Landscape and Urban Planning 78 (2006) 465–474
Relationships between visual landscape preferences and
map-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
b
a Norwegian Institute of Land Inventory, P.O. Box 115, N-1431 Ås, Norway
Department of Landscape Architecture & Spatial Planning, Norwegian University of Life Sciences, P.O. Box 5029, N-1432 Ås, Norway
Received 11 May 2005; received in revised form 14 December 2005; accepted 15 December 2005
Available online 12 July 2006
Abstract
There is increasing awareness of the need to monitor trends in our constantly changing agricultural landscapes. Monitoring programmes often
use 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 whether
aspects of landscape content and configuration could be used as surrogate measures for visual landscape quality in monitoring programmes based
on remote sensing. In this paper, we test whether map-derived indicators of landscape structure from the Norwegian monitoring programme for
agricultural 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, number
of patches and land type diversity. In addition, preference scores were high where water was present within the mapped image area, even if the
water itself was not visible in the images. When the dataset was split into two groups, we found no significant correlation between the preference
scores of the students and locals. Whilst the student group preferred images portraying diverse and heterogeneous landscapes, neither diversity
nor heterogeneity was correlated with the preference scores of the locals. We conclude that certain indicators based on spatial structure also have
relevance in relation to landscape preferences in agricultural landscapes. However, the finding that different groups of people prefer different types
of 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 whether
a particular landscape is aesthetically pleasing or not, and the
role of everyday landscapes in the well being of people is receiving increased focus (Hartig et al., 2003; Kaplan et al., 1998).
Aesthetic issues are controversial in many ways and studies of
landscape aesthetics are no exception (see, for example, Ndubisi,
2002; Parsons and Daniel, 2002). Commonly, criticism in landscape 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.
0169-2046/$ – see front matter © 2006 Elsevier B.V. All rights reserved.
doi:10.1016/j.landurbplan.2005.12.006
in methodology, non-transparent application of values and lack
of replicability (even among experts) (Bruns and Green, 2001;
Daniel, 2001; Ndubisi, 2002; Terkenli, 2001). In spite of efforts
to develop methods that can be accepted throughout the scientific community (see review by Ndubisi, 2002), none has hitherto
gained general acceptance. One unfortunate consequence of this
can be avoidance of the issue by excluding consideration of the
visual aspects of landscape altogether.
Despite these problems, there is an increasing demand for the
visual landscape to be included in landscape policy, management
and planning as well as landscape monitoring (Tahvanainen et
al., 2002; Tress et al., 2001). Landscape issues have recently
moved up the political agenda in Europe (Wascher, 2000), as
expressed by the development and ratification of the European
Landscape Convention (Council of Europe, 2000). The Convention requires each signatory party to identify its own landscapes,
analyse their characteristics and the forces and pressures transforming them and take note of changes (Article 6: Council of
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W.E. Dramstad et al. / Landscape and Urban Planning 78 (2006) 465–474
Europe, 2000). The Landscape Convention emphasizes its relevance to both “landscapes that might be considered outstanding
as well as everyday or degraded landscapes” (Article 2). Furthermore, the Convention outlines that “Landscape must become a
mainstream political concern.” Being able to measure changes
in the visual landscape through already existing monitoring programmes could ease this task.
The lack of an easily accessible methodology to deal with the
visual landscape issue frequently hampers the inclusion of visual
aspects entirely. One possible approach to meet this challenge
is to search for indicators of visual landscape quality that can
be derived from data on landscape structure. Our main aim in
the study presented here was to determine whether aspects of
landscape composition could be used as surrogate measures for
visual landscape quality in monitoring programmes based on
remote sensing.
Studies of human landscape preferences have been based on
several different approaches. Zube (1984) identifies three different paradigms in landscape assessment; ‘professional’ where
the trained expert interprets the landscape, ‘behavioural’ where
biological and evolutionary principles are used to explain landscape preferences and ‘humanistic’ where attitudes, beliefs and
ideas of each individual observer are in focus. Dearden (1987)
questioned whether beauty is inherent in objects, or in the eye of
the beholder and there is discussion about the degree to which
personal attributes and experience influence landscape perception, and the extent to which landscape preferences represent
learned behaviour (Meinig, 1976). Daniel (2001) describes this
history as a controversy of the objective and subjective models,
i.e. whether the aesthetics quality is to be found in properties of
the objective of study or in the subject studying it. More recently,
we have seen approaches to landscape aesthetics that accept a
mixture of cultural and biological forces as explaining human
landscape preference (Tress et al., 2001).
Rapid and wide-ranging changes in landscapes in general,
and agricultural landscapes in particular, have caused politicians
and management authorities to recognise a need for timely information on both landscape state and change. Many countries and
agencies are therefore working to develop indicators and establish 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, the
Norwegian Ministry of Food and Agriculture, in cooperation
with the Ministry of the Environment, initiated a monitoring
programme focusing on agricultural landscapes (Dramstad et
al., 2002). In the Norwegian monitoring programme, as well
as in other work on agri-environmental indicators, the visual
landscape has been a problematic issue since quantitative indicators of visual quality have proven difficult to find (Defra, 2004;
Dramstad and Sogge, 2003; OECD, 2000).
Like numerous other monitoring programmes, the Norwegian monitoring programme for agricultural landscapes (known
as the 3Q-programme) is based on data collected through aerial
photography, i.e. viewing the landscape from a birds-eye perspective. Landscape preference studies, on the other hand, have
to a very large extent used landscape photographs as landscape
surrogates (see, for example, Clay and Daniel, 2000; Daniel and
Meitner, 2001; Scott and Canter, 1997; Wherrett, 2000). The
ability of photographs to represent the dynamic multidimensionality of real landscapes in an adequate way has been doubted
and criticized. However, despite limitations, colour photographs
have been found to represent landscapes in a satisfactory manner
when 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 the
map-based land cover data typical for monitoring programs, and
perceived aesthetic quality as quantified through a photographybased preference study. To achieve this we used the results
from a landscape preference study for Norwegian agricultural
landscapes (Tveit, 2000), linked to a number of 1 km2 squares
included in the 3Q-monitoring programme. Based on the landscape represented in the images, we calculated a number of
map-based landscape indicators, aiming to see whether any of
these indicators were correlated to preference rankings.
2. Methods
2.1. The 3Q-programme
The monitoring programme involves the mapping and analysis of 1400 1 km × 1 km squares. The sample squares are located
using the 3 km × 3 km grid already used for sampling in the
Norwegian forest inventory. Where the grid point falls on land
used for agricultural purposes (as defined by the Economic Map
Series), a 1 km2 surrounding this point has been included in the
sample. The programme follows a 5-year inventory cycle, with
national coverage first available in 2003.
Mapping is based primarily on interpretation of aerial photographs (scale 1:12,500, true colour). Land cover and land use
within each square are digitised according to a hierarchical classification system; including a total of ca. 100 land types at the
third and most detailed level. Among the more common land
types in the area were cereal fields, meadows, built-up land and
both deciduous and coniferous woodlands. In addition, linear
elements and point objects are recorded, including both natural
and cultural features. A number of indicators are calculated from
the resulting maps, addressing the four main issues of interest
defined by the Norwegian Ministries: landscape structure, biodiversity, cultural heritage and accessibility. For further details
on the 3Q-monitoring programme, see Dramstad et al. (2002).
2.2. On-the-ground photography
As a module linked to the 3Q-programme and research
projects related to this programme, ground photography has
been conducted on a 10% sub-sample of the 3Q-monitoring
squares (20–30 squares annually). Around 15–25 photographs
were taken by a professional photographer in each 1 km2 , the aim
being to describe the landscape content and variation throughout
the square. In addition to documenting the present appearance
of the landscape, these photographs also provide a historic landscape archive for the future (Puchmann and Dramstad, 2003).
To enable the same landscape section to be photographed in the
future, the precise location from which each photograph was
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W.E. Dramstad et al. / Landscape and Urban Planning 78 (2006) 465–474
Fig. 1. (a) Map of Norway, with circle surrounding Østfold and Akershus counties. (b) Example monitoring square illustrating the method of photography. Each
arrow 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 use
of GPS.
taken and the direction of the view were recorded on maps in
the 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 mapping the area included in each image (see below).
2.3. Preference study
In the preference study, 30 photographs were selected from
3Q-monitoring squares in Østfold and Akershus counties (see
Fig. 1). The photographs were chosen to illustrate different
degrees of openness in the landscape. Openness was not measured at this stage but pictures were chosen subjectively based on
perceived openness. An open landscape is defined as a landscape
with low vegetation allowing a clear view, as opposed to tall vegetation which obscures the view. Other aspects than openness,
such as light, weather conditions and seasonal differences, were
held constant as far as possible. An attempt was made to keep
the pictures free from features believed to be particularly strong
drivers of preference, such as water (Nasar and Li, 2004), and
man-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 far
distance.
The preference study was conducted using commonly applied
methods (as in, for example, Daniel and Boster, 1976, pp. 9–10;
Hägerhäll, 2001; Herzog, 1984, 1987; Lynch and Gimblett,
1992). Using a projector, landscape slides were shown on a large
screen in random order in a neutral room. Each slide was displayed for 10 s, after which the viewers were given 50 s to answer
questions on a form. The assessment of preferences was conducted by asking participants to give a score to each landscape
according to how much they liked the view (1 for least preferred
and 5 for most preferred). A short break was allowed after every
eight pictures to avoid fatigue effects. A total of 53 people from
Østfold and Akershus and 38 students from other parts of the
country participated in this study (see Table 1). All students
came from the Norwegian University of Life Sciences and, while
their disciplines varied, they were all studying environmentally
related subjects. All participants were invited according to random selection procedures. For the student group this was done
by distributing invitations to every second mailbox, while for
the public group every 50th entry in the local phonebook was
mailed an invitation, also offering to cover travel costs.
2.4. Mapping the view
By comparing a large display of each photograph with the
maps showing the photographer’s location and angle of view,
the boundaries of the area visible in each image were digitised
using ArcViewTM (ESRI). In this paper, we refer to the area
covered by each photograph as a viewshed. The presence of
vegetated boundary-lines, telegraph poles, buildings, etc., aided
the identification of the area represented in the image. An example of three images and their estimated viewsheds is shown in
Fig. 2.
Since, we did not have access to land cover data for the area
outside the monitoring squares, all images that contained clearly
visible area from outside the squares were excluded. This procedure left us with a total of 24 images. A selection of landscape
metrics was calculated for each viewshed (Table 2), including
Table 1
Information about the participants in the preferences study
Number of participants
Gender (M/F)
Average age
Youngest/oldest participant
Locals
Students
53
33/20
50.8
9/76
38
18/20
24.6
20/44
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W.E. Dramstad et al. / Landscape and Urban Planning 78 (2006) 465–474
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) and
spatial configuration (how patches of different land types are
located in relation to one another). Landscape metrics included
the total area of the viewshed, number of different land types
present, number of land type patches present, total length of
patch edges, area of open land types, grain size of the open landscape, landscape heterogeneity and land type diversity measured
as Shannon’s diversity index (Magurran, 1988).
These metrics were chosen because they are commonly
implemented in various forms of landscape monitoring and are
relatively simple to use and to interpret. The heterogeneity index
(see Fjellstad et al., 2001) is less well known but is used in the
3Q-monitoring programme. The index is calculated by recording
land type at a grid of points and then comparing every possible pair of points. The heterogeneity index is the proportion of
points that are on different land types. The minimum heterogeneity value is zero, when all points fall on the same land type
Table 2
The minimum, mean and maximum values for the different landscape metrics
for all estimated viewsheds
Total area (m2 )
Number of land types
Number of patches
Heterogeneity index
Shannon’s diversity index
Open area (m2 )
Percent openness
Grain size of open areas
Length of edge (m)
Minimum
Mean
Maximum
3696
3
3
0.00
0.21
2318
32
0.05
621
31508
5.5
9
0.35
0.87
27432
81
0.40
2335
128861
9
24
0.73
1.65
126828
100
1.73
6580
(large-scale, homogeneous landscape), and the maximum value
is 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 grid
of points was applied when measuring heterogeneity instead of
the 100 m × 100 m grid used in the monitoring programme. The
heterogeneity index is a measure of spatial division that is independent of the number of different land cover types in an area.
As such it complements the Shannon’s diversity index, which is
based on the relative areas of different land types.
To measure the area of open land types and the grain size
of the open landscape, it was necessary to recode all land types
according to whether the land cover would enable a clear view
(open) or would obscure the view (closed). All land types were
transformed to a binary variable, zero representing open areas
such as cereal fields and roads while one represented closed
areas such as forest or built-up land. Grain size was calculated
as the number of patches of open land types divided by the total
area of open land. Since area was measured in metre square this
produced a very small number and we multiplied by 1000 to
make the index easier to grasp at a glance. This gives an index
value, which for a given area, increases evenly with increasing
number of patches. Using grain size in addition to total open area
takes account of the fact that landscape elements such as a narrow
hedgerow or a grass bank between two fields (i.e. that divide the
landscape into more patches) may change the visual impression
of a landscape, even though total area of open landscape may be
almost identical.
Spearman’s correlation coefficient was used to explore relationships between the landscape preference scores and the various spatial metrics.
W.E. Dramstad et al. / Landscape and Urban Planning 78 (2006) 465–474
3. Results
3.1. Landscape metrics
Table 2 shows the degree of variation present in the viewsheds for the different landscape metrics. For comparison, the
average heterogeneity index values calculated for the 1 km2
469
monitoring squares in this region (137 squares) is 0.49 (minimum 0.27, maximum 0.80), whilst the average Shannon’s
diversity index is 1.62 (minimum 0.69, maximum 2.34). The
viewsheds, having been chosen to avoid water and man-made
objects, were thus slightly less heterogeneous and diverse
than 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 from
this group. Photograph (c) received the lowest average preference score from the student group, while photo (d) received the highest average preference score from
this group. Photograph (e) is an example of a viewshed containing water, while photograph (f) shows the most open viewshed. Photographs (g) and (h) were those
over 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 for
the locals, (h) was ranked number 21 by the students and number 10 by the locals.
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W.E. Dramstad et al. / Landscape and Urban Planning 78 (2006) 465–474
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 based
on the ranking (scale 1 to 5) given by the observers. Average preference scores for the two groups of participants were
almost identical (students: 3.25 versus locals: 3.21). Ranges in
scores were from 2.40 to 4.16 for students and from 2.57 to
3.62 for the local group, implying that the student group made
more use of the full scale when giving preference scores. Distribution of preference scores for the 24 images is shown in
Fig. 4. The student group and the locals group differed as to
which image they ranked as highest preferred and lowest preferred (see Fig. 3a–d) and, overall, there was no significant
correlation between the preference scores assigned by the two
groups (Spearman’s rho = 0.358, p = 0.086). The most contested
images – where the two groups showed the most disagreement
– were photographs 8 (Fig. 3g) and 21 (Fig. 3h). Photograph
8 was ranked as number four (i.e. fourth most preferred) for
the students and as number 17 for the locals. Photograph 21
was ranked number 21 by the students and number 10 by
the locals.
3.3. Correlations
Using the total dataset, we found a significant positive correlation 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. We
found no correlation in the total dataset between preferences
and landscape openness or spatial heterogeneity, but found
a significant correlation with land type diversity (Spearman’s
rho: 0.407, p = 0.049) within the mapped image area (measured
by the Shannon index). When the dataset was split into two
groups, we found interesting differences between students and
locals.
For the student group Shannon’s diversity index, the heterogeneity index, and the number of land type patches were all significantly positively correlated with preference score (Table 3).
Percent open area was negatively correlated with preference
score for this group. For the locals, total area and number of
land types in the image were significantly correlated with preference score at the 0.01 significance level, whilst area of open
land types and total length of edge were significant at a 0.05
level. Grain size of the open landscape was not found to be significantly correlated with preference scores for either group.
In interpreting these correlations it is important to consider
that there were also correlations between some of the different
landscape metrics (Table 4). For example, the total area of the
viewsheds was positively correlated with the total open area,
percent open area, length of edge and number of patches and
negatively correlated with grain size. There was no correlation,
on the other hand, between total area of viewshed and heterogeneity, Shannon’s diversity index or number of land types.
A comparison of preference scores for the five images containing the largest proportion of open area (>96% open area),
showed that the student group (average score 2.70) had a significantly lower preference (one-tailed t-test, p < 0.001) for these
large-scale open landscapes than the local group (average score
3.33).
Although we excluded photographs in which water was
clearly visible, water appeared on the list of land types present
within the viewshed for seven of the 24 images (see Fig. 3e).
The water could be seen in only one of the images (in the
background). The five most preferred images in the dataset all
contained water within their viewshed. The other two images
containing water as a land type ranked as number 12 and 22.
Images of viewsheds containing water were significantly more
preferred 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).
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W.E. Dramstad et al. / Landscape and Urban Planning 78 (2006) 465–474
Table 3
Correlations between landscape metrics and average preference scores for the two groups of participants
Landscape metric
Shannon’s diversity index
Heterogeneity index
Number of land types
Number of patches
Percent open area
Total area
Area of open land types
Length of edge
Grain size
Students
Locals
Spearman’s rho
Significance values
Spearman’s rho
Significance values
0.578
0.573
0.534
0.453
−0.452
−0.037
−0.093
0.154
0.327
0.003**
0.138
−0.083
0.583
0.506
0.246
0.532
0.510
0.481
−0.152
0.522 ns
0.701 ns
0.003**
0.012*
0.247 ns
0.007**
0.011*
0.017*
0.479 ns
0.003**
0.007**
0.026*
0.027*
0.864 ns
0.667 ns
0.474 ns
0.119 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 centres
have grown up in those parts of the country with the best agricultural conditions. This means that agricultural landscapes are
the “everyday-landscapes” for a large proportion of the population, and it is therefore important to be able to monitor how
changes affect the visual appearance of these landscapes. A
landscape rich in biodiversity and amenity values is not necessarily a by-product of current agricultural practice (Barnard,
2000; Green and Vos, 2001), and the public have come to realise
that many landscapes are deteriorating (Council of Europe,
2000). In certain cases, the demand for these amenity byproducts is increasing to the extent that food is considered to
be the by-product (see discussion by Hellerstein et al., 2002).
The current lack of scientifically based indicators for measuring changes in the visual landscape, hampers the inclusion of
this topic in monitoring programmes such as the Norwegian
3Q-programme.
The exclusion of landscape aesthetic information from monitoring and management may lead to less soundly based decisionmaking than desirable. When considering the costs and benefits
of landscape changes, for example, economic interests that are
easily measured may receive more attention than visual quality, even though there is an acceptance that this “public good”
also has an economic value in terms of, for example, human
health and tourism income. If alternative development scenarios
could be assessed using simple quantitative methods, the probability that landscape aesthetics would be taken into account in
decision-making could be increased. Methods to capture landscape values are therefore needed so that these values can be
integrated effectively with other kinds of data in designing, planning and managing landscapes (Ndubisi, 2002). Nassauer (1986)
challenges us to work towards an understanding of how elements
fit together in the visual landscape, so that change can be planned
and managed in a way that leads to desirable future landscapes,
instead of focusing too strongly on the past. This is also the message put forward by Green and Vos (2001), underlining the need
to conceive, design, create and maintain new landscapes fit for
the social, economic and environmental needs of the twenty-first
century. To achieve this, we need to be able to quantify and monitor landscape change, including changes in visual appearance.
Landscape preference research points to certain general
“rules of thumb” regarding features that influence preference
scores (Kaplan and Kaplan, 1989; Nassauer, 1995; Ndubisi,
2002; Zube, 1987). Water, for instance, has been shown by many
studies 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). In
our study, water itself was visible in only one of the images.
However, information from maps showed that water was present
in seven of the viewsheds, and the results from the preference
study revealed that these images received significantly higher
preference scores than images of areas without water. In these
images, there are clear vegetation belts that indicate presence of
waterways meandering across the landscape, a landscape feature probably recognised by most people. Our results suggest
that these patterns of vegetation, possibly combined with topographic variation, caused a general positive response (both for
students and locals). The underlying reason for this response
may be an evolutionary adaptation, i.e. that people read the landscape and interpret cues of the presence of water, as proposed in
the information-processing theory of Kaplan and Kaplan (1989).
However, the existence of such a deep, sub-conscious reason for
a preference, presumably applying to all people everywhere, is
extremely difficult to test. Whilst we cannot conclude from this
study whether it is the vegetation that causes positive reactions
or the suggestion of the presence of water, the result is nevertheless that waterways (with their associated vegetation and
topography) are a strong predictor of aesthetic preference.
For the group of students participating in this study, we found
significant positive correlations between landscape preferences
and landscape heterogeneity and diversity. Such relationships
have been mentioned by other authors previously (Hunziker,
1995; Kaplan and Kaplan, 1989; Piorr, 2003; Zube, 1987). It
was interesting to note, however, that these relationships did not
apply for the group of locals. The photographs in Fig. 3 clearly
illustrate the differences between the two groups. Neither the
heterogeneity index, nor Shannon’s diversity index were significantly correlated with preference scores for the locals. The
472
−0.570
0.004**
−0.201
0.347
0.040*
0.852
0.298
0.157
−0.650
0.001**
*
0.498
0.013
0.131
0.541
0.312
0.138
0.515
0.010*
0.921
0.000**
−0.614
0.001**
Total area
0.930
0.000**
0.707
0.000**
−0.050
0.818
0.192
0.368
0.464
0.022*
0.836
0.000**
−0.723
0.000**
Total open
area
Significant at the 0.05 level.
Significant at the 0.01 level.
*
**
Grain size
Total length edge
No. of patches
No. of land types
Shannon’s diversity index
Percent open area
Total area
Total open area
Total closed area
Rho
Significance value
Rho
Significance value
Rho
Significance value
Rho
Significance value
Rho
Significance value
Rho
Significance value
Rho
Significance value
Rho
Significance value
Rho
Significance value
0.536
0.007**
−0.361
0.083
−0.274
0.195
−0.661
0.000**
0.801
0.000**
0.233
0.274
0.183
0.393
−0.045
0.834
0.482
0.017*
−0.049
0.821
0.171
0.424
−0.695
0.000**
0.724
0.000**
0.377
0.070
0.300
0.155
0.312
0.138
0.190
0.375
Total closed
area
Hetero-geneity
index
Table 4
Correlations between the landscape metrics, showing correlation coefficients (Spearman’s rho) and significance values
Percent open
area
0.553
0.005**
0.492
0.015*
0.349
0.095
0.366
0.079
Shannon
diversity index
0.757
0.000**
0.462
0.023*
0.346
0.097
No. of land
types
0.748
0.000**
0.197
0.357
No. of
patches
−0.372
0.073
Total length
edge
W.E. Dramstad et al. / Landscape and Urban Planning 78 (2006) 465–474
correlation between the locals’ landscape preferences and total
length of edge, which could also be considered an aspect of
landscape diversity, is probably attributable to the strong preference of locals for a large view and the fact that total length of
edge is strongly correlated to total viewshed area. One interpretation of the difference between students and locals may be that a
few of the students had attended landscape ecology lectures and
were influenced by what they had learned about the benefits of
diverse and heterogeneous landscapes for biodiversity. Another
possible interpretation is that preference scores reflected different landscape familiarity for the two groups (Kaplan and
Kaplan, 1989; and see, for example, discussion by Armstrong,
2002; Bourassa, 1991; Kjølen, 1998). The significantly higher
preference by locals for the five most open landscapes compared with students may be due to the fact that the local group
live in landscapes that are amongst the most open, large-scale,
intensively managed agricultural landscapes in Norway, whereas
the students came from different parts of the country where
such landscapes are unusual. This also fits with the finding
that locals preferred a large view (total viewshed area) whilst
student preferences were not significantly influenced by size
of view.
More research is needed to investigate the role of familiarity in shaping visual landscape preferences and to explore other
underlying reasons for differences in landscape preferences such
as age and social background. Nevertheless, the results of this
study suggest that care is needed to interpret indicators of landscape change within an appropriate regional context. Regions
defined by landscape type are more likely to provide a necessary and meaningful context for the interpretation of landscape
indicator values than administrative units. In addition, our study
shows that students who may go on to careers in landscape planning and management, may not share the aesthetic preferences of
local people. This is an important consideration when working to
fulfil 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 authorities of the aspirations of the public with regard to the landscape
features of their surroundings” (Article 1: Council of Europe,
2000). Clearly, a more widespread use of methods for public
participation in landscape planning will be required if such landscape quality objectives are to be adequately formulated.
The 3Q-monitoring squares used in this study contained both
low preference and high preference views in close proximity.
Since single photographs cannot represent an entire monitoring
square, the viewshed approach used in this study was necessary to establish connections between map-derived indicators
and landscape preferences. Due to the complexity of underlying
reasons for human landscape preferences, we cannot expect to be
able to accurately predict visual preferences for larger mapped
areas. However, having some general guidelines of the aspects
of landscape content and spatial configuration that are important
in determining preferences will improve our ability to predict
whether landscape changes have positive or negative effects on
the appearance of the landscape, and thus improve interpretation
of regional indicator values. While we fully appreciate the need
to apply such indicators with care (Bell and Morse, 1999; Cale
W.E. Dramstad et al. / Landscape and Urban Planning 78 (2006) 465–474
and Hobbs, 1994; Gustafson, 1998; Leitão and Ahern, 2002;
Turner, 1989), it is no longer possible to deny their need. Further,
there are few methods available for the objective description of
the visual landscape. Highly subjective and expert approaches do
not seem acceptable to policy-makers or the general public and
make it difficult to compare landscapes or quantify changes over
time. This is particularly important if, as our study suggests, the
trained experts have different landscape preferences than local
people. The present situation that excludes visual aspects of a
landscape from consideration due to lack of methods, is not
acceptable either. In this respect, we agree with the statement
that “. . . indicators can distort priorities—those things which
are being measured and reported are viewed as more important,
while things which are less readily measured are omitted and
given lower priority” (Detr, 2000, p. 6). Landscape aesthetics are
among the issues less readily measured. Indicators have a large
number of advantages, and have come to be highly demanded,
for example, by politicians (OECD, 2001). We would therefore
encourage the continued research effort in developing indicators also for these more challenging topics, such as the visual
landscape.
5. Conclusion
By establishing a link between the birds-eye view of remote
sensing data and the on-the-ground perspective of landscape
photographs, this study identified several aspects of landscape
content and spatial configuration that are related to people’s
landscape preferences and that may therefore be suitable as indicators for the visual landscape. In particular, presence of water
and number of land types appear to be useful predictive variables.
However, the finding that different groups of people (students
and locals) prefer different types of landscape underlines the
need for care when interpreting indicator values. There are many
possible underlying reasons for people’s preferences for landscapes. Amongst other things, it may be that an education in
environmental subjects leads to a more complex interpretation
of landscape views, incorporating other values than pure visual
aesthetics (for example, an assessment of conditions for biological diversity) or it may be that preferences are affected by the
degree of familiarity with the landscape in question. Whatever
the causes of differences, this study suggests a need for wide
public participation in landscape planning decisions if visual
aesthetics are to be adequately addressed. We also underline
the importance of interpreting landscape indicator values in an
appropriate regional context, with a focus on landscape types
rather than administrative units.
Acknowledgements
This study was supported by the Norwegian Research Council. The authors would like to thank Oskar Puschmann, Norwegian Institute of Land Inventory (NIJOS) for all his help
with the photos, as well as everyone who participated in the
preference study. We would also like to thank two anonymous
reviewers for valuable comments on an earlier version of the
manuscript.
473
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