Project Abstract

Abstract
Landslides pose a significant threat to human lives, infrastructure, and the environment, making their analysis and prediction crucial for effective risk management. This research focuses on the analysis of landslide susceptibility using Geographic Information Systems (GIS) and Remote Sensing techniques. The study aims to enhance understanding of the factors influencing landslide occurrence and to develop a reliable model for assessing landslide susceptibility in a specific study area. The research begins with a comprehensive review of relevant literature on landslide susceptibility assessment, GIS, and Remote Sensing applications in landslide studies. The review highlights existing methodologies, techniques, and challenges in landslide susceptibility mapping, providing a solid foundation for the research. The research methodology involves the collection of geospatial data, including topographic, geological, land cover, and rainfall data, which are essential for landslide susceptibility analysis. GIS and Remote Sensing tools are employed to process and analyze the data, identifying spatial patterns and relationships that contribute to landslide occurrence. The findings of the study reveal significant correlations between various factors such as slope, land cover, soil type, and proximity to roads or water bodies with landslide susceptibility. A landslide susceptibility model is developed based on these findings, incorporating spatial analysis techniques to assess the level of risk in different areas of the study area. The discussion of the findings emphasizes the importance of integrating GIS and Remote Sensing technologies in landslide susceptibility analysis, highlighting their effectiveness in identifying high-risk areas and supporting informed decision-making for landslide risk mitigation strategies. In conclusion, this research contributes to the field of landslide susceptibility assessment by demonstrating the utility of GIS and Remote Sensing techniques in analyzing and mapping landslide-prone areas. The developed model provides a valuable tool for decision-makers and planners to prioritize areas for landslide risk reduction measures and improve overall disaster preparedness. Keywords Landslide susceptibility, Geographic Information Systems, Remote Sensing, Risk assessment, Geospatial analysis.

Project Overview

The project on "Analysis of Landslide Susceptibility using GIS and Remote Sensing Techniques" aims to investigate and analyze the factors contributing to landslides and develop a predictive model to assess landslide susceptibility in a specific geographic area. Landslides are natural hazards that pose significant risks to human lives, infrastructure, and the environment. By leveraging Geographic Information Systems (GIS) and Remote Sensing technologies, this research seeks to enhance our understanding of landslide susceptibility and improve mitigation strategies. The study will begin with a comprehensive review of existing literature on landslides, GIS applications, and remote sensing techniques related to landslide analysis. This will provide a solid foundation for the research methodology, which will involve data collection, processing, and analysis using GIS software and remote sensing imagery. Various factors influencing landslide susceptibility, such as slope gradient, soil type, land use, and precipitation, will be identified and integrated into the analysis. The research will focus on a specific study area where landslides are prevalent, allowing for a detailed investigation of the contributing factors and the development of a predictive model. GIS will be used to create spatial databases and maps to visualize and analyze the spatial distribution of landslides and related factors. Remote sensing data, such as satellite imagery and LiDAR data, will be utilized to extract valuable information on terrain characteristics and land cover types. The integration of GIS and remote sensing data will enable the development of a landslide susceptibility model using advanced geospatial analysis techniques. The model will predict areas at high risk of landslides based on the identified factors and provide valuable insights for land use planning, disaster risk reduction, and emergency response strategies. By combining the strengths of GIS and remote sensing technologies, this research aims to contribute to the field of geoscience and improve our ability to assess and manage landslide hazards effectively.