relationships between visual landscape preferences and map...

Landscape and Urban Planning 78 (2006) 465–474

Relationships between visual landscape preferences andmap-based indicators of landscape structure

W.E. Dramstad a,∗, M. Sundli Tveit b,1, W.J. Fjellstad a,2, G.L.A. Fry b,3

a Norwegian Institute of Land Inventory, P.O. Box 115, N-1431 As, Norwayb Department of Landscape Architecture & Spatial Planning, Norwegian University of Life Sciences, P.O. Box 5029, N-1432 As, Norway

Received 11 May 2005; received in revised form 14 December 2005; accepted 15 December 2005Available online 12 July 2006

Abstract

There is increasing awareness of the need to monitor trends in our constantly changing agricultural landscapes. Monitoring programmes oftenuse remote sensing data and focus on changes in land cover/land use in relation to values such as biodiversity, cultural heritage and recreation.Although a wide range of indicators is in use, landscape aesthetics is a topic that is frequently neglected. Our aim was to determine whetheraspects of landscape content and configuration could be used as surrogate measures for visual landscape quality in monitoring programmes basedon remote sensing. In this paper, we test whether map-derived indicators of landscape structure from the Norwegian monitoring programme foragricultural landscapes are correlated with visual landscape preferences. Two groups of people participated: (1) locals and (2) non-local students.Using the total dataset, we found significant positive correlations between preferences and spatial metrics, including number of land types, numberof patches and land type diversity. In addition, preference scores were high where water was present within the mapped image area, even if thewater itself was not visible in the images. When the dataset was split into two groups, we found no significant correlation between the preferencescores of the students and locals. Whilst the student group preferred images portraying diverse and heterogeneous landscapes, neither diversitynor heterogeneity was correlated with the preference scores of the locals. We conclude that certain indicators based on spatial structure also haverelevance in relation to landscape preferences in agricultural landscapes. However, the finding that different groups of people prefer different typesof landscape underlines the need for care when interpreting indicator values.© 2006 Elsevier B.V. All rights reserved.

Keywords: Agriculture; Landscape metrics; Monitoring; Photographs

1. Introduction

Most people, if questioned, will have an opinion as to whethera particular landscape is aesthetically pleasing or not, and therole of everyday landscapes in the well being of people is receiv-ing increased focus (Hartig et al., 2003; Kaplan et al., 1998).Aesthetic issues are controversial in many ways and studies oflandscape aesthetics are no exception (see, for example, Ndubisi,2002; Parsons and Daniel, 2002). Commonly, criticism in land-scape aesthetics refers to subjectivity, lack of standardization

∗ Corresponding author. Tel.: +47 64 94 96 84; fax: +47 64 94 97 86.E-mail addresses: [email protected] (W.E. Dramstad),

[email protected] (M.S. Tveit), [email protected] (W.J. Fjellstad),[email protected] (G.L.A. Fry).

1 Tel.: +47 64 96 53 65.2 Tel.: +47 64 94 97 04.3 Tel.: +47 64 96 53 62.

in methodology, non-transparent application of values and lackof replicability (even among experts) (Bruns and Green, 2001;Daniel, 2001; Ndubisi, 2002; Terkenli, 2001). In spite of effortsto develop methods that can be accepted throughout the scien-tific community (see review by Ndubisi, 2002), none has hithertogained general acceptance. One unfortunate consequence of thiscan be avoidance of the issue by excluding consideration of thevisual aspects of landscape altogether.

Despite these problems, there is an increasing demand for thevisual landscape to be included in landscape policy, managementand planning as well as landscape monitoring (Tahvanainen etal., 2002; Tress et al., 2001). Landscape issues have recentlymoved up the political agenda in Europe (Wascher, 2000), asexpressed by the development and ratification of the EuropeanLandscape Convention (Council of Europe, 2000). The Conven-tion requires each signatory party to identify its own landscapes,analyse their characteristics and the forces and pressures trans-forming them and take note of changes (Article 6: Council of

0169-2046/$ – see front matter © 2006 Elsevier B.V. All rights reserved.doi:10.1016/j.landurbplan.2005.12.006

466 W.E. Dramstad et al. / Landscape and Urban Planning 78 (2006) 465–474

Europe, 2000). The Landscape Convention emphasizes its rele-vance to both “landscapes that might be considered outstandingas well as everyday or degraded landscapes” (Article 2). Further-more, the Convention outlines that “Landscape must become amainstream political concern.” Being able to measure changesin the visual landscape through already existing monitoring pro-grammes could ease this task.

The lack of an easily accessible methodology to deal with thevisual landscape issue frequently hampers the inclusion of visualaspects entirely. One possible approach to meet this challengeis to search for indicators of visual landscape quality that canbe derived from data on landscape structure. Our main aim inthe study presented here was to determine whether aspects oflandscape composition could be used as surrogate measures forvisual landscape quality in monitoring programmes based onremote sensing.

Studies of human landscape preferences have been based onseveral different approaches. Zube (1984) identifies three dif-ferent paradigms in landscape assessment; ‘professional’ wherethe trained expert interprets the landscape, ‘behavioural’ wherebiological and evolutionary principles are used to explain land-scape preferences and ‘humanistic’ where attitudes, beliefs andideas of each individual observer are in focus. Dearden (1987)questioned whether beauty is inherent in objects, or in the eye ofthe beholder and there is discussion about the degree to whichpersonal attributes and experience influence landscape percep-tion, and the extent to which landscape preferences representlearned behaviour (Meinig, 1976). Daniel (2001) describes thishistory as a controversy of the objective and subjective models,i.e. whether the aesthetics quality is to be found in properties ofthe objective of study or in the subject studying it. More recently,we have seen approaches to landscape aesthetics that accept amixture of cultural and biological forces as explaining humanlandscape preference (Tress et al., 2001).

Rapid and wide-ranging changes in landscapes in general,and agricultural landscapes in particular, have caused politiciansand management authorities to recognise a need for timely infor-mation on both landscape state and change. Many countries andagencies are therefore working to develop indicators and estab-lish ways to monitor and report on agricultural landscape change(see, for example, Defra, 2004; European Environment Agency,2004; Eurostat, 2003; OECD, 2004; Piorr, 2003). In 1998, theNorwegian Ministry of Food and Agriculture, in cooperationwith the Ministry of the Environment, initiated a monitoringprogramme focusing on agricultural landscapes (Dramstad etal., 2002). In the Norwegian monitoring programme, as wellas in other work on agri-environmental indicators, the visuallandscape has been a problematic issue since quantitative indi-cators of visual quality have proven difficult to find (Defra, 2004;Dramstad and Sogge, 2003; OECD, 2000).

Like numerous other monitoring programmes, the Norwe-gian monitoring programme for agricultural landscapes (knownas the 3Q-programme) is based on data collected through aerialphotography, i.e. viewing the landscape from a birds-eye per-spective. Landscape preference studies, on the other hand, haveto a very large extent used landscape photographs as landscapesurrogates (see, for example, Clay and Daniel, 2000; Daniel and

Meitner, 2001; Scott and Canter, 1997; Wherrett, 2000). Theability of photographs to represent the dynamic multidimen-sionality of real landscapes in an adequate way has been doubtedand criticized. However, despite limitations, colour photographshave been found to represent landscapes in a satisfactory mannerwhen compared to preference rankings made in the field (Daniel,2001 and references therein, Trent et al., 1987; Wherrett, 1998).

The aim of this study was to search for a link between themap-based land cover data typical for monitoring programs, andperceived aesthetic quality as quantified through a photography-based preference study. To achieve this we used the resultsfrom a landscape preference study for Norwegian agriculturallandscapes (Tveit, 2000), linked to a number of 1 km2 squaresincluded in the 3Q-monitoring programme. Based on the land-scape represented in the images, we calculated a number ofmap-based landscape indicators, aiming to see whether any ofthese indicators were correlated to preference rankings.

2. Methods

2.1. The 3Q-programme

The monitoring programme involves the mapping and analy-sis of 1400 1 km × 1 km squares. The sample squares are locatedusing the 3 km × 3 km grid already used for sampling in theNorwegian forest inventory. Where the grid point falls on landused for agricultural purposes (as defined by the Economic MapSeries), a 1 km2 surrounding this point has been included in thesample. The programme follows a 5-year inventory cycle, withnational coverage first available in 2003.

Mapping is based primarily on interpretation of aerial pho-tographs (scale 1:12,500, true colour). Land cover and land usewithin each square are digitised according to a hierarchical clas-sification system; including a total of ca. 100 land types at thethird and most detailed level. Among the more common landtypes in the area were cereal fields, meadows, built-up land andboth deciduous and coniferous woodlands. In addition, linearelements and point objects are recorded, including both naturaland cultural features. A number of indicators are calculated fromthe resulting maps, addressing the four main issues of interestdefined by the Norwegian Ministries: landscape structure, bio-diversity, cultural heritage and accessibility. For further detailson the 3Q-monitoring programme, see Dramstad et al. (2002).

2.2. On-the-ground photography

As a module linked to the 3Q-programme and researchprojects related to this programme, ground photography hasbeen conducted on a 10% sub-sample of the 3Q-monitoringsquares (20–30 squares annually). Around 15–25 photographswere taken by a professional photographer in each 1 km2, the aimbeing to describe the landscape content and variation throughoutthe square. In addition to documenting the present appearanceof the landscape, these photographs also provide a historic land-scape archive for the future (Puchmann and Dramstad, 2003).To enable the same landscape section to be photographed in thefuture, the precise location from which each photograph was

W.E. Dramstad et al. / Landscape and Urban Planning 78 (2006) 465–474 467

Fig. 1. (a) Map of Norway, with circle surrounding Østfold and Akershus counties. (b) Example monitoring square illustrating the method of photography. Eacharrow represents one photograph. Focal length and aperture are recorded next to each arrow. Since 1999, all positions are geo-referenced in the field through the useof GPS.

taken and the direction of the view were recorded on maps inthe field and later digitised and stored in a GIS (see Fig. 1b).Details of the photographic equipment used were also recorded.These mapped positions were used as the starting point for map-ping the area included in each image (see below).

2.3. Preference study

In the preference study, 30 photographs were selected from3Q-monitoring squares in Østfold and Akershus counties (seeFig. 1). The photographs were chosen to illustrate differentdegrees of openness in the landscape. Openness was not mea-sured at this stage but pictures were chosen subjectively based onperceived openness. An open landscape is defined as a landscapewith low vegetation allowing a clear view, as opposed to tall veg-etation which obscures the view. Other aspects than openness,such as light, weather conditions and seasonal differences, wereheld constant as far as possible. An attempt was made to keepthe pictures free from features believed to be particularly strongdrivers of preference, such as water (Nasar and Li, 2004), andman-made features (Kaplan and Kaplan, 1989; Strumse, 1994).However, it was not possible to avoid all man made features,since almost all pictures had some man made features in the fardistance.

The preference study was conducted using commonly appliedmethods (as in, for example, Daniel and Boster, 1976, pp. 9–10;Hagerhall, 2001; Herzog, 1984, 1987; Lynch and Gimblett,1992). Using a projector, landscape slides were shown on a largescreen in random order in a neutral room. Each slide was dis-played for 10 s, after which the viewers were given 50 s to answerquestions on a form. The assessment of preferences was con-ducted by asking participants to give a score to each landscapeaccording to how much they liked the view (1 for least preferredand 5 for most preferred). A short break was allowed after everyeight pictures to avoid fatigue effects. A total of 53 people from

Østfold and Akershus and 38 students from other parts of thecountry participated in this study (see Table 1). All studentscame from the Norwegian University of Life Sciences and, whiletheir disciplines varied, they were all studying environmentallyrelated subjects. All participants were invited according to ran-dom selection procedures. For the student group this was doneby distributing invitations to every second mailbox, while forthe public group every 50th entry in the local phonebook wasmailed an invitation, also offering to cover travel costs.

2.4. Mapping the view

By comparing a large display of each photograph with themaps showing the photographer’s location and angle of view,the boundaries of the area visible in each image were digitisedusing ArcViewTM (ESRI). In this paper, we refer to the areacovered by each photograph as a viewshed. The presence ofvegetated boundary-lines, telegraph poles, buildings, etc., aidedthe identification of the area represented in the image. An exam-ple of three images and their estimated viewsheds is shown inFig. 2.

Since, we did not have access to land cover data for the areaoutside the monitoring squares, all images that contained clearlyvisible area from outside the squares were excluded. This pro-cedure left us with a total of 24 images. A selection of landscapemetrics was calculated for each viewshed (Table 2), including

Table 1Information about the participants in the preferences study

Locals Students

Number of participants 53 38Gender (M/F) 33/20 18/20Average age 50.8 24.6Youngest/oldest participant 9/76 20/44


Fig. 2. Using landscape elements such as telegraph poles and buildings, the area represented in the image was digitised and this ‘viewshed’ used for further analysis.

measures of both content (which land types were present) andspatial configuration (how patches of different land types arelocated in relation to one another). Landscape metrics includedthe total area of the viewshed, number of different land typespresent, number of land type patches present, total length ofpatch edges, area of open land types, grain size of the open land-scape, landscape heterogeneity and land type diversity measuredas Shannon’s diversity index (Magurran, 1988).

These metrics were chosen because they are commonlyimplemented in various forms of landscape monitoring and arerelatively simple to use and to interpret. The heterogeneity index(see Fjellstad et al., 2001) is less well known but is used in the3Q-monitoring programme. The index is calculated by recordingland type at a grid of points and then comparing every possi-ble pair of points. The heterogeneity index is the proportion ofpoints that are on different land types. The minimum hetero-geneity value is zero, when all points fall on the same land type

Table 2The minimum, mean and maximum values for the different landscape metricsfor all estimated viewsheds

Minimum Mean Maximum

Total area (m2) 3696 31508 128861Number of land types 3 5.5 9Number of patches 3 9 24Heterogeneity index 0.00 0.35 0.73Shannon’s diversity index 0.21 0.87 1.65Open area (m2) 2318 27432 126828Percent openness 32 81 100Grain size of open areas 0.05 0.40 1.73Length of edge (m) 621 2335 6580

(large-scale, homogeneous landscape), and the maximum valueis one, when the points in every pair fall on different land types(small-scale landscape with a high degree of spatial division).Since the viewsheds were relatively small, a 10 m × 10 m gridof points was applied when measuring heterogeneity instead ofthe 100 m × 100 m grid used in the monitoring programme. Theheterogeneity index is a measure of spatial division that is inde-pendent of the number of different land cover types in an area.As such it complements the Shannon’s diversity index, which isbased on the relative areas of different land types.

To measure the area of open land types and the grain sizeof the open landscape, it was necessary to recode all land typesaccording to whether the land cover would enable a clear view(open) or would obscure the view (closed). All land types weretransformed to a binary variable, zero representing open areassuch as cereal fields and roads while one represented closedareas such as forest or built-up land. Grain size was calculatedas the number of patches of open land types divided by the totalarea of open land. Since area was measured in metre square thisproduced a very small number and we multiplied by 1000 tomake the index easier to grasp at a glance. This gives an indexvalue, which for a given area, increases evenly with increasingnumber of patches. Using grain size in addition to total open areatakes account of the fact that landscape elements such as a narrowhedgerow or a grass bank between two fields (i.e. that divide thelandscape into more patches) may change the visual impressionof a landscape, even though total area of open landscape may bealmost identical.

Spearman’s correlation coefficient was used to explore rela-tionships between the landscape preference scores and the var-ious spatial metrics.


3. Results

3.1. Landscape metrics

Table 2 shows the degree of variation present in the view-sheds for the different landscape metrics. For comparison, theaverage heterogeneity index values calculated for the 1 km2

monitoring squares in this region (137 squares) is 0.49 (min-imum 0.27, maximum 0.80), whilst the average Shannon’sdiversity index is 1.62 (minimum 0.69, maximum 2.34). Theviewsheds, having been chosen to avoid water and man-madeobjects, were thus slightly less heterogeneous and diversethan a typical landscape view from this region of Norway(Fig. 3).

Fig. 3. Photograph (a) received the lowest average preference score from the locals group and photograph (b) received the highest average preference score fromthis group. Photograph (c) received the lowest average preference score from the student group, while photo (d) received the highest average preference score fromthis group. Photograph (e) is an example of a viewshed containing water, while photograph (f) shows the most open viewshed. Photographs (g) and (h) were thoseover which there was greatest disagreement between students and locals; (g) was ranked number four for the students (i.e., fourth most preferred) and number 17 forthe locals, (h) was ranked number 21 by the students and number 10 by the locals.


Fig. 4. Average preference score for each image for the two groups of participants, showing clear differences in the responses of students and locals to the images.The images are numbered in rank order from left to right according to increasing percent open area in the viewshed. Arrows point to images with water.

3.2. Preference scores

A preference score value was calculated for each image basedon the ranking (scale 1 to 5) given by the observers. Aver-age preference scores for the two groups of participants werealmost identical (students: 3.25 versus locals: 3.21). Ranges inscores were from 2.40 to 4.16 for students and from 2.57 to3.62 for the local group, implying that the student group mademore use of the full scale when giving preference scores. Dis-tribution of preference scores for the 24 images is shown inFig. 4. The student group and the locals group differed as towhich image they ranked as highest preferred and lowest pre-ferred (see Fig. 3a–d) and, overall, there was no significantcorrelation between the preference scores assigned by the twogroups (Spearman’s rho = 0.358, p = 0.086). The most contestedimages – where the two groups showed the most disagreement– were photographs 8 (Fig. 3g) and 21 (Fig. 3h). Photograph8 was ranked as number four (i.e. fourth most preferred) forthe students and as number 17 for the locals. Photograph 21was ranked number 21 by the students and number 10 bythe locals.

3.3. Correlations

Using the total dataset, we found a significant positive cor-relation between preferences and both the number of land types(Spearman’s rho: 0.677, p < 0.001) and the number of patches(Spearman’s rho: 0.623, p = 0.001) within the image area. Wefound no correlation in the total dataset between preferencesand landscape openness or spatial heterogeneity, but founda significant correlation with land type diversity (Spearman’srho: 0.407, p = 0.049) within the mapped image area (measuredby the Shannon index). When the dataset was split into twogroups, we found interesting differences between students andlocals.

For the student group Shannon’s diversity index, the hetero-geneity index, and the number of land type patches were all sig-nificantly positively correlated with preference score (Table 3).Percent open area was negatively correlated with preferencescore for this group. For the locals, total area and number ofland types in the image were significantly correlated with pref-erence score at the 0.01 significance level, whilst area of openland types and total length of edge were significant at a 0.05level. Grain size of the open landscape was not found to be sig-nificantly correlated with preference scores for either group.

In interpreting these correlations it is important to considerthat there were also correlations between some of the differentlandscape metrics (Table 4). For example, the total area of theviewsheds was positively correlated with the total open area,percent open area, length of edge and number of patches andnegatively correlated with grain size. There was no correlation,on the other hand, between total area of viewshed and hetero-geneity, Shannon’s diversity index or number of land types.

A comparison of preference scores for the five images con-taining the largest proportion of open area (>96% open area),showed that the student group (average score 2.70) had a signif-icantly lower preference (one-tailed t-test, p < 0.001) for theselarge-scale open landscapes than the local group (average score3.33).

Although we excluded photographs in which water wasclearly visible, water appeared on the list of land types presentwithin the viewshed for seven of the 24 images (see Fig. 3e).The water could be seen in only one of the images (in thebackground). The five most preferred images in the dataset allcontained water within their viewshed. The other two imagescontaining water as a land type ranked as number 12 and 22.Images of viewsheds containing water were significantly morepreferred than those without water (one-tailed t-test, p = 0.004)and this applied both for students (p = 0.006) and for locals(p = 0.026).


Table 3Correlations between landscape metrics and average preference scores for the two groups of participants

Landscape metric Students Locals

Spearman’s rho Significance values Spearman’s rho Significance values

Shannon’s diversity index 0.578 0.003** 0.138 0.522 nsHeterogeneity index 0.573 0.003** −0.083 0.701 nsNumber of land types 0.534 0.007** 0.583 0.003**

Number of patches 0.453 0.026* 0.506 0.012*

Percent open area −0.452 0.027* 0.246 0.247 nsTotal area −0.037 0.864 ns 0.532 0.007**

Area of open land types −0.093 0.667 ns 0.510 0.011*

Length of edge 0.154 0.474 ns 0.481 0.017*

Grain size 0.327 0.119 ns −0.152 0.479 ns

Ns: not significant.* Significant at the 0.05 level.

** Significant at the 0.01 level.

4. Discussion

In Norway, as in many other countries, population centreshave grown up in those parts of the country with the best agri-cultural conditions. This means that agricultural landscapes arethe “everyday-landscapes” for a large proportion of the popula-tion, and it is therefore important to be able to monitor howchanges affect the visual appearance of these landscapes. Alandscape rich in biodiversity and amenity values is not nec-essarily a by-product of current agricultural practice (Barnard,2000; Green and Vos, 2001), and the public have come to realisethat many landscapes are deteriorating (Council of Europe,2000). In certain cases, the demand for these amenity by-products is increasing to the extent that food is considered tobe the by-product (see discussion by Hellerstein et al., 2002).The current lack of scientifically based indicators for measur-ing changes in the visual landscape, hampers the inclusion ofthis topic in monitoring programmes such as the Norwegian3Q-programme.

The exclusion of landscape aesthetic information from moni-toring and management may lead to less soundly based decision-making than desirable. When considering the costs and benefitsof landscape changes, for example, economic interests that areeasily measured may receive more attention than visual qual-ity, even though there is an acceptance that this “public good”also has an economic value in terms of, for example, humanhealth and tourism income. If alternative development scenarioscould be assessed using simple quantitative methods, the prob-ability that landscape aesthetics would be taken into account indecision-making could be increased. Methods to capture land-scape values are therefore needed so that these values can beintegrated effectively with other kinds of data in designing, plan-ning and managing landscapes (Ndubisi, 2002). Nassauer (1986)challenges us to work towards an understanding of how elementsfit together in the visual landscape, so that change can be plannedand managed in a way that leads to desirable future landscapes,instead of focusing too strongly on the past. This is also the mes-sage put forward by Green and Vos (2001), underlining the needto conceive, design, create and maintain new landscapes fit forthe social, economic and environmental needs of the twenty-first

century. To achieve this, we need to be able to quantify and mon-itor landscape change, including changes in visual appearance.

Landscape preference research points to certain general“rules of thumb” regarding features that influence preferencescores (Kaplan and Kaplan, 1989; Nassauer, 1995; Ndubisi,2002; Zube, 1987). Water, for instance, has been shown by manystudies to be positively correlated with preference scores (see,for example, Herzog and Bosley, 1992; Herzog and Barnes,1999; Kaltenborn and Bjerke, 2002; Purcell et al., 1994). Inour study, water itself was visible in only one of the images.However, information from maps showed that water was presentin seven of the viewsheds, and the results from the preferencestudy revealed that these images received significantly higherpreference scores than images of areas without water. In theseimages, there are clear vegetation belts that indicate presence ofwaterways meandering across the landscape, a landscape fea-ture probably recognised by most people. Our results suggestthat these patterns of vegetation, possibly combined with topo-graphic variation, caused a general positive response (both forstudents and locals). The underlying reason for this responsemay be an evolutionary adaptation, i.e. that people read the land-scape and interpret cues of the presence of water, as proposed inthe information-processing theory of Kaplan and Kaplan (1989).However, the existence of such a deep, sub-conscious reason fora preference, presumably applying to all people everywhere, isextremely difficult to test. Whilst we cannot conclude from thisstudy whether it is the vegetation that causes positive reactionsor the suggestion of the presence of water, the result is nev-ertheless that waterways (with their associated vegetation andtopography) are a strong predictor of aesthetic preference.

For the group of students participating in this study, we foundsignificant positive correlations between landscape preferencesand landscape heterogeneity and diversity. Such relationshipshave been mentioned by other authors previously (Hunziker,1995; Kaplan and Kaplan, 1989; Piorr, 2003; Zube, 1987). Itwas interesting to note, however, that these relationships did notapply for the group of locals. The photographs in Fig. 3 clearlyillustrate the differences between the two groups. Neither theheterogeneity index, nor Shannon’s diversity index were sig-nificantly correlated with preference scores for the locals. The


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vel.

correlation between the locals’ landscape preferences and totallength of edge, which could also be considered an aspect oflandscape diversity, is probably attributable to the strong pref-erence of locals for a large view and the fact that total length ofedge is strongly correlated to total viewshed area. One interpre-tation of the difference between students and locals may be that afew of the students had attended landscape ecology lectures andwere influenced by what they had learned about the benefits ofdiverse and heterogeneous landscapes for biodiversity. Anotherpossible interpretation is that preference scores reflected dif-ferent landscape familiarity for the two groups (Kaplan andKaplan, 1989; and see, for example, discussion by Armstrong,2002; Bourassa, 1991; Kjølen, 1998). The significantly higherpreference by locals for the five most open landscapes com-pared with students may be due to the fact that the local grouplive in landscapes that are amongst the most open, large-scale,intensively managed agricultural landscapes in Norway, whereasthe students came from different parts of the country wheresuch landscapes are unusual. This also fits with the findingthat locals preferred a large view (total viewshed area) whilststudent preferences were not significantly influenced by sizeof view.

More research is needed to investigate the role of familiar-ity in shaping visual landscape preferences and to explore otherunderlying reasons for differences in landscape preferences suchas age and social background. Nevertheless, the results of thisstudy suggest that care is needed to interpret indicators of land-scape change within an appropriate regional context. Regionsdefined by landscape type are more likely to provide a neces-sary and meaningful context for the interpretation of landscapeindicator values than administrative units. In addition, our studyshows that students who may go on to careers in landscape plan-ning and management, may not share the aesthetic preferences oflocal people. This is an important consideration when working tofulfil the requirements of the European Landscape Convention,where a “Landscape quality objective” for a specific landscape,is defined as “the formulation by the competent public authori-ties of the aspirations of the public with regard to the landscapefeatures of their surroundings” (Article 1: Council of Europe,2000). Clearly, a more widespread use of methods for publicparticipation in landscape planning will be required if such land-scape quality objectives are to be adequately formulated.

The 3Q-monitoring squares used in this study contained bothlow preference and high preference views in close proximity.Since single photographs cannot represent an entire monitoringsquare, the viewshed approach used in this study was neces-sary to establish connections between map-derived indicatorsand landscape preferences. Due to the complexity of underlyingreasons for human landscape preferences, we cannot expect to beable to accurately predict visual preferences for larger mappedareas. However, having some general guidelines of the aspectsof landscape content and spatial configuration that are importantin determining preferences will improve our ability to predictwhether landscape changes have positive or negative effects onthe appearance of the landscape, and thus improve interpretationof regional indicator values. While we fully appreciate the needto apply such indicators with care (Bell and Morse, 1999; Cale


and Hobbs, 1994; Gustafson, 1998; Leitao and Ahern, 2002;Turner, 1989), it is no longer possible to deny their need. Further,there are few methods available for the objective description ofthe visual landscape. Highly subjective and expert approaches donot seem acceptable to policy-makers or the general public andmake it difficult to compare landscapes or quantify changes overtime. This is particularly important if, as our study suggests, thetrained experts have different landscape preferences than localpeople. The present situation that excludes visual aspects of alandscape from consideration due to lack of methods, is notacceptable either. In this respect, we agree with the statementthat “. . . indicators can distort priorities—those things whichare being measured and reported are viewed as more important,while things which are less readily measured are omitted andgiven lower priority” (Detr, 2000, p. 6). Landscape aesthetics areamong the issues less readily measured. Indicators have a largenumber of advantages, and have come to be highly demanded,for example, by politicians (OECD, 2001). We would thereforeencourage the continued research effort in developing indica-tors also for these more challenging topics, such as the visuallandscape.

5. Conclusion

By establishing a link between the birds-eye view of remotesensing data and the on-the-ground perspective of landscapephotographs, this study identified several aspects of landscapecontent and spatial configuration that are related to people’slandscape preferences and that may therefore be suitable as indi-cators for the visual landscape. In particular, presence of waterand number of land types appear to be useful predictive variables.However, the finding that different groups of people (studentsand locals) prefer different types of landscape underlines theneed for care when interpreting indicator values. There are manypossible underlying reasons for people’s preferences for land-scapes. Amongst other things, it may be that an education inenvironmental subjects leads to a more complex interpretationof landscape views, incorporating other values than pure visualaesthetics (for example, an assessment of conditions for biolog-ical diversity) or it may be that preferences are affected by thedegree of familiarity with the landscape in question. Whateverthe causes of differences, this study suggests a need for widepublic participation in landscape planning decisions if visualaesthetics are to be adequately addressed. We also underlinethe importance of interpreting landscape indicator values in anappropriate regional context, with a focus on landscape typesrather than administrative units.

Acknowledgements

This study was supported by the Norwegian Research Coun-cil. The authors would like to thank Oskar Puschmann, Nor-wegian Institute of Land Inventory (NIJOS) for all his helpwith the photos, as well as everyone who participated in thepreference study. We would also like to thank two anonymousreviewers for valuable comments on an earlier version of themanuscript.

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