The utility of remote sensing in the study of fluvial systems depends upon the extent to which image data can be used to derive quantitative information of sufficient accuracy and precision for specific applications. In this study, we evaluate the effects of channel morphology on depth retrieval by coupling a radiative transfer model to various morphologic scenarios. Upwelling radiance L-u spectra generated for a range of depths (2-150 cm) and benthic cover types (limestone, periphyton, and gravel) were linearly mixed to simulate subpixel bed topography and substrate heterogeneity. For sloping bottoms, solar-streambed geometry modified L-u relative to a level bottom, especially for beds sloping steeply away from the sun. Aggregate pixel scale L-u spectra were compared to a database of simulated spectra to determine the radiance-equivalent depth of a uniform bottom (REDUB). REDUB spectra for hypothetical stepped streambeds indicated underestimation of the actual area-weighted mean depth, but the ln(L-u,L-560/L-u,L-690) REDUB ratio consistently reproduced the pixel-scale mean for beta distributions of depths. Similarly, when both dark periphyton and bright limestone substrates occurred within a pixel, REDUB spectra produced large errors while the ratio proved robust. Along channel banks, pixels will inevitably be mixed, and our simulations indicated that although bank fractions estimated by spectral mixture analysis were highly accurate for vegetated cutbanks, gravel bars were sensitive to the selection of both aquatic and terrestrial end members and subject to relatively large fraction errors. These theoretical results were tested using a ratio-based relative depth map and two-end member mixture models derived from a hyperspectral image of the Lamar River in Yellowstone National Park, which also served to illustrate the importance and applicability of our simulations. The primary conclusions of this study are that 1) the ratio-based algorithm is well-suited to complex river channels; 2) channel morphology and sensor spatial resolution must be considered jointly during data collection and analysis; and 3) the accuracy and precision of depth estimates are influenced by channel morphology and thus vary spatially.

Improved techniques are needed to characterize complex fluvial systems and monitor ecologically important, yet highly vulnerable riverine environments. This paper explores potential alternatives to traditional mapping of in-stream habitat and presents fuzzy set theory as a means of departing from the rigid. Boolean. object-based framework. We utilize hydrodynamic modeling, remotely sensed data, and fuzzy clustering to obtain classifications that allow for continuous partial membership and gradual transitions among habitat types. Methods of assessing cluster validity are available, but data quality is a crucial consideration. Crisp, vector-based representations can be derived from faster fuzzy classifications by applying a threshold to maximum membership values. This process results in conditional objects separated by ambiguous transition zones. and a compromise must be reached between the proportion of the channel assigned to polygons and the certainty with which this assignment can be made. Spatial patterns of classification uncertainty can also be used to identify areas of confusion, infer boundaries of variable width. and highlight areas of increased habitat diversity. Hydraulic modeling and remote sensing complement one another and, together with field work, could provide a more realistic representation of the fluvial environment.

Although previous research has documented well-organized interactions between the turbulent flow field and an irregular boundary, the spatial variability of turbulent flow characteristics at the reach-scale remains poorly understood. In this paper, we present detailed field measurements of three-dimensional flow velocities and turbulence intensities in a high-gradient, cobble-bed riffle from three discharges; additional data on sediment grain size and bed topography were used to characterize boundary roughness. An acoustic Doppler velocimeter was used to measure velocities along five cross-sections within a 6 in long reach of the North Fork Cache La Poudre River; vertical profiles were also measured along the channel thalweg. We adopted a spatially explicit stochastic hydraulic approach and focused not on coherent flow structures per se but rather time-averaged, reach-scale variability and spatial pattern. Scaling velocities and turbulence intensities by the reach-averaged friction velocity U. accounted for changes in flow depth and enabled comparisons among the three discharges. We quantified the effects of stage and roughness by assessing differences among probability distributions of hydraulic quantities and by examining geostatistical metrics of spatial variability. We computed semivariograms for streamwise and transverse directions and fit parametric models to summarize the spatial structure of each variable at each discharge. Cross-correlograms were also used to describe the local and lagged effects of boundary roughness on flow characteristics. Although the probability distributions yielded some insight, incorporating spatial information revealed important elements of stage-dependent flow structure. The development of secondary currents and flow convergence at higher stages were clearly documented in maps and semivariograms. In general, the spatial structure of the flow field became smoother and more continuous as stage increased and the effects of boundary roughness diminished. Although roughness elements do influence velocities and turbulence intensities, our data suggest that the flow primarily responds to the gross morphology of the channel and that flow depth is the primary control on flow structure. The geostatistical framework proved useful, and our results indicate that a complete stochastic description must also be explicitly spatial. (c) 2006 Elsevier B.V All rights reserved.