Thesis Abstract

Abstract
Landslides are a significant natural hazard that poses risks to lives, properties, and infrastructure in various parts of the world. To effectively mitigate these risks, it is crucial to assess and understand landslide susceptibility using advanced techniques. This thesis focuses on the assessment of landslide susceptibility using remote sensing and GIS techniques, aiming to provide valuable insights for landslide risk management and planning. The study begins with a comprehensive introduction outlining the background of the research, the problem statement, research objectives, limitations, scope, significance, structure of the thesis, and definition of key terms. Chapter two presents a detailed literature review covering ten key aspects related to landslide susceptibility assessment, remote sensing, GIS technologies, and previous studies in the field. Chapter three discusses the research methodology, including data collection, data preprocessing, selection of remote sensing and GIS techniques, model development, validation methods, and statistical analysis. The chapter also highlights the importance of incorporating field surveys and ground truth data to enhance the accuracy of the landslide susceptibility assessment model. Chapter four presents the findings of the study, including the spatial distribution of landslide susceptibility zones, the performance evaluation of the developed model, and the identification of key factors influencing landslide susceptibility. The discussion explores the implications of the findings and compares them with existing literature to provide a comprehensive analysis of the results. In the conclusion and summary chapter, the key findings of the study are summarized, and the implications for landslide risk management and planning are discussed. Recommendations for future research and practical applications of the study findings are also provided. Overall, this thesis contributes to the field of landslide susceptibility assessment by integrating remote sensing and GIS techniques to improve the accuracy and efficiency of the modeling process. The findings of this study have the potential to enhance landslide risk management strategies and inform decision-making processes for sustainable development in landslide-prone areas.

Thesis Overview