Integrating unmanned aerial systems and LSPIV for rapid, cost-effective stream gauging

Lewis, Quinn W.; Lindroth, Evan M.; Rhoads, Bruce L.

doi:10.1016/j.jhydrol.2018.03.008

Cited by 45 publications

(45 citation statements)

References 36 publications

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“…If the camera is not moving, displacements measured from LSPIV grid points on the riverbanks should be zero at all times. Deviations from zero thus indicate camera movement (Lewis et al, ). The same process was performed using the sUAS flying at about 15 m during the same campaign, so wind conditions were identical for each method.…”

Section: Resultsmentioning

confidence: 99%

“…The details of the choice of preprocessing algorithms, number of frames used, size of the LSPIV interrogation area, and smoothing and averaging techniques are all factors that can determine the quality of the resulting surface velocity vector field. However, the effects of altering these parameters can be found elsewhere (Lewis et al, ; Thielicke & Stamhuis, ) and are beyond the scope of this manuscript. Manual manipulation of these factors influences outcomes to a minor degree, but surface seeding, image resolution, and image quality are the greatest limiting factors for LSPIV accuracy (Kantoush et al, ; Kim, ; Lewis & Rhoads, ).…”

Section: Methodology and Experimental Setupmentioning

confidence: 99%

“…Flow measurements in rivers obtained with LSPIV have mainly been used to characterize one‐dimensional streamwise velocities at the surface for the purpose of using measurements to estimate discharge (Lewis et al, ). Recent work using fixed mounts has demonstrated that under certain conditions, LSPIV can capture 2‐D patterns of surface flow in rivers (Lewis & Rhoads, ).…”

Section: Introductionmentioning

confidence: 99%

“…Although sUAS movement often varies with height because of changes in wind direction and magnitude, even if the absolute amount of movement decreases with height, decreases in image resolution can counteract reduced absolute amounts of movement. The net result is an increase in the total displacement of tracers on the water surface (Lewis et al, ). Similarly, for a constant surface velocity, the number of pixels that seeded particles move per frame decreases with camera distance from the water surface.…”

Section: Introductionmentioning

confidence: 99%

“…Another advantage of LSPIV is that it can be deployed rapidly and at low cost, especially when image rectification is not required. Therefore, understanding the conditions under which rectification is not necessary is of considerable value (Lewis et al, ; Muste et al, ). Clearly, sUAS represent a major advance in the acquisition of aerial imagery of rivers, but the extent to which the increased flexibility of sUAS might be offset by the need for detailed image rectification and establishment of extensive ground control remains uncertain.…”

Section: Introductionmentioning

confidence: 99%

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LSPIV Measurements of Two‐Dimensional Flow Structure in Streams Using Small Unmanned Aerial Systems: 1. Accuracy Assessment Based on Comparison With Stationary Camera Platforms and In‐Stream Velocity Measurements

Lewis

Rhoads

2018

Water Resources Research

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Measuring two‐dimensional (2‐D) patterns of flow in rivers at high resolution over large areas is challenging using traditional velocity‐measurement methods, which provide data at specific locations or cross sections. Large‐scale particle image velocimetry (LSPIV) based on imagery obtained from fixed camera platforms can measure flow velocity on the surface of rivers and is generally accurate compared to near‐surface velocity measurements obtained by traditional methods. The proliferation of inexpensive small unmanned aerial systems (sUAS) equipped with high‐resolution cameras and onboard GPS has the potential to facilitate measurements of flow patterns in rivers using LSPIV, but few studies have assessed the accuracy of sUAS‐derived LSPIV compared to fixed‐platform LSPIV and in‐stream velocity measurements. This study assesses the accuracy of sUAS‐based LSPIV for measuring 2‐D mean surface velocities as well as quasi‐instantaneous 2‐D velocities obtained from successive image frames. For persistent 2‐D flow, mean velocities derived from sUAS‐based LSPIV match those obtained by stationary camera platforms, and velocities measured by both LSPIV methods agree with near‐surface velocities measured by an acoustic Doppler velocimeter. Quasi‐instantaneous velocities are degraded by camera movement and low pixel resolution, but capturing the evolution of 2‐D flow structures is possible in certain circumstances. The results confirm that sUAS‐derived LSPIV provides accurate, high‐resolution measurements of mean surface velocities over large spatial areas of persistent 2‐D flow and can characterize evolving 2‐D flow structures under favorable conditions. sUAS‐based LSPIV is a valuable new method for mapping of 2‐D patterns of surface flow in rivers—an issue explored in a companion paper.

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Section: Resultsmentioning

confidence: 99%

Section: Methodology and Experimental Setupmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

LSPIV Measurements of Two‐Dimensional Flow Structure in Streams Using Small Unmanned Aerial Systems: 1. Accuracy Assessment Based on Comparison With Stationary Camera Platforms and In‐Stream Velocity Measurements

Lewis

Rhoads

2018

Water Resources Research

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Integrated UAS and LiDAR reveals the importance of land cover and flood magnitude on the formation of incipient chute holes and chute cutoff development

Lewis

Edmonds

Yanites

2020

Earth Surf Processes Landf

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Meandering river sinuosity increases until the channel erodes into itself (neck cutoff) or forms a new channel over the floodplain (chute cutoff) and sinuosity is reduced. Unlike neck cutoff, which can be measured or modelled without considering overbank processes, chute cutoff must be at least partially controlled by channel‐forming processes on the floodplain. Even though chute cutoff controls meandering river form, the processes that cause chute cutoff are not well understood. This study analyses the morphology of two incipient chute cutoffs along the East Fork White River, Indiana, USA, using high temporal and spatial resolution UAS‐based LiDAR and aerial photography. LiDAR and aerial imagery obtained between 1998 and 2019 reveals that large scour holes formed in the centre of both chutes sometime after chute channel initiation. A larger analysis within the study watershed reveals that scour holes within incipient chutes can be stable or unstable, and tend to stabilize when the chute is colonized by native vegetation and forest. When the scour holes form in farmed floodplain, they enlarge rapidly after initial formation and contribute to complete chute cutoff. In addition, this study shows that the formation of scour holes can occur in response to common, relatively low‐magnitude floods and that the amount of incipient chute erosion does not depend on peak flood magnitude. The role of scour holes in enlarging chute channels could be an important mechanism for chute channel evolution in meandering rivers. This study also confirms that understanding the relationships among flow, land cover, and cutoff morphology is substantially improved with on‐demand remote sensing techniques like integrated UAS and LiDAR. © 2020 John Wiley & Sons, Ltd.

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Refining image‐velocimetry performances for streamflow monitoring: Seeding metrics to errors minimization

Pizarro

Sasso

Manfreda

2020

Hydrological Processes

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River streamflow monitoring is currently facing a transformation due to the emerging of new innovative technologies. Fixed and mobile measuring systems are capable of quantifying surface flow velocities and discharges, relying on video acquisitions. This camera-gauging framework is sensitive to what the camera can "observe" but also to field circumstances such as challenging weather conditions, river background transparency, transiting seeding characteristics, among others. This short communication paper introduces the novel idea of optimizing image velocimetry techniques selecting the most informative sequence of frames within the available video. The selection of the optimal frame window is based on two reasonable criteria: (a) the maximization of the number of frames, subject to (b) the minimization of the recently introduced dimensionless seeding distribution index (SDI). SDI combines seeding characteristics such as seeding density and spatial clustering of tracers, which are used as a proxy to enhance the reliability of image velocimetry techniques. Two field case studies were considered as a proof-of-concept of the proposed framework, on which seeding metrics were estimated and averaged in time to select the proper application window. The selected frames were analysed using LSPIV to estimate the surface flow velocities and river discharge. Results highlighted that the proposed framework might lead to a significant error reduction. In particular, the computed discharge errors, at the optimal portion of the footage, were about 0.40% and 0.12% for each case study, respectively. These values were lower than those obtained, considering all frames available.

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Integrating unmanned aerial systems and LSPIV for rapid, cost-effective stream gauging

Cited by 45 publications

References 36 publications

LSPIV Measurements of Two‐Dimensional Flow Structure in Streams Using Small Unmanned Aerial Systems: 1. Accuracy Assessment Based on Comparison With Stationary Camera Platforms and In‐Stream Velocity Measurements

LSPIV Measurements of Two‐Dimensional Flow Structure in Streams Using Small Unmanned Aerial Systems: 1. Accuracy Assessment Based on Comparison With Stationary Camera Platforms and In‐Stream Velocity Measurements

Integrated UAS and LiDAR reveals the importance of land cover and flood magnitude on the formation of incipient chute holes and chute cutoff development

Refining image‐velocimetry performances for streamflow monitoring: Seeding metrics to errors minimization

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