Analysis of Landslide Susceptibility using Remote Sensing and GIS Techniques

Thesis Abstract

Abstract
Landslides represent a significant natural hazard that poses risks to human lives, infrastructure, and the environment. Understanding the factors contributing to landslide susceptibility is crucial for effective risk assessment and mitigation strategies. This thesis presents an analysis of landslide susceptibility using remote sensing and Geographic Information Systems (GIS) techniques. The study focuses on leveraging the capabilities of these advanced technologies to assess and map the factors influencing landslide occurrence in a specific study area. The introduction provides an overview of the research problem, highlighting the increasing frequency and impact of landslides on communities worldwide. The background of the study discusses the existing literature on landslide susceptibility assessment methods, emphasizing the importance of remote sensing and GIS in enhancing spatial analysis capabilities. The problem statement identifies the gaps in current landslide susceptibility studies and underscores the need for a comprehensive and accurate assessment approach. The objectives of the study are outlined to guide the research process, including the development of a landslide susceptibility model incorporating remote sensing data and GIS techniques. The limitations and scope of the study are defined to establish the boundaries and constraints of the research. The significance of the study is emphasized in terms of its potential contributions to landslide risk management and disaster preparedness efforts. Chapter Two presents a comprehensive literature review, examining previous studies on landslide susceptibility assessment methodologies, remote sensing applications, and GIS techniques. The review synthesizes key findings and methodologies to inform the research framework and methodology development. Chapter Three details the research methodology, encompassing data collection, processing, and analysis procedures. The chapter outlines the selection of study area, acquisition of remote sensing data, generation of thematic layers, and implementation of GIS-based spatial analysis techniques. The research methodology aims to integrate diverse data sources and analytical tools to develop a robust landslide susceptibility model. Chapter Four presents a detailed discussion of the findings derived from the analysis of landslide susceptibility factors using remote sensing and GIS techniques. The chapter explores the spatial distribution of factors such as slope steepness, land cover, soil type, and rainfall patterns in relation to landslide occurrence probabilities. The findings are critically analyzed to identify significant relationships and patterns that contribute to landslide susceptibility. Chapter Five provides a conclusion and summary of the thesis, highlighting the key findings, implications, and recommendations for future research and practical applications. The study contributes valuable insights into the use of remote sensing and GIS for landslide susceptibility assessment, emphasizing the importance of integrating advanced technologies for enhanced risk management strategies. In conclusion, this thesis offers a comprehensive analysis of landslide susceptibility using remote sensing and GIS techniques, demonstrating the potential for advanced technologies to improve our understanding of landslide hazards and support proactive risk mitigation efforts. The findings contribute to the evolving field of geospatial analysis in natural hazard management and provide a foundation for further research in landslide susceptibility assessment methodologies.