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International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B7. Istanbul 2004 
  
During image resampling, nearest neighbor resampling method 
was used. 
3.2 Image Classification 
Supervised classification method was used in order to examine 
digital data extraction possibilities. Training areas were selected 
using ground truth data and visual image interpretation. We 
determined four classes for classification. The classes were 
water, settlement , vegetation and road. Classification was 
carried out using Maximum Likelihood algorithm. 
  
Legend 
Water 
Vegetation 
Settlement 
  
Road 
  
  
  
  
Figure 4 . Classification map of the workspace 
4. RESULTS AND DISCUSSION 
4.1 Combining Data 
Firstly, obtained DEM (Digital Elevation Model) and rectified 
satellite image were joined. In this way 3D model of the 
workspace was achieved. 
  
  
Figure 5. 3D model of the workspace 
531 
4.2 Water Height Elevation And Visual Implementation 
Of Flood 
We got documents about water height of the river of 2002 from 
Directorate of State Hydraulic Works (Primary executive state 
water agency responsible for water resources development in 
Turkey.) obtained from hydrographs. We determined the 
maximum water height in the year. According to the values of 
the height of the water we set 291 cm. and 284 cm. as the 
maximum values. These values belong to on 19 Sept. 2002 and 
17 Apr. 2002. These values are not very high so these values did 
not cause a flood disaster in this year . We add about 2 meters to 
this value to show a probable risk zone. With Erdas Virtual GIS 
module by using a water layer the calculated value of water 
height was shown on the image. For this visual implementation 
classified image was used. Because the aim of this study is to 
show risky residential area. So we used an image showing 
residential area. 
  
Figure 6. Visual implementation for flood disaster 
s. CONCLUSIONS 
The aim of this study is to get a visual risk zone state of an area 
by using Ikonos image. In this project only water factor used but 
for a detailed project addition to this, extra factors such as 
geological characteristics and slope of the area must be taken 
into consideration. 
It is also important to note that high spatial resolution doesn't 
facilitate spectra-based classification. (Kristof, Csato, Ritter, 
2002). But for this study the classes are very general and don't 
need details. So we did not meet such a problem. 
Such a study the topographic state is very important. It means 
DEM of the area must be reliable. Because for this study 
interrogation was based on DEM. 
We investigated only flood disaster for this area but landslide is 
also a problem for the area. For this area such a study can be 
done. 
In this study we investigated only residential areas. With a 
cadastral data this study can be turned into detailed. Flood 
disaster is also very important for cadastral state. Such a study, 
cadastral parcels in the flood risk zone can be defined. A lot of 
interrogations such as; which parcels will be affected from a 
probable flood disaster, who belongs these parcels, if the parcels 
are agricultural land how the crop will be affected can be done. 
At the end of the study we got a visual result. According to this 
result residential area which is in the risk zone was determined. 
This visual result showed us most of the residential area near 
the river in the risk zone. 
After like a result, to take some precautions is the best way. 
The best and useful precaution is to change the position of the 
residential area. Local administrations mustn't allow structuring