Analysis of Landslide Susceptibility using GIS and Remote Sensing Techniques

Thesis Abstract

Abstract
Landslides pose significant risks to human lives, infrastructure, and the environment, making their analysis and prediction crucial for effective disaster risk management. This thesis focuses on the analysis of landslide susceptibility using Geographic Information System (GIS) and Remote Sensing techniques. The study aims to develop a comprehensive understanding of the factors influencing landslide occurrence and to create a predictive model for assessing landslide susceptibility in a given area. The research methodology involves the collection of relevant spatial data, including terrain characteristics, land cover, rainfall patterns, and historical landslide events. GIS tools will be utilized to integrate and analyze these datasets to identify spatial relationships and patterns that contribute to landslide susceptibility. Remote sensing techniques, such as satellite imagery and LiDAR data, will be employed to extract valuable information on land surface features and changes over time. Chapter 1 provides an introduction to the research topic, background information on landslides, the problem statement, research objectives, limitations, scope, significance of the study, structure of the thesis, and definitions of key terms. Chapter 2 presents a comprehensive literature review covering ten key aspects related to landslide susceptibility analysis, including previous studies, methodologies, and applications of GIS and Remote Sensing in landslide research. Chapter 3 outlines the research methodology, detailing the data collection process, data preprocessing techniques, selection of variables, model development, and validation methods. The chapter also discusses the software tools and algorithms used for data analysis and model development. Chapter 4 presents a detailed discussion of the findings obtained from the analysis, including the identification of significant factors influencing landslide susceptibility and the development of the predictive model. The chapter highlights the spatial distribution of landslide susceptibility in the study area and provides insights into the relationships between different variables. Finally, Chapter 5 offers a conclusion and summary of the thesis, presenting the key findings, implications, and recommendations for future research. The study contributes to the advancement of landslide susceptibility analysis by integrating GIS and Remote Sensing techniques to provide a comprehensive understanding of landslide-prone areas and improve disaster risk management strategies. Overall, this thesis contributes to the field of Geo-science by enhancing our knowledge of landslide susceptibility analysis and providing valuable insights for decision-makers and stakeholders involved in disaster risk reduction and land use planning.