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Copyright of Journal of Engineering Sciences is the property of Faculty of Engineering - Assiut University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

For the synoptic assessment of corn plants content of Ca, and K accurate monitoring of land surface dynamics using remote sensing is needed. We looked at a full resolution dataset from the Medium Resolution satellite Imaging (Sentinel-2) as an open source as an alternative to the costly high resolution the more widely used high-resolution satellite Imaging (Worldview2) data for vegetation monitoring. We compared Sentinel-2 image and Worldview 2 data acquired in 2018 with in situ measured hyperspectral data and metal concentrations in plant samples collected from fields in the study area for this purpose. The current research was conducted on the experimental site during the 2018 corn cropping season (Zea Mayz). Results indicated that: The Difference Vegetation Index (DVI), the Enhanced Vegetation Index (EVI), the Green Normalized Difference Vegetation Index (GNDVI), and the Leaf Area Index (LAI) were the more sensitive indicators to Ca and K above ground corn plants content. These VIs had R² values more than 0.5 with the in-situ measurements for the both images. DVI, EVI, and GNDVI performed well in estimating plant dry matter Ca and K content with R² > 0.5, with a high significant level P-value < 0.001and LAI had a statistically significant impact with a P-value < 0.5 for WV2 image. The Sentinel-2 VIs performed well in estimating plant dry matter Ca and K content with R2 values > 0.5, with a high significant level P-value 0.001. LAI had a statistically significant impact with a P-value < 0.5 with Ca concentration and P-value < 0.01 with K concentration. This study suggests that the moderate resolution satellite images can be used for corn plants Ca and K content. [ABSTRACT FROM AUTHOR]

Our paper presents a remote sensing workflow for identifying modern activities that threaten archaeological sites, developed as part of the work of the Endangered Archaeology of the Middle East and North Africa (EAMENA) project. We use open-source Sentinel-2 satellite imagery and the free tool Google Earth Engine to run a per-pixel change detection to make the methods and data as accessible as possible for heritage professionals. We apply this and perform validation at two case studies, the Aswan and Kom-Ombo area in Egypt, and the Jufra oases in Libya, with an overall accuracy of the results ranging from 85–91%. Human activities, such as construction, agriculture, rubbish dumping and natural processes were successfully detected at archaeological sites by the algorithm, allowing these sites to be prioritised for recording. A few instances of change too small to be detected by Sentinel-2 were missed, and false positives were caused by registration errors, shadow and movements of sand. This paper shows that the expansion of agricultural and urban areas particularly threatens the survival of archaeological sites, but our extensive online database of archaeological sites and programme of training courses places us in a unique position to make our methods widely available. [ABSTRACT FROM AUTHOR]

The analysis of contemporary and archival satellite images and archaeological documentations presents the possibility of monitoring the state of archaeological sites in the Near East (for example, Palmyra in Syria). As it will be demonstrated in the case of Upper Egyptian sites, the rapid growth of agricultural lands and settlements can pose a great threat to sites localized on the border of fields and the desert. As a case study, the Qena district was chosen, a region of significance for the history of ancient Egypt. To trace the expansion of agriculture and the development of modern settlements, a synthesis of archival maps (from the last 200 years), and archival and contemporary satellite images was created. By applying map algebra to these documents, it was possible to determine areas which may be marked as "Archaeological Hazard Zones". The analysis helped to trace the expansion of agricultural areas during the last 200 years and the influence of both-ancient Egyptians and the Nile-on the local landscape. [ABSTRACT FROM AUTHOR]

Land use/land cover (LULC) changes can occur naturally or due to human activities. In this study, the maximum likelihood algorithm (MLH) and machine learning (random forest algorithm (RF) and support vector machine (SVM)) were investigated for image classification to oversight spatio-temporal land use changes in El-Fayoum governorate, Egypt. The Google Earth Engine has been utilized to pre-process the Landsat imagery, and then upload it for classification. Each classification method was evaluated using field observations and high-resolution Google Earth imagery. LULC changes were assessed, utilizing Geographic Information System (GIS) techniques, over the last 20 years in three different periods: 2000–2012, 2012–2016, and 2016–2020. The results showed that socioeconomic changes occurred during these transitions. The SVM procedure provided the most accurate maps in terms of the kappa coefficient (0.916) compared to MLH (0.878) and RF (0.909) procedures. Therefore, the SVM technique was adopted to classify all available satellite imagery. The results of change detection showed that urban sprawl has occurred and most of the encroachments were on agricultural land. The results showed that agricultural land area decreased from 26.84% in 2000 to 26.61% in 2020 and urban area increased from 3.43% in 2000 to 5.99% in 2020. In addition, urban land expanded rapidly on account of agricultural lands by a total of 4.78% from 2012 to 2016, while it expanded slowly by a total of 3.23% from 2016 to 2020. Overall, this study offers useful insight into LULC changes that might aid shareholders and decision makers in making informed decisions. [ABSTRACT FROM AUTHOR]

The Egyptian coastal lakes have changes in the water bodies due to the severe anthropogenic activities. In this paper, the Burullus Lake was selected as a case study. It is the second largest of the Egyptian northern coastal lakes along the Mediterranean coast. It has economic and environmental impacts on the nearby society of Kafr El-Sheikh, Egypt. ERDAS IMAGINE and ArcGIS software are used in this study for processing of the images and managing the database of each image. Different classification techniques are tested, the results showed that the maximum likelihood supervised classification technique was more accurate to monitor changes in the water bodies of the Lake. The method is applied to subsets of the Landsat TM, ETM+ and OLI/TIRS images acquired on 1984, 1990, 1998, 2003 and 2015, respectively. Five classes are detected including sea water, lake water, floating vegetation, sand bar and urban, and agriculture land. The results showed that the water bodies of the lake decreased by 44.97% (14,503.68 ha), while floating vegetation area increased mostly by the same amount during the period from 1984 to 2015. This increase in floating vegetation is mainly due to discharging of agriculture wastes and municipal wastes in the lake without adequate treatment. The sea water has minor changes during the period of study. The agriculture area increased by 45.52% (10,529.02 ha), while the sand bar and urban area decreased mostly by the same amount during the period from 1984 to 2015. Statistical models were developed using statistical tools. The models indicated that the water bodies of the lake will be reduced by 58.95% (19,013.42 ha) in 2030. The results of the present study shall help the decision-makers to take the necessary measures to reduce the environmental risk and maintain the lake in order to sustain the lake water area against further reduction. [ABSTRACT FROM AUTHOR]

Detection and monitoring land use/land cover (LULC) changes using historical multi-temporal remote sensing data is greatly important for providing an effective and robust assessment of the human-induced impacts on the environmental conditions. It is extremely recommended for LULC studies related to evaluating the sustainability of changing areas over time. The agricultural sector in Egypt is one of the crucial pillars of the national economy. The amount of traditional agricultural land (Old Lands) in the Nile Delta had a significant decline over the past few decades due to urban encroachment. Consequently, several land reclamation initiatives and policies have been adopted by the Egyptian government to expand agricultural land in desert areas (New Lands) adjacent to both fringes of the Nile delta. Tiba district is one of those newly reclaimed areas located in the western Nile Delta of Egypt with a total area of 125 km2. The primary objective of this article was to identify, monitor and quantify historical LULC changes in Tiba district using historical multi-temporal Landsat imageries for six different dates acquired from 1988 to 2018. The temporal and historical changes that occurred were identified using supervised maximum likelihood classification (MLC) approach. Three major LULC classes were distinguished and mapped: (1) Agricultural land; (2) barren land; and (3) urban land. In 1988, Tiba district was 100% barren land; however, during the 1990s, the governmental reclamation projects have led to significant changes in LULC. The produced LULC maps from performing the MLC demonstrated that Tiba district had experienced significant agricultural land expansion from 0% in 1988 to occupy 84% in 2018, whilst, barren land area has decreased from 100% in 1988 to occupy only 7% in 2018. This reflects the successful governmental initiatives for agricultural expansion in desert areas located in the western Nile Delta of Egypt. [ABSTRACT FROM AUTHOR]

Egypt has one of the largest and fastest growing populations in the world. However, nearly 96% of the total land area is uninhabited desert and 96% of the population is concentrated around the River Nile valley and the Delta. This unbalanced distribution and dramatically rising population have caused severe socio-economic problems. In this research, 24 land use/land cover (LULC) maps from 1992 to 2015 were used to monitor LULC changes in the Nile Delta and quantify the rates and types of LULC transitions. The results show that 74,600 hectares of fertile agricultural land in the Nile Delta (Old Lands) was lost to urban expansion over the 24 year period at an average rate of 3108 ha year−1, whilst 206,100 hectares of bare land was converted to agricultural land (New Lands) at an average rate of 8588 ha year−1. A Cellular Automata-Markov (CA-Markov) integrated model was used to simulate future alternative LULC change scenarios. Under a Business as Usual scenario, 87,000 hectares of land transitioned from agricultural land to urban areas by 2030, posing a threat to the agricultural sector sustainability and food security in Egypt. Three alternative future scenarios were developed to promote urban development elsewhere, hence, with potential to preserve the fertile soils of the Nile Delta. A scenario which permitted urban expansion into the desert only preserved the largest amount of agricultural land in the Nile Delta. However, a scenario that encouraged urban expansion into the desert and adjacent to areas of existing high population density resulted in almost the same area of agricultural land being preserved. The alternative future scenarios are valuable for supporting policy development and planning decisions in Egypt and demonstrating that continued urban development is possible while minimising the threats to environmental sustainability and national food security. [ABSTRACT FROM AUTHOR]