Sample Sidebar Module

This is a sample module published to the sidebar_top position, using the -sidebar module class suffix. There is also a sidebar_bottom position below the menu.

Sample Sidebar Module

This is a sample module published to the sidebar_bottom position, using the -sidebar module class suffix. There is also a sidebar_top position below the search.
قسم الفلك والفضاء

ياسر جاسب بخيت

Email: yassermobark377@gmail.com

 

Abstract:

The main goal of this work is study the land cover changes for "Baghdad city" over a period of (30) years using multi-temporal Landsat satellite images (TM, ETM+ and OLI) acquired in 1984, 2000, and 2015 respectively.  In this work, the methodology adopted took into consideration different image pre-processing techniques including, geometric correction, radiometric correction, atmospheric correction and satellite image clipping. The principal components analysis transform has been utilized as multi operators, (i.e. enhancement, compressor, and temporal change detector). Since most of the image band's information are presented in the first PCs image. Then, the PC1 image for all three years is partitioned into variable sized blocks using quad tree technique. Several different methods of classification have been used to classify Landsat satellite images; these are, proposed method (ant colony system) using proposed system and supervised method (Maximum likelihood Classifier Technique) using ENVI 5.1 software are utilized in order to  get the most accurate results and then compare the results of each method and calculate the land cover changes that  have  been  taken  place in years 2000 and 2015 comparing with 1984. The image classification of the study area resulted into five land cover types: Water bodies, vegetation, open land (Barren land), urban area "Residential I" and urban area "Residential II". The results from classification process indicated that water bodies, vegetation, open land and the urban area "Residential I" were increased, while the second type from urban area "Residential II" in decrease to year 2015 comparable with 1984. Despite use of many methods of classification, results of the proposed method proved its efficiency, where the classification accuracies for the ACS algorithm are 83%, 85% and 84% for years 1984, 2000 and 2015 respectively.