scholarly journals High-resolution underway measurements of phytoplankton photosynthesis and abundance as an innovative addition to water quality monitoring programs

Ocean Science ◽  
2019 ◽  
Vol 15 (5) ◽  
pp. 1267-1285 ◽  
Author(s):  
Hedy M. Aardema ◽  
Machteld Rijkeboer ◽  
Alain Lefebvre ◽  
Arnold Veen ◽  
Jacco C. Kromkamp
Keyword(s):  
High Resolution ◽  
Spatial Patterns ◽  
North Sea ◽  
Phytoplankton Community ◽  
Spatial And Temporal Variability ◽  
Good Representation ◽  
Low Resolution ◽  
Monitoring Programs ◽  
Phytoplankton Communities ◽  
Phytoplankton Photosynthesis

Abstract. Marine waters can be highly heterogeneous both on a spatial and temporal scale, yet monitoring programs currently rely primarily on low-resolution methods. This potentially leads to undersampling. This study explores the potential of two high-resolution methods for monitoring phytoplankton dynamics: fast repetition rate fluorometry for information on phytoplankton photosynthesis and productivity and automated scanning flow cytometry for information on phytoplankton abundance and community composition. These methods were tested in combination with an underway Ferrybox system during four cruises on the Dutch North Sea in April, May, June, and August 2017. The high-resolution methods were able to visualize both the spatial and temporal variability of the phytoplankton community in the Dutch North Sea. Spectral cluster analysis was applied to objectively interpret the multitude of parameters and visualize potential spatial patterns. This resulted in the identification of biogeographic regions with distinct phytoplankton communities, which varied per cruise. Our results clearly show that the sampling based on fixed stations does not give a good representation of the spatial patterns, showing the added value of underway high-resolution measurements. To fully exploit the potential of the tested high-resolution measurement setup, methodological constraints need further research. Among these constraints are accounting for the diurnal cycle in photophysiological parameters concurrent to the spatial variation, better predictions of the electron requirement for carbon fixation to estimate gross primary productivity, and the identification of more flow cytometer clusters with informative value. Nevertheless, the richness of additional information provided by high-resolution methods can improve existing low-resolution monitoring programs towards a more precise and ecosystemic ecological assessment of the phytoplankton community and productivity.

scholarly journals High resolution <i>in situ</i> measurements of phytoplankton photosynthesis and abundance in the Dutch North Sea

2018 ◽  
Author(s):  
Hedy M. Aardema ◽  
Machteld Rijkeboer ◽  
Alain Lefebvre ◽  
Arnold Veen ◽  
Jacco C. Kromkamp
Keyword(s):  
High Resolution ◽  
Spatial Heterogeneity ◽  
Spatial Patterns ◽  
North Sea ◽  
Phytoplankton Biomass ◽  
Spatial Clustering ◽  
Low Resolution ◽  
Phytoplankton Communities ◽  
Phytoplankton Photosynthesis

Abstract. Marine waters can be highly heterogeneous both on a spatial and temporal scale, yet monitoring is currently mainly limited to low-resolution methods. This study explores the use of two high-resolution methods to study phytoplankton dynamics; Fast Repetition Rate fluorometry (FRRf) to study phytoplankton photosynthesis and scanning flowcytometry (FCM) to study phytoplankton biomass and composition. Measurements were conducted during four cruises on the Dutch North Sea in April, May, June, and August of 2017. Both FRRf and FCM data show spatial heterogeneity with monthly variation. Automated unsupervised Hidden Markov Model (uHMM) spatial clustering resulted in the identification of regions with distinct phytoplankton communities. Manual adjustments were necessary to optimize visualization of some distinct phytoplankton communities. Stepwise multiple linear regression (n = 61) revealed that photophysiology (alpha), phytoplankton biomass (total red fluorescence) and abiotic predictors (Turbidity, DIN, time of the day and temperature) determined integrated water column gross primary productivity. Apart from spatial heterogeneity, the diurnal trend is a significant predictor exposing clear trends with other photophysiological parameters. Consequently, spatial patterns are difficult as temporal and spatial patterns occur simultaneously. Nevertheless, high-resolution monitoring is a very useful supplement in addition to regular low-resolution monitoring.

scholarly journals Temporal Stability of Phytoplankton Functional Groups Within Two Agricultural Irrigation Ponds in Maryland, USA

2021 ◽  
Vol 3 ◽  
Author(s):  
Jaclyn E. Smith ◽  
Jennifer L. Wolny ◽  
Matthew D. Stocker ◽  
Robert L. Hill ◽  
Yakov A. Pachepsky
Keyword(s):  
Water Quality ◽  
Functional Groups ◽  
Spatial Patterns ◽  
Functional Group ◽  
Temporal Stability ◽  
Average Concentration ◽  
Spatial And Temporal Variability ◽  
Agricultural Irrigation ◽  
Phytoplankton Communities ◽  
Phytoplankton Functional Groups

Phytoplankton functional groups and their influence on water quality have been studied in various types of water bodies but have yet to be studied in agricultural irrigation ponds. Freshwater sources (e.g., lakes, rivers, and reservoirs) have been previously shown to exhibit high spatial and temporal variability in phytoplankton populations. Improvements in the monitoring of phytoplankton populations may be achieved if patterns of stable spatial variability can be found in the phytoplankton populations through time. The objective of this work was to determine if temporally stable spatial patterns in phytoplankton communities could be detected in agricultural irrigation ponds using a functional group approach. The study was performed at two working agricultural irrigation ponds located in Maryland, USA over two summer sampling campaigns in 2017 and 2018. Concentrations of four phytoplankton groups, along with sensor-based and fluorometer based water quality parameters were measured. Temporal stability was assessed using mean relative differences between measurements in each location and averaged measurements across ponds on each sampling date. Temporally stable spatial patterns of three phytoplankton functional groups were found for both ponds over the two sampling seasons. Both ponds had locations where specific phytoplankton functional group concentrations were consistently higher or lower than the pond's average concentration for each sampling date. Zones of consistently higher or lower than average concentrations were associated with flow conditions, pond morphology, and human activities. The existence of temporally stable patterns of phytoplankton functional group concentrations can affect the outcome of a water quality assessment and should be considered in water quality monitoring designs.

Differences between Nearshore and Offshore Phytoplankton Communities in Lake Ontario

1987 ◽  
Vol 44 (12) ◽  
pp. 2155-2163 ◽  
Author(s):  
I. M. Gray
Keyword(s):  
Chlorophyll A ◽  
Phytoplankton Biomass ◽  
Algal Biomass ◽  
Phytoplankton Community ◽  
Seasonal Pattern ◽  
Principal Component ◽  
Lake Ontario ◽  
Dominant Group ◽  
Phytoplankton Communities ◽  
Eutrophic Species

Differences between nearshore and offshore phytoplankton biomass and composition were evident in Lake Ontario in 1982. Phytoplankton biomass was characterized by multiple peaks which ranged over three orders of magnitude. Perhaps as a consequence of the three times higher current velocities at the northshore station, phytoplankton biomass ranged from 0.09 to 9.00 g∙m−3 compared with 0.10 to 2.40 g∙m−3 for the midlake station. Bacillariophyceae was the dominant group at the northshore station until September when Cyanophyta contributed most to the biomass (83%). Although Bacillariophyceae was the principal component of the spring phytoplankton community at the midlake station, phytoflagellates (49%) and Chlorophyceae (25%) were responsible for summer biomass, with the Chlorophyceae expanding to 80% in the fall. The seasonal pattern of epilimnetic chlorophyll a correlated with temperature. While chlorophyll a concentrations were similar to values from 1970 and 1972, algal biomass had declined and a number of eutrophic species (Melosira binderana, Stephanodiscus tenuis, S. hantzschii var. pusilla, and S. alpinus) previously found were absent in 1982.

scholarly journals Multimodal Low Resolution Face and Frontal Gait Recognition from Surveillance Video

Electronics ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 1013
Author(s):  
Sayan Maity ◽  
Mohamed Abdel-Mottaleb ◽  
Shihab S. Asfour
Keyword(s):  
Face Recognition ◽  
High Resolution ◽  
Gait Cycle ◽  
Gait Recognition ◽  
Recognition System ◽  
Multimodal Fusion ◽  
Surveillance Video ◽  
Low Resolution ◽  
Model Free ◽  
Face Images

Biometric identification using surveillance video has attracted the attention of many researchers as it can be applicable not only for robust identification but also personalized activity monitoring. In this paper, we present a novel multimodal recognition system that extracts frontal gait and low-resolution face images from frontal walking surveillance video clips to perform efficient biometric recognition. The proposed study addresses two important issues in surveillance video that did not receive appropriate attention in the past. First, it consolidates the model-free and model-based gait feature extraction approaches to perform robust gait recognition only using the frontal view. Second, it uses a low-resolution face recognition approach which can be trained and tested using low-resolution face information. This eliminates the need for obtaining high-resolution face images to create the gallery, which is required in the majority of low-resolution face recognition techniques. Moreover, the classification accuracy on high-resolution face images is considerably higher. Previous studies on frontal gait recognition incorporate assumptions to approximate the average gait cycle. However, we quantify the gait cycle precisely for each subject using only the frontal gait information. The approaches available in the literature use the high resolution images obtained in a controlled environment to train the recognition system. However, in our proposed system we train the recognition algorithm using the low-resolution face images captured in the unconstrained environment. The proposed system has two components, one is responsible for performing frontal gait recognition and one is responsible for low-resolution face recognition. Later, score level fusion is performed to fuse the results of the frontal gait recognition and the low-resolution face recognition. Experiments conducted on the Face and Ocular Challenge Series (FOCS) dataset resulted in a 93.5% Rank-1 for frontal gait recognition and 82.92% Rank-1 for low-resolution face recognition, respectively. The score level multimodal fusion resulted in 95.9% Rank-1 recognition, which demonstrates the superiority and robustness of the proposed approach.

scholarly journals Super Resolution Digital Image Correlation (SR-DIC): an Alternative to Image Stitching at High Magnifications

2021 ◽  
Author(s):  
R. S. Hansen ◽  
D. W. Waldram ◽  
T. Q. Thai ◽  
R. B. Berke
Keyword(s):  
High Resolution ◽  
Digital Image Correlation ◽  
Spatial Resolution ◽  
Digital Image ◽  
Super Resolution ◽  
Image Correlation ◽  
Low Resolution ◽  
Strain Measurements ◽  
Resolution Image ◽  
High Resolution Image

Abstract Background High-resolution Digital Image Correlation (DIC) measurements have previously been produced by stitching of neighboring images, which often requires short working distances. Separately, the image processing community has developed super resolution (SR) imaging techniques, which improve resolution by combining multiple overlapping images. Objective This work investigates the novel pairing of super resolution with digital image correlation, as an alternative method to produce high-resolution full-field strain measurements. Methods First, an image reconstruction test is performed, comparing the ability of three previously published SR algorithms to replicate a high-resolution image. Second, an applied translation is compared against DIC measurement using both low- and super-resolution images. Third, a ring sample is mechanically deformed and DIC strain measurements from low- and super-resolution images are compared. Results SR measurements show improvements compared to low-resolution images, although they do not perfectly replicate the high-resolution image. SR-DIC demonstrates reduced error and improved confidence in measuring rigid body translation when compared to low resolution alternatives, and it also shows improvement in spatial resolution for strain measurements of ring deformation. Conclusions Super resolution imaging can be effectively paired with Digital Image Correlation, offering improved spatial resolution, reduced error, and increased measurement confidence.

Factors influencing the growth of Mougeotia in experimentally acidified mesocosms

2000 ◽  
Vol 57 (3) ◽  
pp. 538-547 ◽  
Author(s):  
Jennifer L Klug ◽  
Janet M Fischer
Keyword(s):  
Inorganic Carbon ◽  
Phytoplankton Community ◽  
Dissolved Inorganic Carbon ◽  
Abiotic Factors ◽  
Minor Role ◽  
Little Rock ◽  
Factors Associated ◽  
Phytoplankton Communities ◽  
Mesocosm Experiments ◽  
Potential Factors

Acidification causes profound changes in species composition in aquatic systems. We conducted mesocosm experiments in three northern Wisconsin lakes (Trout Lake, Little Rock - Reference, Little Rock - Treatment) to test how different phytoplankton communities respond to acidification. Major differences exist among these lakes in water chemistry and phytoplankton community composition. In each lake, three pH treatments (control, press (sustained pH 4.7), and pulse (alternating pH 4.7 and ambient pH)) were maintained for 6 weeks. We observed a striking increase in species in the genus Mougeotia in all systems. Mougeotia is a filamentous green alga often found in acidified lakes. The magnitude of the Mougeotia increase differed among lakes and treatments, and we used an autoregressive model to identify potential factors responsible for these differences. Our results suggest that biotic factors such as competition with other algae played a relatively minor role in regulating Mougeotia dynamics. Instead, pH and abiotic factors associated with changes in pH (e.g., dissolved inorganic carbon) were important predictors of Mougeotia dynamics.

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