Typology of Alpine region using spatial-pattern indicators

Ecological Indicators 24 (2013) 37–47

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Ecological Indicators journal homepage: www.elsevier.com/locate/ecolind

Typology of Alpine region using spatial-pattern indicators Caroline Pecher a,∗ , Erich Tasser a , Janette Walde b , Ulrike Tappeiner a,c a b c

Institute for Alpine Environment, European Academy Bozen/Bolzano, Viale Druso 1, 39100 Bozen/Bolzano, Italy Department of Statistics, University of Innsbruck, Universitaetsstrasse 15, 6020 Innsbruck, Austria Institute of Ecology, University of Innsbruck, Sternwartestr. 15, 6020 Innsbruck, Austria

a r t i c l e

i n f o

Article history: Received 15 July 2011 Received in revised form 23 May 2012 Accepted 25 May 2012 Keywords: Alpine region Municipal level Indicators Regional typology PCA Hierarchical clustering

a b s t r a c t The European Alps and their surroundings is a heterogeneous region, where different spatial conditions require appropriate research approaches as well as political and planning strategies. Researchers and decision makers are dependent principally on information relating to environmental and spatial characteristics in their area of interest. Up until now and following the call of Agenda 21, a significant amount of information has already been compiled in a variety of sustainability-indicator systems that also contain information on spatial conditions. The aim of the presented study was to develop a regional typology of the European Alps and their surroundings, on the basis of spatial-pattern indicators. In a first step, a set of 25 spatial-pattern indicators on topography, landscape composition, landscape pattern, and road accessibility were calculated for the 17,504 municipalities in the Alpine-Space cooperation area. The indicator results were subjected to a Principal Components Analysis (PCA) using Varimax rotation with Kaiser normalization. The PCA resulted in five components that explain 71.0% of the total variance. A PCA validation using a sub-sampling approach revealed that the PCA was valid. The PCA results were subsequently employed in a hierarchical clustering-approach using the Ward algorithm with squared Euclidean distance. The number of clusters was chosen by means of the dendrogram, according to the elbow criteria, and by reasons of interpretability. The hierarchical clustering resulted in 6 clusters. Cluster 1 represents “Non-mountainous cultural landscapes”, cluster 2 “Poorly structured agricultural landscapes”, cluster 3 “Agricultural landscapes, interspersed with highly structured semi-natural and natural areas”, cluster 4 “Remote, highly structured cultural landscapes with a high level of insolation”, cluster 5 “Mountainous, forested areas”, and cluster 6 “Mountainous, semi-natural and natural open areas”. Although the presented typology and its underlying analyses have some limitations, they can be applied for various purposes. The spatial-pattern indicators provide individual information for more than 17,000 municipalities in the Alpine Space. Supra-regional relationships of spatial-pattern types are offered by the five extracted components and the six clusters. The results can support researchers and stakeholders from the local to international level. © 2012 Elsevier Ltd. All rights reserved.

1. Introduction Environmental and spatial properties are usually decisive during the selection of appropriate study sites as well as during the discussion and evaluation of results in environmental research. They also offer fundamental information to decision makers in politics, administration, and spatial planning. Sustainability monitoring systems represent a basic source of information for both researchers and decision makers. Besides economic and social indicators, such systems often provide information on spatial characteristics such as topography, infrastructure, landscape composition, and landscape pattern. Following the call launched by

∗ Corresponding author. Tel.: +39 0471 055 321; fax: +39 0471 055 399. E-mail address: [email protected] (C. Pecher). 1470-160X/$ – see front matter © 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.ecolind.2012.05.025

Agenda 21 (UN, 1992), a multitude of sustainability monitoring systems at different administrative levels have been developed (cf. DIAMONT, 2008; EC, 2011b; EEA, 2012; EURAC research et al., 2008; Eustat, 2004; OECD, 2012; Statistisches Bundesamt, 2010; Swiss Federal Statistical Office, 2012; UN, 2007). As Agenda 21 assigns an important role to local authorities (UN, 1992), municipal-level indicator systems, containing the most precise information, have been implemented for several regions (cf. DIAMONT, 2008; EURAC research et al., 2008; Eustat, 2004). However, a comprehensive nation-wide or even international set of sustainability indicators at the municipal level is still exceptional. A weakness of working with indicator sets is, that since single indicators provide only individual information, it is usually difficult to obtain a comprehensive characterization of an area of interest. This would be, however, very important for researchers or decision makers. A valuable source of information consisting of single indicators and aggregated results

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can be created by aggregating single indicators in order to obtain a regional classification. The European Alps is a diversely structured and vulnerable ecoregion, being closely related to its forelands. Hence, the view on the European Alps needs to be enlarged, when carrying out research studies and during the development of political or planning strategies. This basic idea is also underlying the Alpine Space Programme (EU, 2007), a transnational cooperation programme for the promotion of a sustainable regional development in the Alpine region. Its cooperation area, which from this point on we refer to as Alpine Space, spans the European Alps and their surrounding forelands, offering so the opportunity to comprehensively study interactions, similarities, and differences. Recently, several statistics-based regional classifications focussing on environmental and spatial aspects have been developed spanning also the Alpine Space. Within the scope of the FP4 project “SUSTALP”, the European Alps have been clustered into eight agrarian regions supporting an analysis of the relation between agricultural policy and the environment (Tappeiner et al., 2003). Several clustering methods have been applied on the basis of socio-economic, geo-ecological and agricultural-structure data at municipal level. ESPON (2005) developed the so called “Regional Classification of Europe” (RCE), with the aim of supporting policy developments by providing information on territorial structures in Europe. This method comprises the aggregation of 37 indicators from eight thematic fields by multivariate analyses. The aggregated results are available at the administrative level of NUTS 2, (Nomenclature of territorial units for statistics) which are defined as “basic regions for the application of regional policies” (EC, 2011a). Metzger et al. (2005) developed an environmental stratification of Europe by clustering regions on the basis of topography, climate, oceanicity and northing. The aims and applications of the data set are described by Jongman et al. (2006). The data set has a resolution of 1 km2 and has been produced for “stratified random sampling of ecological resources, the selection of sites for representative studies across the continent, and to provide strata for modelling exercises and reporting” (Metzger et al., 2005). Eight regions for the European Alps have been identified within the Interreg IIIB Alpine Space project “DIAMONT” (Tappeiner et al., 2008). This typology, which has been carried out by a clustering of municipallevel sustainability indicators, highlights that different concepts are required for a functional level of sustainable development in the Alps. Mücher et al. (2010) developed a “European Landscape Classification” (LANMAP) on the basis of data on climate, altitude, parent material, and land use. The data set is structured in four hierarchical levels with a resolution of 1 km2 . To the best of our knowledge, a regional typology on the basis of spatial-pattern indicators has not yet been carried out for the Alpine Space. Hence, within this study we aim at developing a regional typology of the Alpine Space based on spatial-pattern indicators at the municipal-level. As a first step, we selected and calculated a set of 25 indicators on topography, infrastructure, landscape composition, and landscape pattern. It is known that indicators referring to entire administrative entities may not be represented well for topographically different regions (Jaeger et al., 2008), and that ecologically delineated regions allow an enhanced interpretation of environmental phenomena (Lausch and Herzog, 2002). According to Pecher et al. (2011), the zones below the potential treeline in the European Alps are comparable to the pre-Alpine regions due to their potentially high human impact. Hence, in order to allow a better comparison of the European Alps and their surrounding regions, the authors defined the areas below the potential treeline in the European Alps as a new reference unit for environmental indicators. In the presented study, we employed this new reference unit for the calculation of 21 indicators. In order to account for differences among municipality sizes, we applied only area-weighted

calculation methods. On the basis of the spatial-pattern indicators, a typology of the Alpine Space was carried out by means of a Principal Components Analysis (PCA) and a subsequently applied hierarchical clustering. 2. Material and methods 2.1. Study area The study area corresponds to the area cooperating in the Alpine Space Program. It spans the seven countries of Austria, France, Germany, Italy, Liechtenstein, Slovenia, and Switzerland, covering an area of 390,000 km2 (EU, 2007). In our study, the Alpine Space area is composed of 17,504 municipalities. The municipal areas range from 0.1 km2 to 767.7 km2 with a mean size of 22.1 km2 . The area cooperating in the Alpine Space Programme covers the Alpine arc and its surrounding administrative regions, including the Alpine foothills, but also some low-mountain ranges and regions not being directly related to the European Alps (cf. Fig. 1). It is topographically a strongly varied region, with the highest altitude at 4810 m a.s.l. in the Western European Alps and the lowest at sea level. 2.2. Base data The calculation of the indicators was carried out on the basis of Corine land cover 2000, Corine land cover Switzerland, a digital elevation model, road-network data, municipal boundaries, and the new reference unit for sustainability indicators defined by Pecher et al. (2011). Corine land cover 2000, which from this point on we refer to as CLC2000, is a pan-European data set on land cover and land use (EEA, 2005b). It is based on Landsat 7 ETM+ imagery and refers to the years 1999–2001 (Nunes de Lima, 2005). Its mapping scale and resolution is 100 m and its minimum mapping unit is 25 ha. Information on land use and land cover is structured with three hierarchical levels; 5 classes at the first level; 15 classes at the second level; and 44 classes at the third level. CLC2000 covers the entire study area, except for Switzerland where Corine land cover Switzerland was applied instead, which we refer to as CLC Switzerland from this point (EEA, 2005a). This data set was developed from the Swiss Land Use Statistics on the basis of aerial photographs from a 1979 to 1985 aerial survey (Nippel and Klingl, 1998). In the Swiss Land Use Statistics, land use was identified at sample points. As a consequence, its legend could only be adapted to the Corine levels 1 and 2. As a result of a generalization process, the spatial resolution of CLC Switzerland was set to 250 m. On a ca. 6–8 km wide belt along 46◦ 33 0 N CLC Switzerland contained data gaps, which we filled using the FocalMajority command in ArcInfo Workstation. We then combined the two CLC data-sets by subordinating CLC Switzerland to CLC2000 in overlapping areas, setting the final resolution at 100 m. Due to the missing third hierarchy level in the Swiss data set, we only used levels 1 and 2 for our analyses. As a digital elevation model we used the CGIAR-CSI SRTM 90m Database (Jarvis et al., 2008). This is a hole-filled pan-European data base with a spatial resolution of three arc seconds. Road network data as well as centre points of settlements were obtained from Tele Atlas MultiNet for the states of Austria, France, Germany, Italy, Liechtenstein, and Switzerland (Tele Atlas, 2008). Roads are categorized into 9 standardized classes for every country. For Slovenia and regions adjacent to the Alpine Space in Czech Republic, Croatia, Hungary, and Slovakia, we digitized the road network on the basis of several roadmaps (CartoTravel, 2005; Google, 2009; ViaMichelin, 2009). For municipal boundaries we applied the Seamless Administrative Boundaries of Europe (SABE), a seamless and harmonized

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Fig. 1. Map of the study area (dark grey) which corresponds to the cooperation area of the Alpine Space Programme.

data-set (EuroGeographics, 2005). It contains administrative units with a scale of 1:100,000, which are maintained by national mapping and cadastral agencies. In our study, the municipal boundaries of Austria, France, Germany, Italy, Liechtenstein, and Switzerland have the reference year 2004. Instead, the Slovenian municipal boundaries refer to the year 2006. We prepared two different reference units for indicators. The first reference unit contains the municipal areas of all 17,504 municipalities in the Alpine Space. Also the second reference unit consists principally of municipal areas, but here, the areas above the potential treeline in the European Alps were cut from the municipal areas according to Pecher et al. (2011). 2.3. Spatial-pattern indicators For a regional typology, we selected a set of 25 indicators on topography, infrastructure, landscape composition, and landscape pattern. Table 1 provides a thorough overview of the selected indicators, the employed reference units, their calculation formulas or procedures, their units and references. We selected the indicators on the premise that they describe important aspects of topography, infrastructure, landscape composition or landscape pattern, that they are individually interpretable and usable, and that they are thematically interrelated. For the calculation of 21 indicators, we used the ecologically derived reference unit according to Pecher et al. (2011). Only the indicator “Municipal area below the potential Alpine treeline” (p(below pt)) was referred to the entire municipal areas. For three indicators, we used the centre points of settlements as reference points. All indicator calculations were performed in ArcGIS 9.3. The topographic condition of the municipalities under study is reproduced by a set of 11 indicators on altitudinal extent, relief forms, slope, exposure and insolation. In addition to a sole topographic description, some of these indicators provide basic information on the suitability of the municipalities for agricultural purposes. For example, the indicator “Slopes ≤9◦ ” (slp(0–9))

represents the percentage of municipal areas that are off-road but accessible by all types of vehicles, whereas the indicator “Slopes >9◦ and ≤27◦ ” (slp(10–27)) represents the percentage that are off-road but accessible by tractors and two-axle mowers. The infrastructural condition of the municipalities under study is represented by information on road density as well as on road accessibility. Landscape composition and landscape patterns are described by 11 indicators, focussing mainly on the state of agricultural and forest areas, as well as the state of semi-natural and natural open areas. Data constraints prevented the calculation of four indicators, as planned. Due to a 25 ha minimum mapping unit of CLC2000, linear land-cover types such as water courses are not always represented by joined raster cells, but rather by isolated raster cells. This is likely to cause over- or underestimations of indicators providing information on patch density and average patch size. Therefore, the Corine “Waterbodies” (CLC values > 500), including rivers, were not considered for calculation of the four indicators on semi-natural and natural areas. 2.4. Aggregated spatial-pattern types For an aggregation of the information contained in the 25 spatial-pattern indicators a Principal Components Analysis (PCA) was chosen. Within PCA, Varimax rotation with Kaiser normalization was applied in order to avoid correlation of the resulting components. The PCA was based on the correlation matrix and the components’ extraction on Eigenvalues greater than 1. Component loadings greater than −0.5 and less than 0.5 are usually not regarded as high (cf. Backhaus et al., 2006) and were therefore not used for the interpretation of the components. Indicators not loading highly on any component were considered as additional sources of information, to the resulting components. The PCA was validated by a sub-sampling approach. Randomly, 1000 sub-samples with a size of 80% of the original sample size were drawn from the original sample and for each sub-sample the PCA was computed. Using these results, the stability of the number of extracted PCA

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Table 1 Spatial-pattern indicators and their calculation formulas/procedures, units, and references. For abbreviations and footnotes cf. descriptions below this table. Indicator

Calculation formula/procedure

Unit

Reference

Altitude of centre of settlement (alt(sett))c

alt(sett) Selection of DEM elevation-value for centre-point of settlement in ArcGIS

m a.s.l.

Identical indicator applied by DIAMONT (2008)

Mean altitude (¯x(alt))a

x¯ =

Mean slope (¯x(slp))a

x¯ is the arithmetic mean of x xi is the value of variable x of administrative entity i n is the sample size

Standard deviation of altitude (std(alt))a Standard deviation of slope (std(slp))a Standard deviation of curvature (std(curv))a

std =

n i=1

xi

m a.s.l. (¯x(alt))

 n 1 n

i=1

(xi − x¯ )

2

◦

(¯x(slp))

m (std(alt))

std is the standard deviation of variable x n is the sample size xi is the value of variable x of administrative entity i x¯ is the arithmetic mean of x

◦

Globaltot = Dirtot + Diftot

kWH/m2

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Average potential annual insolation (insolation)a

1 n

(std(slp)) 1/100 z units (std(curv))

Globaltot global radiation is the total amount of solar radiation for an area Dirtot direct radiation Diftot diffuse radiation Calculation of the incoming solar radiation (insolation) per administrative entity using the function Area Solar Radiation in ArcGIS 9.3 (ESRI, 2012). Input parameters for the function: the mean latitude for the site area was calculated automatically from the input DEM; the calculation was performed for the multiple-day period 1–365 in 2009; all other input parameters were preset by default.

 1 mi 

Slopes ≤9◦ (p(slp0–9 ))a

pi =

Slopes >9◦ and ≤27◦ (p(slp10–27 ))a South-facing areas, exluding areas ≤3◦ slope (p(expsouth ))a North-facing areas, exluding areas ≤3◦ slope (p(expnorth ))a Municipal area below the potential Alpine treeline (p(below pt))b Agricultural areas (p(agri))a Forest areas (p(forest))a Semi-natural and natural open areas (p(opnat))a

pi is the proportion of an administrative entity occupied by patch type i mi is the number of all patches of type i

A

j=1

aij 100

aij is the area of patch number j of patch type i A is the total area of the administrative entity

%

McGarigal and Marks (1995) Similar indicators applied by DIAMONT (2008), EEA (2012), EURAC research et al. (2008), Eustat (2004), OECD (2012), Swiss Federal Statistical Office (2012), UN (2007).

Table 1 (Continued) Indicator

Calculation formula/procedure a

Road density of all roads (d(roads))

d=

1 A

J

j=1

Unit

Reference −2

lj

Cf. edge density by McGarigal and Marks (1995) Identical indicator applied by DIAMONT (2008); similar indicator applied by EURAC research et al. (2008).

min

Identical indicators applied by DIAMONT (2008); similar indicator applied by Eustat (2004).

km2

Moser et al. (2007)

d is the total length of edges inside the administrative entity J is the number of edge segments of the administrative entity lj is the length of edge segment j A is the total area of the administrative entity Driving time by car to nearest municipality with more than 5000 inhabitants (dist(inhab))c Driving time by car to nearest motorway or major road (dist(roads))c

dist Calculation of the Closest Facility within ArcGIS 9.3 Network-Analyst (ESRI, 2012). Starting points: centres of settlements. End points for dist(inhab): centres of settlements with more than 5000 inhabitants. End points for dist(roads): nearest junctions of motorways and major roads.

Effective mesh size of agricultural areas (meff (agri))a Effective mesh size of forest areas (meff (forest))a Effective mesh size of semi-natural and natural open areas (meff (opnat))a

mCBC = eff

Landscape diversity of agricultural areas (ld(agri))a Landscape diversity of semi-natural and natural areas (ld(nat))a

Patch density of agricultural areas (pd(agri))a Patch density of semi-natural and natural areas (pd(nat))a

1 A

m i=1

cmpl

Ai Ai

is the effective mesh size for an administrative entity according mCBC eff

Similar indicators applied by DIAMONT (2008), EURAC research et al. (2008), Swiss Federal Statistical Office (2012).

to the cross-boundary connections (CBC) procedure m is the number of patches A is the total area of the administrative entity Ai is the area of patch i inside the boundaries of the administrative entity cmpl is the area of the complete patch that Ai is part of Ai ldMW =

1 nc

nc

i=1

mi

n km−2

Formula modified after McGarigal and Marks (1995) Similar indicators applied by DIAMONT (2008), EURAC research et al. (2008).

n km−2

Formula developed according to Moser et al. (2007) Similar indicators applied by DIAMONT (2008).

ldMW is the landscape diversity for an administrative entity, calculated by means of a moving window nc is the total number of raster cells inside the administrative entity mi is the number of landscape types within a moving window with a circle radius of 1000 m around cell i pdCBC =

1 A

m

Ai

i=1 Acmpl i

pdCBC is the patch density for an administrative entity according to the cross-boundary connections (CBC) procedure m is the number of patches A is the total area of the administrative entity Ai is the area of patch i inside the boundaries of the administrative entity cmpl is the area of the complete patch that Ai is part of Ai

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m km

agri, CLC-level-1 class “Agricultural areas”; forest, CLC-level-2 class “Forests”; opnat, CLC-level-2 classes “Scrub and/or herbaceous vegetation associations” and “Open spaces with little or no vegetation” as well as CLC-level-1 classes “Wetlands”; nat, forest + opnat. a Reference area, municipal area below the potential treeline according to Pecher et al. (2011). b Reference area, whole municipal area. c Reference point, centre of settlement.

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Table 2 Total variance explained by the 5 extracted components. Component

1 2 3 4 5

Rotation sums of squared loadings Total

% of variance

Cumulative %

6.91 3.95 2.60 2.48 1.74

27.63 15.81 10.41 9.93 6.98

27.63 43.44 53.85 63.78 70.75

components, the corresponding indicators and component loadings, and consequently the interpretation of the components were investigated. Components and indicators not loading on any of the components were used to assign the municipalities into clusters. For clustering the municipalities we used the hierarchical clusteringapproach, using the Ward algorithm with squared Euclidean distance as distance measure for our z-standardized variables. In order to obtain a stable clustering, 5% of municipalities were excluded from cluster analysis, due to their extreme values in the variables. After cluster analysis, discriminant analysis was employed to classify the excluded municipalities. For selecting the number of clusters, the elbow criteria and the dendrogram were employed. Additionally, the focus was put on a profound interpretability of the clusters.

3. Results All indicator results are illustrated cartographically in Fig. 2. The results of the PCA are five components, explaining 71.0% of the total variance (Table 2). The KMO is 0.846 and the Bartlett’s Test of Sphericity is highly significant (p-value < 0.000). The rotation of the components converged in six iterations. Table 3 provides an overview of the final component loadings after Varimax rotation. One indicator did not load highly on any component. “Patch density of semi-natural and natural areas” (pd(nat)) provides additional information to the resulting components (cf. Fig. 2).

The PCA sub-sampling approach with 1000 sub-samples revealed that the PCA was valid. In 445 cases, the rotated component matrix was reproduced identically, i.e. no statistically significant difference in the component loadings (significance level 5%). In 554 cases, the rotated component matrix was statistically identical except from one loading, and in one case except from two loadings. In 90% of these 554 cases, these differences were due to changes in the loading of the indicator “Forest areas” (p(forest)) in Component 3. The loading of p(forest) was originally 0.502 (cf. Table 3) which is at the edge of being considered as a high loading at all. As a consequence, this loading was not used for the interpretation of Component 3. We interpret the components as it follows (cf. Table 3 and Fig. 3). Component 1 – “Mountainous, forested areas”: regions with high values for this component are principally characterized by great altitudinal diversity, a high mean altitude and a high standard deviation of terrain forms. Gentle slopes below 9◦ are rare. The terrain is generally steep, but nevertheless characterized by a high standard deviation of slopes. A high percentage of forest areas with a high effective mesh size are accompanied by a low percentage of agricultural areas with little landscape diversity. The landscape diversity of semi-natural and natural areas is high. Regions with high component values span large sections of the mountainous regions of the study area. Component 2 – “Mountainous, semi-natural and natural open areas”: high component values occur predominantly in regions with a higher percentage of areas above the potential treeline. The effective mesh size of semi-natural and natural open areas is high, as well as the percentage of semi-natural and natural open areas. The mean altitude of such regions is high, as is the altitude of the settlement centres. High component values cover almost exclusively the Alpine arc. Component 3 – “Highly structured cultural landscape”: regions with high component values are characterized by few, highly structured agricultural areas: The effective mesh size, as well as the percentage of agricultural areas, are low, whereas the patch density of agricultural areas is high. High values for this component occur in mountainous or hilly regions, but never in flat regions of the study area.

Table 3 Component loadings of indicators under study after Varimax rotation with Kaiser normalization. The rotation converged in 6 iterations. High component loadings are printed in bold type. For abbreviations cf. Table 1. Indicator

alt(sett) x¯ (alt) x¯ (slp) std(alt) std(slp) std(curv) insolation p(slp0-9 ) p(slp10-27 ) p(expsouth ) p(expnorth ) p(below pt) p(agri) p(forest) p(opnat) d(roads) dist(inhab) dist(roads) meff (agri) meff (forest) meff (opnat) ld(agri) ld(nat) pd(agri) pd(nat)

Component 1

2

3

4

5

0.282 0.537 0.796 0.800 0.777 0.800 −0.027 −0.808 0.708 0.486 0.441 −0.087 −0.657 0.666 0.345 −0.355 0.065 0.142 −0.149 0.690 0.075 −0.662 0.515 0.174 −0.217

0.559 0.626 0.422 0.414 0.297 0.309 0.149 −0.325 0.124 0.076 0.148 −0.872 −0.260 −0.156 0.747 −0.134 0.210 0.109 −0.129 −0.076 0.841 −0.335 0.466 0.030 0.033

0.261 0.216 0.282 0.123 0.319 0.319 0.002 −0.316 0.386 0.242 0.332 −0.006 −0.542 0.502 0.092 0.186 0.161 0.048 −0.720 −0.086 0.003 0.024 0.396 0.667 −0.160

0.389 0.311 0.212 0.080 0.060 0.198 0.172 −0.233 0.259 0.116 0.254 −0.072 −0.084 0.269 0.221 −0.634 0.821 0.742 0.151 0.094 0.065 −0.038 0.308 0.116 −0.079

0.196 0.133 0.025 0.039 −0.021 −0.006 0.911 −0.079 0.150 0.648 −0.533 0.026 −0.074 −0.064 0.200 0.071 0.076 0.118 −0.074 −0.060 −0.025 −0.137 0.132 −0.032 0.021

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Table 4 t values for the clustering results. For abbreviations cf. Table 1. Cluster

pd(nat)

Component 1 – “Mountainous, forested areas”

Component 2 – “Mountainous, semi-natural and natural open areas”

Component 3 – “Highly structured cultural landscape”

Component 4 – “Remote regions”

Component 5 – “High-insolation sites”

1 2 3 4 5 6

−0.113 −0.228 3.088 −0.305 −0.454 −0.386

−0.465 −0.532 −0.695 0.197 1.572 0.472

−0.207 −0.314 −0.088 −0.183 −0.18 3.233

0.181 −1.919 −0.331 0.492 0.04 0.019

−0.296 0.133 −0.303 0.847 −0.265 0.441

−0.142 −0.074 0.025 0.831 −0.558 −0.142

Component 4 – “Remote regions”: long drive-times by car to the next municipality with more than 5000 inhabitants, as well as to the next major road, plus low road density, characterize regions with high component values. High values for this component can be identified in the most elevated massifs of the Alpine arc, French rural regions, as well as in some regions along the eastern Austrian border. Component 5 – “High-insolation sites”: regions with high values for this component have a high average potential irradiation, a high percentage of south-facing non-planar areas, and a low percentage of north-facing non-planar areas. High component values occur mainly in the mountainous regions of the study area with a concentration in the south-western Alps.

Using these components and the indicator “Patch density of semi-natural and natural areas” (pd(nat)) the cluster analysis was done. The cluster analysis resulted in 6 clusters, which were obtained using the dendrogramm (5–7 clusters, cf. Fig. 4), the elbow criteria (6–7 clusters) and reasons of interpretability supported by a discriminant analysis (6 clusters, cf. Fig. 5). For the interpretation of the clustering results, we used t values in Table 4, leading to the following results (cf. also Figs. 4 and 5): Cluster 1 – “Non-mountainous cultural landscapes”: such regions are principally characterized by non-mountainous and largely nonremote regions. These cultural landscapes contain few and highly structured agricultural areas, few semi-natural and natural open areas as well as few forested areas. “Non-mountainous cultural

Fig. 2. Geographic extension of the 25 indicator results classified in tertiles. For indicator abbreviations cf. Table 1.

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Fig. 3. Extracted components and their geographic extension over the study area. Within the white zones the intensity of the phenomenon under study is low, within the grey zones it is medium and within the black zones it is high.

landscapes” cover ca. 40% of the municipalities under study and span large sections of the non-mountainous parts of the study area as well as larger valleys or basins. Cluster 2 – “Poorly structured agricultural landscapes”: regions belonging to this cluster, are principally characterized by poorly structured cultural landscapes with many large agricultural areas. They are non-mountainous, poorly wooded and contain only a few semi-natural and natural areas. “Poorly structured agricultural landscapes” occur nearly exclusively in non-mountainous regions of the study area – especially in the North-Italian Po River plain, the French Rhône delta, the Upper Rhine plain as well as in the regions along the eastern Austrian border. Cluster 3 – “Agricultural landscapes, interspersed with highly structured semi-natural and natural areas”: these regions are characterized particularly by a high patch density of a few semi-natural

and natural areas. These are mainly forest remnants between agriculturally used areas or along river banks. Moreover, these regions span non-mountainous and only poorly structured cultural landscapes containing many and not very highly structured agricultural areas. “Agricultural landscapes, interspersed with highly structured semi-natural and natural areas” are predominantly spread over non-mountainous regions of the study area. Cluster 4 – “Remote, highly structured cultural landscapes with a high level of insolation”: High remoteness along with high insolation can principally be identified for regions belonging to this cluster. For most of the regions belonging to this cluster, long drive-times by car to the next municipality with more than 5000 inhabitants, as well as to the next major road, an additional low road density and a high insolation are typical. Furthermore, these regions are characterized by highly structured cultural landscapes along with a low

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Fig. 4. Dendrogram of the cluster solution.

patch density of semi-natural and natural areas. “Remote, highly structured cultural landscapes with a high level of insolation” span predominantly hilly and mountainous regions in the western and eastern part of the study area. Moreover, they occur in the Apennines as well as in the Dolomites, which are located in the triangle formed by Innsbruck, Verona, and Venice. Cluster 5 – “Mountainous, forested areas”: Above all, such regions are characterized by mountainous, forested areas. They have a low insolation, a low patch density of semi-natural and natural areas, and, in general, they are not remote. “Mountainous, forested areas” cover large sections – especially of the eastern part of the Alps. Large areas can also be identified in the Vosges, the Black Forest, and the Apennine. Cluster 6 – “Mountainous, semi-natural and natural open areas”: Mountainous, semi-natural and natural open areas with a low patch density of semi-natural and natural areas characterize regions belonging to this cluster. Additionally, such regions have many forested areas, and they are remote. “Mountainous, semi-natural and natural open areas” almost exclusively span inner-Alpine regions. 4. Discussion Indicators, components and clustering results draw a heterogeneous picture of the Alpine space. However, the quality of these results is strongly dependent on the analysed base data. Within the presented indicator analysis, fundamental information on spatial patterns from CGIAR-CSI SRTM 90m Database and two Corine

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data-sets were used, the limitations of which will be discussed briefly. As accuracy of SRTM data is known to be correlated with slope and aspect (cf. Gorokhovich and Voustianiouk, 2006; Jarvis et al., 2004), data accuracy is likely to be reduced in mountainous areas. In the case of the Corine data-sets, the fact that land use in CLC Switzerland was identified at sample points, whereas CLC2000 was developed on a mapping scale of 100 m with a minimum mapping unit of 25 ha, might cause uncertainties in their application. Another deficit of these data lies in their reference years, as the most recent information is from 2001, whereas the oldest is from 1979. This might pose a problem, as land-use and land-cover changes are to be expected during such a long period of time. The spatial-pattern indicators, calculated within this study, represented the basis for the subsequent typology. One of the challenges of working with environmental indicators lies in the process of indicator selection. Hence, different selection frameworks and criteria have recently been elaborated (cf. Dale and Beyeler, 2001; Lin et al., 2009, 2012; Niemeijer and de Groot, 2008). According to Dale and Beyeler (2001), indicator sets should be able to represent the complexity of an ecosystem by indicators on its structure, function, and composition. In the presented study, a similar approach was followed, as the indicators had to describe important aspects of topography, infrastructure, landscape composition or landscape pattern in order to comprehensively represent spatial pattern. In this study, an ecologically derived reference unit for indicators was used, consisting of those parts of the municipal areas which are situated below the potential treeline in the European Alps. As this reference unit has only been developed for the European Alps (Pecher et al., 2011), in the regions surrounding this high-mountain range the whole municipal area had to be used for indicator calculation. Hence, it would be desirable to define similar ecologically derived reference units also for other mountain regions like the Black Forest, the Vosges, the Massif Central, the Cévennes, the Apennine and the Jura Mountains in the future. The regional classification within this study has been carried out by employing a PCA and a hierarchical cluster analysis on spatialpattern indicators at the municipal-level. In several other studies, regional classifications have been carried out following different objectives and, therefore, using different base data, reference units, and classification methods (cf. ESPON, 2005; Hazeu et al., 2011; Jongman et al., 2006; Metzger et al., 2005; Mücher et al., 2010; Omernik, 1987; Tappeiner et al., 2003, 2008). In the following, the presented typology shall be discussed with regard to other environmental classifications. The most obvious differences among regional classifications arise from the reference units applied. Administrative entities are usually disregarded during the process of environmental classifications (cf. Metzger et al., 2005; Mücher et al., 2010; Omernik, 1987), as environmental conditions do not correspond to artificially constructed administrative areas. The fact that spatial patterns were classified for administrative entities might thus be considered a drawback of the presented study. However, precisely due to this approach, it was possible to provide aggregated information on environmental conditions and spatial pattern at a spatial level, which is easily to handle for both researchers and decision makers. Moreover, it is one of the greatest advantages of the presented typology that it can be directly supplemented with statistical data for further analyses. Climate is known to be a determining factor for the distribution of global vegetation patterns (Klijn and De Haes, 1994; Metzger et al., 2005). In fact, several classifications of European regions have been developed, being principally based on climatic aspects (cf. Hazeu et al., 2011; Metzger et al., 2005; Mücher et al., 2010). Within the presented study, climatic information was only included in the form of the indicator “insolation” (cf. Table 1). By using additional climatic information on e.g. precipitation (e.g. from CRU, 2012), a

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Fig. 5. Geographic extension of the cluster results over the study area.

more detailed typology might have been possible. As the presented typology was exclusively based on information on topography, infrastructure, landscape composition, and landscape pattern, climatic information per se could, however, not be considered. In general, the cluster results obtained within this study provide a comprehensive overview of spatial-pattern types in the Alpine Space, although some of them might need to contain some additional interpretation content. The clusters 1–3 (cf. Table 4 and Fig. 5) comprising “Non-mountainous cultural landscapes”, “Poorly structured agricultural landscapes”, and “Agricultural landscapes, interspersed with highly structured semi-natural and natural areas” are principally characterized by very similar spatial pattern. The most obvious differences between these regions lie in the intensity as well as the extent of agricultural land-use (cf. Table 4), but these differences might still be more accentuated. Apart from the information used within this study, environmental properties

and spatial patterns are strongly dependent on further environmental conditions, but also to a large extent on socio-economic and political circumstances. Hence, depending on the users’ needs, the results obtained within this study can be supplemented with further environmental data e.g. on additional climatic aspects or on biodiversity, but also with selected socio-economic information. Although we are aware that the presented typology has some limitations, we are convinced that it contributes considerably to a progress in data supply for sustainability issues in the Alpine Space. The set of 25 spatial-pattern indicators at the municipal level can be useful for researchers and decision-makers during locally specific studies and for an evaluation of single municipalities. The five extracted components and the six clusters give an overview of supra-regional relationships between spatial-pattern types, allowing an identification of similarities and differences between Alpine municipalities and regions at a glance. Thanks to

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their reference unit, the information contained in these data can be easily supplemented individually by further environmental and socio-economic data at the municipal level. Responding to the call launched by Agenda 21, a comprehensive spatial-pattern data-base has been created, allowing now – for the first time – a transnational comparison of spatial-pattern information for more than 17,000 municipalities in seven Alpine countries. Acknowledgements This work was funded by the Interreg IIIB-Project DIAMONT, financed by the European Union in the Alpine Space Programme (Project-Nr.: A/III/1.1/29). We would like to thank our colleague Marc Zebisch for his help in developing some indicator calculation procedures and Hilary Solly for revision of the English text. We also wish to thank the editor and two anonymous reviewers for valuable comments on earlier versions of the manuscript. References Backhaus, K., Erichson, B., Plinke, W., Weiber, R., 2006. Multivariate Analysemethoden. Eine Anwendungsorientierte Einführung. Springer, Berlin. CartoTravel, 2005. Slowenien. Die Shell Urlaubs Karte. CRU, 2012 Climatic Research Unit – Home. Available from: http://www.cru. uea.ac.uk/ (accessed 20.05.12). Dale, V.H., Beyeler, S.C., 2001. Challenges in the development and use of ecological indicators. Ecol. Indic. 1, 3–10. DIAMONT, 2008. DIAMONT Database. Available from: http://www.diamontdatabase.eu/. (accessed 20.05.12). EC, 2011a. NUTS – Nomenclature of territorial units for statistics. Available from: http://epp.eurostat.ec.europa.eu/portal/page/portal/nuts nomenclature/ introduction (accessed 20.05.2012). EC, 2011b. Sustainable development indicators. Available from: http://epp.eurostat. ec.europa.eu/portal/page/portal/sdi/indicators (accessed 20.05.12). EEA, 2005a. Corine land cover (CLC1990) Switzerland. EEA, 2005b. Corine land cover 2000 (CLC2000) 100 m - version 5/2005. EEA, 2012. Indicators - EEA. Available from: http://www.eea.europa.eu/data-andmaps/indicators (accessed 20.05.12). ESPON, 2005. In search of territorial potentials. Mid term results by spring 2005. ESRI, 2012. ArcGIS Desktop Help 9.3. Available from: http://webhelp.esri.com/ arcgisdesktop/9.3/ (accessed 20.05.12). EU, 2007. European Territorial Cooperation 2007–2013. Operational Programme Alpine Space. EURAC research, Institute for Economic Research of the Chamber of Commerce of Bolzano/Bozen, Provincial agency for environment, 2008. Nachhaltigkeit Südtirol - Sostenibilità Alto Adige. Available from: http://www.sustainability.bz.it/ (accessed 20.05.12). EuroGeographics, 2005. Seamless Administrative Boundaries of Europe (SABE2004 v1.0). Eustat, 2004. Udalmap. Municipal-Level Indicators of Sustainability. Available from: http://www.eustat.es/ci ci/about/udalmap i.html (accessed 20.05.12). Google, 2009. Google Maps. Available from: http://maps.google.com/ (accessed 19.01.09). Gorokhovich, Y., Voustianiouk, A., 2006. Accuracy assessment of the processed SRTM-based elevation data by CGIAR using field data from USA and Thailand and its relation to the terrain characteristics. Remote Sens. Environ. 104, 409–415. Hazeu, G.W., Metzger, M.J., Mücher, C.A., Perez-Soba, M., Renetzeder, C., Andersen, E., 2011. European environmental stratifications and typologies: an overview. Agric. Ecosyst. Environ. 142, 29–39. Jaeger, J.A.G., Bertiller, R., Schwick, C., Müller, K., Steinmeier, C., Ewald, K.C., Ghazoul, J., 2008. Implementing landscape fragmentation as an indicator in the Swiss

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