Detection Of Algal Blooms Research Articles

Developing a two-step method for retrieving cyanobacteria abundance from inland eutrophic lakes using MERIS data

Cyanobacteria are the main dominant species in inland eutrophic lakes during algae blooms, and measures of cyanobacteria abundance can be used for monitoring and early detection of algal blooms by remote sensing. During May 2013 and August 2016, a total 137 water samples were collected from Lake Taihu and Lake Chaohu. Remote-sensing reflectance was measured, surface water was collected in the field, and chlorophyll-a concentration, phycocyanin concentration, suspended-matter concentration and phytoplankton pigment absorption parameters were measured in the laboratory. The composition and density of planktonic algae were also detected by microscope examination. The remote-sensing reflectance at 15 MERIS bands was simulated based on our measured spectral data, and a two-step method for detecting cyanobacteria abundance using the partial least squares model based on 5 MERIS bands was developed. The results showed that the estimation algorithm can predict cyanobacteria abundance in inland eutrophic lakes with satisfactory accuracy, with RMSE of 7.56 and MAPE of 13.44 %. This algorithm was successfully applied to the MERIS image acquired on August 12, 2010, and showed a reasonable spatial distribution of cyanobacteria abundance in Lake Taihu. It demonstrated that the developed estimation method was an effective way to monitor cyanobacteria abundance in water with a potential to be successfully applied to Sentinel-3 images.

Development of Algal Bloom Removal System Using Unmanned Aerial Vehicle and Surface Vehicle

Recently, owing to changes in weather conditions, cyanobacterial blooms, also known as harmful algal blooms (HABs), have caused serious damage to the ecosystems of rivers and lakes by producing cyanotoxins. In this paper, for the removal of HABs, an algal bloom removal robotic system (ARROS) is proposed. The ARROS has been designed with a catamaran-type unmanned surface vehicle (USV) and an algae-removal device attached below. In addition, electrical control systems and a guidance, navigation, and control (GNC) system are implemented on the ARROS to remove the algal bloom autonomously. Moreover, to increase the efficiency of the work, an unmanned aerial vehicle (UAV) is utilized and the system detects algal blooms with an image-based detection algorithm, which is known as a local binary pattern. The overall mission begins with a command from a server when the UAV detects an algal bloom, and the USV follows the given path autonomously generated by a coverage path planning algorithm. Subsequently, with an electrocoagulation and floatation reactor under the USV, HABs are removed. The performance of the algal bloom detection and HABs removal is verified through outdoor field tests in Daecheong Dam, South Korea.

Application of Unmanned Aerial Vehicle Imagery for Algal Bloom Monitoring in River Basin

The purpose of this study is to enhance control capability of river water quality and environment by developing algal bloom monitoring technology using Unmanned Aerial Vehicle. The study acquired remote sensing data using UAV on the midstream area of Nakdong River, one of four major rivers in South Korea. The study also conducted water quality analysis and spectral reflectance measuring in the field to identify emergence aspect of phytoplankton in waters of the river and to induce algal bloom detection index equation through wavelength mixing of sensors. The result showed that the result values of algal bloom detection obtained from wavelength band mixing had strong positive correlation with algae standing crops. Besides, the results of analyzing spatial distribution of algal bloom of the overall investigated water area showed that the area of 0.22 km2 ~ 0.26 km2 had severity equivalent to the level of warning (5,000 ~ 1,000,000 cells/mL) in 2015 criteria for issuing algae warnings. It is expected that when this remote water quality and pollution monitoring technology is applied in the field, it would be able to improve capabilities to deal with the river water quality and pollution at the early stage.

Operational algae bloom detection in the Baltic Sea using GIS and AVHRR data

During the blooming season, algal colonies can, in extreme cases, cover up to 200 000 square kilometres of the Baltic Sea water surface. Because the position and shape of the blooms may significantly change in a very short time due to the influence of wind and waves, regular monitoring of the blooms’ development is necessary. Currently, the desired monitoring frequency may only be achieved by means of remote sensing. The article presents a novel method of AVHRR data processing for the purpose of detection of algal blooms in the Baltic Sea. Instead of analysing the value of spectral reflectance of the algae, the algorithm analyses the frequency distribution of normalized difference in reflectance between the visible and near-infrared spectral bands. The proposed method has been implemented and tested as part of an operational Geographic Information System.

MODIS-Aqua Data Based Detection and Classification of Algal Blooms along the Coast of India Using RLS Classifier

In the field of marine biology, researches reveal that there exists a constant increase in Algal bloom (AB) count, along the coast of India. This work aims at detecting and classifying six most frequently appearing algal blooms in this region (viz.:Trichodesmium erythraeum, Noctiluca scintillans/miliaris, Cocholodinium ploykrikoides, Chattonella marina and Karenia mikimotoi blooms). The uniqueness of ocean's optical properties such as remote sensing reflectance (Rrs) and normalized water leaving radiance (nLw) during bloom period serve as the underlying features on whose grounds classification is performed. These parameters are acquired from Aqua/MODIS sensor measurements and Regularized least squares classifier is used in GURLS library for classification. An overall classification accuracy of 94.37% is obtained using both Rrs and nLw features, which is superior to the previously conducted studies for monitoring ABs using optical properties of water. Given a MODIS image, a map is developed wherein the pixels with ABs are highlighted and the causative species is recognized. A MODIS image is available every two days and hence frequent generation of AB moitoring maps is possible, which is of great significance in the fisheries industry.

Simulating the Effects of Inelastic Scattering on Upwelling Radiance in Coastal and Inland Waters: Implications for Hyperspectral Remote Sensing

Successful interpretation of spectral images from hyperspectral sensors is particularly important to achieve some far-reaching goals, including the detection and assessment of harmful algal blooms, water quality characteristics, trophic status of water bodies and marine/freshwater primary production. The present work is motivated by the desire to study the effect of inelastic scattering (chlorophyll and gelbstoff fluorescence and water Raman scattering) on upwelling radiance through a direct method. The contribution of inelastic scattering to the emergent (upwelling) radiance from the water column is of significant importance and has relevance for the hyperspectral remote sensing of water colour. The method introduced to simulate inelastic scattering is a straightforward approach and differs from previous work in the way of derivation. The model allows for simulation of upwelling radiance for clear, turbid and productive (phytoplankton-dominated) waters. The results of this model are assessed by comparison with depthdependant upwelling radiance data and results from the Hydrolight numerical model for these three water types. The comparisons show good agreement between measured and simulated values from the present model. The errors of this model are significantly small (when compared with those of the Hydrolight model) in the red wavelength region, where the chlorophyll fluorescence emission tends to peak around 685- 720 nm when the concentration of chlorophyll is high in productive eutrophic waters. The magnitude and peak position at these wavelengths as produced by the present model could be used as precise indicators and predictors for the phytoplankton concentration in marine and inland water bodies. Thus, this work will have important implications in refining hyperspectral bio-optical algorithms for fluorescence and water Raman scattering as well as for the high concentrations and variabilities of water constituents in coastal and inland regions, and in evaluating remote sensing reflectance measurements of these water bodies.

The MERIS Maximum Chlorophyll Index; its merits and limitations for inland water algal bloom monitoring

Satellite remote sensing methods adopting wavelengths in the red and near infra-red have been shown to be superior to the standard blue to green ratio based approaches in the detection of algal blooms under turbid, eutrophic conditions. Here, the MERIS Maximum Chlorophyll Index (MCI) has been explored as a tool for monitoring algal blooms in North America's inland waters where waters range from optically complex, turbid, eutrophic conditions, to low chlorophyll and oligotrophic conditions. Assessment of the MERIS MCI product is made for intense blooms of cyanobacteria in Lake of the Woods, algal blooms in turbid waters of Lake Erie, and low chlorophyll conditions in Lake Ontario. The MCI product is shown to be a versatile tool in monitoring intense surficial algal blooms with chlorophyll concentrations in the 10–300mgm−3 range, while limited in its application to low-biomass conditions as observed in Lake Ontario. Wavelength shifts in the position of the MCI peak for different chlorophyll concentration ranges, as well as variations in the inherent optical properties of water colouring constituents, are anticipated to account for regional variations in MCI–chlorophyll relationships and potentially hinder a universally applicable quantitative MCI product.

Detection of harmful algal blooms of Karenia mikimotoi using MODIS measurements: A case study of Seto-Inland Sea, Japan

The end of Sea-viewing Wide Field-of-view Sensor (SeaWiFS) mission makes harmful algal bloom (HAB) detection with moderate resolution satellite data now relies on Moderate Resolution Imaging Spectroradiometer (MODIS). Based on MODIS and in situ data collected in the coastal region of the western part of Seto-Inland Sea, Japan with HAB-forming algae Karenia mikimotoi, a simpler new satellite remote sensing-based HAB detection method was developed. The strength of this method is, although it does not require indigenous atmospheric correction scheme, it is expected to be able to classify K. mikimotoi blooms, diatom blooms, TSM-dominated waters, gelbstoff-dominated waters, and mixed waters in the optically complex coastal waters. We anticipate that our satellite remote sensing-based HAB detection method can operate as a valuable complementary tool assisting in situ HAB monitoring and as an integrated part of HAB early warning systems to mitigate HAB negative impacts not only in the coastal waters of the western part of Seto-Inland Sea, Japan, but also in the other coastal waters with different HAB-forming algae, providing that backscattering signature and pigment packaging of other HAB-forming algae are similar to those of K. mikimotoi.

Ensemble Methodology Using Multistage Learning for Improved Detection of Harmful Algal Blooms

The available empirical remote sensing techniques for harmful algal bloom (HAB) detection are reliant on prior observations and thresholds. These techniques tend to give high false alarm rate, as they are limited in spatiotemporal contextual information and decision combination techniques. We propose a multistage learning based ensemble methodology addressing the above constraints for performance improvement of HAB detection. Machine learning-based spatiotemporal data mining approach, along with empirical relationships, is used for HAB detection in the first stage of the ensemble, to exploit the potential benefits of each individual detection technique. The decision outputs from these detection techniques are fused in the second stage using nonlinear modeling-based combination techniques unlike conventional weighted averages. The proposed ensemble methodology outperforms all of the individual members and gave a significant overall performance improvement up to 0.8632 kappa accuracy. The performance is evaluated over tenfold cross validation average and compared against various ensemble methods and combination techniques.

Advances on Satellite Remote Sensing Techniquefor Algal Blooms Detection

Remotely-sensed monitoring for algal blooms is one of the important subjects of remote sensing application.With the development of almost 30 years,many algorithms for detection of algal blooms based on satellite data were developed by scholars both at home and abroad.The history of satellite remote sensing detection for algal blooms is reviewed,the remote sensing monitoring algorithms of several sensors are summarized,and their performance and application range are analyzed.Finally,the challenge of remotely-sensed monitoring for algal blooms is discussed,and the prospect of remotely-sensed monitoring for algal blooms is proposed.

A new bio-optical algorithm for the remote sensing of algal blooms in complex ocean waters

[1] A new bio-optical algorithm has been developed to provide accurate assessments of chlorophyll a (Chl a) concentration for detection and mapping of algal blooms from satellite data in optically complex waters, where the presence of suspended sediments and dissolved substances can interfere with phytoplankton signal and thus confound conventional band ratio algorithms. A global data set of concurrent measurements of pigment concentration and radiometric reflectance was compiled and used to develop this algorithm that uses the normalized water-leaving radiance ratios along with an algal bloom index (ABI) between three visible bands to determine Chl a concentrations. The algorithm is derived using Sea-viewing Wide Field-of-view Sensor bands, and it is subsequently tuned to be applicable to Moderate Resolution Imaging Spectroradiometer (MODIS)/Aqua data. When compared with large in situ data sets and satellite matchups in a variety of coastal and ocean waters the present algorithm makes good retrievals of the Chl a concentration and shows statistically significant improvement over current global algorithms (e.g., OC3 and OC4v4). An examination of the performance of these algorithms on several MODIS/Aqua images in complex waters of the Arabian Sea and west Florida shelf shows that the new algorithm provides a better means for detecting and differentiating algal blooms from other turbid features, whereas the OC3 algorithm has significant errors although yielding relatively consistent results in clear waters. These findings imply that, provided that an accurate atmospheric correction scheme is available to deal with complex waters, the current MODIS/Aqua, MERIS and OCM data could be extensively used for quantitative and operational monitoring of algal blooms in various regional and global waters.

Detection of algal blooms in European waters based on satellite chlorophyll data from MERIS and MODIS

A technique for algal-bloom detection in European waters is described, based on standard chlorophyll a concentration (Chl) data from two ocean-colour sensors, the Moderate Resolution Imaging Spectroradiometer (MODIS) and Medium Resolution Imaging Spectrometer (MERIS). Comparison of the two data sources shows good agreement in case 1 waters, whereas the difference is significant in coastal waters including turbid areas. A relationship between the water-leaving reflectance at 667 nm and Chl for case 1 waters was used to eliminate pixels where Chl retrieval is contaminated by backscatter from inorganic suspended matter. Daily Chl data are compared to a predefined threshold map to determine whether an algal bloom has occurred. In this study, a threshold map was defined as the 90th percentile of previous years' data to take account of regional differences in typical Chl levels, with separate maps for each sensor to take account of sensor-specific bias. The algal-bloom detection processing chain is described, and example results are presented.

Electroporation and lysis of marine microalga Karenia brevis for RNA extraction and amplification

We describe here a simple device for dielectrophoretic concentration of marine microalga Karenia brevis non-motile cells, followed by electric field-mediated lysis for RNA extraction. The lysate was purified using magnetic beads and pure RNA extracted. RNA quality was assessed off-chip by nucleic acid sequence-based amplification and the optimum conditions for lysis were determined. This procedure will form part of an integrated microfluidic system that is being developed with sub-systems for performing cell concentration and lysis, RNA extraction/purification and real-time quantitative RNA detection. The integrated system and its components could be used for a large range of applications including in situ harmful algal bloom detection, transcriptomics and point-of-care diagnostics.

First studies of pico- and nanoplankton populations by a laser scanning flow cytometer

The innovative laser scanning flow cytometer (LSFC) CLASS, developed at ENEA Research Center of Frascati, merges the ability to perform very fast analyses on a statistically significant number of particles to the capability of characterizing, from a morphological and optical point of view, cells even if they are not fluorescent. In this work two cultured populations of picoplankton and nanoplankton have been analyzed by means of CLASS to evaluate the future applicability of this system as a tool for the early detection of algal blooms. The analyses were performed on a red pigmented Synechococcus sp. and C hlamydomonas reinhardtii by measuring simultaneously the side scattering, scattering in the wide angular range 5–100°, fluorescence in two spectral bands and depolarization of the laser light by every single cell contained in the sample. The results highlighted the ability of CLASS to characterize cell populations in which standard flow cytometers encounter difficulties and in which the use of traditional time-consuming image analyses is often required, such as those characterized by different morphologies in a small size with little or no fluorescence. In particular the system presented was demonstrated to be suitable for the study of composition and changes in a phytoplankton population.

Harmful algae and their potential impacts on desalination operations off southern California

Seawater desalination by reverse osmosis (RO) is a reliable method for augmenting drinking water supplies. In recent years, the number and size of these water projects have increased dramatically. As freshwater resources become limited due to global climate change, rising demand, and exhausted local water supplies, seawater desalination will play an important role in the world's future water supply, reaching far beyond its deep roots in the Middle East. Emerging contaminants have been widely discussed with respect to wastewater and freshwater sources, but also must be considered for seawater desalination facilities to ensure the long-term safety and suitability of this emerging water supply. Harmful algal blooms, frequently referred to as ‘red tides’ due to their vibrant colors, are a concern for desalination plants due to the high biomass of microalgae present in ocean waters during these events, and a variety of substances that some of these algae produce. These compounds range from noxious substances to powerful neurotoxins that constitute significant public health risks if they are not effectively and completely removed by the RO membranes. Algal blooms can cause significant operational issues that result in increased chemical consumption, increased membrane fouling rates, and in extreme cases, a plant to be taken off-line. Early algal bloom detection by desalination facilities is essential so that operational adjustments can be made to ensure that production capacity remains unaffected. This review identifies the toxic substances, their known producers, and our present state of knowledge regarding the causes of toxic episodes, with a special focus on the Southern California Bight.

Constraints on the use of phytoplankton as a biological quality element within the Water Framework Directive in Portuguese waters

The European Union Water Framework Directive (WFD), a new regulation aiming to achieve and maintain a clean and well-managed water environment, refers to phytoplankton as one of the biological quality elements that should be regularly monitored, and upon which the reference conditions of water quality should be established. However, the use of phytoplankton as a biological quality element will result in several constraints, which are analyzed in this article with examples from Portuguese waters. Specifically, the establishment of reference conditions of water quality may be difficult in some water bodies for which no historical data exists. The sampling frequency proposed for phytoplankton monitoring does not seem suitable to assess phytoplankton succession, and may preclude the detection of algal blooms. Finally, the use of chlorophyll a as a proxy of phytoplankton biomass and abundance has been proposed by some authors, but it may overlook blooms of pico- and small nanophytoplankton, and overestimate the importance of large microphytoplankton. Furthermore, most studies in Portugal have used only inverted microscopy for phytoplankton observation and quantification; this method does not permit the distinction between autotrophic and heterotrophic cells, especially in samples preserved with Lugol’s solution, and does not allow the observation of smaller-sized cells. Finally, some techniques, such as remote sensing and chemotaxonomic analysis, are proposed to be used as supplements in phytoplankton monitoring programs.

High-resolution spatio-temporal distribution of a coastal phytoplankton bloom using laser in situ scattering and transmissometry (LISST)

Development of optical observation technologies provides new insights into harmful algal bloom (HAB) detection and assessment of HAB species dynamics. Based on preliminary laboratory tests, a laser in situ scattering and transmissometry instrument (LISST-100X) was used to monitor a high-biomass phytoplankton proliferation in the field. Short-term spatial and temporal changes in particle size distribution were measured during a recurrent Alexandrium taylori outbreak. Since the bloom was not monospecific, a size-fraction method to discriminate particular species from LISST-100X measurements was proposed. The results were validated to simultaneous microscopic counts of phytoplankton, and the significantly positive correlation obtained between the two methodologies confirmed the instrument's ability to discriminate phytoplankton at the group and species level. The LISST-100X obtains high-resolution in situ data, and is therefore a better alternative than the traditional microscope for assessing temporal and spatial evolution of HABs. Field observations showed high variability over a short time scale associated with diel vertical migration of A. taylori and the whole phytoplankton population (nanoplankton and microplankton). A numerical circulation model was used to investigate the influence of beach hydrodynamics in the observed horizontal variability. Simulations of the model suggested an important role of daily coastal circulation in determining the distribution of A. taylori in coastal environments.

Inter‐comparison of ocean colour data products during algal blooms in the Skagerrak

Several ocean colour (OC) Earth observation (EO) sensors are presently collecting data on an operational basis, including the Envisat MERIS sensor. This study aims at quantifying the differences in performance of the ocean colour EO sensors MERIS, MODIS/Aqua, and SeaWiFS for coastal monitoring and for the particular case of coastal algal bloom situations. The standard chlorophyll a, and radiance and reflectance products from the three sensors have been processed and compared for data acquired during the development of an early‐spring algal bloom in 2004 in the eastern North Sea region. The study shows a high level of consistency between the Case 1 water Chl a products for all sensors. We conclude that the use of MERIS Chl a and other relevant products enable continued and possibly improved performance of an existing system for harmful algal bloom (HAB) detection and monitoring which up to December 2004 has been based on SeaWiFS.

AN ALTERNATIVE PROPOSAL: ACOUSTIC TECHNIQUES TO ASSES DETECTION AND MONITORING OF TOXIC ALGAL BLOOMS

An alternative methodology consisting in the application of acoustic techniques to asses detection and monitoring of toxic algal blooms is presented here. Successive steps in this direction led to already reported results [Blanc et al., 2000] achieved under the frame of a multidisciplinary research programme conducted in the Argentinean Naval Service of Research and Development. First efforts were focused in the examination of the acoustical scattering behaviour of single-species cultures of Skeletonema costatum. For this purpose a pulse-echo electronic equipment was developed, including home-made piezoelectric transducers with an operating frequency centred at 2.6 MHz. A slight modified Johnson's fluid-sphere model was used. Numerical Abundance estimates from at-lab acoustic determinations of Volume Backscattering Strengths were obtained. When they were compared with predicted values computed through the adopted model and traditional optical counting, satisfactory results were obtained. More recently, single cultures of dinoflagellates, namely, Gymnodinium sp., were selected to enable a further stage of acoustic laboratory measurements. Controlled observations of its optimum growth conditions along a six-months period have been performed. The effects of different patterns of light and temperature have been analysed. Simultaneous work on modifying existing models of Backscattering Cross-Sections to provide a realistic description of dinoflagellates physical properties is being performed

High Resolution Spatio-temporal Detection of Potentially Harmful Dinoflagellates in Confined Waters of the NW Mediterranean

A systematic sampling programme was carried out in a large number of confined waters (principally harbours) along the Catalan coast (NW Mediterranean) in the context of a new Monitoring Programme. This Monitoring Programme was associated not only with areas subject to aquaculture activities, and therefore under legislation, but also with confined areas with a high risk of harmful algal blooms (HABs) occurrence, in order to provide an early warning of potentially widespread HABs. The systematic programme was performed weekly in summer and bi-monthly in winter for five years. The main results were: (i) the detection of many harmful species and the presence of high numbers of harmful dinoflagellates, mainly of the genera Alexandrium and Dinophysis; (ii) the detection of Alexandrium catenella, new in the study area, which had hardly ever been detected in the Mediterranean Sea; (iii) the presence of some potentially harmful species, including Dinophysis sacculus, present at all periods of the year; (iv) bloom recurrence in several stations; (v) occasional coincidence of small-scale blooms, such as those confined inside the harbours, with widespread blooms (mesoscale blooms) of the same organism. The implications of this high frequency of HAB detection is discussed in relation to the suitability of this sampling programme (focused on confined waters) for the early detection of algal blooms.