Skip to main content

2023 | Buch

Applications of Remote Sensing and GIS Based on an Innovative Vision

Proceeding of The First International Conference of Remote Sensing and Space Sciences Applications, Egypt 2022

insite
SUCHEN

Über dieses Buch

This book covers various aspects of remote sensing and geographic information systems, from the perspective of earth and environmental sciences. The theme of applications of remote sensing and geographic information systems for the purposes of sustainable development highlights the innovative usage of space imaged spectral data in soil characterization. This book merges the selected contributions to the First International Conference of Remote Sensing and Space Sciences Applications (Egypt 2022) aiming to promote the latest findings on the development of Space Technologies and Applications.

Inhaltsverzeichnis

Frontmatter

Satellite Systems and Their Applications

Frontmatter
Development of Reconfigurable Low-Power Measuring IoT Device for Detecting Common Radioactive Elements for Earth and Space Applications

This research study aims to collect environmental samples from various locations nationwide to analyze the concentration of alpha, beta, and gamma radiation. Initially, a state-of-the-art radiation detector is employed to identify potential radioactive elements. Subsequently, the study focuses on developing a new set of radiation detectors using IoT elements like Raspberry Pi, Arduino, and ESP single-board computing elements, coupled with analog sensors. This aims to create a low-power detector suitable for building a national radiation monitoring network. The network will enable real-time, 24/7 monitoring of radiation activities nationwide. Additionally, the study explores using rad-hard reconfigurable platforms such as FPGA in conjunction with peripheral analog sensors like the BM-20 Geiger-Muller Tube. This combination facilitates the collection of target data on radiation elements, including alpha, beta, and gamma radiation prevalent in outer space. Moreover, the research intends to enhance the proposed detector’s security by incorporating lightweight cryptographic primitives. This involves implementing the Diffie-Hellman key exchange protocol between the local board and an Android app, ensuring secure communication of sensor readings. In summary, this research study involves sample collection, radiation detection using IoT elements, integration with reconfigurable platforms, and the inclusion of cryptographic measures to enhance the security and functionality of the radiation detector. (1) Study the scalability possibilities for extending the design to multiple nodes nationwide for having a low-power runtime radiation network in strategic locations. (2) Extend the possibilities of using such design in multiple space tiny devices such as CubeSat for short space.

Mohamed El-Ashkar, Halima El-Naga, Mohamed El-Hadedy
RECO-FSCA: Reconfigurable Low-Power Implementation of Fprime-Software for CubeSats Applications

The Python Productivity for Zynq (PYNQ) project, developed by Xilinx, is a member of the Zynq class of devices. NASA's Jet Propulsion Laboratory has created the F′ (FPrime) software framework, specifically designed for rapid deployment and development in space applications. FPrime adopts a component-driven approach, enabling the reuse of universal components across multiple projects, thereby reducing development time and costs. In our study, we focused on developing components for FPrime and deploying them on the PYNQ-Z1 and PYNQ-Z2 boards. These components were designed to assess memory performance, critical for flight software, as well as computationally intensive tasks. To test memory-intensive tasks, we designed and implemented an array copying component. For computationally intensive tasks, a modular exponentiation task was developed. Additionally, we integrated the GAGE hash function into FPrime, providing data for computationally intensive operations. Intra-component security features were also incorporated to mitigate the risk of Man-in-the-Middle attacks. Comprehensive measurements were recorded for each aspect of the project. Future work will involve further benchmarking and exploring the feasibility of implementing FPrime on embedded devices within Cube-Sats.

Robert Herndon, Bryan Banta, Giuliano Milan, Yong Zhang, Macade Walker, Ammar Moussa, Darren Chiu, Dhanush Karthikeyan, Mohamed El-Hadedy
Autonomous Payload Imaging System for Remote Sensing Applications

Remote sensing is based on collecting data about an object or phenomenon from a distance without making physical contact with the object to obtain accurate imaging data for performing analyses. Unmanned aerial vehicles (UAVs) are becoming popular in monitoring and remote sensing applications. Due to their robustness, and economical option for daily control operations, UAVs are used more and more for data logger applications. The primary objective of this work is to build a remote sensing payload carried on a drone for collecting images in specified target locations used in full autonomous systems in remote sensing applications. The proposed payload system design is based on an Arduino microcontroller, GPS module, and Memory chip. The system is capable of capturing target images and storing it up in the memory with its associated GPS information. This target image capturing can be repeated for a list of targeted locations in the same route of drone fly. The system was successfully designed, implemented, and tested. The acquired images have been validated and found to be accurate. For easy use of the system, you can enter the target location through google Maps and it will be stored directly on the system before flying. After that the captured images from the mission is stored on the memory of the payload system.

Maha A. Maged, Alaaeldin S. Hassan, Haitham Akah, Mohammed El-Telbany
Plasma Space Debris Removal System—NIRCSAT-X

Space debris has been acknowledged as one of the most significant issues facing the space industry, with efforts now being focused on controlling this problem before it is too late. Current solutions involve expendable single-use vehicles designed to deorbit large pieces of debris. This project aims to develop the structural design of a reusable spacecraft to remove collections of small space debris that will remain undamaged by launch conditions. To annihilate space debris on-site without needing a collection system or earth re-entry for disposing of them. The NIRCSAT-X project proposes a novel technique for space debris removal on-site to reduce cost. The NIRCSAT-X project aims to develop novel technology on the TRL-3 or TRL-4 level. The project aims at a continuous space debris removal system. The paper will present the development of the novel design's mechanical and thermal subsystems.

Gasser Abdelal, Yasser Mahmoudi, Sean McLoone, Adrian Murphy, Andrew May, Callum Connolly
Heat Energy Storage Module for Thermal Management of Small Satellites in Low Earth Orbit Thermal Conditions

Small satellite thermal management aims to keep the satellite hardware components within the ideal operating temperature range. Due to the intermittent heating conditions in low earth orbit (LEO), thermal regulation is a difficult issue. The satellite in LEO occasionally appears in the illumination zone between the earth and the sun, receiving a significant quantity of solar heat flux. During other times, the satellite is in the eclipse zone, when the earth's shadow significantly cools satellite components. The current work looks at using a thermal storage panel (TSP) along with phase change materials (PCM) to regulate the temperatures of satellite subsystems. The TSP was built of aluminium and measured 100 mm in length, 71 mm in width, and 25 mm in height on the outside. The PCMs utilized were organic-based compounds RT 15 and RT 22. The TSP was tested with two different heating loads of 11 and 14 W. These heating loads are typical satellite subsystem heating demands. The TSP was quantitatively evaluated in several scenarios with and without PCM. A numerical model was validated using earlier work linked to the current topic, and the findings fit well. The results revealed that the PCM performed better in terms of thermal control than the cases without the PCM. With an 11 W heating load, the RT 22 case may reduce the maximum temperature by 35.2% while increasing the lowest temperature by 116%.

Abdelrahman M. Elshaer, A. M. A. Soliman, M. Kassab, A. A. Hawwash

Geology and Water Resources

Frontmatter
ASTER and Aerospectrometric Data Analysis for Gold Exploration: Case Study at Um Balad Area, North Eastern Desert, Egypt

The Um Balad area is a part of the Arabian Nubian Shield (ANS), which hosts various gold and base metal deposits. Gold can be prospected in the alterations and sheared zones in the widely exposed metagabbro-diorite rocks in the Um Balad area. In this regard, the integration of ASTER multispectral data processing, gamma-ray spectrometry data analysis, GIS modeling, and fieldwork gave us a good indication of which areas are most likely to contain gold-related alteration zones in the study region. Three remote sensing techniques were applied, including Spectral Angle Mapper (SAM), Spectral Information Divergence (SID), and Mixture Tuned Matched Filtering (MTMF). Key alteration mineral types are mostly related to the spatial distribution of mineral-bearing alteration zones. Careful interpretation of the gamma-ray spectrometry data is reliable for mapping alterations related to hydrothermal activity. The potassium distribution (%) of the study area was analyzed, where the radioelement ratio maps [eTh/K and eU/K] were obtained. The F-parameter and the Th-normalization techniques (Ud % and Kd %) were respectively applied for identifying potential mineralization zones. The GIS (weight overlay analysis) tool was used to combine the results of the three methodologies (remote sensing approaches, gamma-ray spectrometry data interpretation, and automatic lineament extraction) by combining ten layers (ASTER-SAM, ASTER-SID, ASTER-MTMF, lineament density, [K%], [eTh/K], [eU/K], [K*(eU/eTh)], [Kd], [Ud]). A final gold-related alteration zone map was generated. For validation, spectral signatures of the acquired rock samples as well as scanning electron microscopy (SEM) were used for the result verification of such a study. Finally, going forward, the adopted methodologies are considered the basis for gold-related alteration mapping in comparable environments.

Mahmoud Abd El-Rahman Hegab, Salah Eldin Mousa, Salem Mohamed Salem, Marwa Sayed Moustafa
Environmental Impacts of Mining Activities in Um Balad—El Urf Region; Central Eastern Desert

Besides the mining activity and mineral exploitation, the health should always in mind. In this regard, while the radioactive elements are important to our economy, they have remarkable effects of ionizing the groundwater, soils, and plants that are subject to such radiations, being a harmful source. Thus, proper safety measures must be taken to protect their problems. Moreover; Natural radiation comes from a variety of sources, including radioactive substances found in rocks and soil, ingested radioactive substances through food and drink, and cosmic rays that enter the earth’s atmosphere from space. The mining in the central eastern desert is mainly related to the granite intrusions characterized by high radioactivities. The purpose of this study is to assess rock radiation in the area. The exposure rate of radiation is used as a baseline when comparing additional man-made radiation sources, such as radioactive waste disposal, nuclear power generation, and fallout from nuclear weapons. The results: A dose rate model was able to be created to determine the average radiation exposure over the region gold mines. Additionally, the analysis of airborne spectral gamma data makes it easier to distinguish between various rock units quickly. The generated vector maps can be used as a reference for additional mineral and radioactive element exploration. Additionally, for regional environmental monitoring. Additional mining regions should utilize strategies akin to those used in the study area to safeguard and sustain human health.

Sahar Mahmoud, Mahmoud Abd El-Rahman Hegab, Nehal Soliman
Using Treated Wastewater in Groundwater Recharge at Wadi El Farigh Area, Egypt: GIS and Remote Sensing Applications

On the western edges of the Nile Delta, Wadi El-Farigh area occupies agricultural reclamation projects that mainly depend on groundwater for irrigation. Due to over-pumping, groundwater levels have declined, leading to an increase in the cost of pumping water and deteriorating its quality. The study aims to use treated wastewater in artificial recharge of the groundwater aquifers to improve the efficiency of groundwater use and ensure its sustainability. A GIS-weighted overlay model was created for selecting the most suitable sites for groundwater recharge. The model relies on a large amount of data taken from various sources. The sources include Tandem-X and SRTM elevation data images, Landsat 8 satellite image, Sentinel-2 images, geological maps, hydrogeological data, and field measurements. The results showed that the best sites for an artificial recharge experiment are located in the southwest and northwest extensions of Wadi El-Farigh. In addition a channel was proposed to transport water from the treatment station to the recharge location, where the best path for it was determined. This study shows the importance of integrating satellite data and GIS for properly managing groundwater.

Salwa F. Elbeih, Mohammed Hagage, Wael Attia, ElSayed Abd el-sadek
Inland Water Quality Monitoring Using Remote Sensing and GIS Techniques—A Tigris River, Iraq Case Study

Remote sensing and GIS are effective surface water quality assessment and monitoring technologies. These technologies help in making correct decisions that contribute to pollution reduction, its causes, and the time needed for treatment. This study aims to analyze the water quality along the Tigris River in Baghdad City, Iraq by developing mathematical and statistical models to predict water parameters from satellite imagery. In 2018, fourteen different locations along the Tigris River were surveyed. At each location, continuous measurements for eight variables, including temperature (Temp), electrical conductivity (Cond), total dissolved solids (TDS), pH, turbidity (Turb), chlorophyll A, blue-green algae (BGA), dissolved oxygen (DO) were provided. Geometric and radiometric corrections modified the spectral bands from Landsat-8. Then, spectral indices for soil, vegetation, and water were calculated from the corrected bands. Both spectral bands and indices were implemented in the least absolute shrinkage and selection operator (LASSO) for the prediction of those eight water variables. Evaluation of the prediction model showed that the temperature has a maximum root mean square error (RMSE) of 0.093% with 0.8 coefficient of determination (R2), while DO has a minimum RMSE of 0.012% with 0.76 R2. The predictive model for each water variable provides cost-effective alternatives to frequent monitoring of Tigris water quality using field data.

Suhaib Mohammed, Wael Ahmed, Salem Morsy, Adel El-Shazly
Mapping Water Quality and Bathymetry Determination of Burullus Lake Using Remote Sensing and GIS

Lake Burullus is one of Egypt's four northern wetlands located along the Mediterranean Sea coast. Its water quality has deteriorated significantly due to huge agricultural drainage volumes, municipal, and industrial effluents. The main aim of this study is to map the Lake's surface water quality conditions utilizing remote sensing and Geographic Information Systems (GIS) as a tool for water quality monitoring using spectral water indices retrieved from Landsat satellite images. Nitrate, phosphate, turbidity, nitrogen, ammonia, iron, and total phosphorus were all represented on the output maps. Water samples from the lake were collected in October 2020 to demonstrate the lake's deterioration and changes in the measurements of analyses at this time of the year. Furthermore, a series of depth measurements were obtained at appropriate distances from each sample to create a map of the depths (Bathymetry map) inside the lake. Depth measurements followed drilling, disinfection, and dredging activities within the lake when samples were collected. The resulting maps demonstrated a suitable distribution of many water quality metrics. The most worse water quality was found in the eastern and southern extensions of the lake compared with other regions, owing to polluting drainage water and wastewater discharge in these areas. In summary, the study empahsises that remote sensing integrated with GIS are powerful tools to map water quality.

ElSayed Abdelsadek, Salwa Elbeih, Abdelazim Negm

Site Investigations

Frontmatter
Assessment of Extracting Topographic Surface Features Using Different Open Sources DEMs

Digital Elevation Models (DEMs) represent the basis of geographical studies, in general, and geomorphological and hydrological studies, in particular. Generally, many open source DEMs are currently in use, and significant differences have been noted between their results. During the current research, high-accurate DEM was created using modern geomatics techniques (i.e., GNSS). Then, a comparison was carried out between a total of common 100 points, which were carefully chosen and measured in both high-accurate digital model, and open source DEMs extracted from satellite images (e.g. ASTER30 DEM—SRTM1—AlOS PLASTER—DEM “ALOS12.5”), where a number of statistical indicators were applied on these models. Output evaluation results indicate that there were large differences in the accuracy of the height discrepancies. However, open-source model namely: AlOS DEM 12.5 gives the highest accuracy compared to other models (ASTER30—SRTM1), and additionally, is considered the closest accurate height model versus the high-accurate DEM created using GNSS. After that, SRTM1 comes in second place in terms of accuracy. Accordingly, open source AlOS12.5 and SRTM1 DEMs were enhanced using high accurate GNSS-DEM. Finally, analysis of surface topography for the study area (New Cairo City) was carried out using four DEMs (SRTM1—Modified SRTM1—AlOS12.5—Modified AlOS 12.5), resulted in a significant difference for the output parameters before and after the enhancing process.

Mohamed Badawy Farag, Ateaya Bekheet Azeez, Ashraf Mohamed Sharawi, Mohamed Abd El Aziz
Morphological Analysis of Nineteenth-Century Cairo

This study analysed the structure of urban spaces in historic Cairo in the nineteenth century based on the connectivity between streets and living spaces. Conventional studies on the urban fabric of historic Cairo have been based on the concept of an ‘Islamic city’ composed of neighbourhoods (ḥārah). However, the forms of living spaces were diverse and do not necessarily fit into this model. Therefore, this paper discusses the structure of the Islamic city by focusing on connectivity to review the conventional model and present a method for morphological analysis of the urban structure. The analysis was based on a combination of textual and cartographic information. First, the arterial streets in the subject area were identified by the descriptions in al-Khiṭaṭ al-Tawfīqīya. Second, they were located on the 1:500 housing map created in 1938. Third, the degree of the relations within the urban structure was visualised according to the Space Syntax theory. The morphological aggregates were classified into two types: those connected to the arterial streets at only one point and those connected to them at several points. Moreover, for both types, the aggregates may have had only a single name, or multiple names and been segmented by name. Thus, by using the concept of connectivity embodied by an arterial street, we supposed that the morphologically diverse parts of the street became a unit (such as lengths, bends, and cul-de-sacs). Each part connected to the building sites and constituted a mass as a living space. Comparing these masses as spaces with the conventional model showed that they were not necessarily homogeneous aggregates consisting of a set of cul-de-sacs. They were composed of a variety of types of streets. Moreover, the arterial streets that linked these sets created a stratified mass of living spaces and complex urban fabrics.

Naoko Fukami, Susumu Sato, Yuta Arai, Takenori Yoshimura, Yuko Abe, Wakako Kumakura
Suitability of Terrestrial Laser Scanning for Layout Planning of Ship Engine Rooms

The terrestrial laser scanner (TLS) is increasingly in demand in many remote sensing applications, as this type of scanner contains two sensors to produce 3D point clouds and images. This is due to its high efficiency in capturing millions of point clouds in a short period, allowing users to create more accurate and detailed 3D models of the obtained elements. Due to the economic importance of Safaga Port in the Arab Republic of Egypt on the Red Sea, the maintenance of ships in the port, and consequently, the survey of engine rooms, as the main task of the maintenance procedures, are of great importance for Safaga Port Authority. In the current research, the scanning process for the engine of a cargo ship was done using a terrestrial laser scanner Leica P30. Firstly, a field inspection of the ship was done. Then, the fixation and collection of coordinates for ground control points that will be used for georeferencing of different scenes were executed. After that point, clouds and images for different scanning positions were captured. Point clouds were processed (e.g., alignment, cleaning, …) using commercial package software Cyclone. Finally, the required dimensions of engine rooms and complete 3-D models are calculated and offered through the True View visualization tool.

Ateaya Bekheet Azeez, Ashraf Mohammed Ahmed Sahrawi, Asmaa A. Mandouh, Abdelrahman Ali Wahba, Mohammed Elsaeed Elsawwaf
An Evaluation and Accuracy of SRTM and ASTER GDEM to Generation of Contour Lines

DEM is the basis of processing extraction of Digital relief maps. The goal of this study is evaluation of free DEM to extract contour lines, the quality and accuracy of DEM, and comfortable with the standard contour maps of scale 1:50,000 to represent the topography of the land surface, and Cartography comparison to contour from DEM and contour lines from topography maps. This study uses quantitative methods and statistics it is based on Root Mean Square Error (RMSE) and Mean Error (ME). So, this Study focuses on Visualization evaluation of results of the analysis of DEM. Consideration of the accuracy of a degree of simulation of extraction contour lines from DEM and original contour lines. The accuracy was studied using 3330 elevation points using a topographic map. Results indicate that SRTM DEM, and ASTER GDEM both may be used in generation contour maps with a scale 1:50,000, so RMSE to SRTM, ASTER is 6.6 m and 8.6 m and ME is 5.1 m and 5.5 m respectively. the quality analysis showed that agreement output from free DEM with the standard accuracy of contour map 1:50,000 so can you depend on free DEM to extract a contour line with a contour interval 10 m or 20 m with scale 1:50,000 with 5 m to 10 m of the accuracy. The cartographic analysis of contour lines produced from free DEM is characterized by its compatibility with the cartographic rules for contour lines and the absence of distortions and many abnormal phenomena in it, especially when using the Global mapper program, as well as the higher details of the data of the ASTER GDEM model than the SRTM model.

Kariman Ismail, Mohamed Fozy

Environmental Changes, Modeling, and Vulnerability Studies

Frontmatter
Assessment of Desertification Sensitivity Using Interdisciplinary Multi-criteria in GIS-AHP Environment

Geographic Information Systems-Analytical Hierarchy Process (GIS-AHP) was used for desertification sensitivity assessment. Two study areas were selected from the vast desert landscape on both sides of the Nile Delta. Selected soil profiles were field investigated and representative soil samples were collected for laboratory characterization of soil properties relevant to the study. Soil sensitivity to desertification is one of the most important measures supporting decision-making process in agriculture sustainable management. Such sensitivity is an important parameter, considered for horizontal agricultural expansion. The GIS-AHP ensemble technique successfully enables transdisciplinary and multi-inputs in the context of land management adaptation under climate changes, as proven by the case study. Results: For both locations, the GIS-AHP ensemble site rating values are validated by employing long-established soil management areas as markers of favorable site conditions. The results of the interdisciplinary site evaluation are checked for consistency in terms of future soil sustainability. Unlike the conventional GIS-AHP, the novel GIS-AHP ensemble method may take into account different problem perceptions and examine the robustness of produced site rating outcomes in multi-actor situations. The proposed GIS-AHP ensemble methodology can be used to solve any site selection problem and can help to promote integrated environmental management practices.

Mohamed A. E. AbdelRahman, Abd-Allah Gad, Ahmed H. Zaky
Monitoring LU/LC Changes in El-Fayoum Governorate Using Support Vector Machine

Since the 1980s, El-Fayoum has witnessed several changes in land use. Images from Landsat satellites were used to examine the changes in land use/cover in EL-Fayoum from 2000 to 2020. For assessing quantitative data and processing satellite images for this study area’s assessment of land use change, Google Earth Engine (GEE) and ArcGIS Pro were utilized. GEE makes the processing and preprocessing required for satellite images fast and easy. The supervised land use classification process was compared by using a support vector machine algorithm (SVM) and maximum likelihood (MLH). SVM was better in accuracy than MLH, which was a respectable result for monitoring changes. It was discovered that within 20 years, 94.22 and 6.39 km2 of deteriorated agricultural land had been converted into urban areas and desert regions. Another 143.72 and 111.96 km2 of desert land were transformed into agricultural land and urban areas, respectively. The area of the water body decreased from 337.76 to 315.44 km2 with an annual change rate of −1.11%. The urban area increased from 200.70 to 350.34 km2 with an annual change rate of 7.48%. The findings of this study will be useful in organizing and putting into practice crucial management choices in order to preserve El-Fayoum’s biodiversity.

Islam Atef, Wael Ahmed, Ramadan H. Abdel-Maguid
Spectral Environmental Indicators Associated with Mosquito Breeding Habitats Using Satellite Images in Assiut Governorate

Assiut Governorate is characterized by a unique nature that contains arable land, lakes, residential areas and a desert. The risk of Mosquito-borne diseases depends mainly on ecological variables. Recently, GIS and Remote Sensing were used for capturing and analyzing entomological, environmental and landscape features to assessing, modelling and monitoring spatiotemporal distribution habitats of mosquito. This work aims to delineate areas under risk of mosquito; accordingly, priority is given to strategic places to implement integrated control and its management mosquitoes that transmit diseases. Two multispectral Landsat 8 OLI satellite data acquired in 2019-9-21 and 2020-01-11 were freely downloaded for study area. Ranges of Land Surface Temperature (LST), Normalized Difference Moisture Index (NDMI) and Normalized Difference Vegetation Index (NDVI) were calculated at different breeding habitats, which are used as main environmental factors for modelling and prediction of mosquito proliferation risk at different Assiut districts. From surveying results in September, 2019, Culex perexiguus, Culex pipiens, Ochlerotatus detritus and Culex tritaeniorhynchus were present with various density in the study area. Culex perexiguus was found to be the most abundant (50.24%) of all mosquito species. The least mosquito abundant was Culex tritaeniorhynchus (7.19%). During January 2020, where 23 breeding sites visited were checked for larvae. There are 4 species of mosquito larvae distributed in the study area namely; Culex pipiens, Culex perexiguus, Ochlerotatus detritus and Anopheles tenebrosus present with various densities. The mosquito species Culex pipiens was more abundant (89.06%), while Anopheles tenebrosus was least abundant (0.22%). Statistical calculations showed that Cx. pipies is the most dominant species in most of sites surveyed.

Mohamed Sowilem, Ahmed M. El-Zeiny, Hala A. Effat, Kamel Mansour
InSAR Applications of Land Subsidence Over Oil Fields (Case Study Southeast of Republic of Tatarstan, Russia)

Land subsidence risk assessment basically based on current information found that the analyzed subsidence districts are over the top and medium hazards of tormented by subsidence resulted in damages like diminish in runoff and wastewater seepage capacity, disturbance to water transportation structures undermine artificial infrastructure balance. Also, land subsidence may also be caused by oil and gas exploration. Russia is a major oil and gas producer in the globe. The Romashkino field is an oil and gas field located in Tatarstan, Russia. In 1948, the largest oil field in the Volga-Ural Basin was found there. In this study, we focus, application of INSAR analysis for surface deformation by oil and gas extraction and injection cycle with 14 SLC SAR Images of the descending pass from sentinel-1 satellite in C band between 2017 and 2020. The Permanent Scatterers (PS-InSAR) Technique was utilized to study displacement deformation utilizing the SARPROZ software. PSI was applied using interferograms with a super master scene. This approach processes only the coherent pixels with stable phase or amplitude. Due the high-density vegetation cover and long winter time with snow cover, decreased the number of resulting points. Results of the displacement re-sampled price of local land deformation between 2017 and 2020 projected. The displacement rates surrounding the Romashkino oilfield in this study range from 3 mm/yr to -9 mm/yr, according to PS-InSAR measurements. Additionally, we suggest an acceptable cutoff to choose the PSs for the case study when ASI is > 0.70 (DA 0.25).

Yury Razoumny, Javad Hatamiafkoueieh, Sajjad Zeraat Peyma
Applications of Environmental Design for a Renovation of Legacy Radiological Facilities

The fate of nuclear and radiological legacy facilities is rapidly becoming a critical issue. Our proposal is to improve data on existing, legacy radiological facilities so that decisions can be made about whether to treat or replace radiological facilities and exceptional buildings. Using GIS applications and an analytical database, the inherited facilities can be classified based on the seriousness and importance of modernization or treatment, allowing the competent authorities to decide whether to renovate, replace, or decontaminate the radiation buildings. We used the study to develop a database analysis methodology that allows GIS tools to test some facilities quickly and accurately. Buildings that need to be replaced and buildings that can be easily renovated can be identified using these tools, which take into account design and construction criteria. The use of a GIS model for each building represents a significant added value of the method because it allows for the identification of specific buildings or clusters of buildings, as well as their quantification and distribution. The developed GIS tool *Energy Scout* is a tool for estimating the energy impacts of various Energy Conservation Measures (ECMs) residential and commercial buildings. Scout characterizes ECMs using their relative or absolute performance, installed cost, service lifetime, and year of introduction. Probability distributions can be placed on ECM performance, cost, and lifetime inputs, which then propagate through to final energy impacts.

Nadia Mahmoud Sirag

Climate Impacts and Environmental Changes

Frontmatter
Land Cover Patterns and Their Impact on Land Surface Temperature Using Remote Sensing Techniques: A Case Study of EL-Beheira, Governorate, Egypt

Using remote sensing data from Landsat 8 during the winter and summer seasons of 2022, the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Built-up Index (NDBI) have both been derived as one of the land cover elements, and their associations with the Land Surface Temperature (LST) have been examined for EL-Beheira, Governorate, Egypt. Thermal data analysis was used to obtain LST, which indicates the surface temperature's regional, the highest LST ever measured was 40 °C and 65 °C throughout the winter and summer seasons, respectively. NDBI, which evaluated the spacecraft data’s Bands 6 and Band 5, explains the urban accumulated index. The highest NDBI was 0.56 in the winter and 0.61 in the summer, respectively. The maximum NDVI in winter and summer, showed values of 0.84 and 0.58. LST analyses showed that the surface temperatures of the constructed and bare lands were higher than those of the cultivated lands. Significant positive connections between LST and NDBI were discovered, with R2 values of 0.79 in February 2022 and 0.88 in July 2022. However, there were significant negative associations between LST and NDVI, with R2 values of 0.85 and 0.854 in each season. It was also shown that there is a strong negative association between NDVI and NDBI with R2 values of 0.95 and 0.90.

Nagwan Afify, Mohsen Nabil, Eslam Farg, Mohamed Aboelghar, Afify Abass, Sayed Arafat
Projecting Climate and Vegetation Cover Change Impacts on Actual Evapotranspiration Using Time-Series Remote Sensing Data

Recently remote sensing represents a powerful and effective tool to monitor and assess the change in land surface temperature (LST), vegetation cover and evapotranspiration (ET) in agricultural ecosystems. Egypt classifies as a semi-arid region with a moderate to high ET rate from vegetation, especially in newly reclaimed areas far from the Nile delta and its valley. In this study, satellite imagery was used to monitor and determine the changes in ET rates against the increased reclaimed vegetative areas from 2003 to 2020 in the western Nile delta. SEBS model was used to calculate the actual ET Landsat surface temperature. Results showed that the vegetation cover increased moderately from 2003 to 2011 and rapidly increased from 2011 to 2020. Using correlation analysis, 1044 ground truth points GTPs used showed that NDVI negative correlation to LST and high positive correlation to evapotranspiration. The values of the actual ET with a maximum value of the annual ET per each vegetation cover type were 197,849.70, 129,504.63 m3/year/total area (hectare) respectively for tree crops and herbaceous crops in 2020, and predicted values were 535,187.81, 262,560.50 m3/year/total area (hectare) respectively for tree crops and herbaceous crops in 2050. In conclusion, further studies are required to evaluate water resources sustainability management. Otherwise, a rapid increase of vegetation cover will lead to significant overconsumption of the only source of water, which is groundwater. That increases the chances of desertification and drought in the study area.

Eslam Farg, Mohsen Nabil, S. M. Arafat, M. El Sharkawy
Functioning Earth Observations to Monitor the Anthropogenic Greenhouse Gases Emissions in Egypt

Human-driven Greenhouse gases (GHGs) are the most significant contributor to climate change. World countries and Egypt are moving towards achieving sustainable development goals (SDGs) by 2030, and 2050, to reach Net-Zero emissions. This research assesses and monitors the GHGs emissions induced by human activities in Egypt based on satellite observations. Multiple-satellite sensors (OCO2, MODIS, and AIRS) were utilized in this study to obtain Methane (CH4), Carbon Dioxide (CO2), air temperature, and Normalized Difference Vegetation Index (NDVI) data during 2016–2021. To get a deeper insight into the effects of anthropogenic activities on GHGs, temperature, and NDVI were correlated with the GHGs to investigate their effect on the emissions. Results revealed a noticeable increase in CH4 and CO2 emissions over the country, particularly in the Nile Delta, since 2016, with a maximum value in 2021. CO2 concentration was higher in summer, which is characterized by high anthropogenic activities and temperature than in winter, reaching a peak of 0.000417 CO2/mol dry-air in 2021. While, high CH4 concentration fluctuates all the year-round, with a peak of 1902.59 ppbv in 2021. However, the GHGs increase the air temperature, the vegetation cover can play a role to cool the air and absorb excess CO2. As the NDVI has a negative correlation with temperature (R = −0.7). In conclusion, unmanaged human activities in Egypt increased GHGs release and affected environmental sustainability. This study attempts to better understand the ambient environment in Egypt and support the decision-makers with full insight into the GHG emission hotspots in the country to mitigate their release into the atmosphere and achieve Net-Zero emissions.

Naglaa Zanaty
Application of Satellite Rainfall Images for Rainfall Short-Term Forecast Validation

In arid and semi-arid regions, such as Sinai Peninsula, flash floods pose a significant danger to society and individuals, may cause loss of life, property and jobs. An early warning system that relies on accurate short-term forecasts (three days) would warn residents at least to save their lives. Therefore, this paper aims to skill the performance of the Weather Research and Forecasting (WRF), spatial resolution 0.05° and daily temporal resolution, in rainfall short-term forecast on Sinai, reference to the rainfall estimate from satellite images product called Global Precipitation Measurement (GPM), 0.10 and daily spatiotemporal resolution. The evaluation will be done for the most extreme events that occurred since 2010. The GPM will be validated first using the Global Precipitation Climatology Center data (GPCC) at monthly time series, to ensure that GPM could accurately present the rainfall over Sinai. The evaluated criteria for WRF are applied at station and catchment scales using most common criteria used in meteorology;—Percent Correct (PC), Probability of Detection (POD), False Alarm Ratio (FAR), Threat Score (TS), Bias (B). The skill scores are also used to know whether the forecast is better or worse than the control or reference, where the skill scores used are; Heidke Skill Score (HSS), Gilbert Skill Score (GSS). The GPM validated results produce a monthly bias factor for GPM data for all 28 station’s locations in Sinai. The results indicated agreement with the predictions generated by WRF and GPM data in the most selected events especially in occur/not occur detection.

Doaa Amin

Artificial Intelligence and IoT Applications

Frontmatter
Forecasting of Ionospheric Electron Content (TEC) Using a Nonlinear Regression Timeseries Network Over the Northern Crest Region of the Equatorial Anomaly in Egypt

Egypt lies in the crest region of the northern equatorial anomaly which experiences high spatial and temporal ionospheric variations resulting from the equatorial irregularities. This poses a challenge to standard empirical ionospheric models like the International Reference Ionosphere (IRI) to accurately forecast and predict TEC. In this paper, we developed a nonlinear autoregressive (NARX) TEC model that accurately and consistently forecasted ionospheric TEC variation over the anomaly crest region of Egypt during the high solar activity year, 2014. The key factor for model development was the addition of estimated parameters representing equatorial electrojet (EEJ) and $$ExB$$ ExB drift velocity. The EEJ was estimated from the difference in the horizontal component of the magnetic field for two magnetometer stations in the low latitudes (off dip equator and dip equator), $$ExB$$ ExB drift velocity was estimated from the empirical model Dubazane and Habarulema (Sp. Weather 16(6):619–635, [1]) while TEC was derived from a SCINDA GPS receiver installed at Helwan University, Cairo. The TEC model forecasted diurnal TEC in 2014, with a root mean square error of 2.11 TECU, and was highly correlated (~0.99) with GPSTEC. The developed model also forecasted the solstices better than the equinox. However, the IRI-2016 model overestimated GPSTEC for both diurnal and seasonal variation. In comparison, the developed model performed better than IRI in forecasting diurnal and seasonal TEC. This implies that the IRI model is not adequately validated over the Egyptian crest region to reproduce. Therefore, there is a need to increase ionospheric monitoring in the African region which can avail several datasets for validation of empirical models to improve their performance in forecasting TEC over the region. And the addition of the two parameters representing the equatorial irregularities improves the performance of the NARX TEC model.

Wellen Rukundo, Kazuo Shiokawa, Ahmed Elsaid, Ola Abu Elezz, Ayman Mahrous
Assessment of Machine Learning Technique for Depth Estimation in Nile River

Nile River, one of the largest rivers. Bathymetry maps need to be updated frequently since sedimentation and erosion processes typically take place in natural water streams. That is a necessity to conduct an updated bathymetry in order to improve river management strategies and development plans. Using conventional techniques like echo-sounders to map the depth takes time and effort. Remote Sensing Techniques based on the analysis of multispectral satellite images has proven its efficiency in producing bathymetry maps for coastal areas of seas and oceans. Several approaches have been proposed for this matter. This paper aims at assessing the performance of Remote Sensing techniques for deriving bathymetric data from Sentinel-2 multispectral satellite imageries, with the aid of in situ measurements. The regression modelling technique is used for this study. Two regression algorithms are investigated, the Linear Regression, and the Fine Decision Tree Algorithm (FDT) as a Non-Linear Regression Model with Machine Learning regression techniques. Determining the effective Sentinel-2 bands, and the effect of the size of the training data on the results are another aims of this paper. Part of the fourth Reach of the Nile River in Egypt, between Assiut and Delta barrages, with around 23 km length is selected as a study area. Around 82,000 points with depth data are available via field measurements conducted by the Nile Research Institute. The results show that the linear approach is not applicable, (R2 equaled to 0.02), while the R2 equaled to 0.8 when the FDT model is used. The study shows that using the 13 bands of Sentinel-2 is better than the 4 visible and NIR bands (VNIR), and that when in situ measurements covering 10 km, and 3.5 km of the study area were used to develop the regression model, the RMSE of the testing data were 1.74 and 1.6 m, respectively. Which means that the larger the training data does not improve the regression results.

Noha Kamal, Nagwa El-Ashmawy
Convolutional Autoencoder for Remote Sensing Change Detection

Change Detection (CD) is crucial for effectively recognizing and analyzing spatial or spectral changes. Binary change detection uses co-registered images of an area obtained at different times to assign changes and no changes per pixel. Image processing, computer vision, and remote sensing desire more accurate binary CD maps. Deep Learning, notably CNNs, detects the environmental change in binary change systems. This work proposes a heuristic-based Siamese Convolutional Autoencoder for CD problem. Three Siamese architectures are shown. We examined how layer order and pooling layer affect CD map accuracy. LEVIR-CD is used to evaluate the proposed architectures. Experimental data reveal that the suggested technique outperforms Siamese by 3%.

Menna M. Elkholy, Marwa Mostafa, Dina ElSayad, Hala M. Ebeid, Mohamed F. Tolba
An Innovative Evolutionary Computation Strategy for Optimizing Deep Learning Network

Deep learning is one of the subgroups of machine learning widely used in artificial intelligence (AI) fields such as remote sensing (RS) imagery and machine vision. RS is one of the most powerful techniques for understanding and recognizing the pattern of urban growth, and land use/land cover change (LULC) in a given area. Currently, most modern applications of pattern recognition are based on Machine Learning (ML) technologies. Deep learning convolutional neural network (CNN) is widely used as a big data analytics technique, particularly for clustering and/or classification of the RS imagery extracting high-level concepts from low-level features. However, Big data analytics problems are quite difficult to solve due to their large, high-dimensional, and dynamic properties. factually, most CNNs designs are still manually adjusted. Therefore, getting the best-performing CNN model is time-consuming and sometimes unattainable. Researchers recently started using Evolutionary Computation (EC) optimization algorithms to automatically adjust the hyperparameters of CNNs. EC-based optimization method plays a critical role in optimizing CNNs to reduce resource costs and accelerate performance while increasing accuracy. This article proposes a novel evolutionary algorithm named the Learner Performance-Based Behavior Algorithm (LPB) to optimize CNN automatically. The CNN-based-LPB model is proposed for recognizing the urban pattern to make effective and convenient urban pattern recognition.

Shahera Saad Ali, Yehia Mostafa Helmy, Ibrahim Fathy Moawad
Backbones-Review: Satellite Object Detection Using Faster-RCNN

Object detection in remote sensing (RS) images has recently played an important role in many applications, such as environmental monitoring, ship detection, fire detection, autonomous driving, robotic vision, and crowd counting. In addition, object detection in nature images faces challenges such as enhancements in speed and accuracy in training and testing. On the other hand, the challenges in aerial or satellite images include viewpoint variation, occlusion, background cloudiness, shadows, and noise reduction. Deep learning models have helped to solve many challenges in object detection problems, such as the enhancement of speed and accuracy, enhancement of the aerial or satellite image (noise reduction), generation of new examples for the database from previous examples to be a large-scale database, etc. The Faster-RCNN method is one of object detection's most important deep learning models. With the advent of convolutional neural networks (CNNs), feature extraction has become more automated and simpler. This paper investigates four backbones in the remote sensing domain: resnet-50, resnext50_324d, efficientnet_b0, and densenet121. Various experiments were conducted on the NWPU VHR-10 dataset to evaluate these backbones using precision, recall, f1-score, average precision (AP), and mean AP matrices. The obtained results indicate that resnext50_324d has suppressed other backbones by 0.847 in terms of mAP.

Andrew Magdy, Marwa S. Moustafa, Hala Mousher Ebied, Mohamed Fahmy Tolba
Integration of Big Data and Advanced Remote Sensing Techniques to Manage Field Irrigation in Arid Lands

Measuring and observing consumptive water use is critically undertaken in arid regions where water is in charge of the economic prosperity of a large agricultural industry and the food supply of a huge number of people. Egypt suffers from several water challenges, as it is one of the driest arid regions in the entire world. Crops water consumption is considered the largest use of freshwater resources on Egypt. Excessive watering or under irrigation issues could be solved if we are able to calculate crop water demand of plants evapotranspiration precisely. The continuous archive of satellite imagery enables farmers to calculate evapotranspiration continuously. This work presents an approach to enhance the spatial resolution of the evapotranspiration maps using principal component image fusion technique. The Evapotranspiration (ET) maps produced using Landsat imagery and Surface Energy Balance Algorithm for Land (SEBAL). Then the ET maps enhanced by using vegetation indices extracted from high spatial resolution images acquired simultaneously in two selected areas representing orchids and herbaceous crops. The results showed that the use of spectral data fusion has given an accurate information about water consumption for the study area. Moreover, the spatial accuracy of water consumption maps in orchids area was better than herbaceous area. The methodology used in the present study is available to estimate crop water requirements under arid climate with sprinkler and/or drip irrigation systems.

Mohamed M. Elsharkawy, Mohsen Nabil, Eslam Farg, Sayed M. Arafat
AESPIE: Raspberry Pi AES Performance Evaluation Using Image Encryption in C and Python

Image encryption is crucial for safeguarding private and official documents online, while IoT devices and embedded systems handle large volumes of user data, necessitating cryptographic processing to ensure security. Key factors affecting IoT device performance include the chosen encryption algorithm, programming language, hardware acceleration, processor specifications, and data size. Evaluating these parameters for AES, a widely used encryption standard, reveals its effectiveness. AES is a 128-bit block cipher supporting key lengths of 128, 192, and 256 bits (AES128, AES192, and AES256), serving various computing applications for data security in industrial, commercial, and military domains. We employ a 700 × 875 RGB image dataset as a benchmark to evaluate the performance of various hardware and software tools using AES variants (AES128, AES192, and AES256). Encryption and decryption processes are executed in Python and C on Raspberry Pi, a widely adopted platform for IoT applications. Our focus lies in profiling the AES encryption and decryption stages and gathering execution time and overall performance data for the AES symmetric key algorithm. Results from the benchmark indicate that C outperforms Python significantly, with C being 46 times faster at encryption and 6 times faster at decryption. Additionally, in Python, AES decryption is 1.3 times slower than encryption, while in C, AES decryption is 10 times slower than encryption.

William Riddle, G. Quan, S. Salerno, E. Mendez, Valerio Formicola, Nagi El-Naga, Mohamed El-Hadedy

Sustainable Agricultural Development

Frontmatter
Efficiency of Geostatistical Analysis and Kriging for Predicting Soil Available NPK in El-Kawthar Region, Sohag, Egypt

Soil properties vary spatially and knowing the spatial variability of its characteristics is highly essential for the management of these soils and crop cultivation. Fifty-six disturbed surface soil samples picked up from Faculty of Agriculture Farm, El-Kawthar region, Sohag, Egypt. The study aimed to characterize the available NPK spatial variations of soil by fitting the best semi-variogram model; to estimate the properties of unsampled locations using geostatistical tools, and to prepare the spatial variation maps of soil properties by ordinary kriging technique. The soil data were normally distributed using data transformation tool in GIS environment. Different semi-variogram models were applied and among them, the spherical semi-variogram model was found to be the best fit for assessing the spatial variation of the studied properties. The obtained data used to create the spatial variation maps for these soil properties by ordinary kriging tool. The geostatistical and kriging tools could use for estimating the value of unsampled locations and ultimately to develop spatial variability maps for better soil management.

Abdel-Rahman A. Mustafa, Ali R. A. Moursy
Sentinel-2 Satellite Imagery for Retrieving and Mapping Soil Properties Using Machine Learning

One of the most critical techniques to forecast agriculture production is to retrieve soil properties aiming to decrease risk in decision-making since these properties impact the yield amount and quality. Using Sentinel-2 data and machine learning techniques, this work aimed to construct models for soil parameter retrieval over irrigated pivot field in Southwest Ismailia, Egypt. To accomplish this, two machine learning (ML) models, Random Forest (RF) and Linear Regression (LR) were developed to retrieve soil properties and statistically verified. Seven soil properties were investigated, namely clay, silt, bulk density (BD), calcium carbonates (CaCO3), available nitrogen (N), available potassium (K), and electrical conductivity (ECe). Sentinel-2 bands with a spatial resolution of 10 m coupled with measured soil data were employed to build the models. The results showed that the LR model is the best for K retrieval, followed by the RF with R2 values of 0.63 and 0.59 and mean square error (MSE) of 2.33 and 3.21, respectively. While using the LR approach, BD and CaCO3 revealed improved results with R2 of 0.49 and 0.57 and MSE of 0.006 and 0.1, respectively. These findings demonstrate the capability of developed machine learning algorithms for retrieving soil characteristics. They may be used as quick decision-making tools for retrieving soil attributes in other areas with similar conditions.

Mohamed E. S. Amin, M. A. Abdelfattah, E. S. Mohamed, Mohsen Nabil, A. A. Belal, Sayed Ahmed, Ehab Samir, Ali G. Mahmoud
Predictive Mapping of Organic Carbon Content in Soils of Russia Using Ensemble Machine Learning

The study reflects an understanding of individual factors regulating and controlling the content of organic carbon of soils, and shows a modern quantitative assessment of the content of organic carbon of soils in Russia, taking into account its huge variability. Paper presents the results of three-dimensional modeling of the organic carbon content of soils with 500 m spatial resolution at several standard depths (0–5, 5–15) to the territory of the Russian Federation using ensemble machine learning. Automated predictive mapping was based on 4 961 soil horizons from 863 soil profiles, and an extensive set of spatial information, including bioclimatic variables, a digital elevation model and its derivatives, and long-term averaged time series of MODIS data. The results of spatial cross-validation show lower (when compared with randomized) accuracy: the coefficient of determination is 0.46, CCC 0.63, RMSE 1.41 g/kg.

Andrey Chinilin, Igor Savin
Mapping Physiographic and Soil Attributes to Asses Land Suitability for Agricultural Land Use in Red Sea Governorate, Egypt Using Remote Sensing Data

This study's goal was to use remote sensing data to identify a viable location for agricultural growth in the Red Sea Governorate. The research area measures 745,113.9 acres and includes highlands with rock structures and lowlands that line the coastline zone. To identify physiographic units and soil taxa, altered Landsat-8 OLI 2022 remote sensing data were used. The associated soil taxonomic units in these physiographic units but outside of the dissected high rock structures are Typic Haplocacides, loamy skeletal, mixed, hyperthermic in bajadas; Typic Haplocacides sandy skeletal, mixed, hyperthermic in alluvial terraces; Typic Haplcalcids, coarse loamy, mixed, hyperthermic in a deltaic alluvial plain; Typic Torrifluvents, sandy skeletal, mixed (calcareous), hyperthermic in wadis and Gypsic Aquisalids, sandy mixed, hyperthermic in sabkhas. Land suitability assessment for certain land utilization types (LUTs) was conditioned to be under drip irrigation. Cabbage, canola, carrot, citrus, date palm, green pepper, guava, jojoba, maize, mango, potato, sunflower, and tomato are among the chosen crops. There are about 392,891.4 acres of land that can be used for agriculture. The main limiting factors are calcareousness, coarse texture, coarse fragments, and salinity. Alternatives include growing a certain crop in a particular physiographic unit and addressing salinity and fertility issues to manage the best land suitability. However, by controlling the hydraulic activities within the defined watershed areas as a foundation for choosing the locations of the water harvesting sites for micro dams, water resources can be maximized. The objective of the case is to enhance inland water storage and to reduce runoff eastward to the shoreline.

Afify Afify, Nagwan Afify, Hanan El Azab, Mohamed Mokhtar, Sayed Arafat
Development of a Spatial Decision Support System Model for Sustainable Land Management (SLM) in Kafr Elsheikh Governorate, Egypt Using Remote Sensing and GIS

The fundamental driver behind the real-time data collection offered by remote sensing and GIS is the requirement for efficient planning and disaster prevention. Egypt's Nile delta is rapidly becoming more unplanned as a result of population growth and the human influence that results in rapid changes in land use and land cover. Due to the current strain on agricultural land resources, Egypt must create sustainable land management (SLM) methods. The process of creating such systems necessitates the availability of simple tools for evaluating sustainability. To simultaneously satisfy the five pillars of SLM maintaining or improving production services (productivity), lowering the level of production risk (security), protecting the potential of natural resources. In addition, preventing degradation of soil and water quality (protection), being economically viable (viability), and being socially acceptable SLM combines technologies, policies, and initiatives aimed at integrating socio-economic principles with environmental concerns (acceptability). This paper aims to develop SLM indicators under the international Framework for Evaluating Sustainable Land Management (FESLM) by conducting this work in Kafr Elshiekh Governorate. The information and data gathered from the study region were analyzed to create SDSS-SLM indicators that address the five FESLM pillars. The obtained results showed that sustainable land management in the study area was classified into three categories of sustainability: (I) with an area of 51% and (III) with an area of 25%. All rest units do not meet the sustainability requirements of Class IV.

Ehab Hendawy, Mohamed Jalhoum, El-Sayed Mohamed, Abdel-Aziz Belal
Development of Sustainable Agriculture Land-Use Index in Arid Regions Using Analytical Hierarchy Process (AHP) and Remote Sensing

Agriculture is considered the main pillar in the sustainable development processes in African developing countries. The availability and efficiency of land, together with the scarcity of water and energy resources, hinders the expected agriculture expansion now and in the future the resultant is food insecurity. The main objective of this paper is to give more insights in scheming of agriculture land use and management plans through integration of four factors covering the sustainable agriculture activities (land productivity, desertification sensitivity index, irrigation water quality, and renewability). To achieve this goal, a combination of remote sensing (RS) and geographical information system (GIS) database are used for in conjugation analytical hierarchal process (AHP) approach. This sustainability index was applied in pilot area (West Edfu, Egypt) and the most favorable zone for sustainable agriculture uses was defined. The land-use and its change with time showed great usefulness a validation tool for the proposed sustainability index. The results indicate that agricultural lands decreased from an average of 21.87% of the pilot area in 2013 to about 8.06% in 2022; matching the results obtained from the sustainable agriculture land use in the areas of poor class for agriculture activity. It is recommended to add some other variables (social and economic) to that index in the future for maximization of its benefit.

Kamilia Hagagg, Rasha Hussien, Raafat Rayan, Rasha Ewes
Evaluation of the Influence of Three Treatments on Yield and Grain Quality of Winter Wheat Using Remote Sensing in Moscow Region, Russia

Wheat is among the staple cereal crops cultivated worldwide, the need for this product has increased due to the burgeoning population. However, this crop's productivity improvement is agriculture's main task. The current paper aimed at investigating the influence of three treatments: basic, intensive, and highly intensive on wheat growth in the Moscow region, 30 km from the capital using vegetation index (NDVI). The treatments contained fertilizers, fungicides, insecticides, herbicides, and growth regulators at different combinations and concentrations. A Sentinel-2 with a resolution of 10 m was used to monitor the change in the wheat crop during 2019. Yield performances, yield components, grain quality (measured through protein content), and Normalized Difference Vegetation Index (NDVI) were determined according to the tested treatments. The results showed that the high intensive treatment (T3) recorded high values in terms of wheat yield (3 t/ha) more than basic treatment (T1) and grain quality was superior. In addition, reasonable correlations between Normalized Difference Vegetation Index (NDVI), wheat yield, and grain quality were noted. The obtained results open real opportunities for crop management evaluation using remote sensing.

Nazih Yacer Rebouh, Petr Polityko, Petr Dokukin, Dmitry Kucher, Olga Kucher, Solomon Okeke, Elsayed Said Mohamed
Automatic LU/LC Mapping Using Google Earth Engine: A Case Study of Egypt’s New Delta Project

Accurate and up-to-date land use/Land cover (LULC) spatial maps are essential inputs to numerous agriculture, environmental and economic applications. Those maps are also necessary for decision-makers to track illegal land-use activities or plan new projects. Egypt's government is currently running construction and land reclamation mega projects to provide citizens with housing, food, and jobs. Those projects cause significant LULC changes, which must be quantified accurately and frequently to assess their environmental and economic consequences. Remote sensing satellite images can be used for accurate and updated land cover monitoring. Hence, the current study proposes an automated machine-learning algorithm for assessing LULC changes using freely available remote sensing data. The developed approach relays on Sentinel-2 time series imagers and high-quality LULC datasets recently produced for 2020 to train the ML algorithm on Google Earth Engine (GEE). The proposed approach automatically produced five yearly LULC maps for 2018–2022 with an overall accuracy of 89.8% for the 2018 map and 87% for 2022. The accuracy of the change between the 2018 and 2022 maps was also validated and achieved 83% overall accuracy and 0.81 kappa coefficient. The developed algorithm can be run on GEE for any customized area to produce LULC annual maps automatically without the need for expensive and time-consuming field visits.

Mohsen Nabil, Eslam Farg, Marwa S. Mostafa, Nagwan M. Afify, Mohamed M. Elsharkawy, Sayed M. Arafat

Marine and Environmental Resources Management

Frontmatter
Bathymetry Retrieval from Remote Sensing Data in Shallow Water of Marsa Alam, Egypt, Based on Multispectral Satellite Imagery

Satellite imaging provides a whole spectral characterization of a region in digital type. These data are being employed for various vital applications at shallow coastal areas like Bathymetric information which is important for the applications of hydrological engineering like the processes of sedimentary and the studies of coastal. Multispectral satellite imagery has given a great coverage, low price and time-effective resolution for bathymetric measurements. The current study evaluates performance of 2 models to measure water depth within the south of Marsa Alam center—Red Sea Governorate on Halaib and Shalatin road. The models are neural network fitting algorithms (NN) and the bagged regression trees (BAG). Landsat 8 satellite imagery data was utilized to survey the execution of models. The used models were utilized to get the calculation of bathymetric maps in shallow coastal areas from multispectral satellite images using the reflectance values of (red, green) bands and the ratios of (green/red), and (blue/red) bands. The (BAG) resulted in RMSE 0.6219 m and R2 of 0.59 where (NN) yielded RMSE of 0.6911 m and R2 of 0.59 over shallow water depths. The BAG algorithm, produced the foremost reliable results by RMSE 0.6219 m and R2 of 0.59, tested to be the desirable algorithm for bathymetry calculation for study area.

Rania Hassan, Ahmed Saber, Sameh B. ElKafrawy, Mostafa Rabah
Using Remote Sensing Techniques to Determine Environmental Characteristics for Sardinellas-nei in Red Sea, Egypt

Potential fishing zones (PFZ) are those regions where fish aggregate in sea areas; the technique of determining (PFZ) depends on knowing the environmental parameters for a specific type of fish. This manuscript deals with PFZ using a special technique, in which MODIS Aqua satellite data were used to analyze the environment already known as Sardinellas-nei’s shelters, Extracting the distinguished characteristics, then using the extracted characteristics in allocating fishing area of Sardinellas-nei all over the study area. Using remote sensing techniques in allocating Sardinellas-nei’s fishing zones will increase the national income from the fishing sector, and will narrow the food gap between production and consumption from the fish sector, besides aiding in environmental preservation by decreasing the fishing time and effort. Integration between an auxiliary fish yield data belonging to General Authority for Developing Fish Resources (GAFRD), and available products that can be obtained from MODIS Aqua satellite represented in Chlorophyll-a (Chl-a),Normalized Fluorescence Line Height (NFLH), Photosynthetically Available Radiation (PAR), Sea Surface Temperature (SST), Particulate Organic Carbon (POC) and Particulate Inorganic Carbon (PIC) during 2018. These data were used to build a suitability model (SM) to predict other sites on the Red Sea that may contain Sardinellas-nei in its waters, accordingly this manuscript will determine the suitable environment for Sardinellas-nei and support further studies in field of PFZ. The study revealed the environmental characteristics that suited the presence of Sardinellas-nei in 2018, in addition to, the predicted fishing area that resulted from the suitability model is estimated to be 7 times more than the actual fishing landing sites area.

Doaa Naguib, Mohamed Alkuzamy Aziz, Seham Hashem, Sameh El-Kafrawy
Long-Term Detection of Coral Reef Thermal Stress Events Using Daily Satellite Data in the Red Sea

Coral reefs are facing many challenges globally, especially adaptation to global warming. Investigating distributional changes of thermal stress has become an extensive research topic, aiming to help stakeholders adapt to future climate-driven changes. The relationship of time series analysis between sea surface temperature and coral reef bleaching increases our understanding of the coral reef health at the Egyptian Red Sea. Fourteen years of a high-resolution (1 km) daily satellite-derived (MODIS-A) sea surface temperature (SST) are used to analyse the thermal stress, degree of heating week (DHW) and annual patterns of SST variability in the Egyptian Red Sea. Long-term daily data were processed using MATLAB code script to calculate the average, climatology, anomalies, thermal stress, maximum, minimum and DHW. The total mean proportion of dead and bleached corals gradually increased from 2013 to 2015 for all studied cities. The present paper obtained that the three years of the total observed bleaching was positively correlated with sea surface temperature at all studied cities (r = 0.71, 0.96, 0.89 and 0.88) at Ras Ghareb, Hurghada, Qusier and Marsa Alam respectively. The accumulated DHW resulted that there are four thermal stress events during 14 years in 2003, 2007, 2010 and 2012. The annual analysis shows a general upward sea surface temperature trend in the study area.

Mostafa Khaled, Frank Muller-Karger, Ahmad Obuid-Allah, Mahmoud Ahmed, Sameh El-Kafrawy

Management of Cultural Heritage and Natural Disasters

Frontmatter
Remedial Measures for Flash Flood Risk Mitigation for Wadi Abou Sbeiara, Northeast Aswan, Egypt

The overall goals are to develop the capacity for managing Natural Disaster Risk of Flash’s in Egypt to reducing the economic losses and fatalities resulting from flash floods through building capacities for early effective emergency responses. The project work plan included three phases for Wadi Abou Sbeiara dealing with the producing flash flood hazard and risk maps. identification of effective remedial measures for flash flood risk and recommendations for community awareness to increase the institutional capacities. Hydrological and hydraulic models are used for managing the surface water in Wadi Abou-Sbeiara catchment area. The imagine a flood disaster, during the flood events, the model may help to predict when and where there is a risk of flooding by generated Hazard Maps to determine which areas should be evacuated. After the flood, models may use to quantify the risk that a flood of similar or larger magnitude will occur during the coming years and to decide what measures of flood protection may be needed for the future. A combination of proposed management strategies tools is needed to reduce risks, protect natural resources and functions. Due to the fact that the floodplain management is a process, there is no one best set of tools or a single wise use of the floodplain.

D. El-Qusi, A. H. Fahmy, M. Eissa, E. A. Zaghloul
Imaging the Future Threats of the Sand Dunes Along the Northwestern Coast of Nile Delta Using SAR

Although the Sand dunes screen the coastal cities from the sand dunes hazards and their infrastructures from rising sea levels and storms, they might threaten and hide the surrounding developmental projects. Thus, imaging these coastal dunes' internal anatomy and dynamics are crucial to protect the environment. In this study, the Optical and Synthetic Aperture Radar (SAR) images were processed and integrated with field observations and surveys to extract information about the past, current, and future behavior of coastal dunes in the Nile Delta-Northwest sector. A conventional change detection method using the supervised Landsat-8 images for the years 2015 and 2017 were generated to show a rapid change in the sand dune cover, where the high-resolution images of Google Earth were used to digitize the dune crests and measure its encroachment rate, which has been reached about 4 m/year with the NW–SE direction. The full-polarimetric ALOS/PALSAR-2 images from the years 2015 and 2017 show very low coherence for the sand dunes, which means these dunes are dynamic, where the changes in both power and phase imbalances of different dune fields during the investigated years (2015 and 2017) show high differences in values, which reached 40 dB and 0.06°, respectively. Finally, the extracted radar facies from the field survey show two successive groups of coastal sand dunes show a decline in the heavy minerals content to a level that might threaten the properties at the shoreline due to sea level rise and wave storms if mitigation actions are not taken place soon.

Rabab Ramadan, Ehab Hassan, Mostafa A. El-Asser, Ashraf Yahia, Ahmed Gaber
Impact of the Thermal Power Stations on Nile Water Temperature Using Landsat 8 OLI/TIR Case Study: Shubra El Kheima Thermal Power Station, Egypt

The environmental effects of the cooling system of thermal power stations are becoming a major concern. The hot effluents from thermal stations that are being merged into the River Nile water affected water quality especially water temperature (WT). The most common physical assessment of water quality is the measurement of temperature. Temperature impacts both the chemical and biological characteristics of surface water. The present study focuses on utilizing the Remote Sensing (RS) and geographic information systems (GIS) to estimate the change in River Nile temperature in the area of the outlet of cooling water from Shubra El-kheima station (i.e. surrounding area of the station by using remote sensing images and GIS) where Landsat image 8; OLI/TIRS sensor from the enhanced thematic mapper is used. The methodology was based on the simple regression between the water temperatures estimated from satellite images, and actual water temperature measurements. The water samples were taken from the study area around the outlet of Shubra Elkheima station as well as the inlet, Gain and Bias, and radiative transfer equation method are used. The results showed a significant relation between the measurement values and estimated values from the images, which take potentiality of remote sensing in estimating the change in water temperature.

Nadia M. Eshra

New Energy Systems for Terrestrial Applications and Satellite Energy Systems

Frontmatter
Studying the Effect of Fabrication Processes on the Efficiency of Monocrystalline Silicon Solar Cell Fabricated in Egypt-China Laboratory

To achieve the best efficiency of solar cells, it is important to understand the impact of manufacturing stage on their efficiency. This knowledge can also be used to identify and troubleshoot efficiency problem during fabrication. In this paper, we investigate how manufacturing process affect the performance of monocrystalline silicon (mono-Si) solar cells. To reach the final solar cell‚ various number of processing steps were implemented. First, a pseudo-square-type 〈100〉 oriented Czochralski mono-Si wafer with area of 156.75 X *156.75 mm2 and thickness of 180 µm is used. Using Potassium Hydroxide(KOH) and mono-Si texturing additives, the wafer was cleaned and texturized. Then, utilizing the diffusion technique, liquid Phosphorus Oxy chloride (POCl3) was used to form P–N junction. Carbon tetra fluoride (CF4) and (O2) were used to isolate the edges using plasma etching technique. To remove the PSG layer of the cell’s surface, a Phosphorus Silicate Glass (PSG) removal process was used. To improve the efficiency, antireflection layer of silicon nitride is deposited on the surface to reduce the reflection and thus improve the efficiency. Aluminum and silver paste were utilized to achieve back and front side metallization utilizing a screen-printing process. Next, curing the ohmic contact is performed using rapid thermal annealing at high zone temperature. Finally, The efficiency of solar cells as well as their Voc, Isc, FF Rsh, and Rs were all tested using an IV tester. Efficiency up to 18.66% was achieved.

Shymaa Elfiky, Mohamed Zahran, Mai Allam, Ahmed Farghal, Ahmed kassem, Aref Eliwa
Optimization of Monocrystalline Silicon Solar Cells Based on the Phosphorus Diffusion Time

Optimization of diffusion profile is required to obtain junction depth and diffusion layer sheet resistance for P–N junction that effectively separates photogenerated electrons and holes to improve efficiency. POCl3 diffusion technique used to deposit N-layer on P-type substrate. P-type wafers of 156 × 156 mm2 and 180 µm thickness were doped with boron with resistivity of 0.828 Ω.cm. To evaluate influence of diffusion time on sheet resistance and electrical parameters of monocrystalline Si SC, time varied from 600 to 1800 s at constant temperature of 825 ℃ for pre-deposition and 875 ℃ for drive-in and gases flow rate (POCl3/O2) of 1900/2800 SCCM/min for Pre-deposition and 1200/2000 SCCM/min for drive-in. Sentaurus TCAD used to simulate diffusion process to demonstrate the effect of diffusion time on the physical structure of emitter as phosphorous doping profile and junction depth. Results show that sheet resistance decreases while diffusion time increases where it decreased from 33 Ω/□ at 600 s to 25 Ω/□ at 1000 s. However, at 1300 s, sheet resistance started to increase again. The highest efficiency is 18.33% at sheet resistance of 25Ω/□ and 1000 s. This can be explained as followsas deposition time increases, a heavily doped emitter will be formed, leading to low sheet resistance and reducing low contact resistance. But with very long deposition time, phosphorus accumulates at cell surface. The excess phosphorus atoms create a defect source that makes emitter electrically inactive. This dead layer is the major recombination source of photogenerated carriers. So, diffusion time should be appropriate, not too long or too short.

Rabaa Ali, Mohamed Zahran, Mai Allam, Amr Bayoumi, Aref Eliwa
Design and Implementation of Embedded Controller and Software Development for PV on Grid Three Phase Inverter

The performance of the PV grid-connected inverter depends mainly upon inverter controller and its software. An embedded controller can be considered a microcontroller with I/O and internal features targeted to suit the typical needs of a low power platform. The C2000 Delfino LaunchPad LAUNCHXL-F28379D, is a complete low-cost development board from Texas Instruments Delfino F2837xD devices. The LAUNCHXL-F28379D kit includes all the hardware and software needed to develop applications based on the F2837xD microcontrollers. In this paper the concepts of rapid prototyping and digital control techniques in power electronics in the developed laboratory are realized based on using the TI C2000 micro-controller in conjunction with the Matlab/Simulink software based on an integrated development environment. The Matlab/Simulink environment is used for the design, optimization, and off-line simulations of the models and power electronic circuits. The Real Time Workshop converts the Simulink model to C programming code. Subsequently, the executable C code is automatically compiled to the assembly language for the TI C2000 micro-controller, assembled, link-edited, and downloaded. Finally, Matlab Graphical User Interface (GUI) is used to run, tune, and monitor the running process.

Aref Eliwa, Mahmoud Hassanein, Mahmoud Salem, Yousry Atia, Mohamed Zahran
Efficiency Enhancement of Monocrystalline Silicon Solar Cell Based on Optical Losses Reduction on the Surface of the Cell

Optical and electrical losses have a major effect on the conversion efficiency of silicon solar cells where lead to diminish the efficiency. The aim of this work is to reduce the optical losses and improve the monocrystalline silicon solar cell efficiency. In this work, reducing optical losses on the surface of the cell was achieved by forming pyramids randomly on the cell surface and by depositing $${\text{Si}}_{3} {\text{N}}_{4}$$ Si 3 N 4 anti-reflection coating (ARC). Pyramids on the surface was performed by a wet-chemical texturing process using alkaline etching mixture of Potassium Hydroxide (KOH) and (monocrystalline silicon texturing additives) IPA at different time of etching. The silicon nitride (Si3N4) layer is deposited on the surface of the solar cell as a result of ammonia (NH3) and pure silane (SiH4) reaction in plasma-enhanced chemical vapour deposition (PECVD) equipment at different deposition time. The size of the pyramids was varied from no-pyramids, small pyramids, medium pyramids to large pyramids. Si3N4 layer thickness was in the range of 68–102 nm. The effect of the pyramids size and the thickness of Si3N4 layer on the Efficiency of the solar cell were investigated. It is found that, the highest solar cell short circuit current of 8.9 A and the highest efficiency of 18.55% was realized at medium size pyramids and at the Si3N4 thickness of 85 nm. Moreover, it has been found that texturing process reduce the reflection from 35% to 10% and Anti-Reflection coating (ARC) reduce the reflection from 10% to 1%. This study was carried out in the Joint National Egyptian-Chinese Renewable Energy laboratory, Sohag, Egypt.

Shymaa Elfiky, Mohamed Zahran, Mai Allam, Ahmed Kassem, Aref Eliwa, Ahmed Farghal
Effect of PECVD SiNx Deposition Parameters on Efficiency of Monocrystalline Silicon Solar Cells

This study focusses on the optimization of Plasma-enhanced chemical vapour deposition (PECVD) silicon nitride (SiNx) deposition parameters for monocrystalline silicon solar cells antireflection coating. SiNx films were prepared using a gas mixture of high-purity silane (SiH4) and ammonia (NH3). The effect of deposition parameters of SiNx film such as NH3/SiH4 gas flow ratio on the film thickness (th), refractive index (n) and, reflectance (R) of the SiNx samples was investigated. SiNx films was prepared at a deposition temperature of 380 °C, deposition time of 11 min., deposition pressure of 195 Pa, plasma power was set at 3100 W and the NH3/SiH4 gas flow ratio was varied from 7 to 10. We explored the effect of change gas flow ratio of SiNx film on the efficiency of monocrystalline silicon solar cells. It is found that, the optimal flow ratio equal 9. The efficiency (Eff) of solar cell was 18.84%., the short-circuited current (ISC) of 8.96 A, open-circuit voltage (VOC) of 0.633 V and fill factor (FF) of 80.09% are the best parameters of the silicon solar cell at the optimal deposition conditions. This work was carried out in the Joint National Egyptian-Chinese Renewable Energy laboratory, Sohag, Egypt.

Doaa Elkady, Aref Eliwa, Mohamed Zahran, Mai A. Allam, Gaber El-saady Ahmed, El-Nobi A. Ibrahim
Metadaten
Titel
Applications of Remote Sensing and GIS Based on an Innovative Vision
herausgegeben von
Abd Alla Gad
Dalia Elfiky
Abdelazim Negm
Salwa Elbeih
Copyright-Jahr
2023
Electronic ISBN
978-3-031-40447-4
Print ISBN
978-3-031-40446-7
DOI
https://doi.org/10.1007/978-3-031-40447-4