Remote estimation of in water constituents in coastal waters using neural networks

2014 ◽  
Author(s):  
Ioannis Ioannou ◽  
Alexander Gilerson ◽  
Michael Ondrusek ◽  
Soe Hlaing ◽  
Robert Foster ◽  
...  
Keyword(s):  
Neural Networks ◽  
Coastal Waters ◽  
Water Constituents ◽  
Remote Estimation

Application of empirical neural networks to chlorophyll-a estimation in coastal waters using remote optosensors

2003 ◽  
Vol 3 (4) ◽  
pp. 376-382 ◽  
Author(s):  
Yuanzhi Zhang ◽  
S.S. Koponen ◽  
J.T. Pulliainen ◽  
M.T. Hallikainen
Keyword(s):  
Neural Networks ◽  
Chlorophyll A ◽  
Coastal Waters

scholarly journals Can Water Constituents Be Used as Proxy to Map Microplastic Dispersal Within Transitional and Coastal Waters?

2020 ◽  
Vol 8 ◽  
Author(s):  
Sarah Piehl ◽  
Elizabeth C. Atwood ◽  
Mathias Bochow ◽  
Hannes K. Imhof ◽  
Jonas Franke ◽  
...  
Keyword(s):  
Coastal Waters ◽  
Water Constituents

Atmospheric correction over coastal waters using multilayer neural networks

2017 ◽  
Vol 199 ◽  
pp. 218-240 ◽  
Author(s):  
Yongzhen Fan ◽  
Wei Li ◽  
Charles K. Gatebe ◽  
Cédric Jamet ◽  
Giuseppe Zibordi ◽  
...  
Keyword(s):  
Neural Networks ◽  
Coastal Waters ◽  
Atmospheric Correction ◽  
Multilayer Neural Networks

scholarly journals Evaluation of Unoccupied Aircraft System (UAS) Remote Sensing Reflectance Retrievals for Water Quality Monitoring in Coastal Waters

2021 ◽  
Vol 9 ◽  
Author(s):  
Anna E. Windle ◽  
Greg M. Silsbe
Keyword(s):  
Water Quality ◽  
Coastal Waters ◽  
Total Suspended Solid ◽  
Water Quality Monitoring ◽  
Suspended Solid ◽  
Quality Monitoring ◽  
Future Research ◽  
Quality Properties ◽  
Water Constituents

Unoccupied aircraft systems (UAS, or drones) equipped with off-the-shelf multispectral sensors originally designed for terrestrial applications can also be used to derive water quality properties in coastal waters. The at-sensor total radiance a UAS measured constitutes the sum of water-leaving radiance (LW) and incident radiance reflected off the sea surface into the detector’s field of view (LSR). LW is radiance that emanates from the water and contains a spectral shape and magnitude governed by optically active water constituents interacting with downwelling irradiance while LSR is independent of water constituents and is instead governed by a given sea-state surface reflecting light; a familiar example is sun glint. Failure to accurately account for LSR can significantly influence Rrs, resulting in inaccurate water quality estimates once algorithms are applied. The objective of this paper is to evaluate the efficacy of methods that remove LSR from total UAS radiance measurements in order to derive more accurate remotely sensed retrievals of scientifically valuable in-water constituents. UAS derived radiometric measurements are evaluated against in situ hyperspectral Rrs measurements to determine the best performing method of estimating and removing surface reflected light and derived water quality estimates. It is recommended to use a pixel-based approach that exploits the high absorption of water at NIR wavelengths to estimate and remove LSR. Multiple linear regressions applied to UAS derived Rrs measurements and in situ chlorophyll a and total suspended solid concentrations resulted in 37 and 9% relative error, respectively, which is comparable to coastal water quality algorithms found in the literature. Future research could account for the high resolution and multi-angular aspect of LSR by using a combination of photogrammetry and radiometry techniques. Management implications from this research include improved water quality monitoring of coastal and inland water bodies in order to effectively track trends, identify and mitigate pollution sources, and discern potential human health risks.

Remote estimation of respiration rate by optical flow using convolutional neural networks

2021 ◽  
Keyword(s):  
Neural Networks ◽  
Optical Flow ◽  
International Symposium ◽  
Respiration Rate ◽  
Convolutional Neural Networks ◽  
Fast Track ◽  
Electronic Imaging ◽  
Remote Estimation

Fast track article for IS&T International Symposium on Electronic Imaging 2021: Imaging and Multimedia Analytics in a Web and Mobile World 2021 proceedings.

scholarly journals Estimating chlorophyll-A concentration in the Caspian Sea from MODIS images using artificial neural networks

2019 ◽  
Vol 25 (4) ◽  
pp. 515-521
Author(s):  
Siamak Boudaghpour ◽  
Hajar Sadat Alizadeh Moghadam ◽  
Mohammadreza Hajbabaie ◽  
Seyed Hamidreza Toliati
Keyword(s):  
Neural Networks ◽  
Chlorophyll A ◽  
Coastal Waters ◽  
Caspian Sea ◽  
Training Data ◽  
The Caspian Sea ◽  
Feed Forward ◽  
Chlorophyll A Concentration ◽  
Hidden Layer ◽  
Mean Square Errors

Nowadays, due to various pollution sources, it is essential for environmental scientists to monitor water quality. Phytoplanktons form the end of the food chain in water bodies and are one of the most important biological indicators in water pollution studies. Chlorophyll-A, a green pigment, is found in all phytoplankton. Chlorophyll-A concentration indicates phytoplankton biomass directly. Therefore, Chlorophyll-A is an indirect indicator of pollutants, including phosphorus and nitrogen, and their refinement and control are important. The present study, Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images were used to estimate the chlorophyll-A concentration in southern coastal waters in the Caspian Sea. For this purpose, Multi-layer perceptron neural networks (NNs) were applied which contained three and four feed-forward layers. The best three-layer NN has 15 neurons in its hidden layer and the best four-layer one has 5 in each. The three- and four- layer networks both resulted in similar root mean square errors (RMSE), 0.1(<math xmlns="http://www.w3.org/1998/Math/MathML"><mfrac><mrow><mi>&#x3BC;</mi><mi>g</mi></mrow><mi>l</mi></mfrac></math>), however, the four-layer NNs proved superior in terms of R<sup>2</sup> and also required less training data. Accordingly, a four-layer feed-forward NN with 5 neurons in each hidden layer, is the best network structure for estimating Chlorophyll-A concentration in the southern coastal waters of the Caspian Sea.

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