Measuring Channel Planform Change From Image Time Series: A Generalizable, Spatially Distributed, Probabilistic Method for Quantifying Uncertainty

Abstract Channels change in response to natural or anthropogenic fluctuations in streamflow and/or sediment supply and measurements of channel change are critical to many river management applications. Whereas repeated field surveys are costly and time‐consuming, remote sensing can be used to detect channel change at multiple temporal and spatial scales. Repeat images have been widely used to measure long‐term channel change, but these measurements are only significant if the magnitude of change exceeds the uncertainty. Existing methods for characterizing uncertainty have two important limitations. First, while the use of a spatially variable image co‐registration error avoids the assumption that errors are spatially uniform, this type of error, as originally formulated, can only be applied to linear channel adjustments, which provide less information on channel change than polygons of erosion and deposition. Second, previous methods use a level‐of‐detection (LoD) threshold to remove non‐significant measurements, which is problematic because real changes that occurred but were smaller than the LoD threshold would be removed. In this study, we present a new method of quantifying uncertainty associated with channel change based on probabilistic, spatially varying estimates of co‐registration error and digitization uncertainty that obviates a LoD threshold. The spatially distributed probabilistic (SDP) method can be applied to both linear channel adjustments and polygons of erosion and deposition, making this the first uncertainty method generalizable to all metrics of channel change. Using a case study from the Yampa River, Colorado, we show that the SDP method reduced the magnitude of uncertainty and enabled us to detect smaller channel changes as significant. Additionally, the distributional information provided by the SDP method allowed us to report the magnitude of channel change with an appropriate level of confidence in cases where a simple LoD approach yielded an indeterminate result

Editorial for the Special Issue “Remote Sensing of Flow Velocity, Channel Bathymetry, and River Discharge”

River discharge is a fundamental hydrologic quantity that summarizes how a watershed transforms the input of precipitation into output as channelized streamflow [...

Characterizing the spatial structure of river morphology and hydraulics: Remote mapping and geostatistical modeling of dynamic fluvial systems

A river’smorphologic evolution reflects a complex suite of interactions between the form of the channel and the flow and sediment transport processes acting to modify that form. Efforts to better understand these interactions thus require an ability to measure river morphology and characterize its spatial structure. The primary objectives of this dissertation are to develop such a capacity and to apply these methods to the study of channel change. The feasibility of passive optical remote sensing of river bathymetry was examined via radiative transfer modeling, field spectroscopy, and image processing. These analyses indicated the robust performance of a simple, ratio-based depth retrieval algorithm; the conditions under which this technique is appropriate were defined by considering the relativemagnitudes of various radiance components. This physics-based approach allowed the utility of remotely sensed data to be evaluated with a forward image model, which highlighted the influence of both channel and sensor characteristics on depth retrieval accuracy and precision. The potential to obtain a more complete topographic representation of the fluvial environment by combining spectrally-based bathymetry with LiDAR data from bars and floodplains was also demonstrated. Remote sensing thus provides essentially continuous, high resolution data on river morphology, but improved, spatially explicit analytical methods are needed to fully capitalize on this information. This need is addressed by a geostatistical framework consisting of a channel-centered coordinate system and flexible modeling tools for describing reach-scale spatial patterns. Kriging-based spatial prediction techniques can yield geomorphic insight by revealing departures from an a priori expectation of channel form. Similarly, the spatial structure of river morphology and hydraulics can be quantified using a geostatistical metric called the variogram. Survey data from a recently restored channel and three reaches of a dynamic gravel-bed river in Yellowstone National Park documented various styles and degrees of channel change, and variogram models developed from these data were used to examine the relationship between geomorphic context, disturbance history, and the balance between sediment supply and transport capacity. Quantitatively characterizing the variability and spatial organization of channel form will advance our understanding of river morphodynamics

Spatial prediction of river channel topography by Kriging

Topographic information is fundamental to geomorphic inquiry, and spatial prediction of bed elevation from irregular survey data is an important component of many reach-scale studies. Kriging is a geostatistical technique for obtaining these predictions along with measures of their reliability, and this paper outlines a specialized framework intended for application to river channels. Our modular approach includes an algorithm for transforming the coordinates of data and prediction locations to a channel-centered coordinate system, several different methods of representing the trend component of topographic variation and search strategies that incorporate geomorphic information to determine which survey data are used to make a prediction at a specific location. For example, a relationship between curvature and the lateral position of maximum depth can be used to include cross-sectional asymmetry in a two-dimensional trend surface model, and topographic breaklines can be used to restrict which data are retained in a local neighborhood around each prediction location. Using survey data from a restored gravel-bed river, we demonstrate how transformation to the channel-centered coordinate system facilitates interpretation of the variogram, a statistical model of reach-scale spatial structure used in kriging, and how the choice of a trend model affects the variogram of the residuals from that trend. Similarly, we show how decomposing kriging predictions into their trend and residual components can yield useful information on channel morphology. Cross-validation analyses involving different data configurations and kriging variants indicate that kriging is quite robust and that survey density is the primary control on the accuracy of bed elevation predictions. The root mean-square error of these predictions is directly proportional to the spacing between surveyed cross-sections, even in a reconfigured channel with a relatively simple morphology; sophisticated methods of spatial prediction are no substitute for field data

Inferring Surface Flow Velocities in Sediment-Laden Alaskan Rivers from Optical Image Sequences Acquired from a Helicopter

The remote, inaccessible location of many rivers in Alaska creates a compelling need for remote sensing approaches to streamflow monitoring. Motivated by this objective, we evaluated the potential to infer flow velocities from optical image sequences acquired from a helicopter deployed above two large, sediment-laden rivers. Rather than artificial seeding, we used an ensemble correlation particle image velocimetry (PIV) algorithm to track the movement of boil vortices that upwell suspended sediment and produce a visible contrast at the water surface. This study introduced a general, modular workflow for image preparation (stabilization and geo-referencing), preprocessing (filtering and contrast enhancement), analysis (PIV), and postprocessing (scaling PIV output and assessing accuracy via comparison to field measurements). Applying this method to images acquired with a digital mapping camera and an inexpensive video camera highlighted the importance of image enhancement and the need to resample the data to an appropriate, coarser pixel size and a lower frame rate. We also developed a Parameter Optimization for PIV (POP) framework to guide selection of the interrogation area (IA) and frame rate for a particular application. POP results indicated that the performance of the PIV algorithm was highly robust and that relatively large IAs (64–320 pixels) and modest frame rates (0.5–2 Hz) yielded strong agreement ( R 2 > 0.9 ) between remotely sensed velocities and field measurements. Similarly, analysis of the sensitivity of PIV accuracy to image sequence duration showed that dwell times as short as 16 s would be sufficient at a frame rate of 1 Hz and could be cut in half if the frame rate were doubled. The results of this investigation indicate that helicopter-based remote sensing of velocities in sediment-laden rivers could contribute to noncontact streamgaging programs and enable reach-scale mapping of flow fields

Forward and inverse transformations between cartesian and channel-fitted coordinate systems for meandering rivers

The spatial referencing of river channels is complicated by their meandering planform, which dictates that Euclidean distance in a Cartesian reference frame is not an appropriate metric. Channel-fitted coordinate systems are thus widely used in application-oriented geostatistics as well as theoretical fluid mechanics, where flow patterns are described in terms of a streamwise axis s along the channel centerline and an axis n normal to that centerline. A means of transforming geographic (x, y) coordinates to their equivalents in the (s, n) space and vice versa is needed to relate the two frames of reference, and this paper describes a pair of transformation algorithms that are explicitly intended for reach-scale studies of modern rivers. The forward transformation from Cartesian to channel-fitted coordinates involves parametric description of the centerline using cubic splines, calculation of centerline normal vectors and curvature using results from differential geometry, and an efficient local search to find in-channel data points and compute their (s, n) coordinates. The inverse transformation finds the nearest vertices of a discretized centerline and uses a finite difference approximation to the streamwise rates of change of the centerline's Cartesian coordinates to obtain the geographic equivalent of a point in the (s, n) space. The performance of these algorithms is evaluated using: (i) field data from a gravel-bed river to examine the effects of initial centerline digitization and subsequent filtering; and (ii) analytically-defined centerlines and simulated coordinates to assess transformation accuracy and sensitivity to centerline curvature and discretization. Any discrepancy between a point's known coordinates in one frame of reference and the coordinates produced via transformation from the other coordinate system constitutes a transformation error, and our results indicate that these errors are 2-4% and 0.2-0.5% of the channel width for the field case and simulated centerlines, respectively. The primary sources of transformation error are the initial digitization of the centerline and the relationship between centerline curvature and discretization. © Springer Science & Business Media, Inc. 2006

Mapping Benthic Algae and Cyanobacteria in River Channels from Aerial Photographs and Satellite Images: A Proof-of-Concept Investigation on the Buffalo National River, AR, USA

Although rivers are of immense practical, aesthetic, and recreational value, these aquatic habitats are particularly sensitive to environmental changes. Increasingly, changes in streamflow and water quality are resulting in blooms of bottom-attached (benthic) algae, also known as periphyton, which have become widespread in many water bodies of US national parks. Because these blooms degrade visitor experiences and threaten human and ecosystem health, improved methods of characterizing benthic algae are needed. This study evaluated the potential utility of remote sensing techniques for mapping variations in algal density in shallow, clear-flowing rivers. As part of an initial proof-of-concept investigation, field measurements of water depth and percent cover of benthic algae were collected from two reaches of the Buffalo National River along with aerial photographs and multispectral satellite images. Applying a band ratio algorithm to these data yielded reliable depth estimates, although a shallow bias and moderate level of precision were observed. Spectral distinctions among algal percent cover values ranging from 0 to 100% were subtle and became only slightly more pronounced when the data were aggregated to four ordinal levels. A bagged trees machine learning model trained using the original spectral bands and image-derived depth estimates as predictor variables was used to produce classified maps of algal density. The spatial and temporal patterns depicted in these maps were reasonable but overall classification accuracies were modest, up to 64.6%, due to a lack of spectral detail. To further advance remote sensing of benthic algae and other periphyton, future studies could adopt hyperspectral approaches and more quantitative, continuous metrics such as biomass