scholarly journals The Variation on Sediment Reworking with Influencing Factors by a Sand Bubbler Crab, Scopimera globosa, in Intertidal Sediments of the Anmyeon Island, Korea

2021 ◽  
Vol 13 (10) ◽  
pp. 5703
Author(s):  
Jaehwan Seo ◽  
Bon Joo Koo
Keyword(s):  
Chlorophyll A ◽  
Influencing Factors ◽  
Surface Sediment ◽  
Intertidal Sediments ◽  
Daily Production ◽  
Chlorophyll A Concentration ◽  
Sediment Reworking ◽  
Pellet Production ◽  
Crab Density ◽  
Annual Sediment

Though biological and ecological characteristics of Scopimera globosa have been intensively investigated, little has been understood on bioturbation, especially sediment reworking. This study was designed to evaluate variation on sediment reworking of S. globosa based on feeding pellet production (FP) and burrowing pellet production (BP) with influencing factors and estimating the chlorophyll content reduction within the surface sediment by its feeding. The FP and BP largely fluctuated according to chlorophyll a concentration and crab density, but both were not influenced by temperature. The FP was enhanced by chlorophyll a concentration, whereas both FP and BP were restricted by crab density. The daily individual production was highest in spring, followed by fall and summer, with values of 25.61, 20.70 and 3.90 g ind.−1 d−1, respectively, while the total daily production was highest in fall, followed by summer and spring 2150, 1660 and 660 g m−2 d−1, respectively. The daily sediment reworking based on the FP and BP of Scopimera was highest in fall, followed by summer and spring, with values of 1.91, 1.70 and 0.77 mm d-1 and the annual sediment reworking rate of this species was calculated 40 cm year−1 based on its density in this study area. The chlorophyll a reduction ratio was estimated from 11 to 24% in one day by its feeding. These results imply that the sediment reworking of S. globosa is regulated by food abundance and its density, and Scopimera is an important bioturbator, greatly influencing biogeochemical changes in the intertidal sediments.

scholarly journals Retrieval and Evaluation of Chlorophyll-a Concentration in Reservoirs with Main Water Supply Function in Beijing, China, Based on Landsat Satellite Images

2021 ◽  
Vol 18 (9) ◽  
pp. 4419
Author(s):  
Yuequn Lai ◽  
Jing Zhang ◽  
Yongyu Song ◽  
Zhaoning Gong
Keyword(s):  
Remote Sensing ◽  
Water Supply ◽  
Chlorophyll A ◽  
Phosphorus Concentration ◽  
Landsat 8 ◽  
Supply Function ◽  
Estimation Model ◽  
Nitrogen And Phosphorus ◽  
Chlorophyll A Concentration ◽  
Guanting Reservoir

Remote sensing retrieval is an important technology for studying water eutrophication. In this study, Guanting Reservoir with the main water supply function of Beijing was selected as the research object. Based on the measured data in 2016, 2017, and 2019, and Landsat-8 remote sensing images, the concentration and distribution of chlorophyll-a in the Guanting Reservoir were inversed. We analyzed the changes in chlorophyll-a concentration of the reservoir in Beijing and the reasons and effects. Although the concentration of chlorophyll-a in the Guanting Reservoir decreased gradually, it may still increase. The amount and stability of water storage, chlorophyll-a concentration of the supply water, and nitrogen and phosphorus concentration change are important factors affecting the chlorophyll-a concentration of the reservoir. We also found a strong correlation between the pixel values of adjacent reservoirs in the same image, so the chlorophyll-a estimation model can be applied to each other.

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.

Reconstruction of Gap-Free OCM-2 Chlorophyll-a Concentration Using DINEOF

2021 ◽  
Author(s):  
Chiranjivi Jayaram ◽  
J. Pavan Kumar ◽  
T. V. S. Udaya Bhaskar ◽  
I. V. G. Bhavani ◽  
T. D. V. Prasad Rao ◽  
...  
Keyword(s):  
Chlorophyll A ◽  
Chlorophyll A Concentration

scholarly journals Rapid Quantification of Microalgae Growth with Hyperspectral Camera and Vegetation Indices

Plants ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 341
Author(s):  
Pauliina Salmi ◽  
Matti A. Eskelinen ◽  
Matti T. Leppänen ◽  
Ilkka Pölönen
Keyword(s):  
Chlorophyll Fluorescence ◽  
Chlorophyll A ◽  
Near Infrared ◽  
Vegetation Indices ◽  
Hyperspectral Data ◽  
Strong Correlations ◽  
Algae Biomass ◽  
Chlorophyll A Concentration ◽  
Cost Efficient ◽  
Hyperspectral Camera

Spectral cameras are traditionally used in remote sensing of microalgae, but increasingly also in laboratory-scale applications, to study and monitor algae biomass in cultures. Practical and cost-efficient protocols for collecting and analyzing hyperspectral data are currently needed. The purpose of this study was to test a commercial, easy-to-use hyperspectral camera to monitor the growth of different algae strains in liquid samples. Indices calculated from wavebands from transmission imaging were compared against algae abundance and wet biomass obtained from an electronic cell counter, chlorophyll a concentration, and chlorophyll fluorescence. A ratio of selected wavebands containing near-infrared and red turned out to be a powerful index because it was simple to calculate and interpret, yet it yielded strong correlations to abundances strain-specifically (0.85 < r < 0.96, p < 0.001). When all the indices formulated as A/B, A/(A + B) or (A − B)/(A + B), where A and B were wavebands of the spectral camera, were scrutinized, good correlations were found amongst them for biomass of each strain (0.66 < r < 0.98, p < 0.001). Comparison of near-infrared/red index to chlorophyll a concentration demonstrated that small-celled strains had higher chlorophyll absorbance compared to strains with larger cells. The comparison of spectral imaging to chlorophyll fluorescence was done for one strain of green algae and yielded strong correlations (near-infrared/red, r = 0.97, p < 0.001). Consequently, we described a simple imaging setup and information extraction based on vegetation indices that could be used to monitor algae cultures.

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