Field evaluation of a compact, polarizing topo‐bathymetric lidar across a range of river conditions

River Research and Applications
By: , and 

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Abstract

This paper summarizes field trials to evaluate the performance of a prototype compact topo‐bathymetric lidar sensor for surveying rivers. The sensor uses a novel polarization technique to distinguish between laser returns from the water surface and streambed and its size and weight permit deployment from a small unmanned aerial system (sUAS) or a boat. Field testing was designed to identify the range of operational conditions under which the sensor can provide accurate information on river depths. For accuracy assessment, conventional, field‐based depth measurements were collected by wading and sonar. Additionally, optical properties of the rivers were measured in situ. Wading and lidar bathymetry comparisons in relatively shallow channels yielded observed versus predicted (OP) regression R2 values ranging from 0.60 to 0.97. A comparison between sonar and lidar bathymetry in a deeper river resulted in an OP R2 of 0.72. Absorption and attenuation coefficients at the 532 nm wavelength of the lidar were recorded in the field and the highest values of these inherent optical properties were at sites with the highest turbidity and highest concentrations of colored dissolved organic matter, chlorophyll, and suspended sediment. At these sites, which included both sand and gravel/cobble beds, the point density of riverbed returns was not uniform, with areas of sparse coverage occurring primarily in deeper water. However, submerged objects and slopes could be resolved in the lidar point clouds.

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Publication type Article
Publication Subtype Journal Article
Title Field evaluation of a compact, polarizing topo‐bathymetric lidar across a range of river conditions
Series title River Research and Applications
DOI 10.1002/rra.3771
Edition Online First
Year Published 2021
Language English
Publisher Wiley
Contributing office(s) Southwest Biological Science Center, WMA - Integrated Modeling and Prediction Division
Country United States
State Arizona, Colorado
Other Geospatial Lees Ferry, Kremmling, Parshall
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