Thesis Abstract

Abstract
Landslides are natural hazards that pose significant risks to human lives, infrastructure, and the environment. The assessment of landslide susceptibility is crucial for effective risk management and mitigation strategies. This study focuses on the application of remote sensing and Geographic Information System (GIS) techniques to assess landslide susceptibility in a specific study area. The objectives of the study are to analyze the factors contributing to landslide susceptibility, develop a landslide susceptibility model using remote sensing and GIS tools, and evaluate the accuracy of the model through validation. The research methodology involves a comprehensive literature review to establish a theoretical framework for understanding landslide susceptibility factors. Remote sensing data, including satellite imagery and digital elevation models, are utilized to extract relevant information such as land cover, slope, aspect, and elevation. GIS techniques are employed to integrate and analyze these data layers to generate a landslide susceptibility map. The study area is selected based on historical landslide occurrences and susceptibility factors. The findings of the study reveal that factors such as slope steepness, land cover type, and proximity to roads and rivers significantly influence landslide susceptibility. The developed landslide susceptibility model demonstrates good accuracy in predicting areas prone to landslides. The validation of the model shows a high level of agreement between predicted and observed landslide occurrences, indicating the effectiveness of the remote sensing and GIS approach in assessing landslide susceptibility. The significance of this study lies in its contribution to the field of geoscience by providing a practical methodology for assessing landslide susceptibility using remote sensing and GIS techniques. The results can be used by land use planners, disaster management agencies, and policymakers to identify high-risk areas and implement appropriate mitigation measures. The study also highlights the importance of integrating multidisciplinary approaches in geohazard assessment and management. In conclusion, the assessment of landslide susceptibility using remote sensing and GIS techniques offers a valuable tool for understanding and mitigating landslide risks. This study demonstrates the feasibility and effectiveness of such an approach in identifying vulnerable areas and enhancing preparedness for landslide events. Further research could focus on refining the model parameters, incorporating additional data sources, and extending the analysis to other regions prone to landslides.

Thesis Overview