Land cover mapping using remotely sensed observationH. S. Lim School of Physics Universiti Sains Malaysia Minden 11800 Penang, Malaysia. Tel: +604-6577888, Fax: +604-6579150 hslim111@yahoo.com.sg M. Z. MatJafri School of Physics Universiti Sains Malaysia Minden 11800 Penang, Malaysia. Tel: +604-6577888, Fax: +604-6579150 mjafri@usm.my K. Abdullah School of Physics Universiti Sains Malaysia Minden 11800 Penang, Malaysia. Tel: +604-6577888, Fax: +604-6579150 khirudd@usm.my N. M. Saleh School of Physics Universiti Sains Malaysia Minden 11800 Penang, Malaysia. Tel: +604-6577888, Fax: +604-6579150 C. J. Wong School of Physics Universiti Sains Malaysia Minden 11800 Penang, Malaysia. Tel: +604-6577888, Fax: +604-6579150 Sultan AlSultan Remote Sensing Center of Envir. Counsultant ISPRS, commission 7 WG VII/7, Middle East Coordinator Malaz, Al Nurii St., P.O.Box.92038 Riaydh City 11653, Saudi Arabia. Tel: +966504890977 Fax: +96614767828 rsensing_2004@yahoo.com ABSTRACT This study presents the land cover mapping using supervised classification technique namely maximum likelihood, minimum distance-to-mean and parallelepiped. The remotely sensed data used in this study was the Landsat TM image acquired on 20 January 1999 over the Gulf of Saudi Arabia. The objective of this study was to test the feasibility of high spatial resolution Landsat TM image for land cover mapping. Training sites were selected within each scene and land cover classes were assigned to each classifier. Accuracy assessment was performed in this study to determine the quality of the land cover map. The maximum likelihood classifier produced superior result with the highest accuracy in this study. This study clearly classified the land cover features using the multispectral classification technique for urban planning and development purposes. INTRODUCTION The increasing availability of remote-sensing images, acquired periodically by satellite sensors on the same geographical area, makes it extremely interesting to develop the monitoring systems capable of automatically producing and regularly updating land-cover maps of the considered site (Bruzzone, et al., 2002). The objective of this study was to estimate the coverage area of the seasonal agricultural vegetation over AlQassim, Saudi Arabia using two different seasons of satellite Landsat TM images. In this study, we are using three standard supervised classification techniques for the analysis. Finally, the post classification analysis of accuracy assessment was performed based on the Kappa statistic and overall accuracy. STUDY AREA The study area in the Arabian Peninsula, located between latitude 12šN and 32šN and between longitude 20šE and 35šE (Figure 1). This particular geographical position gives the area a great bioclimatic diversity. The desert of the Arabian Peninsular is located as a part of the hot desert, which extends from the Sahara in Africa in the west to the Thar Desert in Indo-Pakistan sub-continent in the east.  Figure 1. The study area in the central of Saudi Arabia
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