Designing a Dynamic Digital Database for Long-Term Monitoring of the Aral Sea Bed Ecosystem Using Artificial Intelligence and Geo-Information Systems

This study presents the development of a centralized, dynamically updateable digital database designed to store, analyze, and visualize ecological information from the dried Aral Sea bed (Orolbo‘yi). The database integrates AI-based classification outputs, satellite image-derived indicators, field survey data, and geospatial attributes into a structured, relational platform. Developed in parallel with vegetation and habitat mapping efforts, the database enables systematic storage of species-specific data, environmental parameters, and spatial coordinates. Its functionality supports user-friendly access, efficient querying, and long-term tracking of biodiversity and restoration progress. This tool is positioned to serve researchers, policymakers, and environmental managers in building adaptive management strategies and ecological forecasting for one of Central Asia’s most vulnerable environments.