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Patterns among local user attitudes toward potential visual changes in Colorado landscapes

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Title:
Patterns among local user attitudes toward potential visual changes in Colorado landscapes
Creator:
Wynne, Diane Simpson
Language:
English
Physical Description:
115 leaves : illustrations ; 28 cm

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Subjects / Keywords:
Landscape assessment -- Colorado ( lcsh )
Landscape assessment ( fast )
Colorado ( fast )
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Academic theses. ( lcgft )
bibliography ( marcgt )
theses ( marcgt )
non-fiction ( marcgt )
Academic theses ( lcgft )

Notes

Bibliography:
Includes bibliographical references (leaves 102-104).
General Note:
Cover title.
General Note:
Submitted in partial fulfillment of the requirements for the degree, Master of Landscape Architecture, College of Design and Planning.
Statement of Responsibility:
[Diane Simpson Wynne].

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Source Institution:
University of Colorado Denver
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Auraria Library
Rights Management:
All applicable rights reserved by the source institution and holding location.
Resource Identifier:
20858336 ( OCLC )
ocm20858336
Classification:
LD1190.A77 1984 .W86 ( lcc )

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Full Text
DIANE SIMPSON WYNNE
HAS SUBMITTED THIS THESIS AS PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR A MASTER OF LANDSCAPE ARCHITECTURE DEGREE AT THE
UNIVERSITY OF COLORADO AT DENVER COLLEGE OF DESIGN AND PLANNING GRADUATE PROGRAM OF LANDSCAPE ARCHITECTURE
ACCEPTED:
Bernie Jones* Associate Director for Jlesearch^fPr the Center for Community Oevelopment and Design; Associate Professor inning and Design
Stephen R. J. Sheppard, Visual Study Manager, WIRTH Envfromental Services
DATE" --------


TABLE OF CONTENTS
Section 1.00 Introduction ............................................ 1
Section 2.00 Background .............................................. 2
Section 3.00 Existing Data Base........................................4
Subsection 3.10 Bureau of Land Management's Visual
Resource Management System ................... 5
Subsection 3.20 Public Sensitivity Workshops .................... 9
Section 4.00 Objectives and Scope of Study............................15
Section 5.00 Hypotheses Statements.....................................18
Subsection 5.10 User Type and Visual Sensitivity.................18
Subsection 5.20 Landscape Type and Visual Sensitivity............19
Subsection 5.30 Activity Type and Visual Sensitivity.............21
Subsection 5.40 Interactions Among Factors and Their
Influence on Visual Sensitivity.................22
Section 6.00 Methodology..............................................23
Subsection 6.10 Study Approach...................................23
Subsection 6.20 Analysis Process.................................31
Section 7.00 Results..................................................36
Subsection 7.10 User Type and Desired Visual Management
Class............................................36
Subsection 7.11 User Type and Reaction to Potential Change . . .39
Subsection 7.12 User Type Summary................................47
Subsection 7.20 Landscape Type and Desired Visual Management
Class............................................50
Subsection 7.21 Landscape Type and Reaction to Potential
Change...........................................54
Subsection 7.22 Landscape Type Summary...........................59
Subsection 7.30 Activity Type and Desired Visual Management
Class............................................65
Subsection 7.31 Activity Type and Reaction to Potential
Change...........................................66
Subsection 7.32 Activity Type Summary............................70
Subsection 7.40 Interactions Between User Type and Activity
Type.............................................74
Subsection 7.41 Interactions Between Landscape Type and
Activity Type....................................78
Subsection 7.42 Influence of User, Landscape and Activity
Types on Visual Sensitivity......................82
Subsection 7.43 Summary of Factors Interaction and Influence
on Visual Sensitivity........................
84


Section 8.0 Interpretation of Results..................................85
Subsection 8.10 User Type and Visual Sensitivity ................85
Subsection 8.20 Landscape Type and Visual Sensitivity............89
Subsection 8.30 Activity Type and Visual Sensitivity.............91
Subsection 8.40 Interactions Among Factors.......................92
Subsection 8.41 User, Landscape and Activity Type's
Influence of Visual Sensitivity.................97
Section 9.0 Conclusions . .............................................98
Bibliography ........................................................ 102
Appendix A............................................................105
Appendix B............................................................109
ii


LIST OF TABLES
No. Page.
1 List of Possible Activities Occurring on BLM 12
Land.
2 List of Possible Public Sensitivity Workshop 13
Participants.
3 Sensitivity Workshop Questionnaire Options With 14
Explanation (Wirth Associates, 1979).
4. Location and Dates of Public Sensitivity Workshops 24
Conducted by the BLM/Wirth Project.
5. List of Activities, Users and Landscape Types Used 29
in Current Study Based on BLM/Wirth Project.
6. Distribution of User Interest Groups Among the Public 30
Sensitivity Workshops Conducted by the BLM/Wirth
Project.
7. Computer Input Format for Existing Data Base. 32
8. Chi Sguare Test of Independence Between User Types 37
and Their Desired Visual Management Class at the
State Level.
9. Weighted Rankings of User Types According to 38
Desired Visual Management Class Responses at the
State and District Levels.
10. Associations Between Pairs of User Types Based 40
on Desired Visual Management Class at the State
Level
11. Chi Sguare Test of Association Between User Types 44
and Reaction to Potential Change at the State Level.
12. Weighted Rankings of User Types According to 45
Their Reaction to Potential Change Responses at
the State and District Level.
13. Associations Between Pairs of User Types Based on 46
Reaction to Potential Change at the State Level.
14. Chi Sguare Test of Independence Between Local 51
Users' Desired Visual Management Class and
Landscape Types at the State Level.
iii


No. Page.
15. Weighted Rankings of Landscape Types According to Local Users' Desired Visual Management Class at the State and District Levels. 52
16. Associations Between Pairs of Landscape Types Based on Desired Visual Management Class at the State Level. 53
17. Chi Square Test of Independence Between Local Users' Reaction to Potential Change and Landscape Types at the State Level. 58
18. Weighted Rankings of Landscape Types According to Local Users' Reaction to Potential Change at the State and District Levels. 60
19. Associations Between Pairs of Landscape Types Based on Reaction to Potential Change at the State Level. 64
20. Chi Square Test of Independence Between Local Users' Reaction to Potential Change and Activity Types at the State Level. 67
21. Weighted Rankings of Activity Types According to Local Users' Reaction to Potential Change, at the State and District Levels. 68
22. Associations Between Pairs of Activity Types Based on Reaction to Potential Change at the State Level. 69
23. Weighted Rankings of Visual Sensitivity Levels by User Type for each Activity Type. 75
24. Weighted Rankings of Visual Sensitivity Levels by Landscape Type for each Activity Type. 79
25. Chi Square Test of Independence Between Local Users' Desired Visual Management Class and BLM's Scenic Qua!ity Rating. 86
26. Critical Combinations of High and Low Visual Sensitivity Levels by Landscape Type and User Type for each Activity Type. 93


LIST OF FIGURES
No, Page,
1. BLM/Wirth Project Study Area (1978-1979). 6
2. Diagram of Bureau of Land Management's Visual 7
Resource Management System (USDI 1980).
3. Locations of Public Sensitivity Workshops Held in 10
Colorado During 1978-1979.
4. Diagram of Relationship Among Landscape, Activity 17
and User Type with Visual Sensitivity
5. Map of the Bureau of Land Management's Districts in 26
Colorado.
6. Response Distribution Curves by Desired Visual 41
Management Class for Each User Type at the
State Level
7. Response Distribution Curves by Reaction to 48
Potential Change for Each User Type at the State
Level.
8. Response Distribution Curves by Desired Management 55
Class for Each Landscape Type at the State Level.
9. Response Distribution Curves by Reaction to 61
Potential Change for Landscape Type at the State
Level.
10. Response Distribution Curves by Reaction to 71
Potential Change for Activity Type at the State
Level.
11. Path Diagram Illustrating Relationships Among User, 83
Landscape and Activity Type with Visual Sensitivity.
v


SECTION 1.0 INTRODUCTION
People are making visual changes in the landscape at ever increasing rates. These changes do not occur in a vacuum, rather they are noticed by the public. People have become more vocal about their concern for scenic resources during the past decade. Government legislation such as the Federal Land Policy and Management Act of 1976 and the National Environmental Policy Act of 1969 have mandated that scenic resources be considered on an equal weight and basis with water, wildlife, historical and other resources. It has therefore become necessary to inventory, evaluate and provide measurable standards for the management of visual resources (USDI, 1980).
People's perceptions of their environment, specifically their attitudes toward changes in the landscape, form an essential part of the visual assessment process. This part is often termed visual sensitivity. Visual sensitivity has been characterized as the most difficult factor to understand and apply, and the one most in need of research and refinement in the visual assessment process (Grden, 1979). Some of the problems in assessing people's concerns stem from the lack of clear specification of the attributes of visual resources.
Existing methodologies for assessing people's concerns about changes in the landscape do not normally address how variations in interest groups, variations in landscapes or variations in activities contribute to people's attitudes toward change. The current state-of-the-art visual resource management processses such as the Bureau of Land Management's Visual Resource Management (VRM) system and the U.S. Forest Service's Visual Management System (VMS) place greater emphasis on user volume than on evaluating public attitudes toward visual change (USDI, 1980; Bacon, 1979). The public is often alluded to but their concerns are not usually incorporated into the management


procedures (Lee, 1976). User volume can be measured objectively, while user attitudes have so far seemed more difficult to obtain, understand and evaluate.
Evaluating and incorporating user attitudes in the assessment of visual sensitivity is critical if people will be affected by proposed changes in the landscape. Sensitivity workshops to identify public attitudes are routinely conducted by some private consulting firms and on occasion by Federal agencies such as the Bureau of Land Management. Budget and personnel cut backs have restricted and lowered the priority of federal visual resource management programs. However, the need to understand and evaluate user attitudes has not diminished. On the contrary, the need has increased due to pressure for development and natural resource extraction, especially in Colorado (Mendosa, 1984; Sheppard, (1984); Taggart, 1984). Therefore, it is important to discover how people's attitudes toward visual change are influenced by various factors. We may then more fully understand and more accurately predict the real impacts that visual change has on people.
SECTION 2.0 BACKGROUND
People's perceptions and expectations direct their attitudes toward changes in the landscape. Without understanding human perception and behavior, it is not possible to determine accurately people's concerns (Sinton and Ginder, 1979). Values, scene meaning and familiarity are important elements in formulating an individual's perception. Physical elements such as topography cannot by themselves ascribe the values of people (Enk, 1980). All values are not universal, but transitory in nature, reflecting what people view as historically and culturally important within their given region (Scheele and Johnson, 1979; Penning-Rowsel1, 1979; Taggart, Tetherow and Bottomly, 1980).
2


People react to visual changes in the landscape based on their "mental images" from previous experiences and knowledge (S. Kaplan, 1979). The meaning of a scene or "image" can change drastically as one moves from one landscape unit to another or from one land use to another. For example, the acceptable level of change may shift predictably when a transmission line is moved from an industrial area to a residential area (Wirth Associates, 1982). In addition, many people's intuitive image of a strip mine in terms of visual impact is quite negative. Yet little research has been done to determine the public's true aesthetic perception of surface mines (Simpson, 1979).
People's expectations also influence their attitudes toward visual change. Evaluating expectations or a person's preconceived ideas about a given landscape, setting or land use will help indicate the degree of acceptable change in the landscape (Blau, Bowie and Hunsaker, 1979; Miller, Jetha and MacDonald, 1979). A person in a wilderness area "expects" to see a visually pristine landscape and not encounter human-induced features such as transmission lines or cooling towers. A person is "conditioned to expect certain visual images associated with specific land uses" (Miller, Jetha and MacDonald, 1979). Any proposed activity that conflicts with these expectations is likely to result in a negative response from that person (Miller, Jetha and MacDonald, 1979; Ady, Gray and Jones, 1979).
Familiarity or an individual's experiences with a given landscape also affects a person's perception and ability to evaluate his/her concern for change. Past experiences and prior information will influence how people will react to change in a given landscape (Hammitt, 1979). The more familiar people are with a landscape, the more capable they are to judge the level of visual change. People who have never encountered a given landscape have no previous experience to evaluate a change in that landscape. The more familiar
3


a person is with a landscape, the more detailed their "mental image" is. A change in that landscape would register as something different from their expectations based on their "mental image" from past experiences.
There is substantial agreement in the literature that incorporating public preference into visual sensitivity evaluation is the appropriate and best way to understand people's visual perception of the landscape. It has been suggested that the exclusive use of professionals to make value decisions is inappropriate. These 'experts' do not have to live with the decisions made but the affected people do (Schomaker, 1978; Penning-Rowsell, 1979; Enk,
1980). The professional who is not familiar with an area may fail to be aware of subtle forms of variety and distinctiveness in that landscape (R. Kaplan, 1979). Professionals should recognize that people do have individual biases concerning the landscape. These biases make public participation in workshops valuable in determining appropriate decisions in the management of visual resources. Without careful selection of participants and awareness of individual biases, the results of the workshop can be skewed (Penning-Rowsell, 1979). Litton (1979) feels that there still needs to be a better agreement "between physical visual landscape criteria used by professionals and perceptual values identified in public workshops".
SECTION 3.00 EXISTING DATA BASE
This study is based on existing data generated from public sensitivity workshops held in Colorado during 1978 and 1979. In October 1978, the Bureau of Land Management (BLM) awarded a contract to Wirth Associates (now Wirth Environmental Service, Division of Dames and Moore) to conduct a visual resource inventory and evaluation of land within the four BLM Districts in Colorado. The project covered 8,504,460 acres of BLM and adjacent land
4


(Figure 1). The visual inventory and evaluation methodology used by Wirth Associates followed BLM's Manual 8411-Upland Visual Resource Inventory and Evaluation. Theis was the first application of BLM's 8411 Manual. The results of the project were to be used in preparing envionmental assessment reports and envionmental impact statements on specific proposals and as a tool for the design of projects. Since the data were collected as part of BLM's Visual Resource Management System, it is important to discuss briefly how their system works (Figure 2).
SUBSECTION 3.10 BUREAU OF LAND MANAGEMENT'S VISUAL RESOURCE MANAGEMENT SYSTEM
As defined by BLM, the "Visual Resource Management System (VRM) is an analytical process that identifies, sets and meets objectives for maintaining scenic values and visual quality" (USDI 1980). The VRM system is divided into three steps. These are Inventory/Evaluation, Management Classes and Contrast Rating (Figure 1). The first step, Inventory/Evaluation, involves the assessment of the three factors: scenic quality, sensitivity levels and distance zones.
Scenic quality is described as "the overal1 impression one retains after driving through, walking through or flying over an area". Land units that are homogeneous in terms of landform and quality are mapped and rated by seven factors. These factors are landform, vegetation, water, color, adjacent scenery, scarcity and color modifications. After a numerical rating, these units are classified as 'A', 'B' and 'C' Scenery, where 'A' Scenery signifies an area of highest scenic quality, 'B' is common and 'C' is minimal.
Sensitivity levels are determined to be high, medium or low based on user volume and user attitude. User volume is based on the number of people who
5


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travel through the area and the number of people who use the area for recreation or other purposes. User attitudes are meant to be determined through public workshops to obtain information on concerns about changes in scenic qualities.
The final Inventory/Evaluation factor assesses the proximity of the user to the landscape being viewed. Distance zones are divided into foreground/middleground, background and seldom seen based on broad thresholds in the perception of forms, textures, and other criteria.
The second step in the VRM system is the derivation of Management Classes based on an overlay/matrix technique using the results of the scenic quality, sensitivity level and distance zone evaluations of the landscape. The Management Classes range from Class 1 to Class 5 and describe the different degrees of modification allowable in the landscape. Classes 2, 3, and 4 allow management activities to become visually dominant to a increasing degree, while retaining the visual characteristics of the natural landscape. Class 1 is an area designated as wilderness, wild and scenic river or other similar area where management activities are very limited. Class 5 is rarely used and reflects an area in need of rehabilitation or enhancement to bring it up to one of the other classes.
To evaluate specific development and management activities, BLM uses a Contrast Rating System to assess the severity of visual impact on a landscape. The Contrast Rating compares the proposed activity's basic visual elements (form, line, color and texture) to the landscape's major visual elements. The degree of contrast (e.g. "does not attract attention",
"attracts attention and begins to dominate", and "demands attention") is then assessed. This information is compared to the appropriate Management Class to determine if the contrast of the proposed development or management activity
8


is acceptable (USDI, 1980; Ross, 1979).
While it is not the intent of this paper to critique BLM's visual assessment procedure, a few major concerns should be stated. In determining the scenic quality of a landscape, the numerical values assigned to the factors (landform, color, scarcity, etc.) are arbitrary and inflexible. The system has numerically pre-weighted these factors regardless of regional context. Although the system states that user attitudes are determined through public workshops, in reality these workshops are seldom held. The workshops held in Colorado in 1978 and 1979 were the only ones of their kind conducted on such a scale in the country. The BLM system usually allows changes to take place in "common" or minimal scenic quality landscapes. There is no mechanism to protect theose intact "common" landscapes which give a region it characteristic flavor (Tetherow, 1984). The Contrast Rating imposes the same numerical rating bias as the factor used in determining scenic quality. Consideration of scale and spatial dominance is not included in the Contrast Rating but should be (Sheppard and Newman, 1979).
SECTION 3.20 PUBLIC SENSITIVITY WORKSHOPS
Wirth Associates designed a series of public visual sensitivity workshops to document local attitudes towards potential changes in surrounding areas as part of their BLM contract to conduct a visual resource inventory and evaluation of BLM and adjacent land. Thirteen workshops were held in eleven different locations between November 1978 and July 1979 (Figure 3). To prepare for a workshop the contractors visited the particular planning unit(s) to become personally familiar with the area. From the reconnaissance survey, they determined and mapped the appropriate landscape units which would be evaluated by the public. Activities appropriate for each planning unit were
9


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selected for evaluation (Table 1). Representatives from public agencies, industry and public interest groups were invited to participate in the workshops (Table 2). To determine workshop participants, a generic list of interest or agency types was developed. From this list, specific representatives were selected from each category by the local BLM District staff. If a planning unit(s) had a national park or monument within its boundaries, representatives from the National Park Service were invited to participate. If there were no national parks or monuments in the planning unit(s), then a representative from the National Park Service was not invited to participate in the workshop. The remaining tasks were to prepare a map of the planning unit(s) to be evaluated and prepare the workshop specific questionnaires.
A brief introduction and overview of the project was given at the beginning of each workshop. A map was displayed to identify the area for evaluation. Slides were shown of each of the landscape units to be evaluated. Next, the participants were shown slides of various potential activities which could occur in the area. The participants were then again shown the landscape unit and asked to indicate their desired visual management class (DVMC), i.e. to indicate the general level of protection that should be given to the visual resource. Table 3 lists the options for the levels of protection with their respective explanations. Examples of the questionnaire, other pertinent information and general statistics on the workshops are included in Appendix B. The participants were next asked to indicate their responses to potential changes caused by each activity for each landscape unit. Finally the participants were given the opportunity to comment in writing on specific issues or projects of concern.
After each workshop, the participants' workshop sheets were reviewed by
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Wirth Associates to determine the validity of their responses. If it was apparent that a participant demonstrated obvious bias by their ratings, their responses were reviewed with BLM and often not included in the evaluation. Summaries of the raw data sheets (a copy of which is included in Appendix B) were completed for each landscape unit by totaling the participants' responses to DVMC and their reactions to potential changes from various proposed activities. A preliminary analysis for determining the user attitude portion of the sensitivity assessment was based on the comparison of the summary responses from the DVMC and reactions to potential change. The user attitude sensitivity level was categorized as high, medium or low. Where it was unclear as to what the sensitivity level should be for a given unit, a temporary high/medium or medium/low designation was given. The results of the preliminary analysis were reviewed by the BLM Area Manager and appropriate staff. The borderline cases of high/medium or medium/low were resolved and the final user attitude sensitivity level for all landscape units decided.
From the information collected at the public workshops, all that was utilized by BLM was a rating of high, medium or low user attitude sensitivity for given landscape units. The ratings were then combined with the corresponding user volume ratings to give a final sensitivity rating. The final sensitivity rating was incorporated into the Inventory/Evaluation portion of BLM's VRM system (Figure 1), (Wirth Associates, 1979). Methods and results of the public sensitivity workshops were summarized in an article in Landscape Architecture (Taggart, Tetherow and Bottomly, 1980).
SECTION 4.00 OBJECTIVES AND SCOPE OF STUDY
For the purpose of this study, visual sensitivity is defined as a measure of people's perceptions of their environment, specifically their concern about
15


human-induced changes in the landscape. In keeping with the conditions under which the data for this study were collected, the local users' desired visual management class and their reaction to potential changes caused by particular activities were used as measures of visual sensitivity. 'Desired visual management class' is defined as the relative sensitivity value the local people placed on the visual resource of a particular landscape unit. Reaction to potential change is defined as the local people's response to possible specific activities which may occur in a particular landscape unit (Wirth Associates, 1979). The scope of this study will be limited to the analysis of the influence that user, landscape and activity type have on visual sensitivity as measured by desired visual management class and reaction to potential change in non-urban settings (Figure 4). There were no transients/vistors included in the data base, but local users only.
Current literature in the field of visual resource assessment is vague in its description of how visual sensitivity may be measured or evaluated. Most research on aesthetics address the assessment of scenic quality and not the concern for change (sensitivity) in the landscape. Visual sensitivity evaluations are often based solely on professional judgement without public input and without sufficient research to substantiate management decisions. There have been some documented local workshops on visual sensitivity which provide considerable data on user attitudes, most notably the BLM/Wirth project in Colorado. No one has, until this paper, analyzed this information systematically for any significant trends, correlations, or relationships which may help direct and improve visual sensitivity evaluation.
The objectives of this study are as follows:
1. To collect and organize the BLM/Wirth project data base into a form that will permit scientific analysis of the existing under-utilized data.
16


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2. To analyze the data base in a systematic process for significant trends, correlations or relationships that might indicate what determines people's concerns for visual changes in the landscape.
3. To suggest implications and specific applications based on the findings of this analysis.
SECTION 5.00 HYPOTHESES STATEMENTS
In order to understand how landscape, user, and activity type influence visual sensitivity, the impact of each of the three factors have been evaluated separately. Three hypotheses have been formulated to suggest the association between each factor and visual sensitivity. A fourth hypothesis addresses the interaction among the three factors and their combined affect on local user attitudes toward change. The hypotheses are as follows:
1. Local users' visual sensitivity varies significantly with user type.
2. Local users' visual sensitivity varies significantly with landscape type.
3. Local users' visual sensitivity varies significantly with activity type.
4. User, landscape and activity type do not independently nor equally influence visual sensitivity.
SUBSECTION 5.10 USER TYPE AND VISUAL SENSITIVITY
For this study, the first hypothesis is that local users' visual sensitivity varies significantly with user type. For the local resident there will most likely be higher levels of concern for the everyday, familiar environment. They are the ones who will have to deal with any changes on a daily basis (R. Kaplan, 1979; Enk, 1980). It is important, therefore, to understand how attitudes differ between local resident types.
18


Investigation into the association between user type and visual sensitivity should provide information on the relative levels of concern by various user types. I suspect recreation and conservation groups are significantly more sensitive than other groups because they generally exhibit a higher concern for environmental protection. Mining, utility and private industry groups might be expected to be least sensitive because of their pro-development attitudes and concern for potential economic gain from the land. I would expect agency representatives (local, state and federal government), elected officials and agricultural groups to be moderate in their levels of concern. The agency group allocates and regulates use of the land, and like the elected officials should represent all people's concerns. I feel the agricultural group will also be moderate in their attitudes, because, although theirs is a consumptive use, they maintain a closeness to and dependency on the land.
The evaluation of specific interest group's sensitivity could be used by professionals in predicting the public's level of sensitivity. Grouping individuals on the basis of shared values or interests and evaluating a social group's concern towards change would be easier than trying to identify and predict the variability of an individual's response (Lee, 1976). In addition, the professional would be able to identify interest groups who are particularly sensitive to certain activities. By having an idea of the concerns of various user types, the professional can invite users who will be a true representation of the public and avoid biasing the result in any particular direction.
SUBSECTION 5.20 LANDSCAPE TYPE AND VISUAL SENSITIVITY
The second hypothesis states that local users' visual sensitivity varies
19


significantly with landscape type. As stated previously, people react to visual changes in the landscape based on their "images" as the result of their acquired set of mental maps from previous experiences (S. Kaplan, 1979). The meaning of a scene can change as one moves from one landscape unit to another.
I feel the highly sensitive areas as expressed by local people are mountains, canyons and river valleys. These three landscapes types are often associated with high scenic quality and are more vulnerable to changes. These types provide a significant portion of recreational use for a wide variety of users in Colorado and are distinctive, dominant landforms with which people can strongly associate. On the other hand, broad valleys, basin-parks and badlands may be considered by local users to be less sensitive than other types. Broad valleys and basin-parks are visually less distinctive, expansive areas with little variety in terms of slope, vegetation or color. Certain cultural intrusions in these landscape types would probably be considered positive improvements in the landscape, adding some variety, color and interest. Although badlands are relatively rare and potentially could be considered to have high scenic quality because of their visual interest and uniqueness, people's "mental images" of badlands may identify those landscapes as areas of low sensitivity.
It would be very useful to visual resource managers and planners to have information on local users' attitudes toward specific physiographic landscape units (Sheppard, 1984; Taggart, 1984). This type of information could be extremely valuable at the initial stages of site planning. Landscape units identified as highly sensitive could be avoided or flagged so that special planning and design efforts could be made.
20


SUBSECTION 5.30 ACTIVITY TYPE AND VISUAL SENSITIVITY
The third major hypothesis for this study states that local users' sensitivity varies significantly with activity type. Any proposed activity that conflicts with a person's expectations is likely to result in a negative response from that person (Miller, Jetha and MacDonald, 1979; Ady, Gray and Jones, 1979). By examining potential changes caused by various activities rather than proposed changes from specific projects, it may be possible to get closer to people's general level of concern, rather than by using their responses to change from an actual project which could hold atypical associations. In specific situations, other factors such as personal economic gain or loss may influence their true concern over the visual change caused by an impending activity (Tetherow, 1984). There is little information available on people's levels of concern for change caused by various activities.
Neither the USFS VMS or BLM's VRM attempt to incorporate user's sensitivity of various activities in their planning process.
In view of the amount of land disturbed, the relative scale of the activity and the loss of the use of the land by the majority of the people, I expect that local users are most sensitive to mining and power plants. By the same reasoning, local users may be viewed as less sensitive to roads/railroads and residential activities. These activities are directly beneficial to a vast majority of the local public. Positive recreation values and minimal surface water resources in this region probably off-set the relatively large scale and loss of land characteristic of water impoundments. For these reasons, I feel people are also less sensitive to water impoundment activities. I expect that off road vehicle (ORV) use is a highly sensitive, controversial activity among users because of the strongly polarized attitudes toward that activity.
21


If it can be demonstrated that sensitivity levels vary with activity type, then the information could be used to substantiate recommendations for specific changes in the two federal management procedures (Lee, 1976) or any other visual resource management prodecure. The information could also be used to discriminate between acceptable and problem activities when assessing visual sensitivity.
SUBSECTION 5.40 INTERACTIONS AMONG FACTORS AND THEIR INFLUENCE ON VISUAL SENSITIVITY
There are two parts to the last hypothesis of this study. The first part of the hypothesis states that user, landscape and activity type do not independently influence visual sensitivity. The second part states that the three factors do not equally influence visual sensitivity. By definition, visual sensitivity is a measure of people's perception of their environment, specifically their concern about changes in the landscape. People's perception of their environment, as suggested by this study, is influenced to a great extent by their values and social interest, the physical aspects of the landscape and the cultural activities potentially imposed in the landscape. It is therefore important to examine the interaction among user, landscape and activity type to try to determine if there are combinations of these factors which may be greater than any single factor's influence of local users' level of sensitivity. These critical combinations could be used to predict the relative level of visual sensitivity and aid in the design of public workshops for more informative results for proposed projects when the activities, users and landscape types are known.
I expect that the most influential factor in determining visual sensitivity is landscape type. Landscapes perceived by the public as having
22


high visual quality such as scenic rivers, national monuments and parks are those most often designated for preservation or high protection (Blair 1980). In other words, people have a high level of concern for any potential change that may occur in landscapes that they perceived as having high scenic quality. I also expect that local people do not necessarily agree with a landscape rated-low in scenic quality by a fixed numerical rating system such as the VRM system used by BLM. These every day or "common" landscapes may evoke a high level of concern and need for protection by the local users. People, to a large extent, feel a part of the land and are basically hesitant to alter the beauty of nature. As Gussow (1979) expressed: "We are not
separate from our landscape____ When we endanger the landscape, therefore, it
is a part of ourselves which we threaten."
SECTION 6.00 METHODOLOGY
SUBSECTION 6.10 STUDY APPROACH
The initial problem involved in this thesis project was the collection of information on the original study and locating the individual workshop data sheets. Table 4 lists the dates, locations and districts of each workshop. Since it has been five years since the original study was conducted, it was difficult to trace all the people who worked on the BLM/Wirth project. Some of the raw data workshop sheets could not be found. As a result, the data for the workshop for the Canyon City District and for the Baxter, Douglas and Mt. Garfield Planning Units in the Grand Junction District are not included in this study. The workshop summary sheets are available for these areas, but were not disaggregated and utilized because of time limitations. The workshop held for the Dolores and Lone Cone Planning Units in the Montrose District were also not included in this study. The Dolores and Lone Cone Planning
23


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24


Units were previously inventoried under an earlier BLM project. It was felt that the classification of landscape types was not consistent with the general types identified in the BLM/Wirth study. With the deletion of the above workshops, this thesis project includes data from eight workshops covering BLM's Craig, Montrose and Grand Junction Districts in Colorado (Figure 5).
The cummulative results from all eight workshops from these three Districts will be referred to as 'State' or 'statewide' throughout this study. A brief description of the Districts follows as an aid in interpreting the findings.
1. Craig. The planning units in the Craig District covered by the Wirth/BLM project were Williams Fork, North Park, Nipple Rim and Browns Park. It should be noted that the planning unit designation is not currently utilized by BLM. Three public workshops were held to evaluate the 3,977 square miles of BLM and adjacent lands. This area is in the Wyoming Basin physiographic province as defined by Fennenman with small segments of the Southern Rocky Mountain province extending into the area. The majority of the general landscape types are found in each of these planning units, with sand dunes and badlands being the most distinct and unique landforms. The largest dunes in the region (with the exception of the Great Sand Dunes National Monument) are located in the North Park Planning Unit. The major rivers flowing through this area are Williams Fork, Yampa, Little Snake, North Platte, Laramie and Green Rivers. Dinosaur National Monument and the Arapaho National Wildlife Refuge are located in this District. The major tourist/resort center is the City of Steamboat and Steamboat Lake. Besides Steamboat, Craig and Hayden are the only other relatively large urban communities. The land uses within these planning units consist mainly of agriculture, ranching and coal, gas and oil mining.
2. Grand Junction. Roaring Fork and Eagle Planning Units were the areas
25


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evaluated by two public workshops in the Grand Junction District. The two planning units cover 1,193 square miles. They lie in the southern Rocky Mountain province with mountains being the most dominant physical and visual element in the landscape. Glenwood Canyon is an extraordinary scenic resource that is located in this District. A portion of the Grand Mesa is also found in this area. The major rivers are the Roaring Fork, Colorado and Eagle Rivers. Interstate-70 is a dominant feature in the landscape in these two planning units. This highway corridor links urban communities and provides an axis around which most of the commercial development is occurring.
Agriculture, gravel mining and recreation/resort areas are the prominent land uses. Aspen, Snowmass, West Vail, Glenwood Springs and Avon are key resort and urban centers.
3. Montrose. The planning units in the Montrose District covered in the Wirth/BLM project were Chromo, Durango, Sacred Mountain, Cimarron, Gunnison Gorge and Escalante Planning Units. Three workshops covered the 3,043 square miles of BLM and adjacent lands. According to Fenneman, the area contains two major physiographic provinces, the southern Rocky Mountain province and the Colorado Plateau province. The Uncompahgre River is generally the interface between these two physiographic regions. The resulting landscape is one of steep, rugged mountains (San Juan Mountains), dominating mesas (Mesa Verde and Log Hill Mesa) and steeply incised canyons (Black Canyon of the Gunnison).
The Mancos shale badlands are also found in this District. The major rivers are the Animas, San Juan, Dolores, Gunnison and Uncompahgre Rivers. Lemon Reservoir, Vallecito Reservoir and Sweitzer Lake are dominant water features. Delta, Pagosa Springs, Chromo, Cortez and Durango are the large urban communities in these planning units. Sacred Mountain Planning Unit is a very historic and archaelogical area containing Mesa Verde National Park and
27


Hovenweep and Yucca House National Monuments. Agriculture, ranching, tourism and some mining are prominent land uses in these planning units (Wirth Associates, 1979).
Table 5 lists the user interest groups, the landscape types, and activities that are used in this study. The user interest groups and activities were predetermined by the BLM/Wirth data base. Table 6 shows how many of each user interest group were represented in each workshop. A description of each landscape type is given in Appendix A.
The assumption is made that the public sensitivity workshops were appropriately designed and conducted consistently from one workshop to another. There is one exception to the assumption that needs to be identified. The participants were asked in all but three workshops to state their reaction to potential change based on the degree of "negative reaction" that they felt that a activity would cause in a specific landscape unit. In the Browns Park/Nipple Rim, Williams Fork and North Park workshops, the participants were asked instead to indicate their "relative preference toward" specific landscape modifications that could potentially take place in a landscape unit. The decision to redirect the participants' response to potential change was made because it was suggested that by asking for the degree of negative response, the guestionnaire had predetermined for the participant that the change was negative and could not have a positive affect in the landscape. To compensate for the changes made in the guestionnaire, I have separated the landscape preference workshops from the negative reaction workshops when analyzing the local users' responses to activities. In other analyses, the two variables are treated as being the same. Fortunately, all three of the landscape preference workshops were located in the Craig District.
28


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SECTION 6.2 ANALYSIS PROCESS
Each landscape unit evaluated in the workshops was categorized into a general landscape type. The categories were based on the general landscape types defined in the BLM/Wirth project and are described in Appendix A. This allowed the responses from the participants to any general landscape type such as mountains to be compared from workshop to workshop. The information associated with each landscape unit, such as the users' responses to each potential activity, DVMC, etc. was coded into the Prime computer at the University of Colorado at Denver. From the one hundred fifteen (115) total participants, 2,717 records of information were generated.
The DVMC was coded on an even integer scale with the number 1 referring to preservation and 4 to low protection. A strong negative reaction to change was coded as a 1 and low adverse reaction as 3. The least preferred location for an activity was coded as a 1 and the most preferred location coded as a 3. An 'other' user type category was formed to combine those individuals whose social affiliation could not be determined. Table 7 is an example of the coded data.
The first step in the data analysis was to determine the basic distributional characteristies of each of the variables, i.e. DVMC, user type, activity response, etc. A one-way frequency distribution was done to check each variable to determine if it had sufficient range to be used in subsequent statistical analyses. It also helps check the data to see if it had been coded correctly.
Chi square tests for independence were used to determine whether a systematic relationship existed between two variables. The test is computed
31


1AL e? L c J
CG LITTlE SNAKE RI V f BROWNS pa=k-nipple 1 p DOUGLAS MOUNTAIN
CG little SNAKE R I V BROWNS park-nipple p. CROSS MOUNTAIN
C 3 LITTLE S \ A t\ l RIV BROWNS PA=K-NICPLE R COLD SPRING MOUNTAIN
CT3 LITTLE â– SNAKE RIV B 0 GW nib PARK-NIPPLE F. DIAMOND MOUNTAIN
C 3 little SNAKE r : v BROWNS PARK-NIPPLE p GODIVA RIM
CIS LITTLE SNAKE C X V BROWNS PARK-NIPDLE ft VERMILLION BLUFFS
CG LITTLE SNAKE P. I V B K 0 W :'i S PA ftK-N IPPLE R NIPPLE ft IM
C 3 LITTLE SNAKE R I V BROWNS PARK-NIPPLE R BLUE HILL-LIMESTONE
cs LITTLE SNAKE RIV BROWNS FARK-MPPLE F POWDER WASH
CG LITTLE SNAKE q ! v BROWNS FARK-NIPPLE R SEVENMILE RIDGE
CG l ITTlE SNAKE F IV BROWNS PARK-NIPPLE F. GRASSLAND-SAGEBRUSH
CG LITTLE SNAKE R IV 6 R G^.mS PARK-NIPPLE p PINYON-JUNIPER,GRASS
C 3 LITTLE S i» h K l R I V B ft OWNS PARK-NIPPLE ft B A 0 L A ft 0 S
CG LITTlE SNAKE. R I V BROWNS PARK-NIPPLE F JOHN [.ELLER MESA
C O little SNAKE R I V BROWNS park-nipple ft POWDER WASH
CG LITTlE snake RIV B ft 0 M\ 4 3 park-nipple f* IRISH CANYON
C O LI'TLE SNAKE RIV BROWNS park-nipple p. CROSS MOUNTAIN CANYG
c s LITTLE SNA KE RIV BROWNS park-nipple p VERMILLION CREEK
CG LITTLE SNAKE R IV B K OU3b park-nipple p BEAVER CREEK CANYON
C a little S yakE P I V BROWNS PA3K-\IPPLa R LITTLE SNAKE R I V :.R
CG LITTLE S \ £ rt E f. : v B R G W ft 3 FARK-.M IPPLE F. YAMPA P.I VcR-SUNBlAM
CG little SNAKE A IV b R 0 W ft 3 P4 = K-N IDPLE c LOWER YAMPA
C 3 LITTLE i ■» A L. RIV B R C «ftS PARK-NIPPLE p sand WASH BASIN-PARK
CG LITTLE SNAKE ft. I V oft GUNS park-nipple D SHEEPHEAD WASH BASIN
u b L I T Tll SNAKE R I V BROW .3 park-nipple ft THOMPSON DRA.
CG LITTLE SNAKE R I V 5 R OWNS FARK-NIPPLE ft BROWNS PARK
C~3 LITTlE SNAkE P I V BROWNS PA°K-NI°PLi p DOUGLAS DRAW-VEft MILL
CG LITTLE SNAKE R IV B R 0 W NS PARK-MPPLE R OOUGLAS MOUNTAIN
C 3 LITTLE SNAKE RIV b R o * .‘J b FARK-MPPLE R DIAMOND MOUNTAIN
C u LITTlE SNAKE c I V BROWNS PARK-NIPPLE F GODIVA RIM
C 3 LITTLE SNAKE R I V 6 R 0Wft3 park-nipple F VERMILLION BLUFFS
C 3 LITTLE snake RIV BROWNS park-nipple P NIPPLE RIM
C 3 LITTLE SNAKE R. I V BROWNS FARK-MPPLE P BLUE HILL/LIMESTONE
C 3 LITTLE SNAKE R I V BROWNS FARK-NIPPLE F POWDER UASH
C 3 LITTlE S i\ m K l. RIV BROWNS PARK-NIPPLE P SEVENMILE RIDGE
CG LITTLE SNAKE P IV BROWNS park-nipple R GRASSLAND-SAGEBRUSH
CG little SNAKE S I V BROWNS PARK-NIPPLE R PINYON-JUNIPER,GRASS
CG little SNAKE RIV BROWNS PARK-MPPLE P BADLANDS
C3 little SNAKE o I V BROWNS park-nipple P VLRMILLION CREEK
CG LITTlE SNAKE P. I V 6 P o W.NS park-nipple R JOHN WELLER MESA
CG LITTLE SNAKE » IV BROwNS park-nipple R POWDER WASH
CG LITTlE SNAKE R I V BROWNS PARK-N IPPLl P IRISH CANYON
C 3 LITTLE snake F. IV BROWNS park-mpple R CROSS MOUNTAIN CANYO
CG LITTLE snake c I V BROWNS PA’K-NIPPLE ft BEAVER CREEK CANYON
CG little SNAKE F. I V BROWNS PARK-NIPPLE R LITTLE SNAKE RIVER
CTG little SNAKE fi IV BROWNS PARK-NIPPLE F YAMPA RIVER-SUNBEAM
CG L ITTLE SNAKE RIV BROWNS PARK-MPPLE R LOWER YAMPA
CG LITTLE SNAKE P I V BROWNS PARK-MPPLE R SAND WASH BASIN-PARK
C 3 little SNAKE R I V BROWNS PARK-MPPLE F SHEEPHEAD WASH BASIN
CG LITTLE SNAKE R I V BROWNS PARK-MPPLE R THOMPSON DRAW
CG LITTLE snake c j y BP. OWNS PARK-NIPPLE R BROWNS PARK
C 3 lITTLE SNAKE RIV BRuWftS PARK-NIPPLE R DOUGLAS ORAW-VERMILL
CG little SNAKE R IV b ROWft 3 PARK-MPPLE P CROSS MOUNTAIN
C 3 LITTlE S N A i\ E P I V BROwNS park-mpple F. DOUGLAS MOUNTAIN
CG LITTLE SIJAKl K I V b * o w i m S park-nipple ft COLO SPRING MOUNTAIN
CG LITTLE snake R I V b ft OWNS park-nipple R DIAMOND MOUNTAIN
CG LITTLE SNaKE RIV 3POWNS PARK- . IPPLa F PINYON-JUNIPER*GRASS
CG LITTLE snake 2 I V BROWNS FARK-NIPPlE P IRISH CANYON
CG LITTlE S N A K l a I V tsROWNS park-;«ipple ft HEAVER CRE-.K CANYON
A t? C. V & - ie=LH Vr~ » m.uuiu^f ujuirce) j = = LULiir UAH6- C = 1 = laucTire- =
f= - C3SS-IF£et? VISUAU MAUAt&^HgHT ^ =

t»'ff *
4—■—-I
MESAS MESAS CANYONS CANYONS BADLANDS CANYONS
MOUNTAIN 312221222392999RECREATI MOUNTAIN 423333333292999REC P.EATI MOUNTAIN 323333332292999RECREATI MOUNTAIN 312323332292999RECREATI RIMS-RIDGE323333331391999RECPEATI RIMS-RIDGE323323331391999RECREATI RIMS-RIDGE313333331391999RECREATI RIMS-RIDGE323333331391999RECREATI BASIN-PARK433333333292999RECREAT I ROLLING HIA23333333292999RECREATI ROLLING HI3C3333331393999RECREATI ROLLING HI393332331393999RECREATI BADLANDS 3C2 33 33312 92999RECF.EATI 333333332392999RECREATI 322322332393999RECREATI 41222211I192999RECREATI 4122231111939R9RECREAT: 2G2222331193999Rt.CREATI 412222111193999RECREATI RIVER VALL212213111193999RECREATI RIVER VALL22 2213111193999PEC RE ATI
RIVER VALL212213111193999RECREATI BASIN-PARK232223222193999RECREATI BAS IN-PARK232223221193999RECREATI BASIN-PARK232223221193999RECREATI
BASIN-PARK222223312193999RECREATT
BASINt-PARK222223312193999RECFEATI MOUNTAIN 31 111 2111393999AGRI CULT MOUNTAIN 311112881398999AGRICULT RIMS-RIDGE32331311 1192999 AGPICULT RIMS-RIDGE 32221322 1192999AGRICUlT RIMS-RIDGE312223111193999AGRICULT RIMS-RIDGE3222132211S2999AGRICULT BASIN-PARK333323221193999AGRICULT ROLLING HI322223221393999AGRICULT ROLLING HI3U2223221293999AGRICULT ROLLING HI3C2223221293999AGRICULT BADLANDS 3 ?• 222222 1896999AUR ICULT 3'C221322 1293999AGR ICULT 332213221293999AGRICULT 322213221293999AGRICULT 311112111291999AGRICULT 311112181291999AGRICULT 311111111291999AGRICULT RIVER VALL31223288 1292999AGR ICULT RIVER VALL322222881293999AGRICULT RIVER VALL122222881293999AGR ICUi_T BASIN-PARK 332223881293999AGRICULT BASIN-PARK332213881293999AGRICULT BASIN-PARK332213881293999AGRICUlT BASIN-PARK322213881293999AGRICULT BASIN-P ARK322213881293999AGRICULT MOUNTAIN 32111211 1393999AGRICUlT MOUNTAIN 312213222291999AGENCY MOUNTAIN 122213112292999AGENCY MOUNTAIN 212213222292999AGENCY ROLLING HI3G3323222292999AGENCY CANYONS 111111221S92999A3ENCY CANYONS 21111211 119 1 999AGENCY
ro\eunM- >cTiviriee
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MESAS
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CANYONS
CANYONS
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32


by the following formula:
= E (pi - fV
' o o'

where f equals the observed frequency in each cell of a two way table
I
and f equals the expected frequency in each cell as calculated by
fi ■ (*>’)
N
where c1 is the frequency in a respective column marginal, r1 is the respective row marginal and N is the total number of valid cases (Nie, 1975). The Null Hypothesis is that there is no association between the two variables. In order for the chi square test to be valid, fewer than twenty percent of the cells should have an expected fequency of less than five and no cell should have an expected frequency of less than one. The variables must also be independent (Siegel, 1956). One-tailed tests of statistical significance were reported for the chi square test at the 0.05, 0.01 and 0.001 levels.
A chi square test does not determine the strength of the relationship between the variables. The contingency coefficient 'C* is a useful measure of the extent of association between variables. It is appropriate for this study because it can be applied to nominal or unordered variables. The contingency
33


coefficient is calculated as follows:
2
where C equals the contingency coefficent, x equals the chi square test value for the variables, and N is the total number of valid cases. A coefficient of zero indicates the lack of any association. A value of one indicates complete dependence or perfect correlation. The limitations of the contingency coefficient are that any limitations that apply to the chi square test also apply to 'C'. Two contingency coefficients are not comparable unless they are of equal table size, i.e. a table 3x7 cannot be compared with a 2 x 2 table (Siegel, 1956).
The contingency coefficients were used as path coefficients for the three factors of landscape, user, and activity type in an attempt to explain their relative degrees of influence on visual sensitivity. This type of analysis is referred to as a path analysis. A path analysis attempts to evaluate linear relationships and determine the causal effects of a given independent variable on a dependent variable (Nie, 1975). This application of path analysis using contingency coefficients may not be statistically valid. It was used here in an attempt to explain only the strength of association and causal effect between the three factors and visual sensitivity.
A weighted mean value was calculated based on the integer coded DVMC and reaction to potential change . These values were then ranked by State and by districts. An example calculation for agency user types' reaction to
34


potential change is as follows:
Strong reaction Moderate reaction Low reaction
621 x 1 668 x 2 239 x 3
621
1338
717
7Z7Z
2676 = 1.75
1528
where 1528 is total number of cases and 1.75 is the adjusted (weighted) mean value of the variable agency by reaction to potential change. This means that for agency user types, their reaction was somewhat less than a moderate reaction, tending toward a strong reaction. The list of weighted values indicated a relative ranking among the variables for the purpose of comparing variables to each other and to other lists by State and by District.
It should be noted at this time that the decision to code both DVMC and reaction to potential change on an ordinal, even interval scale may not actually be a true representation of the relationship between levels of DVMC and reaction to potential change. For example, the actual perceived difference between preservation and high protection is probably not egual to the perceived difference between moderate and low protection. The weighted ranking should be tested for significance between groups. The ranking should only be used as a rough guide to identify extremes and indicate possible trends or patterns and not as the exact ordering of variables.
A wealth of information is available from the analysis of the public sensitivity workshops. This thesis project will not attempt to answer all the guestions or analyze all aspects of the results. The statistically significant analysis of the data was limited to the State level. The information provided at the District level gave only an indication of possible regional differences and was not evaluated for statistical significance.
35


SECTION 7.00 RESULTS
SUBSECTION 7.10 USER TYPE AND DESIRED VISUAL MANAGEMENT CLASS
There is a significant association between user type and local users' desired visual management class (DVMC). This is supported by a chi square test value of 405.31, significant at the 0.001 level (Table 8).
The weighted rankings for user type by DVMC for the State and Districts are given in Table 9. Table 9 indicates for example that conservation groups overall at the State level desired a higher level of protection for the landscape than did the elected officials, mining, utility and private industry. The smaller the weighted mean value, the higher the level of protection indicated. By comparing the extremes (preservation and low protection), one can see that low values in the Craig District ranking are reflecting an indication of higher protection than the other Districts and the State. Montrose's values are higher, indicating lower protection, compared to the other rankings because of the four user types with weighted mean values greater than 3.00.
Unfortunately each user type is not represented in each district. Yet there are interesting variations among those user types which do appear in two or more of the Districts (Table 9). Elected officials in the Montrose District had a weighted mean value of 3.47. The elected officials in the Grand Junction District had a weighted value of 2.40. The agricultural group in Montrose (1.83) expressed concerns considerably different than those in Craig (2.89) or in Grand Junction (2.75). The utility group in Craig District (2.32) expressed a higher level of concern than those in Montrose (3.46) and Grand Junction (3.02). Agency user types in Craig (2.24) differed in their indication of DVMC than those in Montrose (2.90) and Grand Junction (2.51).
To see if there were significant differences in DVMC levels between user
36


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types, each user type at the State level was tested against the other types. Table 10 provides the results of the chi square test between user types. The results indicate that agency, recreation and other types are not significantly different than each other. Mining and private industry types are similar according to their expressed DVMC. Most other types are different, and, as indicated by their weighted mean values, have different levels of desired visual management for the landscape.
Comparing the weighted ranking with the chi square results, it is shown that conservation groups are in the middle of the high level of protection group. However, the chi square tests indicate that the conservation group type is significantly different than the other types. To understand why the conservation group was significantly different, I compared the distribution of the percentage of responses for each DVMC by user type for the State level (Figure 6). The curve for the distribution of responses by the conservation group is different than those for agency, recreation, other and individual. A visual comparison of those curves shows the prominence of high protection ratings by the conservation group relative to the other user groups. The conservation group did not exclusively select preservation ratings as their DVMC. The distribution curves also illustrate that private industry, mining and agricultural interest types expressed high agreement, i.e. greater than 50% of their total response, on moderate levels of protection. Less than 5% of mining, private industry, utility and elected officials user types' responses for DVMC designated any landscape for preservation.
SUBSECTION 7.11 USER TYPE AND REACTION TO POTENTIAL CHANGE
User type and reaction to potential change also have a significant relationship. This association is supported by a chi square test value of
39


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1512.94, significant at the 0.001 level between user type and reaction (Table
11).
The weighted ranking for the user type by reaction to potential change for the State is given in Table 12. The results of the ranking indicated that the conservation and 'other' groups expressed strong negative reaction to potential change. (The smaller the weighted mean value, the stronger the reaction to potential change.) At the other end of the ranking, utility and elected officials indicated an overall low adverse reaction to potential change.
The weighted mean values among the user types in the Districts indicate some of the apparent differences and similarities among the Districts (Table 12). The extremes are easily delineated. Utility (Craig), agriculture (Montrose) and conservation, other, and elected officials (Grand Junction) types are at the strong negative reaction end of the scale. The local recreation (Craig), mining (Grand Junction) and mining, utility, private industry and elected officials (Montrose) types exhibited low adverse reactions. There are clear differences between the elected officials, utility and agricultural types' reactions from Montrose and those from Grand Junction. Both of these Districts' user type rankings are very similar to the State ranking. The Craig District however, varies greatly in comparison to the State ranking and the other two Districts.
Based on the chi square tests among user types and their reaction to potential change, the majority of user types differed statistically from each other (Table 13). Agriculture and recreation types were also similar. Conservation groups and other types have identical weighted mean values but are statistically different in their reaction to potential change. Their response distribution curves appear to explain why they differ significantly
43


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from each other (Figure 7). The conservation group indicated a strong negative reaction in close to 50% of their responses. The 'other' group indicated equally strong or moderate reactions to potential change. The distribution curves indicate an inverse relationship based on their DVMC's between elected officials and conservation groups.
SUBSECTION 7.12 USER TYPE SUMMARY
There are similar relationships between the combined measures of DVMC and reaction to potential change by user type. The statewide weighted rankings are almost identical (Tables 9 and 12). The conservation group differs significantly from other user types in both rankings, and expresses a high level of concern regardless of landscape or activity type. Utility and elected officials differ significantly from the other user types as indicated by their weighted mean values. In the DVMC weighted ranking there appears to be a definite division in the ranking between recreation and agriculture. Although not as obvious, the same break occurs in the weighted statewide ranking of reaction by user type (Table 12). In both rankings, the agricultural user type appears to be the median group between the extremes.
From the results of the analysis between user type and the two measures of visual sensitivity, one can predict with some confidence which user types will have consistently high or low levels of sensitivity. Conservation groups have a high level of sensitivity in comparison to all other user types. The recreational user types did not turn out to be significantly higher in their expressed level of sensitivity. Agency user types tended to indicate higher levels of sensitivity than expected. On a statewide basis, they were in fact identified to be similar statistically to the individual and 'other' type.
The true moderates did indeed turn out to be the agricultural user types.
47



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This is graphically illustrated in the distribution curves for DVMC by user type (Figure 6). The agricultural group indicated their desire for moderate protection in over 70% of their responses. The low levels of concern expected from utility and to a lesser extent private industry and mining were also substantiated by the data.
Based on the chi square test results and weighted mean rankings, the level of sensitivity does vary with user types. Additionally the data indicates that there are also some user types that are statistically similar in their levels of concern for visual change. The data supports the first major hypothesis that local users' visual sensitivity varies significantly with user type.
SUBSECTION 7.20 LANDSCAPE TYPE AND DESIRED VISUAL MANAGEMENT CLASS There is a significant association between landscape type and local users' DVMC. This is supported by a chi square test value of 220.77, significant at 0.001 level (Table 14). The weighted mean values for the State and Districts are given in Table 15.
Visually inspecting the State's weighted ranking, one can discern that there is probably a great deal of overlap and similarity between landscape types based on local users' DVMC. The results of the chi square tests between each landscape type for the State level initially appeared to be as ambiguous as the weighted ranking (Table 16). There is considerable overlap among landscape types around the mean (2.74) in the weighted ranking based on DVMC. This is further demonstrated by the large number of non-significant differences indicated by the chi square tests between landscape type pairs.
The extremes are still however, clearly identified by both the weighted ranking and the chi square tests at the State level. Local users consistently
50


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expressed high levels of protection for mountains, river valleys and reservoirs. They also uniformly designated badlands, mesas and plateaus for low protection. It is not apparent why sand dunes are similar to all but four of the landscape types. These four remaining types (broad valleys, mesas, river valleys and rims/ridges) are significantly different than sand dunes only at the 0.05 level. It is interesting to note that river valleys are only similar to reservoirs.
The distribution curves graphically illustrate that the local users expressed high agreement, i.e. greater than 50% of their total response, on moderate protection for basin/parks, broad valleys and rims/ridges (Figure 8). The distribution curves also show that the local people clearly indicated moderate or low protection for badlands, rather than preservation or high protection.
Comparing the rankings among the State and Districts in Table 15, mountains, river valleys and reservoirs are the three landscapes designated consistently for high levels of protection with the exception of response from the participants in the Grand Junction District. Badlands and mesas were rated at the low end of the scale by the local users in the Craig and Montrose Districts. Grand Junction's ranking varied from the others especially in respect to plateaus, mesas and river valleys.
SUBSECTION 7.21 LANDSCAPE TYPE AND REACTION TO POTENTIAL CHANGE
As with DVMC, landscape type and reaction to potential change exhibit a statistically significant association. This is supported by a chi square test value of 888.78, significant at 0.001 level (Table 17).
Looking at the weighted mean values for each landscape type at the State level, the majority of the landscape types differ statistically from each
54


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other based on local user reaction to potential change (Table 18). The local people expressed a strong negative reaction at the State level toward proposed activities occurring in mountains or sand dunes. The response distribution curves illustrate this level of concern with close to 50% of the people indicating strong negative reaction against activities in mountains (Figure 9). People were in high agreement, i.e. greater than 50% of total responses, that potential change in sand dunes would be visually disturbing to them. In contrast, the high weighted mean values for mesas, badlands and plateaus indicated that the local people did not perceive changes in these landscapes as visually disturbing relative to the other landscape types. The response distribution curves for these landscapes show that close to 50% of the people had little adverse reaction to potential change occurring from activities.
The chi square tests for significance between landscape types based on reaction to potential change at the State level resulted in almost all the landscape types being statistically different than each other (Table 19). The level of significance was 0.001 for almost all of the chi square test results. Rolling hills-broad valleys, reservoirs-rims/ridges, and mesas-badlands were the only statistically similar groups.
SUBSECTION 7.22 LANDSCAPE TYPE SUMMARY
The results of the data supports the second hypothesis that local users' sensitivity varies significantly with landscape type. Comparing the weighted rankings of landscape types based on DVMC and reaction to potential change, the top four (mountains, sand dunes, river valleys and reservoirs) and bottom three (mesas, badlands, plateaus) types are identical. Mountains, river valleys, sand dunes and reservoirs consistently produced a high rating of sensitivity based on these analyses. The data also clearly identify mesas,
59


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badlands and plateaus as those landscape types about which the local users are least concerned.
The weighted ranking and chi square tests at the State level for landscape types based on DVMC indicates considerable overlap of landscape types in the moderate range of local users' visual sensitivity (Tables 12 and 13). The analyses based on reaction to potential change indicates strong statistical difference among landscape types and the overlap of sensitivity levels is minimal. Canyons were not consistently high in visual sensitivity as originally expected, although Grand Junction's workshop participants did indicate a high level of sensitivity for canyons, probably because they evaluated the spectacular Glenwood Canyon. I also hypothesized that broad valleys and basin-parks would be significantly lower in sensitivity relative to the other landscapes. The data indicates that these types were somewhere in the moderate to low range of concern. Instead, mesas, plateaus and badlands were consistently rated low by local users.
SUBSECTION 7.30 ACTIVITY TYPE AND DESIRED VISUAL MANAGEMENT CLASS
The participants in the public workshops had no idea as to which activities would be occurring in each landscape unit when they were asked to indicate their DVMC for that landscape unit. It would be erroneous to suggest that activity type directly influenced local users' DVMC in these workshops. There is however, a significant statistical association between activity type and DVMC because a chi square test was completed and was significant at the 0.001 level. Because of the format of the workshops, any further analysis between these two variables would be conjecture and unsubstantiated with this data base.
65


SUBSECTION 7.31 ACTIVITY TYPE AND REACTION TO POTENTIAL CHANGE
It is however, legitimate to analyze the association between local users' overall reaction to potential change and activity type. There is a significant association at the 0.001 level between these two variables (Table 20).
Some problems in the questionnaire should be mentioned before further discussion of the results of the relationship between activity type and overall reaction to potential change. In several of the workshops, activity types were lumped together. In the Grand Junction and Montrose Districts, mining-coal surface, mining-oil and gas, and 1andfi11/gravel operations were combined as one landscape modification. The ranking of mining-oil and gas by the Craig Distict workshop participants is obviously different than their reaction to mining-coal surface and 1andfi11/gravel operations (Table 21). Roads/railroads, ORV use and pipe lines were also combined in the Grand Junction District and all but one of the Montrose District's workshops. For the Escalante, Cimmaron, and Gunnison Gorge workshop in the Montrose District, ORV use was separately considered. The reactions to ORV use by the Craig workshop and the one workshop in the Montrose District are also considerably different than the level of reaction to pipeline and roads/rail roads.
The difference in the level of reaction between ORV use and roads/railroads-pipelines is evident in the State's weighted ranking (Table
21) . The difference in the level of reaction between oil and gas mining and coal surface mining-landfill/gravel operations is also evident in the weighted ranking at the State level. These observations are statistically substantiated at the State level by the results of the chi square tests (Table
22) .
Power plants and rest areas/campgrounds are the extremes in weighted
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ranking (Table 21). Each of these activities was evaluated at only one workshop. Local users were in high agreement, i.e. greater than 50% of the responses, in their level of negative reaction to power plants as illustrated by the response distribution curve (Figure 10). Close to 50% of the responses by local users toward rest areas/campgrounds indicated that this activity was acceptable in any landscape. The response distribution curves also indicate that almost 50% of the responses toward potential coal surface mining and landfi11/gravel operations were strong negative reactions. The local users were split over their level of reaction to residential activities.
SUBSECTION 7.32 ACTIVITY TYPE SUMMARY
Based on the weighted rankings and the chi square tests, there are statistical differences in the level of concern expressed by local users toward various activity types (Tables 21 and 22). Local users indicated a high level of visual sensitivity toward power plants, landfi11/gravel operations and coal surface activities. They expressed a low level of sensitivity to dams/water impoundments and rest areas/campgrounds. The response to residential activities varied among the Districts and did not consistently reflect a low level of sensitivity as I previous suggested. Moderate levels of visual sensitivity were expressed at the State level towards residential, roads/railroads and residential activities. However, there is considerable variation in the response to that activities from one district to another. The results of the data analyses therefore support the hypothesis that local users' visual sensitivity levels vary significantly with activity type.
70


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SUBSECTION 7.40 INTERACTIONS BETWEEN USER TYPE AND ACTIVITY TYPE
Chi square tests were performed to analyze the interaction between user and activity type. All but two of the tests showed significant results. Rest areas/campgrounds and power plants both had greater than 20% of their cells with expected cell frequency less than 5.0. This occurred because only one workshop evaluated each of these activities. The 'preference response1 workshops (Craig District) were separated from the 'negative response' workshops (Montrose and Grand Junction Districts). The 'other' user type was dropped from the analysis because there is little application of this information to future workshop design or sensitivity assessments. A weighted mean value was calculated for each user type by activity. A ranking of these values was tabulated for each activity (Table 23).
To identify the users who indicated extreme reactions for an activity, a test using the standard deviation was made for each ranking. The extremes were identified as those user types whose weighted mean value was greater than or equal to one standard deviation away from the mean value of the ranking.
The asterisked values in Table 23 indicate the extremes in reaction by user type concerning individual activities. As indicated by the extreme values, there is a clear discrepancy between the 'preference response' workshops (Craig) and the 'negative response' workshops (Montrose/Grand Junction).
In five of the thirteen activities evaluated, agency user types in the Craig District or 'preference response' workshops expressed the highest level of concern toward the specific activities, more than anyone else (Table 23). The agency representatives indicated the least preference in any location for potential changes caused by power line, land treatment, residential, coal surface mining and 1andfi11/gravel operations. The recreation user types most often indicated the least level of concern relative to everyone else in the
74


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Craig workshops toward residential, coal surface mining, roads/railroads, pipelines and landfi11/gravel operations.
The agricultural group in the 'negative response' workshops expressed the highest level of concern toward the most specific activities (Table 23).
These activities were ORV use, power lines, rest area/campgrounds, roads/railroads and pipelines. In eight out of thirteen activities, elected officials and utility user types indicated the least level of concern toward the specific activities. Land treatment, ski areas/resorts and rest areas/campgrounds were the only activities not considered favorable by either the elected officials or utility types or both.
SUBSECTION 7.41 INTERACTIONS BETWEEN LANDSCAPE TYPE AND ACTIVITY TYPE
The same analysis process conducted to show the interaction between user and activity type was also done for landscape and activity type (Subsection 7.40). The chi square tests were significant at 0.001 level for all except residential, ski areas/resorts and rest areas/campgrounds. The ski areas/resorts activity was not significant in the Craig District workshops.
Ski areas/resorts (0.01), residential (0.05), and rest areas/campgrounds(0.05) in the Montrose/Grand Junction Districts have lower levels of significance.
The weighted rankings of landscape type by the reaction to a specific activity are given in Table 24. With one exception there appears to be considerable amount of variation in the rankings between the 'negative response' and 'preference response' workshops. Mesas, badlands and plateaus have been identified in all Districts as being consistently one standard deviation below the mean, indicating a low level of concern by all local users. Table 24 identifies where in the landscape the local people prefer or have the least adverse reactions to a specific activity. This table also
78




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indicates where they least prefer or feel strongest against the activites occurring. For example, the local users in the Craig District preferred to see coal surface minings in basin/parks, badlands or mesas. They were highly concerned about the visual disturbance when this activity takes place near reservoirs.
SUBSECTION 7.42 INFLUENCE OF USER, LANDSCAPE AND ACTIVITY TYPES ON VISUAL SENSITIVITY
Path analysis was used in an attempt to indicate the relative degree of influence of each factor on visual sensitivity. As previously noted, contingency coefficients were used as path coefficients for each factor. As stated in the limitations for the validity of contingency coefficients, only coefficients generated from contingency tables of the same size are comparable. Because there are thirteen landscape and activity types and only ten user types, some of the categories were deleted from landscape and activity types. The selection of the categories to be deleted was based on the chi square test for significance between categories at the State level. Badlands and rolling hills were deleted from the landscape types because they were similar to mesas and broad valleys respectively (Tables 16 and 19). Sand dunes were deleted because they were evaluated in only one workshop.
Pipelines were deleted from activity types because they are similar to roads/railroads (Table 22). Power plants and rest areas/campgrounds were deleted because they were only evaluated at one workshop each.
Figure 11 illustrates the path analysis. The values of all the contingency coefficients are very close to a value of one with the exception of the coefficient between DVMC and activity type. As stated previously, a value of one indicates complete dependence or perfect correlation. These
82



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coefficients are very close to a value of one and therefore it appears that they all correlate highly with visual sensitivity and each other. There is no significant difference among the other contingency coefficients.
SUBSECTION 7.43 SUMMARY OF FACTORS INTERACTION AND INFLUENCE ON VISUAL SENSITIVITY
A three way analysis of the interaction among user, activity and landscape type was not done because it was beyond the scope and time limits of this study. However, the table of critical combinations illustrates the dependent interaction among the variables. If for example, local users expressed a high level of concern towards canyons independent of the proposed activity, then canyons would be listed as an extreme landscape type under each activity type. These same arguments can be made for the relationship between user type and activity type. There would be no extremes if the levels of concern for activity types were independent of landscape or user type. The response by all the users to an activity in any landscape location would be the same. The data support only the first part of the final hypothesis: user, landscape and activity types do not independently influence visual sensitivity.
I expected that landscape type would be the most influential factor in determining local users' sensitivity. However, based on the results of the path analysis, this is not true. Because the path coefficients are almost identical, with one exception, I conclude that the factors equally influence visual sensitivity. Therefore, the findings reject the remaining part of the final hypothesis that landscape, user and activity type do not equally influence visual sensitivity.
I also hypothesized that although the local users' sensitivity would
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agree with BLM's scenic quality ratings on the landscapes rated high in scenic quality, I expected that the data would also show that they would not agree on low scenic quality landscapes. The chi square test did indeed show that there was a significant association between local users' DVMC and BLM's scenic quality rating (Section 3.10) for a given landscape unit (Table 25). The chi square test value was 119.90, significant at 0.001 level. The local users' indicated level of preservation and high protection agreed with BLM's high scenic quality rating . However, the information in the chi square table indicated that low scenic quality landscapes are considered by local users as landscapes for low protection. This suggests, based on this data base, that 'common' or everyday landscapes do not necessarily evoke higher levels of concern toward visual change.
SECTION 8.0 INTERPRETATION OF RESULTS
SUBSECTION 8.1 USER TYPE AND VISUAL SENSITIVITY
Based on the findings of the data analysis, the hypothesis that local users' visual sensitivity varies significantly with user type is supported (Subsections 7.10-7.12). The conservation groups expressed the highest level of sensitivity in comparison to all other known user types (Tables 9 and 12). This finding is not surprising because of their publicized attitudes toward environmental protection. These are also the people likely to voice greater concerns about any landscape modification. Someone from a conservation organization would be a good representative of high visual sensitivity.
Agency, individual and recreation user types are similar in their level of sensitivity. Their level of sensitivity will tend toward the high/moderate protection level. The recreation user types did not turn out to be as significantly high as originally expected by their overall indication of
35


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visual sensitivity. No definite conclusions about them may be drawn because their sensitivity levels can vary based on the type of recreation interest they represent.
At the opposite end of the scale, utility and elected officals express low levels of concern. They are statistically different than each other and except for one other user type (private individual), are statistically different from all other user types (Tables 9 and 12). Because they differ statistically from each, one should not assume that they fully represent one another's interest, and therefore, they should not be combined. However, representatives from any of these two user types would be likely to represent the low end of the scale. The most interesting finding pertaining to user types and visual sensitivity turned out to be the elected officials. County commissioners made up the majority of the elected officials who participated in the workshops. For future sensitivity assessment and workshop design, this user type's expressed level of concern should be kept in mind. To paraphrase one of the county commissioners written comments, progress and developing natural resources are more important than visual quality.
The results also suggest that there are regional variations in the level of concern between the same user types. In the BLM/Wirth study, comments were made that the people in Grand Junction and Craig Distrists had unusually high sensitivity attitudes throughout the highly scenic and tourist/recreation areas. In contrast, some of the participants in the Montrose District workshops expressed an over riding desire for progress. Further investigation with larger samples of each type within each district is required to clarify and identify regional variation as it affects users attitudes toward visual sensitivity. Among the Districts, there were several striking differences in the DVMC weighted values between the same user types (Table 9). The elected
87


officials in Montrose expressed extremely low DVMC levels. Looking at who these elected officials were, I found that six out of eight of the elected officials who participated in the Montrose workshops were county commissioners from the same area. They essentially block voted on every landscape unit evaluated in the workshop. It is not clear why the difference among agriculture and utility types occurred in the Montrose and Grand Junction Districts. The difference in the agriculture user type between the two districts may be because the Montrose agriculture participant was a farmer and the Grand Junction participants were cattlemen. The majority of the Craig District agriculture user types were ranchers and were more closely aligned with the cattlemen of Grand Junction (Tables 8 and 12). This suggests that cattlemen and farmers should be evaluated separated. Farmers tend to be more adversely affected by land withdrawals for power lines, piplelines, etc. It definitely suggests an area that warrants further investigation. The utility group is composed of public service-electric utility representatives and there is no clear explanation for the variation in their expressed levels of concern between the two districts. However, I did find that the high level of concern indicated by agency types in the Craig District can be attributed to the six people from BLM who attended the same workshop. There were only twelve participants overall, so BLM was obviously over represented and skewed the results of the workshop.
The variability among local users' visual sensitivity is substantial.
The selection of the participants for a public workshop as stated by Penning-Rowsel1 (1979) indeed impacts the results of the workshop. Grouping individuals according to their social or special interest appears to be appropriate. The results of the data analysis on user type and their association to visual sensitivity are likely to be applicable to similar
88


environmental and resource situations outside the State of Colorado. The trends among user types and their expressed levels of sensitivity are probably not universal but perhaps reflect a "Western" attitude towards visual resources.
SUBSECTION 8.20 LANDSCAPE TYPE AND VISUAL SENSITIVITY
The analysis of the BLM/Wirth project data base supports the hypothesis that local users' visual sensitivity varies significantly with landscape types (Subsection 7.20-7.22). The results of the analysis relating visual sensitivity and landscape type would be most helpful in site planning. The findings indicated which types of landscapes were more or less suitable for any landscape modification with respect to visual change. In general, the results indicated that river valleys, reservoirs and mountains were only three landscape types for which the local public consistently express high levels of concern. It is interesting to note that two of these three identified landscapes are water related landscapes. It is conceivable to think that they were rated high because of the semi-arid nature of the State's climate and the scarcity of the water resources. The third type, mountains, is the landform most symbolic of the Colorado landscape and may consistently evoke sentimental responses whenever visual changes are suggested.
Mesas, plateaus and badlands were consistently rated low by the local users (Tables 15 and 18). The local people in the Montrose District were not especially sensitive to visual changes in mesas even though the mesas are a dominant landform in the area and have rich historic and archaeologic values. This low level of sensitivity may reflect their desire for economic growth and "progress" as previously stated. The local users may possibly feel that the mesas are not as spectacular or as awesome compared to the Rocky Mountains of
89


Colorado. It is not surprising then, that local users also indicate a low level of sensitivity for plateaus. Mesas are a form of plateaus. Badlands, as originally expected, would be near the low end of the scale because of the mental images that people have when they hear the name "badlands".
The data also showed that although the trends illustrated by the weighted rankings by DVMC (Table 15) and reaction to change (Table 14) were almost identical, the chi sguare test results between pairs of landscape types were not (Tables 16 and 19). In the DVMC-based chi square tests, there was considerable overlap among the landscape types indicated for moderate levels of protection (Table 16). The chi square tests based on reaction to change showed that all but two pairs (rolling hills - broad valleys and reservoirs -rims/ridges) were significantly different. This suggests that when the users indicated their DVMC's, they were taking a very broad appraisal of the landscape. However, by asking for reaction to change caused by various activities additional variables were introduced. People became more discriminating in expressing their level of sensitivity. This may indicate that when evaluating landscapes for local people's level of visual sensitivity, the more "sensitive" or significant question to ask would be their reaction to potential change based on specific activities.
The application of the specific findings of this study on landscape type and visual sensitivity is limited to other similar physiographic areas. In general terms, it appears the most symbolic or distinct landforms such as the San Juan Mountains and those that are scarce or unique such as Glenwood Canyon or the sand dunes are the landscape types that most people want to protect. People in this semi-arid region also express high levels of sensitivity toward water and water resources.
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SUBSECTION 8.30 ACTIVITY TYPE AND VISUAL SENSITIVITY
The hypothesis that local users' visual sensitivity varies with activity type has been supported by the data analysis (Subsection 7.30-7.32). The local users expressed high levels of sensitivity regarding power plants, landfi11/gravel operations and coal surface mining (Table 21). They expressed a low level of sensitivity to dams/water impoundments and rest areas/campgrounds. The grouping of activities under one category and the lack of legitimate information on the effect of specific activities on DVMC has hampered the interpretation of the association between activity type and visual sensitivity. The results of the weighted ranking from the Craig District and chi square test between activities demonstrate that combining coal surface mining and landfi11/gravel operations with oil and gas mining are probably not valid (Tables 21 and 22). In comparison, the first two activities physically and visually disturb larger tracts of land and are much larger in scale than oil and gas mining. The same results occurred when ORV use was separated from roads/raiIroads and pipelines; there again was a large discrepancy among the District rankings. The results of the chi square tests indicate that ORV use is significantly different than all other activities and in the future should be evaluated separately.
As previously stated, the request for reaction to potential change was asked differently in some of the workshops. It is evident by the differences between Craig's ranking order and the other District's ranking order that the variation is to be attributed to variables other than regional landscape characteristics (Table 21). The change in the attitude of the users' response from the degree of negative reaction to the degree of preference in the location of the activity is reflected in the reaction of the Districts to dam and water impoundments. The local users in the Craig District obviously felt
91


that this activity was a positive influence in the landscape in comparison to their reaction to other landscape modifications. Comparing their response to the Montrose and Grand Junction District, one can see that the visual affect of the activity was perceived by the locals very differently. It indicates that because of the implied negative response, the local users in these Districts possibly looked at the activity in terms of possible degradation of fish and wildlife habitat from the loss of instream flow and flooding of land that result from impounding surface waters.
The statewide ranking and significance test indicated a consistently high level of concern, as I hypothesized, by the local users toward mining and power plants (Tables 21 and 22). The response to residential activities varied among the Districts and did not consistently reflect a low level of sensitivity as I previously suggested. The mixed response to residential activities could be due to the public's experience with the energy boom towns which appear and disappear in Colorado and the ever increasing demand for housing. For future sensitivity assessments, it appears that activities such as pipelines, roads/railroads and land treatment will not generate high levels of concern from the local users. These activities could be implemented with good design and adequate mitigation without disturbing the visual resource from the users' point of view.
SUBSECTION 8.40 INTERACTIONS AMONG FACTORS
The final hypothesis states that user, activity and landscape types do not independently nor equally influence visual sensitivity. The first part of the final hypothesis is supported by the analysis of the data base (Subsection 7.40-7.43). Table 26 suggests critical combinations established by identifying the extremes in levels of sensitivity as expressed by local users
92




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Full Text

PAGE 1

DIANE SIMPSON WYNNE HAS SUBMITTED THIS THESIS AS PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR A MASTER OF LANDSCAPE ARCHITECTURE DEGREE AT THE UNIVERSITY OF COLORADO AT DENVER COLLEGE OF DESIGN AND PLANNING GRADUATE PROGRAM OF LANDSCAPE ARCHITECTURE ACCEPTED: DATE

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TABLE OF CONTENTS Section 1.00 Introduction . • 1 Section 2.00 Background •. • • • • • • 2 Section 3.00 Existing Data Base ••••.•.....• Subsection 3.10 Bureau of Land Management•s Visual Resource Management System . . . . • • • Subsection 3.20 Public Sensitivity Workshops . 4 • • 5 9 Section 4.00 Objectives and Scope of Study ..•........... 15 Section 5.00 Hypotheses Statements • . . . • • • • . . . .... 18 Subsection 5.10 User Type and Visual Sensitivity ..•..•.• 18 Subsection 5.20 Landscape Type and Visual Sensitivity. . . 19 Subsection 5.30 Activity Type and Visual Sensitivity . .2T Subsection 5.40 Interactions Among Factors and Their Influence on Visual Sensitivity ......... 22 Section 6.00 Methodology •....•..•••.•.•••...... 23 Subsection 6.10 Study Approach ....•.....••..•.. 23 Subsection 6.20 Analysis Process .••....•.•....•. 31 Section 7.00 Results ......•....•••..•.•.....• 36 Subsection 7.10 User Type and Desired Visual Management Subsection 7.11 Subsection 7. 12 Subsection 7.20 Subsection 7.21 Subsection 7.22 Subsection 7.30 Subsection 7.31 Subsection 7.32 Subsection 7.40 Subsection 7.41 Subsection 7.42 Subsection 7.43 Class ....•.•.......•....... 36 User Type and Reaction to Potential Change •.. 39 User Type Summary .....•.......•.. 47 Landscape Type and Desired Visual Management Class. . . . . . . . . . . . . . . . . .50 Landscape Type and Reaction to Potential Change ••...........••.••.•. 54 Landscape Type Summary • . . . . . • . . . . 59 Activity Type and Desired Visual Management Class .....•..... . . • . 65 Activity Type and Reaction to Potential Change . . . . . . . . . . • . . . 66 Activity Type Summary ..•.....•....• 70 Interactions Between User Type and Activity Type . . . . . . . • . . . . . . . . . . . . 7 4 Interactions Between Landscape Type and Activity Type .........•........ 78 Influence of User, Landscape and Activity Types on Visual Sensitivity. . . .....• 82 Summary of Factors Interaction and Influence on Visual Sensitivity .•...........• 84 ;

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Section 8.0 Interpretation of Results •••••••....••..•• 85 Subsection 8.10 User Type and Visual Sensitivity •.•••.•• 85 Subsection 8.20 Landscape Type and Visual Sensitivity ••.••• 89 Subsection 8.30 Activity Type and Visual Sensitivity .91 Subsection 8.40 Interactions Among Factors • • • . • • .92 Subsection 8.41 User, Landscape and Activity Type's Influence of Visual Sensitivity •• Section 9.0 Conclusions • Bibliography • Appendix A • Appendix B • ii .97 .98 . 102 . . . . . 105 . . 109

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No. 1 2 3 4. 5. 6. 7. 8 . 9 . 10. 11. 12. 13. 14. LIST OF TABLES List of Possible Activities Occurring on BLM Land. List of Possible Public Sensitivity Workshop Participants. Sensitivity Workshop Questionnaire Options With Explanation (Wirth Associates, 1979). Location and Dates of Public Sensitivity Workshops Conducted by the BLM/Wirth Project. List of Activities, Users and Landscape Types Used in Current Study Based on BLM/Wirth Project. Distribution of User Interest Groups Among the Public Sensitivity Workshops Conducted by the BLM/Wirth Project. Computer Input Format for Existing Data Base. Chi Square Test of Independence Between User Types and Their Desired Visual Management Class at the State Leve 1. Weighted Rankings of User Types According to Desired Visual Management Class Responses at the State and District Levels. Associations Between Pairs of User Types Based on Desired Visual Management Class at the State Level Chi Square Test of Association Between User Types and Reaction to Potential Change at the State Level. Weighted Rankings of User Types According to Their Reaction to Potential Change Responses at the State and District Level. Associations Between Pairs of User Types Based on Reaction to Potential Change at the State Level. Chi Square Test of Independence Between Local Users• Desired Visual Management Class and Landscape Types at the State Level. iii Page. 12 13 14 24 29 30 32 37 38 40 44 45 46 51

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No. Page. 15. Weighted Rankings of Landscape Types According to 52 Local Users' Desired V i sual Management Class at the State and District Levels . 16. Associations Between Pairs of Landscape Types Based 53 on Desired Visual Management Class at the State Level. 17. Chi Square Test of Independence Between Local Users' 58 Reaction to Potential Change and Landscape Types at the State Level. 18. Weighted Rankings of Landscape Types According to 60 Local Users' Reaction to Potential Change at the State and District Levels. 19. Associations Between Pairs of Landscape Types Based 64 on Reaction to Potential Change at the State Level. 20. Chi Square Test of Independence Between Local Users' 67 Reaction to Potential Change and Activity Types at the State Level. 21. Weighted Rankings of Activity Types According to 68 Local Users' Reaction to Potential Change, at the State and District Levels. 22. Associations Between Pairs of Activity Types Based 69 on Reaction to Potential Change at the State Level. 23. Weighted Rankings of Visual Sensitivity Levels by 75 User Type for each Activity Type. 24. Weighted Rankings of Visual Sensitivity Levels by 79 Landscape Type for each Activity Type. 25. Chi Square Test of Independence Between Local Users' 86 Desired Visual Management Class and BLM's Scenic Quality Rating. 26. Critical Combinations of High and Low Visual 93 Sensitivity Levels by Landscape Type and User Type for each Activity Type. iv

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LIST OF FIGURES No. 1 . BLM/Wirth Project Study Area (1978-1979). 2 . Diagram of Bureau of Land Management's Visual Resource Management System (USDI 1980). 3 . Locations of Public Sensitivity Workshops Held in Colorado During 1978-1979. 4. Diagram of Relationship Among Landscape, Activity and User Type with Visual Sensitivity 5. Map of the Bureau of Land Management's Districts Colorado. 6. Response Distribution Curves by Desired Visual Management Class for Each User Type at the State Level 7. Response Distribution Curves by Reaction to Potential Change for Each User Type at the State Level. in 8 . Response Distribution Curves by Desired Management Class for Each Landscape Type at the State Level. 9. 10. 11. Response Distribution Curves by Reaction to Potential Change for Landscape Type at the State Level. Response Distribution Curves by Reaction to Potential Change for Activity Type at the State Level. Path Diagram Illustrating Relationships Among User, Landscape and Activity Type with Visual Sensitivity. v Page. 6 7 10 17 26 41 48 55 61 71 83

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SECTION 1.0 INTRODUCTION People are making visual changes in the landscape at ever increasing rates. These changes do not occur in a vacuum, rather they are noticed by the public. People have become more vocal about their concern for scenic resources during the past decade. Government legislation such as the Federa l Land Policy and Management Act of 1976 and the National Environmental Policy Act of 1969 have mandated that scenic resources be considered on an equal weight and basis with water, wildlife, historical and other resources. It has therefore become necessary to inventory, evaluate and provide measurable standards for the management of visual resources (USDI, 1980). People's perceptions of their environment, specifically their attitudes toward changes in the landscape, form an essential part of the visual assessment process. This part is often termed visual sensitivity. Visual sensitivity has been characterized as the most difficult factor to understand and apply, and the one most in need of research and refinement in the visual assessment process (Grden, 1979). Some of the problems in assessing people's concerns stem from the lack of clear specification of the attributes of visual resources. Existing methodologies for assessing people's concerns about changes in the landscape do not normally address how variations in interest groups, variations in landscapes or variations in activities contribute to people's attitudes toward change. The current state-of-the-art visual resource management processses such as the Bureau of Land Management's Visual Resource Management (VRM) system and the U.S. Forest Service's Visual Management System (VMS) place greater emphasis on user volume than on evaluating public attitudes toward visual change (USDI, 1980; Bacon, 1979). The public is often alluded to but their concerns are not usually incorporated into the management

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procedures (Lee, 1976). User volume can be measured objectively, while user attitudes have so far seemed more difficult to obtain, understand and evaluate. Evaluating and incorporating user attitudes in the assessment of visual sensitivity is critical if people will be affected by proposed changes in the landscape. Sensitivity workshops to identify public attitudes are routinely conducted by some private consulting firms and on occasion by Federal agencies such as the Bureau of Land Management. Budget and personnel cut backs have restricted and lowered the priority of federal visual resource management programs. However, the need to understand and evaluate user attitudes has not diminished. On the contrary, the need has increased due to pressure for development and natural resource extraction, especially in Colorado (Mendosa, 1984; Sheppard, (1984); Taggart, 1984). Therefore, it is important to discover how people's attitudes toward visual change are influenced by various factors. We may then more fully understand and more accurately predict the real impacts that visual change has on people. SECTION 2.0 BACKGROUND People's perceptions and expectations direct their attitudes toward changes in the landscape. Without understanding human perception and behavior, it is not possible to determine accurately people's concerns (Sinton and Ginder, 1979). Values, scene meaning and familiarity are important elements in formulating an individual's perception. Physical elements such as topography cannot by themselves ascribe the values of people (Enk, 1980). All values are not universal, but transitory in nature, reflecting what people view as historically and culturally important within their given region (Scheele and Johnson, 1979; Penning-Rowsell, 1979; Taggart, Tetherow and Bottomly, 1980). 2

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People react to visual changes in the landscape based on their "mental images" from previous experiences and knowledge (S. Kaplan, 1979). The meaning of a scene or "image'' can change drastically as one moves from one landscape unit to another or from one land use to another. For example, the acceptable level of change may shift predictably when a transmission line is moved from an industrial area to a residential area (Wirth Associates, 1982). In addition, many people's intuitive image of a strip mine in terms of visual impact is quite negative. Yet little research has been done to determine the public's true aesthetic perception of surface mines (Simpson, 1979). People's expectations also influence their attitudes toward visual change. Evaluating expectations or a person's preconceived ideas about a given landscape, setting or land use will help indicate the degree of acceptable change in the landscape (Blau, Bowie and Hunsaker, 1979; Miller, Jetha and MacDonald, 1979). A person in a wilderness area "expects" to see a visually pristine landscape and not encounter human-induced features such as transmission lines or cooling towers. A person is "conditioned to expect certain visual images associated with specific land uses" (Miller, Jetha and MacDonald, 1979). Any proposed activity that conflicts with these expectations is likely to result in a negative response from that person (Miller, Jetha and MacDonald, 1979; Ady, Gray and Jones, 1979). Familiarity or an individual's experiences with a given landscape also affects a person's perception and ability to evaluate his/her concern for change. Past experiences and prior information will influence how people will react to change in a given landscape (Hammitt, 1979). The more familiar people are with a landscape, the more capable they are to judge the level of visual change. People who have never encountered a given landscape have no previous experience to evaluate a change in that landscape. The more familiar 3

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a person is with a landscape, the more detailed their "mental image" is. A change in that landscape would register as something different from their expectations based on their "mental image•• from past experiences. There is substantial agreement in the literature that incorporating public preference into visual sensitivity evaluation is the appropriate and best way to understand people's visual perception of the landscape. It has been suggested that the exclusive use of professionals to make value decisions is inappropriate. These •experts• do not have to live with the decisions made but the affected people do (Schomaker, 1978; Penning-Rowsell, 1979; Enk, 1980). The professional who is not familiar with an area may fail to be aware of subtle forms of variety and distinctiveness in that landscape (R. Kaplan, 1979). Professionals should recognize that people do have individual biases concerning the landscape. These biases make public participation in workshops valuable in determining appropriate decisions in the management of visual resources. Without careful selection of participants and awareness of individual biases, the results of the workshop can be skewed (Penning-Rowsell, 1979). Litton (1979) feels that there still needs to be a better agreement "between physical visual landscape criteria used by professionals and perceptual values identified in public workshops11• SECTION 3.00 EXISTING DATA BASE This study is based on existing data generated from public sensitivity workshops held in Colorado during 1978 and 1979. In October 1978, the Bureau of Land Management (BLM) awarded a contract to Wirth Associates (now Wirth Environmental Service, Division of Dames and Moore) to conduct a visual resource inventory and evaluation of land within the four BLM Districts in Colorado. The project covered 8,504,460 acres of BLM and adjacent land 4

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(Figure 1). The visual inventory and evaluation methodology used by Wirth Associates followed BLM's Manual 8411-Upland Visual Resource Inventory and Evaluation. Theis was the first application of BLM's 8411 Manual. The results of the project were to be used in preparing envionmental assessment reports and envionmental impact statements on specific proposals and as a tool for the design of projects. Since the data were collected as part of BLM's Visual Resource Management System, it is important to discuss briefly how their system works (Figure 2). SUBSECTION 3.10 BUREAU OF LAND MANAGEMENT'S VISUAL RESOURCE MANAGEMENT SYSTEM As defined by BLM, the "Visual Resource Management System (VRM) is an analytical process that identifies, sets and meets objectives for maintaining scenic values and visual quality" (USDI 1980). The VRM system is divided into three steps. These are Inventory/Evaluation, Management Classes and Contrast Rating (Figure 1). The first step, Inventory/Evaluation, involves the assessment of the three factors: scenic quality, sensitivity levels and distance zones. Scenic quality is described as "the overall impression one retains after driving through, walking through or flying over an area". Land units that are homogeneous in terms of landform and quality are mapped and rated by seven factors. These factors are landform, vegetation, water, color, adjacent scenery, scarcity and color modifications. After a numeri cal rating, these units are classified as 'A', 'B' and 'C' Scenery, where 'A' Scenery signifies an area of highest scenic quality, 'B' is common and 'C' is minimal . Sensitivity levels are determined to be h i gh, medium or low based on user volume and user attitude. User volume is based on the number of people who 5

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travel through the area and the number of people who use the area for recreation or other purposes. User attitudes are meant to be determined through public workshops to obtain information on concerns about changes in scenic qualities. The final Inventory/Evaluation factor assesses the proximity of the user to the landscape being viewed. Distance zones are divided into foreground/middleground, background and seldom seen based on broad thresholds in the perception of forms, textures, and other criteria. The second step in the VRM system is the derivation of Management Classes based on an overlay/matrix technique using the results of the scenic quality, sensitivity level and distance zone evaluations of the landscape. The Management Classes range from Class 1 to Class 5 and describe the different degrees of modification allowable in the landscape. Classes 2, 3, and 4 allow management activities to become visually dominant to a increasing degree, while retaining the visual characteristics of the natural landscape. Class is an area designated as wilderness, wild and scenic river or other similar area where management activities are very limited. Class 5 is rarely used and reflects an area in need of rehabilitation or enhancement to bring it up to one of the other classes. To evaluate specific development and management activities, BLM uses a Contrast Rating System to assess the severity of visual impact on a landscape. The Contrast Rating compares the proposed activity•s basic visual elements (form, line, color and texture) to the landscape•s major visual elements. The degree of contrast (e.g. ••does not attract attention", "attracts attention and begins to dominate••, and ••demands attention") is then assessed. This information is compared to the appropriate Management Class to determine if the contrast of the proposed development or management activity 8

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is acceptable (USDI, 1980; Ross, 1979). While it is not the intent of this paper to critique BLM•s visual assessment procedure, a few major concerns should be stated. In determining the scenic quality of a landscape, the numerical values assigned to the factors (landform, color, scarcity, etc.) are arbitrary and inflexible. The system has numerically pre-weighted these factors regardless of regional context. Although the system states that user attitudes are determined through public workshops, in reality these workshops are seldom held. The workshops held in Colorado in 1978 and 1979 were the only ones of their kind conducted on such a scale in the country. The BLM system usually allows changes to take place in 11Common11 or minimal scenic quality landscapes. There is no mechanism to protect theose intact 11common11 landscapes which give a region it characteristic flavor (Tetherow, 1984). The Contrast Rating imposes the same numerical rating bias as the factor used in determining scenic quality. Consideration of scale and spatial dominance is not included in the Contrast Rating but should be (Sheppard and Newman, 1979). SECTION 3.20 PUBLIC SENSITIVITY WORKSHOPS Wirth Associates designed a series of public visual sensitivity workshops to document local attitudes towards potential changes in surrounding areas as part of their BLM contract to conduct a visual resource inventory and evaluation of BLM and adjacent land. Thirteen workshops were held in eleven different locations between November 1978 and July 1979 (Figure 3). To prepare for a workshop the contractors visited the particular planning unit(s) to become personally familiar with the area. From the reconnaissance survey, they determined and mapped the appropriate landscape units which would be evaluated by the public. Activities appropriate for each planning unit were 9

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selected for evaluation (Table 1). Representatives from public agencies, industry and public interest groups were invited to participate in the workshops (Table 2). To determine workshop participants, a generic list of interest or agency types was developed. From this list, specific representatives were selected from each category by the local BLM District staff. If a planning unit(s) had a national park or monument within its boundaries, representatives from the National Park Service were invited to participate. If there were no national parks or monuments in the planning unit(s), then a representative from the National Park Service was not invited to participate in the workshop. The remaining tasks were to prepare a map of the planning unit(s) to be evaluated and prepare the workshop specific questionnaires. A brief introduction and overview of the project was given at the beginning of each workshop. A map was displayed to identify the area for evaluation. Slides were shown of each of the landscape units to be evaluated. Next, the participants were shown slides of various potential activities which could occur in the area. The participants were then again shown the landscape unit and asked to indicate their desired visual management class (DVMC), i.e. to indicate the general level of protection that should be given to the visual resource. Table 3 lists the options for the levels of protection with their respective explanations. Examples of the questionnaire, other pertinent information and general statistics on the workshops are included in Appendix B. The participants were next asked to indicate their responses to potential changes caused by each activity for each landscape unit. Finally the participants were given the opportunity to comment in writing on specific issues or projects of concern. After each workshop, the participants' workshop sheets were reviewed by 11

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(?): AXTUF-e. ...WIAQk.l (u:'): t?tlLD Jc..llm 1tif= CA.ALP 1\4:. I)J -ro \IJ rfiAl-CF tie?-\ A : ('"'") : i\\)$'f:(f'e-c:q::= K-11\/liY (H) . e.'{ AC:TN\1'{ \Jd.tLt::> Q.,) : Vl?UM-eK 11-1\? 1'{re PCTi" \)'f "iUI-7 -r:1Pe .NJD ee 1HEH.t*') ta:A . ") : lU-I& "l'(f'e. Tl W41LD OE. A. \-k'DIS-12,b.-\l'!.L'-( (.{...} : 1'(fe. 410 1l-\E. taA 'f"Aete. l4rA+-CkllPr-HI H-11). 1 II

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Wirth Associates to determine the validity of their responses. If it was apparent that a participant demonstrated obvious bias by their ratings, their responses were reviewed with BLM and often not included in the evaluation. Summaries of the raw data sheets (a copy of which is included in Appendix B) were completed for each landscape unit by totaling the participants• responses to DVMC and their reactions to potential changes from various proposed activities. A preliminary analysis for determining the user attitude portion of the sensitivity assessment was based on the comparison of the summary responses from the DVMC and reactions to potential change . The user attitude sensitivity level was categorized as high, medium or low. Where it was unclear as to what the sensitivity level should be for a given unit, a temporary high/medium or medium/low designation was given . The results of the preliminary analysis were reviewed by the BLM Area Manager and appropriate staff. The borderline cases of high/medium or medium/low were resolved and the final user attitude sensitivity level for all landscape units decided. From the information collected at the public workshops, all that was utilized by BLM was a rating of high, medium or low user attitude sensitivity for given landscape units. The ratings were then combined with the corresponding user volume ratings to give a final sensitivity rating. The final sensitivity rating was incorporated into the Inventory/Evaluation portion of BLM's VRM system (Figure 1), (Wirth Associates, 1979). Methods and results of the public sensitivity workshops were summarized in an article in Landscape Architecture (Taggart, Tetherow and Bottomly, 1980). SECTION 4.00 OBJECTIVES AND SCOPE OF STUDY For the purpose of this study, visual sensitivity is defined as a measure of people's perceptions of their environment, specifically their concern about 15

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human-induced changes in the landscape . In keeping with the conditions under which the data for this study were collected, the local users• desired visual management class and their reaction to potential changes caused by particular activities were used as measures of visual sensitivity. •Desired visual management class• is defined as the relative sensitivity value the local people placed on the visual resource of a particular landscape unit. Reaction to potential change is defined as the local people•s response to possible specific activities which may occur in a particular landscape unit (Wirth Associates, 1979). The scope of this study will be limited to the analysis of the influence that user, landscape and activity type have on visual sensitivity as measured by desired visual management class and reaction to potential change in non-urban settings (Figure 4). There were no transients/vistors included in the data base, but local users only. Current literature in the field of visual resource assessment is vague in its description of how visual sensitivity may be measured or evaluated. Most research on aesthetics address the assessment of scenic quality and not the concern for change (sensitivity) in the landscape. Visual sensitivity evaluations are often based solely on professional judgement without public input and without sufficient research to substantiate management decisions. There have been some documented local workshops on visual sensitivity which provide considerable data on user attitudes, most notably the BLM/Wirth project in Colorado. No one has, until this paper, analyzed this information systematically for any significant trends, correlations, or relationships which may help direct and improve visual sensitivity evaluation. The objectives of this study are as follows: 1. To collect and organize the BLM/Wirth project data base into a form that will permit scientific analysis of the existing under-utilized data. 16

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2. To analyze the data base in a systematic process for significant trends, correlations or relationships that might indicate what determines people's concerns for visua l changes in the landscape. 3 . To suggest implications and specific applications based on the findings of this analysis . SECTION 5.00 HYPOTHESES STATEMENTS In order to understand how landscape, user, and activity type influence visual sensitivity, the impact of each of the three factors have been evaluated separately. Three hypotheses have been formulated to suggest the association between each factor and visual sensitivity. A fourth hypothesis addresses the interaction among the three factors and their combined affect on local user attitudes toward change. The hypotheses are as follows: 1. Local users• vi sua 1 sensitivity varies significantly with user type. 2. Local users• visual sensitivity varies significantly with landscape type. 3 . Local users• visual sensitivity varies significantly with activity type. 4. User, landscape and activity type do not independently nor equally influence visual sensitivity. SUBSECTION 5.10 USER TYPE AND VISUAL SENSITIVITY For this study, the first hypothesis is that local users• visual sensitivity varies significantly with user type. For the local resident there will most likely be higher levels of concern for the everyday, familiar environment. They are the ones who will have to deal with any changes on a daily basis (R. Kaplan, 1979; Enk, 1980). It is important, therefore, to understand how attitudes differ between local resident types. 18

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Investigation into the association between user type and visual sensitivity should provide information on the relative levels of concern by various user types. I suspect recreation and conservation groups are significantly more sensitive than other groups because they generally exhibit a higher concern for environmental protection. Mining, utility and private industry groups might be expected to be least sensitive because of their pro-development attitudes and concern for potential economic gain from the land . I would expect agency representatives (local, state and federal government), elected officials and agricultural groups to be moderate in their levels of concern. The agency group allocates and regulates use of the land, and like the elected officials should represent all people's concerns. I feel the agricultural group will also be moderate in their attitudes, because, although theirs is a consumptive use, they maintain a closeness to and dependency on the land. The evaluation of specific interest group's sensitivity could be used by professionals in predicting the public's level of sensitivity. Grouping individuals on the basis of shared values or interests and evaluating a social group's concern towards change would be easier than trying to identify and predict the variability of an individual •s response (Lee, 1976). In addition, the professional would be able to identify interest groups who are particularly sensitive to certain activities. By having an idea of the concerns of various user types, the professional can invite users who will be a true representation of the public and avoid biasing the result in any particular direction. SUBSECTION 5.20 LANDSCAPE TYPE AND VISUAL SENSITIVITY The second hypothesis states that local users• visual sensitivity varies 19

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significantly with landscape type. As stated previously, people react to visual changes in the landscape based on their "images" as the result of their acquired set of mental maps from previous experiences (S. Kaplan, 1979). The meaning of a scene can change as one moves from one landscape unit to another. I feel the highly sensitive areas as expressed by local people are mountains, canyons and river valleys. These three landscapes types are often associated with high scenic quality and are more vulnerable to changes. These types provide a significant portion of recreational use for a wide variety of users in Colorado and are distinctive, dominant landforms with which people can strongly associate. On the other hand, broad valleys, basin-parks and badlands may be considered by local users to be less sensitive than other types. Broad valleys and basin-parks are visually less distinctive, expansive areas with little variety in terms of slope, vegetation or color. Certain cultural intrusions in these landscape types would probably be considered positive improvements in the landscape, adding some variety, color and interest. Although badlands are relatively rare and potentially could be considered to have high scenic quality because of their visual interest and uniqueness, people's "mental images" of badlands may identify those landscapes as areas of low sensitivity. It would be very useful to visual resource managers and planners to have information on local users' attitudes toward specific physiographic landscape units (Sheppard, 1984; Taggart, 1984). This type of information could be extremely valuable at the initial stages of site planning. Landscape units identified as highly sensitive could be avoided or flagged so that special planning and design efforts could be made. 20

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SUBSECTION 5.30 ACTIVITY TYPE AND VISUAL SENSITIVITY The third major hypothesis for this study states that local users• sensitivity varies significantly with activity type. Any proposed activity that conflicts with a person•s expectations is likely to result in a negative response from that person (Miller, Jetha and MacDonald, 1979; Ady, Gray and Jones, 1979). By examining potential changes caused by various activities rather than proposed changes from specific projects, it may be possible to get closer to people•s general level of concern, rather than by using their responses to change from an actual project which could hold atypical associations. In specific situations, other factors such as personal economic gain or loss may influence their true concern over the visual change caused by an impending activity (Tetherow, 1984). There is little information available on people•s levels of concern for change caused by various activities. Neither the USFS VMS or BLM•s VRM attempt to incorporate user•s sensitivity of various activities in their planning process. In view of the amount of land disturbed, the relative scale of the activity and the loss of the use of the land by the majority of the people, I expect that local users are most sensitive to mining and power plants. By the same reasoning, local users may be viewed as less sensitive to roads/railroads and residential activities. These activities are directly beneficial to a vast majority of the local public. Positive recreation values and minimal surface water resources in this region probably off-set the relatively large scale and loss of land characteristic of water impoundments. For these reasons, I feel people are also less sensitive to water impoundment activities. I expect that off road vehicle (ORV) use is a highly sensitive, controversial activity among users because of the strongly polarized attitudes toward that activity. 21

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If it can be demonstrated that sensitivity levels vary with activity type, then the information could be used to substantiate recommendations for specific changes in the two federal management procedures (Lee, 1976) or any other visual resource management prodecure. The information could also be used to discriminate between acceptable and problem activities when assessing visual sensitivity. SUBSECTION 5.40 INTERACTIONS AMONG FACTORS AND THEIR INFLUENCE ON VISUAL SENSITIVITY There are two parts to the last hypothesis of this study. The first part of the hypothesis states that user, landscape and activity type do not independently influence visual sensitivity. The second part states that the three factors do not equally influence visual sensitivity. By definition, visual sensitivity is a measure of people's perception of their environment, specifically their concern about changes in the landscape. People's perception of their environment, as suggested by this study, is influenced to a great extent by their values and social interest, the physical aspects of the landscape and the cultural activities potentially imposed in the landscape. It is therefore important to examine the interaction among user, landscape and activity type to try to determine if there are combinations of these factors which may be greater than any single factor's influence of local users• level of sensitivity. These critical combinations could be used to predict the relative level of visual sensitivity and aid in the design of public workshops for more informative results for proposed projects when the activities, users and landscape types are known. I expect that the most influential factor in determining visual sensitivity is landscape type. Landscapes perceived by the public as having 22

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high visual quality such as scenic rivers, national monuments and parks are those most often designated for preservation or high protection (Blair 1980). In other words, people have a high level of concern for any potential change that may occur in landscapes that they perceived as having high scenic quality. I also expect that local people do not necessarily agree with a landscape low in scenic quality by a fixed numerical rating system such as the VRM system used by BLM. These every day or "common" landscapes may evoke a high level of concern and need for protection by the local users. People, to a large extent, feel a part of the land and are basically hesitant to alter the beauty of nature. As Gussow (1979) expressed: "We are not separate from our landscape •••• When we endanger the landscape, therefore, it is a part of ourselves which we threaten." SECTION 6.00 METHODOLOGY SUBSECTION 6 .10 STUDY APPROACH The initial problem involved in this thesis project was the collection of information on the original study and locating the individual workshop data sheets. Table 4 lists the dates, locations and districts of each workshop. Since it has been five years since the original study was conducted, it was difficult to trace all the people who worked on the BLM/Wirth project. Some of the raw data workshop sheets could not be found. As a result, the data for the workshop for the Canyon City District and for the Baxter, Douglas and Mt. Garfield Planning Units in the Grand Junction District are not included in this study. The workshop summary sheets are available for these areas, but were not disaggregated and utilized because of time limitations. The workshop held for the Dolores and Lone Cone Planning Units in the Montrose District were also not included in this study. The Dolores and Lone Cone Planning 23

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Units were previously inventoried under an earlier BLM project. It was felt that the classification of landscape types was not consistent with the general types identified in the BLM/Wirth study. With the deletion of the above workshops, this thesis project includes data from eight workshops covering BLM's Craig, Montrose and Grand Junction Districts in Colorado (Figure 5). The cummulative results from all eight workshops from these three Districts will be referred to as 'State' or 'statewide' throughout this study. A brief description of the Districts follows as an aid in interpreting the findings. 1. Craig. The planning units in the Craig District covered by the Wirth/BLM project were Williams Fork, North Park, Nipple Rim and Browns Park. It should be noted that the planning unit designation is not currently utilized by BLM. Three public workshops were held to evaluate the 3,977 square miles of BLM and adjacent lands. This area is in the Wyoming Basin physiographic province as defined by Fennenman with small segments of the Southern Rocky Mountain province extending into the area. The majority of the general landscape types are found in each of these planning units, with sand dunes and badlands being the most distinct and unique landforms. The largest dunes in the region (with the exception of the Great Sand Dunes National Monument) are located in the North Park Planning Unit . The major rivers flowing through this area are Williams Fork, Yampa, Little Snake, North Platte, Laramie and Green Rivers. Dinosaur National Monument and the Arapaho National Wildlife Refuge are located in this District. The major tourist/resort center is the City of Steamboat and Steamboat Lake. Besides Steamboat, Craig and Hayden are the only other relatively large urban communities. The land uses within these planning units consist mainly of agriculture, ranching and coal, gas and oil mining. 2. Grand Junction. Roaring Fork and Eagle Planning Units were the areas 25

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evaluated by two public workshops in the Grand Junction District. The two planning units cover 1,193 square miles. They lie in the southern Rocky Mountain province with mountains being the most dominant physical and visual element in the landscape. Glenwood Canyon is an extraordinary scenic resource that is located in this District. A portion of the Grand Mesa is also found in this area. The major rivers are the Roaring Fork, Colorado and Eagle Rivers. Interstate-70 is a dominant feature in the landscape in these two planning units. This highway corridor links urban communities and provides an axis around which most of the commercial development is occurring. Agriculture, gravel mining and recreation/resort areas are the prominent land uses. Aspen, Snowmass, West Vail, Glenwood Springs and Avon are key resort and urban center 's. 3. Montrose. The planning units in the Montrose District covered in the Wirth/BLM project were Chromo, Durango, Sacred Mountain, Cimarron, Gunnison Gorge and Escalante Planning Units. Three workshops covered the 3,043 square miles of BLM and adjacent lands. According to Fenneman, the area contains two major physiographic provinces, the southern Rocky Mountain province and the Colorado Plateau province. The Uncompahgre River is generally the interface between these two physiographic regions. The resulting landscape is one of steep, rugged mountains (San Juan Mountains), dominating mesas (Mesa Verde and Log Hill Mesa) and steeply incised canyons (Black Canyon of the Gunnison). The Mancos shale badlands are also found in this District. The major rivers are the Animas, San Juan, Dolores, Gunnison and Uncompahgre Rivers. Lemon Reservoir, Vallecito Reservoir and Sweitzer Lake are dominant water features. Delta, Pagosa Springs, Chromo, Cortez and Durango are the large urban communities in these planning units. Sacred Mountain Planning Unit is a very historic and archaelogical area containing Mesa Verde National Park and 27

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Hovenweep and Yucca House National Monuments. Agriculture, ranching, tourism and some mining are prominent land uses in these planning units (Wirth Associates, 1979). Table 5 lists the user interest groups, the landscape types, and activities that are used in this study. The user interest groups and activities were predetermined by the BLM/Wirth data base. Table 6 shows how many of each user interest group were represented in each workshop. A description of each landscape type is given in Appendix A. The assumption is made that the public sensitivity workshops were appropriately designed and conducted consistently from one workshop to another. There is one exception to the assumption that needs to be identified. The participants were asked in all but three workshops to state their reaction to potential change based on the degree of "negative reaction" that they felt that a activity would cause in a specific landscape unit. In the Browns Park/Nipple Rim, Williams Fork and North Park workshops, the participants were asked instead to indicate their "relative preference toward" specific landscape modifications that could potentially take place in a landscape unit. The decision to redirect the participants• response to potential change was made because it was suggested that by asking for the degree of negative response, the questionnaire had predetermined for the participant that the change was negative and could not have a positive affect in the landscape. To compensate for the changes made in the questionnaire, I have separated the landscape preference workshops from the negative reaction workshops when analyzing the local users• responses to activities. In other analyses, the two variables are treated as being the same. Fortunately, all three of the landscape preference workshops were located in the Craig District. 28

PAGE 35

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PAGE 36

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PAGE 37

SECTION 6.2 ANALYSIS PROCESS Each landscape unit evaluated in the workshops was categorized into a general landscape type. The categories were based on the general landscape types defined in the BLM/Wirth project and are described in Appendix A. This allowed the responses from the participants to any general landscape type such as mountains to be compared from workshop to workshop. The information associated with each landscape unit, such as the users• responses to each potential activity, DVMC, etc. was coded into the Prime computer at the University of Colorado at Denver. From the one hundred fifteen (115) total participants, 2,717 records of information were generated. The DVMC was coded on an even integer scale with the number 1 referring to preservation and 4 to low protection. A strong negative reaction to change was coded as a 1 and low adverse reaction as 3. The least preferred location for an activity was coded as a 1 and the most preferred location coded as a 3. An •other• user type category was formed to combine those individuals whose social affiliation could not be determined. Table 7 is an example of the coded data . The first step in the data analysis was to determine the basic distributional characteristics of each of the variables, i.e. DVMC, user type, activity response, etc. A one-way frequency distribution was done to check each variable to determine if it had sufficient range to be used in subsequent statistical analyses. It also helps check the data to see if it had been coded correctly. Chi square tests for independence were used to determine whether a systematic relationship existed between two variables. The test is computed 31

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1 c;; c:; c.; c:; Ci> CG c;, CG c:; c:; c:; C:i c:; c:; CG c.; CG c:; CG c . :; 1 . t or 'J:.lr JS c?. o11,.:; "i.J;.;, ,s 2 p • J 'IJ s Bilo . n.s 1:) ;.: J\1 •>,; c. DOUGLAS MOUNTAIN CROSS MOUr.TAltl CCLD (;OGIVA VERMILLION BLUFFS NIPPLE. i\IM BLUE. POWDE.P WASH RiuGE BO.DLAI'.DS JCHI'. wELLER PO\IJER jjASH !?.ISh CANYON CROSS CANYO VERMILLION CREEK CANYON LITTLE SNAKE RIVE.R YAMPA RIVER-SUNBE.AM LO".iEP YAMPA BASIN-PARK SHEEPHEAu WASH BASIN ThOF.PSON ORAoi BROWNS PAHK DOUGLAS DOUGLAS DIAMOND GODIVA RIM VEHHILLION BLUFFS NIPPLE RIM cLUE POWDEP w.:.SH SEVENMILE GRASSLAND-SAGEBRUSH B:.OLANDS CREEK \IE.LLE.R MESA POtiDE.R WASH IRISH CANYON CROSS CANYO BEAVER CRE.EK LITTLE. RIVER YAMPA RIVER-SUNBEAM LOWER YAMPA S4ND WASH BASIN-PARK SHEEPHEAD BASIN THOMPSON DRAW BROWN S PARK DOUGLAS DRAw-VERMILL CP.OSS A IN DOUGLAS MOUNTAIN COLD SF RING DIAMOND MOUNTAIN IRISH CA'\YON HEAVER CREEK 32 l 1 MOUI'tTAII 1 31.3333332392999RC:CF.:t.TI ROLLING ROLLING ROLLING 3C333333l293999RE:REAT I CANYONS BADLANDS 412222llll93999RECREATI RIVER VALL212213llll93999RECRE4T! P.IVER R!VER BASIN-PARK232223222193999RECREAT! BASIN-DARK23222322ll93999RECREtT! MOUNTAIN 311112881898999AGR!CULT RIMS-R IDGE323313ll119299 9A&RICULT RIMS-P.IJGE312223111193999AGRICULT RIMS-RIDGE3222132211S2999AGRICULT ' BASIN -PARK333323221193999AGRICULT ROLLING HI322223221393999AGRICULT ROLLING HI3U222322l293999AGRICULT ROLLING H!3t2223221293999AGRICCLT BADLANDS 3C2213221293999AGRICULT MESAS M[SAS 322213221293999AGRICULT CANYONS 3llll2111291999AGRICULT CANYONS 31lllllll291999AGRICULT RIVER VALL312232881292399AGP.ICULT RIVER VALL32222288129j999AGRICULT RIVER BASIN-PARK332213881293999AGRIC JLT BASIN-PARK322213881293999AGRICULT BASIN -PARK32221388l2939 99AGRICULT 321112lll393999AGRICULT MOUNTAIN l22213112292999AGENCY 2122132222S2999AGENCY ROLLihG HI3G3323222292999AGENCY CANYONS llllll221892399AG[NCY CANYONS .I

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by the following formula: I where fe equals the observed frequency in each cell of a two way table I and fe equals the expected frequency in each cell as calculated by N where ci is the frequency in a respective column marginal, ri is the respective row marginal and N is the total number of valid cases (Nie, 1975). The Null Hypothesis is that there is no association between the two variables. In order for the chi square test to be valid, fewer than twenty percent of the cells should have an expected fequency of less than five and no cell should have an expected frequency of less than one. The variables must also be independent (Siegel, 1956). One-tailed tests of statistical significance were reported for the chi square test at the 0.05, 0.01 and 0.001 levels. A chi square test does not determine the strength of the relationship between the variables. The contingency coefficient 1C' is a useful measure of the extent of association between variables. It is appropriate for this study because it can be applied to nominal or unordered variables. The contingency 33

PAGE 40

coefficient is calculated as follows: c = where C equals the contingency coefficent, x 2 equals the chi square test value for the variables, and N is the total number of valid cases. A coefficient of zero indicates the lack of any association. A value of one indicates complete dependence or perfect correlation. The limitations of the contingency coefficient are that any limitations that apply to the chi square test also apply to •c•. Two contingency coefficients are not comparable unless they are of equal table size, i.e. a table 3 x 7 cannot be compared with a 2 x 2 table (Siegel, 1956). The contingency coefficients were used as path coefficients for the three factors of landscape, user, and activity type in an attempt to explain their relative degrees of influence on visual sensitivity. This type of analysis is referred to as a path analysis. A path analysis attempts to evaluate linear relationships and determine the causal effects of a given independent variable on a dependent variable (Nie, 1975). This application of path analysis using contingency coefficients may not be statistically valid. It was used here in an attempt to explain only the strength of association and causal effect between the three factors and visual sensitivity. A weighted mean value was calculated based on the integer coded DVMC and reaction to potential change . These values were then ranked by State and by districts. An example calculation for agency user types• reaction to 34

PAGE 41

potential change is as follows: Strong reaction Moderate reaction Low reaction 2676 = 1. 75 1528 621 X 668 X 239 X 1528 1 = 621 2 = 1338 3 = 717 "2076" where 1528 is total number of cases and 1.75 is the adjusted (weighted) mean value of the variable agency by reaction to potential change. This means that for agency user types, their reaction was somewhat less than a moderate reaction, tending toward a strong reaction. The list of weighted values indicated a relative ranking among the variables for the purpose of comparing variables to each other and to other lists by State and by District. It should be noted at this time that the decision to code both DVMC and reaction to potential change on an ordinal, even interval scale may not actually be a true representation of the relationship between levels of DVMC and reaction to potential change. For example, the actual perceived difference between preservation and high protection is probably not equal to the perceived difference between moderate and low protection. The weighted ranking should be tested for significance between groups. The ranking should only be used as a rough guide to identify extremes and indicate possible trends or patterns and not as the exact ordering of variables. A wealth of information is available from the analysis of the public sensitivity workshops. This thesis project will not attempt to answer all the questions or analyze all aspects of the results. The statistically significant analysis of the data was limited to the State level . The information provided at the District level gave only an indication of possible regional differences and was not evaluated for statistical significance. 35

PAGE 42

SECTION 7.00 RESULTS SUBSECTION 7.10 USER TYPE AND DESIRED VISUAL MANAGEMENT CLASS There is a significant association between user type and local users• desired visual management class (DVMC). This is supported by a chi square test value of 405. 31, significant at the 0.001 level (Table 8). The weighted rankings for user type by DVMC for the State and Districts are given in Table 9. Table 9 indicates for example that conservation groups overall at the State level desired a higher level of protection for the landscape than did the elected officials, mining, utility and private industry. The smaller the weighted mean value, the higher the level of protection indicated. By comparing the extremes (preservation and low protection), one can see that low values in the Craig District ranking are reflecting an indication of higher protection than the other Districts and the State. Montrose's values are higher, indicating lower protection, compared to the other rankings because of the four user types with weighted mean values greater than 3.00. Unfortunately each user type is not represented in each district. Yet there are interesting variations among those user types which do appear in two or more of the Districts (Table 9). Elected officials in the Montrose District had a weighted mean value of 3.47. The elected officials in the Grand Junction District had a weighted value of 2.40. The agricultural group in Montrose (1.83) expressed concerns considerably different than those in Craig (2.89) or in Grand Junction (2.75). The utility group in Craig District (2.32) expressed a higher level of concern than those in Montrose (3.46) and Grand Junction (3.02). Agency user types in Craig (2.24) differed i n their indication of DVMC than those in Montrose (2.90) and Grand Junction (2.51). To see if there were significant differences in DVMC levels between user 36

PAGE 43

w ........ KIUILI6ot LtflUl'f rz:>AJ He.. .. 'for)<_ Z,l z ZJ Zb z \Z. uz 4:7 lP1 44 Gl fse> fA1 ltU 1Z Ia> 44 lA LOZ. '1v IZ4e> Z6 te5 ll l'!? M lv lOZ. '2. Z-16 14 ZIZ. Zll lll zeo 2,1(0 8. \(!9\ c::?r 1Ypt;,? NJD A-1 -r.ue et't...-lt:-

PAGE 44

6:trn.Jl? JUtt::rk:=f.J t'\ n n n mo IUr;tV ll::tlt>-L 4o ff lit z .Vf t .4o IUDIVIc::;u.AL ZIZ.. U11U1Y (4. 1=1 UJDIVI t::UPoL IZ t-.6o ICU. 'Ofl-eF-1 tot <:ft. pqei.JC.'f z.o-r E>t Z.fd't lc..o U> t1'5 171 t.:1c, 'w.> t."'10 -48 'Z.1e> 143 Zft2. Zl'f Z .eef z.t, t-16 u ... u::tt51fFZ-'f 11 Z.'fl f'lq\ll<1 1-\--\.t: NJP ll3/e:th.

PAGE 45

types, each user type at the State level was tested against the other types. Table 10 provides the results of the chi square test between user types. The results indicate that agency, recreation and other types are not significantly different than each other. Mining and private industry types are similar according to their expressed DVMC. Most other types are different, and, as indicated by their weighted mean values, have different levels of desired visual management for the landscape. Comparing the weighted ranking with the chi square results, it is shown that conservation groups are in the middle of the high level of protection group. However, the chi square tests indicate that the conservation group type is significantly different than the other types. To understand why the conservation group was significantly different, I compared the distribution of the percentage of responses for each DVMC by user type for the State level (Figure 6). The curve for the distribution of responses by the conservation group is different than those for agency, recreation, other and individual. A visual comparison of those curves shows the prominence of high protection ratings by the conservation group relative to the other user groups. The conservation group did not exclusively select preservation ratings as their DVMC. The distribution curves also illustrate that private industry, mining and agricultural interest types expressed high agreement, i.e. greater than 50% of their total response, on moderate levels of protection. Less than 5% of mining, private industry, utility and elected officials user types' responses for DVMC designated any landscape for preservation. SUBSECTION 7.11 USER TYPE AND REACTION TO POTENTIAL CHANGE User type and reaction to potential change also have a significant relationship. This association is supported by a chi square test value of 39

PAGE 46

HI tJ IJ-..161 UTIU1'( IJJCNIW,t>.i... ,, ... CffiC.It>t-7 / !ill' *-" 1411 ..:j, .JI.ll it-.ll 1c:t.21P t?{.oq Cf?.40 J,..lb . 12.. ?1 Z?.OI JJ.? . v / JI-lt 4C lUI -lAI -.\1 ..JilJ lJ.t01 101.'7? 12.?3 4Cf.l4 v v / 'Ill .JC.I f(.JJ if.ll 14.% 21.11 IP.4Ct 24.15 / / / / 0 4JI "ll 0 I'JJ UIIU'tY 10.1e> 41,q6 P4.11 1.CfZ., {Pl.11 / / / / / -11-JEll 20.12. t?.e5 Z.24-zo.cB / / / / / / .:OJ '"' Zl.&Z. J..t.?. t::ttM.. / / / v / v I/ 0 8?.1? 6.41 / v / / / I/ / :L_ . *'*" Q Cf'PI Cl/>4.:> I t:f2.'71 / / / / [7 / / / / ""' '22.1(., / / / [7 v 7 / v v v /CJ. PP4f?? /><:1 I

PAGE 47

10 . It' f' ltf" t.r P HI' Hr' U' • • IO t:::HC.. Vl:?1FIW114J 'N'f'e-A-1 1-Ht:: .

PAGE 48

qo 10 i • • ,t? /\. t . /-*?;0 It> /. . \l? /. /. , IW U" , ltr' t' ..... M' HI" !I' t;?'Y'H& 1/VHC.. UiltAiY / +=-N -/ . • / ltV "" ...,. WHC-p ... IW" a:.-1lof...l lP • f'fOf

PAGE 49

1512.94, significant at the 0.001 level between user type and reaction (Table 11). The weighted ranking for the user type by reaction to potential change for the State is given in Table 12. The results of the ranking indicated that the conservation and 'other' groups expressed strong negative reaction to potential change. (The smaller the weighted mean value, the stronger the reaction to potential change.) At the other end of the ranking, utility and elected officials indicated an overall low adverse reaction to potential change. The weighted mean values among the user types in the Districts indicate some of the apparent differences and similarities among the Districts (Table 12). The extremes are easily delineated. Utility (Craig), agriculture (Montrose) and conservation, other, and elected officials (Grand Junction) types are at the strong negative reaction end of the scale. The local recreation (Craig), mining (Grand Junction) and mining, utility, private industry and elected officials (Montrose) types exhibited low adverse reactions. There are clear differences between the elected officials, utility and agricultural types' reactions from Montrose and those from Grand Junction. Both of these Districts' user type rankings are very similar to the State ranking. The Craig District however, varies greatly in comparison to the State ranking and the other two Districts. Based on the chi square tests among user types and their reaction to potential change, the majority of user types differed statistically from each other (Table 13). Agriculture and recreation types were also similar. Conservation groups and other types have identical weighted mean values but are statistically different in their reaction to potential change. Their response distribution curves appear to explain why they differ significantly 43

PAGE 50

l.lllUl'( .. z. 1(9C{IPU '3fiwZ \eG JltPO '3e1 ?tiO lPI<.J Z-ll e, Uk8 LaP I 2Cb ZLA1 1JO(G<;.: J . />U-t . I"U'4UaJci ca.J:VA• BrtZ. Ud/ 444 \ l4{p 461 1ZO 14o leel te4 L04'1 UG l\?1 12?.1? llq? Ur:eB II. le&r *' rk?U NJD V fl/feu1fPL A'f i+l-t::?fP..1t::--l.XYeL-I ,11-(_. 'l052 to,Z20 1059

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'I trrn.Jt/ " V\ Y\ t'\ 6tf" . /1% 1.11? U(JL..I1Y 1-41 &,f"' 2bf ' o/1 zuee '( 1.11!? '"" u.o 'fW 1.1(, 141? I WIVI OtJ.t>.L l.e-t .. J6y 1.-=tt.-:o II.Jt::'\VIt::t.JPL. IZ-117 H:iaX'( I .SUI t.1o? 114, l.l ZVI1 f,C:J1 IUV\VIDl.W-t:>fqVI'
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U:ta-IG'f L -lf.ll 41-ll Z.r34. ?Z. e1.14 / ;7 / t..rnu,y / ;?' / L_ / / / / IJJCNu::ut.t. / / / / / / Cft't:IMS L_ / / 6,ft::::Uf' v v '/ U.\1U1'( [Xtb(. .::::FRC.l t>{..$ 6.FVUP ..IC.I{ JUI -u r:ea.16 Z?.11 J..-t.?. U,(8.Z-1 Z?.ro Aft -I' 212. .liP J.-.1. ? . 14.e(., 1'1.?3 1e.eo i4 *11 '11.1.1 -tl.l 401 \0.40 l4.0f 171.11 / A """' 0 iiJI 40.14 \'U:>. 4o e.ez / L 'll.lf """ .x.lJ o4.D1 "'16.41 6:3.1'9 / / L_ +ll -j()J e . e>? ltoZ.G1 t:;e.\Z .c::t{p,l? / / / / ..w i(JI ?3.14 t1.?'3 / v L_ / / 311 .?2 f.%6 .15C> / / / / / 520 v v v v v v v e-efklet:::Ll F.AIP? L/P V. t?t?fetJ--(/AL . A1'" 1l-le.

PAGE 53

from each other (Figure 7). The conservation group indicated a strong negative reaction in close to 50% of their responses. The ' other ' group indicated equally strong or moderate reactions to potential change . The distribution curves indicate an inverse relationship based on their DVMC's between elected officials and conservation groups. SUBSECTION 7.12 USER TYPE SUMMARY There are similar relationships between the combined measures of DVMC and reaction to potential change by user type. The statewide weighted rankings are almost identical (Tables 9 and 12). The conservation group differs significantly from other user types in both rankings, and expresses a high level of concern regardless of landscape or activity type. Utility and elected officials differ significantly from the other user types as indicated by their weighted mean values. In the DVMC weighted ranking there appears to be a definite division in the ranking between recreation and agriculture. Although not as obvious, the same break occurs in the weighted statewide ranking of reaction by user type (Table 12). In both rankings, the agricultural user type appears to be the median group between the extremes. From the results of the analysis between user type and the two measures of visual sensitivity, one can predict with some confidence which user types will have consistently high or low levels of sensitivity. Conservation groups have a high level of sensitivity in comparison to all other user types. The recreational user types did not turn out to be significantly higher in their expressed level of sensitivity. Agency user types tended to indicate higher levels of sensitivity than expected. On a statewide basis, they were in fact identified to be similar statistically to the individual and 'other' t ype. The true moderates did indeed turn out to be the agricultural user types. 47

PAGE 54

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.:qt' i: t: t: ---j_ /. • .._ • • • • It? IP It> H .... H .... H .... ::.. U11U1Y 0 IO 1 .

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This is graphically illustrated in the distribution curves for DVMC by user type (Figure 6). The agricultural group indicated their desire for moderate protection in over 70% of their responses. The low levels of concern expected from utility and to a lesser extent private industry and mining were also substantiated by the data. Based on the chi square test results and weighted mean rankings, the level of sensitivity does vary with user types. Additionally the data indicates that there are also some user types that are statistically similar in their levels of concern for visual change. The data supports the first major hypothesis that local users• visual sensitivity varies significantly with user type. SUBSECTION 7.20 LANDSCAPE TYPE AND DESIRED VISUAL MANAGEMENT CLASS There is a significant association between landscape type and local users• DVMC. This is supported by a chi square test value of 220.77, significant at 0.001 level (Table 14). The weighted mean values for the State and Districts are given in Table 15. Visually inspecting the State•s weighted ranking, one can discern that there is probably a great deal of overlap and similarity between landscape types based on local users• DVMC. The results of the chi square tests between each landscape type for the State level initially appeared to be as ambiguous as the weighted ranking (Table 16). There is considerable overlap among landscape types around the mean (2.74) in the weighted ranking based on DVMC. This is further demonstrated by the large number of non-significant differences indicated by the chi square tests between landscape type pairs. The extremes are still however, clearly identified by both the weighted ranking and the chi square tests at the State level. Local users consistently 50

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PAGE 59

I.P4Jrr-are. 04J'(OU? I"''LUUSt P.;PfiJ-F-11-($/ Vt>U.ef? ct. u .. u::s / "" .. 0 .lf.>l " 40.1? W.CXP 40. eA "11. II W:?. Q4:ZZ to. 40 to.&+ us. J2105oi5S/ v v *Y-0 -11-0 ... •• ?Jp.I.RZ tl .uo S.41 JB.04 fJ. ? . J.J.?. \2.1? 7 / / 4-Jl.ll "* *" ....., -11Ub. I G. oz. re .roe 1-J.? 12. '71 tz.qz. z.1.ee f.J.?. U:S. / v v I/ e tc * ft.P..1ebUS J..j. ? . JJ.0. q,qq 1-l.? . U.?. \4.11 I.J.? . l-16 . f?Ol.-U/....16, v v v v v * * "" 0 \2.17.? W.? . ll?.qf? J.l.? . '2..CQ.40 t5.ef( u.-s. et"z::',b-0 v v v / / / Jj. ? . l '1/o?o e.qb0 2?.ldJ u . ? . 171.51 f.Jb . / v v v IV 1/ 7 ..,. t(.Of u.-s. 1.-l.-?. \\. ?\-'* * 12.417 I W.?. 1 v v / / v v / v 0 oil* *If 10.61 e .41 171.\4 \"1.1'5 f.J,S. v v v v v v v / v e 0 f'N" \1.:?3 u . ? . B .6Z u.-=::.. v v v v v 1/ v 1/ v v 0 ...v \t'.tV 8:'l5 P4HS/ v v v v v v v / v v v ... \1.'32 \1 .e8 v v v / v 1/ :/ 1/ / v v v v v v 1/ v v 7 v v v v v v t:w .. liP. VJ?UN-A--r -r.Ht:.

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expressed high levels of protection for mountains, river valleys and reservoirs. They also uniformly designated badlands, mesas and plateaus for low protection. It is not apparent why sand dunes are similar to all but four of the landscape types. These four remaining types (broad valleys, mesas, river valleys and rims/ridges) are significantly different than sand dunes only at the 0.05 level. It is interesting to note that river valleys are only similar to reservoirs. The distribution curves graphically illustrate that the local users expressed high agreement, i.e. greater than 50% of their total response, on moderate protection for basin/parks, broad valleys and rims/ridges (Figure 8). The distribution curves also show that the local people clearly indicated moderate or low protection for badlands, rather than preservation or high protection. Comparing the rankings among the State and Districts in Table 15, mountains, river valleys and reservoirs are the three landscapes designated consistently for high levels of protection with the exception of response from the participants in the Grand Junction District. Badlands and mesas were rated at the low end of the scale by the local users in the Craig and Montrose Districts. Grand Junction's ranking varied from the others especially in respect to plateaus, mesas and river valleys. SUBSECTION 7.21 LANDSCAPE TYPE AND REACTION TO POTENTIAL CHANGE As with DVMC, landscape type and reaction to potential change exhibit a statistically significant association. This is supported by a chi square test value of 888.78, significant at 0.001 level (Table 17). Looking at the weighted mean values for each landscape type at the State level, the majority of the landscape types differ statistically from each 54

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/ . . \ / . • 10

PAGE 64

.11 ):) HoUU'f.t>4lb CU!it'l-l? 1 2t; llf!e '\1!6 &1-I..OloJ 4Z4 lPIZ W# 11/b ll.l&utt:112-1U 5mWn I11Z. lzr78 1Zr?1 M1 4Cf>l .mft)'5. W I fie, 114 lC,f:O Z-11 'lJ!R; j(,4f> tc:tl crt &t 104 26 zzo t?=t-(,(,rG4-814 lP11 4111 -wz l1Z. U,1Z4-11. C::::Ul 12? Wlat\IAL N-l /><1 &r"-re..

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other based on local user reaction to potential change (Table 18). The local people expressed a strong negative reaction at the State level toward proposed activities occurring in mountains or sand dunes. The response distribution curves illustrate this level of concern with close to 50% of the people indicating strong negative reaction against activities in mountains (Figure 9). People were in high agreement, i.e. greater than 50% of total responses, that potential change in sand dunes would be visually disturbing to them. In contrast, the high weighted mean values for mesas, badlands and plateaus indicated that the local people did not perceive changes in these landscapes as visually disturbing relative to the other landscape types . The response distribution curves for these landscapes show that close to 50% of the people had little adverse reaction to potential change occurring from activities. The chi square tests for significance between landscape types based on reaction to potential change at the State level resulted in almost all the landscape types being statistically different than each other (Table 19). The level of significance was 0.001 for almost all of the chi square test results. Rolling hills-broad valleys, reservoirs-rims/ridges, and mesas-badlands were the only statistically similar groups. SUBSECTION 7.22 LANDSCAPE TYPE SUMMARY The results of the data supports the second hypothesis that local users' sensitivity varies significantly with landscape type. Comparing the weighted rankings of landscape types based on DVMC and reaction to potential change, the top four (mountains, sand dunes, river valleys and reservoirs) and bottom three (mesas, badlands, plateaus) types are identical. Mountains, river valleys, sand dunes and reservoirs consistently produced a high rating of sensitivity based on these analyses. The data also clearly identify mesas, 59

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PAGE 68

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m w •

PAGE 70

fV'Tet>U!S> M.UU!t e-c:'.b-D F-11-t"E> .. "IU.."E> F-IP6.eCS: t:t.ll-le:!S / *" .II -lUI -iUI ..... JtJJ. 1-W&W-41.1? 111.1> 12-1. 11 2?1. % 111 .4? Zl o '?'? Jt:11.10 v / 4 ..u 0 >fA -lfJf .JfJI t:;;O.I.JIS .q IJ.bf". , Vt>.t... 121.eo -$1.\4-l.:>l-46 1.6G:> tX.11 ?;17.Zf5 11.?.3 )0?.1&> / / / -104 -fill -*-ll .JoOJ 4'4 -C. '141 """ 411' ->1-'J CJ>-U'(al? 4(.,,?3 '1'7.4& 24.14 %10 14G..Z.I 14.te> 41.48 / / / 1/ il.lj •ll 4!11 0 -It -1141 ..lf-\1 I'U>. -re&>U? zu, ;lo "??.eo IZ .e? ffl.l? 14.aJ / / / / 1/ .. f(.lj J.-1.0. ld/.11 l.ee I( ( .e6 11.01 4s;;;:1z. &4.24 "-lUb / / / / / 7 -11'1-.., ..... ,o[J( <> JlV vp.u,.e-6 '20.1? t31foZ. \c.:..e1 Jo?.c:rz 8.1'1 U:O.Zh v / / / / 1/ / il-l/ 0 !.42-J..J,?, / / / / / / 7 / All t41.tt> J...IS. / / / / / / / v / u ** "' .::refit II .CO \6.l.:O '7='f. Z4 / / v / / / / / v / -11 *J.i "J/oJl VJ>Ue'(? Z6.
PAGE 71

badlands and plateaus as those landscape types about which the local users are least concerned. The weighted ranking and chi square tests at the State level for landscape types based on DVMC indicates considerable overlap of landscape types in the moderate range of local users' visual sensitivity (Tables 12 and 13). The analyses based on reaction to potential change indicates strong statistical difference among landscape types and the overlap of sensitivity levels is minimal. Canyons were not consistently high in visual sensitivity as originally expected, although Grand Junction's workshop participants did indicate a high level of sensitivity for canyons, probably because they evaluated the spectacular Glenwood Canyon. I also hypothesized that broad valleys and basin-parks would be significantly lower in sensitivity relative to the other landscapes. The data indicates that these types were somewhere in the moderate to low range of concern. Instead, mesas, plateaus and badlands were consistently rated low by local users. SUBSECTION 7.30 ACTIVITY TYPE AND DESIRED VISUAL MANAGEMENT CLASS The participants jn the public workshops had no idea as to which activities would be occurring in each landscape unit when they were asked to indicate their DVMC for that landscape unit. It would be erroneous to suggest that activity type directly influenced local users' DVMC in these workshops. There is however, a significant statistical association between activity type and DVMC because a chi square test was completed and was significant at the 0.001 level. Because of the format of the workshops, any further analysis between these two variables would be conjecture and unsubstantiated with this data base. 65

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SUBSECTION 7.31 ACTIVITY TYPE AND REACTION TO POTENTIAL CHANGE It is however, legitimate to analyze the association between local users ' overall reaction to potential change and activity type . There is a significant association at the 0.001 level between these two variables (Table 20). Some problems in the questionnaire should be mentioned before further discussion of the results of the relationship between activity type and overall reaction to potential change. In several of the workshops, activity types were lumped together. In the Grand Junction and Montrose Districts, mining-coal surface, mining-oil and gas, and landfill/gravel operations were combined as one landscape modification. The ranking of mining-oil and gas by the Craig Distict workshop participants is obviously different than their reaction to mining-coal surface and landfill/gravel operations (Table 21). Roads/railroads, ORV use and pipe lines were also combined in the Grand Junction District and all but one of the Montrose District's workshops. For the Escalante, Cimmaron, and Gunnison Gorge workshop in the Montrose District, ORV use was separately considered. The reactions to ORV use by the Craig workshop and the one workshop in the Montrose District are also considerably different than the level of reaction to pipeline and roads/rail roads. The difference in the level of reaction between ORV use and roads/railroads-pipelines is evident in the State's weighted ranking (Table 21). The difference in the level of reaction between oil and gas mining and coal surface mining-landfill/gravel operations is also evident in the weighted ranking at the State level. These observations are statistically substantiated at the State level by the results of the chi square tests (Table 22). Power plants and rest areas/campgrounds are the extremes in weighted 66

PAGE 73

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--r 6tm.lt? t/1-:?f. laiVI1Y n l'a)VI1'( -r:(f'e. n 1L11Vl-r-t' n n l'"f-7 .. I"LW1'? 1 .-'f 1-1'1 HIIJII . . HIU/f.-l!:t 1.6(... vH5 1 14 , . HU..JIU6f 11"13 1 .1<::( P\?. HIIJIUGf 6/'0 I.e'? J . (,Z. 1..'-UOf'IU... 1118 1.14 l!5eZ. J.&t 1011 IW. 431" A'-V l.t'Se-J .ee> HU • ez.J J .1l.:> Jl FeSIDeU1lX-l .ert 1031 UUI!!i% 4(6 J.ee WII lt'Jr, M-t:1>6/o>+-iP. ft ...x..r z .-.7 l . "'ff 1.01 1."'11 61e z.,..1 4z.l 2-CO PIPe. l03
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K.1lV11'f I.J>+.JDFIIA-p.a.ws fbi<.Je#C. 1'1"'f'e. IHP . uueo Gtlortl"eUD f..tii..IIIJ6f / w l'l' 44 0 ....,. -ll.lfo .,.., -Z28.11 IOI.f?Z U,, IO 11'1.10 IO.?. U "14.28 ZO'I.E>1 HIUIU!Ot / / -1141 ...... .... "u --1'-lf .., 1-.1.6. lc:rt...IO V?I .et? 42.11 e1e..CJz. Clt.OZ lt:l3 . "t \o1.CJ2-LUJ(;f'IU/ /: / /_ -u :zn .,-;f' ,q '" ft. Ill[ lfJI -11-ll--11-'1 t'e>l .ffl 4"1.81 '312 .?1 B'liZ ti?.Q} 12:l.t:>1 / L / v .1( .. 0 .,K.lj " .... '10/ f.?;8 CP(.qo 164 .DZ ( .J& 114 .'72-. (tO.CV t.CJ.?1 I"''.b05 / / / / / IE .!I lfo!l .11x.}J Jl..Y 4 J.J .-?. '72.40 13l3.{o ws. -tq.44 81.11 / / / / / / -11. "':>I "41 -1l )f.Jt eu..rz r?c?.lk =7q? \O.OZ \e>I. Ol \01.2'4 / L / / / L -lUI ""' f44 "X Cf!:.V 111.61 llP:?.W IU.. .'le> / / / / / / / / :IU' '* 411 UJ..Je<> 11.10 1?<1-• .::t? / / / / / / / / / .y;>f l".'Z:>.OZ. U.UD / / L / / v v / / v 'lt.\1 &J.,.IO L L / / / v L v / / / -f!,JJ 24.l17 \ue.en / / / / / v v / / / v / *lf-v v L_ / / v / / / / v / 1k:te zz . N---rNII'( AI \?ofe:JJ11N-/:<( 11-\f::-LeVeL.

PAGE 76

ranking (Table 21). Each of these activities was evaluated at only one workshop. Local users were in high agreement, i.e. greater than 50% of the responses, in their level of negative reaction to power plants as illustrated by the response distribution curve (Figure 10). Close to 50% of the responses by local users toward rest areas/campgrounds indicated that this activity was acceptable in any landscape. The response distribution curves also indicate that almost 50% of the responses toward potential coal surface mining and landfill/gravel operations were strong negative reactions. The local users were split over their level of reaction to residential activities. SUBSECTION 7.32 ACTIVITY TYPE SUMMARY Based on the weighted rankings and the chi square tests, there are statistical differences in the level of concern expressed by local users toward various activity types (Tables 21 and 22). Local users indicated a high level of visual sensitivity toward power plants, landfill/gravel operations and coal surface activities. They expressed a low level of sensitivity to dams/water impoundments and rest areas/campgrounds . The response to residential activities varied among the Districts and did not consistently reflect a low level of sensitivity as I previous suggested. Moderate levels of visual sensitivity were expressed at the State level towards residential, roads/railroads and residential activities. However, there is considerable variation in the response to that activities from one district to another. The results of the data analyses therefore support the hypothesis that local users ' visual sensitivity levels vary significantly with activity type. 70

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PAGE 79

V1P t:fo 1: • I ----....., :....> • (N . / -IO It> a.. H f'LJi..t(&

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SUBSECTION 7.40 INTERACTIONS BETWEEN USER TYPE AND ACTIVITY TYPE Chi square tests were performed to analyze the interaction between user and act ivity type. All but two of the tests showed significant results. Rest areas/campgrounds and power plants both had greater than 20% of their cells with expected cell frequency less than 5.0. This occurred because only one workshop evaluated each of these act ivities. The 'preference response' workshops (Craig District) were separated from the 'negat ive response' workshops (Montrose and Grand Junction Districts). The 'other' user type was dropped from the analysis because there is little application of this information to future workshop design or sensiti v ity assessments . A weighted mean value was calculated for each user type by activity. A ranking of these values was tabulated for each activity (Table 23). To identify the users who indicated extreme reactions for an activity, a test using the standard deviation was made for each ranking. The extremes were identified as those user types whose weighted mean value was greater than or equal to one standard devi ation away from the mean value of the ranking. The asterisked values in Table 23 indicate the extremes i n reaction by user type concerning individual activities. As indicated by the extreme values, there is a clear discrepancy between the 'preference response' workshops (Craig) and the 'negative response' workshops (Montrose/Grand Junct i on). In five of the thirteen activities evaluated, agency user types in the Craig District or 'preference response' workshops expressed the highest level of concern toward the specific activities, more than anyone else (Table 23). The agency representatives indicated the least preference in any location for potential changes caused by power line, land treatment, residential, coal surface mining and landfill/gravel operations. The recreation user types most often indicated the least level of concern relative to everyone else in the 74

PAGE 81

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PAGE 82

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n JWlY-Illi, n d . n n tLl O.VI CtJM-+ N:.rs..t::f tel v e ... ... t111UW C.. I 1.11..,..,. Ill 1-'11 HIUIU!r !0 t.oz llJDIVIc.tiH-1 -61 l-4t (10 l."le: ISO :z_, IZ. l ."t) 1 UDIVICUN..-4'!Jl tdl M'f ''* HIUill!of o;;l I .I.-I '*" t -1!!1 z . . r-t 12!"1 I.Cl&• I!J!J t.ZI 1m l..n1Ul"( 11.!1 lrttU1'( 18!> HII.IIUS, (.6 t .o6D.JC( liN 1-4 l t1 • I.X.VIruM-"" 1.5z I UDIVIDI.JN.. 11"11. G1 l.t..l Httf!.UC'( t4l, 1 .-4(-:2111 lffl 1.12. &of'. 1-f.o ue UTIU1'( (,f ..., ., 2.11• ILIDtVIDlll'oL 1'0 1..1.!!> .Z.I'J ttiU!lX:f IJ11U1'y 2.2!,1 Mf!U:...'{ let t."l, 11%. Cfn::JJI(S 1.(, t.I..S H ILIIU6, ('.; unuTY (N ':7 • • '1"1 !lc. knleo1JI<'{ 1%-1 -00 .. :!tlf!'t:'U!. .. ......... ?>.CO"' tAU • t
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Craig workshops toward residential, coal surface mining, roads/railroads, pipelines and landfill/gravel operations. The agricultural group in the 'negative response' workshops expressed the highest level of concern toward the most specific activities (Table 23). These activities were ORV use, power lines, rest area/campgrounds, roads/railroads and pipelines. In eight out of thirteen activities, elected officials and utility user types indicated the least level of concern toward the specific activities. Land treatment, ski areas/resorts and rest areas/campgrounds were the only activities not considered favorable by either the elected officials or utility types or both. SUBSECTION 7.41 INTERACTIONS BETWEEN LANDSCAPE TYPE AND ACTIVITY TYPE The same analysis process conducted to show the interaction between user and activity type was also done for landscape and activity type (Subsection 7 . 40). The chi square tests were significant at 0.001 level for all except residential, ski areas/resorts and rest areas/campgrounds. The ski areas/resorts activity was not significant in the Craig District workshops. Ski areas/resorts (0.01), residential (0.05), and rest areas/campgrounds(0.05) in the Montrose/Grand Junction Districts have lower levels of significance. The weighted rankings of landscape type by the reaction to a specific activity are given in Table 24. With one exception there appears to be considerable amount of variation in the rankings between the 'negative response' and 'preference response' workshops. Mesas, badlands and plateaus have been identified in all Districts as being consistently one standard deviation below the mean, indicating a low level of concern by all local users. Table 24 identifies where in the landscape the local people prefer or have the least adverse reactions to a specific activity. This table also 78

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PAGE 87

co ...... " 0:::-FAl!::f n -t1 l . f'l .. CN.N'c:>U5 l!O ft;f r .e-t • 11 '/.1>1-3(, "'Uh t .Of ICS 0-J..J'(G'U";> 105' t..J I WUIJl).llh u:r t..l IZ.I 11 "'1" 1(, .t.11 .. lA 11 IJ•r".>S't .... . n n Zbt• r::w.Je& 1'1 'IN.. . 213 t.c6 VMJ..e'f-:. VAI..l.e'l'S> 14'1 t.IC# 1! 113 "' 48 t.rl M.Ul.l&t I t.tJ ?S' 18 '/,.1:& '" ,..,I.UU6. lo4 .. 1-Js qof, IJ,l..Vl!. c::I!\.L ttlob#J So VH..Ue CtM.tf11DU f'r'cl.{ HebU, I.I!So • .. lut 1.15 l.tft ' ""' 1.1(.. t .c-t t .+t .. f.,(IS. \.-41 " I.!!JI • .. .. 1.11.,. n .::p.A16f • • n 9$ \.eG. '" 1.20 .. -1.'1 I f. II>L. • . VAL. • V"f 't. . IX4 '"3 1.4 1 I'-' t-'1 11 . ... , 174 z.., , 3'J I ."!JI 104 t..ll f{, 14t Z,.ll ZG-r .o;e l9t t.rt. I"O \ . t.C 1'5' Z..Z.1 .. 44 l.(,'f .. U•B14 />FfW7 n n ,, IZ 11 Hlf "I /07.. '/.,., 12 t<, ll'f , ,,c;; 10'1 12.. 1.4Z. 2""1" S'"o 1.14 .. . ..lh "t] 1 .11 ... 411 US'!.S',

PAGE 88

indicates where they least prefer or feel strongest against the activites occurring . For example, the local users in the Craig District preferred to see coal surface minings in basin/parks, badlands or mesas. They were highly concerned about the visual disturbance when this activity takes p lace near reservoirs. SUBSECTION 7.42 INFLUENCE OF USER, LANDSCAPE AND ACTIVITY TYPES ON VISUAL SENSITIVITY Path analysis was used in an attempt to indicate the relative degree of influence of each factor on visual sensitivity. As previously noted, contingency coefficients were used as path coefficients for each factor. As stated in the limitations for the validity of contingency coefficients, only coefficients generated from contingency tables of the same size are comparable. Because there are thirteen landscape and activity types and only ten user types, some of the categories were deleted from landscape and activity types. The selection of the categories to be deleted was based on the chi square test for significance between categories at the State level. Badlands and rolling hills were deleted from the landscape types because they were similar to mesas and broad valleys respectively (Tables 16 and 19). Sand dunes were deleted because they were evaluated in only one workshop. Pipelines were deleted from activity types because they are similar to roads/railroads (Table 22). Power plants and rest areas/campgrounds were deleted because they were only evaluated at one workshop each. Figure 11 illustrates the path analysis. The values of all the contingency coefficients are very close to a value of one with the exception of the coefficient between DVMC and activity type. As stated previously, a value of one indicates complete dependence or perfect correlation. These 82

PAGE 89

Pl6lltze I\. P)<:f.l.+-N..1D PC:riVI"N 1We-kllil-+ ) .

PAGE 90

coefficients are very close to a value of one and therefore it appears that they all correlate highly with visual sensitivity and each other. There is no significant difference among the other contingency coefficients. SUBSECTION 7.43 SUMMARY OF FACTORS INTERACTION AND INFLUENCE ON VISUAL SENSITIVITY A three way analysis of the interaction among user, activity and landscape type was not done because it was beyond the scope and time limits of this study. However, the table of critical combinations illustrates the dependent interaction among the variables. If for example, local users expressed a high level of concern towards canyons independent of the proposed activity, then canyons would be listed as an extreme landscape type under each activity type. These same arguments can be made for the relationship between user type and activity type. There would be no extremes if the levels of concern for activity types were independent of landscape or user type. The response by all the users to an activity in any landscape location would be the same. The data support only the first part of the final hypothesis: user, landscape and activity types do not independently influence visual sensitivity. I expected that landscape type would be the most influential factor in determining local users• sensitivity. However, based on the results of the path analysis, this is not true. Because the path coefficients are almost identical, with one exception, I conclude that the factors equally influence visual sensitivity. Therefore, the findings reject the remaining part of the final hypothesis that landscape, user and activity type do not equally influence visual sensitivity. I also hypothesized that although the local users• sensitivity would 84

PAGE 91

agree with BLM's scenic quality ratings on the landscapes rated high in scenic quality, I expected that the data would also show that they would not agree on low scenic quality landscapes. The chi square test did indeed show that there was a significant association between local users' DVMC and BLM's scenic quality rating (Section 3.10) for a given landscape unit (Table 25). The chi square test value was 119.90, significant at 0.001 level. The local users' indicated level of preservation and high protection agreed with BLM's high scenic quality rating • However, the information in the chi square table indicated that low scenic quality landscapes are considered by local users as landscapes for low protection. This suggests, based on this data base, that 'common' or everyday landscapes do not necessarily evoke higher levels of concern toward visual change. SECTION 8.0 INTERPRETATION OF RESULTS SUBSECTION 8.1 USER TYPE AND VISUAL SENSITIVITY Based on the findings of the data analysis, the hypothesis that local users' visual sensitivity varies significantly with user type is supported (Subsections 7.10-7. 12). The conservation groups expressed the highest level of sensitivity in comparison to all other known user types (Tables 9 and 12). This finding is not surprising because of their publicized attitudes toward environmental protection. These are also the people likely to voice greater concerns about any landscape modification. Someone from a conservation organization would be a good representative of high visual sensitivity. Agency, individual and recreation user types are similar in their level of sensitivity. Their level of sensitivity will tend toward the high/moderate protection level. The recreation user types did not turn out to be as significantly high as originally expected by their overall indication of 85

PAGE 92

t,.Olcj c., 11"1 " If? Z11 4z..z. . t.;.-5 zo.e '5.0 112. 2111 Cll ?Z,f) 24.0 z.?.o lu,q GJ41 I e. Z,?.o 4-1 I 1 61.1 L
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visual sensitivity. No definite conclusions about them may be drawn because their sensitivity levels can vary based on the type of recreation interest they represent. At the opposite end of the scale, utility and elected officals express low levels of concern. They are statistically different than each other and except for one other user type (private individual), are statistically different from all other user types (Tables 9 and 12). Because they differ statistically from each, one should not assume that they fully represent one another's interest, and therefore, they should not be combined. However, representatives from any of these two user types would be likely to represent the low end of the scale. The most interesting finding pertaining to user types and visual sensitivity turned out to be the elected officials. County commissioners made up the majority of the elected officials who participated in the workshops. For future sensitivity assessment and workshop design, this user type's expressed level of concern should be kept in mind. To paraphrase one of the county commissioners written comments, progress and developing natural resources are more important than visual quality. The results also suggest that there are regional variations in the level of concern between the same user types. In the BLM/Wirth study, comments were made that the people in Grand Junction and Craig Distrists had unusually high sensitivity attitudes throughout the highly scenic and tourist/recreation areas. In contrast, some of the participants in the Montrose District workshops expressed an over riding desire for progress. Further investigation with larger samples of each type within each district is required to clarify and identify regional variation as it affects users attitudes toward visual sensitivity. Among the Districts, there were several striking differences in the DVMC weighted values between the same user types (Table 9). The elected 87

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officials in Montrose expressed extremely low DVMC levels. Looking at who these elected officials were, I found that six out of eight of the elected officials who participated in the Montrose workshops were county commissioners from the same area. They essentially block voted on every landscape unit evaluated in the workshop. It is not clear why the difference among agriculture and utility types occurred in the Montrose and Grand Junction Districts. The difference in the agriculture user type between the two districts may be because the Montrose agriculture participant was a farmer and the Grand Junction participants were cattlemen. The majority of the Craig District agriculture user types were ranchers and were more closely aligned with the cattlemen of Grand Junction (Tables 8 and 12). This suggests that cattlemen and farmers should be evaluated separated. Farmers tend to be more adversely affected by land withdrawals for power lines, piplelines, etc. It definitely suggests an area that warrants further investigation. The utility group is composed of public service-electric utility representatives and there is no clear explanation for the variation in their expressed levels of concern between the two districts. However, I did find that the high level of concern indicated by agency types in the Craig District can be attributed to the six people from BLM who attended the same workshop. There were only twelve participants overall, so BLM was obviously over represented and skewed the results of the workshop. The variability among local users• visual sensitivity is substantial. The selection of the participants for a public workshop as stated by Penning-Rowsell (1979) indeed impacts the results of the workshop. Grouping individuals according to their social or special interest appears to be appropriate. The results of the data analysis on user type and their association to visual sensitivity are likely to be applicable to similar 88

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environmental and resource situations outside the State of Colorado. The trends among user types and their expressed levels of sensitivity are probably not universal but perhaps reflect a "Western" attitude towards visual resources. SUBSECTION 8.20 LANDSCAPE TYPE AND VISUAL SENSITIVITY The analysis of the BLM/Wirth project data base supports the hypothesis that local users' visual sensitivity varies significantly with landscape types (Subsection 7.20-7.22). The results of the analysis relating visual sensitivity and landscape type would be most helpful in site planning. The findings indicated which types of landscapes were more or less suitable for any landscape modification with respect to visual change. In general, the results indicated that river valleys, reservoirs and mountains were only three landscape types for which the local public consistently express high levels of concern. It is interesting to note that two of these three identified landscapes are water related landscapes. It is conceivable to think that they were rated high because of the semi-arid nature of the State's climate and the scarcity of the water resources. The third type, mountains, is the landform most symbolic of the Colorado landscape and may consistently evoke sentimental responses whenever visual changes are suggested. Mesas, plateaus and badlands were consistently rated low by the local users (Tables 15 and 18). The local people in the Montrose District were not especially sensitive to visual changes in mesas even though the mesas are a dominant landform in the area and have rich historic and archaeologic values. This low level of sensitivity may reflect their desire for economic growth and "progress'' as previously stated. The local users may possibly feel that the mesas are not as spectacular or as awesome compared to the Rocky Mountains of 89

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Colorado. It is not surprising then, that local users also indicate a low level of sensitivity for plateaus. Mesas are a form of plateaus. Badlands, as originally expected, would be near the low end of the scale because of the mental images that people have when they hear the name "badlands". The data also showed that although the trends illustrated by the weighted rankings by DVMC (Table 15) and reaction to change (Table 14) were almost identical, the chi square test results between pairs of landscape types were not (Tables 16 and 19). In the DVMC-based chi square tests, there was considerable overlap among the landscape types indicated for moderate levels of protection (Table 16). The chi square tests based on reaction to change showed that all but two pairs (rolling hills broad valleys and reservoirs rims/ridges) were significantly different. This suggests that when the users indicated their DVMC's, they were taking a very broad appraisal of the landscape. However, by asking for reaction to change caused by various activities additional variables were introduced. People became more discriminating in expressing their level of sensitivity. This may indicate that when evaluating landscapes for local people's level of visual sensitivity, the more "sensitive" or significant question to ask would be their reaction to potential change based on specific activities. The application of the specific findings of this study on landscape type and visual sensitivity is limited to other similar physiographic areas. In general terms, it appears the most symbolic or distinct landforms such as the San Juan Mountains and those that are scarce or unique such as Glenwood Canyon or the sand dunes are the landscape types that most people want to protect. People in this semi-arid region also express high levels of sensitivity toward water and water resources. 90

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SUBSECTION 8.30 ACTIVITY TYPE AND VISUAL SENSITIVITY The hypothesis that local users' visual sensitivity varies with activity type has been supported by the data analysis (Subsection 7.30-7.32). The local users expressed high levels of sensitivity regarding power plants, landfill/gravel operations and coal surface mining (Table 21). They expressed a low level of sensitivity to dams/water impoundments and rest areas/campgrounds. The grouping of activities under one category and the lack of legitimate information on the effect of specific activities on DVMC has hampered the interpretation of the association between activity type and visual sensitivity. The results of the weighted ranking from the Craig District and chi square test between activities demonstrate that combining coal surface mining and landfill/gravel operations with oil and gas mining are probably not valid (Tables 21 and 22). In comparison, the first two activities physically and visually disturb larger tracts of land and are much larger in scale than oil and gas mining. The same results occurred when ORV use was separated from roads/railroads and pipelines; there again was a large discrepancy among the District rankings. The results of the chi square tests indicate that ORV use is significantly different than all other activities and in the future should be evaluated separately. As previously stated, the request for reaction to potential change was asked differently in some of the workshops. It is evident by the differences between Craig's ranking order and the other District's ranking order that the variation is to be attributed to variables other than reg i onal landscape characteristics (Table 21). The change in the attitude of the users' response from the degree of negative reaction to the degree of preference i n the location of the activity is reflected in the reaction of the Districts to dam and water impoundments. The local users in the Craig District obviously felt 91

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that this activity was a positive influence in the landscape in comparison to their reaction to other landscape modifications. Comparing their response to the Montrose and Grand Junction District, one can see that the visual affect of the activity was perceived by the locals very differently. It indicates that because of the implied negative response, the local users in these Districts possibly looked at the activity in terms of possible degradation of fish and wildlife habitat from the loss of instream flow and flooding of land that result from impounding surface waters. The statewide ranking and significance test indicated a consistently high level of concern, as I hypothesized, by the local users toward mining and power plants (Tables 21 and 22). The response to residential activities varied among the Districts and did not consistently reflect a low level of sensitivity as I previously suggested. The mixed response to res idential activities could be due to the public•s experience with the energy boom towns which appear and disappear i n Colorado and the ever increasing demand for housing. For future sensitivity assessments, it appears that activities such as pipelines, roads/railroads and land treatment will not generate high levels of concern from the local users. These act ivities could be implemented with good design and adequate mitigation without disturbing the visual resource from the users• point of view. SUBSECTION 8.40 INTERACTIONS AMONG FACTORS The final hypothesis states that user, activity and landscape types do not independently nor equally influence visual sensitivity. The first part of the final hypothesis is supported by the analysis of the data base (Subsection 7.40-7.43). Table 26 suggests critical combinations established by identifying the extremes in levels of sensitivity as expressed by local users 92

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l..-...N.Jt/ I"Hetrl tlUUL:... e UJOIVIOLJ;.(... • e 0 HIUIUC:{ e 0 'fi'Pe-e e U11U1Y 0 0 JU.UC. "'f 0 I Ut::IV\t::UP-L • 0 -0 • o • 0 V,A.t... 0 HIU.S. l
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in terms of user, landscape and activity type. It summarizes the information from Tables 23 and 24. This table does not by any means represent all possible critical combinations. The combinations could be used to predict the relative level of local users' visual sensitivity and aid in the design of public workshops when the users, landscape and activities have been identified for a proposed project. The findings could be useful in land use planning decisions. For example, one had to evaluate the feasibility of a proposed ORV activity in a certain area, these findings would be extremely beneficial in the assessment of the local users' visual sensitivity. From a site planning perspective, it suggests locating the activity away from mountains and ridges/narrow valleys to minimize public outcry. According to local user attitudes, the ORV activity would be best suited in mesas, badlands or plateaus. Besides suitability, this information suggests appropriate activities that could occur in the landscape with a minimal amount of public concern. If one discovers that the adjacent land owners can be categorized socially as agricultural or recreational user types, then more care should be taken to mitigate the visual impacts from the proposed activity. For the purpose of designing a public workshop, one should select participants from the elected official, utility, agriculture and recreation user groups to represent the extremes in levels of concern or to avoid them if you want the know the visual sensitivity of those interest groups in the middle ground. The large variations between the 'negative response' workshops and the 'preference response' workshops are clearly evident in Tables 23, 24 and 26. The difference in response to dams/water impoundments is a good example and demonstrates how misleading a questionnaire can be and how the phrasing of questions can alter the results of a workshop. It emphasizes the need for 96

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planners and managers to be aware of the consequences and importance of the format of questions. In my opinion, the 'preference response' is the more appropriate response request because it does not infer a negative reaction and allows the partici pants to make the decision of sensitivity on their own. SUBSECTION 8.41 USER, LANDSCAPE AND ACTIVITY TYPE'S INFLUENCE ON VISUAL SENSITIVITY The second part of the last hypothesis is rejected by the analysis of the data base. It states that user, landscape and activity types do not equally influence visual sensitivity. The path analysis indicates that the factors all correlate highly with visual sensitivity and with each other (Subsections 7.42-7.43). This suggests that user attitudes toward visual sensitivity is not random or based solely on an idividual 's personal bias. The path analysis does show that the association between DVMC and activity type is the weakest correlation. This substantiates my prior decision not to evaluate the association between DVMC and activity type because of the format of the workshop by BLM/Wirth (Subsection 7.30). The close correlation of the remaining factors with visual sensitivity and with each other suggest that any of the three factors could be used to determine local users' sensitivity. If this is true, then the option available to planners and managers are quite numerous. But in reality, this study i s based on data from another project which had very different objectives. Controlled independent tests of each factor with DVMC and with reaction to potential change should be done before any credence is given to this suggestion. However, it does appear valid to ask for DVMC or reaction to potential change to measure the level of sensitivity {although the latter would be preferable if statistically significant discrimination were 97

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necessary) . Additional research should be done to test whether the three factors do indeed equally measure local users• level of sensitivity. I expected that landscape type would be the most influential factor. One reason for this expectation was that those areas of high scenic quality, such as national parks, were also areas considered by most to be highly sensitive. The results of the chi square test supported this expectation 25). I also hypothes ized that local users would not consistently agree with low scenic quality ratings for familiar landscape. The same chi square table as mentioned above does not support this speculation. However, I do not feel that the BLM/Wirth data base is adequately designed to evaluate the association between low scenic quality and local users• levels of sensitivity toward "common" land s capes. One reason is the relatively small numbers of low scenic quality landscapes evaluated in the BLM/Wirth project. Although this study meets all the stated objectives, it is not without its drawbacks . The shortcomings of this project stem initially from utilizing data not specifically gathered for th i s study . The analysis of the data was hampered by combining dissimilar activities into one category. Because the data were collected independent of this study, further research with control groups and independent variable testing should be conducted to confirm the findings. It was also unfortunate that all the data from all the workshops conducted in the BLM/Wirth project were not available to this study. This level of effort on the part of BLM to solicit local users• attitudes on vis u al sensitivity will probably never be repeated. SECTION 9.0 CONCLUSIONS This study was based on my reanalysis of data collected from eight publ ic sensitivity workshops held in Colorado by BLM/Wirth Associates dur ing 98

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1978-1979. The goal of this study was to identify patterns, if any, among local user attitudes toward potential visual changes in the landscape using the information from the eight workshops. The existing data base was organized and loaded into the Prime computer system, thus accomplishing the first objective of the study. The focus of the statistical analysis was on the accumulated data from all eight workshops . However, some work was done evaluating the information at the BLM District level. The second objective was to analyze systematically the data base for significant trends, correlations or relationships that might indicate people•s concern for visual changes in the landscape. Weighted mean rankings, chi square test for independence and contigency coefficients for strength of association were utilized in the analysis process to support or reject the four hypotheses. The analysis of the data supported the hypotheses that local users sensitivity levels varies significantly with landscape type, with user type and with activity type. Conservation groups consistently expressed high levels of sensitivity. Elected officials and utility types generally expressed low levels of concern for visual change. Local users consistently indicated a high level of sensitivity toward changes taking place in mountains, reservoirs and river valleys. They are not as concerned toward mesas, plateaus and badlands. Power plants, landfill/gravel operations and coal surface mining prompted high levels of concern from the local people. The people also expressed low levels of visual sensitivity toward dams/water impoundments and rest areas/campgrounds. These three factors, landscape, user and activity types, are not independent of each other, rather they are highly correlated with each other and with visual sensitivity. It was shown that landscape, user and activity types equally influence visual sensitivity based on the existing data base. QQ

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It is possible that none of these factors alone is a direct determinant of visual sensitivity. There may be a more universal underlying variable which is commmon to all three factors. The true measure of visual sensitivity as Blau, Bowie and Hunsaker (1979) suggest, may be the level of expectation of the scenic quality of a user. The final objective was to suggest implications and specific applications based on the findings of this study. The findings show that the variablility among local users' visual sensitivity is substantial. The results of the study do indicate variations in sensitivity levels among the three BLM Districts. Summarizing the reaction of participants toward potential changes to determine visual sensitivity ratings can be erroneous if the workshop has been over represented by any one group. Once the professional understands the patterns of the local users, i.e. who expresses high levels of sensitivity and who expresses low levels, then perhaps a weighting scheme could be developed to determine a more accurate level of the public's visual sensitivity. Grouping individuals according to their social or special interest types appears to be appropriate. In my opinion, the phrasing of the requested response to specific activities significantly affected the results of the workshops. My recommendations would be to use the preference response to obtain local users level of reaction rather than negative response. The questionnaire then remains neutral and unbiased. I would also use people's reaction to potential change rather than their desired visual management class. Asking people to react to specific activities appears to be a more "sensitive'' or significant test and provides definite indications of their levels of concern. The response from the various Districts indicated that some activity types should not be combined. The combination of oil and gas mining with coal surface mining gave a false indication of the level of 100

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concern toward the oil and gas mining. Therefore, prior to combining activities under one category, care should be given to make sure those activities are similar in their visual impact on the landscape. The summary table of critical combinations (Table 19) can be a valuable guide in visual resource planning and management. The summary table could be used as a starting point i n the planning and designing of a public meeting by indicating where the probable extremes in the levels of visual sensitivity are and where the potentially volatile issues are. The specific findings such as who the users are that consistently express high levels of sensitivity, will also be very useful in programming appropriate activities and site planning. Above all, the findings of this study have empirically demonstrated that there are definite patterns among local user attitudes toward visual change i n landscapes. Local users' levels of visual sensitivity are not random or based solely on individuals' personal biases. Where in the landscape the change is occurring and what activity is causing the change is important to people and affects their levels of visual sensitivity. Public sensitivity workshops should be continued to help identify these key patterns for the professional. There has not been enough research specific to visual sensitivity to provide professionals with information to justify circumventing the public input. This small study has shown that we can systematically measure and have the potential to predict local user attitudes towards visual change in the landscape. 101

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BIBLIOGRAPHY Ady, J., B. A. Gray and G. R. Jones. 1979. A Visual Resource Management Study of Alternative Dams, Reservoirs, and Highway and Transmission Line Corridors near Copper Creek, Washington. In G. Elsner and R. Smardon, Our National Landscape, pp. 590-597. Bacon, W. R. 1979. The Visual Management System of the Forest Service, USDA. In G. Elsner and R. Smardon, Our National Landscape, pp. 660-665. Blair, W. G. E. 1980. Visual Resource Management. Environment Comment, Urban Land Institute, Washington, D.C., June, pp. 6-15. Blau, D. H., M.C. Bowie and F. L. Hunsaker. 1979 Visual Resource I nventory and Imnaha Valley Study: Hells Cnayon National Recreation Area. In G. Elsner and R. Smardon, Our National Landscape, pp. 428-438. Enk, Gordon., et al. 1980. Review of a Methodology for Assessing Impacts of Overhead Tansm1ss1on Lines, Report on Phase I to U.S. Department of Energy. Rensselaerville, N.Y.: The Institute on Man and Science. Grden, B. G. 1979. Evaluation and Recommendations Concerning the Visual Resource Inventory and Evaluation Systems Used within the Forest Service and the Bureau of Land Management. In G. Elsner and R. Smardon, Our National Landscape, pp. 296-304. Gussow, A. 1979. Conserving the Magnitude of Uselessness : A Philosophical G. Elsner and R. Smardon, Our National Landscape, pp. 6-11. Hammitt, W. E. 1979. Measuring Familiarity for Natural Environments through Visual Images. In G. Elsner and R. Smardon, Our National Landscape, pp. 217-226. Kaplan, R. 1979. Visual Resources and the Public: An Empirical Approach. In G. Elsner and R. Smardon, Our National Landscape, pp. 209-216. Kaplan, S. 1979. Perception and Landscape: Conceptions and Misconceptions. In G. Elsner and R. Smardon, Our National Landscape, pp. 241-248 . Lee, R. G. 1976. Research on the Human Sensitivity Level Portion U.S. Forest Service Visual Management System. University of California, Department of Wildland Resource Management, Berkeley, CA. Unpublished draft report.

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Litton, R.B., Jr. 1979. Descriptive Approaches to Landscape Analysis . In G. Elsner and R. Smardon, Our National Landscape, pp. 77-87. Mendosa, F. 1984. Personal Communication. Montrose District, BLM. Miller, C., N. Jetha and R. MacDonald. 1979. Classification of the Visual Landscape for Transmission Planning. In G. Elsner and R. Smardon, Our National Landscape, pp. 507-513. --Nie, N. 1975. Statistical Package for the Social Sciences. McGraw-Hill Book Company, New York. Penning-Rowsell, E.C. 1979. The Social Value of English Landscapes. In G. Elsner and R. Smardon, Our National Landscape, pp. 249-255. Ross, R. W. 1979. The Bureau of Land Management and Visual Resource Management -An Overview. Elsner and R. Smardon, Our National Landscape, pp.666-670. Schomaker, J. H. 1978. Measurements of Preferences for Proposed Landscape Modifications. Landscape Research, Vol. 3, No. 3, pp. 5-9. Sheppard, S. R. J. and S. Newman. 1979. Prototype Visual Impact Assessment Manual. Submitted to BLM and USFS. University of California, Berkeley, CA. Unpublished. Sheppard, S. R. J. 1984. Personal Communication. WIRTH Environmental Service, Division of Dames and Moore. Siegel, S. 1956. Nonparametric Statistics for the Behavioral Sciences. McGraw-Hill Book Company, Inc. New York. Simpson, J. W. 1979. Opportunities for Visual Resource Management in the Southern Appalachian Coal Basin. In G . Elsner and R . Smardon, Our National Landscape, pp.328-334. Sinton, J. W. and G. Ginder. 1979. Visual Resources of the New Jersey Pine Barrens: Integrating Visual Resources into the Planning Process. In G. Elsner and R. Smardon, Our National Landscape, pp.454-461. Taggart, C., T. Tetherow, B. Bottomly. 1980. Visual Values: Colorado Takes Stock. Landscape Architecture, July, pp. 396-400. tm

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Taggart, C. 1984. Personal Communication. EDAW, Inc. Tetherow, T. 1984. Personal Communication. WIRTH Envrionmental Services. USDI, Bureau of Land Management. 1980. Visual Resource Mana5ement Programs. Gen. Tech. Report, GP0-1980-0-302-993, U.s. Govt. Printing ffice, Washington, D.C. Wirth Associates, Inc. 1979. BLM Colorado Visual Resource Inventory. Eight published volumes submitted to BLM. Denver, Colorado. Wirth Associates, Inc. 1982. Fort Peck-Havre Transmission Line Project, Montana: Environmental Report Volume 3: Human Environment. Prepared for U.S. Dept. of Energy. 104

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APPENDIX A 105

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DESCRIPTION OF GENERAL LANDSCAPE TYPES* Mountains are characterized by moderate to steeply sloping lands which are typically very rugged and have extensive areas of rock outcroppings. High elevation ridges, broken talus slopes and smoothly undulating slopes are common to the mountainous terrain. Northfacing slopes tend to be densely forested with mixed alpine conifers and scattered groves of aspen. Southfacing slopes generally support a somewhat sparse cover of pinyon and juniper. Ridge and narrow valleys landscapes are characterized by steeply sloping land which crests in sharp angular ridge lines and drops into steep walled, narrow, v-shaped valleys. Large areas of rock outcroppings may be present along many of the slopes. Vegetation is somewhat diverse and is generally dictated by slope and aspect. Conifers and aspen are found on northern slopes and at higher elevation. Sage, grasslands and scrub oak are common on southern slopes and at lower elevations. Rims and ridges are differentiated from the ridge and narrow valley classification because these areas are characteristically one, independent and isolated ridge rather than the continual 11ridge-valley-ridge11 landscapes. These ridges or rims have moderate to steep slopes with a variety of exposed geological formations. Due to the sandstone, claystone and mudstone makeup of these ridges, colors are extremely diverse. The slopes are highly dissected and eroded due to the geological makeup of the landform. Rolling hills are more rounded and uniform than ridges and narrow *Descriptions taken from Wirth Associates, 1979.

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valleys. These areas are characterized by rounded, undulating uplands often flanked by long and linear elevated benches or terraces. Many small drainages and swales define the individual hillsides. The vegetative cover within these hilly areas range from extensive grasslands to low scrubby stands of pinyon, juniper and scrub oak. Canyons are characterized by nearly vertical, precipitous walls which often exhibit a variety of geological formations. Flowing or intermittently flowing streams generally bisect the canyon floors and are visually dominant elements within the canyons. The streams are bounded by linear bands of riparian vegetation. Further away from the drainage courses and along the sideslopes, the vegetation in the is composed primarily of coniferous species which vary in density with the steepness of the canyon walls. Plateaus are extensive flat land areas which are raised above adjacent lands. Various species of short grasses and extensive areas of sagebrush are common to these areas. Mesas are small high plateaus with steeply sloping sides. Typically mesas are independent from other mesas and are separated by stream courses along the periphery of the mesas. Broad valleys are considered to be wide open expanses of relatively flat to gently sloping land which is bounded by ranges of h ills or mountains. These areas are commonly covered by thick deposits of sediments which originated in the surrounding mountains and typically support vegetative communities of short grasses and sagebrush. Parks are quite similar to broad valleys in respect to their physiography, which is wide, open and flat. The main difference between par k s and broad valleys is that parks are not structurally dependent upon a river or stream as valleys are. Basins have been combined with parks because of their physiographic similarities. Basins are primarily situated in the interior of 107

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a park and are characterized by low, depressed areas often with bodies of standing water. River valleys are lands associated with a major river course such as the Yampa River or Colorado River. Vegetation is the most diverse within these valleys as a result of the dominant water features. Reservoirs are major surface water bodies, and include both natural and man-made impoundments. Badlands are characteristically areas where sandstone, claystone, mudstone and shale have been exposed through erosion. Diverse colors and topography are characteristic of these units and contrast greatly to surrounding landscapes. Little if any vegetation exists within these areas which highlights the intense colors and contrast between this and adjacent landforms. Sand dunes lack vegetative cover other than scattered tufts of tall grasses. These landforms are shifting dunes of gently rolling, light yellow sands. 108

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APPENDIX B

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• 1'-'"''"'-'-'-1 '"'I I I • I I ...,'"' I '"' '-'1 I -------------RE SOURC E AREA ------------PLANNING UNI T RATING AREA DESIRED VISUAL U SER R EACTION T O V ISUAL CHANGE BY: .. flame Ho. HGKT. CLASS Landfonn-1 Landfonn-2 L andt . /Veg • . Vegetation 1Veg./Struct.-1 Veg./Struct . -2 Structures ---. --------I P • Pr e s ervatio n H • VIsual change by this type of activity would c a use a strong negati v e reaction. HPHigh P r otection PARTICIPANT M• VIsual change by this type of activity would cause a REPRESENTING MRHoderate Protection moderate negative reaction. DATE LP •low Protection l• VIsua l change by this type of activity would cause a low adverse reaction. A • Rehabilitation

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RATING AREA DESIRED VISUAL USER REACTION TO VISUAL CHAHGE BY: Uame No. HGHT. CLASS Landfonn-1 I t Lan dfonn-2 L a n d f o rm-) : L a ndf orm-4 Landf ./Veg. Veg./Struct.-; Veg./Structr2 I . ----------------------------------.-. . . . ----. . . ----------1-------. -----. ... """'-----------------------------------------... -----------------. ---------------------. ---------. . ---------... -----------------------------------------------------------------. ------------------------------------------------------. DESIRED VISUAL HANAGEIIEIIT LEVEL USER REACT ION TO VISUAL CltfiHGE BY: VH . Very lllgh Protect ton -the ex Is ttng appearance of thts area should If we assume that vtsual 110dlf1catton of the types I tsted above will occur not be changed. or continue 1n thh region : H . lltgh Protect ton modHtcat ton c o uld take place but would have to A . Thts 1andscape type and rating area would be a1110ng the RIDS t be only slightly visible In this landscape . prefe r able locations. H . Hoderate Protection modificatio n could take place and may b e B . Thh landscape type and rating area wou l d be 1 rod erate l y readily visible but not 1110re d0111lnant than the cxtsttnq preferable l ocation. -_ _ .... _ _ ----............ ... .......... . ... l•ac.t CATEGORIES O f V ISUAL CHANG L a n dfonn1 Resource Extrtction oil & gas) landfonn-2 R e sourc e Extractio n (mtntn c oal operations) Landfol"lll-l landfill Grave l O per ation s DaMs/water (Irrigatio n & sto rage ) landfonn/Vegetatlon a. Roads Railroa d s b . P i pel tn e s Vegetat i o n /Structures-1 a. Power Lines Reflector s /Repeators Substations b. Pow e r P lants Vegetatton/Structures-2 Skt areas/R e s orts

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SUBJECT: BU1 Co 1 orado Vi sua 1 Resource Inventory Vlorks hop, Roaring Fork, November 14 Dear --As a part of an ongoing effort to prepare a resource inventory of public lands, the Bureau of Land Management (BLM) is in the process of directing an inventory of the visual resources for approximately 8.5 million acres within Colorado. Portions of all four BLM districts in Colorado will be involved in this inventory -Canon City, Grand Junction, Montrose and Craig. The inventory will cover all lands within certain Planning Units (PU) in the following BLt1 Resource Areas (RA): CANON CITY DISTRICT Royal Gorge Northeast MONTROSE DISTRICT San Juan RA RA RA Uncompahgre Basin RA San Miguel RA GRAND JUNCTION DISTRICT Glenwood Springs Eagle Roaring Fork RA PU PU Grand Junction RA CRAIG DISTRICT Little Snake River RA Kremmling RA The attached map shows the general locations of these BLM Districts and Resource Areas. Wirth Associates, Inc. has been retained by the BLM to conduct this visual resource inventory in Colorado. A series of workshops with representatives of federal, state, and local agencies, and special interest groups will be held within each Resource Area as a part of the effort. Public concerns for changes to the visual quality within each Resource Area will be the focus of these meetings. ,,.,

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-2-A workshop will be held in the Glenwood Springs Resource Area (highlighted on attached map) at Roaring Fork on Tuesday, November 14, 10 :00 a.m • . Please plan to participate at this meeting and share with us your i nsigh t and understanding of public concerns to visual changes occurring or possibly occurring to landscapes within the Resource Area. With your assistance we can more responsively manage the visual resources on public lands. Additional information will be provided concerning the format and specific content of the workshop at Roaring Fork. A BLM representa t ive wil l be contactin g you concerning this meeting. Sincerely, Alfred W. Wright Area Manager 111

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--....... VISUAL SENSITIVITY WORKSHOP DATA SHEET EXPLANATION AND CONTINUATION GENERAL INSTRUCTIONS: I . \ In filling out the workshop form, please respond from the point of view of the public sector or agency you represent rather than from your personal point of view. This will enable us to better analyze the broad spectrum of public attitudes. Desired Visual Management Class: This is to be a further indication of the value of scenic quality to the group or agency in terms of the level of management you feel should be exercised for scenic values within the area. "Preservation" would restrict all activities which would noticeably alter the landscape. "Rehabilitation" is an indication that from your group or agency•s point of view, the area is already visually degraded and needs some level of corrective measures. User Reaction to Visual Change: The categories of visual change listed on the rating form are intended to repre sent generic types of management actions which would lead to similar kinds of visual change. They are not intended to represent specific management proposals. Desired Visual Management Class Criteria Explanation: Preservation (P) Future landscape modifications should not be detectable. High Protection (HP) Future landscape modifications should be such that they may be visible but not readily evident. Moderate Protection (MP) Future landscape modifications should be such that they may be visible but not obtrusive or . inbarm.ontous. Low Protection (LP) Future landscape modifications could contrast sharply with the existing landscape or could become the dominant feature in that area. Rehabilitation (R} -The landscape has been so degraded that some corrective measures should be take . n to reha .bi 1 ita.te it. (For this category a desired level of protection sh_oU,ld also be gtven, e _.g. R/MP). . 114

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SPECIFIC ISSUES, PROJECTS OR LANDS OF CONCERN TO YOUR GROUP OR AGENCY: In the following space please indicate any issues or concerns your agency or group has about visual resource management in this BLM Resource Area. Specific projects, lands or issues can be addressed. Indicate on the base maps provided, the location of specific lands you are concerned about. Use a code letter/word or other identifiable designation referenced in your narrative. Participant _________________________ __ Date --------------------------------115

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PLANNING UNIT c Desired Rating Units Mgmt. Class Umt: p I :c (..!) V) ....... 4 w:c Scenic Quality ::::> HP ...J Rating Unit{s): c:( > • c ww MP o..::: c:( u V) I ...... 0 ...... Z:?: LP m c:(O ...J ...J R F i n a 1 Rat i n g : \-\.:......;..__ ___ _ Rationale: \Wl VISUAL SENSITIVITY WORKSHOP DATA SUMMARY AND ANALYSIS PARTICIPANT RESPONSES BY LANDSCAPE t 10DIFICATION CATEGORY* 1 LF-2 . (LOW) LF/V (MEO. ). ) • 6 4 z * 0 = High Ratings; II:l:m/1 = Moderate Ratin . gs; Ill = Low Ratings ** See Reverse side for specific types of visual c hanqe within each category • ..&-+..a.. n _ , -.&...!---_ _ _ _,. • ST (MED • .)' 2