Remote Sensing of Ocean Fronts in Marine Ecology and Fisheries

Frontal processes as drivers of floating marine debris in coastal areas

The influence of floating marine debris (FMD) on coastal and marine communities and ecosystems is undeniable, and attention is increasingly focused on ecologically and biologically important coastal areas. To protect marine life and valuable resources from FMD pollution, identifying FMD accumulation zones is recognized as a priority. One of the coastal ocean processes found governing the distribution of FMD is water convergence (frontal zones). These fronts are driven by various oceanographical factors. To date, the transport and accumulation of FMD in relation to fronts in coastal areas is poorly understood. To address this knowledge gap, we reviewed various types of ocean fronts as well as FMD accumulation along frontal zones in coastal areas defined as the region between the coastline and the shelf break. Frontogenesis (physical processes related to frontal formation) were reviewed alongside studies on FMD accumulation in frontal zones to identify physical factors that drive the pathways and accumulation in these areas. This review will contribute to our understanding of accumulation hotspots of FMD within ocean fronts and identify gaps for further research on developing a proxy for FMD hotspot identification in ecologically important coastal areas.

Dynamic causes contribute to the increasing trend of red tides in the east China sea during 2020-2022

Red tide is a marine phenomenon caused by the excessive growth of microscopic algae in the ocean. This study aims to analyze the development trends of red tides in the past 20 years and the dynamic external causes that induce red tides based on existing satellite remote sensing and numerical simulation data. And the changes in dominant species of red tides in different seasons are analyzed. The results show significant temperature fluctuations within the week before the red tide occurs, with an average increase of 1.42 °C. In contrast, the change in salinity is relatively small. Meanwhile, ocean fronts are areas in the ocean where different water masses meet and form boundaries. The average strength of ocean fronts increased by 3.7%, indicating enhanced ocean mixing over a short period of time. Under the combined influence of these factors, the probability of a red tide outbreak in the East China Sea increases rapidly. Therefore, this study has important reference value for further research on the causes of red tides and their response to ocean dynamic changes.

Spatial distribution models for the four commercial tuna in the sea of maritime continent using multi-sensor remote sensing and maximum entropy

The dynamic of marine environmental parameters affects the distribution of commercial tuna in the sea of the maritime continent. Hence, the objectives of this study are to develop spatial distribution models for the four main tuna species in the Maritime continent's sea with reasonable accuracy, identify their correlation with marine environmental parameters, and investigate areas of interaction between those tuna species. The study develops the distribution models for albacore (Thunnus alalunga), bigeye (Thunnus obesus), yellowfin (Thunnus albacares), and skipjack (Katsuwonus pelamis) tuna species, utilizing multi-sensor satellite remote sensing and maximum entropy. The results show models have good performance, focusing on environmental factors such as sea surface temperature (SST), chlorophyll-a (CHL), and sea surface height anomalies (SSHA), combined with eddy kinetic energy (EKE). Seasonal variations in potential tuna habitats are revealed, emphasizing the influence of those marine environmental conditions. From December to May, the four commercial tuna species were distributed in conditions characterized by SST of 26-31.5 °C, CHL levels of 0-3 mg/l, SSHA of -0.3 to 0.2 m, and EKE of 0-1 m2/s2, while from June to November, they experienced SST of 23-31 °C, CHL levels of 0-4 mg/l, SSHA of -0.5 to 0.3 m, and EKE of 0-1.1 m2/s2. The spatial persistence of the four tuna species emerged mainly around the south sea of Java, with skipjack being the most common species found in the sea of the maritime continent. With sufficient and evenly distributed tuna presence records, the results indicate the potential for extrapolation beyond the training data to estimate habitat suitability for the four commercial tuna distributions. The results also suggest potential competition between tuna species sharing ecological niches and highlight possible overlapping areas where different tuna species interact with the same fishing gear.

Global mapping and evolution of persistent fronts in Large Marine Ecosystems over the past 40 years

Ocean fronts, characterized by narrow zones with sharp changes in water properties, are vital hotspots for ecosystem services and key regulators of regional and global climates. Global change is reshaping the distribution of material and energy in the ocean; however, it remains unclear how fronts have varied in the last few decades. Here, we present a global, fine-scale digital atlas of persistent fronts around Large Marine Ecosystems and demonstrate significant global increases in both their occurrence and intensity. In subtropical regions (around boundary currents and upwelling systems) and polar regions, persistent frontal occurrence and intensity are rapidly increasing, while in tropical regions, they remain stable or slightly decrease. These enhancements may be respectively related to changes in boundary currents, upwelling, and sea ice retreat. This spatially heterogeneous trend holds important implications for the redistribution of front-related ecosystem services and air-sea interactions but has not been captured by representative high-resolution climate projections models or observation-assimilated ocean models.

Transport patterns and hydrodynamic context of the MERITE-HIPPOCAMPE cruise: Implications for contaminants distribution and origin

This study aims at characterizing the hydrodynamic context and transport patterns that prevailed during the MERITE-HIPPOCAMPE cruise to assist in the interpretation of in-situ observations. The main physical attributes and structures (mesoscale eddies as well as fine-scale fronts and filaments) are analyzed based on various physical diagnostics. They were computed from satellite data and data-assimilative model outputs to describe ocean dynamics. The Northern and Algerian Currents were prominent features during the cruise while the western basin is divided by the vertically-tilted Balearic front. Temperature and salinity were used to distinguish different water masses at both surface and sub-surface. Sea-level anomalies, relative vorticity, and Okubo-Weiss parameter distributions have shown the presence of marked eddies around stations St10 and St11. Furthermore, Finite-Size Lyaponuv Exponents revealed that the former was rather located on a fine-scale filament near the edge of a cyclonic eddy while the latter was closer to the core of an anticyclone. Nearshore thermal fronts were detected with the Belkin and O'Reilly Algorithm (BOA), especially around stations St17 and St19. The potential coastal sources of contaminants were tested using Lagrangian Origin Maps (LOM), suggesting that stations St1, St2, St4, St11, and St15 were most likely influenced by coastal waters. Additionally, an atmospheric reanalysis combined with a Lagrangian dispersal model allowed for estimating wet deposition events of contaminants while tracking the fate of water masses where rainfall took place. Finally, we provide a set of explanatory quantitative and qualitative variables for future statistical analyses that aim at explaining the distribution of both chemical and biological samples collected during the cruise.

Relationship between floating marine debris accumulation and coastal fronts in the Northeast coast of the USA

Floating marine debris (FMD) is one of the world's most concerning issues due to its potential impact on biodiversity, communities, and ecosystem services. FMD transport and concentrations are driven by fronts, generated by oceanographic processes, and the accumulation of FMD has been reported in gyres, eddies, tidal fronts, salinity fronts, and coastal fronts. This study explores the relationship between fronts and FMD accumulation in the Gulf of Maine (GoM) and the surrounding coastal areas (USA). Frontal edge detection algorithms were applied to sea surface temperature (SST) imagery from the Moderate-resolution Imaging Spectroradiometer (MODIS) between 2002 and 2012. Frontal location is spatially correlated with FMD concentrations collected by the Sea Education Association. Higher concentrations of FMD are associated with frontal frequencies (FF) of 5-10 %. FMD is trapped between fronts and the coastline in accumulation zones. These results highlight the need to consider coastal FMD hotspots, given these are areas of high biodiversity value.

A Novel Correction Methodology to Improve the Performance of a Low-Cost Hyperspectral Portable Snapshot Camera

The development of spectral sensors (SSs) capable of retrieving spectral information have opened new opportunities to improve several environmental and agricultural practices, e.g., crop breeding, plant phenotyping, land use monitoring, and crop classification. The SSs are classified as multispectral and hyperspectral (HS) based on the number of the spectral bands resolved and sampled during data acquisition. Large-scale applications of the HS remain limited due to the cost of this type of technology and the technical difficulties in hyperspectral data processing. Low-cost portable hyperspectral cameras (PHCs) have been progressively developed; however, critical aspects associated with data acquisition and processing, such as the presence of spectral discontinuities, signal jumps, and a high level of background noise, were reported. The aim of this work was to analyze and improve the hyperspectral output of a PHC Senop HSC-2 device by developing a general use methodology. Several signal gaps were identified as falls and jumps across the spectral signatures near 513, 650, and 930 nm, while the dark current signal magnitude and variability associated with instrumental noise showed an increasing trend over time. A data correction pipeline was successfully developed and tested, leading to 99% and 74% reductions in radiance signal jumps identified at 650 and 830 nm, respectively, while the impact of noise on the acquired signal was assessed to be in the range of 10% to 15%. The developed methodology can be effectively applied to other low-cost hyperspectral cameras.

An ocean front dataset for the Mediterranean sea and southwest Indian ocean

Fronts are ubiquitous discrete features of the global ocean often associated with enhanced vertical velocities, in turn boosting primary production. Fronts thus form dynamical and ephemeral ecosystems where numerous species meet across all trophic levels. Fronts are also targeted by fisheries. Capturing ocean fronts and studying their long-term variability in relation with climate change is thus key for marine resource management and spatial planning. The Mediterranean Sea and the Southwest Indian Ocean are natural laboratories to study front-marine life interactions due to their energetic flow at sub-to-mesoscales, high biodiversity (including endemic and endangered species) and numerous conservation initiatives. Based on remotely-sensed Sea Surface Temperature and Height, we compute thermal fronts (2003-2020) and attracting Lagrangian coherent structures (1994-2020), in both regions over several decades. We advocate for the combined use of both thermal fronts and attracting Lagrangian coherent structures to study front-marine life interactions. The resulting front dataset differs from other alternatives by its high spatio-temporal resolution, long time coverage, and relevant thresholds defined for ecological provinces.

Intraspecific variation in reproductive traits and embryo elemental composition of the crab Hemigrapsus crenulatus (Milne Edwards, 1837) across fluctuating coastal environments along Chilean coasts

The estuarine crab Hemigrapsus crenulatus has a wide distribution range along the coast of the South Pacific Ocean (from 20 °S to 53 S°). This decapod is abundant in these coastal and estuarine habitats and plays a key ecological role as prey of the snook fish (Eleginops maclovinus) and kelp gull (Larus dominicanus). Its diet consists of detritus, dead fish, and crustaceans, and the macroalgae Ulva sp. In response to contrasting environmental conditions and anthropogenic impacts along the Chilean coast, H. crenulatus may present intraspecific variations in its reproductive traits and changes in the elemental composition of its embryos, directly affecting its biological fitness. Along the Chilean coast, female individuals were collected during late spring 2019 and early summer 2020 (from November 2019 to February 2020) in six areas of Chile (north: Tongoy (30°S); south-central: Lenga (36°S), Tubul (37°S); south austral: Calbuco (41°S), Castro (42°S), Quellón (43°S). The environmental conditions (i.e. sea surface temperature, precipitation and chlorophyll α) present during each sampling event were also recorded. We evaluated the reproductive parameters of females (fecundity, reproductive output (RO)) as well as their body size (carapace width and dry weight), volume, water content, dry weight, elemental composition (carbon, hydrogen, nitrogen: CHN), and finally the energy content of their embryos. Our results indicated that the environmental conditions of the sea water temperature, precipitation (proxy of water salinity) and chlorophyll α (proxy of food availability) have direct effects on the reproductive parameters of females and the characteristics of their embryos. We observed a low fecundity and high RO in Calbuco and Quellón, where precipitation was high (i.e. diluted salinity) and temperatures and productivity was low. For embryo traits, the highest values of volume and water content were observed in female crabs from the estuarine areas (i.e. Tongoy, Lenga, Tubul), values much higher than those found in the internal sea of Chile (i.e. Calbuco, Castro, Quellón). For the elemental composition, we observed high nitrogen levels and a low C:N proportion in embryos from female crabs from Lenga (a nitrogen enriched area). Fluctuating environmental variables among localities proved to modulate intraspecific variations in females and embryos of H. crenulatus, revealing different reproductive strategies, particularly in the quality and energy investment per embryo, which subsequently influenced successful embryogenesis and larval survival.

Mapping seagrass habitats of potential suitability using a hybrid machine learning model

Seagrass meadows provide essential ecosystem services globally in the context of climate change. However, seagrass is being degraded at an accelerated rate globally due to ocean warming, ocean acidification, aquaculture, and human activities. The need for more information on seagrasses’ spatial distribution and health status is a serious impediment to their conservation and management. Therefore, we propose a new hybrid machine learning model (RF-SWOA) that integrates the sinusoidal chaos map whale optimization algorithm (SWOA) with a random forest (RF) model to accurately model the suitable habitat of potential seagrasses. This study combines in situ sampling data with multivariate remote sensing data to train and validate hybrid machine learning models. It shows that RF-SWOA can predict potential seagrass habitat suitability more accurately and efficiently than RF. It also shows that the two most important factors affecting the potential seagrass habitat suitability on Hainan Island in China are distance to land (38.2%) and depth to sea (25.9%). This paper not only demonstrates the effectiveness of a hybrid machine learning model but also provides a more accurate machine learning model approach for predicting the potential suitability distribution of seagrasses. This research can help identify seagrass suitability distribution areas and thus develop conservation strategies to restore healthy seagrass ecosystems.

Machine Learning to Identify Three Types of Oceanic Fronts Associated with the Changjiang Diluted Water in the East China Sea between 1997 and 2021

Long-term sea surface salinity (SSS) in the East China Sea (ECS) was estimated based on Ocean Color Climate Change Initiative (OC-CCI) data using machine learning during the summer season (June to September) from 1997 to 2021. Changjiang diluted water (CDW) in the ECS propagates northeastward and forms longitudinally-oriented ocean fronts. To determine the CDW’s distribution, three fronts were investigated: (1) a CDW front based on chlorophyll-a concentration (Chl), SSS, and sea surface temperature (SST); (2) a CDW front based on sea surface density (SSD); and (3) a CDW front for nutrient distribution. The Chl fronts matched well with the SSS fronts, suggesting that Chl variation in the ECS is highly correlated with the CDW. Furthermore, the SSD fronts spatially matched well with nitrogen concentration. Sea level anomaly (SLA) variation with SSD was also detected, indicating that CDW had sufficiently large effects on SLA so that they may be detectable by altimeter measurements. This result suggests that the influence of steric height changes and the inflow from rivers are significant in the ECS. Additionally, the continuous long-term SSD developed in this study enables researchers to detect the CDW front and its influence on the ECS marine environment.

Using Satellite-Based Data to Facilitate Consistent Monitoring of the Marine Environment around Ireland

As an island nation, Ireland needs to ensure effective management measures to protect marine ecosystems and their services, such as the provision of fishery resources. The characterization of marine waters using satellite data can contribute to a better understanding of variations in the upper ocean and, consequently, the effect of their changes on species populations. In this study, nineteen years (1998–2016) of monthly data of essential climate variables (ECVs), chlorophyll (Chl-a), and the diffuse attenuation coefficient (K490) were used, together with previous analyses of sea surface temperature (SST), to investigate the temporal and spatial variability of surface waters around Ireland. The study area was restricted to specific geographically delineated divisions, as defined by the International Council of the Exploration of the Seas (ICES). The results showed that SST and Chl-a were positively and significantly correlated in ICES divisions corresponding to oceanic waters, while in coastal divisions, SST and Chl-a showed a significant negative correlation. Chl-a and K490 were positively correlated in all cases, suggesting an important role of phytoplankton in light attenuation. Chl-a and K490 had significant trends in most of the divisions, reaching maximum values of 1.45% and 0.08% per year, respectively. The strongest seasonal Chl-a trends were observed in divisions VIId and VIIe (the English Channel), primarily in the summer months, followed by northern divisions VIa (west of Scotland) and VIb (Rockall) in the winter months.

An Efficient Algorithm for Ocean-Front Evolution Trend Recognition

Marine hydrological elements are of vital importance in marine surveys. The evolution of these elements can have a profound effect on the relationship between human activities and marine hydrology. Therefore, the detection and explanation of the evolution laws of marine hydrological elements are urgently needed. In this paper, a novel method, named Evolution Trend Recognition (ETR), is proposed to recognize the trend of ocean fronts, being the most important information in the ocean dynamic process. Therefore, in this paper, we focus on the task of ocean-front trend classification. A novel classification algorithm is first proposed for recognizing the ocean-front trend, in terms of the ocean-front scale and strength. Then, the GoogLeNet Inception network is trained to classify the ocean-front trend, i.e., enhancing or attenuating. The ocean-front trend is classified using the deep neural network, as well as a physics-informed classification algorithm. The two classification results are combined to make the final decision on the trend classification. Furthermore, two novel databases were created for this research, and their generation method is described, to foster research in this direction. These two databases are called the Ocean-Front Tracking Dataset (OFTraD) and the Ocean-Front Trend Dataset (OFTreD). Moreover, experiment results show that our proposed method on OFTreD achieves a higher classification accuracy, which is 97.5%, than state-of-the-art networks. This demonstrates that the proposed ETR algorithm is highly promising for trend classification.