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Relationships Between Major Ownerships, Forest Aboveground
Biomass Distributions, and Landscape Dynamics in the New
England Region of USA
Daolan Zheng •Linda S. Heath •Mark J. Ducey •
Brett Butler
Received: 16 November 2008 / Accepted: 15 November 2009 / Published online: 5 December 2009
ÓSpringer Science+Business Media, LLC 2009
Abstract This study utilizes remote sensing derived
forest aboveground biomass (AGB) estimates and owner-
ship information obtained from the Protected Areas Data-
base (PAD), combining landscape analyses and GIS
techniques to demonstrate how different ownerships
(public, regulated private, and other private) relate to the
spatial distribution of AGB in New England states of the
USA. ‘‘Regulated private’’ lands were dominated by lands
in Maine covered by a Land Use Regulatory Commission.
The AGB means between all pairs of the identified own-
ership categories were significantly different (P\0.05).
Mean AGB observed in public lands (156 Mg/ha) was 43%
higher than that in regulated private lands (109 Mg/ha), or
30% higher than that of private lands as a whole. Seventy-
seven percent of the regional forests (or about 9,300 km
2
)
with AGB [200 Mg/ha were located outside the area
designated in the PAD and concentrated in western MA,
southern VT, southwestern NH, and northwestern CT.
While relatively unfragmented and high-AGB forests
([200 Mg/ha) accounted for about 8% of total forested
land, they were unevenly proportioned among the three
major ownership groups across the region: 19.6% of the
public land, 0.8% of the regulated private land, and 11.0%
of the other private land. Mean disturbance rates (in
absolute value) between 1992 and 2001 were 16, 66, and
19 percent, respectively, on public, regulated private, and
other private land. This indicates that management prac-
tices from different ownerships have a strong impact on
dynamic changes of landscape structures and AGB distri-
butions. Our results may provide insight information for
policy makers on issues regarding forest carbon manage-
ment, conservation biology, and biodiversity studies at
regional level.
Keywords Biomass accumulation
Forest carbon storage Forest management
Land conservation Ownership behaviors
Introduction
Forest aboveground biomass (AGB) is an important eco-
system property related to carbon cycles, fuel loading, and
biodiversity of flora and fauna (Cleary and others 2005;
Houghton 2005; Ryu and others 2006; Smith and Heath
2007; Zheng and others 2008a). Remote sensing techniques
have become prevalent in estimating AGB in recent years
at various scales (Nelson and others 1988; Franklin and
Hiernaux 1991; Lefsky and others 2002; Zheng and others
2004;Lu2005; Muukkonen and Heiskanen 2007; Zheng
and others 2007). Continuous AGB maps derived from RS
observations include all forest conditions, and are needed
to identify spatial distribution and variation of regional
forest AGB to provide information to support biodiversity
conservation. For example, late-developmental forests are
relatively rare in the Northern Forest region because of
land-use history and have high conservation value, but they
are sparsely represented, especially in sample-based,
D. Zheng (&)M. J. Ducey
Department of Natural Resources and the Environment,
University of New Hampshire, Durham, NH 03824, USA
e-mail: daolan.zheng@unh.edu; dl_zheng@yahoo.com
L. S. Heath
USDA Forest Service, Northern Research Station, Mast Rd,
Durham, NH, USA
B. Butler
USDA Forest Service, Forest Inventory and Analysis, Family
Forest Research Center, Amherst, MA 01003, USA
123
Environmental Management (2010) 45:377–386
DOI 10.1007/s00267-009-9408-3
broad-scale inventories such as that of the USDA Forest
Service, Forest Inventory and Analysis (FIA) program.
Thus, spatially identifying these late-developmental forests
associated with their ownerships could provide necessary
information for ecological and biodiversity studies at
regional levels (Askins and others 1987; Richards and
others 2002).
The spatial pattern of high-conservation-value lands
may vary due to forest ownership. Public and private
owners may consider values of forests from multiple per-
spectives, such as production, conservation, family legacy,
ecological biodiversity, and aesthetics. While private
industrial owners are more likely to emphasize short-term
financial goals, opportunistic harvesting and other finan-
cially motivated behaviors can characterize other private
landowner groups (Jones and others 1995; Egan 2007).
Consequently, owners implement different strategies in
managing their forestlands. Even within private ownership
in the New England (NE) region, individuals owning rel-
atively small forested tracts behave differently from those
individuals and organizations with larger holdings (Butler
2008).
Previous studies have demonstrated that spatial distri-
butions of forest AGB vary substantially with human
introduced disturbances, and various management strate-
gies (Johnson and others 2000; Khera and others 2001). It
is of particular interest to understand the geographic rela-
tionship between late-developmental forests, other rela-
tively high-biomass forests (including younger unmanaged
forests), and their ownership in the NE region. Such
understanding may help inform policy that would influence
landscape management over substantial areas, with impli-
cations for regional biodiversity conservation and carbon
stocks. In addition, a great deal of time, money, and effort
has been invested in the development of spatially explicit
techniques for identifying candidate areas for conservation
assessment and action (Knight and others 2008; Wallace
and others 2008). These techniques enhance the effec-
tiveness of implemented conservation actions and provide
scientifically defensible information for better management
of natural resources, which is ecologically and socially
beneficial to the environment and communities.
The overall goal of this study is to examine relationships
between forest AGB stocks and major ownership groups in
the six NE states to examine if forest AGB differs by owner
group, including landscape dynamics and distribution pat-
terns of AGB. This information could help identify
opportunities for improved management of regional forest
ecosystems. Detailed steps include: (1) stratifying a MO-
DIS-derived 1-km AGB map based on the Protected Areas
Database (PAD) and comparing AGB distribution patterns
among different ownerships; (2) quantifying landscape
structures for different ownerships based on National Land
Cover Dataset (NLCD) and their relationships to regional
AGB distribution; (3) examining owner effect on different
management practices and therefore on landscape dynam-
ics between 1992 and 2001; and (4) illustrating how scaling
process can influence landscape pattern analyses.
Materials and Methods
This study was conducted in the 6 NE states of the USA:
Connecticut (CT), Massachusetts (MA), Maine (ME), New
Hampshire (NH), Rhode Island (RI), and Vermont (VT).
Forestland covered approximately 84% of this total terri-
tory based on the 2001 MODIS land-cover map. Major
inputs for this study include the biomass map, PAD map,
and land-cover maps from different sources.
Biomass Map
The 2001 AGB map used in this study was developed by
Zheng and others (2008b) for the region at 1-km resolution
using multi-scale methodology. In their study, ground-
based measurements from FIA were linked to remotely
sensed spectral information within a 30-m Landsat 7
Enhanced Thematic Mapper plus scene (ETM ?, about
180 9180 km in size) to develop an empirical AGB
model. The model was applied to the entire region using
1-km MODIS data after spectral calibrations with the
ETM ?data. The resulting spatially-explicit biomass map
takes advantage of integrity of spatial variation from RS
and unbiased county means of AGB observations devel-
oped from the larger sample sizes of FIA plots. The esti-
mated total regional forest AGB was 1,867 teragram (10
12
,
dry weight) in 2001, with a mean AGB density (dry
weight) of 120 Mg/ha (Standard deviation =54 Mg/ha)
ranging from 15 to 240 Mg/ha within a 95% percentile
(Zheng and others 2008b). At the state level, the average
difference in mean AGB densities between simulated and
FIA (as reference) was -2.0% ranging from 0% to -4.2%
with a standard error of 3.2% in absolute value.
High-AGB late-developmental secondary forests are of
particular conservation interest in the Northern Forest
region because centuries of land use history have left them
relatively scarce. However, current land use and manage-
ment is leading to an increasing representation of high-
AGB forests, including some forests with more advanced
developmental characteristics, as well as passively man-
aged forests of moderate age. These two categories of
forests may experience different AGB trajectories in the
future, depending not only on stand dynamics but on
landowner objectives and behavior. Spatially identifying
those high-AGB forests associated with major ownership
groups across the region is useful information.
378 Environmental Management (2010) 45:377–386
123
Ownership Identification
General ownership information was obtained from the
PAD map (2006 version 4) that was the result of a col-
laborative effort between the Conservation Biology Insti-
tute and the World Wildlife Fund U.S. (DellaSala and
others 2001). The database was developed as a geographic
information system (GIS) dataset that identifies protected
areas including publicly and privately owned lands in the
conterminous United States (http://www.consbio.org/cbi/
projects/PAD/index.htm). The PAD-US Partnership (www.
protectedlands.net) defines protected areas as, ‘‘lands ded-
icated to the preservation of biological diversity and to
other natural, recreation and cultural uses, and managed for
these purposes through legal or other effective means’’. In
other words, protected areas are in general protected from
land development (permanent conversion to developed
land use) but not from unplanned disturbances or related
to management. The PAD is a national database with
somewhat generalized definitions that may be interpreted
differently from region to region across the country.
1
Regrouping original ownership categories may be neces-
sary depending on study purposes because a perfect naming
system may not even exist. We aggregated the original
PAD ownership classes that occurred in our region into
four broader groups: (1) public (federal, state, and local),
(2) regulated private (protected-private), (3) other private
(private-inholding, tribal, and all other forested lands not
designated in the PAD), and (4) others (joint ownership and
unknown) based on our best knowledge about the regional
land-use history and ownership behavior (Table 1). For
example, ‘‘Tribal’’ land was considered to be similar to
other private than to the public and regulated private cat-
egories based on ownership behavior (not the name). Our
analyses were focused on the first three groups since the
last one represented very small proportion of regional
forestland (0.02%). Such generalization simplified the
analyses because we were not interested in differences of
forest AGB distributions within the public land owned by
government agencies at various levels. In some regions,
differences between government agencies would be
apparent; in this region there is little Federal land (&4%).
Our analyses, instead, were focused on (1) the difference
between public (as a whole) and private in general; and (2)
within the privately owned forests, what are the differ-
ences, in terms of landscape characteristics and AGB dis-
tribution, between the categories of ‘‘regulated private’’
and ‘‘other private’’. Our study design reflects that (1) these
three general groups owned more than 99% of the regional
forested land; and (2) more than 90% of the regional for-
estland were privately owned (Irland 1999).
Landsat-Derived Land-Cover Maps Used
for Landscape Pattern Analyses
We used NLCD 1992 and 2001 maps, developed from
Landsat 5 and 7 imagery at 30-m resolution under the
Multi-Resolution Land Characteristics Consortium
(MRLC) project (http://www.mrlc.gov/). The two maps
were not used for pixel-to-pixel change detection, rather,
they were used for distinguishing forestland from non-
forestland after being aggregated to these 2 very broad
categories for the purposes of (1) landscape pattern anal-
ysis in 2001 to link the AGB distribution patterns in 2001;
(2) quantifying landscape dynamics between 1992 and
2001, and (3) examining how scaling process (from 30 m
to 1 km) can affect landscape pattern analyses. Landscape
pattern related analyses for all three ownerships were
conducted using FRAGSTATS after the NLCD maps were
stratified by the regrouped PAD map. It was noted that
detailed land-cover classes between the 1992 and 2001
NLCD maps were not identical for non-forest categories at
Anderson level II (Anderson and others 1976) with slightly
adjustment for agriculture, urban, and barren, but the
impact of these differences in our heavily forested region
was supposed to be very limited, especially at the broad
categories (i.e., forest versus non-forest) (Vogelmann and
others 2001; Homer and others 2004). We downloaded the
1992 and 2001 NLCD maps for the conterminous U.S.
(http://landcover.usgs.gov/natllandcover.php,www.mrlc.gov/
nlcd_multizone_map.php) and extracted the NE area for
this study. We aggregated the Level II land-cover types of
41, 42, 43 into forest, and all other types into non-forest for
both years.
Table 1 Cross-walk table between original categories in the PAD
and reclassified classes involving forested lands in New England
region
Original records Reclassified classes
Federal Public
State Public
Local Public
Private-protected Regulated private
Private-inholding Other private
Tribal Other private
Forestlands not designated in PAD Other private
Joint Ownership Others
Unknown Others
1
A new version of PAD (although numbered version 1) was released
as this manuscript was through the review process.
Environmental Management (2010) 45:377–386 379
123
Quantifying Landscape Patterns and Dynamics
Using FRAGSTATS
Landscape structures among the ownerships were quanti-
fied using FRAGSTATS – a spatial pattern analysis pro-
gram (version 3.3) (www.umass.edu/landeco/research/
fragstats/downloads/fragstats_downloads.html). Landscape
characteristics and patterns were evaluated in terms of four
representative indices at the class level: (1) patch density
(PD, number per 100 hectares, [0 without limit) calculated
as patch number within the corresponding status or own-
ership divided by total landscape area, larger PD indicating
more fragmented landscape; (2) edge density (ED, meters
per hectare, C0, without limit), as ED increases the land-
scape becomes more fragmented; (3) landscape shape
index (LSI, unitless, C1, without limit), LSI =1 when the
landscape consists of a single square or maximally compact
(i.e., almost square) patch of the corresponding type; LSI
increases without limit as the landscape becomes more
irregular; and (4) mean patch size (MPS, hectare, [0
without limit), larger MPS indicating less fragmented
landscape. The above indices, along with others, are widely
used for quantifying landscape dynamics and spatial pat-
terns (Zheng and others 1997; Buyantuyev and Wu 2007).
Illustration of Scaling Effects on Landscape Pattern
Analyses
A subarea in Maine was used to demonstrate how scaling
process could affect quantifications of landscape charac-
teristics among the ownership categories (Fig. 1a). First,
the 30-m NLCD 2001 map for the subarea (forestland only)
was overlaid with our reclassified PAD map. Second,
landscape characteristics within each of the ownerships
were quantified using FRAGSTATS. The same procedures
were repeated for the analyses after the 30-m NLCD map
was aggregated to 1-km pixel size. Three represented
landscape indices quantified within the subarea based on
different pixel resolutions (30 m versus 1 km) were com-
pared to evaluate scaling effects on landscape pattern
analysis. Majority rule was used for spatial aggregation
from 30-m to 1-km resolution (ESRI 2008), which found
the value that appeared most often within the specified
windows (e.g. 1 91km
2
cells) and sent it to each of the
corresponding cells as the output grid.
Data Analyses and Statistics
Our initial tests in the subarea indicate that scaling process
can substantially affect landscape pattern analyses and
conclusions. Consequently, an appropriate pixel size
should be determined for the entire region. It is also
recognized that there is always a trade off between accu-
racy and efficiency in landscape studies by choosing an
appropriate pixel resolution that is consummated with the
study extent and purpose (Wu 1999). Previous studies have
demonstrated that the ‘noise’ in 30-m classified image can
be reduced by applying a certain cutting value for patch
size (e.g. C1 ha) for landscape structure analyses after a
rule-based merging algorithm is performed to eliminate the
‘salt and pepper’ effect (Ma 1995; Zheng and others 1997).
In forests, a patch is generally equivalent to a stand with a
homogeneous mixture of species, ages, sizes, and/or
stocking of trees (Waring and Running 1998). Heilman and
others (2002) applied minimum of 1 ha in size for
assessing forest fragmentation across the conterminous
U.S. For the above reasons, we aggregated the 30-m NLCD
maps to 90-m resolution (\1 ha) for the entire study area to
quantify landscape characteristics in 1992 and 2001 among
the 3 ownership groups using FRAGSTATS. Landscape
pattern analyses resulting from 90-m resolution data
retained the original patterns obtained from the 30-m data
while decreasing the time required for data processing and
simulations, and simplified the analyses. The FRAG-
STATS results for the 2001 landscape were linked to the
2001 AGB map to examine the relationships between AGB
distribution and landscape characteristics among the three
ownership groups. The FRAGSTATS results for the 1992
and 2001 landscapes were compared to illustrate differ-
ences in landscape dynamics among the 3 ownerships.
Image processing and spatial analyses were performed
using GIS packages (e.g. Imagine, ArcInfo, ArcView,
ArcMap). The Kruskal–Wallis test was used to test overall
significance of AGB distributions among groups, using
a=0.05. Then, the Wilcoxon rank sum test was used to
evaluate differences between each possible pair of groups.
Significance of the Wilcoxon tests was evaluated using a
Bonferroni-adjusted a =0.0083 (three pairs; experiment-
wise error rate was maintained at 0.05).Disturbance rates
in this study were defined as the relative changes for rep-
resentative indices calculated by FRAGSTATS between
the years 1992 and 2001. They are calculated as
(Value
2001
/Value
1992
– 1), and demonstrate how distur-
bance rates caused by different management practices can
affect landscape dynamics.
Results
Privately owned forests accounted for about 90% of the NE
forested land whereas public land (e.g. owned by Federal,
state, and local governments) occupied 9% based on the
PAD map. This is very similar to the estimation of 87%
private ownership from FIA. Within the publicly owned
forest, federal ownership accounted for about 42% based
380 Environmental Management (2010) 45:377–386
123
on the PAD. Spatially, 82% of regional public lands were
within the 3 northern states with the maximum percentage
of 29.2% in NH, followed by 28.8% in ME, and 24.1% in
VT. In the meantime, 98% of regional regulated private
forests were concentrated in northern ME and 71% of other
private forests were in the 3 northern states (30%, 21%, and
Fig. 1 a Reclassified regional
ownership map based on the
national PAD. A sub area in the
rectangular box was used for
testing scaling effects on
landscape pattern analyses; and
b2001 forest aboveground
biomass (dry weight) map at
1-km resolution (Zheng and
others 2008b)
Environmental Management (2010) 45:377–386 381
123
20% for ME, NH, and VT respectively) followed by 17%
in MA, 11% in CT, and 2% in RI (Fig. 1a).
Clear trends of negative relationship were observed
between AGB values and degrees of landscape fragmen-
tation in 2001 between publicly and privately owned lands
in general, but less clear between regulated private and
other private (see discussion). The highest mean AGB was
observed in the public lands (156 Mg/ha), which was 43%
higher than the lowest AGB mean (109 Mg/ha) observed in
regulated private lands, or 30% higher than that in privately
owned lands on average (120 Mg/ha, after area weighting)
(Table 2). Within the private forests, however, mean AGB
density in other private forests was 19% greater than that in
regulated private forests. Mean AGB density in other pri-
vate forests featured the highest spatial variation, followed
by public forestland, with regulated private land showing
the lowest variation where more even-age management
could be expected. Our results agreed with the general
landscape ecological concept that higher forest biomass is
usually associated with more intact and less disturbed
forestlands (Chhetri 1999). A Kruskal–Wallis test sug-
gested that overall difference among AGB groups was
highly significant (K–W chi-squared =13693.64, df =2,
P-value \0.05) (Table 3). All pairs of groups were also
significantly different (P\0.05).
The forested lands with AGB density [200 Mg/ha
represented 7.8% of the total forested area (Fig. 1b). Of
these, 77% or 9,300 km
2
, were located outside the areas
designated in the PAD. These high-AGB forests were
mainly distributed in MA (41%), followed by VT (26%),
NH (19%), CT (11%), ME (3%), and RI (1%). Specifically,
they were concentrated in western MA, southern VT,
southwestern NH, and northwestern CT (Fig. 1b). Fur-
thermore, these high-AGB forests were unevenly propor-
tioned among the three major ownership groups across the
region: 19.6% of the public land, 0.8% of the regulated
private land, and 11.0% of the other private land. This
suggests potential impact of ownership behaviors on forest
carbon storage, conservation biology, and biodiversity
studies in the region.
Scaling processes could substantially affect landscape
pattern analysis. Three representative indices calculated
from 30-m and 1-km based maps within the subarea dif-
fered significantly in magnitude and showed inconsistent
patterns among the ownerships (Fig. 2). For example, 30-m
based results indicated that public land had lower PD than
that of other private land whereas the results from 1-km
based calculations showed an opposite pattern. Also 30-m
based results suggested that landscape shape was more
complex in regulated private land than that in other private
land but only differed slightly based on 1-km calculations.
Furthermore, values calculated from a 1-km map were
much smaller than those calculated from the 30-m map;
thus, a multiplier had to be used for comparison purposes
(Fig. 2).
Our landscape pattern analyses demonstrated that public
land in 2001 across the region had lower values of ED, PD,
and LSI than those in the private lands (Table 2), indicat-
ing a less fragmented landscape. Between the 2 private
ownerships, other private owned lands tended to be more
fragmented than those in the regulated private category.
This may reflect fragmentation and parcelization due to
residential and other development; the regulated private
forests occur in a portion of the region with relatively low
population density, and the regulating authority (Maine
2009) exercises some control over residential conversions
and other land use change.
Similarly, public forests on average experienced the
least disturbances between 1992 and 2001 while the
greatest disturbances were observed in regulated private
forests (Fig. 3). The disturbance rates expressed by the 4
indices in public land ranged from 12% in LSI to 19% in
PD with an average of 16% during the period whereas the
rates in regulated private land ranged from 38% in MPS (in
absolute value) to 85% in ED with an average of 66%.
Meanwhile, disturbance rates in other private land
(unprotected and non-industry related) ranged from 15%
(absolute value) in MPS to 24% in ED averaging 24%.
These results suggested substantial impact of ownership
and different forest management practices on landscape
dynamics in the region.
Table 2 Relationships between forest aboveground biomass (AGB,
Mg/ha) and landscape characteristics (resulting from the FRAG-
STATS) among major and ownership group in New England region
Public Regulated private Other private
ED
a
2.0 7.5 16.4
PD
b
0.015 0.022 0.066
LSI
c
56.4 116.4 189.7
MPS
d
(km
2
) 7 15 9
AGB (Mg/ha) 156 (54) 109 (35
e
) 127 (59)
AGB range 1–483 1–376 1–363
a
Edge density,
b
Patch density,
c
Landscape shape index,
d
Mean
patch size,
e
Standard deviation
Table 3 Comparisons of aboveground biomass frequency distribu-
tions among major ownership groups using Kruskal–Wallis test
Public Regulated private Other private
Public * *
Regulated private *
Other private
* Indicates a significant difference at Pvalue \0.05. We only
marked half of the matrix because of its symmetry
382 Environmental Management (2010) 45:377–386
123
Discussion
Our results agreed well with a previous study that about
90% NE forests were privately owned (Irland 1999).
Linkage between landscape pattern and AGB analyses
should be evaluated with caution due to scaling effects.
Similar results were also reported from other studies in
North America that forest disturbance rates were generally
lower in public lands than in private lands (Spies and others
1994; Turner and others 1996; Sachs and others 1998).
Although the MPS value in public land was smaller (more
fragmented according to the usual definition) than the
values in regulated and other private lands (Table 2), there
was a particular reason for this. Public lands represented a
small portion (9%) of the region, and they were selected
purposively for various considerations including watershed
protection, conservation value, historical significance,
national forest/park/monument/landmark, and recreational
and scenic values. This selection process has led to public
lands occurring as individual small patches scattered across
the region, and some public lands are interwoven with
private owner inholdings. By contrast, the 90% of regional
forests held by private owners were relatively continuous
and dominant over the landscape.
Forest disturbances included harvests in the regeneration
phase of even-aged silvicultural systems, which typically
lead to rapid redevelopment of forest cover and biomass in
this region, and terminal harvests for land use clearing due
to development and associated land cover changes, which
did not. The data used in this study did not allow unam-
biguous separation of the two types of disturbance, though
clearly they implied very different scenarios for future
AGB development in the region.
Between the 2 private ownership groups, forests within
the other private ownership category were more frag-
mented but had higher mean AGB than those in the regu-
lated private category. One should be cautious about causal
inferences, however. A partial explanation may be that
most regulated forests in Maine have in the past been
owned by industrial concerns that usually purchased lands
in large blocks (or aggregated ownerships by assembling
smaller holdings), while many other private forests were
owned by individuals holding small parcels. There has
been a trend in the region (as in the rest of the U.S.) since
the 1980s for industrial lands to be purchased by non-
industrial corporate owners, such as timber investment
management organization (TIMOs) and real estate invest-
ment trusts (REITs). The continuation of the observed
pattern will depend on the future harvesting and ownership
decisions of these organizations. Second, whereas indi-
vidual owners more likely used their properties for multiple
purposes (including forest harvest), forestland under
industrial ownership was managed with a different set of
financial objectives. One would expect a shorter harvesting
interval in such forests. For example, the harvest interval
of spruce-fir in northern ME has been about 70-year old
(http://www.state.me.us/doc/mfs/pubs/htm/supply.htm).
More intense and systematic harvesting pressure would
certainly attenuate high standing biomass accumulation.
AGB frequency curves clearly indicated that many fewer
hectares of forest in the regulated forestlands than in other
private land and public land currently reach AGB values
larger than 200 Mg/ha (Fig. 4).
30 m 1 km
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Patch density (PD, # per 100 ha)
0
50
100
150
200
250
300
350
Public Regulated Privat
eO
ther Private
Landscape shape index (LSI)
0
10
20
30
40
50
60
70
Edge density (ED, meters per ha)
Fig. 2 Evaluation of scaling effects (30 m vs. 1 km) on landscape
pattern analyses among major ownerships in a sub area of Maine,
USA by comparing the relative changes in some representative
landscape indices calculated from FRAGSTATS. Because the index
values calculated from different pixel resolutions are not at the same
magnitude, the values at 1 km have been multiplied by 10 for the
presentation purpose
Environmental Management (2010) 45:377–386 383
123
Another factor affecting AGB distributions is forest type
and composition. Most regulated private forests in northern
ME mainly consists of spruce-fir and northern hardwoods
with a lower expected productivity than in the oak and pine
forests occurring farther south. However, an influence of
ownership and forest practices on AGB distribution
remains evident because most of public forested lands that
have high AGB values are located in the 3 northern NE
states, and share their dominant forest types with the reg-
ulated private forests (Irland 1999). Partitioning the effects
of ownership, forest composition, and land use history on
the regional AGB distribution is necessarily difficult, as
these factors are intertwined in the New England
landscape.
One source of potential error for this study is the use of
2 NLCD maps at different years (1992 and 2001), due
to different classification schemes applied in each year
(Vogelmann and others 2001; Homer and others 2004).
In general, water, urban, and forestedland covers have
relatively high classification accuracies while wetland,
rangeland, and barren have low accuracies (Hollister and
others 2004). Accuracies tend to increase as the classifi-
cation level becomes more broad (Stehman and Wickham
2006). For example, overall accuracies in different regions
across the eastern U.S. increase from 43%–66% at
Anderson Level II to 70%–83% at Level I whereas from
38%–70% to 74%–85% in different regions across the
western U.S. according to the 1992 NLCD map (Stehman
and others 2003; Wickham and others 2004). We expect
even smaller uncertainty from this perspective for this
study because our related analyses are conducted at even
broader categories (forest versus nonforest).
There are also some limitations in the national PAD
(V4) dataset used in this study. First, ideally and theoreti-
cally it should include protected private lands (such as
those under easements or held in fee by conservation
organizations), but it is difficult to maintain consistency
due to differences in various definitions among states. This
-0.5
0
0.5
1
Public Regulated Private Other Private
Patch density Edge density Landscape shape Mean patch
Index size
Fig. 3 Relative disturbance
rates of forest landscapes
between 1992 and 2001 among
major ownerships across the
region by comparing four
representative class-level
indices calculated from
FRAGSTATS
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1 25 49 73 97 121 145 169 193 217 241 265 289 313 337 361 385 409 433 457 481
Above ground biomass density (Mg/ha)
Frequency distribution (%)
Public Regulated Private Other Private
Fig. 4 Frequency distributions
of forest aboveground biomass
density within major
ownerships in New England
region, USA. Statistical
analyses were based on raw data
and the curves drawn based on a
5-point running average
384 Environmental Management (2010) 45:377–386
123
remains true even in the newly available updated PAD-V1,
just released after this study was conducted (http://
www.protectedlands.net/padus/). Second, the PAD is not
designed to distinguish between industrial and non-indus-
trial private owners, although this distinction may be
inferred by forest type in our study in the State of Maine.
Further investigation on the strengths and limitations of the
PAD are needed but they are beyond the scope of this
study. Our experience with this study suggests that labeling
of a management choice in a national dataset should be
carefully implemented at the regional level, especially with
the designation ‘‘protected’’.
Ownership composition in the region is extremely
unevenly distributed, which tends to create confounding
between biophysical and ownership factors in understand-
ing the pattern of AGB distributions. This study, however,
illustrates how different broad ownership categories are
associated with regional landscape dynamics and AGB
distributions. Our results can also be used for comparison
with similar analyses for other regions in the country.
Conclusions
Our results have clearly revealed the impact of major
ownerships on regional biomass accumulation and land-
scape pattern dynamics. Uneven distributions (both spa-
tially and statistically) of high-AGB forests among the
major ownerships provide insight information on regional
forest resources management and policy implication. These
high-AGB forests can contribute important social and
ecological benefits to the community and society as a
whole including: (1) flood and erosion control and man-
agement of water quantity and quality while increasing
carbon storage in forest ecosystems; and, (2) preserving
unique aesthetic value and habitats of late successional
forest for recreational and biodiversity considerations. The
uneven distribution of high-AGB forests by ownership
suggests on the one hand that maintaining such forests on
the landscape may be a worthwhile conservation goal, but
also suggests there may be significant opportunities both to
conserve existing high-AGB forests and to create young
forests on privately-owned lands to provide goods and
services to meet societal demands. In terms of research
methodology, we found that determining a suitable pixel
resolution on raster-version FRAGSTATS simulations is
necessary to achieve meaningful and efficient analyses on
linking regional landscape characteristics and ecosystem
properties (e.g., AGB).
Acknowledgments This study is supported by the USDA Forest
Service, Northern Research Station through grant 05-DG-11242343-
074.
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