Quantifying Below-Water Fluvial Geomorphic Change: The Implications of Refraction Correction, Water Surface Elevations, and Spatially Variable Error

Abstract

Much of the geomorphic work of rivers occurs underwater. As a result, high resolutionquantification of geomorphic change in these submerged areas is important. Currently, to quantify thischange, multiple methods are required to get high resolution data for both the exposed and submergedareas. Remote sensing methods are often limited to the exposed areas due to the challenges imposedby the water, and those remote sensing methods for below the water surface require the collection ofextensive calibration data in-channel, which is time-consuming, labour-intensive, and sometimesprohibitive in dicult-to-access areas. Within this paper, we pioneer a novel approach for quantifyingabove- and below-water geomorphic change using Structure-from-Motion photogrammetry andinvestigate the implications of water surface elevations, refraction correction measures, and thespatial variability of topographic errors. We use two epochs of imagery from a site on the River Teme,Herefordshire, UK, collected using a remotely piloted aircraft system (RPAS) and processed usingStructure-from-Motion (SfM) photogrammetry. For the first time, we show that: (1) Quantification ofsubmerged geomorphic change to levels of accuracy commensurate with exposed areas is possiblewithout the need for calibration data or a dierent method from exposed areas; (2) there is minimaldierence in results produced by dierent refraction correction procedures using predominantlynadir imagery (small angle vs. multi-view), allowing users a choice of software packages/processingcomplexity; (3) improvements to our estimations of water surface elevations are critical for accuratetopographic estimation in submerged areas and can reduce mean elevation error by up to 73%;and (4) we can use machine learning, in the form of multiple linear regressions, and a Gaussian NaïveBayes classifier, based on the relationship between error and 11 independent variables, to generate ahigh resolution, spatially continuous model of geomorphic change in submerged areas, constrained byspatially variable error estimates. Our multiple regression model is capable of explaining up to 54%of magnitude and direction of topographic error, with accuracies of less than 0.04 m. With on-goingtesting and improvements, this machine learning approach has potential for routine application inspatially variable error estimation within the RPAS–SfM workflow.

Methodology for Combining Data Acquired by Unmanned Surface and Aerial Vehicles to Create Digital Bathymetric Models in Shallow and Ultra-Shallow Waters

“…Other factors can also affect the development of any photogrammetric products, such as specific atmospheric conditions affecting the quality of the photo radiometry [16]; blurs appearing on the images [17]; water surface reflections [18]; and sea state, sunglint, and solar elevation angles [19]. In [20], refraction issues are additionally raised. It is claimed that using the SfM approach for submerged areas faces additional challenges, posed by the presence of water and, in particular, the effects of refraction.…”

Applications of Unmanned Aerial Systems (UASs) in Hydrology: A Review

“…Carrivick and Smith [16] reviewed the applications of Sf M photogrammetry and UAS technology in aquatic environments, highlighting the need of automated procedures to correct refraction. The issue of refraction correction has been addressed in several works; Woodget et al [35] quantified above and below-water geomorphic changes in a river through Sf M photogrammetry and analyzed the implications of refraction, water surface elevation and the spatial variability of topographic errors. They demonstrated that it is possible to quantify submerged geomorphic changes with levels of accuracy of less than 4 cm similar to that from exposed areas without the need of calibration data and that, using nadir imagery, the results obtained after different refraction corrections are practically the same.…”

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

“…The third paper focused on remote sensing of water depth using a different, passive optical approach that has seen increasingly widespread application in river research: Structure-from-Motion (SfM) photogrammetry. Woodget et al [9] used a sUAS to acquire multiple, overlapping images from a small river in the United Kingdom for two different time periods and showed that the level of topographic accuracy achieved in submerged areas was similar to that in exposed areas, even without separate calibration data and different SfM processing methods for within the wetted channel. Importantly, these findings imply that multiple techniques are not required to map both the subaqueous channel bed and dry bar surfaces, nor are extensive in-channel survey data.…”

“…Moreover, the selection of a refraction correction method had little impact on results derived from near-nadir imagery. Instead, Woodget et al [9] identified improved estimation of water surface elevations as the most direct means of increasing the accuracy of SfM-based bed elevation measurements. The paper also introduced a machine learning framework for producing continuous, high-resolution maps of geomorphic change that include spatially variable error estimates.…”