VISUAL QUALITY IN A ROCKY MOUNTAIN ENVIRONMENT
By
Kathleen Barry

A THESIS
Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of
Environmental Design- Master of Arts
2017

ABSTRACT
VISUAL QUALITY IN A ROCKY MOUNTAIN ENVIRONMENT
By
Kathleen Barry
The benefits of a mountain landscape touch people physically, emotionally and
psychologically. Often overlooked are the aesthetic benefits mountain landscape features provide
us. This thesis explores the physical landscape of a mountain environment in Aspen, Colorado
and the positive effect mountain landscape features have on the visual quality of the research
area. Utilizing the predictive visual quality equation developed by Dr. Jon Bryan Burley (J. B.
Burley, 1997) and Professor Kendall’s coefficient of concordance statistical test (Brown &
Daniel, 1987) the study proves the positive influence of mountain landscape features on the
visual quality score of the rocky mountain environment.

Key Words: Visual Quality, Landscape Architecture, Mountain Aesthetics

ACKNOWLEDGEMENTS

I would first like to thank Dr. Burley for his aid in development and completion of my
research. His guidance allowed me to explore a topic I am passionate about in an applicable
manner for my graduate degree. He dedicated his time and experience in helping me learn and
grow as a student researcher. I will take with me as I enter my career his enthusiasm for
learning, calm demeanor, and drive to make a positive impact on the world through landscape
architecture.
I would also like to thank my three committee members; Karen Russcher, Paul Nieratko, and
Robert Schutzki for their time, comments and questions. Their varying points of view added
depth and substance, in turn expanding my research.
Finally, I would like to express gratitude to my graduating classmates. Five years at
Michigan State University growing as individual people and as aspiring landscape architects.
The endless hours spent learning together and from each other have been the most meaningful
experiences, I truly thank you for all the help and support in this journey.

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TABLE OF CONTENTS

LIST OF TABLES .......................................................................................................................... v
LIST OF FIGURES ....................................................................................................................... vi
CHAPTER 1: Introduction ............................................................................................................. 1
CHAPTER 2: Literature Review .................................................................................................... 4
2.1 Borrowed Scenery................................................................................................................. 5
2.2 Visual Quality ....................................................................................................................... 8
2.2.1 Shafer & Colleagues ...................................................................................................... 8
2.2.2 Taylor & Colleagues .................................................................................................... 13
2.2.3 Burley & Colleagues .................................................................................................... 16
2.3 Mountain Aesthetics ........................................................................................................... 21
2.4 Viewshed Protection ........................................................................................................... 23
CHAPTER 3: Method ................................................................................................................... 25
3.1 Purpose of Study ................................................................................................................. 25
3.2 Research Area ..................................................................................................................... 26
3.3 Data Collection ................................................................................................................... 29
3.4 Analysis Techniques ........................................................................................................... 32
CHAPTER 4: Results ................................................................................................................... 38
CHAPTER 5: Discussion.............................................................................................................. 41
CHAPTER 6: Conclusion ............................................................................................................. 52
APPENDIX ................................................................................................................................... 54
BIBLIOGRAPHY ......................................................................................................................... 61

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LIST OF TABLES

Table 1: Independent Variables (J.B. Burley, 1997) .................................................................... 34
Table 2: Environmental Quality Index (J. B. Burley, 1997) ......................................................... 35
Table 3: Visual Quality Score by Zoning District ........................................................................ 38
Table 4: Visual Quality Score and Mountain Area Comparison .................................................. 39
Table 5: Average Visual Quality and Area of Mountain by District ............................................ 45
Table 6: Percent of Pavement ....................................................................................................... 47

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LIST OF FIGURES

Figure 1: Colorado State ............................................................................................................... 26
Figure 2: City of Aspen, Roaring Fork River Valley, Colorado ................................................... 27
Figure 3: Downtown Aspen Photograph Locations by Zoning District ....................................... 30
Figure 4: Mixed Use West ............................................................................................................ 33
Figure 5: Lodge West – Full Enclosure ........................................................................................ 43
Figure 6: Commercial Core East – Threshold of Enclosure ......................................................... 43
Figure 7: Commercial Core West – Minimum Enclosure ............................................................ 44
Figure 8: Mixed Use South – Loss of Enclosure .......................................................................... 44
Figure 9: Residential South........................................................................................................... 48
Figure 10: Residential West .......................................................................................................... 49
Figure 11: Mixed Use West .......................................................................................................... 50
Figure 12: Commercial Core East................................................................................................. 51
Figure 13: Commercial Core East................................................................................................. 55
Figure 14: Commercial Core South .............................................................................................. 55
Figure 15: Commercial Core North .............................................................................................. 55
Figure 16: Commercial Core West ............................................................................................... 56
Figure 17: Lodge East ................................................................................................................... 56
Figure 18: Lodge South ................................................................................................................ 56
Figure 19: Lodge West.................................................................................................................. 57
Figure 20: Lodge North ................................................................................................................ 57
Figure 21: Mixed Use East ........................................................................................................... 57

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Figure 22: Mixed Use South ......................................................................................................... 58
Figure 23: Mixed Used West ........................................................................................................ 58
Figure 24: Mixed Use North ......................................................................................................... 58
Figure 25: Residential East ........................................................................................................... 59
Figure 26: Residential North......................................................................................................... 59
Figure 27: Residential West .......................................................................................................... 59
Figure 28: Residential North......................................................................................................... 60

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CHAPTER 1: Introduction
Value in the Rocky Mountain Environment is often measured by the successful
interaction between the natural and built environments. The natural wonders that are the Rocky
Mountains, stand as sights people work very hard to incorporate into their everyday lives. In the
heart of the Rockies, Colorado, these iconic landforms have stood for thousands of years and are
considered to be treasured entities. The city of Aspen, Colorado is known to be a tourist hotspot,
with approximately 300 historic landmarks. These include buildings, structures, parks,
cemeteries, and bridges; the city of Aspen is one full of culture and spirit. The popularity of this
tourist destination is derived from its historical background and surrounding landscape scenery.
The town thrives on the recreational and scenic opportunities that are present thanks to the
natural environment. As time continues and expansion occurs, the matter of preserving these
natural resources becomes ever more pressing. How does a historic place such as Aspen,
Colorado balance preservation of the mountain views with the booming growth of the city itself?
The line of preservation and expansion incorporates many delicate factors. The beautiful natural
landscape and historic entities often stand to be threatened by expansion.
Why exactly do we want to preserve the mountain landscape and possibly restrict further
human expansion? Aside from the obvious physical benefits that are associated with mountain
activities, there is a distinct value in their visual appearance. Often overlooked is the aesthetic
value which is not only concerned with what we see, but what emotions are evoked when we
experience these vast landscapes. Small viewsheds here and there throughout a city can
encourage a feeling of serenity for pedestrians within the hustle and bustle of an urban
environment. Other times there are vast views to the mountain landscape, which can strongly
connect and draw people to nature. The mixture of different views brings a dynamic touch to the

1

structure and layout of the city. When contemplating future development in an environmentally
sensitive area it is important to incorporate these various visual links and view corridors to the
natural world, as well as protect them. As new development encroaches upon the physical
mountain landscape, it threatens to diminish its visual value. This thesis focuses on the aesthetic
value of viewsheds in a Rocky Mountain environment. A viewshed is made up of the areas of
land, water, and other environmental elements that can be seen from a fixed vantage point (Ii et
al., 2012). In a city, such as Aspen, viewsheds should be very carefully considered throughout all
stages of development. There has been some recognition, however there needs to be an increased
emphasis on the importance of these viewsheds.
The natural landscape has been appreciated for thousands of years; dating back to early
Chinese and Japanese civilizations assessment of the landscape has been a topic of conversation.
There are many different views on how to manipulate natural and built structures to exist in a
cohesive manner that benefits both humans and the environment. Theories have evolved over
time and methods have advanced using technology, however there has always been an
underlying theme in respect to the natural environment.
Research studying the physical benefits of the environment has been vast, however until
recent years the visual benefits were difficult to properly assess. Putting a quantitative value on
how people aesthetically appreciate the landscape has limitations due to fact that the visual
quality of landscape is often intangible (Lu, 2011). Opinions on how to accurately evaluate the
landscape reach towards the same goal, yet are derived from different theories or combinations
of theories.
Governmental agencies, psychologists, landscape architects, environmental planners and
many more professionals have worked towards developing a concise method to evaluate and

2

predict the visual and ecological quality of the natural environment (J. B. Burley, 1997). Their
methods have ranged from public surveys to psychological evaluations assessing natural and
photographed landscapes. The varying tools used to evaluate human perception on the natural
landscape have generated four universally recognized paradigms. The expert paradigm,
psychophysical paradigm, experiential paradigm, and the cognitive paradigm. The four models
for assessing visual quality of the landscape each contain strengths and weaknesses when it
comes to complete assessment. These four paradigms have led to positive advancements
concerning visual quality. The research has been able to quantify the aesthetic value of our
natural landscape, and prove there is a need to enhance and protect the viewsheds that overlook
the environment. With focus on the research of Dr. Jon Bryan Burley, who has studied
respondents across South America and has developed a model concerning visual quality, this
thesis studies the aesthetic value of Rocky Mountain viewsheds.

3

CHAPTER 2: Literature Review
As humans, we benefit from the natural environment physically, psychologically and
even visually. The aesthetic value of a landscape is a complicated topic that relies on several
variables. Historically, the landscape has been appreciated and respected as humans have settled
and manipulated the land for their benefit. Early theories and techniques have been developed
which are still relied upon today in landscape design and land planning. These theories and
techniques balance the relationship between the natural and built environments to create the most
pleasing surroundings both physically and visually. Early practices now act as the fundamentals
for design. As landscape architecture and environmental planning have become more and more
established, research within the field has evolved from how to practice to how to improve the
practice. This research specifically focuses on the improvement of visual quality; how to
properly assess and design to achieve optimal visual quality within the landscape.
The visual aspect of the landscape is an often overlooked element when it comes to
design. However recently, within the last 50 years, there has been an increased awareness for the
aesthetics of the designed environment. Specifically, the mountainous regions; the mountain
landscape has been a popular location for settlement based on industrial, recreational and scenic
reasoning. With the recent upswing of the economy, there has been increased settlement and
development in the Rocky Mountain region. With all the newly built structures it is imperative
we actively think about viewsheds during the entire design process. Utilizing views toward the
natural environment creates opportunities for successful design.

4

2.1 Borrowed Scenery
Based upon our past experiences and personal opinions we place a certain value on the
outdoor spaces we encounter. The ability to connect with our natural surroundings strengthens
the value we place on the landscape. To experience a landscape consists of taking in the entirety
of the scene using all senses; sound, taste, touch, smell and sight. The vast landscape we see
before us, the smell of blooming flowers, sound of rushing water are all entities that contribute to
our experience in a landscape. What we take in through our sensory receptors, the resultant
feelings and emotions are what generate this value. Focusing on the sense of sight, various
techniques to enhance the visual experience of our environment have been used throughout time.
Dating back to the late Ming-period is the technique of borrowed scenery, ‘Jienjing’ 借景
(Kuitert, 2015). The method is first explained in a comprehensive guide to garden-making
manual titled The Craft of Gardens, ‘Yuan Ye’ 園冶 (Cheng, 1988).
The technique of borrowed scenery incorporates the use of distant landscape features into
the foreground to appear as one cohesive environment. The illusion of expanding the outdoor
space to encompass distant vistas increases the visual sensation and increases the visual value we
place on the area, overall enhancing the experience. Originally descended from the Chinese, the
technique did not take flight until the Japanese began using ‘Shakkei’ 借景 (Kuitert, 2015).
The evolution of borrowed scenery truly began in Japanese gardens, the practice
reinterpreted the way the landscape was viewed. The technique created an entire composition
where a distinct foreground, middleground and background worked together to create a visually
pleasing environment. For a person standing in the garden, a picture was created as the garden
seemingly expanded to the surrounding landscape.

5

The success of this design method relies on the unity of three elements; foreground,
middleground and background. The foreground is represented by the garden, the area that is
nearest to the observer. Often in a Japanese setting the garden would be surrounded by built
structures and dense urban areas, distracting the viewer from the true beauty of the garden.
Obscuring the nearby built environment allowed people to really connect with nature and enjoy
the garden itself and the views it presented. In order to achieve the illusion of unison between the
garden and distant landscape, the middleground worked to block out unwanted scenery by
framing the desired viewsheds.
The middleground or frame is key in creating a strong relationship between the private
garden and the distant landscape. This element concealed the intermediate space to bring the
natural landscape scene into the confines of the garden. The use of natural and built materials
framed the distant vistas horizontally and vertically (Nakase, 2015). Straight horizontal elements
such as hedges or a fence masked views of human scaled objects such as roadways or built
structures. Perpendicularly, vegetation worked to encapsulate the vertical frame and eliminate
significantly larger gaps. Monumentally scaled entities such as suburbs or villages spanning
many miles seem to disappear through the use of framing objects. These horizontal and vertical
elements worked to obscure spatial depth cues to diminish the sense of distance between the
garden and surrounding landscape (Kuitert, 2015).
The sought after connection to the background of the scene is what truly releases the
garden from its limits. When the relationship is attained in a successful manner; the viewer can
experience the full capacity of not only the garden, but the entire environment. The surrounding
landscape in a Japanese garden consists of earthly features such as mountains, lakes, waterfalls,
rolling hills…etc. In addition to the natural features, man-made elements such as unique

6

architecture, castles, temples, statues and monuments were often time the subject of the picture
view desired (Kuitert, 2015). Borrowing these features gave a certain freedom to the observers,
creating a limitless feeling which allowed the viewers’ mind to wander through the entire
landscape visually and conceptually.
An established connection through the use of borrowed scenery enables visitors to fully
experience the space through a visual medium. The technique of borrowing scenery plays a
significant role in not only designing a specific garden, but in planning the entire landscape area
as well (Nakase, 2015). The ability to fully utilize the entirety of a landscape and allow observers
to fully experience a space leads to success in terms of design. The view of the far-off landscape,
for example mountains, enhances the experience the observer has, therefore denoting the
importance of the mountain landscape features. Utilizing the beauty of our natural landscape in
development leads to effective designs. People wish to be in spaces that function properly and
are pleasing to be in whether physically or visually. The visual aspect of design has been a goal
for hundreds of years, however as a priority it was placed behind functionality. General
techniques such as borrowing scenery have acted as added bonuses of garden and urban design
after the fundamentals of the spaces had been developed. Often an afterthought, aesthetics of the
landscape was not a significant element in terms of design success. Historically, in landscape
design, visually pleasing areas were not evaluated scientifically and therefore they did not signify
much more than an opinion. Spaces were visually assessed in terms of “like” or “dislike”. A
quantitative value had not been placed on the visual quality of a landscape until roughly the
1960’s (J. B. Burley, 1997).

7

2.2 Visual Quality
2.2.1 Shafer & Colleagues
Appreciation for the visual benefits of the outdoor landscape has been a topic for quite
some time, relating back hundreds and hundreds of years. Explorers of the topic were never able
to quantify the visual quality of a landscape until quite recently, within the last 50 years. Visual
quality assessment has been a process based upon the opinion of users and how they feel about
the landscape they are viewing, a qualitative method. Beginning in the late 1900’s, the modern
era of visual quality research sought to assess visual properties of the landscape in order to
evaluate the impact of proposed treatments upon the landscape (J. B. Burley, 1997). Research
during that time was led by Elwood Shafer and his colleagues. They explored a psychophysical
approach in order to obtain a perception based evaluation of visual quality of the landscape. They
worked to find the relationship between the natural environment, trees, water, mountains, etc.,
and what people felt about the outdoor natural environment (Shafer, Hamilton, & Schmidt,
1969).
Project leader for the United States Forest Service and a Professor at the New York State
College of Forestry, Elwood L. Shafer’s research aimed to provide a better basis for planning,
developing and managing natural environments (Shafer, 1969). What is thought to be his
greatest influence on visual quality research was his perception based predictive equation.
Evolved from various studies conducted by him and his colleagues (Shafer & Thompson, 1968,
Shafer et al., 1969), the predictive model determined natural landscape preferences among South
American participants. American participants.
The model began as a survey conducted by Shafer in 1966. He gathered opinions of
Adirondack campers in on what they felt were the most important features of various

8

campgrounds (Shafer, 1966). A great starting point for visual quality research the survey lead to
five features important to the campers; (1) campsites near water, (2) swimming and water sport
facilities, (3) landscape variability surrounding the campground, (4) campground designcampsite spacing, and vegetative screening, (5) tourist attractions nearby (Shafer, 1966). The
survey acted as a platform for a later approach conducted by Shafer & Thompson in 1968. The
purpose of this study was to identify a correlation between physical properties of an environment
and some index of behavior within that environment (Shafer & Thompson, 1968). Building off
the previous findings from 1966, they developed an equation connecting a given behavior pattern
and environmental conditions of a campground.

𝑌𝑌 = 3409 − [0.0183 (𝑋𝑋1 + 𝑋𝑋2)] + [0.157 (𝑋𝑋3^2 )] + [0.0002(𝑋𝑋4 · 𝑋𝑋3^2 )]
Where:
Y = Annual total visitor-days per campground.
X1 = Area (square feet) of land at the developed swimming beach.
X2 = Area (square feet) of water at the developed swimming beach.
X3 = Total number of campsites.
X4 = Number of islands accessible by outboard motorboat

This equation only involves four visual features in the landscape, however accounts for
practically all of the variations in a campground’s use intensity (Shafer, 1969). Although limited,
the equation significantly explained much of how visitors were attracted to the campgrounds.
Such a small study and study area, generated another great stepping stone in the research of
visual quality, but opened a lot of doors for the investigators as well.
Findings from Shafer’s equation demonstrated the amount of variation in a given
environment increases the perceptions it can induce (Shafer, 1969). Different landscape
properties within an environment generate numerous perceptions, compared to a strictly

9

heterogeneous landscape with limited landscape qualities. The uniqueness of an environment
stems from the variation of landscape properties. The question resulting from this study was
“How is the total variation of that environment distributed among its various elements?” (Shafer,
1969). Attempting to identify elements of the landscape that showed importance among
recreational planning, Shafer developed nine factors that described Adirondack campground
environments (Shafer, 1969).“Water-wilderness facilities; Swimming area facility; Campgroundlake accessibility; White birch predominance; Supplementary lake expansiveness; Campsite
picturesqueness; Tourist magnetism; Remoteness; Campsite-lake accessibility” (Shafer, 1969).
These categories explained majority of the physical features outlined by campers, each factor
contributed to the total variance within said environment. These factors are important because
they account for a large portion of the variance and should be acknowledged during the planning
process.
Desiring a mathematical reasoning to environmental perceptions, Shafer and his
colleagues comprised a study. The purpose of this study was to identify quantitative variables
from a photographed landscape that were related to public preference and create a predictive
model. To determine useful quantitative variables that determine significant landscape features,
the study examined black and white photographs. The photographs were taken by multiple
photographers, half of western United States and half were eastern United States landscapes. The
photographs consisted of various combinations of forests, mountains, meadows, and water. To
analyze the photographs, 10 landscape zones including vegetation, nonvegetation, water and sky,
were determined and studied for each landscape scene. A Âź-inch grid was placed on each of the
photographs. The predominant feature of each square determined the landscape zone (Shafer et
al., 1969).

10

Each landscape zone was then divided into four variables including; perimeter, interior,
area and horizontal end-squares which in turn generated 40 variables (10 landscape zones
multiplied by four variables.) In addition to the 40 variables, three tonal variables and three
variables referring to the composite areas of sky, land, or water, there is a total of 46 variables.
The next portion of the study was used to determine if there was a significant relationship
between the quantitative variables of the photographs and the preference score.
To obtain a preference score (Y) of a landscape, interviews with campers from the
Adirondacks in New York State were conducted. 100 photographs were randomly sorted and
ranked among 25 adult participants. The photographs received a preference score which, along
with the photographs was used to generate a predictive model, equation 2.

𝑌𝑌 = 184.8 − [0.5436𝑋𝑋1 − 0.009298𝑋𝑋2] + [0.002069(𝑋𝑋1 · 𝑋𝑋3)^2 ] + [0.0005538(𝑋𝑋1
· 𝑋𝑋4)](2) − [0.06(𝑋𝑋3 · 𝑋𝑋5)] + [0.001634(𝑋𝑋3 · 𝑋𝑋6)] − [0.008441(𝑋𝑋4 · 𝑋𝑋6)]
− [0.0004131(𝑋𝑋4 · 𝑋𝑋5)] + [0.0006666 𝑋𝑋1²] + [0.0001327 𝑋𝑋5² ]

Where:
Y = Landscape preference.
X1 = Perimeter of immediate vegetation - section of the photo where characteristics of individual
leaves and bark are easily distinguishable.
X2 = Perimeter of intermediate nonvegetation - section of the photo where nonvegetation is
visible, but not in fine detail.
X3 = Perimeter of distant vegetation - section of photo where shapes of v vegetation cannot be
distinguished.
X4 = Area of intermediate vegetation - section of photo where vegetation is visible, but not in
fine detail.
X5 = Area of any kind of water - section of photo that includes water.
X6 = Area of distant nonvegetation - section of photo where shapes of nonvegetation cannot be
distinguished
The equation proved useful to quantify aesthetic preferences, however further testing was
required. The investigators designed six field tests using 8 x 10” photographs. Each test consisted

11

of 50 interviews of Adirondack campers. Using a quantitative scaler-value, interviewees ranked
the photographs. The photographs were evaluated using the equation and a comparison was
made between observed and predicted preferences. The field tests statistically related several
landscape properties to the preferences of the campers. The equation was a positive step in terms
of research, it indicated that predictive equations had the possibility of being an effective
assessment method. However, the equation solidified the complexity of landscape perception;
total perception is not the result of simple additive effect of various visual features (Shafer et al.,
1969).
Over the years, Shafer’s research has succumbed to criticism, specifically by Bourassa,
Wienstein, and Carlson. Their criticism stemmed from the lack of formal, predictive theory to
explain the relationships between the variables measured in the photographs and the preferences
of respondents (J. B. Burley, 1997). The lack of theory however, does not dismiss the fact this
general approach had scientific merit and seemed to perform well in predicting landscape quality
(“Environmental Psychology,” 1984). Although the equation has been discarded, Shafer’s initial
technique has been the root of modern day visual quality research.

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2.2.2 Taylor & Colleagues
The expert paradigm supports the thoughts and ideas of professionalism; trained experts
such as landscape planners and designers, forestry managers and ecologists (Zube, Sell, &
Taylor, 1982) are the most qualified to perform and judge landscape assessments. Statistically
speaking this paradigm lacks any formal theory background and is predominately based upon
normative theories (J. B. Burley, 1997).Training in these fields includes the study of
environmental as well as artistically based information, which are the two foundations for the
expert paradigm (Taylor, Zube, & Sell, 1987). Derived from the arts and formal art theories of
past centuries professionals are trained to see through principles and elements of art and design
such as unity, variety, balance, scale, enclosure, form, line, color and texture (Taylor et al.,
1987). The ability to understand these principles which make up a landscape scene allows
professionals to assess the environment thoroughly. Complimenting the artistic background are
the fundamental elements of ecology and resource management practices such as species,
diversity, quality of timber, or lack of evidence of humans (Taylor et al., 1987). The expert
paradigm focuses on categorizing and rating landscapes based on these two fundamental criteria.
Through their studies and training, experts develop a thorough understanding of both artistic and
ecological principles, in turn making them the most suitable judges for assessing visual quality of
the landscape. This paradigm comes with an ease of assessment needing simple photos or site
ratings and the use of a few trained persons (Lu, 2011). The weakness of the expert paradigm is
the lack of public or lay person opinion. To fully understand and assess the visual quality of a
landscape scenario, public preference is an essential component. The strength of the expert
paradigm has led to its popularity among visual quality research studies as well as professional

13

planning and design decisions. The weakness of the expert approach has led to the development
of various assessment techniques.
Development of the natural environment is performed by trained professionals who
consider many different factors/variables and manipulate them to create functional spaces. They
aim to meet the needs of users in an environmentally friendly manner. Relatively speaking, the
general population, or lay person is going to be using these spaces. In terms of understanding the
environment to design the landscape, it is important to understand it from the user’s or public’s
frame of thought. The psychophysical paradigm focuses on the opinions of the public and strives
to understand their perception.
The psychophysical paradigm examines the relationship of landscape variables and the
public perception of said variables. Various visual treatments statistically analyze public
opinions of landscape scenarios (Lu, Di., Burley, Jon., Crawford, Pat., Schutzki, Robert., &
Loures, 2012). Similar to the expert paradigm, the psychophysical approach lacks theoretical
support as to why specific preferences occur, however has led to the discovery of a magnitude of
public preferences. The paradigm is built upon experimental psychology, carefully controlled
experimental studies to measure responses. The experiments developed under the psychophysical
paradigm gathered landscape assessment data from public respondents. According to Taylor and
colleagues, the primary idea behind the psychophysical approach is to understand landscape as
stimuli to respondents (Taylor et al., 1987). Various assessment methods including rating and
comparison surveys measured public responses to photographed landscapes. Statistical
techniques, regression models, and predictive models made progress towards the evaluation and
assessment of visual quality.

14

The expert and psychophysical paradigms focus specifically on physical elements of the
landscape to assess visual quality. The third paradigm recognized is the cognitive paradigm,
which expands the realm of exploration addressing human-landscape interaction. According to
Taylor and his colleagues, landscape quality is seen as a construct built up in the mind, people
are individualists who relate to certain aspects of the landscape which have value to them. The
cognitive paradigm explores the role of perception in human adaptation and evolution. Studies
developed under this paradigm focus on cognitive variables rather than physical landscape
features. Outlined by multiple professionals including Kaplan and Kaplan, Terry and Appletion,
cognitive variables such as legibility, coherence, mystery, complexity, smoothness, density and
degree of naturalness significantly contribute to visual quality. The way people perceive these
elements varies due to past human experiences (Kaplan, R., 1976). Time spent in the landscape
and events we experience throughout our lifetime affect the way we connect and value the
environment. The cognitive paradigm focuses on human interaction with the landscape and why
we perceive it the way we do. The strength with the cognitive approach is that it attempts to
identify and establish a relationship between humans and the landscape. The weakness is the
difficulty in measuring this relationship through cognitive variables in a quantitative manner,
making it a difficult construct to establish (Lu, 2011). The cognitive paradigm lacks a validated
form of measurement, however expands upon the expert and psychophysical paradigms.
Information gathered through this paradigm gives experts an insight as to which landscapes are
highly valued and preferred and develop future designs to meet and surpass that quality.
The fourth technique is the experiential paradigm, in this approach humans are
considered “active participants in the landscape” (Taylor et al., 1987). Their experiences
determine their preferences of landscape values. The experiential paradigm aims to measure

15

attitudes, feelings, and impressions as a person experiences the environment (Lu, Di., Burley,
Jon., Crawford, Pat., Schutzki, Robert., & Loures, 2012). Similar to the cognitive approach, the
experiential paradigm researches the interaction of humans and the landscape. Methods under
this paradigm included psychological measurement, investigation and surveys that recorded not
only visual preference but the respondents’ attitude and feelings toward the landscape
experiences. The studies conducted under this paradigm focused more on particular landscape
values (Yu, 1987) rather than the quality of the landscape. In terms of landscape planning and
management this method of research lacks in effective information (Lu, 2011). The information
of this paradigm is often considered weaker than the other research methods and is not as
frequently researched. However, the human-landscape interaction should be considered during
the overall assessment of landscape quality.
The research from these four paradigms contains valuable information covering the scope
of visual quality research. Each paradigm presents varying strengths and weaknesses, alone
these paradigms reveal information suitable for a portion of visual quality research. However, to
fully understand the entire topic of visual quality, researchers need to address the four paradigms
as a unified approach. Studies that are developed utilizing characteristics from each of the
paradigms present a more thorough approach and have the potential to understand visual quality
completely.
2.2.3 Burley & Colleagues
Incorporating research techniques from various researchers, Dr. Jon Bryan Burley
developed a predictive ecological and environmental quality model. The model was originally
developed as a universal model across South America. The model illustrated its use for
transportation planning and design; which explains 65 to 70 percent of respondent preference to

16

predict human preference (J. B. Burley, 1997). In addition to previous methods, Dr. Burley
added an “environmental quality index” to further assess visual quality of the landscape.
His work, as much of visual quality research, derived from the work of Shafer and his
colleagues. The assessment methods using photographs, gridded squares, physical variables and
other environmental attributes act as the foundation for Burley’s equation. The measure of visual
quality includes much more than just the visual variables; studies revealing a combination of
aesthetic, cultural, economic and ecological variables contribute to visual quality. Several
researchers after Shafer generated material that Burley sampled from in development of his
equation; their research explored and identified these other key informational and experiential
variables beyond the physical attributes of the landscape when measuring visual quality (J. B.
Burley, 1997). Kaplan and colleagues investigated additional variables such as landscape
legibility, coherence, mystery, and complexity. Their studies revealed significant variables which
Dr. Burley adapted into his equation, those variables include openness, smoothness, and
locomotion (Kaplan, R., Kaplan, S., Brown, 1989) In addition to these variables, Dr. Burley
used an environmental quality index similar to the index presented by Carol Smyser (Smyser,
1982). The environmental quality index generated by Carol Smyser is somewhat a qualitative
index, multiple applications over the years have shown its consistency (Jin, 2012). The index,
described as the health index by Burley, references to the presence of air, water, soil resources
and other natural resources. The health index states that the more positive environmental
attributes the higher visual quality score.
Incorporating the variables mentioned above and Shafer’s technique, Dr. Burley
developed his predictive equation explaining 67 percent of the variance. The photographs were
images from South America, the variables contained in the picture were: buildings, automobiles,

17

boats, people, wildlife, roads, fire, smoke, clouds, flowers, vegetation, and nonvegetated
substrate. They were photographs of various landscape types: prairie, woodland, wetland,
agricultural, urban savanna, and cliff detritus, typical aspects contained in almost all of the
environment (J. B. Burley, 1997). The equation revealed two types of regressors, which opposed
each other.
Per Burley, imagine if all elements are zero at the same time, the image is 100 percent
natural elements. The visual quality score would be 68.30, one set of regressors is negative, the
other is positive. The first set being negative, man-made elements represent the noospheric
regressor. Noospheric features including vehicles, human beings, utility structure, etc. lead to
negative visual quality scores. The more noospheric variables contained in an image, the higher
score it would receive and the less preferred it would be, above 68.30. On the other hand,
biospheric attributes including vegetation, wildlife, openness, flowers and mountains generated
positive visual quality scores. The more bioshperic variable present in the landscape, the lower
the score it would receive and the more preferred it would be, below 68.30.
These two regressors were further studied by Burley and his colleagues in 2011. The
study titled Visual and Environmental Quality Perception and Preference in the People’s
Republic of China, France and Portugal utilized Dr. Burley’s equation to compare visual quality
preference similarities and differences of those who participated in the American preference
study (Mo, ClĂŠach, Sales, Deyoung, & Burley, 2011). The study was able to link the two
regressors to theoretical framework. The first theory, which Burley mentioned in his 1997 study
is the “Biospheric Preference Theory.” This theory postulates that respondents have a preference
for biospheric surroundings and a dislike for noospheric surroundings (J. B. Burley, 1997). This
theory is based off the property values of urban and rural residential properties. Rural lots

18

contain quiet streets with few automobiles, have relatively few people, and have fresh air and
clean water (Burley, 1997). This theory states that many South American residents preferred this
type of atmosphere, therefore prefer biospheric conditions.
The study analyzed the responses from the varying regions and divided the Biospheric
Preference Theory three sub-theories; “Theory of Human Intrusion”, Theory of Landscape
Enhancements”, and “Theory of Neutral Modifiers” (Mo et al., 2011). The first sub-theory states
that intrusions are not perceived well by human subjects. Abundance of people, cars, pavement,
eroding soil and related features are signs of intrusion; landscapes displaying an abundance of
these attributes are not scored well. The second sub-theory, ‘landscape enhancements’ correlates
to landscape elements or events that are not often experienced. The blooming of a flower only
occurs in specific time periods and is appreciated when viewed, increasing the landscape value.
Another example being wildlife, sighting of a wild animal can often be rare, so when they are
present value is increased. Distant landscape features are also considered landscape
enhancements. Mountains, buttes, and buildings are generally viewed from specific locations or
directions and increase landscape value just as other enhancements. The third sub-theory pertains
to ‘neutral modifiers.’ These are identified as common spatial elements found in the natural
landscape: sky, clouds, green vegetation and water (Mo et. al., 2011). This theory suggests that
landscapes containing a great quantity of neutral modifiers result in an average (neutral) visual
quality score. Landscapes containing primarily intrusions or enhancements show extremely low
or extremely high scores, displaying their significance. This theoretical framework gives validity
to the study which explained that region is a significant aspect when studying visual quality.
Further research over multiple regions would give insight to visual quality on a global scale. This
information has the potential to increase the productivity and accuracy of design treatments as

19

they appeal to users. Burley continued research as it applies to design treatments on a smaller
scale in 2011, Reinventing Detroit: Reclaiming Grayfields – New Metrics in Evaluating Urban
Environments.
Similar to the formation of his equation, Burley and his colleagues developed this study
to assess a design proposal for Detroit from economic, ecological, aesthetic and cultural
standards (Jin, 2012). Once a booming industrial city, much of Detroit now stands as a grayfield
in a state of redevelopment. The study compared ideas of Frank Lloyd Wright, old designs from
the city (Old Detroit) and new design ideas for Detroit (New Detroit). “Broadacre City” was an
idea proposed by Frank Lloyd Wright, never built, but not forgotten. Much beyond his
architectural training Wright pulled concepts closely related to landscape architecture; the design
considered site hydrology, softscape spaces, recreation, circulation and way-finding, wildlife
habitat, vegetation complexes, aesthetics, socialization, cultural identity, utility corridors,
multiple land-uses, and building footprints (J. Burley, Deyoung, Partin, & Rokos, 2011).
“Broadacre City” was a vision for low density living environments that coexisted with
agriculture and natural spaces, very different from old Detroit. The purpose of this study was to
understand the predicted environmental quality of “Broadacre City” compared with Old Detroit
and the ideas associated with New Detroit. The study used previous methods developed by
Burley and revealed the design for “Broadacre City” and New Detroit are better environments
and more preferred than the conditions of Old Detroit. This study shows that designers and
planners have the opportunity to test their designs before implementation. This model has the
ability to determine if and how much pleasure the public will get from a design.
Visual quality covers many aspects of a person’s opinion of an environment. The
research completed in the field has come a long way since the 1960’s. The modern era of visual

20

quality research can change the way landscape planners and designers process their treatments.
Predictive equations can assist in developing sensitive, efficient and effective landscape
treatments (J. B. Burley, 1997). Being able to assess possible landscape plans gives designers the
chance to alter their designs and make them the best they can possibly be.

2.3 Mountain Aesthetics
Just as taste in clothes, music, literature and art, visual perception of the landscape
changes throughout time. The idea of beauty in landscapes has changed during the history of
civilization (Panagopoulos, 2009). Our preferences will change and evolve as different cultural
norms and values come into play as time progresses.
An on-going conflict in the design and management aspects of landscape, ecological
sustainability and visual aesthetics have had opposing viewpoints concerning the care and
development of our natural environment. The problem lies with the theory of ecologically
sustainable environments tend to be less attractive than environments managed for aesthetics
(Parsons, 1995). During stages of urban development, the aesthetics are often not considered top
priority. Rapid production of urban landscapes including buildings, streets, power lines, largescale power plants etc. have hindered or threatened landscape views. Among experts in planning,
lost or damaged aesthetic qualities can be easily restored (Nohl, 2001). However, this is not the
case, it is usually very difficult or near impossible to revive traditional aesthetic integrity.
Treating the aesthetic issue as casual is a dangerous thought process and must be addressed.
Mentioned by Nohl, a design process known as the aesthetic paradigm has the potential for a
balanced method of developing landscape treatments. This paradigm focuses on the equality of
natural and built landscape features. It is a way of design that respects nature in terms of land use

21

processes. It encourages a stability between the natural environment and an ecologically
sustainable built environment. The nature-compatible land use processes will decisively improve
the aesthetic situation of landscapes (Nohl, 2001). Designing a place that is environmentally and
aesthetically pleasing leads to a successful place that welcomes users and sustainability. It is
important we prioritize aesthetics to ensure proper design methods.
The importance of aesthetics in the environment lies beyond the obvious visual benefits.
Studies conducted assessing aesthetic benefits of the landscape have revealed multiple benefits
including emotional, cognitive, physical, psychosocial, and psychological. Moore (1982) and
West (1985) compared health indicators of prisoners with high and low quality window views
(Choudhry et al., 2015). They discovered prisoners with a view of nature had fewer stress related
physical symptoms such as headaches and indigestion. Cognitive benefits were found in a 1991
study by Hartig and his colleagues. Significant improvements were shown among subjects
exposed to the natural environment (Choudhry et al., 2015). In 1993, Hartig found subjects who
were exposed to the natural environment tended to have greater positive and lesser negative
emotional responses. The study also found that physiological measures including blood pressure
and heart rate positively correlated to the natural environment (Parsons, 1995). In addition to
these visual benefits, exposure to natural wildlife yields psychosocial and physiological benefits
as well (Parsons, 1995). Overall, studies have found interaction with the natural environment and
its components generates a positive impact on humans. It is necessary to preserve these natural
elements, when new development has potentially harmful effects. The ties to nature provide a
sense of belonging which contributes to the culture of a town. The character created through a
connection to nature is a direct link to the neighborhood livability, variety, and quality of life.
These built structures and surrounding landscapes work together in creating the character and

22

identity of the town, preservation is key in ensuring these resources are present for years to
come.

2.4 Viewshed Protection
Beginning as settlement design, the art of urban planning goes as far back at 400 B.C.
(Spreiregen, 1965). From Ancient Athens, to Paris, to Egyptian villages, motivation for
development of urban areas has evolved throughout time. Priorities considered in terms urban
design include people, administrative organization, artistic value, speculation and social welfare.
Majority of these priorities are based upon function rather than aesthetics. It wasn’t until the turn
of the 20th century that an emphasis was placed on aesthetic beautification (Spreiregen, 1965).
During this time period, the profession of Landscape Architecture began to take shape. Major
contributors to the profession including Frederick Law Olmsted, Calvert Vaux, and Frederic
Edwin Church depicted design with an artistic appreciation for the landscape. These individuals
generated awareness and appreciation for the visual aspect of the environment through the terms
of design, planning and evaluations of landscapes (Ii et al., 2012).
Ideals developed during the 1900’s and into the 2000’s progressed with a focus on
aesthetics. Throughout history there has been little emphasis placed on the visual importance of
viewsheds. Urban development originally did not take into consideration the heights of
buildings, signs, towers, rooftop equipment or any other threatening factors concerning
viewsheds (Ii et al., 2012). Scale and mass were often overlooked and over established which
diminished many scenic entities. When compared to a structurally dominant environment, the
untouched wilderness of the mountain environment generates positive emotions. Elements of the
natural landscape such as the vegetation, wildlife and geological features work together in

23

creating a peaceful and pristine environment, which is received in a positive manner when
viewed by humans. These emotions are directly linked to the beauty of the natural landscape and
their positive entities decrease when built structures inhibit views to natural areas. Pedestrian
oriented corridors nestled in the commercial district break up the busy streets. These inner
courtyards provide an escape from all of the built infrastructure that encompasses the city. The
small scale of these areas ties into the history of the space and enhances the character. The use of
natural elements and viewsheds within a city connect humans to nature. It allows for a smooth
transition between the natural and built environment. When designing urban areas, there needs to
be an emphasis placed upon the natural world through the use and protection of viewsheds. This
research explores the aesthetic benefits of viewsheds that incorporate natural mountain landscape
features within an urban environment.

24

CHAPTER 3: Method
3.1 Purpose of Study
A region such as the Rocky Mountains attract endless visitors each year due to recreation
and many other factors. In recent years, the state of Colorado has seen a constant increase in
population (U.S. Department of Commerce, 2016). People are flocking to these environmentally
spectacular areas seeking places where they can surround themselves with the outdoors. This
increase in tourism and long term residents generates the need for new infrastructure. New
development in environmentally sensitive areas such as the Rocky Mountains poses many
difficulties. People want to settle and experience these areas with vast landscapes, however if
numerous people wish to develop in these areas, the wildlife and landscape become threatened. It
is important we consciously develop with preservation in mind. It may become simple to lose
sight of the important qualities that the environment provides, when there is so much growth
present.
Viewsheds in the Rocky Mountain environment are vital to the success of a city. Urban
areas with beautiful surrounding sceneries are desired areas to settle, whether temporarily or
permanently. People often wish to see as much of the natural landscape as possible when they
are choosing locations to visit or live. The visual scenery of these specific viewsheds are often
dominated by mountain landscape features. The purpose of this study is to define a specific
correlation between the visual quality scores of a Rocky Mountain landscape and the area of
mountain features within the landscape.
The Hypothesis for this study is:
Hypothesis 1: The predicted visual quality scores of a Rocky Mountain landscape have a high
concordance with the area of mountain features within the landscape.

25

Null Hypothesis 1: The predicted visual quality scores of a rocky mountain landscape have a low
concordance with the area of mountain features within the landscape.
These mountain features are nice to look at and people generally wish to view them,
however is there a significance where these landscape features are concerned? Do the mountain
landscape features have a significant positive impact the visual quality of Aspen, Colorado?
3.2 Research Area

Figure 1: Colorado State

26

Figure 2: City of Aspen, Roaring Fork River Valley, Colorado

Located in the heart of Colorado’s Rocky Mountains is one of the state’s most historic
and popular tourist destinations. In the picturesque Roaring Fork River Valley is Aspen,
Colorado, an attractive year-round site full of beautiful mountain scenery, endless recreational
activities, historic amenities and high-end retail and restaurants. The town started as a settlement
of the silver mining boom during the 1800’s and gave way to many historical landmarks that are
still standing today. The town became a skier’s dream in the latter half of the 1900’s when the
town hosted the FIS World Alpine Championship (“History,” 2008). The mix of recreational
activities has been complemented by designer boutiques and classy restaurants and has become a
popular second home to many celebrities. The vast array of activities appeals to a wide range of

27

people, triggering the tourist population. The natural landscape is to thank for that; the mountains
provide recreational opportunities, increase property values and contribute natural resources for
the popular town.
Aspen, Colorado was chosen for this research for various reasons. The first reason is the
establishment of the city, it has been successful for over 100 years and includes many historically
significant elements as well as new construction. The second is population, according to the 2010
census the population of Aspen hovers around 6,000 full time residents (U.S. Department of
Commerce, 2016). Permanent residents of the area are extremely passionate about the
preservation of the mountain viewsheds. The City of Aspen is very concerned with the people
that call the town home. Input from the members of the community is strongly encouraged and
community forums are often held to gather input. Citizens are invited to meet with the city
officials to express their opinion on developmental decisions for the city. Input from community
members is vital in any city, especially one with such strong historical ties. In 2012 the
community gathered to discuss height limitations for the commercial core of the city. The public
strongly disliked the use of three story buildings because they obstruct so much of the mountain
viewsheds. The city planning committee then altered the rule according to the opinions expressed
in this forum and the height restriction was placed at a lower height (Supino, 2012). Community
involvement is vital for the prosperity of the town. People who are unhappy are likely to leave
and not return. If the city is able to successfully incorporate the community opinion in the design
of the town, people are more likely to develop a relationship with the city and want to stick
around.
The community has a strong core of full time residents however, the number of
inhabitants increases dramatically during the ski season and the popular summer season. To

28

accommodate the tourist population, infrastructure has been a need in the past and will be a
necessity in the future. This tourist population strongly relies on the mountainous views which
leads to the third reason for selection; the geographic make-up of Aspen. Since the town is
located in the Roaring Fork River Valley the potential for mountain views is increased compared
to a town in the mountains with obstructed views. The grid pattern that Aspen was designed
upon accentuates these mountain views. The streets strictly run North-North and East-West
giving visitors distinct views of the nearby landscape and multiple opportunities for visual
quality research.

3.3 Data Collection
The data used in this research is gathered from photographs taken of downtown Aspen by
myself. The pictures were collected in August 2015; the use of summer photographs ensures the
proper count for vegetation. The visual quality scores would show a variety with a sample from
different seasons. The photographed locations were documented by hand and translated into an
Adobe Photoshop map. Using the City of Aspen zoning map 46 photographs were collected in
four districts; Commercial Core, Lodge, Mixed Use, and Residential.

29

Figure 3: Downtown Aspen Photograph Locations by Zoning District

The picture locations were chosen to ensure a concise sample of the city. The variety of
zoning districts encompasses majority of the land uses for the city of Aspen. The zoning map of
Aspen was developed by the Bureau of Land Management and the City of Aspen Planning &
Zoning committee through the use of Geographic Information Systems. It was updated in 2015,
ensuring proper zoning representation. According to the City of Aspen the districts are defined as
follows (“Planning and Zoning,” 2016). Commercial Core: retail, service, commercial, recreation
and institutional purposes within mixed-use buildings to support and enhance the business and

30

service character. The district also permits a mix of retail, office, lodging, affordable housing,
free-market housing and short term vacation rental uses. The next district is defined as Mixed
Use: variety of lodging, short term vacation rentals, multi-family, single-family and mixed-use
buildings with commercial uses serving the daily or frequent needs of the surrounding
neighborhood, to provide a transition between the commercial core and surrounding residential
neighborhoods and to provide a variety of building sizes compatible with the character of the
Main Street Historic District. The third district known as the Lodge: encourages construction,
renovation and operation of lodges, tourist-oriented multi-family buildings through short term
vacation rentals, high occupancy timeshare facilities and ancillary uses compatible with lodging
to support and enhance the City's resort economy. The City encourages high occupancy lodging
development in this zone district. The final district sampled from is defined as Medium Density
Residential: provide areas for long-term residential purposes, short term vacation rentals, and
customary accessory uses. Recreational and institutional uses customarily found in proximity to
residential uses are included as conditional uses. Lands are generally limited to the original
Aspen Townsite contain relatively dense settlements of predominantly detached and duplex
residences and are within walking distance of the center of the City. The photographs taken in
the sampled districts defined by the city of Aspen cover a wide range of building uses, which is
utilized by a large range of users including temporary and permanent residents.
The photographs were taken with an IPhone 5S, using the panorama 180° feature. Two
pictures were taken from each location to create a 360° viewshed. The pictures were taken from
the viewpoint of a person standing on the ground. The focus of each picture was to capture a
view of the mountain from each location. Other landscape attributes including vehicles,
buildings, people and vegetation were often present in the photographs.

31

A total of 46 pictures were gathered from the four districts. One sample picture from each
district was selected to be analyzed. Each 360° picture was divided into four 8 x 11” photos
pertaining to directional views. Each of the districts had four pictures that were labeled according
to their direction, for example Residential North, Residential South, Residential East and
Residential West. A total of 16 pictures were sampled from and analyzed.

3.4 Analysis Techniques
The photographs were analyzed using Burley’s equation based upon physical variables
and environmental quality developed in 1997. It has been regarded as a universal predictive
equation concerning South American landscapes and has been used in various studies. The
predictive model explores the landscape including total area of noospheric features and total area
of motorized vehicles; presence of humans, wildlife, utility structures, and foreground flowers;
total area of distant nonvegetation landscape features such as mountains and buttes; perimeter of
intermediate nonvegetation; total area of foreground vegetation; and openness, mystery, and
environmental quality index; with an overall p-value for the equation <0.0001 and a p-value
<0.05 for all regressors (J. B. Burley, 1997).
Landscape variables are measured by overlaying a grid on the picture, each 1 x 1 square
grid is recorded as one unit (Figure 4). The numbers used in the equation are obtained by
counting areas or perimeters (J. B. Burley, 1997). The variables (Table 1) in addition to Carol
Smyser’s environmental quality index (Table 2) are implemented into the equation.

32

Figure 4: Mixed Use West

33

Independent Variables
HEALTH

Environmental quality index (Table 2)

X1

perimeter of immediate vegetation

X2

perimeter of intermediate non-vegetation

X3

perimeter of distant vegetation

X4

area of intermediate vegetation

X6

area of distant non-vegetation

X7

area of pavement

X8

area of building

X9

area of vehicle

X10

area of humans

X13

area of herbaceous foreground material

X14

area of wildflowers in foreground

X15

area of utilities

X16

area of boats

X17

area of dead foreground vegetation

X19

area of wildlife

X30 = X2+X4+(2*(X3+X6))

open landscapes

X31 = X2+X4+(2*(X1+X17))

closed landscapes

X32 = X30-X31

openness

X34 = X30*X1*X7/1140

mystery

X52 = X7+X8+X9+X15+ X16

noosphericness

Table 1: Independent Variables (J.B. Burley, 1997)

34

Environmental Quality Index
Variable

Score

A. Purifies Air

+1 0 -1

B. Purifies Water

+1 0 -1

C. Builds Soil Resources

+1 0 -1

D. Promotes Human Cultural Diversity

+1 0 -1

E. Preserves Natural Resources

+1 0 -1

F. Limits Use of Fossil Fuels

+1 0 -1

G. Minimizes Radioactive Contamination

+1 0 -1

H. Promotes Biological Diversity

+1 0 -1

I. Provides Food

+1 0 -1

J. Ameliorates Wind

+1 0 -1

K. Prevents Soil Erosion

+1 0 -1

L. Provides Shade

+1 0 -1

M. Presents Pleasant Smells

+1 0 -1

N. Presents Pleasant Sounds

+1 0 -1

O. Does not Contribute to Global Warming

+1 0 -1

P. Contributes to the World Economy

+1 0 -1

Q. Accommodates Recycling

+1 0 -1

R. Multiple Use

+1 0 -1

S. Accommodates Low Maintenance

+1 0 -1

T. Visually Pleasing

+1 0 -1

Total

______
Table 2: Environmental Quality Index (J. B. Burley, 1997)

35

The visual quality score is obtained through measuring variables and implementing them
into the equation. The lower “Y” scores, the better visual quality of the landscape.

𝑌𝑌 = 68.30 − (1.878 ∗ 𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻𝐻) − (0.131 ∗ 𝑋𝑋1) − (0.064 ∗ 𝑋𝑋6) + (0.020 ∗ 𝑋𝑋9)

+ (0.036 ∗ 𝑋𝑋10) + (0.129 ∗ 𝑋𝑋15) − (0.129 ∗ 𝑋𝑋19) − (0.006 ∗ 𝑋𝑋32)

+ (0.00003 ∗ 𝑋𝑋34) + (0.032 ∗ 𝑋𝑋52) + (0.0008 ∗ 𝑋𝑋1 ∗ 𝑋𝑋1) + (0.00006

∗ 𝑋𝑋6 ∗ 𝑋𝑋6) − (0.0003 ∗ 𝑋𝑋15 ∗ 𝑋𝑋15) + (0.0002 ∗ 𝑋𝑋19 ∗ 𝑋𝑋19) − (0.0009
∗ 𝑋𝑋2 ∗ 𝑋𝑋14) − (0.00003 ∗ 𝑋𝑋52 ∗ 𝑋𝑋52) − (0.0000001 ∗ 𝑋𝑋52 ∗ 𝑋𝑋34)

To identify if the amount of mountainous landscape affects the visual quality score, area
of mountain was recorded among the photographs. Using the variable identified in Dr. Burley’s
equation, the total area of mountain landscape in each picture was recorded. The pictures were
ranked according to visual quality score and area mountain to compare the relationship. In order
to scientifically prove a significant relationship between the visual quality scores and the area of
mountain of each picture, the two columns of ranks were compared using Kendall’s Coefficient
of Concordance (W) (Brown & Daniel, 1987) a nonparametric test which is quite flexible in use
and application (Lu et. Al., 2012). Score i is the given rank ri,I by judge number, j, where there
are in total n (16) scores and m (2) judges. The total rank given to score i is

36

The mean value of these total ranks is

The sum of squared deviations, S, is defined as

Then Kendall’s W is defined as

The test statistic W is between 0 and 1. If W is 0, there is no overall trend of agreement between
the two sets of rankings. If W is 1, the responses may be regarded as random. Intermediate values
of W suggest a degree of concordance among the responses. W will be used to test the agreement
between the visual quality ranking and the area of mountain ranking. Chi-square distribution will
be analyzed by consulting a chi-square table. If W is greater than the Chi-square number, the
results are in accordance with the p-values for that Chi-square number.

37

CHAPTER 4: Results
The visual quality scores were recorded for each location within the zoning districts. The
four sampled pictures from the Mixed Use district scored: 4408495, 25.8621, 31.7065, and
34.7153. The Commercial Core scored: 43.6106, 31.5029, 120.809, 35.5721. The Lodge district
scored: 71.8002, 58.2552, 52.1498, and 36.3071. The Residential scored: 48.1308, 36.6581,
42.1958, and 33.9858. The mean scores of each zoning district is represented below (Table 3).
The lower the score, the better visual quality for that area. The Mixed Use zone scored the best
visual quality and the commercial score had the lowest visual quality. Scores in the 30’s and 40’s
signify highly desirable landscapes. For an urban area such as Aspen, scores in the 50’s are
highly favored and still indicate a preferred landscape. However, according to Burley’s equation
the results dictate Mixed Use district to be the most preferred landscape. The Commercial Core
is shown to be the least preferred landscape for this research area.
Visual Quality Mean Scores by Zoning District
District

Mixed

Residential Commercial

Use

Lodge

Core

44.84948

48.13082

43.61061

71.80002

25.86215

36.65814

31.50291

52.14977

34.71527

42.19577

120.8086

58.25522

31.7065

33.98584

35.57213

36.30715

Average 34.28335

40.242643

57.8735625

54.62804

Table 3: Visual Quality Score by Zoning District

38

Visual

Visual

Area
Area

Picture

Quality Quality

Mountain
Mountain

Score

Rank

Rank

Mixed Use South

31.7065

3

177

1

Residential North

33.9858

4

57

2

Mixed Use West

25.8621

1

55

3

Lodge North

36.3071

7

45

4

Commercial Core West

31.5026

2

36

5

Residential South

42.1958

9

36

6

Mixed Use North

34.7153

5

33

7

Mixed Use East

44.8495

11

17

8

Commercial Core East

43.6106

10

16

9

Commercial Core South

120.809

16

15

10

Residential East

48.1308

12

14

11

Commercial Core North

35.5721

6

13

12

Lodge South

52.1498

13

6

13

Lodge West

58.2552

14

4

14

Residential West

36.6581

8

3

15

Lodge East

71.8002

15

2

16

Table 4: Visual Quality Score and Mountain Area Comparison

39

In Table 4 the pictures are ranked low to high based upon their visual quality scores
120.809 being the highest score and 25.8621 the lowest. The number of squares containing
mountain features of each photograph was recorded in the area mountain column. The visual
quality score and area mountain were assigned an associated rank. In Kendall’s Coefficient of
Concordance, a W value 0.870588 was generated. A corresponding Chi-Square table was
consulted to determine if the derived value for Chi-Square was significant (p≤0.05) at fifteen
degrees of freedom (Brown & Daniel, 1987). Since the derived value of Chi-square 26.11765 is
greater than the table value of 24.996, the null hypothesis was rejected and the hypothesis that
the two sets of numbers are in concordance (p≤0.05) was accepted. It was determined, through
statistical analysis, that the relationship of visual quality scores and the area of mountain features
are in concordance and significant to a high (95%) confidence level. In other words, statistical
analysis has proven the mountain features of a landscape relate to the visual quality scores of that
landscape. Mountain features are significantly important in high visual quality scores.

40

CHAPTER 5: Discussion
The results for this research agree with previous findings which generally state that
humans prefer biospheric (natural-like) landscape environments compared to urban, man-made
(noospheric) environments (J. B. Burley, 1997). The study area samples demonstrate four
different districts with four diverse urban constructs. The urban makeup of the districts
represents a majority of the land use that is present in not only Aspen, Colorado, but most urban
domains. Therefore, this research could potentially be applicable for other similar environmental
regions such as the Alps of Europe or the Andes of North America. Mountain ranges across the
globe are tourist hotspots and face many of the same developmental restrictions and challenges
as those in the North American Rockies.
In an area such as Aspen, Colorado the mountains play a significant role in everyday life,
they provide physical benefits, recreational opportunities and proven from this research, visual
benefits. The objective of this study was to prove mountain landscape features positively affect
visual quality scores in viewsheds of a Rocky Mountain environment. The study used a
combination of Dr. Burley’s predictive equation and Professor Kendall’s Coefficient of
Concordance and suggests a strong relationship between the area of mountain landscape features
and the visual quality of a landscape with a p-value of p≤0.05. The results proved the area of
mountain features present in a photograph demonstrate a lower visual quality score, which means
the area is more visually pleasing to a confidence level of 95%.
The study area for this research was positioned in a valley with surrounding mountain
features on all sides. The grid layout of the city streets was designed to achieve optimal
viewsheds in nearly all directions. The photographs assessed in the study display various
amounts of mountain features. The composition of urban elements in the different districts result

41

in varying levels of enclosure. Throughout the districts there are examples which display each of
the four levels of enclosure; full, threshold, minimal and loss of enclosure (Spreiregen, 1965).
Full enclosure is defined as a ratio of 1:1, one unit of distance away from an enclosing object
which is one unit in height. The upper limit of a person’s field of view is noted as a 45° viewing
angle to the top of the enclosing element. Details of the scene are registered, rather than the view
as a whole. Threshold of enclosure is recognized as a 30° viewing angle with a ratio of 1:2.
Objects are seen as a whole composition; this level is known as the lower limit of enclosure. The
next level is minimum enclosure defined as a viewing angle of 18° and ratio of 1:3. Viewers
perceive the prominent object beyond the space just as much as the space itself. Objects are
viewed as in a relationship to its surroundings. The final level is defined as a loss of enclosure
which is a 14° viewing angle and a ratio of 1:4. Objects in the scene are viewed as urban edges,
the space loses its containing quality. Structural elements that are in the peripheral zone also
function as edges in the viewshed. It is important in urban planning to keep enclosure in mind.
Dynamics of a city rely on the various levels of enclosure to create intricate spaces that appeal to
users. According to Spreiregen as a rule of thumb; for an urban area, the ratio must not exceed
1:3 which falls into the category of minimum enclosure. Plazas that exceed this ratio often lose
their sense of place, it is noted that the space “leaks out” and the plaza loses its general
significance.

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Figure 5: Lodge West – Full Enclosure

Figure 6: Commercial Core East – Threshold of Enclosure

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Figure 7: Commercial Core West – Minimum Enclosure

Figure 8: Mixed Use South – Loss of Enclosure

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Depicting the four levels of enclosure, the photographs generally scored better as the
level of enclosure decreased. This pattern agreed with majority of the photographs, until figure 8
which displayed a loss of enclosure. The fact that figure 8 did not score better than figure 7, it is
safe to assume, the area of mountain landscape features generates positive scores to a certain
point. It also establishes that enclosure is important when considering design where viewsheds
are highly valued.
Figure: District
5: Lodge

Visual Quality Area of
Score
Mountain
71.8002
2

6: Commercial Core

43.6106

16

7: Commercial Core

31.5209

36

8: Mixed Use

31.7065

177

Table 5: Average Visual Quality and Area of Mountain by District

Urban spaces alternate through various levels of enclosure. There are alleyways that
represent full enclosure, shopping corridors that mimic threshold of enclosure and plazas that
display minimum enclosure. The rule of thumb discussed regarding enclosure may correlate to
the visual quality scores of these areas. It may not be true that the more mountain features
present the better visual quality of an area. Aside from the views, there needs to be other features
to balance the natural landscape as well as create a sense of enclosure. If there were endless
amounts of open vistas containing high percentages of mountain features, the visual dynamics of
a cityscape wouldn’t be enjoyable and interesting. The impact that the mountain features have on
the users of the space would be lessened. Present in the data was a hint of a pattern pertaining to
this theory.

45

There is a certain point where the percentage of mountain features becomes less
significant pertaining to the visual quality score. It is safe to say there is a limit on the positive
effects this variable has on the visual quality score, this is referred to as diminishing returns.
There seems to be a pattern as the percentage of mountain features reaches above 3% the visual
quality score is not significantly impacted in a positive manner. This information can be helpful
when creating design guidelines. The large open views are great to see occasionally, however if
the mountains were to be omni-present, they lose their appeal and can become less significant.
Knowing that it only takes roughly 2-3% mountain features in a given frame to increase visual
quality scores creates more opportunities for developers. Design professionals are free to create
dynamic spaces with this information, there is much more room for higher levels of enclosure
within the 0-3% visible mountain feature range. A flow of smaller spaces with minimal mountain
views builds up an anticipation when a person is traveling through an environment. The
sequence of spaces can be heavily weighted with developed structures and when an open view
does present itself, it will be valued higher. This value will register in the minds of the
inhabitants, the mountains will remain significant and protected. Future development will not
need to expand into the wilderness, however will be intelligently expanded through denser
cityscapes.
In addition to levels of enclosure the data was studied was based on viewshed direction.
Rather than focusing on urban makeup, this method for analyzation does not rely on which
district of the city the viewshed is in. This allows the sample to be assessed as a whole rather
than by district. It also allows for another variable, rather than mountain features to be the prime
focus. Originally, the city of Aspen was designed based on a grid system of streets. The streets
are aligned South to North and East to West to achieve ideal views of the surrounding

46

mountains. The grid layout of the city was designed to optimize viewsheds, and the data respects
that grid. In addition to the positive features of the landscape, the negative variables were also
investigated. One individual landscape entity that seemed to have an impact on the visual quality
scores was the amount of pavement. The measurement was computed based on area of pavement
in terms of a percent of the entire landscape scene. The pavement variable includes any
hardscape material; concrete, pavers, stone, etc. These elements are assessed in a negative
manner due to the fact they generally do not improve the natural environment or contribute
positively to human health. To assess the negative impact, I looked at the mean visual quality
scores and the average percentage of pavement for each of the four view directions. The results
are as follows:
Direction

Visual Quality Score

Pavement Percentage

South

61.71528

41.49%

East

52.09778

34.01%

West

38.0695

26.51%

North

35.14508

26.94%

Table 6: Percent of Pavement

The higher average visual quality scores seem to have a correlation to the percentage of
pavement found in each directional viewshed. This is a rather small sample to gather information
from, but it seems that 30% pavement and below produces more visually pleasing landscapes.
Figure 9 below displays a viewshed with a higher percentage of pavement.

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Figure 9: Residential South
The percent of pavement pictured above is 40.04%, percent of mountain features is 2.4% and the
visual quality score is 42.19. Figure 10 below happens to be in the same district, however
displays a lower percentage of pavement. The percent of pavement pictured below is 27.14%,
percent of mountain features is .2% and the visual quality score is 36.67. Comparing these two
viewsheds signifies that even with minimal mountain visibility, a desired viewshed can still be
obtained if the pavement is kept to a minimum.

48

Figure 10: Residential West

Based on the way the city is laid out, generally speaking the West viewshed has very minimal, if
any visible mountain features. Overall the Westward viewsheds displayed the lowest numbers of
mountain area. This is important because the West showed the second best average visual quality
scores in the data sampled. Therefore, it shows that even with a lack of mountain landscape
features, with minimal pavement, there still is a potential to be a visually pleasing landscape.
Looking again into a negatively viewed variable, the third area investigated is the
variable of vehicles present which is similar to the technique used to analyze the pavement
variable. The data collected for vehicles in the photographs was compared to the area of
mountain features and the visual quality score. Basing this investigation off the results of
pavement percentage analyzation, I had planned to find the vehicular variable to behave in a
similar manner. What I found, however was not as clear of a pattern. I found the photograph that

49

scored the best visually, had a higher percent of vehicular presence. For example, figure 11
below scored 25.86 in terms of visual quality, however it presented a 5.08% in terms of the
variable representing vehicles. On the other hand, figure 12 scored a 43.61 visually and
displayed 0% vehicles. The two photos are different in terms of urban make up, directional
viewshed and vehicular presence. The interesting aspect is the opposite results concerning the
comparison of vehicular presence, mountain area and visual quality. I was hoping to find that the
presence of vehicles would significantly decrease the visual quality of the sampled areas. This is
important because there seems to be a negative feeling associated with the presence of vehicles
in urban areas.

Figure 11: Mixed Use West

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Figure 12: Commercial Core East

Generally, the idea is to design with respect to pedestrian usage rather than vehicular in
these types of urban settings. From these results, I gather that the negativity associated with
vehicles has less to do with visual thinking than ecological or physical thinking. Overall,
however the term visual quality takes into account the entire realm of the visual world including
the impacts of variables regarding ecological and physical standpoints. Also, to take into
consideration is a larger sample examining the presence of vehicles may lead to a more reliable
pattern that agrees with the findings of the pavement variable. Future studies regarding mountain
viewsheds could explore the specifics of the visual quality predictive model.

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CHAPTER 6: Conclusion
The study of visual quality is such a newly explored topic there is a vast amount of future
research still to be completed. The opportunities for future research are endless, the amount of
variables, different theoretical backgrounds, and methods combine to create a large area for
research, we have only touched the surface. The past research has proven useful through both
successful and unsuccessful studies. Similar to previous investigations, this research posed some
limitations as well as opportunities for future researchers.
One of the main limiting factors for this research was the sample size. The equation used
for analysis has been proven before, so there wasn’t necessarily a need for a large sample size in
regards to the visual quality of mountain landscape features. However, upon further analysis of
data, it was found that a larger sample size may have led to more precise results, especially when
individual variables were taken into consideration. There wasn’t enough data to outline a
significant pattern for said variables. It did, however open the door for further research. The
small sample size did hint at the possibility for patterns. If the individual variables were studied
with more depth and intensity, I believe there is a possibility to gage the levels of impact each of
the variables place on the visual quality of the landscape.
In addition to the possible potential studies, the data did bring concrete positive
information into the realm of visual quality. This research put a quantitative value on the visual
quality of a rocky mountain landscape. There is now significant meaning to these beautiful
landforms that we are able to experience. This research has the ability to scientifically support
the preservation of the viewsheds and physical mountains that stand to be threatened with further
expansion.

52

Utilizing the findings in this study, policy makers and legislation have the ability to
develop laws for the protection and optimization of rocky mountain viewsheds. Other
professionals involved in development could use this information to support design decisions in
their environmentally sensitive urban settings. Guidelines from this specific research could
potentially recognize levels of enclosure throughout a city, material, amount of pavement and
vehicular presence in the urban core. Just the beginning, there is so much room for growth
regarding this topic. The potential research related to this topic could ensure a safer future
regarding the natural environment. Further development can use this information to create the
most pleasing area visually and ecologically for both humans and our natural environment.

53

APPENDIX

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Figure 13: Commercial Core East

Figure 14: Commercial Core South

Figure 15: Commercial Core North

55

Figure 16: Commercial Core West

Figure 17: Lodge East

Figure 18: Lodge South

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Figure 19: Lodge West

Figure 20: Lodge North

Figure 21: Mixed Use East

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Figure 22: Mixed Use South

Figure 23: Mixed Used West

Figure 24: Mixed Use North

58

Figure 25: Residential East

Figure 26: Residential North

Figure 27: Residential West

59

Figure 28: Residential North

60

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