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Landscape and Urban Planning 120 (2013) 1– 15

Contents lists available at ScienceDirect

Landscape and Urban Planning

jou rn al hom ep age: www.elsev ier .com/ locate / landurbplan

esearch paper

apping wildness for protected area management: A methodologicalpproach and application to the Dolomites UNESCO World Heritageite (Italy)

rancesco Orsia,b,∗, Davide Geneletti a, Axel Borsdorfb,c

Department of Civil, Environmental and Mechanical Engineering, University of Trento, Via Mesiano 77, 38123 Trento, ItalyInstitute of Geography, University of Innsbruck, Innrain 52f, 6020 Innsbruck, AustriaInstitute of Mountain Research: Man and Environment, Austrian Academy of Sciences, Technikerstrasse 21a, ICT, 6020 Innsbruck, Austria

i g h l i g h t s

Unsupervised classification supplies less arbitrary wildness maps.Significant concordance found between classification’s and MCE’s output.Wildness classes can be used by managers as planning units.Wildness variation between and within mountain ranges in the Dolomites was analyzed.Wildness and elevation convey information about recreational opportunities.

r t i c l e i n f o

rticle history:eceived 11 January 2013eceived in revised form 26 July 2013ccepted 29 July 2013

eywords:ildness class

nsupervised classificationoningecreation Opportunity SpectrumIS

a b s t r a c t

Wildness maps may provide valuable information for the management of natural and protected areas(e.g. Recreation Opportunity Spectrum). This requires the adoption of mapping methods that can handlethe relative nature of wildness, providing consistent evaluations for any context of analysis and supplyingoutputs that can be directly applied by park managers.

To this purpose, a novel mapping approach is introduced that uses unsupervised classification to auto-matically cluster land parcels sharing similar wildness characteristics, as described by a set of spatialindicators.

Wildness maps of the Dolomites UNESCO World Heritage Site (Italy) were generated by consideringseven indicators of remoteness, perception and naturalness, and assigning each pixel of the study areato one of three classes (i.e. wild, semi wild, non wild), based on their values for the above-mentionedindicators.

Results of our application showed a good degree of concordance with wildness maps obtained through

Multi Criteria Evaluation (MCE) and emphasized how the class-based output may directly inform zoningactivities and the identification of recreational opportunities. While lack of user’s control is an obstacleto incorporating the views of multiple groups, as it is allowed by MCE-based methods, the proposedapproach supports the idea that land characteristics should define the context of wilderness and drivemanagement decisions. Further applications to a wide set of different contexts can help validate thisapproach.

. Introduction

The concepts of wildness and wilderness are relevant to theanagement of natural areas (Hendee, Stankey, & Lucas, 1990).

∗ Corresponding author at: Department of Civil, Environmental and Mechanicalngineering, University of Trento, Via Mesiano 77, 38123 Trento, Italy.el.: +39 0461 282688.

E-mail address: francesco.orsi@ing.unitn.it (F. Orsi).

169-2046/$ – see front matter © 2013 Elsevier B.V. All rights reserved.ttp://dx.doi.org/10.1016/j.landurbplan.2013.07.013

© 2013 Elsevier B.V. All rights reserved.

The former is the quality of being wild or untrammelled and mayexpress a degree of control of humans over nature (Scott, 2001).The latter broadly refers to “areas where the Earth and its com-munity of life are untrammelled by man, where man himself isa visitor who does not remain” (US Wilderness Act, 1964), thatis places where wildness is found. Today, areas free from human

influence are widely regarded as both a priority target of con-servation actions (Mittermeier, Myers, Thomsen, da Fonseca, &Olivieri, 1998; Mittermeier et al., 2003; Sanderson et al., 2002) anda natural setting for outdoor recreational activities (Cordell, Betz,

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Green, 2008; Farrell, Hall, & White, 2001; Lawson & Manning,002; Manfredo, Driver, & Brown, 1983). While these concepts ini-ially obtained formal recognition in the US (Nash, 1993), theirmportance is being emphasized in Europe as well (Lupp, Höchtl,

Wende, 2011; Zunino, 2007), where institutions are increasinglyupporting the mapping of wild areas (Coleman & Aykroyd, 2009;uropean Parliament, 2009). Over the last thirty years, several GIS-ased approaches have been proposed to mapping wildness (Aplet,homson, & Wilbert, 2000; Carver, Comber, McMorran, & Nutter,012; McCloskey & Spalding, 1989; Sanderson et al., 2002). How-ver, while most of these approaches have been designed to identifyilderness hotspots and inform conservation actions, only few of

hem were specifically tailored for application to the managementf natural areas (Carver et al., 2012; Kliskey, 1994a; Tricker et al.,011). In fact, it is not just wilderness, but the entire set of wildnessonditions of a territory that matters because this set reflects theunctions and recreational opportunities offered by that same ter-itory (Joyce & Sutton, 2009). Wildness maps that can capture suchet of conditions are then used by park managers and administra-ors for activities like zoning and tourism planning.

Mapping wildness for protected area management involves twoajor issues, which are related to the need of identifying a land-

cape’s wildness conditions as levels of an extended environmentalodification spectrum (Hendee et al., 1990; Nash, 1993). The first

roblem is about adaptability. As each area on Earth presents someeculiar/unique conditions (e.g. scale, morphology, human pres-nce, etc.), a method is needed that can detect such conditions,dapt to them and eventually supply replicable results. The secondroblem is related to the type of output required. Park managersnd administrators are not just interested in estimating the gra-ient of wildness across an area: rather they need to identify andluster land parcels characterized by similar wildness conditionsecause these can eventually be assigned a common managementtrategy.

This study aims to develop and test a methodology that helpsractitioners generate wildness maps that may directly informhe management of natural and protected areas. As an alternativeo commonly adopted methods relying on Multi Criteria Evalu-tion (MCE), unsupervised classification is proposed here for itsbility to automatically group land parcels based on their wild-ess conditions. The method is tested in the Dolomites UNESCOorld Heritage Site, a protected area in north eastern Italy made

p of 9 non-contiguous units, thus specifically calling for mappingethods that can adapt to a varying scale of analysis. Through

his application, we intend to highlight the benefits of the pro-osed approach compared to commonly adopted methodologies,nd in particular how it enables users to generate wildness mapshat provide relevant information about the adequacy of a zoningcheme and the potential of an area to offer a balanced set of recre-tional opportunities, with only limited value judgements required.

. The relative nature of wildness and limitations of MCEethods

The context of wilderness is commonly associated with places asemote as Alaska or the jungle of Borneo and not with natural spotsn a developed matrix like most protected areas in Western Europere. Nevertheless, many areas across the globe, though not totallyild, present some of the characteristics that apply to wilderness.s Aplet et al. (2000) pointed out, wild lands can be found in any

andscape at any scale. Today it is widely accepted that wilderness

an be found at the more natural and least developed end of an envi-onmental modification spectrum, which is commonly referred tos the “wilderness continuum” (Lesslie & Taylor, 1985; Nash, 1993).his rationale lets us suppose that, for any area of the world, we

n Planning 120 (2013) 1– 15

should be able to identify the wildest portion of land. However, theproblem is to locate the point, along the continuum, beyond whichwe actually have wilderness as this decision is affected by individ-ual perceptions (“one man’s wilderness is another’s roadside picnicground”, Nash, 1993) as well as the characteristics of the contextof analysis. In other words, the continuum concept highlights therelative nature of wildness and calls for mapping methods that, fora given study area, can assign each location its wildness status ona range that goes from the least wild to the wildest (Carver, Evans,& Fritz, 2002).

Common approaches to map wildness are based on the use ofvarious spatially-explicit indicators (e.g. remoteness, solitude, dis-turbance, natural composition, etc.), which are combined throughMCE-based, Boolean overlay or fuzzy methods to obtain an over-all wildness value. The valuation of indicators and the adoptionof different weighting schemes depend on the scale of analysisand individual perceptions (Aplet et al., 2000; Carver et al., 2002;Comber et al., 2010). Fuzzy approaches may be applied (Comberet al., 2010) to deal with the intrinsic ambiguity of the wildnessconcept, as already proposed by various scholars aiming to limitthe impact of uncertainty in MCE-based analyses (Jiang & Eastman,2000; Malczewski, 2006). However, wildness studies still seemlargely arbitrary, leading to maps that reflect the viewpoint of agroup of scientists and stakeholders (e.g. managers, NGOs) ratherthan some evidence from the field (Comber et al., 2010), eventhough largely agreed upon results can be obtained through sur-veys and other consensus approaches (Carver et al., 2002). In fact,the conversion of an indicator to a common scale (a key step ofMCE, also known as standardization), maintains that the practi-tioner knows which value of the common scale to assign to eachlevel of the indicator. Unfortunately, this is often not the case (i.e.the conversion is commonly performed via mathematical methodslike the maximum standardization method), especially when cate-gorical variables are used (e.g. naturalness expressed through landuse or vegetation cover). Assigning a numerical value to a categoryis evidently not easy: even when expert groups are involved, thetask is potentially subject to issues of consistency (e.g. given thatvirgin forest is assigned a naturalness value of 5, are we sure thatthe value for managed forest is 4 and not, say, 3?).

Beyond the above mentioned issues, however, MCE-basedmethods seem particularly problematic when maps have to begenerated that are suitable for local scale management purposes(e.g. tourist management of a protected area). The goal to clus-ter land parcels showing similar wildness characteristics throughMCE-based methods can be achieved by slicing the overall wild-ness map through the identification of wildness values that reflectchanges in the wildness character of the study area. However, in thiscase, which values should we choose? How could we link a givenvalue to a realistic change in wildness? Despite the use of evolvedtechniques to deal with uncertainty (e.g. fuzzy sets) (Carver et al.,2012; Comber et al., 2010; McMorran, Price, & Warren, 2008), tra-ditional MCE methods can hardly detect information from the landand answer these questions as they require value judgements ininput. While traditional methods are valuable in many situations(e.g. wildness mapping under different groups’ perspective), newapproaches are needed that allow the user to automatically “read”the characteristics of the land and identify portions of territorysharing similar wildness conditions.

3. The study area

This study focuses on the Dolomites UNESCO World HeritageSite, located in north eastern Italy (Fig. 1). The Site, which was estab-lished in 2009, is made up of 9 separate units covering an overallarea of 141,903 ha, surrounded by an additional 89,267 ha of buffer

F. Orsi et al. / Landscape and Urban Planning 120 (2013) 1– 15 3

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Fig. 1. The 9 units of the Dolomites UNESCO World Heritage Site and their

reas protecting the core from external disturbances (Table 1). The units comprehensively feature 18 peaks with an elevation of morehan 3000 m and beautiful mountain landscapes characterized byertical rock faces and deep valleys. The wildness conditions ofhe Site vary considerably both between and within units. Withhe exception of the most remote parts, the presence of humans isenerally perceived across the Site as a consequence of the manyillages lying just outside the buffer area, the high quality of trans-ortation infrastructures and a well established tourism activity.oads as well as cable cars and chairlifts enable visitors to eas-

ly reach high areas, while several huts during the summer seasonffer protection (i.e. accommodation and meals) to hikers even inemote lands.

. Method

The proposed method involves the identification and mappingf some spatially-explicit indicators of wildness, which are even-ually combined through unsupervised classification to group landarcels showing similar wildness conditions. Given the diverse ter-inology existing in literature, some clarification is needed. Weill use the word ‘attribute’ to identify a component of wildness

e.g. remoteness) and the word ‘indicator’ to refer to a specific mea-ure of that attribute (e.g. travel time from roads). Attributes (andndicators) were identified that reflect the multifaceted nature of

ildness. Wildness has an objective and a subjective connotation:t refers to both physical conditions (e.g. presence of anthropogeniclements, naturalness) and peculiar outdoor experiences (e.g. isola-ion, adventure). In fact, the two dimensions overlap each other, as

able 1ist of the 9 units constituting the Dolomites UNESCO World Heritage Site with specificathe core.

Unit Name

1 Pelmo-Croda da Lago

2 Marmolada

3 Pale di San Martino – San Lucano – Dolomiti Bellunesi – Vette Feltrin4 Dolomiti Friulane e d’Oltre Piave

5 Dolomiti Settentrionali

6 Puez-Odle

7 Sciliar – Catinaccio-Latemar

8 Rio delle Foglie

9 Dolomiti di Brenta

Total

on in north eastern Italy. The shape of core and buffer areas is also shown.

highly natural conditions may discourage massive visitation, hencecontributing to the preservation of those same conditions. Theselection of indicators was also driven by the need to obtain mostlyquantitative information (i.e. less value judgement required) thatshowed variability all over the study region (i.e. more uniformresults expected), and it was constrained by data availability forthe study area. Three attributes and seven indicators were even-tually selected for application to the study area (Table 2) and aredescribed in the following sections.

All GIS analyses presented in this study were performed on anarea considerably larger than that of the UNESCO Heritage Site toavoid any edge effect around the boundaries of the Site itself. Indi-cators were mapped in raster format with a resolution of 30 m,which was assumed to guarantee an acceptable precision for thedescription of natural and anthropogenic features.

4.1. Mapping remoteness

Probably the most common indicator of wildness, remotenessgenerally reflects the distance of an area from stable human pres-ence (e.g. cities) or mechanized access (e.g. roads). Roads constitutethe main access to nature and a connection to human settlements,and their absence has been seen as a distinctive sign of wildernessfor long time (Carver & Fritz, 2000; Carver et al., 2012; Leopold,1921). The actual remoteness of a given place within the study

area is fully described by two considerations: how far it is frommechanized access (i.e. roads and cable car stations) and how farit is from points of logistic support (i.e. huts). The latter specifi-cally applies to the selected study area where the presence of huts

ion of the surface of the core and the buffer, and area of the buffer as percentage of

Core (ha) Buffer (ha) Buffer/core (%)

4343.57 2427.25 55.882207.53 577.95 26.18

e 31,665.7 23,668.94 74.7521,460.63 25,027.64 116.6253,585.97 25,182.29 46.99

7930.34 2863.55 36.119302.1 4770.69 51.29

271.6 547.4 201.5511,135.44 4201.05 37.73

141,902.9 89,266.76 62.91

4 F. Orsi et al. / Landscape and Urban Planning 120 (2013) 1– 15

Table 2Attributes and indicators considered for mapping wildness over the study area with specification of data used to generate relevant maps.

Attributes Indicators Data

Remoteness Travel time from mechanized access Digital Elevation Model (DEM)a; road networkb; liftsc; trail networkc

Travel time from huts DEMa; hutsc; trail networkc

Perception Opportunities for solitude Previous study by Orsi and Geneletti (2013)Visibility of human settlements DEMa; land coverd

Naturalness Distance from roads DEMa; road networkb

Distance from human settlements DEMa; land coverd

Distance from artificial land cover DEMa; land coverd

a Source: ASTER Global Digital Elevation Model (resolution: 30 m) available at the Japan Space Systems’ website (http://www.jspacesystems.or.jp).b Source: Province of Bolzano-Bozen (http://gis2.provinz.bz.it/geobrowser), Province of Trento (http://www.territorio.provincia.tn.it), Friuli Venezia Giulia Region

(maps.

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http://irdat.regione.fvg.it), Veneto Region (http://idt.regione.veneto.it).c Source: local administrations (see b) and digitalization of georeferenced hiking

d Source: Corine Land Cover (resolution: 100 m) (http://www.eea.europa.eu).

ven at high elevations considerably alters the actual wildness of alace. Maps of travel times along trails were generated perform-

ng a weighted distance operation in GIS. The weight map wasuilt assuming potential walking speeds on different terrains andssigning pixels of each type of terrain a friction factor inverselyroportional to such speeds (i.e. 1 was assigned to pixels allowinghe highest speed, 2 to pixels allowing half the speed of “fastest”ixels, etc.). Walking speeds in the real world depend on the pres-nce/absence of trails and slope. Table 3 shows the speeds that weressumed along trails: these are compatible with a vertical speed ofbout 200–300 m/h, which is the rate of the average hiker on a nor-al trail (on slopes less than or equal to 30◦). Speeds on off-trail

ells were assumed to be considerably lower than those possiblen trail, consistent with the intrinsic difficulty of travelling off-trailue to the roughness of terrain (e.g. rocks, dense vegetation) as wells the complex orientation. Hence, their value on flat terrain cor-esponds to the lowest speed on trail according to Table 3, and thisalue decreases for increasing slope proportionally to the tangentf the slope angle. Weighted distances were computed from mainoads/cable car stations and huts, respectively, and the resultingistance maps were turned into travel time maps through appro-riate conversion factors (i.e. output maps were multiplied by 60nd divided by the maximum possible speed times 1000 to obtainravel times in minutes).

.2. Mapping perception

Beyond the natural and anthropogenic settings, the wildernessxperience depends on how we feel the environment around us: wehink we are in a wild place when we perceive it that way, and dif-erent people may have different perceptions of wildness (Higham,earsley, & Kliskey, 2000; Kliskey & Kearsley, 1993; Lucas, 1963;ash, 1993). We assumed that two elements primarily contribute to

he perception of wildness in the study area: the probability of get-ing in touch (i.e. encountering/seeing/hearing) with other peoplend the visibility of human signs (e.g. settlements, etc.). Wild areashould offer significant opportunities for solitude (Hendee et al.,990; Manning, 2003; Nash, 1993; US Wilderness Act, 1964). Given

he complexity associated with the estimation of such opportuni-ies, many studies have relied on proxy indicators (e.g. “distancerom urban areas”). While these may provide good estimates overarge areas (i.e. thousands of square kilometres), they are poorly

able 3alking speeds on trails of various slopes used for the computation of travel times.

Slope Speed (km h−1)

Flat or semi-flat (<5◦) 2.5Moderately steep (5–10◦) 2Steep (10–25◦) 1Very steep (>25◦) 0.5

informative over smaller territories, where in fact opportunitiesfor solitude can only be mapped if one knows actual people’s travelpatterns (Aplet et al., 2000). Information on travel patterns wasnot available for our study area and it could hardly be obtainedwith traditional monitoring techniques (e.g. mechanical counters,direct observation, surveys, etc.) for obvious economic and logis-tic reasons. Hence, we took advantage of the geographical materialthat is shared on the web to generate a suitable information ontravel patterns. In particular, we collected 3656 geotagged pho-tographs taken between June 1st and September 30th in the period2000–2011 and posted on Google Earth, and used them to identifypopular hiking destinations all over the study area. Then, by meansof a gravity model, we could estimate the number of visitors mov-ing between the above-mentioned destinations and access points(i.e. roads). This enabled us to roughly estimate the volume of visi-tors (i.e. number of visitors per day) along each section of the studyarea’s trail network. For more information about this approach, seeOrsi and Geneletti (2013).

Opportunities for solitude offered by a pixel were intended asthe probability of getting in touch with another visitor on that pixelat a given second. As off-trail hiking in the study area is generallyneither permitted nor practiced, the probability of encounter-ing/seeing/hearing other visitors is proportional to the visitor flowon that trail and quickly decreasing for increasing distances fromthe trail itself. The probability of encounters on trails was com-puted by considering 80% of the previously estimated visitor flow(i.e. consistent with our direct observations on some trails in thestudy area, we assumed that 80% of visitors move along the trailduring the 6 central hours of the day: 10 am–4 pm) and dividingthat number by the number of seconds in 6 h (i.e. 21,600). In orderto obtain estimates also outside trails, we applied a parabolic dis-tance decay function that reduces the probability obtained on thetrail by 20% at 100 m from the trail and by 80% at 500 m. Param-eters of the decay function were only assumed and not based onreal measurements. Considering that visibility and sound diffusionmay significantly change as a consequence of physical (e.g. forestedvs. bare land) and climatic (e.g. calm vs. windy day) conditions,the selected parameters were expected to realistically representthe decrease of the visual and acoustic impact of people over thedistance.

The perception of wildness is also strongly dependent on thevisibility of signs of the human presence, such as: houses, roads,settlements, etc. (Arriaza, Canas-Ortega, Canas-Madueno, & Ruiz-Aviles, 2004; Habron, 1998; Van den Berg & Koole, 2006). A remoteplace from which one can see a vast portion of urbanized land offersa reduced wilderness experience, just like an area next to a city mayappear considerably wild if no signs of the human presence are

observed. However, different features may have different impacton people’s perception: seeing a village is not the same as seeing aroad. This would imply one map be drawn for each feature consid-ered (e.g. villages, roads, etc.). However, for a matter of potential

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edundancy, only settlements were retained for analysis as theseere assumed to be the elements with the strongest effect on peo-le’s perception. A visibility analysis was performed on the DEM tossess the extent of urban area visible from each cell of the studyrea within a radius of 10,000 m around the cell. This threshold waselected as a compromise between the goal of focusing the analysisn a range within which seeing a settlement may significantly affect

person’s perception and the need to limit computational effort. Inrder to account for the effect of vegetation cover on visibility, theEM was increased by 15 m in the presence of broadleaf forest and0 m in the presence of coniferous or mixed forest, where 15 and0 m were assumed as the average height for broadleaf forest andoniferous/mixed forest, respectively. In addition to that, visibilityalues on forested cells were reduced by 50%, 65% and 80% on areasharacterized by broadleaf forest, mixed forest and coniferous for-st, respectively. These values are just assumptions and are basedn the likely screen effect provided by the three types of forest.

.3. Mapping naturalness

The concept of wildness is intrinsically connected to that of nat-ralness: the more free, unaltered and natural a land the higher

ts wildness value. It is widely accepted that a wild area is onehere natural processes are subject to little or no control (i.e. eco-

ogical processes like flooding or migrations are not controlled byumans), ecosystems show a minimally altered structure (i.e. thepatial configuration of ecosystems does not reflect the interven-ion of humans), human-generated disturbance is almost or totallyacking (i.e. various forms of pollution like light or noise are kepto a minimum) and natural composition is as diverse as possiblei.e. the whole range of native species is maintained, while exoticpecies are absent) (Aplet et al., 2000). In the Alps, naturalness haseen assessed by means of the so called hemeroby index (Grabherr,och, Kirchmeir, & Reiter 1995; Steinhardt, Herzog, Lausch, Müller,

Lehmann, 1999; Tappeiner, Borsdorf, & Tasser, 2008; Tasser,ternbach, & Tappeiner, 2008): a measure of human impact largelyased on the degree of disturbance to soil. The index, which isn evolution of concepts proposed by Jalas (1955), varies from 0completely natural) to 9 (completely unnatural). In general terms,

apping naturalness is a challenging task as it depends on com-lex data (e.g. data about species-area relationship, information onhe pressure of farming) and their availability for a specific scalef analysis. When the analysis is performed over small areas (i.e.everal hundreds to few thousands of square kilometres), in partic-lar, data would require a kind of resolution that is often impossibleo get (e.g. pressure of farming on a 100 m × 100 m grid). Further,

any indicators may show little or no variation over small arease.g. indicators like “modification of the natural environment” orexistence of man-made features” may result in presence/absenceaps), thus providing no useful information.Three indicators of naturalness were selected, consistent with

he characteristics of the study area and the availability of reliableata. Given the scarcity of information on human-related modifi-ations within the study area and the need to generate a suitablenformation over the entire study area, all indicators were builtsing distance-based proxies. Two of them deal with the problemf disturbance/modification of the natural environment and mea-ure the distance from two main sources of disturbance: roads anduman settlements. This is consistent with the wide body of litera-ure on the role of roadless and low urban density areas for natureonservation (Crist, Wilmer, & Aplet, 2005; Mcdonald, Kareiva, &orman, 2008; Selva et al., 2001; Stritthold & Dellasala, 2001). The

hird one instead refers to the control of natural processes and thetructure of ecosystems by considering the presence of modifiedand cover within the landscape matrix. This is measured as theistance from patches of artificial/modified land cover, such as:

n Planning 120 (2013) 1– 15 5

human settlements, agricultural areas, ski trails. This is consistentwith the idea that pressure on ecosystems increases for decreasingdistances from heavily modified patches of land. All distances aresurface distances, that is distances measured considering the slopeof the terrain as well as its roughness.

4.4. Identification of wildness classes through unsupervisedclassification

Once maps were generated for all indicators, the unsupervisedclassification was performed. This operation, which is commonlyapplied for the analysis of satellite images, assigns each cell of thestudy area to one of various classes, whose number is defined bythe user, based on the values of that cell for each of the input mapsin such a way that cells in the same class are more similar to eachother than to cells in any other class (Richards, 2012). Prior to clas-sification, however, standardization is commonly applied to makeinput maps comparable as their value ranges may be considerablydifferent. Standardization, which is a key step in MCE-based anal-yses, allows the user to specify his/her own valuation on indicatorsby converting their original scores to a common unit. Input mapsin this case were standardized by using a common standardiza-tion method (see Eq. (1)), requiring no judgement valuations bythe user. Standardized scores (or z-scores) follow a standard dis-tribution with mean equal to zero and standard deviation equal toone.

Zij = xij − �i

�i(1)

where: Zij = standardized value of cell j in map i; xij = current valueof cell j in map i; �i = mean of all values of map i; �i = standarddeviation of the values of map i.

The classification was performed for the entire study area(simultaneous classification) and for each unit separately (unit-based classification). Hence, 10 classifications, and relatedstandardizations, were done in total. Classifications were per-formed with the Maximum Likelihood Classification tool in ArcGIS(ESRI, 2008).

4.5. MCE-based analysis

In order to test the validity of the proposed methodology,the outputs of the unsupervised classification (both simultaneousand unit-based) were compared to classes of wildness generatedthrough MCE. Maps of the seven indicators were standardized withthe interval standardization (see Eq. (2)) to convert their originalvalue range to a common one (i.e. 0–1) and they were assignedequal weights (i.e. 0.143).

Standardized score = Score − lowest scoreHighest score − lowest score

(2)

Maps of the wildness gradient were obtained by combining thestandardized maps through weighted linear combinations, as fol-lows:

Si =n∑

j=1

wjxij (3)

where Si is the final score of pixel i, wj is the weight assigned to mapj and xij is the standardized value of pixel i in map j. Wildness classeswere eventually extracted from gradient maps through applicationof thresholds. These were defined in a way that ensured consistency

between the share of land of classes obtained through MCE and theshare of land of the equivalent classes obtained with unsupervisedclassification. In order to do that, the histogram of the gradient mapobtained with MCE was analyzed to identify two thresholds (i.e.

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ne for the wild-semi wild boundary and one for the semi wild-on wild boundary) that divided the land in three classes whosehares were equivalent to those obtained through unsupervisedlassification. The wild class was then represented by pixels withalues comprised between the maximum value of the gradient mapnd the higher threshold, the semi wild represented by pixels withalues between the two thresholds and non wild represented byixels with values below the lower threshold.

. Results

Maps of the seven indicators of wildness are shown in Fig. 2.hile wildness values tend to increase constantly when mov-

ng from the boundaries to the interior of the Site, this does notccur with indicators of solitude and visibility. These show a morerregular trend as a consequence of the location of visitor facilitiesnd hiking trails (i.e. the presence of facilities or trails reduces the

pportunities for solitude, leading to a decrease in wildness), andhe complex geomorphology of the land (i.e. the presence of deepalleys or rocky pinnacles may significantly reduce visibility evenear the Site’s boundary, while places at higher elevations ensure

ig. 2. Spatial representation of the 7 indicators used for mapping wildness. Indicator sndicate low wildness.

n Planning 120 (2013) 1– 15

better views). Travel time from huts is also a peculiar indicator asit tends to be high on the outer boundary of the Site and in the veryinterior, and low in intermediate locations.

Classification tests were performed considering both three andfour classes, but a better result in terms of class compactness andfragmentation (i.e. less small and isolated patches) was obtainedwith three classes for both the Site- and the unit-level classification.Classes were labelled: wild, semi wild, non wild. The classificationfor the entire study area (simultaneous) resulted in 20% of landbeing assigned a wild status, 44% a semi wild status and 36% anon wild status. However, classes are unevenly distributed overthe study area with most of the wild land being found in units 3, 4and 9, while units 2, 6 and 8 do not present any wild area (Fig. 3a).Most of the wild as well as semi wild land is concentrated withinthe core of the Site, while the buffer hosts the majority of non wildland (Fig. 4). Table 4 provides a characterization of each of the threeclasses by showing the mean and standard deviation of the input

indicators.

Fig. 5 shows the distribution of wildness classes along the ele-vation gradient over the entire Site for the core area only, whileFig. 6 presents the same information for each unit. Looking at the

cores are standardized so that high values indicate high wildness and low values

F. Orsi et al. / Landscape and Urban Planning 120 (2013) 1– 15 7

Fig. 3. Wildness classes of the study area as obtained with the simultaneous classifi-cation (i.e. a unique unsupervised classification for the entire study area) consideringthree classes (i.e. wild, semi wild, non-wild) (a) and a traditional MCE approach (b).

Fig. 5. Distribution of the three classes along the elevation gradient as emergedfrom the simultaneous classification. The elevation gradient is represented through

Fig. 4. Proportion of the three classes within the core (left) and the buffer (right) of each

100% of one unit’s surface within the core or the buffer.

Table 4Mean and standard deviation of the 7 indicators characterizing the wildness classes obtin minutes from mechanized access and huts, the third column reports the probability, ecolumn reports the area, expressed in hectares, of urban fabric visible, the remaining coluartificial land cover (e.g. agriculture, ski runs).

Classes Time frommechanized access(min)

Time from huts(min)

Solitude (%) Visibil

Mean StD Mean StD Mean StD Mean

Wild 788 485 1092 631 0.45 0.26 5.4

Semi wild 396 229 470 272 3.55 2.05 7.02

Non wild 384 222 1066 621 3.41 1.98 60.3

250-metre classes and only the core is considered in the graph.

entire Site, one can observe that the proportion of wild land isonly predominant at mid elevations (i.e. between 500 and 1500 m),whereas semi wild land has the highest share at mid to high eleva-tions (i.e. above 1500 m). Even though this might be surprising, itcan be explained considering that large extensions of wild land arefound in unit 4 at mid elevations and that wildness is often alteredat high elevations due to the presence of tourist facilities. Lookingat the single units, it is evident that the proportion of semi wild landgenerally increases for increasing elevation with the notable excep-tion of unit 5 where semi wild land tends to decrease at very highelevations with an unexpected increase of non wild land. In fact,this outcome reflects the peripheral location of the highest moun-tains in that unit (i.e. near the boundary of the unit), the presence

of huts and the existence of a cable car reaching the highest peak(i.e. Tofana di Mezzo).

unit according to the simultaneous classification. Each bar in the graphs represents

ained with the simultaneous classification. The first two columns report the timexpressed in percentage, of encountering/hearing/seeing another visitor, the fourthmns report the surface distance, expressed in metres, from roads, urban areas and

ity (ha) Distance fromroads (m)

Distance fromurban areas(m)

Distance fromartificial landcover (m)

StD Mean StD Mean StD Mean StD

3.06 5385 3094 6344 3218 4958 27264.14 3942 2260 7322 2981 4749 2723

37.8 2638 1508 4463 2560 3419 1969

8 F. Orsi et al. / Landscape and Urban Planning 120 (2013) 1– 15

Fig. 6. Distribution of the three classes along the elevation gradient for each unit as emerged from the simultaneous classification. The elevation gradient is representedthrough 250-metre classes and only the core is considered in the graph.

Table 5Change matrix showing transitions across wildness classes between the classification-based and the MCE-based output. The latter was generated by slicing the wildness mapobtained through MCE with thresholds that ensure the same class area proportion resulting from unsupervised classification (i.e. 20% wild, 44% semi wild; 36% non wild).The concordance level shows the percentage of pixels assigned to the same class in both outputs.

Unsupervised classification Concordance level (%)

Wild (ha) Semi wild (ha) Non wild (ha)

MCE Wild (ha) 35,302.86 13,071.33 448.83 68.18Semi wild (ha) 11,557.53 65,608.11 25,407.63Non wild (ha) 105.12 22,877.73 56,495.52

F. Orsi et al. / Landscape and Urba

Fig. 7. Wildness classes of the study area as obtained with unit-based classifications(s

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i.e. one unsupervised classification per unit) considering three classes (i.e. wild,emi wild, non wild) (a) and a traditional MCE approach (b).

The map of the wildness gradient obtained through MCE hadalues ranging between 0.17 (i.e. least wild) and 0.85 (i.e. wildest).hresholds of 0.44 and 0.56 were identified as those enabling theefinition of classes that approximately cover the same share of

and as equivalent classes obtained through unsupervised classifi-ation: 20% for the wild class, 44% for the semi wild class and 36% forhe non wild class. Wildness classes obtained through MCE reflecto certain extent the pattern shown by the unsupervised classi-cation’s output (Fig. 3) with most of the wild land being found

n units 3, 4 and 9, and no wild land in units 1, 2, 7 and 8. TheCE’s output seems more gradual in the passage from non wild to

emi wild to wild, whereas the unsupervised classification’s outputives origin to abrupt changes from non wild to wild. Table 5 showsransitions across classes between the MCE-based and the unsuper-ised classification-based outputs. Roughly two thirds of land (i.e.8.18%) belong to the same class in both outputs, thus confirming aood level of concordance between the two analyses, even thoughransitions from one class to the adjacent one (e.g. from wild toemi wild) are not negligible.

The unit-based classification, which takes each unit as anutonomous entity, supplied a significantly different outcome, asepicted in Fig. 7a. In this case every unit shows all levels of theildness spectrum and there is a greater balance between such lev-

ls within each unit. The entire wildness spectrum is representedoth in the core and the buffer, with the exception of unit 9 (Fig. 8).or all units, the proportion of wild land is generally larger in theore than in the buffer, where non wild land is predominant. Look-ng at the core area, all units seem to offer a balanced set of wildnesslasses, with the exception of unit 1 and unit 4 where the pro-

ortions of wild land and semi wild land are limited, respectively.igs. 7 and 8 also provide interesting information about the ade-uacy of the zoning scheme (i.e. core and buffer). Fig. 7 shows thatome spatial congruence exists between wild land and the core,

n Planning 120 (2013) 1– 15 9

especially for units 3, 5, 7 and 9. However, observing the propor-tions of wild, semi wild and non wild land within the core, one candetect a significant discrepancy between the extent and/or shapeof the core and the occurrence of wild conditions for some units.This is particularly the case of unit 1, where the proportion of wildland is higher in the buffer than it is in the core and the proportionof non wild land is higher in the core than it is in the buffer, and unit8, with more wild land in the buffer. Unit 9 shows the best zoningscheme with all wild land included in the core area. The mean andstandard deviation of input indicators for the three classes and foreach unit are shown in Table 6.

The proportion of wild land increases for increasing elevationeverywhere, except in units 2, 3, 4, 5 (Fig. 9). Units 2, 3 and 5 are allcharacterized by easy accessible high lands due to the location ofthese in proximity of the Site’s boundary and the presence of cablecars. The case of unit 2 is particularly interesting because it hoststhe highest peak of the Site (i.e. Marmolada, 3343 m), but does nothave any wild land in its highest part. The presence of a cable car,huts and summer ski facilities plays a fundamental role towardsthis outcome. The proportion of wild land in unit 4 is considerablyhigh and constant throughout the gradient.

Table 7 shows the value ranges of the maps of wildness gra-dients obtained through MCE for the various units along withthresholds used to slice them according to relevant shares of landobtained through unsupervised classification. A visual compari-son of the unsupervised classification-based and the MCE-basedoutputs emphasises a good degree of similarity in the location ofwildernesses, while discrepancies are observed in the extent ofmoderately wild lands (Fig. 7). An analysis of transitions betweenclasses (Table 8) shows that most pixels (i.e. concordance between53.49% and 77.73%) belong to the same class in both outputs, eventhough some significant transitions are observed, particularly inunits 4 and 5.

6. Discussion

The fact that the area analyzed in this study is not contiguous,but one made up of 9 separate units with different characteristics,posed a peculiar yet common mapping challenge. That is, giventhe relative nature of wildness, the standards adopted for mappingwildness on one unit would not have worked on another unit, norwould the standards adopted for the entire Site have been appro-priate for any of the single units. Under these conditions, methodstraditionally adopted for mapping wildness (e.g. MCE techniques)require such an amount of input (e.g. value judgements) that makestheir use particularly difficult (e.g. complexity associated with find-ing weights and fuzzy sets for many different areas). This studyshowed how unsupervised classification can offer an easier wayto mapping due to the minimal input required. The comparisonof the output obtained through unsupervised classification withthat obtained through traditional MCE-based approach (though asimpler one compared to fuzzy reclassification) revealed a gooddegree of concordance, suggesting room for future applications ofthis novel approach.

6.1. On the method

While unsupervised classification has already been appliedto fields other than remote sensing, like conservation biology(Crossman & Bryan, 2006; Orsi, Church, & Geneletti, 2011) or agri-culture (Fraisse, Sudduth, & Kitchen, 2001), its use for wildness

mapping is new, as far as we are aware, even though Kliskey (1994b)had previously highlighted the benefits of multivariate statisticsfor wilderness perception mapping. As opposed to MCE-basedmethods, which assess wildness and subsequently define wildness

10 F. Orsi et al. / Landscape and Urban Planning 120 (2013) 1– 15

F each us in Tabt

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ig. 8. Proportion of the three classes within the core (left) and the buffer (right) ofeparately, the characteristics of classes differ from one unit to the other as shown

he buffer.

lasses based on some user’s specifications (e.g. standardization,eights, thresholds), unsupervised classification starts from the

haracteristics of the land (expressed through wildness indicators)nd automatically defines classes as clusters of cells sharing similarildness conditions. Classes then reflect natural breaks in a terri-

ory’s wilderness continuum (i.e. cells in a class are more similar to

ach other than to any cell in other classes) and provide park man-gers and administrators with a subdivision that can be readily usedor zoning and the identification of recreational opportunities. Once

able 6ean and standard deviation of the 7 indicators characterizing the wildness classes of ea

time in minutes from mechanized access and huts, the third column report the probabourth column report the area, expressed in hectares, of urban fabric visible, the remaininnd artificial land cover (e.g. agriculture, ski runs).

Unit Classes Time frommechanizedaccess (min)

Time from huts(min)

Solitude (%) V

Mean StD Mean StD Mean StD M

1 Wild 353 118 302 166 2.37 1.94 1Semi wild 252 140 284 164 2.46 1.89

Non wild 212 122 372 213 2.69 1.83

2 Wild 416 118 281 102 0.95 0.62

Semi wild 261 145 152 88 2.56 1.41

Non wild 242 137 411 141 1.14 0.64

3 Wild 431 210 800 462 0.38 0.25 2Semi wild 350 203 992 572 0.38 0.22 6Non wild 279 162 368 219 2.32 1.56 1

4 Wild 901 390 904 521 0.56 0.32

Semi wild 619 356 1291 517 0.16 0.09

Non wild 477 276 908 609 0.87 0.5 2

5 Wild 458 207 481 278 2.45 1.42

Semi wild 263 152 438 253 3.54 2.05

Non wild 363 211 490 283 2.90 1.77 2

6 Wild 345 143 256 148 2.84 1.77

Semi wild 201 116 246 142 1.69 0.97

Non wild 198 116 261 151 3.56 1.96

7 Wild 318 159 254 147 2.80 1.74

Semi wild 351 159 379 201 1.52 0.97 1Non wild 197 115 306 177 2.90 1.81

8 Wild 224 62 178 82 1.39 0.45

Semi wild 134 50 280 69 0.92 0.23

Non wild 138 45 551 127 0.56 0.30

9 Wild 614 213 333 193 0.96 0.57

Semi wild 516 199 574 277 0.53 0.32 1Non wild 295 166 267 154 1.19 0.74

nit according to unit-based classifications. As classifications were run on each unitle 5. Each bar in the graphs represent 100% of one unit’s surface within the core or

superimposed to a map of landscape units, for example, classes mayprovide an overview of the Recreation Opportunity Spectrum for anarea. In fact, a highly diverse spectrum of recreation opportunitiesis assured if the combination of opportunity types (e.g. wild, semiwild, etc.) can be found across the range of environmental settings(e.g. mountain, forest, etc.) (Clark & Stankey, 1979).

The use of unsupervised classification for mapping wildnessinvolves three main steps: namely, the selection of indicators, thestandardization process and the choice of a number of classes.

ch unit as obtained with the unit-based classification. The first two columns reportility, expressed in percentage, of encountering/hearing/seeing another visitor, the

g columns report the surface distance, expressed in metres, from roads, urban areas

isibility (ha) Distance fromroads (m)

Distance fromurban areas(m)

Distance fromartificial landcover (m)

ean StD Mean StD Mean StD Mean StD

8.36 12.51 2901 970 4321 791 3669 6864.77 2.88 2094 1059 4135 981 2903 9305.67 3.51 1229 694 3128 1269 1729 974

5.58 3.6 3195 826 5163 1184 2872 6641.17 0.81 3034 951 5365 860 1715 10235.58 3.51 1601 814 4201 1091 1226 712

2.23 13.68 3208 1381 6478 2229 4390 18970.39 37.89 2157 1262 4429 2493 3186 18242.87 7.92 2686 1530 4182 2325 2722 1553

0.99 0.63 6630 2384 8027 2216 6136 20514.23 2.79 3937 2190 6297 3174 4413 22974.66 17.01 3198 1842 4755 2689 4636 2664

4.5 2.61 4350 2015 7005 2814 4592 25004.59 2.7 3109 1788 6777 3322 4401 25261.15 12.51 2788 1641 5142 2919 3465 2039

2.52 1.71 4174 1165 4862 1585 2563 11272.61 1.62 2074 1178 6472 1604 1339 7509.99 5.94 2639 1402 2696 1430 1490 861

4.68 3.24 3613 1432 5485 1597 2186 12285.03 9.09 2552 962 3228 1112 1804 8896.12 3.78 1695 962 3748 2038 1120 691

4.59 3.69 2660 473 5000 697 1319 6400.81 0.54 1578 466 4525 719 1135 4361.98 1.53 1494 450 2349 713 433 214

3.6 2.16 5296 1286 6097 1201 4248 12103.41 8.46 3513 1410 4699 1686 2883 13282.79 1.8 3775 1812 5190 1891 2577 1470

F. Orsi et al. / Landscape and Urban Planning 120 (2013) 1– 15 11

Table 7Comparison between unsupervised classification and MCE-based classification. The shares of land allocated to the three classes, the range of values in the wildness gradientand the thresholds adopted to slice the gradient maps according to the above-mentioned shares are shown for each unit.

Unsupervised classification MCE-based classification

Classes’ share of land (%) Range Thresholds

Non wild Semi wild Wild Non wild – Semi wild Semi wild – Wild

Unit 1 36 43 21 0.30–0.88 0.58 0.70Unit 2 30 38 32 0.40–0.83 0.54 0.64Unit 3 27 39 34 0.26–0.81 0.5 0.6Unit 4 31 29 40 0.26–0.86 0.52 0.64Unit 5 33 28 39 0.16–0.81 0.48 0.56Unit 6 37 29 34 0.14–0.87 0.49 0.59Unit 7 45 20 35 0.21–0.81 0.52 0.58Unit 8 30 38 32 0.38–0.75 0.52 0.59Unit 9 33 32 35 0.29–0.80 0.57 0.66

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long with some of the characteristics desirable for ecological indi-ators (e.g. measurability, responsiveness, etc.) (Dale & Beyeler,001) and those peculiar of wildness indicators (Aplet et al., 2000),

ndicators suitable for the proposed approach should also featureariability all over the study area and a predominantly quantita-ive nature. In fact, lack of variability over the study area (i.e. thendicator only varies on a portion of the study area and is constantlsewhere) would inevitably result in a portion of the study areaeing assigned a class on its own, while qualitative indicators (e.g.

and use) would call for considerable user’s judgement for interpre-ation (e.g. through standardization), thus reducing the adaptabilityf the approach. These requirements, along with limited data avail-bility at high enough resolution, may result in the adoption ofeveral distance-based indicators, as it occurred in this case study.

hile such proxies are often employed in landscape planning stud-es (Orsi & Geneletti, 2010), their use may oversimplify the analysisnd eventually affect the final result. In this study, data availabilitynd the characteristics of the area (i.e. relative lack of human signsnside the boundaries of the Heritage Site, whose environment isredominantly natural) have led to the use of many distance-basedetrics, which poorly describe the factors they refer to and may

ause problems of correlation and double-counting. Indicators ofaturalness, for example, may have been not particularly suitableo thoroughly describe alterations in the natural environment (i.e.uch alterations may be non existent beyond a certain distancerom the source of disturbance), but they allowed us to generate

relevant information all over the study area.Standardization is not a “mandatory” step in classification but

ne highly recommended to prevent indicators with higher vari-nce from having a stronger influence on the final result, evenhough any standardization does have an impact on the output. Inarticular, standardization can be used to emphasize the relativeap between the scores of an indicator (e.g. the indicator “distancerom roads” can be standardized in such a way that pixels at 2 kmrom a road are assumed 10 times wilder than pixels at 1 km).

The number of classes is the only required input of the method. Apecific number is a compromise between the number of opportu-ity types that the user wants to identify (e.g. a park manager mighte interested in defining three classes because he/she expects toeet the demand of three different kinds of visitor group) and

he quality of classification. Theoretically, more distinct classes (i.e.ittle fragmentation in the output) would reflect better understand-ble wildness levels. High numbers of classes (e.g. 6 or more) wouldeflect peculiar wildness conditions whose interpretation would

e difficult. Assuming that all indicators are standardized consis-ently (e.g. higher values reflect higher wildness or vice versa),lasses should describe increasing (or decreasing) levels of wild-ess. However, the fact that a cell belongs to a given class does not

mean that all of its indicators’ scores are higher (or lower) thanthose of any cell in the lower (or upper) class, as emphasized inTables 4 and 5. To this extent, a class should be intended as a com-pensatory entity whose members (i.e. cells) share similar wildnessconditions, which are provided by the combination of multiple indi-cators with scores falling within a range. In some ways, this is whathappens with MCE-based approaches, which assign cells an overallwildness value given by the weighted sum of multiple indicatorsand therefore the result of a compensation among indicators (e.g.a high overall value can result from the sum of some high valuedindicators and some low valued indicators).

Regarding the classification technique, while this studyemployed the maximum likelihood classification scheme, manyother techniques exist in the literature (e.g. k-means, distribution-based clustering, density-based clustering, etc.) (Richards, 2012)that can be tested for their ability to provide consistent wildnessmaps.

There are at least three key differences between unsupervisedclassification and existing MCE-based methods for wildness map-ping. First, unsupervised classification is essentially an automaticself-learning procedure that “takes a picture” of a study area’s wild-ness based on the indicators selected. While this is consistent withwildness’ relative nature (i.e. for any area of the world it should bepossible to find the wildest, the least wild and all levels in between)and contributes to its applicability to any context, it also prevents athorough user control. Among the implications of that is the impos-sibility to account for multiple groups’ perceptions and views, as itis commonly done with MCE-based approaches by considering dif-ferent weighting schemes. Standardization would be a way to dealwith this issue, but this would inevitably impact the repeatabil-ity and interpretation of the outcome, and eventually make MCEmethods preferable. Second, unsupervised classification, as sug-gested by its name, does not allow the user to specify requirementsthat classes should fulfil (e.g. wild land should be at a distance fromurban areas of not less than 3 km). This is a major limitation, as locallegislation or tourism management may sometimes suggest stan-dards that wildernesses should meet. Third, the proposed methodsupplies wildness classes, not a wildness gradient: if one needs thelatter then MCE methods are to be preferred. For all these reasons,and given the results of our application, unsupervised classifica-tion may represent a valuable tool to obtain preliminary raw (i.e.no value judgements involved) wildness maps, which would helpmanagers rapidly visualize recreational opportunities offered byareas of different size and characteristics (Hendee et al., 1990).

The results obtained can then be verified and improved throughMCE-based methods like those proposed by Comber et al. (2010)or Carver et al. (2012) in an attempt to account for various groups’perspectives on wildness. As shown in this study, unsupervised

12 F. Orsi et al. / Landscape and Urban Planning 120 (2013) 1– 15

Fig. 9. Distribution of the three classes along the elevation gradient for each unit as emerged from unit-based classifications. As classifications were run on each unitseparately, the characteristics of classes differ from one unit to the other as shown in Table 5. The elevation gradient is represented through 250-metre classes and only thec

cdMs

swa

ore is considered in the graph.

lassification could also be used to estimate the shares of land ofifferent wildness conditions: these can then constitute an input toCE-based analyses that slice the territory consistent with those

ame shares.

Despite some promising results, however, the analysis pre-

ented in this paper should be intended as a demonstrative one,hich requires improvements, particularly concerning the type

nd quality of inputs. Future applications should include sensitivity

analyses to check the effect of the technique and data input bias onthe final results (e.g. effect of correlation between indicators).

6.2. On the wildness map of the Dolomites UNESCO World

Heritage Site

Clearly, the characteristics of classes changed significantly fromone classification run to another, according to differences in the

F. Orsi et al. / Landscape and Urban Planning 120 (2013) 1– 15 13

Table 8Change matrix showing transitions across wildness classes between the unsupervised classification-based and MCE-based output for each unit of the UNESCO Site. Theconcordance level shows the percentage of pixels assigned to the same class in both outputs.

Unsupervised classification Concordance level (%)

Wild Semi wild Non wild

MCE-based classification Unit 1 Wild 893.97 456.66 0 77.73Semi wild 419.58 2225.79 306.72Non wild 95.94 227.43 2138.04

Unit 2 Wild 807.57 95.04 5.85 74.24Semi wild 79.92 718.83 289.89Non wild 0 244.26 534.6

Unit 3 Wild 14,557.95 2469.42 432.18 61.91Semi wild 4322.97 11,972.88 6776.46Non wild 30.51 7006.32 7659.72

Unit 4 Wild 13,485.87 5881.77 20.61 68.02Semi wild 4813.74 5989.59 2145.87Non wild 222.39 1783.53 12,144.87

Unit 5 Wild 24,456.6 1990.26 3575.25 56.39Semi wild 5883.48 7111.98 9958.68Non wild 277.92 12,590.19 12,750.57

Unit 6 Wild 2543.31 990.36 20.43 68.74Semi wild 945.63 1534.68 671.49Non wild 157.5 587.97 3341.16

Unit 7 Wild 3311.82 1374.39 258.75 67.56Semi wild 1028.79 874.98 688.23Non wild 586.62 626.76 5318.82

Unit 8 Wild 187.92 76.32 0.09 53.49Semi wild 66.51 123.57 119.61Non wild 8.46 110.79 127.53

844.8449.2

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Unit 9 Wild 3Semi wild 1Non wild

reas of analysis. As summarized by Tables 4 and 5 and shown byigs. 3 and 6, what is wild within unit 8, for example, may not evene semi wild for unit 4, which is the wildest unit of the Site. Perform-

ng the analyses at the Site scale as well as the unit scale alloweds to explore differences both between and within units. Switch-

ng from the Site scale to the single unit scale is like zooming ino let local wildness patterns emerge. Such patterns (Fig. 7) alongith related statistics (Fig. 8) provided relevant insights into the

uality of the adopted zoning scheme and the set of recreationalpportunities offered by each unit. Congruence between the Site’sore and the wild class was observed for some units (3, 5, 7, 9),ven though the proportion of the three classes in the core andhe buffer casted some doubts about the adequacy of the currentoning. A high percentage of semi wild or non wild land withinhe core suggests that for some units (e.g. 1, 2, 7, 8) the core area

ight be oversized or have a partly wrong shape. A comparisonetween the output of the simultaneous classification and that ofhe unit-based classifications suggests a kind of zoning paradox: theildest units (e.g. 3, 4), which are generally exposed to little dis-

urbance, have a much larger buffer area than the least wild unitse.g. 7, 8), which are instead in close proximity to sources of dis-urbance (e.g. urban areas, roads). This is probably the effect of aompromise that had to be achieved for Site designation betweenhe inclusion of the most valuable geomorphologic features in theore and the exclusion of any obnoxious facility from the buffer.

hile the true meaning of the classes shown in Fig. 8 differs from

nit to unit as a consequence of the specificity of unit-based classi-cation (e.g. wild land of unit 1 has not the same characteristics ofild land in unit 2, consistent with wildness’ relative nature), the

raph sheds a light upon the area proportion of each wildness class

1428.84 25.38 62.937 2181.87 1433.887 1323.36 3627.63

on each unit. When this information is coupled with that shownin Fig. 9, one has an overview on the set of recreational opportu-nities offered by each unit. Given the mountainous characteristicsof our study area, elevation classes may in fact represent environ-mental settings. Although one would expect that the proportionof wild land increases for increasing elevation, this does not occurin units 2, 3, 5, where the presence of tourist facilities (includingcable cars) alters wild conditions at high elevations. Unit 4, whichshows a peculiar pattern (i.e. the proportion of wild land is almostconstant across elevation), provides the most diverse RecreationOpportunity Spectrum with a considerable proportion of wild landbeing found in all elevation classes.

7. Conclusions

This paper contributes to the development and application ofwildness maps for protected area management by introducing anew approach: it is no longer the user (e.g. planner, conserva-tion practitioner) who arbitrarily sets rules to define the contextof wilderness, but the land itself that provides information for theidentification of different wildness classes. This approach is moreconsistent with the intrinsic relative nature of wildness (i.e. for eacharea one should be able to find the wildest, the least wild and alllevels in between) and in line with the needs of park managersand administrators involved in activities such as zoning and theidentification of recreational opportunities. An application to the

Dolomites UNESCO World Heritage Site in Italy, though demonstra-tive, showed that the proposed methodology is quickly adaptableto areas of various size and characteristics, and supplies an outputthat can be directly applied for management purposes. Comparison

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ith a traditional MCE-based approach suggested how unsuper-ised classification can be used alternatively to MCE/fuzzy methodso quickly visualize the spatial patterns of an area’s wildness, orn combination with those to provide a preliminary wildness maphat will be fine tuned by incorporating the perspective of differenttakeholders (e.g. conservationists, visitors, etc.). The fact that theethodology requires minimal value judgement supports the idea

hat management decisions should be based, as much as possible,n the characteristics of the land. Nevertheless, human judgementemains fundamental when such decisions are eventually takennd the map of wildness, no matter the method adopted to gen-rate it, must be coupled with all available information to ensurehat local issues are properly addressed (e.g. definition of a zoningcheme that accounts for site-specific socioeconomic constraints).urther applications to a wide variety of contexts and the use of sen-itivity analyses to determine the effect of input bias can validatehe effectiveness of this approach.

cknowledgements

This study was performed as part of the “AcceDo” researchroject, which obtained financial support from the Provinciautonoma di Trento (Italy) through a Marie Curie action, 7th Frame-ork Programme, COFUND-GA2008-226070, “Trentino project –

he Trentino programme of research, training and mobility of post-octoral researchers”. We would like to thank three anonymouseviewers for providing valuable comments on an earlier versionf the manuscript.

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