Geo-Spatial Assessment of Flora and Fauna Dynamics on the Dried Aral Sea Bed Using Artificial Intelligence and Remote Sensing Tools

This study explores the application of geo-information systems and artificial intelligence for assessing the habitat suitability and population dynamics of newly introduced plant and animal species across the dried bed of the Aral Sea. Using data from satellite imagery, drone surveillance, and in-situ field expeditions, the project analyzes spatial vegetation patterns and wildlife habitats. Vegetation recovery was tracked through NDVI indicators, revealing notable progress in zones affected by afforestation efforts using drought-resistant species such as Haloxylon. Additionally, changes in wildlife population structure were interpreted using ecological modeling, emphasizing the influence of environmental variables like salinity, soil type, and microclimate. The findings contribute to a baseline inventory of species and support long-term monitoring strategies for ecosystem restoration in one of Central Asia’s most ecologically degraded landscapes.

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