In recent years, small satellite industry has been a rapid trend and become important especially when associated with operational cost, technology adaptation and the missions. One mission of LAPAN-A2, the 2 nd generation of microsatellite that developed by Indonesian National Institute of Aeronautics and Space (LAPAN), is Earth observation using digital camera that provides imagery with 3.5 m spatial resolution. The aim of this research is to compare between object-based and pixel-based classification of land use/land cover (LU/LC) in order to determine the appropriate classification method in LAPAN-A2 data processing (case study Semarang, Central Java).The LU/LC were classified into eleven classes, as follows: sea, river, fish pond, tree, grass, road, building 1, building 2, building 3, building 4 and rice field. The accuracy of classification outputs were assessed using confusion matrix. The object-based and pixel-based classification methods result for overall accuracy are 31.63% and 61.61%, respectively. According to accuracy result, it was thought that blurring effect on LAPAN-A2 data may be the main cause of accuracy decrease. Furthermore, the result is suggested to use pixel-based classification to be applied in LAPAN-A2 data processing.

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The gum arabic belt in Sudan plays a significant role in environmental, social and economical aspects. This research was conducted in North Kordofan State, which is affected by modifications in conditions and composition of vegetation cover trends in the gum arabic belt as in the rest of the Sahelian Sudan zone. The objective of the paper is to study the classification, changes and analysis of the land use and land cover in the gum arabic belt in North Kordofan State in Sudan. The study used imageries from different satellites (Landsat and ASTER) and multi-temporal dates (MSS 1972, TM 1985, ETM+ 1999 and ASTER 2007 acquired in dry season. The imageries were geo-referenced and radiometrically corrected by using ENVI-FLAASH software. Image classification (pixel-based) and accuracy assessment were applied. Application of multi-temporal remote sensing data demonstrated successfully the identification and mapping of land use and land cover into five main classes. Forest dominated by Acacia senegal class was separated covering an area of 21% in the year 2007. The obvious changes and reciprocal conversions in the land use and land cover structure indicate the trends and conditions caused by the human interventions as well as ecological impacts on Acacia senegal trees. Also the study revealed that a drastic loss of forest resources occurred in the gum arabic belt in North Kordofan during 1972 to 2007 (25% for Acacia senegal trees). The study concluded that, using of traditional Acacia senegal-based agro-forestry as one of the most successful form in the gum belt.