International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 
there is some possibility of local fault line (see Figure 6) in the Drury, S.A., 2001. /mage Interpretation in Geology. Blackwell 
study area. Science Inc., London. F 
Looking at vegetation, dense vegetation can be seen and the Idrisi, 1997. Idrisi Manual. Clark University, Worcester, 
edges of vegetation show formation boundaries. Vegetation Massachusetts. 
anomalies also do not show the sudden linear change that 
indicates a fault line. There is a river channel integrated with Koneeny, G., 2002. Geoinformation: Remote Sensing, 
vegetation showing a geological formation boundary. Photogrammetry and Geographical Information Systems. 
Taylor and Francis Inc., London. 
In this study, we have used Landsat TM multi-spectral imagery 
and a DEM having a 30m horizontal resolution. This is Lillesand, T.M., and Kiefer, R. W., 1994. Remote sensing and KEY W 
satisfactory for working with 1:100 000 - 1:250 000 mapping. image interpretation. John Wiley and Sons Inc., New York. 
But a 30m resolution image is not effective for extracting some 
geological structural features, such as rivers, drainage patterns McGeary, P., 1996. Physical Geology. WCB Publishers, USA. ABSTR 
and faults, given the random errors involved and error 
propagation during the interpretation process. We recommend Pandey, S.N. 1987. Principles and Applications of This stu« 
the use of multi-spectral images having a resolution of at least 5 Photogeology. John Wiley and Sons Inc., New York. July, anc 
to 10m horizontally, depending on the map scale and the crops. TI 
purpose of the interpretation. This will be adequate for 1:5 000 Prost, G. L., 1994. Remote sensing for Geologists: a guide to based on 
- 1: 25 000 mapping. Today, 1 or Sm resolution multi-spectral image interpretation. Taylor and Francis Inc., London. July, anc 
imagery is provided by many commercial imaging companies. compute 
In these high-resolution images, automated interpretation will Ray, R.G., 1960. Aerial photographs in geologic interpretation the: Aus 
be more accurate and reliable because of the finer geometric and mapping. United States Geological Survey. Washington. maskine 
and radiometric detail. complets 
Strahler, A., and Strahler, A., 1994. Introducing physical manner i 
After the results of interpretation and analysis are obtained in a geography. John Wiley and Sons, Inc., New York. pixels we 
map form, ground truth verification should be done using immedia 
ground control points and checked in the field. Turk, A.G., 1990. Towards an understanding of human- aCcurate : 
computer interaction aspects of geographic information 
6. CONCLUSION systems. Cartography, Vol. 19, No. 1, pp. 31-60. 
The purpose of this study was to use a methodology that as Turk, A.G., 1992. GIS Cogency: Cognitive Ergonomics in 
much as possible automatically interpreted and analyzed both a Geographic Information Systems. PhD Thesis, The University Automatc 
multi-spectral image and a DEM of the same area for obtaining of Melbourne. technique 
geological information. In the study, we employed automated sensed ¢ 
extraction of the stream drainage patterns from the DEM, and increasin: 
constructed geomorphology by shading the DEM. We then monitor : 
created the best color composite images and extracted distributi: 
vegetation anomalies by the NDVI method from multi-spectral re Le 
images for automated interpretation of geological structures. sre requit 
S e implemen 
Next, the four criteria were used for photo-geological 
Vol XXXV, Part B4. Istanbul 2004 
crops in le 
interpretation: color, color tones, and vegetation anomalies Classific 
were applied to the color composite images; stream drainage b d ica 
patterns, topography, and morphology have been applied to the ased and 
DEM. A set of rules allowed the computer to undertake a large cover cla: 
amount of the basic computational work unaided. atmospher 
types, anc 
The methodology supports shared-cognitive responsibility for Rods, 19¢ 
decision-making in geological interpretation. It reduces resulting c 
uncertainties in decision making in recognizing geological types. In 
structures and geomorphological features in multi-spectral reliability 
images and DEMs. The methodology enables remote sensing identificat 
li performed 
facilities to locate structural geological features and undertake 
interpretation in an easier, more accurate and straightforward 
way. It also allows existing expertise to be used as efficiently 
as possible. 
In parcel-t 
integrated 
the spatial 
Spatial co 
REFERENCES 
classified i 
Demirkesen, A.C., 2001. Constructing a prior information-base by per-pix 
for river mapping from digital images and DEMS by an accuracy c; 
advanced image interpretation system. Ph.D. Dissertation, The 1990; Joh 
classificatic 
Ohio State University, Columbus, Ohio, USA. 
mote ser 
provide int 
and attribu 
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