Landscape Consequences of Natural Gas Extraction in 

Greene and Tioga Counties, Pennsylvania, 2004–2010

By E.T. Slonecker, L.E. Milheim, C.M. Roig-Silva, and G.B. Fisher

Open-File Report 2012–1220

U.S. Department of the Interior

U.S. Geological Survey
U.S. Department of the Interior

KEN SALAZAR, Secretary

U.S. Geological Survey

Marcia K. McNutt, Director

U.S. Geological Survey, Reston, Virginia: 2012

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Suggested citation:

Slonecker, E.T., Milheim, L.E., Roig-Silva, C.M., and Fisher, G.B., 2012, Landscape 

consequences of natural gas extraction in Greene and Tioga Counties, Pennsylvania, 2004–2010: U.S. Geological Survey Open File Report 2012–1220, 32 p. 

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items must be secured from the copyright owner.
iii

Contents

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

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

Location ..................................................................................................................................................................... 3

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

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

Landscape Metrics and a Landscape Perspective......................................................................................................... 6

Disturbance................................................................................................................................................................ 7

Forest Fragmentation................................................................................................................................................. 8

Interior Forest............................................................................................................................................................. 9

Forest Edge ..............................................................................................................................................................10

Contagion..................................................................................................................................................................10

Fractal Dimension .....................................................................................................................................................11

Dominance................................................................................................................................................................11

Methodology: Mapping and Measuring Disturbance Effects.........................................................................................12

Data ..........................................................................................................................................................................12

Sources .................................................................................................................................................................12

Collection ..............................................................................................................................................................12

Land Cover Update...................................................................................................................................................14

Calculation of Landscape Metrics .............................................................................................................................15

Results: Summary Statistics and Graphics...................................................................................................................16

Disturbed Area..........................................................................................................................................................16

Land Cover Metrics of Interest..................................................................................................................................21

Forest Fragmentation................................................................................................................................................23

Interior and Edge Forest ...........................................................................................................................................25

Conclusion....................................................................................................................................................................28

References Cited..........................................................................................................................................................29

Figures

 

 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 ..................................................................... 2

 2. Examples forested landscapes in Washington County, Pennsylvania, showing the spatial effects of roads, 

well pads, and pipelines related to (a) Marcellus Shale and (b) conventional natural gas development........... 3

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

within Pennsylvania, the focal counties of Greene and Tioga, and their relation to the Marcellus Shale 

Play Interior assessment unit. Base-map data courtesy of the National Map ................................................... 4

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

Land Cover Data .............................................................................................................................................. 5

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

National Land Cover Dataset (NLCD) 2001...................................................................................................... 8
iv

6. Conceptual illustration of interior forest and how critical habitat is affected by linear disturbance ...................10

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

one another .....................................................................................................................................................11

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

geographic information systems (GIS) format .................................................................................................14

9. Workflow diagram for creating an updated land cover map.............................................................................15

 10. Gas extraction-related disturbance identified between 2004 and 2010 in Greene and Tioga Counties, 

Pennsylvania ...................................................................................................................................................17

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

and Tioga Counties, Pennsylvania. Base-map data courtesy of the National Map..........................................25

 12. Change in percent interior forest in Greene and Tioga Counties, Pennsylvania, from 2001 to 2010...............27

 13. Change in percent of edge forest in Greene and Tioga Counties, Pennsylvania, from 2001 to 2010..............28

Tables

1. National Agriculture Image Program (NAIP) dates of acquisition.....................................................................13

2. Cumulative amount of landscape disturbance for natural gas extraction development and infrastructure 

based on disturbance type from 2004 to 2010 by county. MS and non-MS sites refer to Marcellus Shale 

and non-Marcellus Shale sites, respectively....................................................................................................18

3. Percent land cover presented in descending order for each county. Change in percent forest is shown

in bold. MS and non-MS sites refer to Marcellus Shale and non-Marcellus Shale sites, respectively.............20

4. Landscape metrics by county. MS and non-MS sites refer to Marcellus Shale and non-Marcellus Shale 

sites, respectively ............................................................................................................................................22

5. Forest fragmentation metrics by county. MS and non-MS sites refer to Marcellus Shale and non-Marcellus 

Shale sites, respectively ..................................................................................................................................24

6. Change in percent interior forest and percent edge forest by county. MS and non-MS sites refer to 

Marcellus Shale and non-Marcellus Shale sites, respectively .........................................................................26
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)

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

(NAD 83).

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 

Greene and Tioga Counties, Pennsylvania, 2004–2010

By E.T. Slonecker, L.E. Milheim, C.M. Roig-Silva, and G.B. Fisher

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 the area of 

Pennsylvania. Coalbed methane, which is sometimes extracted using the same technique, is commonly

located in the same general area as the Marcellus Shale and is 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 Greene County and Tioga 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 

are also used to quantify these changes and are 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, 2011)].

 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 located 600 to 3,000 meters 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 

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

resources.
3 

However, with all of the development of natural gas wells in the Marcellus Shale it is only part 

of the overall natural gas story in this area. Coalbed methane, which is extracted in similar ways, is 

commonly located in the same general area as the Marcellus Shale. The coalbed methane wells are 

much shallower and less productive but are often located in clusters that dot large areas of the 

landscape, with nearly 60,000 total gas wells. There may be both types of wells in 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 examples 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. Examples forested landscapes in 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 image. Base-map data courtesy of the National Map 

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

Location

This assessment of landscape effects focuses on two counties involved in the Marcellus Shale 

area of development known as the “Play”—Greene County and Tioga County in Pennsylvania. These 
4 

counties were chosen for their position within the “sweet spots” of exceptionally productive Marcellus 

Shale (Stevens and Kuuskraa, 2009). Figure 3 below identifies the selected counties in relation to the 

Marcellus Shale Play 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 Greene and Tioga, and their relation to the Marcellus Shale Play Interior 

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

Geological Survey, 2011)].

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 forest. Pennsylvania forests of the 17th century were 

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

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

century, Pennsylvania became the country’s leading source of lumber, in which 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, 
5 

and flooding became prevalent, especially in the Allegheny Plateau region (Pennsylvania Parks and 

Forests Foundation, 2010). 

The 20th century saw a 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 composed of black cherry, red 

maple, and sugar maple species (Pennsylvania Parks and Forests Foundation, 2010). For the most part, 

except for a very 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 pre-European 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.

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, 2011)].

Pennsylvania forests provide critical habitat to a number of plant species such as the sugar 

maple, the Eastern red cedar, 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 typical skunk to flying 

squirrels, and multiple varieties of snakes and bats. However, a diverse population of birds depends on 
6 

the forests for survival. In the State of Pennsylvania, there are 394 different bird species that are native, 

including endangered species such as the peregrine falcon and the bald eagle (Gross, 2005). 

Key Research Questions

One key aspect of this research is 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 

quantification will be accomplished by extracting the signatures of disturbance from high-resolution 

aerial images and then computing landscape metrics in a geographic information systems (GIS) 

environment. 

This research and monitoring effort will attempt to answer the following key research 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 

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

calibrate the effects of land use and land cover change and the complex interaction between human and 

biotic systems at a variety of natural, geographic, and political scales (Slonecker, 2010).

Land use and land cover change, such as the disturbance and the landscape effects of energy 

exploration, is currently occurring at a relatively rapid pace 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 with 

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

Landscape assessment utilizes spatially explicit imagery and 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, 
7 

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. 

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

others (1997). There are many landscape metrics that can be computed and utilized for some analytical 

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

others, 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 

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 and 

ATtILA (McGarigal and others, 2002; Ebert and Wade, 2004).

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 have been developed that 

allow the objective description of different aspects of landscape structures and patterns (McGarigal and 

others, 2002). They characterize the landscape structure and various processes at both landscape and 

ecosystem level. 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 (Analytical Tools Interface for Landscape Assessments) (U.S. Environmental Protection 

Agency, Las Vegas, Nev.) is an Arcview 3.x extension [Environmental System Research Institute (Esri), 

Redlands, Calif.] developed by the U.S. Environmental Protection Agency (USEPA) 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 (12-digit Hydrologic Unit Codes). 

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 the 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. 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 will result in increased traffic from construction, 

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

maintenance activities. The mere 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 to populations (Gibbs and Shriver, 2005). Forest loss as a result of 
8 

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

runoff (Wickham and others, 2008), alter biosphere-atmosphere dynamics that could contribute to 

climate change (Bonan, 2008; Hayden, 1998), 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. Disturbance in this report is presented as both 

graphic files and tables of summary statistics. This knowledge allows greater focus to be placed on 

specific locations. Figure 5 provides an example of the distribution of natural gas extraction in Bradford 

County, Pennsylvania. The example also shows how that disturbance is placed with respect to the local 

land cover. 

Figure 5. Example of the natural gas disturbance footprint of 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, 2011)].

Forest Fragmentation

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. Habitat loss 
9 

and forest fragmentation can be substantial threats to biodiversity, although research on this topic has 

not been conclusive (With and Pavuk, 2011). 

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 alters 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 towards 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, resulting from 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 (9 acres) per well pad with an 

additional 8.5 hectares (21 acres) of indirect edge effects (Johnson, 2010). This type of extensive and 

long-term 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 meters 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 alteration of habitat into smaller, less 

functional areas. 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. 
10

Figure 6. Conceptual illustration of interior forest and how 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 edges 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 is partially sealed by proliferating vines and second growth underbrush growth, 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 forest. 

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 expresses the degree to which 

adjacent pixel pairs can be found in the landscape. Figure 7 shows the general concept of contagion and 
11

gives examples of low, medium, and high contagion. 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 relationship of a patch by a perimeter-to-area proportion. 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 a similar 

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).
12

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 GIS database, along with additional geospatial data on Marcellus and nonMarcellus 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. 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 watersheds [Hydrological Unit Code (HUC)-12 hydrounits] within and intersecting the boundary 

of each county.

Data 

Sources

High-resolution aerial imagery 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). 

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). 

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

digit (HUC12) for Pennsylvania was downloaded from the USGS National Hydrography Dataset Web 

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

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-meter spatial resolution (Homer and others, 2007). The NLCD may be acquired using the 

Multi-Resolution Land Characteristics Consortium Web site (U.S. Geological Survey, 2011). The 

NLCD 2001 was resampled to 10-meter pixel size. 

Collection

These data were brought into a GIS database for spatial analysis. Using the 2004 imagery as a 

baseline, the imagery was examined for distinct signs of disturbance related to oil and gas drilling and 

development. These mapped features include: 

• Well Pads - Cleared areas related to existing permits or displaying the characteristics of a shale or 

coalbed gas extraction site.

• Roads -Vehicular transportation corridors constructed specifically for shale or coalbed gas 

development. 

• Pipelines - New gas pipelines constructed in conjunction with one or more well pads. 
13

• 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 was a manual process of visually examining highresolution 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, we examined NAIP 

1-meter data composited for the years 2004, 2005/2006, 2008, and 2010, identifying landscape changes 

that occurred after 2004. See table 1 for dates of acquisition used in each year’s composite image. 

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

related or were in the proximity of other natural gas extraction infrastructure, and 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 per 

county, using ArcGIS 10.0 (Esri, Redlands, Calif.). 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 meters, 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.

Table 1. National Agriculture Image Program (NAIP) dates of acquisition.

Date of NAIP Mosaic Dates of collection

Greene County, PA Tioga County, PA

2004 11/11/2004 06/12/2004 08/24/2004

09/01/2004

2005 06/23/2005 08/24/2005

09/07/2005 09/10/2005

09/11/2005

06/23/2005 06/24/2005

07/10/2005

2008 06/07/2008 07/02/2005

07/15/2005 07/18/2005

07/29/2005 09/03/2005

05/29/2005 09/05/2008

09/19/2008 10/07/2008

2010 06/08/2010 06/18/2010

08/31/2010 09/02/2010

06/02/2010 07/05/2010

07/07/2010 07/11/2010

09/01/2010
14

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

geographic information systems (GIS) format.

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 processed to determine its permit status and area. Each 

county's shapefiles were then merged and internal boundaries dissolved resulting in a disturbance 
15

footprint for that county. The disturbance footprint was then rasterized and used to conditionally select 

the pixels in the 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 (Esri, Redlands, 

Calif.).

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

ArcGIS model builder automated scripts to process the digitized data and embed in the resampled NLCD 2001.

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 
16

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 of landscape alterations from natural gas resource development, 

along with the ensuing change in land cover and landscape metrics for each county using metrics 

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

sources of that disturbance: Marcellus sites and roads, non-Marcellus sites and roads, and 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 meters. 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 Greene and Tioga Counties in 

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

In Greene County, the disturbance occurs mostly at the eastern side of the county with some 

activity at the north and south, and minor activity to the west of the county (fig. 10). Tioga County has 

less disturbance than Greene County. The disturbance in Tioga County is concentrated in the eastern 

half and through the central part of the county, almost in a linear fashion, in an east-west direction. The 

detailed insets in figure 10 show the disturbance footprints in the context of the surrounding land cover. 
17

Figure 10. Gas extraction-related disturbance identified between 2004 and 2010 in Greene and Tioga Counties, 

Pennsylvania. Base-map data courtesy of the National Map [(http://viewer.nationalmap.gov/viewer) (U.S. 

Geological Survey, 2011)].

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 broken into two sections: 

disturbance for all sites and their associated roads and disturbance for pipelines. The disturbance area 

for all sites and roads is further broken into disturbance for Marcellus Shale sites and roads, nonMarcellus Shale sites and roads, sites with permits for both Marcellus and non-Marcellus drilling, and 

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

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

greater than 0.4 hectare and for those less than 0.4 hectare. Because land disturbance or access roads 

may be associated with multiple infrastructure (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 roads.” The results 

indicate the following: 
18

• While Tioga County is larger (~730,000 hectares) than Greene County (~370,000 hectares), Greene 

County has 126 Marcellus and 427 non-Marcellus sites compared to 125 Marcellus and 11 nonMarcellus sites in Tioga County. 

• Tioga County has twice the mean hectares of disturbance per site than Greene County (4.0 

hectares/sites compared to 2.0 hectares/sites, respectively). 

• The mean disturbed hectares for Marcellus sites is almost identical for both counties (2.7 

hectares/sites for Greene County and 2.8 hectares/sites for Tioga County), whereas the mean 

disturbed hectares per non-Marcellus sites is almost three times larger in Tioga County than in 

Greene County (3.6 hectares/site compared to 1.6 hectares/site, respectively). A visual examination 

of the Tioga non-Marcellus sites reveals several large sites that include impoundments or multiple 

wells (both Marcellus and non-Marcellus wells). The larger non-MS sites may use hydraulic 

fracturing for the extraction of coalbed methane. 

• Greene County has almost seven times the number of sites that include processing and 

transportation facilities and unpermitted sites than Tioga County; however, these sites are about one 

third the mean size of Tioga County sites (0.9 hectare for Greene County compared to 2.6 hectares 

for Tioga County). 

• Greene County had almost five times the amount of dual sites than Tioga County. The disturbance 

associated with dual sites was included in the disturbance measures for both Marcellus and nonMarcellus sites.

• Greene County has almost twice the number of impoundments than Tioga County. However, the 

mean size of large impoundments in Greene County was almost half the mean size of Tioga County 

(1.1 hectares for Greene versus 1.7 hectares for Tioga), implying a difference in water access, 

storage, and usage. 

Table 2. Cumulative amount of landscape disturbance for natural gas extraction development and infrastructure 

based on disturbance type from 2004 to 2010 by county. MS and non-MS sites refer to Marcellus Shale and nonMarcellus Shale sites, respectively. 

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

Greene County (370,016 hectares)

All infrastructure 663 775.6 1311.2 241.1 126.7 1.17 2.0 0.3

All infrastructure 663 775.6 1311.2 241.1 126.7 1.17 2.0 0.3

All sites and roads 663 775.6 241.1

 MS sites and roads 126 270.4 341.6 56.8 2.14 2.7 0.5

 Non-MS sites and roads 427 457.9 680.8 174.8 1.1 1.6 0.5

 Other 

 infrastructure\nonpermitted

 sites and roads 138 122.17 332.5 63.9 0.9 2.4 0.3

 Dual sites 28 74.9 2.7

Pipelines 53 304.5 288.6 33.0 126.7 5.8 5.44 0.6

Impoundments (>1 acre) 32 33.5 1.1

Impoundments (<1 acre) 119 13.9 0.1

Tioga County (729,701 hectares)

All infrastructure 151 362.1 596.3 46.0 78.1 1.6 4.0 0.3

All sites and roads 151 362.1 44.4

 MS sites and roads 125 300.2 349.6 39.3 2.38 2.8 0.3

 Non-MS sites and roads 11 32.17 39.9 6.0 2.9 3.6 0.6

 Other 

 infrastructure\nonpermitted

 sites and roads 20 51.6 61.1 6.9 2.6 3.1 0.3

 Dual sites 5 21.9 4.37
19

Table 2. Cumulative amount of landscape disturbance for natural gas extraction development and infrastructure 

based on disturbance type from 2004 to 2010 by county. MS and non-MS sites refer to Marcellus Shale and nonMarcellus Shale sites, respectively.—Continued

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

Tioga County (729,701 hectares)—Continued

Pipelines 47 189.3 202.2 12.1 78.1 4.0 4.3 05.0

Impoundments (>1 acre) 18 30.0 1.7

Impoundments (<1 acre) 59 7.9 0.1

Land cover change is the initial impact of disturbance and has long-term effects on ecological 

goods and services. 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 

broken into the values attributable to Marcellus sites; non-Marcellus sites; other infrastructure including 

unpermitted 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 only 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 

forest structure rapidly breaks down into isolated patches, thereby changing forest resilience and habitat 

corridors. The results indicate the following: 

• In both Greene and Tioga Counties, the primary land covers are forest (~72 percent for Greene 

County and 67 percent for Tioga County), agriculture (17 percent and 25 percent, respectively) and 

developed (8 percent and 3 percent, respectively). Natural gas resource development had the greatest 

impact on forest and agricultural land cover. 

• Both counties were above 59.28 percent forest in 2001 and forest has been impacted by recent 

natural gas resource development. Percent forest declined by 0.53 percent (-786 hectares) in Greene 

County and by 0.08 percent (-225 hectares) in Tioga County. 

• In Greene County, forest was the most impacted class by natural gas extraction activities. Of these 

activities, non-Marcellus sites decreased forest area by 0.26 percent (-392 hectares), followed by 

Marcellus sites [0.13-percent decrease (-193 hectares)], then pipelines [0.13-percent decrease (-188 

hectares)], and other infrastructure [0.10-percent decrease (-144 hectares)]. Agriculture was the 

second most impacted class by natural gas extraction activities.

• In Tioga County, agriculture was the most affected by natural gas extraction activities. Of these 

activities, Marcellus sites decreased agriculture areas [0.07-percent decrease 

• (-210 hectares)], pipelines [0.03-percent decrease (-99 hectares)], followed by non-Marcellus sites 

(0.01-percent decrease (-24 hectares)], and other infrastructure (0.01-percent decrease (-16 

hectares)]. Forest was the second most impacted class by natural gas extraction. Forest decreased by 

116 hectares due to Marcellus sites, 89 hectares due to pipelines, 28 hectares due to other sites, and 

14 hectares due to non-Marcellus sites.
20

Table 3. Percent land cover presented in descending order for each county. Change in percent forest is shown in bold. MS and non-MS sites refer 

to Marcellus Shale and non-Marcellus Shale sites, respectively.

Land cover

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

Change Updated with 

pipelines Change

Greene County

Forest 72.61 72.09 -0.53 72.49 -0.13 72.35 -0.26 72.52 -0.10 72.49 -0.13

Agriculture 

Developed 

17.43

8.38

17.14

8.35

-0.30

-0.04

17.35

8.38

-0.09

-0.01

17.27

8.37

-0.17

-0.02

17.40

8.38

-0.04

-0.01

17.38

8.37

-0.05

-0.01

Grassland – 

 herbaceous 0.79 0.78 -0.01 0.79 0.00 0.78 -0.01 0.79 0.00 0.79 0.00

Water 0.57 0.57 0.00 0.57 0.00 0.57 0.00 0.57 0.00 0.57 0.00

Barren 0.12 0.12 0.00 0.12 0.00 0.12 0.00 0.12 0.00 0.12 0.00

Wetlands 

Scrub –

shrub 

Gas 

 extraction

disturbance 

0.07

0.01

 

0.07

0.01

0.88

0.00

0.00

0.88

0.07

0.01

0.23

0.00

0.00

0.23

0.07

0.01

0.45

0.00

0.00

0.45

0.07

0.01

0.14

0.00

0.00

0.14

0.07

0.01

0.19

0.00

0.00

0.19

Tioga County

Forest 67.30 67.23 -0.08 67.26 -0.04 67.30 0.00 67.29 -0.01 67.27 -0.03

Agriculture 

Developed 

25.25

3.44

25.14

3.43

-0.11

-0.01

25.18

3.43

-0.07

0.00

25.24

3.44

-0.01

0.00

25.24

3.44

-0.01

0.00

25.21

3.44

-0.03

0.00

Grassland – 

 herbaceous 0.41 0.40 0.00 0.41 0.00 0.41 0.00 0.41 0.00 0.40 0.00

Water 0.49 0.49 0.00 0.49 0.00 0.49 0.00 0.49 0.00 0.49 0.00

Barren 0.29 0.29 0.00 0.29 0.00 0.29 0.00 0.29 0.00 0.29 0.00

Wetlands 

Scrub –

 shrub 

Gas

 Extraction

disturbance 

0.46

2.36

 

0.46

2.35

0.20

0.00

-0.01

0.20

0.46

2.36

0.12

0.00

0.00

0.12

0.46

2.36

0.01

0.00

0.00

0.01

0.46

2.36

0.02

0.00

0.00

0.02

0.46

2.36

0.07

0.00

0.00

0.07
21

Land Cover Metrics of Interest

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

total edge, mean fractal index, contagion and dominance 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 broken into the values attributable to Marcellus sites; non-Marcellus sites; other 

infrastructure including unpermitted 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 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 

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: 

• Total edge increased by 858.3 kilometers (533.3 miles) and 306.1 kilometers (190.2 miles) for 

Greene and Tioga Counties, respectively. The largest amount of change is attributable to nonMarcellus sites in Greene County, whereas in Tioga County, the largest amount of change is 

attributable to pipeline construction closely followed by Marcellus sites.

• Mean fractal index is intermediate for both counties, reflecting the high percentage (>50 percent) of 

forest coverage for both Greene and Tioga Counties. Mean fractal index remains unaffected when 

considering the individual activities (Marcellus sites, non-Marcellus Sites, other infrastructures, and 

pipelines). Values for mean fractal indexes are similar in both counties (almost identical), and when 

considering all the natural gas extraction activities, the mean fractal index decreases by 0.0080 in 

Greene County, while the mean fractal index decreases by 0.0015 in Tioga County. 

• Contagion shows a moderate level of clumped land cover. Greene County has a slightly higher level 

of contagion than Tioga County. The influence of infrastructure type (all, Marcellus, non-Marcellus, 

other, and pipelines) was similar for Tioga County, but more variable for Greene County. The 

greatest influence (an increase of 1.0564) on contagion in Greene County was from other 

infrastructure; the remaining types of infrastructure had similar effects. However, when considering 

all infrastructure increase in contagion is smaller for both counties (by 0.0491 in Greene County 

compared with 1.0427 in Tioga County). 

• 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. Given that the expected land 

cover is all forest and has an evenness value approaching zero, this value indicates a substantially 

disturbed landscape. 
22

Table 4. Landscape metrics by county. MS and non-MS sites refer to Marcellus Shale and non-Marcellus Shale sites, respectively.

[Note: Categories are not mutually exclusive] 

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

Change

Updated with 

pipelines and 

roads

Change

Greene County

Total area

 (hectares)

Total edge

 (km)

Mean

 fractal

index

149,741.06

14,899.71

1.1385

149,741.06

15,758.03

1.1305

0.00

858.32

-0.0080

149,741.06

150,52.58

1.1363

0.00

152.87

-0.0022

149,741.06

153,61.69

1.1340

0.00

461.98

-0.0045

149,740.84

150,73.65

1.1369

-0.22

173.94

-0.0016

149,740.91

15,179.05

1.1361

-0.15

279.34

-0.0024

Contagion 74.6844 74.7335 0.0491 75.6465 0.9621 75.2797 0.5953 75.7408 1.0564 75.6395 0.9551

Evenness 0.4974 0.4993 0.0019 0.4921 -0.0053 0.4945 -0.0029 0.4913 -0.0061 0.4919 -0.0055

Tioga County

Total area 295,300.80 295,300.80 0.00 295,300.80 0.00 295,300.80 0.00 295,300.80 0.00 295,300.80 0.00

Total edge

 (km)

Mean

 fractal

index

20,470.87

1.1265

20,776.97

1.1250

306.10

-0.0015

20,606.58

1.1258

135.71

-0.0007

20,488.05

1.1264

17.18

-0.0001

20,494.67

1.1263

23.80

-0.0002

20,654.30

1.1257

183.43

-0.0008

Contagion 73.9657 75.0084 1.0427 75.1473 1.1816 75.3287 1.3630 75.3175 1.3518 75.2027 1.2370

Evenness 0.5502 0.5434 -0.0068 0.5426 -0.0076 0.5418 -0.0084 0.5418 -0.0084 0.5423 -0.0079
23

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 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:

• Forests became more fragmented due to natural gas resource development. Both Greene and Tioga

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

• Greene County forest patches increased by 600 patches; most (~324 patches) are attributable to nonMarcellus sites. These patches initially averaged over 75 hectares, but that average was reduced by 

about 14 hectares in 2010.

• Tioga County forest patches increased by almost 213 patches; most (~151 patches) are attributable 

to pipeline construction. These patches initially averaged about 65 hectares and were reduced by

about 4 hectares to a mean of about 60 hectares. Marcellus sites and pipelines had the greatest effect 

on these values.

• The mean patch area in Greene County was greatly reduced for Greene County due to natural gas 

extraction activities—22.8 hectares in Greene County, compared to a decrease in mean forest patch 

area of ~-4.0 hectares in Tioga County.

• The reduction in mean forest patch area can be attributable to non-Marcellus sites in Greene County, 

whereas in Tioga County it can be attributable to pipeline construction. 
24

Table 5. Forest fragmentation metrics by county. MS and non-MS sites refer to Marcellus Shale and non-Marcellus Shale sites, respectively.

[Note: Categories are not mutually exclusive] 

Distribution 

statistics

Original 

land 

cover

Updated with 

all infrastructure Change

Updated with 

MS sites and 

roads Change

Updated 

with nonMS sites 

and roads Change

Updated 

with other 

infrastructure Change

Updated 

with 

pipelines Change

Greene County

Number of

patches

Forest

 patch area

 mean

(hectares)

Forest

 patch area

 median

(hectares)

1,434.00

75.83

0.54

2,034.00

53.07

0.43

600.00

-22.75

-0.11

1,550.00

70.03

0.53

116.00

-5.80

-0.01

1,758.00

61.63

0.45

324.00

-14.20

-0.09

1,539.00

70.56

0.53

105.00

-5.27

-0.01

1,605.00

67.63

0.53

171.00

-8.20

-0.01

Tioga County

Number of

patches

Forest

 patch area

 mean

(hectares)

Forest

 patch area

 median

(hectares)

3,079.00

64.55

0.89

3,292.00

60.31

0.81

213.00

-4.25

-0.08

3,143.00

63.20

0.83

64.00

-1.35

-0.06

3,083.00

64.46

0.89

4.00

-0.09

0.00

3,088.00

64.35

0.89

9.00

-0.20

0.00

3,230.00

61.51

0.82

151.00

-3.05

-0.07
25

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 

increase of more than 50 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 

Green and Tioga Counties, Pennsylvania. Base-map data courtesy of the National Map 

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

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:

• Greene County lost 0.53 percent forest, which contributed to a 1.40-percent loss of interior forest 

and a gain of 0.65 percent in edge forest. 

• Tioga County lost 0.08 percent forest, which contributed to a 0.15-percent loss of interior forest and 

a gain of 0.06 percent in edge forest. 

• Forest loss in Greene County was mainly attributable to non-Marcellus sites, while Marcellus sites 

and pipelines were the major contributors for forest loss in Tioga County. 

• A tentative pattern that appears is that the interior forest loss is approximately twice that of the 

overall forest loss, and the gain in edge forest approximates that overall forest loss. 
26

Table 6. Change in percent interior forest and percent edge forest by county. MS and non-MS sites refer to Marcellus Shale and non-Marcellus Shale sites, 

respectively. 

[Note: Categories are not mutually exclusive]

Distribution 

statistics

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 infrastructureChange Updated with 

pipelines Change

Greene County

Number of

 patches

Percent

 forest

Percent

 interior

forest

Percent

 edge

forest

1,434.00

73.03

47.54

19.46

2,034.00

72.50

46.14

20.11

600.00

-0.53

-1.40

0.65

1,550.00

72.90

47.27

19.55

116.00

-0.13

-0.27

0.09 

1,758.00

72.77

46.79

19.81

324.00

-0.26

-0.75

0.35

1,539.00

72.94

47.22

19.62

105.00

-0.10

-0.32

0.16

1,605.00

72.90

47.10

19.70

171.00

-0.13

-0.44

0.24

Tioga County

Number of

 patches

Percent

 forest

Percent

 Interior

 forest

Percent 

edge forest

3,079.00

67.64

52.72

10.83

3,292.00

67.56

52.57

10.89

213.00

-0.08

-0.15

0.06

3,143.00

67.60

52.66

10.85

64.00

-0.04

-0.07

0.02

3,083.00

67.63

52.71

10.83

4.00

0.00

-0.01

0.00

3,088.00

67.63

52.70

10.84

9.00

-0.01

-0.02

0.01

3,230.00

67.61

52.64

10.88

151.00

-0.03

-0.09

0.05
27

Figure 12. Change in percent interior forest in Greene and Tioga Counties, Pennsylvania, from 2001 to 2010. 

Base-map data courtesy of the National Map [(http://viewer.nationalmap.gov/viewer) (U.S. Geological Survey, 

2011)]. 
28

Figure 13. Change in percent of edge forest in Greene and Tioga Counties, Pennsylvania, from 2001 to 2010. 

Base-map data courtesy of the National Map [(http://viewer.nationalmap.gov/viewer) (U.S. Geological Survey, 

2011)]. 

Conclusion

The results presented here document several landscape metrics that show how natural gas 

extraction in Pennsylvania is affecting the landscape configuration. Agricultural and forested areas are 

being disturbed by natural gas exploration, development and extraction. The disturbance and effects of 

both Marcellus and non-Marcellus development are clearly different over both counties; Greene County 

has higher activity (Marcellus and non-Marcellus) than Tioga County. The effects of non-Marcellus 

sites are greater in Greene County than in Tioga County, where Marcellus sites activities predominate 

over non-Marcellus sites.

The fractal dimension, contagion, and dominance were reported based on the recommendations 

of O’Neill and others (1997); however, they do not appear to be important in these counties. They may 

be of greater importance for other counties and are reported here for consistency. 
29

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