U.S. Department of the Interior

U.S. Geological Survey

Landscape Consequences of Natural Gas Extraction in 

Sullivan and Wyoming Counties, Pennsylvania, 2004–2010

By E.T. Slonecker, L.E. Milheim, C.M. Roig-Silva, and A.R. Malizia

Open-File Report 2013–1261
U.S. Department of the Interior

SALLY JEWELL, Secretary

U.S. Geological Survey

Suzette M. Kimball, Acting Director

U.S. Geological Survey, Reston, Virginia: 2013

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Suggested citation:

Slonecker, E.T., Milheim, L.E., Roig-Silva, C.M., and Malizia, A.R. 2013, Landscape 

consequences of natural gas extraction in Sullivan and Wyoming Counties, Pennsylvania, 2004–2010: U.S. Geological Survey Open File Report 2013–1261, 33 p., http://dx.doi.org/10.3133/ofr20131261. 

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ISSN 2331-1258 (online) 
iii

Contents

Abstract ...................................................................................................................................................................... 1

Introduction: Natural Gas Extraction ........................................................................................................................... 1

Location .................................................................................................................................................................. 4

The Biogeography of Pennsylvania Forests............................................................................................................ 4

Key Research Questions ........................................................................................................................................ 6

Landscape Metrics and a Landscape Perspective...................................................................................................... 7

Disturbance............................................................................................................................................................. 8

Forest Fragmentation.............................................................................................................................................. 9

Interior Forest........................................................................................................................................................ 10

Forest Edge .......................................................................................................................................................... 11

Contagion.............................................................................................................................................................. 11

Fractal Dimension ................................................................................................................................................. 12

Dominance............................................................................................................................................................ 12

Methodology: Mapping and Measuring Disturbance Effects..................................................................................... 13

Data ...................................................................................................................................................................... 13

Sources ............................................................................................................................................................. 13

Collection........................................................................................................................................................... 14

Land Cover Update............................................................................................................................................... 16

Calculation of Landscape Metrics ......................................................................................................................... 17

Results: Summary Statistics and Graphics............................................................................................................... 17

Disturbed Area ...................................................................................................................................................... 17

Land Cover Metrics of Interest .............................................................................................................................. 23

Forest Fragmentation............................................................................................................................................ 25

Interior and Edge Forest ....................................................................................................................................... 27

Conclusion................................................................................................................................................................ 29

References Cited...................................................................................................................................................... 30

Figures

1. Map of the Appalachian Basin Province showing the three Marcellus Shale assessment units ....................... 2

2. Example of forested landscapes from Washington County, Pennsylvania, showing the spatial effects of 

roads, well pads, and pipelines......................................................................................................................... 3

3. The distribution of Marcellus and non-Marcellus natural gas permits issued between 2004 and 2010 within 

Pennsylvania, the focal counties of Sullivan and Wyoming, and their relation to the interior Marcellus Shale 

assessment unit................................................................................................................................................ 4

4. The distribution of percent forest cover by county based on the U.S. Geological Survey 2001 National Land 

Cover Data........................................................................................................................................................ 6

5. Example of a natural gas disturbance footprint from Bradford County, Pennsylvania, embedded within the 

National Land Cover Dataset (NLCD) 2001...................................................................................................... 9

6. Conceptual illustration of interior forest and how this critical habitat is affected by linear disturbance............ 11

7. The concept of contagion is the degree to which similar land cover pixels are adjacent or “clumped” to one 

another............................................................................................................................................................ 12

8. Examples of spatially explicit features of disturbance that were extracted from aerial photographs into a 

geographic information system (GIS) format................................................................................................... 15
iv

9. Workflow diagram for creating an updated land cover map ............................................................................ 16

10. Gas extraction-related disturbance identified between 2004 and 2010 in Sullivan and Wyoming Counties, 

Pennsylvania................................................................................................................................................... 18

11. Change in number of forest patches from 2001 to 2010 showing the increasing fragmentation in Sullivan and 

Wyoming Counties, Pennsylvania................................................................................................................... 27

12. Change in percent interior forest by watershed in Sullivan and Wyoming Counties, Pennsylvania, from 2001 

to 2010............................................................................................................................................................ 29

13. Change in percent of edge forest by watershed in Sullivan and Wyoming Counties, Pennsylvania, from 

2001–2010...................................................................................................................................................... 30

Tables

1. Acquisition dates of National Agriculture Imagery Program (NAIP) source data................................................ 13

2. Cumulative landscape disturbance for natural gas extraction development and infrastructure based on 

disturbance type from 2004 to 2010 by county. ................................................................................................. 20

3. Percent land cover (2001) and land cover change (2004–2010) presented for each county. ............................ 22

4. Landscape metrics by county for 2001 (original land cover) and as updated for natural gas development 

disturbance (2004–2010). .................................................................................................................................. 24

5. Forest fragmentation metrics by county for 2001 (original land cover) and as updated for natural gas 

development disturbance (2004–2010).............................................................................................................. 26

6. Change in percent interior forest and percent edge forest by county for 2001 (original land cover) and as 

updated for natural gas development disturbance (2004–2010)........................................................................ 28
v 

Conversion Factors

Inch/Pound to SI 

Multiply By To obtain

Length

mile (mi) 1.609 kilometer (km)

Area

acre 4,047 square meter (m2)

acre 0.4047 hectare (ha)

SI to Inch/Pound 

Multiply By To obtain

Length

meter (m) 3.281 foot (ft) 

kilometer (km) 0.6214 mile (mi)

Area

square meter (m2) 0.0002471 acre 

hectare (ha) 2.471 acre

Horizontal coordinate information is referenced to the North American Datum of 1983 

(NAD 83).
1 

Landscape Consequences of Natural Gas Extraction in 

Sullivan and Wyoming Counties, Pennsylvania, 2004–2010

By E.T. Slonecker, L.E. Milheim, C.M. Roig-Silva, and A.R. Malizia

Abstract

Increased demands for cleaner burning energy, coupled with the relatively recent technological 

advances in accessing unconventional hydrocarbon-rich geologic formations, have led to an intense 

effort to find and extract natural gas from various underground sources around the country. One of these 

sources, the Marcellus Shale, located in the Allegheny Plateau, is currently undergoing extensive

drilling and production. The technology used to extract gas in the Marcellus Shale is known as hydraulic

fracturing and has garnered much attention because of its use of large amounts of fresh water, its use of 

proprietary fluids for the hydraulic-fracturing process, its potential to release contaminants into the 

environment, and its potential effect on water resources. Nonetheless, development of natural gas 

extraction wells in the Marcellus Shale is only part of the overall natural gas story in this area of 

Pennsylvania. Conventional natural gas wells, which sometimes use the same technique, are commonly 

located in the same general area as the Marcellus Shale and are frequently developed in clusters across 

the landscape. The combined effects of these two natural gas extraction methods create potentially 

serious patterns of disturbance on the landscape. This document quantifies the landscape changes and 

consequences of natural gas extraction for Sullivan County and Wyoming County in Pennsylvania 

between 2004 and 2010. Patterns of landscape disturbance related to natural gas extraction activities 

were collected and digitized using National Agriculture Imagery Program (NAIP) imagery for 2004, 

2005/2006, 2008, and 2010. The disturbance patterns were then used to measure changes in land cover 

and land use using the National Land Cover Database (NLCD) of 2001. A series of landscape metrics is 

also used to quantify these changes and is included in this publication. 

Introduction: Natural Gas Extraction

The need for cleaner burning energy, coupled with the relatively recent technological advances 

in accessing hydrocarbon-rich geologic formations, has led to an intense effort to find and extract 

natural gas from various underground sources around the country. One of these formations, the 

Marcellus Shale, is currently the target of extensive drilling and production in the Allegheny Plateau. 

Marcellus Shale generally extends from New York to West Virginia as shown in figure 1 (Coleman and 

others, 2011). Coleman and others (2011) defined assessment units (AU) of Marcellus Shale production 

based on the geology of the region. 
2 

Figure 1. Map of the Appalachian Basin Province showing the three Marcellus Shale assessment units (AU), 

which encompass the extent of the Middle Devonian from its zero-isopach edge in the west to its erosional 

truncation within the Appalachian fold and thrust belt in the east. The Interior Marcellus Shale AU is expected to be 

a major production area for natural gas (Coleman and others, 2011). Base-map data courtesy of The National Map

[(http://viewer.nationalmap.gov/viewer) (U.S. Geological Survey, 2011a)].

The overall landscape effects of natural gas development have been considerable. Over 9,600 

Marcellus Shale gas drilling permits and over 49,500 non-Marcellus Shale permits have been issued 

from 2000 to 2011 in Pennsylvania (Pennsylvania Department of Environmental Protection, 2011) and 

over 2,300 Marcellus Shale permits in West Virginia (West Virginia Geological and Economic Survey, 

2011), with most of the development activity occurring since 2005. 

The Marcellus Shale is generally 600 to 3,000 meters (m) below the land surface (Coleman and 

others, 2011). Gas and petroleum liquids are produced with a combination of vertical and horizontal 

drilling techniques, coupled with a process of hydraulically fracturing the shale formation, known as 

“fracking,” which releases the natural gas.

The hydraulic-fracturing process has garnered much attention because of its use of large

amounts of fresh water, its use of proprietary fluids for the hydraulic-fracturing process, its potential to 
3 

release contaminants into the environment, and its potential effect on groundwater and drinking-water 

resources.

Development of natural gas wells in the Marcellus Shale is only part of the overall natural gas 

story in this area. Conventional natural gas wells are commonly located in the same general area as the 

Marcellus Shale AU. Conventional wells generally are much shallower and less productive than 

Marcellus Shale wells and commonly are located in clusters that cover large areas of the landscape with 

nearly 60,000 total gas wells established. Both types of wells may affect a given area. With the 

accompanying areas of disturbance, well pads, new roads, and pipelines from both types of natural gas 

wells, the effect on the landscape is often dramatic. Figure 2 shows an example of a pattern of landscape 

change from forest to forest interspersed with gas extraction infrastructure. These landscape effects have 

consequences for the ecosystems, wildlife, and human populations that are collocated with natural gas 

extraction activities. This document examines the landscape consequences of gas extraction for two 

areas of current Marcellus Shale and non-Marcellus Shale natural gas extraction activity.

Figure 2. Example of forested landscapes from Washington County, Pennsylvania, showing the spatial effects of 

roads, well pads, and pipelines related to (a) Marcellus Shale and (b) Conventional natural gas development. Inset 

shows the location of the images. Base-map data courtesy of The National Map

[(http://viewer.nationalmap.gov/viewer) (U.S. Geological Survey, 2011a)]. 
4 

Location

This assessment of landscape effects focuses on two counties, Sullivan County and Wyoming 

County in Pennsylvania, within the Marcellus Shale area of development known as the “Marcellus 

Shale Play” or the Interior Marcellus Shale AU. These counties were chosen for their position adjacent 

to a “sweet spot” of exceptionally productive Marcellus Shale (Stevens and Kuuskraa, 2009). Figure 3 

identifies the selected counties in relation to the Interior Marcellus Shale AU and the distribution of 

Marcellus and non-Marcellus gas extraction permits granted by Pennsylvania.

Figure 3. The distribution of Marcellus and non-Marcellus natural gas permits issued between 2004 and 2010 

within Pennsylvania, the focal counties of Sullivan and Wyoming, and their relation to the interior Marcellus Shale 

assessment unit. Base-map data courtesy of The National Map [(http://viewer.nationalmap.gov/viewer)

(U.S. Geological Survey, 2011a)]. 

The Biogeography of Pennsylvania Forests

Forests are a critical land cover in Pennsylvania. Prior to the European settlements, Pennsylvania 

was almost completely forested and even today, with modern agriculture, urban growth, and population 

growth, Pennsylvania is still roughly 60 percent forested. Pennsylvania forests of the 17th century were 
5 

diverse but were dominated by beech and hemlock, which composed 65 percent of the total forest 

(Pennsylvania Department of Conservation and Natural Resources, 2011). In the late 19th century, 

Pennsylvania became the country’s leading source of lumber, and a number of products, from lumber to 

the production of tannic acid, were generated from the forestry industry (Pennsylvania Department of 

Conservation and Natural Resources, 2011). By the early 20th century, most of Pennsylvania’s forests 

had been harvested. Soon after most of the trees were felled, wildfires, erosion, and flooding became 

prevalent, especially in the Allegheny Plateau region (Pennsylvania Parks and Forests Foundation, 

2010). 

The 20th century saw resurgence in Pennsylvania forests. The Weeks Act of 1911 authorized the 

Federal purchase of forest land on the headwaters of navigable rivers to control the flow of water 

downstream and act as a measure of flood control for the thriving steel industry of Pittsburgh. Slowly, 

the forests began to grow back but with a vastly different composition, this time composed of black 

cherry, red maple, and sugar maple species (Pennsylvania Parks and Forests Foundation, 2010). For the 

most part, except for a few isolated areas in north central Pennsylvania and some State parks, the 

majority of forest cover is currently of the new composition and not of virgin forest. Figure 4 shows that 

today the concentrations of forests in Pennsylvania are highest in the central and north-central parts of 

the State, which is also the main area of hydraulic-fracturing activity in the Marcellus Shale. 
6 

Figure 4. The distribution of percent forest cover by county based on the U.S. Geological Survey 2001 National 

Land Cover Data. Base-map data courtesy of The National Map [(http://viewer.nationalmap.gov/viewer) (U.S. 

Geological Survey, 2011a)].

Pennsylvania forests provide critical habitat to a number of plant and animal species. Plant 

species include the sugar maple, the eastern redcedar, and evergreens that produce berries in the winter. 

There were a number of animal species that have been eradicated from the region, such as elk, moose, 

North American cougar, bison, and grey wolf (Nilsson, 2005). Today, animal species range from the 

more commonly found animals, such as skunks to flying squirrels, and multiple different varieties of 

snakes and bats. However, a diverse population of birds depends on the forests for survival. In the State 

of Pennsylvania, there are 394 different bird species that are native, including endangered species, such 

as the piping plover (Gross, 2005). 

Key Research Questions 

An important aspect of this research was to quantify the level of disturbance in terms of land use 

and land cover change by specific disturbance category (well pads, roads, pipelines, and so forth). This 
7 

quantification was accomplished by extracting the signatures of disturbance from high-resolution aerial 

images and then computing landscape metrics in a geographic information system (GIS) environment. 

This research and monitoring effort focused on answering the following key questions: 

• What is the level of overall disturbance attributed to gas exploration and development activities and 

how has this changed over time? 

• What are the structural components (land cover classes) of this change and how much change can be 

attributed to each class?

• How has the disturbance associated with natural gas exploration and development affected the 

structure, pattern, and process of key ecosystems, especially forests, within the Marcellus Shale 

Play?

• How will the disturbance stressors affect ecosystem structure and function at a landscape and 

watershed scale?

Landscape Metrics and a Landscape Perspective

An important and sometimes overlooked aspect of contemporary gas exploration activity is the 

geographic profile and spatial arrangement of these activities on the land surface. The function of 

ecosystems and the services they provide are due in large part to their spatial arrangement on the 

landscape. Energy exploration and development represents a specific form of land use and land cover 

change (LULCC) activity that substantially alters certain critical aspects of the spatial pattern, form, and 

function of landscape interactions. 

Changes in land use and land cover affect the ability of ecosystems to provide essential 

ecological goods and services, which, in turn, affect the economic, public health, and social benefits that 

these ecosystems provide. One of the great challenges for geographic science is to understand and 

calibrate the effects of LULCC and the complex interaction between human and biotic systems at a 

variety of natural, geographic, and political scales (Slonecker and others, 2010).

Changes in land use and land cover, such as the disturbance and the landscape effects of energy 

exploration, are currently occurring at a relatively rapid pace that is prompting immediate scientific 

focus and attention. Understanding the dynamics of land surface change requires an increased 

understanding of the complex nature of human-environmental systems and requires a suite of scientific 

tools that include traditional geographic data and analysis methods, such as remote sensing and GIS, as 

well as innovative approaches to understanding the dynamics of complex natural systems (O’Neill and 

others, 1997; Turner, 2005; Wickham and others, 2007). One such approach that has gained much 

recent scientific attention is the landscape indicator, or landscape assessment, approach, which has been 

developed within the science of landscape ecology (O’Neill and others, 1997). 

Landscape assessment utilizes spatially explicit imagery; GIS data on land cover, elevation, 

roads, hydrology, vegetation; and in situ sampling results to compute a suite of numerical indicators 

known as landscape metrics to assess ecosystem condition. Landscape analysis is focused on the 

relation between pattern and process and broad-scale ecological relationships such as habitat, 

conservation, and sustainability. Landscape analysis necessarily considers both biological and 

socioeconomic issues and relationships. This research explores these relationships and their potential 

effect on various ecosystems and biological endpoints within the context of natural gas exploration. 

The landscape assessment presented here is based largely on the framework outlined in O’Neill 

and others (1997). Many landscape metrics can be computed and utilized for some analytical purpose. 

However, it has been shown by several researchers (Riitters and others, 1995; Wickham and Riitters, 

1995; Wickham and others, 1997) that many of these metrics are highly correlated, sensitive to 

misclassification and pixel size, and, to some extent, questionable in terms of additional information 
8 

value. The key landscape concepts and metrics reported here are discussed below. The actual formulae 

used to compute these specific metrics can be found in software documentation for FRAGSTATS 

(McGarigal and others, 2002) and Analytical Tools Interface for Landscape Assessments (ATtILA) 

(Ebert and Wade, 2004). Computation details for percent interior forest and percent edge forest are 

documented by Riitters and others (2000). 

The concept of landscape metrics, sometimes called landscape indices, is derived from the field 

of landscape ecology and is rooted in the realization that pattern and structure are important components 

of ecological process. Landscape metrics are spatial/mathematical indices that allow the objective 

description of different aspects of landscape structures and patterns (McGarigal and others, 2002). 

Landscape metrics characterize the landscape structure and various processes at both landscape and 

ecosystem levels. Metrics such as average patch size, fragmentation, and interior forest dimension 

capture spatial characteristics of habitat quality and potential change effects on critical animal and 

vegetation populations. 

Two different geostatistical landscape analysis programs were used to measure the landscape 

metrics presented in this report. FRAGSTATS (University of Massachusetts, Amherst, Mass.) is a 

spatial pattern analysis program for quantifying numerous landscape metrics and their distribution, and 

is available at http://www.umass.edu/landeco/research/fragstats/fragstats.html (McGarigal and others, 

2002). ATtILA (U.S. Environmental Protection Agency (USEPA), Las Vegas, Nev.) is an Esri 

(Environmental Systems Research Institute, Redlands, Calif.) Arcview 3.x extension that computes a 

number of landscape, riparian, and watershed metrics and is available at http://www.epa.gov/esd/landsci/attila/ (Ebert and Wade, 2004). Metrics are presented here at the county level and mapped at the 

watershed level defined by 12-digit Hydrologic Unit Codes (HUC-12). 

Disturbance 

Disturbance is a key concept in a landscape analysis approach and in ecology in general. Gas 

development activities create a number of disturbances across a heterogeneous landscape. In landscape 

analysis, disturbances are discrete events in space and time that disrupt ecosystem structure and function 

and change resource availability and the physical environment (White and Pickett, 1985; Turner and 

others, 2001). When natural or anthropogenic disturbance occurs in natural systems, it generally alters 

abiotic and biotic conditions that favor the success of different species, such as opportunistic invasive 

species over predisturbance organisms. Natural gas exploration and development result in spatially 

explicit patterns of landscape disturbance involving the construction of well pads and impoundments, 

roads, pipelines, and disposal activities that have structural impacts on the landscape (fig. 2). 

Development of multiple sources of natural gas results in increased traffic from construction, 

drilling operations (horizontal and vertical), hydraulic fracturing, extraction, transportation, and 

maintenance activities. The presence of humans, construction machinery, infrastructure (for example,

well pads and pipelines), roads, and vehicles alone may substantially impact flora and fauna. Increased 

traffic, especially rapid increases on roads that have historically received little activity, can have 

detrimental impacts on animal and plant populations (Gibbs and Shriver, 2005). Forest loss as a result of 

disturbance, fragmentation, and edge effects has been shown to negatively affect water quality and 

runoff (Wickham and others, 2008), impact species, alter biosphere-atmosphere dynamics that could 

contribute to climate change (Hayden, 1998; Bonan, 2008), and affect the long-term survival of the 

forest itself (Gascon and others, 2007). 

The initial step of landscape analysis is to determine the spatial distribution of disturbance to 

identify relative hotspots of activity. This knowledge allows greater focus to be placed on specific 

locations. Disturbance in this report is presented as both graphic files and tables of summary statistics. 
9 

Figure 5 provides an example of the distribution of natural gas extraction in Bradford County, 

Pennsylvania, and it also shows how that disturbance is placed with respect to the local land cover. 

Figure 5. Example of a natural gas disturbance footprint from Bradford County, Pennsylvania, embedded within 

the National Land Cover Dataset (NLCD) 2001. Base-map data courtesy of The National Map

[(http://viewer.nationalmap.gov/viewer) (U.S. Geological Survey, 2011a)]. 

Forest Fragmentation

Forest fragmentation is the alteration of forest into smaller, less functional areas. Fragmentation 

of forest and habitat is a primary concern resulting from current gas development. Habitat fragmentation 

occurs when large areas of natural landscapes are intersected and subdivided by other, usually 

anthropogenic, land uses leaving smaller patches to serve as habitat for various species. As human 

activities increase, natural habitats, such as forests, are divided into smaller and smaller patches that 

have a decreased ability to support viable populations of individual species, particularly those in large 

ranges adapted to interior forest conditions. Habitat loss and forest fragmentation can be major threats to 

biodiversity, although research on this topic is inconclusive (With and Pavuk, 2011). 
10

Although many human and natural activities result in habitat fragmentation, gas exploration and 

development activity can be extreme in their effect on the landscape. The development of numerous 

secondary roads and pipeline networks crisscrosses and subdivides habitat structure.

Landscape disturbance associated with shale-gas development infrastructure directly alters 

habitat through loss, fragmentation, and edge effects, which in turn alter the flora and fauna dependent 

on that habitat. The fragmentation of habitat is expected to amplify the problem of total habitat area 

reduction for wildlife species, as well as contribute to habitat degradation. Fragmentation alters the 

landscape by creating a mosaic of spatially distinct habitats from originally contiguous habitat, resulting 

in smaller patch size, greater number of patches, and decreased interior to edge ratio (Lehmkuhl and 

Ruggiero, 1991; Dale and others, 2000). Fragmented habitats generally result in detrimental impacts to 

flora and fauna caused by increased mortality of individuals moving between patches, lower 

recolonization rates, and reduced local population sizes (Fahrig and Merriam, 1994). The remaining 

patches may be too small, isolated, and possibly too influenced by edge effects to maintain viable 

populations of some species. The rate of landscape change can be more important than the amount or 

type of change because the temporal dimension of change can affect the probability of recolonization 

for endemic species, which are typically restricted by their dispersal range and the kinds of landscapes 

in which they can move (Fahrig and Merriam, 1994). 

While general assumptions and hypotheses can be derived from existing scientific literature 

involving similar stressors, the specific impacts of habitat loss and fragmentation in the Marcellus Shale

Play will depend on the needs and attributes of specific species and communities. A recent analysis of 

Marcellus well permit locations in Pennsylvania found that well pads and associated infrastructure 

(roads, water impoundments, and pipelines) required nearly 3.6 hectares (ha) (9 acres) per well pad with 

an additional 8.5 ha (21 acres) of indirect edge effects (Johnson, 2010). This type of extensive and longterm habitat conversion has a greater impact on natural ecosystems than activities such as logging or 

agriculture, given the great dissimilarity between gas-well pad infrastructure and adjacent natural areas 

and the low probability that the disturbed land will revert back to a natural state in the near future (high 

persistence) (Marzluff and Ewing, 2001). Figure 6 shows an example of the concept of the landscape 

metric of forest fragmentation.

Interior Forest 

Interior forest is a special form of habitat that is preferred by many plant and animal species and 

is defined as the area of forest at least 100 m from the forest edge (Harper and others, 2005). Interior 

forest is an important landscape characteristic because the environmental conditions, such as light, wind, 

humidity, and exposure to predators, within the interior forest are different from areas closer to the 

forest edge. Interior forest habitat is related to the size and distribution of forest patches and is closely 

tied to the concept of forest or habitat fragmentation. The amount of interior forest can be dramatically 

affected by linear land use patterns, such as roads and pipelines, which tend to fragment land patches 

into several smaller patches and destroy available habitat for certain species. Figure 6 shows the general 

concept of increased fragmentation and reduced interior forest. 
11

Figure 6. Conceptual illustration of interior forest and how this critical habitat is affected by linear disturbance. A, 

High interior area; B, Moderate interior area; and C, Low interior area (Riitters and others, 1996).

Forest Edge 

Forest edge is simply a linear measure of the amount of edge between forest and other land uses 

in a given area, and especially between natural and human-dominated landscapes. The influence of the 

two bordering communities on each other is known as the edge effect. When edges are expanded into 

natural ecosystems, and the area outside the boundary is a disturbed or unnatural system, the natural 

ecosystem can be affected for some distance in from the edge (Skole and Tucker, 1993). Edge effects 

are variable in space and time. The intensity of edge effects diminishes as one moves deeper inside a 

forest, but edge phenomena can vary greatly within the same habitat fragment or landscape (Laurance

and others, 2007). Factors that might promote edge-effect variability include the age of habitat edges, 

edge aspect, and the combined effects of multiple nearby edges, fragment size, seasonality, and extreme 

weather events.

Spatial variability of edge effects may result from local factors such as the proximity and 

number of nearby forest edges. Plots with two or more neighboring edges, such as smaller fragment 

plots, have greater tree mortality and biomass loss. Edge age also influences edge effects. Over time, 

forest edge can be partially sealed by invasive vines and second growth underbrush, which will 

influence the ability of smaller tree seedlings to survive in this environment. Likewise, the matrix of 

adjoining vegetation plots will have a strong influence on edge effects. Forest edges adjoined by young 

regrowth forest provide a physical buffer from wind and light. Extreme weather events also affect the 

temporal variability in edge effects. Abrupt, artificial boundaries of forest fragments are vulnerable to 

windstorms, snow and ice, and convectional thunderstorms that can weaken and destroy exposed forest 

edges. Periodic droughts can also have a more pronounced effect on forest edges that are exposed to 

drier wind conditions and higher rates of evaporation than interior forests. 

Contagion

Contagion is an indicator that measures the degree of “clumpiness” among the classes of land 

cover features and is related to patch size and distribution. Contagion (0<x≤100, disaggregated to 

aggregated) expresses the degree to which adjacent pixel pairs can be found in the landscape. Figure 7 

shows the general concept of contagion and gives examples of low, medium, and high contagion. 
12

Contagion is valuable because it relates an important measure of how landscapes are fragmented by 

patches. Landscapes of large, less-fragmented patches have a high contagion value, and landscapes of 

numerous small patches have a low contagion value (McGarigal and others, 2002).

Figure 7. The concept of contagion is the degree to which similar land cover pixels are adjacent or “clumped” to 

one another. A, Low contagion; B, Moderate contagion; and C, High contagion (after Riitters and others,1996).

Fractal Dimension

Fractal dimension describes the complexity of patches or edges within a landscape and is 

generally related to the level of anthropogenic influence in a landscape. Fractal dimension generally 

measures the perimeter-to-area proportional relationship of a patch. Human land uses tend to have 

simple circular or rectangular shapes of low complexity and, therefore, low fractal dimensions. Natural 

land covers have irregular edges, complex arrangements and, therefore, higher fractal dimensions. The 

fractal dimension index ranges between 1 and 2, with 1 indicating high human influences in the 

landscape and 2 with natural patterns and low human influence (McGarigal and others, 2002). 

Dominance

Dominance is a measure of the relative abundance of different patch types, typically 

emphasizing either relative evenness or equity in the distribution. Dominance is high when one land 

cover type occupies a relatively large area of a given landscape and is low when land cover types are 

evenly distributed. Dominance is the complement to evenness, which is sometimes used as an 

alternative measure of the relative area of one land cover type over others in the landscape.

Although there are many metrics associated with dominance, here we report on a simple 

landscape metric—the Simpson’s Evenness Index, which is a measure of the proportion of the 

landscape occupied by a patch type divided by the total number of patch types in the landscape 

(McGarigal and others, 2002). 
13

Methodology: Mapping and Measuring Disturbance Effects

High-resolution aerial imagery for each of four timeframes—2004, 2005/2006, 2008, and 

2010—were brought into a geographic information system (GIS) database, along with additional 

geospatial data on Marcellus and non-Marcellus well permits and locations, administrative boundaries, 

ecoregions, and geospatial information on the footprint of the Marcellus Shale Play in Pennsylvania. 

The imagery was examined for distinct signs of disturbance related to oil and gas drilling and 

development as described below. The observable features were manually digitized as line and polygon 

features in a GIS format. The polygons and line features were processed and aggregated into a raster 

mask used to update existing land cover data. Summary statistics for each county were developed and 

reported. Detailed landscape metrics were calculated and mapped over HUC-12 watersheds within or 

intersecting the boundary of each county. All metrics are calculated on the 2001 NLCD and the 2001 

NLCD as updated by disturbance collected from 2004 to 2010 to isolate the natural gas extraction 

disturbance effects.

Data 

Sources

High-resolution aerial imagery (1 m) from the National Agricultural Imagery Program (NAIP) 

was downloaded for each timeframe. NAIP imagery is flown to analyze the status of agricultural lands 

approximately every 2 to 3 years (U.S. Department of Agriculture, Farm Service Agency, 2011). The 

NAIP imagery consists of readily available, high-resolution data that are suitable for detailed analysis of 

the landscape. NAIP imagery is available from the U.S. Department of Agriculture Geospatial Data 

Gateway Web site (U.S. Department of Agriculture, Natural Resources Conservation Service, 2011). 

Table 1 identifies the source imagery dates for each county and year. 

Table 1. Acquisition dates of National Agriculture Imagery Program (NAIP) source data.

Year Source Imagery Dates (chronological from left to right)

Sullivan County

2004 2004-06-21 2004-06-24 2004-06-27 2004-09-23 2004-10-07 2004-11-06

2005 2005-06-21 2005-06-23 2005-06-24 2005-07-10

2006 2006-08-05 2006-08-12 2006-08-13 2006-10-07 2006-10-08

2008 2008-08-04 2008-09-01 2008-09-02 2008-09-05 2008-10-11

2010 2010-07-05 2010-07-08 2010-07-11 2010-09-01

Wyoming County

2004 2004-06-12 2004-06-21 2004-06-24 2004-08-23 2004-10-03

2005 2005-06-23 2005-06-24

2006

2008 2008-08-04 2008-09-02 2008-09-04 2008-09-05 2008-10-10 2008-10-11

2010 2010-07-11 2010-08-07 2010-09-01

Drilling permits for Marcellus Shale and non-Marcellus Shale natural gas were obtained from 

the Pennsylvania Department of Environmental Protection Permit and Rig Activity Reports for 2004–

2010 (Pennsylvania Department of Environmental Protection, Office of Oil and Gas Management, 

2011). 
14

The U.S. Geological Survey (USGS) Watershed Boundary Dataset 12-digit Hydrologic Unit 

Code (HUC-12) for Pennsylvania was downloaded from the USGS National Hydrography Dataset Web 

site (U.S. Geological Survey, 2011b).

The Marcellus Shale Play assessment unit boundaries were downloaded from the USGS Energy 

Resources Program Data Services Web site (U.S. Geological Survey, 2012). 

The 2001 National Land Cover Dataset (NLCD) was acquired for use as the baseline land cover 

map. The NLCD is a 16-class land cover classification scheme applied consistently across the United 

States at a 30-m spatial resolution (Homer and others, 2007) and is released on a 5-year cycle. The 

2001 NLCD was chosen as the baseline because the 2006 NLCD contained some of the landscape 

changes collected during this study. The NLCD may be acquired using the Multi-Resolution Land 

Characteristics Consortium Web site (U.S. Geological Survey, 2011c). The NLCD 2001 was resampled 

to 10-m-pixel size.

Collection

These data were brought into a GIS database for spatial analysis. The imagery was examined for 

distinct signs of disturbance related to oil and gas drilling and development. These features include the 

following: 

• Sites—Cleared areas related to existing permits or displaying the characteristics of a shale or 

conventional gas extraction site.

• Roads—Vehicular transportation corridors constructed specifically for shale or conventional gas 

development. 

• Pipelines—New gas pipelines constructed in conjunction with one or more well pads. 

• Impoundments—Manmade depressions designed to hold liquid and in support of oil and gas drilling 

operations. 

• Other—Support areas or activities such as processing plants, storage tanks, and staging areas.

The collection of gas extraction infrastructure data was a manual process of visually examining 

high-resolution imagery for each county over four dates to identify and digitize (collect) changes in the 

land cover resulting from the development of gas extraction infrastructure. Specifically, NAIP 1-m data 

composited for the years 2004, 2005/2006, 2008, and 2010 were examined using 2004 imagery as a 

baseline, identifying landscape changes that occurred after 2004. 

Changes that correlated with natural gas extraction permits, appeared to be natural gas extraction 

related, or were in proximity to other gas extraction infrastructure were selected and digitized to the 

maximum extent of landscape disturbance. The focus of the data collection was on features attributable 

to the construction, use, and maintenance of gas extraction drill sites, processing plants, and compressor 

stations, as well as the center lines for new roads accessing such sites, plants, and stations, and the 

center lines for new pipelines used to transport the extracted gas. Figure 8 shows examples of digitized 

natural gas extraction features. These data were collected within shapefiles by county, using ArcGIS 

10.0. One shapefile was generated for sites (polygons), one was generated for roads (lines), and one was 

generated for pipelines (lines). Roads and pipelines were generally buffered to 8 and 12 m, respectively, 

for overall area assessments. The buffered distance was selected as the average from measurement of 

roads and pipelines in the counties. All sites were initially classified as gas extraction related or points 

of interest. Points of interest were unlikely to be related to drilling, but were of potential future interest 

and excluded from further processing. All data collected were reviewed by another team member for 

concurrence and consistency. 
15

Figure 8. Examples of spatially explicit features of disturbance that were extracted from aerial photographs into a 

geographic information system (GIS) format.
16

Land Cover Update

Using the collected and reviewed data, the polygons and line features were processed and 

aggregated into a raster format used as a mask to update existing land cover data from NLCD 2001. 

Figure 9 shows the processing flow to accomplish this task consistently across both counties. 

Each feature within the shapefiles was compared to the permit database to determine its permit 

status and its area calculated. A subset of features and roads was selected by infrastructure type (all, 

Marcellus, non-Marcellus, other and pipelines). The selected features were then merged and internal 

boundaries dissolved resulting in a disturbance footprint shapefile for that county. The disturbance 

footprint was then rasterized (10-m-pixel size) and used to conditionally select the pixels in the 

resampled 2001 NLCD to reclassify as a new class: gas extraction disturbance. To consistently perform 

this processing, a set of models was developed using the ArcGIS ModelBuilder. 

Figure 9. Workflow diagram for creating an updated land cover map. The workflow was implemented using 

ArcGIS ModelBuilder scripts to process the digitized data and embed results in the resampled NLCD 2001.
17

Calculation of Landscape Metrics

Landscape-wide and land cover class fragmentation statistics for each county were developed 

and reported using FRAGSTATS, while land cover class-detailed statistics, forest fragmentation 

statistics, including patch metrics and forest condition (interior, edge, and so forth) metrics were 

calculated over smaller watersheds (HUC12) intersecting with the county using ATtILA. The collected 

statistics were then summarized, charted, and mapped for further analysis. 

In addition to the summary of features noted above, a series of landscape metrics was calculated 

for each county based on the change related to gas development activities between 2004 and 2010. To 

do this, the metrics were calculated from the 2001 NLCD dataset (Homer and others, 2007). Following 

that calculation, the 2004–2010 cumulative spatial pattern of disturbance was digitally embedded into

the 2001 NLCD dataset and the metrics were recalculated for each county.

Results: Summary Statistics and Graphics

This section presents a summary for each county of landscape alterations from natural gas 

resource development, along with the ensuing change in land cover and landscape metrics using metrics 

suggested by O’Neill and others (1997). These metrics are then calculated and presented based on the 

sources of that disturbance: Marcellus (MS) sites and roads; non-Marcellus (non-MS) (conventional) 

sites and roads; other infrastructure, which includes nonpermitted sites, processing facilities and their 

associated roads; and pipelines and their associated roads. Nonpermitted sites are defined as disturbed 

areas that appear to be Marcellus or non-Marcellus gas extraction sites that do not have a permit within 

250 m of the disturbance. These data are presented in tabular form with some graphic presentations 

provided where appropriate. Examples of the spatial distribution of selected landscape metrics are 

shown at the watershed level for each county. GIS data of all disturbance features are available upon 

request.

Disturbed Area

Documenting the spatially explicit patterns of disturbance was one of the primary goals of this 

research, and this section describes the extent of disturbed land cover for Sullivan and Wyoming 

Counties in Pennsylvania. The spatial distribution of disturbance influences the impacts of that 

disturbance. Figure 10 shows the distribution of disturbance within Sullivan and Wyoming Counties. 

In Sullivan County, disturbance is sparsely distributed along the northern edge of the county. 

Most of this disturbance is Marcellus related. In Wyoming County, disturbance is dispersed in the 

northwest quadrant of the county and is related to Marcellus Shale natural gas extraction. The detailed 

insets show the disturbance footprints in the context of the surrounding land cover. 
18

Figure 10. Gas extraction-related disturbance identified between 2004 and 2010 in Sullivan and Wyoming

Counties, Pennsylvania. Base-map data courtesy of The National Map [(http://viewer.nationalmap.gov/viewer)

(U.S. Geological Survey, 2011a)]. 

Table 2 lists the disturbance area attributable to all sites and impoundments and their associated 

roads and pipelines. The disturbance area is presented first as a total disturbance for all gas extraction 

infrastructure, including all sites, roads, and pipelines. Total disturbance is then divided into sections: 

the first includes disturbance for all sites and their associated roads and the second includes disturbance 

for pipelines and impoundments. The disturbance area for all sites and roads is further divided into 

disturbance for Marcellus Shale permitted sites and roads, non-Marcellus Shale permitted sites and 

roads, sites lacking an identifiable permit (for example, processing facilities or incomplete permit data), 

and sites with permits for both Marcellus and non-Marcellus drilling, also called dual sites. 

Additionally, the disturbance area associated with impoundments is presented for those impoundments 

greater than 0.4 ha and for those less than 0.4 ha. Because land disturbance or access roads may be 

associated with multiple infrastructural components (for example, pipelines may cross areas also 

disturbed for drill sites), the values for disturbed areas and road miles within break-out categories, such 

as “MS sites and roads,” do not sum up to the higher level category—in this instance, “All sites and 
19

roads.” The results indicate the following changes occurred based on 2004–2010 natural gas 

development: 

• While Sullivan and Wyoming Counties are roughly equal in size (117,126 ha and 105,738 ha, 

respectively), Sullivan County has less than one-half the number of Marcellus sites in Wyoming 

County (8 and 19 sites, respectively). However, each county has one non-Marcellus site.

• The mean number of hectares of disturbance per site is similar in both counties (2.9 ha and 2.4 ha in 

Sullivan and Wyoming, respectively). 

• The mean number of disturbed hectares for Marcellus sites is similar for Wyoming County (2.9 ha) 

and Sullivan (2.5 ha). 

• The limited number (zero or one) of non-Marcellus sites, other infrastructure, pipelines, and 

impoundments renders disturbance analysis of these features challenging. 
20

Table 2. Cumulative landscape disturbance for natural gas extraction development and infrastructure based on 

disturbance type from 2004 to 2010 by county.

[Note: Categories are not mutually exclusive. MS, Marcellus Shale site; non-MS, non-Marcellus Shale site; >, greater than; 

<, less than; ha, hectare]

Land cover update Count Site only 

hectares

Footprint 

disturbed 

hectares

Road 

kilometers

Pipeline 

kilometers

Hectares 

per site

Disturbed 

hectares 

per site

Road 

kilometers 

per site

Sullivan County (117,126 ha)

All infrastructure 8 23.1 24.6 2.4 0 2.9 3.1 0.3

All sites and roads 8 23.1 2.9 

 MS sites and roads 8 23.1 24.6 2.4 2.9 3.1 0.3

 Non-MS sites and 

roads 1 1.6 2.1 0.34 1.6 2.1 0.3

 Other infrastructure/

 non-permitted sites 

 and roads

0 

 Dual sites 1 1.6 2.1 

Pipelines 0 0 

Impoundments (>0.4 ha) 0 

Impoundments (<0.4 ha) 1 0.04 0.0 

Wyoming County (105,738 ha)

All infrastructure 22 55.5 59.7 4.5 2.2 2.5 2.7 0.2

All sites and roads 21 50.2 4.5 2.4 

 MS sites and roads 19 47.8 52.3 4.2 2.5 2.8 0.2

 Non-MS sites and 

 roads 1 2.3 2.6 0.3 2.3 2.6 0.3

 Other infrastructure/

 non-permitted sites 

 and roads

1 0.1 1.24 0.7 

 Dual sites 0 

Pipelines 1 5.3 5.3 0 2.2 

Impoundments (>0.4 ha) 1 0.6 0.6 

Impoundments (<0.4 ha) 0 

Land cover change is the initial impact of disturbance and can have long-term effects on 

ecological integrity and functions. Table 3 lists the percent land cover by county for 2001 and percent 

land cover and change for the updated 2010 landscape. The land cover change for the updated landscape 

is further divided into the values attributable to Marcellus sites; non-Marcellus sites; other infrastructure 

including nonpermitted sites; and pipelines, each with their associated roads. Given that the natural land 

cover of Pennsylvania is forest (Kuchler, 1964), the 2001 land cover provides a measure of the impacts 

prior to most natural gas resource development; the changes between 2004 and 2010 have increased 

these impacts. Of particular interest are the forest cover and its relation to the critical value 59.28 

percent from percolation theory (Gardner and others, 1987; O’Neill and others, 1997). Below this value, 

the landscape structure rapidly breaks down into isolated patches, thereby changing forest resilience and 
21

habitat corridors. The results indicate the following changes based on 2004–2010 natural gas 

development: 

• In both Sullivan and Wyoming Counties, the primary 2001 land covers are forest (81 

percent and 66 percent, respectively), agriculture (9 percent and 23 percent, respectively) 

and developed (2 percent and 4 percent, respectively). 

• Sullivan County had greater than the critical amount of forest in 2001 and that forest has 

been slightly impacted by natural gas resource development. Percent forest declined by 

0.01 percent (11.7 ha). Agriculture declined by the same amount. 

• Wyoming County had greater than the critical amount of forest in 2001. That forest has 

declined by 0.02 percent (21.1 ha) from natural gas resource development. 

• Wyoming County agriculture declined by 0.03 percent (31.7 ha) from natural gas 

resource development. 
22

Table 3. Percent land cover (2001) and land cover change (2004–2010) presented for each county. 

[MS, Marcellus Shale site; non-MS, non-Marcellus Shale site] 

Land cover

Original 

land 

cover

Updated with all 

infra-structure Change

Updated 

with MS 

sites and 

roads

Change

Updated with 

non-MS sites 

and roads

Change

Updated 

with other 

infrastructure 

and roads

Change

Updated 

with 

pipelines 

and roads

Change

Sullivan County

Forest 81.51 81.50 -0.01 81.50 -0.01 81.51 0.00 81.51 0.00 81.51 0.00

Agriculture 9.88 9.86 -0.01 9.86 -0.01 9.88 0.00 9.88 0.00 9.88 0.00

Developed 2.83 2.83 0.00 2.83 0.00 2.83 0.00 2.83 0.00 2.83 0.00

Grassland - 

 herbaceous 0.45 0.45 0.00 0.45 0.00 0.45 0.00 0.45 0.00 0.45 0.00

Water 0.52 0.52 0.00 0.52 0.00 0.52 0.00 0.52 0.00 0.52 0.00

Barren 0.24 0.24 0.00 0.24 0.00 0.24 0.00 0.24 0.00 0.24 0.00

Wetlands 0.56 0.56 0.00 0.56 0.00 0.56 0.00 0.56 0.00 0.56 0.00

Scrub - shrub 4.02 4.02 0.00 4.02 0.00 4.02 0.00 4.02 0.00 4.02 0.00

Gas

 extraction

 disturbance 0.02 0.02 0.02 0.02 0.00 0.00 0.00 0.00

Wyoming County

Forest 66.48 66.46 -0.02 66.46 -0.02 66.48 0.00 66.48 0.00 66.48 0.00

Agriculture 23.68 23.65 -0.03 23.65 -0.03 23.68 0.00 23.68 0.00 23.68 0.00

Developed 4.76 4.75 0.00 4.75 0.00 4.76 0.00 4.76 0.00 4.75 0.00

Grassland - 

 herbaceous 0.23 0.23 0.00 0.23 0.00 0.23 0.00 0.23 0.00 0.23 0.00

Water 1.60 1.60 0.00 1.60 0.00 1.60 0.00 1.60 0.00 1.60 0.00

Barren 0.10 0.10 0.00 0.10 0.00 0.10 0.00 0.10 0.00 0.10 0.00

Wetlands 1.52 1.52 0.00 1.52 0.00 1.52 0.00 1.52 0.00 1.52 0.00

Scrub - shrub 1.64 1.64 0.00 1.64 0.00 1.64 0.00 1.64 0.00 1.64 0.00

Gas

 extraction

disturbance 0.06 0.06 0.05 0.05 0.00 0.00 0.00 0.00 0.01 0.01
23

Land Cover Metrics of Interest

There are numerous landscape metrics, many of which are redundant. Table 4 lists the total area, 

number of patches, total edge, mean fractal index, contagion, and evenness metrics for the 2001 county 

landscape, and the metrics and change for the updated 2010 landscape. The metrics and change for the 

updated landscape are further divided into the values attributable to Marcellus sites; non-Marcellus 

sites; other infrastructure, including nonpermitted sites; and pipelines, each with their associated roads. 

These metrics were chosen for their overall indication of human impacts on the landscape and 

environmental quality (O’Neill and others, 1997). Increase in the edge, especially between unlike land 

covers, indicates declining resilience of the natural land cover and movement of species, while the 

decrease in the mean fractal index (1≤x≤2) indicates an increase in human use. Evenness (0≤x≤1, where 

0 indicates one land cover class and 1 indicates even distribution across land cover classes) indicates the 

relative heterogeneity of the landscape and is the inverse of the dominance measure (McGarigal and 

others, 2002) recommended by O’Neill and others (1997). Contagion (0<x≤100, disaggregated to 

aggregated) is an indicator that measures the degree of “clumpiness” among the classes of land cover 

features. The results indicate the following changes occurred based on 2004–2010 natural gas 

development: 

• Total edge increased by 8.0 kilometers and 22.7 kilometers for Sullivan and Wyoming 

Counties, respectively, with the largest amount attributable to Marcellus development in 

both counties. Marcellus site development constituted the major natural gas development 

in both Sullivan and Wyoming Counties. 

• Fractal index is very low for both counties, indicating a high level of human influence in 

these counties. 

• Contagion shows a moderate level of clumped land cover for both counties. 

• Evenness also shows a moderate level of heterogeneity for both counties with no one 

land cover dominating. 

• Evenness has similar values for each infrastructure type in both counties. Given that the 

expected land cover is forest and an expected evenness value approaching zero, this 

value indicates a substantially disturbed landscape. 
24

Table 4. Landscape metrics by county for 2001 (original land cover) and as updated for natural gas development disturbance (2004–2010). 

[Note: Categories are not mutually exclusive; MS, Marcellus Shale site; non-MS, non-Marcellus Shale site; ha, hectare; km, kilometer]

Metric Original land 

cover

Updated 

with all 

infrastructure

Change

Updated with 

MS sites and 

roads 

Change

Updated with 

non-MS sites 

and roads

Change

Updated with 

other infrastructure and 

roads

Change

Updated 

with 

pipelines 

and roads

Change

Sullivan County

Total area

 (ha) 117,126.31 117,126.31 0 117126.31 0 117,126.31 0 117,126.31 0 117,126.31 0

Total edge

 (km) 6,132.21 6,140.23 8 6,140.23 8 6,133.35 1 6,132.21 0 6,132.21 0

Mean

 fractal

 index 1.1256 1.1254 -0.0002 1.1254 -0.0002 1.1255 -0.0001 1.1256 0.0000 1.1256 0.0000

Contagion 79.3954 80.4566 1.0612 80.4566 1.0612 80.4948 1.0994 79.3954 0.0000 79.3954 0.0000

Evenness 0.3696 0.364 -0.0056 0.364 -0.0056 0.3638 -0.0058 0.3696 0.0000 0.3696 0.0000

Wyoming County

Total area

 (ha) 105738.5 105738.5 0 105738.5 0 105738.5 0 105738.5 0 105738.5 0

Total edge

 (km) 7817.26 7840.02 23 7836.23 19 7818.42 1 7819.22 2 7820.66 3

Mean

 fractal

 index 1.1298 1.1293 -0.0005 1.1294 -0.0004 1.1298 0.0000 1.1298 0.0000 1.1296 -0.0002

Contagion 72.0755 73.4595 1.3840 73.472 1.3965 73.5657 1.4902 73.5679 1.4924 73.5586 1.4831

Evenness 0.5703 0.5618 -0.0085 0.5618 -0.0085 0.5614 -0.0089 0.5614 -0.0089 0.5614 -0.0089
25

Forest Fragmentation

Disturbance in the landscape will affect forests by fragmentation, which is the process of 

dividing large land cover (for example, forest) into smaller segments called patches. A patch is defined 

as adjacent (forest) pixels, including diagonals. A landscape with many small patches is representative 

of a highly fragmented landscape. Fragmented forests provide habitat for edge species, but are poor for 

interior species, and are less likely to provide migration corridors. 

Fragmentation may be evaluated by change in the number of patches and by change in the mean 

and (or) median patch size. Table 5 compares the changing forest patch metrics for the 2001 land cover, 

the updated 2010 land cover, and subsets of the updated 2010 land cover based on Marcellus 

infrastructure, non-Marcellus infrastructure, other infrastructure, and pipelines. The results indicate the 

following changes occurred based on 2004–2010 natural gas development: 

• Forests became slightly more fragmented due to natural gas resource development. Both Sullivan

and Wyoming Counties contained more, but smaller forest patches in 2010 than in 2001. 

• Sullivan County forest patches increased by two attributable to Marcellus development. These 

patches initially averaged about 170 ha, but that average was reduced by almost 1 ha in 2010. 

• Wyoming County forest patches increased by 14 attributable to Marcellus development. These 

patches initially averaged about 68 ha and were reduced by 1 ha to a mean of about 67 ha. 

• Both Sullivan and Wyoming Counties have large differences between the forest patch mean area and 

median area values: 169.6 ha mean to 0.62 ha median and 67.85 ha mean to 0.79 ha median, 

respectively. These large differences indicate a skewed population of forest patch sizes, including 

many small forest patches and few large forest patches.
26

Table 5. Forest fragmentation metrics by county for 2001 (original land cover) and as updated for natural gas development disturbance (2004–2010). 

[Note: Categories are not mutually exclusive; MS, Marcellus Shale site; non-MS, non-Marcellus Shale site]

Metric Original land 

cover

Updated with 

all infrastructure

Change

Updated with 

MS sites and 

roads

Change

Updated with 

non-MS sites 

and roads

Change

Updated with 

other infrastructure and 

roads

Change

Updated with 

pipelines and 

roads

Change

Sullivan County

Number of

 patches 563 565 2.0 565 2.0 563 0.0 563 0.0 563 0.0

Forest patch

 mean area

 (hectares) 169.6 169.0 -0.6 169.0 -0.6 169.6 0.0 169.6 0.0 169.6 0.0

Forest patch

 median area

 (hectares) 0.62 0.62 0.0 0.62 0.0 0.62 0.0 0.62 0.0 0.62 0.0

Wyoming County

Number of

 patches 1036 1050 14.0 1045 9.0 1036 0.0 1036 0.0 1040 4.0

Forest patch

 mean area 

 (hectares) 67.9 66.9 -0.9 67.2 -0.6 67.9 0.0 67.8 0.0 67.6 -0.3

Forest patch

 median area

 (hectares) 0.80 0.76 -0.04 0.79 -0.01 0.80 0.00 0.80 0.00 0.79 -0.01
27

Figure 11 illustrates the spatial distribution of the change in the number of forest patches by 

watershed. Note the relation between disturbance and the change in the number of forest patches. The 

increasing number of forest patches in some watersheds indicates an increasingly fragmented landscape 

with habitat implications for many species.

Figure 11. Change in number of forest patches from 2001 to 2010 showing the increasing fragmentation in 

Sullivan and Wyoming Counties, Pennsylvania. Base-map data courtesy of The National Map

[(http://viewer.nationalmap.gov/viewer) (U.S. Geological Survey, 2011a)]. 

Interior and Edge Forest

Forest condition (interior and edge) is another way to evaluate the state of the forest. In 

particular, interior forest is subject to more rapid decline than other segments of the forest. Table 6 

shows the change in interior forest and edge forest based on natural gas resource development and the 

types of natural gas extraction infrastructure. Figures 12 and 13, respectively, illustrate the spatial 

distribution by watershed of change in percent interior forest and the spatial distribution of change in 

percent edge forest. The results indicate the following changes occurred based on 2004–2010 natural 

gas development:

• While forest fragmentation metrics for both Sullivan and Wyoming Counties show minor changes in 

forest structure, neither county lost appreciable forest area. 
28

Table 6. Change in percent interior forest and percent edge forest by county for 2001 (original land cover) and as updated for natural gas 

development disturbance (2004–2010). 

[Note: Categories are not mutually exclusive; MS, Marcellus Shale site; non-MS, non-Marcellus Shale site]

Metric

Original 

land 

cover

Updated 

with all 

infrastructure

Change

Updated with 

MS sites and 

roads

Change

Updated with 

non-MS sites 

and roads

Change

Updated 

with other 

infrastructure 

and roads

Change

Updated 

with 

pipelines 

and roads

Change

Sullivan County

Percent

 forest 59.0 59.0 0.0 59.0 0.0 59.0 0.0 59.0 0.0 59.0 0.0

Percent

 interior

 forest 35.3 35.3 0.0 35.3 0.0 35.3 0.0 35.3 0.0 35.3 0.0

Percent

 edge forest 17.7 17.7 0.0 17.7 0.0 17.7 0.0 17.7 0.0 17.7 0.0

Wyoming County

Percent

 forest 59.2 59.1 0.0 59.1 0.0 59.1 0.0 59.2 0.0 59.2 0.0

Percent

 interior

 forest 35.6 35.6 0.0 35.6 0.0 35.6 0.0 35.6 0.0 35.6 0.0

Percent

 edge forest 17.4 17.5 0.0 17.5 0.0 17.5 0.0 17.4 0.0 17.4 0.0
29

Figure 12. Change in percent interior forest by watershed in Sullivan and Wyoming Counties, Pennsylvania, from 

2001 to 2010. Base-map data courtesy of The National Map [(http://viewer.nationalmap.gov/viewer)

(U.S. Geological Survey, 2011a)]. 

Conclusion

The results indicate how natural gas extraction in Pennsylvania is affecting the landscape 

configuration. Agricultural and forested areas are being converted to natural gas extraction disturbance. 

While Sullivan and Wyoming Counties had little natural gas development between 2004 and 2010 and, 

therefore, little related disturbance, both counties show evidence of changes in forest structure from this 

low level of development. 

The fractal dimension, contagion, and dominance landscape metrics were reported based on 

recommendations of O’Neill and others (1997); however, these metrics do not appear to be important in 

these counties. They may be of greater importance for other counties and are reported here for 

consistency. 
30

Figure 13. Change in percent of edge forest by watershed in Sullivan and Wyoming Counties, Pennsylvania,

from 2001 to 2010. Base-map data courtesy of The National Map [(http://viewer.nationalmap.gov/viewer) (U.S. 

Geological Survey, 2011a)].

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ISSN 2331-1258

DOI URL http;//dx.doi.org/10.3133/ofr20131261