Solar surface velocity fields determined from small magnetic features

Solar Physics ◽  
1990 ◽  
Vol 130 (1-2) ◽  
pp. 295-311 ◽  
Author(s):  
R. F. Howard ◽  
J. W. Harvey ◽  
S. Forgach
Keyword(s):  
Solar Surface ◽  
Velocity Fields ◽  
Surface Velocity ◽  
Magnetic Features

scholarly journals Are granules good tracers of solar surface velocity fields?

2001 ◽  
Vol 377 (2) ◽  
pp. L14-L17 ◽  
Author(s):  
M. Rieutord ◽  
T. Roudier ◽  
H.-G. Ludwig ◽  
Å. Nordlund ◽  
R. Stein
Keyword(s):  
Solar Surface ◽  
Velocity Fields ◽  
Surface Velocity

Surface velocity fields from tidal interactions

2009 ◽  
Author(s):  
Gloria Koenigsberger ◽  
Edmundo Moreno ◽  
David Harrington ◽  
Ivan Hubeny ◽  
James M. Stone ◽  
...  
Keyword(s):  
Velocity Fields ◽  
Surface Velocity ◽  
Tidal Interactions

scholarly journals Glacier Image Velocimetry: an open-source toolbox for easy and rapid calculation of high-resolution glacier velocity fields

2021 ◽  
Vol 15 (4) ◽  
pp. 2115-2132
Author(s):  
Maximillian Van Wyk de Vries ◽  
Andrew D. Wickert
Keyword(s):  
High Resolution ◽  
Open Source ◽  
Velocity Fields ◽  
Surface Velocity ◽  
Displacement Fields ◽  
Desktop Computer ◽  
Ice Cap ◽  
Image Velocimetry ◽  
Glacier Ice ◽  
Glacier Velocity

Abstract. We present Glacier Image Velocimetry (GIV), an open-source and easy-to-use software toolkit for rapidly calculating high-spatial-resolution glacier velocity fields. Glacier ice velocity fields reveal flow dynamics, ice-flux changes, and (with additional data and modelling) ice thickness. Obtaining glacier velocity measurements over wide areas with field techniques is labour intensive and often associated with safety risks. The recent increased availability of high-resolution, short-repeat-time optical imagery allows us to obtain ice displacement fields using “feature tracking” based on matching persistent irregularities on the ice surface between images and hence, surface velocity over time. GIV is fully parallelized and automatically detects, filters, and extracts velocities from large datasets of images. Through this coupled toolchain and an easy-to-use GUI, GIV can rapidly analyse hundreds to thousands of image pairs on a laptop or desktop computer. We present four example applications of the GIV toolkit in which we complement a glaciology field campaign (Glaciar Perito Moreno, Argentina) and calculate the velocity fields of small mid-latitude (Glacier d'Argentière, France) and tropical glaciers (Volcán Chimborazo, Ecuador), as well as very large glaciers (Vavilov Ice Cap, Russia). Fully commented MATLAB code and a stand-alone app for GIV are available from GitHub and Zenodo (see https://doi.org/10.5281/zenodo.4624831, Van Wyk de Vries, 2021a).

scholarly journals A Combined Approach for Filtering Ice Surface Velocity Fields Derived from Remote Sensing Methods

2017 ◽  
Vol 9 (10) ◽  
pp. 1062 ◽  
Author(s):  
Christine Lüttig ◽  
Niklas Neckel ◽  
Angelika Humbert
Keyword(s):  
Remote Sensing ◽  
Velocity Fields ◽  
Surface Velocity ◽  
Combined Approach ◽  
Ice Surface ◽  
Remote Sensing Methods

On the characterisation of open-flow seeding conditions for image-velocimetry techniques using UASs

2020 ◽  
Author(s):  
Silvano Fortunato Dal Sasso ◽  
Alonso Pizarro ◽  
Salvatore Manfreda
Keyword(s):  
River Discharge ◽  
Velocity Fields ◽  
Velocity Estimation ◽  
Surface Velocity ◽  
Seeding Density ◽  
Acquisition Time ◽  
Flow Conditions ◽  
Open Flow ◽  
Image Velocimetry

<p>In the last years, new technologies have been developed to monitor rivers in a real-time framework opening new opportunities and challenges for the research community and practitioners. Acquiring data in open flow conditions can be performed through the use of Unmanned Aerial System (UAS) to derive surface velocity fields and in consequence, river discharge. Significant work has been done to investigate the reliability of image-velocimetry techniques using numerical simulations and laboratory flume experiments, but, to date, the effects of environmental factors on velocity estimates are not addressed adequately. In this context, a critical variable is represented by the number of particles transiting on the water surface (defined as seeding density) during field surveys and their challenging dynamics along the cross-section, on both time and space. Seeding density has a significant effect on surface velocity estimation and river discharge accuracy. The goal of this study was, therefore, to evaluate the accuracy and feasibility of LSPIV and PTV techniques under different seeding and flow conditions using several footages acquired employing UASs. To this purpose, the seeding behaviour during the whole acquisition time was examined for each case study focusing on the quantification of essential variables such as seeding density, average tracers’ dimension, coefficient of variation of tracers’ area, and spatial dispersion of them in the field of view. For each case study, both image-velocimetry techniques have been applied considering several different sets of images to locally measure the accuracy of velocity estimations in challenging seeding conditions. Results show that the local seeding density, tracers’ dimension and their spatial distribution can strongly influence the reconstruction of velocity fields in natural stream reaches. Therefore, prior knowledge of seeding characteristics in the field can deal with the choice of the optimal image-velocimetry technique to use and the related setting parameters.</p>

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