In this section, the findings of studies into a range of landscape-related subjects are summarized. These cover:
Antarctica Click here
Sounds Click here
Wildlife Click here
Landscape indicators Click here
Fractals Click here
The 1991 Protocol on Environmental Protection to the Antarctic Treaty commits the parties to “the protection of the Antarctic environment…and the intrinsic value of Antarctica, including its wilderness and aesthetic values.” In response, Codling (2001) proposed a landscape character assessment of Antarctica based on the British approach.
In a study of the unique landscapes of Antarctica, Summerson & Bishop (2011) sought to define whether the region was beautiful or sublime. They used semantic scales comprising ten words each for beauty and sublime, words such as beautiful, austere, glorious, bleak, ugly, and spectacular. They classified the Antarctic landscapes into six broad groupings: coastal ice free, mountainous ice free, Antarctic Peninsula ice field, coastal continental margin, ice shelves & floating glaciers, and central Antarctic ice sheet, which occupied much of the continent. Their Internet survey comprising 90 scenes asked the respondent to rate the landscape (1 – 7 scale) and identify adjectives which best described it. From over 300 responses, they scored the adjectives and constructed radar diagrams to illustrate the results (Figure 1). This is of image 128 which is in beauty and in words describing the sublime such as grand, magnificent and breathtaking.
Summerson & Bishop, 2011. Note: Blue shaded area provides results for image 128. Red circle is zero score for all words – further out is positive score. Yellow line is combined scores for all images of mountainous ice-free area.
Figure 1 Radar graph of the semantic assessment of the highest aesthetically rated scene, Antarctica
Combining all the results, they found:
- Distinct difference between responses to coastal ice-free areas and all other Antarctic landscapes;
- Aesthetic value encompasses far more than aesthetic preference; landscapes that are perceived to be “austere” or “barren” and “vast” are just as much part of the authentic Antarctic aesthetic experience as the “beautiful,” “magnificent” and the “grand”;
- Evidence of human activity, especially infrastructure, detracts from aesthetic value;
- Aesthetic responses to Antarctic landscape are predominantly sublime but are more complex than the traditional beautiful/sublime dichotomy (Burke and Kant).
The Antarctica is a region where field assessment of the landscape would not be an option and where photographs must be used for rating purposes.
The Antarctica would be a challenging area in which to undertake landscape quality assessment. The findings of Summerson & Bishop (2011) indicate both beauty and sublimity in the region’s landscapes.
While vision accounts for most of the information gained from the environment, sound is the next most important sense. The interaction of sound with vision, specifically the influence, if any, of sound on landscape preferences has been investigated by several researchers.
The experiment by Hetherington et al, (1993) to use sound as well as video of water movement was summarized in the section on preferences for water. They found that both sound and motion influence preference:
“Motion without sound produces similar results to the static digitized image condition, while the motion with sound and the original video results suggested a consistent polynomial relationship between perceived scenic beauty and flow.” (Figure 2).
Hetherington, Daniel & Brown, 1993
Figure 2 Influence of video & sound on scenic beauty
In Spain, Carles et al, (1998) used combinations of sound and images and subjects rated the sound, then the image and then both together, rating them on a 5-point scale, unpleasant through to pleasant. Images of natural and urban scenes were used together with sounds of a village, stream, park, thunderstorm and residential area. They found that natural sounds, particularly of water, created positive feelings towards the landscape (Figure 3). The combination of the sound and image of the stream gained the highest rating and generally stream sounds increased ratings of all images.
Carles et al, 1998. List of sounds along top. Scenes across bottom, rating of sounds with each scene vertically.
Figure 3 Influence of different sounds on rating of natural and urban scenes
Helicopter flights over the Grand Canyon are very popular but are a major distraction for visitors to the wilderness environment with as many as 43 overflights in a 20-minute period (Horonjev et al, 1993) and 100 can be flying over it at any one time.
Viewing the Grand Canyon
Mace et al, (1999) showed subjects scenes of the Grand Canyon while exposing them to sounds of helicopter flights. They then rated the scenes for naturalness, freedom, preference, annoyance, solitude, scenic beauty, and tranquillity. While each of these was affected by noise, the strongest effects were for annoyance, solitude and tranquillity. It also found that landscape quality and naturalness were adversely affected. Scenic beauty rated 8.02 without noise, 7.66 with 40 dBA helicopter sound (5% ratings decrease) and 7.10 with 80 dBA sound (11.5% decrease). The authors suggested that the effect of noise from helicopters, planes and even cruise ships (e.g. Alaskan inside passage) on landscape enjoyment be considered.
As a follow-up to Mace et al, (1999), Benfield et al, (2010) tested subjects with a range of natural sounds, and ground and air traffic sounds at different levels. He tested the same set of factors as Mace plus serenity and appropriateness. Subjects viewed scenes of national parks while being exposed to different sounds and rated the scenic beauty (Figure 4).
Benfield et al, 2010. Note: Voice sounds omitted. Scale exaggerated.
Figure 4 Effect of sounds on rating of scenic beauty
They found that while natural sounds had a negligible effect on ratings, exposure to ground traffic and especially air traffic noise negatively affected ratings. Exposure to 60 dBA air traffic noise reduced ratings by nearly 11%. They also found that scenes of high scenic beauty were affected more often and to a larger degree than scenes of low scenic beauty and thus measures to reduce human noise in such locations is particularly important. Exposed to natural sounds, the volume affected annoyance, tranquillity, solitude, serenity and preference for scenes of high scenic beauty but had no effect for scenes of low scenic beauty.
In summary, landscape ratings are enhanced by natural sounds, particularly of water, but loud urban and traffic noises, whether from cars or aircraft, can decrease ratings by up to around 11%.
One survey focused on the influence of wildlife on landscape preferences, while several other studies have examined this incidentally to their main focus. Hull & McCarthy (1988) examined the effect of wildlife as part of a study of changeable landscape features. The landscape settings were park-like forests, rivers and grassy fields near Melbourne, Australia. Scenes were photographed with and without wildlife, the wildlife comprising kangaroos, wallabies, deer and swans. The study found that the wildlife had a positive but moderate effect, accounting for less than 10% of the total scenic score. Wildlife tended to have a greater influence in the less attractive scenes. An expectation of seeing wildlife (e.g. by “wildlife feeding area” signs) significantly enhanced the scenic scores.
Nassauer & Benner (1984), found that the presence of birds, porpoises and animals were positive features in the Louisiana oil and gas development area, though of relatively small importance overall (e.g. factor loadings 0.16 in one of the factors identified compared with 0.4 for a small bay and 0.6 for a sand bar).
In his study of the Chicago arboretum, Schroeder (1991) found the presence of birds was one of the frequently mentioned features of the garden. In their study of visitors to Spanish national parks, DeLucio & Mūgico (1994) found that many visitors viewed the park as a zoo in which they could see animals and birds in the wild. In a study of wilderness users in New Zealand, Kliskey (1994) found hunting to be one of their expectations. Van den Berg (2006) in a study of wilderness perception in The Netherlands found that nature study was one of the motives of wilderness users. She found that respondents who indicated that they visited nature for restoration, reflection, and to study plants and animals, displayed higher preferences for wild natural landscapes than respondents for whom these motives were less important.
In western Norway, Strumse (1994) found a scene with a horse rated 4.16 on a 1 (worse) – 5 (best) scale. In a contrary Norwegian finding, Fyhri et al, (2009) found that scenes with sheep rated relatively poorly: 3.05 – 3.16. The finding led to them concluding that agricultural areas and images including domestic animals are somewhat less preferred than other images.
In a study of wetlands in Victoria, Australia, Dobbie & Green (2013) found that animals (i.e. wildlife) were mentioned by 40% of participants in viewing the scenes.
Though not strictly wildlife, the author’s Lake District study (Lothian, 2013) found that the presence of sheep in a scene enhanced the landscape preferences by an average of nearly 10% (Figure 5).
Scene with sheep rated 4.05
Same scene without sheep 3.74
Lothian, 2013b Figure 5 Influence of the presence of sheep on landscape preferences, Lake District
These studies, though limited, indicate that wildlife generally has a positive, if minor, influence on landscape preferences.
People are not wildlife but their influence on landscape preferences has been rarely included in studies. In the study of the Lake District (Lothian, 2013b), only one comparison scene with people was included (Figure 6) and this indicated that the presence of people actually detracted from the scene by 6%. This is surprising given the number of photographs taken of people in tourist photos of places visited.
Scene with people rated 6.92
Same scene without people 7.33
Figure 6 Scenes with and without people (and boats)
In a study in two cities Hangzhou & Suzhou, in China, Zhuo et al, (2013) used ten visual indicators, seven of which were measured objectively and a further three by subjective assessment by participants. These three were years of historical heritage, number of landscape elements, and impression, the latter assessed by the number of the group who remembered the photo. Historical heritage was the age of the buildings, up to 600 years. The results found preference for landscapes with historical heritage of mainly up to 300 years (Figure 7), and a peaking of around 60% (i.e. 0.6 ratio) open space (Figure 8). In addition, they found a preference for 45% water cover, lower plant cover, and greater diversity and naturalness.
Zhua et al, 2013 Figure 7 Preference for age of buildings
Zhua et al, 2013 Figure 8 Preference for open land
In France, Weinstoerffer & Girardin (2000) developed a method to calculate a landscape indicator which would cover both the objective and subjective approach to landscape assessment. It measures the agreement between the supply of landscapes by farmers and the demand for landscapes by society. These are evaluated through four criteria: diversity, upkeep, openness and heritage. An example of the openness criterion is shown in Figure 9. Values for the four criteria are defined by users into five classes and the value of the landscape indicator is the smallest gap between supply and demand for the four criteria. Figure 10 shows the calculation of the landscape indicator for a 99-ha arable farm with 18 fields situated in Alsace in France.
Weinstoerffer & Girardin, 2000.
Figure 9 The openness criterion
Weinstoerffer & Girardin, 2000.
Figure 10 Representation of the landscape supply for a pilot farm
To determine the relationship between landscape spatial patterns and the rating of visual aesthetic quality, de la Fuente de Val et al, (2006) used participants to evaluate eleven visual attributes in photographs from Spain and Chile from which the qualities were identified and their preferences measured. The eleven attributes, based on the aesthetic theories of Kaplan, Appleton, and Bernáldez & Gallardo, were: scenic beauty, coherence, legibility, complexity, mystery, perspective, diversity, risk, colors, pattern and patch-shape. For each photograph, three visual fields were defined: foreground, mid-ground and background. Within each of these fields, nine spatial metrics were analyzed, which included the number of patches and patch diversity, fractal dimension and relief. The authors concluded:
“Scenic beauty shows a limited correlation with landscape pattern indices … However, a number of visual attributes, notably legibility, shape, perspective and mystery, show clearer correlations with landscape spatial pattern indices. These correlations are in agreement with those in related theories concerning the content of available information in the perceived landscape and the pleasure or scenic attraction produced” (de la Fuente de Val et al, 2006).
Landscape metrics refers to studies which use metrics calculated by a program called Fragstats which is freeware developed originally in 1995 at Oregon State University to work with ArcGIS software. It analyzes data for biodiversity, water quality, landscape patterns and aesthetics, and land management and planning. Landscape aesthetics has been a relatively minor use. Uuemaa et al, (2009) provided an overview of landscape metrics, which has emerged largely since 2000. They identified the following studies of landscape aesthetics using landscape metrics: Franco et al, 2003; Palmer, 2004; Dramstad et al, 2006; Lee et al, 2008 and Fry et al, 2009. Sang et al, 2008 has used landscape metrics in the context of the Europe Landscape Convention to provide an “objective” assessment the landscape and the potential impact of changes to it.
Fractals are patterns that are repeated at different scales, self-similarity regardless of scale. Natural examples include river networks, mountain ranges (Figure 11), snowflakes and ocean waves. It has been suggested that the appeal of old cities lies in their organic, fractal nature, which grew in human-scale increments resulting from countless individual decisions and actions, whereas city plans such as Washington, Canberra and Brasilia use simple circular and regular geometries which lack humanness.
Figure 11 Example of a fractal image of two parameters resembling a mountain
The scale invariance of fractals is measured by the fractal dimension, D which is between 1 and 2 for a fractal line and between 2 and 3 for a fractal surface. These measure the extent to which a structure exceeds its base dimension to fill the next dimension. There has been considerable interest in the extent to which aesthetic qualities are a response to fractals – e.g. snowflakes, waves and landscapes.
In order to test this, Hagerhall et al, (2004) used 80 silhouette outlines of landscapes of which Figure 12 is an example.
Original landscape viewSilhouette outline of the landscape
Hagerhall et al, 2004 Figure 12 Example of silhouette outline of the landscape
The ratings of the scenes displayed a wide scatter of preferences in the range of D values from 1.1 to 1.45. The R2 was low, 0.31, suggesting that there was a weak relation between preference and fractal quality. They found that the scenes of high preference and of low D were of water or prominent hilly topography. However, they found that water images occurred across the full range of D values and preferences. Excluding these water images left 52 images, which showed a significant correlation between preference and D (Figure 13) although the R2 is quite low (0.31). The preferences peak at around D = 1.3 which is consistent with previous research.Hagerhall et al, 2004.
Figure 13 Preferences for images (excluding water and hilly topography)
There have been many other studies of fractals both in the urban and natural environment, e.g. Mandelbrot, 1983; Batty & Longley, 1994; Pentland, 1994 and Stamps, 2002. Additional research may further define the influence of fractals on landscape preferences.