Quantifying Submerged Fluvial Topography Using Hyperspatial Resolution UAS Imagery and Structure From Motion Photogrammetry.

Quantifying the topography of rivers and their associated bedforms has been a fundamental concern of fluvial geomorphology for decades. Such data, acquired at high temporal and spatial resolutions, are increasingly in demand for process-oriented investigations of flow hydraulics, sediment dynamics and in-stream habitat. In these riverine environments, the most challenging region for topographic measurement is the wetted, submerged channel. Generally, dry bed topography and submerged bathymetry are measured using different methods and technology. This adds to the costs, logistical challenges and data processing requirements of comprehensive river surveys. However, some technologies are capable of measuring the submerged topography. Through-water photogrammetry and bathymetric LiDAR are capable of reasonably accurate measurements of channel beds in clear water. Whilst the cost of bathymetric LiDAR remains high and its resolution relatively coarse, the recent developments in photogrammetry using Structure from Motion (SfM) algorithms promise a fundamental shift in the accessibility of topographic data for a wide range of settings. Here we present results demonstrating the potential of so called SfM-photogrammetry for quantifying both exposed and submerged fluvial topography at the mesohabitat scale. We show that imagery acquired from a rotary-winged Unmanned Aerial System (UAS) can be processed in order to produce digital elevation models (DEMs) with hyperspatial resolutions (c. 0.02 m) for two different river systems over channel lengths of 50-100 m. Errors in submerged areas range from 0.016 m to 0.089 m, which can be reduced to between 0.008 m and 0.053 m with the application of a simple refraction correction. This work therefore demonstrates the potential of UAS platforms and SfM-photogrammetry as a single technique for surveying fluvial topography at the mesoscale (defined as lengths of channel from c.10 m to a few hundred metres). This article is protected by copyright. All rights reserved.

The Accuracy and Reliability of Traditional Surface Flow Type Mapping: Is It Time for A New Method of Characterising Physical River Habitat?

Surface flow types (SFT) are advocated as ecologically relevant hydraulic units, often mapped visually from the bankside to characterise rapidly the physical habitat of rivers. SFT mapping is simple, non-invasive and cost-efficient. However, it is also qualitative, subjective and plagued by difficulties in recording accurately the spatial extent of SFT units. Quantitative validation of the underlying physical habitat parameters is often lacking, and does not consistently differentiate between SFTs. Here, we investigate explicitly the accuracy, reliability and statistical separability of traditionally mapped SFTs as indicators of physical habitat, using independent, hydraulic and topographic data collected during three surveys of a c. 50m reach of the River Arrow, Warwickshire, England. We also explore the potential of a novel remote sensing approach, comprising a small unmanned aerial system (sUAS) and Structure-from-Motion photogrammetry (SfM), as an alternative method of physical habitat characterisation. Our key findings indicate that SFT mapping accuracy is highly variable, with overall mapping accuracy not exceeding 74%. Results from analysis of similarity (ANOSIM) tests found that strong differences did not exist between all SFT pairs. This leads us to question the suitability of SFTs for characterising physical habitat for river science and management applications. In contrast, the sUAS-SfM approach provided high resolution, spatially continuous, spatially explicit, quantitative measurements of water depth and point cloud roughness at the microscale (spatial scales ≤1m). Such data are acquired rapidly, inexpensively, and provide new opportunities for examining the heterogeneity of physical habitat over a range of spatial and temporal scales. Whilst continued refinement of the sUAS-SfM approach is required, we propose that this method offers an opportunity to move away from broad, mesoscale classifications of physical habitat (spatial scales 10-100m), and towards continuous, quantitative measurements of the continuum of hydraulic and geomorphic conditions which actually exists at the microscale.