A Multi-Source Object-Oriented Framework for Extracting Aquaculture Ponds: A Case Study from the Chaohu Lake Basin, China

Quantifying the extent and distribution of aquaculture ponds has become the key to management in the aquaculture industry, thereby contributing to the sustainable development of the region. However, accurate extraction of individual aquaculture pond boundaries from mesoscale remote sensing images remains a significant challenge. In this work, we developed the Multi-source Object-oriented Framework for extracting Aquaculture ponds (MOFA) to address mapping challenges in the Chaohu Lake basin, China. The MOFA combined Sentinel-1 synthetic aperture radar (SAR) with Sentinel-2 data, applying an object-oriented approach with adaptive threshold segmentation for robust and automated aquaculture pond delineation. Our performance evaluation results showed that the overall accuracy is as high as 90.75%. The MOFA is thus capable of distinguishing seasonal water bodies, lakes, reservoirs, and rivers from individual (non-centralized, contiguous) aquaculture ponds. Our results showed that the central and south sections of the Chaohu Lake basin are characterized by denser aquaculture pond distributions, relative to those in the western basin. The total area of aquaculture ponds across the entire basin decreased from 19,297.86 hm2 in 2016 to 18,262.77 hm2 in 2023, which is likely attributed to local policy adjustments, resource optimization, shifting market demands, or natural environmental changes. The abandonment and unregulated expansion of aquaculture ponds threaten sustainable development. Local governments must implement adaptive governance strategies to balance ecological preservation with economic growth. Overall, the MOFA can quickly and accurately extract and map aquaculture ponds, and further support the scientific planning of sustainable aquaculture development.