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

Carl J Legleiter,Paul J Kinzel

doi:10.3390/rs12081282

Abstract

The remote, inaccessible location of many rivers in Alaska creates a compelling need for remote sensing approaches to streamflow monitoring. Motivated by this objective, we evaluated the potential to infer flow velocities from optical image sequences acquired from a helicopter deployed above two large, sediment-laden rivers. Rather than artificial seeding, we used an ensemble correlation particle image velocimetry (PIV) algorithm to track the movement of boil vortices that upwell suspended sediment and produce a visible contrast at the water surface. This study introduced a general, modular workflow for image preparation (stabilization and geo-referencing), preprocessing (filtering and contrast enhancement), analysis (PIV), and postprocessing (scaling PIV output and assessing accuracy via comparison to field measurements). Applying this method to images acquired with a digital mapping camera and an inexpensive video camera highlighted the importance of image enhancement and the need to resample the data to an appropriate, coarser pixel size and a lower frame rate. We also developed a Parameter Optimization for PIV (POP) framework to guide selection of the interrogation area (IA) and frame rate for a particular application. POP results indicated that the performance of the PIV algorithm was highly robust and that relatively large IAs (64–320 pixels) and modest frame rates (0.5–2 Hz) yielded strong agreement ( R 2 > 0.9 ) between remotely sensed velocities and field measurements. Similarly, analysis of the sensitivity of PIV accuracy to image sequence duration showed that dwell times as short as 16 s would be sufficient at a frame rate of 1 Hz and could be cut in half if the frame rate were doubled. The results of this investigation indicate that helicopter-based remote sensing of velocities in sediment-laden rivers could contribute to noncontact streamgaging programs and enable reach-scale mapping of flow fields.

Highlights

The largest streamflow monitoring network in Alaska is maintained and operated by the U.S Geological Survey (USGS) and currently consists of 111 continuous monitoring stations
Given the impracticality of seeding the flow to support large-scale particle image velocimetry (PIV) of optical images and the various issues associated with thermal PIV, we evaluated the potential to infer surface flow velocities from optical image sequences using a different type of tracer found in great abundance in many Alaskan rivers: sediment
For the hovering image sequence from the Salcha River, data from the global positioning system (GPS)/Inertial Motion Unit (IMU) onboard the helicopter and integrated with the Hasselblad camera, along with surveyed ground control targets placed in the field prior to the flight, allowed us to geo-reference each individual image in the stack without an initial, internal stabilization of the stack

Summary

Introduction

The largest streamflow monitoring network in Alaska is maintained and operated by the U.S Geological Survey (USGS) and currently consists of 111 continuous monitoring stations. In an ongoing effort to improve safety, increase efficiency, and expand the monitoring network, the USGS is developing noncontact methods to estimate streamflow using data collected from a variety of remote platforms [1]. These remote sensing approaches comprise two broad categories: (1) techniques that rely on observations from satellites [2,3,4]; and (2) near-field techniques that include measurements made from bridges [5], small unmanned aircraft systems (sUAS, or drones), helicopters [6], or fixed-wing aircraft. We use the shorter term PIV for brevity

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Discussion

Conclusion