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

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 algor...

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Autores principales:	Dario Gioia, Maria Danese, Giuseppe Corrado, Paola Di Leo, Antonio Minervino Amodio, Marcello Schiattarella
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	automated landform classification geomorphology polygenic terraces Ionian coastal belt southern Italy Geography (General) G1-922
Acceso en línea:	https://doaj.org/article/2e614a6f5eca4e9b937a17844f0040fa
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spelling	oai:doaj.org-article:2e614a6f5eca4e9b937a17844f0040fa2021-11-25T17:52:45ZAssessing the Prediction Accuracy of Geomorphon-Based Automated Landform Classification: An Example from the Ionian Coastal Belt of Southern Italy10.3390/ijgi101107252220-9964https://doaj.org/article/2e614a6f5eca4e9b937a17844f0040fa2021-10-01T00:00:00Zhttps://www.mdpi.com/2220-9964/10/11/725https://doaj.org/toc/2220-9964Automatic 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.Dario GioiaMaria DaneseGiuseppe CorradoPaola Di LeoAntonio Minervino AmodioMarcello SchiattarellaMDPI AGarticleautomated landform classificationgeomorphologypolygenic terracesIonian coastal beltsouthern ItalyGeography (General)G1-922ENISPRS International Journal of Geo-Information, Vol 10, Iss 725, p 725 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	automated landform classification geomorphology polygenic terraces Ionian coastal belt southern Italy Geography (General) G1-922
spellingShingle	automated landform classification geomorphology polygenic terraces Ionian coastal belt southern Italy Geography (General) G1-922 Dario Gioia Maria Danese Giuseppe Corrado Paola Di Leo Antonio Minervino Amodio Marcello Schiattarella Assessing the Prediction Accuracy of Geomorphon-Based Automated Landform Classification: An Example from the Ionian Coastal Belt of Southern Italy
description	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.
format	article
author	Dario Gioia Maria Danese Giuseppe Corrado Paola Di Leo Antonio Minervino Amodio Marcello Schiattarella
author_facet	Dario Gioia Maria Danese Giuseppe Corrado Paola Di Leo Antonio Minervino Amodio Marcello Schiattarella
author_sort	Dario Gioia
title	Assessing the Prediction Accuracy of Geomorphon-Based Automated Landform Classification: An Example from the Ionian Coastal Belt of Southern Italy
title_short	Assessing the Prediction Accuracy of Geomorphon-Based Automated Landform Classification: An Example from the Ionian Coastal Belt of Southern Italy
title_full	Assessing the Prediction Accuracy of Geomorphon-Based Automated Landform Classification: An Example from the Ionian Coastal Belt of Southern Italy
title_fullStr	Assessing the Prediction Accuracy of Geomorphon-Based Automated Landform Classification: An Example from the Ionian Coastal Belt of Southern Italy
title_full_unstemmed	Assessing the Prediction Accuracy of Geomorphon-Based Automated Landform Classification: An Example from the Ionian Coastal Belt of Southern Italy
title_sort	assessing the prediction accuracy of geomorphon-based automated landform classification: an example from the ionian coastal belt of southern italy
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/2e614a6f5eca4e9b937a17844f0040fa
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Assessing the Prediction Accuracy of Geomorphon-Based Automated Landform Classification: An Example from the Ionian Coastal Belt of Southern Italy

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