Inferring Surface Flow Velocities in Sediment-Laden Alaskan Rivers from Optical Image Sequences Acquired from a Helicopter

Legleiter, Carl J.; Kinzel, Paul J.

doi:10.3390/rs12081282

Cited by 28 publications

(65 citation statements)

References 30 publications

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“…Therefore, interpolation and extrapolation may need to be undertaken. This may be achieved using the assumption that the Froude number varies linearly or is constant within a cross section (Le Coz et al, 2010) or based on theoretical flow field distributions (Leitão et al, 2018). Upon a complete profile, unit discharge can be calculated based on the specified water depth at a number of locations in the cross section, and this is then aggregated to provide the total river flow.…”

Section: Image-based Hydrometric Solutions: Existing Workflows and Limitationsmentioning

confidence: 99%

KLT-IV v1.0: Image velocimetry software for use with fixed and mobile platforms

Perks¹

2020

Preprint

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Abstract. Accurately monitoring river flows can be challenging, particularly under high-flow conditions. In recent years, there has been considerable development of remote sensing techniques for the determination of river flow dynamics. Image velocimetry is one particular approach which has been shown to accurately reconstruct surface velocities under a range of hydro-geomorphic conditions. Building on these advances, a new software package, KLT-IV v1.0 has been designed to offer a user-friendly graphical interface for the determination of river flow velocity and river discharge using videos acquired from a variety of fixed and mobile platforms. Platform movement can be accounted for when ground control points and/or stable features are present, or where the platform is equipped with a differential GPS device and inertial measurement unit (IMU) sensor. The application of KLT-IV v1.0 is demonstrated using two case studies at sites in the UK: (i) River Feshie; and (ii) River Coquet. At these sites, footage is acquired from unmanned aerial systems (UAS) and fixed cameras. Using a combination of ground control points (GCPs), and differential GPS and IMU data to account for platform movement, image coordinates are converted to real world distances and displacements. Flow measurements made with a UAS and fixed camera are used to generate a well-defined flow rating curve for the River Feshie. Concurrent measurements made by UAS and fixed camera are shown to deviate by < 4 % under high-flow conditions where maximum velocities exceed 3 m s−1. The acquisition of footage on the River Coquet using a UAS equipped with differential GPS and IMU sensors enabled flow velocities to be precisely reconstructed along a 180 m river reach. In-channel velocities of between 0.2 and 1 m s−1 are produced. Check points indicated that unaccounted for motion in the UAS platform is in the region of 6 cm. These examples are provided to illustrate the potential for KLT-IV to be used for quantifying flow rates using videos collected from fixed, or mobile camera systems.

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Section: Image-based Hydrometric Solutions: Existing Workflows and Limitationsmentioning

confidence: 99%

KLT-IV v1.0: Image velocimetry software for use with fixed and mobile platforms

Perks¹

2020

Preprint

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Section: Accepted Articlementioning

confidence: 99%

“…In addition to the videos, digital aerial photographs of the Salcha River also were acquired from the Robinson R44 on the same day. These images were captured by a Hasselblad A6D-100C 100 megapixel digital mapping camera deployed within a pod mounted on the helicopter's landing gear (Legleiter & Kinzel, 2020b). The data were collected while transiting a series of flight lines designed to provide complete coverage, with ample overlap, of the entire study area.…”

Section: Accepted Articlementioning

confidence: 99%

“…This approach to estimating water depth from averaged river images is synergistic with, and inspired by, parallel efforts to use image sequences to infer surface flow velocities via large‐scale particle image velocimetry (LSPIV). For example, Legleiter and Kinzel (2020b) showed that continuous velocity fields could be derived from time series of passive optical image data in sediment‐laden rivers. An important component of our LSPIV workflow is image stabilization, which serves to align the individual image frames with one another so that any apparent motion can be attributed to the movement of trackable features on the water surface and not the imaging platform.…”

Section: Introductionmentioning

confidence: 99%

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Improving Remotely Sensed River Bathymetry by Image‐Averaging

Legleiter

Kinzel

2021

Water Resources Research

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Basic data on river bathymetry is critical for numerous applications in river research and management and is increasingly obtained via remote sensing, but the noisy, pixelated appearance of image-derived depth maps can compromise subsequent analyses. We hypothesized that this noise originates from reflectance from an irregular water surface and introduced a framework for mitigating these effects by Inferring Bathymetry from Averaged River Images (IBARI). This workflow produces time-averaged images from video frames stabilized to account for platform motion and/or computes a spatial average from an ensemble simulated by randomly shifting images relative to themselves. We used field observations of water depth and helicopter-based videos from a clear-flowing river to assess the potential of this approach to improve depth retrieval. Our results indicated that depths inferred from averaged images were more accurate and precise than those inferred from single frames; observed vs. predicted regression R 2 increased from 0.80 to 0.88. In addition, IBARI significantly enhanced the texture of image-derived depth maps, leading to smoother, more coherent representations of channel morphology. Depth retrieval improved with image sequence duration, but the number of images was more important than the length of time encompassed; shorter acquisitions at higher frame rates would economize data collection. We also demonstrated the potential to scale up the IBARI workflow by producing a mosaic of bathymetric maps derived from averaged images acquired at several hovering waypoints distributed along a 2.36 km reach. This approach is well-suited to data collected from helicopters and small unmanned aircraft systems (sUAS).

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Section: Image-based Hydrometric Solutions: Existing Workflows and LImentioning

confidence: 99%

KLT-IV v1.0: image velocimetry software for use with fixed and mobile platforms

Perks

2020

Geosci. Model Dev.

View full text Add to dashboard Cite

Abstract. Accurately monitoring river flows can be challenging, particularly under high-flow conditions. In recent years, there has been considerable development of remote sensing techniques for the determination of river flow dynamics. Image velocimetry is one particular approach which has been shown to accurately reconstruct surface velocities under a range of hydro-geomorphic conditions. Building on these advances, a new software package, KLT-IV v1.0, has been designed to offer a user-friendly graphical interface for the determination of river flow velocity and river discharge using videos acquired from a variety of fixed and mobile platforms. Platform movement can be accounted for when ground control points and/or stable features are present or where the platform is equipped with a differential GPS device and inertial measurement unit (IMU) sensor. The application of KLT-IV v1.0 is demonstrated using two case studies at sites in the UK: (i) river Feshie and (ii) river Coquet. At these sites, footage is acquired from unmanned aerial systems (UASs) and fixed cameras. Using a combination of ground control points (GCPs) and differential GPS and IMU data to account for platform movement, image coordinates are converted to real-world distances and displacements. Flow measurements made with a UAS and fixed camera are used to generate a well-defined flow rating curve for the river Feshie. Concurrent measurements made by UAS and fixed camera are shown to deviate by < 4 % under high-flow conditions where maximum velocities exceed 3 m s−1. The acquisition of footage on the river Coquet using a UAS equipped with differential GPS and IMU sensors enabled flow velocities to be precisely reconstructed along a 180 m river reach. In-channel velocities of between 0.2 and 1 m s−1 are produced. Check points indicated that unaccounted-for motion in the UAS platform is in the region of 6 cm. These examples are provided to illustrate the potential for KLT-IV to be used for quantifying flow rates using videos collected from fixed or mobile camera systems.

show abstract

Inferring Surface Flow Velocities in Sediment-Laden Alaskan Rivers from Optical Image Sequences Acquired from a Helicopter

Cited by 28 publications

References 30 publications

KLT-IV v1.0: Image velocimetry software for use with fixed and mobile platforms

KLT-IV v1.0: Image velocimetry software for use with fixed and mobile platforms

Improving Remotely Sensed River Bathymetry by Image‐Averaging

KLT-IV v1.0: image velocimetry software for use with fixed and mobile platforms

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