Mapping Substrate Types and Compositions in Shallow Streams

Abstract

Remote sensing of riverbed compositions could enable advances in hydro-morphological and habitat modeling. Substrate mapping in fluvial systems has not received as much attention as in nearshore, optically shallow inland, and coastal waters. As finer spatial-resolution image data become more available, a need emerges to expand research on the remote sensing of riverbed composition. For instance, research to date has primarily been based on spectral reflectance data from above the water surface without accounting for attenuation by the water-column. This study analyzes the impacts of water-column correction for substrate mapping in shallow fluvial systems (depth < 1 m). To do so, we performed three different experiments: (a) analyzing spectroscopic measurements in a hydraulic laboratory setting, (b) simulating water-leaving radiances under various optical scenarios, and (c) evaluating the potential to map bottom composition from a WorldView-3 (WV3) image of a river in Northern Italy. Following the retrieval of depth and diffuse attenuation coefficient ( K d ), bottom reflectances were estimated using a water-column correction method. The results indicated significant enhancements in streambed maps based on bottom reflectances relative to maps produced from above-water spectra. Accounting for deep-water reflectance, embedded in the water-column correction, was demonstrated to have the greatest impact on the retrieval of bottom reflectance in NIR bands, when the water column is relatively thick (>0.5 m) and/or when the water is turbid. We also found that the WV3’s red-edge band (i.e., 724 nm) considerably improved the characterization of submerged aquatic vegetation (SAV) densities from either above-water or retrieved bottom spectra. This study further demonstrated the feasibility of mapping SAV density classes from a WV3 image of the Sarca River in Italy by retrieving the bottom reflectances.

Evaluation of Weighting Average Functions as a Simplification of the Radiative Transfer Simulation in Vertically Inhomogeneous Eutrophic Waters

“…The magnitude and spectrum shape of K d (λ, z) in this study is accordance with the previous studies in turbid productive waters [50][51][52]. However, K d (λ, z) has low value in the blue region, and increases to high values in the red and NIR wavelength in clear waters [53,54]. The relatively high values of K d (λ, z) in the red and NIR region results from the increasing absorption coefficients of pure water in these regions.…”

Physics-based Bathymetry and Water Quality Retrieval Using PlanetScope Imagery: Impacts of 2020 COVID-19 Lockdown and 2019 Extreme Flood in the Venice Lagoon

“…For instance, only bottom-reflected radiance contains bathymetric information. Therefore, water depth can be retrieved only in optically-shallow waters where the light can penetrate the entire water-column and travel back to the sensor after having an interaction with the substrate [27][28][29]. Other radiance components, such as the contributions of water column and surface can complicate depth retrieval particularly if they are spatially variable over the water body.…”

DepthLearn: Learning to Correct the Refraction on Point Clouds Derived from Aerial Imagery for Accurate Dense Shallow Water Bathymetry Based on SVMs-Fusion with LiDAR Point Clouds

“…Empirical SDB methods require certain bands in the visible wavelength-with blue and green being the most widely used-and a set of known in situ depths as the only inputs in simple or multiple linear regressions, which leads to bathymetry estimations in a given area. Accuracies of the calibrated model in the two validation sites reached an RMSE of 1.67 m. In [49], a multiple regression bathymetry model was employed for substrate mapping in shallow fluvial systems having depth <1 m. To do so the authors analyzed spectroscopic measurements in a hydraulic laboratory setting, simulated water-leaving radiances under various optical scenarios and evaluated the potential to map bottom composition from a WorldView-3 image. In [50], authors compare the potential of through-water photogrammetry and spectral depth approaches to extract water depth for environmental applications.…”