Advances in Applied Sociology
2012. Vol.2, No.1, 1-6
Published Online March 2012 in SciRes (http://www.SciRP.org/journal/aasoci) http://dx.doi.org/10.4236/aasoci.2012.21001
Processes Associated with Afforestation near Public Lands
Zola K. Moon, Frank L. Farmer
University of Arkansas, Fayetteville, USA
Received December 21st, 2011; revised January 18th, 2012; accepted February 4th, 2012
This paper empirically examines patterns of afforestation in vicinities immediately surrounding National
Park/National River and National Forest lands. The public lands (Ozark National Forest and Buffalo Na-
tional River) are found on the Ozark Plateau and represent different management mandates. A spatial lag
model is presented comparing two LANDSAT images in conjunction with sociodemographic measures
covering the same time period. The findings here make two important points. First, the public land
boundaries are shown to act as ecological switches. Second, results underscore the importance of under-
standing how publicly managed lands with different mandates function within the larger social as well as
geophysical landscape matrix. Empirical evidence demonstrates that public lands set aside for “preserva-
tion” (Buffalo National River) are associated with greater afforestation, whereas public lands set aside for
“conservation and use of natural resources” (Ozark National Forest) are surrounded by less afforestation.
Keywords: Landcover Change; Afforestation; LANDSAT; Spatial Lag Regression; Public Lands
Introduction
Public lands have been set aside around the world for a vari-
ety of reasons. The rationale for these publicly managed lands
can range from safeguarding a natural landscape or protecting
the habitat of a particular species to conserving forests for fu-
ture utilization. Human interaction with the landscapes in and
around these public lands is a matter of concern in that those
activities may threaten the very resources the public lands are
designed to protect. Stated differently, the very act of setting
aside land may serve to threaten those resources that originally
stimulated the protective effort.
Embedded in the discussion of land use near protected areas
is the impact of changes in human activities near protected
landscapes, especially forests. Mixed empirical results have
been found—in some cases, increases in population result in
more deforestation and development; in other cases, the role of
population is less clear or not important (see for examples
Frentz et al., 2004; Lambin & Geist, 2006). Previous research
on changes in land cover close to public land boundaries has
established that impacts differ by type of public land and oper-
ate in a complex manner (Moon & Farmer, 2010). The current
paper provides an empirical model demonstrating the complex-
ity of afforestation1 patterns resulting from the interaction of
human populations with the surrounding landscape. This re-
search purposefully builds on previous lines of inquiry (Moon
& Farmer, 2010) and specifically considers whether afforesta-
tion, a different type of land cover change, operates differently.
The specific focus of the current research is afforestation rather
than deforestation or forest loss. Deforestation is often the focus
of land cover research projects (see for examples Bhattarai &
Hammig, 2001; Carr, Suter, & Barbieri, 2005; Kok, 2004; Lam-
bin & Geist, 2003; Laurance et al., 2002; Perz, 2002; Radeloff
et al., 2001) because of its often deleterious environmental im-
pacts. However, the processes surrounding afforestation are of
import as well, on the assumption that afforestation at least
partially remediates negative consequences of forest loss, in-
cluding carbon sequestration, erosion control, and non-con-
sumptive use of forest products. The empirical evidence pre-
sented here underscores the importance of understanding how
publicly managed lands function within the larger landscape
matrix.
Analytic Framework
Two main concepts underlie the empirical orientation of this
research. The first is conceptualizing the public land boundaries
as an ecological switch (Wilson & King, 1995). A switch is
defined as a behavior or condition that results in changes in
vegetation such that those changes reinforce the behavior or
condition, forming a positive feedback loop. An example of an
ecological switch would be the processes surrounding a mown
pasture. If a person selectively mows a region in a pasture, the
next mower is likely to follow the evident outline of the previ-
ous mowing session. The “boundary” is provided by the taller
grass that has not been mown. The presence of the boundary
induces mowing up to the boundary but not beyond, maintain-
ing the behavior. With continuation of this pattern, the actual
mix of speciation of grasses will change as shorter grasses in-
crease in the mown area while taller grasses are more success-
ful in the unmown area. Thus the mowing process produces an
ecological switch.
1Afforestation is the establishment of a forest or stand in an area where the
preceding vegetation or land use was not forest while reforestation is the
reestablishment of previously existing forest cover. While some areas in the
study area may be reforested in that prior to 1993 these areas were forest
stands, the definition used in the study is technically the measurement o
f
those lands that were not classified forest in the beginning of the timeframe
(1993) but are by the end of the study timeframe (2004). Given a different
temporal framework, some of these areas could be considered reforesting.
The second concept in the grounding of this research relates
to forest transition theory. Some researchers have presented the
concept of a “forest transition”, in some ways similar to the
demographic transition (Caldwell, 1976), as an explanation for
the pattern of rapid deforestation followed by a slower affore-

Z. K. MOON ET AL.
station (Mather, 1992; Mather & Needle, 1998; Rudel, 1998;
Rudel et al., 2002). The theory proposes that the processes of
economic development, industrialization and urbanization im-
pact forests in foreseeable ways, suggesting a time sequence for
forest cover changes that are prompted by these socioeconomic
changes. According to forest transition theory, a relatively rapid
deforestation takes place initially but then the trend reverses
and a slower increase in forest cover occurs.
According to the theory, the initial stage is deforestation and
is generally linked to agricultural development and expanding
populations. Following this rapid deforestation, populations
stabilize, forest cover remains somewhat static, and then the
afforestation process begins slowly even if the population con-
tinues to grow. The reversal of the trend from deforestation to
recovery of forests is generally explained through two pathways
(Rudel et al., 2005). In the first, the rapid deforestation period is
driven by demands for agricultural lands and forest products,
especially for housing and transportation corridors. After this
first period of relatively rapid change, the local economy de-
velops and diversifies, farm labor moves into off-farm em-
ployment as agricultural production becomes less profitable
compared to other, non-farm economic activities. Some propor-
tion of the rural labor force may migrate to more densely popu-
lated areas. Marginal agricultural land is then abandoned as a
result and reverts to forest cover. Urbanization and economic
development, then, drive this transformation which in the latter
stages results in increasing forest cover. Purchasing abandoned
lands for parks or forest reserves reinforces the momentum of
this transformation. This pathway is exemplified by historic
patterns found in some developed countries, including the USA
(Rudel et al., 2005).
The second explanation is what Rudel et al. (2005) term the
“forest scarcity path”. This explanation relies less on transfor-
mation of the agricultural sector and attributes increases in the
price of scarce forest products as the primary driver for land-
owners to plant trees instead of crops or pasture grasses. The
state increases the momentum along this path through affore-
station programs for marginal lands. Evidence exists (Rudel et
al., 2005) that lower income countries with recent increases in
forest cover follow this pathway.
Regardless of the pathway by which the forest transition may
take place, evidence of a forest transition across the United
States has been found by other researchers (Evans & Kelley,
2008; Rudel, 1998; Rudel & Fu, 1996). More specifically, evi-
dence for either or both pathways to forest transition theory
may be extant in the study area (Rudel & Fu, 1996) as forest
stocks have expanded and changed across the region (Rosson &
Rose, 2010) in the face of significant socioeconomic changes
(Farmer et al., 2010).
In the current model, afforested lands and socio-environment
variables are identified that are associated with change over
approximately a ten year period of time. Population structures,
economic and transportation infrastructure, geophysical char-
acteristics and distance measures to urban centers and public
lands are considered. The primary hypothesis of this paper is
that the presence and location of the public lands has “spillover
effects” into the surrounding landscape. These effects are as-
sumed to be the result of the public land boundaries functioning
as ecological switches on surrounding privately held lands. A
secondary interest is to seek empirical support for the forest tran-
sition process in the study area. An examination of land cover
change in the following sections allows for empirical consid-
eration of these ideas.
Environmental History of the Study Area
The study area is eight counties in the Ozark Plateau Region
within the state of Arkansas and encloses the Buffalo National
River (BNR) and much of the Ozark-St. Francis National For-
est (NF). The Ozark National Forest covers a large percentage
of the study area’s southern portion while the Buffalo National
River lands bisect the study area along a roughly east-west axis.
Between 1906 and 1909, widespread and large-scale timber
cutting took place in the study area, which at that time was the
site of the last noteworthy stretches of virgin forest to be found
east of the Rocky Mountains. In addition to commercial lumber
production, small farmers cleared land during this time with no
intention of replanting trees. In reaction to the thin and highly
erodible soil, the farming strategy was to clear another location
when the first one gave out. Another common practice of the
time was burning woodlands in the fall or early spring, a prac-
tice that was highly damaging to the forest cover. Burning the
woodlands was done to create better forage and pastures for
stock and, according to the local folk wisdom, control ticks and
snakes.
As the federal government reacted to loss of timber reserves
across the entire country, broad areas of forest were withdrawn
from the public domain and placed in the nation’s forest re-
serves. Eventually, these lands became national forests, and
within the study area the Ozark National Forest itself was es-
tablished in 1908. The original understanding among local peo-
ple was that the withdrawal of these lands from the public do-
main into federal ownership to form the Ozark National Forest
would conserve the forests for future production needs of the
local citizens and as such was acceptable policy (Strausberg &
Hough, 1997). Within a year of the establishment of the na-
tional forest lands in Arkansas, however, the public’s attitude
changed dramatically. As the forest rangers began to curb now-
illegal but customary practices of using the land as a de facto
commons for stock forage, timber cutting, and burning and
clearing without regard to title, a public backlash occurred. Local
opposition to management of these lands by the US Forest Ser-
vice has continued from that time to the present, as local inter-
ests have continued to contend with the federally established
management objectives (Strausberg & Hough, 1997).
In 1972 the US House of Representatives established the
Buffalo River as the nation’s first National River. Management
of the Buffalo River fell under the purview of the National Park
Service and was designed to preserve the free-flowing nature of
the Buffalo River. With the establishment of the Buffalo Na-
tional River, the river was “preserved” and people living on
land adjacent to the river were removed over a period of time.
The establishment of the National River, while heralded by con-
servation and environmental activists, was met with “emotional
turmoil in the community regarding the disruption of life for
the Buffalo River residents, whether they were willing or un-
willing sellers” (Rogers, 2010). Administrative boundaries were
established to enforce only approved uses and limit access to
the Buffalo National River lands much as had happened with
US Forest Service lands.
The headwaters of the Buffalo River are included in US For-
est Service lands but most of the rest of the land outside the
National River’s boundaries is privately owned. Consequently,
ongoing concerns by environmental advocacy groups have been
focused on efforts to preserve the clarity and purity of the river
water by advocating restrictions on uses of private lands in the
2

Z. K. MOON ET AL.
river’s drainage system. Activists have also been involved in the
management plans of US Forest Service lands for the headwa-
ters and drainage of the Buffalo River (Rogers, 2010; Straus-
berg & Hough, 1997).
The current analysis considers these two different types of
public lands situated within the study area: the Buffalo National
River, under management by the National Park Service, and the
Ozark-St. Francis National Forest, under management by the
US Forest Service. The history of these two different public
lands captures many of the dilemmas surrounding the estab-
lishment of publicly managed lands. The different public land
management regimes also reflect different originating impulses
for their establishment—on the one hand, preservation of an
existing state is the rationale for the Buffalo National River
while conservation of a resource to be used for the broader
public good is evident for the Ozark-St. Francis National Forest.
Interactions along the boundaries of these two lands are hypothe-
sized to reflect these differences in management objectives.
Creation and continued management of these public lands are
not the only macro-level changes taking place in the study area.
Relatively rapidly growing human populations in the nearby
metropolitan areas have continued pressures on the landscape
matrix surrounding these two public lands through increased
infrastructure development, increased housing and development,
and increased demand for recreational uses. These changes
have impacted both US Forest Service and National Park lands
although not in identical ways (Moon & Farmer, 2010).
In order to understand afforestation patterns a spatial lag re-
gression model is provided to demonstrate the relationship of
socioeconomic, transportation, and geophysical variables with
afforestation at a region-wide level. Evidence is sought for the
hypotheses of a forest transition along with ecological switch-
ing behavior associated with the boundaries of the public lands.
Data and Methods
Data
The models presented below provide an examination of af-
forestation. The endogenous variable (AFFORESTED) is cre-
ated from satellite imagery from 1993 and 2004. The previously
processed imagery2 (Arkansas Natural Resource Conservation
Commission and the University of Arkansas’ Center for Ad-
vanced Spatial Technologies October 31, 2005; US Geological
Survey January 1, 1999) used for the scenes contain both pro-
jection and extent information. The 1993 scene has a resolution
of 29.34 meters/pixel while the 2004 imagery has a resolution
of 28.5 meters/pixel. These images are pre-processed and con-
verted into land cover classes by the original authors. For this
study, the images were clipped to the study area and reclassed
for use in a series of binary maps. The change matrix included a
category called “Afforested” defined as a change from any
other category to forest cover3.
A series of density surfaces were created to generate the ex-
ogenous variables. A random point was generated on the study
area map and a grid created from that point with 1-kilometer
spacing in each cardinal direction across the study area. The
resultant grid contains 11,576 one-kilometer square blocks. These
blocks become the individual records to be analyzed. Density
surfaces were created from socio-demographic data derived from
1990 and 2000 census block data (US Department of Com-
merce Washington DC: Bureau of the Census [producer] 1991,
1993, 2002a, 2002b) using a method developed earlier (see Moon
& Farmer 2001 for full details).
Additional locational information (a database of rural resi-
dential structures obtained from a state agency) was used to
convert areal census data into point data. Then, based on the
population density surface, other socioeconomic data were
allocated across the grid. A difference surface was created for
each of the socioeconomic variables and these differences were
then summed for each 1 square kilometer block and stored in
the data matrix. Direct line distance measures were calculated
for each of the distance variables, using the centroid of the 1
square-kilometer block under consideration to the centroids of
the closest metropolitan and micropolitan polygons (US Census
Bureau—Geography Division 2006), to the center of the near-
est segment of each type of public land (BNR and NF) bounda-
ries (AHTD, 2006) and the center of the nearest major highway
segment (AHTD, 2000). Thirty-meter digital elevation model
data (AHTD, 2001) provided the slope and aspect measures.
Road density was calculated by summing road length (in kilo-
meters, paved and unpaved) in each analytical unit and dividing
by the area of the unit. Thus, a matrix was created with one row
for each of the 1 km × 1 km blocks and columns for the 15
exogenous variables.
AFFORESTED, the measure of the extent of afforestation,
was calculated from a binary map of pixels that changed from
any other category to “Forest”. This binary map was overlaid
with the same 1 km × 1 km block grid. The area within each
block that changed into the AFFORESTED category was sum-
med, the percentage of the total block area calculated, and the
percentages stored. All data was converted to standardized
z-scores, eliminating problems in interpreting parameters in dif-
ferent metrics.
The variables population density (POPDEN), percentage of
working age people (WORKERS), percentage of in-migrants in
the previous five years (MOVERS), percentage of the em-
ployed in natural resource extraction activities such as forestry
or mining (NATRES), percentage of new homes constructed in
the last five years (NEWHOMES), percentage of homes using
electricity as the primary heating source (ELECTRICITY), and
the percentage of homes with phone service (PHONES) were
drawn from the 1990 and 2000 Census data (US Department of
Commerce Washington DC: Bureau of the Census [producer]
1991, 1993, 2002a, 2002b), calculated as change over the time
period. Additional variables include road density (RD_DENS)
calculated as the number of kilometers of road within the 1 km
× 1 km block.
2Imagery was retrieved from GeoStor, a public domain geospatial data re-
pository managed by the Arkansas Geographic Information Office and pilo-
ted by University of Arkansas’ Center for Advanced Spatial Technologies.
Available at http://www.geostor.arkansas.gov.
3As noted earlier, afforestation is used rather than reforestation. Transitional
areas and shrubland were classified as herbaceous and not included in affor-
estation. However, intentional planting (landscaping or plantation) was not
separable from the imagery obtained. Therefore whether the afforestation is
directly the result of natural re-growth or plantation/landscaping growth can
only be suggested by location.
Other distance variables include distance to nearest major,
heavily traveled highway (DIST_HWY), to nearest metropolis-
tan area (DIST_METRO), to nearest micropolitan area (DIST_
MICRO), to the border of the Buffalo National River park lands
(DIST_BNR), and to the Ozark-St. Francis National Forest lands
border (DIST_NF). Completing the variables are percentage of
a given block’s area with southern aspect (PCTSOUTH) and
the percentage of area that has little slope (PCTFLAT). These

Z. K. MOON ET AL.
4
two variables are taken from the DEM data. Table 1 contains
definitions, the first two moments, and Moran’s I for all vari-
ables.
Methods
As noted above, the extent of afforestation is analyzed using
a spatial lag model, which provides an empirical insight into
regional influences. Ordinary least squares regression analysis
was performed and is provided for comparison. Residuals from
the OLS model demonstrated significant spatial autocorrelation,
suggesting the violation of the assumption of independence.
Additionally, diagnostic techniques (Anselin et al., 2006), spe-
cifically the Lagrange Multiplier for the lag variable as well as
for the error term, indicate the appropriateness of using the
spatial lag model. Results for the OLS and spatial lag models
are presented in Table 2.
yafforestation = β1POPDEN + β2WORKERS + β3MOVERS +
β4RD_DENS + β5NATRES + β6NEWHOMES +
β7DIST_ΒNR + β8DIST_NF + β9PCTSOUTH +
β10PCTFLAT + β11DIST_HWY +
β12DIST_METRO + β13DIST_MICRO +
β14ELECTRICITY + β15PHONES +
β16y*lag + ε
Results
Spatial lag regression findings provide a regional overview
of the processes associated with afforestation in the study area.
The endogenous variable is “Afforested” or that percentage of
the area within each 1 square kilometer block analytical unit
that changed from any other type of land cover to “Forest”. As
would be expected from an examination of the Moran’s I values
for the variables, a regression model accounting for spatial
dependencies significantly improves the model fit over an or-
dinary least squares (OLS) approach. Inclusion of the spatial
lag variable substantively improved the adjusted R-square, AIC,
and reduced the spatial autocorrelation in the residuals (see
Table 2 for comparison of OLS and spatial lag regression mod-
els). The spatial lag model explains nearly 60% of the variation
in the model (R-square = 0.59). The lag variable is significant
and the strongest parameter in the model, demonstrating the
spatial association of changes in land cover due to afforestation.
With the exception of population density, the demographic
and economic factors included are not significant. Change in
population density is negative, indicating that increasing popu-
lation densities result in less afforestation. This is as anticipated
given the rapidly growing population levels in this area over the
time period in question.
The road density parameter is positive while the distance to a
major highway is negative. These seemingly contradictory in-
dicators, however, might be better understood as tied to devel-
opment corridors where land once cleared for farming is now
used for residential purposes, increasing the density of roads,
but also resulting in tree plantings around housing and with
nearby cleared lands reverting to forest cover. Housing devel-
opment is likely to take place near transportation arteries, re-
sulting in the relationship of increasing afforestation as distance
to a major highway decreases.
Table 1.
Univariates, Moran’s I, and definitions of variables.
Variable Mean
Standard
Deviation Variance Moran’s I Definition
POPDEN 2.649 15.078 227.35 0.144 Persons per square kilometer, change 1990-2000
MOVERS 0.136 0.596 0.36 0.573 Percentage who lived in different place 5 years ago, change 1990-2000
WORKERS –0.222 0.539 0.29 0.596 Percentage working age (16 - 64), change 1990-2000
NATRES –0.025 0.082 0.01 0.532
Percentage employed in natural resource activities (farming, forestry, mining), change
1990-2000
NEWHOMES 0.035 0.242 0.06 0.430 Percentage housing built within last 5 years, change 1990-2000
RD_DENS 45.757 41.072 1686.94 0.335 Kilometers per square kilometer
PCTFLAT 94.790 8.665 75.09 0.563 Percentage land with less than 20% slope
DIST_BNR 25.066 15.112 228.37 na Distance from centroid of analytical unit to nearest segment of Buffalo National River
boundary
DIST_NF 22.385 14.597 213.08 na Distance from centroid of analytical unit to nearest segment of Ozark National Forest
boundary
PCTSOUTH 36.099 15.362 236.00 0.176 Percentage land with southern aspect
ELECTRICITY 0.084 0.295 0.09 0.494 Percentage homes using electricity as heating source, change 1990-2000
PHONES 0.129 0.598 0.36 0.459 Percentage homes with telephones, change 1990-2000
DIST_METRO 94.125 35.261 1243.34 na Distance from centroid of analytical unit to centroid of nearest metropolitan statistical area
DIST_MICRO 35.785 15.769 248.67 na Distance from centroid of analytical unit to centroid of nearest micropolitan statistical area
DIST_HWY 2.379 1.864 3.47 na Distance from centroid of analytical unit to nearest segment of state highway
AFFORESTED 7.76 7.37 54.36 0.587 Change to forest from any other land cover category

Z. K. MOON ET AL.
Table 2.
Ordinary least squares and spatial lag models compared; standardized
coefficients.
OLS model Spatial lag model
Variable Coefficient Std. Error Coefficient Std. Error
W_REF 0.751** 0.008
CONSTANT 0.000** 0.008 0.000 0.006
RD_DENS 0.084** 0.009 0.104** 0.007
PCTFLAT 0.242** 0.008 0.090** 0.006
DIST_BNR –0.232** 0.012 –0.049** 0.010
DIST_METRO –0.291** 0.010 –0.071** 0.008
DIST_MICRO –0.046** 0.010 –0.007 0.008
DIST_NF 0.331** 0.011 0.073** 0.009
DIST_HWY –0.094** 0.008 –0.017* 0.006
PCTSOUTH –0.055** 0.008 –0.040** 0.006
POPDEN –0.022* 0.008 –0.025** 0.006
MOVERS 0.049* 0.016 –0.005 0.012
NATRES 0.006 0.009 0.005 0.006
NEWHOMES 0.009 0.010 0.012 0.008
ELECTRICITY –0.037* 0.012 –0.015 0.009
PHONES –0.002 0.017 0.012 0.013
WORKERS 0.039* 0.015 0.014 0.011
Log likelihood –14580 –11939
Akaike info criterion 29,192 23,913
Schwarz criterion 29,310 24,038
R-squared 0.273 0.586
Sigma-square 0.72800 0.4143
S.E. of regression 0.85323 0.6437
*p < 0.05; **p < 0.01.
In conjunction with transportation infrastructure, distance
to business centers is considered. Distance to a micropolitan
area—a smaller business center—is not significant. However,
the parameter for distance to the nearest metropolitan center is
negative and significant. This parameter may also reflect the link
to afforestation that concurs with increasing urbanization of the
populace, suggested by forest transition theory.
Topographical features as measured by slope and aspect also
come into play. The greater the percentage of flat lands in an
area, the greater the afforestation. Conversely, the decreasing
amounts of land with southern exposure results in increasing
afforestation. These factors also may reflect development pref-
erences and/or the reversion of land cleared previously for ag-
ricultural uses now returning to forest cover.
Turning now to the relationship between afforestation and
the boundaries of public lands, the model supports the hypothe-
sized relationships. Distance to the Buffalo National River is
negative, meaning that more afforestation is present the closer
the land is to the boundary of the Buffalo National River. The
relationship between the boundary of the Ozark-St. Francis
National Forest lands and afforestation is, however, positive.
This indicates that there is less afforestation on lands closer to
the Ozark-St. Francis National Forest boundaries. These find-
ings suggest that the process of land cover change in this region
is driven by environmental and locational considerations.
Limitations
The methods and data presented here have some limitations.
Land cover images inherently have post-processing errors.
Census data itself has well-known difficulties (US Department
of Commerce Washington DC: Bureau of the Census [producer]
1991, 1993, 2002a, 2002b). However, the process used to dis-
aggregate areal data itself introduces little additional error (Moon
& Farmer, 2001). Measurements of distance between centroids
of polygons or polyline segments provided in publicly available
files may contain inaccuracies in the original data providing an
unknown source of error. Other measures, such as actual trans-
portation networks, may arguably be a better measure of acces-
sibility to metropolitan or micropolitan centers than the use of
polygon centroids used here. Intensity of computation combined
with substantive operationalization questions regarding the
appropriate pathways through a network argued for a simpler
approach.
Conclusion
The findings here make two important points. First, the pub-
lic land boundaries are shown to act as ecological switches.
Second, results underscore the importance of understanding
how publicly managed lands with different mandates function
within the larger social as well as geophysical landscape matrix.
The original approach was to analyze whether the boundaries
of public lands act as ecological switches, inducing some par-
ticular human activities in proximity to the boundary. The an-
swer is yes, and is seen in the empirical demonstration that each
type of public land functions differently in the model and is a
significant contributor in understanding the variation of affore-
station across the study area.
Proximity to the BNR boundary is associated with increases
in afforestation, while proximity to the NF lands is associated
with decreases in afforestation. These opposite influences can
be understood in the context of the different rationales for es-
tablishment of each of the public lands. The socially con-
structed objectives for each area—preservation in the case of
the Buffalo National River and conservation of renewable re-
sources for future use in the case of the Ozark-St. Francis Na-
tional Forest lands—provide the lens through which individuals
may view appropriate use of the land in close proximity to ei-
ther of the different types of public lands. Afforestation takes
place near the National River as persons view that area as ap-
propriate for a “return to a more natural state” whereas timber
is a resource to be used as needed, even if conserved for a pe-
riod of time, in areas associated with the US Forest Service
lands.
Some empirical support for using forest transition theory to
understand afforestation patterns in this region is provided. As
posited by forest transition theory, afforestation appears linked
to areas undergoing urbanizing and development. Beyond po-
pulation density, other socio-economic indicators were not sig-
nificant in explaining afforestation, but locational and topog-
raphical indicators associated with urbanization and develop-
ment were influential in explaining afforestation.
The empirical evidence here underscores the importance of
understanding how publicly managed lands function within the
larger social and environmental landscape matrix. The concept
of ecological switching, coupled with recognition of the so-
cially constructed goals for the publicly managed lands provid-

Z. K. MOON ET AL.
ing the context for decision-making, gives managers and policy
makers a method for anticipating land cover changes in the
future specific to the particular type of public land.
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