Full text: XVIIIth Congress (Part B7)

The classificatıon accuracy were calculated by 
comparing the results obtained from a digital 
classification to the known identity of land 
cover in test areas (75 randomly selected test 
pixels were used) derived from an reference 
area. Due to the effect of the high percentage 
of mixed pixels in urban and suburban classes, 
the overall classification accuracy's for each 
date were obtained 84%, 8496 and 8596 
respectively. 
In Table 3, the areal context of the changes in 
urban and green area obtained from the 
classified 3 data set were given. In Figure 7, 
the land cover changes as per the years are 
shown. 
Table 3. Land Use/Cover Change Assessment. 
  
  
  
  
  
  
  
  
  
  
  
  
Land 1984 1990 1992 
Use/Cover (hectares) | (hectares) (hectares) 
Urban À 319.59 416.34 558.63 . 
Green A. 4850.73 4561.92 4273.92 
Land Cover / Use Change 
5000 -— : 
e 4000 + | 
$ 30004 reine | 
$ 2000 } | —— Green Areal | 
I 1000 + {— O — Urban Areal | 
om =m — 0 el — 
1984 1990 1992 | 
Year | 
  
  
Figure 7. Land cover/use changes 
as per the years. 
4. Conclusion 
Land use patterns change over time in 
response to economic, social and 
environmental forces. Type of any change in 
the use of land resources is essential 
information to proper planning, management 
and regulation of the use of land resources. 
As shown in this study, multi-temporal 
remotely sensed images are capable for 
identifying and delineating the changes 
occurred in land use e.g. new logging areas or 
new land developments such as settlements, 
industrial complex and roads etc. by 
comparing two (or more) 
taken on different dates, pixel by pixel and 
than updating the land use map of the study 
area. It is possible to use many change- 
detection techniques. The procedure that is 
most appropriate to use in a given situation 
Landsat scenes. 
682 
depends on the specific application (type of 
environment, targets of interest), the amount 
of detail required and an extensive knowledge 
of the area to be studied and the logical and 
spectral interrelationships between land use 
classes. 
As a result, it was concluded that since the 
spatial structures of the Tuzla region became 
more rigid and planning alternatives were 
narrowed, the considerable progress must be 
made in the creation of environmental 
awareness and implementation of effective 
legislation. 
REFERENCES 
Campbell, J.B., 1987, Introduction to Remote 
Sensing , The Guilford Press, pp. 499-500. 
Ingebritsen S.E. and R. J. P. Lyon, 1986, 
Principal component analysis of multitemporal 
image pairs, International Journal of Remote 
Sensing, Vol.6, No. 5, pp. 687-696. 
Lillesand, T.M. and R. W. Kiefer, 1987, 
Remote Sensing And Image Interpretation, 
John Wiley & Sons, pp.696-697. 
Michalak, W.Z., 1993. GIS in land use change 
analysis: integration of remotely sensed data 
into GIS, Applied Geography, 13, pp.28-44. 
Ormeci, C., and F. Sunar, 1994, Identification 
and multispectral Analysis of changes of green 
areas and coastal zones in the Kilyos- 
Karaburun coastline of Istanbul, Turkey, 2nd 
Thematic Conference on Remote Sensing for 
Marine and Coastal Environments, New 
Orleans, 31 January-2 February, pp.575-580. 
Virag, LA. and J. E. Colwell, 1987, An 
improved procedure for analysis of change 
inthematic mapper image pairs, Twenty-First 
International Symposium on Remote 
Sensing of Environment, Ann Arbor, 
Michigan, October, pp. 26-30. 
International Archives of Photogrammetry and Remote Sensing. Vol. XXXI, Part B7. Vienna 1996 
 
	        
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