Table 5: Results_of the classified images
Class Tips 1984 1992
ha % ha %
Water 67.32 0.82 63.81 0.77
Wetland 64.62 0.79 * x
Forest 4455.81 54.10 3520.53 42.74
Green 1392.39 16.90 1636.65 19.87
Area
Base Soil 1729.71 21.00 1556.28 18.89
Roads 5.22 0.06 43.74 0.53
Industry 38.25 0.46 151.29 1.84
Urban 441.90 5.36 1264.68 15.36
Building in 41.76 0.51 -
Procesess
Total 8236.98 8236.98
In Remote Sensing Classifications, accuracy refers to the
between the class label assigned to a pixel and the "true"
class. The true class can be observed in the field directly
or indirectly; for example, from a reference map or merge
images (Janssen,1994) In this study classification
accuracy has been assigned by selecting 500 pixels
randamly for both images. As a result the total accuracy
has been calculated from the error matrix. The accuracy
of the 1984 classified Landsat images is 9096 and it is
9296 for the 1992 images.
CONCLUSION
This study provides a current investigation in the
monitoring of water basin area using multitemporal
images. It has always been a big problem to derive land
use information using spectral data.
The classification results obtained in this study have
shown that high resolution Landsat images can be
effectively used for the study of land use-cover. In
addition, it has also pointed out a large scale
urbanisation from 1984 to 1992 in the Elmali Water
Basin.
Therefore, water basin areas in Istanbul should have the
first priority for the environmental protection. Current
status analyses and updating of these should be carried
out by using remote sensing data.
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International Archives of Photogrammetry and Remote Sensing. Vol. XXXI, Part B7. Vienna 1996
