Publications

Zhang, GZ; Niu, LF; Wu, MQ; Kaufmann, H; Li, HY; He, YF; Chen, B (2024). Ecological Impact Patterns and Temporal Cycles of Green Tide Biomass in the Settlement Region: Based on Time-Series Remote Sensing and In Situ Data. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 17, 1610-1622.

Abstract
Recurring green tides (also called Ulva prolifera) cause significant damage to marine ecosystems in the Yellow Sea of China, especially in the settlement region. The settlement region is a critical area for the natural decay and decomposition of green tides, which obviously has ecological consequences. Recent studies in relation to this topic are mostly based on point observation data, which prevents to quantitatively analyze the ecological impact patterns of green tide biomass at large spatial scales. Therefore, we used remote sensing time-series of Geostationary Ocean Color Imager satellite data combined with in situ data to invert marine chlorophyll-a (Chl-a, main indicators representing phytoplankton biomass) concentrations by machine learning methods. Finally, based on a cross-satellite model, we quantified the green tide biomass that occurred in Haiyang City during 2015 and 2016. The main results found are as follows. First, the green tide biomass reveals negative correlations on Chl-a concentration in the settlement region. The Chl-a concentration showed a decreasing trend and remained at a low level (0.1-0.2 mg/m(3)) when the biomass of the green tide increased. After the disappearance of the green tides, the Chl-a concentration accreted rapidly and then began to gradually decrease. Second, combined with the pixel-based statistics grid of the green tide and the average Chl-a concentration, the time cycle of green tides in the settlement region is about 30-35 d. Finally, some special cases (such as typhoon) can change the pattern and temporal cycle in the settlement region. This article provides support for marine ecosystem monitoring.

DOI:
2151-1535

ISSN:
10.1109/JSTARS.2023.3338979