Passive optical remote sensing of river channel morphology and in-stream habitat: Physical basis and feasibility

Successful monitoring of ecologically significant, vulnerable fluvial systems will require improved quantitative techniques for mapping channel morphology and in-stream habitat. In this study, we assess the ability of remote sensing to contribute to these objectives by (1) describing the underlying radiative transfer processes, drawing upon research conducted in shallow marine environments; (2) modeling the effects of water depth, substrate type, suspended sediment concentration, and surface turbulence; (3) quantifying the limitations imposed by finite detector sensitivity and linear quantization; and (4) evaluating two depth retrieval algorithms using simulated and field-measured spectra and archival imagery. The degree to which variations in depth and substrate can be resolved depends on bottom albedo and water column optical properties, and scattering by suspended sediment obscures substrate spectral features and reduces the resolution of depth estimates. Converting continuous radiance signals to discrete digital numbers implies that depth estimates take the form of contour intervals that become wider as depth increases and as bottom albedo and detector sensitivity decrease. Our results indicate that a simple band ratio can provide an image-derived variable that is strongly linearly related to water depth across a broad range of stream conditions. This technique outperformed the linear transform method used in previous stream studies, most notably for upwelling radiance spectra [R-2=0.79 for the ln(560 nm/690 nm) ratio]. Applied to uncalibrated multispectral and hyperspectral images of a fourth-order stream in Yellowstone National Park, this flexible technique produced hydraulically reasonable maps of relative depth. Although radiometric precision and spatial resolution will impose fundamental limitations in practice, remote mapping of channel morphology and in-stream habitat is feasible and can become a powerful tool for scientists and managers. (C) 2004 Elsevier Inc. All rights reserved.