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replaced by coniferous trees trough forestation works in time, and that the Black Sea coastline in particular moved
towards the sea (Table 3). Tree cutting in forest management in the shaving manner is carried out at 20-year intervals
in the woods fit for cutting and in the areas where artificial regeneration process is to be applied. This situation may
lead to errors in assessing the classification results of the satellite data. Although some areas where trees were cut for
the purpose of new plantations existed within the classification results of the year 1997, it was found out, in the checks
made, that most of the areas that appeared as non-forest were transformed into either mining sites or settlement areas.
Another reason of the change in vegetation cover in 1997 is the modification of natural vegetation of the area because
of reforestation works carried out in many places.
Monitoring the temporal changes of the forest lands which are among the natural resources that are very difficult or
sometimes even impossible to retrieve will enable us to find solutions to the potential environment problems on time.
Furthermore; accurate, fast and low cost data/information can be obtained in the studies aimed at determining the
potentials of forests, monitoring their temporal changes and updating relevant information by using remote sensing data
with spectral range fit for the purpose and spatial resolution that are supported from the field check.
ACKNOWLEDGMENT
I would like to thank you very much to Prof. Cankut Ormeci and Prof. Unal Asan who have supported me during my
study and whose information was of much for me.
REFERENCES
Bernstein, R., 1983. Image Geometry and Rectification, The Manual of Remote Sensing, American Society of
Photogrammetry, R. N. Colwell,.
Botkin, D.B., Estes, J.E , MacDonald, R.M., Wilson, M.V., 1984. Studying The Earth Vegetation From Space, Bio
Science, 34181:508-514.
Chavez, P.S., MacKinnon, D.J., 1994. Automatic Detection of Vegetation Changes in the Southwestern U.S Using
Remotely Sensed Images, Photogrammetric Engineering and Remote Sensing, Vol:60, No:5, 571-583.
Cohen, J, 1960. A Coefficient of Aggrement for Nominal Scale, Educational and Physchological Measurement, 20:37-
46.
Congoltan, R.G., 1991. A review of assessing the accuracy of classification of remotely sensed data. Remote Sensing
of Environment, 37:35-46.
Congoltan, R.G., Mead, R., A., 1983. A Quantitative Method to Test for Consistency and Correctness in Phote
Interpretation, PE&RS, 49 (1):69-74.
Co?kun, G., Ormeci, C., Asan, U., Ye°il, A, Musaoëlu, N., Kaya, 2., 1998. Sayysal Uydu Verileri Yle (LandsatTM,
Spot XS) Ystanbul-Gaziosmanpala Orman YHetme Feflióine Baóly Tayakadyn ve *amlar Yórelerinde Me*cere Tipi
Ayyrymynyn Ara]tyrylmasy, Proje raporu, TÜBYTAK Proje no: 1622.
Deckert, C., Bolstad,P.V., 1996. Forest Canopy, Terrain and Distance Effects on Global Positioning System Point
Accuracy, Photogrammetric Engineering& Remote Sensing , Vol: 62, No: 3, pp. 317-321.
Dobson, E. L, Jensen, J.R., Lacy, R.B., Smith, F.G., 1995. A Land Cover Charactarization Methodology for Large Area
Inventories, ACSM/ASPRS Proceedings, Charlotte, North Caroline, pp. 786-795.
Elijah, W., Ramsey, M., Sensen, J.R., 1996. Remote Sensing and Mangrove Wetlands Relating Canopy Spectra to Site -
Specific Data, Photogrammetric Engineering& Remote Sensing , Vol. 62, No: 8, pp. 939-948.
Estes, J. E., 1992, "Technology and Policy Issues Impact Global Monitoring, " GIS World, 5(10):52-55.
Foody, G.M., 1992. On the compensation for Chance Agreement in Image Classification Accuracy Assessment,
Photogrammetric Engineering and Remote Sensing, 58(10):1459-1460.
International Archives of Photogrammetry and Remote Sensing. Vol. XXXIII, Part B7. Amsterdam 2000. 945