Basic Informations

C.V

Master Title

The Use of the Technique of Remote Sensing in the Study of the Agricultural Coverage Change in the Districts of Beni - Suef Governorate from (1986 - 2016)

Master Abstract

The Use of the Technique of Remote Sensing in the Study of the Agricultural Coverage Change in the Districts of Beni - Suef Governorate from (1986 – 2016) The subject of the study deals with the use of remote sensing techniques in studying the change of agricultural cover in the districts of Beni Suef Governorate from 1986-2016, The subject includes five chapters preceded by an introduction and followed by a conclusion with the most important findings and recommendations. The introduction dealt with an introduction to the importance of remote sensing techniques in studying and monitoring land cover change and its importance in geographical studies, and a presentation to define the study area, the reasons for choosing the topic, the study problem, its objectives, its questions, the Approach, the previous studies and the study difficulties, and concluded with the content of the study. The first chapter deals with the study of the natural factors affecting the change of agricultural cover in the districts of Beni Suef Governorate, where it is concerned with studying the geographical location of the study area, studying the geological composition and types of geological structures in Beni Suef Governorate, and studying the surface features and climatic conditions, which include temperature, winds, rain, and relative humidity. Soil types and its impact on agricultural cover. As for the second chapter, it is concerned with studying the human factors affect the change of agricultural cover in the districts of Beni Suef Governorate, represented in the study of the population through the study of population growth, the study of population distribution and density, agricultural density and average per capita share of the agricultural area, and the study of economic activity, where agriculture represents the prevailing craft in the districts of the study area, and urbanization, Irrigation and drainage networks, the road network, and government laws and legislation. The third chapter was devoted to studying the procedural steps for applying remote sensing techniques in the study of the change of agricultural cover in the districts of Beni Suef Governorate during the period 1986-2016 and included a presentation of the concept of remote sensing, applications of remote sensing in the study of land cover, and the remote sensing platform used in the study: Sensors. Satellite images for the study area, Image preprocessing methods, Classification Land cover methods, and Vegetation Indices, in addition to the use of geographic information systems applications in processing, spatial analysis and final output. The fourth chapter entitled Classification of Land Cover in the districts of Beni Suef Governorate between 1986 and2016 covered the concept of classification, and classification interpretation,, systems for land use and land cover classification, types of classification (Supervised -Un Supervised), the results of Supervised classification, monitoring of change in agricultural cover, and monitoring of change in Agricultural cover within the flood plain and reclaimed areas. The fifth chapter was devoted to monitoring the changes in agricultural cover in the districts of Beni Suef Governorate between the years 1986 and2016 through the study of Vegetation indices and the study of the development of the area of vegetation cover in the districts of the study area using the formula of the Vegetation Difference Index (NDVI), and the analysis of the change in the agricultural cover in the districts of the study area between 1986 and 2016 based on the results of the vegetation index Finally, the study ended with a conclusion and included the findings and recommendations of the subject of the study, and a list of references and annexes of the study.

PHD Title

Integration between Remote Sensing Techniques and Artificial intelligence in the Study of Land Cover Change at Fayoum District from 2000-2023 (A Geographical Study)

PHD Abstract

Integration between Remote Sensing Techniques and Artificial intelligence in the Study of Land Cover Change at Fayoum District from 2000-2023 (A Geographical Study) This study focused on the integration of remote sensing techniques and artificial intelligence in analyzing land cover changes in Fayoum District during the period from 2000 to 2023. The study aimed to develop an advanced methodology that employs modern technological tools to monitor spatial and temporal changes in land cover, thereby contributing to more accurate and effective urban planning and natural resource management. The study consisted of four chapters, preceded by an introduction and followed by a conclusion. Chapter One addressed "the theoretical framework: remote sensing and artificial intelligence", It focused on studying the integration of remote sensing techniques and artificial intelligence in analyzing land cover change. The chapter reviewed the remote sensing platforms relied upon in the land cover study, in addition to concepts of remote sensing, land cover classification, the historical development of artificial intelligence, and its components, including machine learning, deep learning, and geographic artificial intelligence (Geo AI). It also presented the procedural steps for analyzing satellite images using the Google Earth Engine platform, which included primary processing operations such as geometric and radiometric correction, atmospheric correction, training sample selection, classification methods, and accuracy assessment. The chapter reviewed the machine learning algorithms adopted in the study, such as Random Forest and Support Vector Machines, explaining their mechanisms for land cover classification. Finally, classification accuracy was evaluated through overall accuracy and Kappa coefficient, with Random Forest demonstrating superior performance in detecting changes and future predictions. Chapter Two discussed "land cover classification in Fayoum District during 2000–2023", beginning with clarifying the concept and systems of land cover classification, with an emphasis on the Food and Agriculture Organization’s Land Cover Classification System (FAO LCCS), which was adopted to define the land cover classes in Fayoum District. Machine learning algorithms were used for supervised land cover classification, including the application of Random Forest (RF) and Support Vector Machines (SVM) across five consecutive time intervals (2000, 2005, 2010, 2015, 2020, 2023). The performance of the algorithms was compared for classifying satellite images for each period, with results clearly indicating the superiority of Random Forest in terms of classification accuracy and consistency across different time periods, leading to its adoption for the final analysis. The classification results showed a notable increase in urban areas, especially in the old regions and around Fayoum city, at the expense of agricultural lands which continuously declined. Additionally, land reclamation for industrial uses such as petroleum extraction was observed in some villages. Despite significant expansion in desert land reclamation, this increase did not compensate for the considerable losses caused by urban sprawl and encroachments on fertile agricultural lands. Chapter Three focused on "land cover and land use changes and future prediction in Fayoum District", emphasizing monitoring these changes using change detection techniques and the methodology employed for detecting changes. The chapter reviewed the rate of change among land cover classes over five consecutive time periods (2000–2005, 2005–2010, 2010–2015, 2015–2020, 2020–2023), as well as analyzing the overall change throughout the entire period from 2000 to 2023. Spatial modeling of land cover changes using Artificial Neural Networks (ANN) was also addressed, applying the MOLUSCE model to predict future changes in land use and land cover and validating the accuracy of these predictions. Results indicated a continued decrease in agricultural land area and increased urban expansion by 2040 compared to 2023. The final section of the chapter analyzed the geographical impacts of these changes, including economic, social, and environmental effects, in addition to challenges related to achieving sustainability and the necessity of developing policies and procedures that contribute to managing these changes sustainably. Emphasis was placed on the importance of coordination among various agencies to conserve natural resources and guide urban growth. Chapter Four detailed "the geographical factors influencing land cover change in Fayoum District", addressing in detail the natural and human factors affecting these changes. The chapter began by analyzing the district’s geographical location and its spatial relationship with the surrounding area, in addition to the geological composition forming the natural base of land cover. It also addressed surface topography regarding elevation and slope, alongside climatic conditions including temperature, relative humidity, wind, precipitation, and their influence on land cover changes. Soil characteristics and types in Fayoum District, which play a pivotal role in agricultural land use, were also reviewed. Furthermore, human factors were discussed, starting with population growth, changes in per capita agricultural land area, and population density which increases pressure on agricultural lands, moving through transportation networks and roads that facilitate urban expansion. The irrigation and drainage networks, which are essential for the preservation and sustainability of agricultural lands, were also studied. The chapter concluded by discussing government policies and related legislation concerning the protection of agricultural lands, noting the impact of policy implementation on reducing encroachments and achieving sustainable development of land cover in Fayoum District. The study concluded by presenting the most important findings reached, followed by formulating a set of recommendations aimed at contributing to mitigating the problem of land cover change in Fayoum District and supporting development and planning efforts.