considered absent in our study. Thus, another aim of the paper
is to extract surrogate measures of vegetation height extraction
through Remote Sensing, in case primary data are not available
or affordable. For the extraction of the texture measures, a mul-
tispectral Quickbird image of the Italian Natura 2000 protected
site of Le Cesine is used.
2 MATERIALS
2.1 Study area
The methods discussed in the paper are tested at the Le Cesine
site. Le Cesine is a Natura 2000 protected site located on the
Adriatic site of the south eastern part of Apulia region, Italy (Fig-
ure 1). It covers around 3.48km? and is one of the oldest pro-
tected areas in Apulia. The area comprises a complex of coastal
lagoons, as well as various canals, marshes and humid grasslands.
Helophytic, halophilous and dry therophytic vegetation alternate
and create interesting mosaics. Cladium mariscus communities
are the most common helophytic vegetation species. The woody
vegetation is mainly characterized by Pinus halepensis and Quer-
cus ilex, while the scrubby vegetation by Erica forskalii.
2.2 Data
The only available data source for height estimation in our study
is a very high resolution multispectral image from the Quickbird
sensor, acquired in mid July 2005. The image is of 2.4m spatial
resolution and contains four bands, lying in the spectral areas of
blue (450-520nm), green (520-600nm), red (630-690nm) and
near-infrared (760—900nm).
For validation purposes, a habitat map of Le Cesine, expressed
in the GHC scheme, from the same period is used. GHC, a tree-
structure classification system proposed to include all European
habitats, is based on life forms. It consists of five main classes,
each of which is further split into several subclasses, resulting
in a total of 160 habitat categories (Bunce et al, 2008). For
certain main classes, e.g. Trees and Shrubs, vegetation height
is not only fundamental for the recognition of the habitat class
of landscape patches from remote sensors, but, often, the only
way, since the spectral reflectance properties of the patches may
not be particularly distinctive. An indicative example includes
the discrimination between the low and mid phanerophyte class
(LPH/MPH), being shorter than 2m, and the tall phanerophytes
(TPH), with above 2m height. The discrimination between these
(semi-)natural habitats is important, since they have distinct char-
acteristics causing or revealing different ecological properties and
functions.
Figure 1 presents the location of Le Cesine in Italy and the avail-
able Quickbird image, where the green band is drawn in gray-
scale. The boundaries of the protected area overlay the Quick-
bird image. The areas of LPH/MPH habitats, as extracted from
the habitat map, are indicated as white dotted patches, the TPH
habitats as dark dotted patches, while all the rest habitats remain
transparent and let the image intensity appear underneath.
3 METHODS
In our study, Quickbird image alone is used to characterize habi-
tats based on their height and discriminate between LPH/MPH
and TPH categories. Vegetation height is approximated indirectly
by quantifying the homogeneity of the ground through the pro-
posed texture analysis measures. The high spatial resolution of
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B8, 2012
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia
322
the image allows the extraction of such measures able to cap-
ture local variations in the ground structure and provide an ac-
curate indication of the homogeneity of the ground. The patches
of interest characterized as LPH/MPH and TPH are detected in
the Quickbird image, based on the GHC habitat map. For these
patches, the proposed texture measures are calculated on a per
pixel basis. For each measure, its average value in the pixels of
the patch is extracted. For the tall phanerophyte patches these
values are expected to be significantly larger than those for the
low/mid phanerophyte patches, reflecting their larger heterogene-
ity. The texture measures are calculated for all bands of the
Quickbird image, in order to examine the discriminatory capa-
bility of each band. Local variance, as a measure of energy, local
entropy and local binary patterns are employed in the calculated
measures.
3.1 Local Variance
The first measure we apply to capture local variations in texture
is based on local variance. Around each pixel of the selected
LPH/MPH or TPH patch, a small neighborhood is considered.
The neighborhood is defined as a square window of predefined
size around the central pixel. The variance of the pixel intensi-
ties in the neighborhood is calculated and assigned to the central
pixel. Since the discrimination of habitats is meaningful on a per
patch basis, the average value of the variance values of the pixels
of the patch is extracted, providing an indication of the intra-patch
heterogeneity and, indirectly, a surrogate of the patch vegetation
height. The same procedure is applied for all Quickbird bands.
3.2 Local Entropy
In order to detect local variations in texture, entropy-based mea-
sures can be employed. Entropy, as introduced in information
theory by Shannon (1949), offers, in general terms, an indication
of randomness in the studied data; in that sense, heterogeneous
patches as far as their pixel brightness is regarded, as the ones
depicting tall vegetation habitats, are expected to have higher en-
tropy values than patches with low vegetation. Similarly to vari-
ance, a local measure of entropy is calculated on a per pixel basis.
For each pixel c of a selected patch, entropy is calculated in a sur-
rounding window, using
k
H(c)=— }_ p() log (pli). (0)
i=l
where k is the total number of different pixel intensities, or gray
values, present in the window and p(?) the frequency of appear-
ance of value i in the window, i.e. the ratio of the number of pix-
els with value i in the window to the total number of pixels of the
window. An interesting trade-off takes place: on the one hand,
the window needs to be rather small in order to increase spatial
resolution and capture local variations in the texture. On the other
hand, in order to have reliable and meaningful statistical analysis,
the number of gray levels has to decrease, through further quan-
tization of the pixel values, as the window size decreases. As an
example, for a window of dimensions 9 x 9 pixels, the image
should be requantized to at most 8 gray levels, so that we have
81 pixels to populate the 8 gray level bins when forming the his-
togram of the window under consideration. Two schemes were
tested, one with the entire region of interest being requantized to
8 gray levels, and one with each window being requantized indi-
vidually.
3.3 Local Entropy Ratio
Aiming at capturing local variations of a small neighborhood
around each pixel compared with the existing variations in the
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