Integration of Remote Sensing and Geographic Information System (GIS) for Land Use Classification and Change Detection

Thesis Abstract

Abstract
The integration of Remote Sensing (RS) and Geographic Information System (GIS) technologies has revolutionized land use classification and change detection studies in recent years. This thesis explores the application of RS and GIS in analyzing land use dynamics through the case study of a specific region. The primary objectives of this research are to develop a comprehensive understanding of land use classification techniques using RS data and to assess the effectiveness of change detection methods within a GIS framework. The study begins with a detailed introduction to the research topic, providing a background of the study area and highlighting the significance of integrating RS and GIS technologies for land use analysis. The problem statement identifies the challenges faced in traditional land use classification and change detection methods, setting the stage for the need to adopt advanced RS and GIS techniques. The objectives of the study are outlined, focusing on the development of accurate land use classification models and the implementation of change detection algorithms. The limitations and scope of the study are clearly defined to provide a framework for the research methodology. By addressing the constraints and boundaries of the study, the research aims to ensure the validity and reliability of the findings. The significance of the study is emphasized, highlighting the potential impact of the research outcomes on land use planning and management practices. The structure of the thesis is outlined to guide the reader through the organization of the research chapters, ensuring a coherent and logical flow of information. The literature review chapter provides a comprehensive analysis of existing research on land use classification and change detection using RS and GIS technologies. The review encompasses ten key themes, including the principles of RS data acquisition, classification algorithms, change detection methods, and the integration of RS and GIS for land use analysis. By synthesizing previous studies, this chapter establishes a theoretical framework for the research methodology. The research methodology chapter details the data collection, processing, and analysis methods employed in the study. Eight key components are outlined, covering the selection of RS data sources, preprocessing techniques, classification algorithms, accuracy assessment methods, and change detection procedures. The methodology aims to demonstrate the systematic approach adopted in conducting the research, ensuring the reproducibility of the results. The findings chapter presents an in-depth discussion of the land use classification results and change detection outcomes obtained through the integration of RS and GIS technologies. The analysis of classified land cover maps and change detection maps reveals the spatial and temporal dynamics of land use patterns in the study area. The implications of the findings are discussed in relation to land use planning and environmental management practices. In conclusion, this thesis summarizes the key findings of the research and provides insights into the potential applications of integrating RS and GIS for land use classification and change detection. The study contributes to the advancement of remote sensing and geospatial analysis techniques in monitoring land use dynamics and supports informed decision-making in land resource management. Overall, this research showcases the importance of leveraging RS and GIS technologies for sustainable land use planning and environmental conservation efforts. Keywords Remote Sensing, Geographic Information System, Land Use Classification, Change Detection, Spatial Analysis, Environmental Management.