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

Remote Sensing

Amy S. Woodget¹  James T. Dietrich²  Robin T. Wilson³ 

[1] Department of Geography and Environment, School of Social Sciences and Humanities, Loughborough University, Loughborough LE11 3TU, UK;Department of Geography, University of Northern Iowa, Cedar Falls, IA 50614, USA;Independent Scholar, Southampton, SO16 6DB, UK;

关键词: fluvial;    geomorphology;    change detection;    remotely piloted aircraft system;    refraction correction;    structure-from-motion photogrammetry;    water surface elevation;    topographic error;    machine learning;   

DOI  :  10.3390/rs11202415

来源: DOAJ

【 摘 要 】

Much of the geomorphic work of rivers occurs underwater. As a result, high resolution
quantification of geomorphic change in these submerged areas is important. Currently, to quantify this
change, multiple methods are required to get high resolution data for both the exposed and submerged
areas. Remote sensing methods are often limited to the exposed areas due to the challenges imposed
by the water, and those remote sensing methods for below the water surface require the collection of
extensive calibration data in-channel, which is time-consuming, labour-intensive, and sometimes
prohibitive in dicult-to-access areas. Within this paper, we pioneer a novel approach for quantifying
above- and below-water geomorphic change using Structure-from-Motion photogrammetry and
investigate the implications of water surface elevations, refraction correction measures, and the
spatial 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 using
Structure-from-Motion (SfM) photogrammetry. For the first time, we show that: (1) Quantification of
submerged geomorphic change to levels of accuracy commensurate with exposed areas is possible
without the need for calibration data or a dierent method from exposed areas; (2) there is minimal
dierence in results produced by dierent refraction correction procedures using predominantly
nadir imagery (small angle vs. multi-view), allowing users a choice of software packages/processing
complexity; (3) improvements to our estimations of water surface elevations are critical for accurate
topographic 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 Naïve
Bayes classifier, based on the relationship between error and 11 independent variables, to generate a
high resolution, spatially continuous model of geomorphic change in submerged areas, constrained by
spatially 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-going
testing and improvements, this machine learning approach has potential for routine application in
spatially variable error estimation within the RPAS−SfM workflow.

【 授权许可】

Unknown