combining vegetation indices, constrained ordination and...

Remote Sensing of Environment 114 (2010) 1155–1166

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Remote Sensing of Environment

j ourna l homepage: www.e lsev ie r.com/ locate / rse

Combining vegetation indices, constrained ordination and fuzzy classification formapping semi-natural vegetation units from hyperspectral imagery

Jens Oldeland a,b,⁎, Wouter Dorigo c, Lena Lieckfeld a,b, Arko Lucieer d, Norbert Jürgens a

a Biocentre Klein Flottbek and Botanical Garden, University of Hamburg, Ohnhorststr. 18, 22609, Hamburg, Germanyb German Aerospace Center, 82203 Oberpfaffenhofen, Germanyc Institute of Photogrammetry and Remote Sensing, University of Technology, Gusshausstrasse 27-29, 1040 Vienna, Austriad School of Geography and Environmental Studies, University of Tasmania, Private Bag 76, Hobart 7001, Tasmania, Australia

⁎ Corresponding author. University of Hamburg, BBotanical Garden, Ohnhorststr. 18, 22609, Hamburg, G407; fax: +49 40 42816 539.

E-mail address: [email protected] (

0034-4257/$ – see front matter © 2010 Elsevier Inc. Aldoi:10.1016/j.rse.2010.01.003

a b s t r a c t
a r t i c l e i n f o
Article history:Received 19 August 2009Received in revised form 4 January 2010Accepted 9 January 2010

Keywords:Cluster analysisRedundancy analysisMultivariateSupervised fuzzy c-meansSemiaridRangelandNamibiaImaging spectroscopy

Vegetation mapping of plant communities at fine spatial scales is increasingly supported by remote sensingtechnology. However, combining ecological ground truth information and remote sensing datasets formapping approaches is complicated by the complexity of ecological datasets. In this study, we present a newapproach that uses high spatial resolution hyperspectral datasets to map vegetation units of a semiaridrangeland in Central Namibia. Field vegetation surveys provide the input to the workflow presented in thisstudy. The collected data were classified by hierarchical cluster analysis into seven vegetation units that reflectdifferent ecological states occurring in the study area. Spectral indices covering vegetation and soil charac-teristics were calculated from hyperspectral remote sensing imagery and used as environmental variables in aconstrained ordination by applying redundancy analysis (RDA). The resulting statistical relationships betweenvegetation data and spectral indiceswere transferred into images of ordination axes, whichwere subsequentlyused in a supervised fuzzy c-means classification approach relying on a k-NN distance metric. Membershipimages for each vegetation unit as well as a confusion image of the classification result allowed a soundecological interpretation of the resulting hard classificationmap. Classification results were validatedwith twoindependent reference datasets. For an internal and external validation dataset, overall accuracy reached 98%and 64% with kappa values of 0.98 and 0.53, respectively. Critical steps during the mapping workflow werehighlighted and compared with similar mapping approaches.

iocentre Klein Flottbek andermany. Tel.: +49 40 42816

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1. Introduction

Vegetation mapping aims to accurately identify the distribution ofdifferent types of vegetation in a defined area. The resultingmaps can beseen as a baseline inventory to assist natural resource or conservationmanagement and land use planning. Depending on the scale and geo-graphical context, vegetation can be described by its physiognomical–ecological characteristics leading to so-called formations such asgrassland, shrubland or forest. These descriptions are based on domi-nant life forms and the main vegetation structure and can be found inmany land cover descriptions suitable for coarse spatial resolutions(McDermid et al., 2005). On the other hand, floristically defined plantcommunities, based on e.g. diagnostic and differential plant species areoften used for vegetation mapping (Chytrý & Tichý, 2003). The plantcommunity based mapping approach is mainly used on a local orregional scale and yields species lists for all existing plant communities,

giving more precise information on plant diversity and conservationstatus (Amarnath et al., 2003; Van Rooyen et al., 2008).

In both cases,field surveys for vegetationmapping are cost- and laborintensive. Especially in remote areas like the polar regions or many aridecosystems, ground basedmapping becomes logistically more challeng-ing. In the last decades, remote sensing has significantly contributed tovegetation mapping of remote areas and for mapping structurally de-finedvegetationunits on global, regional, and local extents (Cihlar, 2000;Gamonet al., 2004;McDermid et al., 2005). Extensive vegetation surveysallow the combination of floristically defined plant communities withsatellite data in order tomap spatial distribution of vegetation (Aragon&Oesterheld, 2008; Zak & Cabido, 2002). Over smaller extents, airbornesensors have been used successfully for the mapping of floristicallydefined vegetation units (Lewis, 2002; Schmidt & Skidmore, 2003;Thomas et al., 2003). These studies used hyperspectral systems with anincreased spectral resolution.

Hyperspectral sensors measure a large number of spectral bands,which provide a near-continuous spectrum covering a large range ofwavelengths from the visual near infrared (VNIR) to the shortwaveinfrared (SWIR). While the VNIR region provides information specifi-cally on leaf pigments and vegetation structure, bands in the SWIR

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1156 J. Oldeland et al. / Remote Sensing of Environment 114 (2010) 1155–1166

region are known to enhance characterization of vegetation, especiallyin semiarid areas, by providing detailed information on woody com-ponents and water content of the vegetation (Asner & Heidebrecht,2002; He et al., 2006; Lucas et al., 2008; Ustin et al., 2004). Many studiesapplying hyperspectral data have used information on the difference inreflectance values in single or combined bands (Liesenberg et al., 2007;Lucas et al., 2008). This approach suffers from two problems. Firstly,reflectance values of single bands often perform poorly when used fordiscriminating vegetation classes with similar species composition(Thomas et al., 2003). Secondly, a high correlation between multiplebands can lead to erroneous results when classification techniques de-pending on regression analysis, such as linear discriminant analysis, areapplied (Hansen & Schjoerring, 2003).

Information from different parts of the measured spectrum is oftencombined to form what is called a spectral vegetation index (VI). Thespectral bands used to form theVI are selected and combined in away toenhance spectral features related to the variable of interest whilereducing undesired effects caused by variations in soil reflectance, sunand view geometry, atmospheric composition, and other leaf or canopyproperties (Dorigo et al., 2007). The normalized difference vegetationindex (NDVI) has become a standard remote sensing product for eco-logical applications (Pettorelli et al., 2005) and it hasbeenwidely appliedfor discriminating and interpreting mapped vegetation units (Honget al., 2004; Rahman & Gamon, 2004). However, only few studies incor-porated other spectral indices for vegetation mapping and these studiesmainly used coarsemultispectral satellite data (de la Cueva, 2008; Honget al., 2004). Indices specifically designed for hyperspectral remotesensing data (hereafter called “hyperspectral indices”) take advantage ofthedetailednarrow-band informationor the largenumber of contiguousbands provided by suchdata.Whilemany hyperspectral indices only usethe bands in the VNIR some also make use of the SWIR region. From theplethora of available spectral indices (Treitz & Howarth, 1999; Ustinet al., 2005) many have not been tested for vegetation mapping.

Twomain strategies can be identified in remote sensingmethods forvegetation mapping: The first strategy involves classification of thespectral information, either on a per-pixel or on a sub-pixel basis. Thetraditional approach is to use supervised classification of remote sensingdata based on a priori knowledge of land cover. Maximum likelihoodclassifiers are commonlyused formultispectral datawhereas the spectralangle mapper is a frequently used method for classifying hyperspectraldata (Richards&Xiuping, 2006). Both classifiers lead to a vegetationmapconsisting of hard boundaries. Yet, for representing vegetation in semi-natural landscapes, where ecotones are important landscape structures(Arnot& Fisher, 2007), a continuous or fuzzy interpretation of vegetationbecomes increasingly important (Foody, 1992; Lees, 2006; Lucieer, 2006;Moraczewski, 1993; Schmidtlein & Sassin, 2004). Fuzzy classificationtechniques have been recognized as a suitable tool to map (semi-)natural vegetation units because they allow a soft overlap of several hardclasses (Foody, 1992; Lu & Weng, 2007; Lucieer, 2006). Despite theirgreat potential to map and identify continuous natural vegetation, su-pervised fuzzy classification algorithms are not frequently employed formapping vegetation units.

The second strategy comprises multivariate techniques, such asCanonical Correspondence Analysis (CCA) (Ter Braak, 1987) or redun-dancy analysis (RDA) (van den Wollenberg, 1977), that create a rela-tionship between detailed quantitative information on vegetation, e.g.species composition, vegetation cover or other structural parametersand spectral information (Brook&Kenkel, 2002; Thomaset al., 2003). Sofar, this strategy has been less frequently used for vegetation mappingthan image classification, although examples are increasingly found inrecent literature (de la Cueva, 2008; Dobrowski et al., 2008; Jensen &Azofeifa, 2006; Malik & Husain, 2008; Yue et al., 2008). The strength ofthe multivariate approach is that it uses the full information on speciescomposition by simultaneously relating each single recorded species tothe datamatrix on spectral information, such as sensor bands or spectralindices. This leads to an ordination space where the ordination axes

reflect the statistical relationship between species and spectralinformation, putting species with a similar relationship to the indicesin order along the axes. There are different views on how vegetationshould be represented in the multivariate approach and how it shouldbe related to spectral information. On the one hand, cluster analysis ofvegetation datasets allows finding discrete units based on floristic data.These discrete groups are easy to handle and can be used in furtheranalysis of spectral data (Lewis, 1998; Thomas et al., 2003). On the otherhand, vegetation can be interpreted as a continuum consisting oftransitions between plant communities. Ordination techniques such asNonmetric Multidimensional Scaling (NMDS) or Detrendend Corre-spondence Analysis (DCA) arrange vegetation data along indirectfloristic gradients displayed by ordination axes, which can be used forfurther analysis (Schmidtlein & Sassin, 2004; Schmidtlein et al., 2007).

Several authors have combined cluster analysis with constrainedordination techniques such as Canonical Correspondence Analysis(CCA) using spectral bands or principal components of the satelliteimage as constraining variables (Armitage et al., 2000; Dirnböck et al.,2003; Ohmann & Gregory, 2002; Thomas et al., 2003). CCA assumesthat species have an optimum along an environmental gradientresulting in a hump-shaped (unimodal) response (Jongman et al.,1995). Therefore, CCA calculates Gaussian canonical regressions, i.e.using polynomials for each explanatory variable, where the species–environment correlation is based onweighted averages using the Chi-square metric. Another constrained ordination technique similar toCCA is redundancy analysis (RDA) (van denWollenberg, 1977), whichrelies on multiple linear regressions calculated from the weightedsums of the Euclidean distances between two matrices. The assumedlinear relationship between species and environment implies amono-tonic increase or decrease of species abundance or occurrence alongan environmental gradient. Depending on the dataset and the under-lying assumptions of the study aim, it can be useful to check whetherRDA or CCA is the better choice. In spite of the possibilities to usespectral indices as explanatory variables in constrained ordination,only a few studies combined other indices other than NDVI in RDA orCCA to create relationships between ground-checked vegetation unitsand canopy properties measured by spectral indices (de la Cueva,2008; Goodin et al., 2004).

In this study, we present an approach for combining hyperspectralremote sensing data with field survey information on plant speciescomposition and plant cover in order to produce a map of floristicallydefined vegetation units. We apply the method to a dwarf shrubsavannah inCentralNamibia at a spatial resolution of 5 mover anareaof19.5 km2. We aim at developing high resolution vegetation maps basedon the relationship between classifiedfield observation data and a set ofhyperspectral vegetation and soil indices, established by a constrainedordination technique. Two independent test datasets are used forvalidation. The potential and shortcomings of our methodology arecritically discussed with regard to other approaches.

2. Material and methods

2.1. Study area

The study area comprises 19.5 km2 of gently undulating rangelandsnorthwest of the town of Rehoboth, Namibia (23° 7′ 13.08″ S, 16° 53′47.40″ W). The climate is semiarid receiving 250 mm annual rainfallwith mean annual temperatures of 20 °C. Vegetation is mainly a dwarfshrub savannah but is heavilymodified by land use. The in situfield datawere sampled on two farms with contrasting management strategies;the farmNarais applies an extensive grazing strategywithmainly cattlein a camp-rotation system. The second farm, Duruchaus, is intensivelygrazed with sheep and goats. Azonal vegetation occurs in and aroundclay pans and some thickets dominated by Acacia mellifera on rocky redsoils occur mainly on Duruchaus.

1157J. Oldeland et al. / Remote Sensing of Environment 114 (2010) 1155–1166

2.2. Methodological overview

Our mapping approach consists of five separate steps. The first twosteps comprise vegetation sampling and identification of vegetationunits as well as acquisition and processing of airborne hyperspectralremote sensing data. This is followed by the calculation of a set ofspectral indices that were already applied in other studies for semiaridsavannahs. Constrained ordination allows vegetation samples to berelated to spectral indices and allows for a derivation of ordination axesreflecting their statistical relationship. Ordination axes that showa goodstatistical fit with the spectral indices were converted to a set ofordination images. Finally, the newdataset is classified into quantitativevegetation distribution maps for each single vegetation unit using asupervised fuzzy classification technique. The whole procedure isexplained in greater detail in the following sections.

2.3. Field sampling

Field samplingwas carriedout inApril 2007onNarais andDuruchausin the Rehoboth district, Central Namibia. 89 vegetation plots wereplaced by a preferential sampling design relyingmainly on image inter-pretation and accessibility from roads or farm tracks. This was done inorder tomaximize the variation in detectable vegetation units accordingto the remotely sensed image andminimizing sampling effort. To ensurespatial compatibility between ground data and pixel resolution weapplied a plot size of 25 m×25m following Justice and Townshend(1981) who provided a formula (Eq. (1)) to calculate the optimal size ofvegetation plots in relation to the pixel size and the geometric accuracyof the imagery. A is the area to be sampled, in this case 625 m2, pixel size(P) was 5 m and geometric accuracy of the image (G) was set to 2 pixelsfollowing (Brogaard & Ólafsdóttir, 1997)

A = P 1 + 2Gð Þð Þ2: ð1Þ

At each vegetation plot, all vascular plant species were recordedand their abundance was estimated visually as percentage cover.Vegetation data were entered into a vegetation database (Mucheet al., 2009) allowing querying and linkage to a GIS. For furtheranalysis, abundance information was extracted from the vegetationdatabase and stored in plot-by-species data matrices that would serveas the response matrix in the constrained ordination (Section 2.7).During the extraction from the database, species with less than threeoccurrences were removed in order to avoid distortion of the clusteranalysis due to rare species (Cao & Larsen, 2001; Marchant, 2002). Fora subset of the study area, the biodiversity network BIOTA-AFRICA(www.biota-africa.org) provided a comparable dataset of 41 vegeta-tion plots of 10 m×10 mwhich were used for accuracy assessment asan external validation dataset.

2.4. Image data and processing

During a flight campaign in October 2005 a hyperspectral imagewastaken using the HyMap airborne imaging spectrometer (Cocks et al.,1998). The image has a spatial resolution of 5 m×5 m and covers 126bands with a 10 nm bandwidth in the wavelength range fromapproximately 450 nm to 2500 nm. The image was orthorectifiedusing the PARGE software (Schläpfer & Richter, 2002) in combinationwith 15 differential GPS measurements (accuracy ∼0.5 m) from theBIOTA-AFRICA network. Errors of the rectified image were less than1 pixel (<5.0 m) in x -and y-directions. ATCOR-4 (Richter & Schläpfer,2002) was used for vicarious calibration and for the removal ofatmospheric effects. For the vicarious calibration, spectroradiometricmeasurements were taken with a portable Fieldspec PRO FR spectrom-eter (Analytical Spectral Devices, Inc.) at four homogeneous dark andbright bare soil targets and converted into reflectance units using aSpectralon™ panel as white reference. Depending on wavelength, the

deviation of ground measured reflectance and HyMap reflectance ob-tained after atmospheric correction varied between 1 and 4% absolutereflectance units.

2.5. Cluster analysis

Since redundancy analysis was planned as an ordinationmethodwechoose Euclidean distance as a distance measure for cluster analysis inorder to be sure to handle data with same distance measures forclustering and for ordination. RDA extends PCA to a constrainedordination technique by allowing multiple regressions of twomatrices:one dependent matrix and one explanatory matrix (van den Wollen-berg, 1977). Hence, RDA also relies on the Euclidean distance.Vegetation data sets are commonly characterized by a high amount ofzeros, for which Euclidean distance is not an appropriate distancemeasure. Therefore, the abundance data was transformed using theHellinger distance (Rao, 1995), which is simply the square root of therow totals divided by the row mean values. It was shown that theperformance of abundance data in Euclidean space using Hellingerdistance gives better results than Chi-square metric or similarapproaches (Legendre & Gallagher, 2001). An agglomerative hierarchi-cal cluster analysis was performed using Euclidean distance andWard'sminimum-variance clusteringalgorithm(Fielding, 2007). It is importantto check cluster structure for validity because cluster analysis tends tofind groups even if there is no clear group structure (Fielding, 2007).Validity of each resulting cluster diagram was assessed using thecophenetic correlation coefficient (rc) which is a widely used measurefor comparing the deviance of a cluster from the original dissimilaritymatrix (Sokal & Rohlf, 1962). According to McGarigal et al. (2000) avalue of rc=0.75 or higher is a good representation of the originaldistance matrix used for cluster analysis. We applied a second qualitymeasure of clustering structure, the Agglomerative Coefficient, which isdefined as the average height of the mergers in a dendrogram and is adimensionless number between zero and one, values closer to oneindicating a better structuring (Kaufman & Rousseeuw, 1990).

It is often a difficult and subjective choice atwhich level of clusteringthe most ecologically meaningful solution can be found. For theinterpretation of the optimal level of clustering, two complementarymethodswere usedwith the restriction that no groups smaller thanfiveplots shall be produced in order to allow a sound statistical analysis.Analysis of similarity (ANOSIM) developed by Clarke (1993) is a non-parametric method for analysing group separability. It compares thedifferenceofmeanranksbetweengroups andwithin groups andyieldsameasure called R (not to be confused with a correlation measure). Rranges from 0 to 1, with values larger than 0.75 indicating a goodseparation. Second, Indicator Species Analysis (ISA), developed byDufrene and Legendre (1997), produces an indicator value for eachspecies,which is ameasure of howwell a species is restricted to a certaincluster. It also calculates the sumof all probability values, which reflectsthe amount of indicator species found. ANOSIM reports the strength ofgroup separabilitywhereas ISA allows an ecological interpretation of theclasses. Results from bothmethodswere used to verify and describe thecommunities derived at cluster levels from two to twenty. Finally, eachvegetation unit resulting from the chosen cluster solution wascharacterized in terms of dominant species and through a generaldescription of its structure. All analysis were performed using thepackages cluster (Maechler et al., 2005), vegan (Oksanen et al., 2008)and labdsv (Roberts 2007) in the statistical environment R 2.8.1 (RDevelopment Core Team, 2008).

2.6. Spectral indices

A review of the literature on hyperspectral indices that are poten-tially suitable for characterizing the biophysical conditions of semiaridrangelands resulted in 30 vegetation and soil indices. With this setof indices, variations in all relevant canopy variables (e.g. pigments,

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canopy structure, canopy water content, woody parts, litter, and soilbackground)were covered. The indiceswere calculated in ENVI 4.2 (RSI2005) using AS-Toolbox (Dorigo et al., 2006). We removed indicesiteratively from the dataset until a general correlation between theindices below Pearson's r=±0.75 was reached. Variance inflationfactor analysis (VIF) (Zuur et al., 2007)was applied to further reduce thelist of indices. According to Montgomery et al. (2001), VIF values largerthan five prove multicollinearity in a regression analysis. Using thisthreshold, a total set of eight indices was selected (Table 1).

The eight selected indices cover a wide range of vegetation and soilcharacteristics. DGVI1, DGVI2, NDLI and CAI were specificallydesigned for hyperspectral sensors and already applied in savannahlandscapes (Chen et al., 1998; He et al., 2006; Miura et al., 2003;Nagler et al., 2003; Serrano et al., 2002); the ratios sensitive tovariations in soil clay and iron content were developed for narrowbandwidths which are usually supported by hyperspectral images(Dorigo et al., 2006). Since vegetation plots are represented as centercoordinates of the plots in a GIS we applied a median filter for5×5 pixels for each index to calculate a single value representing thesize of each vegetation plot. For each vegetation plot, values ofspectral indices were extracted from the images and stored in a plot-by-index data matrix which subsequently served as the predictormatrix in the constrained ordination.

2.7. Constrained ordination

We analyzed the gradient length of the first axis of a DetrendedCorrespondence Analysis (DCA) of the vegetation data, which is acommonway to interpret compositional gradients in species datasets,in order to choose suitable ordination methods (McCune et al., 2002).In DCA, a detrending and non-linear rescaling of ordination axes isperformed so that axes are rescaled in units of average standarddeviation (SD) of species turnover, where an axis value larger than 4SD indicates a complete turnover in species composition (Legendre &Legendre, 1998). For the first axis, we found a gradient length of 3.1SD, indicating an intermediate compositional gradient. Therefore, wechose redundancy analysis (RDA) which is known to be an adequateordination method for short to intermediate compositional gradientsassuming linear species responses along the environmental gradients(Legendre & Legendre, 1998).

In RDA, the abundance and composition of species in the plot-by-species matrix is constrained by the values of the spectral indices inthe plot-by-index matrix such that each ordination axis represents alinear relationship, i.e. a multiple regressionmodel, between response(species) and all predictor variables (spectral indices). For a generalevaluation of ordination results the amount of total varianceexplained by each axis is interpreted as a measure of ordinationsuccess. Ordination diagrams were created using standard scaling andlinear constraints (LC-scores) and were visually inspected for groupseparation along the axes as well as for vector length and direction.Vectors represent the biplot scores for each predictor variable; their

Table 1Final set of spectral indices used in the analysis.

Nr. Index Full name Feature Reference

1 CARI Chlorophyll absorption inReflectance Index

Chlorophyll Kim et al. (1994)

2 LCI Leaf Chlorophyll Index Chlorophyll Datt et al. (2003)3 DGVI1 First-order derivative green

vegetation indexGreenness Chen et al. (1998)

4 DGVI2 Second-order derivativegreen vegetation index

Greenness Chen et al. (1998)

5 NDLI Normalized Difference Lignin Index Lignin Serrano et al. (2002)6 CAI Cellulose Absorption Index Litter Daughtry (2001)7 CLAY Clay ratio Soil Dorigo et al. (2006)8 IRON Iron ratio Soil Dorigo et al. (2006)

length and direction reflect their importance relative to the ordinationaxes. Significance of the ordination was assessed by calculating agoodness of fit test (using function as.mlm.rda in vegan package,Oksanen et al., 2008) yielding R2 and p-values for the relationshipsbetween indices and ordination axes and between vegetation unitsand ordination axes.

2.8. Calculating ordination maps

Since RDA inherently produces linear combinations of predictorvariables to calculate distances in the ordination space, it is possible touse regression coefficients and spectral indices to calculate maps ofordination axes. This produces a new dataset, with one image layerper ordination axis. In order to interpret the ordination images wecompared the relative position of the vegetation units along theordination axes in the ordination diagrams, e.g. we looked for relativeposition of unit 4 on ordination axis one, and used the regressionstatistics of the redundancy analysis to evaluate the fit of the relationbetween the axes, indices and vegetation units.

2.9. Fuzzy classification

In order to finally extract continuous vegetation unit maps from theordination images we applied a supervised fuzzy c-means classifier(SFCM) to estimate the abundance of vegetation unit per pixel (Lucieer,2006; Zhang& Foody, 2001). For the vegetation units, regions of interest(ROI) were created that covered the area of each vegetation plot(625 m2). The ensemble of pixels was divided into a training and a val-idation dataset for each vegetation unit. The degree to which a samplebelongs to a class is expressed by a continuous membership value thatranges between 0.0 and 1.0, where 1.0 indicates perfect similaritywith aclass cluster. The fuzziness component, which determines the amount ofoverlap allowed, was set to 2.0 following various authors (Burroughet al., 2000; Lucieer, 2006). For classificationweused thenon-parametrick-Nearest Neighbor (k-NN) distance metric within the SFCM algorithmfollowing Lucieer (2006) and building onto Zhang and Foody's (2001)Euclidean SFCM algorithm. The k-NN algorithm searches the featurespace for the k nearest pixels within the training sample, whose fielddata vectors are known, applying a distance measure defined in featurespace (Franco-Lopez et al., 2001; Katila & Tomppo, 2001). The k-NNalgorithm does not make any assumptions about the statistical distri-bution of the training pixels, which is advantageous in our situationwhere the number of pixels available for training is limited. FollowingLucieer (2008), we used a number of k=5 nearest neighbors. The SFCMalgorithm produces a fuzzy classification of the ordination imagesresulting in three types of output. First, it computes amembership imagefor each vegetation unit indicating the percent membership of eachpixel. Second, it produces adefuzzifiedhard classification image fromthemembership images based on maximum membership values. Third, itcalculates an image of the Confusion Index (CI), which summarizes theconfusion of class assignment in each pixel. The CI is a ratio of the secondmaximum membership and the maximum membership for each pixel.High values indicate a high classification uncertainty.

2.10. Accuracy assessment

Two datasets were available for assessing the accuracy of the clas-sification result. First, the result was validated with the pixels from theROIs that were not used to train the k-NN classifier, we refer to this asthe internal validation dataset. An independent datasetwasprovided bythe biodiversity network BIOTA-AFRICA (www.biota-africa.org). Vege-tation plots of the independent dataset were assigned labels of alreadyclassified vegetation units according to species composition andabundance. Not all vegetation units were present in the BIOTA-AFRICAdataset, leaving unit four and six empty. The quality of the hard classi-fication vegetation unit map was assessed using a confusion matrix.


Table 2Overview of vegetation units that were derived by cluster analysis. Characteristicspecies for each type are sorted after average abundance in the cluster. Number of plots(n) show cluster size.

Type n Characteristic species Description of the vegetation unit

1 20 Monechma genistifoliumPentzia calvaGeigeria ornativa

Open dwarf shrub with sparse cover,mainly Monechma genistifolium oncalcareous rocky soils

2 5 Stipagrostis ciliataFelicia clavipilosa

Grass and shrub vegetation on outcrops anddeeply incised rocky drainage lines

3 28 Stipagrostis obtusaMonchema genistifoliumMelolobium microphyllum

Sparse grassland and open patches, mainlyStipagrostis obtusa, only few dwarf shrubs

4 10 Acacia melliferaAlbizia anthelminthicaStipagrostis uniplumis

Woody acacia shrub on shallow red soils

5 10 Leucosphaera bainsiiAizoon schellenbergiiEnneapogon desvauxii

Dwarf shrub savanna with many dwarfshrubs, and perennial grasses on darkbiological soil crusts

6 7 Fingerhuthia africanaAizoon giessiiMelhania virescens

Grassland with mainly Fingerhuthiaafricana and few dwarf shrubs on rockysiliceous soil.

7 9 Panicum lanipesEragrostis rotiferRhigozum trichotomumAcacia hebeclada

Shrub vegetation at the border of clay pansand shallow drainage lines with clay soils;grasses and herbs in center of pans


Finally, the overall accuracy of classification (OA) andkappavalueswerecalculated for each dataset and for subsequent numbers of ordinationaxes.

3. Results

3.1. Cluster analysis

The quality of the produced dendrogram, i.e. cluster structure asmeasured by the Agglomerative Coefficient, was 0.86 whereas thecophenetic correlation coefficient reached a value of rc=0.75, bothindicating a considerable amount of structure in the dendrogram. Thecomparison of ANOSIM and ISA for each level of clustering from two totwenty suggested either a level of three or seven clusters. Since thecluster resolution is desired to be as detailed as possible to classify thevegetation into meaningful vegetation units, seven clusters werechosen as the appropriate size for further analysis. Table 2 gives anoverview on the ecological description of the vegetation units basedon constancy of species and relative abundance per group.

3.2. Constrained ordination

A total of 34% variance in the species data was explained by theeight constraining axes. The first three axes explained 12%, 9% and 5%respectively. A simplified ordination diagram of the first and secondRDA axis is shown in Fig. 1. In order to improve interpretability,ellipses showing 95% confidence levels were drawn around the groupcentroids. In the ordination diagram, the vegetation units derived bycluster analysis show distinct clusters separated by spectral indicesalong the first two ordination axes. The first axis represents mainly agradient of vegetation cover along which the different vegetationunits line up. The structurally more complex unit four is positivelyrelated with CLAY, DGVI1 and DGVI2 whereas all other units arenegatively related with those indices along the first axis. The secondaxis is a gradient of chlorophyll concentration, mainly spread by CARIand LCI. CARI is positively correlated with unit 3 and LCI is positivelycorrelated with vegetation unit 5. The third axis (Fig. 1) separatesvegetation units 7, 2 and 1 by different dry matter componentsrepresented by NDLI and CAI. The latter is strongly positively relatedwith vegetation unit 7. NDLI is strongly negatively related with units 2and 7. The fourth axis separates units with high values for CARI and

CLAY (3, 4, 5, and 6) from the groups 7, 2 and 1 which have low valuesfor CARI and CLAY.

The significance of the relationship between spectral indices,vegetation units and the ordination axes performed by RDA is given inTable 3. Spectral indices and vegetation units show high R2 values andlow p-values up to the fifth ordination axis. From ordination axis six toeight, R2 values stay below 0.4, whereas overall p-values were slightlylower than 0.001 but were still significant up to the 5% level. A highsignificance for a spectral index along an axis indicates a strong rela-tionshipwith that axis, whereas a high significance for a vegetation unitreveals a good separation of vegetation units along that specific axis. Forexample, vegetationunit oneand two cannot be separatedwell fromtheother groups along the first axis (Table 3) but there are high signif-icances on the third ordination axis displaying that this axis is bettersuited to separate vegetation units 1 and 2 from the others.

3.3. Fuzzy classification

The supervised fuzzy c-means classification using the k-NN classifierproduced three sets of images: class membership images for eachvegetation unit (Fig. 2a–g), a confusion image (Fig. 2h) and a hardclassification image (Fig. 3). The number of axes leading to the bestclassification results was assessed by comparing overall accuracy andkappa values obtained for the two validation data sets using differentnumbers of axes (Table 4). Starting from the two axes that explainedmost variation we iteratively added the other axes until all eight axeswere included. For the internal validation dataset, best overall accuracyand kappa values were found for a total of six and eight axes. Theindependent BIOTA-AFRICA dataset achieved best values with only fiveaxes, yet the values for the eight axes solution were only slightly lessaccurate. Based on the results obtained for both validation sets, the eightaxes solution was chosen. Membership images, hard classification andconfusion image were checked visually for credibility in order to betterinterpret classification results, e.g. high values in the confusion image(Fig. 2h) indicate overlapping classes, i.e. transition zones and mixedunits, whereas low values show purer classes. The errormatrices for theeight axis solution are shown in Tables 5 and 6 for the internal andindependent validation dataset respectively.

4. Discussion

4.1. Vegetation maps

We were able to map seven vegetation units of a rangeland area inCentral Namibia with a high accuracy but a relatively low samplingeffort (number of vegetation plots=89). Mapped vegetation unitscomprise the main plant communities and their ecological conditionsfor an area of around 19.5 km2 (Table 2). The membership images ofvegetation units 1, 4, and 5 (Fig. 2a, d, and e) and the hard classificationimage show a sharp transition in the center of the study area. This iscaused by a fence line separating both farms and is typical for SouthAfrican rangelands indicating contrastingmanagement strategies (Todd&Hoffman, 1999). Validation of the hard classificationmap (Fig. 3)withthe internal validation dataset shows a very good performance byreaching kappa values of 0.98 and an overall accuracy of 98%. Theconfusion image (Fig. 2h) indicates that vegetation units 4 and 7 onlyhave little confusion with other classes. This can be explained by thehigh proportion of larger shrub species, mainly A. mellifera or A.hebeclada, in both vegetation units, which makes them more distinctdue to higher values in the DGVI indices. The other vegetation unitsshow higher grades of fuzziness. The error matrix of the internalvalidation dataset (Table 5) shows that vegetation units 1 and 3 areslightly confused while the other vegetation units were classified 100%correctly. This confusion is caused by an overlap of high membershipvalues for units 1 and 3 (Fig. 3a, c). This overlap is a goodexample for thefuzziness of the classification. The difference between both vegetation

Fig. 1. Simplified ordination diagram of the RDA analysis. Ellipsoids show 95% interval of vegetation units which are represented by number codes. Vectors show direction andimportance of spectral indices. First two constrained RDA axes (left) explain 21%, RDA axes three and four (right) explain additional 9% variation in species composition throughspectral indices.


units is mainly a difference in stoniness and the cover fraction of grassand shrubs, whereas species composition is mainly the same. Both veg-etation units principally are found on the southern farm and are mostlyneighboring, which increases the possibility of overlap.

The independent dataset shows a moderate performance with akappa value of 0.53 and an overall accuracy of 64%. In the classificationresults again some confusion exists between vegetation units 1 and 3(Table 6), but also between vegetation units 1 and 2, aswell as between2and5 (Fig. 2a, b, c ande). The reasons for the less accurate classificationresults based on the external dataset are various; firstly, the indepen-dent dataset was not collected for the special purpose of vegetationmapping but for biodiversity monitoring. This means that vegetationplots are constantly placed in the center of selected hectares, belongingto a grid of 1×1 km with a mesh width of 100 m, according to thesampling scheme of the monitoring project (Jürgens, 1998). Thus,vegetation plots are restricted to center points of hectares andmight bein the vicinity of different vegetation units. This seems to be the maincause for the confusion of the classification. Secondly, there were only

Table 3Significance of relationships between spectral indices, vegetation units and ordination axes

RDA1 RDA2 RDA3 R

Spectral indicesCARI * * *** **LCI *** *DGVI1 . **DGVI2 *** **NDLI * *** ***CAI * . ***CLAY ** **IRON ** *R2 0.6323 0.6305 0.6209 0F-value 17.41 17.28 16.58 1p-value <0.001 <0.001 <0.001 <

Vegetation unit1 * *** **2 ** ***3 *** *** *** **4 *** * *** **5 *** *** *** **6 *** . **7 *** *** ***R2 0.9027 0.6716 0.7651 0F-value 126.7 27.95 44.52 2p-value <0.001 <0.001 <0.001 <

Signif. codes: <0.001***; 0.01=**; 0.05=* 0.05; ‘.’=0.1.

few vegetation plots in total (n=41) and plot sizewas smaller than ourdesign, e.g. 10 m×10 m versus our 25 m×25 m, capturing fewerspecies. Thirdly, with the same sampling approach different surveyteams usually yield different estimates of percent cover or differ inspecies identification (Kercher et al., 2003) which can lead to differentclassifications, making it more difficult to compare vegetation datasetsbetween different projects. Finally, two vegetation units did not fall intothe sampling scheme of the external dataset and vegetation unit two isunderrepresented with only one vegetation plot.

On a comparable scale, Thomas et al. (2003) mapped borealpeatlands in Canada using 600 vegetation plots of 1 m2 by which theywere able to distinguish up to nine types of fen vegetation. By applyinga maximum likelihood classification to a hyperspectral image, theyyielded maximum kappa values between 0.32 and 0.55. The moderateperformance was explained by the low spectral separability of theproduced vegetation classes which also could be due to the very smallplot size and the small total area (600 m2) sampled. In fact, the hypo-thesis that plant communities can be clearly separated by their spectral

.

DA4 RDA5 RDA6 RDA7 RDA8

* **** ** ***** .*** ** ** *. *

* ****

.5326 0.4206 0.3217 0.2604 0.17791.54 7.35 4.802 3.564 2.1920.001 <0.001 <0.001 <0.05 <0.05

* *****

* . ** ** ** ** . ** * ***

* ** **.6206 0.4873 0.2643 0.3226 0.16562.36 12.99 4.91 6.509 2.7130.001 <0.001 <0.01 <0.001 <0.05

Fig. 2. Resulting membership images derived by the supervised fuzzy c-means classification. Vegetation units 1–7 are displayed from a–g. Bright pixels indicate high membershipvalues, dark pixels indicate low values. Confusion image (h) shows areas of high confusion in white and more pure classes in black.


Fig. 3. Classified vegetation map based on a supervised fuzzy c-means classificationresult, hard class labels according to identified vegetation units are assigned based onmaximum membership values in each pixel. The colors represent different vegetationunits, see Table 2 for explanation.

Table 5Error matrix for eight axes solution using k-NN algorithm on internal validation dataset.Values represent percent of pixels classified into class.

Unit Pixels 1 2 3 4 5 6 7 Total

Unclassified 0 0 0 0 0 0 0 0 01 116 93.1 0 0 0 0 0 0 15.12 31 0 100 0 0 0 0 0 4.343 195 1.72 0 97.44 0 0 0 0 26.854 110 0.86 0 0 100 0 0 0 15.525 84 4.31 0 2.05 0 100 0 0 13.016 71 0 0 0.51 0 0 100 0 10.077 108 0 0 0 0 0 0 100 15.1Total 715 100 100 100 100 100 100 100 100

Overall accuracy=98.18%; kappa=0.98.


features is based on the assumption that boundaries between plantcommunities are hard. This might be acceptable for intensively usedagricultural or urban landscapes with crisp boundaries between landcover types, but in semi-natural savannah systems transitions betweenplant communities are gradual. Thus, from the vast amount of image

Table 4Effect of number of bands on overall accuracy (OA) and kappa values for the internaland the independent validation datasets. Important values are highlighted in bold.

Axes Internal Independent

OA Kappa OA Kappa

1 39.02 0.24 38.16 0.192 64.06 0.57 43.59 0.253 86.01 0.83 52.52 0.384 93.29 0.92 56.52 0.435 94.54 0.93 64.00 0.536 98.18 0.98 61.56 0.497 97.90 0.98 58.70 0.468 98.18 0.98 63.82 0.52

classification algorithms available (see Lu and Weng (2007) for anoverview of classification algorithms), supervised fuzzy classificationapproaches seem to be most promising for natural landscapes. Foody(1992, 1996) pioneered the application of fuzzy classification ofvegetation with remote sensing data. Malik and Husain (2006) alsouseda supervised fuzzy classification todiscriminate between fourplantcommunities and five land cover classes on a subset of a SPOT XS scenecovering 6 km2 of a valley in Pakistan. They reached overall accuraciesbetween 65% and 72%, yet also reported the problem of spectralseparability between vegetation classes. Lucieer (2006) used supervisedfuzzy c-means classification applying the Mahalanobis distance metricon IKONOSpanchromatic bands for classifying sub-Antarctic vegetation.Lucieer did not usefloristically defined vegetationunits but seven ratherbroad categories (four non-vegetated), which lead to an overallaccuracy of 73% and a kappa value of 0.69. In comparison with theabove mentioned studies, our approach yielded equal or even betterresults depending on the dataset used for validation.

4.2. Constrained ordination using spectral indices

Vegetation mapping usually relies on the classification of remotelysensed images. Inour case, classificationwasdoneon thebasis of imagesof ordination axes, which reflect the relationship between vegetationunits and spectral indices. Visualization was possible since the RDAproduces a linear combination of predictor variables, as in multipleregressions,which can be combinedwith images of spectral indices. Theordination diagram showed a clear separation of vegetation units by theconstraining spectral indices (Fig. 2), where the spectral indicesenhanced the ordination, i.e. explained 34% of the overall variation inthe species data. Apparently constrained axes show only low eigenva-lues, yet this is due to the large amount of unconstrained axes availableto explain the variation, i.e. one for each species (n=79). The regressionresults clearly show that there is an overall good quality of theordination up to the fifth or sixth ordination axes (Table 3). The highestR2 value achieved was R2=0.63 for the regression of indices andordination axes and R2=0.92 for the regression of vegetation units and

Table 6Error matrix for eight axes solution using k-NN algorithm on independent validationdataset. Values represent percent of pixels classified into class. No vegetation plots fromindependent dataset did fit into classes four and six leaving them empty.

Unit Pixels 1 2 3 4 5 6 7 Total

Unclassified 0 0 0 0 – 0 – 0 01 68 97.06 42.86 35.00 – 4.13 – 0 35.882 21 0 0 0 – 13.22 – 0 4.713 121 2.94 57.14 51.67 – 6.61 – 0 24.714 – – – – – – – – –

5 131 0 0 4.17 – 65.29 – 0 24.716 – – – – – – – – –

7 10 0 0 9.17 – 10.74 – 100 10Total 351 100 100 100 – 100 – 100 100

Overall accuracy=63.82%; kappa=0.52.


ordination axes. Nevertheless, using all ordination axes produced thebest classification results, whichmeans that also informationwith a lowsignificance can improve the overall classification outcome.

In comparison, Thomas et al. (2003) used a CCA and were able toexplain a total amount of 44% variation in the species data using spectralbands, but their highest R2 was 0.47. Brook and Kenkel (2002) alsoapplied an RDA on scores of four ordination axes derived by acorrespondence analysis of the vegetation data and Landsat TMchannels 3, 4, 5 and 7. They were able to explain 47% of the variationin the species data and interpreted the relationship between spectralreflectance and vegetation mainly as a structural rather than a floristic(species composition) effect. As mentioned earlier, RDA and CCA differin the underlying species response model (Austin, 1987; McCune et al.,2002), hence the question whether RDA or CCA is themore appropriatemultivariate analysis depends on the ecological gradients coveredwithin the dataset and the aim of the analysis. In our case, an approachbased on CCA would have resulted in a total variance explained in 24%and less interpretable ordination diagrams, meaning that RDA leads tomore meaningful results.

The application of hyperspectral indices in multivariate analysis ofrelationships between vegetation and spectral information seems to bea logical step as the use of vegetation indices makes the approach morerobust to uncertainties in atmospheric correction and changingillumination and observation conditions compared to the directinclusion of spectral bands. Moreover, the use of vegetation indicesreduces the large amount of highly collinear data to a reasonableamount of less correlated features that can be directly linked tovegetation properties relevant for the observed canopies. However, wefound only one study that used spectral indices other than NDVI in amultivariate analysis, namely the three tasseled cap indices brightness,greenness and wetness derived from Landsat ETM+ data (de la Cueva,2008). Hyperspectral signatures of vegetation canopies are rich ininformation (Ustin et al., 2004) and many vegetation indices areavailable to exploit this information. The explanatory power of spectralindices is quite high as these indices are strongly related to certainaspects of canopy information, such asdrymatter, chlorophyll and otherplant pigments, water, nitrogen, or cellulose (He et al., 2006; Treitz &Howarth, 1999; Ustin et al., 2004). In this study, the vegetation indicesDGVI1 and DGVI2 were highly correlated with the first ordination axiswhich can be interpreted as an increase in vegetation cover but alsoin vegetation height. Unit 4, consisting of woody acacia thicket waspositively correlatedwithboth indices,whereas the sparsevegetationofunit 3 was negatively correlated. This correlation indicates that theDGVI1 and DGVI2 are sensitive to an increase in vegetation cover also insemiarid dwarf shrub savannah. Miura et al (2003) showed this for aCerrado savannah in Brazil. However, vegetation on Namibian range-lands is much sparser than Cerrado vegetation. The LCI was positivelyrelated with unit 5, which was identified on the northern farmland(Fig. 3h). Here, the vegetation composition is much different from thatin units 1 and 3 which both are negatively correlated with LCI. Thevegetation in unit 5 is dominated by dense stands of the dwarf shrubLeucosphaera bainsii (Table 3). However, it is hard to define causalitywith LCI here because there is a considerable amount of dark biologicalsoil crusts which alsomight contribute to forming this relationship. Thedry matter, or litter indices, CAI and NDLI, both point at unit 7, whichshows a considerable amount of dry biomass originating from dry grassmaterial from the last year. Regarding the soil indices, it is notable thatthe clay index is positively correlated with axis three and four and notwith the first axis as it might be interpreted from the ordinationdiagram. The iron index is related to iron induced absorptions in theNIRwhich lead to the reddish coloring of the soil with increasing ironcontent. Yet, a clear interpretation remains difficult. The iron index isnegatively related with ordination axis two, helping to separate unit 5from theother units. Itsmost significant contribution is foundon axis six(Table 3) where it separates the red soils of the unit 4 from all othergroups. It is important to notice that the set of eight indices selected in

this study equally samples from the VNIR and SWIR region, each stres-sing different properties of vegetation (Asner & Heidebrecht, 2002;Nagler et al., 2003). Transferring the approach presented here to otherstudy areas should include a sound selection of spectral indicesappropriate for the studied ecosystem and the applied spectral sensor.Hyperspectral indices provide information on a wide range of canopyrelated variables and seem suitable to be used by ecologists as variablesin multivariate ecosystem experiments or for vegetation mapping ap-proaches relying on multivariate relationships between spectral andcompositional data.

4.3. Pitfalls of cluster analysis

A source of uncertainty thatmight influence classification results liesin the means of cluster analysis which is the complicated task ofstructuring data by grouping objects according to their similarity.Complicated, because there are many subjective choices to make, suchas the choice of the overall clustering strategy, e.g. hierarchical or parti-tioning, an appropriate distance measure, and the clustering algorithm(Fielding, 2007). The result is heavily dependent on the origin andquality of the data used, aswell as the available expert knowledge of theanalyst. Ecological datasets in particular require special transformationsin order to be applicable with cluster analysis or ordination techniques(Legendre&Gallagher, 2001). For example,whendealingwith remotelysensed data, it seems more realistic to apply a quantitative distancemeasure in order to take species abundance information into consid-eration. Distance measures based on presence–absence, like thefrequently used Jaccard or Sörensen Index, can lead to a very differentresult (Legendre& Legendre, 1998),making interpretation of vegetationclassifications in relation with spectral data more difficult. In otherwords, if a species occurs only with 1% cover, and is transformed topresence, then the plot is treated in the sameway as it would have beenwhen the species had shown a cover of 100%. This is an unrealisticassumption in a vegetation mapping context based on spectral pro-perties of dominant plant canopies.

Yet, the greatest challenge is the interpretation of a resultingdendrogram. Although methods exist for checking its quality anddecidingup towhich level the cluster canbe interpreted (Fielding, 2007;Pillar, 1999), thesemethods are rarely reported in the literature (Aho etal., 2008). We applied a thorough assessment of cluster structure usingtwo measures of cluster quality, the cophenetic correlation coefficientand theAgglomerative Coefficient,which reachedhighvalues indicatingthat the clustering is based on a highly structured dataset. Furthermore,before trying to test for spectral separability between vegetation units,one should verify that vegetation units are already separated asmuch aspossible. We proposed the combination of two separability measuresANOSIM and ISA for choosing an optimal level of clustering. This turnedout to be an efficient way of interpreting group separation. We are notaware of any other vegetationmapping study using cluster analysis andremote sensing data reporting one of the above mentioned qualitychecks. Dendrograms with low structure quality produced by clusteranalysismight be themain cause for the low spectral separability foundin other studies (Malik & Husain, 2006; Thomas et al., 2003).

Interestingly, the method most frequently applied to delineatevegetation units in remote sensing studies is the Two-way IndicatorSpecies Analysis (TWINSPAN), a polythetic divisive clustering algorithmdeveloped in vegetation science by Hill (1979), for examples see Malikand Husain (2006), Peel et al. (2007), Ravan et al. (1995), and Thomaset al. (2003). This method has been criticized in ecological literaturemainly for two reasons: first, it mainly detects one large gradient due toits statistical restriction by using correspondence analysis to span afloristic gradient (Belbin & Mcdonald, 1993; Kent, 2006). McCune et al.(2002) suggested that this method should not be applied at all, exceptin situations where there is a known large one-dimensional gradientin the dataset. This can be the case when the floristic gradient reflectsthe major gradient in vegetation cover (Nilsen et al., 1999). Second,


TWINSPAN uses the Chi-square metric, which gives high weight tospecies with low abundance (Faith et al., 1987). For delineatingvegetation units based on abundance of dominant species this is notnecessarily useful. Several modifications of the TWINSPAN algorithmare reported in the literature, e.g. Roleček et al. (2009) have improvedTWINSPAN classifications by incorporating a measure of clusterheterogeneity into the algorithm, available in the free software packageJUICE (Tichy, 2002). We suggest using either an improved version ofTWINSPAN or alternative clustering techniques since they might con-tribute to a better discrimination between vegetation units and help toavoid the shortcomings of the original method.

4.4. Problems during field sampling

A sound sampling design is most important because success is onlypossiblewhen the baseline data is a realistic representation of the studyarea. In this study, the decision for a preferential samplingdriven by costand time factors led to a satisfying result. However, amore sophisticateddesign, such as a systematic or stratified random sampling, thatadditionally minimizes the effects of spatial autocorrelation, could bea more efficient approach for vegetation mapping studies (Fortin et al.,1989; McGwire et al., 1993; Stohlgren, 2007). As Nilsen et al. (1999)already pointed out, a sound sampling design matched to the sensorspecifications is required to avoid error propagationand shouldbe takeninto considerationduring theplanningphase. For example, the extent ofthe image affects the number of plots needed to capture the variety ofvegetation units (Marignani et al., 2007). Sensor resolution, i.e. pixelsize, predetermines a reasonable plot size (Nagendra & Rocchini, 2008;Rahman et al., 2003), which is known to affect different properties ofspecies data in vegetation classification, such as species constancy andnumber of species (Dengler, 2009b; Dengler et al., 2009). We havechosen a plot size of 25 m×25 m,which sufficiently covered a pixel sizeof 5 m×5m and was appropriate for the homogeneous vegetation andlevel of geometric correction of the HyMap image. In addition to plotsize, plot shape, e.g. quadratic, rectangular, circular or hexagonal shape,can affect those vegetation parameters (Dengler, 2009a; Stohlgren,2007). As stressed by Stohlgren (2007), quadratic vegetation plotsfacilitate the comparison of sampled vegetation data with pixel infor-mation of remotely sensed images. However, rectangular plots, e.g.20 m×50 m have been identified to be more appropriate for relatingspectral data to biodiversitymeasurements since they are able to catch awider range of ecological gradients than quadratic shaped vegetationplots (Oldeland et al., 2009).

5. Conclusion

Remote sensing approaches for vegetationmapping usingmultivar-iate analyses have been increasingly applied over the last years. Theseapproaches combine detailed ground data from ecological field surveyswith remotely sensed data, showing great potential in the field of finescale vegetationmapping (Alexander &Millington, 2000). In this study,we extended the multivariate approach for vegetation mapping byconnecting field data and spectral indices with different multivariateanalysis techniques. First, hierarchical cluster analysis was applied todelineate meaningful vegetation units. This is a crucial step in themethodology since all following analyses are built on proper groupidentification. Checking dendrograms for structure and quality istherefore a necessary step. Second, ordination of species dataconstrained by hyperspectral indices leads to images representing thestatistical relationship betweenvegetation units and spectral data. Here,the ability to relate spectral indices with vegetation data allows for agood interpretation of the spectral properties of each vegetation unit.The power of hyperspectral indices for multivariate ecological applica-tions is still relatively untouched. In our opinion, there is a greatpotential for the communities of remote sensing and ecologicalscientists to use these types of predictor variables for improving

vegetation mapping approaches based on multivariate relationships.Finally, we used supervised fuzzy classification technique to createabundancemaps for each vegetation unit aswell as a hard classificationimage suitable for conservationmanagement or landscape planning. Toconsider fuzziness is especially important in semi-natural landscapeswhere transitions between different plant communities are oftengradual. As Kerr and Ostrovsky (2003) have stated, ecologists havebegun to recognize the potential of remotely sensed data. Conversely,one could state that the remote sensing community should follow byrecognizing the potential of ecological datasets and methods and beaware of the potential pitfalls during field sampling and ecological dataanalysis in order to produce accurate vegetation maps.

Acknowledgements

We thank the farm owners of Narais and Duruchaus for providingaccess to their rangelands, Dirk Wesuls for helping with plant iden-tification and interpretations of the vegetation classification, ourcolleagues at the DLR for the assistance during pre-processing, JariOksanen for comments on ordination, the BIOTA-AFRICA project forproviding infrastructure and the external dataset and finally theHelmholtz-EOS PhD Programme for funding this research project. Wealso thank the handling editor and two anonymous reviewers forsignificantly improving the manuscript.

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