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Agriculture, Ecosystems and Environment 142 (2011) 205– 212

Contents lists available at ScienceDirect

Agriculture, Ecosystems and Environment

jo ur n al homepage: www.elsev ier .com/ lo cate /agee

simplified model to assess landscape quality from rural roads in Spain

lvaro Ramírez ∗, Esperanza Ayuga-Téllez, Eutiquio Gallego, José María Fuentes, Ana Isabel GarcíaIPREE Research Group, Universidad Politécnica de Madrid, Ciudad Universitaria s/n, 28040 Madrid, Spain

r t i c l e i n f o

rticle history:eceived 30 November 2010eceived in revised form 4 May 2011ccepted 4 May 2011vailable online 8 June 2011

eywords:andscapessessment methodsural roads

a b s t r a c t

The present work proposes a simplified model for assessing the quality of landscapes from rural roads inSpain. The proposed model is based on the full Canas method but takes into account only four of the 16factors, the latter contemplates: vegetation, land use, form (elements differing from the background) andtexture (reflection of light from surfaces). The procedure was tested by assessing the landscapes capturedin 109 photographs taken from sections of rural roads around Spain and comparing the results with (a)those obtained using the full Canas model, and (b) the landscape preferences of the evaluators determinedusing the Likert scale. The correlation between the Canas method and the proposed model was strong(R2 = 98.5%), indicating that the simplified model is sufficient for landscape quality assessment in thepresent setting. A strong correlation (R2 = 96.24%) was also found between the landscape quality values

ignificant factorspain

obtained using the simplified model and the landscape preferences of the evaluators recorded via theLikert scale. The proposed model differentiates between the landscapes seen from the rural roads betterthan the Canas method and could be used in the planning of rural roads and other transport infrastructures(greenways, railways, highways, etc.) around Spain and even in other parts of the Mediterranean afterfurther validation. It might also be employed in the decision-making process surrounding the investmentsto be made in existing but deteriorated rural roads with agricultural and/or leisure uses.

. Introduction

The assessment of landscape quality is a multidisciplinary activ-ty that can be approached in different ways depending on theims of such assessment and the training of the people involvedPalmer and Hoffman, 2001). Experts in landscape architecture,ngineering, psychology and planning, all approach assessmentrom different angles (Carlson, 1977; Ribe, 1982; Dearden, 1987;yuga, 2001; Tassinari and Torreggiani, 2006). However, the litera-

ure shows there to be two clearly differentiated main approacheshat can be followed (Briggs and France, 1980; Lothian, 1999;rriaza et al., 2004). The first assumes that landscapes have anbjective or intrinsic beauty that can be quantified via the pres-nce of physical or aesthetic components or factors (Daniel andoster, 1976; Smardon, 1983; Schauman, 1986; Coeterier, 1996).he main criticism of this is that it does not take into account theubjective, psychological components particular to each observeri.e., the observer’s emotions, motivations, memories and prefer-nces) (Dunn, 1976). The second is based on arriving at an average

or the evaluations made by a number of persons (general publicr experts), each of which must perforce bring his or her own sub-ective components into the assessment procedure (Arthur et al.,

∗ Corresponding author. Tel.: +34 91 336 5620; fax: +34 91 336 5625.E-mail address: alvaro.ramirez@upm.es (Á. Ramírez).

167-8809/$ – see front matter © 2011 Elsevier B.V. All rights reserved.oi:10.1016/j.agee.2011.05.007

© 2011 Elsevier B.V. All rights reserved.

1977; Briggs and France, 1980; Bernáldez et al., 1989; Pérez, 2002).In an attempt to unify these approaches, some authors have advo-cated the joint use of physical, aesthetic and psychological factors intheir evaluations (Russell and Pratt, 1980; Zube et al., 1982; Kaplan,1988; Hernández et al., 2004a; Canas et al., 2009), investing eachwith more or less importance depending on the purpose of theassessment.

In the 1970s and 1980s a number of US agencies (USDA ForestService, 1974; USDA Soil Conservation Service, 1978; USDI Bureauof Land Management, 1980; USDOT, 1981) proposed practicalmethods for assessing the visual quality of landscapes taking intoaccount physical (land form of the terrain, vegetation, water andman-made structures) and aesthetic (colour, form, line, texture,scale and spatial dominance) factors. The possible modification ofthese factors and the introduction of others has been a matter of dis-cussion (Coeterier, 1996; Ayuga, 2001; Sevenant and Antrop, 2009).Although these methods base their assessment on expert opinion,Smardon et al. (1988) argues that the subjectivity of the public’spreferences should be incorporated via the surveying of differentlocal people.

The most exhaustive form of assessment involves evaluatorsmaking direct observations in the field. The use of photographs,

however, allows assessment work to be performed in the office,reducing costs (Dunn, 1976; Shafer and Brush, 1977; Shuttleworth,1980; Stewart et al., 1984; Hull and Stewart, 1992; Wherrett, 2000;Pérez, 2002). Notwithstanding, Palmer and Hoffman (2001), who

206 Á. Ramírez et al. / Agriculture, Ecosystems and Environment 142 (2011) 205– 212

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Fig. 1. Locations of the 38 rural roads w

iscuss the validity of the use of photographs, express doubtsegarding the impossibility of evaluating certain factors, such asounds, smells and perhaps certain nuances of colour. Geographicnformation systems and computer-aided design also have provedo be useful tools for landscape assessment (Lee et al., 1999;ernández et al., 2004b, 2005; Otero et al., 2007; Ghadirian andishop, 2008).

In recent years a number of authors have tried to adapt thebove-mentioned methods of assessment to the particular char-cteristics of Mediterranean landscapes (their relief, vegetation,ridity, etc.), investigating the factors that should be taken intoccount in this region (Arriaza et al., 2004; Otero et al., 2007;anas et al., 2009) and incorporating psychological variables intohe process of evaluation. The method proposed by Canas et al.2009), which is further explained in Section 2, assigns values to2 aspects defining 16 physical, aesthetic and psychological land-cape factors via the viewing of photographs. The present workroposes a simplification of the Canas method for the assessmentf landscapes from Spanish rural roads. The landscapes surround-ngs rural roads in Spain make up an important sub-class that merit

pecial assessment methods since they receive substantial num-ers of recreational travellers, a consequence of the promotion ofural tourism over the last decade. The simplified model was vali-ated by assessing the landscapes visible from 109 stretches of rural

the landscape assessments were made.

road from around Spain and comparing the results obtained withthose provided by the full Canas method and with the evaluators’landscape preferences as determined using the Likert scale (Likert,1932).

2. Materials and methods

This work was undertaken within the framework of a largerresearch project aimed at developing systems for managing andconserving Spain’s rural road network (Gallego et al., 2008a,b). Thepresent work involved the selection of 109 sections of 38 rural roads(see Fig. 1) used for agricultural and recreational purposes, madeby considering the land use in the areas they served, their pave-ment type, their traffic intensity and their importance within thelocal network. Most of the rural roads analysed provided accessto parcels of agricultural land or farms. Thus, the predominantlandscape was one of cultivated land; neither forestry nor waterbodies were normally present. The areas adjacent to the roads werepredominantly flat. These kinds of road are usually laid close to vil-lages and form dense but isolated networks that generally form

a network, the centre of the town appearing as the origin of thisnetwork.

The mean length of the rural roads analysed was 2.71 km. Mostof them were unpaved (72.63%). Some 54.3% of the total road dis-

Á. Ramírez et al. / Agriculture, Ecosystems and Environment 142 (2011) 205– 212 207

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Fig. 2. Representative exam

ance analysed was represented by rural roads running throughrrigated lands. Most of the roads laid in non-irrigated lands pro-ided access to land parcels usually devoted to cereal crops. Thoseoads laid in irrigated areas provided access to a wider variety ofrops, e.g., maize or different vegetables grown under plastic.

Abundant documentation was collected on each of the selectedural roads, including numerous photographs of the landscapes vis-ble from them, plus information regarding elements that might bemportant in landscape assessment, e.g., topography, road surfaces,he size of the earthworks involved in making the roads, and theresence of manufactured items such as pipes, culverts and trafficigns, etc. Each photograph was georeferenced for the easy input ofts position into a geographical information system. The length ofach rural road varied between 3 and 11 km. Nonetheless, the land-cape was evaluated at intervals of 1 km (sufficient to adequatelyover any changes in landscape over these short roads), resulting in

total of 109 assessments. The authors considered the photographsaken in the direction of the travelled road (i.e., from the viewpointf a driver or passenger in a vehicle) (Fig. 2) as representative of theandscape associated with the sampled road segments.

.1. The Canas assessment method

The Canas method (Canas et al., 2009) was developed fromhose proposed by different US agencies (USDA Forest Service,974; USDA Soil Conservation Service, 1978; USDI Bureau of Landanagement, 1980; Smardon, 1983). Descriptors used by the US

orest Service are specific for mountain landscapes characterizedy a dense vegetation, while the ones used by the US Bureau of Landanagement are suitable for semi-arid areas or areas with thin veg-

tation and open spaces. The Canas method is an adaptation of thexperts’ assessment method proposed by Smardon et al. (1988)

o the particular characteristics of Mediterranean landscapes. Itlassifies landscapes into different categories via the assessmentf 16 factors: water (type, shore vegetation, movement and quan-ity), form of the terrain, vegetation (complete coverage, diversity,

of the landscapes assessed.

quality and type), snow (coverage), fauna (presence, interest andvisibility), land use (type and intensity), views (amplitude andtype), cultural resources (presence, type, interest and visibility),alterations (intrusion, landscape fragmentation, horizontal line andobstruction of view), sounds, smells (neither of which are takeninto account when working from photographs), form (diversity,contrast and compatibility), colour (diversity, contrast and com-patibility), texture (diversity, contrast and compatibility), unity(structural lines and proportion) and expression (affectivity, stimu-lation and symbolism). A more detailed description of the meaningof each of these factors and their aspects can be found in Canas et al.(2009). This assessment provides a final landscape quality score of0–100 (<20 deteriorated; 20–32 deficient; 32–44 mediocre; 44–56acceptable; 56–68 good; 68–80 very good; >80 excellent), accord-ing to the classification of Canas (1995).

In addition, the type of resources to which the quality of a land-scape is most owed can be determined since physical, aesthetic andpsychological factors are considered separately. The score for eachfactor is determined according to an additive model, i.e., by addingup the scores for each of its different aspects (Canas et al., 2009):

V = v1(x1) + v2(x2) + ... + vn(xn) (1)

where V is the landscape quality score; vi (xi) is value of factor xi.For the present work, technical information files were prepared

using spreadsheets to collect the scores awarded for the differentfactor variables using the photographs (Fig. 3). This informationautomatically provided the final landscape quality value (totalscore) for each landscape (Fig. 4).

2.2. Development of the proposed model from the Canas method

The aim of this work was to obtain a simplified version of theCanas method (i.e., one that reduces the number of factors requiredfor accurate landscape quality assessments to be made) suitable foruse with rural roads.

208 Á. Ramírez et al. / Agriculture, Ecosystems and Environment 142 (2011) 205– 212

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Fig. 3. Technical information sheet for assessin

.3. Reducing the number of factors to be assessed

Prior to reducing the number of factors, it is necessary tonow the number of evaluators required for reliable results toe obtained. To determine this figure, 21 evaluators (10 men, 11

omen, aged between 20 and 50, educated to university level) used

he Canas method to asses the landscapes in the 109 photographs.hese evaluators were not experts in landscape assessment, andeceived instructions for filling in the technical information files.

able 1endall � values for the different factors used to assess the landscapes using theanas method.

Factor �

Form of the terrain 0.23Vegetation 0.37Land use 0.30Views −0.03Cultural resources 0.02Alterations 0.13Form 0.39Colour 0.37Texture 0.35Unity 0.34Expression 0.41

quality of a landscape with the Canas method.

The results of this preliminary step suggested that at least 12 eval-uators would be required for reliable results (an error of <10% witha 95% level of confidence) to be obtained. The assessments of these21 evaluators were also used to identify the most important fac-tors in landscape analysis. More than half the scores awarded for‘water’, ‘snow’, ‘fauna’, ‘sounds’ and ‘smells’ were zero (data notshown), a consequence of their absence in the photographs or theimpossibility of their evaluation. Table 1 shows the Kendall � rankcorrelation coefficients for the remaining factors.

2.4. Statistical procedures used to identify the factors to includein the simplified model

The mean scores awarded by the group of 21 evaluators wereanalysed using the Tukey multiple range test to determine theirlevel of homogeneity (Zar, 1999).

The contribution of each factor to the total landscape qualityscore was established by calculating the corresponding Kendall� rank correlation coefficients (Hollander and Wolfe, 1999). Theresults were confirmed using the Kruskal-Wallis test (Conover,

1971).

Multiple regression models were produced bearing in minddifferent combinations of factors as independent variables. Theminimum number of factors for each was then determined. The

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very similar to the scores awarded by the group of 21 evaluators

ig. 4. Landscape quality score for a road section and classification scale using theanas method.

ifferent regression models were then compared by calculatinghe coefficient of multiple determination (R2) and diagnosing theesidues. This allowed a model involving only the most importantactors to be identified.

.5. Validation of the simplified model

To validate the proposed model, which makes use of just fouractors – land use, vegetation, form and texture – it was used by

new group of 19 evaluators (again aged between 20 and 50 andducated to university level) to assess the same 109 rural road sec-ions. As before, none of these evaluators was an expert in landscapessessment, and all received instructions for completing the tech-ical information files. The results obtained by the 19 evaluatorsere compared to those obtained by the 21 evaluators using the

ull Canas method.In addition, to verify whether the proposed model represented

bserver preferences well, a simple regression model was estab-ished relating the mean scores for each road section to the Likertcores awarded for the same by these 19 evaluators. The good-ess of fit was determined by calculating the coefficient of multipleetermination (R2). The residuals were also analysed.

Finally, cluster analysis was used to classify the landscape qual-ty results of these 19 evaluators into categories (Lei and Koehly,003; Pohar et al., 2004). The algorithm employed to obtain these

nd Environment 142 (2011) 205– 212 209

categories was that of Ward (using squared Euclidian distances)(Kuiper and Fisher, 1975; Overall et al., 1993).

2.6. Statistical methods used to analyse the influence of sex inlandscape assessment

Two-way ANOVA was used to identify differences between themean landscape quality scores awarded by men and women (fac-tors: sex and road section identity). The Kolmogorov–Smirnov test(Lilliefors, 1967) was used to determine whether the mean scoresfor each section of road given by each sex fitted normal distribu-tions, and the Barlett test (Snedecor and Cochran, 1989) was usedto determine whether the variance was constant. Finally, the Stu-dent t test for paired samples was used to compare the scores ofthe two sexes (Student, 1908).

Two-way ANOVA was used to identify differences between themean landscape quality scores awarded by men and women (fac-tors: sex and road section identity). The Kolmogorov–Smirnov testwas used to determine whether the mean scores for each sectionof road given by each sex fitted normal distributions, and the Bar-lett test was used to determine whether the variance was constant.Finally, the Student t test for paired samples was used to comparethe scores of the two sexes.

3. Results and discussion

3.1. Factors included in the simplified model

Using the results provided by the 21 evaluators, a regressionmodel involving just four factors – vegetation, land use, form andtexture – was found to be the most adequate (R2 = 91%). Thesefactors, plus their weightings as determined by the appropriateregression model, were those used in the simplified model. Eq. (2)shows how the total mean landscape quality score is calculatedusing the simplified model.

Total mean = −2.98 + 1.58 × vegetation + 1.01 × land use

+ 2.87 × form + 2.05 × texture (2)

The mean score for each factor showed good correlation withthe total mean landscape quality score (r > 0.3).

The simplified model included neither ‘water’ nor ‘snow’ sincethe analysed rural roads do not provide views of water bodiesor of snow-covered landscapes. Many authors have highlightedthe importance of ‘water’ in landscape assessment (Ulrich, 1983;Purcell and Lamb, 1984; Purcell, 1987; Ulrich, 1986; Steinitz, 1990).In contrast, few have examined the importance of ‘snow’, althoughthe importance of seasonal changes has been examined (Buhyoffand Wellmann, 1979; Mudrak, 1983; Pearce and Waters, 1983;Misgav, 2000). In Mediterranean landscapes, however, snow is notvery common, and most rural roads do not pass by large bodies ofwater; the assessment of such landscapes would demand specialtreatment. Thus, the simplified application of the Canas methodthat includes just the four factors in Eq. (2) would appear adequatefor assessing landscapes from Spanish rural roads.

3.2. Validation of the simplified model via its testing by the groupof 19 evaluators

The landscape quality scores awarded by the group of 19 eval-uators for each of the 38 roads using the simplified models were

using the full Canas (Fig. 5).The correlation between the Canas method and the proposed

model was strong (R2 = 98.5%), indicating that, for the present set-

210 Á. Ramírez et al. / Agriculture, Ecosystems a

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ig. 5. Linear regression for the landscape quality scores using the Canas assessmentethod and the simplified model.

ing, the simplified model is sufficient for determining landscapeuality.

The scores awarded by both methods were confirmed to beffected by the sex of the evaluator (Fig. 6a and b). The corre-ation between the Canas method and the proposed model wastronger for male observers (R2 = 98.3%) than for female observersR2 = 94.23%) but not significantly so. The scores awarded for eachoad section analysed by the male evaluators were higher (gener-lly by about 3 points) than those awarded by the female evaluatorsp = 0).

The landscape preferences of the 19 evaluators were also deter-ined for each of the 109 road sections using the Likert scale. A

trong correlation was seen between these scores and the land-cape quality scores awarded by the proposed model (R2 = 96.24%).his result is similar to that seen in comparisons involving the fullanas method (Canas et al., 2009).

.3. Influence of sex on landscape quality scores

The influence of sex in landscape studies is controversial. Tipsnd Savasdisara (1986) and Yu (1995) indicate there to be no greatifferences between men’s and women’s appreciation of landscape.owever, other authors report the opposite (Macia, 1979; Abellond Bernáldez, 1986; Bernáldez et al., 1989; Fischer and Shrout,006). The results of the present work suggest that the sex of thevaluator needs to be taken into account, at least in the presentontext.

Two-way ANOVA showed the scores awarded by the sexes toe different (p = 0) in both cases. The interaction sex x road section

dentity, however, had no effect on the final mean landscape qualitycores awarded (p > 0.8). The results of the Kolmogorov–Smirnov

ig. 6. Scatter plots showing the relationship between the landscape quality scores obbservers (b).

nd Environment 142 (2011) 205– 212

test did not allow a normal distribution to be rejected since themean scores for each section of road given by each sex fitted a nor-mal distribution (p = 0.6). The Barlett test detected no significantdifferences between the standard deviations of the mean scores ofthe men and women (p = 0.11). The Student t test for paired sam-ples showed the scores of the two sexes to be significantly different(p = 0); the difference between the means was 4.02 (95% confidenceinterval 3.19–4.86).

3.4. Classification of landscapes using the simplified model

Compared to the full Canas method, the simplified method pro-vided a new classification of the quality of the landscapes for the 38rural roads (Table 2). Cluster analysis of the means for the factors‘vegetation’, ‘land use’, ‘form’ and ‘texture’ grouped the roads intoseven categories (Table 3). In fact, the simplified model differenti-ates between the landscapes seen from the rural roads better thanthe Canas method. Using the Canas method, 32 of the rural roadswere classified as mediocre or deficient, and only one fell into the‘good’ category. This occurred because the factors ‘sounds’, ‘smells’,‘water’ and ‘snow’ have to be given a value in the full Canas method– zero in this case – but are given no value in the simplified model.This introduces a downward bias of the overall Canas scores – aproblem not suffered by the simplified model. This therefore sug-gests that the simplified model is not only adequate for assessingSpanish landscapes from rural roads, but that it does so better thanthe Canas method.

3.5. Applications of the simplified model

The proposed, simplified model is appropriate for assessing thequality of landscapes observed from rural roads in Spain. It couldalso be used to assess the quality of landscapes visible from otherSpanish transport routes, such as highways, motorways of railways.However, when used with these longer infrastructures, which crossdifferent types of landscape, the distance between the points wherephotographs are taken would have to be first determined to ensurethe different visual characteristic of these landscapes were cap-tured in a representative manner. In the present work photographswere taken at 1 km intervals. The speed at which travellers movealong these other infrastructures would also need to be taken intoaccount; the faster an observer travels, the greater the landscapecontemplation distance becomes.

The simplified model was validated for use with Spanish land-

scapes, which are largely agricultural, relatively flat, and lacking interms of forest masses and water bodies. Such characteristics arerelatively common throughout the Mediterranean; the simplifiedmodel may, therefore, be of use right across this region. In other

tained with the Canas method and the simplified model for male (a) and female

Á. Ramírez et al. / Agriculture, Ecosystems and Environment 142 (2011) 205– 212 211

Table 2Percentage of rural roads belonging to each landscape quality class. Comparison between the simplified model and the Canas assessment method.

Landscape quality Simplified model Canas method

Total mean (see Eq. (2)) Percentage of rural roads Total mean of Vxi (see Eq. (1)) Percentage of rural roads

Deteriorated <16 13.1 <20 0Deficient 16–24 28.9 20–32 31.6Mediocre 24–27 34.2 32–44 52.6Acceptable 27–30 13.1 44–56 13.1Good 30–33 5.2 56–68 2.6Very good 33–36 2.6 68–80 0Excellent >36 2.6 >80 0

Table 3Landscape quality categories determined by cluster analysis and mean scores for the factors in each category.

Landscape quality Total mean (see Eq. (2)) Percentage of rural roads Vegetation Land use Form Texture

Deteriorated <16 14.68 4.57 4.45 0.81 2.72Deficient 16–24 26.61 6.51 9.20 1.41 3.54Mediocre 24–27 15.60 7.49 9.44 4.18 4.41Acceptable 27–30 11.01 7.73 10.62 4.80 5.09

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Good 30–33 19.27

Very good 33–36 7.34

Excellent >36 5.50

egions it would be necessary to determine the degree of influencen landscape quality of the different factors defining the landscape;his could be performed using the same methodology described inhe present work.

Finally, this model may be of use in decision-taking in ruraload maintenance and conservation projects, helping to establishnvestment priorities for the transformation of, for example, dis-sed railway lines or future recreational greenways.

. Conclusions

The quality of Spanish landscapes crossed by rural roads can bessessed using the proposed, simplified model which involves justour factors: ‘vegetation’, ‘land use’, ‘form’, and ‘texture’.

The methodology followed to determine the most importantactors could be used to decide which are the most important oneshen trying to adapt the model to other settings.

The proposed model is represented by the linear equation:

otal mean = − 2.98 + 1.58 × vegetation + 1.01 × land use

+ 2.87 × form + 2.05 × texture

A strong correlation exists between the quality value awardedo a landscape when using the simplified model and the actualreferences of the evaluator (Likert scale).

Further validation of the proposed model may allow for its use inhe design of other transport infrastructures (highways, railways,tc.), in order to best preserve or take advantage of the landscape.

Given the influence of sex, groups of evaluators should includequal numbers of men and women if comparisons of assessmentsetween groups are to be made.

Planners, engineers and maintenance technicians in Spain maynd the proposed model a useful way of determining the visualuality of landscapes visible from existing and projected ruraloads. The proposed model might be used with other transportnfrastructures and in other parts of the Mediterranean.

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