To what extent can Ulva and Sargassum be detected and separated in satellite imagery?

Current status and prospects of algal bloom early warning technologies: A Review

In recent years, frequent occurrences of algal blooms due to environmental changes have posed significant threats to the environment and human health. This paper analyzes the reasons of algal bloom from the perspective of environmental factors such as nutrients, temperature, light, hydrodynamics factors and others. Various commonly used algal bloom monitoring methods are discussed, including traditional field monitoring methods, remote sensing techniques, molecular biology-based monitoring techniques, and sensor-based real-time monitoring techniques. The advantages and limitations of each method are summarized. Existing algal bloom prediction models, including traditional models and machine learning (ML) models, are introduced. Support Vector Machine (SVM), deep learning (DL), and other ML models are discussed in detail, along with their strengths and weaknesses. Finally, this paper provides an outlook on the future development of algal bloom warning techniques, proposing to combine various monitoring methods and prediction models to establish a multi-level and multi-perspective algal bloom monitoring system, further improving the accuracy and timeliness of early warning, and providing more effective safeguards for environmental protection and human health.

Interannual variations of Sargassum blooms in the Yellow Sea and East China Sea during 2017-2021

Golden tide, caused by Sargassum horneri, is becoming another periodic and trans-regional harmful macroalgal bloom in the Yellow Sea (YS) and East China Sea (ECS) other than the green tide. In this study, we employed high-resolution remote sensing, field validations, and population genetics to investigate the spatiotemporal development pattern of Sargassum blooms during the years 2017 to 2021 and explore the potential environmental factors that influence them. Sporadic floating Sargassum rafts could be detected in the middle or northern YS during autumn and the distribution area then occurred sequentially along the Chinese and/or western Korean coastlines. The floating biomass amplified significantly in early spring, reached its maximum in two to three months with an evident northward expansion, and then declined rapidly in May or June. The scale of the spring bloom was much larger than the winter one in terms of coverage, suggesting an additional local source in ECS. The blooms were mostly confined to waters with a sea surface temperature range of 10-16℃, while the drifting pathways were consistent with the prevailing wind trajectory and surface currents. The floating S. horneri populations exhibited a homogenous and conservative genetic structure among years. Our findings underscore the year-round cycle of golden tides, the impact of physical hydrological environments on the drifting and blooming of pelagic S. horneri, and provide insights for monitoring and forecasting this emerging marine ecological disaster.

Remote detection of marine debris using Sentinel-2 imagery: A cautious note on spectral interpretations

Remote detection of marine debris (also called marine litter) has received increased attention in the past decade, with the Multispectral Instruments (MSI) onboard the Sentinel-2A and Sentinel-2B satellites being the most used sensors. However, because of their mixed band resolutions and small sub-pixel coverage of debris within a pixel (e.g., <10 %), caution is required when interpreting the spectral shapes of MSI pixels. Otherwise, the spectrally distorted shapes may be misused as spectral endmembers (signatures) or interpreted as from certain types of floating matters. Here, using simulations and MSI data, I show the origin of the spectral distortions and emphasize why both pixel averaging and pixel subtraction are critical in algorithm design and spectral interpretation for the purpose of remote detection of marine debris using Sentinel-2 MSI sensors.

Temporal and spatial dynamics of tropical macroalgal contributions to blue carbon

Blue carbon ecosystems are a vital part of nature-based climate solutions due to their capacity to store and sequester carbon, but often exclude macroalgal beds even though they can form highly productive coastal ecosystems. Recent estimates of macroalgal contributions to global carbon sequestration are derived primarily from temperate kelp forests, while tropical macroalgal carbon stock in living biomass is still unclear. Here, using Singapore as a case study, we integrate field surveys and remote sensing data to estimate living macroalgal carbon stock. Results show that macroalgae in Singapore account for up to 650 Mg C biomass stock, which is greater than the aboveground carbon found in seagrass meadows but lower than that in mangrove forests. Ulva and Sargassum dominate macroalgal assemblages and biomass along the coast, with both genera exhibiting distinct spatio-temporal variation. The annual range of macroalgal biomass carbon is estimated to be 450 Mg C yr^-1, or 0.77 Mg C ha^-1 yr^-1. Noting the uncertainties of the fate of macroalgal biomass carbon, we estimate the potential sequestration rate and find that it is comparable to mature terrestrial ecosystems such as tropical grasslands and temperate forests. This study demonstrates that macroalgal seasonality allows for a consistent amount of biomass carbon to either be exported and eventually sequestered, or harvested for utilization on an annual basis. These findings on macroalgal growth patterns and their considerable contributions to tropical coastal carbon pool add to the growing support for macroalgae to be formally included in blue carbon assessments.

Slow Pyrolysis of Ulva lactuca (Chlorophyta) for Sustainable Production of Bio-Oil and Biochar

Ulva Lactuca is a fast-growing algae that can be utilized as a bioenergy source. However, the direct utilization of U. lactuca for energy applications still remains challenging due to its high moisture and inorganics content. Therefore, thermochemical processing such as slow pyrolysis to produce valuable added products, namely bio-oil and biochar, is needed. This study aims to conduct a thorough investigation of bio-oil and biochar production from U. lactuca to provide valuable data for its further valorization. A slow pyrolysis of U. lactuca was conducted in a batch-type reactor at a temperature range of 400–600 °C and times of 10–50 min. The results showed that significant compounds obtained in U. lactuca’s bio-oil are carboxylic acids (22.63–35.28%), phenolics (9.73–31.89%), amines/amides (15.33–23.31%), and N-aromatic compounds (14.04–15.68%). The ultimate analysis revealed that biochar’s H/C and O/C atomic ratios were lower than feedstock, confirming that dehydration and decarboxylation reactions occurred throughout the pyrolysis. Additionally, biochar exhibited calorific values in the range of 19.94–21.61 MJ kg−1, which is potential to be used as a solid renewable fuel. The surface morphological analysis by scanning electron microscope (SEM) showed a larger surface area in U. lactuca’s biochar than in the algal feedstock. Overall, this finding provides insight on the valorization of U. lactuca for value-added chemicals, i.e., biofuels and biochar, which can be further utilized for other applications.

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.