Investigation of landslide susceptibility using remote sensing and GIS techniques in a selected region.

Thesis Abstract

**Abstract
** Landslides pose significant risks to communities and infrastructure worldwide. This study focuses on investigating landslide susceptibility in a selected region using remote sensing and Geographic Information Systems (GIS) techniques. The aim is to develop a comprehensive understanding of the factors contributing to landslide occurrence and to create a reliable susceptibility model for effective risk management. The research begins with a detailed introduction to the problem of landslides and the importance of studying susceptibility to mitigate potential hazards. The background of the study provides context for the research by reviewing existing literature on landslide susceptibility assessment methodologies and previous studies in similar regions. The problem statement highlights the need for advanced techniques such as remote sensing and GIS to improve accuracy in assessing landslide susceptibility. The objectives of the study are outlined to guide the research process, focusing on identifying key factors influencing landslide occurrence and developing a robust susceptibility model. The limitations of the study are acknowledged, including data availability constraints and potential uncertainties in remote sensing analyses. The scope of the study defines the geographical extent and specific methodologies that will be employed to achieve the research objectives. The significance of the study lies in its potential to enhance landslide risk management strategies, leading to improved disaster preparedness and response measures. The structure of the thesis is outlined to provide a roadmap of the subsequent chapters, detailing the organization of the research findings. Chapter two presents a comprehensive literature review, covering ten key aspects related to landslide susceptibility assessment, including remote sensing applications, GIS techniques, terrain analysis, and statistical modeling approaches. The review synthesizes existing knowledge to inform the research methodology. Chapter three describes the research methodology, detailing the data collection process, remote sensing data acquisition, GIS analysis techniques, and the development of the landslide susceptibility model. The chapter includes discussions on data preprocessing, feature selection, and model validation methods. Chapter four presents a detailed discussion of the research findings, including the factors influencing landslide susceptibility identified through remote sensing and GIS analyses. The results of the susceptibility model are interpreted to assess the accuracy and reliability of the predictions. In the final chapter, chapter five, the conclusions drawn from the study are summarized, highlighting the key findings, implications for landslide risk management, and recommendations for further research. The thesis concludes with a reflection on the research process and its contribution to the field of landslide susceptibility assessment. In conclusion, this thesis contributes to the advancement of landslide susceptibility assessment through the integration of remote sensing and GIS techniques. By improving our understanding of landslide dynamics and susceptibility factors, this research aims to support informed decision-making and enhance resilience to landslide hazards in the selected region and beyond.