scholarly journals Primary Productivity and heterotrophic activity in an enclosed marine area of central Patagonia (Puyuhuapi channel; 44° S, 73° W)

2012 ◽  
Vol 9 (5) ◽  
pp. 5929-5968 ◽  
Author(s):  
G. Daneri ◽  
P. Montero ◽  
L. Lizárraga ◽  
R. Torres ◽  
J. L. Iriarte ◽  
...  
Keyword(s):  
Chlorophyll A ◽  
Mixed Layer ◽  
Secondary Production ◽  
Winter Season ◽  
High Pco2 ◽  
Two Phase ◽  
Chl A ◽  
Phase Cycle ◽  
Over The Top ◽  
Bacterial Secondary Production

Abstract. We assessed temporal variability in phytoplankton biomass, Chlorophyll a, nutrient availability, Gross Primary Production (GPP), community respiration (CR), and bacterial secondary production (BSP) over a year of monthly observations (October 2007 to October 2008) at a fixed station in the Puyuhuapi fjord, Chilean Patagonia (44° S, 73° W). A set of in situ observations gathered over two consecutive spring-summer seasons, and one autumn-winter season in the middle, has made it possible to connect the two-phase (i.e. productive season/non-productive season) pattern of Chlorophyll a (Chl a) variability shown by satellite data with a two-phase cycle in GPP, CR, and the composition of phytoplankton assemblages. Estimates of annual GPP and CR, integrated over the top 20 meters of the water column, were 533 and 537 g C m−2 yr−1, respectively. Low values of pCO2 were measured in mixed layer autotrophic waters (GPP/CR > 1) while high pCO2 levels were measured in mixed layer heterotrophic waters (GPP/CR < 1). Bacterial Secondary Production (BSP) was significantly and positively correlated with GPP (r = 0.6, p < 0.05, n = 24) and Chl a (r = 0.4, p < 0.05, n = 24) on an annual cycle basis. The winter drop in bacterioplankton (both bacteria and archea) activity (from 0.9 ± 0.6 g C m−2 d−1 to 0.6 ± 0.3 g C m−2 d−1) was not as pronounced as the winter drop in phytoplankton activity (from 1.1 ± 1.12 g C m−2 d−1 to 0.1 ± 0.09 g C m−2 d−1). It is hypothesized that dissolved organic matter (DOM) of terrestrial origin plays an important role (especially in winter) supporting bacterial activity in the Puyuhuapi fjord.

scholarly journals Global distribution and variability of subsurface chlorophyll a concentration

2021 ◽  
Author(s):  
Sayaka Yasunaka ◽  
Tsuneo Ono ◽  
Kosei Sasaoka ◽  
Kanako Sato
Keyword(s):  
Chlorophyll A ◽  
Mixed Layer ◽  
Net Primary Production ◽  
Thermal Structure ◽  
Mixed Layer Depth ◽  
Subsurface Layer ◽  
Spatiotemporal Variability ◽  
Shortwave Radiation ◽  
Concentration Data ◽  
Chl A

Abstract. Chlorophyll a (Chl-a) often retains its maximum concentration not at the surface but in the subsurface layer. The depth of the Chl-a maximum primarily depends on the balance between light penetration from the surface and nutrient supply from the deep ocean. However, a global map of subsurface Chl-a concentrations based on observations has not been presented yet. In this study, we integrate Chl-a concentration data not only from recent biogeochemical floats but also from historical ship-based and other observations, and present global maps of subsurface Chl-a concentration with related variables. The subsurface Chl-a maximum deeper than the mixed layer depth was stably observed in the subtropics and tropics (30° S to 30° N), only in summer in midlatitudes (30–40° N/S), and rarely at 45–60° S of the Southern Ocean and in the northern North Atlantic (north of 45° N). The depths of the subsurface Chl-a maxima are deeper than those of the euphotic layer in the subtropics and shallower in the tropics and midlatitudes. In the subtropics, seasonal oxygen increases below the mixed layer implied substantial biological new production, which corresponds to 10 % of the net primary production there. During El Niño, the subsurface Chl-a concentration in the equatorial Pacific is higher in the middle to the east and lower in the west than that during La Niña, which is opposite that on the surface. The spatiotemporal variability of the Chl-a concentration described here would be suggestive results not only for the biogeochemical cycle in the ocean but also for the thermal structure and the dynamics of the ocean via the absorption of shortwave radiation.

scholarly journals Assessment of chlorophyll-a and sea surface temperature variability around the Mascarene Plateau, Nazareth Bank (Mauritius) using satellite data

2017 ◽  
Vol 64 (4) ◽  
Author(s):  
V. Ramchandur ◽  
Soonil D. D. V. Rughooputh ◽  
R. Boojawon ◽  
B. A. Motah
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Chlorophyll A ◽  
Satellite Data ◽  
Phytoplankton Biomass ◽  
Winter Season ◽  
Sea Surface ◽  
Chl A ◽  
The Past ◽  
Western Side

The Mascarene Plateau is characterised by shallow banks namely Saya de Malha and Nazareth which are known to harbour high phytoplankton biomass along the slope down to the ridge. Correlation between sea surface temperature (SST) and Chlorophyll-a (Chl-a) distribution surrounding the plateau was investigated. Higher Chl-a concentration was observed during the period July to September, indicating higher productivity due to upwelling. The regions east (61-630E) and west (57-590E) of the Mascarene Plateau were also studied along latitudes 130S up to 180S in the exclusive economic zone of Mauritius, where most of the fishing activities are concentrated. In general, 2008 was observed to be less warm during the past 14 years registering a drop with respect to the maximum monthly mean records, whilst 2006 was the most productive during winter season in the region of study. Chl-a bloom was observed after cyclone Imelda in April 2013 showing Chl-a concentration above 0.3 mg m-3 along latitude 130S and longitude 570E. The study reveals that the western side of the plateau is more productive with relatively warmer surface temperature compared to the eastern side of the plateau.

Two-Phase Cycle DBA (TCDBA) for Differentiated Services on EPON

2009 ◽  
Vol E92-B (9) ◽  
pp. 2823-2837 ◽  
Author(s):  
Hye Kyung LEE ◽  
Won-Jin YOON ◽  
Tae-Jin LEE ◽  
Hyunseung CHOO ◽  
Min Young CHUNG
Keyword(s):  
Differentiated Services ◽  
Two Phase ◽  
Phase Cycle

scholarly journals A novel two-phase cycle algorithm for effective cyber intrusion detection in edge computing

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Yiguang Gong ◽  
Yunping Liu ◽  
Chuanyang Yin
Keyword(s):  
Intrusion Detection ◽  
Optimal Parameter ◽  
Edge Computing ◽  
Cloud Services ◽  
Second Phase ◽  
Pareto Optimal ◽  
Two Phase ◽  
Multi Objective ◽  
Phase Cycle ◽  
Positive Rate

AbstractEdge computing extends traditional cloud services to the edge of the network, closer to users, and is suitable for network services with low latency requirements. With the rise of edge computing, its security issues have also received increasing attention. In this paper, a novel two-phase cycle algorithm is proposed for effective cyber intrusion detection in edge computing based on a multi-objective genetic algorithm (MOGA) and modified back-propagation neural network (MBPNN), namely TPC-MOGA-MBPNN. In the first phase, the MOGA is employed to build a multi-objective optimization model that tries to find the Pareto optimal parameter set for MBPNN. The Pareto optimal parameter set is applied for simultaneous minimization of the average false positive rate (Avg FPR), mean squared error (MSE) and negative average true positive rate (Avg TPR) in the dataset. In the second phase, some MBPNNs are created based on the parameter set obtained by MOGA and are trained to search for a more optimal parameter set locally. The parameter set obtained in the second phase is used as the input of the first phase, and the training process is repeated until the termination criteria are reached. A benchmark dataset, KDD cup 1999, is used to demonstrate and validate the performance of the proposed approach for intrusion detection. The proposed approach can discover a pool of MBPNN-based solutions. Combining these MBPNN solutions can significantly improve detection performance, and a GA is used to find the optimal MBPNN combination. The results show that the proposed approach achieves an accuracy of 98.81% and a detection rate of 98.23% and outperform most systems of previous works found in the literature. In addition, the proposed approach is a generalized classification approach that is applicable to the problem of any field having multiple conflicting objectives.

scholarly journals Spatiotemporal Variability of Surface Phytoplankton Carbon and Carbon-to-Chlorophyll a Ratio in the South China Sea Based on Satellite Data

2020 ◽  
Vol 13 (1) ◽  
pp. 30
Author(s):  
Wenlong Xu ◽  
Guifen Wang ◽  
Long Jiang ◽  
Xuhua Cheng ◽  
Wen Zhou ◽  
...  
Keyword(s):  
South China Sea ◽  
Chlorophyll A ◽  
South China ◽  
Satellite Data ◽  
Phytoplankton Biomass ◽  
The South China Sea ◽  
Spatiotemporal Variability ◽  
The South ◽  
Chl A ◽  
China Sea

The spatiotemporal variability of phytoplankton biomass has been widely studied because of its importance in biogeochemical cycles. Chlorophyll a (Chl-a)—an essential pigment present in photoautotrophic organisms—is widely used as an indicator for oceanic phytoplankton biomass because it could be easily measured with calibrated optical sensors. However, the intracellular Chl-a content varies with light, nutrient levels, and temperature and could misrepresent phytoplankton biomass. In this study, we estimated the concentration of phytoplankton carbon—a more suitable indicator for phytoplankton biomass—using a regionally adjusted bio-optical algorithm with satellite data in the South China Sea (SCS). Phytoplankton carbon and the carbon-to-Chl-a ratio (θ) exhibited considerable variability spatially and seasonally. Generally, phytoplankton carbon in the northern SCS was higher than that in the western and central parts. The regional monthly mean phytoplankton carbon in the northern SCS showed a prominent peak during December and January. A similar pattern was shown in the central part of SCS, but its peak was weaker. Besides the winter peak, the western part of SCS had a secondary maximum of phytoplankton carbon during summer. θ exhibited significant seasonal variability in the northern SCS, but a relatively weak seasonal change in the western and central parts. θ had a peak in September and a trough in January in the northern and central parts of SCS, whereas in the western SCS the minimum and maximum θ was found in August and during October–April of the following year, respectively. Overall, θ ranged from 26.06 to 123.99 in the SCS, which implies that the carbon content could vary up to four times given a specific Chl-a value. The variations in θ were found to be related to changing phytoplankton community composition, as well as dynamic phytoplankton physiological activities in response to environmental influences; which also exhibit much spatial differences in the SCS. Our results imply that the spatiotemporal variability of θ should be considered, rather than simply used a single value when converting Chl-a to phytoplankton carbon biomass in the SCS, especially, when verifying the simulation results of biogeochemical models.

scholarly journals Research on Inversion Mechanism of Chlorophyll—A Concentration in Water Bodies Using a Convolutional Neural Network Model

Water ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 664
Author(s):  
Yun Xue ◽  
Lei Zhu ◽  
Bin Zou ◽  
Yi-min Wen ◽  
Yue-hong Long ◽  
...  
Keyword(s):  
Neural Network ◽  
Regression Model ◽  
Convolutional Neural Network ◽  
Chlorophyll A ◽  
Language Processing ◽  
Water Bodies ◽  
Inversion Effect ◽  
Least Squares Regression ◽  
Chlorophyll A Concentration ◽  
Chl A

For Case-II water bodies with relatively complex water qualities, it is challenging to establish a chlorophyll-a concentration (Chl-a concentration) inversion model with strong applicability and high accuracy. Convolutional Neural Network (CNN) shows excellent performance in image target recognition and natural language processing. However, there little research exists on the inversion of Chl-a concentration in water using convolutional neural networks. Taking China’s Dongting Lake as an example, 90 water samples and their spectra were collected in this study. Using eight combinations as independent variables and Chl-a concentration as the dependent variable, a CNN model was constructed to invert Chl-a concentration. The results showed that: (1) The CNN model of the original spectrum has a worse inversion effect than the CNN model of the preprocessed spectrum. The determination coefficient (RP2) of the predicted sample is increased from 0.79 to 0.88, and the root mean square error (RMSEP) of the predicted sample is reduced from 0.61 to 0.49, indicating that preprocessing can significantly improve the inversion effect of the model.; (2) among the combined models, the CNN model with Baseline1_SC (strong correlation factor of 500–750 nm baseline) has the best effect, with RP2 reaching 0.90 and RMSEP only 0.45. The average inversion effect of the eight CNN models is better. The average RP2 reaches 0.86 and the RMSEP is only 0.52, indicating the feasibility of applying CNN to Chl-a concentration inversion modeling; (3) the performance of the CNN model (Baseline1_SC (RP2 = 0.90, RMSEP = 0.45)) was far better than the traditional model of the same combination, i.e., the linear regression model (RP2 = 0.61, RMSEP = 0.72) and partial least squares regression model (Baseline1_SC (RP2 = 0.58. RMSEP = 0.95)), indicating the superiority of the convolutional neural network inversion modeling of water body Chl-a concentration.

scholarly journals Response of Total Suspended Sediment and Chlorophyll-a Concentration to Late Autumn Typhoon Events in the Northwestern South China Sea

2021 ◽  
Vol 13 (15) ◽  
pp. 2863
Author(s):  
Junyi Li ◽  
Huiyuan Zheng ◽  
Lingling Xie ◽  
Quanan Zheng ◽  
Zheng Ling ◽  
...  
Keyword(s):  
South China Sea ◽  
Continental Shelf ◽  
Suspended Sediment ◽  
Chlorophyll A ◽  
South China ◽  
Total Suspended Sediment ◽  
Offshore Area ◽  
Chl A ◽  
Late Autumn ◽  
China Sea

Strong typhoon winds enhance turbulent mixing, which induces sediment to resuspend and to promote chlorophyll-a (Chl-a) blooms in the continental shelf areas. In this study, we find limited Chl-a responses to three late autumn typhoons (typhoon Nesat, Mujigae and Khanun) in the northwestern South China Sea (NWSCS) using satellite observations. In climatology, the Chl-a and total suspended sediment (TSS) concentrations are high all year round with higher value in autumn in the offshore area of the NWSCS. After the typhoon passage, the Chl-a concentration increases slightly (23%), while even TSS enhances by 280% on the wide continental shelf of the NWSCS. However, in the southern area, located approximately 100 km from the typhoon tracks, both TSS and Chl-a concentrations increase 160% and 150% after typhoon passage, respectively. In the deeper area, the increased TSS concentration is responsible for the considerable increase of the Chl-a. An empirical analysis is applied to the data, which reveals the TSS and Chl-a processes during typhoon events. The results of this study suggest a different mechanism for Chl-a concentration increase and thus contribute toward further evaluation of typhoon-induced biological responses.

scholarly journals Temporal Variation of Chlorophyll-a Concentrations in Highly Dynamic Waters from Unattended Sensors and Remote Sensing Observations

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2699 ◽  
Author(s):  
Jian Li ◽  
Liqiao Tian ◽  
Qingjun Song ◽  
Zhaohua Sun ◽  
Hongjing Yu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Chlorophyll Fluorescence ◽  
Chlorophyll A ◽  
Temporal Variations ◽  
Lake Area ◽  
Short Term ◽  
Chl A ◽  
Spatio Temporal

Monitoring of water quality changes in highly dynamic inland lakes is frequently impeded by insufficient spatial and temporal coverage, for both field surveys and remote sensing methods. To track short-term variations of chlorophyll fluorescence and chlorophyll-a concentrations in Poyang Lake, the largest freshwater lake in China, high-frequency, in-situ, measurements were collected from two fixed stations. The K-mean clustering method was also applied to identify clusters with similar spatio-temporal variations, using remote sensing Chl-a data products from the MERIS satellite, taken from 2003 to 2012. Four lake area classes were obtained with distinct spatio-temporal patterns, two of which were selected for in situ measurement. Distinct daily periodic variations were observed, with peaks at approximately 3:00 PM and troughs at night or early morning. Short-term variations of chlorophyll fluorescence and Chl-a levels were revealed, with a maximum intra-diurnal ratio of 5.1 and inter-diurnal ratio of 7.4, respectively. Using geostatistical analysis, the temporal range of chlorophyll fluorescence and corresponding Chl-a variations was determined to be 9.6 h, which indicates that there is a temporal discrepancy between Chl-a variations and the sampling frequency of current satellite missions. An analysis of the optimal sampling strategies demonstrated that the influence of the sampling time on the mean Chl-a concentrations observed was higher than 25%, and the uncertainty of any single Terra/MODIS or Aqua/MODIS observation was approximately 15%. Therefore, sampling twice a day is essential to resolve Chl-a variations with a bias level of 10% or less. The results highlight short-term variations of critical water quality parameters in freshwater, and they help identify specific design requirements for geostationary earth observation missions, so that they can better address the challenges of monitoring complex coastal and inland environments around the world.

scholarly journals Can chlorophyll-a in meso-oligotrophic shallow waters be estimated using statistical approaches and empirical models from MODIS imagery?

RBRH ◽  
2018 ◽  
Vol 23 (0) ◽  
Author(s):  
Andres Mauricio Munar ◽  
José Rafael Cavalcanti ◽  
Juan Martin Bravo ◽  
David Manuel Lelinho Da Motta Marques ◽  
Carlos Ruberto Fragoso Júnior
Keyword(s):  
Linear Regression ◽  
Spatial Heterogeneity ◽  
Multiple Linear Regression ◽  
Chlorophyll A ◽  
Linear Regression Analysis ◽  
Multiple Linear Regression Analysis ◽  
Accurate Estimation ◽  
Shallow Waters ◽  
Chl A ◽  
Input Variables

ABSTRACT Accurate estimation of chlorophyll-a (Chl-a) concentration in inland waters through remote-sensing techniques is complicated by local differences in the optical properties of water. In this study, we applied multiple linear regression (MLR), artificial neural network (ANN), nonparametric multiplicative regression (NPMR) and four models (Appel, Kahru, FAI and O14a) to estimate the Chl -a concentration from combinations of spectral bands from the MODIS sensor. The MLR, NPMR and ANN models were calibrated and validated using in-situ Chl -a measurements. The results showed that a simple and efficient model, developed and validated through multiple linear regression analysis, offered advantages (i.e., better performance and fewer input variables) in comparison with ANN, NPMR and four models (Appel, Kahru, FAI and O14a). In addition, we observed that in a large shallow subtropical lake, where the wind and hydrodynamics are essential factors in the spatial heterogeneity (Chl-a distribution), the MLR model adjusted using the specific point dataset, performed better than using the total dataset, which suggest that would not be appropriate to generalize a single model to estimate Chl-a in these large shallow lakes from total datasets. Our approach is a useful tool to estimate Chl -a concentration in meso-oligotrophic shallow waters and corroborates the spatial heterogeneity in these ecosystems.

Analysis of within- and between-day chlorophyll-a dynamics in Mantua Superior Lake, with a continuous spectroradiometric measurement

2013 ◽  
Vol 64 (4) ◽  
pp. 303 ◽  
Author(s):  
M. Bresciani ◽  
M. Rossini ◽  
G. Morabito ◽  
E. Matta ◽  
M. Pinardi ◽  
...  
Keyword(s):  
Chlorophyll A ◽  
Continuous Monitoring ◽  
Phytoplankton Growth ◽  
Key Factors ◽  
Eutrophic Lakes ◽  
Chl A ◽  
Metabolic Adaptations ◽  
Horizontal Migration ◽  
Radiometric System ◽  
Growth Distribution

Eutrophic lakes display unpredictable patterns of phytoplankton growth, distribution, vertical and horizontal migration, likely depending on environmental conditions. Monitoring chlorophyll-a (Chl-a) concentration provides reliable information on the dynamics of primary producers if monitoring is conducted frequently. We present a practical approach that allows continuous monitoring of Chl-a concentration by using a radiometric system that measures optical spectral properties of water. We tested this method in a shallow, nutrient-rich lake in northern Italy, the Mantua Superior Lake, where the radiometric system collected data all throughout the day (i.e. every 5 min) for ~30 days. Here, specifically developed algorithms were used to convert water reflectance to Chl-a concentration. The best performing algorithm (R2 = 0.863) was applied to a larger dataset collected in September 2011. We characterised intra- and inter-daily Chl-a concentration dynamics and observed a high variability; during a single day, Chl-a concentration varied from 20 to 130 mg m–3. Values of Chl-a concentration were correlated with meteo-climatic parameters, showing that solar radiance and wind speed are key factors regulating the daily phytoplankton growth and dynamics. Such patterns are usually determined by vertical migration of different phytoplankton species within the water column, as well as by metabolic adaptations to changes in light conditions.

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