Mapping of recreation suitability in the Belgrad Forest Stands

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Mapping of recreation suitability in the Belgrad Forest Stands € _ Inci Caglayan a, *, Ahmet Yes¸il a, Chris Cieszewski b, Fatmagül Kılıç Gül c, Ozgür Kabak d a

Istanbul University-Cerrahpas¸a, Faculty of Forestry, Istanbul, Turkey University of Georgia, Warnell School of Forestry and Natural Resources, 180 East Green Street, Athens, GA, 30602, USA c Yıldız Technical University, Department of Geomatic Engineering, Istanbul, Turkey d Istanbul Technical University, Faculty of Management, Istanbul, Turkey b

A R T I C L E I N F O

A B S T R A C T

Keywords: MCDM GIS Analytic hierarchy process Recreation suitability Scenic beauty Forest stands

Istanbul is a fast-growing city with a more than 15 million population and limited possibilities of recreational opportunities within its perimeter. The Belgrad Forest, one of the largest forests in Istanbul, currently have only 3.18% of the area designated for recreation. The aim of this research is to assess and map of recreation area (RA) suitabilities in Belgrad Forest stands to enhance forest planning. By this way, the recreation potential of Belgrad Forest will be revealed for meeting the needs of Istanbul residents. We consider nine main criteria for deter­ mining the recreation suitability of forest stands (n ¼ 1118). We use the Analytic Hierarchy Process approach to find the importance weights of criteria and sub-criteria as well as total suitability scores of the stands. Finally, we sort all the stands according to their suitability scores and assign them to three classes of recreational suitability: High, Moderate, and Low. Results suggest that the high suitability potential RAs in the Belgrad Forest constitute 8.25% of the total area. Therefore, there is an opportunity to extend the forest recreational uses in this area to at least 8.25%, which would be beneficial to the residents of Istanbul within an environment of expansive urbanization.

1. Introduction Public institutions and private sector companies need to consider in their strategic planning various options of forest management. The recreation is an important aspect of forest ecosystem services provided to societies increasingly looking for more contacts with nature and counterbalancing the stressful life in urban developments. The impor­ tance of tourism and recreation has been especially growing in recent decades. According to Kindler (2016), the German notion of forest functions is similar in principle, despite some differences in back­ grounds, to the American understanding of ecosystem services, as they both address similar conceptual ideas based on choices of services, or functions, and their human-centered accommodations. Forest ecosys­ tems and forest functions produce for modern societies various goods and services, collectively called ecosystem services. These include the production of biomass, clean air, habitats for plants and animals, pro­ vision of visual beauties, recreation, carbon sequestration, soil protec­ tion and water regulating water circulation (Costanza et al., 1997; Martínez-Harms & Balvanera, 2012). These ecosystem services are divided into four main categories as Cultural, Regulating, Provisioning

and Supporting services (Martínez-Harms & Balvanera, 2012; MEA, 2005). Provisions of recreation and scenic beauty of forestlands are impor­ tant cultural ecosystem services (Martínez-Harms & Balvanera, 2012; MEA, 2005), and the recreation is the most prominent among them (Kienast et al., 2009; Plieninger, Dijks, Oteros-Rozas, & Bieling, 2013). In general, recreation refers to the various activities carried out in the outdoors by tourists and visitors. These activities include hiking, bird watching, horse riding, swimming, plant and mushrooms collecting, hunting, outdoor fitness, picnicking, etc. Often such activities are called outdoor recreation (Bettinger & Wing, 2004; van Riper, Kyle, Sutton, Barnes, & Sherrouse, 2012). However, since many of them frequently take place in the forests, forests are broadly considered to be the major source of attractive Recreational Areas, (Paracchini et al., 2014), thereafter called RAs, in addition to camping and swimming areas, such as lakes and rivers, which alike provide opportunities for popular rec­ reational activities (Plieninger et al., 2013). Given the significance of recreation in modern societies, the selection of forest RAs is an impor­ tant part of the consideration in sustainable forest management plan­ ning. For this reason, we use RA suitability analyses, based on different

* Corresponding author. _ Caglayan), [email protected] (A. Yes¸il), [email protected] (C. Cieszewski), [email protected] (F.K. Gül), E-mail addresses: [email protected] (I. € Kabak). [email protected] (O. https://doi.org/10.1016/j.apgeog.2020.102153 Received 6 August 2018; Received in revised form 16 December 2019; Accepted 21 January 2020 0143-6228/© 2020 Published by Elsevier Ltd.

Please cite this article as: İnci Caglayan, Applied Geography, https://doi.org/10.1016/j.apgeog.2020.102153

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decision-making techniques, to identify the forest areas that may be especially relevant when considered for recreational functions. Lev­ insohn, Langford, Rayner, Rintoul, and Eccles (1987) suggested the spatial analysis, such as computing the recreation suitability indices and developed a Geographic Information System (GIS) technique, which considered biophysical and land covers and conservation resources, to assess recreational appropriateness. Other authors have discussed such spatial approaches as Recreation Opportunity Spectrum (ROS) (Chhetri & Arrowsmith, 2008; Clark & Stankey, 1979, p. 32; Merry et al., 2018; Paracchini et al., 2014) and Recreation Policy Developments (Kliskey, 2000). ROS presented an approach dealing with the suitable site selec­ tion problem in RAs. Clark and Stankey (1979, p. 32) designed the ROS to help managers that awareness of the combination of physical, bio­ logical, social, and managerial conditions that give value to a place. Most of the published site suitability analyses are based on Multiple Criteria Decision Making (MCDM) approaches. Frequently the criteria for such analysis are rooted in social and ecological considerations, many of which do not account for economic values. The role of recre­ ation in regional development and the effects on land-use planning have led to the formulation of various disciplinary approaches for the spatial assessment using diverse techniques. Many models of site suitability assessment focus on recreation suitability. Caspersen and Olafsson (2010) presented an Opportunity Spectrum approach to deal with the suitable site selection problem in recreation framework. In their study, they divided their criteria into three types, and seven experience classes. Curtis (2004) and Nahuelhual, Carmona, Lozada, Jaramillo, and Aguayo (2013) also used Opportunity Spectrum approach with the Delphi method in defining recreation opportunities. Chan, Shaw, Cameron, Underwood, and Daily (2006) have mapped six ecosystem services, considering the recreation services the land cover and the level of public ~ as-Ortega, Can ~ as-Maduen ~ o, and access criteria. Similarly, Arriaza, Can Ruiz-Aviles (2004); Chan et al. (2006); Martínez-Harms and Balvanera (2012) used the land cover and the level of public access criteria to map recreation suitability. Chhetri and Arrowsmith (2008) used a GIS-based technique measuring the recreational potential of tourist sites. Based on the data obtained from the technique, they have improved the predictors of scenic attractiveness using regression modeling of questionnaire data. They have also determined the “recreational opportunity potential” by overlaying their maps at top of each other. Finally, using the output maps of overall geographical area, they have developed a spatial model of “recreational potentials”. Various proponents of the recreational po­ tential approach (Caspersen & Olafsson, 2010; Chan et al., 2006; Gimona & van der Horst, 2007; Kienast, Degenhardt, Weilenmann, €ger, & Buchecker, 2012; Kliskey, 2000; Liu, Luo, & Li, 2012; Wa Nahuelhual et al., 2013) mapped all the accessibility of the RA with measurements of their distances from the residences and main roads. Finally, Kliskey (2000) used principal component analysis (PCA) in the recreational terrain suitability index approach with data derived from Public Access GIS on slope and canopy closure measurements. Other existing studies use variables, such as the number of tourist visits, to map RAs, the distance to the roads, and the proximity of the tourist services. According to the above-given literature, the reported studies of the RAs selections use various social and ecological criteria customized to individual cases, locations, and altitudes. There is no common standard or methodology for such a process. We set out considering the entire multitude of various criteria from the literature with the intention of selecting a set of the most commonly used criteria by various authors. Such criteria would comprise consistently applied indicators in the problems of recreation site suitability and applicable to our problem conditions. Given the general trend in the world of growing urbanization and ever-increasing city populations, most of the societies living in the urban areas face increasing challenges in meeting various needs of the society for diverse uses of RAs. Dense city areas generally do not have many attractive RAs within their limits, requiring their inhabitancies to travel into remote locations with RAs, which vary in their suitability. The

challenge of finding and mapping suitability of RAs of forest stands in locations adjacent to large cities is, therefore, an essential existential need for many urban areas in the world. Istanbul is a fast-growing city with a more than 15 million population and limited possibilities of recreational opportunities within its perimeter. The Belgrad Forest, one of the largest forests in Istanbul, has a great potential to serve recreation areas for Istanbul residents. However currently only 3.18% of the area designated for recreation. Therefore, Belgrad Forest represents a good example of a need for an assessment and mapping of recreational area suitabilities in the context of modern society needs within an environ­ ment of expansive urbanization. The main objective of this study is to determine the most suitable RAs in the Belgrad forest in the Istanbul in the manner that is compatible with, and suitable for, functional planning that operates on the smallest management units, which are forest stands. For this purpose, we use a method combining MCDM and GIS. For scrutinizing the criteria selected from the literature, we used weighted pairwise comparisons. 2. Materials and methods 2.1. Study area We used as our study area the 5660 ha Belgrad Forest, geographically located northwest of Istanbul, between 28� 530 24" – 29� 010 0200 eastern longitudes, and 41� 090 33" – 41� 140 2200 northern latitudes (Fig. 1), with elevations varying from 24 m to 231 m. Its dominant tree species are beech (Fagus Orientalis Lipsky.), oak (Quercus spp.) and hornbeam (Carpinus spp.). The stands of the Belgrad Forest consist of approxi­ mately 38% old mixed stands and 31% semi-matured mixed stands. The Belgrad Forest is divided into three sections called Bentler, Kurtkemeri, and Atatürk Arboretum. The forest area includes historical water res­ ervoirs originated in the Ottoman Empire period. While the study area is covered with trees, there are some substantial temperature differences between its southern and northern slopes. The terrain varies between different stands with the predominant slopes of about 18% on west ex­ posures, 15% east, southeast, and northwest, 13% on the southwest, 11% on south and northeast, and 2% and more on the north exposures. 2.2. Determination of criteria in the recreation suitability analysis To determine effective criteria for the recreation suitability stand selection, we first review the most common criteria in the literature. These initially included: scenic beauty, elevation, slope, aspect, distance to RAs, intensive activity area, accessibility, land cover, canopy closure, population density (Chan et al., 2006; Liu et al., 2012), and the number of visitors (Sugimura & Howard, 2008). As the data for these criteria were instable and subjective, we discarded the social criteria such as population density and number of visitors. Table 1 lists the considered outdoor activities, data sources, and literature pertinent to the selected activities and the recreation site selection criteria. For the scenic beauty criterion, we defined four sub-criteria, which determine the type of the scenic beauty (Fig. 1): water amount, water movement, positive man-made effects (e.g., buildings of high historical value), and negative man-made effects (e.g., houses or villas). We added these sub-criteria for measuring the scenic beauty criterion using the photographic pictures. We presented the general decision hierarchy for the Analytic Hier­ archy Process (AHP) in Fig. 2. In the hierarchy, at the top, the objective is defined as recreation site suitability, the nine main criteria are at the second level, and for the scenic beauty criterion, there are four subcriteria. Finally, the 1118 stands are at the bottom of the hierarchy (Fig. 2). 2.3. Determination of weighting and score with the AHP approach AHP is one of the most widely used methods in spatial multi-criteria 2

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Fig. 1. Study area location in Turkey, Istanbul city, Belgrad Forest.

decision analysis (Kordi & Brandt, 2012). We use AHP in this study to determine the weight of the criteria. Saaty (1990) developed Pairwise Comparison, which integrates various aspects of the decision problem into a single objective function, allowing the selection of a management option with the greatest value of the objective function (Linkov et al., 2004, pp. 15–54). AHP uses pairwise comparisons and expert opinions to capture qualitative measures and intangible qualifiers to derive their priorities. The relative importance of each criterion is determined by considering the priorities of the decision makers through this method. The decision maker compares the total number of criteria with the help of the comparison matrix (A) in the n�n dimension. There exists aij ¼ 1/aji relations between the matrix elements, i ¼ 1, 2, 3, …, n; and j ¼ 1, 2, 3, …, n. For the elements on the diagonal of this matrix (i ¼ j), the corresponding factor is 1. In this study, an expert provided information for finding the weights of the criteria. The selection of suitable RAs requires knowledge about all criteria considered in this study. Besides as application is made for the Belgrad Forest, the expert should have knowledge about the topography of this area. As a result, in order to prevent irrelevant evaluations, we decided to get information from one expert who has deep knowledge about the Belgrad Forest and academic background related to the criteria considered. The expert evaluated nine main criteria and four sub-criteria in order of importance by pairwise comparisons of the criteria using 1 (equal importance) to 9 (extreme importance) scale. Based on these evalua­ tions, we calculated the weighting scores of each factor of the 9 main criteria and 4 sub-criteria using Equations (1) and (2) and then normalized them using linear normalization. Tables 2 and 3 show the main weights of all the criteria and sub-criteria. Normalized weighted score values were obtained by normalizing the weighted scores. n .X bij ¼ aij ð aij Þ

2.4. Data collection and processing in GIS Due to geographically varying conditions and values of recreational parameters, it is desirable in the absence of existing standards to develop new and comprehensive localized approaches to mapping recreation functions integrated with GIS, to study the spatial interactions between different recreational activities (Kliskey, 2000). Therefore we used data provided from GIS to generate values of recreational parameters in our case. In these calculations and analyses, we used the base data from the maps, storing all the information in ArcGIS 10.2 format. Our base data was stand polygons, roads, and some points. We used previouslydelineated stand polygons to be compatible with the current practices of forestry applications in Turkey. The stand data were extracted from the Forest Management Plans (FMP) database. The plans were produced by the Turkish Forestry Office on a scale of 1:25,000. We calculated seven spatial criteria values for the recreational suitability of each stand by using ArcMap functions. These criteria are elevation, slope, aspect, scenic beauty, intensive activity area, accessibility, and distance to RAs. Besides, data of two criteria (land cover and canopy closure) were also allocated to stands from the FMP. One of the other base data was the digital elevation model (DEM) data that was created from contour lines of topographic maps with a scale of 1:25,000 obtained from the General Directorate of Mapping. We also have produced another base data using point data obtained by our field observations for scenic beauty and intensive activity areas. The boundary of districts and highway base data with a scale of 1:1000 were obtained from Istanbul Metropolitan Mu­ nicipality and used to determine distance to RAs. The dataset and sources were given in Table 4. In accordance with the schematic models presented in Fig. 3, the spatial data was processed in four ways to calculate the values of rec­ reational parameters (Fig. 3). For scenic beauty and intensive activity areas, the point features collected by field observations were converted to raster data as kernel density surfaces (Fig. 3a). For values of elevation, aspect, and slope, DEM created from contour lines of topographic maps

(1)

i¼1 n . X wi ¼ ð bij Þ n

(2)

j¼1

3

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raster distance values were re-classified to conduct spatial analysis (Fig. 3c). Distance to recreational area criterion values were calculated by the network analysis based on forest roads and highways. The input data of the centers of districts and stands were specified by ArcMap (Fig. 3d). Details of calculations and conversions are given in the following subsections.

Table 1 The selected criteria for stand suitability evaluation for outdoor recreation in the literature. Outdoor recreation

Data search for this study

Literature

Scenic beauty

Photos

(Burkhard, Kroll, Nedkov, & Müller, 2012; Caspersen & Olafsson, 2010; Chhetri & Arrowsmith, 2008; Gr^et-Regamey, Weibel, Kienast, Rabe, & Zulian, 2015; Kienast et al., 2012; Nahuelhual et al., 2013) (Caspersen & Olafsson, 2010; Chan et al., 2006; Gimona & van der Horst, 2007; Gr^et-Regamey et al., 2015; Kienast et al., 2012; Kliskey, 2000; Liu et al., 2012; Nahuelhual et al., 2013; Paracchini et al., 2014) Caspersen and Olafsson (2010)

Distance to RAs (Level of public access)

Road maps (transportation from the central districts)

Intensive activity area

Footpaths and tracks, picnics area, etc. GPS points Roads map (In-forest road network)

Accessibility (primary and secondary road concentration distance to road) Aspect

Topography

Elevation

Topography

Slope

Topography

Land cover

Forest inventory

Canopy closure

Forest inventory

2.4.1. Scenic beauty and intensive activity areas As point data geographic information with coordinates and related attributes is useful in spatial analysis and it is simple to create such data (Bailey & Gatrell, 1995), we assessed with point features both scenic beauty effects and the intensive activity areas. We used photographic pictures of various areas to select suitable RAs according to the scenic beauty. First, we took panoramic photos 55 that have accessible by forest roads in the Belgrad forest and we recorded the coordinates of the points by using GPS. Second, we evaluated and rated each photo with respect to the sub-criteria according to the evaluation scales given in Table 3. This criterion was calculated based on four subcriteria: water movement, water amount, negative man-made effects (road, houses, villas), and positive man-made effects (buildings of high historical value). After rating each photo, finally, we multiplied the scores by the sub-criteria weights to obtain weighted scores of the photobased scenic beauty assessments. Examples of assessment of photos are given in Fig. 4. To find out intensive activity areas, first, we visited the most popular picnicking areas and recorded their GPS coordinates. Then, we assigned “100 as a point feature value for intensive activity areas to these coordinates. We transmitted weighted scenic beauty scores and intensive activity area scores of the points to ArcMap as point feature data. Then we converted the point data to raster data related to both scenic beauty and intensive activity areas to create continuous density surface of the criteria using the kernel density tool of ArcMap analysis (Radius ¼ 500 m). Finally, the kernel surfaces represented as raster data were utilized to calculate mean criteria scores of stands as polygons by using zonal statistic as table tool in ArcMap (Fig. 3a).

(Gr^et-Regamey et al., 2015; Kliskey, 2000; Liu et al., 2012; Nahuelhual et al., 2013; Syrbe & Walz, 2012; van Riper et al., 2012) € (Gül, Orücü, & Karaca, 2006; Kliskey, 2000) (Casado-Arzuaga, Onaindia, Madariaga, & Verburg, 2014; Chhetri & Arrowsmith, 2008; Gül et al., 2006; Kliskey, 2000; Liu et al., 2012; van Riper et al., 2012) (Chhetri & Arrowsmith, 2008; Gül et al., 2006; Kliskey, 2000; Liu et al., 2012; van Riper et al., 2012) (Casado-Arzuaga et al., 2014; Caspersen & Olafsson, 2010; Chan et al., 2006; Gr^et-Regamey et al., 2015; Kienast et al., 2012; Kliskey, 2000; Liu et al., 2012; Nahuelhual et al., 2013) Kliskey (2000)

2.4.2. Elevation, slope, and aspect The topography is an important factor influencing the land recrea­ tional uses, its development, and the density of its inhabitation. DEM raster data for elevation criterion was created from contour lines and boundary data by using topo to raster tool of ArcMap with 10 m pixel size. Before that, the contour lines were checked topologically and some values were edited. Raster data for slope and aspect were produced from DEM with the same pixel size. Later, elevation, slope and aspect raster data were reclassified into predetermined classes to specify the class of the stands. We divided the elevations into four classes, the slopes into

were used. The line vertex values were interpolated and converted to a continuous raster surface (DEM). The slope and aspect raster data were derived from DEM (Fig. 3b). For accessibility criteria, forest roads in line form were used to calculate the distance of a stand from forest roads. The

Fig. 2. The hierarchical structure of recreation site suitability. 4

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boundary of each stand. An example of how we defined the most prevalent class of elevation according to maximum pixel counts for ten examples vector stands in Table 5.

Table 2 Determination of weighted scores. Main Criteria

Main Criteria Weights

Values

Weighted Score of Values

Normalized Weighted Score

Distance to Recreational Areas

0.040

0–5 km 5–10 km 10–30 km

0.724 0.193 0.083

1 0.267 0.115

Intensive activity area

0.082

Activity No activity

– –

1 0

Accessibility

0.061

0–100 km 100–200 km >200 km

0.724 0.193 0.083

1 0.267 0.115

Aspect

0.025

North South East West SE SW NE NW Flat

0.164 0.046 0.074 0.091 0.060 0.047 0.180 0.214 0.125

0.764 0.216 0.343 0.423 0.278 0.218 0.840 1 0.583

Elevation

0.021

0–50 50–100 100–150 >150

0.466 0.277 0.161 0.096

1 0.595 0.346 0.206

Slope

0.354

Little or no slope: 0–3% gradient Gentle slopes: 4–10% gradient Moderate slopes: 11–20% gradient Steep slopes: 21–30% gradient Extremely steep slopes >30% gradient

0.542

1

0.246

0.453

0.117

0.215

0.059

0.109

0.037

0.069

Pasture Young conifer stand Semi-matured conifer stand Old conifer stand Young deciduous stands Semi-matured deciduous stand Old deciduous stand Young mixed stand Semi-matured mixed stand Old mixed stand Irregular stand

0.025 0.020

0.093 0.075

0.052

0.195

0.091

0.341

0.025

0.093

0.108

0.402

0.216

0.806

0.033

0.123

0.140

0.523

0.268 0.022

1 0.080

Bare (0–10%) Sparse (11–40%) Moderate (41–70%) Dense (71–100%)

0.055 0.114

0.097 0.200

0.260

0.456

0.571

1

Land Cover

Canopy closure

0.093

0.131

2.4.3. Accessibility Accessibility measures the reachability to stands from forest roads. We assume that the visitors can access various areas from road edges or from parking lots, which both define our access points for the forest stands. For this consideration, we selected four categories of roads from the Belgrad Forest Road Database: unpaved roads, country roads, asphalt roads, and highway roads. First, for each stand, raster data was calculated by the euclidean distance tool of ArcMap to find the distance of the pixels between the stand and its nearest forest road. The pixels in each stand were reclassified into three distance classes:1 (0–100 m), 2 (100–200 m), and 3 (>200 m). Then, pixel counts of every class for the stand were calculated by using tabulated area tool (Fig. 3c). Finally, each stand was assigned the value of accessibility as the most prevalent class based on the raster data that was contained within the boundary of each stand similarly as in case of the elevation aspect and slope. 2.4.4. Distance to RAs Many authors in the literature discuss impacts of distances between urban settlements and RAs, which for different considerations can vary vastly between 0.5 km and 80 km depending on local conditions and geographic regions (H€ ornsten & Fredman, 2000; Kliskey, 2000; Liu et al., 2012; Maes et al., 2012; Paracchini et al., 2014). In our analysis, we assumed that the acceptable distances from urban settlements to RAs, are between 0 and 30 km, which is 10 km more than observed in case of majority of the tourism activities taking place in this area ac­ cording to Pehlivanoglu (1986). To determine the distances to the forest stands at local scales, we used the highways and other road data avail­ able from the Istanbul base map data. To calculate the distances, we first determined the centroids of polygons of districts and forest stands, by using the ArcMap functions. Meanwhile, only the stands’ centers that were accessible by vehicles were selected. Then we combined all the roads defined by highways, the forest roads, and other road types and established network datasets. The distances from all district centers to the stand centroids were calculated, using the Arcmap network analysis-closest facility tool (Fig. 3d). The stands were grouped into three classes based on their distance to their nearest district: group one 0–5 km, group two 5–10 km, and group three 10–30 km (Fig. 6). Scores of the stands for this criterion were calculated based on their groups. Finally, we assigned the normalized weighted score to each stand based on one of the three criteria classes. 2.4.5. Land cover and canopy closure Land cover and canopy closure criteria data are defined as vector data based on previously delineated stand parameters. We obtained these data as a table data from FMP for each stand polygons. We clas­ sified the land cover into the following categories: pasture, young conifer stands, semi matured conifer stands, old conifer stand, young deciduous stands, semi-matured deciduous stands, old deciduous stands, young mixed stands, semi-matured mixed stands, old mixed stands, and irregular stands. We grouped the land cover stands into the following three age classes: Young, 0–60 yrs; Middle, 61–140 yrs; and Old, 141–180 yrs. Finally, we assigned the normalized weighted score to each stand based on one of the eleven criteria classes. The canopy closure information was included in the forest inventory data, which defines four classes of canopy closure: Bare (%0–10), Sparse (%11–40), Mod­ erate (%41–70), Dense (%71–100). Finaly, we assigned the normalized weighted score to each stands matched one of the four criteria class.

five classes, and the aspects into nine classes (see Table 2). The total pixel counts for every class per stand were calculated by using the tabulated area tool (Fig. 3b). We then assigned to each vector stand the normalized weighted score of elevation, slope, and aspect that was most prevalent based on the raster data that was contained within the

2.5. Calculation of total suitability score At the end of the analyses performed in Fig. 3, after developing da­ tabases for scenic beauty, intensive activity areas, elevation, slope, 5

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Table 3 Sub-criteria and scores used in weighting photos. Main Criteria

Main Criteria Weights

Sub-criteria

Weights

Values

Score

Scenic beauty

0.193

Water movement

0.148

Movement No movement

1 0

Water amount

0.283

pond - little lake lake no water

0.5 1 0

Negative man-made effects (road, houses, villas)

0.047

No man-made Man-made

1 0

Positive man-made effects (buildings of high historical value)

0.522

No positive man-made Positive man-made

0 1

landscape with the highest suitable areas with just a few exceptions (Fig. 7b). Finally, according to the scenic beauty criterion, there are 6 suitable regions as can be seen in Fig. 7c. Most of the Belgrad Forest consists of mixed stands with high preference and visual impact. Fig. 4 shows examples of the “best” and the “worst” scenic beauty scores derived from the photo assessment. The scenic beauty and intensive activity areas show similarities in the spatial distributions. The geographic locations of the scenic beauty areas are approximately overlapping with the areas of the highest suitability according to the intensive activities areas, which is likely not coincidental because given a choice people will choose to attend more eagerly the most scenic areas. Table 7 shows the criteria weights and 10 example sets of the normalized individual criterion scores and corresponding to them weighted overall scores calculated for 10 sample stands. We calculated the overall stand scores as weighted product sums of the normalized scores multiplied by each criteria weight. We used the cluster method in ArcMap that depends on Jenks Nat­ ural Breaks algorithm to classify the values belonging to stands in various groups. All the stands were sorted according to the considered weighted criteria and assigned to three classes of recreational suitability according to the weighted stand scores: High, 0.484–0.834; Moderate, 0.371–0.484; and Low, 0.371–0.127 (Table 8). We preferred to use three classes to present the results for simplification of interpretations and ease of application in forestry practices. The results are illustrated in Fig. 8, which contains 8.25% of the high suitability weighted scores denoted by red color; the high suitability potential RAs in Belgrad Forest constitute 8.25% of our entire study area. The area and percentages of the low and moderate suitability levels are summarized in Table 8. In the current management plans, only 3.18% of the area is designated for recreation. With our methodology, we show that this percentage can be increased to 8.25%. The percentage of the suitable RAs in land cover, slope and aspect, and elevation and canopy closure are presented in Figs. 9–11, respec­ tively. The percent of suitable in land cover analysis showed that the sites high suitable for RAs mostly consisted of sites with the deciduous and mixed stands (Fig. 9). The pasture and irregular stand consisted of sites with ranking as low suitability scores. Therefore, respectively, 96.8% and 100% had a low potential for RAs (Fig. 9). Concerning the slope, 98.7% of 0–3% gradients area had high suitability RAs (Fig. 10a). The high suitable sites for RAs were distributed approximately equally in all aspects (Fig. 10b). The high suitable sites for RAs were distributed in all elevations (Fig. 11a). Concerning the canopy closure, 90.9% of the sparse area is high suitability RAs. However, the scores increase from 0.1 to 1 when canopy closure increases from bare (0-10) to dense (71–100%) (Fig. 11b). The criterion weight of the canopy closure was lower than the other criteria so the scores of the canopy closure levels did not affect the outcome.

Table 4 Dataset and sources. Dataset

Scale

Form

Source

Contour lines

1/ 25,000 1/ 25,000

Line shapefile Polygon shapefile

Topographic maps-General Directorate of Mapping Forest management plansTurkish Forestry Office-2012

1/1000

Line shapefile

Districts boundary

1/1000

Polygon shapefile

Scenic beauty and Intensive activity areas

–

Point shapefile

Base maps- Istanbul Metropolitan Municipality2015 Base maps-Istanbul Metropolitan Municipality2015 Field work-2017

Forest stands, Land cover and Canopy closure Forest roads and Highways

aspect, accessibility, and distance to RAs, we created normalized weighted scores as a table for each stand polygon and each criteria after various data conversion for all criteria using different tools as described in Fig. 3 and sections above. Then, we multiplied normalized weighted scores of stands for each criteria with the main criteria weights. Finally, we summed the multiplied values and obtained the recreation suitability values of each stand. Table 6 summarizes how the total suitability score is calculated for a single stand as a result of all these suitability analyses. 3. Results Figs. 5–7 illustrate the maps developed in this study using stan­ dardized criteria layers and weights, applying the AHP to determine the recreational suitability of individual stands. The maps represent stan­ dardized values for all stands according to each of the main criterion. In these maps, each color represents a suitability degree of an indi­ vidual criterion. Fig. 5a indicates the level of public access in terms of the distances between residences and the RAs. The highest values of potential for RA suitability of individual stands according to the distance criterion are concentrated on the east part of the map. Intensive activity areas comprise seven clusters (Fig. 5b). Only a few areas show intensive activities; although, the whole study area is accessible by roads. Accessibility and aspect landscapes show that according to these criteria there are many areas suitable for recreation (Figs. 5c and 6a). They are dispersed throughout the entire study area and all aspect directions (Figs. 5c and 6a). The best area for recreational use seems to be located in the northwest part of the landscape (Fig. 6a). Suitable areas according to the elevation are in the southwest part of the landscape and partly in the central south of the landscape (Fig. 6b). According to the slope cri­ terion, the suitable areas are fragmented throughout the landscape; although, most of them are in the south-west half of the landscape below the northeast diagonal (Fig. 6c). The land cover suitability criteria stands are concentrated on the north part of the landscape (Fig. 7a). Only canopy closure has the nearly complete coverage of the entire

3.1. Sensitivity analysis In order to show the robustness of the results and analyze the effect of the criteria weights on the classification of the stands, a sensitivity 6

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Fig. 3. Flowchart of data processing (a) Scenic beauty and Intensive activity area, (b) Aspect, Elevation, Slope, (c) Accessibility, (d) Distance to RAs “criteria are in red”. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

analysis was conducted. We developed 18 scenarios. In 9 of them, we increased the weight of a criterion by 10% at a time. In the other 9 scenarios, we decreased the weight of a criterion by 10% at a time. In a scenario, when the weight of a criterion is increased or decreased, the weights of the other criteria are also modified proportionally to their original weights to maintain the total weights of criteria equal to 1. Changes of the stand levels in the scenarios compared to the original solution are presented in Table 8. According to the results, no significant change from low to high or high to low was detected in the level of the stands. In most of the scenarios, the number of stands that changed their class is less than 5%. In only two scenarios, Canopy closure - Increased by 10% and Scenic beauty - decreased by 10%, it is more than 5%. However, in these scenarios, the levels of the stands improved system­ atically. If the ranges of the levels are changed, probably their levels will not change that much. Results of the sensitivity analysis indicate that suitability levels of the stands determined by the methodology are robust and reliable, as the levels of the stands do not change dramatically with changes in the weights of the criteria (Table 9).

criteria developments we formulated and executed in this study a transparent methodology for mapping recreational suitability. We focus primarily on field parameters, societal characteristics, and landscape features that we considered contributory to social, ecological, and eco­ nomic values. Various recreational activities require individual sets of criteria that might not be compatible with other types of recreational activities, so we considered carefully the range of possible activities that are likely to take place in our study area. The most common classification methods to define single ecosystem services hotspot are top richest cells, threshold and cluster methods €ter & Remme, 2016). Most researchers use these classification (Schro methods to define overall priority or suitability areas of ecosystem ser­ €ter & Remme, 2016). We used cluster methods to delineate vices (Schro hotspots with the help of Jenks natural breaks in this study. However, there is no consensus on the choice of the number of classes and what a €ter & Remme, 2016). These un­ hotspot is in the literature (Schro certainties can lead to relying on intuition for decision-maker. Invariably the most important single criterion for RA suitability, which holds constant across a broad range of activities, is the scenic beauty. In the literature, scenic beauty is frequently described as scenic attractiveness, landscape quality, visual quality, or aesthetics. At times, the perception of scenic beauty can be associated with water availability and its amount, which are some of the most important factors having

4. Discussion Based on the literature research in the areas of tourism and indicator 7

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Fig. 4. “Best” and “Worst” panoramic photos and weighting scores. Table 5 Pixel counts of elevation class of ten a sample stands (from tabulated area tool). Stand ID 1 2 3 4 5 6 7 8 9 10

Pixel counts for four elevation classes (0–50 m)

(50–100 m)

(100–150 m)

(>150 m)

0 0 0 0 0 0 0 0 0 0

0 0 0 0 7200 0 7400 0 0 7700

5000 0 51700 19400 0 300 0 42600 32900 40000

13300 14500 11000 64500 0 35800 0 6000 0 11400

Main criteria weights

Evaluation of Stand ID 1 Values

Normalized weighted score

Distance to RAs Intensive activity area Accessibility Aspect Elevation Slope Land cover

0.040 0.082

10–30 km No activity

0.115 0

0.061 0.025 0.021 0.354 0.093

1 0.840 0.206 0.453 0.123

Canopy closure Scenic beauty

0.131 0.193

0–100 km NE >150 4–10 Young mixed forest 71–100 0 TOTAL score

Assigned of normalized weighted score

>150 m >150 m 100–150 m >150 m 50–100 m >150 m 50–100 m 100–150 m 100–150 m 100–150 m

0.206 0.206 0.346 0.206 0.595 0.206 0.595 0.346 0.346 0.346

positive effects on recreational activities (Hammitt, Patterson, & NOE, 1994; Paracchini et al., 2014). Researchers consider scenic beauty or visual impact as an important factor in the determination of RAs (Table 1), while in some studies it is treated as a separate feature in its own right. For example, in the studies conducted by Arriaza et al. (2004), Bulut and Yilmaz (2008) and Clay and Daniel (2000), the au­ thors used the rating photographs for scenic beauty assessment as the main criterion. Blasco et al. (2009) applied a paired comparison method, taking advantage of the expert opinion examining photographs of stands for evaluation of the visual effects of each of them. Schirpke, Tasser, and Tappeiner (2013) identified views that could be seen from one point for mountainous areas to determine their visual impact indirectly from the photographs. Vizzari (2011) used a combination of GIS, AHP, and scenic beauty to perform a kernel density analysis with score values given to visual impact points for a visual impact assessment. The positive man-made effects are desirable in scenic beauty assessment, but the negative man-made effects have a negative impact on such assessment (Kienast et al., 2012). In this study, we assessed the scenic beauty using a combination of GPS coordinates, ground photography, AHP, and GIS-kernel density analysis. Consistently with other studies, our findings

Table 6 A-sample calculation for stand 1. Criteria

Most prevalent elevation class

1 0 0.394

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Fig. 5. Standardized criterion layers for Distance to RAs (a), Intensive activity area (b) and Accessibility (c).

Fig. 6. Standardized criterion layers for Aspect (a), Elevation (b) and Slope (c).

in accordance with the intensive activity areas map, suggest that for recreation purposes people generally favor the high scenic beauty areas and neighboring with them locations. The preferences of people choosing RAs are influenced largely by the topography of the area. Polat and Akay (2015) notice that the slope grade is associated with the function of RAs. They state that the optimal slope grades vary according to the intended recreational activities, which is evident from comparisons of different preferences of topog­ raphy for, say, skiing versus picnicking. The slope has an adverse impact on the soil properties and it limits the recreational uses (Liu et al., 2012). When we examine the stands of the Belgrad Forest, we can see that about 65% of them have 4%–10% of slope grades and 32% of them have 11%– 20% slope grades. Almost the entire area is included in the gentle and moderate slope classes. In general, our results suggest that when selecting the suitable RAs, the human activities in the Belgrad Forest are negatively correlated with the elevations (Fig. 6b). Despite the differ­ ences in temperatures, we found the aspect to have no decisive impact on selecting the suitable recreation stands since its selections didn’t correlate with any significant RAs of the Belgrad Forest. The RAs are affected by population densities of the regional urban areas and proximity to main roads and population centers (Chan et al.,

2006), which in this study we define as the level of public access. Par­ acchini et al. (2014) define the distance from the city as remote if it is greater than 10 km. Kliskey (2000), on the other hand, using participant views for snowmobiling terrain suitability, suggests that acceptable distances from highways can be 10–80 km. A similar result obtained Paracchini et al. (2014), who conducted a survey to determine the close-to-home and daily maximum traveled distance for outdoor recre­ ation and found it to be between 8 and 80 km, while Liu et al. (2012) based his study on a distance assumption of 0.5–10 km. In general, the distance that people are willing to travel depends on the specificity of the recreational activities they’re looking for and on the attractiveness of the RA they want to reach, which could be related to the availability of more natural habitats (Paracchini et al., 2014). The willingness to travel depends also on the local culture and tra­ €rnsten and Fredman (2000) found that the preferred distance ditions. Ho for recreational activities to forests of more than 40% of the Swedish population is 1 km or less. In Finland, 80% of participants travel up to 8 km for recreation (Maes et al., 2012). The criterion of the distance-to-RA was very important in our study and was directly corresponding with the mapping of the present recreational uses. Pehlivanoglu (1986) already noticed that people prefer to access the RAs in Belgrad Forest from 9

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Fig. 7. Standardized criterion layers for Land cover (a), Canopy closure (b) and Scenic beauty (c). Table 7 Examples of stand scores. Main Criterion

Distance to RAs

Intensive activity area

Accessibility

Aspect

Elevation

Slope

Land cover

Canopy closure

Scenic beauty

Criterion Weight

0.040

0.082

0.061

0.025

0.021

0.354

0.093

0.131

0.193

Overall Stand score

0.115 0.115 0.115 0.115 0 0.115 0 0.115 0.115 0

0 0 0 0 0 0 0 0 0 0

1 1 1 1 1 1 1 1 1 1

0.840 0.423 0.216 0.218 0.840 1 0.423 1 0.343 0.423

0.206 0.206 0.346 0.206 0.595 0.206 0.595 0.346 0.346 0.346

0.453 1 0.453 0.453 0.453 0.453 0.215 0.453 0.215 0.453

0.123 0.075 0.523 0.523 0.093 0.075 0.075 0.123 0.195 0.075

1 1 1 1 1 1 1 1 1 1

0 0 0 0 0 0 0 0 0 0

0.394 0.573 0.418 0.415 0.395 0.393 0.298 0.401 0.307 0.377

Stand ID 1 2 3 4 5 6 7 8 9 10

Table 8 Suitability level, percentage, and scores of suitable RAs. Suitability level

Area (ha)

Percent (%)

Suitability scores

Low Moderate High

1820 3373 467

32.16 59.59 8.25

(0.126–0.371] (0.372–0.484] (0.485–0.834]

distances not much greater than about 20 km. Given the increasing Istanbul population, we assumed that in current date and age people would be willing to travel up to 25 km to the forest entrance from where they would go further up to 5 km to specific RAs in the forest. More research is needed in the future to determine the exact values of these parameters. A questionnaire can be designed targeting the visitors of Belgrad Forest in order to find the distribution of distance the visitors travel to reach Belgrad Forest and characteristics of the distance they travel and time they spend within the forest. Land cover depends on land usage, and it influences the recreational uses by affecting the visual impacts. The visual appeal of any cover type is likely to increase with the richness of plant species (Kienast et al., 2012) because diversity in vegetative communities can produce spatial patterns that translate into higher esthetical effects and values favored by the visitors. Borders, edges, and ecotones, between diverse biotic communities, are more attractive for tourism and recreation than monolithic landscapes created by vegetative monocultures (Hammitt

Fig. 8. Recreation suitability for stands.

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Fig. 9. Percent of suitable RAs in Land cover.

Fig. 10. Percent of suitable RAs in Slope (a) and Aspect (b).

Fig. 11. Percent of suitable RAs in Elevation (a) and Canopy closure (b).

et al., 1994). For example, pure coniferous stands, compared to mixed stands, are much less admired by people not associated with the forest production (Kienast et al., 2012), while they might be more profitable and desirable for such reasons as carbon sequestration and biomass and fiber production. There are various examples of land cover classifica­ tions in the literature. For example, Casado-Arzuaga et al. (2014) clas­ sified the land covers into urban areas, plantations, and natural forests, while Kienast et al. (2012) classified the land covers into coniferous forests, mixed forests, deciduous forests, and other types. In this study, we classified the land covers as pasture, young conifer stands,

semi-matured conifer stand, old conifer stand, young deciduous stands, semi-matured deciduous stand, old deciduous stand, young mixed for­ est, semi-matured mixed forest, old mixed forest, and irregular stands. Here we focused on the class of the vegetation cover known as the de­ ciduous, conifer or mixed. Our results show that land use criteria appear to greatly be effective for selection moderate suitability of recreation. In warm climates, such as northwestern Turkey, people favor shaded areas for their recreational activities and getting away from city life. We considered such preferences as an evaluation criterion for choosing the areas with recreational designation based on the different degrees of 11

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Table 9 Number of stands that changed their levels. Scenario

Low to Moderate

Low to High

Moderate to Low

Moderate to high

High to Low

High to Moderate

Total Change

Percentage

Increased by 10% decreased by 10%

0

0

3

0

0

1

4

0.3%

6

0

0

3

0

0

9

0.8%

Intensive Activity Areas

Increased by 10% decreased by 10%

0

0

10

0

0

1

11

0.9%

19

0

0

8

0

0

27

2.3%

Accessibility

Increased by 10% decreased by 10%

8

0

1

6

0

0

15

1.3%

6

0

5

0

0

3

14

1.2%

Increased by 10% decreased by 10%

1

0

1

2

0

1

5

0.4%

4

0

1

1

0

0

6

0.5%

Increased by 10% decreased by 10%

1

0

0

0

0

0

1

0.1%

2

0

0

0

0

0

2

0.2%

Increased by 10% decreased by 10%

17

0

21

14

0

3

55

4.7%

14

0

12

5

0

2

33

2.8%

Increased by 10% decreased by 10%

16

0

0

12

0

0

28

2.4%

0

0

9

1

0

1

11

0.9%

Increased by 10% decreased by 10%

41

0

0

25

0

0

66

5.7%

0

0

47

1

0

9

57

4.9%

Increased by 10% decreased by 10%

0

0

43

1

0

9

53

4.5%

37

0

0

36

0

0

73

6.3%

Distance to RAs

Aspect

Elevation

Slope

Land Cover

Canopy Closure

Scenic Beauty

shading in the stands with various canopy closures; generally, the higher canopy closure the cooler the area. The predominant canopy closure in the Belgrad Forest approaches 96%, which is very dense. Since people favor high canopy closure in our study region, almost all Belgrad Forest is therefore very suitable for recreation according to this criterion. This might be also the reason why the aspect in our analysis had no signifi­ cant impact on the selections of RAs, since most of the areas were already relatively cool due to the dense canopies, which typically respond to higher solar radiation by developing denser leave apparatus in areas of higher sun exposure.

developed in this study provide new information relevant to the expansion and geographic orientation of all potential RAs in Belgrad Forest. They show optimal locations of all the potential RAs according to various criteria of selection and the aggregate weighted integrator of all the discussed above criteria. The main overall conclusion emerging from the presented here study is that the Belgrad Forest, officially designated for recreational activities only on 3.18% of its area, can further extend its recreational uses to at least 8.25% of its area, which should be beneficial to the local com­ munities, especially in the highly populated Istanbul. The methodology used in this study that is conducted for the Belgrad Forest can also be applied to other forest areas in Turkey or in other countries. Suitability of the RAs can be calculated for the stands of other forest areas with the same criteria set and hierarchy. However, the criteria weights would be different in these applications. Criteria weights are better to be found by the evaluations of the experts of the specific area in terms of the topology of the area and recreational issues. In order to prevent irrelevant evaluations, we decided to get infor­ mation from one expert who has deep knowledge about the Belgrad Forest and academic background related to the criteria considered. The information was obtained from one expert, as experts who knew the study area better could not be reached. If it is possible to reach such experts, it may be possible to compare the results obtained with those expert opinions with this study. In this way, the confidence and vali­ dation of the study may be possible. In this study, we received opinions from one expert, but we performed sensitivity analysis and showed that the results were robust.

5. Conclusions We have presented in this report development of criteria indicators for the selection of suitable RAs in the Belgrad Forest that should be helpful in the regional planning and management work. Our study area constitutes one of the main recreation centers nearby Istanbul, Turkey, which is a major urban area with a population of about 15 million people. Selection of suitable RAs is a multi-criteria decision problem, which needs to meet the functional planning requirements and contribute to the forest management stewardship and its sustainability. Since economic criteria are often implicit in studies of recreational suitability, we have not considered it explicitly here. Nonetheless, as the role of recreation in regional development is important and necessarily associated with economic development and activities, the presented analysis implicitly include some associated with it economic consider­ ations through promoting the membership in using the RAs. The maps 12

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One limitation of the study is the use of previously-delineated stands given at the beginning of the application. If the stands are not predefined, the methodology cannot be applied directly. In this case, the raster data can be defined as a decision-making unit (similar to stand) or a preliminary study can be conducted to form the stands in the forest area. This will allow a continuous assessment to take advantage of changes in landscape variables. Please see Zhang et al. (2019) as an example of this approach.

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