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Remote sensing for resources development and environmental management
Damen, M. C. J.

Landunits on these scale levels can be recognized and
delineated easily by visual interpretation of small
scale imanery, such as those produced by satellite
remote sensing (V.Ackerson & E.Fish, 1980 ; C.M.
Gerards & M.C.Girard, 1973, 1975, 1985 ; H.Antrop &
L.Gaels, 1977 ; M.Antrop, 1982).
Landelements and -facets can be recognized indivi
dually upon a satellite image when their size, shape
ant orientation can be resolved and when their spec
tral signature at a given time differs sufficiently
from the one of the adjacent features. When this is
not the case, the landscape structure to which they
belong, will be distorted and may not be recognized.
Landscape ecology and -planning often group the
landscape elements and -facets according to two pro
perties : the biotic significance and the importance
of their spatial dimensions, i.e. the height and
the size of the objects. Thus five groups are
defined : biotic and abiotic volumes, biotic and
abiotic spaces and biotic screens. Examples of each
group are : forests, built-up areas, agricultural
land, water- and barren surfaces, hedges and tree-
rows. These groups are fairly independent from the
phenology and remain constant throughout the year.
They also reflect the typological composition of
the landscape as well for the content (genotypical
composition) as for the physiognomic appearance or
the "scenery" (phenotypical composition). For the
image interpretation these groups are also signifi
cant. Volumes and screens are shadow giving objects.
Biotic volumes occupy mostly vast areas and thus
are registered with a large proportion of pure
pixels. Biotic screens are seldom resolved in the
image but cause a lot of interference with the spec
tral reflectance of the adjacent fields. Biotic
space constists of the vast areas of agricultural
land containing complex structures of field patterns
and a varying diversity of land uses. Both this
group and the one formed by the abiotic volumes give
large proportions of mixed pixels. Although these
groups are not completely significant for land use
interpretation (all agriculture in one group), they
have the advantage of being fairly constant in time
(no seasonal variation) and being good indicators
for the landscape structure.
Even with a well differentiated spectral signature,
the possibilities of identification of each object
category and of inventoring its areal distribution
on a remote sensed image depend upon three additio
nal factors :
1. - the size of the objects vs. the pixel size ;
2. - its shape (compactness and orientation) vs.
the pixel shape ;
3. - its areal proportion in the scene.
The first two factors determine the proportion
between the pure and mixed pixels for each object.
The probability of having pure pixels in a given
category increases with the object size, the com
pactness of its shape and the proportion of the
area it covers. These three parameters may vary a
lot in the geographical space.
In visual image interpretation, a remote sensed
image is to be considered as an holistic feature
showing more or less structured image primitives.
These are made of aggregates of adjacent pixels
having the same photographical density. In analogy
with the airphoto-interpretation these primitives
form the texture of the image and may be called
texels. Their significance is fundamentally diffe
rent from the concept of a pixel and from the con
cept of texture in digital image processing. They
are formed by a real spatial structure and are not
mathematical constructions between pixels.
The size, shape orientation of the texels as well as
the patterns they may form, contain distorted infor
mation about the landscape structures.
A pixel should be considered as a discrete sample
out of the landscape continuum. No classification
problems with the pure pixels as far as the diffe
rent categories have distinct image properties.
Figure 1. Hierarchical relatioship between land
scape structure, structure of the image, texels and
pixels. Landscape elements : A = hedge- & tree-
rows ; B = woodland ; C = fields ; M = buildings ;
D = wasteland. Components of the landscape :
BM = biotic volumes ; AM = abiotic volume ; BR =
biotic space ; AR = abiotic space ; S = biotic
screens. Texels formed by adjacent pixels having
the same DN.
Texture formed by a spatial pattern of texels.
Micro-structure formed by a spatial pattern of
textures. Macro-structure of the image correspond
to the main landscape types : P = polderland ;
H = Houtland ; M = Meetjesland ; G = region of
Ghent ; L = Land van Lokeren ; W = Land van Waas.
On the contrary, mixed pi els do not contain
any real information about the landscape. Their
classification can be improved only by adding con
textual information. This is achieved automatially
in the deductive procedure followd in visual inter
pretation, using textural and structural informa
tion from all over the scene.
There is an space-dependent hierarchival rela
tion between pixels, texels, textures and struc
tures in the image and in the landscape.
Fig. 1 illustrates this.
The integration of structural information about the
landscape with the other ground truth could be
achieved in the following scheme :
1. - make a typological classification in the
area of study, combining classical methods
of landclassification and geographical lands
cape analyss. Attributes significant for
the structural properties of the landscape
should be used ;
2. - sample the remote sensed image for the
landscape units obtained using mainly tex
tural parameters. Estimate the proportion
of the pure pixels for the different land
scape components ;
3. - use the
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