Thesis Abstract

Abstract
Landslides represent a significant natural hazard that poses a threat to human lives, infrastructure, and the environment. In recent years, advancements in remote sensing and Geographic Information System (GIS) technologies have provided valuable tools for assessing and analyzing landslide susceptibility. This thesis aims to investigate the susceptibility of landslides using remote sensing and GIS techniques in a study area with a history of landslide occurrences. The research begins with a comprehensive introduction that sets the context for the study, providing background information on landslides, the significance of the research, and the limitations and scope of the study. The problem statement highlights the need to develop effective methods for landslide susceptibility assessment to mitigate the risks associated with these natural disasters. The literature review in Chapter Two explores existing studies related to landslide susceptibility assessment, remote sensing applications, GIS techniques, and methodologies used in similar research projects. The review synthesizes key findings and identifies gaps in the current knowledge, providing a foundation for the research methodology. Chapter Three outlines the research methodology, including data collection, processing, and analysis procedures. The chapter details the selection of remote sensing data sources, GIS techniques, and statistical models used to assess landslide susceptibility in the study area. The methodology is designed to integrate multiple data layers and variables to create a comprehensive landslide susceptibility map. Chapter Four presents a detailed discussion of the findings obtained from the analysis of landslide susceptibility using remote sensing and GIS techniques. The chapter examines the spatial distribution of landslide susceptibility zones, identifies key factors contributing to landslide occurrence, and assesses the accuracy of the susceptibility model developed in the study. Finally, Chapter Five provides a summary of the research findings and conclusions drawn from the study. The research highlights the effectiveness of remote sensing and GIS techniques in assessing landslide susceptibility and provides recommendations for future research and practical applications in landslide risk management. In conclusion, this thesis contributes to the field of geo-science by demonstrating the utility of remote sensing and GIS technologies in analyzing landslide susceptibility. The findings of this study have implications for disaster risk reduction efforts and land use planning in landslide-prone areas. By integrating advanced technologies and methodologies, this research offers insights into improving landslide hazard assessment and management strategies for a more resilient future.

Thesis Overview

The project titled "Analysis of Landslide Susceptibility using Remote Sensing and GIS Techniques" aims to investigate and evaluate the factors influencing landslide occurrences through the application of remote sensing and Geographic Information System (GIS) technologies. Landslides are natural hazards that pose significant risks to human lives, infrastructure, and the environment. By understanding the spatial characteristics and susceptibility of landslide-prone areas, effective mitigation strategies can be developed to minimize the impacts of landslides. The study will focus on leveraging remote sensing data, such as satellite imagery and digital elevation models, to identify and map areas susceptible to landslides. GIS tools will be used to analyze various terrain parameters, land cover types, rainfall patterns, and other relevant factors that contribute to landslide occurrences. By integrating these datasets and applying spatial analysis techniques, the research aims to create a comprehensive landslide susceptibility map that can help in risk assessment and management. The research methodology will involve data collection, preprocessing, analysis, and modeling. Various statistical and geospatial techniques will be utilized to identify the relationships between landslide occurrences and influencing factors. Machine learning algorithms may also be employed to develop predictive models for landslide susceptibility assessment. The study will be conducted in a selected study area known for frequent landslide events, allowing for a detailed investigation of the factors contributing to landslide susceptibility. The significance of this research lies in its potential to enhance our understanding of landslide dynamics and improve landslide risk assessment and management practices. By employing advanced remote sensing and GIS technologies, this study aims to provide valuable insights into the spatial distribution and susceptibility of landslides, ultimately contributing to more effective disaster preparedness and response strategies. Overall, the project "Analysis of Landslide Susceptibility using Remote Sensing and GIS Techniques" seeks to advance the field of geoscience by integrating cutting-edge technologies to study and mitigate the impacts of landslides. The research outcomes are expected to contribute to the development of sustainable land use planning and disaster risk reduction measures in landslide-prone areas, ultimately promoting the safety and resilience of communities facing landslide hazards.