Remote Sensing of Harmful Algal Blooms in the Mississippi Sound and Mobile Bay: Modelling and Algorithm Formation

The incidence and severity of harmful algal blooms have increased in recent decades, as have the economic effects of their occurrence./The diatom Pseudo-nitzschia spp. caused fisheries closures in Mobile Bay during 2005 due to elevated levels of domoic acid. In the previous 4 years Karenia brevis counts of \u3e5,000 cells L 1 have occurred in Mobile Bay and the Mississippi Sound. Population levels of this magnitude had previously been recorded only in 1996. Increases in human populations, urban sprawl, development of shoreline properties, sewage effluent and resultant changes in NP ratios of discharge waters, and decline in forest and marsh lands, will potentially increase future harmful algal bloom occurrences in the northern Gulf of Mexico. Due to this trend in occurrence of harmful algal populations, there has been an increasing awareness of the need for development of monitoring systems in this region. Traditional methods of sampling have proven costly in terms of time and resources, and increasing attention has been turned toward use of satellite data in phytoplankton monitoring and prediction. This study shows that remote sensing does have utility in monitoring and predicting locations of phytoplankton blooms in this region. It has described the composition and spatial and temporal relationships of these populations, inferring salinity, total nitrogen and total phosphorous as the primary variables driving phytoplankton populations in Mobile Bay and the Mississippi Sound. Diatoms, chlorophytes, cryptophytes, and dinoflagellates were most abundant in collections. Correlations between SeaWiFS, MODIS and in situ data have shown relationships between Rrs reflectance and phytoplankton populations. These data were used in formation of a decision tree model predicting environmental conditions conducive to the formation of phytoplankton blooms that is driven completely by satellite data. Empirical algorithms were developed for prediction of salinity, based on Rrs ratios of 510 nm/ 555 nm, creating a new data product for use in harmful algal bloom prediction. The capacity of satellite data for rapid, synoptic coverage shows great promise in supplementing future efforts to monitor and predict harmful algal bloom events in the increasingly eutrophic waters of Mobile Bay and the Mississippi Sound

Toward predicting Dinophysis blooms off NW Iberia: a decade of events

Dinophysis acuminata and Dinophysis acuta are recurrent species off NW Iberia but their outbreaks occur under different conditions. A decade (2004-2013) of weekly data for each species at two sentinel stations located at the entrance of Rias de Aveiro-AV (NW Portugal, 40 degrees 38.6' N) and Pontevedra-PO (Galicia, Spain, 42 degrees 21.5' N), were used to investigate the regional synchronism and mesoscale differences related to species detection, bloom (>200 cells L-1) initiation and development. Results highlight the high interannual variability of bloom events and summarize the associated meteorological/oceanographic conditions. D. acuta blooms were observed in 2004-2008 and 2013, and the species highest maxima at AV occurred after the highest maxima of its prey Mesodinium, with a time-lag of 2-3 weeks. D. acuminate blooms were observed every year at both stations. The cell concentration time series shows that the blooms generally present a sequence starting in March with D. acuminata in PO and three weeks later in AV, followed by D. acuta that starts at AV and three months later in PO. Exceptionally, D. acuminate blooms occurred earlier at AV than PO, namely in high spring upwelling (2007) or river runoff (2010) years. A four-year gap (2009-2012) of D. acuta blooms occurred after an anomalous 2008 autumn with intense upwelling which is interpreted as the result of an equatorward displacement of the population core. Numerical model solutions are used to analyze monthly alongshore current anomalies and test transport hypotheses for selected events. The results show a strong interannual variability in the poleward/equatorward currents associated with changes in upwelling forcing winds, the advection of D. acute blooms from AV to PO and the possibility that D. acuminata blooms at AV might result from inocula advected southward from PO. However, the sensitivity of the results to vertical position of the lagrangian tracers call for more studies on species distribution at the various bloom stages. (C) 2015 Elsevier B.V. All rights reserved

Establishing National Ocean Service Priorities for Estuarine, Coastal, and Ocean Modeling: Capabilities, Gaps, and Preliminary Prioritization Factors

This report was developed to help establish National Ocean Service priorities and chart new directions for research and development of models for estuarine, coastal and ocean ecosystems based on user-driven requirements and supportive of sound coastal management, stewardship, and an ecosystem approach to management. (PDF contains 63 pages

Image processing for smarter browsing of ocean color data products: investigating algal blooms

Remote sensing technology continues to play a significant role in the understanding of our environment and the investigation of the Earth. Ocean color is the water hue due to the presence of tiny plants containing the pigment chlorophyll, sediments, and colored dissolved organic material and so can provide valuable information on coastal ecosystems. We propose to make the browsing of Ocean Color data more efficient for users by using image processing techniques to extract useful information which can be accessible through browser searching. Image processing is applied to chlorophyll and sea surface temperature images. The automatic image processing of the visual level 1 and level 2 data allow us to investigate the occurrence of algal blooms. Images with colors in a certain range (red, orange etc.) are used to address possible algal blooms and allow us to examine the seasonal variation of algal blooms in Europe (around Ireland and in the Baltic Sea). Yearly seasonal variation of algal blooms in Europe based on image processing for smarting browsing of Ocean Color are presented

An Integrative Remote Sensing Application of Stacked Autoencoder for Atmospheric Correction and Cyanobacteria Estimation Using Hyperspectral Imagery

Hyperspectral image sensing can be used to effectively detect the distribution of harmful cyanobacteria. To accomplish this, physical- and/or model-based simulations have been conducted to perform an atmospheric correction (AC) and an estimation of pigments, including phycocyanin (PC) and chlorophyll-a (Chl-a), in cyanobacteria. However, such simulations were undesirable in certain cases, due to the difficulty of representing dynamically changing aerosol and water vapor in the atmosphere and the optical complexity of inland water. Thus, this study was focused on the development of a deep neural network model for AC and cyanobacteria estimation, without considering the physical formulation. The stacked autoencoder (SAE) network was adopted for the feature extraction and dimensionality reduction of hyperspectral imagery. The artificial neural network (ANN) and support vector regression (SVR) were sequentially applied to achieve AC and estimate cyanobacteria concentrations (i.e., SAE-ANN and SAE-SVR). Further, the ANN and SVR models without SAE were compared with SAE-ANN and SAE-SVR models for the performance evaluations. In terms of AC performance, both SAE-ANN and SAE-SVR displayed reasonable accuracy with the Nash???Sutcliffe efficiency (NSE) > 0.7. For PC and Chl-a estimation, the SAE-ANN model showed the best performance, by yielding NSE values > 0.79 and > 0.77, respectively. SAE, with fine tuning operators, improved the accuracy of the original ANN and SVR estimations, in terms of both AC and cyanobacteria estimation. This is primarily attributed to the high-level feature extraction of SAE, which can represent the spatial features of cyanobacteria. Therefore, this study demonstrated that the deep neural network has a strong potential to realize an integrative remote sensing application

PICES Press, Vol. 12, No. 1, January 2004

The state of PICES science - 2003 (pdf 281 KB) 2003 Wooster Award (pdf 764 KB) The state of the eastern North Pacific through summer 2003 (pdf 448 KB) The Bering Sea: Current status and recent events (pdf 951 KB) The state of the western North Pacific in the first half of 2003 (pdf 684 KB) The status of oceanic zooplankton in the eastern North Pacific (pdf 390 KB) The precautionary approach to the PDO (pdf 976 KB) Photo highlights of PICES XII (pdf 2.79 MB) William G. Pearcy: Renaissance oceanographer (pdf 2.86 MB) KORDI/PICES/CoML Workshop on "Variability and status of the Yellow Sea and East China Sea ecosystems (pdf 785 KB) PICES/IOC Workshop on "Harmful algal blooms - Harmonization of data" (pdf 330 KB) From physics to predators: Monitoring North Pacific ecosystem dynamics (pdf 270 KB) Toward a coast-wide network of Northeast Pacific coastal-ocean monitoring programs - a brief workshop report (pdf 640) PICES publications (pdf 103 KB) PICES calendar (pdf 45 KB

A data-driven modeling approach for simulating algal blooms in the tidal freshwater of James River in response to riverine nutrient loading

Algal blooms often occur in the tidal freshwater (TF) of the James River estuary, a tributary of the Chesapeake Bay. The timing of algal blooms correlates highly to a summer low-flow period when residence time is long and nutrients are available. Because of complex interactions between physical transport and algal dynamics, it is challenging to predict interannual variations of bloom correctly using a complex eutrophication model without having ahigh-resolution model gridto resolve complexgeometryand anaccurate estimate of nutrientloading to drive the model. In this study, an approach using long-term observational data (from 1990 to 2013) and the Support vector machine (LS-SVM) for simulating algal blooms was applied. The Empirical Orthogonal Function was used to reduce the data dimension that enables the algal bloom dynamics for the entire TF to be modeled by one model. The model results indicate that the data-driven model is capable of simulating interannual algal blooms with good predictive skills and is capable of forecasting algal blooms responding to the change of nutrient loadings and environmental conditions. This study provides a link between a conceptual model and a dynamic model, and demonstrates that the data-driven model is a good approach for simulating algal blooms in this complex environment of the James River. The method is very efficient and can be applied to other estuaries as wel

The Town Lake Report, Volumes I and II

This report makes brief references to sediment and other trends seen in Waller Creek.EXECUTIVE SUMMARY: Town Lake’s importance as a natural resource is growing in tandem with Austin’s rapid population. The lake is a source of drinking water for the City, and its greenbelt and open waters are widely used for recreation and as a focal-point for public events. In 1992, under the Clean Lakes program, a comprehensive report entitled the “Town Lake Study” (COA 1992a; COA 1992b; COA 1992c) was prepared. It examined the condition of the lake (Volume I), water quality control alternatives (Volume II) and a feasibility study (Volume III). This report updates the diagnostic study, Volume I (COA 1992a), including the current status of water quality with data analyzed through the year 2000. It also includes a summary of measures taken to reduce pollution from urban runoff since 1990.Waller Creek Working Grou

Florida Bay Science Program: a synthesis of research on Florida Bay

This report documents the progress made toward the objectives established in the Strategic Plan revised in 1997 for the agencies cooperating in the program. These objectives are expressed as five questions that organized the research on the Florida Bay ecosystem: Ecosystem History What was the Florida Bay ecosystem like 50, 100, and 150 years ago? Question 1—Physical Processes How and at what rates do storms, changing freshwater flows, sea level rise, and local evaporation and precipitation influence circulation and salinity patterns within Florida Bay and exchange between the bay and adjacent waters? Question 2—Nutrient Dynamics What is the relative importance of the influx of external nutrients and of internal nutrient cycling in determining the nutrient budget for Florida Bay? What mechanisms control the sources and sinks of the bay’s nutrients? Question 3—Plankton Blooms What regulates the onset, persistence, and fate of planktonic algal blooms in Florida Bay? Question 4—Seagrass Ecology What are the causes and mechanisms for the observed changes in the seagrass community of Florida Bay? What is the effect of changing salinity, light, and nutrient regimes on these communities? Question 5—Higher Trophic Levels What is the relationship between environmental and habitat change and the recruitment, growth, and survivorship of animals in Florida Bay? Each question examines different characteristics of the Florida Bay ecosystem and the relation of these to the geomorphological setting of the bay and to processes linking the bay with adjacent systems and driving change.This report also examines the additional question of what changes have occurred in Florida Bay over the past 150 years

Comparison of Spatial and Temporal Genetic Differentiation in a Harmful Dinoflagellate Species Emphasizes Impact of Local Processes

Population genetic studies provide insights into intraspecific diversity and dispersal patterns of microorganisms such as protists, which help understanding invasions, harmful algal bloom development and occurrence of seafood poisoning. Spatial genetic differentiation has been reported in many microbial species indicating significant dispersal barriers among different habitats. Temporal differentiation has been less studied and its frequency, drivers, and magnitude are thus relatively poorly understood. The toxic dinoflagellate species Gambierdiscus caribaeus was sampled during 2 years in the Florida Keys, and repeatedly from 2006 to 2016 at St. Thomas, US Virgin Islands (USVI), including a 3-year period with monthly sampling, enabling a comparison of spatial and temporal genetic differentiation. Samples from the USVI site showed high temporal variability in local population structure, which correlated with changes in salinity and benthic habitat cover. In some cases, temporal variability exceeded spatial differentiation, despite apparent lack of connectivity and dispersal across the Greater Caribbean Region based on the spatial genetic data. Thus, local processes such as selection might have a stronger influence on population structure in microorganisms than geographic distance. The observed high temporal genetic diversity challenges the prediction of harmful algal blooms and toxin concentrations, but illustrates also the evolutionary potential of microalgae to respond to environmental change