Preprints
https://doi.org/10.5194/gmd-2023-57
https://doi.org/10.5194/gmd-2023-57
Submitted as: model description paper
 | 
27 Jun 2023
Submitted as: model description paper |  | 27 Jun 2023
Status: this preprint was under review for the journal GMD but the revision was not accepted.

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

Taesam Lee, Jaewoo Park, Sunghyun Hwang, and Vijay Singh

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 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. Therefore, a nonparametric 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 synthetically simulated trapezoidal, U-shape, and V-shape channels. In addition, the proposed KLR model was compared with the traditional polynomial regression model and another nonparametric technique, locally weighted scatterplot smoothing (LOWESS). The experimental study was performed with the river experiment center in Andong, South Korea. Furthermore, the KLR model was applied to two real case studies in the Migok-cheon stream on Hapcheon-gun and Pori-cheon stream on Yecheon-gun and compared to the other models. With the extensive applications to the feasible river channels, the results indicated 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 different shapes. Finally, the limitation of the UAV-driven demarcation approach was also discussed due to the penetrability of RGB sensors to water.

Taesam Lee et al.

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2023-57', Anonymous Referee #1, 31 Jul 2023
    • AC1: 'Reply on RC1', Taesam Lee, 04 Sep 2023
  • RC2: 'Comment on gmd-2023-57', Anonymous Referee #2, 04 Aug 2023
    • AC2: 'Reply on RC2', Taesam Lee, 04 Sep 2023
  • RC3: 'Comment on gmd-2023-57', Anonymous Referee #3, 05 Aug 2023
  • RC4: 'Comment on gmd-2023-57', Anonymous Referee #4, 10 Aug 2023
    • AC5: 'Reply on RC4', Taesam Lee, 04 Sep 2023

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2023-57', Anonymous Referee #1, 31 Jul 2023
    • AC1: 'Reply on RC1', Taesam Lee, 04 Sep 2023
  • RC2: 'Comment on gmd-2023-57', Anonymous Referee #2, 04 Aug 2023
    • AC2: 'Reply on RC2', Taesam Lee, 04 Sep 2023
  • RC3: 'Comment on gmd-2023-57', Anonymous Referee #3, 05 Aug 2023
  • RC4: 'Comment on gmd-2023-57', Anonymous Referee #4, 10 Aug 2023
    • AC5: 'Reply on RC4', Taesam Lee, 04 Sep 2023

Taesam Lee et al.

Taesam Lee et al.

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Short summary
The current study presents a novel method to demarcate the cross-section of a river channel using a very flexible regression model, called K-nearest neighbor local linear regression (KLR). The proposed method draws the cross-section automatically based on the point cloud data taken from unmanned aerial vehicles (UAVs). The proposed model can provide a further development of 4th industy innovation by employding the UAV-based photogrammetry.