Geography Reference
In-Depth Information
Fig. 5.48 LANDSAT-MSS-June-1975 imagery subsetting based on the spatial extent of the
ERB in Syria
The general extracted and classified LULC-classes based on LCCS-scheme (see
Sect. 5.3 ) were: Cultivated and managed terrestrial area(s); Natural and semi-
natural terrestrial vegetation; Artificial surfaces and associated area(s); Bare
area(s); and Natural water-bodies, snow, and ice.
The three period LANDSAT-imageries (MSS-June-1975, TM-May-1987 and
TM-May-2007) and the one period ASTER-imagery (ASTER-May-2005 fused
with the LANDSAT-ETM+/SLC-Off corrected/-May-2005-data) (see Chap. 4.1 )
were classified using the supervised classification technique MLC to generate the
general LULC-classes of the first LCCS-classification scheme.
The concept used to produce the thematic maps of the general classes was
divided into two ways: preprocessing steps and the mosaic-process (see Sect. 5.2 ).
The classification process was carried out for all defined classes on the one mosaic-
image that covered the whole spatial distribution of the study area. This mosaic-
image was generated from more than one remote sensing image, in which the
temporal, spatial, spectral and radio-metrical characteristics of each image were
deemed to be compatible with each other, or when it was possible to enhance and/
or correct the distortion in these characteristics. The second method was performed
when it was impossible to generate a correct and suitable mosaic-image with no, or
an acceptable level of, distortion in the above referred characteristics, or when it
was possible to generate a more suitable mosaic-image which gave more accuracy
in classification. For example, the second method was performed on the LAND-
SAT-MSS-June-1975 data, where subsetting of each of the seven images using the
ERB-borders-vector-file extracted from the SRTM-data was conducted (see
Sect. 5.1 ) (Fig. 5.48 ). The classification procedures were performed for each
subsetted image after which mosaicing was carried out on all the individual
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