The classification of late seral forests in the Pacific Northwest, USA using Landsat ETM+ imagery

www.elsevier.com/locate/rse

Remote Sensing of Environment 91 (2004) 320–331

The classification of late seral forests in the Pacific Northwest,

USA using Landsat ETM+ imagery

Hong Jianga,b,*, James R. Strittholta, Pamela A. Frosta, Nicholas C. Slossera

aConservation Biology Institute, 260 SW Madison Avenue Suite 106, Corvallis, OR 97333, USAb International Institute of Earth System Science, Nanjing University, Hankou Road 22, Nanjing 210093, China

Received 2 December 2003; received in revised form 9 March 2004; accepted 13 March 2004

Abstract

To conserve the Earth’s most extraordinary expressions of temperate biodiversity in the Pacific Northwest (PNW), USA, the mapping of

late seral (old and mature) conifer forests plays a critical role. For this paper, we define old conifer forests as >150 years and mature conifer

forest between 50 and 150 years. We offer a new Optimal Iterative Unsupervised Classification (OIUC) procedure for mapping late seral

conifer forests over an eight-ecoregion area. The key steps of the OIUC classification were: (1) fully using the Landsat 7 Enhanced Thematic

Mapper Plus (ETM+) 15 m panchromatic channel merged with other 30 m bands 3 and 5 to make a pan-sharpened false color composite for

high resolution image interpretation; (2) splitting the ETM+ scene by ecologically distinct areas, or ecoregions, to create relatively

homologous images for classification; (3) using a procedure similar to cluster busting where multiple iterative manipulation of the ISODATA

clusters was employed; and (4) edge matching of sub-scenes to form ecoregions, then later merged together to form a map for the entire

PNW. Supporting data and information included ancillary spatial GIS data layers, aerial photos, Digital Ortho Quad images (DOQs), field

investigations, and previously reported forest age results. Classification accuracy was assessed using 2081 stratified random locations on 105

individual DOQs covering the entire region. Approximately 4.7 million ha (f 19%) of the PNW was classified as old conifer forest (>150

years). Another 4.8 million ha (f 19%) was classified as mature conifer forest (50–150 years). Over 9.48 million ha (f 38%) of the PNW

was late seral conifer forest. The extent of late seral forests (old and mature conifer cover classes) varied greatly between the eight ecoregions.

The Central and Southern Cascades and Klamath–Siskiyou ecoregions contained the highest amount of late seral forest in the region. The

results showed high accuracy of the late seral forest classification for the PNW with an overall accuracy of 90.72% and KAPPA test K value

0.8534. Producer’s (Omission) accuracy for old and mature forests were 91.36% and 80.40%, User’s (Commission) accuracy were 89.42%

and 80.59%, respectively. Accuracy levels differed for the different ecoregions examined. In general, mature conifer forests exhibited higher

levels of confusion than did old conifer forests, due to the spectral influences of high-density young conifer stands and terrain shadow effects.

The results fill an important data gap needed for ongoing conservation planning purposes throughout the region. We found that for relatively

large geographic areas the OIUC method is an efficient and cost-effective alternative that yields high quality results.

D 2004 Elsevier Inc. All rights reserved.

Keywords: Conifer forest; Pacific Northwest; Landsat ETM+

1. Introduction and mature forest as 50–150 years. Substantial effort, both

The Pacific Northwest (PNW), USA, ranks among the

most ecologically diverse regions of North America (Frank-

lin & Dyrness, 1988), and late seral (old and mature) conifer

forests play an important role in maintaining that diversity

(Whittaker, 1960; Defenders of Wildlife, 1998). For this

study, old conifer forest was defined as being >150 years

0034-4257/$ - see front matter D 2004 Elsevier Inc. All rights reserved.

doi:10.1016/j.rse.2004.03.016

* Corresponding author. Conservation Biology Institute, 260 SW

Madison Avenue Suite 106, Corvallis, OR 97333, USA. Tel.: +1-541-

757-0687; fax: +1-541-752-0518.

E-mail address: [email protected] (H. Jiang).

political and scientific, has been directed toward conserva-

tion of old-growth conifer forests and associated species in

this region. However, the extent of these forests, which once

dominated much of the PNW, has been drastically reduced

by logging and other forest clearing activities, especially

since 1940 over much of the region (Franklin & Spies,

1991). Consequently, the conifer forests of the PNW have

become a heterogeneous mosaic of original old-growth

forests, second-growth stands, young forest, plantations of

various ages, and clearcuts (Spies et al., 1994, Tunner et al.,

1996). Successful conservation of late seral forests through-

out the Pacific Northwest requires spatially explicit data on

H. Jiang et al. / Remote Sensing of Environment 91 (2004) 320–331 321

its current extent so it can be utilized in conservation

planning efforts at the regional level.

Regional conservation planning is becoming increasingly

sophisticated (i.e., more inclusive of different conservation

values which are then incorporated into quantitative analyt-

ical and modeling tools such as nature reserve selection

algorithms) as more accurate and extensive spatially explicit

data become available and the computing capacity to use

these data more robust and affordable. Despite the many

advances, important data layers (e.g., extent and condition

of forest cover) are still needed to adequately address

important regional conservation questions, and remote sens-

ing technology is important in filling at least some of the

existing data gaps. For example, possessing spatially ex-

plicit data on forest condition is fundamental to developing

regional forest sustainability plans.

The PNW continues to be a major focal area for remote

sensing research, particularly as it is applied to forests.

Some regional studies have addressed changes in land cover

over time. Tunner et al. (1996), for example, used the

Landsat Multispectral Scanner (MSS) and Thematic Mapper

(TM) imagery for four time periods (from 1975 to 1991) and

interpreted land cover in the Olympic area of Washington

using the unsupervised ISODATA method. Similarly, Sachs

et al. (1998) used Landsat Multispectral Scanner (MSS) and

TM imagery to map forest cover and detect major distur-

bances between 1975 and 1992 in British Columbia, Can-

ada. The researchers also used unsupervised ISODATA

results to differentiate between closed conifer, semi-open

conifer, deciduous, and mixed forest classes, as well as three

closed conifer age classes—young, mature, and old.

In two separate studies (Eby, 1987; Green & Congalton,

1990), mapped old-growth forests in western Washington

and Oregon using Landsat MSS and TM data. Morrison et

al. (1991) and Congalton et al. (1993) used satellite imagery

to quantify and map the locations of late seral forests on

nine national forests in the PNW region. These two studies

used the same Forest Service data to conduct their analyses,

but reported very different late seral forest areal extent

estimates. Overall, the two studies differed by approximate-

ly 25%, but for a few forests, they differed by as much as

100%. Cohen and Spies (1992) and Cohen et al. (1995,

2001) used 1988 Landsat TM imagery to model forest

vegetation attributes, including age, across western Oregon

using a multi-image mosaic. Similarly, Ohmann and Greg-

ory (2002) employed direct gradient analysis and nearest-

neighbor imputation to predict detailed ground attributes of

vegetation in a digital landscape map of the Oregon Coast.

While many of these studies focused on forest age, they

differ in terms of method, areal extent, ownership focus, and

age class definitions. For example, Boyd et al. (2002) used

three methods: vegetation indices, regression analysis, and

neural networks to estimate conifer forest cover (approxi-

mately 30–40 years and older) in the western Cascade

Mountains. Cohen et al. (1995) estimated the age and

structure of forests using two ( < 200 and >200), three

( < 80, 80–200, and >200), and five ( < 40, 40–80, 80–

120, 120–200, and >200) age classes. Cohen et al. (2001)

later modeled continuous variables instead of discrete age

classes. Alternatively, Ohmann and Gregory (2002) de-

scribed the Oregon Coast forests in terms of vegetation

density, species composition, and size class, rather than by

age or age class.

Additionally, differences in the underlying data sources

(dates of acquisition, resolution, and ancillary data support)

represent other sources of divergence among the studies.

Consequently, despite the wealth of data available, reason-

ably accurate and timely spatial database for late seral

conifer forests for the entire PNW region for conservation

planning purposes was not available, The goal of this

project was to provide such a data layer filling an important

data gap. However, our study was intentionally restricted

and in no way was intended to compete with ongoing

government mapping efforts (e.g., Interagency Vegetation

Mapping Project), which will undoubtedly add greater depth

to our understanding of the regional vegetation complexity.

2. Methods

2.1. Study area

The study area in this assessment covered approximately

25 million hectares across portions of Washington, Oregon,

and California. We elected to subdivide the study area using

ecoregions as defined by World Wildlife Fund (WWF) for

forest assessment purposes (Fig. 1). An ecoregion is defined

as a relatively large area of land or water that contains a

geographically distinct assemblage of natural communities.

These communities (1) share a large majority of their

species, dynamics, and environmental conditions, and (2)

function together effectively as a conservation unit at global

and continental scales (Olson et al., 2001). In addition to

being a useful ecological unit of analysis for conservation

purposes, ecoregions are also useful in mapping vegetation

as focusing on ecologically distinct areas usually minimizes

confusion of discrete land cover classes that appear spec-

trally similar on an image (Lachowski et al., 1996).

We examined eight ecoregions (Fig. 1). They included

Cascade Mountains Leeward Forests (CMLF), Northern

Cascades Forests (NCF), Puget Lowland Forests (PLF),

Central Pacific Coastal Forests (CPCF), Central and South-

ern Cascades Forests (CSCF), Eastern Cascade Forests

(ECF), Klamath–Siskiyou Forests (KSF), and Willamette

Valley Forests (WVF). Ecoregion boundaries were simpli-

fied in two instances by removing island polygons. The

Olympic Mountains were incorporated into the CPCF ecor-

egion instead of being an island polygon of the NCF

ecoregion and two small CSCF island polygons were

removed and incorporated in the ECF ecoregion. A detailed

description of these eight ecoregions can be found in

Ricketts et al. (1999).

Fig. 1. Map of study area showing the WWF ecoregions in the PNW, USA [Cascade Mountains Leeward Forests (CMLF), Northern Cascades Forests (NCF),

Puget Lowland Forests (PLF), Central Pacific Coastal Forests (CPCF), Central and Southern Cascades Forests (CSCF), Eastern Cascade Forests (ECF),

Klamath–Siskiyou Forests (KSF), and Willamette Valley Forests (WVF)].

H. Jiang et al. / Remote Sensing of Environment 91 (2004) 320–331322

2.2. Data

The study was based on the classification of 23 Landsat 7

Enhanced TM plus (ETM+) satellite images acquired from

July to September 2000. The projection system used was

Universal Transverse Mercator (UTM) Zone 10; Spheroid

was Clarke 1866; and Datum was NAD27. ERDAS Imagine

software (version 8.5) was used to carry out all classification

and mapping tasks. In addition to the ETM+ satellite

imagery, we acquired and utilized numerous other ancillary

datasets.

Ecoregion boundaries were obtained as Arc/Info poly-

gons (1:2,000,000 scale) from World Wildlife Fund (WWF).

A GIS coverage for roads (1:24,000) was acquired from the


Bureau of Land Management (BLM) and used for image

rectification. Digital Elevation Model (DEM) data at 30 m

resolution was acquired from the USGS. Aspect results were

generated from the DEM and used to stratify the imagery

further (fully illuminated aspects versus less illuminated

aspects in areas with rugged terrain (CMLF, NCF, CSCF,

KSF, and portions of CPCF) prior to conducting the

unsupervised classification. GAP vegetation data (Jennings,

2000, http://www.gap.uidaho.edu) layers were used to help

discern the coarse level plant community differences across

the region.

Oregon Digital Ortho Quad (DOQ) images, created by

the U.S. Geological Survey (USGS) and U.S. Forest Service

(USFS), were acquired from the Oregon Geographic Infor-

mation Council (OGIC, http://www.sscgis.state.or.us/), and

DOQ imagery available for Washington and California were

purchased from the USGS.

2.3. Image processing

The Landsat 7 ETM+ imagery was georeferenced to the

UTM zone 10 projection. To improve subregional accuracy,

the image was rectified using the 1:24,000 roads data

acquired from the BLM to less than 0.5 pixel root mean-

square error (RMSE) using the third-order polynomial and

resampled to 30 m pixels using the nearest-neighbor option.

Rather than relying solely on RMSE values to assess the

quality of the rectification, which relies on a pixel-by-pixel

correction, we elected to use the roads coverage (1:24,000)

to refine the image rectification. Once satisfied with the

results, we proceeded to actual image classification.

We also decreased the heterogeneity of the spectral

classification results in the ETM+ imagery by dividing

individual scenes into pieces based on ecoregion bound-

aries. These pieces, similar ecologically, formed the basic

analytical units for running the unsupervised classification

process, thus avoiding the influences of more complicated

spectral, geographic, and forest community variations. At

the macro level, having the entire study area subdivided into

ecologically meaningful subregions allowed for greater

concentration on just the forest communities present in

any given area. For example, the CPCF ecoregion is

dominated by various Douglas-fir (Pseudotsuga menziesii)

communities with a high level of hardwood intermixing.

Very different conifer community types (e.g., Jeffrey pine;

Pinus jeffreyi or Ponderosa pine Pinus ponderosa) were not

present to confound classification. The most challenging

ecoregion from the standpoint of forest community variabil-

ity was the Klamath–Siskiyou which contained a wide

range of conifer forest communities.

Visual interpretation of Landsat imagery has been dem-

onstrated as a useful tool in land cover and vegetation

mapping (Scott et al., 1993; Lillesand & Kiefer, 2000;

Zheng et al., 1997; Cohen et al., 1995; Wilson & Sader,

2002). Therefore, we merged the 30-m-resolution multi-

spectral data (bands 3 and 5) and 15-m-resolution panchro-

matic data (band 8), yielding effectively a 15-m-resolution

multispectral image (Lillesand & Kiefer, 1999). We used the

pan-sharpened false color composite (bands 5, 8, and 3; see

Fig. 2) along with approximately 50 out of 155 DOQ

images (1992–1995), over 500 aerial photos (1993–

1999), and limited ground truth data (Midcoast OR in CPCF

ecoregion) to assign the various classes.

The main advantage of frequently referring to higher

resolution data is in deciphering complexities in the land-

scape that would be nearly impossible for computer soft-

ware to interpret alone. Using these data has been reported

to be particularly useful for broad area assessments where

classification of individual pixels would not be appropriate

(Lachowski et al., 1996; Zheng et al., 1997; Jensen, 1999;

Lillesand & Kiefer, 2000; Cohen et al., 2001; Wilson &

Sader, 2002).

Unsupervised classification provides the most compre-

hensive information on the spectral characteristics of an

area, presents spectrally pure clusters for labeling, and gives

the analyst the freedom to group similar clusters together.

However, the unsupervised classification method can also

potentially mismatch spectral signature clusters and themat-

ic classes (Cihlar, 2000). Other classification problems can

result depending on how certain parameters (e.g. number of

clusters and allowable dispersion around a cluster mean) are

controlled since changes in these parameters by the analyst

can produce very different final clusters for the same dataset

(Cihlar, 2000; Lachowski et al., 1996). A recent method for

trying to minimize this problem entails producing a large

number of clusters, typically 100–400 (Homer et al., 1997;

Wayman et al., 2001), and then reducing this broad classi-

fication by well-defined merging steps.

We chose the unsupervised classification approach for

mapping late seral conifer forests in the PNW using an

Optimal Iterative Unsupervised Classification (OIUC) meth-

od developed for this project to overcome the stated

limitations of unsupervised classification. The OIUC

includes the following steps: (1) development of reference

data sets; (2) optimal iterative classification using ISODATA

clustering; and (3) post classification treatment. The first

step was to construct a useful reference dataset upon which

to base the satellite image classification. The importance of

including ancillary datasets is widely recognized for im-

proving the accuracy and quality of remote sensing-derived

land cover classification (Jensen, 1996a; Lachowski et al.,

1996). In this case, we utilized the DOQ data, aerial photos,

and limited field investigation.

The OIUC method strives to overcome the problems of

parameter variability and mismatching of spectral clusters

and thematic classes in the least number of iterations. In the

OIUC method, the homogeneity of a cluster is produced

through an iterative approach (Fig. 3). In the initial unsu-

pervised classification by Iterative Self-Organizing Data

Analysis Technique (ISODATA), 60–80 clusters (using

bands 1–5 and band 7) were produced using the ISODATA

command in ERDAS Imagine. These clusters were then

http:\\www.gap.uidaho.edu

http:\\www.sscgis.state.or.us\

Fig. 2. Pan-sharpened false color composite mosaic for PNW study area (bands 5, 8, and 3) and WWF ecoregion boundaries (orange).


matched with reference imagery and data. Spectral classes

that agreed with observed age classes observed using higher

resolution imagery were considered good matches, labeled,

and removed from further consideration. Spectral classes

that were less clear were put back into the remaining pixel

pool and rerun again. This was repeated as many times as

needed to assign our final classification result. In some

instances, the first iteration was adequate to delineate a

particular land cover class. Other pixels were more difficult

to assign the proper class and required multiple iterations.

Fig. 3. Flow chart diagram of Optimal Iteration Unsupervised Classification (OIUC) used in the study.


Before combining the sub-scenes together forming the

final result, the pixel-based classification results were gen-

eralized slightly. For this application, we were more inter-

ested in patch-level spatial patterns than by results of

individual pixels. Therefore, using the CLUMP and ELIM-

INATE commands, we removed all pixel clusters that were

smaller than five cells (or 0.45 ha).

2.4. Forest cover classes

Mapping forest age is complex and difficult, especially

over large geographic extents such as the PNW. In fact, the

multispectral signatures derived from sensors such as Land-

sat ETM+ does not measure age at all but some composite

of stand structure, tree density, and cover condition impact-

ed to varying degrees by atmospheric and terrain influences.

Because of this complexity, we elected to concentrate on the

conifer-dominated component of the forest vegetation as

have other researchers. For conservation planning purposes,

however, this is the majority of the regional forest cover and

of greatest region-wide ecological concern. That is not to

say that mapping old growth Oregon white oak (Quercus

garryana) is not important, it is just far more difficult than

mapping closed-canopy conifer forests.

Published definitions for late seral forests throughout the

region vary greatly based on forest type, environmental

conditions, and other factors, both ecological and economic

(Cohen et al., 1995; Fierst, 1992; Franklin & Spies, 1991;

Sachs et al., 1998). For example, Douglas-fir stands older

than 40 years were defined as late seral by Sachs et al.

(1998). For other cases, Douglas-fir forests were defined as

late seral if they were older than 80 years (Franklin &

Spies, 1991; Spies & Franklin, 1991; Spies, 1991) and

even 100 years (Bingham & Sawyer, 1991). Similarly,

definitions of ‘‘old-growth’’ Douglas-fir have ranged from

older than 150 years to older than 250 years (Hayens,

1986). Of course, the PNW is more than just Douglas-fir

forests. In one review, Fierst (1992) summarized old

growth thresholds for a number of different regional forest

types (Table 1).

Table 1

Forest age and old growth threshold for forest types within Region 6 of the

USDA Forest Service (Fierst, 1992)

Location Forest type Site condition Main canopy

age (years)

Central Oregon White fir/Grand fir Low and medium 150

High 150

Blue Mountains White fir/Grand fir Low and medium 150

High 150

Westside Douglas-fir Low 200

Medium 205

High 190

Western Pacific silver fir Sites 2 and 3 180

Site 4 200

Site 5 260

Site 6 360

Westside Western hemlock Site 1 200

Site 2 200

SW Oregon Port-Orford cedar All 240

Eastside Douglas-fir All 150

Eastside Ponderosa pine Low 150–200

Medium and high 150–200

Eastside Subalpine fir Low 150

High 150


To avoid the extreme difficulty of trying to account for

each separate mature and old growth threshold for each

community type throughout the region, we chose a uniform

definition that would apply to the entire study area: 50–150

years for mature forests, and older than 150 years for old-

growth forests. We originally intended to define mature

forest as being between 80 and 150 years to fit the majority

of published definitions available for the region based

largely on ecological criteria. However, with logging rota-

tions falling well below the 80-year mark over much of the

region, we elected to drop the threshold to 50 years instead

since the final product was to be used for conservation

planning purposes. In this paper, we focus on late seral

forest (mature and old growth); however, we mapped other

land cover classes in the study as outlined below, but lump

them here as ‘‘other lands’’. The classes included:

(1) Old conifer forest (> 150 years)

(2) Mature conifer forest (50–150 years)

Table 2

Area (ha) and percent area of old conifer and mature conifer forests in the Pacifi

Ecoregion Total ecoregion

area (ha)

Old conifer

area (ha)

Cascade Mountains Leeward Forests (CMLF) 1,600,444 420,394

Northern Cascades Forests (NCF) 1,278,056 511,396

Puget Lowland Forests (PLF) 1,719,726 106,149

Central Pacific Coastal Forests (CPCF) 4,267,988 668,281

Central Southern Cascades Forests (CSCF) 4,481,279 1,328,796

Eastern Cascade Forests (ECF) 5,398,139 687,836

Klamath–Siskiyou Forests (KSF) 5,033,181 946,394

Willamette Valley Forests (WVF) 1,487,769 34,993

Entire PNW 25,266,582 4,704,240

(3) Other lands (including early regeneration (0–10 years),

young conifer (10–50 years), deciduous forest, open

forest/woodland, non-forest, clouds, and shadows)

(4) Water

Assigning age to conifer forest image data can take the

form of continuous values or discrete classes. Both methods

have utility and value to different users. We chose the later

because we wanted to generate results quickly and we knew

that an extensive field effort would not be possible.

2.5. Accuracy assessment

Map accuracy assessment was conducted using the

DOQ image data. A total of 2081 random distribution

points were assigned and evaluated. Because of the time

difference between the satellite imagery (2000 and 2001)

and DOQ images (early and mid-1990s), we removed all

accuracy assessment sample points where obvious human

or natural disturbance had occurred between dates (e.g.,

older forest in 1992 and open field in 2000). Dozens of

sample points were removed during the accuracy assess-

ment process. We used the KAPPA statistic, which

expresses the proportionate error generated by a classifi-

cation process relative to the error of completely random

classification, to evaluate accuracy. KAPPA analysis is a

discrete multivariate technique that is widely used (Con-

galton, 1991; Ma & Redmond, 1995; Jensen, 1996b;

Elmore et al., 2000). The accuracy and error matrices

showing errors of commission and omission were gener-

ated for all mapped cover classes.

3. Results and discussion

Nearly 19% of the PNW (4.7 million ha) was mapped as

old conifer forest and another 19% mapped (4.8 million ha)

as mature conifer forest. Thus, a total of over 9.48 million

ha (f 38%) of the PNW were mapped as late seral conifer

forest. The extent of late seral forests (old and mature

conifer cover classes) varied greatly between the eight

ecoregions (Table 2; Fig. 4). The Central and Southern

c Northwest, USA according to ecoregion

Percent of

ecoregion

Mature conifer

area (ha)

Percent of

ecoregion

Combined

area (ha)

Percent of

ecoregion

26.27 285,441 17.84 705,835 44.10

40.01 203,994 15.96 715,391 55.97

6.17 248,698 14.46 354,847 20.63

15.66 683,098 16.01 1,351,379 31.66

29.65 899,266 20.07 2,228,063 49.72

12.74 947,215 17.55 1,635,051 30.29

18.80 1,239,586 24.63 2,185,980 43.43

2.35 270,362 18.17 305,355 20.52

18.62 4,777,659 18.91 9,481,900 37.53

Fig. 4. Late seral forest mapping results for the PNW, USA.


Cascades Forests (CSCF) led all other ecoregions with

approximately 1.3 million ha of old conifer forest followed

by the Klamath–Siskiyou Forests (KSF) with approximate-

ly 0.95 million ha. The Northern Cascades Forests (NCF)

contained the highest percent area of old conifer forest by

ecoregion than any other ecoregion (f 40%) followed by

the Central and Southern Cascades Forests (CSCF) with

nearly 30% and the Cascade Mountains Leeward Forests

(CMLF) with over 26%.

The Klamath–Siskiyou Forests (KSF) contained the

largest amount of mature conifer forest with over 1.2 million

ha followed by 0.95 million ha in the Eastern Cascade

Forests (ECF) and 0.89 million ha in Central and Southern

Cascades Forests (CSCF).


The percent area of mature conifer forest by ecoregion

was quite uniform for the eight ecoregions examined rang-

ing from 25% for the Klamath–Siskiyou Forests (KSF) to

14% for the Puget Lowland Forests (PLF).

Extent of old and mature conifer forest is not the only

important factor to consider when planning for forest

conservation. Spatial pattern is also extremely important

and is discussed in detailed by Jiang et al. (in review) using

this database. Previous studies have found spatial pattern to

vary widely across the forest landscapes of the PNW based

on differing land ownership, management strategies, and

human-induced disturbance regimes (Spies et al., 1994;

Tunner et al., 1996; Staus et al., 2002). Furthermore, what

the mapped results mean for forest conservation with

regards to management and policy in the PNW is currently

being examined as part of this research.

3.1. Map accuracy

A total of 2081 sample points were used including 662

for old conifer forests, 444 for mature conifer forests, and

975 for other lands. The classification of late seral forests

had an overall accuracy of 90.72% with a KAPPA test K

value of 0.8534. User (commission) accuracy results for old

and mature conifer forests were 89.42% and 80.59%,

respectively (Table 3). Results also showed high producer

(omission) accuracy for old conifer forests (91.36%) and

mature conifer forests (80.40%). User and producer accu-

racy was highest for the ‘‘other lands’’ class, 96.21% and

94.94% respectively.

Accuracy varied with the different ecoregions. Overall

accuracy was highest (93.96%) in the Puget Lowland

Forests (PLF), and the lowest (89.33%) in the Central

Pacific Coastal Forests (CPCF). The overall accuracy for

most ecoregions remained around 90% (Table 4).

In general, user accuracy for old conifer forest was

higher than that of mature conifer forest. In most ecor-

egions (except in CMLF where it was 76%), the user

accuracy for old conifer forest was >85%. User accuracy

for mature conifer forest ranged from 73.33% to 86.21%.

Producer accuracy showed a similar trend. Producer accu-

racy of old conifer forest ranged from 72.22% to 93.54%,

Table 3

Error matrix for late seral forest classification results for the Pacific Northwest, U

Classified data Reference data

Class Old conifer forests Mature conife

Old conifer forests 592 57

Mature conifer forests 49 357

Other lands 7 30

Total 648 444

Producer’s (%) 91.36% 80.40%

Overall accuracy (%) 90.72%

K 0.8534

with three ecoregions having an accuracy of over 90%

(PLF, NCF, and CSCF). Mature conifer forest producer

accuracies ranged from 72.58% to 91.66%, with one

ecoregion (WVF) having accuracy >90%. This could be

explained by the fact that the WVF ecoregion is relatively

flat eliminating shadow influence characteristic of the other

ecoregions in the study area. Producer accuracy for mature

conifer forest was >80% in only two ecoregions (NCF and

PLF). Lower producer accuracy for mature conifer forest

was more typical throughout the region, due primarily to

the relatively complex terrain, forest composition variabil-

ity, and the forest management history. For example,

mature conifer forest was most often confused with either

old conifer forest or high-density, younger conifer forest,

which was common in many of the more heavily managed

landscapes. This was especially true for planted Douglas-

fir plantations throughout large portions of the CPCF

ecoregion. Fiorella and Ripple (1993) also found the

highest level of confusion between mature and old-growth

forests. In their study, the mature category had the lowest

accuracy (69%) and the highest percent commission error

(55%). Similarly, Cohen et al. (1995) found user accuracy

for mature conifers low (56% correct) compared to old

conifers (75%).

Our results showed a similar pattern to these two

regional studies (old conifer class showing higher accu-

racy than the mature conifer class); however, our study

also showed a higher overall accuracy for both classes

compared to existing remote sensing studies in the region.

We believe this can be explained in two ways. First,

previous studies relied heavily on spectral features of the

imagery. Our OIUC procedure tried to take advantage of

the spectral features as well as the broader spatial pattern

delineated by using the pan-sharpened false color com-

posite imagery and other ancillary data and by subdivid-

ing the region into meaningful subregions for analysis.

Also, we intentionally allowed younger conifer stands

(50–80 years) to be included as mature conifer forest

unlike most previous remote sensing studies in the region

that usually used a cutoff of 80 years. For our purpose,

using the 50-year cutoff was more useful for input into

regional conservation planning efforts because of short

SA

r forests Other lands Total User’s (%)

13 662 89.42%

37 443 80.59%

939 976 96.21%

989 2081

94.94%

Table 4

Error matrices for late seral forest classification results for the Pacific Northwest, USA by ecoregion


Class Old conifer forests Mature conifer forests Other lands Total User’s (%)

(1) Cascade Mountains Leeward Forests (CMLF)

Old conifer forests 22 6 1 29 75.86%

Mature conifer forests 5 20 0 25 80.00%

Other lands 0 0 34 34 100.00%

Total 27 26 35 88

Producer’s (%) 81.48% 76.93% 97.14%


K 0.7934

(2) Northern Cascades Forests (NCF)



Other lands 1 5 136 142 95.77%

Total 102 68 146 316

Producer’s (%) 93.13% 88.24% 93.15%


K 0.8761

(3) Puget Lowland Forests (PLF)



Other lands 0 2 56 58 96.55%

Total 31 30 59 120

Producer’s (%) 93.54% 83.33% 94.91%


K 0.8679

(4) Central Pacific Coastal Forests (CPCF)



Other lands 2 13 207 222 93.24%

Total 118 97 211 426

Producer’s (%) 88.13% 78.35% 98.10%


K 0.8523

(5) Central and Southern Cascades Forests (CSCF)



Other lands 1 3 166 170 97.64%

Total 178 70 178 426

Producer’s (%) 92.69% 78.57% 93.26%


K 0.8502

(6) Eastern Cascade Forests (ECF)



Other lands 0 3 191 194 98.45%

Total 99 62 193 354

Producer’s (%) 87.87% 72.58% 98.96%


K 0.8521

(7) Klamath–Siskiyou Forests (KSF)



Other lands 3 3 91 97 93.81%

Total 89 55 91 235

Producer’s (%) 86.51% 78.18% 100.00%


K 0.8428


Table 4 (continued)


Class Old conifer forests Mature conifer forests Other lands Total User’s (%)

(8) Willamette Valley Forests (WVF)



Other lands 0 1 58 59 98.30%

Total 18 36 62 116

Producer’s (%) 72.22% 91.66% 93.55%


K 0.8263


forest rotations throughout much of the region. Lowering

the minimum age for mature forest also resulted in higher

accuracies for this class compared to previous studies.

3.2. Optimal Iteration Unsupervised Classification (OIUC)

methodology

The objective of vegetation classification using remote

sensing is to assign the digital image data (in the form of

pixels) into discrete and meaningful categories (Barrent &

Curtis, 1999). A correctly classified image will represent

areas of vegetation that share particular spectral character-

istics as specified by an established classification scheme

(Jensen, 1999; Richards & Jia, 1999; Lillesand & Kiefer,

2000; Dustan et al., 2001). Individual, or single-scene

classification potentially offers better accuracy for mapping

vegetation cover types than a mosaic of multiple scenes,

because of reduced pixel sample size and variability (Homer

et al., 1993; Jensen, 1996a; Franklin et al., 2000). Similarly,

our results showed that subdividing a scene into sub-scenes

provides even greater accuracy in vegetation classification

and forest seral stage identification.

The number of iterations of OIUC needed to produce

relatively pure clusters depends on the variability of spectral

traits within a given image. Sub-scenes covered by high-

density young conifer forests, shadows over variable terrain,

or some high-elevation forest types, generally required more

iterations to get pure clusters. Typically, one sub-scene

required three to four iterations.

Ten years ago, some remote sensing scientist (e.g., Cohen

& Spies, 1992) indicated the Landsat 7 ETM+ sensor, with a

15 m panchromatic channel, would likely be the next most

powerful source of satellite imagery for analysis of forest

stand attributes in the Pacific Northwest. Our results affirmed

the utility of applying ETM+ pan-sharpened 15 m imagery in

identifying the forest seral stages in the Pacific Northwest

when combined with other ancillary data sources.

The OIUC method was demonstrated to produce rela-

tively quick results that are useful to many conservation

applications. As larger government vegetation mapping

efforts continue, more detail will be available and welcomed

by the conservation community. In the meantime, we now

have a useful data layer that will help in regional conserva-

tion planning that covers all ownerships; is of high accuracy

for the classes chosen; and easily repeatable as the regional

forest landscape continues to change.

Acknowledgements

Funding was provided by The Paul G. Allen Forest

Protection Foundation, David and Lucille Packard Founda-

tion, and World Wildlife Fund. We are grateful for free

access the auxiliary GIS data layer from USFS, USGS,

BLM, Oregon Department of Forestry. We thank ERDAS

and the Environment Systems Research Institute (ESRI) for

their continuing support. We also thank R. Robinson and J.

Bergquist for their helpful assistance with image processing,

field survey and early manuscript revision as well as

anonymous reviewers of this manuscript whose comments

greatly improved the clarity of the final paper.

References

Barrent, E. C., & Curtis, L. F. (1999). Introduction to environmental remote

sensing (4th ed.) p. 452. London: Stanley Thornes.

Bingham, B. B., & Sawyer Jr., J. O. (1991). Distinctive features and defi-

nitions of young, mature, and old-growth Douglas-fir/hardwood forests.

In L. F. Ruggiero, K. B. Aubry, A. B. Carey&M.H. Huff (Eds.).Wildlife

and vegetation of unmanaged Douglas-fir forests. US Department of

Agriculture, Forest Service, Pacific Northwest Research Station, Port-

land, Oregon. General Technical Report PNW-GTR-285. pp. 363–378.

Boyd, D. S., Foody, G. M., & Ripple, W. J. (2002). Evaluation of

approaches for forest cover estimation in the Pacific Northwest, USA

using remote sensing. Applied Geography, 22, 375–392.

Cihlar, J. (2000). Land cover mapping of large areas from satellites: Status

and research priorities. International Journal of Remote Sensing, 21(6),

1093–1114.

Cohen, W. B., Maiersperger, T. K., Spies, T. A., & Oetter, D. R. (2001).

Modeling forest cover attributes as continuous variables in a regional

context with Thematic Mapper data. International Journal of Remote

Sensing, 22(12), 2279–2310.

Cohen, W. B., & Spies, T. A. (1992). Estimate structural attributes of

Douglas fir/western hemlock forest stands from Landsat and SPOT

imagery. Remote Sensing of Environment, 41, 1–17.

Cohen, W. B., Spies, T. A., & Fiorella, M. (1995). Estimating the age and

structure of forests in a multi-ownership landscape of western Oregon

U.S.A. International Journal of Remote Sensing, 16(4), 721–746.

Congalton, R. G. (1991). A review of assessing the accuracy of classifica-

tions of remote sensed data. Remote Sensing of Environment, 37, 35–46.

Congalton, R. G., Green, K., & Teply, J. (1993). Mapping old growth

forests on National Forests and Park Lands in the Pacific Northwest


from remotely sensed data. Photogrammetric Engineering and Remote

Sensing, 59, 529–535.

Defenders of Wildlife (1998). Oregon’s living landscape: Strategies and

opportunities to conserve biodiversity (p. 210). Washington, DC:

Defenders of Wildlife.

Dustan, P., Dobson, E., & Nelson, G. (2001). Landsat thematic mapper:

Detection of shifts in community composition of coral reefs. Conser-

vation Biology, 15(4), 892–902.

Eby, J. R. (1987). The use of sun incidence angle and infrared reflec-

tance levels in mapping old-growth coniferous forests. Proceeding,

American Society of Photogrammetry and Remote Sensing. Reno,

NV: Fall Convention.

Elmore, A. J., Mustard, J. F., Manning, S. J., & Lobell, D. B. (2000).

Quantifying vegetation change in semiarid environments: Precision

and accuracy of spectral mixture analysis and the Normalized difference

vegetation index. Remote sensing of environment, 73, 87–102.

Fierst, J. (1992). Region 6, interim old growth definition for Douglas-fir

series, Grand fir/White fir series, interior Douglas-fir series, Lodgepole

pine series, Pacific Silver fir series, Ponderosa pine series, Port-Orford-

Cedar and Tanoak (Redwood) series, Subalpine fir series, and Western

hemlock series. Forest Service, USDA, 1993.

Fiorella, M., & Ripple, W. J. (1993). Determining successional stage of

temperate coniferous forests with Landsat satellite data. Photogrammet-

ric Engineering and Remote Sensing, 59, 239–246.

Franklin, J. F., & Dyrness, C. T. (1988). Natural vegetation of Oregon and

Washington (p. 452). Corvallis, OR: Oregon State University Press.

Franklin, S. E., Moskal, L. M., Lavigne, M. B., & Pugh, K. (2000).

Interpretation and classification of partially harvests forest stands in

the Fundy model forest using multitemporal Landsat TM digital data.

Canadian Journal of Remote Sensing, 26(4), 318–333.

Franklin, J. F., & Spies, T. A. (1991). Ecological definitions of Old-growth

Douglas-fir forests. In L. F. Ruggiero, K. B. Aubry, A. B. Carey & M.

H. Huff (Eds.). Wildlife and vegetation of unmanaged Douglas-fir for-

ests. Oregon: US Department of Agriculture, Forest Service, Pacific

Northwest Research Station Portland. General Technical Report

PNW-GTR-285. pp. 61–70.

Green, K., & Congalton, R. G. (1990). Mapping potential old growth

forests and other resources on national forest and parklands in Ore-

gon and Washington. Proceedings, GIS/LIS’ 90, annual convention

( pp. 712–723), Anaheim, CA.

Hayens, R. W. (1986). Inventory and value of old-growth in the Doug-

las-fir region. Research Note PNW-437. Portland, OR: US. Depart-

ment of Agriculture, Forest Service, Pacific Northwest Research

Station. 19 p.

Homer, C. G., Edwards, T. C., Ramsey, R. D., & Price, K. P. (1993). Use of

remote sensing methods in modelling sage grouse winter habitat. Jour-

nal of Wildlife Management, 48, 123–130.

Homer, C. G., Ramsey, R. D., Edwards Jr., T. C., & Falconer, A. (1997).

Landscape cover-type modeling using a multi-scene Thematic Map-

per mosaic. Photogrammetric Engineering and Remote Sensing, 63,

671–677.

Jennings, M. D. (2000). Gap analysis: Concepts, methods, and recent

results. Landscape Ecology, 15, 5–20.

Jensen, J. R. (1996a). Introductory digital image processing: A remote

sensing perspective (p. 325). New Jersey, USA: Prentice-Hall.

Jensen, J. R. (1996b). Introductory digital image processing: A remote

sensing perspective. Upper Saddle River: Prentice-Hall (318 pp.).

Jensen, J. R. (1999). Remote sensing of the environment: An earth resource

perspective (p. 450). New Jersey, USA: Prentice-Hall.

Lachowski, H., Maus, P., Golden, M., Johnson, J., Landrum, V., Powell,

J., Varner, V., Wirth, T., & Gonzales, J. (1996). Guidelines for the use

of digital imagery for vegetation mapping. New York: USDA, Forest

Service, EM-7140-25.

Lillesand, T. M., & Kiefer, R. W. (2000). Remote sensing and image

interpretation (4th ed.) (p. 725). New York: Wiley.

Ma, Z., & Redmond, R. L. (1995). Tau coefficients for accuracy assessment

of classification of remote sensing data. Photogrammetric Engineering

and Remote Sensing, 61, 435–439.

Morrison, P. H., Kloepeer, D., Leversee, D. A., Socha, C. M., & Ferber, D.

(1991). The ancient forests in the Pacific Northwest. Washington, DC:

Report by The Wilderness Society.

Ohmann, J. L., & Gregory, M. J. (2002). Predictive mapping of forest

composition and structure with direct gradient analysis and nearest-

neighbor imputation in coastal Oregon, U.S.A. Canadian Journal of

Forest Research, 32, 725–741.

Olson, D. M., Dinerstein, E., Wikramanayake, E. D., Burgess, N. D.,

Powell, G. V. N., Underwood, E. C., D’Amico, J. A., Itoua, I., Strand,

H. E., Morrison, J. C., Loucks, C. J., Allnutt, T. F., Ricketts, T. H.,

Kura, Y., Lamoreux, J. F., Wettengel, W. W., Hedao, P., & Kassem, K.

R. (2001). Terrestrial ecoregions of the World: A new map of life on

Earth. Bioscience, 51(9), 933–938.

Richards, J. A., & Jia, X. (1999). Remote sensing and digital image ana-

lysis (p. 362). New York: Springer-Verlag.

Ricketts, T. H., Dinerstein, E., Olson, D. M., Loucks, C. J., Eichbaum, W.,

Dellasala, D., Kavanagh, K., Hedao, P., Hurley, P. T., Carney, K. M.,

Abell, R., & Walters, S. (1999). Terrestrial ecoregions of North Amer-

ican, a conservation assessment. Washington, DC: Island Press.

Sabol Jr., D. E., Gillespie, A. R., Adms, J. B., Smith, M. O., & Tucker,

C. J. (2002). Structural stage in Pacific Northwest forest estimated

using simple mixing models of multispectral images. Remote Sensing

of Environment, 80, 1–16.

Sachs, D. L., Sollins, P., & Cohen, W. B. (1998). Detecting landscape

changes in the interior of British Columbia from 1975 to 1992 using

satellite imagery. Canadian Journal of Forest Research, 28, 23–36.

Scott, J. M., Davis, F., Csuiti, B., Noss, R., Butterfield, B., Groves, C.,

Anderson, H., Caicco, S., D’erchia, F., Edwards Jr., T. C., Ulliman, J.,

& Wright, R. G. (1993). Gap analysis: A geographic approach to pro-

tection of biological diversity. Wildlife Monographs. Moscow, ID: A

Publication of the Wildlife Society, University of Idaho (41 pp.).

Spies, T. A. (1991). Plant species diversity and occurrence in young, mature,

and old-growth Douglas-fir stands in western Oregon andWashington. In

L. F. Ruggiero, K. B. Aubry, A. B. Carey & M. H. Huff (Eds.). Wildlife

and vegetation of unmanaged Douglas-fir forests. US Department of

Agriculture, Forest Service, Pacific Northwest Research Station, Port-

land, Oregon. General Technical Report PNW-GTR-285. pp. 111–122.

Spies, T. A., & Franklin, J. F. (1991). Composition, function, and structure

of old-growth Douglas-fir forests. In L. F. Ruggiero, K. B. Aubry, A. B.

Carey & M. H. Huff (Eds.). Wildlife and vegetation of unmanaged

Douglas-fir forests. US Department of Agriculture, Forest Service, Pa-

cific Northwest Research Station, Portland, Oregon. General Technical

Report PNW-GTR-285. pp. 71–82.

Spies, T. A., Ripple, W. J., & Bradshaw, G. A. (1994). Dynamics and

pattern of a managed coniferous forest landscape. Ecological Applica-

tions, 4, 555–568.

Staus, N. L., Strittholt, J. R., Dellasala, D. A., & Robinson, R. (2002). Rate

and pattern of forest disturbance in the Klamath–Siskiyou ecoregion,

USA between 1972 and 1992. Landscape Ecology, 17, 455–470.

Tunner, M. G., Wear, D. N., & Flamm, R. O. (1996). Land ownership and

land-cover change in the southern Appalachian highlands and the

Olympic Peninsula. Ecological Applications, 6, 1150–1172.

Wayman, J. P., Wynne, R. H., Scrivani, J. A., & Reams, G. A. (2001).

Landsat TM-based forest area estimation using iterative guided spectral

class rejection. Photogrammetric Engineering and Remote Sensing, 67,

1155–1166.

Whittaker, R. H. (1960). Vegetation of the Siskiyou Mountains Oregon and

California. Ecological Monographs, 30, 279–338.

Wilson, E. H., & Sader, S. A. (2002). Detection of forest harvest type using

multiple dates of Landsat TM imagery. Remote Sensing of Environment,

80, 385–396.

Zheng, D., Wallin, D. O., & Hao, Z. (1997). Rates and patterns of land-

scape change between 1972 and 1988 in the Changbai Mountain area of

China and North Korea. Landscape Ecology, 12, 241–254.

Documents

The classification of late seral forests in the Pacific Northwest, USA using Landsat ETM+ imagery