Jove
Visualize
Contact Us

Related Concept Videos

Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

202
To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
202
Uniform Depth Channel Flow01:27

Uniform Depth Channel Flow

301
Uniform depth channel flow keeps fluid depth consistent along channels such as irrigation canals. In natural channels, such as rivers, approximate uniform flow is often assumed. This condition occurs when the channel’s bottom slope matches the energy slope, balancing potential energy lost from gravity with head loss due to shear stress. This balance prevents depth changes along the channel length, resulting in a steady, uniform flow.Uniform flow in open channels with a constant cross-section...
301

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same journalSame Topic

RETRACTED: Ndaguba et al. Operability of Smart Spaces in Urban Environments: A Systematic Review on Enhancing Functionality and User Experience. <i>Sensors</i> 2023, <i>23</i>, 6938.

Sensors (Basel, Switzerland)·2026
Same journal

Correction: Ma et al. A Lightweight, Low-Frequency, Broadband Underwater Acoustic Transducer with Ternary Symmetric Excitation: Integrating KNN and Terfenol-D for Enhanced Performance. <i>2026</i>, <i>26</i>, 3645.

Sensors (Basel, Switzerland)·2026
Same journal

Correction: He et al. An Edge-Computing-Based Emotion-Aware Adaptive Lighting System for Intelligent Cockpits. <i>Sensors</i> 2026, <i>26</i>, 3489.

Sensors (Basel, Switzerland)·2026
Same journal

Correction: Tu et al. Lower Limb Motion Recognition with Improved SVM Based on Surface Electromyography. <i>Sensors</i> 2024, <i>24</i>, 3097.

Sensors (Basel, Switzerland)·2026
Same journal

Real-Time Detection System for Road Roughness Based on Ultrasonic Technology.

Sensors (Basel, Switzerland)·2026
Same journal

FedHSFV: Federated Learning for Finger Vein Recognition via Hierarchical Decoupling and Subspace Metric.

Sensors (Basel, Switzerland)·2026
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Nov 10, 2025

Confocal Imaging of Confined Quiescent and Flowing Colloid-polymer Mixtures
10:56

Confocal Imaging of Confined Quiescent and Flowing Colloid-polymer Mixtures

Published on: May 20, 2014

12.3K

Development of an Image Registration Technique for Fluvial Hyperspectral Imagery Using an Optical Flow Algorithm.

Hojun You1, Dongsu Kim2

  • 1IIHR-Hydroscience and Engineering, University of Iowa, Iowa City, IA 52242, USA.

Sensors (Basel, Switzerland)
|April 3, 2021
PubMed
Summary

This study introduces an efficient optical flow algorithm for precise image registration in unmanned aerial vehicle (UAV)-based hyperspectral fluvial remote sensing. The new method significantly reduces spatial errors, improving riverine property monitoring.

Keywords:
fluvial remote sensinghyperspectral imageryimage registrationoptical flow

More Related Videos

Automated 3D Optical Coherence Tomography to Elucidate Biofilm Morphogenesis Over Large Spatial Scales
09:56

Automated 3D Optical Coherence Tomography to Elucidate Biofilm Morphogenesis Over Large Spatial Scales

Published on: August 21, 2019

7.1K
Spatial Temporal Analysis of Fieldwise Flow in Microvasculature
09:39

Spatial Temporal Analysis of Fieldwise Flow in Microvasculature

Published on: November 18, 2019

6.1K

Related Experiment Videos

Last Updated: Nov 10, 2025

Confocal Imaging of Confined Quiescent and Flowing Colloid-polymer Mixtures
10:56

Confocal Imaging of Confined Quiescent and Flowing Colloid-polymer Mixtures

Published on: May 20, 2014

12.3K
Automated 3D Optical Coherence Tomography to Elucidate Biofilm Morphogenesis Over Large Spatial Scales
09:56

Automated 3D Optical Coherence Tomography to Elucidate Biofilm Morphogenesis Over Large Spatial Scales

Published on: August 21, 2019

7.1K
Spatial Temporal Analysis of Fieldwise Flow in Microvasculature
09:39

Spatial Temporal Analysis of Fieldwise Flow in Microvasculature

Published on: November 18, 2019

6.1K

Area of Science:

  • Environmental Science
  • Geospatial Science
  • Remote Sensing Technology

Background:

  • Traditional fluvial remote sensing using RGB sensors offers limited quantitative accuracy due to narrow spectral bands.
  • Hyperspectral imaging provides extensive spectral data for detailed riverine property characterization.
  • UAV-based hyperspectral imaging faces challenges in image registration due to data complexity and high spatial resolution.

Purpose of the Study:

  • To develop a practical and accurate image registration technique for UAV-based fluvial hyperspectral data.
  • To enhance the spatial accuracy of hyperspectral images acquired from unmanned aerial vehicles for riverine studies.

Main Methods:

  • Implementation of an optical flow algorithm for efficient image registration of hyperspectral data.
  • Comparison of the proposed method against no registration and traditional template matching algorithms.

Main Results:

  • The optical flow algorithm achieved an average reduction in spatial errors of 91.9% compared to no registration.
  • A significant 78.7% reduction in spatial errors was observed compared to template matching methods.
  • Demonstrated high spatial accuracy for UAV-based fluvial hyperspectral imaging.

Conclusions:

  • The proposed optical flow-based image registration is a practical and effective solution for UAV hyperspectral fluvial remote sensing.
  • This technique overcomes limitations of traditional methods, enabling more accurate quantitative monitoring of riverine environments.
  • Advances in UAV hyperspectral imaging registration pave the way for improved riverine property analysis.