Thesis Abstract

Abstract
Landslides are natural hazards that pose significant risks to communities, infrastructure, and the environment. Understanding landslide susceptibility is crucial for effective risk assessment and mitigation strategies. This thesis focuses on the analysis of landslide susceptibility using remote sensing and Geographic Information System (GIS) techniques in a specific region. The study area selected for this research is characterized by a history of landslides, making it an ideal location for investigating susceptibility factors. The research begins with a comprehensive literature review on landslide susceptibility assessment methods, remote sensing technologies, and GIS applications in landslide studies. The review highlights the importance of integrating remote sensing data and GIS tools for accurate and efficient landslide susceptibility mapping. The methodology chapter outlines the research design, data collection procedures, and analysis techniques employed in the study. Remote sensing data, including satellite imagery and digital elevation models, are utilized to extract relevant terrain parameters such as slope, aspect, elevation, and land cover. GIS tools are then used to integrate these data layers and analyze the spatial relationships between the variables. The findings chapter presents the results of the analysis, including the identification of landslide susceptibility zones based on the integrated remote sensing and GIS approach. The spatial distribution of susceptibility classes is mapped, highlighting areas at high risk of landslides within the study region. The factors contributing to landslide susceptibility, such as slope gradient, land cover type, and precipitation patterns, are examined in detail. The discussion chapter provides a critical analysis of the findings, discussing the implications for landslide risk management and future research directions. The limitations of the study, such as data availability and scale issues, are acknowledged, and recommendations for improving the methodology are suggested. In conclusion, this thesis contributes to the field of landslide susceptibility assessment by demonstrating the effectiveness of remote sensing and GIS techniques in identifying and mapping landslide-prone areas. The research findings have practical implications for land use planning, disaster preparedness, and infrastructure development in regions vulnerable to landslides. Keywords Landslide susceptibility, Remote sensing, Geographic Information System, Risk assessment, Spatial analysis.

Thesis Overview

The project titled "Analysis of landslide susceptibility using remote sensing and GIS techniques in a specific region" aims to investigate and assess the factors contributing to landslide susceptibility within a defined geographic area by utilizing advanced remote sensing and Geographic Information System (GIS) technologies. Landslides pose significant hazards to communities, infrastructure, and the environment, making their accurate identification and assessment crucial for risk mitigation and disaster management. The research will begin with an extensive literature review to establish the current understanding of landslide susceptibility assessment methodologies, remote sensing techniques, and GIS applications in landslide studies. This comprehensive review will provide a solid theoretical framework for the subsequent research activities. The primary objective of the study is to develop a robust methodology for analyzing landslide susceptibility by integrating remote sensing data, such as satellite imagery and LiDAR data, with GIS spatial analysis techniques. This integrated approach will enable the identification of key landslide triggers and factors contributing to susceptibility, including topography, land cover, soil properties, and rainfall patterns. The specific region chosen for this study will be carefully selected based on its susceptibility to landslides and the availability of relevant data sources. The research will involve the collection and processing of remote sensing data to create detailed land cover maps, terrain models, and other spatial layers essential for landslide susceptibility analysis. Furthermore, the research methodology will include statistical analyses, such as logistic regression and multicriteria decision analysis, to model and assess landslide susceptibility based on the identified factors. The integration of field surveys and validation techniques will also be employed to validate the accuracy of the susceptibility models developed. The findings of this study are expected to provide valuable insights into the spatial distribution of landslide susceptibility in the specific region under investigation. By identifying high-risk areas prone to landslides, authorities and stakeholders can implement targeted mitigation measures and land use planning strategies to reduce the potential impact of landslides on communities and infrastructure. In conclusion, this research project on the analysis of landslide susceptibility using remote sensing and GIS techniques represents a critical contribution to the field of geohazards assessment and management. The integration of advanced technologies and spatial analysis methods will enhance our understanding of landslide dynamics and support informed decision-making for disaster risk reduction and sustainable development in landslide-prone regions.