Effects of forest ownership and change on forest harvest rates,

types and trends in northern Maine

Suming Jin *, Steven A. Sader 1

Department of Forest Management, 260 Nutting Hall, University of Maine, Orono, ME 04469-5755, United States

Received 12 December 2005; received in revised form 1 March 2006; accepted 2 March 2006

Abstract

Forest ownership maps from 1994, 2000 and 2004 were analyzed with land cover maps (early 1990s) and forest change detection maps derived

from Landsat imagery (1991, 2000, 2004). Between 1994 and 2000, roughly 80% of the Industrial forest ownership in a northern Maine study area

changed hands. Approximately 75% of these forestlands were sold to Timber Investment Management Organizations (TIMOs) and 25% to other

Industrial owners. Non-Government Organizations (NGOs) and Logger/Short-term Investors (LDs) purchased smaller parcels of forestland from

the Industrial and TIMO sellers, from 1994 to 2004. Landsat change detection methods indicated a general trend in landowners’ preference to

harvest softwood and softwood–hardwood in the 1980s, softwood–hardwood and hardwood–softwood stands in the 1990s, and nearly a balanced

proportion of four forest types between 2000 and 2004; however, these trends varied among individual landowners. Industrial ownership type

harvested the highest percentage of forest in the 1980s, but not in the 1990s and early 2000s. The TIMOs and LDs harvested a higher percentage of

forest in the 1990s and early 2000s, while the NGOs harvested less. The Non-Industrial Private Forest (NIPF) held more stable ownership through

time and had more equal and intermediate harvest rates through time. Forest land that experienced no ownership change had significantly lower

harvest rates than land that changed ownership between 1994 and 2000. Given the rates of past harvesting and current composition of forestland,

the estimated average forest disturbance rotation on the 2004 Industrial ownership would be 51 years, compared to 70 years for NIPF forestlands.

# 2006 Elsevier B.V. All rights reserved.

Keywords: Forest ownership type; Ownership change; Forest harvest rate; Forest harvest type; Landsat change detection

www.elsevier.com/locate/foreco

Forest Ecology and Management 228 (2006) 177–186

1. Introduction

Maine is the most heavily forested state in the U.S. with 90%

of the land area in forest cover, and 97% of Maine’s forestlands

are classified as productive timberland (McWilliams et al.,

2005). Maine is unique in that over 95% of this forest is

privately owned (Maine Forest Service, 1999; Laustsen et al.,

2003). Extensive clearcutting (and salvage logging) occurred in

the late 1970s and throughout the 1980s, in the wake of the

massive spruce budworm outbreak (Griffith and Alerich, 1996).

The Maine Forest Practices Act, implemented in 1991, limited

the size of clearcuts (Maine Forest Service, 1995; Hagan and

Boone, 1997). Clearcutting steadily declined to less than 5% of

total harvested volume by the late 1990s and early 2000s, and

partial harvesting became dominant (Maine Forest Service,

1999; McWilliams et al., 2005).

* Corresponding author. Tel.: +1 540 2573602.

E-mail addresses: sumingjin@hotmail.com (S. Jin),

Sader@apollo.umenfa.maine.edu (S.A. Sader).1 Tel.: 1 207 -5812845; Fax: 1 207 5812875.

0378-1127/$ – see front matter # 2006 Elsevier B.V. All rights reserved.

doi:10.1016/j.foreco.2006.03.009

The extensive private forests in northern Maine have

experienced not only dramatic change in silvicultural harvest

practice but also significant ownership changes over the past

two decades (Irland, 2000; Maine Forest Service, 1999;

Laustsen et al., 2003; McWilliams et al., 2005). In 1988, public

concern was aroused by the sale of a large block of Maine

timberland, formerly owned by Diamond International Cor-

poration, to entities focused on land development (NFLC,

1994). This was unlike previous transactions where timberland

was transferred from one large industrial owner to another, and

the land remained in ‘‘working forest’’, which was not

considered threatening to the long-term production of wood

fiber, maintaining mills and woods jobs or providing traditional

public recreation access to the private, corporate forest

(Phillips, 1993). The Northern Forest Lands Council and its

predecessors, the Northern Forest Lands Study and Governors’

Task Force, were created in response to concerns that the large,

privately owned northern forest was at grave risk of break-up

and land use conversion (NFLC, 1994; NEFA, 2004).

The ownership change pattern in Maine reflects the dramatic

changes of forest industry in recent years on larger scale.

S. Jin, S.A. Sader / Forest Ecology and Management 228 (2006) 177–186178

In New England, large industrial owners dominated forestland

ownership for years. Their management focused on using the

forestland base as a supply for manufacturing facilities.

Beginning in the 1990s, several forest products companies

(industrial) sold their forestland to other types of landowners

(NFLC, 1994; Yin et al., 1998; Irland, 2000; Yale Forest Forum

Review, 2002; McWilliams et al., 2005). For example, two

classes of timberland owners emerged in the 1990s that

previously did not exist. The first was the real estate investment

trusts (REITs), and the second was institutional investors, who

tend to cede forest management responsibilities to timberland

investment management organizations (TIMOs). These orga-

nizations represented financial institutions that were buying and

selling large tracts of timberlands in Maine and elsewhere

(Maine Tree Foundation, 2005, unpublished). Non-government

organizations (NGOs) were a small but emerging buyer in the

late 1990s (Yale Forest Forum Review, 2002; NEFA, 2004).

Because the NIPF, TIMO, REIT, NGO and other landowners do

not own manufacturing facilities, like industrial owners, they

have different incentives and management strategies for their

timberlands. For example, TIMOs and REITs focus primarily

on maximizing the return on their timberland assets (Yale

Forest Forum Review, 2002). In the 2002 Yale Forest Forum

‘‘Institutional Timberland Investment Balancing Ecology,

Finance and the Public Interest’’, Eva Greger, managing

partner of GMO Renewable Resources (a TIMO) emphasized

that the TIMOs are long-term total return investors, and are not

overly focused on annual cash flows from timber sales;

therefore they can time timber markets and make investments in

the land (Yale Forest Forum Review, 2002).

Regardless of the vast tracts of Maine forest being bought

and sold during the 1990s and early 2000s, most of the land has

remained in working forests. Also, the major industrial and

NIPF landowners, and some of the TIMO and REIT landowners

in Maine, have attained forest certification under either the

Scientific Forestry Initiative or Forest Stewardship Council. A

few of the NIPFs have entered into no-development,

conservation easements (Maine Forest Service, 1999; Sader

et al., 2002). However, the dramatic forest ownership changes,

especially in northern Maine, have raised public concerns about

how the new ownership mix will affect future forest

sustainability, and particularly, the traditional recreation access

to the extensive, privately owned forest that Maine citizens have

enjoyed for over 100 years. According to the recent U.S. Forest

Service, Forest Inventory and Analysis (FIA) report (McWil-

liams et al., 2005) ‘‘It is not known how changes in land

ownership in Maine will affect long-term timber management

and availability. There is a need to evaluate the effect of

changes in ownership (e.g. number of small landowners and

shift from industry owners to investors) on timber supply/

availability and the stability of the land based managed actively

for forestry.’’

Harvest rates over time and silvicultural practices (e.g.

partial harvest, clearcut) may have a considerable influence on

forest sustainability (Grigal and Bates, 1997; Larsen, 1995;

McWilliams et al., 2005). Using satellite imagery to evaluate

how different land ownership affects harvest rates and trends

may improve our understanding of current and future forest

composition and structure over multiple ownerships. This is not

easily accomplished in a timely and cost effective manner using

ground based or aerial based methods. Landsat images have

been widely used to map forest cover and monitor forest

disturbances, including harvesting activities (Kushwaha, 1990;

Coppin and Bauer, 1996; Franklin et al., 2000; Cohen et al.,

1998, 2002; Sader et al., 2003). Few studies have examined

forest disturbance in relation to land ownership. Cohen et al.

(2002) used multi-date Landsat data to characterize the

relationships among disturbance rates and patterns between

mostly federal and private owners to determine the impact of

land management activities and wildfire in western Oregon.

Turner et al. (1996) studied land ownership patterns between

1975 and 1991 related to land cover change in the Southern

Appalachians and the Olympic Peninsula. They found some

differences in land cover transitions through time, between

ownerships (public and private lands) and between the two

regions. A literature review did not reveal any large landscape

scale, spatially explicit studies that have analyzed nearly three

decades of forest disturbances over multiple private owner-

ships.

1.1. Objectives

This research will analyze forest cover and change maps

derived from satellite imagery to understand the relationship of

changing ownership on timber harvest rates (area of forest

disturbance expressed as a percentage) and trends for a large

study area in northwestern Maine. Given the extensive

clearcutting of the 1980s following the spruce budworm

outbreak, coupled with forestry legislation (e.g. 1991 imple-

mentation of the Maine Forest Practices Act) and major

ownership changes that occurred from the late 1980s to early

2000s, some interesting landscape level research questions

arise. The core question is ‘‘Does ownership type and

ownership change have a significant effect on forest harvest

rates over time’’? Specifically, have some forest holdings

experienced disproportionately high or low harvest rates

compared to other holdings? Is there a trend over time? Are

there differences in landowner’s preference to harvest particular

forest types? The research will provide a new analysis and a

landscape scale perspective of forest harvesting activity across

multiple ownerships.

1.2. Study area

The study site is approximately 1.8 million ha and includes

portions of five counties in northern Maine, USA (Fig. 1). The

study area was selected because it contained a mix of ownership

types that were representative of the unorganized townships of

northern Maine. Also the area was contained wholly within one

Landsat scene (Path 12 Row 28 of the Worldwide Reference

System), making data processing and analysis more convenient.

This Acadian forest (a matrix of woods and waterways with

hardwood and softwood trees typical of the Northern

Appalachians and the Boreal Forest) in Maine occupies the

S. Jin, S.A. Sader / Forest Ecology and Management 228 (2006) 177–186 179

Fig. 1. The northern Maine study area (gray area with white township bound-

ary) encompasses approximately 1.8 million ha and portions of five counties.

Black lines are Maine county boundaries.

northern boundary of temperate forest and southern edge of

boreal forest (Loo and Ives, 2003). The upland vegetation is

composed of conifer (>75% softwood), deciduous (>75%

hardwood) and mixed stands (softwood–hardwood = 51–75%

softwood; hardwood–softwood = 51–75% hardwood) with a

variety of age classes from early successional to mature forest

types. Soils are derived mainly from glacial till. The area is

relatively flat to rolling with occasional low mountains,

abundant lakes, ponds, streams and associated wetland

vegetation and essentially no urban development (Hepinstall

et al., 1999).

2. Methods

2.1. Satellite data acquisition and preprocessing

To make all of the data compatible for processing and

analysis, some preliminary steps were followed. Radiome-

trically corrected and geo-referenced 1991 and 2004 Landsat

Thematic Mapper (TM), and 2000 Landsat Enhanced Thematic

Mapper Plus (ETM+) images (in the data archive of the Maine

Image Analysis Laboratory at the University of Maine) were

available to support the forest change mapping. All images

were collected in the summer months (hardwood leaf-on).

Clouds and cloud shadows in the 1991, 2000 and 2004 Landsat

imagery were removed through screen digitizing polygons

around the affected areas. These data were supplemented with

1993 and 1999 Landsat TM and 2001 and 2002 Landsat ETM+

images for areas covered by clouds in the 1991, 2000 and 2004

datasets. The 2001 Landsat imagery was cloud free.

2.2. Preparation of forest change maps

A forest cover and change map derived from 1991 and 2000

Landsat imagery was available to this study. Based on

comparison with 178 randomly sampled photo interpretation

plots, the accuracy of the forest-no change, forest cover loss,

and forest cover gain classes of the map was 90%, 88%, and

92%, respectively (Sader et al., 2005). An unsupervised

classification approach (isodata clustering) was used to identify

forest areas represented by pixels that experienced some level

of disturbance or canopy loss (e.g. light to heavy harvesting

activity for the purposes of this study) from areas were no

disturbance (equated with no change) was detected. Note that

the terms forest disturbance and forest cover loss are used

interchangeably throughout the manuscript. Pixels indicating

forest cover gain represented mostly natural forest regeneration

following harvesting activity that occurred at least a few years

before the 1991 image was recorded. The visible red, near

infrared and middle infrared wavebands of the Landsat sensor

(TM 3, 4, 5) and the normalized difference moisture index for

both dates were used in the classification approach. The

detailed methods used to prepare the 1991–2000 forest change

map were reported by Sader et al. (2005).

Recent investigations into the use of vegetation indices in

Maine forest change detection studies have indicated that the

time-series normalized difference moisture index (NDMI),

derived from Landsat data (Eq. (1)), produce accurate results

for the Maine forest conditions (Wilson and Sader, 2002; Sader

et al., 2003; Jin and Sader, 2005). The forest change classes for

the 2000, 2001 and 2004 Landsat data sets were processed

using a method called RGB_NDMI, described by Sader et al.

(2003). Wilson and Sader (2002) demonstrated that the

RGB_NDMI classification method can detect partial or clearcut

harvests with high overall accuracy (>90%) when Landsat

image acquisitions were less than 3 years apart:

NDMI ¼ ðNIR�MIRÞðNIRþMIRÞ ; (1)

where NIR is the near infrared spectral band 4 (0.76–0.90 mm)

of Landsat and MIR is mid infrared spectral band 5 (1.55–

1.75 mm).

For the 2000, 2001 and 2004 Landsat datasets (with 1999

and 2001 replacement for cloud affected areas), three-date

NDMI images were used as input data to unsupervised

classification (Isodata clustering). Clusters were interpreted

into four classes using visual interpretation of the original TM

color composite images (Cohen et al., 1998; Wilson and Sader,

2002; Sader et al., 2003). The four classes were: forest cover

loss (pixels indicating disturbance or harvest) before 2000,

forest cover loss between 2000 and 2001, forest cover loss

between 2001 and 2004, and forest-no change (pixels with no

disturbance or cover loss detected). These were later grouped

into two classes: forest loss 2000–2004, forest-no change.

S. Jin, S.A. Sader / Forest Ecology and Management 228 (2006) 177–186180

Cohen et al. (1998) demonstrated that that there was no

significant difference in accuracy of detecting clearcuts

between field visits combined with photo interpretation

methods and visual interpretation of Landsat color composites.

Also Sader et al. (2003) reported the method to be accurate and

not significantly different compared to photo interpretation

plots of partial and clearcut harvest areas in Maine.

2.3. Forest ownership type and ownership change class

Maine forest ownership maps from 1994, 2000 and 2004

were acquired from a private company. These maps identified

individual landowners and ownership type at each date (names

of all private landowners will remain confidential). An alpha-

numeric labeling system was used to identify each landowner in

the database. The three dates of ownership identities were

recoded into four general groups: no change in ownership from

1994 to 2004 (NC940004), ownership change between 1994

and 2000 (C1_9400), ownership change between 2000 and

2004 (C1_0004), and ownership change between 1994 and

2000, and between 2000 and 2004 (C2_940004). The 2004

forest ownership map was categorized into nine ownership

types: Industrial (I), NIPF, TIMO, Logger/Short-Term Investor

(LD), Non-Government Organization (NGO), State (S), Indian

(IN), Federal (F) and Others (O). One of the major landowners

is not a TIMO but is a publicly traded REIT. Nevertheless, we

put the REIT into the TIMO group because its major assets in

Maine are timberlands. The ‘‘Others’’ ownership type are a

mixed category of smaller NIPFs and other unknown owners,

altogether representing a much smaller proportion of the study

area. Organizing the ownership data in this way will facilitate

the comparison of harvest activity over time by ownership type

and ownership change class.

2.4. Forest area available for harvest

To normalize the different topographic characteristics and

ownership size effects, the forest cover loss (harvest)

estimates were based on the percentage of the ‘‘accessible’’

forest in each ownership type. The forest harvest rates were

calculated as ‘‘forest cover loss area/accessible forest

area � 100’’ (i.e. forest cover loss percentage in time period).

The proportion of forest considered accessible was based on

the percentage of the forest types present in the 1993 Maine

Gap land cover map (Hepinstall et al., 1999) minus elevations

over 3200 feet, slopes greater than 40%, and land in reserve or

protected status. The State of Maine and forest management

companies generally do not harvest forests under these three

constraints; therefore we excluded these forest areas from

consideration so that the percentage of harvested forest would

be based on the forestland base with harvest potential.

Collectively, the forest excluded from the analysis repre-

sented approximately 8.5% of the total forest area, of which

7.0% was contained within Baxter State Park. The total

accessible forest represented is approximately 1.5 million ha.

The accuracy of forest versus non forest cover types in the

Maine Gap map was 94% (Hepinstall et al., 1999).

2.5. Forest cover loss and gain over three decades

To prepare the datasets for analysis of forest harvest rates in

the different time periods, we needed to combine information

from different maps. The Maine Gap land cover map

(Hepinstall et al., 1999) included two regeneration stand types:

early regeneration and late regeneration. Late regeneration

represented older harvesting, mainly clearcuts (spruce bud-

worm, salvaged stands), in the late 1970s through the early

1980s. Early regeneration represented stands harvested from

approximately the mid 1980s to 1990. The two regeneration

types from the Maine Gap map were combined to represent the

forest stands harvested from late 1970s and throughout the

1980s. Because nearly all of the study area is under working

forest ownerships (Industrial, NIPF, State, Other), we assumed

that natural regeneration was occurring and the land has

remained in forest use. Tree planting is a small component of

the total forest regeneration area in Maine (Laustsen et al.,

2003; McWilliams et al., 2005). The 1991–2000 forest cover

loss from the change detection map (Sader et al., 2005)

represented forests harvested throughout the 1990s. The forest

cover loss area between 2000 and 2004 (from the 2000-01-04

RGB-NDMI change detection mapping) represented forests

harvested in the early 2000s.

To improve the visualization and localization of the harvest

activity over the study area, we calculated a ‘‘disturbance

index’’, which we define operationally as the cumulative

disturbance detected over the three time periods (percentage of

forest pixels in each township with forest canopy loss detected).

The disturbance rotation (time span/disturbance index) for

major selected major landowner types was also calculated as

another metric to compare among landowner types. This metric

was calculated on each ownership type area and not on the

township level.

2.6. Forest type harvested by ownership type and

ownership change class

The forest cover loss areas of 1991–2000 and 2000–2004

were intersected with the Maine Gap map (Hepinstall et al.,

1999) to determine the forest types harvested in each time

period—hardwood (H), hardwood–softwood (HS), softwood–

hardwood (SH) and softwood (S). The regeneration areas in the

Maine Gap map were intersected with a 2004 forest cover map

to estimate the forest type harvested during the 1980s. The

methods used to prepare the 2004 forest cover map have been

reported elsewhere (Metzler and Sader, 2005). The assumption

was made that the regenerated forest composition (although not

age class or structure) is representative of the forest

composition before harvesting. The regeneration stands derived

from the Maine Gap map represent mostly the legacy of the

massive spruce budworm infestation and subsequent salvage

logging practiced by all major landowners. Although the name

of the insect implies otherwise, the spruce trees (Picea sp.) are

not the most preferred host as judged by higher mortality of

balsam fir (Abies balsamea (L.) Mill.) following past epidemics

(Miller, 1963; Royama, 1984). According to Baskerville

S. Jin, S.A. Sader / Forest Ecology and Management 228 (2006) 177–186 181

Fig. 2. Forestland ownership change between two time periods, 1994–2000 and

2000–2004. The three bars depict the percentage of forest that remained in the

same ownership type (stable) and the percentage that an ownership gained

(bought) or lost (sold). See Table 1 for ownership codes. The total of three

percentages (stable, gain and loss) for each ownership type is 100%.

(1975), mature fir stands are destroyed in a budworm

infestation and this seems to favor regeneration from fir from

abundant seedlings that were established prior to the outbreak.

When the new fir forest matures to form a dense canopy they

again become susceptible to the next epidemic (Baskerville,

1975).

To understand whether landowners were harvesting a forest

type in proportion to its abundance, we calculated the ratio of

the percentage of each type harvested to the percentage of each

type available. The intent of this analysis was to determine if

there was any ‘‘preference’’ for landowners (ownership type,

individual ownership, and ownership change class) to harvest

certain forest types in greater proportion to other types. For

example, if landowners had no preference for harvesting one

particular cover type over another, it would be expected that the

percentage of each type harvested would be in equal proportion

to the availability of that forest type on the landscape (Hayes

and Sader, 2002).

3. Results and discussion

3.1. Forestland ownership pattern

Table 1 indicates that TIMOs owned more than 35% of the

forestland in the study area in 2004 and Industrial ownership

type, followed by NIPFs, held approximately 23% and 19%,

respectively, in the study area. The remaining ownership types

together held about 22% of the total forestland. According to

our statewide database, for comparison, the Industrial, TIMO

and major NIPF ownership types held 34%, 24%, and 23% of

the forest (excluding unknown ownership forest area, mostly in

southern Maine) at the state level, therefore the three ownership

cumulative percentages in our study area appear to be generally

representative of the commercial forestland ownership of

Maine.

More than 95% of the forestland controlled by TIMOs in the

study area was acquired between 1994 and 2004. Around 93%

of the TIMO lands (by 2004) was bought from industrial

Table 1

Forest area (ha) distribution of 2004 ownership type in each ownership change ca

2004 Ownership type Ownership change class 1994–20

NC940004 C1_9400

Timber Investment Management

Organization (TIMO)

29938 353992

Industrial (I) 160674 174524

Non-Industrial Private Forest (NIPF) 254254 8304

Logger/Short-Term Investor (LD) 1554 887

Non-Government Organization (NGO) 1996 52996

State (S) 65230 566

Indian (IN) 20217 0

Federal (F) 1632 0

Others (O) 70456 18873

Total (ha) 605953 610143

NC940004: no change in ownership from 1994 to 2004; C1_9400: ownership change

C2_940004: ownership change between 1994 and 2000, and between 2000 and 20a Note that totals may not add to 100% due to rounding.

companies and 7% was purchased from other owner types. The

TIMOs obtained most of their forestland between 1994 and

2000 (Fig. 2), and they continued to acquire more forestland

between 2000 and 2004. However, between 2000 and 2004, one

TIMO sold approximately half of its forestland to another

TIMO (representing around 100,000 ha originally acquired

from an industrial ownership between 1994 and 2000)

accounting for 60% of the area in the Ownership Change

Twice class (Table 1). A third TIMO sold smaller parcels to the

other ownership types such as NIPF, LD, State and NGO from

1994 to 2004.

The Industrial ownership sold their forestlands throughout

the time period (1994–2004). Between 1994 and 2000, around

80% of industrial forest experienced landowner change, with

around 75% sold to the TIMOs, and 25% to other industrial

ownership. Approximately one-half of the industrial land

remaining in 2004 had experienced ownership change between

1994 and 2000, and the majority of these transactions occurred

tegory of available forested area

00–2004 Total Percent

C1_0004 C2_940004

35685 107478 527093 35.7

768 0 335966 22.8

20056 1445 284059 19.3

14478 5586 22505 1.5

4467 40616 100075 6.8

2277 21324 89397 6.1

272 0 20489 1.4

45 6 1683 0.1

1464 2930 93722 6.4

79509 179385 1474990 100.0a

between 1994 and 2000; C1_0004: ownership change between 2000 and 2004;

04.

S. Jin, S.A. Sader / Forest Ecology and Management 228 (2006) 177–186182

Fig. 3. The 2004 forest ownership map is displayed on the left; disturbance index map is on the right side. The disturbance index represents the cumulative sum of

percent forest disturbed (harvested) of available forest land throughout the 1980s, 1990s, and early 2000s for each township in the study area.

among the Industrial ownership themselves (Table 1, Fig. 2). In

contrast, nearly 90% of NIPF land remained in single

ownership (stable over the time periods). The 10% of NIPF

area change was attributed to one purchase by a TIMO between

2000 and 2004. The NGOs acquired several parcels of forest

from Industrial, NIPFs, TIMOs, and LDs through 1994–2004.

The LDs and State acquired more forestland during the 1994–

2000 time frame.

3.2. Forest harvest rates by ownership type

The 2004 forest ownership map and the disturbance index

map (cumulative% of 1980s, 1990s and 2000s harvest area by

township) are depicted in Fig. 3. Note that 2004 is the base year

for ownership identification and comparison for reporting

convenience, however some landowners (e.g. TIMOs, LD,

NGO) did not own a large percentage of the land at earlier dates

Fig. 4. Left image is an example of the combined forest change map for three time pe

is forest harvested from the late 1970s through 1980s, dark green is forest-no change

color composite.

(1970s and 1980s). Much of the harvesting may have occurred

prior to the purchase dates of the 2004 owners and therefore the

disturbance measured is largely a legacy of the earlier owners.

There are 47% of Industrial townships and 37% TIMO

townships based on 2004 ownership type, which represented

the higher disturbance categories (>50% of available forest

harvested). Compared to the Industrial and TIMO owned lands

in 2004, the stable NIPF ownership had many more townships

represented by the low to medium disturbance categories (30–

50%), and only 8% of the townships had high cumulative

disturbance (>50%). More than half of the State, Federal and

Indian owned townships were lightly disturbed (0–30%) up

through 2004.

A full resolution example of the combined forest change

map (Fig. 4, left map) for three time periods is provided with the

2004 Landsat color composite images (Fig. 4, right map) for

visual reference. For example, the harvested areas during 1980s

riods. Red is forest disturbed in 1990s, yellow is forest disturbed in 2000s, green

since the late 1970s. The right image is the 2004 Landsat TM band 4, 5, 3 (RGB)

S. Jin, S.A. Sader / Forest Ecology and Management 228 (2006) 177–186 183

Table 2

Forest harvest percentage by time period and by 2004 ownership type

Type No

change

Late

1970–1980s

1990s 2000sa Area (ha)

TIMO 51.0 22.4 16.4 10.2 527093

I 51.1 27.5 15.1 6.3 335966

NIPF 64.5 18.0 10.9 6.7 284059

LD 69.1 3.7 18.5 8.7 22505

NGO 61.4 20.0 13.8 4.7 100075

S 79.1 9.9 7.6 3.4 89397

F 83.7 6.4 7.3 2.6 1683

IN 75.1 18.3 4.7 1.9 20489

O 60.7 20.9 13.3 5.1 93722

Total accessible

forestb

57.3 21.3 14.0 7.3 1474990

a Note that the 2000–2004 time interval represents less than one-half of the

interval of the 1980s and 1990s data. See Table 1 for identification of ownership

types.b Total accessible forest does not add to 100 due to rounding.

Fig. 5. Forest harvest percentage of each individual Industrial and NIPF owner

(based on 2004 ownership status) for three time periods. An alpha-numeric

labeling system was used to identify each landowner.

were composed of larger patches (Fig. 4, left map, point A) and

those areas were regenerated by 2004 (Fig. 4, right map). By

2004, the regeneration stands on those harvested areas were

between 15 to approximately 25 years old. In contrast, some of

the harvested areas during early 1990s were composed of

clustered, smaller clearcut patches (Fig. 4, left map, point B),

likely and artifact of the 1991 implementation of the Maine

Forest Practices Act (Maine Forest Service, 1995).

Table 2 depicts the harvest rate (based on all pixels detected

as forest cover loss) and no change forest area percentage of the

accessible forested area by 2004 ownership type. Fifty seven

percent of forestlands were not disturbed (harvested) and 21%

of forestlands were harvested from the end of the 1970s and

throughout the 1980s. Around 14% of the forestlands were

harvested during 1990s and 7% were harvested from 2000 to

2004. The forest harvest rates for land held by 2004 ownership

types were higher in 1980s than 1990s except for the Federal,

State and LD owners. The overall harvest rates for 2000–2004

were equal or slightly higher compared to the 1990s

considering that there were only 4 years in this time period

compared to 9 years in the 1990s. Greater than 75% of Federal,

State and Indian forestlands accessible were not harvested

during the three time periods.

Industrial and TIMOs were the largest forest landowners by

2004, and both still had approximately half (6% below the

average) of their forestland not disturbed within the time period

of the database. Harvest rates on 2004 Industrial owned lands

were above the average for the 1980s and 1990s; however, the

rates decreased below the average for the 2000s. The Industrial

owned land had an average 51 years of disturbance rotation.

Nearly all of this land was in the industrial ownership type

(although some was transferred between individual owners)

throughout the three time periods. The TIMOs harvested a

slightly higher percent of their forestland during 1990s (16.4%

compared to average 14.0%) and 2000s (10.2% compared to

average 7.3%). If the 10.2% TIMO harvest rate is projected

linearly to the end of the decade (6 more years), the projected

harvest rate would be higher than the 1990s rate.

The LDs acquired most of their land between 1994 and 2004

(Fig. 2). They harvested at higher rates (4.5% and 1.4% higher

than average during 1990s and 2000s, respectively); however,

they had a high percentage (69%) of undisturbed forestland

remaining. Loggers purchased forestland that experienced low

harvest rates (3.7%) during the end of 1970s and throughout

1980s (Table 2). The harvest rates on land owned by NIPFs (by

2004) for the three time periods were lower than land owned by

the Industrials and TIMOs (whose lands were mainly bought

from Industrial after 1994). The land owned by NIPFs were

more stable through time, had intermediate harvest rates, and an

average 70 years of disturbance rotation. The NGOs purchased

most of their land throughout 1994–2004 and the harvest

percentage was close to the average during 1990s; however,

2.6% less than the average during the 2000s. Given the past

harvest trends on forest land owned by NGOs by 2004, the

100% disturbance rotation of the ownership land base is

predicted to be about 65 years. Again, it should be noted that

this is strongly affected by the harvesting legacy on these lands

before the NGOs purchased them.

Within each forestland ownership type (based on 2004

ownership), harvest rates varied from one individual ownership

to another. Fig. 5 presents the forest harvest percentage of

individual ownership within the Industrial and NIPF types.

Almost all of these forestlands were kept under the Industrial or

NIPF ownership type during the three time periods. The

variance of forest harvest percentage (averaged by year) for

Industrials is significantly greater than NIPFs (P = 0.001),

though there is no statistically significant ownership type effect

on the averaged forest harvest percentage.

3.3. Forest harvest rates by ownership change class

Using a pairwise ‘‘T’’-test, the forest harvest rate on the

stable forestland (NC940004) was significantly lower than the

forest harvest rate on the Ownership Change between 1994 and

2000 (C1_9400) forestland through the three time periods

(Table 3, p-value = 0.047). The forest harvest rate on the

S. Jin, S.A. Sader / Forest Ecology and Management 228 (2006) 177–186184

Table 3

Forest harvest percentage by time period and by ownership change class

Ownership

change class

No

change

Late

1970–1980s

1990s 2000s Area (ha)

NC940004 63.43 18.63 11.58 6.35 615520.0

C1_9400 49.83 25.86 15.88 8.43 604280.4

C1_0004 59.00 17.60 15.90 7.50 78674.7

C2_940004 60.81 16.89 15.14 7.16 176515.3

NC940004: no change in ownership from 1994 to 2004; C1_9400: ownership

change between 1994 and 2000; C1_0004: ownership change between 2000 and

2004; C2_940004: ownership change between 1994 and 2000, and between

2000 and 2004.

Table 4

Percent forest type available by ownership change class

Forest type Ownership change class 1994–2000–2004

NC940004 C1_9400 C1_0004 C2_940004

S 25.6 25.5 16.2 19.8

SH 24.7 24.7 27.4 26.0

HS 23.8 25.6 22.7 21.2

H 25.9 24.3 33.7 33.0

Total (%) 100.0 100.0 100.0 100.0

NC940004, no change in ownership from 1994 to 2004; C1_9400, ownership

change between 1994 and 2000; C1_0004, ownership change between 2000 and

2004; C2_940004, ownership change between 1994 and 2000, and between

2000 and 2004.

Fig. 6. The ratio of harvested forest type to available forest through time for

four ownership change classes. 100 indicates equal percentage harvested in

proportion to available.

C1_0004 and C2_940004 forestland was slightly lower than

NC940004 during the late 1970s and 1980s, however higher

during the 1990s and 2000s. These results suggest a trend that

the forestland that experienced ownership change by 2004 also

experienced higher harvest rates.

3.4. Forest type available and harvested by ownership type

Table 4 is the percentage of forest type available by

ownership change class. In the early 1990s there was nearly an

equal proportion of four forest types (S, SH, HS, H) available in

the two ownership change classes (NC940004 and C1_9400).

There was a higher proportion of hardwood than softwood in

C1_0004 and C2_940004 classes (Table 4). Table 5 indicates

the percentage of forest type available at the beginning of the

1990s based on the 2004 ownership status. By 2004, there was

roughly an equal proportion of four forest types (H, HS, SH, S)

available in the TIMO, Industrial, Non-industrial, State and

Table 5

Percent forest type available at the beginning of 1990s for 2004 ownership types

Forest type TIMO I NIPF LD

S 20.5 28.8 25.5 19.7

SH 26.5 22.6 24.2 26.1

HS 25.8 23.4 23.8 20.2

H 27.2 25.2 26.6 34.1

Total (%)a 100.0 100.0 100.0 100.0

a Totals may not add to 100% due to rounding.

Others ownership types. Loggers purchased forestland with a

higher proportion of hardwood (34%) than softwood (20%)

compared to the proportions of these forest types available on

the other ownerships (Table 5). This may be because those

forestlands with higher proportion of hardwood experienced

much lower harvest rates during 1980s (Table 2), and the

hardwood stands that had matured were marketable by the

2000s. The NGOs appeared to purchase forestlands with a

higher proportion of softwood (Table 5). It is unclear whether

this indicates that the NGOs valued softwood dominant stands

for conservation or if this stand composition was just a function

of the land available and negotiated for purchase at the time.

Indian and Federal owners had the two highest percentages of

hardwood composition.

Fig. 6 suggests the forest type preferred for harvest under the

ownership change classes during the three time periods. During

the end of 1970s and 1980s for all forestland area in the study

area (whether ownership was kept stable or not), softwood

(120–139%) and softwood–hardwood (110–123%) stands were

harvested in much higher proportion while less hardwood–

softwood (50–66%) and much less hardwood (28–58%) were

harvested than was proportionally available on the landscape.

Most of the 1980s harvesting occurred in softwood and

softwood–hardwood stands, especially those containing balsam

fir (A. balsamea (L.) Mill.), as these were the preferred host of

the spruce budworm, leading to the extensive salvage logging

(Maine Forest Service, 1995; McWilliams et al., 2005).

The general trend of forest type harvested over time

(Fig. 6) indicates a shift from softwood to hardwood dominant

stands. During 1990s, softwood and hardwood–softwood were

NGO S IN F O

34.4 24.4 12.0 17.0 20.2

22.7 29.7 20.8 24.9 24.7

17.4 23.3 28.4 20.8 26.4

25.5 22.6 38.8 37.3 28.6

100.0 100.0 100.0 100.0 100.0

S. Jin, S.A. Sader / Forest Ecology and Management 228 (2006) 177–186 185

Table 6

The ratio of harvested forest type to available forest for 2004 ownership type

Time Forest type TIMO I NIPF LD NGO S IN F O

1980s S 127.8 127.2 116.0 89.5 118.2 101.2 133.8 92.0 119.2

SH 127.0 130.3 126.2 157.1 123.3 109.5 149.9 132.2 132.2

HS 52.6 43.4 56.6 70.7 42.5 81.6 47.0 92.6 54.6

H 43.1 31.3 50.0 53.5 33.7 76.6 10.1 50.0 35.6

1991–2000 S 95.9 115.4 86.7 94.7 118.1 89.5 91.4 52.3 98.8

SH 140.5 162.3 167.8 152.2 161.0 171.4 198.5 200.1 147.4

HS 91.3 72.6 88.5 104.3 57.8 69.6 105.5 132.2 88.8

H 71.9 52.0 61.2 60.6 50.2 48.8 45.8 37.1 70.3

2000–2004 S 89.8 106.7 87.2 78.6 145.1 72.5 87.4 85.5 88.0

SH 105.6 113.1 110.5 107.9 116.8 120.3 79.8 179.5 86.1

HS 99.6 104.0 103.4 83.0 59.3 89.3 183.5 87.6 106.1

H 102.5 76.9 99.7 116.4 52.1 114.0 53.6 60.5 114.9

Note: 100 means equal percentage harvested in proportion to available, more than 100 means higher percentage harvested in proportion to available.

harvested in roughly equal proportion to the percentage of

available forest types and softwood–hardwood stands became

the preferred forest type harvested (109–163%). Hardwood

harvesting (39–67%) increased though still in lower proportion

to that available. In the recent 4 years (2000s), all of the forest

types were harvested in almost equal proportion. Rice (2003)

reported that markets for hardwood products in Maine were

generally unfavorable for many years, but this began to change

in the 1990s. The 2003 Maine Forest Inventory (McWilliams

et al., 2005) stated ‘‘recent increases in demand for hardwoods,

both pulp and sawlogs, have created opportunities for managing

and developing deciduous forests.’’

The same general trend of forest type harvested over time

was also shown in the data of the forest type preferred (by 2004

forestland ownership type) for harvest during the three time

periods (Table 6). From 2000 to 2004, Industrials and NGOs

were the only owners who harvested more softwood in

proportion to the amount available. Industrial ownership may

have harvested softwood in higher proportion because they

needed softwood to supply their own mills. NGOs had the

highest percentage of softwood available (34%, Table 5). One

NGO purchased forestland from an Industrial ownership

between 2000 and 2004 but the Industrial ownership held

the logging rights and harvested mostly softwood and

softwood–hardwood stands (Dr. Jeremy Wilson, University

of Maine, personal communication, 4/05). This helps to explain

an anomaly of the NGO class showing a high proportion of

softwood harvested (to available) between 2000 and 2004 that

otherwise might not be expected from this type of owner

(Table 6). The LDs harvested hardwood in higher proportion

from 2000 to 2004 (and they bought lands with a much higher

percentage of hardwood). The TIMOs preference of forest type

harvested was similar to the NIPF.

4. Conclusions

The results indicated that forest ownership type and

ownership change did influence forest harvest intensity and

trend over time in northern Maine. One cautionary note is that

these results should not be extrapolated beyond the study area

where they may not be representative of other commercial

forests of Maine or other states where the individual forest

owners and forest type conditions (among other variables) are

different. The forest change detection methods and merging of

older land cover maps with updated forest change maps should

be widely applicable to other regions and landscapes.

Both TIMOs and LDs harvested forest at slightly higher

rates than other owners in the 1990s and 2000s. The NGO

ownership had harvest rates that were slightly lower than the

average mostly on lands that they purchased between 1994 and

2004. The NIPF owners held more stable ownership and had

more equal and intermediate harvest rates through time. Most

TIMOs, NGO and LDs have held the land for only a short time

(mostly less than 10 years). Sales of some lands formally owned

by TIMOs to LDs (between 2000 and 2004) suggest that

TIMOs may be willing to turn over some of their timber

holdings in shorter time compared to other owners, like NIPFs.

There are many possible explanations for differences

detected in harvest rates and trends among landowner types.

A socio-economic survey of the forest landowners would be

necessary to explain the underlying drivers and elucidate the

individual and group ownership harvest trends reported here,

however this is beyond the scope of this study. Readers are

referred to other authors (e.g. NFLC, 1994; Irland and Lloyd,

1999; Irland, 2000; Yale Forest Forum Review, 2002; Rice,

2003) for discussions on forest industry trends, wood markets,

forest policy issues and socio-economic factors that may

influence landowner forest management strategies and land

sales in Maine and the northeastern U.S.A. Significant trends in

forest management and harvest rates may emerge after more

time evolves under the new ownership mix in northern Maine.

Thus an update of the harvesting data at the end of the 2000s

decade is recommended to assess these new spatial patterns and

longer term trends.

Time-series medium spatial resolution Landsat imagery

offers a large landscape perspective to monitor forest disturbance

over multiple ownerships which is generally not feasible or cost-

effective using ground based methods or extensive high

resolution aerial imagery. Satellite remote sensing analysis

incorporating socio-economic and other ground-based data

S. Jin, S.A. Sader / Forest Ecology and Management 228 (2006) 177–186186

(e.g. forest inventory) may be a promising approach for

predicting future forest change or timber supply availability

under different ownership change scenarios on a statewide or

regional basis.

Acknowledgments

This research was supported by the Maine Agricultural and

Forest Experiment Station (MAFES)-Grant ME-09608.

MAFES External Publication 2860.

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