Analysis of Landslide Susceptibility using Remote Sensing and Geographic Information Systems (GIS) in a Mountainous Region

Thesis Abstract

Abstract
The increasing frequency and intensity of landslides in mountainous regions pose significant threats to the environment and human settlements, necessitating effective strategies for landslide susceptibility analysis and mitigation. This thesis focuses on the application of remote sensing and Geographic Information Systems (GIS) in assessing landslide susceptibility in a mountainous region. The study aims to develop a comprehensive understanding of the factors influencing landslide occurrences and to create a reliable model for predicting landslide susceptibility. The research begins with an introduction that outlines the background of the study, defines the problem statement, and sets the objectives of the research. The limitations and scope of the study are also discussed to provide a clear framework for the research. The significance of the study is highlighted to emphasize its potential impact on disaster risk reduction in mountainous areas. Additionally, the structure of the thesis is presented to guide the reader through the research process, and key terms are defined to enhance understanding. Chapter Two presents a detailed literature review that explores existing studies on landslide susceptibility analysis, remote sensing techniques, GIS applications, and relevant methodologies. The review covers various factors contributing to landslides, such as topography, geology, land use, and rainfall, highlighting the importance of integrating remote sensing and GIS technologies for accurate susceptibility mapping. In Chapter Three, the research methodology is outlined, detailing the data collection process, including satellite imagery acquisition, topographic data extraction, and field surveys. Various analytical techniques, such as statistical analysis and spatial modeling, are employed to assess landslide susceptibility factors and develop a predictive model. The chapter also addresses the validation methods used to evaluate the accuracy and reliability of the susceptibility model. Chapter Four presents a comprehensive discussion of the findings derived from the analysis of landslide susceptibility factors in the mountainous region. The results of the spatial modeling and predictive mapping are interpreted to identify high-risk areas prone to landslides. The implications of these findings for disaster management and land-use planning are discussed, emphasizing the importance of proactive measures to reduce landslide risks. In Chapter Five, the conclusion and summary of the thesis are provided, highlighting the key findings, implications, and contributions of the research. Recommendations for future research directions are also suggested to enhance the effectiveness of landslide susceptibility analysis using remote sensing and GIS technologies in mountainous regions. Overall, this thesis contributes to the field of geoscience by providing valuable insights into the application of remote sensing and GIS for landslide susceptibility analysis. The research findings have implications for disaster risk reduction strategies and land-use planning in mountainous areas, emphasizing the importance of proactive measures to mitigate landslide hazards and safeguard communities and the environment.