Evolutionary trends and analysis of the driving factors of Ulva prolifera green tides: A study based on the random forest algorithm and multisource remote sensing images

Understanding the prolonged spatiotemporal evolution and identifying the underlying causes of Ulva prolifera green tides play pivotal roles in managing such occurrences, restoring water ecology, and fostering sustainable development in marine ecosystems. Satellite remote sensing represents the primary choice for monitoring Ulva prolifera green tides due to its capability for extensive, long-term ocean monitoring. With the use of multisource ecological and environmental big data, such as GOCI Ⅰ/Ⅱ images, meteorological data, runoff data, and data on various water quality parameters (SST, ocean current speed, wind speed, precipitation, DO, PAR, Si, NO3-, PO4-, and N/P), pertaining to the highly impacted South Yellow Sea of China, an innovative remote sensing extraction model was established. This model involved a hybrid approach, merging the robustness of the random forest (RF) model and the optical algae cloud index (ACI) to map Ulva prolifera distribution patterns. Moreover, we analysed the evolutionary trends and the driving factors determining these distribution patterns. The ACI-RF method exhibited exceptional performance, with an F1 score surpassing 0.95, outperforming alternative methods such as the support vector machine (SVM) and K-nearest neighbour (KNN) methods. Over the period from 2011 to 2022, excluding 2021, there was a notable decline in the area of Ulva prolifera green tides, varying between 397 and 2689.9 km2, with an average annual reduction rate of 3%. The maximum annual biomass varied between 0.12 and 15.9 kt. Notably, more than 75% of the area of Ulva prolifera green tides exhibited northward drift, which was significantly influenced by northern currents and wind fields. The regions most susceptible to Ulva prolifera green tides were observed in areas of water eutrophication. The analysis of driving factors shows that factors such as average sea surface temperature, eastward wind speed, northward wind speed, precipitation, PO4-, and N/P/Si significantly influence the biological growth rate of Ulva prolifera. Furthermore, coastal land use change and surface runoff in Jiangsu Province, particularly surface runoff in June, significantly impacted the growth rate of Ulva prolifera, with Pearson correlation coefficients of 0.74 and 0.67, respectively. Against the background of global warming and severe deterioration in the marine environment, Ulva prolifera blooms persist. Consequently, two distinct management strategies were proposed based on the distribution patterns and cause analysis results for addressing Ulva prolifera green tides: establishing a continuous protection framework for rivers, lakes, and nearshore areas to mitigate pollutant inputs and implementing precise environmental monitoring measures in urban expansion areas and farmlands to combat overgrowth-induced green tides. Leveraging the inherent advantages of remote sensing big data for rapid mapping, this methodology could be applied in other regions affected by marine ecological disasters. Additionally, the criteria for selecting influencing factors offer a valuable reference for designing tailored measures aimed at controlling Ulva prolifera green tides.