The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B6b. Beijing 2008 
TM images 1990. 
TM images 2000. 
Removing of banding in 2 i 3 
(PCA) 
ISOCHJST channels 1,3,4,5 
Extraction of classes 
AREA calculation 
Contrast 
adjustment 
ISOCLUST channels 1,3,4,5 
Extraction of classes 
AREA calculation 
Forming test areas 
Forming test areas 
Maxlike supervised 
-o 
Maxlike supervised 
Figure 1 : Flow chart with IDRISI functions 
6. CONCLUSION 
The application of any change detection technique may be 
unsuccessful if user does not have enough knowledge about its 
characteristics in relation to the conditions over the area of 
study. Generally, the use of more than one technique is 
preferred by many researchers, because they can compare the 
results derived, and finally select the best ones for their project 
(Sangavongse 1995.) 
The technique of change detection based on two-date 
classification and two-class operation has been found to be fast 
and simple to use with satisfactory results. In order to reduce 
errors in change detection it would be also required to have 
phenological codata about the images. The most important 
factors that should be taken into account when performing 
change detecting, as recommended by Jensen (1996), have 
involved the familiarity with the study area, the quality of the 
data set, and the characteristics of change detection algorithms. 
It may be concluded that the use of Landsat TM for mapping 
forest change area provided satisfactory results which can 
potentially be improved. Land use/land cover should be 
conducted on a regular interval, to have reliable and usable 
results with conclusions about size and direction of change 
specially on large areas. The use of remote sensing is widely 
applicable and cost and time effective to their users. 
REFERENCE 
Bajic, M., 1999. Daljinska istrazivanja(Remote Sensing), 
Faculty of Geodesy, Zagreb, Croatia 
Clark Labs,2003. IDRISI Kilimanjaro Guide to GIS and Image 
Processing, Worcester, USA 
Jensen, J. R., 1996. Introductory Digital image processing: A 
remote sensing perspective. New Jersey: Prentice-Hall Inc., 
New Jersey. 
Lillesand, Kiefer ,1994. Remote sensing and image 
interpretation. Wiley and Sons, Inc., New York. 
Sangawongse, S. (1993). 'Land Use Change in the Chiang Mai 
Area from Two-data classification analysis on Landsat TM 
Imagery 
Schmitt, U. & Ruppert, S.G.,1996. Forrest classification of 
multispectral mosaicked satellite images, Archives of 
Photogrammetry and Remote Sensing, Vienna, Austria 
www: http://glcf.umiacs.umd.edu