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Remote Sensing of Algal Blooms: An Overview with Case Studies
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KLEMAS, V., 2012. Remote sensing of algal blooms: an overview with case studies. Journal of Coastal Research, 28(1A), 34–43. West Palm Beach (Florida), ISSN 0749-0208. High concentrations of nutrients from agricultural and urban runoff, or those produced by coastal upwelling, are causing algal blooms in many estuaries and coastal waters. Algal blooms induce eutrophic conditions, depleting oxygen levels needed by organic life, limiting aquatic plant growth by reducing water transparency, and producing toxins that can harm fish, benthic animals, and humans. The magnitude and frequency of phytoplankton blooms have increased globally in recent decades, as shown in data from ocean-color sensors on-board satellites. Satellite and airborne measurements of spectral reflectance (ocean color) represent an effective way for monitoring phytoplankton by its proxy, chlorophyll-a, the green pigment that is present in all algae. This article reviews the use of remote sensing techniques for detecting phytoplankton and mapping algal blooms. Two case studies are presented, illustrating the advantages and limitations of satellite and airborne remote sensing. www.JCRonline.org ADDITIONAL INDEX WORDS: Harmful algal blooms, remote sensing, eutrophication, phytoplankton blooms.
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... Properly monitoring algal blooms is crucial to protecting aquatic 338 life . Algal blooms can be caused by high nutrient 339 concentrations from urban and agricultural runoff; hence, accurate nutrient concen-340 tration monitoring is crucial for the research on algal blooms (Klemas 2012). ...
... Since their unpredictable flight path and unstable height and velocity, balloons and 454 kites are currently infrequently employed in remote sensing investigations. How-455 ever, the data collection cost using balloons and kites is incredibly inexpensive when 456 compared to data collected from airplanes(Klemas 2012). ...
Artificial intelligence can aid in effective aquatic ecosystem management, and advance sustainable water management techniques through several modelling tools. This review chapter examines AI applications in aquatic ecosystem monitoring and management, focusing on the effects of water contamination by different algal species. Accelerating the detection of toxic algae might lead to the development of an early warning system that could predict the occurrence and location of blooms. AI has the potential to make ecosystem recovery plans more effective. AI uses machine learning methods to improve prediction models to provide accurate and timely notices of algal blooms. As always, the major goal is to reduce the negative effects of harmful algal blooms on aquatic communities, and ecosystems by providing decision-makers with useful information for anticipatory and effective response plans. AI-based image processing algorithms have significantly advanced monitoring and forecasting of algal blooms which is crucial to reducing the harmful effects of eutrophication. While promising solutions are available to tackle such environmental concerns, their implementation becomes unsustainable when most of the challenges remain unaddressed. More research is needed to address the challenges of using AI technologies independently, including their implementation, to safeguard environmental sustainability and the importance that AI can create for current and upcoming generations. By examining various progressive applications, it will advance our understanding of how AI could be a key factor in ensuring a future that is ecologically conscious for aquatic ecosystem management.
... Monitoring phytoplankton through remote sensing is important to study their distribution, abundance, and productivity across the world oceans and inland waters [10]. Remote sensing enables the acquisition of high-resolution data on large spatial and temporal scales, providing a comprehensive understanding of the processes governing phytoplankton dynamics [11]. ...
This work introduces the use of Bayesian Neural Networks (BNNs) for inferring chlorophyll-a concentration ([CHL-a]) from remotely sensed data. BNNs are probabilistic models that associate a probability distribution to the neural network parameters and rely on Bayes’ rule for training. The performance of the proposed probabilistic model is compared to that of standard ocean color algorithms, namely ocean color 4 (OC4) and ocean color index (OCI). An extensive in situ bio-optical dataset was used to train and validate the ocean color models. In contrast to established methods, the BNN allows for enhanced modeling flexibility, where different variables that affect phytoplankton phenology or describe the state of the ocean can be used as additional input for enhanced performance. Our results suggest that BNNs perform at least as well as established methods, and they could achieve 20–40% lower mean squared errors when additional input variables are included, such as the sea surface temperature and its climatological mean alongside the coordinates of the prediction. The BNNs offer means for uncertainty quantification by estimating the probability distribution of [CHL-a], building confidence in the [CHL-a] predictions through the variance of the predictions. Furthermore, the output probability distribution can be used for risk assessment and decision making through analyzing the quantiles and shape of the predicted distribution.
... For large river basins, nutrients and algal dynamics are monitored with various methods, including remote sensing, automatic instruments, and in-situ measurements (Blaen et al., 2016;Chang et al., 2015;Fink et al., 2020). While remote sensing offers the ability to monitor large spatial areas with high temporal resolution, the major disadvantages are difficulty in detecting benthic algal blooms and estimating nutrients, which do not have measurable optical properties and issues with vegetation obscuration (Chang et al., 2015;Huang et al., 2018;Klemas, 2012;Sagan et al., 2020;Soomets et al., 2022). Automatic sampling allows for continuous and real-time monitoring of nutrients (e.g., total phosphorus) with high-frequency data collection (Blaen et al., 2016). ...
Eutrophication persists in many freshwater systems despite extensive efforts to control nutrient emissions from point and diffuse sources. While intensely studied at local or regional scales, the joint response of benthic and pelagic algae to nutrient loading across entire river networks remains poorly understood. Here, we assessed spatial patterns of pelagic and benthic algal biomass in response to point source and diffuse phosphorus loading in the Elbe River Basin, a temperate, transboundary river network, based on extensive monitoring data and with the parsimonious hydro-ecological model C n ANDY (Coupled Complex Algal-Nutrient Dynamics). We referenced our simulations to median river discharge data and phosphorus inputs from point (1,900 wastewater treatment plants) and diffuse sources, determined with the MoRE model and CORINE land cover analysis. We found distinct spatial eutrophication patterns across the river network and complex responses to local and cumulative anthropogenic nutrient emissions. Lower stream orders, particularly those in urban and agricultural areas, showed the highest dissolved phosphorus concentration and benthic algae density. Conversely, pelagic algae dominated higher stream orders, influenced by nutrient transport from lower-order streams to downstream reaches. The validated C n ANDY model effectively identified eutrophication hotspots, enabling prioritized nutrient and eutrophication management. Although extensive monitoring data were available, systematic gaps in established monitoring schemes limited the model calibration and validation. Therefore, we advocate for a revision and propose model-aided eutrophication monitoring at the river basin scale with representative coverage of all stream orders from up to downstream and the algal biomass in the benthic and pelagic compartments.
Satellite remote sensing provides a cost-effective and large-scale alternative to traditional methods for retrieving water quality parameters for inland waters. Effective water quality parameter retrieval via optical satellite remote sensing requires three key components: (1) a sensor whose measurements are sensitive to variations in water quality; (2) accurate atmospheric correction to eliminate the effect of absorption and scattering in the atmosphere and retrieve the water-leaving radiance/reflectance; and (3) a bio-optical model used to estimate water quality from the optical signal. This study provides a literature review and an evaluation of these three components. First, a review of decommissioned, active, and upcoming satellite sensors is presented, highlighting their advantages and limitations, and a ranking method is introduced to assess their suitability for retrieving chlorophyll-a, colored dissolved organic matter, and non-algal particles in inland waters. This ranking can aid in selecting appropriate sensors for future studies. Second, the strengths and weaknesses of atmospheric correction algorithms used over inland waters are examined. The results show that no atmospheric correction algorithm performed consistently across all conditions. However, understanding their strengths and weaknesses allows users to select the most suitable algorithm for a specific use case. Third, the challenges, limitations, and recent advances of machine learning use in bio-optical models for inland water quality parameter retrieval are discussed. Machine learning models have limitations, including low generalizability, low dimensionality, spatial/temporal autocorrelation, and information leakage. These issues highlight the importance of locally trained models, rigorous cross-validation methods, and integrating auxiliary data to enhance dimensionality. Finally, recommendations for promising research directions are provided.
The recent increase in algal blooms in lakes, potentially exacerbated by climate warming, is of global concern. However, a spatially and temporally detailed characterization of algal bloom trends at a global scale has been lacking, posing challenges to definitively attribute warming as a primary driver. Here, we used daily MODIS satellite observations from 2003 to 2022 to analyze algal bloom trends in 1,956 large freshwater lakes worldwide. Among these lakes, 620 have experienced algal bloom events in over half of the years during the past two decades, with an upward trend in bloom frequency observed in 504 lakes. This trend is particularly prominent in subtropical regions and has become most pronounced after 2015. The global median annual bloom frequency has significantly increased at a rate of +1.8%/yr over the past two decades, showing a significant correlation with air temperatures (r2=0.43, P<0.05). Furthermore, in 44.8% of the bloom-affected lakes, we observed a strong correlation between air temperature and bloom frequency. Our study helps clarify the factors contributing to the global expansion of algal blooms and emphasizes the urgent need to recognize and address this growing environmental challenge within the context of climate warming.
This study develops an artificial intelligence (AI) model to forecast harmful algal blooms (HABs) in the Persian Gulf and Gulf of Oman using freely available remote sensing data, including chlorophyll-a (Chl-a), sea surface temperature (SST), salinity, and wind. The model introduces novel features such as spatial and temporal standard deviations of Chl-a concentration and a derived gradient feature. Correlation analysis indicated that these features enhance predictive capability. A multi-layer artificial neural network (ANN) was trained using a 66%/34% data split for training and testing, achieving 88.7% accuracy in binary classification (bloom/non-bloom) with an area under the ROC curve (AUC) of 90.1%. Overfitting was mitigated by monitoring training and validation loss, both of which consistently decreased over epochs, confirming robust model generalization. The use of standard deviation in SST and salinity highlights their influence on bloom dynamics, providing key insights into algal bloom drivers. The focus on freely available data enables stakeholders to better manage the environmental challenges posed by HABs.
Phytoplankton blooms are closely coupled to physical processes. Increased access to wind, sea surface temperature, and ocean color datasets aids in retrospective analyses of bloom events and provides vital linkages of physical and biological processes. From such analyses, conceptual models of bloom initiation, transport, maintenance, and dissipation may help identify the timing of bloom initiation and define environmental conditions conducive to growth. The advent of on-line, real-time environmental data, new ocean color sensors, and hyperspectral scanners may allow enough predictive capability so that harmful or toxic bloom conditions can be detected, research efforts focused, and reliable information made available, before effects of these blooms are manifest in near shore areas.
Between January 18-20, 1995, a dinoflagellate algal bloom occurred in the Swan River, Western Australia. An airborne Digital Multi-Spectral Video (DMSV) system was used to acquire data on the bloom several times a day for three days. Simultaneous in-water data were gathered to determine the temporal and spatial dynamics of the algal concentrations. The study demonstrated the highly dynamic nature of algal blooms in space, depth, and time (within and between days), but the DMSV, limited by the existing band combinations, failed to show a strong qualitative relationship between physical and biological measurements. The results indicate that, to describe the bloom best, the timing of airborne data acquisition must be timed to coincide with movements of phytoplankton within the water column (such as daily afternoon migration of cells to the surface waters) and times of the day that will minimize sun-glint. Furthermore, ground truthing to calibrate or validate images must be carried out within a short period of airborne data acquisition because of the dynamic nature of the distributions of the phytoplankton cells. When blooms are rapidly changing, in-water sampling over large spatial scales, within short time spans, may be logistically impossible. In these situations, remote sensing provides an effective tool to capture the dynamic nature of the blooms in a qualitative sense.
Coastal zone, as defi ned by the Coastal Institute of the University of Rhode Island, includes areas of continental shelves, islands, or partially enclosed seas, estuaries, bays, lagoons, beaches, and terrestrial and aquatic ecosystems within watersheds that drain into coastal waters. The coastal zone is the most dynamic interface between land and sea and represents the most challenging frontier between human civilization and environmental conservation. Worldwide, over 38% of human population lives in the coastal zones (Crossett et al., 2004). In the United States, about 53% of the human population lives in the coastal counties (Small and Cohen, 2004). An increasing proportion of the global population lives within the coastal zones of all major continents that require increasing attention to agricultural, industrial, and other human-related effects on coastal habitats and water quality and their impacts on ecological dynamics, ecosystem health, and biological diversity.
Beginning systematically with the fundamentals, the fully-updated third edition of this popular graduate textbook provides an understanding of all the essential elements of marine optics. It explains the key role of light as a major factor in determining the operation and biological composition of aquatic ecosystems, and its scope ranges from the physics of light transmission within water, through the biochemistry and physiology of aquatic photosynthesis, to the ecological relationships that depend on the underwater light climate. This book also provides a valuable introduction to the remote sensing of the ocean from space, which is now recognized to be of great environmental significance due to its direct relevance to global warming. An important resource for graduate courses on marine optics, aquatic photosynthesis, or ocean remote sensing; and for aquatic scientists, both oceanographers and limnologists.
