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Article

Calculating and Mapping the Naturalness of Peri-Urban Greenways

by
Aitor Àvila Callau
1,*,
Yolanda Pérez-Albert
1 and
Jesús Vías Martínez
2
1
Grup de Recerca d’Anàlisi Territorial i Estudis Turístics (GRATET), Department of Geography, Universitat Rovira i Virgili, 43480 Vila-seca, Spain
2
Department of Geography, Universidad de Málaga, 29071 Málaga, Spain
*
Author to whom correspondence should be addressed.
Forests 2023, 14(6), 1181; https://doi.org/10.3390/f14061181
Submission received: 9 May 2023 / Revised: 4 June 2023 / Accepted: 5 June 2023 / Published: 7 June 2023
(This article belongs to the Topic Nature-Based Solutions)
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Abstract

:
Peri-urban forests often have extensive greenway networks that allow for outdoor recreation. However, information associated with these greenways often does not include their degree of naturalness, which is usually reduced to descriptions of the flora and fauna and often overlooks the factors that reduce naturalness. Therefore, in some cases, the naturalness of these greenways is lower than expected. Quantifying their naturalness would be helpful, especially for hikers interested in appreciating and enjoying nature. Additionally, this information would help outdoor recreation managers to design trails or decide which ones to promote as “greenways”. The objectives of this study are (1) to design a method to calculate and map the naturalness of greenways using two approaches, one based on perceptual fieldwork and the other on geographic information systems (GIS); (2) to apply the designed method to a specific greenway; and (3) to compare both methodological approaches. The results show that, for the greenway studied, the naturalness scores obtained are low in all three types of analyses used. Around 70% of the greenway sections in the GIS visibility analysis and 80% in the GIS proximity analysis have a low naturalness index. In comparison, this value is reduced to 40% with the fieldwork analysis. Although the results of the GIS approach (proximity and visibility) generate naturalness indices and spatial patterns that are very similar, they differ significantly from those derived from the fieldwork analysis. The discussion of the results suggests that the three methodologies used are valid for analyzing the degree of naturalness of the trails. However, if used together, it could add flexibility to the type of variables incorporated in the analysis.

1. Introduction

In the present day, urban populations are subjected, on the one hand, to an overload of work motivated by their work or academic activity, and on the other hand, to living in a degraded environment resulting, among other factors, from pollution. The confluence of these two situations can cause negative impacts on people’s health: higher levels of physiological and psychological stress and a higher risk of chronic pathologies, including cardiovascular diseases and mental health problems [1].
In order to counteract these adverse effects on health, the World Health Organization (WHO) recommends regular physical activity as it is beneficial for preventing and treating non-communicable diseases (such as cardiovascular diseases) and for mental health [2]. Additionally, according to Kerr et al. [3], the amount of time spent outdoors is related directly to the improvement of individuals’ physical and emotional states. The combination of these two elements, physical activity and outdoor exposure, gives rise to outdoor recreation, which provides psychological, sociological, educational, and physical benefits to people’s health [4].
When physical activity is performed outdoors in natural environments, it is generally referred to as “green exercise”. Several authors [1,5,6,7] consider that “green exercise”, when carried out in pleasant natural environments, may positively affect mental health and blood pressure, an important indicator of cardiovascular health. In addition, “green exercise” in natural green spaces may have advantages over that carried out in urban green spaces, especially concerning mental health [1,8]. This may be because natural vegetation provides a sense of privacy, encourages personal relationships and physical exercise, and allows one to distance oneself from routines, which implies isolating oneself from sources of stress [9,10]. The COVID-19 pandemic has encouraged outdoor activities, and, therefore, the use of hiking trails has increased. Obradović and Tešin’s work [11] states that, as a result of the pandemic, there was an increase in the number of people who practiced hiking, that people became more involved in this activity, but that perhaps this situation was motivated because other types of activities could not be carried out. Despite this, a large percentage of those surveyed stated that they would continue to practice outdoor hiking when the situation normalized.
Peri-urban forests often feature extensive networks of greenways, paths, or trails that offer a unique opportunity to escape the stress of urban life through sport and recreation [12,13,14,15,16]. It has also been found that the availability and accessibility of trails promote an active lifestyle, increasing the likelihood that users will meet physical activity recommendations prescribed by experts [17,18,19,20,21,22,23,24]. Moreover, from a semi-rural or peri-urban location perspective, trails allow for interaction with the natural environment, which, along with the benefits of physical activity, can improve the quality of life for its users [18,21,25].
The satisfying experience of a hike is conditioned by whether the trail’s characteristics align with the user’s motivations or preferences [11]. If a trail features elements preferred by the user, such as a pleasant landscape, their motivation to walk the trail will increase [8]. Several authors [13,18] have established that trails are mainly used for physical activity. However, in addition to physical exercise, other motivations, such as culture, heritage, or contact with nature drive physical activity on trails [18,22,26,27,28,29,30]. For example, Farías-Torbidoni [26] analyzed the motivations of hikers in natural areas and concludes that the main reason for walking the trail is related directly to nature (connecting with nature, relaxing and disconnecting, and enjoying the landscape). Cordeiro and Alves [31] obtained a similar result by establishing that hikers’ most important motivating factors are relaxation in the first place and exploration of nature in the second place. A study by Mayer and Lukács [32] revealed the desire to escape from everyday life and be in a natural environment as the main motivational factors for hiking trails. The results of Keith et al. [13] established that, after physical activity, relaxing and escaping from city life, spending time with friends or family, and discovering and experiencing nature are the main motivations for using a trail. Thus, contact with nature is one of the recurring motivations for trail use. Therefore, hiking in forests or natural environments is one of the most sought-after hiking experiences [33]. As shown, several studies confirm a high level of preference for environments with abundant natural vegetation, with natural beauty and contact with nature being one of the main reasons people use trails [31,33,34,35]. Therefore, evaluating the degree of naturalness or natural interest of the trails would allow potential users to plan their open-air activities based on their primary motivation. This way, their level of satisfaction would increase, since their expectations would be adjusted to the experience along the trail.
Naturalness is one of the dimensions of human perception of the environment [36]. In addition, naturalness is a subjective concept with aesthetic and biophysical dimensions [36,37]. However, it can be quantified through scientific assessment, for example, based on the number of native versus non-native species in a forest [37]. In outdoor recreation, naturalness relates to protected nature, nature-based recreational experiences, or wildlife [38]. In terms of trails, naturalness is deeply linked to the degree of conservation of the natural environments around them [39].
According to Fredman et al. [38], naturalness is synonymous with predominantly natural rather than built or artificial, and the degree of naturalness can increase depending on the distance from a human settlement. In addition, the impacts (such as visual, odorous, or acoustic) derived from the activities in urban settlements and their peripheries can compromise a landscape’s perceived degree of naturalness. For example, in the case of sound, it is an element of the landscape that, on the one hand, forms part of the place’s identity and its attractiveness, both in natural and cultural terms. On the other hand, noise (for example, associated with road or rail traffic) not only masks the characteristic sounds of the region but also represents a threat to the nature and naturalness of the place, causing environmental fragmentation and general ecological stress [40].
Traditional methodologies that study the characteristics of trails use a series of factors such as the attractiveness of the environment, the type of ground, the conditions of the trail itself, or the natural monuments and attractions present [41]. This method is in line with those applied by hiking federations, such as the European Rambler’s Association that issues trail quality seals, such as the “Leading Quality Trails—Best of Europe”, and that, to certify them, relies on the quality seal acquisition guide proposed by the “Deutsche Wanderverband Service”. The collection and evaluation of these factors have traditionally been based on fieldwork, relying mainly on observation and perception, but in recent decades, these methods have been supplanted by geographic information systems (GIS) [37,42,43,44,45].
In summary, not all hikers have the same interests and motivations, with some prioritizing the athletic aspect, others the ecological and naturalistic aspect, and others the recreational and cultural motivation [29,46]. Additionally, user experience improves if the trail characteristics align with their preferences, so evaluating and communicating the degree of naturalness of the trails is a priority for users motivated by contact with nature. Thus, a detailed understanding of the relationships between users’ motivations and trail characteristics can contribute to the design of strategies that increase user satisfaction and provide greater health benefits [27,46]. However, in analyzing and evaluating trails, the traditional fieldwork method has been substituted by GIS tools. The research question posed by this article is whether GIS tools effectively replace traditional fieldwork methods or whether they are complementary.
This study aims to calculate the naturalness of a greenway, that is, its affinity for the “nature-minded hiker” profile [26], whose main motivation is to appreciate, enjoy, and have direct contact with nature. The objectives of the study are: (1) to design a method that allows the calculation and mapping of the naturalness of greenways based on two approaches, one based on fieldwork and the other on GIS; (2) to apply the designed method to the selected greenway and assess its degree of naturalness; and (3) to compare both methodological approaches to contrast the different results, highlighting their respective strengths and weaknesses.
The study is applied to a local and circular greenway called Anella Verda (“Green Ring”), which is 34 km long and mainly runs through the peri-urban forests of the city of Tarragona (Spain). This was chosen because it is one of the most used greenways in the area [47] and because the responsible administration (Tarragona city council) promotes it as “green”, meaning that it has a strong ecological and naturalness component.

2. Materials and Methods

The methodology is divided into 4 steps (Figure 1). Except for the first step, the following steps are commonly used in methodologies based on multi-criteria analysis techniques in GIS environments. The first step consists of selecting a greenway and defining its sections to operationalize the analysis of its degree of naturalness. The second step is to determine a list of factors that influence the naturalness of the selected greenway, either positively or negatively. The third step refers to collecting data on the selected factors, and in this phase, two different approaches are proposed: GIS and perceptual fieldwork. We compare these two methodologies because they are some of the most widely used for collecting data on factors in multi-criteria analysis. In turn, the GIS method is subdivided into two approaches: proximity analysis and visibility analysis. It is considered that a certain factor, although not directly visible from the greenway, may be nearby and impact the user’s perception in some way. For example, no direct visual contact between a greenway user and a nearby wastewater treatment plant may exist; however, this element will potentially produce a different negative impact (acoustic and/or odoriferous) that the user will perceive equally, compromising the degree of naturalness of the greenway. Therefore, it is appropriate to use proximity and visibility analyses referring to the GIS method in this study. Finally, the fourth and last methodological step consists of calculating the naturalness index of each section of the greenway according to the three approaches and mapping it.
The segmentation of the greenway into sections (step 1 of the methodology) and its reference length as well as the selection of most of the factors that influence the naturalness of a greenway (step 2 of the methodology) are based on the Qualitätsweg Wanderbares (Quality Hiking Trail) guide (Deutscher Wanderverband Service GmbH, Kassel, Germany), promoted by the Deutscher Wanderverband (German Hiking Association, DWV) which is available online at https://www.wanderbares-deutschland.de/service/qualitaetsinitiativen/qualitaetswege (accessed on 4 February 2021). This guide details the requirements that a trail in Germany must meet to be certified as Qualitätsweg Wanderbares Deutschland (Quality Hiking Trail of Wanderbares Deutschland). To achieve this, different parameters of the trail are measured (materials, location, capacity to be enjoyed), the trail’s development as a means of communication, signage, the landscape and natural environment, cultural aspects, and the humanization of the route, among others. This guide is widely recognized and used by prestigious outdoor recreation associations such as the European Ramblers Association (ERA), which awards a seal called Leading Quality Trails—Best of Europe (ERA-EWV-FERP, Praha, Czech Republic) to the highest quality trails in Europe with the support of this guide (https://www.era-ewv-ferp.org/lqt/, accessed on 4 February 2021).

2.1. Study Area

The Anella Verda (“Green Ring”) runs through the peri-urban area of Tarragona (Catalonia, Spain). This space is a highly fragmented traditional agricultural landscape due to the proliferation of industrial, logistical, commercial, and leisure areas and a dense network of infrastructures. The environment of this greenway is considered a chaotic area with many interstitial spaces, although with great potential for outdoor recreation as it has an extensive network of trails [48].
Through two variants, the selected greenway connects the rivers that flow into the municipality of Tarragona (the Francolí and the Gaià), resulting in a great excursion of 34 km in length. Its interior variant mostly runs through peri-urban forests, while the other variant runs along the coastline (Figure 2). The route covers a large part of the extensive historical and cultural heritage of the municipality, passing through a Roman aqueduct (Figure 2a), medieval farmhouses, defense towers (Figure 2c), Roman quarries, orchards, river mouths, villages of interest (Figure 2e), and coves or beaches of high natural value (Figure 2b,d). Although there are specific locations within the itinerary with the presence of tourists (such as the Roman aqueduct), the entire greenway is used mainly by the inhabitants of its surroundings [47].

2.2. Trail Segmentation

The greenway layer has been downloaded from the website of the Tarragona City Council in GPX format and has been subdivided into different sections in order to operationalize data collection. For this purpose, the greenway has been segmented using satellite images through photointerpretation based on two criteria. The first (1) considers the morphology of the path and is based on detecting sudden changes, for example, when its width or type of surface is radically modified. The second (2) establishes a travel distance of around 4 km, following the Qualitätsweg Wanderbares guide.

2.3. Factor Selection and Sources

For analyzing the naturalness of the sections of the Anella Verda, a set of factors has been selected that are mainly based on the Qualitätsweg Wanderbares guide and other studies that evaluate naturalness using multi-criteria analysis techniques integrated into GIS, such as those by Vías and Ocaña [45] and Khazaee Fadafan et al. [43]. The selected factors are grouped into two sets (Table 1), similar to the approach used by Khazaee Fadafan et al. [43]. On the one hand, there are factors related to the intensive use of space that may reduce the naturalness of the greenway, which could be considered negative factors. On the other hand, there are factors that add naturalness, i.e., those that shape natural landscapes and their figures of protection that determine their relevance from a natural perspective, and all of them are positive factors.
Although there are many factors to use in the analysis, for this study, only those present in the study area have been considered. Specifically, through a previous exploration of the cartography associated with the Landscape Catalog of Camp de Tarragona from the Landscape Observatory of Catalonia [49], only the factors within a 500 m influence area from the greenway are included. Those that cannot be measured by the three approaches (GIS visibility, GIS proximity, and fieldwork) at the same time are excluded. In the case of approaches that require the use of GIS (proximity and visibility), the corresponding layers in ESRI shapefile format have been obtained from the different official organizations detailed in Table 1.

2.4. Factor Data Collection

The fieldwork was entirely perceptual or experiential. For each section, the presence of each factor was analyzed, considering a single scale of values from 1 to 5, with 1 indicating very little presence and 5 the opposite situation. The fieldwork was carried out in July 2021 by two of the authors of this article, having printed support cartography of all of the factors to be assessed. During data collection, the weather conditions were favorable, i.e., no episodes of wind or rain were experienced. The collected data were entered on analog fieldwork sheets by both operators independently and then the simple average of the evaluations made by each of them was calculated.
In the case of the GIS-based approaches, the analysis was restricted to an influence area of 500 m. According to Booth et al. [50], landscape characteristics can be separated into two categories based on their proximity to the viewer: aesthetic of view and aesthetic of landscape. The former is determined by the perceived characteristics at great distances, while the latter is limited to the perceived characteristics within a much closer range, typically by viewers within a natural area looking at a patch of landscape around them. Their landscape aesthetic model is limited to evaluating the characteristics of the 1000 m around the observer, based on their professional judgment and knowledge of the study area. However, they argue for the possibility of adapting this distance to the characteristics of the analyzed zone. Following the same criteria, and taking into account the characteristics of the study area, this work considers it appropriate to restrict both GIS analyses to the 500 m around each section of the greenway.
For the visibility analysis, visual basins were calculated using the “visibility analysis” plugin, accessible from the free and open-source software QGIS 3. As a previous step, a layer of viewpoints or observers was created for each of the greenway sections. To achieve this, the line layer, which represents the different sections, was transformed into a point layer based on its vertices, which generated 3716 observation points distributed, according to the section, as shown in Table 2. Attributes related to the observer’s height (the default program value which is 1.60 m) and visibility radius (which is 500 m) are associated with this point layer.
After configuring the observation points, the visual basins were calculated for each section using the default program values for the sphericity of the Earth and the atmospheric refraction index. In this study, due to its advantages in terms of precision and accuracy in modeling the terrain [51,52,53], a digital surface model (DSM) was used for calculating the visual basins, as they integrate the height of trees and buildings in the visibility calculations. This DSM was downloaded from the Spanish National Geographic Institute website.
This calculation process was performed 10 times, one for each set of points representing a section of the greenway. The resulting values were transformed into binary values: 0 (not visible) and 1 (visible). Then, the visible surface area in square meters was calculated for each of the 10 sections of the greenway. The visible surface area results from multiplying the number of pixels with a value of 1 by the surface area of each pixel, which in this study was 100 square meters. Finally, to determine the percentage of the visible surface area of each factor, its surface area was calculated by overlaying the layers representing the factors and the visual basin from each section.
In the case of the proximity analysis, the percentage of the surface area of each factor within a 500 m buffer around each section was calculated. For this, the “buffer” algorithm of QGIS 3 was used to calculate the 10 buffers for each section of the greenway. Next, all of the layers related to the different factors were clipped using each buffer as the clipping entity. Once the factors were clipped, they were overlaid with the buffers of each section to calculate the proportion of the surface area of each factor relative to the total surface area of each buffer. However, for the factor “F4b_transport”, the percentage of the distance of each section that directly intersects with the layer of noise from interurban roads and railways was calculated. Therefore, unlike in the visibility analysis, proximity to the communication routes is not measured, but rather to the noise they generate, one of the negative impacts these infrastructures can cause [40].

2.5. Naturalness Index Calculation and Mapping

To calculate the naturalness index, the ideal point method is used, adapted from Malczewski [54] and expressed by the following equation:
N a t u r a l n e s s   i n d e x = 1 − ∑ 1 n x i − p 2 N ( a − p ) 2
where xi is the value of factor n in section i; p is the ideal point; a is the anti-ideal point (opposite to the ideal), and N is the total number of factors.
The ideal point method reflects the statistical distance of each case from the ideal situation. It is used in this study because, like in Vías and Ocaña [45], it is more appropriate than a mere linear aggregation since the compensation derived from using the arithmetic mean balances situations with different dissimilarity values.
In the analyses derived from the GIS method, the value considered the ideal point for all factors is 100%, while for the data collected in the fieldwork, this value is 5. As a preliminary step to calculating the formula, the values of the factors belonging to group 1 (intensive use—negative factors) are inverted since, being factors that harm naturalness, the lower the original value collected, the closer they will be to the ideal point of naturalness. For example, if the original value of factor 1 (F1_Industry) was 4.52%, it is inverted and becomes 95.48%, considering 100% as the ideal point.
Considered as a measure of similarity, the result of calculating the part of the formula included in the parentheses means that the value 0 is the ideal point, while 1 is its antithesis. However, following the study of Vías and Ocaña [45], and in order to maintain a logical order of scores (0 indicating lower degree of naturalness and 1 indicating higher degree of naturalness), the value resulting from calculating the parentheses is inverted.
Once the naturalness index of each section was calculated, they were mapped using an equal interval classification to be comparable: low naturalness degree (from 0 to 0.33), medium naturalness degree (greater than 0.33 to 0.66), and high naturalness degree (greater than 0.66 to 1).

3. Results

3.1. Trail Sections’ Characteristics

The segments that make up the analyzed greenway can be classified into three main groups: those that run along the river courses and are located at the eastern and western ends (S1, S2, and partially S6); those in the interior (S3, S4, S5, and partially S6); and finally, the coastal ones (S7, S8, S9, and S10) (Figure 3). In the first group, the presence of the river is the main factor shaping the characteristics. However, there are some differences: S1 is an entirely urban section that has been configured as a river park and is equipped with urban furniture (fountains, benches, trash cans, etc.) and roads with different surface characteristics (artificial, dirt, etc.) or users (bike lane or pedestrian lane); the S2 section corresponds to a wide riverbed, with little riparian vegetation and multiple trail and path options; while the second part of S6 coincides with the final stretch of the Gaià river and has a dense and well-preserved riparian vegetation since it is included in the protected natural space of the mouth of the Gaià river.
The interior sections display differences in land use, unique cultural elements, and the distance to fast traffic routes. Thus, in S3, there is the Roman aqueduct of Ferreres, and in the first part of S6, the Roman quarry of Mèdol, both of which are first-rate historical and cultural attractions. In addition, examples of scattered traditional architecture (farmhouses) appear throughout the set of interior sections. S3 runs mostly through wooded areas and is located away from fast traffic routes, while in S4 and S5, and the first part of S6, there is a mosaic of forests, abandoned or non-abandoned agricultural areas, and low-intensity residential areas. Finally, it is noteworthy that the entirety of S4 runs parallel and adjacent to fast-traffic routes.
The coastal sections are arranged from east (S7) to west (S10) and are characterized by a greater or lesser degree of urbanization in their immediate hinterland and by the distance to the railway line. Thus, S7 starts at Tamarit Castle and corresponds to a beach surrounded by low-density urbanization. S8 runs through the protected natural space of the Bosc de la Marquesa, which has been preserved thanks to the railway line acting as a barrier to urbanization. S9 and S10 correspond to beaches, with the railway line parallel to the coast and very close, with an urban or practically urban hinterland.

3.2. Factor Mapping

Figure 4a shows the factors used to assess the naturalness of the greenway, which have been described previously. The factors that detract from the naturalness of the extensive surface landscape have different spatial distributions. Industry (F1_industry) is concentrated in the western sector, while urban areas (F5_urban) have a large surface area corresponding to the city of Tarragona adjacent to the industry and a series of randomly distributed patches in the entire study area (low-density residential area). The linear factors (F2_voltage, F4_transport in both variants) cross the territory horizontally, from east to west, although the network of communication routes is denser in the western sector. It should be noted that the transportation factor used in the proximity analysis (F4b_transport) corresponds to a polygon that, in its western section, occupies a good part of the territory. The factors that add naturalness to the landscape are mostly superficial in nature, from F6 to F9, with a greater profusion in the eastern sector of the study area, most of them located in the interior zone, except for the aesthetic coastal landscape located on the coast, also in the east. Finally, it should be noted that in the river factor (F10_rivers), they are located at the ends and have a vertical (N–S) disposition.
Since the GIS analysis is carried out using two approaches, visibility and proximity, a visual basin has been generated for the first case (Figure 4b), which covers an area of 17.53 km2, and an influence area or buffer for the second case that delimits a polygon equidistant to 500 m from the Anella Verda and has an area of 37.98 km2 (Figure 4c).

3.3. Trail Naturalness Assessment

The results of the naturalness index calculated for each section are different for the three methodological approaches proposed (Figure 5). First, according to the GIS visibility analysis, 70% of the sections have a naturalness score of less than 0.33 and the remaining 30% (S7, S8, and S9) have an intermediate naturalness. No section with a high naturalness index is identified. Spatially, the three sections with an intermediate level of naturalness are contiguous and entirely on the coastal axis of the greenway.
Secondly, the proximity GIS analysis results follow a very similar pattern to the previous one, with 80% of the sections having a low degree of naturalness. The remaining 20% (S7 and S8) do not exceed a score of 0.66, placing them in an intermediate level of naturalness. With this analysis, no sections appear to have a naturalness index higher than 0.66. The spatial pattern is identical to the previous one, except for S9, with a score of 0.32, which is located at a low level of naturalness.
Thirdly, in the fieldwork, more significant variability is observed, both in the sections’ scores and their spatial pattern. Specifically, less than half of the sections (40%) obtain a low naturalness score, half obtain an intermediate level of naturalness, and the remaining section (S8) is the only one that obtains a high naturalness score (0.70). From the spatial pattern, it can be deduced that the coastal sections have higher naturalness indices than the rest, with S8 standing out, having several positive factors (F6_forest, F7_n2000, F8_coast, F9_faunaflora, and F11_sea) that coincide spatially while lacking negative factors that reduce naturalness.

3.4. Comparison of Methods

The results obtained through the three methodological approaches to the naturalness of the greenway sections show, in general, low values of naturalness, which are consistent with the degree of human intervention in this territory. This area is located near the city of Tarragona, with many urbanized areas occupied by various human activities.
Out of the 10 sections analyzed, in half of them (S1, S2, S4, S5, and S7), the three methodological approaches coincide in the naturalness class (Figure 6). In contrast, at least two methodological approaches coincide in the other half of the sections (S3, S6, S8, S9, and S10). There was no section where the three approaches offered a different naturalness class in each one. In all sections, at least two approaches coincide.
Sections S1, S2, S4, and S5 have the same value of naturalness, which is low, meaning they have little naturalness. The main feature that identifies them is the high presence of low-density residential areas, except in S2 where the low naturalness is due to industrial areas and power lines.
In section S7, the three methodological approaches also agree. However, this time the naturalness increases to the medium class due to the lower residential urbanization and the increase in positive elements such as the presence of forests, protected areas, coastal landscape, and relevant species of flora and fauna.
The coincidence of the degree of naturalness, according to the three approaches, has been observed primarily in the eastern area (highly urbanized), the northern area, and one section in the southern area. On the other hand, the southern area is the most heterogeneous in terms of naturalness results. Of the four sections in this area, only one is coincident in the three approaches, while the other three (S8, S9, and S10) only coincide in two. S10 and S8 are coincident in the methodological approaches with GIS (proximity and visibility), in which the values of natural and water landscapes dominate. In contrast, in S9, where the results of visibility and fieldwork coincide, the presence of the sea is significant.

4. Discussion

The information associated with an official trail usually does not provide an indicator that quantifies its degree of naturalness; the signs, brochures, and other available documentation typically only describe the characteristics of the fauna and flora of the greenway in question [55]. The availability of information on the degree of naturalness of a trail when choosing a greenway or a section of it to travel would be very useful for users, but especially for the profile of hiker interested in appreciating and enjoying nature. Despite the advantages that knowledge of the degree of naturalness of a trail could provide its users, a limited number of studies address this topic (e.g., [36,37,39,40,45,56]).
This study demonstrates that, although the administration promotes the analyzed greenway as “green” and “natural”, it does not have the degree of naturalness that a nature-minded hiker would likely expect to find. In this regard, similarities have been found with the study by Vías and Ocaña [45], in which a multi-criteria evaluation model integrated into a GIS approach was used to assess the suitability of a network of trails for hiking. One of the criteria included in the generated model was natural interest. As a result of this criterion, they found that more than three-quarters of the analyzed sections had a below-average natural interest, which can be considered a low value in the context of that study area (the Sierra de las Nieves natural protected area in southern Spain). Similarly, in the work by Pavão et al. [37], the hypothesis was made that natural forests could have a relevant role as a tourist attraction for trails in the Azores (Portugal). To demonstrate this hypothesis, a quantitative analysis based on GIS was applied to determine the predominant types of cover found along these trails. They concluded that the predominant ground cover along the routes in this nature tourism destination correspond to anthropogenic landscapes of low naturalness (e.g., reforested forests, artificial pastures, or invasive forests).
Table 3 summarizes the applied methodologies in this study. Regarding the analysis speed, establishing the perception of naturalness through fieldwork is considered the slowest method. On the other hand, the fastest method is proximity to the trail using GIS tools. More layers are needed for the visibility analysis (the third method used), and the calculation of visual basins requires more processing time. The cost behavior also shows differences between fieldwork and GIS methods. The former requires a high and continuous budget for personnel to carry out data collection campaigns, while in GIS, the investment is initial and dedicated to the purchase of hardware, since free GIS software can be used, and the necessary information layers are usually available free of charge from institutions responsible for cartography. This cost difference is accentuated if we consider that, although computer equipment is not necessary during the collection of information in perceptual fieldwork, it is used in the subsequent treatment of the data.
One crucial aspect to consider is the selection of factors that will be part of the analysis model for the degree of naturalness. In the case of perceptual fieldwork, the limitations in selecting factors are very low since, based on perception, any factor can be collected, such as bad smells or the presence of garbage. The only limitation detected is based on the time dedicated to this task, which is very demanding. In the case of GIS, the selection of factors is based on technical criteria considering the availability and scale of cartography, which for some factors are difficult to find or there is an absence of information or it is not at an appropriate scale. Furthermore, in visibility analysis with GIS, these limitations are increased since, in addition to obtaining information layers, an additional layer, the digital surface model (DSM), is required to calculate the visual basins. In the selection of factors, it is considered that fieldwork is more versatile since it can incorporate factors easily since it is only based on perception. Due to the characteristics of perception-based fieldwork, the risk of variations in the assessment of factors is very high since, despite having detailed descriptors for each category of the range, external factors such as weather conditions or the experience and judgment of the technicians performing the assessment can be different. This situation is diametrically opposed to the use of GIS, which obtains equivalent results for any section and situation by applying the same parameters.
Regarding the initial research question, the results obtained from comparing the methods show that if a quick analysis is required, the best option is to use a GIS-based proximity analysis. However, if the time and budget are available, the fieldwork technique can be used, providing greater flexibility when selecting factors.
According to the results obtained from this study, it is not possible to affirm that GIS can be a complete substitute for fieldwork, but rather that both methodologies are complementary. The three methods applied are valid independently when measuring the degree of naturalness of a trail. However, GIS would provide a quick and objective analysis in an integrated methodology. At the same time, fieldwork could introduce, on the one hand, factors that are not usually available in cartographic layers, such as the presence of trash or foul odors and, on the other hand, information related to the user’s emotions or experiences, such as the attractiveness of a picturesque place or the tranquility of a forest. Other authors have already used this complementarity between GIS and fieldwork in other contexts, leading to satisfactory results (e.g., [57,58,59]).
In any case, it is considered that the administration should expand the available information on the degree of naturalness of the trails with the intention that when the user makes their choice, it fits their motivations and preferences to increase their level of satisfaction [11]. In addition to increasing satisfaction, having this information can encourage outdoor recreational and sports activities with potentially positive effects on the health and well-being of people.
The main methodological limitations of this study are related to the segmentation of the original greenway into sections and the area of influence used. In this work, criteria related to the longitudinal homogeneity of the sections and their characteristics are used to segment the trail. Other works, such as Vías and Ocaña [45], use intersections with other trails to establish the different section breakpoints. Therefore, depending on how the criterion is established to locate these section breakpoints, the segmentation of the itinerary can directly influence the final calculations of naturalness for each section. Additionally, in the specific case of the GIS approach, determining the area of influence (both for visibility analysis and proximity analysis), which in this study was 500 m, can also affect the results.
Additionally, it is essential to consider that the adaptation of the factors to each specific case study limits the replicability of this method. That is, depending on the characteristics of the analyzed greenway and the study area in which it is located, the factors to include, both for GIS analysis and fieldwork analysis, may vary. This makes it difficult to replicate the method in another study area without first conducting a diagnosis of the factors present to customize the selection. For example, the same factors cannot be used to measure the degree of naturalness of a path that runs through a desert area, a forest, or an inland area versus a coastal area.

5. Conclusions

This study contributes to the international literature by adapting multi-criteria analysis techniques to calculate and map the level of naturalness of greenways, a field scarcely explored so far. It also provides a comparative analysis of the data collection methods used to detect and discuss their strengths and weaknesses.
The three analyses applied in this study (two through GIS and the other with fieldwork) are shown to be valid for calculating the degree of naturalness of a greenway. However, from the discussion of the results, it is concluded that integrating both techniques (GIS and fieldwork) can lead to more precise and detailed results in trail naturalness assessment.
The two GIS analyses obtain similar naturalness indices for this case study, although the values are consistently lower than those derived from the fieldwork analysis. Additionally, although the greenway analyzed in this study is promoted as “green”, the resulting naturalness indices are generally low in all three methods. This suggests that this type of itinerary or trail only sometimes meets expectations, meaning it does not have the degree of naturalness that users would expect to find. Moreover, it would be interesting for the local administration to consider these results to, on the one hand, transfer the level of naturalness of each section to the users of this greenway (for example, with the implementation of informative panels or signs on the greenway). On the other hand, if appropriate, implement the necessary measures to increase the levels of naturalness of the sections that require it, primarily to mitigate the negative impacts potentially produced by the factors considered in this study as negative or intensive use.
The results obtained from both the analyzed greenway and the replication of this methodology in other study areas could be of great use for land and outdoor recreation managers. On the one hand, it could help them design trails by selecting those segments with the highest naturalness, and on the other hand, it could help them promote these trails. For hikers, it means having more and higher quality information about the naturalness of greenways for their decision-making process before using a particular greenway or section thereof.

Author Contributions

All authors (A.À.C., Y.P.-A. and J.V.M.) contributed equally to conceptualizing, writing, and reviewing this work. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by MCIN/AEI/10.13039/501100011033 (grant number PID2020-114363GB-I00) and the GRATET Research Group, which is funded by the Catalan Government under code 2021-SGR-00657.

Data Availability Statement

Not applicable.

Acknowledgments

This article has been possible with the support of the grant PRE2018-084802 funded by MCIN/AEI/10.13039/501100011033 and by “ESF Investing in your future”.

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

References

  1. Li, H.; Zhang, X.; Bi, S.; Cao, Y.; Zhang, G. Psychological benefits of green exercise in wild or urban greenspaces: A meta-analysis of controlled trials. Urban For. Urban Green. 2022, 68, 127458. [Google Scholar] [CrossRef]
  2. WHO. Guidelines on Physical Activity and Sedentary Behaviour; World Health Organ: Genève, Switzerland, 2020; pp. 1–582. [Google Scholar]
  3. Kerr, J.; Marshall, S.; Godbole, S.; Neukam, S.; Crist, K.; Wasilenko, K.; Golshan, S.; Buchner, D. The relationship between outdoor activity and health in older adults using GPS. Int. J. Environ. Res. Public Health 2012, 9, 4615–4625. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  4. Breitenstein, D.; Ewert, A. Health benefits of outdoor recreation: Implications for health education. Health Educ. 1990, 21, 16–21. [Google Scholar] [CrossRef]
  5. Barton, J.; Pretty, J. The beneficial effects of green exercise on health. In Forest Medicine; Nova Science Publishers, Inc.: Hauppauge, NY, USA, 2013; pp. 201–219. ISBN 9781626184626. [Google Scholar]
  6. Calogiuri, G.; Elliott, L.R. Why do people exercise in natural environments? Norwegian adults’ motives for nature-, gym-, and sports-based exercise. Int. J. Environ. Res. Public Health 2017, 14, 377. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  7. Hill, E.; Gómez, E. Perceived health outcomes of mountain bikers: A national demographic inquiry. J. Park Recreat. Adm. 2020, 38, 31–44. [Google Scholar] [CrossRef]
  8. Davies, N.J.; Lumsdon, L.M.; Weston, R. Developing Recreational Trails: Motivations for Recreational Walking. Tour. Plan. Dev. 2012, 9, 77–88. [Google Scholar] [CrossRef]
  9. Fumagalli, N.; Maccarini, M.; Rovelli, R.; Berto, R.; Senes, G. An exploratory study of users’ preference for different planting combinations along rural greenways. Sustainability 2020, 12, 2120. [Google Scholar] [CrossRef] [Green Version]
  10. Senes, G.; Rovelli, R.; Bertoni, D.; Arata, L.; Fumagalli, N.; Toccolini, A. Factors influencing greenways use: Definition of a method for estimation in the Italian context. J. Transp. Geogr. 2017, 65, 175–187. [Google Scholar] [CrossRef]
  11. Obradović, S.; Tešin, A. Hiking in the COVID-19 era: Motivation and post-outbreak intentions. J. Sport Tour. 2022, 26, 147–164. [Google Scholar] [CrossRef]
  12. Chen, X.; de Vries, S.; Assmuth, T.; Dick, J.; Hermans, T.; Hertel, O.; Jensen, A.; Jones, L.; Kabisch, S.; Lanki, T.; et al. Research challenges for cultural ecosystem services and public health in (peri-)urban environments. Sci. Total Environ. 2019, 651, 2118–2129. [Google Scholar] [CrossRef]
  13. Keith, S.J.; Larson, L.R.; Shafer, C.S.; Hallo, J.C.; Fernandez, M. Greenway use and preferences in diverse urban communities: Implications for trail design and management. Landsc. Urban Plan. 2018, 172, 47–59. [Google Scholar] [CrossRef]
  14. Komossa, F.; Wartmann, F.M.; Kienast, F.; Verburg, P.H. Comparing outdoor recreation preferences in peri-urban landscapes using different data gathering methods. Landsc. Urban Plan. 2020, 199, 103796. [Google Scholar] [CrossRef]
  15. Komossa, F.; van der Zanden, E.H.; Verburg, P.H. Characterizing outdoor recreation user groups: A typology of peri-urban recreationists in the Kromme Rijn area, the Netherlands. Land Use Policy 2019, 80, 246–258. [Google Scholar] [CrossRef]
  16. Žlender, V.; Ward Thompson, C. Accessibility and use of peri-urban green space for inner-city dwellers: A comparative study. Landsc. Urban Plan. 2017, 165, 193–205. [Google Scholar] [CrossRef] [Green Version]
  17. Brownson, R.C.; Housemann, R.A.; Brown, D.R.; Jackson-Thompson, J.; King, A.C.; Malone, B.R.; Sallis, J.F. Promoting physical activity in rural communities: Walking trail access, use, and effects. Am. J. Prev. Med. 2000, 18, 235–241. [Google Scholar] [CrossRef]
  18. Corning, S.E.; Mowatt, R.A.; Chancellor, H.C. Multiuse trails: Benefits and concerns of residents and property owners. J. Urban Plan. Dev. 2012, 138, 277–285. [Google Scholar] [CrossRef]
  19. Evenson, K.R.; Herring, A.H.; Huston, S.L. Evaluating change in physical activity with the building of a multi-use trail. Am. J. Prev. Med. 2005, 28, 177–185. [Google Scholar] [CrossRef] [PubMed]
  20. Neff, L.J.; Ainsworth, B.E.; Wheeler, F.C.; Krumwiede, S.E.; Trepal, A.J. Assessment of trail use in a community park. Fam. Community Health 2000, 23, 76–84. [Google Scholar] [CrossRef]
  21. Schasberger, M.G.; Hussa, C.S.; Polgar, M.F.; McMonagle, J.A.; Burke, S.J.; Gegaris, A.J., Jr. Promoting and developing a trail network across suburban, rural, and Urban communities. Am. J. Prev. Med. 2009, 37, S336–S344. [Google Scholar] [CrossRef] [PubMed]
  22. Wan Omar, W.R.; Patterson, I.; Pegg, S. A Green Pathway for Future Tourism Success: Walking Trails in Kuala Lumpur. Tour. Plan. Dev. 2012, 9, 57–76. [Google Scholar] [CrossRef]
  23. Wang, G.; Macera, C.A.; Scudder-Soucie, B.; Schmid, T.; Pratt, M.; Buchner, D. Cost effectiveness of a bicycle/pedestrian trail development in health promotion. Prev. Med. 2004, 38, 237–242. [Google Scholar] [CrossRef] [PubMed]
  24. Xie, B.; Lu, Y.; Zheng, Y. Casual evaluation of the effects of a large-scale greenway intervention on physical and mental health: A natural experimental study in China. Urban For. Urban Green. 2022, 67, 127419. [Google Scholar] [CrossRef]
  25. Park, T.; Eyler, A.A.; Tabak, R.G.; Valko, C.; Brownson, R.C. Opportunities for Promoting Physical Activity in Rural Communities by Understanding the Interests and Values of Community Members. J. Environ. Public Health 2017, 2017, 8608432. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  26. Farías-Torbidoni, E.I. Managing for recreational experience opportunities: The case of hikers in protected areas in Catalonia, Spain. Environ. Manag. 2011, 47, 482–496. [Google Scholar] [CrossRef]
  27. Farías-Torbidoni, E.I.; Grau, R.; Camps, A. Trail preferences and visitor characteristics in Aigüestortes i Estany de Sant Maurici National park, Spain. Mt. Res. Dev. 2005, 25, 51–59. [Google Scholar] [CrossRef]
  28. Farías-Torbidoni, E.I.; Mas-Alòs, S.; Gil-Moreno-de-Mora, G.; Lavega-Burgués, P.; Castañer, M.; Lorente-Catalán, E.; Seguí-Urbaneja, J.; Lacasa-Claver, E. Health and Well-Being in Protected Natural Areas—Visitors’ Satisfaction in Three Different Protected Natural Area Categories in Catalonia, Spain. Int. J. Environ. Res. Public Health 2020, 17, 6746. [Google Scholar] [CrossRef] [PubMed]
  29. Vías, J.; Rolland, J.; Castillo, S.; del Campo-Ávila, J.; Luque, A. Creación de rutas senderistas mediante análisis de redes y algoritmo multicriterio. Aplicación en el parque natural Sierra de las Nieves. In Proceedings of the XVI Congreso Nacional de Tecnologías de Información Geográfica, Alicante, Spain, 25–27 June 2014; pp. 947–953. [Google Scholar]
  30. Watts, G.; Bauer, J. Tranquillity trails–design, implementation and benefits for healthy leisure. World Leis. J. 2021, 64, 156–165. [Google Scholar] [CrossRef]
  31. Cordeiro, B.; Alves, L. Protected areas, tourism and hiking trails. The hiker’s profile in Serra da Lousã (Portugal). PASOS Rev. Tur. Y Patrim. Cult. 2022, 20, 939–949. [Google Scholar] [CrossRef]
  32. Mayer, K.; Lukács, A. Motivation and mental well-being of long-distance hikers: A quantitative and qualitative approach. Heliyon 2021, 7, e06960. [Google Scholar] [CrossRef]
  33. Saayman, M.; Viljoen, A. Who are wild enough to hike a wilderness trail? J. Outdoor Recreat. Tour. 2016, 14, 41–51. [Google Scholar] [CrossRef]
  34. Gaffar, V.; Yuniawati, Y.; Ridwanudin, O. A Study of Outdoor Recreation Motivation and Activity Preferences. Xinan Jiaotong Daxue Xuebao/J. Southwest Jiaotong Univ. 2019, 54, 3. [Google Scholar] [CrossRef] [Green Version]
  35. Lee, J.; Lee, H.-S.; Jeong, D.; Shafer, C.S.; Chon, J. The relationship between user perception and preference of greenway trail characteristics in urban areas. Sustainability 2019, 11, 4438. [Google Scholar] [CrossRef] [Green Version]
  36. Chon, J.; Shafer, C.S. Aesthetic responses to urban greenway trail environments. Landsc. Res. 2009, 34, 83–104. [Google Scholar] [CrossRef]
  37. Pavão, D.C.; Porteiro, J.; Ventura, M.A.; Borges Silva, L.; Medeiros, A.; Moniz, A.; Moura, M.; Moreira, F.; Silva, L. Land cover along hiking trails in a nature tourism destination: The Azores as a case study. Environ. Dev. Sustain. 2021, 23, 16504–16528. [Google Scholar] [CrossRef]
  38. Fredman, P.; Wall-Reinius, S.; Grundén, A. The Nature of Nature in Nature-based Tourism. Scand. J. Hosp. Tour. 2012, 12, 289–309. [Google Scholar] [CrossRef]
  39. Oishi, Y. Toward the improvement of trail classification in national parks using the recreation opportunity spectrum approach. Environ. Manag. 2013, 51, 1126–1136. [Google Scholar] [CrossRef]
  40. Malec, M.; Kędzior, R.; Ziernicka-Wojtaszek, A. The Method of Soundscape Naturalness Curves in the Evaluation of Mountain Trails of Diversified Anthropopressure—Case Study of Korona Beskidów Polskich. Sustainability 2023, 15, 723. [Google Scholar] [CrossRef]
  41. Molokáč, M.; Hlaváčová, J.; Tometzová, D.; Liptáková, E. The Preference Analysis for Hikers’ Choice of Hiking Trail. Sustainability 2022, 14, 6795. [Google Scholar] [CrossRef]
  42. Beeco, J.A.; Hallo, J.C.; Brownlee, M.T.J. GPS Visitor Tracking and Recreation Suitability Mapping: Tools for understanding and managing visitor use. Landsc. Urban Plan. 2014, 127, 136–145. [Google Scholar] [CrossRef]
  43. Khazaee Fadafan, F.; Soffianian, A.; Pourmanafi, S.; Morgan, M. Assessing ecotourism in a mountainous landscape using GIS MCDA approaches. Appl. Geogr. 2022, 147, 102743. [Google Scholar] [CrossRef]
  44. Peterson, B.A.; Brownlee, M.T.J.; Marion, J.L. Mapping the relationships between trail conditions and experiential elements of long-distance hiking. Landsc. Urban Plan. 2018, 180, 60–75. [Google Scholar] [CrossRef]
  45. Vías, J.; Ocaña, C. Multicriteria evaluation by GIS to determine trail hiking suitability in a natural park. Bol. La Asoc. Geogr. Esp. 2014, 66, 323–339. [Google Scholar]
  46. Vías, J.; Rolland, J.; Gómez, M.L.; Ocaña, C.; Luque, A. Recommendation system to determine suitable and viable hiking routes: A prototype application in Sierra de las Nieves Nature Reserve (southern Spain). J. Geogr. Syst. 2018, 20, 275–294. [Google Scholar] [CrossRef]
  47. Àvila Callau, A. El Análisis de los Servicios Ecosistémicos Culturales A Partir de Información Geográfica Voluntaria. El Caso del Periurbano De Tarragona; Universitat Rovira i Virgili: Tarragona, Spain, 2022. [Google Scholar]
  48. Àvila Callau, A.; Pérez-Albert, Y.; Serrano Giné, D. Quality of GNSS Traces from VGI: A Data Cleaning Method Based on Activity Type and User Experience. ISPRS Int. J. Geo-Inf. 2020, 9, 727. [Google Scholar] [CrossRef]
  49. Observatori del Paisatge. Catàleg de Paisatge del Camp de Tarragona; Observatori Del Paisatge: Olot, Spain, 2010. [Google Scholar]
  50. Booth, P.N.; Law, S.A.; Ma, J.; Buonagurio, J.; Boyd, J.; Turnley, J. Modeling aesthetics to support an ecosystem services approach for natural resource management decision making. Integr. Environ. Assess. Manag. 2017, 13, 926–938. [Google Scholar] [CrossRef] [PubMed]
  51. Klouček, T.; Lagner, O.; Šímová, P. How does data accuracy influence the reliability of digital viewshed models? A case study with wind turbines. Appl. Geogr. 2015, 64, 46–54. [Google Scholar] [CrossRef]
  52. Noblejas, H.C.; Martínez, J.V.; Rodríguez, M.F.M. Relation between the Views and the Real Estate Application to a Mediterranean Coastal Area. ISPRS Int. J. Geo-Inf. 2022, 11, 365. [Google Scholar] [CrossRef]
  53. Pellicer, I.; Estornell, J.; Martí, J. Application of airborne LiDA R data in viewshed analysis. Rev. Teledetec. 2014, 41, 9–18. [Google Scholar] [CrossRef] [Green Version]
  54. Malczewski, J. GIS and Multicriteria Decision Analysis; John Wiley & Sons: New York, NY, USA, 1999; ISBN 0471329444. [Google Scholar]
  55. Weis, K.; Hronček, P.; Tometzová, D.; Gregorová, B.; Přibil, M.; Jesenský, M.; Čech, V. Analysis of notice boards (Panels) as general information media in the outdoor mining tourism. Acta Montan. Slovaca 2019, 24, 269–283. [Google Scholar]
  56. Getzner, M.; Meyerhoff, J. The benefits of local forest recreation in austria and its dependence on naturalness and quietude. Forests 2020, 11, 326. [Google Scholar] [CrossRef] [Green Version]
  57. Babaie-Kafaky, S.; Mataji, A.; Sani, N.A. Ecological capability assessment for multiple-use in forest areas using GIS- based multiple criteria decision making approach. Am. J. Environ. Sci. 2009, 5, 714–721. [Google Scholar] [CrossRef] [Green Version]
  58. Duran-Llacer, I.; Arumí, J.L.; Arriagada, L.; Aguayo, M.; Rojas, O.; González-Rodríguez, L.; Rodríguez-López, L.; Martínez-Retureta, R.; Oyarzún, R.; Singh, S.K. A new method to map groundwater-dependent ecosystem zones in semi-arid environments: A case study in Chile. Sci. Total Environ. 2022, 816, 151528. [Google Scholar] [CrossRef] [PubMed]
  59. Gupta, M.; Srivastava, P.K. Integrating GIS and remote sensing for identification of groundwater potential zones in the hilly terrain of Pavagarh, Gujarat, India. Water Int. 2010, 35, 233–245. [Google Scholar] [CrossRef]
Forests 14 01181 g001 550
Figure 1. Outline of the methodological steps.
Figure 1. Outline of the methodological steps.
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Figure 2. Study area with the representation of the analyzed greenway and some relevant photographs. (a) Roman aqueduct, (b) Platja Llarga beach, (c) La Mora defense tower, (d) La Mora coves and (e) Ferran village.
Figure 2. Study area with the representation of the analyzed greenway and some relevant photographs. (a) Roman aqueduct, (b) Platja Llarga beach, (c) La Mora defense tower, (d) La Mora coves and (e) Ferran village.
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Figure 3. Mapping of the greenway sections and some representative photographs (own elaboration) of each one.
Figure 3. Mapping of the greenway sections and some representative photographs (own elaboration) of each one.
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Figure 4. Mapping of the factors used (a), visual basin (b), and buffer (c) of all sections.
Figure 4. Mapping of the factors used (a), visual basin (b), and buffer (c) of all sections.
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Figure 5. The naturalness index (NI) obtained for each section (S) according to the three methodological approaches used.
Figure 5. The naturalness index (NI) obtained for each section (S) according to the three methodological approaches used.
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Figure 6. Coincidence in naturalness class of the sections according to the different methodological approaches used.
Figure 6. Coincidence in naturalness class of the sections according to the different methodological approaches used.
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Table
Table 1. Factors included in the analysis.
Table 1. Factors included in the analysis.
GroupFactor CodesFactor DescriptionSource (Geographic Information Systems—GIS)
Group 1: intensive use (negative factors)F1_industryIndustrial areasUrbanistic Map of Catalonia (MUC)
F2_voltageHigh-voltage towers and lines Topographic Map (IGN)
F3_treatmentWater treatment plantsHypermap (Generalitat de Catalunya)
F4a_transportRoadways and railwaysUrbanistic Map of Catalonia (MUC)
F4b_transport *Acoustic pollution from roads and railwayNoise Pollution Information System (SICA)
F5_urbanUrban centersUrbanistic Map of Catalonia (MUC)
Group 2: natural landscapes (positive factors)F6_forestWoodland and forest Hypermap (Generalitat de Catalunya)
F7_n2000Protected natural areas (Natura 2000 network)Hypermap (Generalitat de Catalunya)
F8_coastScenic coastal landscapesLandscape Catalogue of the Camp de Tarragona (Landscape Observatory of Catalonia)
F9_faunafloraAreas of faunal and floral interestHypermap (Generalitat de Catalunya)
F10_riversWatercourses (rivers, torrents, etc.)Urbanistic Map of Catalonia (MUC)
F11_seaSea Hypermap (Generalitat de Catalunya)
In the factor codes column, the “x” of “Fx” means the number assigned to each factor. * For the analysis of the transportation factor, with proximity analysis (F4b_transport), the layer indicating the influence area of noise from interurban roads and railway lines has been used (Ldia: average noise level during the day from 7 a.m. to 7 p.m.).
Table
Table 2. Number of observation points for each section (S).
Table 2. Number of observation points for each section (S).
SectionsS1S2S3S4S5S6S7S8S9S10
Number of vertices209201790509517444196270144436
Table
Table 3. Comparison of the three analyses used.
Table 3. Comparison of the three analyses used.
Perceptual FieldworkGIS VisibilityGIS Proximity
Implementation speedSlowModerateFast
CostsHigh and continuousMedium and initialMedium and initial
Conditioning factors in the selection of factorsLowHighMedium
Risk of variation in the evaluation of the factorsHighLowLow
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Àvila Callau, A.; Pérez-Albert, Y.; Vías Martínez, J. Calculating and Mapping the Naturalness of Peri-Urban Greenways. Forests 2023, 14, 1181. https://doi.org/10.3390/f14061181

AMA Style

Àvila Callau A, Pérez-Albert Y, Vías Martínez J. Calculating and Mapping the Naturalness of Peri-Urban Greenways. Forests. 2023; 14(6):1181. https://doi.org/10.3390/f14061181

Chicago/Turabian Style

Àvila Callau, Aitor, Yolanda Pérez-Albert, and Jesús Vías Martínez. 2023. "Calculating and Mapping the Naturalness of Peri-Urban Greenways" Forests 14, no. 6: 1181. https://doi.org/10.3390/f14061181

APA Style

Àvila Callau, A., Pérez-Albert, Y., & Vías Martínez, J. (2023). Calculating and Mapping the Naturalness of Peri-Urban Greenways. Forests, 14(6), 1181. https://doi.org/10.3390/f14061181

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