THESYS { V lllllllllllllllll”HUI“IllHIlllllUlllllllllllllllllllll 1293 01812 1446 This is to certify that the thesis entitled PREDICTIVE VISUAL QUALITY ASSESSMENT IN AGGREGATE MINE RECLAMATION presented by Peter S. Keefe has been accepted towards fulfillment of the requirements for Mdegree in Mgional Planning E Major professor Date 18 April 1996 0-7639 MS U is an Affirmative Action/Equal Opportunity Institution N; LIBRARY Q lchlgan State ‘ ll 3n [”F‘ 4. University ‘- ”if?" 3" thlmlfiy PREDICTIVE VISUAL QUALITY ASSESSMENT IN AGGREGATE MINE RECLAMATION By Peter S. Keefe A THESIS Submitted to Michigan State University in partial fulfillment of the requirements for the degree of MASTERS OF URBAN AND REGIONAL PLANNING Department of Urban and Regional Planning 1997 ABSTRACT PREDICTIVE VISUAL QUALITY ASSESSMENT IN AGGREGATE MINE RECLAMATION By Peter S. Keefe The study of landscape aesthetics has recently been brought into the forefront of research through the passage of various federal legislative acts which mandate the consideration of the quality of surroundings as a natural resource. I applied models that have been developed to meet these national program requirements to a local land use, aggregate mining. I evaluated if current reclamation procedures improve the visual quality of aggregate mines. Using a perception-based, predictive visual quality formula on two surface mine sites, I determined the effect of applying four different reclamation treatments: open water, natural revegetation, agriculture and housing development in comparison to the operating site. The visual quality model predicted with a 95% confidence level that reclaiming the mine sites using open water or natural revegetation does significantly increase the visual quality of mine sites. Conversely, reclaiming by using housing development or agriculture had no significant effect on the visual quality of the mine sites. Copyright by Peter Scannell Keefe 1 997 DEDICATION To Veda, who gracefully accepted the burdens of my schooling with support, loving and a smile. iv ACKNOWLEDGMENTS To Anthony Bauer whose enthusiasm for landscape architecture first encouraged my interest in land reclamation. To Jon Burley who taught me a variety of ways to view landscape architecture and to Terry Brown who unselfishly added to my thesis. N .91— I: MM: 2' I .. . 7.- I If I I i ' .rq'i' :3 {I Sn“, SS *6 I E I. I‘ Jr“ —v ‘ I} 2:1,)": ’IJ‘SSLJI III 3011. .r. ..-..-....__ I -w—m Ii". II ‘III‘... E . -" p". -s‘ fr :v-q TABLE OF CONTENTS LIST OF TABLES ............................................................................ VIII LIST OF FIGURES AND AN EQUATION ............................................ IX INTRODUCTION ................................................................................. 1 LITERATURE REVIEW ........................................................................ 4 LANDSCAPE QUALITY MODELS ......................................................................... 4 Ecological Model ................................................................................... 4 Formal Aesthetic Model ........................................................................... 5 Psychophysical Model ............................................... _ ............................. 5 Psychological Model .............................................................................. 6 Phenomenological Model ......................................................................... 7 VISUAL QUALITY APPLICATIONS ....................................................................... 7 MODEL COMPARISONS .................................................................................. 8 Validity .............................................................................................. 8 Quantification ....................................................................................... 9 Public versus Expert Opinion .................................................................... 9 Landscape Representation ...................................................................... 10 MODEL CONSIDERATIONS ............................................................................. 12 FUTURE MODEL DEVELOPMENT ...................................................................... 13 [NW ................................................................................................ 15 Purpose of Research ............................................................................. 15 The Existing Problem ........................................................................... 15 Visual Quality Modeling as a Decision Making Tool ........................................ l6 METHODOLOGY ............................................................................... I7 APPROACH ............................................................................................ 17 GEOLOGY OF MICHIGAN .............................................................................. 17 THE Srnas ............................................................................................. 18 STUDY DESIGN ........................................................................................ l9 ANALYSIS ............................................................................................. 24 vi TABLE OF CONTENTS CONT. RESULTS ......................................................................................... 26 KEY SAND AND GRAVEL DATA/ HARDROCK DATA .................................................. 29 STATTSTTCAL ANALYSIS ............................................................................... 29 DIS C USSION .................................................................................... 3 1 INTERPRETATTON ...................................................................................... 32 MODEL VARIABLE GROUPlNGS ........................................................................ 34 PATTERNS IN TESTING RESULTS ....................................................................... 35 APPLICATIONS OFTHEVlSUAL QUALITY EQUATTON ................................................... 36 CONCLUSION .......................................................................................... 38 APPENDICES .................................................................................... 42 EQUATTON VARIABLE CLASSIFICATTON ............................................................... 41 CONDENSED TESHNG RESULTS ....................................................................... 42 GRAPH OF VISUAL QUAerY SCORES ................................................................. 44 RANKED VISUAL QUALrIY SCORES ................................................................... 45 VISUAL QUALITY TESTING RESULTS ............................................. 51 BIBLIOGRAPHY .............................................................................. 107 GENERAL REFERENCES ................................................................. 1 l 1 vii LIST OF TABLES TABLE 1 VISUAL MODEL VARIABLES ............................................................... 23 TABLE2 ENVIRONMENTAL QUALITYINDEX ........................................................ 24 TABLE 3 VISUAL QUALITY SCORE BY RECLAMATION TREATMENT ................................ 27 TABLE 4 ADJUSTMENT TO EXISHNG SITE SCORE BY TREATMENT ................................. 29 TABLE 5 MULTIPLE COMPARISON OF RECLAMATION TREATMENT BY SITE ........................ 3O viii LIST OF FIGURES AND AN EQUATION FIGURE 1 MINE SITE LOCATIONS .................................................................. 19 FIGURE 2 SET OF MINE SITE IMAGES .............................................................. 22 EQUATION 1 BURLEY'S VISUAL QUALITY EQUATION ............................................... 23 FIGURE 3 GRAPH OF VISUAL QUAerY BY Sm: TREATMENT .................................... 28 INTRODUCTION Only recently has the aesthetic quality of a space has become a “mainstream” concern. With a series of legislative actions the federal government brought the topic of environmental scenic quality to the forefront. Laws such as the Wilderness Act of 1964, National Wild and Scenic Rivers Act of 1968, the National Trails Act of 1968, the National Environmental Protection Act (NEPA) of 1970, and the Coastal Zone Management Act of 1972 all contain articles that pertain to aesthetic quality (Ruddell, Gramann, Ruddis and Westphal, 1989, Leopold, 1982, Brown and Daniel, 1991, Latimer, H090 and Daniel, 1981, Arthur, 1977). The NEPA states “it is the responsibility of the federal government to use all practical means (to) assure for all Americans aesthetically and culturally pleasing surroundings” (NEPA, sec. 101 (b)). The passage of NEPA marked the turning point in acknowledging the landscape as a visual resource (Brown, 1994). Many government agencies needed to adopt this new attitude which led to new goal setting policies. The Forest Service now has in it’s mission statement to treat the visual landscape “as a basic resource, to be treated as an essential part of‘and receive equal consideration with other basic resources of the land” (USDA Forest Service, 1977) and “one of the management goals for New England’s forests is the consideration of aesthetics” (USDA Forest Service, 1973). With the need to preserve scenic values, the scenic quality of an area now had to be defined, measured and manipulated in order to preserve these qualities. New management models have, and still are, emerging to aid in the assessment of the visual landscape. The purpose of this study is to apply these techniques used in federal projects and apply them to local and private projects. These methods of predicting visual impact could be used as design and management tools on the local level to mitigate the effects of high impact development. I have chosen to utilize these methodologies in aggregate mining. Aggregate mining is a local land use that is widely distributed across the country. Aggregate is a basic construction commodity that accounts for 43% of all mineral commodities produced in the United States (Dietrich, 1986). Michigan has an estimated 5, 000 total mine sites (Wyckoff, 1992) with 357 operating mines in 1994 (US Department of Interior, Bureau of Mines, 1995). On average this accounts for an average of 60 total mine sites, with 4.3 being active, in every county across the state. LITREATURE REVIEW The first step in being able to analyze landscape quality is the ability to define it. Landscape quality has been defined by the features that make up the landscape, the characteristic elements and attributes, and then the degree of excellence which that landscape possesses (Daniel and Vinning, 1983). Questions pertaining to landscape definition and landscape assessment have led to differing forms of landscape assessment models. In their review of various landscape models, Daniel and Vinning (Daniel and Vinning, 1983) categorized all landscape quality models into five classes. Within these classes some apply directly to landscape visual assessment while other models do not. Looking at the full range of classes is helpful in understanding the theoretical nature of the work. Landscape Quality Models Ecological Model The ecological models are typified by McHarg’s model that defines the landscape in terms of its biology. It places a high value on natural functions such as diversity and biomass production, while placing a low value on cultural values such as appropriateness and visual human impact (Daniel and Vinning, LITREATURE REVIEW The first step in being able to analyze landscape quality is the ability to define it. Landscape quality has been defined by the features that make up the landscape, the characteristic elements and attributes, and then the degree of excellence which that landscape possesses (Daniel and Vinning, 1983). Questions pertaining to landscape definition and landscape assessment have led to differing forms of landscape assessment models. In their review of various landscape models, Daniel and Vinning (Daniel and Vinning, 1983) categorized all landscape quality models into five classes. Within these classes some apply directly to landscape visual assessment while other models do not. Looking at the full range of classes is helpful in understanding the theoretical nature of the work. Landscape Quality Models Ecological Model The ecological models are typified by McHarg’s model that defines the landscape in terms of its biology. lt places a high value on natural functions such as diversity and biomass production, while placing a low value on cultural values such as appropriateness and visual human impact (Daniel and Vinning, 1983). This class of model predisposes against human interference in the landscape and assumes that most human activities will have a negative impact. While this model has great ramifications for ecologically sensitive design, it only has limited applications in the field of visual quality modeling. Formal Aesthetic Model The formal aesthetic model is the most commonly utilized landscape visual assessment model as it is used by the Forest Service (USDA Forest Service, 1984) and the Canadian Ministry of Forests (Ministry of Forests, 1981). This model relies on the design principles to guide the designer to find the most appropriate solution. The appeal of this approach is that it allows agencies to utilize existing personnel, skills and often existing data to implement the model (Brown, 1994) making it cost effective (Leopold, 1982). The formal aesthetic model has severe limitations in that it is capable of rating and comparing various landscape development alternatives only in a very rudimentary way. This model is a set of principles used to guide the designer. Psychophysical Model The psychophysical model creates a quantitative relationship between physical environmental stimuli and perceptual responses (Hull, Buhyoff and Cordell, 1987). This approach selects individual stimuli in the landscape and then develops mathematical models in order to explain the human response to the stimuli. Many of these models are oriented toward measuring the effect of a single-factor stimulus such as waterflow quantity (Brown and Daniel, 1991), atmospheric optical quality (Landphair, 1979) or forest visual quality (Ruddell et al., 1989). Other models have expanded this concept in order to determine the visual quality of entire landscapes (Shafer, 1969, Burley, 1995). The strength of the psychophysical approach lies in it’s ability to relate change in manageable site characteristics to resulting impacts on visual quality (Ruddell et. al., 1989). This model has direct applications to the field of visual quality management due to it’s ability to identify the portions of the landscape that elicit positive or negative responses and gauge the magnitude of change, allowing various landscape alternatives to be compared. Psychological Model The psychological models attempt to determine the users response to the landscape in terms of their feelings and perceptions. This model rates landscapes on informational variables, such as how space organization is interpreted and if the user understands this organization (Kaplan and Kaplan, 1989). The most notable psychological models have been developed by the Kaplans (Kaplan, 1979) and Appleton (Appleton, 1984). This model incorporate the feelings that the landscape evokes within the viewer, expressing the landscape in terms of security, relaxation, warmth, freedom happiness, stress fear, insecurity gloom, constraint, prospect and refuge. Although the psychological model is strong theoretically, it’s use of conceptual variables makes it difficult to apply in predicting scenic quality. Phenomenological Model The phenomenological model places the greatest emphasis on individual feelings, expectations and interactions between the user and the landscape. The model typically elicits responses from the participant in the form of a questionnaire. The model then assesses the person-landscape-context interaction. This results in assessments that are extremely complex and too variable for this model to be used as a landscape management tool (Daniel and Vinning, 1983). Visual Quality Applications In order for any model to be useful in assessing landscape visual quality, it must be possible to use it as a development tool which guides the designer to find visually pleasing solutions. As a development tool the model must be predictive in nature, allowing the designer or manager to determine the visual quality before the landscape is altered (Arthur, 1977). Scenic resources should be evaluated in an objective and quantitative fashion (Carlson, 1977). The only models that have the qualities for determining landscape visual quality are the psychophysical and the formal aesthetic models. Model Comparisons Validity Although the formal aesthetic model is the most widely used form of visual quality modeling, it does present serious drawbacks. The model presents serious reliability concerns as this model is the most dependent on expert judgment (Carlson, 1977) and it does not present a standard methodology for testing results. The results of applying the formal aesthetic model are not reproducible, so the outcome of applying the model cannot be duplicated to test it’s validity. Therefore, the validity of this model is solely dependent on the expertise of the designer. The psychophysical model overcomes the validity problem associated with formal aesthetic model, the application is more objective, being less dependent on the skills of the designer, and utilizes a mathematical model to determine the magnitude of the visual quality. This model allows different landscape alternatives to be quantified and tested against each other. This testing of alternatives removes the subjectivity from the process that that is inherent in the formal aesthetic model (Miller, 1984). Quantification Both the psychophysical and the formal aesthetic models are predictive landscape visual quality models, that is, they both forecast the net result of landscape alterations on visual quality before the changes occur. However, the formal aesthetic model can predict only what the net effect should be, not the magnitude of the change; it can only suggest that the resulting view will improve or degrade visual quality. The psychophysical model can also predict the direction of change as well as quantify the significance of the change. This allows the designer or manager to make informed decisions on the relative visual quality of the proposed changes. Public versus Expert Opinion The models split with regards as to whose interpretation of a landscape is the more appropriate to use. Though, the expert may have the greater understanding of the landscape, the local public probably has the greater attachment to the land. The formal aesthetic model is clearly dependent on expert opinion, but the psychophysical model, such as Shafer’s, is based on public opinion and public interpretation of the landscape. 10 The research surrounding public versus expert opinion is confusing and often contradictory. A summary of 11 different studies that compared results of surveys of both professional and public opinion found that one third of the time they strongly agreed, one third of the time they strongly disagreed and one third of the time they were in moderate agreement, suggesting that there is no correlation between the two groups. This study did determine that the public tends to decide on perceived naturalism while professionals tended to be biased according to their own professional perspectives (Palmer, 1984). This problem becomes more involved with the question of which public to use, tourist or resident? Rachel Kaplan (Kaplan, 1979) compared the results of testing residents versus tourists on visual quality. She found tourists were more interested in preserving the regional characteristics and the residents were interested in that create regional flavor. The questions of who the arbiter of landscape visual quality should be is confusing. No definitive study has been conducted to determine this. It could well be that the determining group could be dependent on the location, the type, and intent of the landscape modification. Landscape Representation The model that has required the most validation for the techniques it uses is the psychophysical model. While many other models may use photography and 11 computer generated depictions, the psychophysical model is dependent on them. The validity of using landscape representations in place of the actual landscape has been an area of active research. The spectator of the natural environment is in that environment in a way which the spectator of a photograph is not in the photograph (Carlson, 1977). In early work Shafer even states that “complete understanding of the perceptual process requires the inclusion of experience and of its lasting traces in the memory (Shafer, 1969). A wide variety of studies have determined that black and white photographs and color slides are accurate representations of a landscape and participants react to the images in the same way they would react to the landscape itself (Stamps, 1992, Waztek and Ellsworth, 1994). Using photographs in modeling has advantages and disadvantages. The use of photographs allows for techniques such as photomontage and photomanipulation so that accurate representations of the proposed changes can be constructed. The most important term here is “accurate”. The models are a valid representation if the respondent cannot detect that the photo has been altered (Orland, 1994) and if representational deviations are less than 6% (Waztek and Ellsworth, 1994). 12 Other landscape representational techniques such as hand rendering or computer generated images, such as from CAD programs, do not elicit equal responses as the actual landscape and therefor are not valid substitutes for the landscape (Zube, 1984). Model Considerations Shafer’s equation in the psychophysical model includes three primary implementation concerns. First, Shafer makes the assumption that aesthetic quality is correlated with a preference for that landscape. In fact, Shafer seems to use these terms almost interchangeably (Carlson, 1977). A preference for a landscape might, or might not, be directly related to the perceived beauty of a landscape. A second concern of this model is that it lacks any theoretical basis. This psychophysical approach has received criticism as these models are developed without any theoretical basis (Weinstein, 1976). Although these criticism are valid, I do not believe that this invalidates the results, as statistical relationships are considered strong enough to validate an equation in other fields (Burley, 1995). The third concern is the inherent negative attributes of this form of equation. When one considers the wide range of elements that occur in landscapes, it becomes clear that an equation in this form could never account for them all. To attempt to accomplish this would mean an infinite number of variables that 13 could be added to the equation to account for all possible situations. But without testing for all of these variables it is impossible to know their effect on visual quality. Using this logic it may be possible to predict the primary influences in visual quality, but it becomes inherently impossible to account for all of the factors that may play a role. Future Model Development Landscape quality models seem to be moving in two clear directions. First is the theoretical basis. These researchers tend to discount current models for any long-term use as they fail to have any theoretical basis (Bourussa, 1991, Weinstein, 1976, Carlson, 1977). The models that do have strong theoretical bases are developing into biological models. They attempt to explain man’s interpretation of his surroundings in terms of inbred biological responses. Appleton (1984) has attempted to create a holistic approach to explain human aesthetic responses by inbred biological needs. This model there has two basic forms. First is the prospect is an environment that allowed primitive man to hunt by viewing his prey without being spotted. Conversely, the refuge is a landscape where primitive man was able to find shelter and refuge from the environment and other predators (Appleton, 1984). Modern man interprets these as spaces that may elicit feels of security or exploration. The Kaplans have conducted research in a similar direction. They tested for similar inbred traits from our ancestry to determine if responses to landscapes l4 landscapes are influenced by man’s ability to understand the landscape, to comprehend the surroundings, and to gather information (Zube, 1984). A second direction is being called for in model development. “Much of the validity testing has been done; predicting for limited subjects, testing the validity of simulations, biases in research methods etc. What is needed is a more elaborate and theoretical model that predicts scenic beauty magnitude and estimates the change in value resulting from landscape modification. Planners need to ask how much better... . Landscape quality models need to become landscape utility models that are equations that clearly show cause and effect relationships in landscape alterations (Hamilton, et. al., 1979). In this article the authors call for further development of the psychophysical models. The existing predictive equations were a first step but they now believe that it is time to move past these models. Researchers believe that these models could be used to move toward finding a theoretical basis for visual quality (Hull et. al., 1987). Within the limits of the existing models, the psychophysical appears to be the most capable of estimating the magnitude of visual quality changes. This is the only model that is capable of directly comparing landscapes or landscape alternatives, to determine their relative visual quality. This allows the landscape manager to determine the significance in visual quality that alterations on the landscape will have. 15 Intent Purpose of Research The purpose of this study is to determine the visual qualitative effects that reclamation may have on aggregate mine sites. People commonly assume that a mine site will have a detrimental visual quality impact on the surrounding community. They also assume that reclamation, in any form, will improve this. Until recently this has been demonstrated only through heuristic judgments by the community, regulators, miners and designers. l utilized a format in which these assumptions could be either proven or dispelled in a more objective format using Scientific methodology. The Existing Problem When a new aggregate operation is proposed within a community, the opposition that it faces can be severe. The local citizens are concerned about the negative impacts that the mine could have on the community. Some of these impacts, such as groundwater contamination, noise pollution, and increased truck traffic, are relatively easy to predict and monitor. Other impacts, such as visual degradation, have been difficult to monitor and measure. Impacts that are ambiguous and ill defined can result in arguments that are highly emotional, which tend to lead away from an objective decision making process. 16 Visual Quality Modeling as a Decision Making Tool Until recently, techniques for determining and measuring visual quality have not existed, and they are still developing. Although they may not have reached a high degree of sophistication they do provide a reliable yardstick against which proposed changes to the landscape can be measured. These models offer a methodology that takes visual quality out of the heuristic and personal judgment stages and places them in a form that can be quantified, analyzed, and compared to determine their quality within the setting. This approach allows all of the parties involved to make more rational decisions, decisions that are based on sound principles. It also allows them to determine if their existing assumptions regarding visual quality of mine sites and reclaimed sites are correct or how sites could be altered to improve their visual quality. METHODOLOGY Approach The approach employed in this experiment determined measurable visual quality differences between various landscape reclamation treatments and the existing mining conditions. To accomplish this research, photographs of the mine sites were altered to simulate various proposed post-mining conditions. The visual quality of the existing and post-mining views were then determined by applying Burley’s visual quality equation and statistically analyzed using Friedman’s two-way analysis. Geology of Michigan Michigan is primarily divided into two areas geographically, the northern and southern peninsulas. To a considerable degree these two areas are geologically separate and distinct (Heinrich, 1976). Although the geology is not absolutely divided along these geographic parts, a gradient of change occurs through the state. The northern peninsula 'is generally underlain by rock from the Precambrian Age while the southern peninsula is underlain by much younger Ordovician or Pennsylvanian materials. 17 18 Throughout the southern peninsula much of this bedrock is covered by the surficial geology that is composed of glaciofluvial deposits. These sediments are typically tills, gravels, sands silts, and clays. These high quality deposits are the class of deposits that are needed to obtain a quality source of sand and gravel (Michigan Limestone Corp., 1987). The Sites For this study, I selected two operating aggregate mine sites that would demonstrate the widest possible diversity in conditions that occur at aggregate mines within Michigan. The first site is a sand and gravel operation located in Brighton Michigan. This mine is located within a growing suburban area located 36 miles west of Detroit (Figure 1). This relatively small operation encompasses approximately 250 acres and is used primarily by a single contractor as a source for construction base materials (Hayes, 1995). The site is also used as a deposit site for out soils that are generated from these same construction sites. The second site is the world’s largest operating limestone quarry, where the open pit is approximately five miles by two miles. The limestone quarried here is used primarily in glass and cement production (Michigan Limestone Corp., 1987). This quarry is located in Rogers City Michigan, 210 miles north of Detroit, in a rural community of 4,000 on the shore of Lake Huron. 19 Hardfock Site Sand and Gravel Site 0 Ejggrg 1 Mine Site Locations These two sites demonstrate the wide range of conditions that aggregate operations can present: the material being mined, the scale of the operation, the setting of the operation, the equipment used for mining, and the conditions within and surrounding the mines. Study Design I took a series of black and white photographs at each mine site using a SLR camera fitted with a 50 mm lens. This camera configuration was chosen as it best reproduces a view as seen by the human eye (Schaefer, 1992). Black- and-white photography was used because color is not a variable within Burley’s visual-quality equation. Also black-and-white images require less memory when entered into a computer (Adobe, 1994). 20 The mining photos were typically taken from the perimeter of the operations area so that the resulting views are generally oriented into the active pit. The photos depict the conditions that can exist within an active pit including views of crushers, screeners, trucks, cars, cranes, shovels, waste piles, utilities, vegetation, standing water, reclaimed areas, steep eroded banks and sheer rock faces. I chose thirty photographs to represent the two sites. The sixty photographs demonstrate the wide range of conditions possible between the two sites. I scanned these photographs into a computer using a flat bed scanner at a moderate resolution of 150 lines per inch. Along with the mining photos, I scanned other landscape images at this time. These other landscape photographs were taken throughout the lower peninsula and were used to create a library of scenes that could be used to construct post-mining treatments representing the reclaimed mine sites. I scanned all of these images and imported them into Adobe Photoshop. With the mining views in Adobe Photoshop. I could then construct images to represent the four different post-mining treatments. These reclamation treatments include the existing mine site, agriculture, single-family housing, 21 natural revegetation, and open water (Figure 2). All of these post-mining reclamation images assume a 10 to 20 year time lapse from the time of mining cessafion. The 300 images (5 treatments x 60 samples) used in this study (see enclosed CD ROM) were then exported from Photoshop and written to slide film to create a permanent hard copy. I chose the slide format as a cost and time effective method for enlarging the images to the 8” x 10” format that is necessary for applying the visual quality formula. I projected these slides onto the rear of a translucent screen. The screen had an 8” x 10”, 1/4” grid drawn on it for the tabulation of the visual-quality equation. The translucent screen allowed me to work in front of the screen without blocking the projection of the image. From this grid I counted each variable and entered the resulting values into Burley’s equation (Equation 1). The variables for this equation were developed by Shafer and Burley (Table 1) 22 Figure 2 Set of Mine Site Images Sand and Gravel — Existing Condition Reclaimed for Agriculture Reclaimed for Housing Reclaimed by Natural Revegetation Reclaimed for Open Water 23 Y = 68.3 - (1.878 * Health) - (0.131 * X1) - (0.064 * X6) + ( 0.020 * X9) + ( 0.036 * X10) + (0.129 * x15) - ( 0.129 * X19) - (0.006 * xa2) + (0.00003 * X34) + (0.032 * X52) + (0.0006 * X1 * X1 ) + (0.00006 * X6 * X6) - (0.0003 * X15 * X15) + (0.0002 * X19 * X19) - (0.0009 * X2 * X14) - (0.00003 * xs2 * X62) - (0.0000001 * xs2 * X34) mam; Burley’s Visual Quality Equation 19121;; Visual Model Variables Health = from the environmental quality index X1 = perimeter of immediate vegetation X2 = perimeter of intermediate non-vegetation X3 = perimeter of distant vegetation X4 = area of intermediate vegetation X6 = area of distant non-vegetation X7 = area of pavement X8 = area of buildings X9 = area of vehicles X10 = area of humans X14 = area of wildflowers in foreground X15 = area of utilities X16 = area of boats X17 = area of dead foreground vegetation X19 = area of wildlife X30 = open landscape: X2 + X4 + (2 * (X3 + X6)) X31 = closed landscape: X2 + X4 + (2 * (X1 + X17)) X32 = openness: X30 - X31 X34 = mystery: (X30 * X1 * X7) /1140 X52 = noosphericness: X7 + X8 + X9 + X15 + X16 Within this equation, one variable requires further computation in order to gain a resultant. The environmental quality index is calculated from Table 2. 24 113912 Environmental Quality Index Purifies air +1 0 -1 Purifies water +1 0 -1 Builds soil resources +1 0 -1 Promotes human cultural diversity +1 0 -1 Preserves natural resources +1 0 -1 Limits use of fossil fuels +1 0 -1 Minimizes radioactive contamination +1 0 -1 Promotes biological diversity +1 0 -1 Provides food +1 0 -1 Ameliorate wind +1 0 -1 Prevents soil erosion +1 0 -1 Provides shade +1 0 -1 Presents pleasant smells +1 0 -1 Presents pleasant sounds +1 0 -1 Does not contribute to global warming +1 0 -1 Contributes to the world economy +1 0 -1 Accommodates recycling +1 0 -1 Accommodates multiple use +1 0 -1 Accommodates low maintenance +1 0 -1 Visually pleasing +1 0 -1 Using this formula, I calculated the value for each component by counting the number of squares that each variable occupied on the screen. I calculated the visual quality value by entering these values into the equation. Analysis In order to determine the significance of the results from the visual quality formula I utilized the Friedman two-way analysis of variance by ranks test (Daniel, 1978). l organized the raw scores from the visual quality formula into table form, labeling the reclamation treatment as the treatment and the scene as the subject. I then ranked these raw scores with the low score being ranked as 25 1, to the high being ranked 5, within each subject (Appendix D). I then totaled these rankings according to treatment. I made adjustments to the test statistic to compensate for ties that occurred in the ranking process. From these treatment totals, I calculated the test statistic. I used the test statistic to determine that the null hypothesis could be rejected, demonstrating that at least one treatment was significantly different from the others. I employed a multi-comparison procedure to determine which treatments were significantly different. RESULTS This experiment resulted in visual quality scores for each existing view and for each proposed reclamation treatment (Appendix B, Appendix C and Figure 3). The testing generated raw scores that ranged from a least preferred view score of 82 to a most appealing view score of 28. The mean scores were 63 for the sand and gravel site and 56 for the hardrock site. To place these scores within context, a score of 70 is a neutral score; making the high score of 82 represents a moderately unpleasant view, while the mean scores of 63 and 56 are neutral to pleasant views, and the low score of 28 is an extremely pleasing view. The application of each reclamation treatment resulted in low rates of variance in the visual quality, when scores were grouped by treatment. The resulting mean and standard deviations, by treatment, are synopsized in Table 3. In order todeterrnine the net effect of each reclamation treatment, I compared the score for each treatment to the existing score for that site (Table 4). The agriculture and housing development treatments had little effect on the visual quality scores of the existing site, improving the score as little as one point (-1) or degrading the view at most by three points (3). Conversely the natural 26 27 revegetation and open water had significant impacts on the existing site score, improving the score by as much as 29 points (-29). 19M Visual Quality Score by Reclamation Treatment Wm Mean. We. Existing 74/ 65 2.91 /7.96 Agriculture 73 / 68 1.48 / 5.60 Housing 76 / 67 1.97 / 6.43 Natural Revegetation 45/39 1.37/6.10 Open Water 48/42 1.74 / 7.26 Key: Sand and Gravel Data / Hard Rock Data 28 EMILE—3 Graph of Visual Quality by Site Treatment Sand and Gravel Site Treatment Existing Agriculture Housing Natural Revegetation Open Water Hardrock Site Treatment Existing Agriculture Housing Natural Revegetation Open Water Good Poor Visual Quality 29 IflhlLfl. Adjustment to Existing Site Score by Treatment Agriculture -1 /3 2.58 / 5.48 Housing 2 / 2 2.94 / 4.17 Natural Revegetation -29 / -26 2.79 / 4.80 Open Water -26 / -23 2.87 / 4.34 Key Sand and Gravel data/ Hardrock data Statistical Analysis The Friedman two-way analysis of variance test revealed statistically significant difference between at least two treatments using a confidence level of 99.5% (p 3 0.005). The multiple comparison procedure produced a test statistic of 34.42. By using multiple comparison 1 was then able to determine which treatments produced significantly different scores from other treatments, using a 95% confidence level (p s 0.05). These results are outlined in Table 5. These results confirm what was interred in Table 3 and Appendix C. The visual quality scores for the existing condition, housing development, and the agriculture treatments are all closely related and are, in fact, not significantly different. The natural revegetation and the open water treatments are also so 3O closely related that they are not statistically different. These two groupings of treatments do vary greatly from each other and are statistically very different. This trend of two groupings of reclamation treatments is nearly identical at both the hardrock and the sand and gravel sites, with a single exception at the sand and gravel site; the agriculture and the housing treatments were closely related but were statistically different. Table; Multiple Comparison of Reclamation Treatment by Site Sand and Gravel Ireatmem Ream Iastfitatistic Result Site Existing and Agriculture 1 6.50 Not significant Existing and Housing 23.50 Not significant Existing and Revegetation 88.00 Significant Existing and Water 58.00 Significant Agriculture and Housing 40.00 Significant Agriculture and Revegetation 71 .50 Significant Agriculture and Water 41 .50 Significant Housing and Revegetation 1 1 1.50 Significant Housing and Water 81 .50 Significant Revegetation and Water 30.00 Not significant Hardrock Site Existing and Agriculture 20.00 Not significant Existing and Housing 1 5.50 Not significant Existing and Revegetation 74.00 Significant Existing and Water 52.00 Significant Agriculture and Housing 4.50 Not significant Agriculture and Revegetation 94.00 Significant Agriculture and Water 72.00 Significant Housing and Revegetation 89.50 Significant Housing and Water 67.50 Significant Revegetation and Water 22.00 Not significant DISCUSSION Perhaps the most unanticipated result of this testing were the factors that did not influence visual quality. I presumed that the differences between the two sites would influence the results of performing the reclamation, but this was not the case. The sand and gravel mine is a small site that has been sequentially reclaimed, with many of the pit’s banks having established vegetation. The hardrock site is a large open quarry with no vegetation within the pit. I believed that as the hardrock site presents a larger and less vegetated view, it would accentuate the relative magnitude of improvement the reclamation would result. Without specifically testing for the impact of the views between the sites, it appears that if the differences between the two sites had any influence at all, the influence was minor. At both sites the scores of the existing site were comparable and when the treatments were applied, the visual quality results were similar. Another surprising feature of the results was that the existing site ranked as the third most preferred view. I believe that prevailing opinion would have rated the existing site as the least preferred, as the perception of mining is being so destructive to the landscape. These existing views depicted mining as it 31 32 commonly occurs, and include views of buildings, equipment, material stockpiles waste heaps and roadways presumably would make them rate quite low. This was the first indication that not all reclamation improves the visual quality of the mine sites The results of the experiment demonstrate the relationships that occur between the reclamation treatments (Appendix C). The grouping that received the more preferred scores were the open water and natural revegetation treatments. These two treatments had mean scores that that improved on the existing visual quality score by up to 29 points. The two treatments generated visual quality scores that differed between each other by an average of only three points. In the second, less preferred, grouping were the existing conditions, agriculture and housing treatments. These two reclamation treatments generated scores that closely related to the score of the existing site, resulting in scores that only varied from the existing score by an average of three points. Interpretation Reclamation Improving Visual Quality These groupings of treatments raise the question, is all reclamation good reclamation in terms of visual quality? If one of the primary goals of reclamation is to improve the appearance of the mine site, then two of these reclamation treatments do not achieve this goal. The results suggest that not all reclamation 33 treatments create views that are significantly better than that of the operating mine site. The results of the analysis demonstrate that when the mine sites were reclaimed using the agriculture and housing reclamation treatments, the resulting views did not have a significantly different visual quality than that of the operating mine site. A result of these groupings of treatments is that although the intended end use of the mine site may be very different under different reclamation plans, the resulting visual quality of the reclaimed site may not be different. At both mine sites the resulting visual quality from applying the open water and the natural revegetation treatments resulted in views that were not significantly different. This is also the result from applying the housing and the agriculture treatments. This close relationship of resulting visual quality could allow for reclamation planning that is broader in scope and allow a greater variety of end uses. A case in point 'would be that if housing development were the approved end use then sequential reclamation of the mine site would be unimportant from a visual quality standpoint. lf open water were the end use, then sequential reclamation would be very important as it would significantly increase the visual quality of the site. These groupings of treatments could be used to accommodate very different uses of the site without impacting the visual quality of the site. This discounts 34 the practice of choosing a particular end use to regulate the visual quality of a mine site. What seems to be more important is to choose a grouping of reclamation treatments that have similar visual quality results and then apply the one that is the most appropriate land use. Model Variable Groupings As noted by Burley (Burley, 1995) the visual quality equation (Appendix A) includes three sets of variables. The first set of variables are those that have a positive effect on visual quality. These variables tend to be perceived as those that have naturalistic qualities. The second grouping of variables are those that have a negative effect on the visual quality. These variables can be interpreted as being man’s intrusion onto the landscape. The third category contains variables that are considered to be neutral within the equation. While these neutral factors may not be significant in the equation, they do impact the resulting visual quality by limiting the quantity of positive or negative variables within the scene. For example, if a lake were to be constructed, making water (a neutral variable) the dominant feature, it would exclude other elements such as flowers (a positive variable) or pavement (a negative variable). The result of identifying these variable groupings is that they can then be used as criteria in the design process. If one of the goals of the designer is to manipulate the visual quality to achieve the highest possible level, then the inclusion of positive variables needs to be optimized while the impact of the 35 negative variables needs to be mitigated. Therefore landscape manager do not need to have a full understanding of Burley’s equation, they only need to understand the principle of the three variable groupings in order to increase visual quality. Patterns in Testing Results Throughout this testing, resulting visual quality scores seem to result in reoccurring patterns. If the scores resulting from the application of a treatment are rated on a scale using the existing score as the baseline, then the magnitude in the change as a result of having applied the treatment appears to be predictable (Table 4). The effect on the existing score will be the existing score +- score adjustment +- standard deviation. For example, when natural revegetation was applied in SG 4, the existing score of 74 was lowered by 30 points to a reclaimed score of 44. This score adjustment of 30 points is within the range of the mean change (29 points) +- standard deviation (2.97). These results demonstrate that when a particular reclamation treatment is applied to an existing view, the direction and the magnitude of the change in visual quality could be forecast within the range of the standard deviation. Since the resulting standard deviations are relatively small, this allows for a fairly accurate prediction of applying a treatment. If future testing were to yield similar results, it could become unnecessary to test the visual quality results of applying many reclamation treatments. In its place 36 an accurate forecasting model could be constructed that could predict the results of applying treatments, without the need to test each alternative. The visual quality equation could then be applied as a confirmation tool at the end of the design process. Applications of the Visual Quality Equation The most direct application of this quantitative visual quality equation is it’s use as a design tool. By utilizing the equation and maximizing the variables that have a positive effect while minimizing the negative effect variables, the visual quality of a view can be increased. Therefore it is not important to have a detailed understanding of the model. What is important is to determine what elements will raise visual quality, what elements will lower the visual quality, and then to use these variables to the design’s advantage. In the past these design decisions have generally been relegated to expert opinion. When any aesthetic issue was involved, the site manager deferred those questions to the architect, landscape architect, or the designer. Many have believed that the professionals who have been trained in the design principles have a deeper understanding of their surroundings. With the development of the visual quality equation this no longer needs to be the case. The site manager could use this model to gain insight into design and have a greater ability to work with the designer to find the most appropriate solution. 37 An example of how the manager and designer could collaborate is in the design process. Many municipalities currently have landscape or aesthetic ordinances that regulate the quantity, density, and specie of plant material that are required. The shortcoming of this approach is that they attempt to apply a standardized solution to situations that vary widely. The resulting landscapes are often inappropriate. Although they may serve the intended purpose, they may also create new conflicts because they cannot account for the variety of site variables. The alternative is to set quantitative visual quality standards. In place of specifying planting plans the municipality could mandate that the existing visual quality could not be altered by more than a specified range. This would allow the designer to determine the most appropriate and economical method to achieve the standard. The designer would have the freedom to use site characteristics, such as topography to develop creative solutions in order to mitigate the visual quality impact of the mine site. The municipality could be included in the process and have a better understanding of the constraints and tools that the designer used to reach the design solution. One concern of this approach is that visual quality may not be the primary concern of the municipality. If the objective of the community master plan is 38 economic development, then applying strict visual quality standards could be argued as being inappropriate since there is a predisposition in the equation to favor natural settings. This should not be interpreted as meaning that the equation would be irrelevant though. The visual quality equation could still be used as a design tool to mitigate the effects of the development. lt’s principles could be utilized to reduce the blighted appearance that many industrial zones now have. The effect could be one of the industrial campus that many firms are now promoting. Conclusion By quantifying visual quality both deSigners and regulators are now able to predict the visual impact that pit mining and various reclamation treatments will create. This ability to predict and systematically analyze the effect of proposed changes is important because it adds rational and objective decision making to a process that is currently highly subjective and emotional. The objective of this experiment is to determine whether the most common reclamation practices do in fact increase the visual quality of active mine sites. The results determine that two common reclamation treatments do not yield statistically significant different visual quality than that of the operating mine. Reclaiming for housing development and for agriculture both resulted in visual 39 quality levels that were not statistically different from the existing view. Reclamation that utilizes open water or natural revegetation do significantly increase the visual quality of the mine site however. Another important finding from this study was that some reclamation treatments yield identical visual quality results. Using heuristic methodology one could assume that all reclamation treatments are unique and, therefore would yield unique visual quality levels. This was shown to be not true. Using these reclamation treatments, both development for housing and reclamation for agriculture were found to have the same visual quality as the existing site. Also, open water and the natural revegetation treatments yielded similar results. The results of applying various reclamation treatments were surprisingly consistent. When a treatment was applied to a site, the resulting visual quality scores occurred within a small well defined range. This was also true when the same treatment was applied between sites. If this observation were confirmed in future testing, it could potentially lead to a model that could forecast the result of applying a specified treatment. This could negate the need for much of the testing that has been performed in this experiment and streamline the visual quality analysis procedure. The ability to identify and manipulate variables within the landscape is important in promoting visual quality. By exploiting the variables within the 4O equation that cause positive changes in visual quality and suppressing the variables that cause negative changes in visual quality, the equation can be manipulated as a design tool. During the design process this could be incorporated during the inventory and analysis phases in the mapping of positive and negative visual elements and then designing the proposed landscape to accentuate the positive and mask the negative elements. This paper demonstrates that visual quality procedures developed for use on public lands can be applied to local land uses through this application using open pit mining. The use of these procedures would benefit all participants in the mining process. The goal of the regulatory process is to ensure that mining will not have significant ill effects on the natural and cultural community surrounding it. This should also be one of the mining industries goals. This methodology is one step in ensuring that mining need not be a burden on a community and, in fact, could be used to improve the visual quality of the area. APPENDICES APPENDIX A Equation Variable Classification Variables having a negative effect on visual quality humans vehicles utility structures buildings pavement air and water pollution eroded land Variables having a positive effect on visual quality foreground vegetation distant non-vegetation wildlife openness presence of flowers Variables having a neutral effect on visual quality foreground herbaceous vegetation intermediate vegetation distant vegetation sky clouds sun moon water ice snow 41 APPENDIX B Condensed Testing Results Visual Quality Raw Scores for Sand and Gravel Site Existing Agricultural Housing Revegetation Water 36 1 71 73 74 45 47 SG 2 75 73 74 45 48 SG 3 69 71 74 45 49 SG 4 74 73 76 44 48 SG 5 .69 71 74 45 47 SG 6 73 73 75 45 47 SG 7 73 73 77 45 47 SG 8 73 73 76 45 48 SG 9 74 73 74 45 48 SG 10 77 77 80 49 53 SG 11 77 73 74 45 48 SG 12 73 73 74 45 47 SG 13 72 72 74 44 48 SG 14 73 73 75 45 48 SG 15 73 73 74 45 48 SG 16 75 73 75 45 49 SG 17 73 73 76 45 49 SG 18 73 73 75 45 46 SG 19 73 73 75 45 48 SG 20 74 72 73 44 48 SG 21 75 73 78 45 49 SG 22 77 77 80 49 52 SG 23 75 77 79 49 52 SG 24 79 77 81 49 52 SG 25 83 73 73 45 47 SG 26 77 73 75 45 46 SG 27 75 73 75 44 48 SG 28 75 73 76 45 48 SG 29 75 73 75 45 48 SG 30 79 73 76 45 48 42 43 Visual Quality Raw Scores for Hardrock Site Existing Agricultural Housing Revegetation Water HR 1 72 73 74 45 49 HR 2 68 72 73 43 47 HR 3 77 73 73 45 52 HR 4 80 78 79 50 53 HR 5 78 78 79 49 53 HR 6 82 77 77 50 54 HR 7 70 65 66 37 40 HR 8 69 7O 7O 4O 44 HR 9 73 73 7O 41 45 HR 10 65 74 72 42 45 HR 11 72 68 70 4O 48 HR 12 65 66 66 34 38 HR 13 65 61 63 35 36 HR 14 60 62 59 32 34 HR 15 57 60 58 28 31 HR 16 58 63 60 31 35 HR 17 61 58 59 31 33 HR 18 60 67 63 37 39 HR 19 54 71 68 41 44 HR 20 58 67 63 37 34 HR 21 60 64 61 34 36 HR 22 66 74 70 42 46 HR 23 65 73 73 44 48 HR 24 61 64 61 34 36 HR 25 51 61 58 31 27 HR 26 55 65 63 35 41 HR 27 71 71 73 43 47 HR 28 67 73 71 45 42 HR 29 65 7O 70 42 38 HR 30 58 62 59 32 33 APPENDIX C Graph of Visual Quality Scores Sand and Gravel Slte - Raw Scores '3 g 7 3 .2. z z ’3. 3 8‘: §§§:§ 2 Z ‘5 a > a) . o ' ' I . z I I : i I I : o I | n I ' I I I I , - 02m ,. osos ' H f-~ Ei «szos !. “m '. l' .. 1. «3205 3’ an» .‘ " up 1: “L208 ~,\ a“ o \‘ .- 3 “9205 H‘ 92.! ~ “9 «L .I «pugs I, 93.1 o, ' .’! -- 9:06 ,4, " ’7' '1 E !! "nos {1‘ 022.1 .- 1 11 1 5 “am R ”a” O '9 ‘9‘ 1 ‘, thDS 8 i "m" I. L- !! "0208 g I) “OZ". '1 .. l‘i «6105 g ‘1‘ “'1 1' a Q. +9131 4 q-IIDS ' .\ 1: "1.106 3 i! "U" 1' °' :71 f' "91.1 . «910s " a! £9 3 1" 1| “mos: a (31"“51 lg «nos 3 g «mu 1‘ 3'; v .3 "SIDS ii "fill-l Ii «mos j} 0ZlI-l .1 "”95 {I «III-l {{ notes if "0"" 1 '.1 8 -4m ll'“* Ii "cos ‘(1 "u" g "(.08 [El "LII-I 5 "9% r? "9* u .| +906 [5 "can !! IP’OS i‘ "’m I: :- !i "203 ‘\. «him 1, ‘.1 '. was 2; ""1 4.1:“. 195 ‘24 c“: nu 88288338 88238988 one. ones APPENDIX D Ranked Visual Quality Scores Ranked Visual Quality Scores at the Sand and Gravel Site Existing Agricultural Housing Revegetation Water SG 1 3.0 4.0 5.0 1.0 2.0 $6 2 5.0 3.0 4.0 1.0 2.0 SG 3 3.0 4.0 5.0 1.0 2.0 SG 4 4.0 3.0 5.0 1.0 2.0 $6 5 3.0 4.0 5.0 1.0 2.0 SG 6 3.5 3.5 5.0 1.0 2.0 $6 7 3.5 3.5 5.0 1.0 2.0 SG 8 3.5 3.5 5.0 1.0 2.0 $6 9 4.5 3.0 4.5 1.0 2.0 SG 10 3.5 3.5 5.0 1.0 2.0 SG 11 5.0 3.0 4.0 1.0 2.0 SG 12 3.5 3.5 5.0 1.0 2.0 SG 13 3.5 3.5 5.0 1.0 2.0 SG 14 3.5 3.5 5.0 1.0 2.0 86 15 3.5 3.5 5.0 1.0 2.0 SG 16 4.5 4.0 4.5 1.0 2.0 $6 17 3.5 3.5 5.0 1.0 2.0 $6 18 3.5 3.5 5.0 1.0 2.0 SG 19 3.5 3.5 5.0 1.0 2.0 SG 20 5.0 3.0 4.0 1.0 2.0 SG 21 4.0 3.0 5.0 1.0 2.0 SG 22 3.5 3.5 5.0 1.0 2.0 SG 23 3.0 4.0 5.0 1.0 2.0 $61 24 4.0 3.0 5.0 1.0 2.0 SG 25 5.0 3.5 3.5 1.0 2.0 SG 26 5.0 3.0 4.0 1.0 2.0 86 27 4.5 3.0 4.5 1.0 2.0 SG 28 4.0 3.0 5.0 1.0 2.0 SG 29 4.5 3.0 4.5 1.0 2.0 SG 30 5.0 3.0 4.0 1.0 2.0 45 46 Ranked Visual Quality Scores at the Hardrock Site Existing Agricultural Housing Revegetation Water HR 1 3.0 4.0 5.0 1.0 2.0 HR 2 3.0 4.0 5.0 1.0 2.0 HR 3 5.0 3.5 3.5 1.0 2.0 HR 4 5.0 3.0 4.0 1.0 2.0 HR 5 3.5 3.5 5.0 1.0 2.0 HR 6 5.0 3.5 3.5 1.0 2.0 HR 7 5.0 3.0 4.0 1.0 2.0 HR 8 3.0 4.5 4.5 1.0 2.0 HR 9 4.5 4.5 3.0 1.0 2.0 HR 10 3.0 5.0 4.0 1.0 2.0 HR 11 5.0 3.0 4.0 1.0 2.0 HR 12 3.0 4.5 4.5 1.0 2.0 HR 13 5.0 3.0 4.0 1.0 2.0 HR 14 4.0 5.0 3.0 1.0 2.0 HR 15 3.0 5.0 4.0 1.0 2.0 HR 16 3.0 5.0 4.0 1.0 2.0 HR 17 5.0 3.0 4.0 1.0 2.0 HR 18 3.0 4.5 4.0 1.0 2.0 HR 19 3.0 5.0 4.0 1.0 2.0 HR 20 3.0 5.0 4.0 2.0 1.0 HR 21 3.0 5.0 4.0 1.0 2.0 HR 22 3.0 5.0 4.0 1.0 2.0 HR 23 3.0 4.5 4.5 1.0 2.0 HR 24 3.5 5.0 3.5 1.0 2.0 HR 25 3.0 5.0 4.0 2.0 1.0 HR 26 3.0 5.0 4.0 1.0 2.0 HR 27 3.5 3.5 5.0 1.0 2.0 HR 28 3.0 5.0 4.0 2.0 1.0 HR 29 3.0 4.5 4.5 2.0 1.0 1.0 2.0 HR 30 3.0 4.0 5.0 SG1 Variable heahh X1 X2 X3 X4 X6 X7 X8 X9 X10 X14 X15 X16 X17 X19 X30 X31 X32 X34 X52 Score Existing -4 50 30 84 221 0000000000 419 351 68 71 APPENDIX E Agriculture -5 50 0 79 326 0000000000 484 426 58 73 47 Visual Quality Testing Results Housing -5 50 53 79 262 ooooooofioo 473 415 58 41 74 Natural Revegetation 10 50 0 79 326 0000000000 484 426 58 45 Open Water 50 1 9 79 307 0000000000 484 426 58 47 SG2 Variable heaflh X1 X2 X3 X4 X6 X7 X8 X9 X10 X14 X15 X16 X17 X19 X30 X31 X32 X34 X52 Score Existing -6 48 99 91 0 6000000000 281 195 86 75 Agriculture -5 48 0 91 328 0000000000 510 424 86 73 Housing -5 48 97 91 272 ooooooogoo 551 465 86 56 74 Natural Revegetation 10 48 0 91 328 OOOOOOOOOO 510 424 86 45 Open Water 48 94 91 140 (3000000000 416 330 86 48 SG3 Variable heahh X1 X2 X3 X4 X6 X7 X8 X9 X10 X14 X15 X16 X17 X19 X30 X31 X32 X34 X52 Score Existing -2 29 0 50 317 0000000000 417 375 42 69 Agriculture -4 54 0 50 317 0000000000 71 49 Housing -5 54 47 50 276 0000000200 423 431 41 74 Natural Revegetation 10 54 0 50 317 0000000000 45 Open Water 54 48 50 227 OOOOOOOOOO SG4 Variable heahh X1 X2 X3 X4 X6 X7 X8 X9 X10 X14 X15 X16 X17 X19 X30 X31 X32 X34 X52 Score Existing -6 94 83 93 78 0000000000 74 50 Agriculture Housing Natural Revegetation -5 -6 1 0 52 52 52 0 91 0 93 93 93 330 265 330 0 0 0 0 0 0 0 65 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 51 6 542 516 434 460 434 82 82 82 0 0 0 0 65 0 73 76 44 Open Water 52 82 93 175 0000000000 443 361 82 48 51 SG 5 Variable Existing Agriculture Housing Natural Open Revegetation Water health -3 -4 -5 1 0 9 X1 89 48 48 48 48 X2 36 0 37 0 34 X3 66 66 66 66 66 X4 186 180 143 180 161 X6 0 0 0 0 0 X7 0 0 0 0 0 X8 0 0 37 0 0 X9 0 0 0 0 0 X10 0 0 0 0 0 X14 0 0 0 0 0 X15 0 0 0 0 0 X16 0 0 0 0 0 X17 0 0 0 0 0 X19 0 0 0 0 0 X30 354 312 312 312 327 X31 400 276 276 276 291 X32 -46 36 36 36 36 X34 0 0 0 0 0 X52 0 0 37 0 0 Score 69 71 74 45 47 52 SG 6 Variable Existing Agriculture Housing Natural Open Revegetation Water health -5 -5 -5 1 0 9 X1 42 42 42 42 42 X2 90 0 73 0 28 X3 80 80 73 80 80 X4 146 241 207 241 218 X6 0 0 0 0 0 X7 0 0 0 0 0 X8 0 0 50 0 0 X9 0 0 0 0 0 X10 0 0 0 0 0 X14 0 0 0 0 0 X15 0 0 0 0 0 X16 0 0 0 0 0 X17 0 0 0 0 0 X19 0 0 0 0 0 X30 396 401 426 401 406 X31 320 325 364 325 330 X32 76 76 62 76 76 X34 0 0 0 0 0 X52 0 0 50 0 0 Score 73 73 75 45 47 SG7 Variable heahh X1 X2 X3 X4 X6 X7 X8 X9 X10 X14 X15 X16 X17 X19 X30 X31 X32 X34 X52 Score Existing -5 94 85 84 29 0000000000 282 302 -20 73 53 Agriculture Housing Natural Revegetation -5 -6 1 0 44 44 44 0 61 0 84 84 84 259 204 259 0 0 0 0 0 0 0 55 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 427 433 427 347 353 347 80 80 80 0 0 0 0 55 0 73 77 45 Open Water 44 83 84 1 37 0000000000 388 308 80 47 SGB Variable heahh X1 X2 X3 X4 X6 X7 X8 X9 X10 X14 X15 X16 X17 X19 X30 X31 X32 X34 X52 Score Existing -5 37 82 46 89 3003000 0000 263 245 18 73 Agriculture -5 47 0 46 535 30000:: 0000 Housing -5 47 49 46 431 0 0 104 0000300 572 574 104 76 Natural Revegetation 10 47 0 46 535 45 Open Water 47 93 46 388 :ooooo COCO 55 SG 9 Variable Existing Agriculture Housing Natural Open Revegetation Water health -6 -5 -5 10 8 X1 83 47 47 47 47 X2 90 0 74 0 97 X3 89 89 89 89 89 X4 50 399 344 399 230 X6 0 0 0 0 0 X7 0 0 0 0 0 X8 0 0 55 0 0 X9 0 0 0 0 0 X10 0 0 0 0 0 X14 0 0 0 0 0 X15 0 0 0 0 0 X16 0 0 0 0 0 X17 0 0 0 0 0 X19 0 0 0 0 0 X30 318 577 596 577 505 X31 306 493 512 493 421 X32 12 84 84 84 84 X34 0 0 0 0 0 X52 0 0 55 0 0 Score 74 73 74 45 48 SG 10 Variable heahh X1 X2 X3 X4 X6 X7 X8 X9 X10 X14 X15 X16 X17 X19 X30 X31 X32 X34 X52 Score Existing -5 0 86 56 210 0000000000 408 296 1 12 77 Agriculture -5 0 0 56 880 0000000000 992 880 1 12 77 56 Housing -5 0 105 56 793 ooooooo‘goo 1010 898 112 87 80 Natural Revegetation 0‘ —L 01°00 0 00000000008 992 880 1 12 49 Open Water 8 0 46 56 286 0000000000 444 332 1 12 53 SG11 Variable heanh X1 X2 X3 X4 X6 X7 X8 X9 X10 X14 X15 X16 X17 X19 X30 X31 X32 X34 X52 Score Existing -5 0 107 74 210 00000010000 465 317 148 77 Agriculture -5 50 0 74 568 0000000000 716 668 48 73 57 Housing -5 50 78 74 518 ooooooogoo 744 696 48 50 74 Natural Revegetation 10 50 0 74 568 0000000000 716 668 48 45 Open Water 50 78 74 296 0000000000 522 474 48 48 SG 12 Variable heanh X1 X2 X3 X4 X6 X7 X8 X9 X10 X14 X15 X16 X17 X19 X30 X31 X32 X34 X52 Score Existing -5 42 151 79 84 0000000000 393 319 74 73 Agriculture -5 42 0 79 385 0000000000 543 469 74 73 Housing -5 42 75 79 353 ooooooogoo 586 512 74 32 74 Natural Revegetation 10 42 0 79 385 0000000000 543 469 74 45 Open Water 42 85 79 279 0000000000 522 448 74 47 SG 13 Variable heahh X1 X2 X3 X4 X6 X7 X8 X9 X10 X14 X15 X16 X17 X19 X30 X31 X32 X34 X52 Score Existing -5 54 89 95 00000000000 279 197 82 72 59 Agriculture Housing Natural Revegetation -5 -5 1 0 54 54 54 0 59 0 95 95 95 269 199 267 0 0 0 0 0 0 0 38 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 459 448 457 377 366 375 82 82 82 0 0 0 0 38 0 72 74 44 Open Water ooooooooooggg‘gm 377 295 82 48 SG 14 Variable heanh X1 X2 X3 X4 X6 X7 X8 X9 X10 X14 X15 X16 X17 X19 X30 X31 X32 X34 X52 Score Existing -5 49 132 82 0 000000-‘000 73 Agriculture -5 49 0 82 365 0000000000 60 Housing -5 49 47 82 303 0000000300 514 448 66 62 75 Natural Revegetation 1 0 49 0 82 365 0000000000 529 463 66 45 Open Water 49 94 82 172 0000000000 430 364 66 48 SG 15 Variable heauh X1 X2 X3 X4 X6 X7 X8 X9 X10 X14 X15 X16 X17 X19 X30 X31 X32 X34 X52 Score Existing -5 47 91 90 00000000000 271 185 86 73 61 Agriculture Housing Natural Revegetation -5 -5 1 0 47 47 47 0 84 0 90 90 90 394 342 394 0 0 0 0 0 0 0 52 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 574 606 574 488 520 488 86 86 86 0 0 0 0 52 0 73 74 45 Open Water 47 93 90 220 0000000000 SG 16 Variable heahh X1 X2 X3 X4 X6 X7 X8 X9 X1 0 X14 X1 5 X16 X1 7 X1 9 X30 X31 X32 X34 X52 Score Existing -6 43 91 92 0000000\I000‘l 280 182 98 13 75 62 Agriculture Housing -5 -5 43 43 0 81 92 92 315 257 0 0 0 0 7 65 0 0 0 0 0 0 0 0 0 0 0 0 0 0 499 522 401 424 98 98 0 0 7 65 73 75 Natural Revegetation 10 43 0 92 315 0000000\100 499 401 98 45 Open Water 8 43 88 92 155 0000000V00 427 329 98 49 SG 17 Variable heahh X1 X2 X3 X4 X6 X7 X8 X9 X1 0 X14 X1 5 X1 6 X1 7 X1 9 X30 X31 X32 X34 X52 Score Existing -5 39 92 90 000000-*\l000 272 170 102 73 Agriculture -5 39 0 90 254 0000000\100 434 332 102 73 Housing -5 39 82 90 173 ooooooogoo 435 333 102 88 76 Natural Revegetation 10 39 0 90 254 0000000\I00 434 332 1 02 45 Open Water 39 79 90 121 0000000\100 380 278 1 02 49 SG 18 Variable Existing Agriculture Housing Natural Open Revegetation Water health -5 -5 -5 1 0 9 X1 91 91 91 91 91 X2 83 0 78 0 83 X3 81 81 81 81 81 X4 10 335 276 335 1 51 X6 0 0 0 0 0 X7 0 0 0 0 0 X8 7 7 67 7 7 X9 0 0 0 0 0 X10 0 0 0 0 0 X14 0 0 0 0 0 X15 0 0 0 0 0 X16 0 0 0 0 0 X17 0 0 0 0 0 X19 0 0 0 0 0 X30 255 497 516 497 396 X31 275 517 536 517 416 X32 -20 -20 -20 -20 -20 X34 0 0 0 0 0 X52 7 7 67 7 7 Score 73 73 75 45 46 SG 19 Variable heahh X1 X2 X3 X4 X6 X7 X8 X9 X10 X14 X15 X16 X17 X19 X30 X31 X32 X34 X52 Score Existing -5 47 95 81 000000-‘0000 257 189 68 73 65 Agriculture Housing Natural Revegetation -5 -5 10 47 47 47 0 91 0 81 81 81 391 328 391 0 0 0 0 0 0 0 63 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 553 581 553 485 513 485 68 68 68 0 0 0 0 63 0 73 75 45 Open Water 47 89 81 211 0000000000 462 SG 20 Variable heaflh X1 X2 X3 X4 X6 X7 X8 X9 X10 X14 X15 X16 X17 X19 X30 X31 X32 X34 X52 Score Existing -6 60 98 87 00000000000 272 218 54 74 Agriculture -5 60 0 87 242 0000000000 416 362 54 72 Housing -5 60 76 87 208 ooooooo‘fioo 458 404 54 34 73 Natural Revegetation 10 60 0 87 242 0000000000 416 362 54 44 Open Water 8 60 73 87 92 0000000000 339 285 54 48 SG 21 Variable heaflh X1 X2 X3 X4 X6 X7 X8 X9 X10 X14 X15 X16 X17 X19 X30 X31 X32 X34 X52 Score Existing -6 37 92 89 00000000000 270 166 104 75 Agriculture -5 40 0 89 568 0000000000 746 648 98 73 Housing -6 4O 1 17 89 481 ooooooofioo 776 678 98 87 78 Natural Revegetation 10 40 0 89 568 0000000000 746 648 98 45 Open Water 40 96 89 172 0000000000 446 348 98 49 SG 22 Variable heahh X1 X2 X3 X4 X6 X7 X8 X9 X1 0 X14 X1 5 X1 6 X1 7 X1 9 X30 X31 X32 X34 X52 Score Existing -5 0 167 82 361 0000000000 692 528 164 77 Agriculture -5 0 38 82 832 0000000000 1 034 870 1 64 77 68 Housing -5 0 98 82 734 ooooooogoo 996 832 164 98 80 Natural Revegetation 1 0 0 38 82 832 0000000000 1 034 870 1 64 49 Open Water 42 82 272 0000000000 478 314 164 52 SG 23 Variable heanh X1 X2 X3 X4 X6 X7 X8 X9 X10 X14 X15 X16 X17 X19 X30 X31 X32 X34 X52 Score Existing -4 0 47 84 375 0000000000 590 422 168 75 Agriculture -5 0 0 84 941 0000000000 1109 941 168 77 69 Housing -5 0 76 84 862 ooooooogoo 1 106 938 168 93 79 Natural Revegetation CO oooooooooocsmoo‘ _,,:> o 1109 941 168 49 Open Water 54 84 854 0000000000 1 076 908 1 68 52 SG 24 Variable health X1 X2 X3 X4 X6 X7 X8 X9 X10 X14 X15 X16 X17 X19 X30 X31 X32 X34 X52 Score Existing -6 0 89 81 46 0 000000\100 297 135 162 79 70 Agriculture Housing Natural Revegetation -5 -5 1 0 0 0 0 0 120 0 81 81 81 744 625 774 0 0 0 0 0 0 0 149 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 906 907 936 744 745 774 162 162 162 0 0 0 0 149 0 77 81 49 Open Water 55 81 319 0000000000 536 374 162 52 71 SG 25 Variable Existing Agriculture Housing Natural Open Revegetation Water health -6 -5 -5 1 0 8 X1 1 82 45 45 45 45 X2 86 0 80 0 93 X3 82 82 82 82 82 X4 43 251 193 251 123 X6 0 0 0 0 0 X7 0 0 0 0 0 X8 0 0 58 0 0 X9 0 0 0 0 O X10 0 0 0 0 0 X14 0 20 20 20 20 X15 0 0 0 0 0 X16 0 0 0 0 0 X17 0 0 0 0 0 X19 0 0 0 0 0 X30 293 415 437 415 380 X31 493 341 363 341 306 X32 -200 74 74 74 74 X34 0 0 0 0 0 X52 0 0 58 0 0 Score 83 73 73 45 47 SG 26 Variable health X1 X2 X3 X4 X6 X7 X8 X9 X1 0 X14 X15 X16 X17 X1 9 X30 X31 X32 X34 X52 Score Existing -5 0 70 87 1 12 0000000000 356 182 174 77 Agriculture -5 48 0 87 219 0000000000 393 315 78 73 72 Housing -5 48 66 58 175 ooooooogoo 357 337 20 66 75 Natural Revegetation 10 48 0 87 219 0000000000 393 315 78 45 Open Water 48 88 87 150 0000000000 412 334 78 46 SG 27 Variable heanh X1 X2 X3 X4 X6 X7 X8 X9 X10 X14 X15 X16 X17 X19 X30 X31 X32 X34 X52 Score Existing -4 0 91 67 165 0000000000 390 256 134 75 Agriculture -5 62 0 67 607 0000000000 741 731 10 73 73 Housing -5 62 48 67 380 Natural Revegetation 1 0 62 0 67 448 0000000000 582 572 .5 000 44 Open Water Nous-o: ooooooooooflflhmoo 255 245 10 48 74 SG 28 Variable Existing Agriculture Housing Natural Open Revegetation Water health -4 -5 -5 10 8 X1 0 48 48 48 48 X2 40 0 66 0 83 X3 86 86 53 86 86 X4 130 166 99 166 127 X6 0 0 0 0 0 X7 0 0 0 0 0 X8 0 0 93 0 0 X9 0 0 0 0 0 X10 0 0 0 0 0 X14 0 0 0 0 0 X15 0 0 0 0 0 X16 0 0 0 0 0 X17 0 0 0 0 0 X19 0 0 0 0 0 X30 342 338 271 338 382 X31 170 262 261 262 306 X32 172 76 1 0 76 76 X34 0 0 0 0 0 X52 0 0 93 0 0 Score 75 73 76 45 48 75 SG 29 Variable Existing Agriculture Housing Natural Open Revegetation Water health -6 -5 -5 10 8 X1 58 58 58 58 58 X2 95 0 73 0 95 X3 52 52 52 52 52 X4 107 463 391 463 156 X6 0 0 0 0 0 X7 0 0 0 0 0 X8 0 0 72 0 0 X9 2 0 0 0 0 X10 0 0 0 0 0 X14 0 0 0 0 0 X15 0 0 0 0 0 X16 0 0 0 0 0 X17 0 0 0 0 0 X19 0 0 0 0 0 X30 306 567 568 567 355 X31 318 579 580 579 367 X32 -12 -12 -12 -12 -12 X34 0 0 0 0 0 X52 2 0 72 0 0 Score 75 73 75 45 48 SG 30 Variable heahh X1 X2 X3 X4 X6 X7 X8 X9 X10 X14 X15 X16 X17 X19 X30 X31 X32 X34 X52 Score Existing -6 0 100 88 398 00000001000 674 498 176 79 Agriculture -5 47 0 88 620 0000000000 796 714 82 73 76 Housing -5 47 118 88 513 0 0 107 0000000 807 725 82 107 76 Natural Revegetation 10 47 0 88 620 0000000000 796 714 82 45 Open Water 47 97 88 235 0000000000 508 426 82 48 HR1 Variable heaflh X1 X2 X3 X4 X6 X7 X8 X9 X10 X14 X15 X16 X17 X19 X30 X31 X32 X34 X52 Score Existing -6 43 48 71 0 48 000000000 286 134 152 72 77 Agriculture Housing Natural Revegetation -5 -5 10 43 43 43 0 36 0 71 71 71 708 681 708 0 0 0 0 0 0 0 27 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 850 859 850 794 803 794 56 56 56 0 0 0 0 27 0 73 74 45 Open Water 43 52 71 708 0000000000 902 846 56 49 HR2 Variable heanh X1 X2 X3 X4 X6 X7 X8 X9 X10 X14 X1 5 X16 X1 7 X19 X30 X31 X32 X34 X52 Score Existing -4 57 62 67 0 45 000000000 306 196 110 68 78 Agriculture Housing Natural Revegetation -5 -5 10 57 57 57 0 35 0 67 67 67 550 520 550 14 14 14 0 0 0 0 30 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 712 717 712 664 669 664 48 48 48 0 0 0 0 30 0 72 73 43 Open Water 57 89 145 ooooooooo; 396 348 48 47 HRS Variable heahh X1 X2 X3 X4 X6 X7 X8 X9 X10 X14 X1 5 X1 6 X1 7 X1 9 X30 X31 X32 X34 X52 Score Existing Agriculture -5 -5 1 66 48 39 0 79 79 54 197 32 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 315 355 425 293 -1 10 62 0 0 0 0 77 73 Housing -5 48 21 79 197 0 0 1 1 0000000 376 314 62 11 73 Natural Revegetation 10 48 0 79 186 0000000000 344 282 62 45 Open Water 48 49 79 104 0000000000 31 1 249 62 52 HR4 Variable heanh X1 X2 X3 X4 X6 X7 X8 X9 X1 0 X14 X1 5 X1 6 X1 7 X19 X30 X31 X32 X34 X52 Score Existing -6 #OOQEOOOhOOOOOOOOHO 80 80 Agriculture Housing Natural Revegetation -5 -5 10 0 0 0 70 32 70 41 41 41 513 482 513 0 0 0 0 0 0 0 37 0 0 0 0 0 0 0 0 0 0 4 4 4 0 0 0 0 0 0 0 0 0 665 596 665 583 514 583 82 82 82 0 0 0 4 41 4 78 79 50 Open Water fifiooo coon-0000008 163 82 53 HRS Variable heahh X1 X2 X3 X4 X6 X7 X8 X9 X10 X14 X1 5 X16 X17 X19 X30 X31 X32 X34 X52 Score Existing -5 PP P P0006000P0000000000 78 Agriculture -5 0 64 58 499 000P000000 679 563 1 16 78 Housing -5 0 100 40 466 coo-nooogoo 646 566 80 39 79 Natural Revegetation 1 0 0 64 58 499 000P000000 679 563 1 16 49 Open Water ooo¢ooooooo$f3om 158 116 53 HR6 Variable heahh X1 X2 X3 X4 X6 X7 X8 X9 X1 0 X14 X1 5 X1 6 X17 X19 X30 X31 X32 X34 X52 Score Existing -6 0 43 86 ooooooaoooo 215 43 172 78 82 82 Agriculture Housing Natural Revegetation -5 -5 9 0 0 0 98 131 98 86 86 86 380 257 380 0 0 0 0 0 0 0 23 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 650 560 650 478 388 478 172 172 172 0 0 0 0 23 0 77 77 50 Open Water oooooooooooggow 215 172 54 HR7 Variable heanh X1 X2 X3 X4 X6 X7 X8 X9 X1 0 X14 X1 5 X1 6 X1 7 X1 9 X30 X31 X32 X34 X52 Score Existing -5 96 70 Agriculture -5 57 32 0 447 127 000000000 733 593 140 65 Housing -5 57 93 0 408 125 ooooooofio 751 615 136 41 66 Natural Revegetation 10 57 32 0 447 127 0 00000000 733 593 140 37 Open Water 57 92 83 127 000000000 429 289 140 40 HR8 Variable heaflh X1 X2 X3 X4 X6 X7 X8 X9 X10 X14 X15 X16 X17 X19 X30 X31 X32 X34 X52 Score Existing -5 78 Agriculture -6 17 0 0 663 113 000000000 889 697 192 70 84 Housing -5 17 73 0 615 105 ooooooogo 898 722 176 56 70 Natural Revegetation 10 ‘0) _L \l 000000000-‘000 (DOD 889 697 1 92 40 Open Water a—L U‘I—lm ‘0‘] 0000000006300 285 192 44 HR9 Variable heahh X1 X2 X3 X4 X6 X7 X8 X9 X10 X14 X15 X16 X17 X19 X30 X31 X32 X34 X52 Score Existing Agriculture -5 -7 0 43 93 92 0 0 0 248 71 71 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 235 482 93 426 142 56 0 0 0 0 73 73 Housing -5 43 127 0 197 71 0000000830 466 410 56 33 70 Natural Revegetation 10 43 92 0 228 oooooooooj 462 406 56 41 Open Water ooooooooojooggm 234 178 56 45 HR 10 Variable heaflh X1 X2 X3 X4 X6 X7 X8 X9 X10 X14 X15 X16 X17 X19 X30 X31 X32 X34 X52 Score Existing -5 0 71 0 0 208 000000500 487 416 12 65 Agriculture -7 0 41 86 553 00000000091 960 594 366 74 86 Housing -5 0 74 103 509 97 000000030 983 583 400 62 72 Natural Revegetation 10 0 41 86 553 97 000000000 960 594 366 42 Open Water ooooooooo‘gogfiom 408 366 45 HR11 Variable heaflh X1 X2 X3 X4 X6 X7 X8 X9 X10 X14 X15 X16 X17 X19 X30 X31 X32 X34 X52 Score Existing -5 \I 0) 000000000010000 72 Agriculture -5 50 109 0 151 cacoooocooocac‘,‘1 410 360 50 68 Housing -5 52 128 0 44 75 ooooooogo 322 276 46 59 70 Natural Revegetation 10 50 109 0 151 6500000000,} 410 360 50 40 Open Water oooooooooaoogom 88 HR 12 Variable Existing Agriculture Housing Natural Open Revegetation Water health -5 -7 -5 1 0 8 X1 0 53 53 53 53 X2 93 0 86 0 96 X3 1 5 15 1 5 1 5 15 X4 0 51 1 434 511 0 X6 201 164 156 164 164 X7 0 0 0 0 0 X8 0 0 85 0 0 X9 0 0 0 0 0 X10 0 0 0 0 0 X14 0 0 0 0 0 X15 0 0 0 0 0 X16 0 0 0 0 0 X17 0 0 0 0 0 X19 0 0 0 0 0 X30 525 869 862 869 454 X31 93 617 626 617 202 X32 432 252 236 252 252 X34 0 0 0 0 0 X52 0 0 85 0 0 Score 65 66 66 34 38 HR 13 Variable heahh X1 X2 X3 X4 X6 X7 X8 X9 X1 0 X14 X1 5 X1 6 X1 7 X1 9 X30 X31 X32 X34 X52 Score Existing -5 88 Agriculture -5 51 0 0 279 200 000000000 679 381 298 61 89 Housing -5 51 72 0 227 183 ooooooogo 665 401 264 52 63 Natural Revegetation 9 51 0 0 279 200 000000000 679 381 298 35 Open Water 10 0 oooooooooooogflm 490 192 298 36 HR 14 Variable health X1 X2 X3 X4 X6 X7 X8 X9 X10 X14 X15 X16 X17 X19 X30 X31 X32 X34 X52 Score Existing -5 121 101 0 0 250 000000000 601 343 258 60 Agriculture -7 53 96 0 385 250 000000000 981 587 394 62 90 Housing -5 53 57 0 355 250 000000080 912 518 394 30 59 Natural Revegetation 9 53 0 0 385 250 000000000 885 491 394 32 Open Water N com oooooooooconoomwoo 599 205 394 34 HR15 Variable heahh X1 X2 X3 X4 X6 X7 X8 X9 X10 X14 X15 X16 X17 X19 X30 X31 X32 X34 X52 Score Existing -5 42 98 0 0 298 000000000 694 182 512 57 Agriculture -7 50 96 0 520 298 000000000 1212 716 496 60 91 Housing -5 50 193 0 481 298 ooooooogo 1270 774 496 39 58 Natural Revegetation 10 50 96 0 520 298 000000000 1212 716 496 28 Open Water 50 90 208 313 000000000 924 398 526 31 HR16 Variable heahh X1 X2 X3 X4 X6 X7 X8 X9 X10 X14 X15 X16 X17 X19 X30 X31 X32 X34 X52 Score Existing -5 45 93 0 0 257 000000000 607 1 83 424 58 Agriculture -7 31 0 0 524 257 000000000 1038 586 452 63 Housing -5 31 89 0 495 257 000000080 1 098 646 452 29 60 Natural Revegetation 10 31 0 0 524 257 000000000 1038 586 452 31 Open Water 62m N 000000000200 599 147 452 35 HR 17 Variable heanh X1 X2 X3 X4 X6 X7 X8 X9 X10 X14 X15 X16 X17 X19 X30 X31 X32 X34 X52 Score Existing -5 0 86 0 0 280 000000000 646 86 560 61 Agriculture -5 42 0 0 696 280 000000000 1256 780 476 58 93 Housing -5 42 95 0 636 278 ooooooogo 1287 815 472 60 59 Natural Revegetation 9 42 0 0 696 280 000000000 1256 780 476 31 Open Water 10 0 ooooooooomoogfim 656 180 476 33 HR 18 Variable heaflh X1 X2 X3 X4 X6 X7 X8 X9 X10 X14 X15 X16 X17 X19 X30 X31 X32 X34 X52 Score Existing -5 69 0 0 0 218 000000000 436 138 298 60 Agriculture -7 69 0 31 0 149 000000000 360 138 222 67 94 Housing -5 69 0 43 0 149 0 10 0000000 384 138 246 10 63 Natural Revegetation 9 69 0 31 0 149 000000000 360 1 38 222 37 Open Water 0 com «.5 0000000003000 298 138 160 39 95 HR 19 Variable Existing Agriculture Housing Natural Open Revegetation Water health -5 -7 -5 9 8 X1 0 0 0 0 0 X2 0 0 0 0 0 X3 0 77 169 77 23 X4 0 0 0 0 0 X6 694 134 134 134 134 X7 0 0 0 0 0 X8 0 0 32 0 0 X9 0 0 0 0 0 X10 0 0 0 0 0 X14 0 0 0 0 0 X15 0 0 0 0 0 X16 0 0 0 0 0 X17 0 0 0 0 0 X19 0 0 0 0 0 X30 1 388 422 606 422 314 X31 0 0 0 0 0 X32 1 388 422 606 422 314 X34 0 0 0 0 0 X52 0 0 32 0 0 Score 54 71 68 41 44 HR 20 Variable heahh X1 X2 X3 X4 X6 X7 X8 X9 X10 X14 X15 X16 X17 X19 X30 X31 X32 X34 X52 Score Existing -5 50 0 0 0 258 000000000 516 100 416 58 Agriculture -7 50 0 57 0 152 0 00000000 418 100 318 67 96 Housing -5 50 0 77 0 152 0 1 1 0000000 458 100 358 11 63 Natural Revegetation 9 50 0 57 0 152 000000000 418 100 318 37 Open Water 01 000 IO 00 00000000001000 516 100 416 34 HR 21 Variable heahh X1 X2 X3 X4 X6 X7 X8 X9 X10 X14 X15 X16 X17 X19 X30 X31 X32 X34 X52 Score Existing -5 76 118 0 159 207 000000000 691 429 262 60 Agriculture -7 76 0 88 410 1 86 000000000 958 562 396 64 97 Housing -5 76 88 88 378 186 0000009930 1014 618 396 32 61 Natural Revegetation 9 76 32 88 410 186 000000000 990 594 396 34 Open Water 76 124 88 186 000000000 672 276 396 36 HR 22 Variable health X1 X2 X3 X4 X6 X7 X8 X9 X10 X14 X15 X16 X17 X19 X30 X31 X32 X34 X52 Score Existing -5 52 87 0 0 1 1 1 000000000 309 191 118 66 98 Agriculture Housing Natural Revegetation -7 -5 10 49 49 49 0 46 0 0 0 0 255 239 285 50 50 50 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 355 385 385 353 383 383 2 2 2 0 0 0 0 1 1 0 74 70 42 Open Water 01 (DP 0000300 ‘03 —L—L 00N00000000000 .h 0) HR 23 Variable heahh X1 X2 X3 X4 X6 X7 X8 X9 X10 X14 X15 X16 X17 X19 X30 X31 X32 X34 X52 Score Existing -5 51 98 0 217 134 000000000 583 417 166 65 Agriculture -5 50 0 92 162 0000000000 346 262 84 73 99 Housing -5 50 74 94 146 ooooooo§oo 408 320 22 73 Natural Revegetation 10 50 0 92 162 0000000000 346 262 84 44 Open Water cocoa: mono“ 00000000000 282 198 84 48 HR 24 Variable heaflh X1 X2 X3 X4 X6 X7 X8 X9 X10 X14 X15 X16 X17 X19 X30 X31 X32 X34 X52 Score Existing -5 58 93 0 0 200 000000000 493 209 284 61 100 Agriculture Housing Natural Revegetation -7 -5 9 58 58 58 0 82 38 0 0 0 422 389 422 200 200 200 0 0 0 0 22 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 822 871 860 538 587 576 284 284 284 0 0 0 0 22 0 64 61 34 Open Water 58 106 200 00 0000000 506 222 284 36 HR 25 Variable heanh X1 X2 X3 X4 X6 X7 X8 X9 X10 X14 X15 X16 X17 X19 X30 X31 X32 X34 X52 Score Existing -5 50 0 0 0 470 000000000 940 100 840 51 Agriculture -7 50 0 1 36 0 240 000000000 752 100 652 61 101 Housing -5 50 0 171 0 240 0 19 0000000 822 1 00 722 19 58 Natural Revegetation 9 50 0 1 36 0 240 000000000 752 1 00 652 31 Open Water 0'1 coo "‘ a) ooooooooogoéo 978 100 878 27 HR 26 Variable heanh X1 X2 X3 X4 X6 X7 X8 X9 X1 0 X14 X1 5 X1 6 X1 7 X1 9 X30 X31 X32 X34 X52 Score Existing -5 0" APO cacaoooc3c3c>c>$o\1mm 743 227 516 55 Agriculture -7 40 0 17 178 194 000000000 600 258 342 65 102 Housing -5 40 63 17 134 194 000000030 61 9 277 342 44 63 Natural Revegetation 9 40 0 17 178 194 000000000 600 258 342 35 Open Water —L cgojfiooo 000000000 463 422 41 103 HR 27 Variable Existing Agriculture Housing Natural Open Revegetation Water health -5 -5 -5 1 0 8 X1 51 51 51 51 51 X2 86 82 150 82 93 X3 0 0 0 0 0 X4 1 2 373 327 373 177 X6 36 36 36 36 36 X7 0 0 0 0 0 X8 0 0 52 0 0 X9 0 0 0 0 0 X10 0 0 0 0 0 X14 0 0 0 0 0 X15 0 0 0 0 0 X16 0 0 0 0 0 X17 0 0 0 0 0 X19 0 0 0 0 0 X30 170 527 549 527 342 X31 200 557 579 557 372 X32 -30 -30 -30 -30 -30 X34 0 0 0 0 0 X52 0 0 52 0 0 Score 71 71 73 43 47 HR 28 Variable health X1 X2 X3 X4 X6 X7 X8 X9 X10 X14 X15 X16 X17 X19 X30 X31 X32 X34 X52 Score Existing -5 54 67 Agriculture -5 0 61 77 317 ooooooooog 644 378 266 73 104 Housing -5 0 61 120 291 94 0 32 0000000 780 352 428 32 71 Natural Revegetation 10 0 61 77 317 56 000000000 644 378 266 45 Open Water u—L g‘ooo 000000000300 400 54 346 42 HR 29 Variable heaflh X1 X2 X3 X4 X6 X7 X8 X9 X1 0 X14 X15 X16 X17 X19 X30 X31 X32 X34 X52 Score Existing ‘ -5 38 82 88 19 128 000000000 533 177 356 65 Agriculture -5 38 90 83 308 ooooooooog 644 474 1 70 70 105 Housing -5 38 126 1 13 284 61 000000030 758 486 272 40 70 Natural Revegetation 10 38 90 83 308 40 000000000 644 474 1 70 42 Open Water 38 82 83 164 000000000 576 158 418 38 HR 30 Variable heauh X1 X2 X3 X4 X6 X7 X8 X9 X10 X14 X15 X16 X17 X19 X30 X31 X32 X34 X52 Score Existing -5 77 96 0 0 260 000000000 616 250 366 58 Agriculture -7 55 88 o 542 260 000000000 1150 740 410 62 106 Housing -5 55 164 0 508 260 0000000320 1192 782 410 34 59 Natural Revegetation 9 55 88 O 542 260 000000000 1150 740 410 32 Open Water 55 99 138 260 000000000 757 347 410 33 BIBLIOGRAPHY BIBLIOGRAPHY Adobe Systems Inc. W9. California. 1984. Appleton, Jay. “Prospects and Refuges Re-Visited” Leneseaee Jeemel 3 (Fall, 1984): 91-103 Arthur, Louise M. “Predicting Scenic Beauty of Forest Environments: Some Empirical Tests.”_Eer_e_s_t_$_Qiene_e 23 (June, 1977): 151-160 Bourssa, Steven C. The Aesthetics ef Leneeeepe. London: Belhaven Press, 1991. Brown, Terry. “Conceptualizing Smoothness and Density as landscape Elements in Visual Resource Management.” Leneseeeeegm Planning 30 (October, 1994): 49-58. Brown, Thomas C. and Daniel, Terry C. “Landscape Aesthetics of Riparian Environments: Relationship of Flow Quantity to Scenic Quality Along a Wild and Scenic River" WM 27 (August, 1991): 1 787-1 874 Burley, Jon Bryan. “A Visual and Ecological Environmental Quality Model for Transportation Planning and Design” unpublished 1995 Burley, Jon Bryan and Brown, Terry. “Visual Quality / Aesthetics Modeling for Reclamation / Landscape Disturbance Applications” Preeeeding of the 'II nn --.|N :: I0.- h:-II: I 1| 0 3\ fr 1 =IlII0 m (June, 1992): 519-531. Carlson, A. A. “On the Possibility of Quantifying Scenic Beauty” Leneeeepe Plenm‘ng 4 (June, 1977): 131-172 Daniel, Terry C. and Vinning, Joanne. “Methodological Issues in the Assessment of Landscape Quality.” in W EMLQDLDQDI. eds. lAltman and Wohlhill. New York: Plenum, 1983. 107 108 Daniel, Wayne W. Apelied Nonparametric Statistics. Boston: Houghton Mifflin, 1978. Dietrich, Norman L. “Visual Landscape Analysis of Rural Iowa Limestone Quarries,” American ocie for urface Minin and R clamation (March, 1986): 1-7. Hamilton, John W., Buhyoff, Gregory J. and Wellman J. Douglas. “The Derivation of Scenic Utility Functions and Surfaces and Their Role in Landscape Management.” In ur National andsca 'A onf r n A liedTechniuesfrA ' Mn m n fh V' Reeoeree Washington D. C.: U. S. Government Printing Office (September, 1979): 271-278. Hayes, Lew. Telephone Interview. October, 1995. Heinrich, William A. The in r I of Mi h' n. Lansing: State of Michigan, 1976 Hull, Bruce, Buhyoff, G. J. and Cordell, Ken. “Psychophysical Models: An Example with Scenic Beauty Perceptions of Roadside Pine Forests” W 6 (Fall, 1987): 113-122 Kaplan, Rachel. “Visual Resources and the Public: An Empirical Approach.” ur National Land a ° A onference on A lie Techni In Anelyeis ene Menagement ef the Vieual Reeeeree Washington D. C.: U. S. Government Printing Office (September, 1979): 209-216. Kaplan, Rachel, Kaplan, Stephen and Brown, Terry. “Environmental Preference A Comparison of Four Domains of Predictors.“ Enyimnment W 21 (September, 1989): 509-531. Landphair, Harlow C. “Texas Lignite and the Visual Resource: An Objective Approach to Visual Resource Evaluation and Management In M tional Lands a e A onfer no on A lie Te hni i ene Manegement ef the Vieuel Reeeetee Washington D. C.: U. S. Government Printing Office (September, 1979): 312-322. Latimer, Douglas A., Hogo, Henry and Daniel, Terry C. “The Effects of Atmospheric Optical Conditions on Perceived Scenic Beauty,” MW 150981): 1865- 1874- Leopold, Robert E. “Planning Design and Management of Visual Resources,” Iifo i Iivi i - LII : S “It! III ‘0 «n. E: -_II,- i Mensa. (1982): 91-97. 109 Michigan Limestone Corp., Calcite Plant. Promotional Material. 1987. Miller, Patrick A. “A Comparative Study of the BLM Scenic Quality Rating Procedure and Landscape Preference Dimensions” Landecape Joemel 3 (Fall, 1984): 123-134 Ministry Of Forests. Forest Lendscaee Handbeok. Victoria, Canada: lnforrnation Services Branch, Ministry of Forests, 1981. Orland, Brian. “Visualization Techniques for Incorporation in Forest Planning Geographic lnforrnation Systems.” W Planning 30 (October, 1994): 83-97. Palmer, James F. Th P rce tion of L n s Visu I all Envirenmentel Erofeesionals and Leeel Qitizene. Publisher unknown, 1984. Ruddell, Edward J., Gramann, James H., Rudis, Victor A. and Westphal, Joanne M. "The Psychological Utility of Visual Penetration in Near- View Forest Scenic-Beauty Models.” W 21 (July, 1989): 392-412 Schaefer, John P. Baeie Teehnigeee ef Photegreehy. Boston: Little, Brown and Company, 1992. Shafer, Elwood L. “Perception of Natural Environments” WW Bebexi_0_r1 (June, 1969): 71-82 Stamps Ill, Arthur E. ”Perceptual and Preferential Effects of Photomontage Simulations of Environments.“ WM 74 (June, 1992): 675-688 USDA Forest Service, U. S. Department of Agriculture. W Wad Washington D- C: U- 5- Govemment Printing Office, 1973. USDA Forest Service, U. S. Department of Agriculture. Netienel Fereet W Washington D. C.: U. S. Government Printing Office, 1977. U. S. Department of the Interior, Bureau of Mines. Minetel lndestty Semeye, W Washington DC: US Government Printing Office, 1995. 110 Watzek, Kurt A. and Ellsworth John C. “Perceived Scale Accuracy of Computer Visual Simulations,” Landscaee Journal 1 (Spring, 1994): 21-36 Weinstein, Neil David. “The Statistical Prediction of Environmental Preferences, Problems of Validity and Application” Envirenment and Behavier 8 (December, 1976): 611-625 Wyckoff, Mark A. Min r I ra ti n Me s Plannin onin . Presentation at Michigan State University, 1995 Zube, Ervin H. “Themes in Landscape Assessment Theory” Lemdseeee Journal 3 (Fall, 1984): 105-109 GENERAL REFERENCES GENERAL REFERENCES Angelo, Mark. “The Use of Computer Graphics in the Visual Analysis of the Proposed Sunshine Ski Area Expansion.” In Our National Landscepe; A onference on A lied Techni f rAnal si n na em of the Vieual Reeeeree Washington D. C.: U. S. Government Printing Office (September, 1979): 439-446. Arthur, Louise M. and Boster, Ron S. Meaeuring Seenie Beauty; A Seleeted Annetated Bipliegrephy. USDA Forest Service, 1976. Bacon, Warren R. “The Visual Management System of the Forest Service, USDA.” In Qer Natienel Landscape; A Qonferenee on Applied T hni r s Mna m ft i alRe r Washington D. C.: U. S. Government Printing Office (September, 1979): 660-665. Betchel, Robert B., Marans, Robert W. Michelson, William. Metneeeip Envimnmeptel end Behevier Reeeetch. New York: Van Nostrand Reinhold Co., 1987. Berleant, Arnold. IheAesthetjeeeLLengeeepe. Philadelphia: Temple Press, 1992. Bishop, Ian D. and Hulse, David W., “Prediction of Scenic Beauty Using Mapped Data and Geographic lnforrnation Systems,” W9. Weeping 30 (October, 1994): 59-69 Buhyoff, Gregory J., Wellman, Douglas J. and Daniel, Terry C. Predicting Scenic Quality for Mountain Pine Beetle and Western Spruce Budworrn Damaged Forest Vistas,” Eoiestfiejepee 28 (December, 1982): 827-838 Chenoweth, Richard. “Visitor Employed Photography: A Potential Tool for Landscape Architecture” Lendseapfleymej 3 (Fall, 1984): 137-143 Crawford, Doug. “Using Remotely Sensed Data in Landscape Visual Quality Assessment.” WPlanning 30 (October, 1994): 71-81. 111 112 Cutler, M. Rupert. “Resource Policy and Esthetics: The Legal Landscape” In Our National Landscape; A Conference on Applied Technigpee fer Analyeie and Menagement of the Visuel Resouree Washington D. C.: U. S. Government Printing Office (September, 1979): 12-15. Earickson, Robert J. and Harlin, John M. Geegrephie Measurement and Qeantitetive Anelysis. New York: Macmillian, 1994. Elsner, Gary H. “Computers and the Landscape.” In Our Netienal Lendeeepe; onf r nce on a lied T chni n I i an Mana em n the Viepal Reeepree Washington D. C.: U. S. Government Printing Office (September, 1979): 88-92. Fitzgerald, Randall Boyd. “Visual Analysis as a Design and Decision- Making Tool in the Development of a Quarry.” In Our Nationel n sca e' A nference on A lied Techni u s f r A I si nd Menagement 91 the Vieeal Reeouree Washington D. C.: U. S. Government Printing Office (September, 1979): 335-339. Gree. Dee F. ed.. W Washington: USDA Forest Service, 1975. Hatfield, Michael A, Balzer, J. LeRoy and Nelson, Roger E. “Computer-Aided Visual Assessment In Mine Planning and Design. ” In Our Natiehel a A nfrenc onA lidT hniu r W Washington 0- C-= U- 8- Govemment Printing Office (September, 1979): 323-327. Hudsoath. Thomas 8. WWW Watefimhte unpublished. Kaplan, Stephen. “Perception and Landscape: Conceptions and Misconceptions.” In Our Natipnal Landeeape; A Qonferenee eh lie T i u for Anal i Man h Vi l Bespytce Washington D. C.: U. S. Government Printing Office (September, 1979): 241-248. Kaplan, Stephen and Kaplan, Rachel. Wm W New York Praogor 1982 Kaplan, Stephen and Kaplan, Rachel. MW fleepje. California: Wadsworth, 1978. 113 Kent, Richard L. “Determining Scenic Quality Along Highways: A Cognitive Approach.” Landeeepe end Urben Plenning 27 (November, 1993): 29-45. Land Reelamation: A Repdrt on Research into Problems ef Reelaiming Derelict Lend England: IPC Business Press. Leopold, Robert, Rowland, Bruce and Stadler, Reed. “Surface Mining.” In Our National Landscape; A Conference on Applied Techniddee 19!: Anelyeie end Management of the Vieual Resodree Washington D. C.: U. S. Government Printing Office (September, 1979): 20-24. Litton, R. Burton. “Descriptive Approaches to landscape Analysis.” In QuL N ional Landsca e' A onference on A lied Techni ue r Anelysis ehd Mehegemeht of the Visdal Reeodree Washington D. C.: U. S. Government Printing Office (September, 1979): 77-87. Lowenthal, David. ed. nvir m n IP r ti n an v' r. Chicago: Department of Geography The University of Chicago, 1967. Mehrabin, Albert. W New York: Basic Books Inc., 1976. Paulson, M. J. and Scott, Robert D. “Visualization of Change from Mining and Land Disturbance Computer-aided Photographic Simulations, Site Selection, Reclamation, Impact Assessment” 19th Natienel Meeting 9! WWW (Spring. 1993): 642- 649. Perlman, Michael. W. Dallas: Spring Publications, 1994. Rowe, Robert D. and Lauraine G. Chestnut, ed. MehegthtLQdeJMhd =I is 0 - kt 0| -II =I- I ssIIs - - Colorado: Westview Press, 1983. Ross, Robert W. “The Bureau of Land Management and Visual Resource Management-An Overview.” In Our Netionel Landecepe; A anfeLenee Ali Tchni r l' nM em tof ' I Beeggce Washington D. C.: U. S. Government Printing Office (September, 1979): 666-670. Schauman, Sally. “Scenic Value of Countryside Landscapes to Local Residents: A Whatcom County, Washington Case Study,” Leadseepe deemel 7 (Spring, 1988): 40-46 114 Schauman, Sally. “The Countryside Visual Resource.” In Our Natiohel L d ca 'A onference on A lied Techni for An I 3' Management pf the Visual Resource Washington D. C.: U. S. Government Printing Office (September, 1979): 48-54. Schauman, Sally and Adams, Carolyn. “ Soil Conservation Service Landscape Resource Management.” In er National Landscape; A ference on A lied Techni f r I is nd Mana em n of the Viedal Beeeuree Washington D. C.: U. 8. Government Printing Office (September, 1979): 671-673. Shafer, Elwood L, Hamilton, John F. and Schmidt, Elizabeth A. “Natural Landscape Preferences: A Predictive Model,” deumal pf Leiedre fleseehch 1 (Winter, 1969): 1-19 Shafer, Elwood L., and Tooby, Michael. Landscape Preferences: An lntemational Replication.” deumel dt Leiedre Reeeemh 5 (Summer, 1973): 60-65 Sheppard, Stephen R. J. “Predictive Landscape Portrayals: A Selective Research Review,” Leadeeemdemet 1 (Spring, 1982): 9-13 Stamps III, Arthur E. "Pre- and Postconstruction Environmental Evaluations.” Pereeptdel end Motor Skills 75 (October, 1992): 481-482 Stevenson, A. E., Conley, J. A. and Carey, J. B. “A Computerized System for Portrayal of Landscape Alterations.” In Our Natienel Landeeepe; A nf renc nA lie Techni l ' M n the Vieuel Reeedtee Washington D. C.: U. S. Government Printing Office (September, 1979): 151-156. Stilgoe, John R. “Popular Photography, Scenery Values, and Visual Assessment” Lendeeepeieemet 3 (Fall, 1984): 111-121 Sedeee Mining of Nee-Qeel Minerele. Washington: The National Academy of Sciences, 1980. USDA Forest Service, U. S. Department of Agriculture. Netjehejfepefi W Washington D- 0.: U- 8- Govemment Printing Office, 1974. USDA Forest Service, U. S. Department of Agriculture. MMEQLQSL LaadsaangaaagamsatflMnfl Washington D- C-2 U- 8- Govemment Printing Office, 1973. 115 U. S. Department of the Interior, Bureau of Land Management. Visdel Simdletidn Teehnigdee Washington DC: US Government Printing Office U. S. Department of the Interior, Bureau of Land Management,. Visual R§§QLILQ§ Menegement Progrem Washington DC: US Government Printing Office, 1980. U. S. Department of the Interior, Bureau of Mines. Mineral lhddethi Semeye, Qtdehed Stene Washington DC: US Government Printing Office, 1993. U. S. Department of the Interior, Bureau of Mines. Minerel Indesthi Survexe, W Washington DC: US Government Printing Office, 1993. U. S. Department of the Interior, Bureau of Mines. Mmeflhddethifiuhieyep W Washington DC: US Government Printing Office, 1994. U. S. Department of the Interior, Bureau of Mines. W Washington DC: US Government Printing Office, 1990. Werth, Joel T. “Sand and Gravel Resources: Protection, Regulation, and Reclamation” W American Planning Association, 1980 Wicker. Allan A. WWW California: Brooks/Cole Publishing, 1979 Zube, Ervin H. “Cross-Disciplinary and lnterrnode Agreement on the Description and Evaluation of Landscape Resources” Engagement mfleheflet 6 (March, 1974): 69-89 Zube, Ervin H. W California: Brooks/Cole Publishing Company, 1980. Zube, Ervin H., Brush, RobertO. and Fabos, Julius Gy. Lendeeepe WWW Pennsylvania: Halstead Press, 1975. Zunn, Leo E. “Landscape Depiction and Perception: A Transactional Approach” Lendeeepededmei 3 (Fall, 1984): 144-145