Eddy Detection of Lakes in the Qinghai–Tibet Plateau Based on Optical Remote Sensing and Deep Learning

Lake eddies are dynamic phenomena prevalent in large lake systems, playing a critical role in affecting lake physics, nutrient transport, and ecological balance. Efficient and precise detection of these features is essential for advancing scientific understanding of lake dynamics and for improving environment management strategies. Despite their importance, the study of lake eddies remains under-explored, and current detection methods are insufficient. This study addresses this gap by proposing a lake eddy identification framework based on the YOLOv7 deep learning model, utilizing Landsat 8/9 satellite imagery acquired from April 2013 to December 2023. Qinghai Lake, Selin Co, and Nam Co on the Qinghai–Tibet Plateau were selected as the study areas. The method involves the development of an image enhancement process to optimize the visibility of lake eddies in remote sensing imagery, followed by the implementation of the YOLOv7 model for detection. The framework identified 912 cyclonic eddies and 102 anticyclonic eddies on the three aforementioned lakes, providing detailed information on their spatial distribution and morphology. This approach demonstrates significant potential for advancing the study of lake hydrodynamics and energy transport substances in aquatic ecosystems.