[ 
t 
anbul 2004 
47.90 « 
2.087050 
1.992803 
1.438374 
0.000645 
1024.343038 
-525.664732 
1141160 
1141158 
m 
"om n ow ow o" m 
and Linear 
another and 
elation was 
ap and the 
n results are 
lihood-linear 
discriminant 
d classifiers 
'se results is 
lihood and 
efficient than 
t classifiers 
parellelpiped 
elpiped map 
surface. It is 
on algorithm 
PTS: 
= 57.72 + 
= 2.087050 
= 2.111239 
= 1.272756 
0.000616 
"onu 
' 
e 
- 
co 
o 
ne 
= 
= 1141160 
= 1141158 
ind. Minimum 
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B4. Istanbul 2004 
  
1933054 Q05578040X — re 
  
Regression Parameters: 
    
  
X axis: fisher 
Y &xis: mindist 
Coeff. of Det. = 27.73 % 
Std. Dev. of X = 1.992803 
Std. Dev. of Y = 2.111293 
S.E. of Estinate = 1.794294 
Std. Error of Beta = 0.000943 
t Stst for r or Beta 661.656027 
t Stat for Beta <> 1 = -524 351238 
Saxple Size (nj = 1141160 
Apparent df = 1141158 
high frequency 
Figure 4. Regression of Linear discriminant and Minimum 
distances classifiers. 
| Y= 1165727+ 0.763452X t= 0762671 
Regression Parameters: 
  
X axis: maxlike 
Y axis: piped 
   
el Coeft. of Det. = $8.20 1 
Std. Dev. of X = 2.087050 
| Std. Dev. of Y = 2.088639 
154- S.E. of Estinate = 1.350412 
i Std. Error of Beta = 0.000606 
ly t Stat for r or Beta = 1260.436523 
| t Stat for Beta <> ] = -350.534486 
| Sauple Size (n) = 1141160 
3 Apparent df = 1141158 
  
  
  
high frequency 
l'igure 5. Regression of Maximum likelihood and Parellelpiped 
classifiers. 
4. CONCLUSIONS 
For more effective use of the satellite remote sensing, landuse 
managers should be aware of the limitations and advantages of 
satellite data and should chose from their avaible landuse 
mapping options accordingly. Remote sensing is especially 
proper for initial reconnaissance mapping and continued 
monitoring of landuse over large areas. In this context, 
techniques for improving the classification of landuse with 
satellite remote sensing data include the use of appropriate 
digital data. In order to achieve this task, selection of the most 
proper satellite image, band combination, and the classifier are 
Very important. Additionaly, the image processing is important 
and different stages of it such as filtering of bands and principal 
Component analysis should be applied before evaluation. All 
these points were applied to this study and it has been seen that 
Maximum likelihood classifier was the most suitable 
1095 
classification method for landuse mapping purpose. Minimum 
distances classifeir was also determined as suitable as the 
maximum likelihood classifier. 
4.1 References 
Butera, M.K., 1983, Remote sensing of wetlands, [EEE 
Transactions on Geoscience and Remote Sensing GE-21,pp. 
383-392 
Campbell, 2002, Introduction to Remote Sensing, 
CORINE Land Cover Technical Guide, European Commission, 
Luxemburg, pp. 21-53. 
Dean, A.M and Smith, G.M., 2003, An evaluation of per — 
parcel land cover mapping using maximum likelihood class 
probabilities, /nternational Journal of Remote Sensing, 24 (14), 
pp. 2905-2920 
Ernst, C.L. and Hoffer, R.M., 1979, Digital processing of 
remotely sensed data for mapping wetland communities. LARS 
Technical Report 122079. Laboratory -for Applications of 
Remote Sensing. Purdue University, West Lafayette, 119 pp. 
Liu, X.H., Skidmore,A.K. and Oosten, V.H., 2002, Integration 
of classification methods for improvement of land-cover map 
accuracy, ISPRS Journal of Photogrammetry&Remote Sensing, 
56, pp 257-268. 
Lo, C.P. and Watson, L.J., 1998, The influence of geographic 
sampling methods on vegetation map accuracy evaluation in a 
swampy environment. Photogrammetric Engineering and 
Remote Sensing 64:1189-1200. 
Ozesmi, S.L and Bauer, M., 2002, Satellite Remote Sensing of 
Wetlands, Wetlands Ecology and Management, 10, pp. 381-402 
Richards, J.A., 1995, Remote Sensing Digital Image 
Analysis: An Introduction, Springer-Verlag, pp. 265-290. 
IDRISI Klimanjaro, 2004, Guide to GIS and Image Processing 
Volume 2, Idrisi Production, Clark Labs,USA, pp.57-82 
4.2 Acknowledgments 
The authors would like to thank to Dokuz Eylul University 
Scientific Research Fund for providing financal support for this 
project (Project no: 02.K B.FEN.052).