Assessing the Prediction Accuracy of Geomorphon-Based Automated Landform Classification: An Example from the Ionian Coastal Belt of Southern Italy

Abstract

Automatic procedures for landform extraction is a growing research field but extensive quantitative studies of the prediction accuracy of Automatic Landform Classification (ACL) based on a direct comparison with geomorphological maps are rather limited. In this work, we test the accuracy of an algorithm of automatic landform classification on a large sector of the Ionian coast of the southern Italian belt through a quantitative comparison with a detailed geomorphological map. Automatic landform classification was performed by using an algorithm based on the individuation of basic landform classes named geomorphons. Spatial overlay between the main mapped landforms deriving from traditional geomorphological analysis and the automatic landform classification results highlighted a satisfactory percentage of accuracy (higher than 70%) of the geomorphon-based method for the coastal plain area and drainage network. The percentage of accuracy decreased by about 20–30% for marine and fluvial terraces, while the overall accuracy of the ACL map is 69%. Our results suggest that geomorphon-based classification could represent a basic and robust tool to recognize the main geomorphological elements of landscape at a large scale, which can be useful for the advanced steps of geomorphological mapping such as genetic interpretation of landforms and detailed delineation of complex and composite geomorphic elements.

Land surface diversity: A geomorphodiversity index of Italy

“…On a totally different account, in the last decades, the field of geomorphometry particularly focused on the study of automated or unsupervised classification of landforms, which can be suitable for a fast growth and analysis on larger areas (Gioia et al, 2021). Methods of this type only require a DEM but, since the translation from continuous morphometric variables to their derivatives is subordinated to scale‐dependence, many quantitative and automated approaches have been proposed (Drăguţ et al, 2011; Drăguţ & Eisank, 2011; Evans, 2003; Liucci et al, 2017; Liucci & Melelli, 2017), using secondary topographic attributes (Shary et al, 2005; Wilson & Gallant, 2000), the MRS method (Baatz & Schäpe, 2000), or using ALCoM statistical technique (Van Niekerk, 2010).…”

“…The tool is easy to use, and can detect up to ten possible landform geometries, classified either as flat, summit, ridge, shoulder, spur, slope, hollow, footslope, valley and depression. As highlighted by Gioia et al (2021) this tool uses 'a computer vision approach' that self-adapts to the topographic characteristic of the area.…”

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

“…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].…”

Quantification of Surface Pattern Based on the Binary Terrain Structure in Mountainous Areas

“…However, these images are not sufficient to reflect topographic characteristics, which is significant for landform research. Digital elevation models (DEMs) are a significant type of remote sensing data and record elevation and terrain relief information [40][41][42][43]. In the field of geomorphology, DEMs have supported in-depth studies on the Earth's surface, including surface characterization [25,44], local terrain segmentation [45][46][47] and global landform classification [48,49].…”