Chlorophyll-a Concentration Data Research Articles

Algal Bloom Prediction Using Extreme Learning Machine Models at Artificial Weirs in the Nakdong River, Korea.

In this study, we design an intelligent model to predict chlorophyll-a concentration, which is the primary indicator of algal blooms, using extreme learning machine (ELM) models. Modeling algal blooms is important for environmental management and ecological risk assessment. For this purpose, the performance of the designed models was evaluated for four artificial weirs in the Nakdong River, Korea. The Nakdong River has harmful annual algal blooms that can affect health due to exposure to toxins. In contrast to conventional neural network (NN) that use backpropagation (BP) learning methods, ELMs are fast learning, feedforward neural networks that use least square estimates (LSE) for regression. The weights connecting the input layer to the hidden nodes are randomly assigned and are never updated. The dataset used in this study includes air temperature, rainfall, solar radiation, total nitrogen, total phosphorus, N/P ratio, and chlorophyll-a concentration, which were collected on a weekly basis from January 2013 to December 2016. Here, upstream chlorophyll-a concentration data was used in our ELM2 model to improve algal bloom prediction performance. In contrast, the ELM1 model only uses downstream chlorophyll-a concentration data. The experimental results revealed that the ELM2 model showed better performance in comparison to the ELM1 model. Furthermore, the ELM2 model showed good prediction and generalization performance compared to multiple linear regression (LR), conventional neural network with backpropagation (NN-BP), and adaptive neuro-fuzzy inference system (ANFIS).

Using 3D modeling and remote sensing capabilities for a better understanding of spatio-temporal heterogeneities of phytoplankton abundance in large lakes

Lake biological parameters show important spatio-temporal heterogeneities. This is why explaining the spatial patchiness of phytoplankton abundance has been a recurrent ecological issue and is an essential prerequisite for objectively assessing, protecting and restoring freshwater ecosystems. The drivers of these heterogeneities can be identified by modeling their dynamics. This approach is useful for theoretical and applied limnology. In this study, a 3D hydrodynamic model of Lake Geneva (France/Switzerland) was created. It is based on the Delft3D suite software and includes the main tributary (Rhone River) and two-dimensional high-resolution meteorological forcing. It provides 3D maps of water temperature and current velocities with a 1 h time step on a 1 km horizontal grid size and with a vertical resolution of 1 m near the surface to 7 m at the bottom of the lake. The dynamics and the drivers of phytoplankton heterogeneities were assessed by combining the outputs of the model and chlorophyll-a concentration (Chl-a) data from MERIS satellite images between 2008 and 2012. Results highlight physical mechanisms responsible for the occurrence of seasonal hot-spots in phytoplankton abundance in the lake. At the beginning of spring, Chl-a heterogeneities are usually caused by an earlier onset of phytoplankton growth in the shallowest and more sheltered areas; spatial differences in the timing of phytoplankton growth can be explained by spatial variability in thermal stratification dynamics. In summer, transient and locally higher phytoplankton abundances are observed in relation to the impact of basin-scale upwelling.

Landsat TM/ETM+ and machine-learning algorithms for limnological studies and algal bloom management of inland lakes

Accumulating remotely sensed and ground-measured data and improvements in data mining such as machine-learning techniques open new opportunities for monitoring and managing algal blooms over large spatial scales. The goal of this study was to test the accuracy of remotely sensed algal biomass determined with machine-learning algorithms and Landsat TM/ETM+ imagery. We used chlorophyll-a concentration data from the 2007 National Lake Assessment (NLA) (lake N = 1157) by the US Environmental Protection Agency to train and test Landsat TM/ETM+ algorithms. Results showed significant improvements in chlorophyll-a retrieval accuracy using machine-learning algorithms compared with traditional empirical models using linear regression. Specifically, the results from boosted regression trees and random forest explained, respectively, 45.8% and 44.5% of chlorophyll-a variation. Multiple linear regression could only explain 39.8% of chlorophyll-a variation. The chlorophyll-a concentration derived from Landsat TM/ETM+ and a simple to use Google Earth Engine application, accurately characterized a 2009 algal bloom in western Lake Erie to show the model worked well for the analysis of temporal changes in algal conditions. Compared with chlorophyll-a data from the NLA, chlorophyll-a measurements with our Landsat TM/ETM+ model had almost the same correlation with lake’s total phosphorus concentrations, especially when using multiple Landsat images. Therefore, Landsat measurements of chlorophyll-a have value for ecological assessments and managing algal problems in lakes.

Temperature and food-mediated variability of European Atlantic sardine recruitment

The influence of the environmental conditions during larval development on the resulting recruitment strength was investigated for European sardine (Sardina pilchardus) at Atlanto-Iberian waters. Satellite-derived Sea Surface Temperature (SST) and Chlorophyll-a concentration (Chla) data from the previous spawning seasons (January to March/April and October to December of the previous year) were related to recruitment success data in the main recruitment hotspots. Recruitment data was taken from yearly acoustic scientific cruises and from the ICES recruitment index estimated by an age-structured model for the entire stock. A linear discriminant analysis model using SST, Chla, and the abundance of spawners during the spawning season identified years of high and low recruitment for all the recruitment hotspots with an accuracy of ≥79%. In general, high recruitment years were associated with high Chla and low SST, although the most important variables to discriminate between the groups were area-specific. High recruitment years were mostly related to high food availability (Chla), particularly during the last quarter of the previous year. In Western Iberia and in the Gulf of Cadiz, high recruitment years were also associated to lower SST, whereas in the Bay of Biscay, where SST during the winter was generally below the optimal range ≈11–12 °C for sardine larval development, higher recruitment was associated with high SST. For ICES data of the southern European sardine stock, lower SST and higher Chla during the last quarter of the previous year were associated with high recruitment years and SST alone was able to discriminate between the two recruitment groups with 73% accuracy. Although the time-series of available data are still small, these significant relationships are consistent with field and laboratory studies relating larval growth and mortality with main environmental drivers. These relationships should be further investigated in the following years to evaluate if they can be used to construct reliable indicators to predict the level of recruitment and abundance with sufficient advance to help in the management of this important fishing resource.

Water quality before and after watershed-scale implementation of stormwater wet ponds in the coastal plain

Wet ponds have been used extensively for stormwater control throughout the US, including coastal areas. Despite the widespread application of these water control structures, few studies have investigated how watershed-scale implementation of wet ponds affects downstream water quality or how the pollutant removal efficacy of wet ponds changes over time in a coastal setting. This study utilizes a seven year data set of nutrient, total suspended solid, and chlorophyll-a concentration data collected during baseflow and stormflow from two coastal headwater streams draining a developed (28% impervious) and an undeveloped (1.2% impervious) watershed. The seven year record encompasses before, during, and after a large construction project and concurrent implementation of wet ponds in the developed watershed that drain 97% of the watershed area. Additional nutrient, total suspended solid, and chlorophyll-a concentration data were collected from within a wet pond in the developed watershed during baseflow over a single spring and summer. A comparison of stream water quality before and after the construction project and wet pond implementation in the developed watershed showed that mean chlorophyll-a, nitrate-nitrite (NOx−), organic nitrogen, and total suspended solid concentrations significantly increased, the mean orthophosphate (PO43−) concentration significantly decreased, and the mean ammonium (NH4+) concentration did not change. Over a three year time period after construction and pond implementation, developed stream chlorophyll-a, ammonium, and organic nitrogen concentrations decreased, and nitrate-nitrite, orthophosphate, and total suspended solid concentrations increased compared to the reference stream during the same period, indicating changes in pollutant removal capacity. A comparison of baseflow and stormflow samples during the Post period and samples from a wet pond in the developed watershed indicated that ponds were functioning as sources of chlorophyll-a and total suspended solids to the stream and sinks for nitrate-nitrite. Overall, watershed-scale implementation of wet ponds in the developed watershed failed to mitigate many negative water quality impacts caused by increased development. This study suggests that centralized stormwater management may not be optimal for maintaining water quality in coastal environments, and that pond retrofits combined with frequent excavation could improve pollutant removal by wet ponds. Further research on the effects of nutrient cycling in coastal wet ponds and wet pond maintenance is needed.

Phytoplankton abundance and distribution on the Yucatan shelf (June 1979 and April 1983)

Chlorophyll-a concentration and phytoplankton abundance data obtained during two cruises (April 1983 and June 1979) were used to study phytoplankton horizontal and vertical distributions in the waters of the Yucatan shelf. Waterbottle samples were taken to 200 m depth at 60 stations. The maximum mean (fr om different depths per station) phytoplankton abundance (734000 cells/l in April and 632900 cells/l in June) and chlorophyll concentration (1.63 mg/m3 in April and 0.60 mg/m3 in June) occurred near the region of Cape Catoche (approximately at 22°152N and 87°452W), wh ere upwelling occurs regularly. Along the vertical profile, in April chlorophyll ranged between 0.05 and 1.73 mg/m3; in general, the highest values were observed at the 2 m depth (1.73 mg/m3) to the 25 m depth (up to 1.50 mg/m3). On the basis of the chlorophyll content, phytoplankton abundance and species composition distribution, three regions were distinguished on the Yucatan shelf: 1) the upwelling region located to the north of Cape Catoche; 2) the region of possible upwellings, with one area located along the coast between Rнo Lagartos and Progreso and with another located at the central edge zone of the shelf; 3) and the transitional zone that includes most of the central and western parts of the study area. The conclusions of this study support those based on the Soviet-Cuban plankton surveys of the 1960s–1980s.

Interannual and Seasonal Dynamics in Lipidic Signatures of Trichiurus lepturus

ABSTRACTInterannual and seasonal (premonsoon, monsoon, and postmonsoon) variability in the lipid, fatty acids, and total cholesterol content of ribbon fish, Trichiurus lepturus were determined for a period of 4 years (2008–2011) along the southwest (SW) and southeast (SE) coasts of India. The lipidic compositions were studied along with chlorophyll-a concentration and sea surface temperature (SST) data obtained from MODIS and Sea WiFS. The lipid content shows seasonal fluctuations with higher values in the postmonsoon season along the SE coast and during the monsoon in the SW coast. Among polyunsaturated fatty acids (PUFAs), docosahexaenoic acid (DHA) dominated over eicosapentaenoic acid (EPA) throughout the seasons except in winter on the SE coast. The health indices such as atherogenic/thrombogenicity indices, total cholesterol, and hypocholesterolemic/hypercholesterolemic ratio of ribbon fish were found to be ideal, which contributed toward its parameters to be qualified as a potential health food.

Merging Satellite Ocean Color Data With Bayesian Maximum Entropy Method

Merging multiple satellite ocean color data is one of the ways to create a unified ocean color product and improve the spatial coverage. In this paper, the Bayesian maximum entropy (BME), a probabilistic method, is used to integrate chlorophyll-a (chl-a) concentration data obtained by the seaviewing wide field-of-view sensor (SeaWiFS) on Orbview-2, the medium-resolution imaging spectrometer instrument (MERIS) on ENVISAT and the moderate-resolution imaging spectroradiometer (MODIS) on Aqua. MODIS chl-a concentration on current day is considered as the accurate hard data. A probabilistic model is developed to link hard data and chl-a concentration of other sensors on previous days. The latter are processed as soft data by this probabilistic model to take into account the differences between mission-specific products. The semivariogram of chl-a concentration, which presents the spatial variability and provides a priori knowledge, is developed to improve the spatial coverage. The average daily coverage of the merged chl-a field is 74% for the 1-day temporal integration which is about six times higher than any single mission, and 95% for the 3-day temporal integration which achieves basically a complete global coverage. Root-mean-square error (RMSE) and correlation between in situ chl-a measurements and the BME-merged chl-a from 1-day data are 0.42 and 0.72, and from 3-day data are 0.44 and 0.70, respectively. Compared with the existing GSM method and the weighted averaging (AVW) method, the BME method can greatly improve the spatial coverage and preserve the high accuracy, which demonstrates the potential advantages of the BME method to merge ocean color products from multiple sensors.

Unique Sequence of Events Triggers Manta Ray Feeding Frenzy in the Southern Great Barrier Reef, Australia

Manta rays are classified as Vulnerable to Extinction on the IUCN Red List for Threatened Species. In Australia, a key aggregation site for reef manta rays is Lady Elliot Island (LEI) on the Great Barrier Reef, ~7 km from the shelf edge. Here, we investigate the environmental processes that triggered the largest manta ray feeding aggregation yet observed in Australia, in early 2013. We use MODIS sea surface temperature (SST), chlorophyll-a concentration and photic depth data, together with in situ data, to show that anomalous river discharges led to high chlorophyll (anomalies: 10–15 mg∙m−3) and turbid (photic depth anomalies: −15 m) river plumes extending out to LEI, and that these became entrained offshore around the periphery of an active cyclonic eddy. Eddy dynamics led to cold bottom intrusions along the shelf edge (6 °C temperature decrease), and at LEI (5 °C temperature decrease). Strongest SST gradients (>1 °C∙km−1) were at the convergent frontal zone between the shelf and eddy-influenced waters, directly overlying LEI. Here, the front intensified on the spring ebb tide to attract and shape the aggregation pattern of foraging manta rays. Future research could focus on mapping the probability and persistence of these ecologically significant frontal zones via remote sensing to aid the management and conservation of marine species.

Locality of Chlorophyll-A Distribution in the Intensive Study Area of the Ariake Sea, Japan in Winter Seasons based on Remote Sensing Satellite Data

Mechanism of chlorophyll-a appearance and its locality in the intensive study area of the Ariake Sea, Japan in winter seasons is clarified by using remote sensing satellite data. Through experiments with Terra and AQUA MODIS data derived chlorophyll-a concentration and truth data of chlorophyll-a concentration together with meteorological data and tidal data which are acquired for 6 years (winter 2010 to winter 2015), it is found that strong correlation between the chlorophyll-a concentration and tidal height changes. Also it is found that the relations between ocean wind speed and chlorophyll-a concentration. Meanwhile, there is a relatively high correlation between sunshine duration a day and chlorophyll-a concentration. Furthermore, it is found that there are different sources of chlorophyll-a in the three different sea areas of Ariake Sea area in the back, Isahaya bay area, and Kumamoto offshore area.

Relation Between Chlorophyll-A Concentration and Red Tide in the Intensive Study Area of the Ariake Sea, Japan in Winter Seasons by using MODIS Data

Relation between chlorophyll-a concentration and red tide in the intensive study area of the back of Ariake Sea, Japan in the recent winter seasons is investigated by using MODIS data. Mechanism of red tide appearance is not so clarified. On the other hand, chlorophyll-a concentration can be estimated with satellite remote sensing data. An attempt is made for estimation of the location and size of red tide appearance. In particular, severe damage due to red tide is suspected in the winter seasons now a day. Therefore, 6 years (winter 2010 to winter 2015) data of MODIS data derived chlorophyll-a concentration and truth data of red tide appearance (the location and the volume) which are provided by Saga Prefectural Fishery Promotion Center: SPFPC (once/10 days of shipment data) have been investigated. As the results of the investigation, it is found that a strong correlation between the chlorophyll-a concentration and red tide appearance together with the possible sources of the red tide.

Trend Analysis of Relatively Large Diatoms Which Appear in the Intensive Study Area of the Ariake Sea, Japan in Winter (2011-2015) based on Remote Sensing Satellite Data

Behavior of relatively large size of diatoms which appear in the Ariake Sea areas, Japan in winter based on remote sensing satellite data is clarified. Through experiments with Terra and AQUA MODIS data derived chlorophyll-a concentration and truth data of chlorophyll-a concentration together with meteorological data and tidal data which are acquired for 5 years (winter 2011 to winter 2015), it is found that strong correlation between the chlorophyll-a concentration and tidal height changes. Also it is found that the relations between ocean wind speed and chlorophyll-a concentration. Meanwhile, there is a relatively high correlation between sunshine duration a day and chlorophyll-a concentration.

Band Selection of Hyperspectral Chlorophyll-A Concentration Inversion Based on Parallel Ant Colony Algorithm

In chlorophyll-A concentration inversion which based on hyperspectral remote sensing image, must use ground synchronous spectrum measurement data to find the best three band combination by iterative-method, we proposed the problem to analysis the existing band selection methods of chlorophyll-A concentration inversion and put forward a band selection method of the hyperspectral chlorophyll-A concentration inversion three-band model which based on parallel ant colony algorithm. The method is based on analysis of the optical properties and three band model theory, determines the interval of the sensitive band from hundreds of bands，then takes band reflectance of the sampling points and Correlation coefficient of sample points of chlorophyll-A concentration as the initial pheromone list, and updates distance parameter list constantly to let the parallel work ants find the best band combinations. Experimental results show that the method can select the optimal three-band fast and efficient by the remote sensing data and samples of chlorophyll-A concentration data, and the steps of chlorophyll-A concentration inversion can be simplified greatly.

Assimilation of MODIS Chlorophyll-a Data Into a Coupled Hydrodynamic-Biological Model of Taihu Lake

MODIS chlorophyll-a concentration (Chla) data were assimilated into a coupled hydrodynamic-biological model using an Optimal Interpolation method. Simulations were conducted using MODIS data covering Taihu Lake in May 2009, when algal blooms typically begin to occur. The results of the assimilation approach showed improvements in the estimation of Chla distributions in spatial coherency and temporal continuity. Bias of assimilation (model run after assimilation) was 5.1%, with a RMSE of 49.7%. In comparison, the free run (model run without assimilation) had a bias of -34.9% and RMSE of 176.5%. In situ data used for comparison showed reduced RMSE and the Bias for assimilation. Two sensitivity experiments were used to determine the suitable correlation length scale with respect to observation data accuracy. The result showed that 500m is the optimum scale to construct the background error covariance matrix. The sensitivity experiment of observational data accuracy also showed that more accurate observation data allowed for better assimilation results.

Role of sea ice on satellite-observed chlorophyll-a concentration variations during spring bloom in the East/Japan sea

The relationship between the spring bloom along the Primorye coast and the sea ice of the Tatarskiy Strait in the northern region of the East/Japan Sea, a semi-enclosed marginal sea in the North Pacific, was investigated using the ten-year SeaWiFS chlorophyll-a concentration data and DMSP/SSMI sea ice concentration data from 1998 to 2007. Year-to-year variations in the chlorophyll-a concentrations in the spring were positively correlated with those of the sea ice concentrations in the Tatarskiy Strait in the previous winter with a correlation coefficient of 0.77. Abrupt increases in nutrients, essential for the spring bloom in the upper ocean during spring, were supplied from sea ice-melted waters. Time series of vertical distributions of the nutrients indicated that phosphate concentrations were extremely elevated in the upper ocean (less than 100m) without any connection to high concentrations in the deep waters below. The water mass from sea ice provided preferable conditions for the spring bloom through changes in the vertical stratification structure of the water columns. Along-coast ratios of stability parameters between two neighboring months clearly showed the rapid progression of the generation of a shallow pycnocline due to fresh water originating from sea ice. This study addressed the importance of the physical environment for biogeochemical processes in semi-enclosed marginal seas affected by local sea ice.

Comparison of Composite Methods of Satellite Chlorophyll-a Concentration Data in the East Sea

To produce a level-3 monthly composite image from daily level-2 Sea-viewing Wide Field-of-view Sensor (SeaWiFS) chlorophyll-a concentration data set in the East Sea, we applied four average methods such as the simple average method, the geometric mean method, the maximum likelihood average method, and the weighted averaging method. Prior to performing each averaging method, we classified all pixels into normal pixels and abnormal speckles with anomalously high chlorophyll-a concentrations to eliminate speckles from the following procedure for composite methods. As a result, all composite maps did not contain the erratic effect of speckles. The geometric mean method tended to underestimate chlorophyll-a concentration values all the time as compared with other methods. The weighted averaging method was quite similar to the simple average method, however, it had a tendency to be overestimated at high-value range of chlorophyll-a concentration. Maximum likelihood method was almost similar to the simple average method by demonstrating small variance and high correlation (r=0.9962) of the differences between the two. However, it still had the disadvantage that it was very sensitive in the presence of speckles within a bin. The geometric mean was most significantly deviated from the remaining methods regardless of the magnitude of chlorophyll-a concentration values. Its bias error tended to be large when the standard deviation within a bin increased with less uniformity. It was more biased when data uniformity became small. All the methods exhibited large errors as chlorophyll-a concentration values dominantly scatter in terms of time and space. This study emphasizes the importance of the speckle removal process and proper selection of average methods to reduce composite errors for diverse scientific applications of satellite-derived chlorophyll-a concentration data.

Episodic phytoplankton bloom events in the Bay of Bengal triggered by multiple forcings

Two episodic phytoplankton bloom events were identified in the southeastern and southwestern Bay of Bengal in January 1998 and December 2005, respectively, from SeaWiFS-derived chlorophyll-a concentration data collected between September 1997 and December 2010. Possible causes were examined using time series datasets of satellite-derived sea surface height anomalies, sea surface temperature, wind stress and Ekman pumping velocity data. It was found that strong cyclonic eddies with long residence time transported cold, nutrient-rich water from the subsurface layer to the euphotic zone, and were responsible for sustaining the continuous growth of phytoplankton that led to the two aforementioned phytoplankton blooms. Moreover, regarding the event in December 2005, three tropical cyclones passed through the region of the phytoplankton bloom and could have been additional factors that enhanced the bloom. Regarding the event in January 1998, transportation from the adjacent Andaman Sea by the geostrophic current and an anomalous zonal easterly wind contributed to the phytoplankton bloom in the southeastern Bay of Bengal. In addition, both blooms occurred during Indian Ocean Dipole (IOD) years. The absence of the second downwelling Kelvin waves in positive IOD year of 1997–1998 favouring the long residence of the strong cyclonic eddy in the eastern bay, and the strong wind-induced mixing in negative IOD year of 2005 might contribute to enhancing productivity.

Phytoplankton light absorption of cultures and natural samples: comparisons using two spectrophotometers

Here we present laboratory measurements of phytoplankton absorption for cultures and natural water samples using two different spectrophotometers, an Ultrapath system and a double beam spectrophotometer equipped with an integrating sphere (Lambda 850). The Ultrapath system provides simplified optics with high throughput efficiency, portability, and is relatively less expensive in comparison to conventional spectrophotometers. A more robust algorithm for correction of pathlength amplification (β) for particles retained on filter paper was determined for Lambda 850 in comparison to the Ultrapath. The Lambda 850 β algorithm (ODs(λ) = 0.405 [ODf(λ)] + 0.475 [ODf(λ)]2; r2 = 0.973; n = 7395) showed species and size dependence as indicated by the LISST 100X and HPLC chlorophyll-a concentration data. A better agreement was observed between the two spectrophotometers for filter paper measurements (r2 = 0.991; slope = 0.958; n = 130 for cultures and r2 = 0.978; slope = 0.957; n = 349 for natural samples), than for suspensions (r2 = 0.960; slope = 0.915; n = 92 for cultures and r2 = 0.960; slope = 0.921; n = 27 for natural samples). The differences in measurement of suspensions between the spectrophotometers could be attributed to volume scattering function and acceptance angle of the waveguide detector.

Outlier detection in satellite data using spatial coherence

Satellite data sets often contain outliers (i.e., anomalous values with respect to the surrounding pixels), mostly due to undetected clouds and rain or to atmospheric and land contamination. A methodology to detect outliers in satellite data sets is presented. The approach uses a truncated Empirical Orthogonal Function (EOF) basis. The information rejected by this EOF basis is used to identify suspect data. A proximity test and a local median test are also performed, and a weighted sum of these three tests is used to accurately detect outliers in a data set. Most satellite data undergo automated quality-check analyses. The approach presented exploits the spatial coherence of the geophysical fields, therefore detecting outliers that would otherwise pass such checks. The methodology is applied to infrared sea surface temperature (SST), microwave SST and chlorophyll-a concentration data over different domains, to show the applicability of the technique to a range of variables and temporal and spatial scales. A series of sensitivity tests and validation with independent data are also conducted.

Detection of cyclone-induced rapid increases in chlorophyll-a with sea surface cooling in the northwestern Pacific Ocean from a MODIS/SeaWiFS merged satellite chlorophyll product

We performed a comprehensive analysis of satellite chlorophyll-a concentration (chl-a) data to detect all events in which chl-a rapidly increased on a time scale of 10 days or less. Our analysis could successfully detect cases in which chl-a increased and sea surface temperature (SST) decreased rapidly after the passages of tropical and extratropical cyclones. The events with large SST decreases tended to occur south of Japan, in the Sea of Okhotsk, and in the regions between 35° and 45° N, especially the Kuroshio–Oyashio Extension (KOE) region. Although the contribution of cyclones on the yearly total chl-a increase was basically small, the cyclone-induced chl-a increases accounted for a few tens of per cent of the total chl-a increase in some areas. In oligotrophic regions, the increases in chl-a tended to become larger as the corresponding SST decrease became larger, although the relationship between them is opposite in mesotrophic and eutrophic regions.