Analysis of the Use of Geomorphic Elements Mapping to Characterize Subaqueous Bedforms Using Multibeam Bathymetric Data in River System

Abstract

Riverbed micro-topographical features, such as crest and trough, flat bed, and scour pit, indicate the evolution of fluvial geomorphology, and have an influence on the stability of underwater structures and overall scour pits. Previous studies on bedform feature extraction have focused mainly on the rhythmic bed surface morphology and have extracted crest and trough, while flat bed and scour pit have been ignored. In this study, to extend the feature description of riverbeds, geomorphic elements mapping was used by employing three geomorphic element classification methods: Wood’s criteria, a self-organization map (SOM) technique, and geomorphons. The results showed that geomorphic element mapping can be controlled by adjusting the slope tolerance and curvature tolerance of Wood’s criteria, using the map unit number and combination of the SOM technique and the flatness of geomorphons. Relatively flat bed can be presented using “plane”, “flat planar”, and “flat” elements, while scour pit can be presented using a “pit” element. A comparison of the difference between parameter settings for landforms and bedforms showed that SOM using 8 or 10 map units is applicable for land and underwater surface and is thus preferentially recommended for use. Furthermore, the use of geomorphons is recommended as the optimal method for characterizing bedform features because it provides a simple element map in the absence of area loss.

Beaver-Driven Peatland Ecotone Dynamics: Impoundment Detection Using Lidar and Geomorphon Analysis

“…Geomorphon analysis [32], a technique that we employ in this research, is a machine vision terrain classifier of rasterized terrain models. Geomorphons have been often used to represent and even predict geomorphological and hydrological changes in river valleys [33][34][35][36]. In the form of input to a physically based hydrological model, geomorphons were successfully represented in the spatially distributed hydrological parameters of the upper reaches of a montane watershed in Brazil [33].…”

“…In the form of input to a physically based hydrological model, geomorphons were successfully represented in the spatially distributed hydrological parameters of the upper reaches of a montane watershed in Brazil [33]. Yan et al (2020) found geomorphons to be optimal in the characterization of subaqueous riverbed features on the Yangtze River, China [35]; while the work of Gioia et al (2021) showed geomorphon-based classification to be a robust tool for the identification of geomorphological landscape elements at a large scale in southern Italy [37].…”

Automatic Mapping and Characterisation of Linear Depositional Bedforms: Theory and Application Using Bathymetry from the North West Shelf of Australia

“…[23] analysed longitudinal profiles to identify a change in derivatives signs while [24], also using longitudinal profiles, used the position of local minima and maxima. Other authors have attempted to use spatial classes (e.g., [25][26][27][28][29]), building on concepts such as geomorphons [30], topographic signatures [31] or index [32], that are then converted into individual features. Finally, other authors explored the delineation of crestline areas by skeletonization using triangular irregular network [33] and polygon breaking algorithms [34,35].…”

Spatial Analysis for Landscape Changes

“…Yan et al (2020) [2] describe an interesting approach of the semi-automatic extraction of subaqueous landforms using multibeam bathymetric data. The comparison of three different methods of landform classification (i.e., Wood's criteria, SOM, and geomorphons) highlights that the geomorphon method has the highest degree of accuracy for the automatic extraction of the bedforms of a delta system in China.…”