Spatiotemporal dynamics and multidimensional drivers of laver aquaculture in Haizhou Bay: insights from U-net-based remote sensing monitoring

The ecological impacts of expanding nearshore aquaculture demand accurate monitoring and a mechanistic understanding of underlying drivers. This study employed Landsat remote sensing images spanning 2000 to 2023 and a U-Net deep learning model to extract spatiotemporal patterns of laver aquaculture in Haizhou Bay, China, while also investigating the natural, technological, and socioeconomic factors influencing its growth. Key findings include: The U-Net model achieved an overall accuracy of approximately 98.9% and an F 1 score of around 0.887, significantly outperforming traditional classification methods (MLE, SVM, NN) by effectively reducing spectral confusion. The aquaculture area followed a “growth-peak-decline” pattern, peaking in 2018 at 10,872.45 hm², with a strong correlation to local government data. Among natural factors, only the 2-meter temperature showed a significant positive correlation with aquaculture expansion, while other factors like sea surface temperature and wind speed had minimal impact, suggesting that the region’s environmental stability supports large-scale production. Technological advancements, such as deep-sea farming and shellfish-algae intercropping, contributed to industry growth, while policy changes after 2019 resulted in a reduction of aquaculture area. Economic and policy interactions played a central role in spatial restructuring, with GDP positively correlating with aquaculture expansion during the growth phase (2000-2018), but negatively decoupling during the policy adjustment phase (2019-2023). This research provides a comprehensive framework for the sustainable management of coastal aquaculture by integrating remote sensing data with an analysis of multiple driving forces.