Thesis Abstract

Abstract
Landslides are a significant natural hazard that poses a threat to lives, properties, and infrastructure in many regions around the world. The ability to predict and map landslide susceptibility is crucial for effective risk management and mitigation strategies. Geographic Information System (GIS) and Remote Sensing techniques have emerged as powerful tools for analyzing and mapping landslide susceptibility by integrating various spatial data layers. This thesis focuses on the analysis of landslide susceptibility mapping using GIS and Remote Sensing techniques to enhance our understanding of landslide occurrences and provide valuable insights for risk assessment and land use planning. The research begins with an introduction that outlines the background of the study, problem statement, objectives, limitations, scope, significance, and structure of the thesis. Chapter two presents a comprehensive literature review that discusses ten key aspects related to landslide susceptibility mapping, including previous studies, methodologies, data sources, and modeling techniques. Chapter three details the research methodology, covering data collection, preprocessing, analysis techniques, modeling approaches, and validation methods. The methodology section includes eight key components that guide the process of mapping landslide susceptibility accurately and efficiently. Chapter four presents the findings of the study, showcasing the results of the GIS and Remote Sensing analysis in mapping landslide susceptibility. The discussion delves into the factors influencing landslide occurrences, the accuracy of the susceptibility models, and the spatial distribution of high-risk areas. The chapter also highlights the significance of the findings in improving landslide risk assessment and management practices. Finally, chapter five provides a comprehensive conclusion and summary of the thesis, emphasizing the key findings, implications, and recommendations for future research and practical applications. Overall, this thesis contributes to the field of geoscience by demonstrating the effectiveness of GIS and Remote Sensing techniques in analyzing and mapping landslide susceptibility. The research outcomes offer valuable insights for stakeholders, policymakers, and land use planners to make informed decisions and implement proactive measures to reduce the impact of landslides on vulnerable communities and infrastructure. The findings of this study have the potential to enhance disaster preparedness, response, and resilience in landslide-prone regions, ultimately contributing to a safer and more sustainable environment for all.

Thesis Overview

The project titled "Analysis of Landslide Susceptibility Mapping Using GIS and Remote Sensing Techniques" aims to investigate and analyze the factors influencing landslide occurrences in a specific geographical area. Landslides are significant natural hazards that can cause devastating impacts on human lives, infrastructure, and the environment. By utilizing Geographic Information Systems (GIS) and Remote Sensing techniques, this research seeks to enhance understanding of landslide susceptibility and develop accurate mapping models to assess and predict landslide risks effectively. The research will commence with a comprehensive literature review to examine existing studies, methodologies, and technologies related to landslide susceptibility mapping. This review will provide a solid foundation for understanding the key concepts, challenges, and advancements in the field, guiding the subsequent research methodology and analysis. The methodology will involve collecting relevant geospatial data, including topography, land cover, soil types, rainfall patterns, and historical landslide records. GIS will be used to integrate and analyze these datasets, while Remote Sensing techniques, such as satellite imagery and LiDAR data, will provide valuable information for identifying potential landslide-prone areas. The analysis phase will focus on developing a landslide susceptibility mapping model using advanced spatial analysis techniques within the GIS environment. By incorporating various factors that contribute to landslide occurrences, such as slope steepness, soil characteristics, and land cover types, the model aims to predict areas at high risk of landslides accurately. The findings of this research will be presented in a detailed discussion, highlighting the effectiveness of the GIS-based mapping model in assessing landslide susceptibility. The accuracy and reliability of the model will be evaluated through comparison with actual landslide occurrences and validation using statistical measures. In conclusion, this research project will contribute to the field of geoscience by providing valuable insights into the application of GIS and Remote Sensing technologies for landslide susceptibility mapping. The developed mapping model has the potential to assist decision-makers, urban planners, and disaster management authorities in implementing proactive measures to mitigate landslide risks and enhance disaster resilience in vulnerable regions.