The production of topographic datasets is of increasing interest and application throughout the geomorphic sciences, and river science is no exception. Consequently, a wide range of topographic measurement methods have evolved. Despite the range of available methods, the production of high resolution, high quality digital elevation models (DEMs) requires a significant investment in personnel time, hardware and/or software. However, image‐based methods such as digital photogrammetry have been decreasing in costs. Developed for the purpose of rapid, inexpensive and easy three‐dimensional surveys of buildings or small objects, the ‘structure from motion’ photogrammetric approach (SfM) is an image‐based method which could deliver a methodological leap if transferred to geomorphic applications, requires little training and is extremely inexpensive. Using an online SfM program, we created high‐resolution digital elevation models of a river environment from ordinary photographs produced from a workflow that takes advantage of free and open source software. This process reconstructs real world scenes from SfM algorithms based on the derived positions of the photographs in three‐dimensional space. The basic product of the SfM process is a point cloud of identifiable features present in the input photographs. This point cloud can be georeferenced from a small number of ground control points collected in the field or from measurements of camera positions at the time of image acquisition. The georeferenced point cloud can then be used to create a variety of digital elevation products. We examine the applicability of SfM in the Pedernales River in Texas (USA), where several hundred images taken from a hand‐held helikite are used to produce DEMs of the fluvial topographic environment. This test shows that SfM and low‐altitude platforms can produce point clouds with point densities comparable with airborne LiDAR, with horizontal and vertical precision in the centimeter range, and with very low capital and labor costs and low expertise levels. Copyright © 2012 John Wiley & Sons, Ltd.

12Successful monitoring of ecologically significant, vulnerable fluvial systems will require improved quantitative techniques for 13 mapping channel morphology and in-stream habitat. In this study, we assess the ability of remote sensing to contribute to these 14 objectives by (1) describing the underlying radiative transfer processes, drawing upon research conducted in shallow marine 15 environments; (2) modeling the effects of water depth, substrate type, suspended sediment concentration, and surface turbulence; (3) 16 quantifying the limitations imposed by finite detector sensitivity and linear quantization; and (4) evaluating two depth retrieval 17 algorithms using simulated and field-measured spectra and archival imagery. The degree to which variations in depth and substrate can 18 be resolved depends on bottom albedo and water column optical properties, and scattering by suspended sediment obscures substrate 19 spectral features and reduces the resolution of depth estimates. Converting continuous radiance signals to discrete digital numbers 20 implies that depth estimates take the form of contour intervals that become wider as depth increases and as bottom albedo and detector 21 sensitivity decrease. Our results indicate that a simple band ratio can provide an image-derived variable that is strongly linearly related 22 to water depth across a broad range of stream conditions. This technique outperformed the linear transform method used in previous 23 stream studies, most notably for upwelling radiance spectra [R 2 =0.79 for the ln(560 nm/690 nm) ratio]. Applied to uncalibrated 24 multispectral and hyperspectral images of a fourth-order stream in Yellowstone National Park, this flexible technique produced 25 hydraulically reasonable maps of relative depth. Although radiometric precision and spatial resolution will impose fundamental 26 limitations in practice, remote mapping of channel morphology and in-stream habitat is feasible and can become a powerful tool for 27 scientists and managers.

At watershed extents, our understanding of river form, process and function is largely based on locally intensive mapping of river reaches, or on spatially extensive but low density data scattered throughout a watershed (e.g. cross sections). The net effect has been to characterize streams as discontinuous systems. Recent advances in optical remote sensing of rivers indicate that it should now be possible to generate accurate and continuous maps of in‐stream habitats, depths, algae, wood, stream power and other features at sub‐meter resolutions across entire watersheds so long as the water is clear and the aerial view is unobstructed. Such maps would transform river science and management by providing improved data, better models and explanation, and enhanced applications. Obstacles to achieving this vision include variations in optics associated with shadows, water clarity, variable substrates and target–sun angle geometry. Logistical obstacles are primarily due to the reliance of existing ground validation procedures on time‐of‐flight field measurements, which are impossible to accomplish at watershed extents, particularly in large and difficult to access river basins. Philosophical issues must also be addressed that relate to the expectations around accuracy assessment, the need for and utility of physically based models to evaluate remote sensing results and the ethics of revealing information about river resources at fine spatial resolutions. Despite these obstacles and issues, catchment extent remote river mapping is now feasible, as is demonstrated by a proof‐of‐concept example for the Nueces River, Texas, and examples of how different image types (radar, lidar, thermal) could be merged with optical imagery. The greatest obstacle to development and implementation of more remote sensing, catchment scale ‘river observatories’ is the absence of broadly based funding initiatives to support collaborative research by multiple investigators in different river settings. Copyright © 2007 John Wiley & Sons, Ltd.

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