Remote sensing of early-stage green tide in the Yellow Sea for floating-macroalgae collecting campaign

Effective capping of dissolved sulfide generated in Ulva prolifera-rich marine sediments by iron-rich red soil

Bloom-induced macroalgal enrichment on the seafloor can substantially facilitate dissolved sulfide (DS) production through sulfate reduction. The reaction of DS with sedimentary reactive iron (Fe) is the main mechanism of DS consumption, which however usually could not effectively prevent DS accumulation caused by pulsed macroalgal enrichment. Here we used incubations to investigate the performance of Fe-rich red soil for buffering of DS produced from macroalgae (Ulva prolifera)-enriched sediment. Based on our results, a combination of red soil additions (6.8 kg/m2) before and immediately after pulsed macroalgal deposition (455 g/m2) can effectively cap DS within the red soil layer. The effective DS buffering is mainly due to ample Fe-oxide surface sites available for reaction with DS. Only a small loss (4 %) of buffering capacity after 18-d incubation suggests that the red soil is capable of prolonged DS buffering in macroalgae-enriched sediments.

New diagnostic sea surface current fields to trace floating algae in the Yellow Sea

The new velocity fields based on the Generalized Ekman (GE) theory to trace floating algae were derived and verified by drifter observations and compared to reanalysis datasets in the Yellow Sea (YS). Two velocity fields using diagnostic approaches and two velocity fields from reanalysis datasets were examined. The results revealed that the diagnostic velocity fields had comparable accuracy to the reanalysis datasets, even locally better. Then, we applied each velocity field to trace green algae, Ulva prolifera, in July 2011 and brown algae, Sargassum horneri, in May 2017 using particle tracking experiments. In addition, drifter trajectories were simulated, and error accumulation speed was estimated for each velocity field. Simulation results using the diagnostic velocity fields consistently showed better agreement with satellite images and in situ observations than those using reanalysis datasets, demonstrating that the diagnostic velocity could be a superior tool for simulating surface-floating substances and organisms. The approach to derive diagnostic velocity fields can be easily applied instead of relying on heavy computing numerical models.

The ecological effect of large-scale coastal natural and cultivated seaweed litter decay processes: An overview and perspective

Seaweeds are important components of marine ecosystems and can form a large biomass in a few months. The decomposition of seaweed litter provides energy and material for primary producers and consumers and is an important link between material circulation and energy flow in the ecosystem. However, during the growth process, part of the seaweed is deposited on the sediment surface in the form of litter. Under the joint action of the environment and organisms, elements enriched in seaweed can be released back into the environment in a short time, causing pollution problems. The cultivation yield of seaweed worldwide reached 34.7 million tons in 2019, but the litter produced during the growth and harvest process has become a vital bottleneck that restricts the further improvement of production and sustainable development of the seaweed cultivation industry. Seaweed outbreaks worldwide occur frequently, producing a mass of litter and resulting in environmental pollution on coasts and economic losses, which have negative effects on coastal ecosystems. The objective of this review is to discuss the decomposition process and ecological environmental effects of seaweed litter from the aspects of the research progress on seaweed litter; the impact of seaweed litter on the environment; and its interaction with organisms. Understanding the decomposition process and environmental impact of seaweed litter can provide theoretical support for coastal environmental protection, seaweed resource conservation and sustainable development of the seaweed cultivation industry worldwide. This review suggests that in the process of large-scale seaweed cultivation and seaweed outbreaks, ageing or falling litter should be cleared in a timely manner, mature seaweed should be harvested in stages, and dried seaweed produced after harvest and washed up on shore should be handled properly to ensure the benefits of environmental protection provided by seaweed growth and sustainable seaweed resource development.

The drifting and spreading mechanism of floating Ulva mass in the waterways of Subei shoal, the Yellow Sea of China - Application for abating the world's largest green tides

The large-scale green tides have been prevailing in the Yellow Sea over a decade. Prevention and control techniques in the source region (Subei Shoal) are urgently needed to minimize its adverse ecological and social impacts. Drifting and spreading mechanism of Ulva mass was investigated in the Subei Shoal in order to develop the early containment measures. The multidisciplinary surveys suggested twelve major waterways transporting the initial Ulva mass which was closely related to the basin topology and water circulation in the shoal. The epiphytic algal mass from the northern and eastern raft regions contributed 82.7 % of the initial floating biomass, and moved out in 4-6 days with an average drifting velocity of 0.28 m s^-1. Accordingly, two series of algae-blocking lines were proposed to remove floating mass from the shoal. And the primary field trial in 2018 confirmed the feasibility of this strategy to abate the green tides.

A new method based on additive vegetation index for mapping Huangtai algae coverage in Lake Ulansuhai

Huangtai algal blooms are key indicators of eutrophication and lake-ecosystem damage. Understanding the spatiotemporal heterogeneity of their growth is critical for preserving the ecological environment. The dimidiate pixel model is commonly used to estimate vegetation coverage; however, indices such as the normalized difference vegetation index have not been specifically constructed for the Huangtai algae spectrum and thus are not specific or sufficiently precise for use as indicators. Therefore, we propose a new dimidiate pixel model based on a novel additive vegetation index to calculate the Huangtai algal coverage for each pixel using Landsat multispectral satellite images with 30-m resolution. The results showed that the additive vegetation index with R² = 0.994 is a better indicator than the normalized difference vegetation index, enhanced vegetative index, and ratio vegetative index, with the accuracy of the new model reaching 86.61%. Monthly Landsat images from 2006 to 2016 were used to calculate the Huangtai algal coverage. Analysis of the inter-monthly variation indicated increased coverage from May to July, with an annual maximum and minimum of 14.43% and 0.33% in 2008 and 2013, respectively. This study provides a new reference map of Huangtai algal cover, which is important for monitoring and protecting the Lake Ulansuhai environment.

A new assessment of the algal biomass of green tide in the Yellow Sea

The annually recurring Yellow Sea green tide causes significant economic, social, and ecological impacts in China. Currently, the magnitude of Yellow Sea green tide is usually evaluated according to the snap shot maximum algal coverage area or artificially removed algal biomass. However, this method ignores growth of the alga Ulva prolifera and thus needs improvement. We build a model to predict algal growth in drifting from upstream and the potential muaximum biomass of green tide. The results suggest that the potential maximum biomass is significantly higher than those estimated merely from maximum algal coverage area, particularly for years with extended period of algal loading in the upstream. Our method improves the evaluation of the magnitude of green tide and provides a scientific basis for developing effective countermeasures to reduce the persistent disaster.

Adaptive Threshold Model in Google Earth Engine: A Case Study of Ulva prolifera Extraction in the South Yellow Sea, China

An outbreak of Ulva prolifera poses a massive threat to coastal ecology in the Southern Yellow Sea, China (SYS). It is a necessity to extract its area and monitor its development accurately. At present, Ulva prolifera monitoring by remote sensing imagery is mostly based on a fixed threshold or artificial visual interpretation for threshold selection, which has large errors. In this paper, an adaptive threshold model based on Google Earth Engine (GEE) is proposed and applied to extract U. prolifera in the SYS. The model first applies the Floating Algae Index (FAI) or Normalized Difference Vegetation Index (NDVI) algorithm on the preprocessed remote sensing images and then uses the Canny Edge Filter and Otsu threshold segmentation algorithm to extract the threshold automatically. The model is applied to Landsat8/OLI and Sentinel-2/MSI images, and the confusion matrix and cross-sensor comparison are used to evaluate the accuracy and applicability of the model. The verification results show that the model extraction of U. prolifera based on the FAI algorithm has higher accuracy (R2 = 0.99, RMSE = 5.64) and better robustness. However, when the average cloud cover is more than 70% in the image (based on the statistical results of multi-year cloud cover information), the model based on the NDVI algorithm has better applicability and can extract the algae distributed at the edge of the cloud. When the model uses the FAI algorithm, it is named FAI-COM (model based on FAI, the Canny Edge Filter, and Otsu thresholding). And when the model uses the NDVI algorithm, it is named NDVI-COM (model based on NDVI, the Canny Edge Filter, and Otsu thresholding). Therefore, the final extraction results are generated by supplementing NDVI-COM results on the basis of FAI-COM extraction results in this paper. The F1-score of U. prolifera extracted results is above 0.85. The spatiotemporal distribution of U. prolifera in the South Yellow Sea from 2016 to 2020 is obtained through the model calculation. Overall, the coverage area of U. prolifera shows a decreasing trend over the five years. It is found that the delay in recovery time of Porphyra yezoensis culture facilities in the Northern Jiangsu Shoal and the manual salvage and cleaning-up of U. prolifera in May are among the reasons for the smaller interannual scale of algae in 2017 and 2018.

Effects of Spatial Resolution on the Satellite Observation of Floating Macroalgae Blooms

Satellite images with different spatial resolutions are widely used in the observations of floating macroalgae booms in sea surface. In this study, semi-synchronous satellite images with different resolutions (10 m, 16 m, 30 m, 50 m, 100 m, 250 m and 500 m) acquired over the Yellow Sea, are used to quantitatively assess the effects of spatial resolution on the observation of floating macroalgae blooms of Ulva prolifera. Results indicate that the covering area of macroalgae-mixing pixels (MM-CA) detected from high resolution images is smaller than that from low resolution images; however, the area affected by macroalgae blooms (AA) is larger in high resolution images than in low resolution ones. The omission rates in the MM-CA and the AA increase with the decrease of spatial resolution. These results indicate that satellite remote sensing on the basis of low resolution images (especially, 100 m, 250 m, 500 m), would overestimate the covering area of macroalgae while omit the small patches in the affected zones. To reduce the impacts of overestimation and omission, high resolution satellite images are used to show the seasonal changes of macroalgae blooms in 2018 and 2019 in the Yellow Sea.

From macroalgae to porous graphitized nitrogen-doped biochars - Using aquatic biota to treat polycyclic aromatic hydrocarbons-contaminated water

Enhanced macroalgal biochars with large specific surface areas (up to 399 m² g^-1), partly graphitized structure, high nitrogen doping (up to 6.14%), and hydrophobicity were fabricated by co-carbonization of macroaglae, ferric chloride, and zinc chloride. These biochars were used as sorbents for the removal of polycyclic aromatic hydrocarbons from water. The sorption capacity of polycyclic aromatic hydrocarbons onto macroalgal biochars was high (up to 90 mg g^-1), and recycling by thermal desorption was practicable. We revealed the physical-dominated multilayer sorption process, based on results from characterization and sorption experiments. Pore filling, mass transfer, π-π stacking, and the partition effect were found to be possible sorption mechanisms. This study suggests that porous graphitized nitrogen-doped biochars may be synthesized from macroalgae with simple one-pot carbonization and display promising applicability for sorption removal of organic pollutants from water.

Hydro-biogeochemical modeling of the early-stage outbreak of green tide (Ulva prolifera) driven by land-based nutrient loads in the Jiangsu coast

The outbreak of a large-scale green tide (Ulva prolifera) will have a serious impact on marine environment, ecological functions, landscape, and coastal social economy. Eutrophication is generally considered to be the most important driving factor of this phenomenon. It is difficult to obtain the pressure-impact relationship between land-based loading and green tides by only surveying or monitoring, whereas modeling can perform this task easily. In this study, therefore, a hydro-biogeochemical model was established and verified by the measured hydrodynamic and water quality variables. In the initial outbreak area of Jiangsu coast, China, we studied the relationship between U. prolifera bloom and the driving factors of nutrient loads and structures by modeling different scenarios of land source inputs. It was found that the ratio of nitrogen to phosphorus could be affected significantly, which triggered the bloom of U. prolifera. When the land-based input doubled or halved, the dissolved inorganic nitrogen concentration increased 20.6% or decreased 9.5%, respectively, which might result in 14.5% increase or 46.3% decrease in the green tide, respectively. It was also found that the nutrient distribution and structure was affected by the land-based load, which caused the outbreak of U. prolifera. Moreover, the total nutrient load must be controlled to prevent the outbreak of green tide in the Jiangsu coast.

Spatiotemporal Patterns and Morphological Characteristics of Ulva prolifera Distribution in the Yellow Sea, China in 2016–2018

The world’s largest macroalgal blooms, Ulva prolifera, have appeared in the Yellow Sea every summer on different scales since 2007, causing great harm to the regional marine economy. In this study, the Normalized Difference of Vegetation Index (NDVI) index was used to extract the green tide of Ulva prolifera from MODIS images in the Yellow Sea in 2016–2018, to investigate its spatiotemporal patterns and to calculate its occurrence probability. Using the standard deviational ellipse (SDE), the morphological characteristics of the green tide, including directionality and regularity, were analyzed. The results showed that the largest distribution and coverage areas occurred in 2016, with 57,384 km2 and 2906 km2, respectively and that the total affected region during three years was 163,162 km2. The green tide drifted northward and died out near Qingdao, Shandong Province, which was found to be a high-risk region. The coast of Jiangsu Province was believed to be the source of Ulva prolifera, but it was probably not the only one. The regularity of the boundary shape of the distribution showed a change that was opposite to the variation of scale. Several sharp increases were found in the parameters of the SDE in all three years. In conclusion, the overall situation of Ulva prolifera was still severe in recent years, and the sea area near Qingdao became the worst hit area of the green tide event. It was also shown that the sea surface wind played an important part in its migration and morphological changes.

A simple and effective method for monitoring floating green macroalgae blooms: a case study in the Yellow Sea

Several algorithms have been proposed to detect floating macroalgae blooms in the global ocean. However, some of them are difficult or even impossible to routinely apply by non-experts because of performing a sophisticated atmospheric correction scheme or due to the mismatch in spectral bands from one sensor to another. Here, a generic, simple and effective method, referred to as the Floating Green Tide Index (FGTI), was proposed to detect floating green macroalgae blooms (GMB). The FGTI was defined as the difference between greenness and wetness features extracted from digital number (DN) observation through Tasseled Cap Transformation analysis, providing the advantage of bypassing the atmospheric correction procedure. Through cross-index and cross-sensor comparisons, the FGTI showed similar performance to the existing VB-FAH (Virtual-Baseline Floating macroAlgae Height) and FAI (Floating Algae Index) algorithms but proved more robust than the traditional NDVI (Normalized Difference Vegetation Index) in terms of response to perturbations by environmental conditions, viewing geometry, sun glint, and thin cloud contamination. Given the requirement for spectral bands in the current and planned satellite sensors, the FGTI design can easily be extended to any satellite sensor, and therefore provide an excellent data resource for studying GMB in any part of the global ocean.

Automatic method to monitor floating macroalgae blooms based on multilayer perceptron: case study of Yellow Sea using GOCI images

Timely and accurate information about floating macroalgae blooms (MAB), including their distribution, movement, and duration, is crucial in order for local government and residents to grasp the whole picture, and then plan effectively to restrain economic damage. Plenty of threshold-based index methods have been developed to detect surface algae pixels in various ocean color data with different manners; however, these methods cannot be used for every satellite sensor because of the spectral band configuration. Also, these traditional methods generally require other reliable indicators, and even visual inspection, in order to achieve an acceptable mapping of MAB that appears under diverse environmental conditions (cloud, aerosol, and sun glint). To overcome these drawbacks, a machine learning algorithm named Multi-Layer Perceptron (MLP) was used in this paper to establish a novel automatic method to monitor MAB continuously in the Yellow Sea, using Geostationary Ocean Color Imager (GOCI) imagery. The method consists of two MLP models, which consider both spectral and spatial features of Rayleigh-corrected reflectance (R_rc) maps. Accuracy assessment and performance comparison showed that the proposed method has the capability to provide prediction maps of MAB with high accuracy (F1-score approaching 90% or more), and with more robustness than the traditional methods. Most importantly, the model is practically adaptable for other ocean color instruments. This allows customized models to be built and used for monitoring MAB in any regional areas. With the development of machine learning models, long-term mapping of MAB in global ocean is conducive to promoting the associated studies.