Evaluating the capabilities of the CASI hyperspectral imaging system and Aquarius bathymetric LiDAR for measuring channel morphology in two distinct river environments

Abstract

Remote sensing is a powerful tool for examining river morphology. This study used detailed field surveys to assess the capability of the CASI hyperspectral imaging system and Aquarius bathymetric LiDAR to measure bed elevations in rivers with disparate optical characteristics. Field measurements of water column optical properties in the clear Snake River, the more complex Blue and Colorado, and highly turbid Muddy Creek were used to calculate depth retrieval precision and dynamic range. Differences in depth of a few centimeters were detectable via passive optical techniques in the clearest stream, but precision was greatly reduced under turbid conditions. The bathymetric LiDAR evaluated in this study could not detect shallow depths or differences in depth smaller than 11 cm owing to the difficulty of distinguishing water surface and bottom returns in laser waveforms. In clear water and with high radiometric resolution, hyperspectral systems such as CASI could detect depths approaching 10 m, but semi-empirical analysis of the Aquarius LiDAR indicated that maximum detectable depths were of the order of 2-3 m in the clear-flowing Snake River, and closer to 1 m in the more turbid streams. Turbidity also constrained spectrally based depth retrieval, and depth estimates from the Blue/Colorado were far less reliable than on the Snake. Both sensors yielded positively biased (0.03 m for CASI, 0.08 m for Aquarius) bed elevations on the Snake, with precisions of 0.16-0.17 m. For the Blue/Colorado, mean errors were of the order of 0.2 m, biased shallow for optical data and biased deep for LiDAR, although no Aquarius laser returns were recorded from the deepest parts of these channels; precisions were reduced to 0.29-0.32 m. Both approaches have advantages and limitations, and prospective users must understand the capabilities and constraints associated with various types of remote sensing to ensure efficient use of these evolving technologies.

Capability Assessment and Performance Metrics for the Titan Multispectral Mapping Lidar

“…A total of 423 ADC samples and coincident lidar-derived depths were utilized. Figure 10b shows a dispersion plot of the validation and lidar depth samples; the agreement between these data sets is good with an R 2 of 0.92 and an root mean square error (RMSE) of 0.26 m. This RMSE value is consistent with other various bathymetric depth or elevation accuracy values reported for the Aquarius bathymetric Lidar system [16,42,43], and for the Experimental Advanced Airborne Research Lidar-B (EAARL-B) system [44]. From Figure 10b it should be noted that there is a higher dispersion between the lidar-derived water depths and the reference ADCP data at larger depths.…”

Mapping river bathymetries: Evaluating topobathymetric LiDAR survey

“…Multi‐ and hyper‐spectral image analyses (Marcus et al, ; Legleiter et al, ) have also been used to map water depths (rather than submerged topography), but their success depends on accurately defining local water optical properties and linking those to changes in water depth and turbidity of stream water. These passive optical surveys have generally been limited to short reaches (Legleiter et al, ). Multibeam sonar (Conner and Tonina, ) has been shown to be very effective in large, navigable streams and rivers where sufficient flow depth is available for power boats.…”

“…Whereas topobathymetric LiDAR performance in littoral systems is well documented and has become an established technique (Nayegandhi et al, 2006(Nayegandhi et al, , 2009, this approach has not been systematically tested (McKean et al, 2009cMandlburger et al, 2015;Pan et al, 2015) and accepted in riverine systems (Kinzel et al, 2013;Legleiter et al, 2015). Nonetheless, recent investigations showed the advantages of bathymetric LiDAR in ecohydraulics applications that aim to understand the interaction between biotic and abiotic processes in lotic systems (Tonina and McKean, 2010;Tonina et al, 2011;McKean and Tonina, 2013).…”

A framework for modeling connections between hydraulics, water surface roughness, and surface reflectance in open channel flows

“…We considered three distinct morphologic units: pool, run, and riffle. Water surface slopes for each unit were calculated using LiDAR‐derived water surface elevations [ Legleiter et al , ]. Field measurements of flow depth and depth‐averaged velocity within the three morphologic units were collected in August 2015 by personnel from the University of Wyoming along the reach located at 43.808781°N, 110.557866°W.…”