Thesis Abstract

Abstract
Urbanization is a global phenomenon that has significant implications for land use and land cover changes. The integration of remote sensing and Geographic Information Systems (GIS) has emerged as a powerful tool for monitoring urban land use classification and change detection. This thesis aims to explore the application of remote sensing and GIS technologies in classifying urban land use and detecting changes over time. The study focuses on utilizing satellite imagery and spatial analysis techniques to achieve accurate and timely information on urban land use dynamics. The research begins with an introduction that provides background information on the study area and the importance of urban land use classification and change detection. The problem statement highlights the challenges associated with traditional methods of monitoring urban land use changes and the need for more efficient and accurate approaches. The objectives of the study are outlined to guide the research process towards achieving specific goals. The limitations and scope of the study are discussed to provide a clear understanding of the boundaries and constraints within which the research is conducted. The significance of the study is emphasized to highlight the potential contributions to the field of urban planning and environmental management. The structure of the thesis is presented to provide an overview of the organization of chapters and sections. Chapter two presents a comprehensive literature review on remote sensing, GIS, urban land use classification, and change detection. The review covers key concepts, methodologies, and previous studies that have explored the integration of these technologies in urban environments. Chapter three details the research methodology, including data collection, image processing techniques, classification algorithms, and change detection methods. The chapter also discusses the validation of results and the statistical analysis used to assess the accuracy of the classification and change detection outputs. Chapter four presents a detailed discussion of the findings, including the classification results, change detection maps, and spatial patterns of urban land use changes. The analysis highlights the effectiveness of the remote sensing and GIS approach in monitoring urban land use dynamics and identifying significant changes over time. Finally, chapter five provides a conclusion and summary of the thesis, emphasizing the key findings, implications, and recommendations for future research. The study demonstrates the potential of integrating remote sensing and GIS for urban land use classification and change detection, offering valuable insights for sustainable urban planning and management. Keywords Remote Sensing, Geographic Information Systems, Urban Land Use, Classification, Change Detection

Thesis Overview

The project titled "Integration of Remote Sensing and GIS for Urban Land Use Classification and Change Detection" aims to leverage the combined power of remote sensing and Geographic Information Systems (GIS) to enhance the classification of urban land use and monitor temporal changes in urban environments. This research seeks to address the growing need for accurate, timely, and detailed information on urban land use patterns and dynamics, which are essential for effective urban planning, environmental management, and sustainable development. The integration of remote sensing, which involves the acquisition of data from satellite or aerial platforms, and GIS, which enables the storage, analysis, and visualization of spatial data, offers a powerful toolset for mapping and monitoring urban land use. By combining these technologies, the project aims to overcome the limitations of traditional land use classification methods, which often rely on manual interpretation or limited ground-based data collection. Through the utilization of remote sensing imagery and GIS techniques, the project will focus on the classification of different land cover types within urban areas, such as residential areas, commercial zones, industrial sites, green spaces, and transportation networks. By employing advanced image processing algorithms and spatial analysis tools, the research will strive to achieve high accuracy in land use classification and effectively capture the spatial heterogeneity of urban landscapes. Furthermore, the project will investigate the temporal dynamics of urban land use by analyzing multi-temporal remote sensing data to detect and quantify changes occurring in urban environments over time. By comparing historical land use maps with current imagery, the research aims to identify patterns of urban growth, land cover changes, encroachment on natural areas, and other transformations that may impact urban sustainability and resilience. The outcomes of this research are expected to provide valuable insights for urban planners, policymakers, and environmental practitioners, enabling them to make informed decisions regarding land use management, infrastructure development, and environmental conservation in urban areas. By enhancing our understanding of urban land use dynamics and change processes, this project seeks to contribute to more sustainable and resilient urban environments that can accommodate future growth while preserving essential ecological functions and resources.