Linking scenery and users’ perception analysis of Italian beaches (case studies in Veneto, Emilia-Romagna and Basilicata regions)

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Linking scenery and users’ perception analysis of Italian beaches (case studies in Veneto, Emilia-Romagna and Basilicata regions) Ilaria Rodellaa,∗, Corinne Corbaub a b

University of Padova, Via Martiri della libertà 8, Padova, 35122, Italy University of Ferrara, Department of Physics and Earth Sciences, Via Saragat 1, Ferrara, 44122, Italy

ARTICLE INFO

ABSTRACT

Keywords: Landscape Beach management Fuzzy logic assessment Questionnaires Physical and human parameters

The main purpose of this paper is to provide a scenic assessment of Italian beaches considering both physical and social aspects useful for defining coastal management strategies. Scenic values were calculated for 25 sites along Italian coastline using of a Coastal Scenic Evaluation System (CSES), a fuzzy logic containing 26 physical/human factors. The sites were categorized into five classes from Class I (top grade scenery) to Class V (poor scenery). Furthermore, a survey through questionnaires on Users' Perception (UP) was performed to obtain social assessment of the beaches and to define the beachgoer's characteristics for each scenic class. Five parameters (beach cleanliness, sea-water, beach width, landscape and crowding) were also combined to obtain a scenic and social analysis of the beaches. Three beaches belonged to Class I, e.g. remote or resort areas with a low impact of human activities and high score of natural parameters. Two Class II beaches were located at remote or rural areas having sand beaches, turquoise water and vigorous vegetation together with a low impact of tourist developments. Classes III, IV and V presented a wide distribution and their lower scores were linked to a poor environmental setting. These beaches were generally located in urban localities. Three aspects that were considered by the beach's users as the most important were beach cleanliness, good sea water and high quality of the services. However, UP assessment showed negative judgments on sea-water and landscape especially for Class IV and V beaches. On the other hand, cleanliness, recreational activities and facilities were well-judged for the Class III, IV and V beaches mainly due to the presence of private beach establishments. Scenic and social analysis of the beaches indicated that beach width was the best parameter both for scenic and social assessment; while crowding factor was a contradictory parameter presenting opposite trends of CSES and UP. Beach cleanliness, sea-water and landscape were depended to scenic Class of the beaches, both for CSES and UP. The excellent scenic values were associated to the environmental settings whereas human parameters usually showed low scores especially for urban beaches due to marine litter and the presence of coastal defense structures like groins and breakwaters. Nevertheless, users frequented both beaches with high and low parameters, due to their habitually frequentation and proximity to the beach. Finally, the results indicate that management strategies are also needed to improve the scenic quality and users' judgement of some beach features.

1. Introduction Coastal landscapes can be described as “a littoral area, as perceived by people, whose character results from the numerous interactions of natural and/or human factors” (Council of Europe, 2000). Scenery may also be defined as “the appearance of an area” (Council of Europe, 2000; RangelBuitrago et al., 2013). The first studies on scenery date back to the 60s in USA and France (Zube et al., 1982), when a body of legislation was enacted to the identification and management of scenic resources. In Britain, the Countryside Act of 1968 stated: “in the exercise of these functions relating to land under enactment every Minister, government

∗

department and public body shall have regard to the desirability of conserving the natural beauty and amenity of the Countryside.” Attention was subsequently directed to aesthetic concerns associated with agriculture, forestry, recreation, and designated Areas of Outstanding Natural Beauty, National Parks and Heritage Coasts. For coastal environment, scenery is a resource because of its economic value and because it is a recognized component of resource assessment programs (Kay and Alder, 2005). Coastal development has always been strongly dependent on natural resource exploitation and in particular on its scenery Additionally, the evaluation of coastal scenery is an important instrument for coastal preservation (identifying the value to society of particular

Corresponding author. E-mail address: [email protected] (I. Rodella).

https://doi.org/10.1016/j.ocecoaman.2019.104992 Received 17 July 2019; Received in revised form 9 September 2019; Accepted 13 September 2019 0964-5691/ © 2019 Elsevier Ltd. All rights reserved.

Please cite this article as: Ilaria Rodella and Corinne Corbau, Ocean and Coastal Management, https://doi.org/10.1016/j.ocecoaman.2019.104992

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areas/views), protection (identifying high quality landscapes and controlling development) and improvements (identification of components that can detract from views), as evaluation outcomes provides a scientific basis for any envisaged management plan (Ergin et al., 2004). Indeed, “landscape and scenery management” means actions from a perspective of sustainable development to ensure the landscape preservation, to guide and harmonize changes carried out by social, economic and environmental processes. Many techniques have been used for the assessment of landscape values, including photographs, landscape assessment numbers, scenic uniqueness, best worse scores from grid squares, statistical techniques obtained from site observations and others (Carlson, 1984; Gregory and Davis, 1993; Penning-Rowsell, 1982). Some scenery evaluations reported subjective analysis considering perception assessments or public preference inventories (Semeoshenkova and Williams, 2011). Other assessments considered both subjective and objective analysis (Roca and Villares, 2008), using for instance questionnaire with photographs and images (Cervantes et al., 2008; Krelling et al., 2017). Indeed, the preference approach is an integrative approach for studying the human-landscape relationship (Reimann et al., 2018), because many aspects are associated on public perception and “subjective” associations among the natural, aesthetic and cultural landscape features. Moreover, several studies about beach users' preferences include scenery within the main aspects of managing a beach, based on its importance for beach users (Williams and Micallef, 2009). Indeed, in some places, such as UK, Wales, Turkey, USA, Colombia, scenery is the first or second main reason for choosing a beach (Botero et al., 2013b; Morgan and Williams, 1995; Unal and Williams, 1999; Williams, 2011). The same authors report that scenery ranked in the enjoyment of a beach-based holiday. Results of these evaluations also indicate that beach users' relationships with the environment and the scenic perception are complex. Recreational behavior is indirectly affected by environmental quality; reciprocally, people affect the natural environment through their individual behavior, which may depend on their perceptions of the environment (Pendleton et al., 2001). As a consequence, many coastal countries must increase their tourist derived economic profits to be competitive and for this reason, it is critical to know beach users' preferences about landscape (da Costa Cristiano et al., 2018). Preferences and perceptions can be influenced by sociodemographic factors as well as a variety of other psychological variables, needs, personality and environmental factors (Botero et al., 2013b; Cervantes et al., 2008; Reimann et al., 2018; Roca et al., 2009). Because of these uncertainty factors, scenery assessment is not easy to obtain and understand. Furthermore, almost all checklists used to quantify scenic values have two major weaknesses: no weighing of parameters and no users' preferences are taken into account, they are usually experts’ opinion (Williams, 1987). In order to obtain a complete evaluation of scenery, we investigated the Italian coastal scenery using two complementary approaches, quantitative and qualitative:

applied methodologies in Colombian case studies (Botero et al., 2013a). Therefore, since no similar data have ever been collected in the Italian regions, an exploratory investigation was carried out in Italy. The study purpose is to classify the sceneries of 25 sites along the Italian coast highly frequented by tourists during the summer season. Moreover, the obtained classifications were used as a basis for understanding the users' preference and perception and to define the beachgoer's characteristics for each scenic class individuated. The study further aims to evaluate strategies for beach management and preservation for each scenery class. 2. Study area The study areas include 25 coastal stretches along the Adriatic and Ionian coasts of Italy and are located at Rosolina Mare in Veneto region, Lidi di Comacchio in Emilia – Romagna region and Metaponto Lido in Basilicata region. Beaches were selected according to the physical configuration (waves, wind, slope, width, vegetation cover, etc.), and anthropic features (touristic centres, uses, landscape configuration, management policy, environmental protection figures, etc.) in order to better represent the different regions. 2.1. Rosolina Mare (Veneto region) Rosolina Mare littoral, located inside the Veneto Regional Park, is 8 km-long from Adige river month at North and Porto Caleri lagoon month at South (Fig. 1 a). The littoral is characterized by a semi-urbanized area at North, an urbanized area in the middle of the littoral (whit 16 beach establishments) and southward a natural area with free beaches (corresponding to “Giardino Botanico di Porto Caleri” Site of Community Importance S.C.I. IT3270001, S.C.I. IT3270004). This natural area is characterized by the presence of dunes (incipient foredunes in particular), pinewood, saltmarsh and wetland. The coastline is characterized by low sandy beaches, with beach width varies from 20 to 210 m, gentle beach slope (0.5–3°). Coastal tourism has gradually increased since 1955 with the construction of the first beach establishment and the growing seaside tourism industry. In 2016, the number of tourist arrivals was 141,013 of which 69,494 were foreign tourists in prevalence of Germany (32,446), Poland (6,016), Czech Republic (more than 4,900) (Veneto Region, 2016). Tourist presences in 2016 were 1,104,733 of which 535,807 were foreign tourists (Veneto Region, 2016). 2.2. Lidi di Comacchio (Emilia-Romagna region) The Lidi di Comacchio North littoral is 16 km-long: It extends from Po di Goro to Porto Garibaldi and includes five coastal localities: Lido di Volano, Lido di Nazioni, Lido di Pomposa, Lido degli Scacchi and Porto Garibaldi, for a total of 71 beach resorts (Fig. 1 b). The coastal area consists of low sandy beaches derived from ancient swampy and alluvial deposits (Martinelli et al., 2011). The seabed slope is generally very gentle, about 0.4° (Bondesan et al., 1995). The width of the emerged beach is variable and ranges from 20 to 70 m from Lido di Volano to Lido degli Scacchi and from 80 to more than 120 m at Porto Garibaldi (Rodella et al., 2017a,b,c). Erosion, principally due to upstream structures and storm surges, affects this coastal stretch, which is protected by hard defence systems i.e. groins, revetments, breakwaters and dykes (Martinelli et al., 2010; Scarelli et al., 2017). Despite the strong human impact, several elements still characterize the landscape, such as wetlands (at Lido di Volano), lake basins (i.e. Nazioni Lake), rivers (Po river and Porto Garibaldi channel) and artificial maritime pine forests. This littoral is also characterized by many protected areas that are: Sites of Community Interest and Protected Zones (SCI-PZs IT4060002 Valli di Comacchio), naturalistic areas, areas of landscape interest (Lido di Volano and Delta Po Park of Emilia Romagna region) and redevelopment beach

- a scenery evaluation using a well-known fuzzy logic analysis called “Coastal Scenic Evaluation System (CSES)” that answers to 26 natural and human scenery parameters; - an evaluation of users' perception, carried out by questionnaire survey. As reported by Williams (2019) human-landscape interaction suggests that aesthetic quality can occur in both the objective qualities of landscape and the subjective meaning of landscape (Lewis et al., 1973; Tuan, 1977). Our ultimate goal is to integrate the methodologies because in some cases the social vision can enrich the CSES even if methods have different purposes. Several studies presented CSES applications, some of them with focus on litter grade (Rangel-Buitrago et al., 2017a), on marine protected areas (Mooser et al., 2018) and others. A huge amount of studies aims to present UPA application. However, there is only a study in literature that try to integrate the 2

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Fig. 1. Location of the studied sites according to their coastal scenic classes: a) beaches of Rosolina Mare; b) beaches of Lidi di Comacchio; c) beaches of Metaponto Lido. Labels correspond to coastal sites indicated in Table 3. Bing Aerial Map QGis®; Datum - WGS-84.

areas (e.i. Porto Garibaldi, Lido degli Scacchi, Lido di Pomposa and Lido di Nazioni).

and removable services (umbrellas, cabins, walkways, sports facilities) are present. This coastal area is affected by intense erosion as indicated by Aiello et al. (2013) and Spilotro et al. (2006). Despite the presence of anthropic elements, this littoral includes natural and semi-natural areas of Natura 2000 Network having high biological and naturalistic values. These areas include Sites of Community Interest (SCI) for preserving the Mediterranean maquis considered as a priority habitat by the environment legislation of the European Community (Habitat directive 92/43 CEE) for high vulnerability and exposure of extinction risk for anthropic reasons (urban settlements, industrial areas, touristic enjoyment, uncontrolled fires, erosion). The SCIs are: Costa jonica Foce Agri (IT9220085, Policoro, Scanzano Jonico); Costa jonica Foce Basento

2.3. Metaponto Lido (Basilicata region) Metaponto Lido littoral, in Basilicata region, is 7 km-long along the Gulf of Taranto in the Ionian Sea, bounded on the West by the Basento River and on the East by Bradano River (Fig. 1 c). The Metaponto Lido is characterized by low sandy beaches, gently sloping off shore by 1–2% (Greco and Martino, 2014), and is subjected to strong anthropogenic pressure (Tropeano et al., 2013). Along the littoral, 32 beach establishments with fixed facilities (bars, restaurants, bathrooms, accesses) 3

BEACH FACE

4 5 6 7 8 9 10 11 12 13 14

4

VEGETATION DEBRIS

DUNES VALLEY SKYLINE LANDFORM TIDES COASTAL LANDSCAPE FEATUREb VISTAS WATER COLOUR & CLARITY VEGETATION COVER

ACCESS TYPE

SKYLINE UTILITIESd

22 23

24

25 26

Height (m) Slope (°) Special Featuresa Type Width (m) Colour Slope (°) Extent (m) Roughness

No buffer zone/heavy traffic Very unattractive >3 Unattractive 3

No buffer zone/light traffic

Heavy tourism and/or urban

Full strand line

Continuous accumulations Sewage evidence None Heavy Industry

Tolerable

Open on two sides Milky blue/green/opaque Scrub/garigue (marram/gorse, bramble, etc.) Full strand line

1

Mud W < 5 m or W > 100 m Dark tan < 5° <5m Distinctly jagged Remnants Dry valley Flat

5 m ≤ H < 30 m 45°–60° 1

2

Intolerable

Open on one side Muddy brown/grey Bare (< 10% vegetation only) Continuous (> 50 cm high)

Absent Absent Dark Absent Absent Absent Absent Absent Not visible Macro (> 4m) None

Absent (< 5 m) < 45° Absent

1

Rating

Sensitively designed 2

Hedgerow/terracing/monoculture Light tourism and/or urban and/ or sensitive

Same sewage evidence

Single accumulation

Single accumulation

Green/grey/blue Wetlands/meadow

Cobble/Boulder 5 m ≤ W < 25 m Light 5°–10° 5 m–10 m Deeply pitted and/or irregular Fore-dune Stream (< 1 m) Undulating Meso (2 m–4 m) 2

30 m ≤ H < 60 m 60°–75° 2

3

Parking lot visible from coastal area Very sensitively designed 1

Sensitive tourism and/or urban

Few scattered items

Little

Open on three sides Clear blue//dark blue Coppices, maquis (mature trees bushes) Few scattered items

3

Pebble/Gravel 25 m ≤ W < 50 m Light tan/bleached 10°–20° 10 m–20 m Shallow pitted Secondary ridge Stream (1 m–4 m) Highly undulating

60 m ≤ H < 90 m 75°–85° 3

4

Natural/historic features None

Parking lot not visible from coastal area

Field mixed cultivation ± trees/natural Historic and/or none

No evidence of sewage

Virtually absent

None

None

Open on four sides Very clear turquoise Varity of mature trees/mature natural cover

Sand 50 m ≤ W ≤ 100 m White/gold > 20° > 20 m Smooth Several >4m Mountainous Micro (< 2m) >3

H ≥ 90 m circa vertical Many (> 3)

5

b

Cliff Special Features: indentation, banding, folding, screes, irregular profile. Coastal Landscape Features: Peninsulas, rock ridges, irregular headlands, arches, windows, caves, waterfalls, deltas, lagoons, islands, stacks, estuaries, reefs, fauna, embayment, tombola, etc. c Non-built environment: When there is no agricultural activity.If the natural vegetation cover parameter (17) has scored a 5, then tick the 5 box here.If the natural vegetation cover parameter (17 has scored 2, 3, 4 then tick the 3 box here.Built Environment: Caravans will come under tourism, grading 2: Large intensive caravan site, Grading 3: Light, but still intensive caravan sites, grading 4: Sensitively designed caravan sites. d Utilities: Power lines, pipelines, street lamps, groins, seawalls, revetments.

a

SEWAGE (DISCHARGE EVIDENCE) NON_BUILT ENVIRONMENTc BUILT ENVIRONMENTd

21

Human parameters 19 DISTURBANCE FACTOR (NOISE) 20 LITTER

18

15 16 17

CLIFF

1 2 3

ROCKY SHORE

Physical parameters

Num.

Table 1 Coastal scenic evaluation system. Physical and human parameters (Ergin, 2019; Ergin et al., 2004).

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• CLASS IV: Mainly urban - Poor sites with medium landscape value and light development (0.40 > D ≥ 0.00); • CLASS V: Urban - Very unattractive urban elements, intensive de-

(IT9220080, Bernalda, Pisticci); Costa Ionica Foce Bradano (IT9220090, Bernalda); Costa Ionica Foce Cavone (IT9220095, Pisticci, Scanzano Jonico); Bosco Pantano di Policoro e Costa jonica Foce Sinni (SIC and Special Protected Zone- SPZ- IT9220055, Policoro, Rotondella).

velopment with a low landscape value (D < 0.00).

Final assessment matrices elaborated for all sites are graphically presented as histograms, weighted average of attributes and membership degree of attributes. These histograms (Fig. 2 a, d, g, l, o) provide a visual summary of both physical and human parameters obtained from Table 1 and are useful for immediate assessment of high and low rated attributes. Weighted averages of attributes delineated relative comparison of physical and human parameters (Fig. 2 b) and membership degree vs. attribute curve present overall scenic assessment over the attributes (Fig. 2 c) (Rangel-Buitrago et al., 2013). Beaches can be classified in many ways and for this study the optimum classification was the anthropogenic one (Table 3) since it regards user's preferences for beach choice (Williams et al., 2016a). Each site was classified into one of five categories: resort, urban, village, rural, remote (Williams and Micallef, 2009; Rangel-Buitrago et al., 2017, Table 3):

3. Methods 3.1. Coastal Scenic Evaluation System (CSES) and beach typology The Coastal Scenic Evaluation System (CSES; Ergin et al. (2004) via fuzzy logic analysis is a checklist technique that utilizes 26 coastal scenic assessment parameters grouped as physical and human factors sets (Table 1). CSES surveys were planned by the analysis of Bing aerial images in QGis environment, measuring beach dimensions and editing in maps the survey area of each beach. During the field surveys researchers also used a handheld GPS device (WGS-84 datum) to locate the beaches and to view areas to be investigated (with a horizontal accuracy of 0.20–0.40 m, depending on GPS satellite signal). Experts filled the checklist between 10 a.m. and 3 p.m. under normal calm summer weather conditions (June and September 2016, Mooser et al., 2018) over a 100 m range along the sites (Pranzini et al., 2019). At each location, discussion was encouraged on each parameter. Attribute values range from a low (1) to high rating (5) and a fuzzy logic program calculates an assessment parameter ‘D’ of the 26 parameters. Five classes of scenery could be defined according to the D value (Ergin, 2019), i.e.:

1. Resort: include areas related to an accommodation complex (i.e., hotel/condominium), where a substantial proportion of beach users are resident, and management is the complex responsibility. A series of facilities is usually prevalent because recreation is the main aim of this kind of beaches; 2. Urban: serves large populations which have well-established public services such school(s), bank(s), among others. Within this typology are clearly demarcated central business district with commercial activities, e.g. harbors. Urban beaches are freely open to the public; 1. Village: located outside the central urban environment and have a small and permanent population reflecting an organized but smallscale service structure, such as, a school(s), church(es), shop(s) and public/private transport. These beaches include ‘ribbon

• CLASS 1: Top natural - Extremely attractive natural site with a very high landscape value (D value ≥0.85); • CLASS 1I: Natural - Attractive natural site with a very high landscape value (0.85 > D value ≥ 0.65); • CLASS III: Natural - Many natural elements with little outstanding landscape features (0.65 > D value ≥ 0.40);

Fig. 2. Scenic evaluation rating histograms, scenic histogram of weighted averages and membership degree curves for each scenic class.

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Table 2 Parameters used for assessing physical and social evaluation of scenery (CSES: coastal scenic evaluation system; UP: users’ perception analysis). Num

Parameter

Physical factors (from CSES)

Social factors (from UP)

1 2 3 4 5

Beach cleanliness Sea-water quality Beach width Landscape Crowding (space, noise)

Litter (20) Water colour (16) Width (5) Coastal landscape features (14), skyline (25) Noise disturbance (19)

Quality and cleaning of the beach Sea quality Beach width Landscape Crowding

Table 3 Beach typologies (use: FB: free beach; BE: beach establishment) and CSES results. Location

N.

Beach

Use

Type (Williams and Micallef, 2009)

D value

Class

ROSOLINA MARE (RO)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25

Casoni Rosapineta Camping (North) Tizè Perla Marina di Porto Caleri Porto Caleri 1 Porto Caleri 2 Porto Caleri 3 Ipanema–Lido di Volano Lido di Volano (South) Lido di Nazioni Cristallo –Lido di Nazioni Aloha–Lido di Nazioni Pic Nic–Lido Pomposa Sagano–Lido degli Scacchi Lido Scacchi Nettuno–Porto Garibaldi Lido Marinella Riva dei Greci Magna Grecia Blumen Bad Ermitage Mondial Le Dune Basento sx

FB FB BE BE BE FB FB FB BE FB FB BE BE BE BE FB BE FB BE BE BE BE BE BE FB

Rural Village Village Village Village Remote Remote Remote Village Remote Rural Urban Urban Urban Urban Urban Urban Rural Rural Urban Urban Urban Urban Urban Remote

−0,06 0,2 0,15 0,27 0,53 0,92 0,77 1,02 0,43 −0,26 0,17 −0,61 −0,36 −0,48 −0,19 0,11 −0,24 1,04 0,67 0,5 0,3 0,11 0,19 0,39 0,55

V IV IV IV III I II I III V IV V V V V IV V I II III IV IV IV IV III

LIDI DI COMACCHIO (FE)

METAPONTO LIDO (MT)

The first section regarded the socio-demographic variables that include age, gender, residence, education level, income, daily expenses at the beach, company, reason for choosing the beach. The second section of the questionnaire included questions related to perception of physical and recreational features of the beach, including available services and equipments. Several variables have been take into consideration such as: quality of services/facilities (bar, showers, beach huts, etc.), cleanliness of beach and sea, beach width, crowding, landscape, presence of swimming pool, safety, recreational activities. For the attitude scale, the items were measured on ordinal rank like “good/adequate”, “sufficient/medium” and “low/insufficient”. Data were collected from June to September 2015. Only people over 16 years old were randomly selected and interviewed. In the case of a group visit, one person was interviewed in order to avoid the risk of doubling answers. Interviewers walked freely through the beach area and the closest adult, resting under one of the sunshades, was asked as to whether they would like to fill in the questionnaire. In case they were not interested, interviewers followed to the next sunshade and conducted the same procedure. They were also informed that there was no right or wrong answer and their sincere responses would be appreciated.

development’ between urban and rural environments; 2. Rural: located outside the urban/village environment and not readily accessible by public transport having virtually no facilities perhaps a small shop, car park and toilet. Housing is limited in number (generally 0–10 but may be more depending on the size of the coastal stretch) and usually of a temporary nature, but no permanent community focal centre exists (churches, schools, shops, among others); 3. Remote: are defined by access difficulty largely by boat or foot after a walk of 300 m or more. They can be adjacent to either village or rural areas but rarely with urban ones. They are not supported by public transport and have very limited (< 5 if any) temporary housing. Furthermore, beaches were divided in two categories according to their principal use: 1. free beaches without recreational and cleaning services; 2. beach establishments, i.e. beach concessions providing recreational activities, facilities and services. 3.2. Users’ perception (UP) Questionnaires were conducted in order to assess user's profile, the reasons for choosing a certain beach, priorities and perceptions for each scenic class beach. The questionnaire was prepared after a literature review on the users' attitudes and perceptions of the beach (AlmeidaGarcía et al., 2016; Botero et al., 2013b; Cervantes et al., 2008; Marin et al., 2009) and was based on those used by Rodella et al. (2017c) and Rodella et al., 2019. Questionnaire was structured in two sections.

3.3. Data and statistical analysis Statistical and descriptive analyses were performed using the Statistical Package for Social Sciences (SPSS) version 20 (Statistics Solutions) and Microsoft Excel version 2017 (Microsoft Office, Redmond, Washington, USA). Analysis considered separately the totality of answers provided for each question. Respondents were free to 6

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common user preferences for beach landscapes and facilities. The Null Hypotheses for these tests consider that variables are independent of scenic class. When significant effects were observed, additional chisquared tests for independence within each class were performed separately Chi-squared tests of independence compare the squared deviations between observed and expected frequencies. When a large difference occurs, the Chi-squared value is high, which suggests dependence between the variables. In order to assess if this difference is significant, the test statistic is compared to the value of the Chi-squared distribution for a specific significance level (the critical value). When the test statistic exceeds the critical value, the null hypothesis of independence between the variables is rejected. In this case, the p-value of the test is lower than the significance level. Finally, users' perception was also analyzed through frequency diagrams. 3.4. Comparison of CSES and UP parameters Five parameters were chosen to compare the physical and social assessment of the selected beaches (Table 2) using some parameters carried out from CSES and user's perception (UP). These parameters are: beach cleanliness (1), sea-water quality (2), beach width (3), landscape (4) and crowding (5). Coastal scenic evaluation and users’ perception were integrated through sector analysis modifying the sectorial table proposed by Rangel-Buitrago et al. (2017) and Williams et al. (2016a). The sectorial analysis was constructed for each beach class, with scenic class in column (5) and UP evaluation in row (4 sectors of 25% of good responses). The upper right quadrant (denominated AB sector) involved four cells and represents beaches with positive UP and good scenic assessments. The lower left quadrant (denominated CD sector), which also has four cells, describes beaches having negative UP and poor scenic values. The two remaining corners regroup beaches with contradictory results, e.g. good UP and low scenic classes or vice versa.

Fig. 3. Examples of Class 1: a - Porto Caleri free beach 3; b – Porto Caleri lagoon (Google Earth photo: https://lh5.googleusercontent.com/p/AF1QipNeuSAUhqEzgtG5_YryewxLYXe2cwoVf6B20p5H=h720) – Rosolina Mare, Veneto; c- Lido Marinella; d) Bradano river mouth (Google Earth photo: https://plus. google.com/photos/photo/107595651368390563629/ 6465582732609662834) – Metaponto Lido).

leave questions that they were unwilling or not comfortable to answer. As a consequence, absolute numbers used for analysis in each question varied. For instance, only 299 respondents of 380 provided information to characterize their income. For some questions respondents selected more than one answer, so the total number of answers was sometimes bigger than the number of questionnaires. An example is the case of preferred beach features for users (3 choices per user), 952 answers were obtained. Taking those aspects into account, the results were mostly presented showing the relative frequency (%). The data set was analyzed using Pearson chi-squared (χ2) tests for independence to determine if answers were dependent on scenic class of beach where respondents were interviewed. Contingence tables were built considering interaction between beaches and user's characteristics and preferences. Consequently, each column of the table represents the scenic class corresponding to CSES. Metrics for the evaluation of the perceptual variables were built using the relative percentages of user opinions for each scenic class. The results allow us to identify the most

4. Results and discussion 4.1. Investigated sites characteristics and distribution The results of the CSES for the 25 selected sites are reported in Fig. 1 and Table 3. The median D values for each beach category are: 0.77 for remote, 0.44 for rural, 0.27 for village and 0.11 for urban type. The maximum D value is observed for rural beaches (1.28) with a standard deviation of 0.49. On the other hand, urban beaches show the minimum Fig. 4. Examples of Class III: a) Marina di Porto Caleri; b) Magna Grecia; c) Basento sx beaches; d) Argonauti harnor seawalls and groins near Basento sx beach (https://plus.google.com/photos/photo/ 108612300778670295252/ 6627034051021576850); e) abandoned beach establishment in a dunes' field at Marina di Porto Caleri beach.

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Fig. 5. Class V: a) Rosolina Mare, Casoni – free beach; b) Lido di Volano South – free beach.

D value (−0.61) with a standard deviation of 0.137. Class IV and V are the main dominant class with 66% of the coastal sites, while class III and I-II represent the 19% and only the 14% respectively. As already observed by Rangel-Buitrago et al. (2013) and Williams et al. (2012) in similar studies performed in Colombia and Andalusia (Spain), the classification of analyzed sites is strongly dependent on the setting and level of human occupation. In this study, the study areas have similar beach settings (parameters between 1 and 9 in Table 1, e.g. sediment type, width, slope) and urbanization level but different beach management to evaluate the influence of local administration and their relation with some physical (e.g. dune preservation, vegetation cover) and human parameters (e.g. litter, sewage discharge evidence, built environment, access type, utilities). Furthermore, our purpose was to especially highlight the users’ preferences about human parameters. In fact, physical factors, as seen in other studies on these coasts (Rodella et al., 2017b; Simeoni et al., 2016), do not affect tourist attendance. On the contrary, some human parameters, such as litter, are important to evaluate users' preferences of a certain scenic class. At Rosolina Mare three beaches are classified as Class IV, two beaches as Class I, one beach into Class II, into Class III and into Class V (Fig. 1 and Table 3). From a geographical point of view, beaches show a progressive increase of scenic value from North to South (Fig. 1) due to the increase of natural parameters like vegetation cover and dune presence (Rodella et al., 2017a). The prevalent scenic classes of Lidi di Comacchio are IV (two beaches) and V (five beaches) mainly due to human parameters like disturbance factors, access type and built environment (Fig. 2 l-o). Only one beach is classified as Class III in the northern stretch of Lido di Volano (Figs. 1 and 2 g) due to a high level of vegetation cover and a low presence of utilities. At Metaponto Lido, one beach is classified into Class I and one into Class II, two into Class III and four into Class IV (Fig. 1 and Table 3). The central zone of the littoral, which is the urban area of Metaponto Lido, presents the lowest scenic values characterized by an intense touristic development and high intensity of coastal defence structures like submerged barriers and revetments (Aiello et al., 2013). Scenic value increases northward and southward and especially to the North near the natural area of Bradano river (Fig. 1c). Fig. 2 reports histograms, weighted averages and membership degrees for each scenic class. Despite the non-homogeneous distribution of investigated sites, the following general trends can be highlighted for each scenic Class. The assignment of a high score, such as 4 or 5, indicates a high scenic (high rating) value like for Lido Marinella (Fig. 2 -a). Fig. 2 b, e and h, corresponding to Class I, II and III beaches and representing the scenic histogram of weighted averages, show that beaches have high attribute values (4 and 5) and consequently low scoring on attributes 1 and 2. These classes show curves skewed to the right indicating a high scenic quality (Fig. 2 c, f, i) and positive impact of the physical/human parameters. Similar trends are reported by numerous studies around the world, e.g. for the most attractive scenic sites

along the Andalusia Coast (SW Spain; Mooser et al., 2018). On the other hand, beaches of Class IV and V (Fig. 2 n, q), present not well oriented curves and curve skewed to the left. These trends indicate a high weighted averages at lower attribute values (i.e. 1 and 2) and reflect the adverse impact of the physical or human parameter (Williams et al., 2012). 4.2. Scenic class description 4.2.1. Class I sites Three sites having a D value ≥ 0.85 (Table 3 and Fig. 2 a-b-c) are classified as Class I: Porto Caleri free beach 1 and Porto Caleri free beach 3 at Rosolina Mare; Lido Marinella – free beach at Metaponto (Fig. 3). These beaches are located in natural or semi-urban littorals, characterized by the absence or a low-level degree of human occupation. At Porto Caleri beach (Fig. 3 a) all human parameters score five (excellent) except for litter presence (score four; Fig. 2 a). The natural environment provides an attractive vista, open on four sides characterized by presence of pinewood, saltmarsh, wetland of Porto Caleri lagoon (Fig. 3 a) and dunes (about 342,160 m2 of dunes; Rodella et al., 2017a). This coastal stretch is also characterized by the presence of protected flora and fauna species typical of sand littorals, freshwater and brackish water wetlands, e.g. Phragmites australis, Zostera noltii, Zostera noltii (http://www.parcodeltapo.org/index.php/it/musei-estrutture/90-vivere-il-delta/188-il-giardino-botanico-di-porto-caleri. html). Lido Marinella beach (beach num. 18; Table 3; Figs. 1 and 3 c) is classified as extremely attractive natural environment due to high human parameters (score five). Indeed, the high score (four score) is related to the absence of disturbance factor, litter, built environment and the good access. Furthermore, this beach is generally not crowded and distant from traffic roads. Physical parameters present an attractive vista, open almost on three sides. During the survey, the water colour was clear blue. Few scattered seaweed remnants were observed on the beach while scrub vegetation was present behind the high tide mark. Lido Marinella beach, as all the other beaches studied at Metaponto Lido, is located inside the National Biogenetic Nature Reserve of Metaponto. This beach, located near the mouth of the Bradano river and the Salinella salt lake, presents shows the greatest naturalness of the coastline with typical species such as the marine straw and rush. The Caretta caretta turtles may also be observed. 4.2.2. Class II sites Coastal area with D value between 0.67 and 0.77 (Table 3) corresponds to natural or semi-natural/urban site with high landscape values and low human presence. Two beaches are classified within this category (Porto Caleri 2 – free beach and Riva dei Greci beach). They are respectively located in remote and rural coastal stretches in the edge of protected areas (e.g. Pollino National Park at Metaponto Lido). These 8

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Fig. 6. Beach appreciation and assessment: quality and cleaning of the beach (a), sea-water quality (b), beach width (c), beach crowding (d), safety (e), recreational activities (f), equipments/services (g), landscape (h), landscape importance (i). Significant differences in the interaction among classes are showed as results of partial chi-squared tests (χ2), degree of freedom (df), p-value (p) with its significative level (N.S.: not significative) and the absolute number of answers analyzed (n).

sites generally rate lower than Class I due to the lower scoring of skyline landform and landscape special features (Table 1), but have high scores for the dunes, the vegetation cover, the water and the beach sediment. The human parameter's influence is limited mainly because of the absence or low level of noise and litter. Porto Caleri 2 beach (num. 7; Fig. 1 a and Table 3) presents the same physical setting of the near beaches Porto Caleri 1 and 3 (num. 6 and 8; Fig. 1 a and Table 3) but with a higher presence of vegetation debris (wood in particular) and litter. Riva dei Greci beach (num. 19; Fig. 1 c and Table 3), located in front of a camping village, presents high physical and human parameters even if disturbance factors like noise had low score. On the other hand, this beach has excellent cleaning services explaining the absence of litter and sewage.

4.2.3. Class III sites Class III includes four sites (2 villages, 1 urban, 1 remote), which present an attractive scenario, flawed by features such as non-attractive buildings, no buffer zone, and presence of streams bringing pollution and litter to the beaches. Marina di Porto Caleri (Rosolina), Ipanema Lido di Volano and Magna Grecia beach establishment, Basento – free beach (Metaponto Lido) belong to this category (Table 3 and Fig. 4 a, b, c). These sites have intermediate values for both natural and human features (Fig. 2 g-h). In general, human activities/developments have no severe impact on these areas and are mainly related essentially limited to the presence of scattered marine debris litter items or litter abandoned by beach users (usually local or national visitors). Low scores are mostly due fundamentally linked to the absence of attractive 9

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Fig. 7. Dispersion graphs of the selected parameters: beach cleanliness, sea-water quality, beach width, landscape, crowding, for each CSES class. Y Axes report the score evaluation carried out by CSES, while X axes show the percentages of good (quality and cleaning of beach, sea water and beach width), beautiful (landscape) and low (crowding level) judgments.

vista and presence of disturbance factors like crowding, noise (at Marina di Porto Caleri (Rosolina), Ipanema Lido di Volano, Magna Grecia beach establishments), litter items due the absence of cleaning operations, and abundant vegetation debris linked to the lush vegetation cover along the Basento – free beach (Metaponto Lido). Furthermore, Basento - free beach is located near the Argonauti harbor affecting the natural value of the beach (Fig. 4 d).

However, Camping Rosapineta – free beach and Tizè beach, Perla beach (Rosolina Mare), do not present defence structures but the low scores are related to sewage and noise disturbance due to the high crowding level during the summer season. 4.2.5. Class V sites Includes seven sites, usually very unattractive urban beaches with intensive development and landscape values lower than zero (Table 3). This class regroups 5 sites corresponding to urban beaches (i.e. Aloha beach establishment Lido di Nazioni, Comacchio), 1 rural (Casoni – free beach; Fig. 5 a), 1 remote (Lido di Volano South – free beach, Comacchio; Fig. 5 b). Class V (Fig. 2 m, p) has the lowest lower attribute values (i.e. 1 and 2), and a left hand skew (Fig. 2 q) reflecting the adverse impact of the physical or human parameters. Human parameters present low scores especially in Lidi di Comacchio (Fig. 1 b and Table 3), strongly impacted by the absence of a buffer zone and high level of human constructions, such as high buildings, coastal roads, etc. and the presence of coastal protection structures, i.e. groins and breakwaters, e.g. at Lido di Pomposa Pic Nic (Fig. 2 o). Other negative features include high amounts of litter, high noise levels due to heavy coastal traffic and degraded natural environments. Beaches in northern area of Rosolina Mare and in southern Lido di Volano (remote and rural beaches, Fig. 1a–b) obtain low values essentially due to the presence of coastal protection structures, (i.e. groins and revetments) and very narrow beach (almost absent in small stretches to 10–15 m). In particular, the lowest values are observed in erosional coastal sectors. Indeed, as reported by Rangel-Buitrago et al. (2013a) erosional processes induce emplacement of different structures that affected the scenic value. Crowding (especially during the weekend) is also one of the main significant factor characterizing the Class V beaches of Lidi di Comacchio and Metaponto Lido (Corbau et al., 2015; Rodella et al., 2017b; Trivisani et al., 2017).

4.2.4. Class IV sites Beaches of Class IV are Camping Rosapineta – free beach, Tizè beach, Perla beach (RO), Lido di Nazioni and Lido degli Scacchi– free beach (FE), Blumen Bad, Ermitage and Mondial beach establishments (MT) (Table 3) and correspond to villages (3), rural (1) and urban (5) beaches with poor landscape values due to undesirable anthropogenic activities. Rural and village areas present few human constructions, in general houses or bars, located close to the back beach with a small or no buffer zone. Litter items accumulated mainly because of the absence or the low frequency of cleaning activities (e.g. Lido degli Scacchi– free beach) reflecting an irresponsible environmental human behavior.). Urban sites present major anthropic impacts impact due to high buildings visible from large distances, a reduced buffer zone due to deteriorated dunes and vegetation cover, coastal defense structures and massive presence of beach chairs and umbrellas, as observed at Lidi di Comacchio and Metaponto Lido urban sites. Negative aspects are dominated by encroaching urbanization associated to poor skyline quality, litter, noise disturbance and a degradation of natural features. Similar international examples are Santa Marta, Cartagena de Indias, Tolú and Turbo beaches (Colombia; Rangel-Buitrago et al., 2013a) Konyaalti middle (Turkey), Kercem Cliffs (Malta), Saunderfoot (UK) (Ergin, 2018), where the scenery assessment was conditioned by the presence of utilities such as groins, breakwaters and revetments and negative scores related to sediment beach color, water color and quality. 10

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Table 4 Main results of users' profile and comparisons of respondents’ perceptions between scenic classes. Significant differences in the interaction among classes are showed as results of partial chi-squared tests (χ2), degree of freedom (d.f.), p-value (p) with its significative level (N.S.: not significative) and the absolute number of answers analyzed (n). QUESTIONS

SEX AGE

EDUCATIONAL LEVEL

RESIDENCE COMPANY

INCOME

DAILY EXPENSE ON THE BEACH MOTIVATION FOR THE VISIT

FIRST TIME IN THE LOCALITY

Scenery class

Male Female No answer < 25 26–40 41–65 > 65 Mean age St. dev. Secondary school College Academic degree No answer Resident Not resident No answer Only In couple Family (with children) Friends Other < 20,000 € 20,000–31,000 € 31,000–41,000 € > 41,000 € No answer Mean value (€) Standard dev. (€) Sea/beach Nature and landscape Cultural heritage (handicraft/folklore/cooking) Economic reasons Play sport/amusement Relax/quiet Have a holiday home Proximity to residence Other Yes I came regularly in this beach I went sometimes in this beach

Total (%)

I (%)

II (%)

III (%)

IV (%)

V (%)

60.5 39.5 0.0 18.4 34.2 36.8 10.5 42.11 15.84 26.3 39.5 34.2 0.0 65.8 34.2 0.0 7.9 13.2 44.7 34.2 0.0 60.5 23.7 2.6 0.0 13.2 5.97 0.75 34.2 5.3 2.6 0.0 0.0 7.9 13.2 36.8 0.0 2.6 86.8 10.5

50.0 50.0 0.0 16.7 38.9 44.4 0.0 38.06 15.59 33.3 38.9 22.2 5.6 44.4 55.6 0.0 0.0 22.2 50.0 22.2 5.6 55.6 33.3 11.1 0.0 0.0 16.50 2.13 61.1 0.0 0.0 0.0 0.0 22.2 0.0 11.1 0.0 5.6 61.1 33.3

56.9 43.1 0.0 16.7 33.3 45.8 4.2 40.53 17.34 19.4 59.7 20.8 0.0 18.1 81.9 0.0 0.0 8.3 73.6 16.7 1.4 20.8 31.9 13.9 11.1 22.2 12.49 1.39 16.7 6.9 0.0 1.4 1.4 18.1 25.0 30.6 0.0 2.8 81.9 15.3

46.1 52.5 1.4 23.6 23.6 44.3 8.6 40.04 19.33 19.1 54.6 24.8 1.4 24.8 75.2 0.0 3.5 11.3 63.8 17.7 3.5 29.1 29.1 11.3 12.1 18.4 13.65 0.88 27.7 .7 0.0 1.4 3.5 12.1 29.1 21.3 3.5 8.5 78.0 11.3

50.5 49.5 0.0 31.5 25.9 36.1 6.5 37.46 17.36 32.4 42.3 25.2 0.0 16.2 82.9 .9 9.9 13.5 48.6 26.1 .9 21.6 23.4 11.7 12.6 30. 15.64 1.79 12.6 0.0 .9 2.7 7.2 6.3 32.4 27.0 9.9 7.2 75.7 16.2

4.3. Users’ perception

51.1 48.4 .5 23.7 27.9 41.5 6.9 39.51 17.87 24.5 49.7 25.0 .8 26.1 73.7 .3 5.0 12.1 58.7 21.8 2.1 29.7 27.6 11.1 10.3 21.3 13.38 0.69 23.4 2.1 .5 1.6 3.7 11.6 26.3 25.8 4.2 6.3 78.2 14.5

Between scenic classes X2

d.f.

p

n

6.812

8

0.557 (N.S.)

380

13.176

12

0.356 (N.S.)

376

19.927

12

0.068 (N.S.)

380

44.586

8

0.000

380

34.644

20

0.37 (N.S.)

380

42.246

16

0.000

380

131.199

108

0.035

380

95.978

40

0.000

380

13.700

12

0.320 (N.S.)

380

incomes. Daily expenses (p-value = 0.035; Table 4) highlight similar trend with lowest value at class I beaches (5.97 €) compared to about 13–15€ of beaches of Class IV and V. The results are probably due to the fact that beaches of class I and II are prevalently natural and consequently without possibility of spending for users; contrarily urban beaches of class III, IV and V allow the beach goers to pay for recreational activities, restaurants etc. In a future application we could also consider the costs related to travel urban beaches can be reached by foot or by bus while remote ones by car or train. Therefore, daily expenses could be affected both from recreational activities/services and beach distance. The interviewers were predominantly not resident in the locality (73.7%), especially in beaches of Class III, IV and V (respectively 81.9%, 75.2% and 82.9%; p-value 0.000) but generally have a holiday home (25% in Class III, 27% in Class IV and 31.5% in Class V). On the other hand, in beaches of Class I and II beaches people were resident in the localities or beaches are near to their residence (about 34%). The users habitually frequented high and low scenic class beaches for different reasons. The main reasons for choosing the beach were principally sea and beach (particularly in Class II 61.1% and Class I 34.2%; Table 4); even if a considerable percentage of users answered “relax/quiet” (an average of 11.1%) and play sport/amusement (3.7%). Only 1.8% of users choose “nature and landscape” and consequently not a main reason for choosing the beach.

A total of 380 surveys were carried out in 2015 with 123 questionnaires collected at Rosolina, 145 at Lidi di Comacchio and 112 at Metaponto Lido. 4.3.1. Beach users’ profile Gender balance was an average of 51.1% of male and 48.4% of female, but a prevalence of male in Class I (about 60% of users) and female in Class IV (52.2%) may be noted. Interviewers were predominantly between 41 and 65 years old (41.5%), with young visitors (< 25 years old) more frequent in Class V (31.5%) and elderly person in Class I (10.5%). Tourism was principally of family type with children in Class III (73.6%; Table 4), group of friends in Class I (34.2%) and couples in Class II (22.2%). The prevalent educational level was college (particularly in Classes III and IV; Table 4), followed by academic (prevalent in Class I with 34.2%) and secondary school (prevalent in Class II with 33.3% and Class V with 32.4%). Average annual income (per person) was low than 20,000 € (29.7%) or between 20,000 and 31,000 € (27.6%). Annual income (per person) showed a significative level (p-value = 0.000; Table 4) between classes, especially observed the lowest (< 20.000€ for Class I and II with 60.5% and 55.6%) and highest (> 41.000 € for Class IV and V with 12.1% and 12.6%) 11

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Table 5 The preferred beach features for users of each scenic class. Significant differences in the interaction among classes are showed as results of partial chi-squared tests (χ2), degree of freedom (d.f.), p-value (p) with its significative level (N.S.: not significative) and the absolute number of answers analyzed (n). Which features do you prefer better at a beach?

Others Good quality of services/facilities (bar, showers, beach huts, etc.) Clean sea Landscape Swimming pool Clean beach Safety Recreational activities No answer

Scenery Class

Total (%)

I (%)

II (%)

III (%)

IV (%)

V (%)

1.75 9.65 28.07 14.91 0.00 21.93 7.89 0.00 15.79

1.85 22.22 20.37 12.96 0.00 16.67 9.26 1.85 14.81

3.70 17.59 20.37 7.41 0.46 25.00 8.33 3.24 13.89

3.55 19.86 21.04 4.96 0.95 22.70 8.75 4.49 13.71

3.60 17.42 16.82 8.41 1.50 21.92 5.71 2.40 22.22

4.3.2. Beach appreciation and assessment The overall holiday satisfaction was high (86.1%) independently from scenic class of the beaches (x2 6.673, d.f. 8, p-value 0.572). Table 5 reports the users’ preferences about some features on the beach. As already observed for others beaches (Cabezas-rabadán et al., 2019), the preference is variable and is not directly related to the beach type and scenic class. The most appreciated features are clean beach and clean sea (respectively an average of 22.54% and 20.35%) depending from the scenic class (x2 42,761, d.f. 28, p-value 0.03). In fact, beach clean is preferred in urban beaches of class III and IV (about 25% and 22%; Table 5) while clean sea in natural beaches of Class I (28.07%; Table 5). The third reason for choosing is the good quality of services and facilities (an average of 17.81%; Table 5), especially for beaches of Class II and IV. Landscape was preferred only by an average of 7.81% of users (Table 5), mostly for Class I and II suggesting a relation between these scenic classes and the reasons for beach choice. Quality and cleaning of the beach were generally good (average of 52.4%; Fig. 6 a), in particular for Class II and IV (61.1% and 63.8% respectively). Low quality and cleaning of the beaches were observed in beaches of Class I (31.6%). Sea quality was generally bad (33.7%) or sufficient (36.1%), especially in beaches of Class V (low for 60.9% of users; Fig. 6 b). On the average beach width was adequate for more than 73% of users (especially in Class V beaches with 85.6%, excessive for 4% and insufficient for 18% (Fig. 6 c). Crowding during the weekend was high for an average of 51.1% in all sites (Fig. 6 d) and, crowding was considered high during the weekend in beaches of Class II with a percentage of more than 61% (Fig. 6 d). About 37% of tourists considered crowding to be at a medium level at beaches of Class III, while only 6.9% of visitors considered crowding to be elevated in Class III. Finally about 10% of the users indicated a low level of crowding at beaches of Class I and V (Fig. 6 d). Safety was principally good or sufficient, with a prevalence of good answer in Class IV and V beaches (respectively 68.1% and 50.5%; Fig. 6 e). On the contrary, poor safety is reported for beaches of Class I (26.3% in Fig. 6 e). The recreational activities were poor for 44.7% for Class I beaches, sufficient for 42.1% for Class I and 44.1% for Class V and good for 38.3% at Class IV (Fig. 6 f). An average of 67.9% of users were satisfied with the available services/equipments (Fig. 6 g), particularly at beaches of Class IV (74.5% and Class V (73.9%). Landscape was generally judged beautiful (57.6%), especially for Class I beaches (beautiful for 81.6%). On the contrary, landscape was considered indifferent (38.7%) or bad (23.4%) for Class V beaches, (Fig. 6 h). The landscape importance for users was high for 60.5% at Class I beaches, medium for 49.6% at Class IV beaches and low for 6.3% at Class V beaches (Fig. 6 i). Users of each class are generally definite as follow. Beachgoers of class I were prevalently resident in the localities or near the littoral, therefore they habitual frequent the beaches. Moreover, people frequented these littorals for the sea and beach (Table 4). The Class I beaches of are not very popular and are generally difficult to reach because located in front of wide dune systems (Porto

3.33 17.81 20.35 7.81 0.88 22.54 7.72 3.07 16.49

Between scenic class X2

d.f.

p

n

42,761

28

0.037

380

Caleri, Rosolina Mare, Fig. 1 a) or artificial pinewood (Metaponto Lido, beach num. 18; Fig. 1 c) that may explain why only residents or tourists, looking for tranquillity, habitually frequent these beaches. Users of class II were prevalently not resident in the localities, even if they habitual frequent the beaches mainly for sea and beach, relax and quiet (Table 4). Users of class III were prevalently not resident in the localities but habitual frequented the beaches because they have a holiday home or due to the proximity to the beach (Table 4). Therefore, sea, beach and landscape are not the first reason for choosing these beaches as declared by users. Furthermore they indicated that the landscape had medium importance (Fig. 6 i). Users of class IV and V have holiday home or are coastal residents. In this context, although the beach and the sea were important, the main reason for choosing the beach was the proximity to the sea (Table 4) and the good quality of services like safety and recreational activities and equipments (Fig. 6). The results are in agreement with similar studies on stakeholder and user perspectives of beach in Emilia-Romagna, Italy (Bernini et al., 2015; Rodella et al., 2017a, 2017b), in Veneto, Italy (Parente et al., 2017; Rodella et al., 2017c) and in Basilicata, Italy (Trivisani et al., 2017). Indeed, these studies demonstrated that for similar recreational context, natural parameters and landscapes feature do not influence users’ perception and beach frequentation. 4.4. Comparison of CSES and UP parameters Quality and cleaning of the beach (parameter num. 1 in Table 2, Fig. 7) have generally high scores both for the CSES and UP analysis, particularly in beaches of Class II, III and IV (sector AB). These results indicate that the coastal studied localities commonly have cleaning services (beach concessions and free beaches), that could be locally due to a strategic management plan of the local Administrations. The local administration of Comacchio Municipality, for instance, provides a cleaning service for all beaches in both high and low bathing seasons (Municipality of Comacchio, 2018). On the other hand, beaches of Class I and V showed contradictory results corresponding to high CSES scores but low UP evaluation (BC cell). These beaches include different typologies, mainly remote and urban (Table 3). Remote beaches of Class I present high density of vegetation debris and sewages discharged (e.g. Porto Caleri 3 beach) while urban beaches of Class V have low quality of recreational activities and services (e.g. Lido di Pomposa beach). Sea-water quality (parameter num. 2 in Table 2, Fig. 7) shows CSES evaluations ranging from medium (Class I, II and III) to low (Class IV and V) and UP evaluations ranging from sufficient to low in the same classes. Water color and clarity parameter values were low, often muddy/brown/grey (scoring 1 or 2 at point 16; Table 2) due to the presence of algae and sediment in suspension. In particular, at Lidi di Comacchio beaches and the northern-central zones of Rosolina Mare littoral, the Po river and Adige river respectively discharge their waters very closed to these littorals. However, the sea-water quality parameter 12

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is more variable than the other parameters, due to the influence and variability of the river flow and as a consequence, both scenic evaluation and users’ perception may vary. Beach width (parameter num.3 in Table 2, Fig. 7) is the only parameter located in AB sector indicating a good/excellent evaluation both for CSES and UP. In particular, the Class V urban beaches present the best value corresponding to AA cell. In fact, in these beaches, even the tourist presences are often high, the risk of overcrowding phenomenon and carrying capacity issues are mitigated by constant human interventions. For instance, in Emilia-Romagna and Veneto littorals, artificial beach nourishments are frequently realized to face erosion and contribute to a certain level of beach stability over time (Rodella et al., 2017a,b,c; Utizi et al., 2016). Landscape (parameter num. 4; Fig. 7) is considered good (classes I, II, III) or sufficient (classes IV, V) by CSES assessment and good (classes I, II, III, IV) or sufficient (Class V) by UP assessment. CSES considers the specific natural features characterizing the beaches, such as coastal landscape features and skyline, while UP landscape evaluation includes both natural and anthropogenic features. Nevertheless, results indicate a relative agreement of the evaluations, with the worst results for the Class IV and V beaches (cells DB and CD respectively). However, in some cases, these urban beaches are appreciated by users because structures and buildings are parts of urban landscapes. In this context, the distinction between natural scenes and human-influenced scenes might sometimes be problematic or unclear as observed by Fyhri et al. (2009) and a number of studies indicate that landscapes, perceived as natural, are considered more scenic than human-influenced landscapes (Zube et al., 1982; Hull and Revell, 1989; Kent and Elliot, 1995; Ulrich et al., 1991; Real et al., 2000). Crowding (parameter num. 5 in Table 2, Fig. 7) is the most ambiguous parameter. It has been assessed as good (Class I and II) to medium/sufficient (from Class III to V) for CSES assessment and poor/ bad for UP assessment for all scenic classes. These discrepancies are principally due to the crowding issues considered in CSES and UP. In fact, in CSES crowding is evaluated through the level of noise at the beach; while UP considers the available space per person and therefore the number of persons at the beach. Furthermore, CSES considers noise due not only by tourists but also to playing loud radio/CD music, jet skis, heavy traffic, airport or highway, etc. (Pranzini et al., 2019). Results indicate that noise disturbance are generally acceptable in Class I and II beaches, because of their remote or village typologies and consequently less frequentation. Otherwise, urban beaches of Class III, IV and V have medium/high level of noise level and therefore parameter evaluation tend to 2. In all beaches, UP indicates a high level of crowding especially during the weekends as already explained.

of Landscape Interest protected by Legislative Decree 63/2008, art. 142), indeed, is locally greater than fifty per (Falco, 2017). The Italian National Code of Cultural Heritage and Landscape, issued in 2004, precisely aims to identify measures for the rehabilitation of degraded areas and lines of urban development and construction, on the basis of their compatibility with the landscape values recognized and protected (Italian Parlament, 2008). However, for several surveyed beaches, specifically Class V, the application of Landscape protocols and National Plan can only preserve the current state without recovering the naturalness of the coast. In many places, utilities (parameter num. 26 of CSES; Table 1) are linked to the presence of coastal defence structures. The lowest rated values are observed in erosional coastal sectors and it is important to point out that most of the studied sites are in erosion requiring the realization of different protective structures. At Metaponto Lido and Lidi di Comacchio, for instance, numerous defence structures were built during the last decades (Montanari and Marasmi, 2014; Trivisani et al., 2017) Today, from a scenic point of view, it is mandatory to reduce their visual impact because coastal protection structures often look unnatural or unsightly negatively affecting beach recreational experiences (Stamski, 2005). Furthermore, in order to increase values of physical versus human parameters, beach nourishments and dune restorations should be preferred versus construction of hard protective structures especially in protected areas (Corbau et al., 2015) considering that well vegetated dune ridges constitute a buffer between beach and built environment, reducing noise disturbance, visual impact of buildings as indicated by Williams et al. (2012). Considering UP results of other studies in Mediterranean coast (Rodella et al., 2017c; Simeoni et al., 2017, 2016), peoples were not worried about the effects of coastal defence structures on landscape and scarcely known them. Furthermore, interviewed beach's users were little aware of the tools necessary to manage beaches, indeed, they were not familiar to the ICZM protocol (Koutrakis et al., 2011). This is a key element in management of beaches because beach visitors are private stakeholders, whose contribution may be essential to identify sound practices for pursuing sustainable coastal development (Dahm, 2003). In particular, users' participation in ICZM may reduce local conflicts and make decision-making about coastal management more appropriate to provide sustainable beach services. Thus, surveying visitors' preferences and opinions provide important information for policymakers involved in coastal management (Marzetti et al., 2016)., It is therefore crucial to develop an appropriate information strategy in these Mediterranean coastal zones promoting public awareness on ICZM in order to increase visitors' probability of paying for beach conservation. Indeed, knowledge of ICZM is a significant variable that affects the willingness to pay (WTP) of users because those who are informed about ICZM have a higher probability of paying for beach preservation particularly in natural beaches (Marzetti et al., 2016; Rodella et al., 2019b). While safety and landscape are not decisive for users, facilities, water quality and cleanness are the most preferred parameters and are strongly related to the scenic evaluation of the beaches. This result may be important considering the Italian case studies in order to guarantee an optimum match between scenery and users’ preferences. This highlights the importance given to recreational activities and facilities (Parente et al., 2017; Rodella et al., 2017c; Simeoni et al., 2017), water quality as observed in Canary Islands (Peña-Alonso et al., 2018) and in Spain (Roca and Villares, 2008), and clean beaches. Regarding this last parameter, however, several studied sites have parameters (e.g., vegetation debris) for which local coastal managers can do little or nothing to improve their scenic impact. Accumulations of litter and vegetation debris on the beach may be due to waves and currents actions, tourist behaviours and the absence of periodic cleaning operations in some free beaches. For instance, the establishment of cleaning operations at Marina di Porto Caleri (Class III) would upgrade the site to Class II, as well as at Basento sx beach (beach num.25, Metaponto Lido). Studies of Spanish beaches show that there is a statistical relationship between

4.5. Management implication and improvements Most tourists are interested in the bathing area (Botero et al., 2013a,b; Williams, 2011; Williams et al., 2016) and numerous surveys have showed that five parameters are of crucial importance with respect to beach choice: safety, facilities, water quality, no litter and scenery/landscape (Williams and Micallef, 2009). The results of this study partially agree with those obtained by the previous authors. In fact, we found that the beach choice is not directly related to safety and landscape preferences but is more influenced by residence and proximity to the beach with significant differences between scenic classes. Landscape and attractive vista therefore play a secondary role in the choice even if people of natural beaches (particularly of Class I) appreciated them. These results are in agreement with Roca and Villares (2008) and Rodella et al. (2019b) that found that attractive views and landscapes, quiet and comfort are more appreciated in natural and semi-urban beaches than in urban beaches. Unfortunately, most of surveyed beaches are strongly affected by the human activities and have intensively urbanized the coastal zone. Urbanization of the protected 300-m strip from shoreline (coastal zone 13

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certified beaches and cleaning operation (Mir-Gual et al., 2015), therefore this operation could positive affect the environmental status of the beaches. Solving the problems of marine litter, however, is not easy and it is not sufficient to apply clean-up measures at few beaches (Asensio-Montesinos et al., 2019). For marine litter comes from landbased sources, measures should be focused on the retention of waste before it arrives to the sea, for example, along the river banks or at river mouths (Asensio-Montesinos et al., 2019). On the other hand, for marine litter comes from sea-based sources, measures should be taken for reducing fisheries and aquaculture discharges (besides merchant shipping, ferries and cruise liners, military fleets and research vessels, pleasure craft, offshore oil, gas platforms, drilling rigs; (AsensioMontesinos et al., 2019; Utizi et al., 2018). From a policy point of view, the implementation of the Marine Strategy Framework Directive (MSFD) - the environmental pillar of the Integrated Maritime Policy of the European Union (EU)- represent a tool for reducing litter impact on European coasts achieving a Good Environmental Status (GES) in European marine waters by 2020.

Acknowledgments We would like to express our appreciation to Prof. Umberto Simeoni, Dr. Antonio Trivisani, Dr. Stefano Paganin. The authors would like to thank three anonymous reviewers for their valuable comments on the originally submitted manuscript. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.ocecoaman.2019.104992. References Aiello, A., Canora, F., Pasquariello, G., Spilotro, G., 2013. Shoreline variations and coastal dynamics: a space-time data analysis of the Jonian littoral. Italy. Estuar. Coast. Shelf Sci. 129, 124–135. https://doi.org/10.1016/j.ecss.2013.06.012. Almeida-García, F., Peláez-Fernández, M.Á., Balbuena-Vázquez, A., Cortés-Macias, R., 2016. Residents' perceptions of tourism development in Benalmádena (Spain). Tour. Manag. 54, 259–274. https://doi.org/10.1016/J.TOURMAN.2015.11.007. Asensio-Montesinos, F., Anfuso, G., Corbí, H., 2019. Coastal scenery and litter impacts at Alicante (SE Spain): management issues. J. Coast. Conserv. 185–201. https://doi.org/ 10.1007/s11852-018-0651-8. Bernini, C., Urbinati, E., Vici, L., 2015. Visitor Expectations and Perceptions of Sustainability in a Mass Tourism Destination. 01/2015. Bologna. Bondesan, M., Castiglioni, G.B., Elmi, C., Gabbianelli, G., Marocco, R., Pirazzoli, P., Tomasin, A., 1995. Coastal areas at risk from storm surges and sea-level rise in northeastern Italy. J. Coast. Res. 11, 1354–1379. https://doi.org/10.2307/4298437. Botero, C., Anfuso, G., Williams, A.T., Palacios, A., 2013a. Perception of coastal scenery along the Caribbean littoral of Colombia. J. Coast. Res. 165, 1733–1738. https://doi. org/10.2112/SI65-293.1. Botero, C., Anfuso, G., Williams, A.T., Zielinski, S., Pereira da Silva, C., Cervantes, O., Silva, L., Cabrera, J.A., 2013b. Reasons for beach choice : European and Caribbean perspectives. J. Coast. Res. 880–885. https://doi.org/10.2112/SI65-149.1. Cabezas-rabadán, C., Rodilla, M., Pardo-pascual, J.E., Herrera-racionero, P., 2019. Land Use Policy Assessing users ’ expectations and perceptions on di ff erent beach types and the need for diverse management frameworks along the Western Mediterranean. Land Use Policy 81, 219–231. https://doi.org/10.1016/j.landusepol.2018.10.027. Carlson, A.A., 1984. On the possibility of quantifying scenic beauty - a response to Ribe. Landsc. Plan. 11, 49–65. https://doi.org/10.1016/0304-3924(84)90017-0. Cervantes, O., Espejel, I., Arellano, E., Delhumeau, S., 2008. Users' perception as a tool to improve urban beach planning and management. Environ. Manag. 42, 249–264. https://doi.org/10.1007/s00267-008-9104-8. Corbau, C., Simeoni, U., Melchiorre, M., Rodella, I., Utizi, K., 2015. Regional variability of coastal dunes observed along the Emilia-Romagna littoral, Italy. Aeolian Res 18, 169–183. https://doi.org/10.1016/j.aeolia.2015.07.001. da Costa Cristiano, S., Portz, L.C., Anfuso, G., Rockett, G.C., Barboza, E.G., 2018. Coastal scenic evaluation at Santa Catarina (Brazil): implications for coastal management. Ocean Coast Manag. 160, 146–157. https://doi.org/10.1016/j.ocecoaman.2018.04. 004. Council of Europe, 2000. European landscape convention. Rep. Conv. Florence ETS No. 17, 8. http://conventions.coe.int/Treaty/en/Treaties/Html/176.htm. Dahm, C., 2003. Beach User Values and Perceptions of Coastal Erosion. Environment Waikato Regional Council. Ergin, A., 2019. Coastal Scenery Assessments by means of a fuzzy logic approach. In: Rangel-Buitrago, N.G. (Ed.), Coastal Scenery. Springer International, pp. 65–106. Ergin, A., Karaesmen, E., Micallef, A., Williams, A.T., 2004. A new methodology for evaluating coastal scenery: fuzzy logic systems. Area 36, 367–386. https://doi.org/ 10.1111/j.0004-0894.2004.00238.x. Falco, E., 2017. Protection of coastal areas in Italy: where do national landscape and urban planning legislation fail? Land Use Policy 66, 80–89. https://doi.org/10.1016/ J.LANDUSEPOL.2017.04.038. Fyhri, A., Jacobsen, J.K.S., Tømmervik, H., 2009. Tourists' landscape perceptions and preferences in a Scandinavian coastal region. Landsc. Urban Plan. 91, 202–211. https://doi.org/10.1016/J.LANDURBPLAN.2009.01.002. Greco, M., Martino, G., 2014. Modelling of coastal infrastructure and delta river interaction on ionic Lucanian littoral. Procedia Eng 70, 763–772. https://doi.org/10. 1016/j.proeng.2014.02.083. Gregory, K.J., Davis, R.J., 1993. The perception of riverscape aesthetics: an example from two Hampshire rivers. J. Environ. Manag. 39, 171–185. https://doi.org/10.1006/ jema.1993.1062. Hull, R.B., Revell, G.R., 1989. Cross-cultural comparison of landscape scenic beauty evaluations: A case study in Bali. Journal of Environmental Psychology 9 (3), 177–191. https://doi.org/10.1016/S0272-4944(89)80033-7. Italian Parlament, 2008. Decreto Legislativo - 26 marzo 2008, n. 63 Ulteriori disposizioni integrative e correttive del decreto legislativo 22 gennaio 2004. 42, in relazione al paesaggio. Kay, R.C., AlderJ, J., 2005. Coastal Planning and Management. E&F Spon., London; Sterling. Kent, R.L., Elliot, C.L., 1995. Scenic routes linking and protecting natural and cultural landscape features: a greenway skeleton. Landscape and Urban Planning 33,

5. Conclusion The methodology used in this study seeks to address the following arguments: - difficulties involved to with establishing empirical relationships between landscape assessment and people's perceptions; - analysis of objective and subjective evaluation of some beach parameters useful to understand management implication of coastal areas; - point out how scenic areas may be improved by judicious intervention relating to physical and anthropogenic parameters chosen for assessment (Rodella et al., 2019a, 2019b). This methodology allowed to classify twenty-five beaches for type, scenery, users’ perception along the Italian coastline and is a first step in the direction of evaluation of Italian coastal scenery, a very attractive landscape component for national and international tourists. Furthermore, we obtain for each scenic class a framework of users that frequented the corresponding beach, their preferences about beach characteristics and their attitudes to environmental issues. Therefore, this study indicates for each scenic class a related typology of user and consequently that beaches should be managed considering both environments and specific types of user. In general beach users were satisfied with their recreational experiences. The high degree of satisfaction with the provision of services in urban and overcrowded beaches and the global approval of natural characteristics and conservation status in natural beaches suggest that public perception is not only influenced by the specific characteristics of each beach but also depends on the user profile. Furthermore, even if people of this study were habitually users of the beaches, natural beaches were choosing for sea and beach while semi-urban and urban beaches for facilities, holiday home and proximity to the beach. From the social point of view, since many visitors are essentially interested in beach attractions because they have a holiday home and well known the beaches, it should be mandatory to promote a diversification of activities under a more ecotourism perspective that is linked to the great biodiversity, nature, and landscape conservation of the investigated sites. Finally, it is well known that human parameters, such as built environment, noise disturbance, litter, may change in a short time. Therefore, coastal scenic assessment surveys are recommended to be carried out periodically yielding the changes in the classification of the coastal sites, both for physical and social point of view, which can be used to guide coastal planners and managers.

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Mediterranean Coastal Areas: Problems and Measurement Techniques.”. Rodella, I., Corbau, C., Simeoni, U., Utizi, K., 2017b. Assessment of the relationship between geomorphological evolution, carrying capacity and users perception: case studies in Emilia-Romagna (Italy). Tour. Manag. 59, 7–22. https://doi.org/10.1016/ j.tourman.2016.07.009. Rodella, I., Simeoni, U., Corbau, C., 2017c. La percezione dell’offerta turistico-balneare nei lidi di Comacchio (Emilia-Romagna). Studi Costieri 25, 39–48. http://www. gnrac.it/rivista/Numero25/Articolo3.pdf. Rodella, I., Madau, F., Mazzanti, M., Corbau, C., Carboni, D., Utizi, K., Simeoni, U., 2019. Data for the analysis of willingness to pay for Italian beaches. Data in Brief 23, 103815. https://doi.org/10.1016/j.dib.2019.103815. Rodella, I., Madau, F., Mazzanti, M., Corbau, C., Carboni, D., Utizi, K., Simeoni, U., 2019b. Willingness to pay for management and preservation of natural, semi-urban and urban beaches in Italy. Ocean Coast Manag. 172, 93–104. https://doi.org/10. 1016/j.ocecoaman.2019.01.022. Scarelli, F.M., Sistilli, F., Fabbri, S., Cantelli, L., Barboza, E.G., Gabbianelli, G., 2017. Seasonal dune and beach monitoring using photogrammetry from UAV surveys to apply in the ICZM on the Ravenna coast (Emilia-Romagna, Italy). Remote Sens. Appl. Soc. Environ. 7, 27–39. https://doi.org/10.1016/J.RSASE.2017.06.003. Semeoshenkova, V.S., Williams, a T., 2011. Beach quality assessment and management in the sotavento (eastern) algarve, Portugal. J. Coast. Res. 1282–1286. Simeoni, U., Rodella, I., Corbau, C., 2016. Risultati preliminari sulla percezione turistica delle spiagge italiane. Ambiente Italia 2016, 127–140. Simeoni, U., Corbau, C., Rodella, I., 2017. La percezione dell’offerta turistico-balneare in Italia: aspettative, soddisfazione e conoscenze degli aspetti fisico-gestionali delle spiagge. Studi Costieri 25, 5–24. http://www.gnrac.unifi.it/rivista/Numero25/ Articolo1.pdf. Spilotro, G., Pizzo, V., Leandro, G., 2006. Evoluzione della Costa Ionica della Basilicata e gestiona della complessità. In: “L’arretramento Della Costa Ionica Della Basilicata: Complessità, Studi, Azioni.” Unibas,Reg.Bas. SIGEA, pp. 27. Stamski, R., 2005. Impacts of Coastal Protection Structures in California's Monterey Bay National Marine Sanctuary. NOAA Technical Memorandum - Marine Sanctuary Division (MSD). Trivisani, A., Simeoni, U., Corbau, C., Rodella, I., 2017. La percezione dell’offerta turistico-balneare delle spiagge della Costa del Metapontino (Basilicata). Studi Costieri 25, 71–80. http://www.gnrac.it/rivista/Numero25/Articolo6.pdf. Tropeano, M., Cilumbriello, A., Sabato, L., Gallicchio, S., Grippa, A., Longhitano, S.G., Bianca, M., Gallipoli, M.R., Mucciarelli, M., Spilotro, G., 2013. Surface and subsurface of the Metaponto coastal plain (Gulf of taranto-southern Italy): present-day- vs LGM-landscape. Geomorphology 203, 115–131. https://doi.org/10.1016/j. geomorph.2013.07.017. Tuan, Y., 1977. Space and Place: the Perspectives of Experience. University of Minnesota Press, Minnesota. Ulrich, R., Simons, R., Losito, B., Fiorito, E., Miles, M., 1991. Stress recovery during exposure to natural and urban environments. Journal of Environmental Psychology 201–230. https://doi.org/10.1016/S0272-4944(05)80184-7. Unal, O., Williams, A.T., 1999. Beach visits and willingness to pay: cesme peninsula, Turkey. In: Ozhan, E. (Ed.), Land Ocean Interactions: Monitoring Coastal Ecosystems. Ankara, Turkey. MEDCOAST, Middle East Technical University, pp. 1149–1162. Utizi, K., Corbau, C., Rodella, I., Nannini, S., Simeoni, U., 2016. A mixed solution for a highly protected coast (Punta Marina, Northern Adriatic Sea, Italy). Mar. Geol. 381, 114–127. https://doi.org/10.1016/j.margeo.2016.09.002. Utizi, K., Notti, E., Sala, A., Buzzi, A., Rodella, I., Simeoni, U., Corbau, C., 2018. Impact assessment of EMFF measures on Good Environmental Status ( GES ) as de fi ned by Italy. Mar. Policy 88, 248–260. https://doi.org/10.1016/j.marpol.2017.12.003. Veneto Region, 2016. Movimento turistico. WWW Document. http://statistica.regione. veneto.it/banche_dati_economia_turismo.jsp September, 09 2019. Williams, A.T., 1987. Coastal conservation policy development in England and Whales with special reference to the Heritage Coast concept. J. Coast. Res. 3, 99–106. Williams, A.T., 2011. Definitions and typologies of coastal tourism destinations. In: Jones, A., Phillips, M. (Eds.), Disappearing Destinations: Climate Change and Future Challenges for Coastal Tourism. CABI, Wallingford, Oxford, pp. 47–66. Williams, A.T., 2019. Some scenic evaluation techniques. In: Rangel-Buitrago, N.G. (Ed.), Coastal Scenery Evaluation and Management. Springer International Publishing AG, part of Springer Nature, pp. 43–66. 2019. https://doi.org/10.1007/978-3-31978878-4. Williams, A.T., Micallef, A., 2009. Beach Management Principles and Practice. (London). Williams, A.T., Micallef, A., Anfuso, G., Gallego-Fernandez, J.B., 2012. Andalusia, Spain: an assessment of coastal scenery. Landsc. Res. 37, 327–349. https://doi.org/10. 1080/01426397.2011.590586. Williams, A.T., Rangel-Buitrago, N.G., Anfuso, G., Cervantes, O., Botero, C.M., 2016. Litter impacts on scenery and tourism on the Colombian north Caribbean coast. Tour. Manag. 55, 209–224. https://doi.org/10.1016/j.tourman.2016.02.008. Zube, E.H., Sell, J.L., Taylor, J.G., 1982. Landscape perception: research, application and theory. Landsc. Plan. 9, 1–33. https://doi.org/10.1016/0304-3924(82)90009-0.

341–355. https://doi.org/10.1016/0169-2046(94)02027-D. Koutrakis, E., Sapounidis, A., Marzetti, S., Marin, V., Roussel, S., Martino, S., Fabiano, M., Paoli, C., Rey-Valette, H., Povh, D., Malvárez, C.G., 2011. ICZM and coastal defence perception by beach users: lessons from the Mediterranean coastal area. Ocean Coast Manag. 54, 821–830. https://doi.org/10.1016/j.ocecoaman.2011.09.004. Krelling, A.P., Williams, A.T., Turra, A., 2017. Differences in perception and reaction of tourist groups to beach marine debris that can influence a loss of tourism revenue in coastal areas. Mar. Policy 85, 87–99. https://doi.org/10.1016/j.marpol.2017.08.021. Lewis, P., Lowenthal, D., Yi-Fu, T., 1973. Visual blight in America. Assoc. Am. Geogr 48. Marin, V., Palmisani, F., Ivaldi, R., Dursi, R., Fabiano, M., 2009. Users' perception analysis for sustainable beach management in Italy. Ocean Coast Manag. 52, 268–277. https://doi.org/10.1016/j.ocecoaman.2009.02.001. Martinelli, L., Zanuttigh, B., Corbau, C., 2010. Assessment of coastal flooding hazard along the Emilia Romagna littoral. IT. Coast. Eng. 57, 1042–1058. https://doi.org/ 10.1016/j.coastaleng.2010.06.007. Martinelli, L., Zanuttigh, B., De Nigris, N., Preti, M., 2011. Sand bag barriers for coastal protection along the Emilia Romagna littoral, northern adriatic sea. Italy. Geotext. Geomembranes 29, 370–380. https://doi.org/10.1016/j.geotexmem.2010.11.010. Marzetti, S., Disegna, M., Koutrakis, E., Sapounidis, A., Marin, V., Martino, S., Roussel, S., Rey-Valette, H., Paoli, C., 2016. Visitors' awareness of ICZM and WTP for beach preservation in four European Mediterranean regions. Mar. Policy 63, 100–108. https://doi.org/10.1016/j.marpol.2015.10.005. Mir-Gual, M., Pons, G.X., Martín-Prieto, J.A., Rodríguez-Perea, A., 2015. A critical view of the Blue Flag beaches in Spain using environmental variables. Ocean Coast Manag. 105, 106–115. https://doi.org/10.1016/j.ocecoaman.2015.01.003. Montanari, R., Marasmi, C., 2014. Il Sistema Gestionale Delle Celle Litoranee SICELL Aggiornamento 2006-2012 Tavole. Mooser, A., Anfuso, G., Mestanza, C., Williams, A.T., 2018. Management implications for the most attractive scenic sites along the Andalusia coast (SW Spain). Sustainability 10, 1328. https://doi.org/10.3390/su10051328. Morgan, R., Williams, A.T., 1995. Socio-demographic parameters and user priorities at Gower Beaches, UK. In: Healy, M.G., Doody, J.P. (Eds.), Directions in European Coastal Management. EUCC & Samara Publishing Ltd, Cardigan, pp. 83–90. Municipality of Comacchio, 2018. Carta dei servizi di igiene ambientale. http://www. comune.comacchio.fe.it/index.php/content/download/10260/12. Parente, L., Rodella, I., Simeoni, U., 2017. La percezione dell’offerta turistico-balneare di due spiagge del Veneto: Lido di Venezia (VE). e Rosolina Mare (RO) 25, 25–38. Peña-Alonso, C., Ariza, E., Hernández-Calvento, L., Pérez-Chacón, E., 2018. Exploring multi-dimensional recreational quality of beach socio-ecological systems in the Canary Islands (Spain). Tourism Management 64, 303–313. https://doi.org/10. 1016/j.tourman.2017.09.008. Pendleton, L., Martin, N., Webster, D.G., 2001. Public perceptions of environmental quality: a survey study of beach use and perceptions in Los Angeles County. Mar. Pollut. Bull. 42, 1155–1160. https://doi.org/10.1016/S0025-326X(01)00131-X. Penning-Rowsell, E.C., 1982. A public preference evaluation of landscape quality. Reg. Stud. 16, 97–112. https://doi.org/10.1080/09595238200185091. Pranzini, E., Williams, A.T., Rangel-Buitrago, Nelson, 2019. Coastal scenery assessment: definitions and typology. In: Rangel-Buitrago, N. (Ed.), Coastal Scenery, Coastal Research Library 26. Springer International Publishing AG, part of Springer Nature, pp. 257. 2019. https://doi.org/10.1007/978-3-319-78878-4_5. Rangel-Buitrago, N., Correa, I.D., Anfuso, G., Ergin, A., Williams, A.T., 2013. Assessing and managing scenery of the caribbean coast of Colombia. Tour. Manag. 35, 41–58. https://doi.org/10.1016/j.tourman.2012.05.008. Rangel-Buitrago, N., Williams, A., Anfuso, G., Arias, M., Gracia, C.,A., 2017. Magnitudes, sources, and management of beach litter along the Atlantico department coastline, Caribbean coast of Colombia. Ocean Coast Manag. 138, 142–157. https://doi.org/10. 1016/j.ocecoaman.2017.01.021. Real, E., Arce, C., Sabucedo, M.J., 2000. Classification of landscapes using quantitative and categorical data, and prediction of their scenic beauty in north-western Spain. Journal of Environmental Psychology 355–373. https://doi.org/10.1006/JEVP.2000. 0184. Reimann, M., Ehrlich, Ü., Tõnisson, H., 2018. Recreational Preferences of Estonian Coastal Landscapes and Willingness-to-pay in Compari- son – a good tool for creating national beach man- agement strategy. In: Botero, Camilo M., Cervantes, Omar D., Finkl, C.W. (Eds.), Beach Management Tools - Concepts, Methodologies and Case Studies. Springer, pp. 895–912. https://doi.org/10.1007/978-3-319-58304-4_46. Roca, E., Villares, M., 2008. Public perceptions for evaluating beach quality in urban and semi-natural environments. Ocean Coast Manag. 51, 314–329. https://doi.org/10. 1016/j.ocecoaman.2007.09.001. Roca, E., Villares, M., Ortego, M.I., 2009. Assessing public perceptions on beach quality according to beach users' profile: a case study in the Costa Brava (Spain). Tour. Manag. 30, 598–607. https://doi.org/10.1016/j.tourman.2008.10.015. Rodella, I., Corbau, C., Simeoni, U., 2017a. The relationship between beach geomorphology ansd sustainable tourism in sandy beaches: a case study of Rosolina Mare littoral (Veneto Region). In: Sixth International Symposium “Monitoring of

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