Thesis Abstract

Abstract
Urban areas are undergoing rapid changes due to various factors such as population growth, urbanization, and economic development. Monitoring these changes and understanding urban land use patterns are essential for sustainable urban planning and management. This thesis explores the integration of remote sensing and Geographic Information Systems (GIS) for urban land use classification and change detection. The study begins with an introduction that outlines the background of the research, the problem statement, objectives, limitations, scope, significance, and structure of the thesis. A comprehensive literature review in Chapter Two examines existing studies on remote sensing and GIS applications in urban land use classification and change detection. The review highlights the various methods, techniques, and tools used in previous research, providing a foundation for the current study. Chapter Three details the research methodology, including data collection, preprocessing, image classification techniques, change detection algorithms, accuracy assessment methods, and validation procedures. The methodology aims to integrate remote sensing data with GIS to classify urban land use and detect changes over time accurately. Chapter Four presents a thorough discussion of the findings obtained from the integration of remote sensing and GIS for urban land use classification and change detection. The results showcase the effectiveness of the proposed methodology in accurately classifying urban land use and detecting changes, providing valuable insights for urban planners and decision-makers. Finally, Chapter Five concludes the thesis by summarizing the key findings, discussing the implications of the study, and suggesting recommendations for future research. The study demonstrates the potential of remote sensing and GIS integration in enhancing urban land use classification and change detection, contributing to sustainable urban development practices. In conclusion, the integration of remote sensing and GIS for urban land use classification and change detection offers innovative approaches to urban planning and management. By leveraging the capabilities of these technologies, urban areas can be monitored, analyzed, and managed more effectively, leading to sustainable development and improved quality of life for urban residents.

Thesis Overview

The project titled "Integration of Remote Sensing and GIS for Urban Land Use Classification and Change Detection" focuses on the combined use of remote sensing techniques and Geographic Information Systems (GIS) to analyze urban land use patterns and detect changes over time. Urban areas are dynamic environments with complex land use patterns that are constantly evolving due to various factors such as population growth, urbanization, and economic development. Understanding these changes is crucial for urban planning, environmental management, and sustainable development. Remote sensing technologies, such as satellite imagery and aerial photography, provide valuable data for monitoring and analyzing land use changes in urban areas. These technologies allow for the collection of high-resolution spatial data over large areas, enabling researchers to identify and classify different land cover types, such as residential areas, commercial zones, industrial sites, and green spaces. GIS software plays a key role in processing and analyzing this spatial data, allowing for the creation of detailed maps and spatial models that can be used to track land use changes over time. The integration of remote sensing and GIS techniques offers a powerful and comprehensive approach to studying urban land use dynamics. By combining the spatial information obtained from remote sensing with the analytical capabilities of GIS, researchers can gain valuable insights into the drivers of land use changes, assess the impact of urban development on the environment, and support evidence-based decision-making in urban planning and management. Key components of this research project include data collection through remote sensing technologies, image processing and analysis using GIS software, land use classification and change detection algorithms, spatial modeling techniques, and the interpretation of results for urban planning purposes. The research will involve the application of advanced geospatial tools and methods to study urban land use patterns in a selected study area, identify trends and changes over time, and assess the implications for urban sustainability and resilience. Overall, the project aims to contribute to the field of surveying and geo-informatics by demonstrating the effectiveness of integrating remote sensing and GIS technologies for urban land use classification and change detection. The findings of this research will provide valuable insights for urban planners, policymakers, and researchers working in the fields of urban development, environmental management, and sustainable land use planning.