Review of Existing Methods of Landscape Assessment and Evaluation
Landscape should be recognised as a resource and is therefore a variable to be considered in land use decisions (Dearden, 1985). When evaluating landscapes one should use an interdisciplinary approach, communicate with other evaluators and, importantly, recognise the academic respectability of the elementary (Appleton, 1975).
"Just as the Brisbane wicket after rain used to be said to reduce all batsmen to an equal level of incompetence, so this absence of aesthetic theory brings the professional down to the same plane as the man in the street. It is true that the theory underlying the judging of a fine wine or a good piece of sculpture is probably as obscure as that which underlies the evaluation of landscape. In those arts, however, we still have some faith - possibly misplaced - in the ability of the expert to recognise excellence, however defined (Appleton, 1975)."
A structured method of landscape assessment, linking description, classification, analysis and evaluation, will provide an integrated framework within which decisions on land use management and advice can be debated (Cooper and Murray, 1992). One of the biggest problems in developing quantitative assessment methods for scenic impacts is that of measuring the contributions of specific landscape elements to overall preference (Buhyoff and Riesenmann, 1979).
During the late 1960's through to the '70s, there was an emphasis in landscape assessment to produce 'objective' and quantitative methods of attaching a numerical value for the 'subjective' responses to aesthetic or scenic quality. These methods were developed to act as evaluative tools to enable an evaluation to be repeated by different observers, or carried out in different areas and still produce comparable results (Robinson et al., 1976) that is they were expected to give reliable and consistent information about the observers' responses to landscape quality.
Unwin (1975) describes three phases of landscape evaluation.
Definitions in landscape evaluation
Before the subject of landscape evaluation can be reviewed, it is necessary to define several key words. In the course of this report the term "total landscape" refers to the less tangible properties of the landscape as well as the more obvious visual properties and should not be confused with "landscape" which refers to the visual properties only. Unfortunately, this differentiation is not always apparent in the literature.
Hull and Revell (1989) define landscape and scenes as:
"The outdoor environment, natural or built, which can be directly perceived by a person visiting and using that environment. A scene is the subset of a landscape which is viewed from one location (vantage point) looking in one direction ..."
The term landscape clearly focuses upon the visual properties or characteristics of the environment, these include natural and man-made elements and physical and biological resources which could be identified visually; thus non-visual biological functions, cultural/historical values, wildlife and endangered species, wilderness value, opportunities for recreation activities and a large array of tastes, smells and feelings are not included (Daniel and Vining, 1983; Amir and Gidalizon, 1990).
Often landscape quality is defined as including a wide range of environmental/ecological, socio-cultural and psychological factors. According to Jacques (1980) the distinction between 'value' and 'quality' is meaningless, since both terms refer to the comparison of the landscape in front of your eyes to an idealised landscape in one's mind.
Visual impact on landscape quality is concerned with physical changes introduced to a site by a new development activity (Amir and Gidalizon, 1990).
Visual quality - a phrase synonymous with beauty, but intended to convey an impression of objectivity; landscape evaluation - ascertaining of a single, often numerical, measure of visual quality, more appropriately would be " landscape quality survey"; judgement - the presumed ability by the design professions to evaluate 'visual quality', as distinct from value (Jacques, 1980).
Landscape value - a personal and subjective assessment of aesthetic satisfaction derived from a landscape type; landscape appraisal - the study of the effect of landscape changes upon landscape value; preference - the liking of one landscape type better than another (Jacques, 1980).
Subjectivity versus objectivity
There is a fundamental, theoretical, divergence of opinion over the question of whether landscapes have an intrinsic or objective beauty which may in some way be measurable or comparable, or whether scenic beauty is a value that can only be subjectively attributed to an area or specific landscape (Shuttleworth, 1980b). While physical geographers have devised ways of measuring landscape parameters to reflect visual quality; human geographers have probed individual and societal attitudes toward landscape (Dearden, 1985).
Orland et al. (1995) have described qualitative approaches as those which focus upon the evaluation the complexity of landscape using the judgements of panels of human subjects, and quantitative approaches as those which measure physical characteristics of the visual field directly.
On the physical/objective side, Buhyoff and Riesenmann (1979) have presented evidence that certain landscape dimensions can be used successfully to prepare an evaluation, and that aesthetic impact can be measured from specific landscape dimensions.
There is an increasing interest in the use of mapped data and geographic information systems (GISs) to assess visual landscape variables using reproducible methods over a wide area (Bishop and Hulse, 1994). Recent research efforts have shown that the public's scenic preferences can be assessed objectively and quantitatively (Dearden, 1980). This research has also demonstrated that public perceptions can be related to and, in fact, predicted from environmental attributes of a more tangible nature (Buhyoff et al., 1994).
The assessment and/or quantification of scenic quality is mandatory for proper consideration of the aesthetic consequence of management actions (Buhyoff et al., 1994). The Belgian experience with landscape evaluation, especially in rural re-allotment projects, indicates, and international literature from a great number of disciplines or research field confirms, the necessity to speak of scenic or visual resource management (Tips, 1984).
Landscape evaluation methods
Numerous techniques of landscape evaluation have been devised in recent years (Crofts and Cooke, 1974). They form a spectrum in which the extremes are represented on the one hand by techniques based unequivocally on the subjective assessments of landscape quality by individuals or groups (e.g. Shafer et al., 1969) and on the other by techniques using physical attributes of landscape as surrogates for personal perception (e.g. Linton, 1968; Land Use Consultants, 1971).
The various models can be subdivided several ways. Arthur et al. (1977) splits them into descriptive inventories and public preference models, both categories being further split into non-quantitative and quantitative methods. Briggs and France (1980) use direct and indirect methods to subdivide the models; Crofts (1975) describe two sorts of techniques - preference and surrogate component techniques; Daniel and Vining (1983) split the methods into ecological, formal aesthetic, psychophysical, psychological and phenomenological models. For this review the methods will be split into descriptive inventories, public preference methods (after Arthur et al., 1977) and a third category of quantitative holistic techniques.
It is important to examine the reliability and validity of landscape
evaluation models and to identify any assumptions central to the models.
Internal and external validity are of concern in the development of any
landscape visual assessment system. External validity reflects, in part, how
well the system-generated assessments correspond to other, known measures of
visual quality. Internal validity reflects how well the system's internal logic
withstands testing and violation of assumptions (Buhyoff et
Descriptive inventories comprise the largest category of techniques for assessing scenic resources; they include both quantitative and qualitative methods of evaluating landscapes by analysing and describing their components (Arthur et al., 1977). Classification methods are those which first attempt to classify the survey units on the basis of their overall similarity, and then to grade or evaluate the resulting clusters - formal aesthetic models are an example of this method. Non-classificatory methods, such as ecological models, attempt to identify the relationships between selected landscape components and environmental quality, then use these relationships to predict landscape quality (Briggs and France, 1980).
Descriptive inventory methods rely on combinatorial functions (such as addition, subtraction and multiplication) to value, compare and aggregate landscape components which have been identified and measured by an individual expert or team of experts. The components in the inventory may consist of physical landscape elements (Tandy, 1971) or design elements (Bureau of Land Management (BLM), 1980). Implicit in the quantitative design inventories approach to landscape evaluation is the assumption that scenic quality of the whole landscape can be explained in terms of aggregation of the values of the landscape components.
Although these surrogate methods of landscape evaluation can provide general assessments of landscape quality and a landscape inventory based on subjectively-selected but objectively-applied criteria, the objectivity of their application, and their precise, often quantitative, results disguise their underlying subjectivity (Crofts and Cooke, 1974).
The descriptive inventory approach contains several assumptions. One is that the value of a landscape can be explained in terms of the values of its components. Another is that scenic beauty is embedded in the landscape components, that it is a physical attribute of the landscape; however, scenic beauty depends on the observer as well as that which is being observed (Arthur et al., 1977). Descriptive inventories methods have been criticised for the way landscape components are arbitrarily identified then subjectively scored by the design professionals, and the lack of empirical research to justify the inclusion of these components as determinants of scenic quality (Robinson et al., 1976, Arthur et al., 1977).
Formal aesthetic models
The basic theory of the formal aesthetic model is that aesthetic values are inherent in the formal properties of the landscape. These properties are defined as basic forms, lines, colours and textures and their interrelationships. The relationships between these elements are then inspected to classify each area in terms of variety, unity, integrity or other complex formal characteristics. Due to the formal training required for this, the method is almost always applied by an expert, usually a landscape architect (Daniel and Vining, 1983).
Because the landscape-quality assessment results in ordered categories, and not in cardinal or interval measures, it is difficult to relate these assessments to economic or trade-off types of valuation processes. Thus, valuing landscape quality relative to other social values is rather restricted. The models have been found to be seriously deficient with regard to the fundamental criteria of sensitivity and reliability (Daniel and Vining, 1983).
Within the ecological model, the environmental features that are relevant to landscape quality are primarily biological or ecological. The landscape is characterised in terms of species of plants and animals present, ecological zones, successional stage or other indicators of ecological processes. In the context of such modelling humans are characterised as users of the landscape (Daniel and Vining, 1983).
Ecological models tend to be designed for specific areas and are therefore difficult to apply to landscapes in general; they are also more sensitive in distinguishing between natural and human-influenced environments than in making distinctions within either of those classes. If the alternatives for land management are to manipulate or not manipulate the environment, the ecological models will almost invariably indicate against any manipulation (Daniel and Vining, 1983).
A major underlying assumption of the ecological model is that landscape quality is directly related to naturalness, or ecosystem integrity. The validity of this model depends upon the assumption that "natural" areas undisturbed by humans are highest in landscape quality. Reliability depends on the consistency and accuracy of the individual applying the method as the assessments are usually carried out by an "ecological expert" (Daniel and Vining, 1983).
An example of a formal aesthetic model is the Visual Management System (VMS) developed by the USDA Forest Service. It has the purpose of evaluating scenic resources within a land-management framework and assumes that scenic quality is directly related to landscape diversity or variety (Daniel and Vining, 1983). VMS uses character classification (such as gorges, mountains, foothills and plateaus), variety classification (form, line, colour and texture) and sensitivity level (referring to the relative importance of the landscape as a visual or recreational resource). In some cases VMSs are more quantitative holistic techniques that descriptive inventories.
Leopold's "uniqueness ratio" illustrates a landscape assessment methodology based primarily on ecological measures of the landscape. The uniqueness of a given landscape is defined by multiple physical, biological, and human-use dimensions that reflect the implicit assumption that aesthetic value is primarily a function of ecological criteria (Daniel and Vining, 1983).
Cooper and Murray (1992) used local patterns of land class distribution and land class clusters to divide a region into geographically distinct landscape units. High-elevation and upland areas were differentiated from lowlands and unit boundaries were then drawn in relation to selected physiographic and landform features such as watershed boundaries and specified juxtapositions of land classes.
In the BLM's (1980) scoring scheme for scenic quality, landscape features were valued using different criterion. The first was in terms of the four basic design elements, namely: form, line colour and texture (R. Kaplan, 1985). High valuations are given to the five natural landscape components (landform, vegetation, water, colour and adjacent scenery), whilst cultural modification is assumed to have negative effects. It is the only component that can receive a negative score. The other criterion is to value landscape in terms of variety, scarcity, vividness and distinctiveness. Landscapes with the greatest variety in landform, vegetation, and colour patterns are given the highest scores.
Brabyn (1996) desribes an automated classification process which uses a GIS to determine uniqueness and variety. The landscape character classification process used national digital databases to classify vegetation, naturalness, water and landforms in an objective manner. The resultant hierarchical classification, which is based on different levels of generalisation, enables the classification to cope with the different levels of perception that people experience. Brabyn (1996) also notes that a landscape character classification does not identify quality and points out that such a classification needs to evolve as the understanding of the nature of landscapes becomes more sophisticated.
Public preference models
The recent upsurge in public interest in preserving the beauty of public lands has resulted in the development of scenic assessment based on public input (Arthur et al., 1977), indeed, it can be argued logically that the best source of data upon such a subjective issue as landscape quality is the general public. Although planners may claim that it is their duty to guide public taste in these matters, the visual attractiveness of the landscape is ultimately a product of the aggregated opinions of all the individuals concerned with that landscape (Briggs and France, 1980).
The visual quality (or value) of a landscape is rated on the basis of an observer's individual preference of the whole landscape. Those techniques that are based on subjective assessments of scenery and attempt to encompass the diverse and changing perceptions of individuals are likely to be most successful. The essence of the preference approach is the judgement of the landscape in totality, as opposed to the measurement techniques, which rely on the definition of factors to explain variation in landscape quality (Dunn, 1976).
Questionnaires or verbal surveys are the most commonly used non-quantitative method for sampling scenic preference of various groups. They are a valuable source of quick information but accuracy can be sacrificed for speed. They are useful for determining preferences for extremely divergent categories of landscape (Arthur et al., 1977). Alternative to questionnaires, one can provide visual stimuli for evaluation, such as photographs (e.g.Shuttleworth, 1980a; Wade, 1982) or one can use other stimuli, such as sound (Anderson et al., 1983). Although perceptions still vary, the variation is less than with verbal descriptions.
There are various difficulties when carrying out such evaluations. Past studies show that the personality of the observer, and their location affect what they observe, as does the duration of observation, the socio-economic profile of the observers, the type of physical characteristics of the landscape, the dynamics of its components and its complexity (Amir and Gidalizon, 1990). Two concerns are noted by Hull and Stewart (1992) - regarding the ecological validity of photo-based assessments caused by differences between on-site and photo-based contexts and that the individual rater, rather than the group average, is the more appropriate unit of analysis for tests of validity of photo-based assessments. The techniques have other problems - their psychological basis is at best uncertain; the validity of their quantitative or semi-quantitative results is invariably questionable; and in order to be representative of society's views, they require extensive, time-consuming surveys (Crofts and Cooke, 1974).
The psychological approach has been used in many studies where dimensional analyses of people's preferences for different landscapes are performed. These studies have demonstrated that various psychological constructs such as complexity, mystery, legibility and coherence are important predictors of human landscape preferences (Buhyoff et al., 1994). The psychological model refers to the feelings and perceptions of people who inhabit, visit, or view the landscape. A high-quality landscape evokes positive feelings, such as security, relaxation, warmth, cheerfulness or happiness; a low-quality landscape is associated with stress, fear, insecurity, constraint, gloom, or other negative feelings (Daniel and Vining, 1983).
Because psychological methods use multiple observers and yield one or more quantitative scale values for each assessed landscape, their reliability and sensitivity can be precisely determined. This is an important advantage, since users of these assessments can know the degree of precision and to prove confidence in the landscape values produced. The methods base landscape assessments on the reactions and judgements of the people who experience and/or use the landscapes. In this regard there is an important element of validity inherent in the method (Daniel and Vining, 1983).
Without clear relationships to objectively determine environmental features, the psychological methods leave landscape assessment in a correlational feedback loop; psychological reactions to the landscape are explained only in terms of other psychological reactions:-
"From a practical perspective, this leaves the landscape manager with both feet firmly planted in midair" (Daniel and Vining, 1983).
The phenomenological model places even greater emphasis on individual subjective feelings, expectations, and interpretations. Landscape perception is conceptualised as an intimate encounter between a person and the environment (Daniel and Vining, 1983). The principal method of assessment is the detailed personal interview or verbal questionnaire. Phenomenological models tend not to be used to rank landscapes in terms of scenic beauty.
Phenomenological approaches have largely sacrificed reliability in favour of achieving high levels of sensitivity; by emphasising very particular personal, experiential and emotional factors, the visual properties of the landscape become only very tenuously associated with landscape experience. This model represents the extreme of subjective determination of relevant landscape features. It fails to establish systematic relationships between psychological responses and landscape features. However, by emphasising the unique role of individual experiences, intentions, and expectations, the phenomenological model serves to point out the importance of the human context in which landscapes are encountered (Daniel and Vining, 1983).
Most landscape techniques proceed on the assumption that there is a broad consensus within our society upon what is considered to be of high landscape value. This assumption is linked to another: that "visual quality" is an intrinsic property of landscape and can be stated objectively (Jacques, 1980). The issue of observer consensus is a major topic in landscape perception and preference.
Landscape preference studies should not rely exclusively on general rankings of preference, but should also consider other trends of variation and eventually compare individual patterns of selection. If only consensus aspects are examined (e.g. group preference rank), idiosyncratic features remain ignored. The subjects' variance in the relative evaluation of appraisal characteristics may have very different origins. It may be related to sociocultural or psychological factors that affect landscape preference as described by a number of authors (Abello et al, 1986).
Reasons for the real variation in consensus levels remain elusive. Evidence suggests little overall correlation between perceived attractiveness and consensus levels, although the more 'extreme' evaluations rarely attract majority support. Consensus does not increase with greater familiarity. Indeed those admitting less knowledge of local landscapes show greater consensus in their generally cautious and conservative evaluations. Those with the greatest landscape knowledge are more critical of its various qualities so that their responses show greater variance. The degree of consensus on evaluation, therefore, generally declines with increasing landscape familiarity, although not always sufficiently to be statistically significant given the available sample sizes (Penning-Rowsell, 1982).
A series of studies by Kaplan and Kaplan illustrated the psychological model of landscape assessment. A basic method in these studies is to identify relevant psychological variables on photographs of landscapes. Preference ratings and ratings on the landscape dimensions are then obtained from naive observers (Daniel and Vining, 1983).
The essence of Fines' technique is the classification by field observers of subjective responses to the attractiveness of views according to a single, comprehensive and predetermined scale (Crofts and Cooke, 1974). Most literature on phenomenological methods is devoted to studies of developed landscapes or to perception of environmental hazards. There are only a few specific studies seeking to assess natural landscapes (Daniel and Vining, 1983).
Quantitative holistic methods
Quantitative holistic methodologies combine two approaches: quantitative public preference surveys and landscape features inventories. Measures of landscape quality should be systematically related to physical / biological and social features of the environment so that accurate predictions of the implications of environmental change can be made (Arthur et al., 1977).
Models, such as that of Shafer et al. (1969) represent a compromise between techniques which assess the effects of landscape elements on overall preference by summing evaluations of individual dimensions (descriptive methods) and techniques which emphasise the interactions of landscape elements by evaluating the scenic quality of the entire image (preference models); this compromise creates the quantitative holistic models such as the psychophysical and surrogate component models (Buhyoff and Riesenman, 1979; Arthur et al., 1977).
A prominent feature of this method is the use of a statistical technique known as multiple regression analysis to establish a mathematical relationship, between components of the landscape and the scenic preferences of observers. Weights for landscape components are estimated from preference ratings collected from the public. The weights, multiplied with a set of measurements of landscape components, produce an overall scenic quality score for the other similar landscapes.
These predictive models have tended to be more a tool for research than for impact assessment. Their orientation is to predict scenic quality based on the presence of quantifiable landscape attributes (Palmer, 1983). Psychophysical modelling uses measurements of physical landscape features to predict people's preferences for the overall visual quality of the landscapes (Daniel and Vining, 1983).
Implicit in the psychophysical approach is the principle that a model of an observer's perceptive processes need not be complete in all possible respects in order to obtain meaningful results. Although this assumption is at variance with the Gestalt school of thought (regarding the sum of parts to make the whole), it has nevertheless been largely upheld by modern empirical psychophysical work (McAulay, 1988).
Traditional psychophysical models, while not "classifying" landscapes, are developed to make predictions of scenic preference or visual quality from variables which are often selected for their predictive, rather than "genuine" explanatory ability (Buhyoff et al., 1994). Surrogate component techniques are based on the identification of physical landscape components which can be compared with preference ratings. Visual management systems aim to be able to both predict and explain scenic preference; their essential purpose is the prediction and assessment of impacts resulting from potential management alternatives (Bishop and Hulse, 1994).
Psychophysical methods of landscape assessment seek to determine mathematical relationships between the physical characteristics of the landscape and the perceptual judgements of human observers (Daniel and Vining, 1983). The relationships of interest are those between physical features of the environment (e.g. topography, vegetation, water, etc.) and psychological responses (typically judgements of preference, aesthetic value or scenic beauty). Landscape features such as land cover, land use, forest stand structure, and arrangement are measured and then statistically related to scenic quality judgements. Models such as paired comparisons, Likert scales, and sorting and ranking scales are a means to evaluate scenes quantitatively (Arthur et al., 1977); multiple linear regression has recently been the most commonly used techniques to determine these relationships (Buhyoff et al., 1994).
Of all landscape assessments, these methods have been subjected to the most rigorous and extensive evaluation. They have been shown to be very sensitive to subtle landscape variations and psychophysical functions have proven very robust to changes in landscapes and in observers (Daniel and Vining, 1983). Relying on ordinal or interval scales of measurement, psychophysical methods have consistently been able to provide different landscape-quality assessments for landscapes that vary only subtly. However, they require the full range of scenes to be selected to represent all of the physical characteristics used as predictors of scenic beauty (Hull and Revell, 1989). They also provide good assessments of public perceptions of the relative scenic quality differences between landscapes (Buhyoff et al., 1994) based on the assumption that the aesthetic judgements of public panels provide an appropriate measure of landscape quality (Daniel and Vining, 1983).
However, the models can be expensive and time consuming to develop and are restricted to a particular landscape type and to a specified viewer population and perspective; in the short term they are not highly efficient (Daniel and Vining, 1983). The very structure of these models is often a limiting factor in their explanatory value and wide generalisation (Buhyoff et al., 1994).
Psychophysical assessments are useful in many management contexts - features such as quantitative precision, objectivity, and a basis in public perception and judgement are important. The assessments are not based on one expert's opinion, but reflect a measured consensus among observers representative of the public that views landscapes and is affected by management actions (Daniel and Vining, 1983).
Surrogate component models
The basis of component techniques is the identification and measurement of those physical components of the landscape which are regarded as surrogates of scenic quality. The individual components are isolated, their identification and measurement discussed and their combined utility within existing techniques evaluated. Because component ratings are compared to overall preference ratings in these models, the contribution of particular components to scenic beauty can be measured in terms of explained variance (Arthur et al., 1977).
These components can be assigned to three groups in relation to their assumed importance in determining scenic quality. The major components comprise the landscape skeleton as expressed by macro relief (measured by terrain types), relative relief and water presence (measured by drainage density). To these can be added the minor but permanent components which are the variations of the macro forms at smaller scales. They are the overall variations such as surface texture and ruggedness, particular features such as the irregularity of two-dimensional outlines and three-dimensional forms, and the singularities such as isolated features. Finally, there are the transitory components with regard to the characteristics of water bodies and surface textures (Crofts, 1975).
Visual Management Systems
Another approach to the evaluation/assessment of visual resources is the design-based classification/assessment. Visual management systems (VMS) are straightforward systems that use intuitive constructs and easily observable physical landscape attributes to arrive at landscape classification decisions. Because expert, or knowledge-based, computer systems are capable of carrying out reasoning and analysis functions in narrowly defined subject areas at proficiency levels approaching that of humane experts, they have characteristics that can be used to develop not just a method of predicting visual landscape quality but also a system that explains why certain levels of quality exist. In fact, the specification of knowledge may well be the most important contribution of a scenic quality assessment or prediction system (Buhyoff et al., 1994).
The prolific work of Shafer and colleagues (Arthur et al., 1977) is an example of quantitative holistic methodologies. They have measured areas, perimeters, and tones of the differentiated landscape zones of photographs and related them to preference rankings using factor analysis and multiple regression techniques (e.g. Shafer et al., 1969; Shafer and Tooby, 1973; Brush and Shafer, 1975). Shafer's studies illustrate a sound and systematic approach to relating components to preferences (Arthur et al., 1977).
The Scenic Beauty Estimation (SBE) method requires that landscapes be observed and judges by panels of persons representative of targeted populations. To develop models using this system, a number of different landscapes must be assessed and their physical characteristics evaluated (Daniel and Vining, 1983). This can be done using colour photographs or slides (Arthur, 1977) or on-site at the landscapes (Schroeder and Daniel, 1981).
Psychophysical models have been developed for landscape vistas by relating measured characteristics of the vistas to scaled landscape preference (Daniel and Vining, 1983). These have often used colour slides of forest or panoramic views and gained preference ratings using paired-comparison formats (Buhyoff and Wellman, 1980; Buhyoff and Riesenmann, 1979).
In Eleftheriadis and Tsalikidis's (1990) model, coastal landscape quality was expressed in terms of scenic beauty preferences of the resource users, and these preferences were related to quantitative measures of land use designations, and of forest stand and site characteristics. Carls (1974) used the landscape zones of Shafer et al. (1969) together with a people zone, and low and high development zones to look at the effects of people and man-induced conditions of preferences for outdoor recreation landscapes.
Task Four illustrates the application of descriptive inventory techniques to areas around three existing windfarms in Wales and a proposed site in Scotland.
Methodological problems and error in models
All these types of models are complicated by methodological problems that can affect interpretation of results. One such problem is whether numerical ratings of landscape beauty represent people's preferences for the landscapes, their judgements of scenic beauty of the landscapes, or both. There is considerable support for the argument that scenic beauty judgements differ from scenic preferences (Arthur et al., 1977); according to Jacques (1980) public preferences tend to give a measure of 'value', 'quality' is discerned through judgement. When asked to indicate their preference for various landscapes, observers tend to apply criteria for use of those areas (recreation, residence etc) rather than for inherent beauty (Arthur et al., 1977).
There are some persistent errors in the evaluation of landscape, as identified by Hamill (1985). Examples of the following seven types of error have been found in the literature: incorrect use of numbers derived from place in a classification; incorrect use of numbers to stand for words; use of spurious numbers in simple mathematical operations; use of bad data in complex mathematical and statistical operations; use of data that does not satisfy requirements of the model; use of numbers to support, derive, or demonstrate meaningless, spurious or useless concepts; and use of concepts without adequate operational definitions.
Economics of landscape evaluation
How do preference values determined from models relate to economic values of the same landscapes? Results of an exploratory study (Brush and Shafer, 1975) suggest that a consumer's evaluation of real estate that overlooks a given natural scene correlates highly with the scene's predicted preference scores. It should be possible to develop an equation that ties scenic preference values to economic land values. The results of such research should be useful in benefit-cost and environmental impact analyses of the effect of proposed man-made changes in natural environments.
Traditional economic analyses have generally failed to account for unmarketed (nonpecuniary) resources, such as aesthetics. The effect of excluding nonpecuniaries from trade-off (economic) decisions is that they have entered the system as if they were free. Recognition of this problem has, in part, motivated attempts to evaluate scenic resources. If applied to aesthetic resources, redefinition would require putting a price on scenic beauty or charging for its "use". However, putting a price on aesthetic resources is probably not feasible for several reasons. First, aesthetic experiences are difficult to define, second, there is the problem of placing charges on aesthetic experiences (Arthur et al.,1977).
Methods of scenic resource financial valuation
Several methods have been used to obtain values for scenic resources. "Willingness-to-pay" values (how much will a commuter pay to preserve the trees), revealed demand (does he take a different highway?) and opinion tallies (does he complain to his MP?) are only a few of the methods used (Arthur et al., 1977). Opinion tallies, often result in undervaluation. Revealed demand is complicated by the necessity of identifying all of the variables acting on the situation.
The hedonic price method (HPM) is a less subjective way of scoring landscape components; components of landscape are valued against people's willingness to pay to live in particular types of landscape, defined as comprising of different bundles of components (Willis and Garod, 1993). HPM is a process of constrained maximisation in which systems of equations involving both prices and quantities for the composite commodity and its attributes are constructed and then solved (Price, 1994).
The travel cost method (TCM) uses a sample of visitors to a site which embodies desired environmental attributes and asks them factual questions about the origin of their journey to the site, their mode of transport and perhaps about other costs incurred and their own socio-demographic characteristics (Bergin and Price, 1994).
Contingent Valuation Technique and Willingness-to-Pay
Contingent valuation techniques (CVT) in landscape evaluation are seen as a natural evolution from landscape evaluation methods based on the scoring of landscape components and other public preference techniques such as landscape ranking. By valuing landscape as an entity, CVT avoids many of the problems, such as those of separability and collinearity, often associated with travel cost and hedonic price methods of landscape valuation (Willis and Garrod, 1993).
Willingness-to-pay (WTP) studies can assist in valuing today's landscape, they also attempt to value the benefits which residents and visitors might derive from alternative landscapes which could arise at some time in the future (Willis and Garrod, 1993).
WTP's linearity means that it is a linear or other predetermined function of the quantity of the feature in the landscape. However, evidence suggests that the impact of a landscape feature does not increase in proportion to its size (Willis and Garrod, 1993). Unfortunately, WTP values are often too high (Arthur et al., 1977) and WTP to gain a commodity is generally less than willingness to accept compensation for losing it (Price, 1994).
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