Thesis Abstract

Abstract
Landslides are significant natural hazards that can cause devastating impacts, including loss of life, property damage, and disruption of infrastructure. To mitigate these risks, understanding the factors contributing to landslide susceptibility is crucial. This thesis presents an in-depth analysis of landslide susceptibility using remote sensing and Geographic Information Systems (GIS) techniques. The study focuses on identifying and mapping areas prone to landslides, aiming to provide valuable insights for effective hazard management and land-use planning. The research begins with a comprehensive review of existing literature on landslide susceptibility assessment methods, remote sensing technologies, and GIS applications in landslide studies. This review highlights the importance of integrating remote sensing data and GIS tools for accurate and efficient landslide mapping and analysis. Various factors influencing landslide occurrence, such as topography, geology, land cover, precipitation, and human activities, are examined to establish a robust foundation for the research. The methodology section outlines the step-by-step approach employed in the study, including data collection, preprocessing, analysis techniques, and model development. Remote sensing data sources, such as satellite imagery and digital elevation models, are utilized to extract relevant information for landslide susceptibility mapping. GIS software is employed for data integration, spatial analysis, and model implementation to generate a landslide susceptibility map. The findings of the study reveal the spatial distribution of landslide susceptibility in the study area, highlighting high-risk zones that require immediate attention. The results demonstrate the effectiveness of remote sensing and GIS techniques in identifying vulnerable areas and predicting potential landslide occurrences. By integrating various spatial data layers and applying statistical modeling, the research provides a detailed understanding of the factors influencing landslide susceptibility. The discussion section interprets the research findings in the context of existing knowledge and discusses the implications for landslide risk assessment and management strategies. The limitations of the study, such as data availability constraints and model uncertainties, are acknowledged, and recommendations for future research directions are provided. The study emphasizes the significance of incorporating remote sensing and GIS technologies into landslide susceptibility assessments for enhanced accuracy and reliability. In conclusion, this thesis contributes to the field of geoscience by advancing the understanding of landslide susceptibility using remote sensing and GIS approaches. The research underscores the importance of proactive measures in mitigating landslide risks and underscores the potential of technology-driven solutions for effective hazard management. The insights gained from this study can inform decision-makers, planners, and stakeholders in developing sustainable land-use policies and disaster preparedness initiatives to reduce the impacts of landslides on vulnerable communities.

Thesis Overview

The project titled "Analysis of Landslide Susceptibility Using Remote Sensing and Geographic Information Systems (GIS)" aims to investigate and analyze the factors contributing to landslide susceptibility in a specific geographic area using advanced technologies such as remote sensing and GIS. Landslides are significant natural hazards that pose risks to human life, infrastructure, and the environment. By applying remote sensing techniques and GIS technology, this research seeks to enhance the understanding of landslide susceptibility, improve prediction accuracy, and facilitate better disaster management strategies. The research will begin with a comprehensive review of existing literature on landslides, remote sensing applications, GIS technologies, and previous studies related to landslide susceptibility assessment. This literature review will provide a solid foundation for understanding the current state of knowledge in the field and identifying gaps that need to be addressed. The methodology for the research will involve data collection through remote sensing techniques such as satellite imagery, aerial photography, and LiDAR data. GIS will be used for data processing, spatial analysis, and modeling to identify factors influencing landslide susceptibility, such as slope gradient, soil type, land cover, and precipitation patterns. Statistical analysis and machine learning algorithms will be applied to develop a landslide susceptibility model that can predict areas at high risk of landslide occurrence. The findings of this research are expected to contribute to the field of geoscience by providing valuable insights into the factors influencing landslide susceptibility and the effectiveness of remote sensing and GIS technologies in landslide hazard assessment. The results will be presented and discussed in detail in Chapter Four of the thesis, highlighting the key findings, trends, and implications for future research and practical applications. In conclusion, this research project on the analysis of landslide susceptibility using remote sensing and GIS holds great significance in improving our understanding of landslide hazards and enhancing disaster preparedness and response measures. By integrating advanced technologies and spatial analysis techniques, this study aims to contribute to the development of more accurate and reliable landslide susceptibility models for better risk assessment and mitigation strategies.