Combining vegetation indices, constrained ordination and fuzzy

Transcription

Remote Sensing of Environment 114 (2010) 1155–1166
Contents lists available at ScienceDirect
Remote Sensing of Environment
j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / r s e
Combining vegetation indices, constrained ordination and fuzzy classification for
mapping 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, Germany
German Aerospace Center, 82203 Oberpfaffenhofen, Germany
Institute of Photogrammetry and Remote Sensing, University of Technology, Gusshausstrasse 27-29, 1040 Vienna, Austria
d
School of Geography and Environmental Studies, University of Tasmania, Private Bag 76, Hobart 7001, Tasmania, Australia
b
c
a r t i c l e
i n f o
Article history:
Received 19 August 2009
Received in revised form 4 January 2010
Accepted 9 January 2010
Keywords:
Cluster analysis
Redundancy analysis
Multivariate
Supervised fuzzy c-means
Semiarid
Rangeland
Namibia
Imaging spectroscopy
a b s t r a c t
Vegetation mapping of plant communities at fine spatial scales is increasingly supported by remote sensing
technology. However, combining ecological ground truth information and remote sensing datasets for
mapping approaches is complicated by the complexity of ecological datasets. In this study, we present a new
approach that uses high spatial resolution hyperspectral datasets to map vegetation units of a semiarid
rangeland in Central Namibia. Field vegetation surveys provide the input to the workflow presented in this
study. The collected data were classified by hierarchical cluster analysis into seven vegetation units that reflect
different ecological states occurring in the study area. Spectral indices covering vegetation and soil characteristics were calculated from hyperspectral remote sensing imagery and used as environmental variables in a
constrained ordination by applying redundancy analysis (RDA). The resulting statistical relationships between
vegetation data and spectral indices were transferred into images of ordination axes, which were subsequently
used in a supervised fuzzy c-means classification approach relying on a k-NN distance metric. Membership
images for each vegetation unit as well as a confusion image of the classification result allowed a sound
ecological interpretation of the resulting hard classification map. Classification results were validated with two
independent 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 were
highlighted and compared with similar mapping approaches.
© 2010 Elsevier Inc. All rights reserved.
1. Introduction
Vegetation mapping aims to accurately identify the distribution of
different types of vegetation in a defined area. The resulting maps can be
seen as a baseline inventory to assist natural resource or conservation
management and land use planning. Depending on the scale and geographical context, vegetation can be described by its physiognomical–
ecological characteristics leading to so-called formations such as
grassland, shrubland or forest. These descriptions are based on dominant life forms and the main vegetation structure and can be found in
many land cover descriptions suitable for coarse spatial resolutions
(McDermid et al., 2005). On the other hand, floristically defined plant
communities, based on e.g. diagnostic and differential plant species are
often used for vegetation mapping (Chytrý & Tichý, 2003). The plant
community based mapping approach is mainly used on a local or
regional scale and yields species lists for all existing plant communities,
⁎ Corresponding author. University of Hamburg, Biocentre Klein Flottbek and
Botanical Garden, Ohnhorststr. 18, 22609, Hamburg, Germany. Tel.: +49 40 42816
407; fax: +49 40 42816 539.
E-mail address: Oldeland@botanik.uni-hamburg.de (J. Oldeland).
0034-4257/$ – see front matter © 2010 Elsevier Inc. All rights reserved.
doi:10.1016/j.rse.2010.01.003
giving more precise information on plant diversity and conservation
status (Amarnath et al., 2003; Van Rooyen et al., 2008).
In both cases, field surveys for vegetation mapping are cost- and labor
intensive. Especially in remote areas like the polar regions or many arid
ecosystems, ground based mapping becomes logistically more challenging. In the last decades, remote sensing has significantly contributed to
vegetation mapping of remote areas and for mapping structurally defined vegetation units on global, regional, and local extents (Cihlar, 2000;
Gamon et al., 2004; McDermid et al., 2005). Extensive vegetation surveys
allow the combination of floristically defined plant communities with
satellite data in order to map spatial distribution of vegetation (Aragon &
Oesterheld, 2008; Zak & Cabido, 2002). Over smaller extents, airborne
sensors have been used successfully for the mapping of floristically
defined vegetation units (Lewis, 2002; Schmidt & Skidmore, 2003;
Thomas et al., 2003). These studies used hyperspectral systems with an
increased spectral resolution.
Hyperspectral sensors measure a large number of spectral bands,
which provide a near-continuous spectrum covering a large range of
wavelengths from the visual near infrared (VNIR) to the shortwave
infrared (SWIR). While the VNIR region provides information specifically on leaf pigments and vegetation structure, bands in the SWIR
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J. Oldeland et al. / Remote Sensing of Environment 114 (2010) 1155–1166
region are known to enhance characterization of vegetation, especially
in semiarid areas, by providing detailed information on woody components and water content of the vegetation (Asner & Heidebrecht,
2002; He et al., 2006; Lucas et al., 2008; Ustin et al., 2004). Many studies
applying hyperspectral data have used information on the difference in
reflectance 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 for
discriminating vegetation classes with similar species composition
(Thomas et al., 2003). Secondly, a high correlation between multiple
bands can lead to erroneous results when classification techniques depending on regression analysis, such as linear discriminant analysis, are
applied (Hansen & Schjoerring, 2003).
Information from different parts of the measured spectrum is often
combined to form what is called a spectral vegetation index (VI). The
spectral bands used to form the VI are selected and combined in a way to
enhance spectral features related to the variable of interest while
reducing undesired effects caused by variations in soil reflectance, sun
and view geometry, atmospheric composition, and other leaf or canopy
properties (Dorigo et al., 2007). The normalized difference vegetation
index (NDVI) has become a standard remote sensing product for ecological applications (Pettorelli et al., 2005) and it has been widely applied
for discriminating and interpreting mapped vegetation units (Hong
et al., 2004; Rahman & Gamon, 2004). However, only few studies incorporated other spectral indices for vegetation mapping and these studies
mainly used coarse multispectral satellite data (de la Cueva, 2008; Hong
et al., 2004). Indices specifically designed for hyperspectral remote
sensing data (hereafter called “hyperspectral indices”) take advantage of
the detailed narrow-band information or the large number of contiguous
bands provided by such data. While many hyperspectral indices only use
the bands in the VNIR some also make use of the SWIR region. From the
plethora of available spectral indices (Treitz & Howarth, 1999; Ustin
et al., 2005) many have not been tested for vegetation mapping.
Two main strategies can be identified in remote sensing methods for
vegetation mapping: The first strategy involves classification of the
spectral information, either on a per-pixel or on a sub-pixel basis. The
traditional approach is to use supervised classification of remote sensing
data based on a priori knowledge of land cover. Maximum likelihood
classifiers are commonly used for multispectral data whereas the spectral
angle mapper is a frequently used method for classifying hyperspectral
data (Richards & Xiuping, 2006). Both classifiers lead to a vegetation map
consisting of hard boundaries. Yet, for representing vegetation in seminatural landscapes, where ecotones are important landscape structures
(Arnot & Fisher, 2007), a continuous or fuzzy interpretation of vegetation
becomes increasingly important (Foody, 1992; Lees, 2006; Lucieer, 2006;
Moraczewski, 1993; Schmidtlein & Sassin, 2004). Fuzzy classification
techniques have been recognized as a suitable tool to map (semi-)
natural vegetation units because they allow a soft overlap of several hard
classes (Foody, 1992; Lu & Weng, 2007; Lucieer, 2006). Despite their
great potential to map and identify continuous natural vegetation, supervised fuzzy classification algorithms are not frequently employed for
mapping vegetation units.
The second strategy comprises multivariate techniques, such as
Canonical Correspondence Analysis (CCA) (Ter Braak, 1987) or redundancy analysis (RDA) (van den Wollenberg, 1977), that create a relationship between detailed quantitative information on vegetation, e.g.
species composition, vegetation cover or other structural parameters
and spectral information (Brook & Kenkel, 2002; Thomas et al., 2003). So
far, this strategy has been less frequently used for vegetation mapping
than image classification, although examples are increasingly found in
recent literature (de la Cueva, 2008; Dobrowski et al., 2008; Jensen &
Azofeifa, 2006; Malik & Husain, 2008; Yue et al., 2008). The strength of
the multivariate approach is that it uses the full information on species
composition by simultaneously relating each single recorded species to
the data matrix on spectral information, such as sensor bands or spectral
indices. This leads to an ordination space where the ordination axes
reflect the statistical relationship between species and spectral
information, putting species with a similar relationship to the indices
in order along the axes. There are different views on how vegetation
should be represented in the multivariate approach and how it should
be related to spectral information. On the one hand, cluster analysis of
vegetation datasets allows finding discrete units based on floristic data.
These discrete groups are easy to handle and can be used in further
analysis of spectral data (Lewis, 1998; Thomas et al., 2003). On the other
hand, vegetation can be interpreted as a continuum consisting of
transitions between plant communities. Ordination techniques such as
Nonmetric Multidimensional Scaling (NMDS) or Detrendend Correspondence Analysis (DCA) arrange vegetation data along indirect
floristic gradients displayed by ordination axes, which can be used for
further analysis (Schmidtlein & Sassin, 2004; Schmidtlein et al., 2007).
Several authors have combined cluster analysis with constrained
ordination techniques such as Canonical Correspondence Analysis
(CCA) using spectral bands or principal components of the satellite
image as constraining variables (Armitage et al., 2000; Dirnböck et al.,
2003; Ohmann & Gregory, 2002; Thomas et al., 2003). CCA assumes
that species have an optimum along an environmental gradient
resulting 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 on weighted averages using the Chisquare metric. Another constrained ordination technique similar to
CCA is redundancy analysis (RDA) (van den Wollenberg, 1977), which
relies on multiple linear regressions calculated from the weighted
sums of the Euclidean distances between two matrices. The assumed
linear relationship between species and environment implies a monotonic increase or decrease of species abundance or occurrence along
an environmental gradient. Depending on the dataset and the underlying assumptions of the study aim, it can be useful to check whether
RDA or CCA is the better choice. In spite of the possibilities to use
spectral indices as explanatory variables in constrained ordination,
only a few studies combined other indices other than NDVI in RDA or
CCA to create relationships between ground-checked vegetation units
and canopy properties measured by spectral indices (de la Cueva,
2008; Goodin et al., 2004).
In this study, we present an approach for combining hyperspectral
remote sensing data with field survey information on plant species
composition and plant cover in order to produce a map of floristically
defined vegetation units. We apply the method to a dwarf shrub
savannah in Central Namibia at a spatial resolution of 5 m over an area of
19.5 km2. We aim at developing high resolution vegetation maps based
on the relationship between classified field observation data and a set of
hyperspectral vegetation and soil indices, established by a constrained
ordination technique. Two independent test datasets are used for
validation. The potential and shortcomings of our methodology are
critically discussed with regard to other approaches.
2. Material and methods
2.1. Study area
The study area comprises 19.5 km2 of gently undulating rangelands
northwest of the town of Rehoboth, Namibia (23° 7′ 13.08″ S, 16° 53′
47.40″ W). The climate is semiarid receiving 250 mm annual rainfall
with mean annual temperatures of 20 °C. Vegetation is mainly a dwarf
shrub savannah but is heavily modified by land use. The in situ field data
were sampled on two farms with contrasting management strategies;
the farm Narais applies an extensive grazing strategy with mainly cattle
in a camp-rotation system. The second farm, Duruchaus, is intensively
grazed with sheep and goats. Azonal vegetation occurs in and around
clay pans and some thickets dominated by Acacia mellifera on rocky red
soils occur mainly on Duruchaus.
2.2. Methodological overview
Our mapping approach consists of five separate steps. The first two
steps comprise vegetation sampling and identification of vegetation
units as well as acquisition and processing of airborne hyperspectral
remote sensing data. This is followed by the calculation of a set of
spectral indices that were already applied in other studies for semiarid
savannahs. Constrained ordination allows vegetation samples to be
related to spectral indices and allows for a derivation of ordination axes
reflecting their statistical relationship. Ordination axes that show a good
statistical fit with the spectral indices were converted to a set of
ordination images. Finally, the new dataset is classified into quantitative
vegetation distribution maps for each single vegetation unit using a
supervised fuzzy classification technique. The whole procedure is
explained in greater detail in the following sections.
2.3. Field sampling
Field sampling was carried out in April 2007 on Narais and Duruchaus
in the Rehoboth district, Central Namibia. 89 vegetation plots were
placed by a preferential sampling design relying mainly on image interpretation and accessibility from roads or farm tracks. This was done in
order to maximize the variation in detectable vegetation units according
to the remotely sensed image and minimizing sampling effort. To ensure
spatial compatibility between ground data and pixel resolution we
applied a plot size of 25 m × 25 m following Justice and Townshend
(1981) who provided a formula (Eq. (1)) to calculate the optimal size of
vegetation plots in relation to the pixel size and the geometric accuracy
of 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 pixels
following (Brogaard & Ólafsdóttir, 1997)
2
A = ðP ð1 + 2GÞÞ :
ð1Þ
At each vegetation plot, all vascular plant species were recorded
and their abundance was estimated visually as percentage cover.
Vegetation data were entered into a vegetation database (Muche
et al., 2009) allowing querying and linkage to a GIS. For further
analysis, abundance information was extracted from the vegetation
database and stored in plot-by-species data matrices that would serve
as the response matrix in the constrained ordination (Section 2.7).
During the extraction from the database, species with less than three
occurrences were removed in order to avoid distortion of the cluster
analysis due to rare species (Cao & Larsen, 2001; Marchant, 2002). For
a subset of the study area, the biodiversity network BIOTA-AFRICA
(www.biota-africa.org) provided a comparable dataset of 41 vegetation plots of 10 m × 10 m which were used for accuracy assessment as
an external validation dataset.
2.4. Image data and processing
During a flight campaign in October 2005 a hyperspectral image was
taken using the HyMap airborne imaging spectrometer (Cocks et al.,
1998). The image has a spatial resolution of 5 m × 5 m and covers 126
bands with a 10 nm bandwidth in the wavelength range from
approximately 450 nm to 2500 nm. The image was orthorectified
using the PARGE software (Schläpfer & Richter, 2002) in combination
with 15 differential GPS measurements (accuracy ∼0.5 m) from the
BIOTA-AFRICA network. Errors of the rectified image were less than
1 pixel (<5.0 m) in x -and y-directions. ATCOR-4 (Richter & Schläpfer,
2002) was used for vicarious calibration and for the removal of
atmospheric effects. For the vicarious calibration, spectroradiometric
measurements were taken with a portable Fieldspec PRO FR spectrometer (Analytical Spectral Devices, Inc.) at four homogeneous dark and
bright bare soil targets and converted into reflectance units using a
Spectralon™ panel as white reference. Depending on wavelength, the
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deviation of ground measured reflectance and HyMap reflectance obtained after atmospheric correction varied between 1 and 4% absolute
reflectance units.
2.5. Cluster analysis
Since redundancy analysis was planned as an ordination method we
choose Euclidean distance as a distance measure for cluster analysis in
order to be sure to handle data with same distance measures for
clustering and for ordination. RDA extends PCA to a constrained
ordination technique by allowing multiple regressions of two matrices:
one dependent matrix and one explanatory matrix (van den Wollenberg, 1977). Hence, RDA also relies on the Euclidean distance.
Vegetation data sets are commonly characterized by a high amount of
zeros, for which Euclidean distance is not an appropriate distance
measure. Therefore, the abundance data was transformed using the
Hellinger distance (Rao, 1995), which is simply the square root of the
row totals divided by the row mean values. It was shown that the
performance of abundance data in Euclidean space using Hellinger
distance gives better results than Chi-square metric or similar
approaches (Legendre & Gallagher, 2001). An agglomerative hierarchical cluster analysis was performed using Euclidean distance and Ward's
minimum-variance clustering algorithm (Fielding, 2007). It is important
to check cluster structure for validity because cluster analysis tends to
find groups even if there is no clear group structure (Fielding, 2007).
Validity of each resulting cluster diagram was assessed using the
cophenetic correlation coefficient (rc) which is a widely used measure
for comparing the deviance of a cluster from the original dissimilarity
matrix (Sokal & Rohlf, 1962). According to McGarigal et al. (2000) a
value of rc = 0.75 or higher is a good representation of the original
distance matrix used for cluster analysis. We applied a second quality
measure of clustering structure, the Agglomerative Coefficient, which is
defined as the average height of the mergers in a dendrogram and is a
dimensionless number between zero and one, values closer to one
indicating a better structuring (Kaufman & Rousseeuw, 1990).
It is often a difficult and subjective choice at which level of clustering
the most ecologically meaningful solution can be found. For the
interpretation of the optimal level of clustering, two complementary
methods were used with the restriction that no groups smaller than five
plots shall be produced in order to allow a sound statistical analysis.
Analysis of similarity (ANOSIM) developed by Clarke (1993) is a nonparametric method for analysing group separability. It compares the
difference of mean ranks between groups and within groups and yields a
measure called R (not to be confused with a correlation measure). R
ranges from 0 to 1, with values larger than 0.75 indicating a good
separation. Second, Indicator Species Analysis (ISA), developed by
Dufrene and Legendre (1997), produces an indicator value for each
species, which is a measure of how well a species is restricted to a certain
cluster. It also calculates the sum of all probability values, which reflects
the amount of indicator species found. ANOSIM reports the strength of
group separability whereas ISA allows an ecological interpretation of the
classes. Results from both methods were used to verify and describe the
communities derived at cluster levels from two to twenty. Finally, each
vegetation unit resulting from the chosen cluster solution was
characterized in terms of dominant species and through a general
description of its structure. All analysis were performed using the
packages cluster (Maechler et al., 2005), vegan (Oksanen et al., 2008)
and labdsv (Roberts 2007) in the statistical environment R 2.8.1 (R
Development Core Team, 2008).
2.6. Spectral indices
A review of the literature on hyperspectral indices that are potentially suitable for characterizing the biophysical conditions of semiarid
rangelands resulted in 30 vegetation and soil indices. With this set
of indices, variations in all relevant canopy variables (e.g. pigments,
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canopy structure, canopy water content, woody parts, litter, and soil
background) were covered. The indices were calculated in ENVI 4.2 (RSI
2005) using AS-Toolbox (Dorigo et al., 2006). We removed indices
iteratively from the dataset until a general correlation between the
indices below Pearson's r = ±0.75 was reached. Variance inflation
factor analysis (VIF) (Zuur et al., 2007) was applied to further reduce the
list of indices. According to Montgomery et al. (2001), VIF values larger
than five prove multicollinearity in a regression analysis. Using this
threshold, a total set of eight indices was selected (Table 1).
The eight selected indices cover a wide range of vegetation and soil
characteristics. DGVI1, DGVI2, NDLI and CAI were specifically
designed for hyperspectral sensors and already applied in savannah
landscapes (Chen et al., 1998; He et al., 2006; Miura et al., 2003;
Nagler et al., 2003; Serrano et al., 2002); the ratios sensitive to
variations in soil clay and iron content were developed for narrow
bandwidths which are usually supported by hyperspectral images
(Dorigo et al., 2006). Since vegetation plots are represented as center
coordinates of the plots in a GIS we applied a median filter for
5 × 5 pixels for each index to calculate a single value representing the
size of each vegetation plot. For each vegetation plot, values of
spectral indices were extracted from the images and stored in a plotby-index data matrix which subsequently served as the predictor
matrix in the constrained ordination.
2.7. Constrained ordination
We analyzed the gradient length of the first axis of a Detrended
Correspondence Analysis (DCA) of the vegetation data, which is a
common way 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 is
performed so that axes are rescaled in units of average standard
deviation (SD) of species turnover, where an axis value larger than 4
SD indicates a complete turnover in species composition (Legendre &
Legendre, 1998). For the first axis, we found a gradient length of 3.1
SD, indicating an intermediate compositional gradient. Therefore, we
chose redundancy analysis (RDA) which is known to be an adequate
ordination method for short to intermediate compositional gradients
assuming linear species responses along the environmental gradients
(Legendre & Legendre, 1998).
In RDA, the abundance and composition of species in the plot-byspecies matrix is constrained by the values of the spectral indices in
the plot-by-index matrix such that each ordination axis represents a
linear relationship, i.e. a multiple regression model, between response
(species) and all predictor variables (spectral indices). For a general
evaluation of ordination results the amount of total variance
explained by each axis is interpreted as a measure of ordination
success. Ordination diagrams were created using standard scaling and
linear constraints (LC-scores) and were visually inspected for group
separation along the axes as well as for vector length and direction.
Vectors represent the biplot scores for each predictor variable; their
Table 1
Final set of spectral indices used in the analysis.
Nr. Index
1
2
3
4
5
6
7
8
CARI
Full name
Chlorophyll absorption in
Reflectance Index
LCI
Leaf Chlorophyll Index
DGVI1 First-order derivative green
vegetation index
DGVI2 Second-order derivative
green vegetation index
NDLI
Normalized Difference Lignin Index
CAI
Cellulose Absorption Index
CLAY
Clay ratio
IRON
Iron ratio
length and direction reflect their importance relative to the ordination
axes. Significance of the ordination was assessed by calculating a
goodness of fit test (using function as.mlm.rda in vegan package,
Oksanen et al., 2008) yielding R2 and p-values for the relationships
between indices and ordination axes and between vegetation units
and ordination axes.
2.8. Calculating ordination maps
Since RDA inherently produces linear combinations of predictor
variables to calculate distances in the ordination space, it is possible to
use regression coefficients and spectral indices to calculate maps of
ordination axes. This produces a new dataset, with one image layer
per ordination axis. In order to interpret the ordination images we
compared the relative position of the vegetation units along the
ordination axes in the ordination diagrams, e.g. we looked for relative
position of unit 4 on ordination axis one, and used the regression
statistics of the redundancy analysis to evaluate the fit of the relation
between the axes, indices and vegetation units.
2.9. Fuzzy classification
In order to finally extract continuous vegetation unit maps from the
ordination 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 validation dataset for each vegetation unit. The degree to which a sample
belongs to a class is expressed by a continuous membership value that
ranges between 0.0 and 1.0, where 1.0 indicates perfect similarity with a
class cluster. The fuzziness component, which determines the amount of
overlap allowed, was set to 2.0 following various authors (Burrough
et al., 2000; Lucieer, 2006). For classification we used the non-parametric
k-Nearest Neighbor (k-NN) distance metric within the SFCM algorithm
following Lucieer (2006) and building onto Zhang and Foody's (2001)
Euclidean SFCM algorithm. The k-NN algorithm searches the feature
space for the k nearest pixels within the training sample, whose field
data vectors are known, applying a distance measure defined in feature
space (Franco-Lopez et al., 2001; Katila & Tomppo, 2001). The k-NN
algorithm does not make any assumptions about the statistical distribution of the training pixels, which is advantageous in our situation
where the number of pixels available for training is limited. Following
Lucieer (2008), we used a number of k = 5 nearest neighbors. The SFCM
algorithm produces a fuzzy classification of the ordination images
resulting in three types of output. First, it computes a membership image
for each vegetation unit indicating the percent membership of each
pixel. Second, it produces a defuzzified hard classification image from the
membership images based on maximum membership values. Third, it
calculates an image of the Confusion Index (CI), which summarizes the
confusion of class assignment in each pixel. The CI is a ratio of the second
maximum membership and the maximum membership for each pixel.
High values indicate a high classification uncertainty.
2.10. Accuracy assessment
Feature
Reference
Chlorophyll Kim et al. (1994)
Chlorophyll Datt et al. (2003)
Greenness
Chen et al. (1998)
Greenness
Chen et al. (1998)
Lignin
Litter
Soil
Soil
Serrano et al. (2002)
Daughtry (2001)
Dorigo et al. (2006)
Dorigo et al. (2006)
Two datasets were available for assessing the accuracy of the classification result. First, the result was validated with the pixels from the
ROIs that were not used to train the k-NN classifier, we refer to this as
the internal validation dataset. An independent dataset was provided by
the biodiversity network BIOTA-AFRICA (www.biota-africa.org). Vegetation plots of the independent dataset were assigned labels of already
classified vegetation units according to species composition and
abundance. Not all vegetation units were present in the BIOTA-AFRICA
dataset, leaving unit four and six empty. The quality of the hard classification vegetation unit map was assessed using a confusion matrix.
Table 2
Overview of vegetation units that were derived by cluster analysis. Characteristic
species for each type are sorted after average abundance in the cluster. Number of plots
(n) show cluster size.
Type n
1
2
3
4
5
6
7
Characteristic species
20 Monechma genistifolium
Pentzia calva
Geigeria ornativa
5 Stipagrostis ciliata
Felicia clavipilosa
28 Stipagrostis obtusa
Monchema genistifolium
Melolobium microphyllum
10 Acacia mellifera
Albizia anthelminthica
Stipagrostis uniplumis
10 Leucosphaera bainsii
Aizoon schellenbergii
Enneapogon desvauxii
7 Fingerhuthia africana
Aizoon giessii
Melhania virescens
9 Panicum lanipes
Eragrostis rotifer
Rhigozum trichotomum
Acacia hebeclada
Description of the vegetation unit
Open dwarf shrub with sparse cover,
mainly Monechma genistifolium on
calcareous rocky soils
Grass and shrub vegetation on outcrops and
deeply incised rocky drainage lines
Sparse grassland and open patches, mainly
Stipagrostis obtusa, only few dwarf shrubs
Woody acacia shrub on shallow red soils
Dwarf shrub savanna with many dwarf
shrubs, and perennial grasses on dark
biological soil crusts
Grassland with mainly Fingerhuthia
africana and few dwarf shrubs on rocky
siliceous soil.
Shrub vegetation at the border of clay pans
and shallow drainage lines with clay soils;
grasses and herbs in center of pans
Finally, the overall accuracy of classification (OA) and kappa values were
calculated for each dataset and for subsequent numbers of ordination
axes.
3. Results
3.1. Cluster analysis
The quality of the produced dendrogram, i.e. cluster structure as
measured by the Agglomerative Coefficient, was 0.86 whereas the
cophenetic correlation coefficient reached a value of rc = 0.75, both
indicating a considerable amount of structure in the dendrogram. The
comparison of ANOSIM and ISA for each level of clustering from two to
twenty suggested either a level of three or seven clusters. Since the
cluster resolution is desired to be as detailed as possible to classify the
vegetation into meaningful vegetation units, seven clusters were
chosen as the appropriate size for further analysis. Table 2 gives an
overview on the ecological description of the vegetation units based
on constancy of species and relative abundance per group.
3.2. Constrained ordination
A total of 34% variance in the species data was explained by the
eight constraining axes. The first three axes explained 12%, 9% and 5%
respectively. A simplified ordination diagram of the first and second
RDA axis is shown in Fig. 1. In order to improve interpretability,
ellipses showing 95% confidence levels were drawn around the group
centroids. In the ordination diagram, the vegetation units derived by
cluster analysis show distinct clusters separated by spectral indices
along the first two ordination axes. The first axis represents mainly a
gradient of vegetation cover along which the different vegetation
units line up. The structurally more complex unit four is positively
related with CLAY, DGVI1 and DGVI2 whereas all other units are
negatively related with those indices along the first axis. The second
axis is a gradient of chlorophyll concentration, mainly spread by CARI
and LCI. CARI is positively correlated with unit 3 and LCI is positively
correlated with vegetation unit 5. The third axis (Fig. 1) separates
vegetation units 7, 2 and 1 by different dry matter components
represented by NDLI and CAI. The latter is strongly positively related
with vegetation unit 7. NDLI is strongly negatively related with units 2
and 7. The fourth axis separates units with high values for CARI and
1159
CLAY (3, 4, 5, and 6) from the groups 7, 2 and 1 which have low values
for CARI and CLAY.
The significance of the relationship between spectral indices,
vegetation units and the ordination axes performed by RDA is given in
Table 3. Spectral indices and vegetation units show high R2 values and
low p-values up to the fifth ordination axis. From ordination axis six to
eight, R2 values stay below 0.4, whereas overall p-values were slightly
lower than 0.001 but were still significant up to the 5% level. A high
significance for a spectral index along an axis indicates a strong relationship with that axis, whereas a high significance for a vegetation unit
reveals a good separation of vegetation units along that specific axis. For
example, vegetation unit one and two cannot be separated well from the
other groups along the first axis (Table 3) but there are high significances on the third ordination axis displaying that this axis is better
suited to separate vegetation units 1 and 2 from the others.
3.3. Fuzzy classification
The supervised fuzzy c-means classification using the k-NN classifier
produced three sets of images: class membership images for each
vegetation unit (Fig. 2a–g), a confusion image (Fig. 2h) and a hard
classification image (Fig. 3). The number of axes leading to the best
classification results was assessed by comparing overall accuracy and
kappa values obtained for the two validation data sets using different
numbers of axes (Table 4). Starting from the two axes that explained
most variation we iteratively added the other axes until all eight axes
were included. For the internal validation dataset, best overall accuracy
and kappa values were found for a total of six and eight axes. The
independent BIOTA-AFRICA dataset achieved best values with only five
axes, yet the values for the eight axes solution were only slightly less
accurate. Based on the results obtained for both validation sets, the eight
axes solution was chosen. Membership images, hard classification and
confusion image were checked visually for credibility in order to better
interpret classification results, e.g. high values in the confusion image
(Fig. 2h) indicate overlapping classes, i.e. transition zones and mixed
units, whereas low values show purer classes. The error matrices for the
eight axis solution are shown in Tables 5 and 6 for the internal and
independent validation dataset respectively.
4. Discussion
4.1. Vegetation maps
We were able to map seven vegetation units of a rangeland area in
Central Namibia with a high accuracy but a relatively low sampling
effort (number of vegetation plots = 89). Mapped vegetation units
comprise the main plant communities and their ecological conditions
for an area of around 19.5 km2 (Table 2). The membership images of
vegetation units 1, 4, and 5 (Fig. 2a, d, and e) and the hard classification
image show a sharp transition in the center of the study area. This is
caused by a fence line separating both farms and is typical for South
African rangelands indicating contrasting management strategies (Todd
& Hoffman, 1999). Validation of the hard classification map (Fig. 3) with
the internal validation dataset shows a very good performance by
reaching kappa values of 0.98 and an overall accuracy of 98%. The
confusion image (Fig. 2h) indicates that vegetation units 4 and 7 only
have little confusion with other classes. This can be explained by the
high proportion of larger shrub species, mainly A. mellifera or A.
hebeclada, in both vegetation units, which makes them more distinct
due to higher values in the DGVI indices. The other vegetation units
show higher grades of fuzziness. The error matrix of the internal
validation dataset (Table 5) shows that vegetation units 1 and 3 are
slightly confused while the other vegetation units were classified 100%
correctly. This confusion is caused by an overlap of high membership
values for units 1 and 3 (Fig. 3a, c). This overlap is a good example for the
fuzziness of the classification. The difference between both vegetation
1160
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 and
importance of spectral indices. First two constrained RDA axes (left) explain 21%, RDA axes three and four (right) explain additional 9% variation in species composition through
spectral indices.
units is mainly a difference in stoniness and the cover fraction of grass
and shrubs, whereas species composition is mainly the same. Both vegetation units principally are found on the southern farm and are mostly
neighboring, which increases the possibility of overlap.
The independent dataset shows a moderate performance with a
kappa value of 0.53 and an overall accuracy of 64%. In the classification
results again some confusion exists between vegetation units 1 and 3
(Table 6), but also between vegetation units 1 and 2, as well as between
2 and 5 (Fig. 2a, b, c and e). The reasons for the less accurate classification
results based on the external dataset are various; firstly, the independent dataset was not collected for the special purpose of vegetation
mapping but for biodiversity monitoring. This means that vegetation
plots are constantly placed in the center of selected hectares, belonging
to a grid of 1 × 1 km with a mesh width of 100 m, according to the
sampling scheme of the monitoring project (Jürgens, 1998). Thus,
vegetation plots are restricted to center points of hectares and might be
in the vicinity of different vegetation units. This seems to be the main
cause for the confusion of the classification. Secondly, there were only
few vegetation plots in total (n = 41) and plot size was smaller than our
design, e.g. 10 m × 10 m versus our 25 m × 25 m, capturing fewer
species. Thirdly, with the same sampling approach different survey
teams usually yield different estimates of percent cover or differ in
species identification (Kercher et al., 2003) which can lead to different
classifications, making it more difficult to compare vegetation datasets
between different projects. Finally, two vegetation units did not fall into
the sampling scheme of the external dataset and vegetation unit two is
underrepresented with only one vegetation plot.
On a comparable scale, Thomas et al. (2003) mapped boreal
peatlands in Canada using 600 vegetation plots of 1 m2 by which they
were able to distinguish up to nine types of fen vegetation. By applying
a maximum likelihood classification to a hyperspectral image, they
yielded maximum kappa values between 0.32 and 0.55. The moderate
performance was explained by the low spectral separability of the
produced vegetation classes which also could be due to the very small
plot size and the small total area (600 m2) sampled. In fact, the hypothesis that plant communities can be clearly separated by their spectral
Table 3
Significance of relationships between spectral indices, vegetation units and ordination axes.
Spectral indices
CARI
LCI
DGVI1
DGVI2
NDLI
CAI
CLAY
IRON
R2
F-value
p-value
Vegetation unit
1
2
3
4
5
6
7
R2
F-value
p-value
RDA1
RDA2
RDA3
RDA4
*
*
***
***
.
***
*
**
***
*
*
**
***
.
0.6323
17.41
< 0.001
**
0.6305
17.28
<0.001
*
***
***
***
***
***
0.9027
126.7
< 0.001
**
***
*
***
***
0.6716
27.95
<0.001
Signif. codes: < 0.001***; 0.01 = **; 0.05 = * 0.05; ‘.’ = 0.1.
***
***
**
*
0.6209
16.58
< 0.001
***
***
***
***
***
.
***
0.7651
44.52
< 0.001
RDA5
RDA6
RDA7
***
***
***
*
.
**
**
*
.
*
**
*
***
0.5326
11.54
<0.001
RDA8
*
*
0.4206
7.35
< 0.001
***
0.3217
4.802
<0.001
***
0.2604
3.564
< 0.05
0.1779
2.192
<0.05
**
***
***
***
***
***
0.6206
22.36
<0.001
.
**
**
*
0.4873
12.99
< 0.001
.
*
**
0.2643
4.91
<0.01
*
*
*
***
**
0.3226
6.509
< 0.001
0.1656
2.713
<0.05
1161
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 membership
values, dark pixels indicate low values. Confusion image (h) shows areas of high confusion in white and more pure classes in black.
1162
Table 5
Error 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
1
2
3
4
5
6
7
Total
0
116
31
195
110
84
71
108
715
0
93.1
0
1.72
0.86
4.31
0
0
100
0
0
100
0
0
0
0
0
100
0
0
0
97.44
0
2.05
0.51
0
100
0
0
0
0
100
0
0
0
100
0
0
0
0
0
100
0
0
100
0
0
0
0
0
0
100
0
100
0
0
0
0
0
0
0
100
100
0
15.1
4.34
26.85
15.52
13.01
10.07
15.1
100
Overall accuracy = 98.18%; kappa = 0.98.
classification algorithms available (see Lu and Weng (2007) for an
overview of classification algorithms), supervised fuzzy classification
approaches seem to be most promising for natural landscapes. Foody
(1992, 1996) pioneered the application of fuzzy classification of
vegetation with remote sensing data. Malik and Husain (2006) also
used a supervised fuzzy classification to discriminate between four plant
communities and five land cover classes on a subset of a SPOT XS scene
covering 6 km2 of a valley in Pakistan. They reached overall accuracies
between 65% and 72%, yet also reported the problem of spectral
separability between vegetation classes. Lucieer (2006) used supervised
fuzzy c-means classification applying the Mahalanobis distance metric
on IKONOS panchromatic bands for classifying sub-Antarctic vegetation.
Lucieer did not use floristically defined vegetation units but seven rather
broad categories (four non-vegetated), which lead to an overall
accuracy of 73% and a kappa value of 0.69. In comparison with the
above mentioned studies, our approach yielded equal or even better
results depending on the dataset used for validation.
4.2. Constrained ordination using spectral indices
Fig. 3. Classified vegetation map based on a supervised fuzzy c-means classification
result, hard class labels according to identified vegetation units are assigned based on
maximum membership values in each pixel. The colors represent different vegetation
units, see Table 2 for explanation.
features is based on the assumption that boundaries between plant
communities are hard. This might be acceptable for intensively used
agricultural or urban landscapes with crisp boundaries between land
cover types, but in semi-natural savannah systems transitions between
plant communities are gradual. Thus, from the vast amount of image
Table 4
Effect of number of bands on overall accuracy (OA) and kappa values for the internal
and the independent validation datasets. Important values are highlighted in bold.
Axes
1
2
3
4
5
6
7
8
Internal
Independent
OA
Kappa
OA
Kappa
39.02
64.06
86.01
93.29
94.54
98.18
97.90
98.18
0.24
0.57
0.83
0.92
0.93
0.98
0.98
0.98
38.16
43.59
52.52
56.52
64.00
61.56
58.70
63.82
0.19
0.25
0.38
0.43
0.53
0.49
0.46
0.52
Vegetation mapping usually relies on the classification of remotely
sensed images. In our case, classification was done on the basis of images
of ordination axes, which reflect the relationship between vegetation
units and spectral indices. Visualization was possible since the RDA
produces a linear combination of predictor variables, as in multiple
regressions, which can be combined with images of spectral indices. The
ordination diagram showed a clear separation of vegetation units by the
constraining spectral indices (Fig. 2), where the spectral indices
enhanced the ordination, i.e. explained 34% of the overall variation in
the species data. Apparently constrained axes show only low eigenvalues, yet this is due to the large amount of unconstrained axes available
to explain the variation, i.e. one for each species (n = 79). The regression
results clearly show that there is an overall good quality of the
ordination up to the fifth or sixth ordination axes (Table 3). The highest
R2 value achieved was R2 = 0.63 for the regression of indices and
ordination axes and R2 = 0.92 for the regression of vegetation units and
Table 6
Error matrix for eight axes solution using k-NN algorithm on independent validation
dataset. Values represent percent of pixels classified into class. No vegetation plots from
independent dataset did fit into classes four and six leaving them empty.
Unit
Pixels
1
2
3
4
5
6
7
Total
Unclassified
1
2
3
4
5
6
7
Total
0
68
21
121
–
131
–
10
351
0
97.06
0
2.94
–
0
–
0
100
0
42.86
0
57.14
–
0
–
0
100
0
35.00
0
51.67
–
4.17
–
9.17
100
–
–
–
–
–
–
–
–
–
0
4.13
13.22
6.61
–
65.29
–
10.74
100
–
–
–
–
–
–
–
–
–
0
0
0
0
–
0
–
100
100
0
35.88
4.71
24.71
–
24.71
–
10
100
Overall accuracy = 63.82%; kappa = 0.52.
ordination axes. Nevertheless, using all ordination axes produced the
best classification results, which means that also information with a low
significance can improve the overall classification outcome.
In comparison, Thomas et al. (2003) used a CCA and were able to
explain a total amount of 44% variation in the species data using spectral
bands, but their highest R2 was 0.47. Brook and Kenkel (2002) also
applied an RDA on scores of four ordination axes derived by a
correspondence analysis of the vegetation data and Landsat TM
channels 3, 4, 5 and 7. They were able to explain 47% of the variation
in the species data and interpreted the relationship between spectral
reflectance and vegetation mainly as a structural rather than a floristic
(species composition) effect. As mentioned earlier, RDA and CCA differ
in the underlying species response model (Austin, 1987; McCune et al.,
2002), hence the question whether RDA or CCA is the more appropriate
multivariate analysis depends on the ecological gradients covered
within the dataset and the aim of the analysis. In our case, an approach
based on CCA would have resulted in a total variance explained in 24%
and less interpretable ordination diagrams, meaning that RDA leads to
more meaningful results.
The application of hyperspectral indices in multivariate analysis of
relationships between vegetation and spectral information seems to be
a logical step as the use of vegetation indices makes the approach more
robust to uncertainties in atmospheric correction and changing
illumination and observation conditions compared to the direct
inclusion of spectral bands. Moreover, the use of vegetation indices
reduces the large amount of highly collinear data to a reasonable
amount of less correlated features that can be directly linked to
vegetation properties relevant for the observed canopies. However, we
found only one study that used spectral indices other than NDVI in a
multivariate 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 in
information (Ustin et al., 2004) and many vegetation indices are
available to exploit this information. The explanatory power of spectral
indices is quite high as these indices are strongly related to certain
aspects of canopy information, such as dry matter, chlorophyll and other
plant pigments, water, nitrogen, or cellulose (He et al., 2006; Treitz &
Howarth, 1999; Ustin et al., 2004). In this study, the vegetation indices
DGVI1 and DGVI2 were highly correlated with the first ordination axis
which can be interpreted as an increase in vegetation cover but also
in vegetation height. Unit 4, consisting of woody acacia thicket was
positively correlated with both indices, whereas the sparse vegetation of
unit 3 was negatively correlated. This correlation indicates that the
DGVI1 and DGVI2 are sensitive to an increase in vegetation cover also in
semiarid dwarf shrub savannah. Miura et al (2003) showed this for a
Cerrado savannah in Brazil. However, vegetation on Namibian rangelands is much sparser than Cerrado vegetation. The LCI was positively
related with unit 5, which was identified on the northern farmland
(Fig. 3h). Here, the vegetation composition is much different from that
in units 1 and 3 which both are negatively correlated with LCI. The
vegetation in unit 5 is dominated by dense stands of the dwarf shrub
Leucosphaera bainsii (Table 3). However, it is hard to define causality
with LCI here because there is a considerable amount of dark biological
soil crusts which also might contribute to forming this relationship. The
dry matter, or litter indices, CAI and NDLI, both point at unit 7, which
shows a considerable amount of dry biomass originating from dry grass
material from the last year. Regarding the soil indices, it is notable that
the clay index is positively correlated with axis three and four and not
with the first axis as it might be interpreted from the ordination
diagram. The iron index is related to iron induced absorptions in the NIR
which lead to the reddish coloring of the soil with increasing iron
content. Yet, a clear interpretation remains difficult. The iron index is
negatively related with ordination axis two, helping to separate unit 5
from the other units. Its most significant contribution is found on axis six
(Table 3) where it separates the red soils of the unit 4 from all other
groups. It is important to notice that the set of eight indices selected in
1163
this study equally samples from the VNIR and SWIR region, each stressing different properties of vegetation (Asner & Heidebrecht, 2002;
Nagler et al., 2003). Transferring the approach presented here to other
study areas should include a sound selection of spectral indices
appropriate for the studied ecosystem and the applied spectral sensor.
Hyperspectral indices provide information on a wide range of canopy
related variables and seem suitable to be used by ecologists as variables
in multivariate ecosystem experiments or for vegetation mapping approaches relying on multivariate relationships between spectral and
compositional data.
4.3. Pitfalls of cluster analysis
A source of uncertainty that might influence classification results lies
in the means of cluster analysis which is the complicated task of
structuring data by grouping objects according to their similarity.
Complicated, because there are many subjective choices to make, such
as the choice of the overall clustering strategy, e.g. hierarchical or partitioning, an appropriate distance measure, and the clustering algorithm
(Fielding, 2007). The result is heavily dependent on the origin and
quality of the data used, as well as the available expert knowledge of the
analyst. Ecological datasets in particular require special transformations
in order to be applicable with cluster analysis or ordination techniques
(Legendre & Gallagher, 2001). For example, when dealing with remotely
sensed data, it seems more realistic to apply a quantitative distance
measure in order to take species abundance information into consideration. Distance measures based on presence–absence, like the
frequently used Jaccard or Sörensen Index, can lead to a very different
result (Legendre & Legendre, 1998), making interpretation of vegetation
classifications in relation with spectral data more difficult. In other
words, if a species occurs only with 1% cover, and is transformed to
presence, then the plot is treated in the same way as it would have been
when the species had shown a cover of 100%. This is an unrealistic
assumption in a vegetation mapping context based on spectral properties of dominant plant canopies.
Yet, the greatest challenge is the interpretation of a resulting
dendrogram. Although methods exist for checking its quality and
deciding up to which level the cluster can be interpreted (Fielding, 2007;
Pillar, 1999), these methods are rarely reported in the literature (Aho et
al., 2008). We applied a thorough assessment of cluster structure using
two measures of cluster quality, the cophenetic correlation coefficient
and the Agglomerative Coefficient, which reached high values indicating
that 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 as much as
possible. We proposed the combination of two separability measures
ANOSIM and ISA for choosing an optimal level of clustering. This turned
out to be an efficient way of interpreting group separation. We are not
aware of any other vegetation mapping study using cluster analysis and
remote sensing data reporting one of the above mentioned quality
checks. Dendrograms with low structure quality produced by cluster
analysis might be the main cause for the low spectral separability found
in other studies (Malik & Husain, 2006; Thomas et al., 2003).
Interestingly, the method most frequently applied to delineate
vegetation units in remote sensing studies is the Two-way Indicator
Species Analysis (TWINSPAN), a polythetic divisive clustering algorithm
developed in vegetation science by Hill (1979), for examples see Malik
and Husain (2006), Peel et al. (2007), Ravan et al. (1995), and Thomas
et al. (2003). This method has been criticized in ecological literature
mainly for two reasons: first, it mainly detects one large gradient due to
its statistical restriction by using correspondence analysis to span a
floristic gradient (Belbin & Mcdonald, 1993; Kent, 2006). McCune et al.
(2002) suggested that this method should not be applied at all, except
in situations where there is a known large one-dimensional gradient
in the dataset. This can be the case when the floristic gradient reflects
the major gradient in vegetation cover (Nilsen et al., 1999). Second,
1164
TWINSPAN uses the Chi-square metric, which gives high weight to
species with low abundance (Faith et al., 1987). For delineating
vegetation units based on abundance of dominant species this is not
necessarily useful. Several modifications of the TWINSPAN algorithm
are reported in the literature, e.g. Roleček et al. (2009) have improved
TWINSPAN classifications by incorporating a measure of cluster
heterogeneity into the algorithm, available in the free software package
JUICE (Tichy, 2002). We suggest using either an improved version of
TWINSPAN or alternative clustering techniques since they might contribute to a better discrimination between vegetation units and help to
avoid the shortcomings of the original method.
vegetation mapping approaches based on multivariate relationships.
Finally, we used supervised fuzzy classification technique to create
abundance maps for each vegetation unit as well as a hard classification
image suitable for conservation management or landscape planning. To
consider fuzziness is especially important in semi-natural landscapes
where transitions between different plant communities are often
gradual. As Kerr and Ostrovsky (2003) have stated, ecologists have
begun to recognize the potential of remotely sensed data. Conversely,
one could state that the remote sensing community should follow by
recognizing the potential of ecological datasets and methods and be
aware of the potential pitfalls during field sampling and ecological data
analysis in order to produce accurate vegetation maps.
4.4. Problems during field sampling
Acknowledgements
A sound sampling design is most important because success is only
possible when the baseline data is a realistic representation of the study
area. In this study, the decision for a preferential sampling driven by cost
and time factors led to a satisfying result. However, a more sophisticated
design, such as a systematic or stratified random sampling, that
additionally minimizes the effects of spatial autocorrelation, could be
a 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 sensor
specifications is required to avoid error propagation and should be taken
into consideration during the planning phase. For example, the extent of
the image affects the number of plots needed to capture the variety of
vegetation units (Marignani et al., 2007). Sensor resolution, i.e. pixel
size, predetermines a reasonable plot size (Nagendra & Rocchini, 2008;
Rahman et al., 2003), which is known to affect different properties of
species data in vegetation classification, such as species constancy and
number of species (Dengler, 2009b; Dengler et al., 2009). We have
chosen a plot size of 25 m × 25 m, which sufficiently covered a pixel size
of 5 m × 5 m and was appropriate for the homogeneous vegetation and
level of geometric correction of the HyMap image. In addition to plot
size, 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 plots
facilitate the comparison of sampled vegetation data with pixel information of remotely sensed images. However, rectangular plots, e.g.
20 m × 50 m have been identified to be more appropriate for relating
spectral data to biodiversity measurements since they are able to catch a
wider range of ecological gradients than quadratic shaped vegetation
plots (Oldeland et al., 2009).
5. Conclusion
Remote sensing approaches for vegetation mapping using multivariate analyses have been increasingly applied over the last years. These
approaches combine detailed ground data from ecological field surveys
with remotely sensed data, showing great potential in the field of fine
scale vegetation mapping (Alexander & Millington, 2000). In this study,
we extended the multivariate approach for vegetation mapping by
connecting field data and spectral indices with different multivariate
analysis techniques. First, hierarchical cluster analysis was applied to
delineate meaningful vegetation units. This is a crucial step in the
methodology since all following analyses are built on proper group
identification. Checking dendrograms for structure and quality is
therefore a necessary step. Second, ordination of species data
constrained by hyperspectral indices leads to images representing the
statistical relationship between vegetation units and spectral data. Here,
the ability to relate spectral indices with vegetation data allows for a
good interpretation of the spectral properties of each vegetation unit.
The power of hyperspectral indices for multivariate ecological applications is still relatively untouched. In our opinion, there is a great
potential for the communities of remote sensing and ecological
scientists to use these types of predictor variables for improving
We thank the farm owners of Narais and Duruchaus for providing
access to their rangelands, Dirk Wesuls for helping with plant identification and interpretations of the vegetation classification, our
colleagues at the DLR for the assistance during pre-processing, Jari
Oksanen for comments on ordination, the BIOTA-AFRICA project for
providing infrastructure and the external dataset and finally the
Helmholtz-EOS PhD Programme for funding this research project. We
also thank the handling editor and two anonymous reviewers for
significantly improving the manuscript.
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Combining vegetation indices, constrained ordination and fuzzy

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