Analysis of Landslide Susceptibility Using GIS and Remote Sensing Techniques

Thesis Abstract

Abstract
Landslides are natural hazards that pose significant risks to communities and infrastructure worldwide. Understanding the factors that contribute to landslide susceptibility is crucial for effective risk management and mitigation strategies. This thesis presents an in-depth analysis of landslide susceptibility using Geographic Information System (GIS) and Remote Sensing techniques. The study focuses on identifying the key factors influencing landslide occurrence and developing a predictive model to assess landslide susceptibility. The research begins with a comprehensive review of existing literature on landslides, GIS, and Remote Sensing techniques. This review provides a theoretical foundation for the study and highlights the importance of integrating spatial data analysis tools in landslide susceptibility assessment. The methodology chapter outlines the data collection process, including the acquisition of satellite imagery, digital elevation models, land cover data, and other relevant spatial data layers. Various GIS and Remote Sensing techniques, such as terrain analysis, image classification, and spatial modeling, are employed to analyze the data and identify landslide-prone areas. The results chapter presents the findings of the study, including the identification of landslide-prone areas based on the developed susceptibility model. The analysis reveals the key factors contributing to landslide occurrence, such as slope steepness, land cover type, and soil characteristics. The spatial distribution of landslide susceptibility is mapped using GIS tools, providing valuable insights for land use planning and disaster risk reduction efforts. The discussion chapter critically evaluates the study findings and compares them to existing research in the field. The implications of the results are discussed in the context of sustainable land management practices and disaster risk reduction strategies. The limitations of the study are also acknowledged, and recommendations for future research are provided. In conclusion, this thesis contributes to the understanding of landslide susceptibility assessment using GIS and Remote Sensing techniques. The results highlight the importance of integrating spatial data analysis tools in landslide risk management and provide valuable insights for policymakers, land use planners, and other stakeholders involved in disaster risk reduction efforts. Further research in this area is essential to improve the accuracy and reliability of landslide susceptibility models and enhance the resilience of communities facing landslide hazards.