Remote detection of harmful algal blooms (original) (raw)
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Ocean Colour Remote Sensing of Harmful Algal Blooms in the Benguela System
Remote Sensing of the African Seas, 2014
The Benguela, as a highly productive upwelling system, suffers from the occurrence of a variety of harmful algal blooms, most of which are associated with elevated biomass; a feature common to the shelf environment of upwelling systems. Most harmful blooms have in the past been attributed to one or another dinoflagellate species, but more recently harmful impacts have also been ascribed to other groups of phytoplankton, including diatom and autotrophic ciliate species. Typical bloom assemblages, forcing mechanisms and harmful impacts are outlined, and bloom types most amenable to detection with ocean colour radiometry are identified. Inherent and apparent optical properties of these algal assemblage types are described, and a preliminary evaluation is made of the suitability of available ocean colour data and algorithms. The evolution of several bloom events is described using various algorithms applied to ocean colour data from the MERIS sensor, and recommendations are made regarding optimal ocean colour usage for high biomass algal blooms in the coastal zone.
Ocean Sensing and Monitoring II, 2010
The detection and monitoring of harmful algal blooms using in-situ field measurements is both labor intensive and is practically limited on achievable temporal and spatial resolutions, since field measurements are typically carried out at a series of discrete points and at discrete times, with practical limitations on temporal continuity. The planning and preparation of remedial measures to reduce health risks, etc., requires detection approaches which can effectively cover larger areas with contiguous spatial resolutions, and at the same time offer a more comprehensive and contemporaneous snapshot of entire blooms as they occur. This is beyond capabilities of in-situ measurements and it is in this context that satellite Ocean Color sensors offer potential advantages for bloom detection and monitoring. In this paper we examine the applications and limitations of an approach we have recently developed for the detection of K. brevis blooms from satellite Ocean Color Sensors measurements, the Red Band Difference Technique, and compare it to other detection algorithm approaches, including a new statistical based approach also proposed here. To achieve more uniform standards of comparisons, the performance of different techniques for detection are applied to the same specific verified blooms occurring off the West Florida Shelf (WFS) that have been verified by in-situ measurements.
2011
We present a simple algorithm to identify Karenia brevis blooms in the Gulf of Mexico along the west coast of Florida in satellite imagery. It is based on an empirical analysis of collocated matchups of satellite and in situ measurements. The results of this Empirical Approach is compared to those of a Bio-optical Technique-taken from the published literature-and the Operational Method currently implemented by the NOAA Harmful Algal Bloom Forecasting System for K. brevis blooms. These three algorithms are evaluated using a multi-year MODIS data set (from July, 2002 to October, 2006) and a long-term in situ database. Matchup pairs, consisting of remotely-sensed ocean color parameters and near-coincident field measurements of K. brevis concentration, are used to assess the accuracy of the algorithms. Fair evaluation of the algorithms was only possible in the central west Florida shelf (i.e. between 25.75°N and 28.25°N) during the boreal Summer and Fall months (i.e. July to December) due to the availability of valid cloud-free matchups. Even though the predictive values of the three algorithms are similar, the statistical measure of success in red tide identification (defined as cell counts in excess of 1.5 × 10 4 cells L −1) varied considerably (sensitivity-Empirical: 86%; Bio-optical: 77%; Operational: 26%), as did their effectiveness in identifying non-bloom cases (specificity-Empirical: 53%; Bio-optical: 65%; Operational: 84%). As the Operational Method had an elevated frequency of false-negative cases (i.e. presented low accuracy in detecting known red tides), and because of the considerable overlap between the optical characteristics of the red tide and non-bloom population, only the other two algorithms underwent a procedure for further inspecting possible detection improvements. Both optimized versions of the Empirical and Bio-optical algorithms performed similarly, being equally specific and sensitive (~70% for both) and showing low levels of uncertainties (i.e. few cases of false-negatives and false-positives: ~30%)-improved positive predictive values (~60%) were also observed along with good negative predictive values (~80%).
2010
The detection and monitoring of harmful algal blooms using in-situ field measurements is both labor intensive and is practically limited on achievable temporal and spatial resolutions, since field measurements are typically carried out at a series of discrete points and at discrete times, with practical limitations on temporal continuity. The planning and preparation of remedial measures to reduce health risks, etc., requires detection approaches which can effectively cover larger areas with contiguous spatial resolutions, and at the same time offer a more comprehensive and contemporaneous snapshot of entire blooms as they occur. This is beyond capabilities of in-situ measurements and it is in this context that satellite Ocean Color sensors offer potential advantages for bloom detection and monitoring. In this paper we examine the applications and limitations of an approach we have recently developed for the detection of K. brevis blooms from satellite Ocean Color Sensors measurements, the Red Band Difference Technique, and compare it to other detection algorithm approaches, including a new statistical based approach also proposed here. To achieve more uniform standards of comparisons, the performance of different techniques for detection are applied to the same specific verified blooms occurring off the West Florida Shelf (WFS) that have been verified by in-situ measurements.
Ocean Opt, 1998
Harmful algal blooms are becoming more frequent phenomena in the coastal environment. As this escalating trend continues, an early warning system based upon noninvasive rapid detection of harmful algal blooms is desired. A discoloration of water is often associated with these blooms, suggesting the feasibility of detection via ocean color measurements. Previous research has focused on absorption characteristics and pigment compositions of the algae as being responsible for the unique optical signatures observed. Harmful algal species do not contain unique pigments, and thus absorption alone can not explain the distinct changes in water color. However, taxon-specific optical properties, in particular scattering and backscattering, in combination with high concentrations of a monodispersed population may be responsible for the significant changes in ocean color during bloom events. We measured the inherent optical properties (IOPs) of several harmful algal species to determine the source for the frequently observed changes in ocean color during blooms. Four common harmful algal species (Prorocentrum minimum, Gymnodinium splendens, Heterosigma akashiwo, and Aureococcus anophagefferens) were investigated under controlled growth conditions to determine IOPs and particle size distributions (PSD) for exponential and stationary phase cells in order to understand how ocean color might change over the course of a bloom. Only slight distinctions in the shape of the absorption spectra were observed between species or growth phase, indicating that pigments are not responsible for the distinct ocean colors associated with blooms. The exception was G. splendens which was in a heterotrophic mode, and therefore contained few pigments, resulting in low absorption. The data demonstrate that absorption is not the source of distinct ocean color during harmful algal blooms, but rather the algal scattering and backscattering properties. The scattering and backscattering spectra are affected by cell size and growth phase, providing a major contribution to the changes in water color associated with blooms as the PSD changes from a polydispersed to a monodispersed population and as the phytoplankton physiology changes. Therefore our ability to detect these blooms optically will depend upon our ability to determine and interpret accurately scattering and backscattering spectra.
On the calibration and use of in situ ocean colour measurements for monitoring algal blooms
International Journal of Remote Sensing, 2001
Simultaneous in situ measurements of chlorophyll concentration and water colour are reported at three diverse sites: a sea loch, the west Scottish shelf and the north Atlantic. A good (R2 5 0.97) log-log relation exists between the ratio, c, of the irradiance re ection coe cients at 490 and 570 nm and the chlorophyll concentration, C, over a range of chlorophyll concentrations from 0.01 to nearly 50 mg mÕ 3 . This relation can be expressed as C5 1.87 cÕ 1 .8 1 . The equation is very similar to that used in remote sensing algorithms for the conversion of ocean colour data into chlorophyll concentrations. The root mean square (RMS) variation of observed chlorophyll about the values predicted by this equation is 33%. The robustness of this algorithm implies that this colour ratio can be used to monitor chlorophyll concentrations in case 1 waters with a minimum of additional calibration information. As an illustration, a time series is presented of colour-ratioderived chlorophyll concentration during the spring bloom in a Scottish sea loch in 1994. The data show reasonable agreement with the chlorophyll time series measured by a recording uorometer, and a comparison of the two time series serves to highlight the advantages and disadvantages of each instrument.
Inter-Comparison of Ocean Colour Data Products During Algal Blooms in the North Sea
2005
Several satellite-borne ocean colour Earth observation (EO) sensors are presently collecting data on an operational basis, including the Envisat MERIS sensor. This study aims at detecting differences in ocean colour EO sensor performances for ocean monitoring and in particular the study of algal bloom situations. The motivation for the present study is to explore how data from different sensors can be utilized in one system for HAB detection and monitoring. Ocean products from the MERIS, MODIS/Aqua, and SeaWiFS sensors have been processed and intercompared for data acquired during the development of an early spring algal bloom in 2004 in the North Sea region. The study assesses the comparability of these OC sensors in the cases of bloom and non-bloom situations. 1.
Frontiers in Marine Science, 2020
Early detection of dense harmful algal blooms (HABs) is possible using ocean colour remote sensing. Some algorithms require a training dataset, usually constructed from satellite images with a priori knowledge of the existence of the bloom. This approach can be limited if there is a lack of in situ observations, coincident with satellite images. A laboratory experiment collected biological and bio-optical data from a culture of Karenia mikimotoi, a harmful phytoplankton dinoflagellate. These data showed characteristic signals in chlorophyll-specific absorption and backscattering coefficients. The bio-optical data from the culture and a bio-optical model were used to construct a training dataset for an existing statistical classifier. MERIS imagery over the European continental shelf were processed with the classifier using different training datasets. The differences in positive rates of detection of K. mikimotoi between using an algorithm trained with purely manually selected areas...