Investigate the effect of tides on the internal wave mophorlogy and generation sites in the Sulu Sea using satellite images

2016 ◽  
Author(s):  
Bingqing Liu
Keyword(s):  
Internal Wave ◽  
Satellite Images ◽  
Sulu Sea

Detecting Internal Waves From Satellite Ocean Color Imagery

2006 ◽  
Author(s):  
Chung-Ru Ho ◽  
Feng-Chun Su ◽  
Nan-Jung Kuo ◽  
Shih-Jen Huang ◽  
Chun-Te Chen ◽  
...  
Keyword(s):  
Internal Wave ◽  
Internal Waves ◽  
Spatial Resolution ◽  
Satellite Images ◽  
Chlorophyll Concentration ◽  
Theoretic Model ◽  
Near Surface ◽  
Sulu Sea ◽  
High Resolution Satellite Images ◽  
Wave Phenomena

Internal waves have been observed by lots of high resolution satellite images, such as Synthetic Aperture Radar (SAR) and optical images of SPOT and Landsat. These images are usually expensive. In this study, some free but lower spatial resolution satellite images are applied to observe the internal wave phenomena. The internal waves in the Sulu Sea are detected from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) onboard the Orbview-2 satellite. The SeaWiFS image has a spatial resolution of 1.1 km. It is acceptable to observe the internal wave phenomena while the soliton width is larger than the image resolution. The results show that the internal solitary in the Sulu Sea can be observed successfully with SeaWiFS chlorophyll images. The internal waves in the Sulu Sea have amplitudes of 10 to 90 m and wavelengths of 5 to 16 km. The large-amplitude internal solitary waves may significantly influence the near-surface chlorophyll concentration. The chlorophyll concentration would be lower when the depression internal waves passed through. A theoretic model is proposed and tested to estimate the amplitudes of internal waves from chlorophyll concentration images.

Nonlinear Internal Wave Evolution in the Sulu Sea

1985 ◽  
Vol 15 (12) ◽  
pp. 1613-1624 ◽  
Author(s):  
Antony K. Liu ◽  
James R. Holbrook ◽  
John R. Apel
Keyword(s):  
Internal Wave ◽  
Nonlinear Internal Wave ◽  
Wave Evolution ◽  
Sulu Sea

Internal wave refraction observed from sequential satellite images

2008 ◽  
Vol 29 (21) ◽  
pp. 6381-6390 ◽  
Author(s):  
Y. Zhao ◽  
A. K. Liu ◽  
M.‐K. Hsu
Keyword(s):  
Internal Wave ◽  
Satellite Images ◽  
Wave Refraction

The machine training in problems of satellite images’s processing

Metrologiya ◽  
2020 ◽  
pp. 15-37
Author(s):  
L. P. Bass ◽  
Yu. A. Plastinin ◽  
I. Yu. Skryabysheva
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Computer Vision ◽  
Satellite Images ◽  
Vision Systems ◽  
Earth Remote Sensing ◽  
Practical Applications ◽  
Convolution Neural Networks ◽  
Computer Vision Systems ◽  
Trained Neural Network

Use of the technical (computer) vision systems for Earth remote sensing is considered. An overview of software and hardware used in computer vision systems for processing satellite images is submitted. Algorithmic methods of the data processing with use of the trained neural network are described. Examples of the algorithmic processing of satellite images by means of artificial convolution neural networks are given. Ways of accuracy increase of satellite images recognition are defined. Practical applications of convolution neural networks onboard microsatellites for Earth remote sensing are presented.

Multitemporal distribution analysis of Dodonaea viscosa (L.) Jacq. by remote sensing in Durango, Mexico

2020 ◽  
Vol 13 (8) ◽  
Author(s):  
Marco, A. Márquez-Linares ◽  
Jonathan G. Escobar--Flores ◽  
Sarahi Sandoval- Espinosa ◽  
Gustavo Pérez-Verdín
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Design Methodology ◽  
Supervised Classification ◽  
Satellite Images ◽  
Ground Cover ◽  
Distribution Analysis ◽  
Dodonaea Viscosa ◽  
Artificial Neural ◽  
Landsat Satellite

Objective: to determine the distribution of D. viscosa in the vicinity of the Guadalupe Victoria Dam in Durango, Mexico, for the years 1990, 2010 and 2017.Design/Methodology/Approach: Landsat satellite images were processed in order to carry out supervised classifications using an artificial neural network. Images from the years 1990, 2010 and 2017 were used to estimate ground cover of D. viscosa, pastures, crops, shrubs, and oak forest. This data was used to calculate the expansion of D. viscosa in the study area.Results/Study Limitations/Implications: the supervised classification with the artificial neural network was optimal after 400 iterations, obtaining the best overall precision of 84.5 % for 2017. This contrasted with the year 1990, when overall accuracy was low at 45 % due to less training sites (fewer than 100) recorded for each of the land cover classes.Findings/Conclusions: in 1990, D. viscosa was found on only five hectares, while by 2017 it had increased to 147 hectares. If the disturbance caused by overgrazing continues, and based on the distribution of D. viscosa, it is likely that in a few years it will have the ability to invade half the study area, occupying agricultural, forested, and shrub areas

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