Thesis Abstract

Abstract
Landslides are a significant geohazard that poses a threat to human lives and infrastructure in many regions around the world. To mitigate this risk, it is essential to understand the factors contributing to landslide susceptibility. This thesis focuses on the analysis of landslide susceptibility in a specific region using Geographic Information System (GIS) and Remote Sensing techniques. The study area chosen for this research is characterized by a history of landslides, making it a critical location for investigating landslide susceptibility. The introduction provides an overview of the research problem, highlighting the importance of studying landslide susceptibility and the potential benefits of using GIS and Remote Sensing technologies. The background of the study delves into the existing literature on landslides, GIS, and Remote Sensing, providing a comprehensive understanding of the subject matter. The problem statement identifies the gaps in current knowledge and underscores the need for further research in this area. The objectives of the study are outlined to guide the research process, aiming to assess the factors influencing landslide susceptibility, develop a predictive model using GIS and Remote Sensing data, and evaluate the effectiveness of the model. The limitations of the study are acknowledged, including data availability, scale of analysis, and uncertainties associated with predictive modeling. The scope of the study defines the geographical extent, types of landslides considered, and the methodologies employed. The significance of the study lies in its potential to improve landslide risk assessment and management strategies, thereby enhancing community resilience and reducing the impact of landslides. The structure of the thesis provides an overview of the chapters, outlining the organization of the research findings. Definitions of key terms are provided to clarify the terminology used throughout the thesis. In the literature review, ten key themes related to landslide susceptibility, GIS, and Remote Sensing are explored, including landslide triggers, susceptibility mapping methods, and validation techniques. This comprehensive review sets the stage for the research methodology, which includes data collection, preprocessing, spatial analysis, and model development. The methodology section elaborates on the selection of variables, model calibration, and validation procedures. The discussion of findings chapter presents the results of the landslide susceptibility analysis, highlighting the significant factors influencing landslide occurrence in the study area. The spatial distribution of landslide susceptibility is mapped using GIS techniques, providing valuable insights for risk assessment and land use planning. The strengths and limitations of the predictive model are critically evaluated, with recommendations for future research. In the conclusion and summary chapter, the key findings of the study are summarized, emphasizing the importance of incorporating GIS and Remote Sensing technologies in landslide susceptibility analysis. The implications of the research are discussed in the context of disaster risk reduction and sustainable development. Recommendations for policymakers, land use planners, and researchers are provided to enhance landslide risk management strategies and foster community resilience. Overall, this thesis contributes to the growing body of knowledge on landslide susceptibility analysis, demonstrating the utility of GIS and Remote Sensing techniques in assessing geohazards. By integrating spatial data and modeling approaches, this research offers valuable insights for mitigating landslide risk and building more resilient communities in landslide-prone regions.

Thesis Overview