Thesis Abstract

Abstract
Landslides are natural hazards that pose significant risks to human lives, infrastructure, and the environment. The ability to assess and predict landslide susceptibility is crucial for effective disaster risk reduction and land use planning. This thesis presents a comprehensive analysis of landslide susceptibility using Geographic Information Systems (GIS) and Remote Sensing techniques. The study aims to evaluate the spatial distribution of landslide susceptibility factors and develop a reliable model for predicting landslide occurrence in a specific study area. The research methodology involves the collection and analysis of various data sources, including topographic maps, satellite imagery, geological maps, and land use/land cover data. GIS software is used to integrate and manipulate these datasets to generate thematic maps of landslide susceptibility factors such as slope gradient, lithology, land cover, and distance to roads. Remote sensing techniques are employed to extract relevant information from satellite images, such as vegetation cover and soil moisture content, which are important indicators of landslide susceptibility. The study area selected for this research is the XYZ region, known for its high incidence of landslides. A detailed field survey is conducted to validate the landslide susceptibility model developed using GIS and Remote Sensing data. The results of the analysis reveal the spatial distribution of landslide susceptibility factors in the study area and highlight areas at high risk of landslide occurrence. The findings of this research contribute to the existing knowledge on landslide susceptibility assessment and provide valuable insights for land use planners, disaster management authorities, and policymakers. The developed model can be used as a decision support tool for identifying vulnerable areas and implementing mitigation measures to reduce the impact of landslides. In conclusion, the integration of GIS and Remote Sensing techniques offers a powerful approach for analyzing landslide susceptibility and enhancing disaster risk management strategies. The results of this study provide a valuable framework for future research on landslide hazard assessment and mitigation efforts in similar regions prone to landslide events.

Thesis Overview

The research project titled "Analysis of Landslide Susceptibility Using GIS and Remote Sensing Techniques" aims to investigate and assess the factors contributing to landslide susceptibility in a specific geographic area by utilizing Geographic Information System (GIS) and Remote Sensing technologies. Landslides pose a significant threat to communities, infrastructure, and the environment, making it crucial to understand and predict their occurrence to mitigate risks effectively. The study will begin with a comprehensive literature review to explore existing research on landslide susceptibility assessment methods, GIS applications, Remote Sensing techniques, and factors influencing landslides. This review will provide a foundation for understanding the current state of knowledge in the field and identify gaps that the research aims to address. The research methodology will involve collecting relevant data, including topographic, geological, hydrological, and land cover information, which will be integrated into a GIS environment for analysis. Remote Sensing data, such as satellite imagery, will also be utilized to monitor land surface changes and identify potential landslide-prone areas. By applying spatial analysis techniques within the GIS framework, the study will develop a landslide susceptibility model that incorporates various influencing factors. These factors may include slope steepness, soil type, land cover, rainfall patterns, and proximity to roads or infrastructure. The model will be validated using historical landslide data and statistical analysis to assess its accuracy and reliability in predicting landslide susceptibility. The findings of the research will be presented and discussed in detail, highlighting the spatial distribution of landslide susceptibility within the study area and the key factors contributing to landslide occurrence. The implications of these findings for risk assessment, land use planning, and disaster management will be addressed, emphasizing the importance of incorporating GIS and Remote Sensing techniques in landslide mitigation strategies. In conclusion, the research will provide valuable insights into understanding landslide susceptibility and improving risk assessment and management practices through the integration of GIS and Remote Sensing technologies. The study aims to contribute to the body of knowledge on landslide hazard assessment and provide practical recommendations for enhancing resilience to landslides in vulnerable areas.