summaries by Federal Land Ownerships
1

Northwest Forest Plan—The First 20 Years (1994-2013): Status and Trends of Late-Successional and Old-Growth Forests

Introduction

Slightly more than two decades have passed since the implementation of the Northwest 

Forest Plan (hereafter referred to as “NWFP” or “the Plan”). The Plan amended 19 

existing U.S. Department of Agriculture Forest Service (USFS) and 7 U.S. Department 

of the Interior Bureau of Land Management (BLM) resource management plans across 

three states and two Forest Service regions within the range of the northern spotted 

owl (Strix occidentalis caurina). An interagency effectiveness monitoring framework 

was implemented in the late 1990s to meet the Plan requirements for tracking the 

status and trends of late-successional and old-growth (LSOG) forests, northern spotted 

owl populations and habitat, marbled murrelet (Brachyramphus marmoratus) populations and habitat, watershed condition, social and economic conditions, and tribal 

relationships (Mulder et al. 1999). Beginning in 2005, monitoring reports have been 

published at 5-year intervals and made available at http://www.reo.gov/monitoring/. 

This report is the third in the series of LSOG monitoring reports outlined by 

the interagency monitoring plan (Hemstrom et al. 1998) and covers the time period 

from 1993 to 2012. This report summarizes the assessment of LSOG for federally 

administered lands (“federal lands”) affected by the Plan. Information on other 

ownerships (“nonfederal lands”) was provided for context. In previous monitoring 

reports (Moeur et al. 2005, 2011), the term “older forest” was used interchangeably 

with the terms “late-successional” and “old-growth” forest. This was done to allow 

flexibility for assessing and displaying results based on a variety of definitions. We 

continued the use of this term in the same manner. Because of updates in information sources and improvements in analytical techniques, the results in this report 

are not directly comparable to those in previous reports.

The goal of this monitoring is to evaluate the success of the Plan in reaching 

the desired amount and distribution of older forests on federal lands. Three specific 

monitoring questions were addressed in this report (Hemstrom et al. 1998: 7):

• What was the amount and distribution of older forest at the large-landscape 

scale (e.g., NWFP area, state, physiographic province)?

• What was the spatial arrangement of older forest stands, interior areas, 

edges, and interstand distances across the NWFP landscape?

• How did these things change as a result of disturbance and ingrowth starting with the year of the NWFP analyses in 1993?

Our monitoring relied on two types of data to answer these questions—maps 

and forest inventory plots. The NWFP covers a very large geographic area of over 

50 million acres, with about half of it managed by the federal government. Currently, 

one of the more practical ways to make monitoring maps for such large areas is to 

integrate forest inventory plot data that is collected periodically with satellite remote 
2

GENERAL TECHNICAL REPORT PNW-GTR-911
3

sensing data that is compiled annually. While maps allow us to monitor amounts 

and patterns across broad landscapes, the plot data enable us to monitor stand-level 

changes on the ground that satellites cannot see and also make area-based estimates. 

Using both, we can compare the results from each to better inform ourselves.

The old saying “a picture is worth a thousand words” might also apply to maps. 

A map of the forest allows us to see its abundance and distribution at multiple 

spatial scales. For monitoring purposes, maps from different time periods help us 

to understand how the forested landscape has changed. As stated by W.B. Greeley, 

former Chief of the Forest Service in the early 20th century, “The story is told in 

the maps...” (Greeley 1925).

History of Mapping Old-Growth Forests 

in the Pacific Northwest

The first mapping of forest resources in the Pacific Northwest was accomplished in 1873 

as part of the 9th National Census map atlas (Walker 1874). That map was a hand-drawn 

representation of the land covered by forest; represented in density classes based on the 

number of 40-ac blocks per square mile (640 ac) that were wooded (Liknes et al. 2013). 

It did not focus on any specific timber type or seral stage; those forest attributes became 

more well-defined in the early 20th century when a statewide map of forest resources 

in Oregon was produced in 1914 (ODF 1914). Shortly thereafter, the 1928 McSweeneyMcNary Act directed the Secretary of Agriculture to “make and keep current an 

inventory and analysis of the Nation’s forest resources.” That effort produced maps that 

included the first comprehensive assessments of older forests across the entire NWFP 

area (Andrews and Cowlin 1940, Cowlin et al. 1942, Wieslander and Jensen 1946).

The next map of older forests for the NWFP area was not produced until over 

a half century later (FEMAT 1993) and was used for planning purposes for the 

NWFP. Since the Plan’s implementation, there have been two additional federal 

efforts to map the older forests within its boundary (Moeur et al. 2005, 2011) and 

other efforts have mapped older forest within the general area (Strittholt et al. 

2006). This report marks the third federal effort. 

While not intended for monitoring purposes at the time of their development, 

the historical maps in conjunction with the newer maps provide us with some idea of 

how forests have changed over the last century (fig. 1). When monitoring older forest 

where losses can occur rapidly, but growth and recruitment operate on much longer 

time scales, it can be easier to see the changes between maps separated by decades 

or a century than maps separated by years. The longer historical perspective can be 

important for setting the context for monitoring forest change (e.g., establishing a 

baseline) and adapting to the information it provides (Liknes et al. 2013).
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GENERAL TECHNICAL REPORT PNW-GTR-911

Figure 1—Historical and current maps of older forest within the Northwest Forest Plan area. Map sources for the 

historical map are Andrews and Cowlin (1940) in Washington and Oregon, and Weislander and Jensen (1946) in California and are extrapolated from stand-level surveys. The current map is from this report and based on remotely sensed 

data. OGSI refers to the “old-growth structure index” mapped at two different stand-age thresholds: 80 and 200 years.
5

Northwest Forest Plan—The First 20 Years (1994-2013): Status and Trends of Late-Successional and Old-Growth Forests

Defining Late-Successional/Old-Growth Forests

The first step in making a map of old-growth forest requires that old growth be defined 

in a discrete and mappable way. Yet, old-growth forests are complex and hard to 

ascribe to terms that separate them distinctly from the intricacy that is forest succession (Franklin and Spies 1991, Frelich and Reich 2003, Marcot et al. 1991, Thomas et 

al. 1988). Human perceptions of what is an old-growth forest are just as complex, if not 

more so (Pesklevits et al. 2011, Spies and Duncan 2009). Experience has taught us that 

defining old-growth forests is problematic; yet, for purposes of monitoring, necessary.

Early attempts at defining old growth in this region date back to the first forest 

inventory (Andrew and Cowlin 1940, Wieslander and Jensen 1946). Back then, the 

term “old growth” was a relative one used to differentiate slower-growing older 

forests from the faster-growing younger forests (Andrews and Cowlin 1940). In 

addition to a general sense of stand age, it was largely based on the diameter at 

breast height (dbh) of the largest dominant and co-dominant live trees. 

Beginning in the 1980s, ecological definitions that included more elements 

than just the size of the large live trees began to be formulated (Franklin et al. 1981, 

Franklin and Spies 1984, SAF 1984). In 1985, an interagency group composed of 

technical experts from the USFS (management and research station), BLM, and 

Oregon State University began developing interim definitions of old growth for 

Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) forests for the Pacific Northwest (Old-Growth Definition Task Group 1986). Thomas et al. (1988) concluded 

that old growth was best perceived as, “a stage of forest development characterized 

by more diversity of structure and function than that found in younger successional 

stages.” Furthermore, they recognized that old-growth characteristics differed by 

forest type, such that a single definition was not feasible. However, they acknowledged that definitions were needed for management purposes (Thomas et al. 1988).

By the end of the decade, the Chief of the Forest Service sent out a memo to 

regional foresters outlining the Forest Service’s position on old growth as well as 

a generic definition and description of old-growth forests (USDA FS 1989). This 

definition encompassed the later stages of forest stand development that are usually 

distinguished by the presence of larger, older trees and structural attributes such as 

multiple canopy layers, decadence in the form of standing dead trees (snags), and 

accumulations of fallen trees (logs). It also noted that these characteristics differed 

by forest types, such that one definition would not fit all. And finally, it made clear 

that old-growth forest was not necessarily “virgin” or “primeval” forest, but could 

be developed through thoughtful forest management. This generic definition was 

to serve as the starting framework for the development or modification of more 

specific definitions that related to structural components that could be readily 
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GENERAL TECHNICAL REPORT PNW-GTR-911

identified or measured (USDA FS 1989). Shortly thereafter, interim definitions were 

established that provided discrete classifications based on minimum amounts of oldgrowth elements such as snags and logs (USDA FS 1992, 1993). 

For monitoring purposes, the definition of “older forest” has been fairly simple 

in the past, and focused on the average diameter of live overstory trees (Moeur et 

al. 2005, 2011). However, the effectiveness monitoring plan called for the eventual development of a more refined definition or indices to help assign plots and 

remotely sensed stands to a position along a continuum of late-successional and 

old-growth structure and composition development (Hemstrom et al. 1998).

Here, we used an index called the “old-growth structure index” (OGSI) conceptually developed by Spies and Franklin (1988) and further refined as a method to 

overcome some of the problems with using categorical interim definitions (Franklin 

and Spies 1991). This index has been found to be useful for monitoring the abundance of old-growth forest across large landscapes (Franklin et al. 2005, Gray at al. 

2009, Ohmann et al. 2012). The OGSI consists of measurable forest structure 

elements, in our case (1) density of large live trees, (2) diversity of live tree size 

classes, (3) density of large snags, and (4) percentage of cover of down woody 

material. These are elements commonly considered as key ecological and structural 

Old-growth structure index

Stand age (years)

Low

High

Younger

Young with 

old growth 

remnants

Mapping threshold at 200 years

Mapping threshold 

at 80 years

Mature with 

old growth 

remnants

Early 

old growth

Mid 

old growth

Late 

old growth

0 70 150 250 500 750

Older

Snags 

and 

logs

Figure 2—The old-growth structure index from Spies and Franklin (1988) is represented by the solid 

black line. The dashed line represents the modified index curve used for mapping older forests in 

this report. It minimized inclusion of young forests with high structural diversity (e.g., post-wildfire 

snags and logs) and focused on older forests.
7

Northwest Forest Plan—The First 20 Years (1994-2013): Status and Trends of Late-Successional and Old-Growth Forests

attributes of old-growth forests within the NWFP area. Low index values represented younger or less structurally complex forests, and high index values represented older or more structurally complex forests (fig. 2). 

The Plan’s Expectations

At its implementation in 1994, the NWFP anticipated that the rate of loss of older 

forests on federally managed lands that had been observed in prior decades would 

lessen, eventually stabilize, and then begin to increase as younger forests in reserved 

land use allocations developed into older forests. A continued loss of existing older 

forests of about 5 percent per decade from timber harvesting and wildfires was 

expected, but recruitment was expected to eventually exceed these losses. It was 

estimated that it would take at least 50 to 100 years to restore the amount of older 

forest on federal lands (fig. 3) to within the typical range that occurred during 

previous centuries, and closer to what they had been prior to logging and extensive 

fire suppression (FEMAT 1993, USDA and USD1 1994: chaps. 3 and 4: 36–46).

Older forest on federal lands (percent area)

Time period

Outcome 1:

The amount of old

forests on federal lands 

is at levels near or above 

the long-term average 

that occurred prior to 

logging and extensive 

fire suppression.

Outcome 2:

The amount of old 

forests on federal lands 

is within the estimated 

natural range.

Outcome 3:

The amount of old 

forests on federal lands 

is beIow the estimated 

natural range.

Outcome 4:

The amount of old 

forests on federal lands 

is very low.

1940 1990 2040 2090 2140

Observed trend

Future

trend?

Figure 3—The expectation of the Northwest Forest Plan was that it would take 50 to 100 years to achieve 

the abundancy, diversity, and connectivity outcomes for older forests on federal lands within the northern 

spotted owl’s range (curve derived from FEMAT (1993) and fig. 5 in Hemstrom et al. (1998: 22).
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GENERAL TECHNICAL REPORT PNW-GTR-911

Table 1—Thresholds for measuring success in achieving older forest abundance and diversity outcomesa

Outcome

Percentage of federal land 

covered by older forest

Percentage of federal land in 

older forest standsb more than 

1,000 acres in patch size

Percentage of provinces that 

meet both amount and stand 

patch size criteria

------------------------------------- Percent -------------------------------------

1 60 to 100 80 to 100 80 to 100

2 40 to 60 5 to 80 5 to 80

3 5 to 40 1 to 5 1 to 5

4 Less than 5 Less than 1 Less than 1

a

 Summarized from Hemstrom et al. (1998) (see fig. 3).

b

 Patches of older forest that consist of core plus core-edge pixels (see bottom of fig. 7).

Table 2—Thresholds for measuring success in achieving older forest connectivity outcomesa

Outcome

Average distance between 

old-forest stands of more 

than 1,000 acres 

Percentage of federal land 

covered by old forest standsb

Percentage of adjacent provinces 

connected with large stands of 

old forest

------------ Miles ------------ -------------------------- Percent --------------------------

1 Less than 6 60 to 100 100

2 6 to 12 50 to 60 100

3 12 to 24 25 to 50 Less than 100

4 More than 24 Less than 25 Less than 100

a

 Summarized from Hemstrom et al. (1998) (see fig. 3).

b Consists of patches and fingers of older forest, which include riparian buffers and green tree retention.

General outcomes for abundance, diversity, and connectivity of older forest 

were described in the Forest Ecosystem Management Assessment Team (FEMAT) 

report (FEMAT 1993), the Plan (USDA and USDI 1994: chaps. 3 and 4: 36–46), 

and refined into “measureable” outcomes by Hemstrom et al. (1998: 19–21). These 

measurable outcomes were based on a long-term average, defined as a period 

of at least 200 to 1,000 years, over which the full potential range of older forest 

communities could develop following a severe disturbance. The Forest Ecosystem Management Assessment Team (1993) estimated that 60 to 70 percent of the 

forested area of the region was typically covered by older forests, ranging higher in 

moister forests and lower in drier forests. The team (FEMAT 1993) also estimated 

the average centurial low (average of the lows that occur in 100-year periods) at 

40 percent, setting the lower limit of the “typical” range for older forest coverage. 

These averages were to apply to a wide range of stand sizes, from less than 1 ac, 

to hundreds of thousands of acres. Measurable outcomes were defined in terms of 

the abundance and diversity (amount and patch size) of older forest (table 1) and 

connectivity (distance between patches) of older forest (table 2).
9

Northwest Forest Plan—The First 20 Years (1994-2013): Status and Trends of Late-Successional and Old-Growth Forests

Monitoring allows us to evaluate whether or not the Plan is meeting these 

expected outcomes. The overall expectation (based on expert opinion) was that the 

Plan had a 77-percent likelihood of achieving outcomes 1 or 2 in moister provinces, 

and 63-percent likelihood in drier provinces (FEMAT 1993). But, Hemstrom et al. 

(1998) cautioned that older forest development takes many decades and these outcomes, which were based on the understanding of long-term reference conditions at 

the time of the Plan’s implementation, might need to be adjusted as climate changes 

and our scientific understanding of forest ecology evolves.

Data Sources and Methods

Many, but not all, of the data sources used in this report were initially developed 

and used for the 10- and 15-year monitoring reports. During each 5-year monitoring 

cycle, previously used data sources are updated to incorporate new research findings and other information, or to correct errors. While more detailed descriptions of 

these data sources can be found in previous monitoring reports (Davis et al. 2011; 

Lint 2005; Moeur et al. 2005, 2011), we briefly describe them here, and discuss any 

updates made from previous versions.

Physiographic Provinces

The NWFP boundary was based on the geographic range of the northern spotted 

owl. Because the range of the owl was so large, it was divided into 12 physiographic 

provinces for analytical purposes (FEMAT 1993, Thomas et al. 1990, USDA and 

USDI 1994). Physiographic provinces were delineated in an attempt to reduce the 

complex and diverse nature of the owl’s range into broad areas that represented different forest zones, plant communities, and disturbance regimes that vary geographically with climate, topography, soils, and geology. These physiographic provinces 

were largely based on subdivisions by Franklin and Dyrness (1973). We use the same 

physiographic provinces that were used for the 15-year report (Moeur et al. 2011).

Forest Vegetation Zones

The concept of “vegetation zones” (areas dominated by a plant community type) 

has existed for well over a century (Humboldt 1816) and many efforts to map them 

have been, and continue to be, made (Franklin and Dyrness 1973, Henderson et al. 

2011). Similar to physiographic provinces, potential vegetation zones reflect the 

physical and climatic conditions of the area and are useful for ascribing ecological 

processes of forest development and disturbance. Vegetation zones often are drawn 

at a much finer spatial resolution than provinces. Here, we used vegetation zones to 

group forest inventory plots from across the NWFP area for analysis and development of unique old-growth structure indices for each zone (fig. 4).
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GENERAL TECHNICAL REPORT PNW-GTR-911

Figure 4—Potential forest vegetation zones used in deriving old-growth structure index equations 

(Simpson 2013). CA = California, OR = Oregon, WA = Washington.
11

Northwest Forest Plan—The First 20 Years (1994-2013): Status and Trends of Late-Successional and Old-Growth Forests

Our forest vegetation zone map was a 30-m raster map developed by the USFS, 

Pacific Northwest Region (Region 6), Ecology Program (Simpson 2013). Other maps 

existed for portions of the region, but this one provided consistent coverage for the 

entire NWFP area. This layer was derived from understory species composition 

information in existing Gradient Nearest Neighbor (GNN) maps developed by the 

Landscape Ecology, Modeling, Mapping, and Analysis group in the Pacific Northwest 

(http://lemma.forestry.oregonstate.edu/). Understory species serve as indicators for 

what type of forest will dominate in the absence of disturbance. Multiple map dates 

(1996, 2000, and 2006) were used to account for species composition changes owing to 

recent disturbances such as fire or timber harvest. Presence or absence of tree species 

was used to infer potential forest vegetation zones. Where imputations from different 

map years disagreed, the more mesic vegetation zone was assigned, with one exception: plots with both mountain hemlock and Pacific silver fir present were assigned 

to the silver fir zone when mountain hemlock (Tsuga mertensiana (Bong.) Carriere) 

accounted for less than 10 percent of the plot basal area. Independent Ecology Program 

plots (http://ecoshare.info/category/data-sets/) were used for classification assessment. 

The most common forest vegetation zones were low- to moderate-elevation 

types such as western hemlock, Douglas-fir, and grand fir/white fir, which 

accounted for about 63 percent of the NWFP forested areas (fig. 4). Higher-elevation 

types like Pacific silver fir and mountain hemlock covered about 17 percent of the 

NWFP forests. Coastal areas were dominated by Sitka spruce (Picea sitchensis

(Bong.) Carr.) in the north and redwoods (Sequoia sempervirens (D. Don) Endl.) in 

the south, which comprised about 7.4 percent of the forested area. The remaining 

12.6 percent is composed of various other vegetation zones.

Land Use Allocations

Federal land use allocations (LUAs) have specific management directions under 

the NWFP. This report groups these allocations into two categories: (1) reserved 

and (2) nonreserved. Reserved allocations are areas where the maintenance and 

restoration of older forests over time is expected under the current land use plans. 

They include the following land use allocations:

Congressionally reserved (CR) areas: Lands reserved by the U.S. Congress such 

as wilderness areas, wild and scenic rivers, and national parks and monuments.

Late-successional reserves (LSRs): Lands reserved for the protection and restoration of LSOG forest ecosystems and habitat for associated species; including marbled 

murrelet reserves (LSR3) and northern spotted owl activity core reserves (LSR4).

Managed late-successional areas (MLSAs): Areas for the restoration and maintenance of optimum levels of LSOG stands on a landscape scale, where regular and 
12

GENERAL TECHNICAL REPORT PNW-GTR-911

frequent wildfires occur. Silvicultural and fire hazard reduction treatments are allowed 

to help prevent older forest losses from large wildfires or disease and insect epidemics.

Administratively withdrawn (AW) areas: Areas identified in local forest and 

district plans; they include recreation and visual areas, back country, and other 

areas where management emphasis does not include scheduled timber harvest.

Adaptive management area in reserves (AMR): Identified to develop and test 

innovative management to integrate and achieve ecological, economic, and other 

social and community objectives. Emphasis on restoration of late-successional 

forests and managed as an LSR.

Nonreserved LUAs were designed for multiple land use objectives including 

sustained-yield management for timber production. They include:

Matrix (other): Federal lands outside of reserved allocations where most timber 

harvest and silvicultural activities were expected to occur.

Adaptive management area (AMA)–Nonreserved (AMA): Identified to develop 

and test innovative management to integrate and achieve ecological, economic, 

and other social and community objectives. Some commercial timber harvest was 

expected to occur in these areas, but with ecological objectives.

The geographic information system (GIS) layer representing these LUAs was 

originally delineated during the analysis for the NWFP (USDA and USDI 1994). 

The LUA GIS layer has been updated three times since it was first developed. 

Each update was done prior to the monitoring cycle, 

including this one. Previous updates were described 

in the 10- and 15-year monitoring reports. The latest 

update mainly involved addition of congressionally 

reserved allocations (364,000 ac) as a result of a few 

new wilderness designations since the 15-year report. 

Other updates included land exchanges and acquisitions as well as minor editing to correct errors and 

clean up map features. About 71,000 ac remain without assigned allocations. We attributed these areas as 

“no data” (ND). Since NWFP implementation, LUA 

updates document a slight overall increase in federal 

lands (1.5 percent) with a 0.4 percent increase in 

reserved land use allocations (fig. 5).

As in previous monitoring reports, riparian 

reserves, another NWFP LUA, were not delineated 

because of a lack of consistency in defining and 

delineating the stream network at the Plan, scale 

Figure 5—Federal land use allocation by percentage of area at the 

Northwest Forest Plan’s implementation in 1994 (from USDA and 

USDI 1994) and two decades later (2013). AW = administratively 

withdrawn, AMA = adaptive management area, MLSA = managed 

late-successional areas, LSR = late-successional reserve, CR = 

congressionally reserved.
13

Northwest Forest Plan—The First 20 Years (1994-2013): Status and Trends of Late-Successional and Old-Growth Forests

and varying site-specific definitions (Moeur et al. 2005). Thus, our estimates for 

“reserved” federal lands were biased low, and “nonreserved” estimates were biased 

slightly higher than they would be if riparian reserves had been accounted for, owing 

to unmapped riparian reserves on “nonreserved” LUAs.

Forest Inventory Plots

The forest attributes used in this report were derived from field measurements of forest 

conditions on forest inventory plots distributed across the region. We used these plot 

data as imputed by GNN map production to estimate vegetation conditions for specific 

years of interest (Ohmann and Gregory 2002). We also used the plot data directly for 

the range of years they were measured for comparison to our map-based estimates. 

Plot data from several inventory efforts were used in this assessment (table 3). 

On BLM and Pacific Northwest Region (USFS R6) USFS lands, Current Vegetation Survey (CVS) (Max et al. 1996, USDA FS 2001, USDI BLM 2001) plots were 

established on a systematic grid in the mid-1990s and remeasured in the late 1990s 

and early 2000s (table 3). Outside of wilderness on Region 6 lands, the CVS grid 

was established on a 1.7-mi spacing (one plot per 1,850 ac) on BLM lands and 

Region 6 Wilderness, the CVS grid was on a 3.4-mi spacing (one plot per 7,400 ac). 

On Pacific Southwest Region (R5-FIA) USFS lands, plots were randomly installed 

within predefined strata in the mid-1990s (USDA FS 2000). Beginning in 2001, 

the Forest Inventory and Analysis (FIA) program of the Forest Service Pacific 

Northwest Research Station installed plots across all ownerships with a spacing of 

one plot per 6,000 ac on a 10-year remeasurement cycle (FIA annual; Betchtold and 

Patterson 2005). There were 11,511 forested plots in the NWFP area used to assess 

recent condition.

Every inventory plot sampled lands within a 2.47-ac area, with four subsample 

points for the FIA annual sample and five subsample points for the CVS and 

R5-FIA. We only used plots that contained areas classified as forested (currently 

or previously ≥10-percent stocked with tree species not primarily managed for a 

nonforest land use, ≥1 ac, and ≥120 ft wide). 

The FIA annual plot data (2002–2011) were used to compare against map 

estimates for all landowners in the NWFP area. To do this, we averaged the mapbased estimates across time periods (1993 and 2012), which spanned the FIA plot 

sampling period. We also compared mapped change estimates with plot-based 

change estimates using CVS plots, which were remeasured twice with the same plot 

design, and between R5-FIA and FIA annual plots, where the plot design differed 

between measurements. However, as found in the 15-year report (Moeur et al. 2011), 

substantial unexplained differences were found between R5-FIA, FIA annual, and 
14

GENERAL TECHNICAL REPORT PNW-GTR-911

Table 3—Distribution and sampled area of plots on U.S. Forest Service (USFS) and western Oregon Bureau of 

Land Management (BLM) lands in the Northwest Forest Plan area by measurement year 

CVS occasion 1 CVS occasion 2 R5-FIA FIA annual

Year

Number 

of plots

Area 

sampled

Number 

of plots

Area 

sampled

Number 

of plots

Area 

sampled

Number 

of plots

Area 

sampled

--------------------------------------------------- Thousand acres ---------------------------------------------------

1993 384 984 19 13

1994 927 2,862 2 8

1995 1,792 4,940 22 49

1996 2,114 4,861 309 1,924

1997 552 520 542 1,123 352 1,342

1998 595 1,207 505 1,934

1999 594 1,326 22 85

2000 283 548 3 8

2001 449 914

2002 759 2,466 79 488

2003 639 1,403 81 527

2004 579 2,246 90 563

2005 491 1,127 76 464

2006 406 927 102 626

2007 432 880 89 533

2008 73 484

2009 103 647

2010 92 588

2011 87 544

Totals 5,769 14,167 5,769 14,167 1,234 5,363 872 5,464

Current Vegetation Survey (CVS) = USFS Pacific Northwest Region (Region 6) and BLM; Pacific Southwest Region (Region 5) Forest Inventory and 

Analysis (FIA) and FIA annual = USFS R5.

GNN map estimates that make them incomparable on account of the differences in 

the plot sampling designs, so the R5-FIA results were not shown here.

For a general sense of how our map-based estimates of older forest compared to plotbased estimates at the large-landscape scale, we estimated older forest area for federal, 

nonfederal, and all ownerships combined using FIA annual inventory plots measured 

between the years 2002 to 2011 and standard FIA inventory procedures (e.g., Campbell 

et al. 2010). These plot measurements occurred between the time periods of our maps 

(time 1 = 1993 and time 2 = 2012) with the midpoint being around 2006, so for map-toplot comparisons, we used the averaged mapped areal estimate for time 1 and time 2. 
15

Northwest Forest Plan—The First 20 Years (1994-2013): Status and Trends of Late-Successional and Old-Growth Forests

For a comparison of map- and plot-based estimates of older forest change, we 

used CVS data from remeasured plots from USFS and BLM lands. Plots were first 

measured from 1993 to 1997, and then remeasured from 1997 to 2007 (table 4). On 

average, the time between the measurements was 7.1 years. The USFS CVS data 

were initially compiled by Waddell and Hiserote (2005), with additional compilation, 

land classification, and disturbance attribution done by Gray and Whittier (2014). 

Standard stratification layers were built in GIS using National Land Cover Database 

(NLCD) classified satellite data, vegetation zone GIS layers, and USFS parcel 

ownership layers for inventory plot compilation. Standard errors were calculated for 

all plot-based estimates using post-stratification and double-sampling statistics to 

calculate sample error (Bechtold and Patterson 2005).

Table 4—Distribution and sample area of forest inventory plots by sample occasions used for the plot-based 

assessment of older forest change

First sample occasion

(1993–1997)

Second sample occasion

(1997–2007)

State and physiographic province Number of plots Area sampleda Number of plots Area sampled

Thousand acres Thousand acres

Washington:

Olympic Peninsula 291 631.2 291 631.2

Western Lowlands 0 0.0 0 0.0

Western Cascades 1,153 2,948.4 1,153 2,948.4

Eastern Cascades 1,004 3,413.7 1,004 3,413.7

Total 2,448 6,993.3 2,448 6,993.3

Oregon:

Coast Range 430 1,426.6 430 1,426.6

Willamette Valley 3 25.9 3 25.9

Western Cascades 1,795 4,355.3 1,795 4,355.3

Klamath 675 2,105.8 679 2,133.0

Eastern Cascades 641 1,560.8 641 1,560.8

Total 3,544 9,474.4 3,548 9,501.5

California:

Coast Range 17 91.4 12 70.6

Klamath 973 4,357.8 742 4,348.6

Cascades 315 1,038.1 193 1,103.0

Total 1,305 5,487.4 947 5,522.3

Northwest Forest Plan total 7,297 21,955.1 6,943 22,017.1

a

Area sampled based on plot expansion factors and not the actual area of the plots. 
16

GENERAL TECHNICAL REPORT PNW-GTR-911

Old-Growth Structure Index

The concept of the old-growth structure index was a departure from the normal 

approach to defining old growth. It was designed to reflect the continuous nature of 

ecological succession as opposed to identifying one point along the continuum to 

separate old growth from younger forests (Spies and Franklin 1988). It is a composite 

index that simply sums the values of old-growth characteristics so that the highest 

index values occur in the later stages of forest succession. The first step in developing an OGSI is to determine the proper old-growth characteristics (elements) to sum.

As stated previously, our OGSI was calculated using one to four measurable 

old-growth structure elements including (1) density of large live trees, (2) diversity 

of live-tree size classes, (3) density of large snags, and (4) percentage cover of down 

woody material. These elements vary in size (e.g., what diameter constitutes a “large” 

tree) and importance by forest vegetation zone. To account for these differences, we 

used forest inventory plots and the Random Forest algorithm (Breiman 2001) as the 

first step to determining the appropriate elements to use for each forest vegetation 

zone. Random Forests is a common method used for variable selection (Genuer 

2010). We used it to rank potential structure elements based on their relationship 

(explained variance) to stand age. For example, in the ponderosa pine vegetation 

zone, the density of large conifer explained most of the variance in the plot data for 

older forests, whereas dead wood elements explained little. In drier forests, such as 

ponderosa pine, the natural disturbance regime and relatively high decomposition 

rates result in very low levels of snags and down wood. Thus, in that forest zone, the 

OGSI was simply based on the density of large live trees. 

Once structure elements were selected, we used forest inventory plot data to examine the age class distributions (e.g., <25, 25 to 50, 50 to 75, 75 to 100, 100 to 150, 150 to 

200, ≥200 years) for different diameter classifications (e.g., ≥9.8, 19.7, 29.5, and 39.4 in 

dbh) of the large live tree, snag, and down wood elements within each mapped forest 

vegetation zone (fig. 4). Based on the distributions of the means (taking into consideration the standard deviations), we used expert opinion and guidance from interim definitions (USDA FS 1992, 1993) to select minimum diameters for each structure element 

within each forest vegetation zone (table 5). The diameter size classes were chosen 

such that the structure element consistently increased with age and distinguished well 

between the older age classes (e.g., >150 year) and the younger ones (app. A).

Next, an OGSI was calculated for every forest inventory plot using methods 

described in Pabst (2005). Each element was scored on a continuous scale from 0 

to 100 determined from regression equations for single or segmented lines linked 

to statistical distributions from the plot data (app. B). For the live tree element, 

we used the minimum, 25th, 75th, and 99th percentiles) as our regression inflexion 
17

Northwest Forest Plan—The First 20 Years (1994-2013): Status and Trends of Late-Successional and Old-Growth Forests

points. For dead wood elements, we used the minimum, median, 75th , and 99th percentiles. To develop these regressions, we only used forest inventory plots for stands 

that were older than 100 to 200 years, depending on the forest zone in which they 

occurred (table 5). For the stand structure element, we used a diameter diversity 

index (a measure of the structural diversity of a forest stand, based on tree densities 

in different dbh classes) as implemented in Pabst (2005). Each element was given 

equal weighting and our OGSI was the sum of the element scores divided by the 

total number of elements in the equation.

The OGSI reflected the continuous nature of forest succession, but for monitoring purposes, we are required to identify thresholds along that continuum to 

produce binary maps and plot estimates of older forests (0 = not old forest, 1 = old 

forest) for analysis of abundance and distribution. To select mapping and plot analysis thresholds, we used forest inventory plot data to fit a nonparametric curve to the 

relationship between OGSI and stand age using locally weighted polynomial regression in R version 3.0.2. (R Core Team 2013) (app. C). The first threshold we chose 

Table 5—Minimum old-growth structure index (OGSI) element thresholds and minimum stand ages for 

selection of plots used to calculate OGSI by forest vegetation zonea

Forest vegetation 

zone

Percentage 

of NWFP 

forest area

Large 

live tree 

element 

min dbh

Snag 

element 

min dbh

Down 

wood 

element 

min dia.

Plot 

minimum 

stand age 

threshold

Mean plot 

age ± 1 SD

General 

interim 

definition 

agesb

Percent ---------------- Inches ---------------- ---------------- Years ----------------

Western hemlock 28.0 39.4 29.5 9.8 200 273 ± 62 200

Douglas-fir 19.5 29.5 19.7 9.8 150 200 ± 55 180-295

White fir/grand fir 15.8 29.5 19.7 9.8 200 249 ± 47 150-300

Pacific silver fir 9.8 29.5 19.7 9.8 200 293 ± 74 180-360

Mountain hemlock 7.2 29.5 19.7 9.8 200 272 ± 62 150-200

Tanoak 3.9 39.4 19.7 9.8 200 242 ± 38 240

Redwood 3.7 39.4 29.5 9.8 150 245 ± 127 None

Sitka spruce 3.7 39.4 29.5 9.8 200 305 ± 116 None

Western redcedar 2.2 39.4 29.5 9.8 200 250 ± 38 200

Oak woodland 1.9 19.7 n/a n/a 100 118 ± 18 None

Ponderosa pine 1.5 29.5 n/a n/a 150 201 ± 71 150-200

Subalpine 1.2 19.7 19.7 9.8 150 200 ± 50 150-200

Other pine <1 19.7 n/a n/a 100 124 ± 22 120-200

Port Orford cedar <1 29.5 19.7 9.8 200 247 ± 42 240

Shasta red fir <1 29.5 19.7 9.8 200 231 ± 23 150-200

Juniper <1 19.7 n/a n/a 100 132 ± 22 None

NWFP = Northwest Forest Plana. dbh = diameter at breast height. SD = standard deviation. b The last two columns compare mean and standard deviation (±1 SD) of plot stand ages used in the calculations of OGSI to existing interim definition guidance.

Source: USDA FS 1992,1993.
18

GENERAL TECHNICAL REPORT PNW-GTR-911

was based on stand age of 80 years for all forest vegetation zones except ponderosa 

pine, which was 120 years. We called this threshold the “OGSI-80” (even though 

ponderosa pine was 120) and used it to describe the general point on the forest 

succession time scale (fig. 2) at which young forests in this region generally begin to 

“mature” and start exhibiting stand structure associated with older forests (FEMAT 

1993, Franklin and Johnson 2013, USDA and USDI 1994). The second analytical 

threshold used in this report was called the “OGSI-200” and was based on a 200-

year stand age (160 years for oak woodlands), which generally corresponds to the 

range of stand ages used to define the “old-growth” condition in this region (table 

5). We also provided thresholds at 120 and 160 years in appendix C, table C-1.

We intentionally excluded stand age from the equations used to calculate OGSI 

as in Pabst (2005) because, as can be seen in the scatter graphs in appendix C, 

forests develop old-forest structure at different rates depending on site conditions 

and many other factors (Zenner 2004). For example, Franklin and Spies (1991) illustrated that a young stand with high amounts of dead wood (snags and down wood) 

inherited from the previous old stand could achieve an index value in the mid range 

of the scale. Our analysis of the plot and map data verified this, as we noted midrange index values in several unsalvaged postfire areas with high levels of snags and 

down wood. As our focus was to estimate amounts of older forest, not structurally 

complex early-seral forest, we modified the binary older forest thresholds to only 

include stands with ≥10-percent tree canopy cover and either the presence of at least 

one large live-tree exceeding the diameter threshold or an average stand diameter 

greater than half the size of the live tree diameter threshold for that vegetation zone.

Thus, our classified maps of “older forest” based on these thresholds are not maps 

of age per se. Rather, they are maps of old-growth structure that represent two different points in a continuum of forest succession and stand development: one at which 

forests begin to have elements of mature forest structure, and one occuring later when 

the characteristics of old growth are well established (Franklin et al. 2002, Spies 2004).

Forest-Capable Area

This data source is a 30-m resolution raster coverage used for map analyses that represents areas within the NWFP boundary that are capable of developing into forests. This 

map was developed for the 15-year monitoring reports (Davis et al. 2011, Raphael et 

al. 2011) and was not updated for this report. It is largely based on the U.S. Geological 

Survey (USGS) Gap Analysis Program (GAP) and the “impervious layer” from NLCD 

(Herold et al. 2003, Vogelmann et al. 2001). It excludes urbanized areas, major roads, 

agricultural areas, water, lands above tree line, snow, rock, and other nonforested 

features. We used this map to “mask out” nonforested areas for each time period map. 

Map areal estimates and other analyses in this report only apply to forest-capable areas.
19

Northwest Forest Plan—The First 20 Years (1994-2013): Status and Trends of Late-Successional and Old-Growth Forests

LandTrendr Maps

LandTrendr maps identified where, when, how much, and how long disturbance had 

occurred between 1993 and 2012. They also showed us areas where the forest vegetation had been stable or was recovering (fig. 6). These annual time-series maps of forest 

vegetation disturbance and recovery were similar to what was used in the 15-year 

monitoring reports. They were developed following methods in Kennedy et al. (2010, 

Figure 6—LandTrendr data (1993–2012) provide us with a picture of both forest disturbance (left) 

and stable/recovering areas (right). CA = California, OR = Oregon, WA = Washington.
20

GENERAL TECHNICAL REPORT PNW-GTR-911

2012) and verified for accuracy using the TimeSync method (Cohen et al. 2010). The 

LandTrendr maps represented three aspects of vegetation change: (1) year of disturbance, (2) magnitude of disturbance, and (3) duration of disturbance. We classified 

these maps to produce a map of where timber harvesting, wildfire, insect and disease, 

and other natural disturbances (e.g., blowdown, floods, landslides) have occurred 

between 1993 and 2012 (app. D). Where this map overlapped losses of older forest, it 

helped to explain the causes for older forest loss since the Plan’s implementation. 

Landsat imagery that covered the NWFP area was acquired from the USGS 

Glovis website (glovis.usgs.gov) for the summer period (usually July and August) from 

1984 to 2012. Images were atmospherically corrected using Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) methodology (Masek et al. 2008). 

This was different from the multivariate alteration detection and calibration (MADCAL) radiometric normalization methods (Canty et al. 2004) used in the 15-year 

report (Moeur et al. 2011). To minimize cloud coverage, multiple image dates within a 

given season were used to produce a clear-pixel composite image for that year. 

The composite imagery was then processed using the LandTrendr segmentation 

algorithm (Kennedy et al. 2010), which computes the normalized burn ratio (NBR) 

(Key and Benson 2006, van Wagtendonk et al. 2004) spectral index for each pixel in 

the time series. The algorithm performed a temporal segmentation for each pixel to 

find year-dates (vertices) where changes in NBR had occurred (normally associated 

with changes in vegetation). Between sets of any two vertices, temporal segments were 

established and each segment was labeled as disturbance, recovery, or stable based 

on spectral direction. For disturbance segments, the year of disturbance declared was 

always one year after the first (or start) vertex of the segment. The “year of detection” did not always represent the “year of disturbance.” Sometimes the disturbance 

occurred after the Landsat image was acquired for that year. The disturbance was then 

detected in the imagery from the following year (or years) depending on image quality and other factors. Usually, the disturbance was detected within 1 to 2 years of the 

event. Because many disturbances can last more than one year, the number of years 

of the disturbance segment was used as the duration of the segment. Both duration 

and magnitude were also provided for recovery segments. Where there was no change 

in NBR over a segment, the segment was declared as stable, with duration equal to 

the number of years of the segment and magnitude equal to zero. Each segment was 

subsequently fitted with a linear regression to smooth the NBR response over the segment length, thereby removing unwanted spectral noise. This is referred to as temporal 

normalization. For each temporally normalized segment, we estimated the percentage 

of vegetation cover for the beginning and ending vertices using a statistical model that 

relates NBR to vegetation cover (Cohen et al. 2010). The difference between vertex 

predictions represented the magnitude of the disturbance or recovery segment in terms 
21

Northwest Forest Plan—The First 20 Years (1994-2013): Status and Trends of Late-Successional and Old-Growth Forests

of percentage vegetation cover. We scaled the absolute magnitude to the starting value 

to compensate for varying predisturbance forest cover values, such that all reported 

magnitudes were expressed as a proportion of the starting condition. The final step was 

to spatially filter the pixels to a minimum mapping unit of 11 pixels, or about 2.47 ac. 

Occasionally, there were temporally overlapping disturbances detected during our 

monitoring period. However, Kennedy et al. (2012) found that overlapping secondary 

disturbances within the NWFP area covered less than 5 percent of the area of primary 

disturbances. For simplicity, our LandTrendr products and analyses were based on the 

highest magnitude disturbance that occurred throughout the entire Landsat time series. 

We used duration and magnitude to reclassify the three LandTrendr disturbance map 

products (year, duration, and magnitude) into one map that represented the cause of 

most significant disturbance between 1993 and 2012. An analysis of wildfire and timber harvest unit polygons showed an average disturbance duration signal of about 1.5 ± 

1.0 years (mean ± 1 SD) for regeneration harvests, 1.7 ± 1.7 years for wildfires, and 2.2 

± 2.2 years for thinning harvests. Based on this information, we classified the duration 

map into a binary map of fast (1 to 4 years) and slow (>4 years) disturbance. Fast disturbance represented abrupt events such as a wildfire, timber harvest, or debris flow. 

Slow disturbances represented insects or disease, or postfire mortality. To attribute the 

cause of disturbance, we used a map of wildfire perimeters (see Davis et al. 2011: chap. 

4) that was updated to include mapped wildfires that occurred between 1993 and 2012 

to classify disturbances caused by wildfire. We used aerial insect damage detection 

GIS data (USDA FS 2008) to help classify disturbance that was likely associated with 

spruce budworm (Choristoneura occidentalis) and bark beetles (Dendroctonus spp.). 

Finally, we used LUAs for congressionally reserved or administratively withdrawn 

lands to help classify areas where timber harvesting was not likely to be the cause of 

the disturbance. See appendix D, table D-1 for the complete classification rule set used 

to produce our map of cause of disturbance and classification accuracy assessment. 

Our maps of wildfire disturbance are fairly inclusive of all major wildfires exceeding a 

few acres, but there are many wildfires each year that are smaller than this, which were 

not mapped. We used agency “forest activity” harvest GIS layers, where available, but 

did not have similar GIS layers for harvesting on nonfederal lands. 

Given the disturbance detection time lag previously discussed, our disturbance 

maps did not capture all disturbances that occurred during the final year of our 

analysis (2012), particularly for those disturbances that occurred later in that year 

after the satellite image acquisition date, or were obscured by smoke from wildfires. 

Those changes will be captured in subsequent monitoring efforts, and, in general, 

our classified disturbance map provided us a general sense of the amount of and 

change agents behind older forest losses between 1993 and 2012.
22

GENERAL TECHNICAL REPORT PNW-GTR-911

Gradient Nearest Neighbor Maps

We used GNN maps of forest composition and structure (Ohmann and Gregory 

2002) on forest-capable lands from two different dates, 1993 and 2012. We 

developed GNN maps specifically for landscape- and regional-scale analysis and 

monitoring (Moeur et al. 2005, 2011; Ohmann and Gregory 2002; Spies et al. 

2007). They have also been used for other broad-scale vegetation analyses across a 

wide range of forest ecosystems for multiple objectives (Ohmann et al. 2007, 2011, 

2012; Pierce et al. 2009; USDI FWS 2011). Along with each map, a large suite of 

diagnostics detailing model reliability and map accuracy were provided and are 

summarized in appendix E.

Our mapping method for this report was the single neighbor (k = 1) GNN 

imputation as described by Ohmann and Gregory (2002) and Ohmann et al. (2014), 

and similar to the method used for the 15-year monitoring (Moeur et al. 2011, 

Ohmann et al. 2012). The process uses the nearest-neighbor plot identified for each 

map pixel based on the weighted Euclidean distance within a multivariate gradient 

space as determined from canonical correspondence analysis (CCA) (ter Braak 

1986), a method of constrained ordination (direct gradient analysis). All of the forest 

attributes from that plot are then assigned (or imputed) to each map pixel. The k 

= 1 approach produces realistic representations of the range of variation of forest 

conditions over broad regions, including the “extreme tails” of the distributions 

(Ohmann et al. 2014), which can represent rare conditions (e.g., very large conifer 

forest). It also maintains the covariance structure of plot attributes within each map 

pixel such that there will be no illogical combinations of species or structures not 

found in the plot data. 

We only used plots that were at least 50 percent forest. For each plot, tree-level 

data from all forest subplots were combined and converted to per-hectare (per-acre) 

values that described the forest portion of the plot, which was treated as homogeneous. We did not average conditions across forest and nonforest land classes within 

a plot because our modeling and mapping approach applied only to the forested 

component of the landscape. Imputation was applied to pixels of all land classes, 

but nonforest areas were masked in the final vegetation map using the previously 

described forest-capable layer.

To determine values for spatial predictors associated with each plot observation, we used a 3- by 3-pixel “plot footprint” that encompassed the outer extent of 

the forested area sampled by the field plot. We intersected the 3- by 3-pixel block 

with the LandTrendr data described in the previous section for the same year as 

plot measurement, and the means of the tasseled cap indices (composite images 

representing brightness, greenness, and wetness based on weighted combinations 
23

Northwest Forest Plan—The First 20 Years (1994-2013): Status and Trends of Late-Successional and Old-Growth Forests

of Landsat TM image bands) and fitted NBR for this footprint were associated with 

the plot observation. Values for the abiotic explanatory variables (e.g., climate) were 

similarly extracted for the plot footprints, but assumed constant over the range of 

plot measurement dates.

For both the 15- and 20-year reports, the GNN maps were developed using Landsat time-series data that were temporally normalized using the LandTrendr algorithm 

as described in the previous section. The LandTrendr data allowed us to develop 

GNN maps for multiple years with improved consistency across years. For the 20-year 

report maps, we made several incremental improvements to our data and methods as 

summarized below, some of which are also documented in Ohmann et al. (2014):

New plots added: The 15-year maps were based on plots measured through 2007. 

For the 20-year maps, we added four more years of field plots, measured through 2011.

Screening for plot outliers: This process benefited immensely from having 

yearly LandTrendr time-series mosaics from 1984 to 2012, as well as LandTrendr 

disturbance maps, to aid in determination of timing of disturbances relative to plot 

measurement. In screening, potential outlier plots were flagged using various algorithms that compared observed plot attributes to predicted map attributes to identify 

mismatches. These plots were viewed in the LandTrendr imagery and digital aerial 

photography to identify and exclude plots that straddled contrasting forest conditions (e.g., older forest and clearcut), or that had been disturbed between plot measurement and imagery dates (uncommon with the yearly plot-imagery matching).

New spatial predictor: Spatial predictor variables normally used in the GNN 

process include maps of abiotic variables such as climate, topography, latitude, and 

longitude, as well as Landsat imagery of tasseled cap brightness, greenness, and 

wetness. For the 20-year report, we added the NBR from the LandTrendr data. The 

LandTrendr segmentation algorithms for creating disturbance maps were based on 

NBR, and we hoped to gain more consistency between the older forest and disturbance maps by adding this variable.

Matching of plots to imagery: For the 15-year report, we had only two 

LandTrendr imagery dates to work with, and plots were matched either to imagery 

time 1 or time 2. This resulted in as much as a 6-year difference between plot 

measurement date and imagery date. Many plots were excluded from modeling 

because of disturbance between plot and imagery dates. For this 20-year report, we 

implemented yearly matching of field plots to LandTrendr data, with plots matched 

to the same year as plot measurement. This was made possible by having yearly 

LandTrendr mosaics available from 1984 to 2012. This resulted in many fewer plots 

excluded because of disturbance and effectively eliminated differences between plot 

and imagery dates associated with growth.
24

GENERAL TECHNICAL REPORT PNW-GTR-911

From these GNN maps, we derived maps of “older forest” for time 1 (1993) and 

time 2 (2012) using the established “LSOG” definition based on quadratic mean 

diameter (QMD ≥ 20 in and conifer cover ≥ 10 percent) from previous monitoring 

reports (Moeur et al. 2005, 2011) and two additional definitions based on the previously described age thresholds (80 years and 200 years) applied to the OGSI maps. 

We termed these classified maps of older forest at time 1 and 2 as “bookend” maps. 

They portrayed older forest conditions at the beginning and ending of the Plan 

monitoring period covered by this report (1993 to 2012).

While GNN was designed for regional-scale analyses, the maps portray forest 

structure and composition at finer spatial scales. In a recent assessment of the OGSI, 

GNN performed well at the scale of a 30-km (18.6-mi) hexagon (distance from the 

center of one hexagon to the next), which covered slightly more than 190,000 ac 

(Ohmann et al. 2014). In addition to conducting our analyses at regional, state, and 

physiographic scales, we used 30-km (18.6-mi) hexagons to summarize our maps 

for interpretation and visual displays of older forest abundance, diversity, and connectivity at times 1 and 2, as well as changes between these time periods.

Bookend Analysis

We first analyzed the change in areal frequency distribution of OGSI as a continuous index divided into 10 equal-interval bins (e.g., 0 to 10, 10 to 20, 20 to 30, 90 

to 100) for both time periods (1993 and 2012). This allowed us to look at changes 

along the entire spectrum of older forest development, instead of some threshold set 

to portray just the older forest portion. 

We then analyzed abundance and distribution of older forests using GNN data to 

produce binary bookend maps of older forest for the two time periods, and for three 

different definitions (LSOG, OGSI-80, and OGSI-200). We analyzed the spatial 

patterns of each binary map using software GUIDOS v1.4, which was developed for 

analysis of forest spatial patterns extracted from satellite images (Soille and Vogt 

2009). GUIDOS assigned each older forest pixel an attribute that described its 

landscape membership by segmenting binary map patterns into seven mutually 

exclusive classes: core, perforation, edge, bridge, branch, loop, and islet. We then 

combined these morphological classes into five general landscape categories (fig. 7):

Core—The interior portion of a group of older forest pixels that is large enough 

to contain at least one pixel 98 ft from the edge.

Core-edge—Pixels along the edge of group of older forest pixels large enough to contain at least one core pixel. Given the resolution of the binary maps, edges are 98 ft wide.

Patch—Consists of core plus core-edge pixels. Represents stands of older forest 

that contain some interior area and are at least 2.47 ac (the approximate size of a 

forest inventory plot).
25

Northwest Forest Plan—The First 20 Years (1994-2013): Status and Trends of Late-Successional and Old-Growth Forests

Finger—Stringers of older forest pixels that bridge, branch, or 

loop out from a patch, but are not wide enough to contain any core 

pixels (1 to 2 pixels wide but can be several pixels in length).

Scatter—Isolated pixels or groups of older forest (islet) pixels 

that are too small (<2.47 ac) to contain core pixels and do not connect 

to any patches of older forest.

From each bookend, we produced areal estimates of older forest 

amounts and patterns for both time periods (1993 and 2012). We 

then differenced the bookend maps to quantify net changes in both 

amount and distribution between the two time periods. Areal map 

estimates were prepared for each OGSI threshold (80 and 200 year) 

by physiographic province, state, and NWFP area.

Finally, geographic representations of the conditions in 1993 and 

1992, as well as the net changes between these two time periods, 

were provided using 30-km (18.6-mi) hexagons. For these geographical representations, areal amounts were summarized as the percentage area of each hexagon (192,000 ac) regardless of the area within 

the hexagon that was forest capable. This produced maps where the 

percentage value of each hexagon was comparable to the next. 

Outcome Analysis

To determine the Plan’s effectiveness in achieving outcomes 1 or 2 for 

abundance, distribution, and connectivity of older forest, we summarized four things: (1) area covered by old forest, (2) area covered by 

patches and fingers only (>2.47 ac), (3) area covered by large patches 

only (>1,000 ac), and (4) average distance between large patches. We 

used 30-km (18.6-mi) hexagons (center to center) to represent “relatively large areas” as described in FEMAT (1993: IV–50). Each hexagon covered about 192,000 ac. Using zonal statistics in ArcGIS, each 

hexagon was attributed with the percentage of federal forest lands, 

older forest amounts (total, patches and fingers only, and large patches 

only), and mean distance to large patches of older forest as calculated 

in ArcGIS using the Euclidean Distance tool in Spatial Analyst. 

These statistics were based on the entire area of the hexagon, 

and not just the forest-capable portions within them. Thus, they 

were comparable between all hexagons across the Plan area. Only 

hexagons that contained at least 10 percent federally managed forest 

lands (or about 19,200 ac, which is roughly equivalent to one sixthfield watershed) were attributed with outcomes outlined in tables 1 

Figure 7—Morphologic spatial patterns and landscape categories used to assess old-forest patterns 

and connectivity.
26

GENERAL TECHNICAL REPORT PNW-GTR-911

and 2 for each time period. We then differenced the time periods to determine if the 

trend was toward improving or degrading the outcome. Finally, we determined which 

hexagons met the threshold criteria for outcomes 1 or 2 for abundance and diversity 

(table 1) as well as connectivity (table 2) for each time period.

Results

Bookend Map Analysis

Observed frequency distributions of OGSI for all lands across the NWFP area 

varied between different land manager groupings (fig. 8). The national parks 

showed a larger proportion of their forests with higher OGSI scores; whereas 

nonfederal forest lands had the largest proportion of lower OGSI scores. Forests 

managed by the BLM and USFS were intermediate, with the USFS having slightly 

larger proportions of higher OGSI scores than BLM. These OGSI distributions did 

not change much between 1993 and 2012, with the exception of increases in the first 

bin (0 to 10) that likely resulted from losses from disturbances in all other bins.

Overall, the amount of older forest on all lands within the NWFP boundary has 

decreased by 5.9 to 6.2 percent between 1993 and 2012, depending on which older 

forest definition was used (fig. 9a). The amount of decrease was less on federal lands 

(2.0 to 2.9 percent) than on nonfederal lands (11.6 to 18.1 percent) (figs. 9b and 9c, 

respectively). When subdivided into smaller landscape areas, we noted net gains in 

certain portions of the NWFP area (app. F). Most of the areas with net gains coincided 

0

10

20

30

40

50

60

70

80

1993 2012 1993 2012 1993 2012 1993 2012

Nonfederal Bureau of Land

Management

Forest Service National Park Service

Percentage of forested area

Land manager

0 to 10

10 to 20

20 to 30

30 to 40

40 to 50

50 to 60

60 to 70

70 to 80

80 to 90

90 to 100

OGSI

range

Figure 8—Frequency distributions of the old-growth structure index (OGSI) based on 10 equal-interval bins for the major federal land 

managers between bookend time periods (1993 and 2012).
27

Northwest Forest Plan—The First 20 Years (1994-2013): Status and Trends of Late-Successional and Old-Growth Forests

with locations of historical large wildfires that burned in mid-19th or early-20th centuries (e.g., Yaquina Fire, Nestucca Fire, Yacolt Fire). Landscapes that exhibited net 

losses were mainly coincident with recent large wildfires (e.g., Biscuit Fire, Megram 

Fire, B&B Fire) or nonfederal timber lands. The physiographic provinces that incurred 

the largest losses of older forest (OGSI-80 and OGSI-200) on federal lands (based on 

area) were the Oregon Western Cascades, Oregon Klamath, and California Klamath. 

0

5

10

15

20

1993 2012 1993 2012 1993 2012

OGSI-80 LSOG OGSI-200

Older forest area

(million acres)

All lands

-5.9% -6.2% -6.1%

19.0 17.9

11.3 10.6

8.3 7.7

Figure 9—Change in area of older forests by ownership class from the map-based bookend analysis 

of binary maps of old forest using three definitions; old-growth structure index at the 80-year 

threshold (OGSI-80) (left), late-seral old growth (LSOG) (b), and OGSI at the 200-year threshold 

(OGSI-200) right. Percentage net changes in old-forest area between bookends (1993 and 2012) are 

shown in italics.

0

5

10

15

20

Older forest area

(million acres)

1993 2012 1993 2012 1993 2012

OGSI-80 LSOG OGSI-200

Federal lands

-2.9% -2.0% -2.8%

12.6 12.2

7.4 7.3 6.4 6.3

0

5

10

15

20

Older forest area

(million acres)

Core Core-edge Finger Scatter

1993 2012 1993 2012 1993 2012

OGSI-80 LSOG OGSI-200

Nonfederal lands

-11.6% -14.2% -18.1%

6.5 5.7

3.9 3.3 1.8 1.5

A

B

C
28

GENERAL TECHNICAL REPORT PNW-GTR-911
29

Northwest Forest Plan—The First 20 Years (1994-2013): Status and Trends of Late-Successional and Old-Growth Forests

The spatial arrangement of older forest is distinctly different on federal versus nonfederal lands. Core and core-edge account for a smaller proportion on nonfederal forests (fig. 9c), but account for half or more of the total amount of older forest on federal 

lands (fig. 9b). Combined, these two components (core and core-edge) form relatively 

contiguous patches of older forest ranging from 2.47 to >10,000 ac. The remaining older 

forest consists of fragmented fingers and small pieces or remnants scattered throughout 

a matrix of younger forests. Over the last 20 years, older forests have become slightly 

more fragmented from disturbances on federal lands at the scale of the Plan, but again, 

in some smaller landscapes such as the Siuslaw National Forest, the older forests have 

not only increased in abundance, but have also become somewhat less fragmented. 

In the above analysis, we compared amounts and spatial patterns of older forest 

using three different definitions: (1) the OGSI threshold at 80 years, (2) the LSOG 

definition used in previous monitoring reports (QMD ≥ 20 in and conifer cover ≥10 

percent), and (3) the OGSI threshold at 200 years. As noted by Moeur et al. (2011) and 

Ohmann et al. (2012), the LSOG definition’s reliance on QMD rendered it susceptible 

to blinking on or off as the result of loss or gain of just a few trees on a plot (e.g., an 

underburn or thinning from below can create instant older forest). The OGSI definitions avoided this problem by not using QMD, but small changes in the live-tree tally 

may still affect the diameter diversity index component of the index. Based on these 

definitions, the amount of LSOG fell in between the amounts for OGSI at the 80- and 

200-year thresholds. Because LSOG was bracketed by the OGSI thresholds (fig. 9), we 

discontinued its use to simplify our analyses and also for the reasons stated above. For 

the remainder of this report, we framed the analyses using only the OGSI at the 80- 

and 200-year thresholds shown in appendix C. Time period areal map estimates and 

estimates of net change for both map thresholds are shown by physiographic province, 

state, and NWFP area and displayed in tables 6 through 9.

Geographical representations of federal land management agencies, federal 

forest, and reserved federal forest were visually catagorized using 30-km (18.6-mi) 

hexagons (fig. 10). Estimates of losses by disturbance category for both map thresholds are shown by physiographic province, state, and NWFP area and displayed in 

tables 6 through 9. Geographical representation of the information in these tables is 

shown in figures 11, 12, and 13. Over all federal lands, the leading disturbance agent 

for loss of older forest on federal lands was wildfire, which accounted for 71 to 73 

percent of the loss in OGSI-80 and OGSI-200, respectively (fig. 14). Federal timber 

harvesting accounted for 16 to 19 percent (OGSI-200 and OGSI-80, respectively). 

Other natural disturbances and insects and disease accounted for about 10 to 11 

percent (OGSI-80 and OGSI-200, respectively). At the Plan scale, the rate of loss of 

older forests on federal lands from timber harvesting was 1.2 to 1.3 percent (tables 6 
30

GENERAL TECHNICAL REPORT PNW-GTR-911 Table 6—Bookend map areal estimates of older forests (OGSI-80) on federal lands (left), net area and percentage changes from 1993 to 2012, and assigned losses from LandTrendr disturbance maps (right) Older forest area estimates from bookend maps LandTrendr disturbance assignment for losses State and physiographic province 1993 2012 Net area change Net percent change Harvest Wildfire Insects Other Total explained loss Percentage loss from 1993 ----------- Thousand acres ----------- Percent -------------------------- Thousand acres -------------------------- Percent Washington: Olympic Peninsula 909.2 910.8 1.6 0.2 2.5 1.2 1.2 2.6 7.4 -0.8 Western Lowlands 38.0 40.3 2.3 6.1 0.0 0.0 0.1 1.5 1.5 -4.0 Western Cascades 1,685.8 1,704.9 19.1 1.1 8.4 2.5 1.3 5.3 17.5 -1.0 Eastern Cascades 1,427.8 1,395.9 -31.9 -2.2 25.3 78.3 36.2 5.2 145.1 -10.2 Total 4,060.7 4,060.7 -8.8 -0.2 36.2 82.0 38.8 14.6 171.5 -4.2

Oregon:

Coast Range 633.5 639.7 6.3 1.0 12.2 0.1 0.5 0.1 12.9 -2.0

Willamette Valley 5.7 5.7 0.0 -0.4 0.3 0.0 0.0 0.0 0.3 -5.0

Western Cascades 2,640.9 2,512.4 -128.5 -4.9 35.5 72.3 4.1 1.6 113.5 -4.3Klamath 1,132.8 1,020.5 -112.3 -9.9 17.8 138.5 1.3 0.4 158.0 -13.9Eastern Cascades 789.5 767.1 -22.4 -2.8 17.8 35.1 9.6 1.0 63.5 -8.0

Total 5,202.4 4,945.3 -257.0 -4.9 83.5 246.0 15.6 3.0 348.2 -6.7

California:

Coast Range 157.9 163.2 5.3 3.4 0.4 3.9 0.1 0.6 5.1 -3.2Klamath 2,683.3 2,573.0 -110.3 -4.1 13.8 186.7 3.1 3.3 206.9 -7.7Cascades 478.8 487.7 8.9 1.9 14.2 7.9 3.3 0.8 26.3 -5.5

Total 3,320.0 3,224.0 -96.0 -2.9 28.4 198.6 6.5 4.8 238.2 -7.2

Northwest Forest Plan total 12,583.0 12,221.2 -361.8 -2.9 148.1 526.6 60.9 22.4 757.9 -6.0
31

Northwest Forest Plan—The First 20 Years (1994-2013): Status and Trends of Late-Successional and Old-Growth Forests

Table 7—Bookend map areal estimates of older forests (OGSI-80) on nonfederal lands (left), net area and percentage changes from 1993 to 2012, and assigned losses from LandTrendr disturbance maps (right)

Older forest area estimates from bookend maps LandTrendr disturbance assignment for losses

State and physiographic province 1993 2012

Net area 

change

Net 

percentage 

change Harvest Wildfire Insects Other

Total 

explained 

loss

Percentage 

loss from 

1993

----------- Thousand acres ----------- Percent -------------------------- Thousand acres -------------------------- Percent

Washington:

Olympic Peninsula 294.0 233.5 -60.5 -20.6 80.7 0.0 2.5 0.0 83.2 -28.3

Western Lowlands 866.4 666.2 -200.2 -23.1 289.4 0.0 5.9 0.0 295.3 -34.1Western Cascades 527.6 462.2 -65.4 -12.4 131.6 0.3 2.2 0.0 134.0 -25.4

Eastern Cascades 584.9 514.9 -70.0 -12.0 106.0 10.6 9.1 0.0 125.7 -21.5

Total 2,272.9 1,876.9 -396.0 -17.4 607.7 10.9 19.6 0.0 638.2 -28.1

Oregon:

Coast Range 815.3 596.6 -218.7 -26.8 285.1 0.4 4.8 0.0 290.3 -35.6

Willamette Valley 127.6 107.8 -19.8 -15.5 27.1 0.0 0.5 0.0 27.5 -21.6

Western Cascades 476.7 370.5 -106.2 -22.3 158.7 3.3 2.8 0.0 164.9 -34.6

Klamath 390.5 351.0 -39.5 -10.1 90.5 3.4 2.1 0.0 95.9 -24.6

Eastern Cascades 236.5 203.2 -33.3 -14.1 48.8 7.3 3.1 0.0 59.2 -25.0

Total 2,046.6 1,629.1 -417.5 -20.4 610.2 14.4 13.2 0.0 637.8 -31.2

California:

Coast Range 1,267.0 1,358.6 91.6 7.2 90.8 6.6 3.8 0.0 101.2 -8.0Klamath 503.2 494.2 -9.0 -1.8 50.6 10.1 3.8 0.0 64.5 -12.8

Cascades 372.3 350.3 -22.0 -5.9 49.1 5.4 4.4 0.0 58.9 -15.8

Total 2,142.6 2,203.2 60.6 2.8 190.5 22.0 12.0 0.0 224.5 -10.5

Northwest Forest Plan total 6,462.0 5,709.2 -752.8 -11.7 1,408.3 47.3 44.9 0.0 1,500.5 -23.2

OGSI-80 = old-growth structure index at the 80-year threshold. 
32

GENERAL TECHNICAL REPORT PNW-GTR-911 Table 8—Bookend map areal estimates of older forests (OGSI-200) on federal lands (left), net area and percentage changes from 1993 to 2012, and assigned losses from LandTrendr disturbance maps (right) Older forest area estimates from bookend maps LandTrendr disturbance assignment for losses State and physiographic province 1993 2012 Net area change Net percentage change Harvest Wildfire Insects Other Total explained loss Percentage loss from 1993 ---------- Thousand acres ---------- Percent -------------------------- Thousand acres -------------------------- Percent Washington: Olympic Peninsula 720.0 727.4 7.4 1.0 1.8 1.0 1.3 2.5 6.6 -0.9 Western Lowlands 9.5 8.9 -0.7 -7.0 0.0 0.0 0.0 0.6 0.6 -6.7 Western Cascades 1,104.8 1,110.8 6.0 0.5 5.6 1.8 0.9 3.6 11.9 -1.1 Eastern Cascades 534.2 536.3 2.0 0.4 14.6 38.0 29.2 4.2 86.1 -16.1 Total 2,368.5 2,383.3 14.8 0.6 22.0 40.9 31.5 10.8 105.2 -4.4

Oregon:

Coast Range 373.6 415.6 42.0 11.2 6.2 0.1 0.3 0.0 6.6 -1.8

Willamette Valley 1.7 1.8 0.1 6.8 0.1 0.0 0.0 0.0 0.1 -6.2

Western Cascades 1,556.3 1,463.3 -92.9 -6.0 26.0 58.5 2.8 1.3 88.6 -5.7

Klamath 447.3 402.1 -45.3 -10.1 9.5 64.1 0.8 0.1 74.5 -16.6

Eastern Cascades 298.1 295.7 -2.4 -0.8 7.0 17.9 3.8 0.7 29.4 -9.9

Total 2,676.9 2,578.4 -98.5 -3.7 48.8 140.6 7.6 2.2 199.2 -7.4

California:

Coast Range 49.7 48.3 -1.5 -3.0 0.2 2.3 0.0 0.2 2.8 -5.6

Klamath 1,239.8 1,141.8 -98.0 -7.9 9.0 161.9 3.4 2.7 177.0 -14.3

Cascades 103.7 108.4 4.6 4.5 2.7 3.2 0.8 0.3 7.0 -6.8

Total 1,393.3 1,298.5 -94.8 -6.8 11.9 167.4 4.3 3.2 186.9 -13.4

Northwest Forest Plan total 6,438.7 6,260.2 -178.5 -2.8 82.7 348.9 43.4 16.2 491.2 -7.6

OGSI-200 = old-growth structure index at the 200-year threshold. NWFP = Northwest Forest Plan.
33

Northwest Forest Plan—The First 20 Years (1994-2013): Status and Trends of Late-Successional and Old-Growth Forests

Table 9—Bookend map areal estimates of older forests (OGSI-200) on nonfederal lands (left), net area and percent changes from 1993 to 2012, and assigned losses from LandTrendr disturbance maps (right)

Older forest area estimates from bookend maps LandTrendr disturbance assignment for losses

State and physiographic province 1993 2012

Net area 

change

Net 

percentage 

change Harvest Wildfire Insects Other

Total 

explained 

loss

Percentage 

loss from 

1993

---------- Thousand acres ---------- Percent -------------------------- Thousand acres -------------------------- Percent

Washington:

Olympic Peninsula 137.8 102.7 -35.1 -25.5 31.8 0.0 1.3 0.0 33.1 -24.0

Western Lowlands 240.6 160.9 -79.6 -33.1 109.9 0.0 1.9 0.0 111.8 -46.5Western Cascades 248.2 210.4 -37.8 -15.2 58.9 0.2 1.1 0.0 60.1 -24.2

Eastern Cascades 138.3 106.6 -31.7 -22.9 31.3 4.7 3.4 0.0 39.5 -28.6

Total 764.9 580.7 -184.2 -24.1 231.9 4.9 7.8 0.0 244.6 -32.0

Oregon:

Coast Range 254.3 190.5 -63.8 -25.1 105.3 0.3 1.6 0.0 107.2 -42.1

Willamette Valley 15.9 13.8 -2.1 -12.9 5.1 0.0 0.1 0.0 5.2 -32.8

Western Cascades 178.4 122.0 -56.4 -31.6 72.8 1.4 1.0 0.0 75.2 -42.1

Klamath 100.4 86.1 -14.3 -14.2 34.7 1.1 0.8 0.0 36.6 -36.5

Eastern Cascades 55.8 44.8 -10.9 -19.6 13.1 4.1 1.0 0.0 18.2 -32.5

Total 604.8 457.3 -147.5 -24.4 231.0 6.9 4.5 0.0 242.3 -40.1

California:

Coast Range 235.8 247.3 11.5 4.9 29.9 1.9 1.2 0.0 33.0 -14.0

Klamath 153.7 147.5 -6.2 -4.0 26.1 4.5 1.9 0.0 32.4 -21.1

Cascades 59.0 56.4 -2.6 -4.5 10.2 1.6 1.1 0.0 13.0 -22.0

Total 448.5 451.2 2.7 0.6 66.3 8.0 4.1 0.0 78.4 -17.5

Northwest Forest Plan total 1,818.2 1,489.2 -329.0 -18.1 529.2 19.8 16.4 0.0 565.3 -31.1

OGSI-200 = old-growth structural index at the 200-year threshold. 
34

GENERAL TECHNICAL REPORT PNW-GTR-911

Figure 10—Geographical patterns of federally managed lands and forest using 30-km (18.6-mi) hexagons. To illustrate the spatial distribution of federal land managers, hexagons containing at least 1 percent federal land ownerships were displayed and based on the majority of ownership. BLM = Bureau of Land Management, NPS = National Park Service, USFS = U.S. Forest Service, CA = California, WA = Washington, OR = Oregon.
35

Northwest Forest Plan—The First 20 Years (1994-2013): Status and Trends of Late-Successional and Old-Growth Forests Figure 11—Geographical patterns for all lands showing old-growth structure index at the 80-year threshold for bookend time periods of 1993 and 2012, as well as patterns of net change between the bookends. CA = California, OR = Oregon, WA = Washington.
36

GENERAL TECHNICAL REPORT PNW-GTR-911

Figure 12—Geographical patterns for all lands showing old-growth structure index at the 200-year threshold for bookend time periods of 1993 and 2012, as well as patterns of net change between the bookends. CA = California, OR = Oregon, WA = Washington.
37

Northwest Forest Plan—The First 20 Years (1994-2013): Status and Trends of Late-Successional and Old-Growth Forests Figure 13—Geographical patterns for all lands showing forest disturbance between 1993 and 2012 as detected by LandTrendr. CA = California, OR = Washington, WA = Washington.
38

GENERAL TECHNICAL REPORT PNW-GTR-911

and 8). This was less than the anticipated 

loss of 5 percent loss over two decades 

(FEMAT 1993). Losses from wildfire were 

also anticipated to be about 5 percent over 

two decades (FEMAT 1993) and were close 

to that expectation, at 4.2 to 5.4 percent 

(tables 6 and 8). Losses from wildfire were 

highest in fire-prone physiographic provinces, specifically the Washington Eastern 

Cascades (5.5 to 7.1 percent) and the Klamath Provinces of Oregon and California (12.2 

to 14.3 percent and 7.0 to 13.1 percent, 

respectively). Most of the losses from 

wildfire occurred in reserved land use 

allocations. Losses from insects were notable 

in the Washington Eastern Cascades at 2.5 

to 5.5 percent and Oregon Eastern Cascades 

at 1.2 to 1.3 percent (tables 6 and 8). 

Outcome Analysis

At the implementation of the Plan, about 80.9 percent of hexagons that contained 10 

percent or more federal lands met the older forest abundance and diversity outcomes 

1 or 2 as represented by OGSI-80 (fig. 15). Using the same threshold and for the 

same time, about 56.8 percent met older forest connectivity outcome 1 or 2 (fig. 16). 

Twenty years later, slightly less (78.6 percent and 54.5 percent, respectively) met 

these outcomes. Overall, we estimated a 2.3 percent decrease in both the abundance/

diversity outcome and connectivity outcome for OGSI-80 on the federal landscape.

For older forests represented by OGSI-200, about 16.8 percent of the federal 

landscape met outcomes 1 or 2 for abundance and diversity in 1993, which decreased 

to 12.7 percent (a 4.1 percent decrease) by 2012 (fig. 17). Very little (3.2 percent) of the 

federal landscape met connectivity outcomes 1 or 2 for OGSI-200. There was some loss 

balanced by gains to keep this percentage stable across the monitoring period (fig. 18). 

Small portions of the federal landscape (hexagons with at least 10 percent 

federal forest lands by area) improved (e.g., moved into or closer to outcomes 1 or 2) 

in these terms, over the last 20 years, but not to the extent that they contributed to 

the overall measures for abundant and well-distributed older forests at this time. 

Most of these improved areas were for OGSI-200 and occurred within historical old 

wildfire perimeters, but also in some high elevations (figs. 15 to 18).

0

10

20

30

40

50

60

70

80

90

100

Federal

Nonfederal

Federal

Nonfederal

OGSI-80 OGSI-200

Percentage of older forest loss

Insect and other

Wildfire

Timber harvest

Figure 14—LandTrendr explained causes for losses of older forests between 

1993 and 2012. Old-growth structure index at the 80-year (OGSI-80) and 200-

year threshold (OGSI-200).

0

10

20

30

40

50

60

70

80

90

100

Federal

Nonfederal

Federal

Nonfederal

OGSI-80 OGSI-200

Percentage of older forest loss

Insect and other

Wildfire

Timber harvest
39

Northwest Forest Plan—The First 20 Years (1994-2013): Status and Trends of Late-Successional and Old-Growth Forests

Figure 15—Geographical patterns for the abundance and diversity outcomes for old-growth structure index at the 80-year threshold.
40

GENERAL TECHNICAL REPORT PNW-GTR-911

Figure 16—Geographical patterns for the connectivity outcomes for old-growth structure index at the 80-year threshold.
41

Northwest Forest Plan—The First 20 Years (1994-2013): Status and Trends of Late-Successional and Old-Growth Forests

Figure 17—Geographical patterns for the abundance and diversity outcomes for old-growth structure index at the 200-year threshold.
42

GENERAL TECHNICAL REPORT PNW-GTR-911

Figure 18—Geographical patterns for the connectivity outcomes for old-growth structure index at the 200-year threshold.
43

Northwest Forest Plan—The First 20 Years (1994-2013): Status and Trends of Late-Successional and Old-Growth Forests

Maps Versus FIA Annual Plots

At the large-landscape scale, averaged (average for time 1 and time 2) map estimates for OGSI-80 on federal lands were within the bounds of error estimation of 

the FIA annual plot-based estimates in 4 of the 10 physiographic provinces that 

contain significant amounts of federally managed lands (excluding the Washington 

Lowlands and the Oregon Willamette Valley provinces). The averaged map estimates were also within the plot error bounds for California (fig. 19a). Averaged 

map-based estimates by physiographic province were 6 to 12 percent lower than 

Figure 19—Comparison results for map-based and Forest Inventory and Analysis (FIA) plot-based areal 

estimates of old-growth structure index at the 80-year threshold. GNN = Gradient Nearest Neighbor.
44

GENERAL TECHNICAL REPORT PNW-GTR-911

plot-based estimates for Washington (62,000 to 193,000 ac, respectively). Some of 

this difference was due to the use of the forest-capable mask in the mapping, which 

excluded some forested lands at or near the high-elevation timber lines, particularly 

in the Eastern Cascades of Oregon and Washington. Averaged map-based estimates 

were 12 to 17 percent lower than plot-based estimates for the Oregon Eastern 

Cascades and California Cascades provinces (about 100,000 ac less for each). For 

the California Coast, our averaged map estimates were slightly higher (29,000 ac) 

than plot estimates. The Plan area estimate from averaged maps was about 4 

percent, or 584,000 ac, less than the plot-based estimate. 

Averaged map estimates for OGSI-200 on federal lands were within the error 

bounds of FIA annual plot-based estimates in 5 of 10 physiographic provinces 

containing significant amounts of federally managed lands. They were also within 

the bounds for both Washington and California (fig. 20a). Map-based estimates 

were again lower than plot-based estimates for the Washington Western Cascades 

(8 percent or 90,000 ac) and also for the Oregon Klamath and Eastern Cascades 

(12 to 18 percent or 59,000 to 65,000 ac, respectively) as well as the California 

Cascades (29 percent or 44,000 ac). For the California Klamath, our averaged map 

estimate was slightly higher (8 percent or 83,000 ac) than the plot estimate. 

The differences between map-based and plot-based areal estimates were 

smaller on federal lands than for nonfederal lands (figs. 19b, 20b). Our maps tended 

to estimate more acres of older forest (both OGSI-80 and -200) on nonfederal lands 

than the FIA plots did. At the broadest spatial extent (all forested lands within the 

NWFP area), the similarity between map-based and plot-based areal estimates was 

the greatest. At this scale, the maps were within plot error bounds for half of all 

physiographic provinces and the NWFP area scale, and for the OGSI-200 definition, within the error bounds for all three states (figs. 19c and 20c).

Maps Versus Remeasured CVS Plots

A comparison of map- versus plot-based forest change estimates on USFS and 

BLM lands was done for Oregon and Washington, where we had remeasured CVS 

plot data. Similar to map-based versus FIA plot-based areal estimates, our OGSI80 map estimates were mostly lower than CVS plot estimates for both time 

periods (fig. 21a); the exceptions being the Washington Olympic Peninsula 

(WaOLY) physiographic province, where map estimates were within plot error 

bars for both time periods, and time 1 map estimates for Oregon Western Cascades (OrWCO) and Klamath (OrKLA) provinces were also within the plot error 

bars (fig. 21a). The combined map-based OGSI-80 estimates showed a net loss for 

Washington and Oregon of 2.5 percent, ranging from 0.1 percent in Washington 

to 5.0 percent in Oregon. For the same combined area, plots showed a net increase 
45

Northwest Forest Plan—The First 20 Years (1994-2013): Status and Trends of Late-Successional and Old-Growth Forests

of 0.3 percent, ranging from a 1.2 percent increase in Washington to a 0.3 percent 

decrease in Oregon (fig. 21a). 

For OGSI-200, map-based estimates were more similar to plot-based estimates 

than for OGSI-80 (fig. 21b); the exception being the Oregon Eastern Cascades 

(OrECO) province where map estimates showed a marked increase in OGSI-200 compared to plot estimates. For the combined area of Washington and Oregon, the maps 

estimated a net decrease of 1.9 percent; whereas, the plots showed a net gain of 3.2 

percent. Most of the mapped decrease occurred in Oregon (-3.8 percent) while maps 

Figure 20—Comparison results for map-based and Forest Inventory and Analysis (FIA) plot-based 

areal estimates of OGSI-200. GNN = Gradient Nearest Neighbor.
46

GENERAL TECHNICAL REPORT PNW-GTR-911

estimated a 1.0 percent increase in Washington. The plot-based net changes showed a 

3.3 and 3.2 percent increase in Washington and Oregon, respectively (fig. 21b).

Most of the remeasured plot-based increases in older forest using either OGSI 

threshold occurred in Oregon on BLM CVS plots, with a 1.8 percent and 4.8 

percent increase of BLM forested lands qualifying as OGSI-80 and OGSI-200, 

respectively. For USFS forested lands, the net changes based on plot data were 

small, with an estimated 0.3 percent loss of OGSI-80 and a 0.1 percent gain of 

OGSI-200. For the BLM and USFS plots that crossed the OGSI-80 or OGSI-200 

thresholds, the largest changes were increases in the down wood element scores, 

with slight increases in the live tree element scores for both large tree density and 

diameter diversity. Thirty-six percent of the USFS plots that qualified as OGSI-200 

at time 1, but not at time 2, had experienced burning or cutting between measurements. For the remainder, the biggest changes in OGSI scores were due to lower 

snag and down wood element scores at time 2.

Figure 21—Comparison results for map-based and current vegetation survey (CVS) plot-based areal 

estimates for both bookend time periods. OGSI-80 and -200 = old-growth structure index at the 80- 

and 200-year threshold, respectively. GNN = Gradient Nearest Neighbor.
47

Northwest Forest Plan—The First 20 Years (1994-2013): Status and Trends of Late-Successional and Old-Growth Forests

Discussion

The main focus of our monitoring was on status and trends of older forest on federal lands managed under the NWFP. How older forests were defined was important. We compared definitions from previous monitoring reports (Moeur et al. 2005, 

2011) with a new definition based on OGSI that better represented the continuous 

nature of forest succession and development. Our baseline (1993) map estimate 

of older forest using the old definition (LSOG) was 7.4 million acres; slightly 

higher than the 7.3-million-ac baseline (1994/1996) estimate in Moeur et al. (2011). 

Because our baseline time period preceded the baseline time period in Moeur et 

al. (2011) by 1 to 3 years, this is not surprising. Our map-based estimate of LSOG 

acres was bracketed by mapped acres from our new definitions, OGSI-80 (12.6 

million ac) and OGSI-200 (6.3 million ac), which represented forests at the time 

they begin to exhibit older-forest attributes (OGSI-80) and well into the older-forest 

development stage (OGSI-200). Given the simplistic nature of the LSOG definition, 

and the difficulty in interpreting changes based on it, we discontinued its use for 

older-forest monitoring. 

Based on the new definitions, the map-based analysis results in this report suggest that at the scale of the Plan, area of older forests have decreased over the last 

two decades on federal lands by 2.8 to 2.9 percent. The amount and rate of mapped 

change varied by federal ownership and geographic scale (app. F). On federal land, 

wildfire was the leading cause for older-forest losses, while on nonfederal lands, 

timber harvesting, was the main cause for loss (fig. 14). Losses of older forest area 

were highest on lands managed by the USFS, especially in reserved LUAs (table 

10). The highest percentage loss (-8.6 percent) was on reserved allocations on USFS 

lands in northwest California (table 10). 

Table 10—Summary of net percentage changes from 1993 to 2012 for federal lands managed by the Forest 

Service, Bureau of Land Management, and National Park Service

Nonreserved Reserved

Federal land management agency OGSI-80 OGSI-200 OGSI-80 OGSI-200

Net percent change

Forest Service–Region 6 -2.1 -3.5 -4.0 -1.8

Forest Service–Region 5 -0.81 -3.9 -5.3 -8.6

Bureau of Land Management–Oregon +0.7 +0.9 -1.3 +1.1

Bureau of Land Management–Northern 

California +9.3 -1.8 +2.6 -5.8

National Park Service NA NA -1.1 -0.3

NA = not applicable. OGSI-80 and -200 = old-growth structural index at the 80- and 200-year threshold, respectively. 
48

GENERAL TECHNICAL REPORT PNW-GTR-911

Comparisons between map-based estimates and plot-based estimates showed 

that map estimates of older forests were usually slightly lower than plot estimates 

on federal lands. Reasons for this include differences in how maps versus plots 

account for forest-capable area along the tree line of the high elevations and other 

large nonforested areas (e.g., agricultural, urban). Another potential cause for differences might be due to changes in the dead wood elements (e.g., snags and down 

wood) that are not easily detectable with Landsat technology. We did not have a 

plot-based estimate of old-forest change for the entire Plan area, but for federal 

lands (USFS and BLM) in Washington and Oregon we were able to estimate 

changes at the physiographic province and state scales using CVS plots that were 

measured at two different time periods. For that analysis, again our map-based net 

changes differed from plot-based net changes, sometimes in the opposite direction (loss vs. gain), with maps showing more net losses and plots showing more 

net gains. We suspect that net change differences were due in part to the reasons 

above, but perhaps more so to the difference in the bookend time periods between 

the maps (1993–2012) and plots (based on mid-points of measurement occasions: 

1995–2002). The 2012 satellite image used for our time 2 map showed millions of 

acres of forest that had burned or was harvested since 2007, the last measurement 

year for CVS plots. 

While overall changes in older forest have mostly been negative, we observed 

portions (e.g., individual forests, 30-km [18.6-mi] hexagons) of the federal landscape where recruitment of older forests was beginning to occur. Areas where we 

observed net gains in older forest sometimes overlaid extremely large wildfires that 

burned over a century ago, such as the Yaquina Fire (1843) and the Nestucca Fire 

(1846) in the Siuslaw National Forest. For the most part, areas showing net gains of 

older forest are occurring outside of the areas prone to more frequent large wildfires. As witnessed by the historical record (including the above fires), those areas 

appear to experience extremely large wildfires that happen relatively infrequently 

compared to the rest of the NWFP area.

Expected outcomes for abundance, diversity, and connectivity of older forests on 

federal lands have not yet been achieved. In addition to reducing the abundance of 

older forests, large wildfires also contributed to declines in its connectivity. The vast 

majority of these large wildfires have occurred in the more fire-prone portions of the 

NWFP area as mapped in the 15-year reporting period (see Davis et al. 2011: chap. 

4). However, not all wildfire removes older forests. In fact, low- to moderate-severity 

wildfires can positively influence the development of old-growth stand structure and 

often add structural elements (e.g., snags and down wood) important for its development (Donato et al. 2009, Spies and Franklin 1991, Taylor and Skinner 1998, Tepley 
49

Northwest Forest Plan—The First 20 Years (1994-2013): Status and Trends of Late-Successional and Old-Growth Forests

et al. 2013) and function (Clark et al. 2011, Franklin et al. 2000). High-severity 

wildfire currently is causing most of the loss of older forests on federal lands.

Following methods in Kennedy et al. (2012), we used LandTrendr disturbance 

magnitude data from inside the perimeters of wildfires that burned since the Plan’s 

implementation to analyze patterns of fire severity. We classified LandTrendr into 

three equal intervals of disturbance magnitude (low = 0 to 33 percent, moderate = 

33 to 66 percent, and high = 66 to 100 percent). We compared this to Monitoring 

Trends in Burn Severity (MTBS) classified data (http://www.mtbs.gov/methods.

html). Comparing our three disturbance magnitude classes to the four MTBS 

burn-severity classes (unburned to low, low, moderate, and high severity), we found 

that our “high magnitude” class comprised mostly MTBS “moderate” and “high” 

burn-severity classes (fig. 22). Between 1994 and 2011, on average, forests burned 

mostly at lower magnitudes (57 ± 14 percent; mean ± 1 SD). Moderate-burn magnitudes accounted for 21 ± 7 percent, while high-burn magnitudes occurred on 23 ± 

11 percent of the burned area (see wildfire inset). 

Although wildfires accounted for the majority of older forest loss on federal 

lands, timber harvesting accounted for about 17 to 20 percent. Using LandTrendr 

data, we analyzed the pattern of timber harvest disturbance magnitude between 1993 

and 2012. We used the same disturbance magnitude classes as for the wildfire analysis above (low, moderate, and high), where a low magnitude was associated with 

thinning and a high magnitude with regeneration harvest. In addition to proportion of 

magnitude, we also looked at amount of timber harvest by magnitude. Since 1993, the 

amount of timber harvesting on federal lands increased (fig. 23c) concurrent with an 

Figure 22—Frequency histogram comparing Monitoring Trends in Burn Severity (MTBS) and 

LandTrendr (LT) disturbance magnitude within large wildfire perimeters in the Northwest Forest Plan 

area. Percentages above bars show what percentage of the LT class consisted of each MTBS class.
50

GENERAL TECHNICAL REPORT PNW-GTR-911
51

Northwest Forest Plan—The First 20 Years (1994-2013): Status and Trends of Late-Successional and Old-Growth Forests

increase in the amount of low-magnitude harvest (e.g., thinning) (fig. 23d). The 

amount of harvest on nonfederal lands increased shortly after the implementation of 

the Plan, then decreased around 2008 (fig. 23a). The proportions of nonfederal harvest 

by magnitude did not change significantly over this time period (fig. 23b). Similar 

patterns of timber harvesting trends were also noted by Kennedy et al. (2013).

Uncertainty in Mapping and Estimating Losses, 

Gains, and Net Change

There are many sources of potential error when making map-based or plot-based 

areal estimates for large geographic areas that cover thousands or millions of acres. 

This is confounded when making estimates for two different time periods and analyzing the differences between them. Remote sensing techniques used in this report 

are relatively new and constantly being improved upon as research progresses. Still, 

they are subject to errors. The following issues noted during our analyses are briefly 

discussed here to give the reader a general sense of the uncertainties involved in 

large-scale forest monitoring.

Figure 23—Timber harvesting patterns of harvesting on nonfederal land (a and b), and federal land (c and d) during the monitoring 

period as detected by LandTrendr. Low magnitude = 0 to 33 percent vegetation cover loss, moderate = 33 to 66 percent cover loss, and 

high = 66 to 100 percent cover loss. Note: Y-axis scale for graph (c) is not the same as for graph (a).
52

GENERAL TECHNICAL REPORT PNW-GTR-911

When using time-series maps for monitoring purposes, a challenge is to make 

each annual map as consistent with all other annual maps as possible. This reduces 

“artificial” changes caused only by mapping errors, such as spatial misregistration 

where pixels and edges of mapped stands do not exactly line up from year to year. 

The LandTrendr disturbance map production process that precedes the GNN map 

production attempted to geometrically rectify and radiometrically normalize (through 

time) the Landsat imagery used in the GNN mapping process. While this reduced 

artificial differences between annual imagery dates, it did not totally remove them. 

In portions of our maps at the pixel scale, we observed some erroneous mapped 

losses of older forest, particularly along eastern and northeastern edges of clearcuts 

and in other areas with distinct stand boundaries. This appeared to be caused by 

the combined effects of shadows cast by adjacent older forest into the clearcut and 

a slight geographic misregistration of one to two pixels in the LandTrendr imagery 

between 1993 and 2012 (shifting from southwest to northeast). Shadow differences 

between annual maps are often due to satellite images for the same area being 

taken at different times of the day or month in each year. Shadows in some recent 

clearcuts were (erroneously) mapped as older forest in 1993, then as those plantations developed into dense, closed-canopy young forests, shadows diminished by 

2012 resulting in false losses of older forest in the bookend analysis. These problems likely resulted in a bias toward loss of older forest in our analyses. However, 

these errors can be diluted by summarizing the map information to broader spatial 

extents than the modeled 30-m (98-ft) pixels (Ohmann et al. 2014). 

Uncertainty in plot-based estimates can arise from general sampling errors 

and is sensitive to differences in plot design, size, or transect length (Gray 2003). 

Additionally, field-based decisions on which trees belong to the dominant or codominant class, which was a key attribute in the calculation of the LSOG attribute 

used in previous reports, can have significant impacts on many stand attributes.

Because the OGSI variables consider snags and down dead wood, older forest may 

blink on and off as a result of changes in the dead wood population (e.g., as a result 

of pulses of mortality and subsequent decay or consumption by fire). This problem is 

exacerbated by the large sampling errors associated with dead wood on the plots. However, application of the index at the regional scale rather than at the plot level should 

reflect overall changes in the abundance of older-forests attributes in the NWFP area. 

Because of the uncertainties involved with large-scale monitoring, we included 

both map- and plot-based estimations in this report. In general, the summarization 

of older forest change in this report may be more readily interpreted by the differences in the maps because each bookend map is based on one specific Landsat 

image year, whereas the plot estimates were based on plot measurements that 

spanned multiple years.
53

Northwest Forest Plan—The First 20 Years (1994-2013): Status and Trends of Late-Successional and Old-Growth Forests

Conclusions

Twenty years after implementation of the NWFP in 1994, net changes in amount of 

older forests on federal lands managed under the Plan’s guidance have been small 

(a 2.8 to 2.9 percent decrease). This occurred despite losses from wildfire (4.2 to 5.4 

percent), timber harvest (1.2 to 1.3 percent), and insects or other causes (0.7 to 0.9 

percent), suggesting that processes of forest succession have compensated for some 

of these losses. Losses from wildfire were about what was expected when the Plan 

was designed, but losses from timber harvesting were about one quarter of what 

was anticipated. 

Overall, these results are consistent with the Plan’s expectations for older forest 

outcomes for this period of time. Nothing in these findings suggested that overall 

achievement of abundance, diversity, and connectivity outcomes over the next few 

decades is not feasible; however, we noted that some areas, represented by 30-km 

(18.6-mi) hexagons, have moved further away from those outcomes at this spatial 

resolution and at this time (figs. 15 through 18). At the implementation of the 

Plan, the connectivity of older forests, especially those represented by OGSI-200, 

was and is now further below the outcomes set forth in the Plan, and recent large 

wildfires are largely to blame for localized degradation of this condition. Nonetheless, we found that some areas, most notably in the central Coast Range of Oregon 

because of its fire history, are now showing redevelopment of connected older 

forest conditions. 

Losses of older forest to large wildfires were anticipated as part of the 

disturbance regimes of the NWFP landscape, and these losses were considered 

in the Plan’s reserved network design. However, recent findings by Westerling 

et al. (2006) and Miller et al. (2012) reported an increased frequency of large 

wildfire occurrence and area burned annually, compared to the recent decades 

preceding the development of the Plan. Consistent with this, monitoring has 

shown an increased occurrence of large wildfire within the NWFP area (Davis 

et al. 2011). Most of these large wildfires have occurred primarily in the warmer 

and drier, fire-prone landscapes (see Wildfire Inset). So, while wildfire-related 

loss of older forest within the reserve network was anticipated, and monitoring 

results show some reserved areas have been set back by decades to centuries 

with respect to achieving Plan outcomes, the increased frequency of these large 

wildfires is concerning. 

Whereas the amount of annual area burned has increased, it is unclear how 

much if any has burned at uncharacteristically high severity that might be due to 

fire exclusion during the 20th century or climate change. It is also unclear if the 

observed trend in large wildfire occurrence will continue or change as a result of 
54

GENERAL TECHNICAL REPORT PNW-GTR-911

climate change or changes in forest management and fire suppression policies on 

federal lands. The effect large wildfires are having on the current reserve network 

warrants further monitoring. 

It will take continued monitoring to answer these questions and track the 

abundance and patterns of older forests and the processes (disturbance, growth, and 

mortality) that shape them throughout the next decades. While net changes have so 

far been small, it is possible that more rapid changes could be coming as large areas 

of forest develop into structural conditions that are near the threshold of our older 

forest definitions, or if rates of wildfire increase. 

Monitoring older forests is a long-term commitment to remeasurement of permanent field plots; management of field, remotely sensed, and GIS data layers; and 

modeling and inventory analyses of change over time. The federal agencies have 

maintained that commitment for 20 years. In that time, recent technologies and 

methodologies have made some significant advancements, allowing us to accomplish our monitoring tasks with increased accuracy and efficiency. In contrast, 

reductions in field effort have resulted in fewer measurements of large trees (on the 

hectare (acre) plot on USFS lands) and down wood (shorter transects on all lands). 

There is still some uncertainty in our estimates that we will continue to address in 

future monitoring cycles. One focus for future monitoring might be the inclusion of 

finer resolution remote sensed imagery, along with regional forest inventory plots in 

our mapping framework. Some exploration of this has already been accomplished, 

with the inclusion of light detection and ranging (lidar) data in the GNN process 

(Zald et al. 2014). Every year that passes brings new data to help address this issue, 

such as additional inventory plots and more remeasured plots that will allow us 

to track trends in species-specific tree mortality and ingrowth patterns. This finer 

ecological resolution will help us to understand the changes we see in remote sensing. When combined with new remote sensing technologies, our ability to anticipate 

and understand forest change is likely to increase in the future.

We have made progress transitioning from discrete definitions of older forest to 

an index that represents the continuum of forest development. However, this is just 

the first step in that direction. Continuing research to improve upon the concept of 

the OGSI and its use to project expected trends of forest succession into the future 

is important. In doing so, we would allow ourselves a consistent method for not 

only mapping and monitoring older forests, but also the entire forest mosaic including recently disturbed forests and younger regenerating and rapidly growing forests, 

especially those with complex structure, that are as ecologically important as older 

forests. The loss of older forests to wildfire is also a gain in early seral habitat—the 

question is, what mix and pattern of these forests do we want on our landscapes?
55

Northwest Forest Plan—The First 20 Years (1994-2013): Status and Trends of Late-Successional and Old-Growth Forests

Acknowledgments

We thank Justin Braaten, Daniel Donato, Andrew Merschel, Rob Pabst, Matt Reilly, 

Harold Zald, and Andrew Yost for helping with the development of the old-growth 

structure index used in this report. We thank Mike Simpson for developing and 

sharing the forest vegetation zone map used in this report. Vicente Monleon produced the OGSI mapping and analysis thresholds in appendix C. Maria Fiorella 

who was instrumental in screening outlier plots for our GNN maps and Time Syncing for our LandTrendr maps. This report benefited greatly from reviews by Daniel 

Donato (Washington Department of Natural Resources), Michael Haske (BLM), 

Miles Hemstrom (Oregon State University), Ashley Steel (PNW), and Andrew Yost 

(Oregon Department of Forestry). The development and testing of LandTrendr was 

partially supported by the National Aeronautics and Space Administraton Terrestrial Ecology and Applied Sciences Programs. 

Metric Equivalents

When you know: Multiply by: To find:

Inches (in) 2.54 Centimeters (cm)

Feet (ft) 0.3048 Meters (m)

Acres (ac) 0.405 Hectares (ha)

Basal area (ft2

/ac) 0.2296 Basal area (m2/ha)

Square miles (mi2

) 2.59 Square kilometers (km2)

Trees per acre (trees/ac) 2.47 Trees per hectare (trees/ha)

Tons (ton) 907.0 Kilograms (kg)

Tons per acre (ton/ac) 2.24 Megagrams per hectare (Mg/ha)

Cubic feet per acre (ft3

/ac) 0.07 Cubic meters per hectare (m3/ha)

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64

GENERAL TECHNICAL REPORT PNW-GTR-911

Figure A-1—Western hemlock forest vegetation zone. DBH = diameter at breast height. DIA = 

diameter at large end and ≥3 m long.

Appendix A: Forest Inventory Plot Summaries of 

Old-Growth Structure Elements
65

Northwest Forest Plan—The First 20 Years (1994-2013): Status and Trends of Late-Successional and Old-Growth Forests

Figure A-2—Douglas-fir forest vegetation zone. DBH = diameter at breast height. DIA = diameter at 

large end and ≥3 m long.
66

GENERAL TECHNICAL REPORT PNW-GTR-911

Figure A-3—Grand fir/white fir forest vegetation zone. DBH = diameter at breast height. DIA = 

diameter at large end and ≥3 m long.
67

Northwest Forest Plan—The First 20 Years (1994-2013): Status and Trends of Late-Successional and Old-Growth Forests

Figure A-4—Pacific silver fir forest vegetation zone. DBH = diameter at breast height. DIA = 

diameter at large end and ≥3 m long.
68

GENERAL TECHNICAL REPORT PNW-GTR-911

Figure A-5—Mountain hemlock forest vegetation zone. DBH = diameter at breast height. DIA = 

diameter at large end and ≥3 m long.
69

Northwest Forest Plan—The First 20 Years (1994-2013): Status and Trends of Late-Successional and Old-Growth Forests

Figure A-6—Tanoak forest vegetation zone. DBH = diameter at breast height. DIA = diameter at 

large end and ≥3 m long.
70

GENERAL TECHNICAL REPORT PNW-GTR-911

Figure A-7—Redwood forest vegetation zone. DBH = diameter at breast height. DIA = diameter at 

large end and ≥3 m long.
71

Northwest Forest Plan—The First 20 Years (1994-2013): Status and Trends of Late-Successional and Old-Growth Forests

Figure A-8—Sitka spruce forest vegetation zone. DBH = diameter at breast height. DIA = diameter at 

large end and ≥3 m long.
72

GENERAL TECHNICAL REPORT PNW-GTR-911

Figure A-9—Western redcedar forest vegetation zone. DBH = diameter at breast height. DIA = 

diameter at large end and ≥3 m long.
73

Northwest Forest Plan—The First 20 Years (1994-2013): Status and Trends of Late-Successional and Old-Growth Forests

Figure A-10—Oak woodland forest vegetation zone. DBH = diameter at breast height. DIA = 

diameter at large end and ≥3 m long.
74

GENERAL TECHNICAL REPORT PNW-GTR-911

Figure A-11—Ponderosa pine forest vegetation zone. DBH = diameter at breast height. DIA = 

diameter at large end and ≥3 m long.
75

Northwest Forest Plan—The First 20 Years (1994-2013): Status and Trends of Late-Successional and Old-Growth Forests

Figure A-12—Subalpine forest vegetation zone. DBH = diameter at breast height. DIA = diameter at 

large end and ≥3 m long.
76

GENERAL TECHNICAL REPORT PNW-GTR-911

Figure A-13—Other pine forest vegetation zone. DBH = diameter at breast height. DIA = diameter at 

large end and ≥3 m long.
77

Northwest Forest Plan—The First 20 Years (1994-2013): Status and Trends of Late-Successional and Old-Growth Forests

Figure A-14—Port Orford cedar forest vegetation zone. DBH = diameter at breast height. DIA = 

diameter at large end and ≥3 m long.
78

GENERAL TECHNICAL REPORT PNW-GTR-911

Figure A-15—Shasta red fir forest vegetation zone. DBH = diameter at breast height. DIA = diameter 

at large end and ≥3 m long.
79

Northwest Forest Plan—The First 20 Years (1994-2013): Status and Trends of Late-Successional and Old-Growth Forests

Figure A-16—Juniper woodlands forest vegetation zone. DBH = diameter at breast height. DIA = 

diameter at large end and ≥3 m long.
80

GENERAL TECHNICAL REPORT PNW-GTR-911

Appendix B: Old-Growth Structure Index Element Curves

0

25

50

75

100

Live tr

0 10 20 30 40 50 60

ee element score

Trees per hectare (TPH) ≥ 100 cm DBH

25th 

75th

99th

Min

Plot 

statistic

TPH 

(x-axis)

Score 

(y-axis) Slope Intercept

Minimum 0 0 n/a n/a

25th 8.9 50 5.64 0.00

75th 24.1 75 1.64 35.45

99th 52.8 100 0.87 53.98

Figure B-1—Western hemlock curves for stands ≥200 years of age (n = 1,151). DBH = diameter at breast height. DIA = diameter at large 

end and ≥3 m long. NA = not applicable.

0

25

50

75

100

0 5 10 15 20 25 30 35 40 45

Median 

75th

99th

Min

Snag element score 

Snags per hectare (SPH) ≥ 75 cm DBH 

Plot 

statistic

TPH 

(x-axis)

Score 

(y-axis) Slope Intercept

Minimum 0 0 n/a n/a

25th 6.2 50 8.00 0.00

75th 11.5 75 4.76 20.24

99th 39.1 100 0.91 64.59

0

25

50

75

100

Down wood element score

Percentage cover (COV) ≥ 25 cm DIA

0 2 4 6 8 10 12 14 16 18 20

Median 

75th

99th

Min

Plot 

statistic

TPH 

(x-axis)

Score 

(y-axis) Slope Intercept

Minimum 0 0 n/a n/a

25th 4.3 50 11.76 0.00

75th 7.0 75 9.12 11.20

99th 18.4 100 2.19 59.67
81

Northwest Forest Plan—The First 20 Years (1994-2013): Status and Trends of Late-Successional and Old-Growth Forests

Figure B-2—Douglas-fir curves for stands ≥150 years of age (n = 635). DBH = diameter at breast height. DIA = diameter at large end and 

≥3 m long. NA = not applicable.

0

25

50

75

100

Snag element score

Snags per hectare (SPH) ≥ 50 cm DBH

Min

75th

99th

0 5 10 15 20 25 30 35 40 45 50

Median 

Plot 

statistic

TPH 

(x-axis)

Score 

(y-axis) Slope Intercept

Minimum 0 0 n/a n/a

Median 3.3 50 15.24 0.00

75th 9.9 75 3.79 37.57

99th 46.8 100 0.68 68.32

0

25

50

75

100

Down wood element score

Percentage cover (COV) ≥ 25 cm DIA

0 1 2 3 4 5 6 7 8 9

Min

75th

99th

Median 

Plot 

statistic

TPH 

(x-axis)

Score 

(y-axis) Slope Intercept

Minimum 0 0 n/a n/a

Median 1.1 50 45.96 0.00

75th 2.3 75 21.43 6.68

99th 8.4 100 4.06 65.85

0

25

50

75

100

Live tree element score

Trees per hectare (TPH) ≥ 75 cm DBH 

0 20 40 60 80 100 120

25th

Min

75th

99th Plot 

statistic

TPH 

(x-axis)

Score 

(y-axis) Slope Intercept

Minimum 0 0 n/a n/a

25th 17.4 50 2.87 0.00

75th 44.3 75 0.93 33.83

99th 97.6 100 0.47 54.22
82

GENERAL TECHNICAL REPORT PNW-GTR-911

Figure B-3—Grand fir/white fir curves for stands ≥200 years of age (n = 714). DBH = diameter at breast height. DIA = diameter at large 

end and ≥3 m long. NA = not applicable.

0

25

50

75

100

Snag element score 

Snags per hectare (SPH) ≥ 50 cm DBH 

Min

75th

Median

99th Plot 

statistic

TPH 

(x-axis)

Score 

(y-axis) Slope Intercept

Minimum 0 0 n/a n/a

Median 9.9 50 5.06 0.00

75th 18.4 75 2.94 20.93

99th 68.3 100 0.50 65.80

0 10 20 30 40 50 60 70 80

0

25

50

75

100

Down wood element score

Percentage cover (COV) ≥ 25 cm DIA

Min

75th

Median

99th

0 2 4 6 8 10 12

Plot 

statistic

TPH 

(x-axis)

Score 

(y-axis) Slope Intercept

Minimum 0 0 n/a n/a

Median 2.2 50 23.22 0.00

75th 4.0 75 13.46 1.01

99th 10.6 100 3.77 59.89

0

25

50

75

100

Live tree element score

Trees per hectare (TPH) ≥ 75 cm DBH 

Min

75th

25th

99th

0 20 40 60 80 100 120

Plot 

statistic

TPH 

(x-axis)

Score 

(y-axis) Slope Intercept

Minimum 0 0 n/a n/a

25th 17.5 50 2.85 0.00

75th 44.7 75 0.92 33.86

99th 99.0 100 0.46 54.41
83

Northwest Forest Plan—The First 20 Years (1994-2013): Status and Trends of Late-Successional and Old-Growth Forests

Figure B-4—Pacific silver fir curves for stands ≥200 years of age (n = 1,270). DBH = diameter at breast height. DIA = diameter at large 

end and ≥3 m long. NA = not applicable.

0

25

50

75

100

Snag element score 

Snags per hectare (SPH) ≥ 50 cm DBH 

Min

75th

Median

99th

0 10 20 30 40 50 60 70 80 90

Plot 

statistic

TPH 

(x-axis)

Score 

(y-axis) Slope Intercept

Minimum 0 0 n/a n/a

Median 22.0 50 2.27 0.00

75th 33.5 75 2.17 2.17

99th 82.0 100 0.51 57.76

0

25

50

75

100

Down wood element score

Percentage cover (COV) ≥ 25 cm DIA

Min

75th

Median

99th

0 2 4 6 8 10 12 14 16 18

Plot 

statistic

TPH 

(x-axis)

Score 

(y-axis) Slope Intercept

Minimum 0 0 n/a n/a

Median 4.9 50 10.15 0.00

75th 8.1 75 7.76 11.78

99th 17.0 100 2.82 52.06

0

25

50

75

100

Live tree element score

Trees per hectare (TPH) ≥ 75 cm DBH 

Min

75th

25th

99th

0 20 40 60 80 100 120

Plot 

statistic

TPH 

(x-axis)

Score 

(y-axis) Slope Intercept

Minimum 0 0 n/a n/a

25th 22.2 50 2.25 0.00

75th 50.3 75 0.89 30.22

99th 105.3 100 0.45 52.13
84

GENERAL TECHNICAL REPORT PNW-GTR-911

Figure B-5—Mountain hemlock curves for stands ≥200 years of age (n = 900). DBH = diameter at breast height. DIA = diameter at large 

end and ≥3 m long. NA = not applicable.

Min

75th

Median

99th

0 10 20 30 40 50 60 70

Plot 

statistic

TPH 

(x-axis)

Score 

(y-axis) Slope Intercept

Minimum 0 0 n/a n/a

Median 13.1 50 3.80 0.00

75th 23.0 75 2.54 16.55

99th 60.4 100 0.67 59.65

0

25

50

75

100

Snag element score 

Snags per hectare (SPH) ≥ 50 cm DBH 

Min

75th

Median

99th

0 2 4 6 8 10 12 14

Plot 

statistic

TPH 

(x-axis)

Score 

(y-axis) Slope Intercept

Minimum 0 0 n/a n/a

Median 2.6 50 19.14 0.00

75th 4.9 75 11.03 1.18

99th 12.4 100 3.32 58.79

0

25

50

75

100

Down wood element score

Percentage cover (COV) ≥ 25 cm DIA

0

25

50

75

100

Live tree element score

Trees per hectare (TPH) ≥ 75 cm DBH 

Min

75th

25th

99th

0 10 20 30 40 50 60 70 80 90

Plot 

statistic

TPH 

(x-axis)

Score 

(y-axis) Slope Intercept

Minimum 0 0 n/a n/a

25th 10.2 50 4.92 0.00

75th 37.0 75 0.93 40.55

99th 82.4 100 0.55 54.58 
85

Northwest Forest Plan—The First 20 Years (1994-2013): Status and Trends of Late-Successional and Old-Growth Forests

Figure B-6—Tanoak curves for stands ≥200 years of age (n = 140). DBH = diameter at breast height. DIA = diameter at large end and ≥3 

m long. NA = not applicable.

Min

75th

Median

99th

0 5 10 15 20 25 30 35 40 45

Plot 

statistic

TPH 

(x-axis)

Score 

(y-axis) Slope Intercept

Minimum 0 0 n/a n/a

Median 5.2 50 9.53 0.00

75th 12.7 75 3.38 32.28

99th 41.0 100 0.88 63.84

0

25

50

75

100

Snag element score 

Snags per hectare (SPH) ≥ 50 cm DBH 

Min

75th

Median

99th

0 2 4 6 8 10 12

Plot 

statistic

TPH 

(x-axis)

Score 

(y-axis) Slope Intercept

Minimum 0 0 n/a n/a

Median 1.6 50 30.84 0.00

75th 2.8 75 20.46 6.84

99th 10.4 100 3.33 65.54

0

25

50

75

100

Down wood element score

Percentage cover (COV) ≥ 25 cm DIA

Min

75th

25th

99th

0 10 20 30 40 50 60

Plot 

statistic

TPH 

(x-axis)

Score 

(y-axis) Slope Intercept

Minimum 0 0 n/a n/a

25th 10.2 50 4.92 0.00

75th 24.7 75 1.72 32.55

99th 49.5 100 1.01 50.09

0

25

50

75

100

Live tree element score

Trees per hectare (TPH) ≥ 100 cm DBH 
86

GENERAL TECHNICAL REPORT PNW-GTR-911

Figure B-7—Redwood curves for stands ≥150 years of age (n = 35). DBH = diameter at breast height. DIA = diameter at large end and ≥3 

m long. NA = not applicable.

Min

75th

Median

99th

0 2 4 6 8 10 12 14 16

Plot 

statistic

TPH 

(x-axis)

Score 

(y-axis) Slope Intercept

Minimum 0 0 n/a n/a

Median 2.3 50 21.70 0.00

75th 4.9 75 9.49 28.13

99th 13.8 100 2.82 61.08

0

25

50

75

100

Snag element score 

Snags per hectare (SPH) ≥ 75 cm DBH 

Min

75th

Median

99th

0 2 4 6 8 10 12 14

Plot 

statistic

TPH 

(x-axis)

Score 

(y-axis) Slope Intercept

Minimum 0 0 n/a n/a

Median 3.1 50 16.21 0.00

75th 5.4 75 10.62 7.26

99th 12.7 100 3.44 56.31

0

25

50

75

100

Down wood element score

Percentage cover (COV) ≥ 25 cm DIA

Min

75th

25th

99th

0 10 20 30 40 50 60 70 80

Plot 

statistic

TPH 

(x-axis)

Score 

(y-axis) Slope Intercept

Minimum 0 0 n/a n/a

25th 10.9 50 4.59 0.00

75th 35.8 75 1.00 39.07

99th 68.2 100 0.77 47.41

0

25

50

75

100

Live tree element score

Trees per hectare (TPH) ≥ 100 cm DBH 
87

Northwest Forest Plan—The First 20 Years (1994-2013): Status and Trends of Late-Successional and Old-Growth Forests

Figure B-8—Sitka spruce curves for stands ≥200 years of age (n = 21). DBH = diameter at breast height. DIA = diameter at large end and 

≥3 m long. NA = not applicable.

Min

75th

Median

99th

0 5 10 15 20 25 30

Plot 

statistic

TPH 

(x-axis)

Score 

(y-axis) Slope Intercept

Minimum 0 0 n/a n/a

Median 5.6 50 8.93 0.00

75th 14.8 75 2.72 34.78

99th 24.3 100 2.63 36.05

0

25

50

75

100

Snag element score 

Snags per hectare (SPH) ≥ 75 cm DBH 

Min

75th

Median

99th

0 2 4 6 8 10 12 14 16 18 20

Plot 

statistic

TPH 

(x-axis)

Score 

(y-axis) Slope Intercept

Minimum 0 0 n/a n/a

Median 3.9 50 12.82 0.00

75th 7.1 75 7.81 19.53

99th 17.4 100 2.43 57.77

0

25

50

75

100

Down wood element score

Percentage cover (COV) ≥ 25 cm DIA

Min

75th

25th

99th

0 10 20 30 40 50 60 70 80

Plot 

statistic

TPH 

(x-axis)

Score 

(y-axis) Slope Intercept

Minimum 0 0 n/a n/a

Median 12.1 50 4.13 0.00

75th 24.7 75 1.98 25.99

99th 68.1 100 0.58 60.77

0

25

50

75

100

Live tree element score

Trees per hectare (TPH) ≥ 100 cm DBH 
88

GENERAL TECHNICAL REPORT PNW-GTR-911

Figure B-9—Western redcedar curves for stands ≥200 years of age (n = 26). DBH = diameter at breast height. DIA = diameter at large 

end and ≥3 m long. NA = not applicable.

Min

75th

Median

99th

0 5 10 15 20 25 30 35

Plot 

statistic

TPH 

(x-axis)

Score 

(y-axis) Slope Intercept

Minimum 0 0 n/a n/a

Median 3.8 50 13.16 0.00

75th 7.8 75 6.25 26.25

99th 28.9 100 1.18 65.76

0

25

50

75

100

Snag element score 

Snags per hectare (SPH) ≥ 75 cm DBH 

Min

75th

Median

99th

0 1 2 3 4 5 6

Plot 

statistic

TPH 

(x-axis)

Score 

(y-axis) Slope Intercept

Minimum 0 0 n/a n/a

Median 0.7 50 71.43 0.00

75th 2.3 75 15.63 9.06

99th 5.4 100 8.06 56.45

0

25

50

75

100

Down wood element score

Percentage cover (COV) ≥ 25 cm DIA

Min

75th

25th

99th

0 10 20 30 40 50 60 70

Plot 

statistic

TPH 

(x-axis)

Score 

(y-axis) Slope Intercept

Minimum 0 0 n/a n/a

25th 14.2 50 3.52 0.00

75th 37.6 75 1.07 34.83

99th 61.8 100 1.03 36.16

0

25

50

75

100

Live tree element score

Trees per hectare (TPH) ≥ 100 cm DBH 
89

Northwest Forest Plan—The First 20 Years (1994-2013): Status and Trends of Late-Successional and Old-Growth Forests

Figure B-10—Subalpine curves for stands ≥150 years of age (n = 191). DBH = diameter at breast height. DIA = diameter at large end and 

≥3 m long. NA = not applicable.

Min

75th

Median

99th

0 10 20 30 40 50 60 70

Plot 

statistic

TPH 

(x-axis)

Score 

(y-axis) Slope Intercept

Minimum 0 0 n/a n/a

Median 5.2 50 9.53 0.00

75th 13.3 75 3.10 33.72

99th 61.4 100 0.52 68.07

0

25

50

75

100

Snag element score 

Snags per hectare (SPH) ≥ 50 cm DBH 

Min

75th

Median

99th

0 2 4 6 8 10 12 14

Plot 

statistic

TPH 

(x-axis)

Score 

(y-axis) Slope Intercept

Minimum 0 0 n/a n/a

Median 1.6 50 31.48 0.00

75th 3.9 75 10.78 32.87

99th 12.6 100 2.89 63.71

0

25

50

75

100

Down wood element score

Percentage cover (COV) ≥ 25 cm DIA

Min

75th

25th

99th

0 20 40 60 80 100 120

Plot 

statistic

TPH 

(x-axis)

Score 

(y-axis) Slope Intercept

Minimum 0 0 n/a n/a

25th 9.2 50 5.43 0.00

75th 52.0 75 0.58 44.63

99th 113.1 100 0.41 53.72

0

25

50

75

100

Live tree element score

Trees per hectare (TPH) ≥ 50 cm DBH 
90

GENERAL TECHNICAL REPORT PNW-GTR-911

Figure B-11—Port Orford cedar curves for stands ≥200 years of age (n = 59). DBH = diameter at breast height. DIA = diameter at large 

end and ≥3 m long. NA = not applicable.

Min

75th

25th

99th

0 5 10 15 20 25 30 35 40 45 50

Plot 

statistic

TPH 

(x-axis)

Score 

(y-axis) Slope Intercept

Minimum 0 0 n/a n/a

Median 9.8 50 5.08 0.00

75th 18.4 75 2.93 21.15

99th 45.7 100 0.91 58.20

0

25

50

75

100

Snag element score 

Snags per hectare (SPH) ≥ 50 cm DBH 

Min

75th

25th

99th

0 1 2 3 4 5 6 7 8

Plot 

statistic

TPH 

(x-axis)

Score 

(y-axis) Slope Intercept

Minimum 0 0 n/a n/a

Median 1.6 50 30.50 0.00

75th 3.4 75 13.96 27.11

99th 7.4 100 6.34 53.25

0

25

50

75

100

Down wood element score

Percentage cover (COV) ≥ 25 cm DIA

Min

75th

25th

99th

0 10 20 30 40 50 60

Plot 

statistic

TPH 

(x-axis)

Score 

(y-axis) Slope Intercept

Minimum 0 0 n/a n/a

25th 15.9 50 3.14 0.00

75th 34.1 75 1.37 28.16

99th 53.8 100 1.27 31.76

0

25

50

75

100

Live tree element score

Trees per hectare (TPH) ≥ 75 cm DBH 
91

Northwest Forest Plan—The First 20 Years (1994-2013): Status and Trends of Late-Successional and Old-Growth Forests

Figure B-13—Oak woodland curves for stands ≥100 years of age (n = 40). DBH = diameter at breast height. NA = not applicable.

Figure B-12—Shasta red fir curves for stands ≥200 years of age (n = 25). DBH = diameter at breast height. DIA = diameter at large end 

and ≥3 m long. NA = not applicable.

Min

75th

Median

99th

0 5 10 15 20 25 30 35 40 45 50

Plot 

statistic

TPH 

(x-axis)

Score 

(y-axis) Slope Intercept

Minimum 0 0 n/a n/a

Median 7.9 50 6.33 0.00

75th 14.8 75 3.62 21.43

99th 44.3 100 0.83 62.42

0

25

50

75

100

Snag element score 

Snags per hectare (SPH) ≥ 50 cm DBH 

Min

75th

Median

99th

0 2 4 6 8 10 12 14

Plot 

statistic

TPH 

(x-axis)

Score 

(y-axis) Slope Intercept

Minimum 0 0 n/a n/a

Median 1.8 50 28.30 0.00

75th 2.9 75 22.07 11.02

99th 12.8 100 2.52 67.70

0

25

50

75

100

Down wood element score

Percentage cover (COV) ≥ 25 cm DIA

Min

75th

25th

99th

0 20 40 60 80 100 120

Plot 

statistic

TPH 

(x-axis)

Score 

(y-axis) Slope Intercept

Minimum 0 0 n/a n/a

25th 11.6 50 4.30 0.00

75th 46.9 75 0.71 41.75

99th 100.7 100 0.46 53.19

0

25

50

75

100

Live tree element score

Trees per hectare (TPH) ≥ 75 cm DBH 

Min

75th

Median

99th

0 10 20 30 40 50 60 70 80

Plot 

statistic

TPH 

(x-axis)

Score 

(y-axis) Slope Intercept

Minimum 0 0 n/a n/a

25th 9.0 50 5.53 0.00

75th 36.7 75 0.91 41.81

99th 75.9 100 0.64 51.65

0

25

50

75

100

Live tree element score

Trees per hectare (TPH) ≥ 50 cm DBH 
92

GENERAL TECHNICAL REPORT PNW-GTR-911

Figure B-14—Ponderosa pine curves for stands ≥150 years of age (n = 39). DBH = diameter at breast height. NA = not applicable.

Figure B-15—Other pine species curves for stands ≥100 years of age (n =1 6). DBH = diameter at breast height. NA = not applicable.

Figure B-16—Juniper woodland curves for stands ≥100 years of age (n = 10). DBH = diameter at breast height. NA = not applicable.

Min

75th

Median

99th

0 5 10 15 20 25 30

Plot 

statistic

TPH 

(x-axis)

Score 

(y-axis) Slope Intercept

Minimum 0 0 n/a n/a

25th 2.5 50 19.63 0.00

75th 12.3 75 2.56 43.47

99th 26.3 100 1.79 53.04

0

25

50

75

100

Live tree element score

Trees per hectare (TPH) ≥ 75 cm DBH 

Min

75th

Median

99th

0 10 20 30 40 50 60

Plot 

statistic

TPH 

(x-axis)

Score 

(y-axis) Slope Intercept

Minimum 0 0 n/a n/a

25th 5.7 50 8.77 0.00

75th 23.7 75 1.39 42.08

99th 50.6 100 0.93 52.97

0

25

50

75

100

Live tree element score

Trees per hectare (TPH) ≥ 50 cm DBH 

Min

75th

Median

99th

0 5 10 15 20 25 30 35

Plot 

statistic

TPH 

(x-axis)

Score 

(y-axis) Slope Intercept

Minimum 0 0 n/a n/a

25th 3.3 50 15.24 0.00

75th 19.2 75 1.57 44.84

99th 29.5 100 2.43 28.34

0

25

50

75

100

Live tree element score

Trees per hectare (TPH) ≥ 50 cm DBH 
93

Northwest Forest Plan—The First 20 Years (1994-2013): Status and Trends of Late-Successional and Old-Growth Forests

Appendix C: Old-Growth Structure Index Mapping and 

Analysis Thresholds

Table C-1—Old-growth structure index (OGSI) mapping thresholds (80, 120, 160, and 200 years) by forest 

vegetation zone 

Scatter 

plot

Pseudoa 

R2 Forest vegetation zone OGSI-80 OGSI-120 OGSI-160 OGSI-200

A 0.57 Western hemlock 23.9 30.9 37.7 44.9

B 0.33 Douglas-fir 21.6 30.9 40.8 49.3

C 0.51 Grand fir/white fir 19.8 29.9 40.6 49.2

D 0.67 Pacific silver fir 18.0 25.2 34.1 43.3

E 0.52 Mountain hemlock 14.3 22.9 32.8 43.5

F 0.53 Tanoak 22.8 31.2 40.4 49.1

G 0.28 Redwood 30.0 40.7 47.9 53.4

H 0.55 Sitka spruce 30.8 47.7 56.2 59.8

I 0.45 Western redcedar 25.7 36.6 42.6 47.6

J 0.27 Oak woodlands 26.2 57.3 74.0 n/a

K 0.28 Ponderosa pine NA 19.1 45.5 67.7

L 0.26 Subalpine 19.1 32.4 42.0 43.5

M 0.10 Other pine 29.8 35.0 40.5 49.9

N 0.46 Port Orford cedar 20.7 28.5 39.7 47.9

O 0.39 Shasta red fir 17.2 24.8 34.5 41.3

P 0.53 Juniper 10.8 49.4 88.2 71.9

NA = not applicable.

a

 See Schabenberger and Pierce (2002: 213).
94

GENERAL TECHNICAL REPORT PNW-GTR-911

Figure C-1—Scatter plot of OGSI and stand age for forest inventory plots showing a locally weighted polynomial regression line used to 

develop mapping thresholds at 80 and 200 years (dashed lines).
95

Northwest Forest Plan—The First 20 Years (1994-2013): Status and Trends of Late-Successional and Old-Growth Forests

Figure C-1 (continued)—Scatter plot of OGSI and stand age for forest inventory plots showing a locally weighted polynomial regression 

line used to develop mapping thresholds at 80 and 200 years (dashed lines).
96

GENERAL TECHNICAL REPORT PNW-GTR-911

Figure C-1 (continued)—Scatter plot of OGSI and stand age for forest inventory plots showing a locally weighted polynomial regression 

line used to develop mapping thresholds at 80 and 200 years (dashed lines).
97

Northwest Forest Plan—The First 20 Years (1994-2013): Status and Trends of Late-Successional and Old-Growth Forests

References:

Cleveland, W.S. 1979. Robust locally weighted regression and smoothing 

scatterplots. Journal of the American Statistical Association. 74: 829-836.

R Core Team. 2013. R: A language and environment for statistical computing. R 

Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.

org/. (12 November 2014).

Schabenberger, O.; Pierce, F.J. 2002. Contemporary statistical models for the 

plant and soil sciences. Boca Raton, FL: CRC Press. 738 p.
98

GENERAL TECHNICAL REPORT PNW-GTR-911

Appendix D: Attributing and Accuracy of LandTrendr 

Disturbance Maps

LandTrendr disturbance maps were attributed with a “cause agent” through the 

interpretation of the duration of the disturbance, the location of the disturbance in 

relationship to federal land use allocations, relationship to aerial detection survey 

(ADS) maps for insects (USDA FS 2011), spatial relationship to mapped wildfire 

perimeters, and inside wildfire perimeters, year of disturbance in relationship to 

the wildfire year. If a disturbance inside a wildfire perimeter predated the wildfire year by more than 2 years, it was attributed to some other cause, not wildfire 

(e.g., insects or timber harvest). We classified disturbance into four general 

classes as follows:

Timber harvest—Represents timber harvesting including thinning and regeneration harvests. Classified as fast (duration ≤4 years) disturbances outside of 

congressionally reserved or administratively withdrawn (CRAW) lands and also 

outside of wildfire perimeters; or if within a wildfire perimeter, then predating 

the fire year by more than 2 years. Some fast disturbances meeting these criteria 

occurred within ADS mapped areas for insects. Visual inspection with highresolution aerial imagery showed that most of these disturbances were from timber 

harvesting, likely related to insect damage salvage.

Wildfire—Fast or slow (duration >4 years) disturbances inside a mapped 

wildfire perimeter, but only when no other disturbance preceded the fire year. Slow 

disturbances inside wildfire perimeters likely represent postfire mortality.

Insect and disease—Slow-duration disturbances that occurred outside of 

wildfire perimeters, or if inside a wildfire perimeter, preceded the fire year. Also 

includes fast disturbances that occurred in ADS maps of insects, where mapped for 

2 or more consecutive years.

Other disturbance —Fast disturbances that occurred on CRAW lands and 

outside of wildfire perimeters, or if inside fire perimeters, then preceded fire year.

All disturbance maps were produced at a minimum mapping unit of 11 map 

pixels (30-m resolution). This equates to about 2.5 ac as the minimum patch size 

for a disturbance. This removes some “noise” from the satellite imagery, but there 

is still some amount of uncertainty with our classification. Our highest confidence 

is for the wildfire classification, which is largely corroborated with mapped fire 

perimeters acquired from various sources (e.g., GEOMAC 2012, MTBS 2012). 

We also have harvest unit maps for much of the federal (but not all) landscape, 

but lack these data for nonfederal lands. We suspect that some of the “harvest” 

class includes fast disturbances related to nontimber harvesting causes (e.g., 

urbanization, landslides, blowdown, floods, etc.). This is likely more the case on 
99

Northwest Forest Plan—The First 20 Years (1994-2013): Status and Trends of Late-Successional and Old-Growth Forests

nonfederal lands. To get a general sense for the accuracy of our harvest classification, we compared the amount of harvest to timber harvest records from the 

Forest Service and Bureau of Land Management (fig. D-1). Overall, LandTrendr 

estimated 8,955 MBF (thousand board feet), compared to agency records of 8,064 

MBF for the 17-year period 1995–2011.

LandTrendr

Timber harvest

Harvest records

(MMBF)

Year

800

1995199619971998199920002001200220032004200520062007200820092010

700

600

500

400

300

200

100

0

Figure D-1—Comparison between LandTrendr timber harvest estimate and agency records from the 

Forest Service and Bureau of Land Management within the Northwest Forest Plan area. LandTrendr 

magnitude was converted into MMBF using the following assumption: low (0 to 33 percent) = 10 

MBF/ac, mod (33 to 66 percent) = 20 MBF/ac, high (66 to 100 percent) = 30 MBF/ac.
100

GENERAL TECHNICAL REPORT PNW-GTR-911

Table D-1—Classification table for LandTrendr cause agents

All possible map overlay combinations First classification Final classificationa

slow - outside of CRAW - outside of fire Insect and disease Insect and disease

slow - inside of CRAW - outside of fire Insect and disease Insect and disease

slow - outside of CRAW - outside of fire - in ADS Insect and disease Insect and disease

fast - inside of CRAW - outside of fire - in ADS Insect and disease Insect and disease

slow - inside of CRAW - outside of fire - in ADS Insect and disease Insect and disease

slow - outside of CRAW - inside of fire - prefire Insect and disease then wildfire Insect and disease

slow - inside of CRAW - inside of fire - prefire Insect and disease then wildfire Insect and disease

slow - outside of CRAW - inside of fire - in ADS - prefire Insect and disease then wildfire Insect and disease

fast - inside of CRAW - inside of fire - in ADS - prefire Insect and disease then wildfire Insect and disease

slow - inside of CRAW - inside of fire - in ADS - prefire Insect and disease then wildfire Insect and disease

fast - inside of CRAW - outside of fire Other disturbance Other disturbance

fast - inside of CRAW - inside of fire - prefire Other disturbance then wildfire Other disturbance

fast - outside of CRAW - outside of fire Timber harvest Timber harvest

fast - outside of CRAW - outside of fire - in ADS Insect–possible harvest Timber harvest

fast - outside of CRAW - inside of fire - prefire Timber harvest then wildfire Timber harvest

fast - outside of CRAW - inside of fire - in ADS - prefire Insect or harvest then wildfire Timber harvest

fast - outside of CRAW - inside of fire Wildfire Wildfire

slow - outside of CRAW - inside of fire Wildfire Wildfire

fast - inside of CRAW - inside of fire Wildfire Wildfire

slow - inside of CRAW - inside of fire Wildfire Wildfire

fast - outside of CRAW - inside of fire - in ADS Wildfire Wildfire

slow - outside of CRAW - inside of fire - in ADS Wildfire Wildfire

fast - inside of CRAW - inside of fire - in ADS Wildfire Wildfire

slow - inside of CRAW - inside of fire - in ADS Wildfire Wildfire

a

 General classes used for monitoring reporting. Final classifications reviewed visually with high-resolution color aerial imagery.
101

Northwest Forest Plan—The First 20 Years (1994-2013): Status and Trends of Late-Successional and Old-Growth Forests

Accuracy Assessment:

The following accuracy matrices are based on TimeSync (TS) analyses of the 

LandTrendr (LT) disturbance map.

TS DISTURB TS NOT DISTURB

LT DISTURB 556 817 1,373

LT NOT DISTURB 174 32,019 32,193

730 32,836 33,566

Overall accuracy was 97 percent, Kappa = 0.52

LT INSECT/

DISEASE LT FIRE

LT 

HARVEST LT OTHER

TS INSECT/DISEASE 2 0 1 0 3

TS FIRE 0 38 1 0 39

TS HARVEST 1 1 124 1 127

TS OTHER 2 0 5 0 7

5 39 131 1 186

Overall accuracy was 93 percent, Kappa = 0.84

References:

Monitoring Trends in Burn Severity [MTBS]. 2012. Data accessible through the 

Forest Service’s Remote Sensing Applications Center (RSAC). http://www.mtbs.

gov/compositfire/mosaic/bin-release/burnedarea.html. (2 February 2014).

Geospatial Multi-Agency Coordination Group [GeoMAC]. 2012. An internetbased mapping application originally designed for fire managers to access online 

maps of current fire locations and perimeters in the conterminous 48 states. 

http://rmgsc.cr.usgs.gov/outgoing/GeoMAC/. (2 February 2014).

U.S. Department of Agriculture, Forest Service [USDA FS]. 2011. Forest health 

protection aerial survey data. http://www.fs.fed.us/r6/nr/fid/as/index.shtml. (4 

January 2011).
102

GENERAL TECHNICAL REPORT PNW-GTR-911

Appendix E: Gradient Nearest Neighbor (GNN) Older 

Forest Map Accuracy Report

A large suite of diagnostics detailing GNN model reliability and map accuracy 

is produced as a standard part of GNN modeling, and a report is provided with 

all data downloads. For local- (plot-) scale accuracy assessment, we used a modified leave-one-out cross-validation for all plots used in the model (Ohmann and 

Gregory 2002). Predicted map values for vegetation attributes at plot locations 

were compared to the field-measured values. For evaluation of the bookend models, 

the predicted value was from the bookend model date closest to the year of plot 

measurement. Because none of the plot inventories provide a valid, representative 

sample of forest conditions across all ownerships at either of the bookend dates, 

it was not possible to assess the accuracy of each bookend model independently. 

Rather, the cross-validation provides a general indication of the reliability of both 

bookend models.

To quantify old-forest map accuracy for this report, we summarized the crossvalidation data (predicted-observed pairs) by GNN model region (fig. E-1). For each 

bookend map, we compared plot-observed old-forest classification to independent 

GNN prediction at the plot’s location and constructed a binary error matrix of 

observed (plot) and predicted (mapped) late-successional and old-growth (LSOG) 

designations to derive several map diagnostics (tables E-1 and E-2).

Map accuracy as a percentage correct is the percentage of plots where the 

observed and predicted agree (either LSOG present or LSOG absent). Sensitivity 

is based on the percentage of field plots where the map correctly predicted LSOG 

presence, and specificity is the percentage of plots where the map correctly predicted LSOG absence. The kappa statistic takes into account the agreement occurring by chance (Cohen 1960) but still is not independent of prevalence (Kappas tend 

to be lower where LSOG comprises a smaller percentage of the forest landscape). 

The assessment of “overall map agreement” in tables E-1 and E-2 is a subjective 

classification of Kappa by Landis and Koch (1977).

Overall, the bookend maps had fair to moderate agreement with the plot data. 

The bookend 2 (2012) map was slightly more accurate than bookend 1 (1993). Drier 

model regions (MR 222 and MR 226) had lower map accuracies than moister 

model regions.
103

Northwest Forest Plan—The First 20 Years (1994-2013): Status and Trends of Late-Successional and Old-Growth Forests

Figure E-1—Gradient Nearest Neighbor (GNN) modeling regions shown in color (from Moeur et al. 2011).
104

GENERAL TECHNICAL REPORT PNW-GTR-911

Table E-1—Map versus plot accuracy statistics for bookend map 1 (1993)

Model 

region

Old-forest 

definition

Number of 

plots Prevalence

Percent 

correct Sensitivity Specificity Kappa

Overall 

map 

agreement

221 OGSI 80 2937 0.41 78.6 0.73 0.83 0.56 Moderate

221 OGSI 200 2937 0.24 82.4 0.61 0.89 0.51 Moderate

222 OGSI 80 2650 0.47 63.7 0.62 0.65 0.27 Fair

222 OGSI 200 2650 0.16 80.8 0.36 0.89 0.26 Fair

223 OGSI 80 2024 0.34 80.4 0.67 0.87 0.55 Moderate

223 OGSI 200 2024 0.16 88.9 0.61 0.94 0.57 Moderate

224 OGSI 80 5124 0.49 71.7 0.71 0.72 0.43 Moderate

224 OGSI 200 5124 0.22 79.5 0.50 0.88 0.39 Fair

225 OGSI 80 3703 0.52 72.0 0.74 0.70 0.44 Moderate

225 OGSI 200 3703 0.20 80.0 0.40 0.90 0.32 Fair

226 OGSI 80 975 0.40 63.8 0.53 0.71 0.24 Fair

226 OGSI 200 975 0.11 88.8 0.36 0.95 0.34 Fair

OGSI = old-growth structure index.

Table E-2—Map versus plot accuracy statistics for bookend map 2 (2012)

Model 

region

Old-forest 

definition

Number of 

plots Prevalence

Percentage 

correct Sensitivity Specificity Kappa

Overall 

map 

agreement

221 OGSI 80 2937 0.42 78.2 0.73 0.82 0.55 Moderate

221 OGSI 200 2937 0.25 82.6 0.63 0.89 0.53 Moderate

222 OGSI 80 2650 0.46 64.3 0.62 0.66 0.28 Fair

222 OGSI 200 2650 0.16 80.5 0.37 0.89 0.27 Fair

223 OGSI 80 2024 0.34 80.2 0.67 0.87 0.55 Moderate

223 OGSI 200 2024 0.17 88.6 0.61 0.94 0.58 Moderate

224 OGSI 80 5124 0.49 71.7 0.71 0.72 0.43 Moderate

224 OGSI 200 5124 0.22 79.9 0.51 0.88 0.40 Moderate

225 OGSI 80 3703 0.51 72.3 0.74 0.71 0.45 Moderate

225 OGSI 200 3703 0.20 79.3 0.40 0.89 0.31 Fair

226 OGSI 80 975 0.40 64.9 0.54 0.72 0.27 Fair

226 OGSI 200 975 0.10 88.9 0.33 0.95 0.34 Fair
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Northwest Forest Plan—The First 20 Years (1994-2013): Status and Trends of Late-Successional and Old-Growth Forests

References: 

Cohen, J. 1960. A coefficient of agreement for nominal scales. Educational and 

Psychological Measurement. 20: 37–46.

Landis, J.R.; Koch, G.G. 1977. The measurement of observer agreement for 

categorical data. Biometrics. 33: 159–174.

Moeur, M.; Ohmann, J.L.; Kennedy, R.E.; Cohen, W.B.; Gregory, M.J.; Yang, 

Z.; Roberts, H.M.; Spies, T.A.; Fiorella, M. 2011. Northwest Forest Plan–

status and trends of late-successional and old-growth forests from 1994 to 2007. 

Gen. Tech. Rep. PNW-GTR-853. Portland, OR: U.S. Department of Agriculture, 

Forest Service, Pacific Northwest Research Station. 48 p.
106

GENERAL TECHNICAL REPORT PNW-GTR-911

Appendix F: Older Forest Summaries by Federal Land 

Ownerships

The following summaries cover the Northwest Forest Plan (NWFP or the Plan) area 

by major land management agency (region and state) for the time period between 

1993 and 2012. These trend lines are based on annual time series maps produced 

for NWFP monitoring. Individual federal unit (e.g., forest or Bureau of Land 

Management [BLM] district) summary sheets can be downloaded from the NWFP 

Interagency Monitoring website at: http://www.reo.gov/monitoring/. 

Trend lines for two different older forest definitions are provided:

• OGSI-80–old-growth structure index threshold of ≥80 years average 

stand age.

• OGSI-200–old-growth structure index threshold of ≥200 years average 

stand age.

Summary sheets are interpreted as follows:

• Top graph (a) shows older forest trends on all federal lands, showing beginning and end period areal estimates and net percentage change in parenthesis.

• The two middle graphs show older forest trends for the same area, but partitioned into nonreserved (b) and reserved (c) land use allocations.

• The bottom graphs (d) and (e) describe landscape pattern changes in old 

forest over the last 20 years since the Plan’s implementation. OGSI-80 

graphs for nonreserved and reserved land use allocations (LUAs) on the 

left (d), and OGSI-200 graphs on the right (e). Core and core edge represent 

intact stands of old forest; whereas, fingers and scatter represent fragmented old forest. A decrease in core and core edge and increase in fingers 

and scatter represents an increase in landscape fragmentation.
107

Northwest Forest Plan—The First 20 Years (1994-2013): Status and Trends of Late-Successional and Old-Growth Forests

Figure F-1—Older forest trends summary for U.S. Department of Agriculture Forest Service, Pacific Northwest Region. 

OGSI = old-growth structure index at the 80- and 200-year threshold. NWFP = Northwest Forest Plan Area.

A

B

D

C

E
108

GENERAL TECHNICAL REPORT PNW-GTR-911

Figure F-2—Older forest trends summary for U.S. Department of Agriculture, Forest Service, Pacific Southwest Region. 

OGSI = old-growth structure index at the 80- and 200-year threshold.

A

B

D

C

E
109

Northwest Forest Plan—The First 20 Years (1994-2013): Status and Trends of Late-Successional and Old-Growth Forests

Figure F-3—Older forest trends summary for U.S. Department of the Interior, Bureau of Land Management, Oregon. 

OGSI = old-growth structure index at the 80- and 200-year threshold. NWFP = Northwest Forest Plan Area.

A

B

D

C

E
110

GENERAL TECHNICAL REPORT PNW-GTR-911

Figure F-4—Older forest trends summary for U.S. Department of the Interior, Bureau of Land Management, Northern California. OGSI = old-growth structure index at the 80- and 200-year threshold. NWFP = Northwest Forest Plan Area.

A

B

D

C

E
111

Northwest Forest Plan—The First 20 Years (1994-2013): Status and Trends of Late-Successional and Old-Growth Forests

Figure F-5—Older forest trends summary for U.S. Department of the Interior, National Park Service. OGSI = old-growth 

structure index at the 80- and 200-year threshold. NWFP = Northwest Forest Plan Area. n/a = not applicable.

A

B

D

C

E
112
113

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114

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