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Landscape Quality Assessment Dr Andrew Lothian Scenic Solutions Flinders University Research Colloquium, 13 August, 2014.

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Presentation on theme: "Landscape Quality Assessment Dr Andrew Lothian Scenic Solutions Flinders University Research Colloquium, 13 August, 2014."— Presentation transcript:

1 Landscape Quality Assessment Dr Andrew Lothian Scenic Solutions Flinders University Research Colloquium, 13 August, 2014

2 Scope Why measure landscape quality? How to measure landscape quality Acquiring the data Respondents Overall findings Mapping Lessons & Applications The presentation focuses on the study of the Lake District in England but also draws on other studies conducted in South Australia Dr Andrew Lothian, Scenic Solutions2

3 Who is Andrew Lothian? 3 Dr Andrew Lothian, Scenic Solutions I worked in environmental policy in SA Government for many years in Australia. Lectured at Flinders in policy. Long interest in how to quantify landscape aesthetics. During 1990s, undertook PhD in landscape quality assessment at the University of Adelaide. Since then I have conducted 10 consultancy studies on landscape quality & visual impact assessment of developments including wind farms. Flinders Ranges S.A. CoastRiver Murray Barossa & Eden Valleys

4 Why measure landscape quality? Unlike biophysical assets, landscape aesthetics is a qualitative asset, as perceived by people. The European Landscape Convention defines landscape as “an area, as perceived by people, whose character is the result of the action and interaction of natural and/or human factors.” Landscape quality is the human subjective aesthetic response to the physical landscape. Beautiful landscapes attract millions of tourists throughout the world to areas such as the Swiss Alps, the Canadian Rockies, the Italian lakes and Amalfi coast. The Lake District in England attracts 20 million visitors annually. Australia’s Great Barrier Reef, Kakadu and the Kimberlies, Uluru and Kangaroo Island attract many overseas visitors. They come to see the wild and natural landscapes, not the cities. Many World Heritage areas are outstanding landscapes. Exposure to natural landscapes provides significant health and restorative benefits. Views of attractive landscapes adds significant value to properties. Dr Andrew Lothian, Scenic Solutions4

5 How not to measure landscape quality There have been many attempts to measure landscape quality by recording all the physical features – land forms, land cover, land use, water, geology, etc, in the expectation that by analysing all of this data, the landscape quality would emerge. It never did! The reason is that this process is a cognitive activity involving analysis and thinking. But landscape quality involves making judgements about what we like – i.e. preferences. This is an affective process. Example: We know whether or not we like chocolate by tasting it, not by analysing its content, origin, colour etc. These can inform us but do not define its taste. Similarly we judge music by whether we like it, not by analysis of the instruments, score, etc. Landscape character units defined and mapped Scenic quality indicators mapped Weightings applied Scores of attributes applied Subjective judgements made Scenic quality comparisons made Scenic quality described and/or mapped 5Dr Andrew Lothian, Scenic Solutions

6 Psychophysics – basis for measuring landscape quality Preferences are our likes and dislikes and are based on affect, not cognition. The dictionary define aesthetics as “things perceptible by the senses as opposed to things thinkable or immaterial.” This clearly differentiates thinking from the senses. Researchers fell into the trap of assuming cognition was the same as affect. They are completely different. IN the 19 th century, Gustav Fechner, a German physicist, developed psychophysics – the science of measuring the brain’s interpretation of information from the senses (sight, sound, smell, taste, touch). Over recent decades, psychologists have applied its methods to measuring human landscape preferences. Gustav Fechner 1801 - 1887 6Dr Andrew Lothian, Scenic Solutions

7 Only by applying the affective paradigm can the attractiveness of a landscape be determined. Attractiveness is determined by measuring preferences. As it relies on preferences it is a subjective quality but preferences can be analysed objectively. Common elements in research methodologies are: Selection of scenes for rating. Rating scale – e.g. 1 to 10. Rating instrument – i.e. a means for showing scenes with a rating scale. Participants who rate the scenes – a sufficient number of raters for statistical analysis. They should be disinterested in the subject – i.e. have no stake in the outcome. Applying the affective paradigm 7Dr Andrew Lothian, Scenic Solutions

8 1. Photograph region 2. Classify region’s landscape units 3. Select survey photographs 4. Identify & score landscape quality components 5. Prepare & implement Internet survey 6. Prepare data set and analyse results 7. Map region’s landscape quality The method I use involves photographing the area, classifying the area into units of similar landscape characteristics, selecting photographs representative of these characteristics, rating of the photographs, analysing the results, and using the understanding gained to map the landscape quality. Community Preferences Method 8Dr Andrew Lothian, Scenic Solutions

9 Use of Photographs Advantages of photographs: Avoids transporting large groups of people through large region. Enables widely separated locations to be assessed on comparable basis. Can cover seasonal changes. Can assess visual impact of hypothetical developments. Many studies have shown that photographs will provide similar ratings as field assessments providing certain criteria are met. A meta-analysis of studies found a correlation of 0.86 between on- site and photo assessments. Criteria for photographs Standardised horizontal format 50 mm focal length (digital equivalent) Colour Non-artistic composition Sunny cloud-free conditions (ideal) Avoid strong side lighting of early morning or evening Good lateral & foreground context to scenes Avoid distracting and transitory features including people The principle is standardisation so that respondents judge the landscape, not the photograph 9Dr Andrew Lothian, Scenic Solutions

10 Landscape Units Areas of similar characteristics e.g. land form, land cover, land use, water, texture, colour – as shown in the map. Simple classification of Lake District: – Coastal estuaries, marshes and beaches – Plains – Low fells – Valleys without lakes – Valleys with lakes – High fells – High mountains Base the selection of photographs on sampling the landscape units. Lake District Landscape Typology Chris Blandford Associates 10 Dr Andrew Lothian, Scenic Solutions

11 11

12 Landscape components Dr Andrew Lothian, Scenic Solutions12 In addition to having photographs rated for landscape quality, a small group scored the scenes for a range of components that might contribute to landscape quality. 1 – 5 scale used to score the visual significance of the component in each scene. For the Lake District, components covered: Water Land forms Land cover – shrubs and trees Naturalness – absence of human influence Diversity – total busyness of the scene Cultural elements – artificial features Stone walls & hedgerows By combining these scores with the ratings the strength of their contribution to landscape quality can be determined. Scores: Stone walls & hedgerows 3.31, naturalness 2.54, land cover 3.57 Scores: Land cover 4.22, water 3.10, land form 4.11, diversity 3.90

13 Photography March, June and July, 2013 covering winter, spring & summer Over 4000 photographs 145 photos selected and Internet survey prepared in August 1500 invitations emailed to potential participants Acquiring the Data – Lake District Routes travelled for photography Progress in survey participation 13 Dr Andrew Lothian, Scenic Solutions

14 Survey data 540 responses 314 rated all 145 scenes, 73% 34 rated 0 scenes 4 displayed strategic bias – mostly 10s Net 430 UK-born respondents & 72 non- UK born Analysis covered only UK-born Comparison of ratings by non-UK born included. Number of completed surveys Histogram of scene means DataNumberMeanSD Respondents4306.141.23 Scenes1456.111.24 14Dr Andrew Lothian, Scenic Solutions

15 Characteristics covered Age Gender Education Birthplace Postcode Familiarity Residence Respondent characteristics The respondents were generally middle aged, with many more males participating than females, and most were very well educated. 15

16 Comparison of respondents with UK population 16Dr Andrew Lothian, Scenic Solutions Compared with the general UK population, the respondents were: Older More males Higher levels of education The differences were statistically different.

17 Similarity of ratings The respondents differed significantly from the UK population. Does this matter? It would matter if preferences varied widely across age, gender & education. But they don’t vary significantly. The top graph compares the average preferences on a 1 – 10 scale, indicating their similarity. The bottom graph exaggerates the scale to show the differences. The range is only 0.32 or +/- 0.16. So regardless of their characteristics, people rated the scenes similarly. 17Dr Andrew Lothian, Scenic Solutions

18 Respondent origins & familiarity Many of the respondents came from the north- west, 64% lived in Lancashire and Cumbria. 57% lived in the Lake District Familiarity increased ratings by as much as 14% Familiarity might breed contempt, but in respect of landscapes it has the opposite effect. This is due to “place attachment”. CategoryRating% increase Extremely familiar6.2614.21 Very familiar6.039.98 Somewhat familiar5.999.25 Visited but not familiar6.1011.25 Never visited5.48100.00 18Dr Andrew Lothian, Scenic Solutions

19 Overall ratings by landscape type 19Dr Andrew Lothian, Scenic Solutions LandscapeScenesMean Mountains227.05 Valleys with lakes257.02 Rockfaces106.81 Streams46.47 Valleys without lakes96.27 High fells225.87 Low fells115.66 Coast35.56 Dense trees55.24 Quarries34.95 Pines84.39 Plains104.15

20 Mountains #122 8.36 #44 7.55 #141 6.51 #26 7.20 22 scenes Mean rating 7.05 Range 5.43 to 8.36, a wide range of 2.93 Strong skew to higher ratings – histogram Diversity & naturalness have quite strong influence on ratings y = 0.78x + 4.20, R² = 0.37y = 0.86x + 4.43, R² = 0.48 20Dr Andrew Lothian, Scenic Solutions Histogram

21 Rockfaces 21 #81 6.38 #99 6.91 #17 7.02 #111 6.02 10 scenes Mean rating 6.81 Range 5.73 to 7.73, a moderate range of 2.00 Strong skew to higher ratings – histogram Surprisingly, neither height or steepness influenced ratings y = -0.49x + 8.85, R² = 0.26 y = 0.19x + 5.92, R² = 0.09 Dr Andrew Lothian, Scenic Solutions

22 High Fells 22 #28 7.14 #30 5.04 #77 4.39 #59 4.39 22 scenes Mean rating 5.87 Range 3.85 to 7.39, a wide range of 3.54 Low to high ratings – histogram Diversity & naturalness have strong influence on ratings y = 1.47x + 2.51, R² = 0.46y = 0.61x + 3.94, R² = 0.16 Dr Andrew Lothian, Scenic Solutions

23 Low fells 23 #5 5.50 #55 5.41 #100 5.85 #109 6.04 11 scenes Mean rating 5.66 Range 4.36 to 6.64, a wide range of 2.28 Middle rating – histogram For those low fells with stone walls, their presence actually decreased ratings Highest influence of tree spacing on ratings was for scattered trees y = -0.26x+ 6.79, R² = 0.14 2 = isolated, 3 = scattered, 4 = scat-dense, 5 = dense Dr Andrew Lothian, Scenic Solutions

24 24 #11 5.88 #120 6.93 #57 6.19 #63 6.18 9 scenes Mean rating 6.27 Range 5.55 to 6.93, a narrow range of 1.38 Middle to higher ratings – histogram Land cover & naturalness have moderate influence on ratings Valleys without lakes y = 0.54x + 4.45, R² = 0.44 y = 0.80x + 4.00, R² = 0.36 Dr Andrew Lothian, Scenic Solutions

25 Valleys with lakes 25 #16 8.12 #38 7.34 #89 7.59 #136 7.47 25 scenes Mean rating 7.02 Range 5.51 to 8.66, a wide range of 3.15 Mainly higher ratings – histogram Even a glimpse of water increased ratings Naturalness has a strong influence on ratings y = 1.20x + 2.98, R² = 0.40 Dr Andrew Lothian, Scenic Solutions

26 Influence of water on ratings Dr Andrew Lothian, Scenic Solutions26 The scores of water in the scenes was compared with the area of water as measured on each photo. There was a reasonable correlation (0.52) but other factors were clearly involved in determining the visual significance of water in a scene The area of water as a % of the non-sky portion of each scene was measured and related to the ratings. Surprisingly this found virtually no relationship between the percentage of the scene that was water and the ratings, which suggests that any amount of water, small or large, increases ratings.

27 River Murray Study Dr Andrew Lothian, Scenic Solutions27 A similar finding was made in the study of the River Murray. Scenes without water rated 4.43 but the presence of even a small glimpse of water (score 1) raised this to 5.78. The difference in ratings between a glimpse and extensive water was only 1 unit. Water scoreRating 15.78 26.03 36.28 46.53 56.78 Water score 1.05, Rating 6.08

28 Plains 28 #18 3.74 #64 4.05 #107 4.74 #75 3.89 10 scenes Mean rating 4.15 Range 3.11 to 5.77, a wide range of 2.66 Low to middle ratings – histogram Abundance of land cover has slight influence Plains are low in diversity but it has a strong influence. y = 0.44x + 2.77, R² = 0.53y = 1.45x + 1.40, R² = 0.60 Dr Andrew Lothian, Scenic Solutions

29 Components vs components 29 Revised cultural y = 0.75x + 0.64, R² = 0.37 y = 0.79x + 1.09, R² = 0.40 y = 0.48x + 1.59, R² = 0.33 Landscape components were scored on a 1 – 5 scale. Comparing the scores of one component with another brings out some interesting relationships. Dr Andrew Lothian, Scenic Solutions

30 Components vs ratings 30 y = 1.29x + 1.93, R² = 0.78 y = 1.14x + 2.52, R² = 0.43 ScoreRating 13.61 25.05 36.50 47.95 59.40 y = 1.45x + 2.16, R²= 0.63 y = 0.19x + 5.78, R = 0.01 Cultural elements include farming, sheep and cattle, stone walls and hedgerows, fields, narrow winding roads, and farmhouses. It indicates that cultural elements had little influence on ratings. Dr Andrew Lothian, Scenic Solutions Comparing ratings with scores shows their influence

31 Dr Andrew Lothian, Scenic Solutions31 Barossa Study The Barossa study made an interesting discovery through comparing factor scores with scenic ratings. It might be thought that the vines enhance scenic quality but this is not so, they actually reduce it. It is the presence of trees around the vineyards that enhance scenic quality.

32 Comparison scenes – with & without features 32 With poles Without polesDiff.% 3.134.311.1837.70 4.025.881.8646.27 2.924.731.8161.99 3.274.751.4745.00 2.92 4.73 Powerlines Colour With colour Without colourDiff.% 6.655.670.9814.74 6.3946.3850.0090.14 5.794.840.9516.41 6.285.630.6410.25 4.05 3.74 5.79 4.84 With sheep Without sheepDiff.% 6.475.880.599.12 5.54.870.6311.45 4.053.740.317.65 5.344.830.519.55 Sheep Dr Andrew Lothian, Scenic Solutions

33 33 Stone walls & hedgerows 5.50 4.83 With walls Without wallsDiff.% 5.504.830.6712.18 6.976.720.253.59 5.404.890.519.44 4.314.050.266.03 5.555.120.437.75 8.31 8.00 SnowSummerDiff.% 7.306.291.0113.84 8.318.000.313.73 6.856.830.020.90 7.497.040.455.95 Seasonal change Water With water Without waterDiff.% 6.516.240.274.15 7.346.061.2817.44 7.486.930.557.30 7.116.410.709.85 7.34 6.06 Dr Andrew Lothian, Scenic Solutions

34 34 5.02 4.80 7.14 7.17 With trees Without treesDifference % difference 6.767.140.385.62 7.177.14-0.03-0.42 5.946.110.162.84 Trees were inserted into 4 scenes to assess the effect of revegetating the fells on the landscape. 3 were rated higher without the trees & one was higher with the trees. Respondents may have rejected trees on familiar fells. Or they rejected the dense trees as scattered trees received a positive rating. Or they prefer the fells to be bare rather than vegetated. Trees 5.12 5.04 Dr Andrew Lothian, Scenic Solutions

35 Mapping Mapping proceeded area by area, 40 in all, to build up the complete map. The generic ratings that were derived from the survey were applied to each area. LandscapeRating Plains4 Pines4 Low fells5 Rivers6 Valleys without lakes6 Valleys with lakes6/7 High rounded fells5 High steep (≥30%) fells6 High fells with rockfaces6 Mountains (≥700 m – 850 m)7 Mountains ≥ 850 m8 Dr Andrew Lothian, Scenic Solutions35 The map shows the main rating to be 5 (yellow) with ribbons & areas of 6 (light red - rivers, valleys without lakes, steep fells). Many lakes and mountains from 700 – 850 m were 7 (darker red) and inside those were small areas of 8 (darkest red).

36 Landscape quality ratings Dr Andrew Lothian, Scenic Solutions36

37 Dr Andrew Lothian, Scenic Solutions37 Why do we like what we like? What generates the appeal of landscapes? – why do we like what we like? Hierarchy of influences – innate individual Most landscape theory is based on evolutionary perspective – what we like is survival enhancing. We like what aids our survival as a species. This might explain our preference for water but doesn’t explain liking for the sea which we cannot drink. Or survival in mountains. DEMOGRAPHIC Individual Indi FAMILIARITY Regional CULTURE Society INNATE All people It may however explain preferences for scattered trees – like African savannah - rather than dense trees which can hide predators & be difficult to climb. Dearden’s Pyramid of Influences

38 Dr Andrew Lothian, Scenic Solutions38 Restorative benefits of viewing nature 2012 Cumbria Visitor Survey found that the top reasons for visiting the area was because of the physical scenery and landscape of the area (69%) followed by the “atmospheric character of the area being peaceful, relaxing, beautiful and so on (54%).” Studies from experiencing natural environments: Reduced anger and violence among residents of Chicago apartments and reduced crime in their neighbourhood Less fatigue and more rapid recovery from fatigue Reduced blood pressure Lower heart rates and reduced stress for students swotting for exams Even viewing posters of natural scenes is beneficial. Intuitive understanding of the restorative benefits of viewing nature helps explain the popularity of the Lake District which attracts 20 million visitors a year. The landscape survey found that the naturalness correlated highly with ratings, as did land form and diversity, both part of naturalness.

39 Dr Andrew Lothian, Scenic Solutions39 What is the economic value of Lake District landscape? A century ago, the Swiss landscape was judged to be worth $200m/annum 2009 – 2012 visitation averaged 22.05 million visitor days. Average expenditure of £980 million/year = £44.44/visitor/day. The area of the Lake District National Park is 2219.68 km 2 Annual expenditure = £441,505/km 2 or £4,415/ hectare. Farmgate income £59m = £31,536/sq km or £315/ha = 7% of its value for visitors. Total: £473,041/sq km or £4,730/ hectare.

40 Applications Dr Andrew Lothian, Scenic Solutions40 Possible applications include: 1.Incorporating landscape quality provisions in policies and planning to ensure its recognition, protection and enhancement; 2.Defining scenic quality objectives for the management, protection and enhancement of landscape quality in the region; 3.Assisting in the definition and substantiation of nominations of areas for World Heritage and National Park status; 4.Promoting the tourism and recreational opportunities of the region; 5.Assisting in the selection of routes for transmission lines and roads and for minimizing developmental impacts, e.g. wind farms.

41 Conclusions The project provides insights and understanding of how the community view the Lake District’s scenic assets. Measuring and mapping the landscape quality of the Lake District is a first for the UK which abandoned landscape quality assessment decades ago. However the project demonstrates that a robust and credible method of measuring community preferences is available. 41Dr Andrew Lothian, Scenic Solutions

42 Dr Andrew Lothian Director, Scenic Solutions PO Box 3158, Unley, Adelaide South Australia, 5061, AUSTRALIA Mobile: 0439 872 226 Phone/fax: (618) 8272 2213 Email: Internet: Dr Andrew Lothian, Scenic Solutions42

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