Improved automatic classification of litho-geomorphological units by using raster image blending, Vipava Valley (SW Slovenia)

Debevec Jordanova, Galena (author), Verbovšek, Timotej (author)

Automatic landslide classification based on digital elevation models has become a powerful complementary tool to field mapping. Many studies focus on the automatic classification of landslides’ geomorphological features, such as their steep main scarps, but in many cases, the scarps and other morphological features are difficult for algorithms to detect. In this study, we performed an automatic classification of different litho-geomorphological units to differentiate slope mass movements in field maps by using Maximum Likelihood Classification. The classification was based on high-resolution lidar-derived DEM of the Vipava Valley, SW Slovenia. The results show an improvement over previous approaches as we used a blended image (VAT, which included four different raster layers with different weights) along with other common raster layers for morphometric analysis of the surface (e.g., slope, elevation, aspect, TRI, curvature, etc.). The newly created map showed better classification of the five classes we used in the study and recognizes alluvial deposits, carbonate cliffs (including landslide scarps), carbonate plateaus, flysch, and slope deposits better than previous studies. Multivariate statistics recognized the VAT layer as the most important layer with the highest eigenvalues, and when combined with Aspect and Elevation layers, it explained 90% of the total variance. The paper also discusses the correlations between the different layers and which layers are better suited for certain geomorphological surface analyses.

• Language: English
• Keywords: slope deposits, geomorphometry, automatic classification, Maximum Likelihood Classification, multivariate statistics, PCA
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: NTF - Faculty of Natural Sciences and Engineering
• Publication status: Published
• Publication version: Version of Record
• Year: 2023
• Number of pages: 19 str.
• Numbering: Vol. 15, iss. 2, art. 531
• PID: 20.500.12556/RUL-154138
• UDC: 55
• ISSN on article: 2072-4292
• DOI: 10.3390/rs15020531
• COBISS.SI-ID: 138060547
• Publication date in RUL: 26.01.2024

Record is a part of a journal

• Title: Remote sensing
• Shortened title: Remote sens.
• Publisher: MDPI
• ISSN: 2072-4292
• COBISS.SI-ID: 32345133

Licences

• License: CC BY 4.0, Creative Commons Attribution 4.0 International
• Link: http://creativecommons.org/licenses/by/4.0/
• Description: This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.