Your browser does not allow JavaScript!
JavaScript is necessary for the proper functioning of this website. Please enable JavaScript or use a modern browser.
Open Science Slovenia
Open Science
DiKUL
slv
|
eng
Search
Browse
New in RUL
About RUL
In numbers
Help
Sign in
Improved automatic classification of litho-geomorphological units by using raster image blending, Vipava Valley (SW Slovenia)
ID
Debevec Jordanova, Galena
(
Author
),
ID
Verbovšek, Timotej
(
Author
)
PDF - Presentation file,
Download
(48,60 MB)
MD5: C9D776E18C0A9D8A2556CD28FB46E75F
URL - Source URL, Visit
https://www.mdpi.com/2072-4292/15/2/531
Image galllery
Abstract
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
Views:
361
Downloads:
28
Metadata:
Cite this work
Plain text
BibTeX
EndNote XML
EndNote/Refer
RIS
ABNT
ACM Ref
AMA
APA
Chicago 17th Author-Date
Harvard
IEEE
ISO 690
MLA
Vancouver
:
Copy citation
Share:
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.
Projects
Funder:
ARRS - Slovenian Research Agency
Project number:
P1-0195
Name:
Geookolje in geomateriali
Funder:
ARRS - Slovenian Research Agency
Funding programme:
Young researchers
Funder:
ARRS - Slovenian Research Agency
Project number:
J1-2477
Name:
Erozijski procesi na obalnih flišnih klifih z oceno tveganja
Similar documents
Similar works from RUL:
Similar works from other Slovenian collections:
Back