Orienting the camera and firing lasers to enhance large scale particle image velocimetry for streamflow monitoring

Abstract

Large scale particle image velocimetry (LSPIV) is a nonintrusive methodology for continuous surface flow monitoring in natural environments. Recent experimental studies demonstrate that LSPIV is a promising technique to estimate flow discharge in riverine systems. Traditionally, LSPIV implementations are based on the use of angled cameras to capture extended fields of view; images are then orthorectified and calibrated through the acquisition of ground reference points. As widely documented in the literature, the identification of ground reference points and image orthorectification are major hurdles in LSPIV. Here we develop an experimental apparatus to address both of these issues. The proposed platform includes a laser system for remote frame calibration and a low-cost camera that is maintained orthogonal with respect to the water surface to minimize image distortions. We study the feasibility of the apparatus on two complex natural riverine environments where the acquisition of ground reference points is prevented and illumination and seeding density conditions are challenging. While our results confirm that velocity estimations can be severely affected by inhomogeneously seeded surface tracers and adverse illumination settings, they demonstrate that LSPIV implementations can benefit from the proposed apparatus. Specifically, the presented system opens novel avenues in the development of stand-alone platforms for remote surface flow monitoring.

A novel permanent gauge-cam station for surface-flow observations on the Tiber River

“…Left-side recordings analyzed through LSPIV lead to extremely low values, less than one-tenth radar velocities, whereas right-side videos yield velocity overestimations, up to twice radar velocities. This fact is consistent with previous studies (Tauro et al, 2014b(Tauro et al, , 2016b, and is mainly attributed to the high sensitivity of LSPIV to illumination conditions and to the irregular presence and distribution of floaters in the field of view (Tauro et al, 2016b). In the video captured early in the morning, the river mirror-like surface is highly detrimental for velocity estimation.…”

“…As anticipated in (Tauro et al, 2014b) and further supported in this work, image-based algorithms such as LSPIV tend to be highly affected by varying flow settings. To overcome this issue, we plan on thoroughly studying the relationship between time variations of the velocity field and flow conditions.…”

“…In Tauro et al (2014b), a modified setup has been proposed to allow for remote digital image acquisition and calibration based on the use of low-power laser modules. This implementation has proved convenient for observations in difficult-to-access environments and during flood events; however, image processing through LSPIV has been found to be highly affected by the occurrence and spatial distribution of tracers.…”

“…Video storage is enabled through a 1 Tb solid state external hard drive, which features ext4 journaling file system for high storage limits. The sensing unit is based on the portable prototype for LSPIV observations developed in Tauro et al (2014b). Specifically, it is connected to the control unit and suspended underneath the bridge through an aluminum bar at an elevation of approximately 15 m from the water surface; see Fig.…”

“…This results in simplified image preparation; for instance, image matching is rarely required before LSPIV processing of videos captured from stationary implementations (Tauro et al, 2014b). In the future, we plan on testing video data recorded at the gauge-cam station through an array of image-based algorithms, spanning from LSPIV to particle tracking velocimetry (Tang et al, 2008), long-term tracking (Pervez and Solomon, 1994), and optical flow (Quénot et al, 1998).…”