Study area

Jahan-Nama Protected area was taken as an investigation site. It is situated in the Northern piece of Iran and falls within Golestan Province with a territory of 31747 ha of which more than 15000 ha is occupied by rangelands and mixed forest-rangelands and 11000 ha by dense and semi-dense forest (Fig 1). Gravely soils are predominant in the area. Average yearly precipitation in the western and northern pieces on the investigation site is around 640 mm and 350 mm for eastern part. Elevation ranges between 590 m and 3033 m. Average slope of the area is about 21.5 percent. It enjoys a mid-climate during spring and summer. Natural interesting landscapes such as springs, rough mountains, forest routs; permanent and seasonal waterways and waterfalls make it as one of the main recreational destinations in the Golestan Province. Numerous valuable species of mammal and birds use this region as a habitat. Large number of travelers visits the area every year. In light of most-recent information recorded by Statistical Center of Iran (SCI) the number of inhabitants at the study site is 2819.

Figure 1: Map of the study area and observed points

Waterways, track roads in forest, waterfalls, springs, green landscapes, caves, histo-cultural heritages and other natural attractions are considered as the leisure destinations by travelers. Two destinations in Jahan-Nama protected area namely Ziarat and Jahan-Nama villages are the main tourist residential places.

MCE process

Multi criteria evaluation is a technique wherein multiple discrete and continues criterion could be combined to achieve a single composite basis for decision making (Eastman, 2012). This technique consists of series of consecutive stages included setting the goals, criteria identification, database establishment, calculation of criteria weights, standardization of maps and finally weighted aggregation of the layers. Figure 2 depicts succession stages of multi criteria evaluation.

Criteria identification proceeds according to past works within the field of tourism and additionally through expert knowledge. Some biophysical attributes determine the suitability of landscape for tourism, including, among others, climatic condition, topography of landscapes, flora and fauna, attractions and so on. Criteria which considered during present work were proposed during previous studies and are included: Visibility (Bunruamkaew and Murayam (2011);(Suryabhagavan et al., 2015)) Vegetation (Bali et al., 2015; Chhetri and Arrowsmith, 2008; Nino et al., 2017; Samanta and Baitalik, 2015), Biodiversity (Bunruamkaew and Murayam, 2011; Dhami et al., 2014), Elevation (Aklıbaşında and Bulut, 2014; Bunruamkaew and Murayam, 2011; Chhetri and Arrowsmith, 2008; Nahuelhual et al., 2013; Nino et al., 2017; Pareta, 2013; Samanta and Baitalik, 2015), Slope (Aklıbaşında and Bulut, 2014; Bunruamkaew and Murayam, 2011; Chhetri and Arrowsmith, 2008; Suryabhagavan et al., 2015), Distance to cultural heritages (Bunruamkaew and Murayam, 2011; Pareta, 2013; Suryabhagavan et al., 2015), Distance to roads ((Nahuelhual et al., 2013);(Samanta and Baitalik, 2015); (Aklıbaşında and Bulut, 2014); Distance to rural area ((Nino et al., 2017), Mahiny et al., 2009), Soil erosion (Bali et al., 2015), Temperature (Suryabhagavan et al., 2015; (Aklıbaşında and Bulut, 2014); Faults (Shojaee et al., 2012); Distance to water resources ((Chhetri and Arrowsmith, 2008); (Bunruamkaew and Murayam, 2011); Aspect (Mahiny et al., 2009; (Bali et al., 2015)), Distance to rivers (Mahiny et al., 2009), Precipitation (Aklıbaşında and Bulut, 2014); (Suryabhagavan et al., 2015), Scenic beauty ((Nahuelhual et al., 2013); Soil texture (Bali et al., 2015) and Number of sunny days ((Makhdoom, 2013). Moreover, Geology and distance to attractive points were also considered based on expert knowledge. All considered criteria and sub-criteria are shown in Table 1.

Visibility was acquired from the viewshed map which created from attractive points and digital elevation model. All cells visible to defined points over the surface were determined. Possible attractive points and tracks (for example, springs, rivers, and histo-cultural points) were imported as the source layer in viewshed. Visible cells were considered suitable and nonvisible ranked unsuitable in the final layer.

Greenery is one of the main tourist attractions all around the world. NDVI is one of the key indicators that implies on the density of vegetation. This index was calculated from Landsat 8 images band 4 (red) and 5 (near-infrared) as Eq. 1 (Carlson and Ripley, 1997).

NDVI= (NIR-Red)/ (NIR+Red)         (Eq.1)

Unlike green spaces, bare lands are not so much appealing for travellers; consequently, vegetation density lower than 0.05 ranked as unsuitable. Although vegetation can play a decisive role in attracting tourists, they often avoid areas with highly dense cover (Pérez-Maqueo et al., 2017); thereby density higher than 0.8 was also considered as unsuitable.

Presence of wildlife resources may increase the suitability of area for tourism activities. Wild animals are so interesting not only for visitors seeking natural habitats but also for those scientists coming to study some particular species (Dye and Shaw, 2007). Proximity to wildlife was recommended by experts to take into account fauna bio diversity in this study. In this way, distance operator was applied on wildlife hotspots map. A distance of 250 m was considered as buffer for natural habitats to prevent human impacts on the valuable species.

Topographies of landscapes along with flora and fauna are of the main resources in attracting tourists (Bahaire and Elliott-White, 1999). Slope, aspect and elevation are three primary components of topography, which consider in this study. These components were derived from Digital Elevation Model (DEM). Area with slight slopes is safer for tourists and the impact of tourist trampling on soil erosion may be diminished there (Yang et al., 2014). Slight slopes are also more feasible for all travellers from different ages. Steep slopes could increase the risk of soil erosion; hence slopes greater than 50 percent were classified as constraint. Aspect map was classified based upon expert knowledge thereby north – and west-faced slopes ranked as the most suitable followed by south- and east-faced slopes. Elevation was also classifies based upon expert knowledge and previous literatures. Due to possible physiological problems like respiratory disorders which occur in some visitors, altitude greater than 2500 m was considered as limited range.

Cultural heritages include known historical buildings, holy shrines and ancient hills were considered as the source point layer to calculate distance to cultural heritages. Maximum distance of 15 km regarded as the minimum suitability.

Accessibility refers to the ease with which a site or service may be reached or obtained (Nicholls, 2001). Accessibility of landscapes to visitors can affect the suitability of these areas for tourism activities so that the location which provides easy access to attractions may have the greater potential than sites with difficult access (Chhetri and Arrowsmith, 2008). The plans of tourists are also limited by accessibility of destinations (Ólafsdóttir and Runnström, 2009). To calculate accessibility of landscape, all roads (both asphalts and dirt roads) were extracted on Google’s earth and transferred to GIS to create the road layer. Distance operator was applied on road layer to estimate distance from the extracted roads. Distance greater than 5000 m from roads was considered as unsuitable.

Villages serve as service providers to visitors in many natural areas. Many of the histo-cultural destinations are located near the villages. On the one hand, the life-style of the indigenous people inhabited in rural areas is appealing for many of the travellers. On the other hand, tourism activities close to the villages might support local economy. Therefore, proximity to villages ought to incorporate in land allocation for tourism. Distance map was created around main villages located within the study area. Distance greater than 5000 m from villages thought-about as unsuitable based on expert knowledge and literature.

Where the soil erosion potential (erodibility) is in critical condition, tourism activities could result in enhanced soil loss. So taking into consideration soil erodibility in land evaluation for tourism is of a great importance. In the present work, soil erosion potential at the study site was predicted by RUSLE  following the Eq. 2 (Renard et al., 1997).

A=R.K.L.S.C.P               (Eq.2)

Where A is mean annual soil loss (ton/hectare/year),

R is rainfall and runoff erosivity factor (MJ/ha/mm/yr.),

K is soil erodibility factor (ton/MJ/mm),

L is slope length,

S is slope steepness,

C is Cropping-management factor,

P is erosion control factor

Soil erosion greater than 100 (ton/ha/year) was considered as unsuitable.

Climate plays important role in tourism (Day et al., 2013). Tourism demand is considerably influenced by the climatical factors, so that the selection of destinations strongly governed by weather conditions (Liu, 2016; Ridderstaat et al., 2014). Accordingly, temperature, rainfall and sunshine duration are three climatic parameters that incorporate during this study. Temperature is one of the explanatory factors in tourism, in order that the number of tourists in the future can be predicted by temperature (Liu, 2016). To create temperature map, monthly average was calculated over the tourism period (March to October). Temperature 37 C and 4 C was deemed as the thresholds for suitability. Sunshine duration, also, conjointly plays a decisive role in selecting destinations. Location within which the number of sunny days was lower than 7 days thought of unsuitable.

Tourism significantly depends on water resources, both in providing basic human needs and as an asset for a wide range of tourist activities (Gössling et al., 2012). Accessibility to high quality drinking and potable water is vital for tourism development (Koç et al., 2017).  Future Viability and sustainability of tourist destinations depends upon adequate water supply of sufficient quality and quantity (Essex et al., 2004). Therefore, considering the water resources in land allocation for tourism has a crucial importance. All water resources including springs, rivers and wells were choose as source point. A distance of 2000 m defined as the final suitability based upon the expert team. To protect the water resources in term of quality and surrounding ecosystem, a distance of 30 m from the source point was considered as the buffer.

Rivers are used for touristic purposes in many parts of the world (van Balen et al., 2014). Main perennial and ephemeral streams serve as scenic landscapes by a large number of visitors. Accordingly, proximity to rivers takes into account in land planning for tourism. A distance of 30 m was reserved as the buffer to ensure the protection of riparian ecosystems and river physical stability.

One of the most important attributes of the landscapes which determine the suitability of tourism is landscape scenery (Nahuelhual et al., 2013; Weyland and Laterra, 2014). Scenic beauty layer was  adopted from (Sakieh et al., 2017). This layer is a MCE output of biophysical attributes including spot with high topographic variability, vegetation density, vegetation ecotone, summit visibility, river visibility, vegetation type and diversity of vegetation.

To prevent landscape degradation in term of stability, soil texture and geology was classified primarily based on expert knowledge. Loamy texture has been considered as those with best suitability followed by loamy clay, clay loam, sandy clay loam, sandy and clay.

We also considered distance from main faults as a conservative sub criterion in this work. A minimum distance of 150 m from the main faults served as buffer.

Proximity to possible attractions including, among others, springs, rivers, forest roads and vegetation ecotons were also calculated using distance operator. As distance to attraction points increased, the suitability of landscape for tourism decreased.

Criteria weighting: Calculating the weight of each factor which affects the land suitability for tourism is a critical and essential step in land allocation. Different criteria have unequal degree of importance in land allocation. Analytical hierarchy process (AHP) as superior and effective method (Zhang et al., 2015) employed in MCE to weight multiple factors. AHP provides the weights of each factor through pairwise comparison of multiple factors based on matrix calculations. To implement AHP a two-layer structure was constructed in which in the first layer five criteria and the 20 sub-criteria in the second layer. AHP questionnaires fill out by 30 experts of tour operators, academic members and tourism students. Finally, questionnaires were imported into Expert Choice 11 to calculate the factors weight and consistency ratio. All criteria and their relative weights are shown in Table 1.

Standardization: As each factor has its own dimension with different value, using fuzzy sets the input layers are standardized in a 0 – 255 range. Variety of fuzzy membership functions employed including monotonically linear increasing, monotonically linear decreasing, linear symmetric and user defined. All constraints (the range in which there is no suitability for targeted land use) were prepared using Boolean logic; whereby all suitable values gives 1 and values exceeds the suitability thresholds gives 0. All fuzzy membership functions and thresholds are shown in Table 1.

Weighted Linear Combination (WLC): Weighted linear combination is an aggregation method whereby all layers should be standardized to a common numeric range (0-255). Then, fuzzified layer regarding to their relative weights, which are calculated by AHP, and constraints are integrated using Eq. 3.

SI=(∑i=1nWiXi)*Ci

(3)

Where:

SI is the suitability index,

W_i is the weight of factor i,

X_i is the fuzzified factor i,

C_i is the constraint i,

Table 1: Criteria, sub-criteria and their relative fuzzy membership functions, thresholds and weighting score