Development of a Remote Sensing GIS Platform for Rapid Geological Hazard Assessment

Thesis Abstract

The increasing frequency and severity of geological hazards such as landslides, earthquakes, and volcanic eruptions pose significant threats to communities, infrastructure, and economic stability, particularly in regions characterized by rapid urbanization and variable topography. Traditional hazard assessment methods often rely on manual field surveys and isolated data sources, which can be time-consuming, labor-intensive, and inadequate for rapid response requirements. This study aims to develop an integrated Remote Sensing Geographic Information System (GIS) platform to facilitate real-time, efficient, and accurate geological hazard assessment and management. The specific objectives are to design a user-friendly GIS-based framework incorporating remote sensing data, develop algorithms for hazard detection and risk mapping, validate the platform with case studies, and evaluate its operational cost-effectiveness compared to existing methods. The research adopts a mixed-methods approach, combining quantitative spatial analyses with qualitative user evaluation. The population of the study includes satellite imagery, aerial photographs, geological and topographical datasets obtained from open-source agencies, and selected hazard-prone sites within a representative geological region encompassing approximately 10,000 square kilometers. A stratified sampling technique is employed to select 50 hazard-prone locations for detailed case studies, ensuring diverse terrain and hazard types. Data collection instruments include high-resolution multispectral satellite images from Sentinel-2, DEM (Digital Elevation Model) datasets, geological maps, and incident reports. These data sources are integrated into the platform using GIS software such as ArcGIS Pro and QGIS, and supplemented with field validation through targeted ground-truth surveys at selected sites. Analytical techniques employed include supervised classification using Maximum Likelihood Estimation, Change Detection Analysis to identify recent hazard developments, and spatial overlay analysis to generate hazard and risk maps. Regression analysis and machine learning algorithms, such as Random Forests, are applied to develop predictive models for hazard susceptibility based on key geospatial variables. The validation phase involves cross-validation of the hazard maps with historical incident reports and ground-truth data, with accuracy assessed through metrics such as Kappa coefficient and Receiver Operating Characteristic (ROC) curves. The operational feasibility and cost-benefit analysis are performed through comparative evaluation with traditional hazard assessment approaches. Expected findings from this research include a robust, scalable GIS platform capable of efficiently processing multi-source remote sensing data for hazard detection, offering high spatial accuracy (above 85%) in risk zone delineation. The platform's algorithms are anticipated to detect changes in geological features within a 24- to 48-hour window, significantly reducing response times relative to conventional methods. The predictive models are expected to demonstrate an accuracy of over 80% in hazard susceptibility classification and be effective across diverse geological and climatic contexts. These outputs will support rapid decision-making, early warning dissemination, and targeted mitigation measures. The contribution to knowledge lies in advancing GIS-based remote sensing methodologies for geological hazard assessment, integrating cutting-edge machine learning techniques within a real-time, user-oriented platform. It fills existing gaps related to the operational deployment of remote sensing data for hazard management in resource-constrained settings and demonstrates the feasibility of leveraging freely available satellite imagery and open-source GIS tools for disaster risk reduction. The study’s main conclusion emphasizes that the developed platform enhances the speed, accuracy, and comprehensiveness of hazard assessments, augmenting current practices and supporting proactive disaster preparedness strategies. Recommendations include scaling the platform for regional and national applications, integrating it with existing early warning systems, and fostering collaborations with disaster management agencies. Future studies are suggested to incorporate additional datasets such as seismic sensors and social vulnerability indices, and to explore the use of deep learning models for enhanced hazard prediction accuracy. Overall, the research underscores the pivotal role of ICT-driven solutions in advancing resilient, data-informed approaches to geological hazard management, ultimately contributing to safer communities and sustainable development in hazard-prone regions.