Abstract
The interpretation techniques, used in remote sensing applications, have exploited the wealth of information of the remotely sensed data. These techniques include methods of extracting information from spectral, temporal, and spatial domains of this data. In this paper the remote sensing satellites are considered the primary sources of digital images. Two interpretation algorithms (spectral and temporal) have been designed and implemented on different Landsat images. The first algorithm is directed to the statistical classification of both the Landsat low resolution multispectral scanner (MSS) images and principal component (PC) transformed images whilst the second one is an albedo algorithm to monitor land changes. The algorithm is designed and implemented on MSS and thematic mapper (TM) images. It has been used successfully for land management of the eastern area in Egypt. The results are eventually investigated, argued, and evaluated.
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