Analysis of Landslide Susceptibility Using Remote Sensing and GIS Techniques

Thesis Abstract

Abstract
Landslides are natural hazards that pose significant threats to human lives, infrastructure, and the environment. The ability to predict and mitigate landslide occurrences is crucial for effective disaster management and land use planning. This study focuses on the analysis of landslide susceptibility using Remote Sensing and Geographic Information System (GIS) techniques in order to enhance our understanding of the factors influencing landslide occurrences and to develop accurate susceptibility maps for risk assessment. The research methodology involved the collection of remote sensing data, such as satellite imagery and digital elevation models, as well as geological, topographical, and land cover data. These data were processed and integrated within a GIS platform to analyze the spatial relationships between landslide occurrences and various influencing factors. The study area selected for this research is a region with a history of landslide events, providing a suitable context for the investigation. Chapter One introduces the research topic, provides the background of the study, states the problem statement, outlines the objectives, discusses the limitations and scope of the study, explains the significance of the research, and presents the structure of the thesis. Chapter Two reviews relevant literature on landslide susceptibility assessment, remote sensing applications, GIS techniques, and previous studies related to the topic. Chapter Three details the research methodology, including data collection procedures, data processing techniques, and the application of statistical and spatial analysis methods. The chapter also describes the development of a landslide susceptibility model based on the integration of various data layers within the GIS environment. Chapter Four presents a comprehensive discussion of the findings, including the identification of significant factors influencing landslide susceptibility, the accuracy assessment of the susceptibility model, and the spatial distribution of landslide-prone areas within the study area. The results are interpreted and compared with existing studies to highlight the novelty and contributions of this research. Chapter Five concludes the thesis by summarizing the key findings, discussing the implications of the research outcomes for landslide risk management, and suggesting recommendations for future studies. The study contributes to the field of landslide susceptibility assessment by demonstrating the effectiveness of Remote Sensing and GIS techniques in analyzing and mapping landslide-prone areas. The findings provide valuable insights for land use planning, disaster preparedness, and sustainable development practices in landslide-affected regions. Overall, this research enhances our understanding of landslide susceptibility and contributes to the development of proactive measures for mitigating the impact of landslides on vulnerable communities and ecosystems.