Estimating Ulva prolifera green tides of the Yellow Sea through ConvLSTM data fusion

Abstract

Green tides, a worldwide problem, are harmful to aquaculture, tourism, marine ecosystems, and maritime traffic. Currently, green tide detection relies on remote sensing (RS) images, which are often missing or unusable. Thus, the observation and detection of green tides cannot be performed daily, which makes it difficult to improve environmental quality and ecological health. To address this problem, this study proposed a novel green tide estimation framework (GTEF) through convolutional long short-term memory, which learned the historical spatial-temporal seasonal and trend patterns of green tides from 2008 to 2021 and fused the previously observed or estimated data and biological (optional) and physical (optional) data over the preceding seven days when RS images were absent or unusable for daily observation and detection tasks. The results showed that the overall accuracy (OA), false-alarm rating (FAR), and missing-alarm rating (MAR) of the GTEF were 0.9592 ± 0.0375, 0.0885 ± 0.1877 and 0.4315 ± 0.2848, respectively. The estimated results described the green tides in terms of attributes, geometry and position features. Especially in the latitudinal features, the Pearson correlation coefficient of the predicted data and observed data were over 0.8 (P < 0.05), which showed a strong correlation. In addition, this study also discussed the role of biological and physical factors in the GTEF. Sea surface salinity may be the dominant factor in the early stages of green tides; in the late stage, solar irradiance may be the dominant factor. Sea surface winds and sea surface currents also played a significant role in green tide estimation. Results showed the OA, FAR and MAR of the GTEF which, with physical factors but without biological factors, were 0.9556 ± 0.0389, 0.1311 ± 0.3338 and 0.4297 ± 0.3180, respectively. In short, the proposed approach could generate a daily map of green tides, even if RS images were missing or unusable.

Keywords: Biological factors; Estimation and forecast; Green tides; Historical data; Physical factors.