Soil salinity is a significant challenge in numerous regions across the globe, including Egypt. The potential consequences encompass negative impacts on crop yield, human well-being, and eco-logical systems. The utilization of remote sensing and machine learning techniques is increasingly becoming recognized as cost-effective methodologies for the cartographic representation of soil salinity. The present work employed Landsat 8 satellite imaging data and sophisticated machine learning techniques to delineate and assess soil salinity levels in Sharkia Governorate, Egypt. In this work, several machine learning techniques were employed to forecast the salinity values of Total Dissolved Solids (TDS) in the designated geographical region. These algorithms encompassed support vector machines (SVM), regression trees, Gaussian linear regression, and tree-based ensemble in addition to linear regression analysis. A variety of instances were generated to develop an optimal model that accurately characterizes the salinity TDS values within the study area. This was achieved by utilizing the band values extracted from the Landsat 8 satellite imagery. The approach that demonstrated the highest performance was observed when employing the Blue, Red, and shortwave infrared bands in conjunction with the SVM-Quadratic SVM model. This particular configuration yielded an R2 value of 0.86 and an RMSE value of 175.98. The findings of this work demonstrate the feasibility of precisely mapping soil salinity through the utilization of Landsat 8 satellite imaging data and machine learning techniques. The provided data can be utilized to identify regions characterized by elevated levels of soil salinity, as well as for the formulation of effective approaches aimed at addressing this issue.

This study offers a siting approach and major help for decision-makers in Egypt and other nations when it comes to airport location choices. This research is about how a multi-criteria approach was employed in the decision-making process for a new airport in Egypt. It presents a practical way for discovering suitable locations in urban planning using remote sensing and Geographic Information Systems, which benefits both sustainable and economic development. The methodology of site detection depends on multiple-criteria analysis for choosing the optimum suitable sites. The most significant factors considered for this analysis are topography, urban areas, and geological characteristics. The study's constraints, such as distance from existing airports and land use, have been taken into account and will be explained later. Finally, a sensitivity analysis was conducted to decide the validity of the criteria used and to select the best site among the suggested locations based on the highest suitability score for each proposed location. The study results obtained five optimum suitable locations which are mentioned respectively Fayoum, the new Administrative Capital, Ras Sedr in South Sinai governorate, Siwa and Al Minya, which could help in the improvement of the aviation field. According to this study, a multi-criteria evaluation approach was utilized to assist decision-makers in selecting acceptable airport sites in Egypt. GIS allows for multi-criteria evaluation, which combines various sorts of criteria to arrive at the optimal decision. The weighted linear combination was used consistently in the multi-criteria evaluation in order to generate a suitability map.

Soil salinity is a major issue that causes land degradation, especially in arid regions which affects negatively on soil properties and reduces agricultural productivity. Remote sensing is an essential tool for detecting and monitoring salinity changes upon time. In this research an assessment was carried out to determine the best representative salinity index based on filed measurements of soil salinity in El-Sharqiyah governorate in the northeast of Egypt. The index was validated in accord with other field soil salinity data in term of total dissolved salts (TDS) measured through 3-years intervals in 2015, 2018 and 2021. Landsat8 satellite images were used to calculate NDSI (Normalized Difference Salinity Index). Seven indices were used to determine soil salinity, where the best correlated index was based on 2015 field work and used to produce salinity map of the study area. The best correlated index on 2015 was validated upon data of 2018 and 2021. Finally, this approach led to the sensitivity of remote sensing to soil salinity and the ability of its indices for soil salinity predication.

Multipath is one of major sources of error in GPS code and carrier phase measurements in the differential mode of operation. This paper includes four studies. The first one is the detection of the multipath errors from five tests carried out by setting up different material types of the vertical reflective surface (Aluminium Panel, Glass Panel, Ceramic Panel, Wood Board, and without reflective surface). The effects of different material types of reflective surface on the pseudo-range multipath are analysed using TEQC Software. The results obtained in this paper prove that different material types of vertical reflective do not give the same multipath error performance in Aluminium gave large Multipath Error, followed by glass, followed by Ceramic, then wood. In the second study is the detection of the multipath errors from three tests carried out by setting up at aluminium Panel dimensions difference (0.75X0.75 m, 1X1 m and1.25X1.25 m) as a vertical reflective surface and one test is without Multipath then analysed by using TEQC, the results obtained in this paper prove that the multipath error of GPS increases with the increase in aluminium Panel dimensions. Due to the occurrence of the largest multipath error in aluminium, in the third study, a multipath error was detected in four tests by positioning the 1.25 x 1.25 m aluminium panel at different distances (1 m, 2 m, 3 m, and without panel) from the antenna receiver, and it was found that the more the distance between the antenna and the aluminium receiver reduces the multipath error. In the fourth study, four tests were carried out by positioning the aluminium panel so that it inclines from the horizontal at different angles (10°, 20°, 30°, 40°) at a fixed distance of 1 m from the antenna, each test separately and found the multipath error decreases with the increase in the angle of inclination of the panel, as the statistical method for comparison and tests using the statistical T-test were presented in the previous studies. يعد خطأ تعدد المسارات مصد ًرا رئيسيًا في ارصاد نظام تحديد الموقع العالمي، مما قد يمنع تحقيق أعلى مستويات الدقة. سيناقش هذ ا البحث اربع دراسات، األولى يتم الكشف عن أخطاء تعدد المسارات من خمسة اختبارات تم إجراؤها عن طريق إعداد أنواع مختلفة من المواد للسطح العاكس الرأسي )الواح األلمنيوم، الزجاج، السيراميك، الخشب، بدون لوح(. يتم تحليل تأثيرات أنواع مختلفة من المواد للسطح العاكس الرأسي على خطا تعدد المسارات بواسطة برنامج TEQC .النتائج التي تم الحصول عليها في هذا البحث تثبت أن االنواع ال مختلفة من المواد العاكسة الرأسية ال تعطي نفس أداء خطأ تعدد المسارات في جميع االختبارات الخمسة ولكنها أعطت األلمنيوم ذو التأثير العالى على خطأ تعدد المسارات، يليه الزجاج، يليه السيراميك...