Estimation Model of Chlorophyll-a Concentration Based on Continuous Wavelet Coefficient

2021 ◽  
Vol 58 (8) ◽  
pp. 0828002
Author(s):  
彭咏石 Peng Yongshi ◽  
陈水森 Chen Shuisen ◽  
陈金月 Chen Jinyue ◽  
赵晶 Zhao Jing ◽  
王重洋 Wang Chongyang ◽  
...  
Keyword(s):  
Chlorophyll A ◽  
Wavelet Coefficient ◽  
Continuous Wavelet ◽  
Estimation Model ◽  
Chlorophyll A Concentration

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 ESTIMASI PRODUKTIVITAS PRIMER PERAIRAN BERDASARKAN KONSENTRASI KLOROFIL-A YANG DIEKSTRAK DARI CITRA SATELIT LANDSAT-8 DI PERAIRAN KEPULAUAN KARIMUN JAWA

2017 ◽  
Vol 14 (1) ◽  
Author(s):  
Mulkan Nuzapril ◽  
Setyo Budi Susilo ◽  
James Parlindungan Panjaitan
Keyword(s):  
Chlorophyll A ◽  
Attenuation Coefficient ◽  
Primary Productivity ◽  
Chlorophyll Concentration ◽  
Euphotic Zone ◽  
Landsat 8 ◽  
Secchi Disk ◽  
Estimation Model ◽  
Chlorophyll A Concentration ◽  
Average Value

Sea primary productivity is an important factor in monitoring the quality of sea waters due to his role in the carbon cycle and the food chain for heterotrophic organisms. Estimation of sea primary productivity may be suspected through the values of chlorophyll-a concentration, but surface chlorophyll-a concentration was only able to explain 30% of the primary productivity of the sea. This research aims to build primary productivity estimation model based on chlorophyll-a concentration value of a surface layer of depth until depth compensation. Primary productivity model of relationships with chlorophyll concentration were extracted from Landsat-8 imagery then it could be used to calculated of sea primary productivity. The determination of the depth classification were done by measuring the attenuation coefficient values using the luxmeter underwater datalogger 2000 and secchi disk. The attenuation coefficient values by the luxmeter underwater, ranges between of 0.13-0.21 m-1 and secchi disk ranged, of 0.12 – 0.21 m-1. The penetration of light that through into the water column where  primary productivity is still in progress or where the depth of compensation ranged from 28.75 – 30.67 m. The simple linier regression model between average value of chlorophyll- concentration in all euphotic zone with sea primary productivity has high correlation, it greater than of surface chlorophyll-a concentration (R2 = 0.65). Model validation of sea primary productivity has high accuracy with the RMSD value of 0.09 and satellite-derived sea primary productivity were not significantly different. The satellite derived of chlorophyll-a could be calculated into sea primary productivity.Abstrak Produktivitas primer perairan merupakan faktor penting dalam pemantauan kualitas perairan laut karena berperan dalam siklus karbon dan rantai makanan bagi organisme heterotrof. Estimasi produktivitas primer perairan dapat diduga melalui nilai konsentrasi klorofil-a, namun konsentrasi klorofil-a permukaan laut hanya mampu menjelaskan 30% produktivitas primer laut. Penelitian ini bertujuan untuk membangun model estimasi produktivitas primer berdasarkan nilai konsentrasi klorofil-a dari lapisan kedalaman permukaan sampai kedalaman kompensasi. Model hubungan produktivitas primer dengan konsentrasi klorofil-a yang diekstrak dari citra satelit Landsat-8 kemudian dapat digunakan untuk mengestimasi produktivitas primer satelit. Penentuan klasifikasi kedalaman dilakukan dengan mengukur nilai koefisien atenuasi menggunakan luxmeter underwater datalogger 2000  dan secchi disk. Nilai koefisien atenuasi dengan menggunakan luxmeter underwater berkisar antara 0,13 -0,21m-1 dan secchi disk berkisar antara 0,12 – 0,21 m-1. Penetrasi cahaya yang masuk ke kolom perairan dimana produksi primer masih berlangsung atau kedalaman kompensasi berkisar antara 28,75 – 30,67 m. Model regresi linier sederhana antara konsentrasi klorofil-a rata-rata seluruh zona eufotik dengan produktivitas primer perairan memiliki korelasi yang lebih tinggi dibandingkan konsentrasi klorofil-a permukaan dengan R2= 0,65. Validasi model produktivitas primer memiliki keakuratan yang tinggi dengan RMSD sebesar 0,09 dan produktivitas primer satelit secara signifikan tidak berbeda nyata dengan produktivitas primer data insitu. Sehingga  nilai konsentrasi klorofil-a satelit dapat ditransformasi menjadi produktivitas primer satelit.

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 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
Sign in / Sign up

Export Citation Format

Share Document