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Multi-criteria evaluation to select sitesfor ecotourism facilities: a case studyMiankaleh PeninsulaManijeh Ghahroudi Tali a , Seyed Hasan Sadough a , Mohammad AliNezammahalleh a & Sakineh Khatoon Nezammahalleh aa Department of Geography, Shahid Beheshti University, Tehran,Islamic Republic of IranPublished online: 15 Aug 2012.

To cite this article: Manijeh Ghahroudi Tali , Seyed Hasan Sadough , Mohammad Ali Nezammahalleh& Sakineh Khatoon Nezammahalleh (2012): Multi-criteria evaluation to select sites for ecotourismfacilities: a case study Miankaleh Peninsula, Anatolia: An International Journal of Tourism andHospitality Research, 23:3, 373-394

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Multi-criteria evaluation to select sites for ecotourism facilities:a case study Miankaleh Peninsula

Manijeh Ghahroudi Tali*, Seyed Hasan Sadough, Mohammad Ali Nezammahalleh and

Sakineh Khatoon Nezammahalleh

Department of Geography, Shahid Beheshti University, Tehran, Islamic Republic of Iran

(Received 29 December 2011; final version received 12 July 2012)

To select sites for ecotourism facilities in Miankaleh Peninsula, the study used multi-criteria evaluation (MCE). In the Peninsula, ecotourism is in its incipient stage ofexpansion. It is essential to identify, in advance, suitable sites for the facilities as theobjective of this research. Thus, three sets of environmental, economic, and geodatacriteria were included. MCE helps integrate the conflict goals. We devised some novelmethods, in addition to common methods, for assigning weights to 17 criteria. Theweights were determined by analytic hierarchy process and then combined thanks toArcGIS Spatial Analysis. This combination suggested seven sites as the most suitable.The results indicate that these sites qualify the included conditions.

Keywords: marine protected area; Miankaleh Peninsula; multi-criteria evaluation;ecotourism

Introduction

Human life is dependent on the environment (Wallis, Michelle, & Anneke, 2010) and we

should protect it for our survival and welfare; and for protecting it, the capabilities and

weaknesses of each given point of the earth surface must accurately be detected. Thus, it is

important to provide the human society with development and welfare and to conserve the

environment. The importance of the environment, protection of plant and animal life and

biodiversity are a wise and accepted fact, as at the 2002 World Summit on Sustainable

Development in Johannesburg, it was agreed by 190 countries to achieve a reduction in the

rate of biodiversity loss (Bertzky & Stoll-Kleemann, 2009). Protected areas are well

efficient in achieving the goal. There are more than 170,000 protected areas worldwide till

now that were determined by different authority sources (Bertzky & Stoll-Kleemann,

2009). Until 2010 management effectiveness at least in 35,000 of these areas must be

evaluated. Therefore, in environmental dimension it is necessary that managers of these

areas try to minimize physical and social impacts of human activities, including tourism,

on the environment (Wunder, 2000).

According to the World Tourism Organization (WTO), tourism as the third largest

industry, after oil and automobile industries, will continue to grow in the coming decades. It

is believed that tourism is an industry with fastest growth in the world. Ecotourism is the

most rapidly growing segment of the industry (Campbell, 1999). Ecotourism is a responsible

travel to natural environments which conserves the environment and provides locals with

ISSN 1303-2917 print/ISSN 2156-6909 online

q 2012 Taylor & Francis

http://dx.doi.org/10.1080/13032917.2012.712872

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*Corresponding author. Email: m-ghahroudi@sbu.ac.ir

Anatolia – An International Journal of Tourism and Hospitality Research

Vol. 23, No. 3, November 2012, 373–394

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welfare (international ecotourism society). Therefore, it is a travel for sustainable

recreational use of nature and visiting natural manifestations. Fennel (2001) identified 85

definitions for ecotourism and from those, Weaver and Lawton (2007) presented three

criteria for the definition of ecotourism: (1) nature-based attractions, (2) education of visitors

in interaction with attractions, and (3) following human and biological sustainability in

experience and product management. Ecotourists or nature-based tourists are people who

travel to the natural environments for visit and use of nature or sport; their travel is with

education from nature and they are responsible for conserving the environment.

Ecotourism can support human life, bring about materials and energy, and absorb

waste products (Daily, 1997). It is claimed that ecotourism can integrate environmental

responsibility with economic benefits so that it can be considered as both an establishment

for economic development and a motive for environmental protection. To protect the

environment, economic stimulus is necessary particularly in remote regions with weak

government supervision (Wunder, 2000). Estimates reveal that in 1988, there were

between 157 and 236 million international ecotourists with economic impacts of $233

billion (Campbell, 1999). In developing countries, this rapidly growing industry was

proved to be a means for foreign exchanges and it can be described as an essential mean to

provide employment and generate income particularly in underdeveloped areas with

natural richness because it requires low initial investments (Wunder, 2000). Developing

countries are dealing with issues such as rapid population growth, pressure in labour force,

foreign debts, and lack of capital (Gossling, 1999). Thus, ecotourism is capable of

redistributing economic resources, mitigating harsh socio-economic circumstances, and

preserving biodiversity (Boo, 1990; Gossling, 1999).

Therefore, ecotourism with the highly fast rate of growth worldwide generates much

income, helps poor regions develop, and protects the environmental conditions. Iran, as a

developing country, has many different natural attractions with a wide variety of

topographic and climatic situations from very humid to arid climatic conditions and from

very high mountain summits to elevations under the sea level. Due to these natural

conditions and historical heritage, Iran is among the top 10 countries in tourism potentials

and among the top 5 in biodiversity. Despite these potentials, Iran is situated in 68th order

out of 150 members of WTO. To decrease the dependence of the country on the income

from oil industry, it is recommended to assume some other industries and activities; one of

these is ecotourism emphasizing the sustainable use of natural resources and conservation

of the environment. For such an activity some preparations and infrastructures are needed

to help well obtain the real goals with least possible harmful effects. Therefore, ecotourism

development is recommended in this country and the development needs establishment of

some facilities, and the location of these facilities is important for successful planning.

Marine and coastal protected areas can be defined as

any confined area within or adjacent to the marine environment, together with its overlyingwaters and associated flora, fauna, and historical and cultural features, which has beenreserved by legislation or other effective means, including custom, with the effect that itsmarine and/or coastal biodiversity enjoy a higher level of protection than its surroundings.(Yagi, Takagi, Takada, & Kurokura, 2010)

Miankaleh Peninsula, as a marine and coastal protected area, according to the International

Union of the Conservation of Nature (IUCN) categorization (1990), falls into the wildlife

sanctuary category that must be managed for conservation in which tourism can be

allowed (IUCN, 1990). In the study area, ecotourism is in its initial stage of development.

Given the notable natural attractions of Iran and the special location of the area in a

province, Mazandaran, with the highest percentage of ecotourists in the country, it is

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inevitable that ecotourism activities in the near future will expand more over the study

area. This expansion will be through high demands of ecotourists as well as through

government development plan policies (Alipour & Heydari, 2005).

On the other hand, according to the sustainable development indicators (UNESCO,

2005), the goals of biodiversity, and the high willingness to pay of the region visitors for

the nature (Amirnejad, Khalilian, Assareh, & Ahmadian, 2006), planning for ecotourism

can be recommended for the aims of social and economic development, visitor welfare,

and reduction in biodiversity loss; as Wunder (2000) stated that such “local incomes are

both a goal in itself and an instrument for conservation”. In planning for ecotourism

development in the area, it is necessary to establish some infrastructures for visitors in

natural environments. The location of these facilities is important in order to minimize

human impacts on the environment and, on the other hand, keep visitors satisfied along

their stay. Therefore, it is essential to select, before any expansion, suitable sites for the

purpose of development of ecotourism facilities (e.g. campgrounds, ecolodge, toilets, and

restaurants) in order to prevent the establishment of these facilities in sensitive points

where may have some devastating impacts on the environment or may dissatisfy visitors

(Abdelhamied, 2011). Thus, the objective of this study was to determine suitable sites for

possible establishment and development of ecotourism facilities and infrastructures

including camping sites, toilets, and sport facilities on this study area so that these facilities

have least impacts on the natural environment and most ability to satisfy ecotourists.

Literature review

A proper decision-making can result by the presence of an appropriate evaluation system

that helps well obtain the purposes of conservation and development in protected areas

(Bertzky&Stoll-Kleemann, 2009; Tucker et al., 2005).Multi-criteria evaluation (MCE) is a

method for decision-making with many different and conflict criteria in choosing a suitable

site (Hossain & Nani, 2010; Voogd, 1983; Zucca, Sharifi, & Fabbri, 2008). Accomplishing

the MCE involves creation of consistent alternatives and subsequent ranking of them based

on their importance. There are some disadvantages of MCE, for example, it does not

adequately address the facilitation issue of interaction between analyst and decisionmakers

to elicit and revise preferences as part of the iterative process particularly with multiple

decision makers. So a jury process must be applied. The integration of the analytical

techniques designed to deal withMCE issues with Geographical Information System (GIS)

capabilities results in more functionality. GIS applications are useful in classification,

processing, andmanipulation of spatial databases and, as a result, the integration ofMCE in

GIS environment helps the user improve decision-making processes. Despite the recently

widespread application of MCE in the context of GIS-based decision-making, such an

application in tourism research is slight and GIS was not well utilized in strategic decision

problems (Brown, 2006; Feick &Hall, 1999). In this study, for the first time, we applied the

MCEmethod for selecting the best sites for ecotourism facilities.Many criteria that onemay

find effective in this site selection are incorporated. The appropriate methods to determine

weights are illustrated so concise and applicable that they can be conducted in any other area

with similar circumstances.

Study area

In the east of Mazandaran Province, in Iran, Miankaleh (located on 368520N 538420E) is

stretched almost straight into the Caspian Sea along an east-west direction as a peninsula

in the south of which there is Gorgan Bay. The study area is up to 14,600 hectares and has a

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flat topography with no considerable reliefs and with gentle slope and composed of mainly

old and recent sand dunes (Iran Geology Map, 2003). The average monthly temperature is

about 178C and the average relative humidity is 82% and the average annual precipitation

is about 560mm (Iran Meteorological Organization). The area is covered approximately

by 73% of Punica granatum, Rhamnus pallasii, and Rabus persicus P. granatum and

overlapping with other species. Annually 144 kinds of birds migrate to the habitat where

there are eight prevalent animal species. Based on Emberger coefficient it has a subhumid

climate. The population in the protected area changes seasonally from nearly 200 to 1000

people who engaged to a large extent in fishing, cattle ranching, and partly in agriculture

(Iran Statistical Center, 2011). Almost all the people who are owners of livestock or are

herders stay temporarily in the region and rarely may stay overnight. They dwell in the

surrounding rural or urban centres. There are 388 corrals for sheep, buffaloes, and horses

and 23% of the whole area is under agricultural. The area has natural wildlife with very

little human intervention. When inside the area we can see a beautiful view to the sea

towards both the north and the south and looking towards the south we can see the Alborz

Mountain Range far away with dense tree coverage. Natural silence prevails over the area.

This silence and wildness next to the sea and mountain is an important attraction of the

area. As Kroonenberg (2007) said, these coastal areas require awareness and accurate

management (Figure 1).

Methodology

In this study, we used two types of questionnaires: the first questionnaire measures 70

ecotourists’ satisfaction about attractions and facilities. The second questionnaire was

designed to ask 35 experts questions about their preferences of all criteria, and alternatives

to six criteria (Figure 2) to determine the appropriate weights for the analytic hierarchy

process (AHP). The experts mainly university professors and Environmental Organization

practitioners have specialties in environmental, tourism planning studies. They were asked

to suggest their own preferences about each pair of criteria as ratio. For reliability and

validity, we used Cronbach’s alpha coefficient and Kaiser-Meyer-Olkin (KMO) index.

The first questionnaire has alpha coefficient of 0.74 and KMO index of 0.63 that is

acceptable for the purpose of the study (Kristof, 1963).

Scanned maps including the geology map at scale of 1:100,000, a sheet of topographic

map, at scale of 1:250,000, and three sheets of topographic maps, at scale of 1:50,000, as

well as eight sheets of digital maps, at scale of 1:25,000, were collected. Images of Landsat

ETM þ 2005 (USGS, 2001) and panchromatic Indian Remote Sensing (IRS) of 2006 with

30 and 5m spatial resolution, respectively, were preprocessed for subsequent analyses.

Statistical data collected from the nearby climatic and meteorological stations helped

interpolate the climate spatially.

The published studies including Miankaleh Comprehensive Project (MCP, 2003) by

Mazandaran Environmental Organization were also used. IUCN presents a method based

on which it is possible to identify the categories of plant and animal species in the red that

are in the near future may be in danger of extinction (Keith, Auld, Ooi, & Mackenzie,

2000). As stated by Rodrigues et al. (2006), in spite of red list limitations, it provides a

strong management tool for conservation of species. Therefore, the eight categories of the

list in a new method are used as a tool to determine weights for both plant and animal

habitats as two environmental criteria for site selection in the study. The conducted survey

had two stages of fieldwork: the first for observing and collecting data and the second to

analyse the produced results and to understand the analysis accuracy. In the first stage,

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some data were collected to measure dune vulnerability parameters in predesigned sheets

and to collect questionnaire data.

Assigning within subject weights for criteria

In this research, a process was followed to achieve a final suitability map. This map

resulted from combining a set of 17 map layers as criteria. As illustrated in Figure 2, for

creation of the map we followed some procedures. Weights are assigned to spatial units of

a layer (criterion) in an order. In order to identify the trade-offs in the decision-making

process, with the ultimate goal of achieving a compromise, the area of each spatial layer

after getting weights was classified based on a certain objective into 10 classes from the

most suitable with high values to the least suitable with low values. Then after weighting

each layer, all were combined. In this study, for the ordering of each different criterion we

used an appropriate method. After normalization and application of AHP and consistency

calculation, these criteria were combined to reveal the selected sites (Figure 2). In the

Figure 1. Position of the study area.

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following section, we explain the characteristics and ordering of each of these 17 criteria.

Environmental criteria were weighted with the objective to minimize adverse impacts of

visitors on the environment. The economic criteria were weighted with the objective both

to maximize visitor satisfaction and to minimize costs of building facilities. Geodata

criteria were weighted with the objective of optimizing structural foundation and

considering building standards. As mentioned in the study area section, since there are not

Figure 2. The criteria and the procedure for obtaining the suitability map.

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many residents inside the area, it was difficult to include some social criteria; but in the

layer of distance from buildings and municipal services, it was tried to take this component

of ecotourism into account.

Plant and animal

The existing species in the area get weights based on the red list categorization for the

extent to which they are exposed to the threat of extinction as well as on their national

conservation status (Jalili & jamzad, 1999). The eight categories of red list and their

suggested weights for site selection of ecotourism facilities are represented in Table 1. The

species falling in category of least concern (LC) are assigned the value of 90 in this study,

near threatened (NT) the value of 70, vulnerable (VU) the value of 50, endangered (EN)

the value of 30, critically endangered (CR) the value of 10, the unknown species get the

value of 40, as the average of all assigned values, due to their unknown status. The

categories of extinct in the wild (EW) and extinct (EX) have not been already observed in

the area but we can, respectively, assign the value of 2 and 1, respectively, for them,

because they must get the least suitable value as possible. The whole study area as a layer

for each of these two criteria is composed of some spatial habitats (polygons) which each

has some prevailing species and the borders of these habitats are not precise. The spatial

extents of the species habitat were extracted from the animal and vegetation map in MCP

(2003), with some updated modifications by unsupervised classification of ETM þ

Images on vegetation layer. The classified image was compared with the vegetation map

from that project. Some field observations were then carried out for parts that had

differences between coincident polygons of the two layers from image and from the

project. The average of defined values of species inside the area of each habitat was

assigned as the weight of that species (Table 2).

In this method, the final value for each habitat in a criterion is the sum of the values,

following categorization, Table 1, of species in a given habitat divided by the number of

species in that habitat. In Table 2, for example, the animal habitat number 1 has five

prevailing species of Vulpes, Canis aureus, Order Lagomorph, Family Suidae, and Felis

catus and according to categorization in Table 1, the first four species fall into LC category

and get value of 90 and the fifth species falls into VU category and gets the value of 50, and

that the final average value, as a result, for this habitat would be 82. Other habitat values

will be determined in the same way. For the habitats that have no values in the list, the

value of 40 was considered as a cautious measure. It is notable that the habitats are not very

exact in the borders. In the whole area, seven prevailing vegetation types can be found.

Table 1. Red list categories for species status and their corresponding weights assigned in thisstudy.

Thecategoryname Unknown Extinct

Extinct inthe wild

Criticallyendangered Endangered Vulnerable

Nearthreatened

Leastconcern

Categorystatus

NV EX EW CR EN VU NT LC

Definedvalue

40 1 2 10 30 50 70 90

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Security zone

The secure area is a very sensitive portion in the Miankaleh Peninsula, being a habitat for a

majority of migrating birds. Infrastructure development and facilities establishment are

not allowed throughout the zone (MCP, 2003). Therefore, two separate portions of the area

are excluded from final combination.

Land use

Land use criterion is the forth in priority in selecting suitable sites. The land use criterion

(layer) that is composed of 12 elements (polygon) was provided from the land use map of

MCP. The elements are dense rangeland (DR), fisheries units (FU), low dense forest

(LDF), mine industrial units (MIU), orchard forest (OF), orchard woods (OW), sandy land

(SL), semi-dense forest (SDF), semi-dense rangeland (SDR), vegetated wetland (VW),

Shrub (SH), and irrigated farming (IF). The area of each element gets a weight that

indicates its suitability. In the ordering for some criteria, including land use, we used the

AHP weighting method with pairwise comparison based on questionnaire data (Figure 2).

Geometric mean of different ranks from experts was used as the rank of each criterion and

the elements. In the pairwise comparison, all the elements of land use criterion were

compared in pairs through a matrix to assess their relative importance. Here, 12 land use

elements were compared pairwise with each other in the matrix. In the diagonal of the

matrix each element coincides with itself and gets the value of 1 for each entity. The values

of land use elements arranged from 1/9 to 9 were compared to detect the priorities of

elements in terms of their suitability for the research objective (Brent, Rogers, Tsmsbitsa-

Siimane, & Rohwer, 2007; Kuo, Liang, & Huang, 2006). The greater the assigned value of

land use type of A than that of B, the more the importance of the land use type of A than

that of B (Table 3).

In Table 3, for example, in the fifth row and the first column, the value of OF by DR is

3/1 in that the importance of OF is three times more than DR. The priorities of importance

are based on expert preferences. Finally, we calculated the consistency index of the matrix

Table 2. Within subject weights for animal habitats and vegetation habitats in Miankaleh followingvalues in Table 1.

Habitat No. Staple types Weight calculation methodFinalweight

Vegetation 1 J. aculeatus (mixture) 9LC þ 2NT þ 2VU þ EN þCR ¼ 1090/15 ¼ 72.66

73

2 A. tschernieviana NT ¼ 70 703 P. granatum þ R. pallasii VU þ LC ¼ 70 704 R. persicus þ P. granatum 70 þ 90 ¼ 160/2 ¼ 80 805 J. aculeatus VU ¼ 50 506 Halophyte LC ¼ 90 907 Marshy plants – 40

Animal 1 Vulpes Vulpes, C. aureus,Order lagomorph, (FamilySuidae) Sus scrofa, F. silvestris

LC þ LC þ LC þ LC þVU ¼ 410/5 ¼ 82

82

2 The secure area for migratingbirds (145 types)

LC*104 þ VU*35 þCR*6 ¼ 11170/145 ¼ 77.03

77

3 P. colchicus LC ¼ 90 904 N. tenuirostris CR ¼ 10 105 F. chaus, Testudo horsfieldii LC þ VU ¼ 70 70

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Table

3.

Pairw

isecomparisonofdifferentlanduse

entities

forlanduse

criterionandspreadsheetcalculationsforobtainingweights.

Decim

alvalues

DR

FU

LDF

MIU

OF

OW

SL

SDF

SDR

VW

SH

IF

DR

1.00

2.00

2.00

3.00

0.33

0.33

0.33

2.00

1.00

3.00

2.00

0.50

FU

0.50

1.00

2.00

2.00

0.33

0.33

0.33

1.00

1.00

2.00

1.00

1.00

LDF

0.50

0.50

1.00

3.00

0.50

0.33

0.25

1.00

0.50

2.00

0.50

0.33

MIU

0.33

0.50

0.33

1.00

0.33

0.33

0.50

1.00

1.00

1.00

1.00

0.50

OF

3.00

3.00

2.00

3.00

1.00

1.00

0.50

3.00

3.00

3.00

2.00

1.00

OW

3.00

3.00

3.00

3.00

1.00

1.00

0.50

3.00

3.00

3.00

2.00

1.00

SL

3.00

3.00

4.00

2.00

2.00

2.00

1.00

4.00

3.00

3.00

3.00

1.00

SDF

0.50

1.00

1.00

1.00

0.33

0.33

0.25

1.00

0.50

1.00

1.00

0.33

SDR

1.00

1.00

2.00

1.00

0.33

0.33

0.33

2.00

1.00

1.00

1.00

0.50

VW

0.33

0.50

0.50

1.00

0.33

0.33

0.33

1.00

1.00

1.00

0.50

0.33

SH

0.50

1.00

2.00

1.00

0.50

0.50

0.33

1.00

1.00

2.00

1.00

0.50

IF2.00

1.00

3.00

2.00

1.00

1.00

1.00

3.00

2.00

3.00

2.00

1.00

Sum

15.67

17.50

22.83

23.00

8.00

7.83

5.67

23.00

18.00

25.00

17.00

8.00

Norm

alized

values

Weight

DR

0.06

0.11

0.09

0.13

0.04

0.04

0.06

0.09

0.06

0.12

0.12

0.06

0.08

FU

0.03

0.06

0.09

0.09

0.04

0.04

0.06

0.04

0.06

0.08

0.06

0.13

0.06

LDF

0.03

0.03

0.04

0.13

0.06

0.04

0.04

0.04

0.03

0.08

0.03

0.04

0.05

MIU

0.02

0.03

0.01

0.04

0.04

0.04

0.09

0.04

0.06

0.04

0.06

0.06

0.05

OF

0.19

0.17

0.09

0.13

0.13

0.13

0.09

0.13

0.17

0.12

0.12

0.13

0.13

OW

0.19

0.17

0.13

0.13

0.13

0.13

0.09

0.13

0.17

0.12

0.12

0.13

0.14

SL

0.19

0.17

0.18

0.09

0.25

0.26

0.18

0.17

0.17

0.12

0.18

0.13

0.17

SDF

0.03

0.06

0.04

0.04

0.04

0.04

0.04

0.04

0.03

0.04

0.06

0.04

0.04

SDR

0.06

0.06

0.09

0.04

0.04

0.04

0.06

0.09

0.06

0.04

0.06

0.06

0.06

VW

0.02

0.03

0.02

0.04

0.04

0.04

0.06

0.04

0.06

0.04

0.03

0.04

0.04

SH

0.03

0.06

0.09

0.04

0.06

0.06

0.06

0.04

0.06

0.08

0.06

0.06

0.06

IF0.13

0.06

0.13

0.09

0.13

0.13

0.18

0.13

0.11

0.12

0.12

0.13

0.12

Notes:Divisionofeach

valuein

decim

almatrixbytheSum

ofthecolumnresultsin

thevalues

innorm

alized

matrix,then

theaverageofeach

rowin

thenorm

alized

matrixgives

the

weightsforelem

entsoflanduse.CI¼

ðlmax2

nÞ=ðn

21ÞwhereCIisconsistency

index,nisnumber

ofelem

ents,andlmax

¼P

SW,whereSistheSum

valueandW

istheobtained

weight.CR¼

CI/RI,CIcanbefoundin

Saaty

andVargas

(1991).In

thediagonalofthematrix(thebold

values)each

entity

iscompared

byitself.Thevalues

onthetwosides

ofthe

diagonal

aremirrored.

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of 12 elements (Brent et al., 2007; Contreras, Hanaki, Aramaki, & Connore, 2008; Saaty &

Vargas, 1991) and the index value turned out to be 0.05 in spreadsheet program which

indicates an acceptable consistency for the matrix assigned values. In Table 3, after

comparing elements pairwise, we calculated the weights of elements in the Normalized

values section of the table.

Shoreline

About 94% of the peninsula (148 km) is circumscribed by shoreline. About 82% of

ecotourists who were asked about their goal and about the sport which attracted them to

the destination responded to swimming and visiting the shoreline beauty. Furthermore,

about 69% of visitors stated a demand for coastal sports facilities. The shoreline, thus, is an

attraction that less the distance of the points from the line, the more suitable the points are

for site selection. Distances of all points from the shoreline were analysed by the straight-

line method in Spatial Analyst Extension of ArcGIS. The method was used for analysing

all the criteria with distance buffer.

Dune vulnerability

About 71% of the entire the study area is covered by active and inactive sand dunes with

different conditions and under a variety of human activities. Sand dunes are complex

dynamic systems that respond rapidly to driving forces forming them (Garcia-Mora,

Gallego-Fernandez, Williams, & Garcia-Movo, 2001). These soft landforms are the result

of interactions between climatic pressures, marine processes, vegetation, geomorphology,

and particularly human activities. The vulnerability of sand dunes is an outcome of the

integration of these forces (Garcia-Mora et al., 2001). Following Davies, Williams, and

Curr (1995) and Garcia-Mora et al. (2001), the method based on which we calculate the

dune vulnerability index (DVI), five groups of variables must be measured as

geomorphology condition of dune (GCD), marine influence (MI), aeolian influence (AI),

vegetation condition (VC), and human effect (HE). As Garcia-Mora et al. (2001) noted,

because of the complexity of coastal dunes, the number of parameters for the five groups

will be determined regarding the magnitude of influence by each category and the

purpose of the research. After rating for each parameter records in a checklist on a scale

of 0–4 and after calculating the percentages in columns, the average of the first five

columns gives a value as dune vulnerability (Davies et al., 1995; Williams, Alveirinho-

Dias, Garcia-Movo, & Garcia-Mora, 2001) for a certain segment (Figure 4). The full

explanation for calculation of DVI is available in detail in the studies of Davies et al.

(1995), Garcia-Mora et al. (2001), and Williams et al. (2001), and here we use this index

just as one criterion in site selection. Sand dunes in Miankaleh initially based on

morphological, biological, and human characteristics have been classified into four

different segments that DVI is calculated for. Measurements of the involving parameters

are carried out using different materials. In Figure 4, for example, the computation of

vulnerability index for the five categories of 4th segment and also the results for the four

segments are represented.

For GCD, five parameters were identified: (1) the length of homogeneous active sand

dune system (km), (2) the width of dynamic dune system (km), (3) average height of

secondary dunes (m), (4) slope steepness (8), and (5) particle size (mm). For MI, we just

included three parameters that were found more effective in the area: (1) berm slope (8),

(2) width of internal zone (km), and (3) coastal orientation to wave direction (8).

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AI includes three parameters: (1) pebble cover as % surface, (2) % seaward dune

vegetated, and (3) % of the system unvegetated. The VC plays an important role in

developing dune systems in Miankaleh. There are three kinds of vegetation in sand dune

systems (Mora et al., 2001): type I is mainly annual plants of very soft leaves that are not

adaptable to such environments. Type II is perennial plants that have advantages of

enduring surrounding pressures. Type III predominantly is plants capable of resisting

against burying by coastal sands and removing by seawater. The parameters for this

category are (1) % cover of type III plants in beach, (2) % cover of type III plants in the

seaside of frontal dune, (3) relative proportion of type II plants in the seaside of frontal

dune, (4) relative proportion of type I plants in the seaside of frontal dune, (5) relative

proportion of type II and type III plants in 100m inland of dune front, and (6) relative

proportion of vigorous or plants with normal vitality in the seaside of frontal dune (%).

The pressure due to HE is varying in different segments and we chose seven parameters

for this category: (1) visitor pressure, (2) access difficulty, (3) path network as % of frontal

dune, (4) relative cover of agriculture in the system (%), (5) grazing on the active system,

(6) number of rabbits, and (7) relative surface forested in the system 200m inland from

fore dune. The less the DVI value is for a segment the lower the vulnerability is for that

and more suitable is for the goal of the study.

Roads

There are 156.85 km of trails and paths in the area. To select a suitable point, access and

proximity to the paths is of special importance. In addition to a reduction in construction

costs for facilities, the proximity to the routes will meet ecotourists satisfaction in visiting

the nature. Thus, the less the distance from the trails and paths, the more suitable the points

are for the purpose of the study.

Soil, geology, and geomorphology

The entire area is consisted predominantly of five different soil types. In the Shahkileh

soil, Solonchak soil with pH about 7.5, ground water level is high and salty and the soil is

deep. Zaghmarz soil, Solonetz soil with some mottling, is covered most of the year by

water and gets a low suitability score. The marshy soils are never suitable and only the old

and recent sands, Regosol, have proper soils to give higher suitability score.

The whole study area is approximately homogeneous in terms of geology and

geomorphology and as mentioned earlier, the area consists about 71% of sand dunes.

These dunes are moderate while the recent lagoonal deposits and recent shore deposits

are, respectively, the most and the least suitable. Five geomorphological landforms are

distinguishable in the area out of which 76 square kilometres are sand dune reliefs. The

end of few alluvial fans and the smooth SLs, about 13 square kilometers, received the

most suitable values. Digital elevation model (DEM) of the area was developed from

the extracted data of digital map, at a scale of 1:25,000. According to this DEM, the

highest point is 218.51m and the lowest point is up to 229.84m. Given the

probability of sea-level rises and consequently inundating the coastal lands and the little

tide, very high and very low elevations are not suitable. Given the urban drainage

standards (Manual, 2001), the slope range of 2–48 was chosen as the most suitable for

the facilities (Table 4).

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Attractions, corals, building, and municipal services

Data for these criteria were extracted from digital map, questionnaire, and national

statistical data. Distance from the points of attractions, corrals, buildings, and municipal

services each in a separate layer file were analysed in GIS Software by Spatial Analyst

Extension. There are 388 corrals in the area which are used by local people to keep cattle.

The establishment of ecotourism facilities near the traditional corrals is never suitable in

terms of both health problems and ecotourism satisfaction. Thus, from the location of each

corral, buffers were defined in distances of 100m, 500m, and 2 and 5 km. A distance

buffer of 100m from these points has the least value. By increasing the distance the

suitability value increases correspondingly for this layer. Since the presence of municipal

services such as electricity and fresh water facilitates the development and reduces the

construction costs, proximity to these services is more desirable. The pixels near some

historical and natural attractions have higher suitability value. The pixels in proximity to

the buildings are more valuable (Santana-Jimenez, Suarez-Vega, & Hernandez, 2011).

Climate

In order to determine the Emberger index (Q2) for each individual pixel in the area, we

used Kringing interpolation method in GIS and statistics from 15 meteorological stations

in nearby regions. In the innovative method, the mean daily maximum temperature

(MaxT) in the hottest month of the year in Kelvin, the mean daily minimum temperature

(MinT) in the coolest month in Kelvin, and the average annual precipitation (P) were

interpolated as separate raster layers. These parameters were determined for each

individual pixel. By plugging these raster layers in the Emberger formula in GIS calculator

the output layer shows the quotient for each pixel.

Q2 ¼2000 £ Ras P

RasMaxT2 RasMin T:

Table 4. The weights of sub-groups in geodata by AHP.

Criterion Sub-group Weight

Geology Recent lagoonal deposit 0.45Sand dune deposits 0.35Recent shore deposit 0.20

Soil Old dunes 0.26Zaghmarz 0.18Marshy 0.17Shahkileh 0.18Embryo dune 0.22

GeoM Swamp 0.15Sand with low slope 0.22Sand with active dune 0.14Sand on the end of fan 0.32Releafs with old inactive dune 0.18

Elevation 30—26m 0.2826–22m 0.3322–18m 0.39

Slope 0–28 0.292–48 0.344–68 0.38

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The result is a raster layer that each pixel in the layer has a Q2 value and is representative

of a climate. Given the relative vegetation dynamics as a result of climate impacts

(Roerink et al., 2003), the lower values of Q2 are less suitable due to more aridity and

consequent sensitivity. The least pixel value of Q2 is 50.5 and the highest is 61.12 so that

the eastern parts have lower Q2 than the western parts. Although climate of the whole area

is temperate subhumid, the climate of eastern parts is close to temperate semiarid and the

climate of western parts is mainly close to temperate humid. The area has considerably

same climatic parameters. Some other parameters have not considerable spatial variations

throughout the study area but are important in ecotourism camping. The mean annual

values during a period from 1990 to 2005 in Gorgan Synoptic Station for three parameters

including the number of cloudy days, sunshine hours, and wind speed in knots are,

respectively, 116, 2100, and 2.1. The criterion of climate has the least priority following

that of slope in the final suitability map.

Normalization of criteria

All layers that got values in the form of polygon vectors were converted into raster layers

with pixel size of 10m. The values of individual criteria that are in different ranges and

calculated in different units must be normalized into a standard scale to integrate into the

final analysis. In normalization, each given pixel that is assigned a grade of membership

using Fuzzy membership functions and other methods assumes a degree of suitability. The

spatial points where greater membership values were assigned for them are more desirable.

Here, the values range from 0 to 10 and the value 0 means unsuitable and the value 10

represents the most suitable in terms of environment, economics, and geodata. All layers

with different range and units of pixel values were normalized by reclassification of Spatial

Analyst Extension.

Weights for criteria

The AHP method, readily applicable in spreadsheet program, was used to determine

appropriate weights for layers (Saaty, 1980). In the AHP method, the decision-making

problem is decomposed into a hierarchy of sub-problems and the relative importance

of the elements can be evaluated in a pairwise comparison with respect to the main

goal above that hierarchy. In this method, each goal has some criteria, and

each criterion has some elements; the elements are compared with each other one by

one and take a ratio of preference for each comparison and the same procedure is

continued for the criteria to reach a final decision about the main goal. The pairwise

comparison, as previously explained in land use section, for criteria of the study is

shown in Table 5.

In order to calculate weights, the ratio of each criterion to the other was added to the

column, then each individual value was divided by the sum in the right section of the

table of that indicator. The average of criteria in the row gives the weights for each

criterion. The CR for each group must be (0.1 (Saaty & Vargas, 1991). The authors with

research and execution experiences made little modifications in ratios of some criteria to

achieve an acceptable CR (Saaty & Vargas, 1991). The three groups of criteria are

similarly compared pairwise to obtain the corresponding weights for indicators. The

weights of the three indicators of environmental, economic, and geodata are 0.5, 0.32,

and 0.18, respectively.

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Table

5.

Pairw

isecomparisonofthecriteria

ineach

separateindicatorandtheweightsofeach

layer.

Environmental

Anim

alVeg

Secure

Dune

Climate

Anim

alVeg

Secure

Dune

Climate

Weight

Anim

al1.00

1.00

0.50

3.00

5.00

Anim

al0.22

0.21

0.19

0.32

0.33

0.25

Veg

1.00

1.00

0.50

2.00

3.00

Veg

0.22

0.21

0.19

0.21

0.20

0.21

Secure

2.00

2.00

1.00

3.00

3.00

Secure

0.44

0.41

0.38

0.32

0.20

0.35

Dune

0.33

0.50

0.33

1.00

3.00

Dune

0.07

0.10

0.13

0.11

0.20

0.12

Climate

0.20

0.33

0.33

0.33

1.00

Climate

0.04

0.07

0.13

0.04

0.07

0.07

SUM

4.53

4.83

2.67

9.33

15.00

CR:0.05

1.00

Economic

Lan

use

Shoreline

Attraction

Corral

Service

Building

Road

Lan

duse

Shoreline

Attraction

Corral

Service

Building

Road

Weight

Landuse

1.00

0.50

4.00

0.33

3.00

4.00

0.50

Landuse

0.11

0.06

0.22

0.09

0.20

0.22

0.14

0.15

Shoreline

2.00

1.00

2.00

0.50

3.00

2.00

0.33

Shoreline

0.23

0.13

0.11

0.14

0.20

0.11

0.09

0.14

Attraction

0.25

0.50

1.00

0.33

1.00

0.50

0.20

Attraction

0.03

0.06

0.06

0.09

0.07

0.03

0.06

0.06

Corral

3.00

2.00

3.00

1.00

3.00

5.00

1.00

Corral

0.34

0.26

0.17

0.27

0.20

0.27

0.28

0.25

Service

0.33

0.33

1.00

0.33

1.00

1.00

0.33

Service

0.04

0.04

0.06

0.09

0.07

0.05

0.09

0.06

Building

0.25

0.50

2.00

0.20

1.00

1.00

0.20

Building

0.03

0.06

0.11

0.05

0.07

0.05

0.06

0.06

Road

2.00

3.00

5.00

1.00

3.00

5.00

1.00

Road

0.23

0.38

0.28

0.27

0.20

0.27

0.28

0.27

SUM

8.83

7.83

18.00

3.70

15.00

18.50

3.57

CR:0.09

1.00

Geodata

Geology

Soil

GeoM

Elevation

Slope

Geology

Soil

GeoM

Elevation

Slope

Weight

Geology

1.00

0.50

0.50

0.50

1.00

Geology

0.13

0.09

0.17

0.10

0.14

0.13

Soil

2.00

1.00

0.50

1.00

1.00

Soil

0.25

0.18

0.17

0.20

0.14

0.19

GeoM

2.00

2.00

1.00

2.00

2.00

GeoM

0.25

0.36

0.33

0.40

0.29

0.33

Elevation

2.00

1.00

0.50

1.00

2.00

Elevation

0.25

0.18

0.17

0.20

0.29

0.22

Slope

1.00

1.00

0.50

0.50

1.00

Slope

0.13

0.18

0.17

0.10

0.14

0.14

SUM

8.00

5.50

3.00

5.00

7.00

CR:0.02

1.00

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Integration of layers and the final suitability map

After reclassification in GIS, the products of criteria and their corresponding weights,

obtained by AHP, were added to generate a map in which each pixel is a sum of the

products of all included criteria and their own weights in that given pixel. Therefore, the

greater a pixel sum value, the more suitable is that pixel for the objective of this study.

ENV ¼ ðAnimal £ 0:25Þ þ ðVegetation £ 0:21Þ þ ðSecure £ 0:35Þ þ ðDune £ 0:12Þ

þ ðClimate £ 0:07Þ;

ECON ¼ ðLand use £ 0:15Þ þ ðShoreline £ 0:14Þ þ ðAttraction £ 0:06Þ þ ðCorral £ 0:25Þ

þ ðService £ 0:06Þ þ ðBuilding £ 0:06Þ þ ðRoad £ 0:27Þ;

GEO ¼ ðGeology £ 0:13Þ þ ðSoil £ 0:19Þ þ ðGeoM £ 0:33Þ þ ðElevation £ 0:22Þ

þ ðSlope £ 0:14Þ:

The result from the combination of these groups of criteria is a raster layer for each

group. Then these raster layers as indicators are combined in the same way with their

corresponding weights to give the final suitability map.

Final SuitabilityMap ¼ ðENV £ 0:5Þ þ ðECON £ 0:32Þ þ ðGEO £ 0:18Þ:

After classification of the final raster map into nine classes, from the class with highest

pixel values, seven sites were selected as the most suitable for the objective (Figure 3).

Discussion of findings

Given the lack of tourism facilities in the area, 99% demand of ecotourists for providing

such facilities, the potential capability of the developing countries for rapid growth in

ecotourism, and the national government policies, the penetration and development of

these facilities will be inevitable in the future. Therefore, it is essential to conduct in

advance a research before any tourism development, because development without a

primary study takes only the economic and safety criteria into account regardless of the

ecological criteria (Parolo, Ferrarini, & Rossi, 2009), to base the establishment and

development of ecotourism facilities on some sustainable development indices.

From the 17 criteria, we selected seven best suitable sites. It is necessary to note that all

the selected points have the same soil cover, geomorphology, and geology. All the sites are

covered by old dune soil type that has the best condition. All the sites also coincide

partially with vegetation cover of P. granatum þ R. persicus and P. granatum þ R.

pallasii. In geological terms, all the sites are located in coastal sand dunes that are sensitive

areas (Mansuroglu, Ortacesme, & Karaguzel, 2006), but the DVI in all these sites is (0.5

which indicates low vulnerability (Garcia-Mora et al., 2001) (Figure 4). Except for site

number 7 located in LDF land use, none of these sites is in forest areas (Mansuroglu et al.,

2006). All the sites, but the site number 6, are in an animal habitat and that four species out

of the five are in LC status and just F. catus is in VU status. For this habitat as a whole,

there is relatively the LC (Lindenmayer, Franklin, & Fischer, 2006). None of the sites is

located within the excluded secure area. All the sites were selected within the 100m buffer

around the routes.

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Figure 3. Integration of criteria in the three groups. In each layer, the white areas represent suitablejust in terms of that given criterion; the darker the areas are in each criterion, the less suitable they arein site selection.

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Site 1 has an area of 93.5 hectares (Figure 5) and in the site as mentioned F. catus is not

in the vicinity and so it is environmentally more suitable. The vegetation in the site is P.

granatum þ R. persicus that has NT status in red list and (Schlacher, Jager, & Nielsen,

2011) received a value of 70 in the study (Table 2). The site is 5.6 km distance from secure

area and has orchard land use that according to Schlacher et al. (2011) is consistent with

tourism activities and to expert opinions is the second in priority. The distance from the

shoreline as a factor attracting ecotourists is about 1 km towards north and 0.55 km

towards the south. The only unsuitable criterion in the site is the relatively high DVI (0.49)

among the four segments that, however, based on Garcia-Mora et al. (2001) classification

is low. The high DVI in the segment is as a result of the pressure of human influences

including agriculture and fisheries. This segment in the south is stretched into the Gorgan

Bay over which about 28% is covered by SDF land use that reduces DVI in VC group for

the segment; the relative proportion of type II plants in the seaside of frontal dune is over

34%, mainly Juncus aculeatus. Human activities including agriculture, grazing, and

fisheries are much more in the segment than those in other segments that augment its DVI

value (Figure 4). Distance to available routes is up to 5m that is suitable due to

conservation measures on one hand (Schlacher et al., 2011) and due to reduction in

Figure 4. The polygons indicate DVI for each four segments. The more the polygon area, thegreater is the vulnerability. The axes divided into 100 units represent the influence of each categoryof parameters. Below the figure it can be seen how the values are obtained for polygon of segment 4as an example.

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construction and transportation costs and in subsequent pollution on the other. Distance

from the corrals and domesticated cattle keeping places is over 1 km and according to

Chaiyarat and Srikosamatara (2009) and the questionnaire data, the distance is suitable

enough to keep the facilities safe from dissatisfaction (Abdelhamied, 2011). The site is

relatively in an acceptable distance from the available municipal services, buildings of

local residents, and attractions in which respondents are interested. This proximity reduces

the costs in providing services such as electricity and fresh water and improves the social

awareness and economic circumstances for local people and subsequently the

environmental conservation (Chaiyarat & Srikosamatara, 2009). The elevation is

223.63 and for sea-level fluctuations in the Caspian (Kroonenberg, 2007), it has more

appropriate settings than other points. The average slope for the site is up to 3.58 and in a

moderate level of suitability.

Site 2, with area of 6 hectares, is covered by P. granatum and R. pallasii that according

to Table 2 based on IUCN gets the value 70. The distance of the site from the secure area is

over 500m. The point on which the site is located has the land use type of orchard and as

site 1 is consistent with the purpose. The shoreline from which the distance of the site is

over 550m is an attraction where about 85% of ecotourists are interested. Although this

site is located at a distance from site 1, both are located on the same segment in DVI and

are weak regarding the criterion. The distance of the site from routes is 33m and from

corrals and cattle keeping places is more than 1 km and enough to keep ecotourists safe

and satisfied. Distance of the site from buildings and available services is 7 and 4.65 km,

respectively. The elevation of 220m is the highest among the selected sites and is also

suitable. Q2 is 53.3 for the pixel in which the site is located.

Site 3 with an area of 9 hectares was selected in the vicinity of site 2, as it can be seen

in Figure 5, and covered predominantly by Artemisia annua and the land use type of the

area the site is located on is SL that is the most preferred by the experts, however, with a

little difference from the orchard land use. Position of the site is about 600 and 46m

distance, respectively, from the shoreline and routes and both are appropriate

economically, the former for an increase in the satisfaction of probably established

facilities and the latter for a decrease in the costs of construction and extra trail

development. Segment 3 on which the site is located has a moderate DVI (0.435). The

relative proportion of type II and type III plants in 100m inland of dune front is nearly 45

Figure 5. Location of the seven sites selected for the purpose of the study.

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and scored 2, following Garcia-Mora (2001), and consists of P. granatum þ R. pallasii

and J. Aculeatus. The SDF (semi-dense forest) in the northern part of the segment

decreases its vulnerability. In the northern part, there are some vigorous plants and is

densely covered by P. granatum and a great percentage of the portion is under irrigated

agriculture and an integration of orchard and forest so that it diminishes DVI. The site is

the closest to the places keeping domesticated cattle and, thus, least desirable.

Site 4, 24 hectares in area, was selected on an average distance of 4 km to the east of

sites 2 and 3. The plant habitat in which the site is located (Table 2) has the vegetation

cover of P. granatum þ R. pallasii with mixture of LC and VU status that get the value of

70 in the study. Therefore, there are no considerable restrictions in exposing to extinction.

The distance of the site from the shoreline and from the trails is, respectively, 610 and

58.5m. The site was selected in the same segment as site 3 in DVI and the land use of this

site like that of site 3 is SL. The site is 1890m from the services and 1 km from the places

of corrals and both are relatively suitable. It is located at a suitable distance from

attractions and local buildings (respectively, 943 and 724). The site with 5.58 slope is the

greatest amongst the selected sites and it is suitable for construction purposes (Manual,

2001). The site is 223.4m in elevation and 56.27 in Q2. The weak point about the site is

that it is located at a distance of 184m from the boundary of Numenius tenuirostris habitat

with CR status. The site is somewhat disadvantageous because it has some rough

topography.

Site 5, with an area of 16 hectares, is in the same habitat as site 4 (Table 2). The

distance of the site from the security zone is about 3200m, from the shoreline the site is

nearly 700m, from the trails the site is up to 75m, and from corrals the site is about 5 km.

The site located in SDR and according to Mansuroglu et al. (2006) is not well suited. The

site as the two previous sites is on segment 3. It has the least distance of 316m from the

rare traditional local buildings and is also the most suitable of the selected sites in this

criterion. It is acceptable in elevation and slope angle and Q2 is 55.74 for the pixel the site

is inside. Site 4 is in a relative vicinity of 420m from the habitat of N. tenuirostris.

Site number 6, with highest pixel value in the final raster map, has an area of 49

hectares. The site is located in the habitat of Phasianus colchicus with LC status (valued

90) and in vegetation habitat of P. granatum þ R. persicus (valued 70). The only negative

point about the area is its relative proximity to the secure area (154m). Distance of the site

from shoreline is about 400m and the land use type inside which it was determined is SL.

The DVI for the segment on which the site is located is 0.37 and has the least vulnerability

among the other segments. The width of dynamic dune system in segment 2 is 0.48 km and

the width of internal zone and the coastal orientation in wave direction is, respectively,

1.18 km and 168. Although the percentage cover of type III plants in beach is 8.3, with

score of 3, which increases the vulnerability, due to the secure area contained within the

segment, access difficulty, low pressure of visitors, and less agricultural activities, a

considerable decrease can be seen in the total DVI of the segment. Low human activities

and consequent decrease in value of vulnerability in the segment are in exact

accordance with the study of Mora et al. (2001). Distance from paths and from the places

of corrals is, respectively, 84 and 1000m and is suitable. It is located in the approximate

boundary of recent coastal sediments and sand dune deposits. The elevation and

slope of the site is not much suitable relative to those of other sites (226.91 and 0.38). The

site is at an appropriate distance from attractions and buildings to keep ecotourists

coming back and local people changing their attitudes towards the environment

(Amirnejad et al., 2006). Q2 is 53.89 for the pixel containing the site which is at a

moderate suitability level.

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Site number 7, with 11 hectares in area, has vegetation of P. granatum þ R. persicus.

The site, indeed, is situated in an approximate distance of 316m from the shoreline, i.e. the

nearest distance from the shoreline among the seven sites, and 70m from somewhat paved

routes. The site is located in the same segment as site 6 and with the least DVI and in an

acceptable distance from attractions and buildings and close to municipal services. Both

the elevation and Q2 here are of least values out of the seven sites, i.e. up to228.57m and

51.86, respectively.

Conclusion and implications

The results of the study for the first time indicate that the MCE method, thanks to the

software providing spatial analysis, can be useful in site selection for ecotourism facilities

as requisites for successful ecotourism development planning. From the combination of

criteria, it is possible to accurately evaluate the inputs and obtain the suitable options. The

AHP method also proved to be applicable in ecotourism facilities site selection. In this

study, after assigning within-subject weights to each layer and combining the normalized

criteria, seven sites were selected and the weighting was assessed to demonstrate that the

methodology is well able to take all the criteria and goals of the study into account, and

finally to select the best options in environmental and economic terms. The results of the

red list classification in this study demonstrate that the method of average values of species

in a given habitat based on their exposure to extinction can be used for assigning weights

to elements of each criterion in a site selection analysis. The study also indicated that the

DVI method is applicable in areas with similar situations and that it may be investigated as

a criterion for the MCE method. According to the new procedure carried out for climate

criterion, for the first time, we showed that by using the GIS Kringing interpolation

method, each parameter of the Emberger quotient can be interpolated to determine the Q2

for each pixel of an area as a criterion for site selection. The resulted map identifies the

most suitable sites for the purpose of establishment and development of ecotourism

facilities as well as the most disadvantageous areas in environmental and economical

terms.

Acknowledgements

The financial support of Shahid Beheshti University is greatly acknowledged. The authors would liketo give their sincere thanks to anonymous referees for their helpful comments.

References

Abdelhamied, H. (2011). Customers perceptions of floating restaurants in Egypt. Anatolia, 22(1),1–15.

Alipour, H., & Heydari, R. (2005). Tourism revival and planning in Islamic Republic of Iran:Challenges and prospects. Anatolia, 16(1), 39–61.

Amirnejad, H., Khalilian, S., Assareh, M., & Ahmadian, M. (2006). Estimating the existence valueof north forests of Iran by using a contingent valuation method. Ecological Economics, 58,665–675.

Bertzky, M., & Stoll-Kleemann, S. (2009). Multi-level discrepancies with sharing data in protectedareas: What we have and what we need for the global village. Journal of EnvironmentalManagement, 90, 8–24.

Boo, E. (1990). Ecotourism: The potentials and pitfalls, Vol. 1. Washington, DC: WWF.

392 M. Ghahroudi Tali et al.

Dow

nloa

ded

by [

Uni

vers

ity o

f T

ehra

n] a

t 12:

18 0

1 M

ay 2

013

Brent, A., Rogers, D., Ramabitsa-Siimane, T., & Rohwer, M. (2007). Application of analyticalhierarchy process to establish health care waste management systems that minimise infectionrisks in developing countries. European Journal of Operational Research, 181, 403–424.

Brown, G. (2006). Mapping landscape values and development preferences: A method for tourismand residential development planning. International Journal of Tourism Research, 8, 101–113.

Campbell, L. (1999). Ecotourism in rural developing communities. Annals of Tourism Research,26(3), 534–553.

Chaiyarat, R., & Srikosamatara, S. (2009). Populations of domesticated cattle and buffalo in theWestern Forest Complex of Thailand and their possible impacts on the wildlife community.Journal of Environmental Management, 90, 1448–1453.

Contreras, F., Hanaki, K., Aramaki, T., & Connore, S. (2008). Application of analytical hierarchyprocess to analyse stakeholders preferences for municipal solid waste management plans.Conservation and Recycling, 52, 979–991.

Daily, D. (1997). Natures services. Sosietal dependence on natural ecosystems. Washington, DC:Island press.

Davies, P., Williams, A., & Curr, R. (1995). Decision making in dune management: Theory andpractice. Journal of Coastal Conservation, 1, 87–96.

Feick, R., & Hall, G. (1999). Consensus building multi participant spatial decision support system.URISA Journal, 2, 17–23.

Fennel, D. (2001). A content analysis of ecotourism definitions. Current Issues in Tourism, 4,403–421.

Garcia-Mora, M., Gallego-Fernandez, J., Williams, A., & Garcia-Movo, F. (2001). A coastal dunevulnerability classification. A case study of the SW Iberian Peninsula. Journal of CoastalResearch, 17, 802–811.

Gossling, S. (1999). Ecotourism: A means to safeguard biodiversity and ecosystem functions?Ecological Economics, 29, 303–320.

Hossain, M., & Nani, G. (2010). GIS-based multi criteria evaluation to land suitability modelling forgiant prawn (Macrobrachium rosenbergii) farming in Companigonj Upazila Noakhali,Bangladesh. Computer and Electronics in Agriculture, 70, 172–186.

IUCN (1990). United Nations list of national parks and protected areas. Switzerland: IUCN, Gland.Jalili, A., & jamzad, Z. (1999). Red data book of Iran: A preliminary survey of endemic, rare and

endangered plant species in Iran. Tehran: Research Institute of Forests and Rengelands.Keith, D., Auld, T., Ooi, M., &Mackenzie, B. (2000). Sensitivity analyses of decision rules in World

Conservation Union (IUCN) Red list criteria using Australian plants. Biological Conservation,94, 311–319.

Kristof, W. (1963). The statistical theory of stepped-up reliability coefficients when a test hasdivided into several equivalent parts. Psychometrika, 28, 221–238.

Kroonenberg, S.A. (2007). Solar-forced 2600 BP and Little Ice Age highstands of the Caspian Sea.Quaternary International, 173–174, 137–143.

Kuo, M., Liang, G., & Huang, W. (2006). Extensions of multicriteria analysis with pairwisecomparison under a fuzzy environment. International Journal of Approximate Reasoning, 43,268–285.

Lindenmayer, D., Franklin, J., & Fischer, J. (2006). General management principles and a checklistof strategies to guide forest biodiversiry conservation. Biological Conservation, 131, 433–445.

Mansuroglu, S., Ortacesme, V., & Karaguzel, O. (2006). Biotope mapping in an urban environmentand its implications for urban management in Turkey. Journal of Environmental Management,81, 175–187.

Manual, U.D. (2001). Urban Drainage Design Manual, Hydraulic Engineering Manual No. 22,2nd ed., Publication FHWA-NHI-01-021.

Mazandaran Environmental Organization (2003). Miankaleh Comprehensive Project (in Farsi),Report.

Parolo, G., Ferrarini, A., & Rossi, G. (2009). Optimization of tourism impacts in protected areas bymeans of genetic algorithms. Ecological Modelling, 220, 1138–1147.

Rodrigues, A., Pilgrim, J., Lamoreux, J., Huffmann, M., & Brooks, T. (2006). The value of the IUCNred list for conservation. TRENDS in Ecology and Evolution, 21, 71–76.

Roerink, G., Menenti, M., Soepboer, W., & Su, Z. (2003). Assessment of climate impact onvegetation dynamics by using remote sensing. Physics and Chemistry of the Earth, 28, 103–109.

Saaty, T. (1980). The analytic hierarchy process. New York: McGraw Hill.

Anatolia – An International Journal of Tourism and Hospitality Research 393

Dow

nloa

ded

by [

Uni

vers

ity o

f T

ehra

n] a

t 12:

18 0

1 M

ay 2

013

Saaty, T., & Vargas, L. (1991). Prediction, projection and forecasting. Dordrecht: Kluwer AcademicPublishers.

Santana-Jimenez, Y., Suarez-Vega, R., & Hernandez, J. (2011). Spatial and environmentalcharacteristics of rural tourism lodging units. Anatolia, 22(1), 89–101.

Schlacher, T., Jager, R., & Nielsen, T. (2011). Vegetation and ghost crabs in coastal dunes asindicators of putative stressors from tourism. Ecological Indicators, 11, 284–294.

Tucker, G., Bubb, P., Deheer, M., Miles, L., Lawrence, A., & Bajracharya, S. (2005). Guidelines forbiodiversity assessment and monitoring for protected areas. Kathmandu: KMTNC.

UNESCO (2005). United Nations Decade of Education for Sustainable Development (2005–2014):International implementation scheme. Paris: UNESCO.

USGS (2001). Global Land Survey, Landsat ETM þ , 28.5m scene p163r034_7t20010730 & 308mscene L71163034_03420050420., Retrieved from http://www.glcf.com, 2011.

Voogd, H. (1983). Multi criteria evaluation for urban and regional planning. London: Pion.Wallis, A., Michelle, L., & Anneke, J. (2010). Significance of the environment in the assessment of

the sustainable development: The case for south west Victoria. Ecological Economics, 10,1016–1027.

Weaver, D., & Lawton, L. (2007). Twenty years on: The state of contemporary ecotourism research.Tourism Management, 28, 1168–1179.

Williams, A., Alveirinho-Dias, J., Garcia-Movo, F., & Garcia-Mora, M. (2001). Integrated coastaldune management: Checklists. Continental Shelf Research, 21, 1937–1960.

Wunder, S. (2000). Ecotourism and economic incentives an empirical approach. EcologicalEconomics, 32, 465–472.

Yagi, N., Takagi, A., Takada, Y., & Kurokura, H. (2010). Marine protected areas in Japon:Institutional background and management framework. Marine Policy, 34, 1300–1306.

Zucca, A., Sharifi, A., & Fabbri, A. (2008). Application of spatial multi-criteria analysis to siteselection for a local park: A case study in Bergamo Province, Italy. Journal of EnvironmentalManagement, 88, 752–769.

394 M. Ghahroudi Tali et al.

Dow

nloa

ded

by [

Uni

vers

ity o

f T

ehra

n] a

t 12:

18 0

1 M

ay 2

013