VAT method for visualization of mass movement features : an alternative to hillshaded DEM

Verbovšek, Timotej (author), Popit, Tomislav (author), Kokalj, Žiga (author)

Hillshaded digital elevation models are a well-known information layer used to determine the geomorphological properties of landslides. However, their use is limited because the results are dependent on a particular sun azimuth and elevation. Approaches proposed to overcome this bias include positive openness, sky-view factor, red relief image maps, and prismatic openness. We propose an upgrade to all these methods, a method named Visualization for Archaeological Topography (VAT). The method is based on a fusion of four information layers into a single image (hillshaded terrain, slope, positive openness, and sky-view factor). VAT can be used to enhance visibility of features of varied scale, height, orientation, and form that sit on terrain ranging from extremely flat to very steep. Besides this, the merits of VAT are that the results are comparable across diverse geographical areas. We have successfully tested the method for landslide recognition and analysis in five different areas in the Vipava Valley (SW Slovenia). Geomorphology of the area is very diverse and holds various types of mass movements. In contrast to classical hillshaded digital elevation models (DEMs), the geomorphological features of landslides obtained by the VAT method are very clearly seen in all studied mass movements.

• Language: English
• Keywords: landslides, geomorphology, VAT, SVF, lidar, aerial laser scanning, Vipava Valley
• 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: 2019
• Number of pages: 14 str.
• Numbering: Vol. 11, iss. 24, art. 2946
• PID: 20.500.12556/RUL-133065
• UDC: 55
• ISSN on article: 2072-4292
• DOI: 10.3390/rs11242946
• COBISS.SI-ID: 1529950
• Publication date in RUL: 10.11.2021

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.
• Licensing start date: 09.12.2019