Promises and uncertainties in remotely sensed riverine hydro-environmental attributes: Field testing of novel approaches to unmanned aerial vehicle-borne lidar and imaging velocimetry

Abstract

Recent advancements in remotely sensed techniques have markedly expanded data acquisition potential in riverine studies, but the techniques' applicability must be validated and improved because of uncertainties associated with diverse field conditions. This study is the first experimental evidence of using a newly designed unmanned aerial vehicle (UAV)-borne green lidar system (GLS) and deep learning-automated space–time image velocimetry (STIV) for remote investigation of hydraulic and vegetation quantities of the gravel-bed Asahi River in Okayama Prefecture, Japan. In addition to identifying bed deformation in waters shallower than 2 m, the GLS point clouds characterized the submerged infrastructure with block detailing patterns, thereby identifying positional displacement and severely damaged parts. This paper also presents a noncontact method of estimating incremental river discharge. Compared to benchmarked flow model estimates, remotely sensed discharges for three transects covering shallower, deeper, and partially submerged woody vegetation areas were overestimated by 1–11%, with 4% underestimation for another cross-section. The STIV analysis also showed complicated flow patterns that were reasonably confirmed by flow vectors from depth-averaged modeling. Ultimately, depth-averaged flow model estimates validated hydraulic parameters derived remotely from GLS and STIV, and vice versa. In addition to approximating vegetation growth rates, the study using GLS attributes accurately identified riparian vegetation types as herbaceous (70%), woody (86%), and bamboo groves (65%). Finally, our findings provide insight into the management of shallow clear-flowing vegetated rivers and remote sensing of streamflow to validate hydrodynamic-numerical methods.