Research article

Mapping alpine vegetation using vegetation observations and topographicattributes

Karin Pfeffer*, Edzer J. Pebesma and Peter A. Burrough1Utrecht Centre for Environment and Landscape Dynamics, Faculty of Geographical Sciences, University ofUtrecht, 3508 TC Utrecht, The Netherlands; *Author for correspondence (e-mail: k.pfeffer@uva.nl)

Received 9 July 2002; accepted in revised form 6 May 2003

Key words: Alpine vegetation, Classification, Digital elevation model, Ordination, Universal kriging

Abstract

Local planning in mountain areas requires spatial information on site factors such as vegetation that is commonlylacking in rugged terrain. This study demonstrates a procedure for the efficient acquisition of a vegetation mapusing topographic attributes and nominal vegetation data sampled in the field. Topographic attributes were de-rived from a digital elevation model �DEM� and nominal vegetation data were reduced to normalised scores bydetrended correspondence analysis �DCA�. The procedure for mapping vegetation types addressed the relationsbetween DCA scores and topographic attributes, spatial correlation of DCA scores and classification of predictedDCA scores based on a cluster analysis of DCA scores at observation locations. The modelled vegetation classescorresponded with the impression obtained in the field. We also showed that the final result is rather sensitive towhich samples are included in the analysis.

Introduction

The establishment of socio-economic infrastructuressuch as ski runs in alpine areas may have consider-able impact on the natural vegetation cover, going asfar as its total removal. This should be avoided be-cause the presence of vegetation protects the slopeand reduces the risks of erosion, landslides and ava-lanches. If the establishment of new infrastructureaims to use vegetation cover to minimise theenvironmental impact and the risk of natural hazards,then it is useful to know species associations andspecies preferences, to avoid the disturbance of eco-logically valuable sites and to identify locations thatcan be easily revegetated. However, spatial informa-tion on site conditions is commonly lacking in moun-tain areas �Hörsch et al. 2002�, and in most cases tosatisfy planning criteria the mapping of vegetationhas to be carried out from scratch.

For many years vegetation has been mapped byusing information from the external aspects of the

landscape, such as changes in elevation or rock type,and by manually drawing boundaries between dissim-ilar units. Initially, this procedure was carried outmerely in the field. With the establishment andprogress of aerial photography and satellite remotesensing, boundaries could be drawn by visual inter-pretation of images and updated by field visits. How-ever, identifying vegetation associations and locatingtheir boundaries according to visual changes in thelandscape is rather subjective. Studies in related fielddisciplines have shown that even when using thesame images, different scientists are likely to producedifferent thematic maps of the same study area �Bieand Beckett 1974; Janssen and Middelkoop 1991�.Moreover, conventional mapping neglects short-rangevariation. Satellite images are a possible alternativefor the direct retrieval of certain spatially distributedvegetation characteristics, but with current technol-ogy a clear identification of individual plant speciesother than trees is hardly possible �Franklin 1995;Nagendra 2001�.

© 2003 Kluwer Academic Publishers. Printed in the Netherlands.759Landscape Ecology 18: 759–776, 2003.

Conventional mapping of vegetation in the field,from aerial photographs or satellite images might besufficient for projects at a regional scale. However,when considering projects at a local scale, forinstance the design of an environmentally sound skirun, the spatial resolution of data obtained by aerialphoto interpretation, generalised field mapping ordata retrieved from satellite images is insufficient, es-pecially for critical areas such as gullies and steepslopes. But high resolution vegetation data areexpensive and difficult to collect so other methods areneeded to support the quick and cheap acquisition ofa vegetation map with the right balance between dataaccuracy and costs.

It is well known that both plant growth and speciescomposition of vegetation depend to a certain extenton ecological site factors such as elevation, slope, as-pect of the slope �a surrogate for the amount of directreceived solar radiation�, slope curvature, soil mois-ture, or a meaningful combination of several of these.Today, these factors can be easily derived from agridded digital elevation model �DEM�, which mayhave any spatial resolution we need �Burrough et al.2001�. This enables the use of the derivatives of thedigital elevation model to support the mapping ofhigh resolution vegetation data, as shown by Bur-rough et al. �2001�, Gottfried et al. �1998�, Hörsch etal. �2002� and Tappeiner et al. �1998�. These studies,however, merely examined the deterministic relationsbetween vegetation data �species and types� and thederived topographic attributes, but the interactionsbetween the spatially correlated structures of the veg-etation and geographical distribution of the ecologi-cal factors were hardly addressed. As Bio �2000� hasshown for individual species, however, the spatialcorrelation structures of plant species response shouldbe an integral part of exploratory data analysis andmapping. The inclusion of speciessite factor relationsand spatial interactions in a mapping campaign mayprovide useful extra information at a high spatialresolution, compared to the conventional techniquesalready mentioned.

Accordingly, the aim of this study was to developand test a quick and reliable procedure for moderateto high resolution vegetation mapping in Alpine areaswhere access is difficult and field data collection isexpensive. Our source data are ecological siteattributes computed from a high resolution, continu-ous, gridded digital elevation model �DEM� derivedfrom large scale maps, and vegetation species abun-dance collected in the field. Since the actual vegeta-

tion patterns are not only determined by topography,but from a complex interaction between historical andrecent environmental, human and disturbance factors�Hörsch et al. 2002� and interactions among speciessuch as competition, we also incorporate the spatialautocorrelation structures of field observations of thevegetation data to improve the information content.

Figure 1 gives a flowchart of the procedures used.In stage 1, ecologically important derivatives arecomputed for each grid cell of the DEM. Because oftime constraints, species abundance data cannot becollected for each individual cell on the DEM, butmust be collected from a much smaller sample ofquadrats of equal shape and size. These quadrats arelocated in the terrain with reference to a lower reso-lution grid. Detrended correspondence analysis�DCA� is used to reduce these vegetation scores to alimited number of major axes �Jongman et al. 1995�.

In stage 2 we used multiple linear regression to re-late the DCA axes scores to the primary and second-ary topographical attributes that were computed forthe sampled quadrats. The spatial correlation struc-tures of the regression residuals at the sampled quad-rats are then examined using semivariograms �Bur-rough and McDonnell 1998, Pebesma and Wesseling1998�.

In stage 3, we combine the regression models andsemivariograms to carry out a universal kriging inter-polation of the DCA scores from the sampled quad-rats to the whole area covered by the DEM. The DCAvegetation scores are classified into vegetation classesusing k-means clustering �MacQueen 1967�: theseclasses are used to allocate all grid cells to classes ina map of vegetation that covers the whole area.

In this paper we describe the mapping procedurein detail and demonstrate its application for a ski areain the Tyrolean Alps, Austria. This area had alreadybeen chosen as a case study within a European project�GETS 2001� for the development of quantitativemethods of environmental impact assessment for skiruns. The end result of the work reported in this pa-per is a detailed vegetation map that can be used formulticriteria decision making to improve the planningof new ski runs �Pfeffer et al. 2002�.

Study area

The study area is located in the Ötztal, a north-southvalley in the region of Tyrol, on the upper westernslopes of the village of Sölden, which is a popular ski

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area in the Austrian Alps. It covers an area ofapproximately 3.6 km2, and has an elevation rangefrom the timberline, at about 1900 m, up to 2650 m�Figure 2�.

Geologically, the study area belongs to the Ötztalmassif, whose formation is the result of a sequenceof processes going back more than 450 million years�Purtscheller 1978�. The present land formation ismainly determined by alpidic fold and uplift, degra-dation and glacial activity. Typical geomorphologicfeatures are rock glaciers, protalus ramparts and taluscones, which occur predominantly at higher altitudes.The presence of morainic arcs, cirques, smooth landforms and a dominant cover of morainic deposits tes-tify to the glacial activity. The underlying materialconsists for the greater part of different kinds of

gneiss and schist of variscic and prevariscic origin.The amphibolites at the northern border of the studyarea originated from volcanic tuffs by metamorphismand are typical for the area to the north of Sölden.

Due to the rather homogeneous mineralogicalcharacteristics of the underlying material, the soil ofthe area consists largely of podzols, interspersed byloamy and marshy soils. These differences in soil arereflected by certain indicator species. However, it isdifficult to trace these singularities due to the unpre-dictable occurrence of these soil differences and dueto their short range variation. Less extreme climaticconditions and human interaction engender a greaterand more developed content of soil organic materialin the lower areas.

Figure 1. Flowchart of mapping procedure.

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The Ötztal is an inner alpine valley with a dry cli-mate characterised by an average annual precipitationof 700 to 800 mm in the valley, concentrated over fewdays annually. The area is characterised by large os-cillations in temperature. The small amount ofprecipitation, especially in winter, is for the greaterpart caused by its location in a north-south-valley. Atthe lower boundary of the study area the mean yearlytemperature is about 2 °C with a minimum of� 24.5 °C and a maximum of 20 °C. Higher areasreceive more precipitation and are generally colder,since precipitation increases and temperature de-creases with elevation �about 30% and 6 °C per 1000m�. The percentage of sunny days �more than 50%�favours tourism both in winter and summer.

The vegetation of the area can be roughly dividedinto ski piste vegetation, dwarf shrub heaths, alpinegrassland dominated by sedge and different kinds ofgrass and pioneer vegetation.

The wide, smooth east-facing slopes above thetimberline are intensively used, both for skiing inwinter and hiking and grazing in the summer.

Much artificial snow is produced, particularly onthe ski pistes below 2300 m. This increases the ca-pacity of the area for winter tourism, but it also af-fects natural processes like growth of the vegetationand snowmelt rate.

Data

The DEM and topographic attributes derived fromthe DEM

A digital elevation model having a grid cell of 10 mwas generated from digital isolines in vector formathaving altitude intervals of 20 m �source: Bundesamtfür Eich- und Vermessungswesen �BEV�, Austria�.First the isolines were rasterised to grid cells of 10m. Then linear interpolation was used to calculate el-evation values for the pixels between the isolines onthe basis of the elevation values at the isolines �Ilwis,1998�.

Topographic attributes, in particular altitude, slope,planform curvature, profile curvature, �potential an-nual� solar radiation, distance to ridges, mean wetnessindex and mean sediment transport were derived fromthe digital elevation model using PCRaster �PCRaster2002; Wesseling et al. 1996�. Slope was calculated onthe basis of the elevation of its nearest neighbours ina 3�3 cell window. The third-order finite differencemethod was used, suggested by Horn �1981� and alsoused by Skidmore �1989�. The curvature transverse tothe slope direction and the curvature in the directionof the slope, defined as planform curvature and pro-file curvature, were determined according to theequations given by Zevenbergen �1987�. The meanwetness index and the mean sediment transport were

Figure 2. Geographical location of the study area. © BEV – 2003, Vervielfältigt mit Genehmigung des BEV - Bundesamt für Eich- undVermessungswesen in Wien, ZI. EB 2003/00151.

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computed using the surface topology of the DEM asdetermined by the well-known D8 algorithm �Bur-rough and McDonnell 1998; Moore et al. 1993� andalso used by Burrough �2001�. Received solar radia-tion in MJ/m2, which is preferred to aspect because itis measured on a circular scale and is an ecologicallymore useful and interpretable variable �Burrough2001�, was derived from the DEM with a radiationmodel implemented in PCRaster �Van Dam, 2001�.All results were stored in raster maps having a gridcell size of 10 m.

Vegetation data

During the summer of 2000 the occurrence of plantspecies in the study area was recorded at 223 plots,each representing an area of 10 m�10 m. Plots wereselected using a regular sampling scheme with a gridof 100 m�100 m as a reference. All species growingon the sampling plot were recorded on the basis of asimple ordinal abundance scale ranging from 1 to 3,where 1 indicates just the presence of a plant speciesat that plot, 2 that a species occurred frequently and 3that the sampling plot was dominated by a certainplant species. In total 147 species were identified, ne-glecting detailed specification of some grass speciesand all fungi and ferns. From the 223 sampling plots208 sites were used for the analysis. The 15 rejectedsampling plots fell on roads or tracks. The speciesTable was used as the input for the detrended corre-spondence analysis. Topographic attributes for eachsampled site were derived from the high resolutionDEM.

The vegetation records show that the study areacontains many common species, that is to say speciesthat were found at many of the sampling sites. Thesecommon species, listed in Table A1, are known to betypical for alpine grassland and alpine heaths �Reisigland Keller 1987�. Although each species has its ownpreferences, some species are rather tolerant. Toler-ant species frequently occur at many different kinds

of sites making it difficult to identify an unambigu-ous correlation between these species and topo-graphic attributes. On the other hand some specieswere recorded which were characteristic for sites withspecific conditions like a certain elevation range, ex-posure or moisture content.

Vegetation data reduction

Detrended correspondence analysis �DCA� �Jongmanet al. 1995� was used to reduce the 147 recorded spe-cies to four independent ordination axes. This ordina-tion technique constructs a theoretical variable thatbest explains the species data by maximising the dis-persion of species scores; DCA also corrects for thearch effect by detrending �Jongman et al. 1995�. Theordination identified those sites having similar plantsand those species that tend to occur together.

We used the computer program Canoco 4.02 �TerBraak and Smilauer 1998� to carry out DCA. Table 1illustrates the importance of each ordination axis withrespect to the original vegetation pattern as indicatedby the eigenvalue. Only one fifth of the total varia-tion of the species data was explained by the four ex-tracted axes, so a large part of the variation in theoccurrence pattern of plant species is unexplained.The eigenvalues show, however, that the explainedvariability in the vegetation data is for the greater partexpressed by the first two dimensions; these do notgive a clear separation of the species along one singleaxis, however, for which the eigenvalue should beabove 0.5 �Jongman 1995�. The small amount of ex-plained variation is partly caused by the use of abun-dance data whose frequency was measured on asimple abundance scale instead of actual count data.Also, the species data are generally rather noisy �TerBraak 1998� due to complex interactions between nu-merous influence factors �Hörsch et al. 2002�. In ad-dition the tolerance of plants to their typical growingconditions and the occurrence of many common spe-

Table 1. Summary Table of DCA applied to vegetation data.

DCA1 DCA2 DCA3 DCA4 Dispersion ofall EV*

Eigenvalue �EV� 0.432 0.195 0.119 0.098 4.186Cumulative percent of dispersion 10.32 14.98 17.82 18.05Range of DCA scores �-1.9;2.9� �-2.4;4.8� �-3.0;6.0� �-5.1;5.2�

* Dispersion of all eigenvalues �EV� represents the total variance in the species data

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cies might have disturbed a clear response to thetheoretical variable constructed by DCA.

The range of the DCA scores along the first ordi-nation axis is rather small compared to the rangesalong subsequent ordination axes; it does not haveoutlying values as found in subsequent axes. It seemsthat the first ordination axis reflects the major patternof common species, while subsequent axes accountfor rare species, because sites with extreme scores inthe subsequent axes contain rare species.

Mapping of vegetation classes

Outline

To map vegetation classes we need to combine topo-graphic information with vegetation data. As alreadyexplained in the introduction, the starting points aretopographic attributes derived from the DEM and re-duced vegetation data in the form of DCA scores�Figure 1�. The mapping approach covers two mainstages, regression and residuals analysis �stage 2 inFigure 1�, and derivation of vegetation classes fromboth DCA scores and topographic attributes �stage 3in Figure 1�. First, the correlation between vegetationscores and topographic attributes was identified andthe spatial dependence of the scores was modelled.Second, DCA scores were estimated for unsampledlocations from the DCA scores at the observed loca-tions, using the topographic attributes specified in thefirst part and the spatial dependence of the observa-tion points �universal kriging�. Then k-means clusteranalysis was applied to the DCA scores at theobserved locations, resulting in cluster centres thatwere used to convert the predicted DCA maps tovegetation classes that characterised specific speciesassociations.

Regression analysis and selection of variables

Different techniques were used to identify those to-pographic variables that have a spatial impact on theextracted vegetation axes. First a visual interpretationof the species-topography relation was performed onthe basis of scatterplots of topographic attributesagainst extracted vegetation axes and bivariate corre-lations. Scatterplots of the selected topographicattributes against DCA scores are shown in Figure 3.Several non-linear transformations of the topographicattributes were explored in a vain attempt to enhance

the linearity of their relationship to the DCA scores.Table 2 shows that height, slope, solar radiation, pro-file curvature of the slope, mean wetness index andmean sediment transport play the main role inexplaining the variation in the DCA scores.

Since mean wetness index and mean sedimenttransport were highly correlated, the sensitivity of thespecies-environment relation to the exclusion ofeither of them was tested. The exclusion of one ofthose attributes hardly changed the species-environ-ment relation. Assuming that mean wetness index hasa higher impact on the occurrence of vegetation spe-cies than mean sediment transport, sediment transportwas excluded from further analysis.

To select a relevant subset of the topographic var-iables for linear regression, a stepwise variable selec-tion was carried out for each DCA axis using thedefault settings in SPSS 10.0. Table 3 shows the to-pographic attributes that were selected for each axis.For example elevation, slope and solar radiation arethe variables that contribute most to the variation inthe first DCA axis.

Residual analysis

Linear regression between the vegetation axes and theselected topographic attributes yielded regression re-siduals for each vegetation axis. We used variogramanalysis �Burrough and McDonnell 1998; Pebesmaand Wesseling 1998� to analyse the spatial structureof these regression residuals. To model spatial corre-lation, we fitted the spherical model,

��h� � �c0 � c1�3h

2a�

1

2�h

a�3� for 0 � h � a

c0 � c1 for h � a

��0� � 0 for h � 0�1�

where ��h� is the semivariance, a is the range of thevariogram defining the spatial scale of variation, h thelag, and c0 the nugget variance which represents thespatially uncorrelated part of the variance withoutspatial component, and with c1 the maximum valueof the semivariance.

Figure 4 shows the residual variograms for eachvegetation axis, computed with the computer programGstat �Pebesma and Wesseling 1998�. Table 2

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Figure 3. Scatterplot of selected topographic variables against DCA scores.

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displays the parameters of the fitted variogram mod-els.

The spatial prediction of vegetation scores

The prediction of vegetation scores at unobserved lo-cations was based on the DCA scores at the observedlocations and the results of the regression and residualanalysis. To predict DCA scores at unobserved loca-tions we used universal kriging, because this ad-dresses both the spatial dependence of the observa-tions and their linear relation to topographicattributes. Universal kriging models the DCA scoresat all locations Z(x) as the sum of an unknown inter-cept �0, a non-stationary trend and an intrinsicallystationary error ��x� �Cressie, 1993�, where the trendis modelled as a linear function of known base func-tions fj (x) and unknown constants �j:

Z�x� � �0 � �j�1

p

f j�x� �j � �x� �2�

The topographic attributes that were identified instepwise linear regression �Table 3� were used as base

functions. The spatial dependence in the error term��x� was modelled by the variogram of the residualsfor each vegetation axis. It is assumed that theweighted least square residual variograms, shown inFigure 4, can be used for universal kriging of theDCA scores. As an example, the model for the firstvegetation axis is:

Z�x� � �0 � Delev�x��elev � Dslope�x��slope

� Drad�x��rad � �x� �3�

where Delev �x� and Dslope �x� and Drad �x� are the se-lected base functions, expressing that elevation, slopeand solar radiation are significantly correlated withthe first vegetation axis. �0 is an unknown intercept,and �elev, �slope and �rad are unknown constants.

Universal kriging of the four vegetation axes on thebasis of DCA scores, selected topographic attributesand the fitted residual variograms in Figure 4 wasdone with Gstat �Pebesma and Wesseling 1998�. Thefour maps of resulting predicted DCA scores areshown in Figure 5. They clearly reflect the trend ofthe base functions specified for each ordination axis.

Cluster analysis and classification of predicted DCAscores

Cluster analysis was applied to standardised andweighted DCA scores at the sampled locations. Thescores were standardised to zero mean and variancesequal to their corresponding eigenvalue to includethat each vegetation axis explains a certain amount ofvariation.

Cluster analysis requires the specification of thenumber of vegetation clusters, which in this case waschosen such that it matched the impression obtainedin the field and general knowledge about growingpreferences of alpine vegetation. The clustering wasrepeated for several numbers of classes to explore thecorrespondence of computed vegetation clusters withthe impression obtained in the field. Eventually weconcluded that in this case seven classes would suf-fice to reflect the vegetation variability due to varia-tion in elevation, slope, aspect, average wetness, landuse and the location of the timber line.

We used k-means clustering �MacQueen 1967� tocreate a legend for a vegetation map with the sevenclasses. K-means clustering was chosen because it ispossible to compute the class centres and dispersionfrom the data. It can also allocate individual observa-

Table 2. Pearson correlations of DCA axes with single topographicattributes.

Axis 1 Axis 2 Axis 3 Axis 4

Elevation �m� 0.762 � 0.089 � 0.155 0.125Slope �degrees� � 0.580 � 0.217 � 0.123 � 0.083Planform curvature�m–1 * 100�

0.048 0.127 � 0.063 � 0.114

Profile curvature�m–1 * 100�

0.073 � 0.061 0.027 0.149

Solar radation�MJ/m2�

� 0.259 � 0.143 � 0.523 � 0.056

Ridge proximity�ln �m��

0.130 0.043 � 0.058 0.039

Mean wetness in-dex �lne�

0.267 0.252 � 0.042 � 0.002

Table 3. Selected topographic attributes to explain the variance ineach vegetation axis.

Depen-dent vari-able

Independent variables MultipleR2

DCA1 DEM, Slope, Solar radiation 0.7466DCA2 Mean wetness index, DEM, Slope 0.1095DCA3 Solar radiation 0.2733DCA4 Profile curvature 0.0221

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tions to the classes once they have been defined. Thevegetation clusters of the observation locations areshown on the left hand of Figure 5. These clustercentroids were used to classify the transformed andweighted predicted �kriged� DCA scores to one of theseven vegetation classes, where the scores of the pre-diction maps were standardised and weighted in thesame way as the DCA scores at the sampling sites.

The vegetation class map so obtained is shown onthe right hand of Figure 6. Cluster analysis and theallocation of the predicted DCA scores �Figure 6� toa vegetation class were done with SPSS 10.0.

Interpreting the vegetation classes

Each vegetation class was defined by sampling siteshaving a similar species composition and topographicattributes that had been identified to have an impacton the observed species. To analyse the ecologicalrepresentation of each computed vegetation class, theclass membership of the sampling sites was linked tothe original Table of field data with species recordsper sampling site. This allowed the identification ofcommon and typical species for each vegetation class,which are listed in Table A2 and Table A3.

It seems that vegetation classes 1 and 2 weremainly determined by the presence of species thatwere not observed at other locations, for the greaterpart reflected by extreme values along the secondDCA axis, and rather homogeneous conditions at the

Figure 4. Fitted variograms for each extracted ordination axis.

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sampling site and its neighbouring area. Frequentlyobserved species of class 1 are common forrevegetated ski runs and alpine meadows. Local ob-served species in this class are species which are usu-ally found in moist areas. The occurrence of speciessuch as Trifolium badium can be explained by thepresence of small springs due to the presence of im-permeable soil layers �loamy spots�, that the area isthe source area for the mountain rivers resulting in ahigh mean wetness index, and that it is partly an areawhere snowmelt water creates marshy soils. Class 2represents species that are characteristic for alpinegrassland in the central Alps. Additionally, it containsspecies introduced by the use of this area for farm-ing, for example Geranium pratense or Chrysanthe-mum leucanthemum, and also species with a rather

low frequency at other sites. The higher east-facingslopes at the south-western part of the study area,which are much used for skiing, were occupied byclass 3. Due to the extreme growing conditions,caused by the elevation and the impact of skiing, thespecies diversity is rather low. These areas are cov-ered by moss species such as Polytrichium and Pohlianutans, grass and lichen species, interspersed withsome flowers such as Primula glutinosa and Sibbal-dia procumbens. Class 4, which for the greater partcovers north-facing slopes up to an elevation of 2250m, was created by the significant correlations of boththe first and third vegetation axes with solar radiationand the specific species pattern of sites at north-fac-ing slopes below 2250 m. In particular, this includesspecies of alpine heaths dominated by Rhododendron.

Figure 5. Maps with predicted DCA scores obtained with universal kriging.

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South-facing slopes, ranging from 2000-2500 m wereassigned to class 5. That class is mainly determinedby the similar species composition at the sampled lo-cations and the topographic uniformity of that areawith respect to aspect and slope. It is dominated byalpine species that are characteristic for these slopesabove 2000 m, with a gradual change from alpineheaths dominated by Erica to Nardetum and Curvul-etum, interspersed with Juniper, Campanula bar-barta, Lotus cornulates and Antennaria dioeca in thelower elevation ranges of that class. Class 6 occupiesmainly east-facing slopes and south-facing slopesabove 2350 m. These gentle slopes with little plan-form and profile curvature cover a large part of thenatural area, and also a part of the ski runs, and theycontain walking paths. The observed species compo-sition of class 6, consisting of Nardus stricta, Carex,Polytrichium, Cladonia rangifera, Centraria island-ica and other lichens, interspersed with somecommon flowers such as Homogynae alpina, Trifo-lium alpinum, Leucanthemopsis alpina and Geumrepens, is characteristic for the site conditions of thatalpine area together with the human interference, in-cluding grazing. The occurrence of tree species in thelower area of the study area, related to elevation, de-termined the configuration of class 7. This class alsoindicates the location of the timberline.

Consistency of vegetation classes

When reducing multiple vegetation species to vegeta-tion axes, the observation at each observed locationis represented by a set of scores, which is used tomeasure similarity to other sampled sites. Thereforethe DCA scores and maps derived from the DCAscores depend on the whole set of observationsincluded in the analysis. To analyse the sensitivity ofthe mapping procedure to sampling variability, thewhole procedure for mapping vegetation types wasrepeated five times, each time for a different subsetcontaining 80 percent of the whole data set. The con-figuration of the subsets is based on a random divi-sion of all observed locations into a five groups,where each group contained 20 percent of the fulldata set. For each subset one group was excluded.From Figure 7 one can see how different the map at-tained for each of the subsets �Figure 7b-7f� is fromthe reference map �Figure 6, repeated in 7a�. Thesubset maps show that predicted DCA scores clusterpredominantly according to a common spatial pattern.However, the spatial clusters differ in size, shape andpattern. A cluster that is dominant in the original mapmay disappear if it is part of a very small cluster con-taining few data points with an eccentric speciescomposition. Table 5 shows that in every submap 50to 65 percent of all grid cells correspond to the ref-erence map of vegetation classes of Figure 5.

Figure 6. Clusters assigned to observation locations �left� and classification of the predicted DCA scores �right�.

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Figure 7. The map in the upper left corner shows the vegetation classes obtained by using the full data set for the mapping procedure. Theother maps illustrate the result of the mapping procedure when just using 80% of the full data set. Colours were assigned such that maximalvisual correspondence to the reference map �upper left corner was obtained.

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Discussion

Alpine vegetation is the result of a complex interac-tion between historic and recent natural processes andhuman interaction, and it is impossible to completelyexplain the response of the vegetation to a limited setof attributes. Additionally, some species are quite tol-erant with respect to varying conditions, so there willnever be a perfect species-environment correlation.However, the analysis of the correlation betweenvegetation and topographic attributes showed thatabout one third of the vegetation pattern that was en-compassed by the extracted vegetation axes could beexplained by the set of selected topographic at-tributes. Thus the overall influence of topography onthe spatial pattern of alpine vegetation types was evi-dent, despite the impact of natural and artificial dis-turbance factors. The expected correlation of alpinevegetation especially with elevation and solar radia-tion was explicitly reflected by the first and thirdvegetation axes �Table 2; Figure 3a, Figure 3g�.Though not included in this study, the integration ofadditional environmental attributes, for instance soilvariables, may increase the species-environment cor-relation. In our case this information was not avail-able. Moreover, the study aimed at mapping vegeta-tion pattern using generally available data such as adigital elevation model.

Despite the considerable deviation of some obser-vations from the fitted regression line, the linear re-lationship was used for mapping the DCA scores.Non-linear approaches such as generalized additivemodels �GAMs� could have been used to deal withnon-linear relationships between the response and theset of explanatory variables �Guisan et al. 2002�.However, GAMs cannot deal with correlated residu-als, and because they did not perform much betterthan linear regression, we decided to use linearregression.

The identified correlation of vegetation and topo-graphic attributes made it possible to map vegetation

at unobserved locations using the established relationbetween vegetation and topography as also reportedin other studies �Gottfried et al. 1998; Burrough et al.2001; Hörsch et al. 2002; Meentemeyer and Moodry2000�, but with the differences, that we addressedspatial correlation between sampling sites, thus opti-mising spatial predictions. Spatial correlation of theobservations adds useful information especially in ar-eas with spatially variable species composition, buthomogeneous site conditions. The sample variogramsof the DCA score regression residuals showed thatthere are other spatial structures in the vegetation datain addition to a pattern explained by topography witha dominant long-range correlation. Choosing a finerresolution of the sampling scheme might haveresulted in a more accurate description of the vegeta-tion pattern, but would have been more costly. A veryfine resolution mapping such as used in the vegeta-tion study by Gottfried et al. �1998� requires a verydetailed digital elevation model and a sophisticatedglobal positioning system to georeference the preciselocation of the sampling plots in the field. These con-ditions are not appropriate for socio-economic plan-ning, where a suitable balance must be found betweenthe cost of data collection and data accuracy �Meen-temeyer and Moodry 2000�. However, our procedurecould have been improved by including intermediatesamples in areas where topography varies rapidlyover short distances, or by stratified sampling, wheresampling density increases with vegetation variabil-ity in attempt to find the right balance between dataaccuracy and costs. Nevertheless, a stratified sam-pling strategy by including environmental stratifica-tion as for example suggested by the gradsectsampling method �Gillison and Brewer, 1985� is incontradiction with the aim to obtain an unbiased es-timate, but could be implemented in a second step toimprove the correlation analysis.

The DCA scores and therefore the generation ofvegetation classes are determined by the samples in-cluded in the analysis. The incidence of a few sampleshaving an excentric species composition, possiblycaused by local differences of sites conditions, willcause extreme DCA scores along one of the vegeta-tion axes. When classifying the DCA scores, sampleswith excentric scores will be assigned to singleclasses. Therefore fewer classes remain for the bulkof observations. This happened in the classification ofthe second subset of the DCA scores �Figure 7c�,where one sample had very exceptional DCA scoresbecause of the occurrence of species that were not

Table 5. Success rate of classification.

Classified sub maps Success rate in %

Sub map1 61.5Sub map2 49.8Sub map3 53.4Sub map4 53.3Sub map5 62.9

771

found at another location. This sample occupied oneclass and therefore only six classes were left to clas-sify the other sites of the study area. The comparisonof the computed vegetation classes using the wholedata set with the classes derived from a random sub-set showed that the classification procedure is quitesensitive to which samples are included and it is wiseto have an equal distribution of samples over unitswith dissimilar topographic conditions.

Conclusion

The paper shows that the procedure proposed is auseful approach for the rapid mapping of vegetationunder the given circumstances. It integrates theestablished relation between spatial information onsite conditions and abundance of plant species, mea-sured on a simplified abundance scale, with the spa-tial structure in the vegetation data. It was confirmedthat topographic variables correlate to a certain extentwith the spatial pattern of alpine vegetation, and thatthis information can be used to map alpine vegetation.DCA supported the illustration of the variability ofthe species composition at observed locations. Thecomparison of the vegetation classes so obtained withthe original data set showed that the derived classesencompassed the general structure of the vegetationpattern. Repeating the procedure for different subsetsof the vegetation data demonstrated the consistencyof the mapping procedure. A strong persistence of thelarge-scale features indicates the necessity for furtherresearch with respect to short-range features.

Acknowledgements

This work was financed by the European UnionProject GETS, a European Research Network for thrApplication of Geomorphology and EnvironmentalImpact Assessment to Transportsystems, GETSproject contract No. ERBFMRXCT 970162.

Appendix

Table 4. Variogram parameters for each DCA axis. Variogrammodels for DCA 1, 3 and 4 are effectively linear.

c0 c1 a

DCA 1 0.11 1.33 10823DCA 2 0.42 0.80 612DCA 3 0.48 3.49 12779DCA 4 0.58 1.23 2522

Table A1. The presence of common species and their classificationinto general vegetation classes.

Common species General vegetation class

Antennaria dioica Alpine grassland, Alpine heathsCalluna vulgaris Alpine heathsCampanula barbarta Alpine grasslandCarex species (above all Carexcurvula)

Alpine grassland, Alpine heaths

Cladonia rangifera Alpine heathsCalluna vulgaris Alpine heathsFestuca species(above all Fes-tuca varia)

Alpine grassland, Alpine heaths

Geum repens Alpine grasslandHelictotrichon versicolor Alpine grassland, Alpine heathsHomogynae alpina Alpine grasslandIslandica cetraria Alpine heathsLeontodan helveticus Alpine grassland, AlpineLeucanthemum alpinum Alpine grasslandLuzula campestris Alpine grassland, Alpine heathsNardus stricta Alpine grassland, Alpine heathsPhyteuma orbiculare Alpine grassland, AlpinePolytrichium species Pioneer vegetationPotentilla aurea Alpine grassland,Rhododendron ferrugineum Alpine heathsTrifolium alpinum Alpine grasslandVaccinium myrtillus Alpine heathsVaccinium uliginosum Alpine heathsVaccinium vitas-ideae Alpine heaths

772

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774

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