Preprints
https://doi.org/10.5194/gmd-2021-309
https://doi.org/10.5194/gmd-2021-309

Submitted as: model description paper 17 Nov 2021

Submitted as: model description paper | 17 Nov 2021

Review status: this preprint is currently under review for the journal GMD.

Nonparametric-based estimation method for river cross-sections with point cloud data from UAV photography URiver-X version 1.0 -methodology development

Taesam Lee and Kiyoung Sung Taesam Lee and Kiyoung Sung
  • Department of Civil Engineering, ERI, Gyeongsang National University, 501 Jinju-daero, Jinju, Gyeongnam, South Korea, 660-701

Abstract. Aerial surveying with unmanned aerial vehicles (UAVs) has been popularly employed in river management and flood monitoring. One of the major processes in UAV aerial surveying for river applications is to demarcate the cross-section of a river. From the photo images of aerial surveying, a point cloud dataset can be abstracted with the structure from motion (SfM) technique. To accurately demarcate the cross-section from the cloud points, an appropriate delineation technique is required to reproduce the characteristics of natural and manmade channels, including abrupt changes, bumps, and lined shapes, even though the basic shape of natural and manmade channels is a trapezoidal shape. Therefore, a nonparametric-based estimation technique, called the K-nearest neighbor local linear regression (KLR) model, was tested in the current study to demarcate the cross-section of a river with a point cloud dataset from aerial surveying. The proposed technique was tested with a simulated dataset based on trapezoidal channels and compared with the traditional polynomial regression model and another nonparametric technique, locally weighted scatterplot smoothing (LOWESS). Furthermore, the KLR model was applied to a real case study in the Migok-cheon stream, South Korea. The results indicate that the proposed KLR model can be a suitable alternative for demarcating the cross-section of a river with point cloud data from UAV aerial surveying by reproducing the critical characteristics of natural and manmade channels, including abrupt changes and small bumps, as well as the overall trapezoidal shape.

Taesam Lee and Kiyoung Sung

Status: open (until 12 Jan 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Taesam Lee and Kiyoung Sung

Model code and software

URiver-X ver1 Matlab Code Taesam Lee http://dx.doi.org/10.17632/xdw4cgnvhm.1

Taesam Lee and Kiyoung Sung

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Short summary
A nonparametric-based estimation technique, called the K-nearest neighbor local linear regression (KLR) model, was proposed in the current study to demarcate the cross-section of a river with a point cloud dataset from UAV photogrammetry. The results indicate that the proposed KLR model can be a suitable alternative by reproducing the critical characteristics of natural and manmade channels, including abrupt changes and small bumps, as well as the overall trapezoidal shape.