Analysis of Landslide Susceptibility in a Mountainous Region Using Remote Sensing and GIS Techniques

Thesis Abstract

Abstract
Landslides pose significant threats to lives, infrastructure, and the environment in mountainous regions worldwide. In this thesis, an in-depth analysis of landslide susceptibility in a mountainous region is conducted using advanced Remote Sensing and Geographic Information System (GIS) techniques. The study aims to enhance the understanding of landslide susceptibility factors and develop a robust model for landslide risk assessment and management. The thesis begins with a comprehensive introduction that provides the background of the study, highlights the problem statement, outlines the objectives, discusses the limitations and scope of the study, emphasizes the significance of the research, and provides the structure of the thesis. The definitions of key terms related to landslides, susceptibility, Remote Sensing, and GIS are also presented to ensure clarity and understanding throughout the research. Chapter Two delves into the literature review, examining existing studies on landslide susceptibility assessment, Remote Sensing applications in landslide studies, GIS techniques for analyzing landslide data, and relevant risk assessment models. The review synthesizes key findings, identifies research gaps, and establishes the theoretical framework for the current study. Chapter Three details the research methodology employed, including data collection methods, Remote Sensing data acquisition techniques, GIS data processing procedures, selection of landslide susceptibility factors, model development, validation techniques, and statistical analyses. The chapter also discusses the study area characteristics, data sources, and tools utilized in the research process. Chapter Four presents a thorough discussion of the findings obtained from the analysis of landslide susceptibility in the mountainous region. The chapter evaluates the effectiveness of the developed model, identifies the most influential factors contributing to landslide susceptibility, and assesses the accuracy of the predictive model. Detailed maps, charts, and statistical analyses are used to illustrate the results and facilitate interpretation. In Chapter Five, the conclusion and summary of the research thesis are provided, highlighting the key findings, implications for landslide risk management, recommendations for future research, and the overall contribution of the study to the field of geoscience. The thesis concludes with a call to action for improved landslide monitoring, early warning systems, and sustainable land use planning in mountainous regions. In conclusion, this thesis contributes to the advancement of knowledge in landslide susceptibility analysis through the integration of Remote Sensing and GIS technologies. The findings offer valuable insights for policymakers, land managers, and researchers to mitigate landslide risks and enhance disaster resilience in mountainous regions. Further research is recommended to refine the model, incorporate real-time data, and assess the long-term effectiveness of landslide mitigation strategies.