Detection of macroalgae blooms by complex SAR imagery

SLWE-Net: An improved lightweight U-Net for Sargassum extraction from GOCI images

The Sargassum bloom has severely impacted the ecological environment of the East China Sea and the Yellow Sea, causing significant economic losses. In recent years, deep learning has seen extensive development due to its outstanding feature extraction capabilities. However, the deep learning process typically involves a large number of parameters and computations. To address this issue, this paper proposes a lightweight deep learning network based on the U-Net framework, called SLWE-NET, which uses lightweight modules to replace the feature extraction modules in U-Net. In this experiment, SLWE-Net performed the best in both extraction accuracy and model lightweight. Compared to the formal U-Net, the number of parameters decreased by 65.83 %, the model size reduced from 94.97 MB to 32.51 MB, and the mIoU increased to 93.81 %. Therefore, the method proposed in this paper is beneficial for Sargassum extraction and provides a basis for operational monitoring.

Investigating Spatial Distribution of Green-Tide in the Yellow Sea in 2021 Using Combined Optical and SAR Images

Optical remote sensing is limited to clouds and rain. It is difficult to obtain ground object images in severe weather. Microwave remote sensing can penetrate clouds and rain to obtain ground object images. Therefore, this paper combines optical and microwave data to analyze the time and space of the green-tide in the Yellow Sea in 2021. Compared with a single data source, the distribution characteristics increase the frequency of time observation and show the green-tide changes in more detail. The continuous remote sensing observation time is 80 days. Ulva prolifera has experienced discovery (mid-late May), development (mid-late May to early June), outbreak (early June to mid-late June), decline (late June to mid-July), and extinction (late July to mid-August) in five stages; the development period drifts along the northeast direction, the outbreak period drifts along the northwest direction, the decline and extinction periods are mainly in the Rizhao and Qingdao waters. Ulva prolifera has a tendency to drift northward as a whole, drifting through Yancheng, Lianyungang, Linyi, Rizhao and Qingdao waters eventually landing on the coast of Qingdao and gradually disappearing.

Using 1st Derivative Reflectance Signatures within a Remote Sensing Framework to Identify Macroalgae in Marine Environments

Macroalgae blooms (MABs) are a global natural hazard that are likely to increase in occurrence with climate change and increased agricultural runoff. MABs can cause major issues for indigenous species, fish farms, nuclear power stations, and tourism activities. This project focuses on the impacts of MABs on the operations of a British nuclear power station. However, the outputs and findings are also of relevance to other coastal operators with similar problems. Through the provision of an early-warning detection system for MABs, it should be possible to minimize the damaging effects and possibly avoid them altogether. Current methods based on satellite imagery cannot be used to detect low-density mobile vegetation at various water depths. This work is the first step towards providing a system that can warn a coastal operator 6–8 h prior to a marine ingress event. A fundamental component of such a warning system is the spectral reflectance properties of the problematic macroalgae species. This is necessary to optimize the detection capability for the problematic macroalgae in the marine environment. We measured the reflectance signatures of eight species of macroalgae that we sampled in the vicinity of the power station. Only wavelengths below 900 nm (700 nm for similarity percentage (SIMPER)) were analyzed, building on current methodologies. We then derived 1st derivative spectra of these eight sampled species. A multifaceted univariate and multivariate approach was used to visualize the spectral reflectance, and an analysis of similarities (ANOSIM) provided a species-level discrimination rate of 85% for all possible pairwise comparisons. A SIMPER analysis was used to detect wavebands that consistently contributed to the simultaneous discrimination of all eight sampled macroalgae species to both a group level (535–570 nm), and to a species level (570–590 nm). Sampling locations were confirmed using a fixed-wing unmanned aerial vehicle (UAV), with the collected imagery being used to produce a single orthographic image via standard photogrammetric processes. The waveband found to contribute consistently to group-level discrimination has previously been found to be associated with photosynthetic pigmentation, whereas the species-level discriminatory waveband did not share this association. This suggests that the photosynthetic pigments were not spectrally diverse enough to successfully distinguish all eight species. We suggest that future work should investigate a Charge-Coupled Device (CCD)-based sensor using the wavebands highlighted above. This should facilitate the development of a regional-scale early-warning MAB detection system using UAVs, and help inform optimum sensor filter selection.

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.

Developing a Quality Index Associated with Rain for Hurricane Winds from SAR

Differences in synthetic aperture radar (SAR)-retrieved hurricane wind speeds from co-polarization and cross-polarization measurements are found to be correlated with rain rate. A quality index is proposed for the SAR-retrieved wind speed product to recognize heavy rain- affected areas by taking account of the different imaging mechanisms of the radar backscattering from the ocean surface via cross-polarization and co-polarization observations. A procedure is proposed to rectify wind retrievals in the rain-contaminated areas within the hurricane core, based on the theoretical physical profile for hurricanes. The effectiveness of the proposed methodology for heavy rain area recognition and wind speed reconstruction in the rain-affected areas is validated against step frequency microwave radiometer measurements from hurricane reconnaissance missions and the hurricane surface wind analysis product (HWIND). The quality flags provide confidence levels of hurricane surface winds from SAR, which together with the proposed method to correct wind retrievals in rain-contaminated areas, can contribute to improved operational applications of SAR-derived winds under hurricane conditions.

Spatio-temporal patterns of Ulva prolifera blooms and the corresponding influence on chlorophyll-a concentration in the Southern Yellow Sea, China

The world's largest macroalgal blooms (MABs) caused by the Ulva prolifera outbreaks have occurred every summer since 2007 in the Southern Yellow Sea, China. Accumulating evidence showed that MABs may deteriorate the regional marine environment and influence the growth of some primary producers such as phytoplankton. In this study, we investigated the spatio-temporal patterns of U. prolifera green tides and chlorophyll-a concentration in the Southern Yellow Sea in 2015 using satellite images obtained from HJ-1 CCD, MODIS, and GOCI. The correlation between the distributions of U. prolifera abundance and chlorophyll-a concentration was analyzed quantitatively by setting up a series of 5 × 5 km experimental grids, and we also discussed the possible mechanisms about the influence of U. prolifera blooms on the other floating microalgae. The results showed that the development of U. prolifera blooms in the Southern Yellow Sea in 2015 could be featured as "appearance - development - outbreak - decline - disappearance", while the concentration of chlorophyll-a showed "increase - sharp decline - slow recovery - stabilization" from April to August. We also found that the concentration of chlorophyll-a had the following relationships with U. prolifera temporally: (1) the concentration of chlorophyll-a increased with the growth of U. prolifera from April to mid-May; (2) the chlorophyll-a concentration decreased sharply with the dramatically increased coverage of U. prolifera in June; and (3) the chlorophyll-a concentration slowly recovered and finally stabilized as U. prolifera decreased in July. Generally, there was a negative correlation between the occurrence of U. prolifera and chlorophyll-a concentration in the Southern Yellow Sea, China. Our results showed that the outbreak of U. prolifera does have a certain impact on the growth and reproduction of planktonic microalgae, and it suggests that U. prolifera blooms have potentially altered the ecological balance in the coastal waters of the Southern Yellow Sea.

Spatial and temporal variability in macroalgal blooms in a eutrophied coastal estuary

All three macroalgal clades (Chlorophyta, Rhodophyta, and Phaeophyceae) contain bloom-forming species. Macroalgal blooms occur worldwide and have negative consequences for coastal habitats and economies. Narragansett Bay (NB), Rhode Island, USA, is a medium sized estuary that is heavily influenced by anthropogenic activities and has been plagued by macroalgal blooms for over a century. Over the past decade, significant investment has upgraded wastewater treatment from secondary treatment to water-quality based limits (i.e. tertiary treatment) in an effort to control coastal eutrophication in this system. The goal of this study was to improve the understanding of multi-year macroalgal bloom dynamics through intensive aerial and ground surveys conducted monthly to bi-monthly during low tides in May-October 2006-2013 in NB. Aerial surveys provided a rapid characterization of macroalgal densities across a large area, while ground surveys provided high resolution measurements of macroalgal identity, percent cover, and biomass. Macroalgal blooms in NB are dominated by Ulva and Gracilaria spp. regardless of year or month, although all three clades of macroalgae were documented. Chlorophyta cover and nutrient concentrations were highest in the middle and upper bay. Rhodophyta cover was highest in the middle and lower bay, while drifting Phaeophyceae cover was patchy. Macroalgal blooms of >1000g fresh mass (gfm)/m² (max=3510gfm/m²) in the intertidal zone and >3000gfm/m³ (max=8555gfm/m³) in the subtidal zone were observed within a heavily impacted embayment (Greenwich Bay). Macroalgal percent cover (intertidal), biomass (subtidal), and diversity varied significantly between year, month-group, site, and even within sites, with the highest species diversity at sites outside of Greenwich Bay. Total intertidal macroalgal percent cover, as well as subtidal Ulva biomass, were positively correlated with temperature. Dissolved inorganic nitrogen concentrations were correlated with the total biomass of macroalgae and the subtidal biomass of Gracilaria spp. but not the biomass of Ulva spp. Despite seasonal reductions in the nutrient output of wastewater treatment facilities emptying into upper Narragansett Bay in recent years, macroalgal blooms still persist. Continued long-term monitoring of water quality, macroalgal blooms, and ecological indicators is essential to understand the changes in macroalgal bloom dynamics that occur after nutrient reductions from management efforts.