Analysis of Landslide Susceptibility Using Remote Sensing and GIS Techniques in a Mountainous Region

Thesis Abstract

Abstract
Landslides pose significant risks to communities residing in mountainous regions worldwide, necessitating effective strategies for their identification and mitigation. This thesis presents a comprehensive analysis of landslide susceptibility using Remote Sensing (RS) and Geographic Information System (GIS) techniques in a mountainous region. The study area selected for this research is characterized by diverse topographic features and land cover types, contributing to varying degrees of landslide susceptibility. The primary objective of this research is to develop a reliable and accurate landslide susceptibility model that integrates RS and GIS technologies to enhance spatial analysis and decision-making processes. The research methodology encompasses a multi-stage process, beginning with data collection and preprocessing, followed by the application of statistical and spatial analysis techniques to identify landslide-susceptible areas. The RS data utilized include high-resolution satellite imagery, aerial photographs, and LiDAR data, enabling the extraction of relevant terrain parameters and land cover information. GIS software is employed to integrate and analyze these datasets, facilitating the identification of key factors influencing landslide occurrence. The literature review provides a comprehensive overview of existing research on landslide susceptibility assessment, highlighting the significance of RS and GIS technologies in improving accuracy and efficiency. Key themes explored in the literature review include the identification of landslide causative factors, the development of susceptibility models, and the application of remote sensing data for landslide analysis. The findings of this study reveal a strong correlation between terrain attributes, land cover types, and landslide occurrence in the study area. The landslide susceptibility model developed demonstrates high accuracy in predicting areas prone to landslides, thereby providing valuable insights for land use planning and disaster management practices. The discussion of findings delves into the implications of the research outcomes, emphasizing the importance of integrating RS and GIS techniques for effective landslide risk assessment. In conclusion, this thesis contributes to the advancement of landslide susceptibility analysis through the integration of RS and GIS technologies in a mountainous region. The significance of this research lies in its practical implications for enhancing landslide risk management strategies and promoting sustainable development practices in vulnerable areas. Recommendations for future research include the refinement of predictive models and the exploration of emerging technologies for landslide monitoring and early warning systems.