A deep-learning framework to detect green tide from MODIS images - Zhu, Weidong; Xu, Yuelin; Zhang, Lei; Liu, Zitao; Liu, Shuai; Li, Yifei (Frontiers in Remote Sensing, 2025)
IntroductionMonitoring Ulva prolifera blooms over the long term is crucial for maintaining marine ecological balance. MODIS images, with their wide spatial coverage, high temporal resolution, and rich historical data, are commonly used for this purpose. However, their relatively low spatial resolution may lead to inaccuracies in precisely defining the bloom extents, thereby impeding the formulation of effective management strategies.MethodsTo address this issue, our study developed the WaveNet model. This model integrates VGG16 with the Bidirectional Feature Pyramid Network (BiFPN) and is further enhanced with a Convolutional Block Attention Module (CBAM). We applied this framework to MODIS imagery for the detection and monitoring of U. prolifera.ResultsWaveNet demonstrated superior performance in long-term sea surface U. prolifera monitoring compared to traditional methods, achieving an accuracy of 97.14% and an F1 score of 93.26%. This represents a significant improvement over existing techniques.DiscussionThese results highlight WaveNet’s improved capacity for accurate spatial recognition and classification, overcoming the limitations of previous methods. Applying this approach, we analyzed the spatiotemporal distribution of U. prolifera blooms in the Yellow Sea of China from 2018 to 2024. Our framework offers valuable insights for early prevention and targeted management of green tides, contributing to the development of more effective mitigation strategies.