Project Abstract

Abstract
Landslides are natural hazards that pose significant threats to human lives and infrastructure, particularly in mountainous regions. In this study, we conducted an analysis of landslide susceptibility in a mountainous region using remote sensing and Geographic Information System (GIS) techniques. The primary objective was to develop a robust methodology for assessing landslide susceptibility that integrates remote sensing data and GIS technology. This research utilized a combination of satellite imagery, digital elevation models, land cover data, and geological information to identify factors contributing to landslide occurrence. The research methodology encompassed several key steps. Firstly, landslide inventory mapping was conducted to identify historical landslide locations within the study area. Subsequently, relevant thematic layers such as slope, aspect, land cover, lithology, and rainfall were generated and analyzed to delineate areas prone to landslides. A landslide susceptibility map was then developed using a suitable model, such as the Analytical Hierarchy Process (AHP) or a machine learning algorithm like Random Forest or Support Vector Machine. The findings revealed significant correlations between various factors and landslide occurrences in the study area. High slope gradients, specific lithologies, and intense rainfall events were identified as major contributors to landslide susceptibility. The integration of remote sensing and GIS techniques provided a comprehensive understanding of the spatial distribution and characteristics of landslides in the mountainous region. The developed landslide susceptibility map can serve as a valuable tool for land use planning, disaster risk reduction, and emergency response activities. The results of this study contribute to the body of knowledge on landslide susceptibility assessment and highlight the importance of utilizing advanced technologies for hazard mapping and mitigation. The significance of this research lies in its potential to enhance landslide risk management strategies and improve the resilience of communities living in landslide-prone areas. The study also underscores the effectiveness of remote sensing and GIS techniques in analyzing complex geological phenomena and supporting decision-making processes related to natural hazard management. In conclusion, the analysis of landslide susceptibility in a mountainous region using remote sensing and GIS techniques represents a critical advancement in understanding landslide dynamics and mitigating associated risks. By combining spatial data analysis with advanced modeling approaches, this research offers valuable insights into the factors influencing landslide occurrences and provides a basis for developing proactive measures to reduce vulnerability to landslides. The outcomes of this study have practical implications for land use planning, disaster preparedness, and sustainable development in mountainous regions prone to landslides.

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