Artificial Intelligence Analysis of Satellite Tracking Data from Migratory Waterbirds

Abstract

Migratory birds make extensive travel through various ecosystems, hence the significance of studying their migration paths, movements, and habitat usage in order to conserve biological diversity and manage ecology. Satellite technology gives out vast amounts of spatiotemporal data relating to movement of birds; however, manual analysis of such complicated data may consume time, and even inhibit the ability to derive any significant patterns of migration. This paper will investigate the use of Artificial Intelligence (AI) technology to analyze satellite tracking data for migratory birds. The paper will concentrate on the application of machine learning methods to process the tracking data, migration paths, movement patterns, key stopping grounds, and predict future migration patterns. The AI-assisted analysis method can be used to incorporate the temporal and spatial tracking data to detect repeated movement patterns and give us an idea about the use of habitat and factors affecting the migration. The suggested method will also help us in identifying the key ecologically important areas that need increased conservation effort. With the use of satellite tracking and AI-assisted analysis methods, it is clear that there exists potential in analyzing the movement data of migratory waterbirds in an efficient manner