Analysis of landslide susceptibility in a specific region using GIS and remote sensing techniques.

Thesis Abstract

Abstract
Landslides pose a significant threat to communities and infrastructure in various regions around the world. Understanding the factors that contribute to landslide susceptibility is crucial for effective risk assessment and mitigation strategies. This study focuses on analyzing landslide susceptibility in a specific region using Geographic Information System (GIS) and remote sensing techniques. The research aims to identify the key factors influencing landslide occurrence and to develop a predictive model for assessing landslide susceptibility. The thesis begins with an introduction that provides an overview of the research topic and its importance. The background of the study highlights the existing knowledge gaps in landslide susceptibility assessment and the need for advanced techniques like GIS and remote sensing. The problem statement emphasizes the challenges posed by landslides and the significance of developing proactive measures to mitigate their impact. The objectives of the study outline the specific goals that the research aims to achieve, including identifying landslide-prone areas and assessing the spatial distribution of susceptibility factors. The limitations of the study are also discussed to acknowledge any constraints that may affect the research outcomes. The scope of the study defines the geographical area under investigation and the specific methodologies employed. The significance of the study emphasizes the potential impact of the research findings on land-use planning, disaster management, and infrastructure development. The structure of the thesis provides a roadmap of the chapters and their contents, guiding the reader through the research process. Chapter two presents a comprehensive literature review that synthesizes existing studies on landslide susceptibility assessment, GIS applications, and remote sensing techniques. The review highlights the current methodologies, challenges, and advancements in the field, laying the foundation for the research methodology. Chapter three details the research methodology, including data collection, preprocessing, and analysis techniques. The chapter outlines the steps involved in GIS-based spatial analysis and remote sensing data interpretation. The selection of landslide susceptibility factors and the development of a susceptibility model are described in detail, providing insights into the research process. Chapter four presents a thorough discussion of the research findings, including the spatial distribution of landslide susceptibility in the study area and the validation of the susceptibility model. The chapter analyzes the factors contributing to landslide occurrence and assesses the accuracy of the predictive model. Chapter five concludes the thesis by summarizing the key findings, discussing their implications, and highlighting the contributions of the research. The conclusions drawn from the study inform recommendations for future research directions and practical applications in landslide risk management. In conclusion, this thesis contributes to the field of landslide susceptibility assessment by integrating GIS and remote sensing techniques to analyze landslide-prone areas in a specific region. The research findings provide valuable insights for decision-makers, planners, and researchers working towards mitigating the impact of landslides and enhancing community resilience in vulnerable areas.