Next Article in Journal
Data-driven Bicycle Network Analysis Based on Traditional Counting Methods and GPS Traces from Smartphone
Next Article in Special Issue
Eigenvector Spatial Filtering-Based Logistic Regression for Landslide Susceptibility Assessment
Previous Article in Journal
Geo-Referencing and Mapping 1901 Census Addresses for England and Wales
Previous Article in Special Issue
Monitoring Ground Instabilities Using SAR Satellite Data: A Practical Approach
Open AccessArticle

Multi-Aspect Analysis of Object-Oriented Landslide Detection Based on an Extended Set of LiDAR-Derived Terrain Features

1
Institute of Geodesy and Geoinformatics, Wrocław University of Environmental and Life Sciences, 50-375 Wrocław, Poland
2
Department of Land, Environment, Agriculture and Forestry, University of Padova, Agripolis, viale dell’Università 16, 35020 Legnaro (PD), Italy
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2019, 8(8), 321; https://doi.org/10.3390/ijgi8080321
Received: 10 May 2019 / Revised: 27 June 2019 / Accepted: 20 July 2019 / Published: 24 July 2019
(This article belongs to the Special Issue Geospatial Approaches to Landslide Mapping and Monitoring)
Landslide identification is a fundamental step enabling the assessment of landslide susceptibility and determining the associated risks. Landslide identification by conventional methods is often time-consuming, therefore alternative techniques, including automatic approaches based on remote sensing data, have captured the interest among researchers in recent decades. By providing a highly detailed digital elevation model (DEM), airborne laser scanning (LiDAR) allows effective landslide identification, especially in forested areas. In the present study, object-based image analysis (OBIA) was applied to landslide detection by utilizing LiDAR-derived data. In contrast to previous investigations, our analysis was performed on forested and agricultural areas, where cultivation pressure has degraded specific landslide geomorphology. A diverse variety of aspects that influence OBIA accuracy in landslide detection have been considered: DEM resolution, segmentation scale, and feature selection. Finally, using DEM delivered layers and OBIA, landslide was identified with an overall accuracy (OA) of 85% and a kappa index (KIA) equal to 0.60, which illustrates the effectiveness of the proposed approach. In the end, a field investigation was performed in order to evaluate the results achieved by applying an automatic OBIA approach. The advantages and challenges of automatic approaches for landslide identification for various land use were also discussed. Final remarks underline that effective landslide detection in forested areas could be achieved while this is still challenging in agricultural areas. View Full-Text
Keywords: landslide detection; OBIA; segmentation; DEM; LiDAR landslide detection; OBIA; segmentation; DEM; LiDAR
Show Figures

Figure 1

MDPI and ACS Style

Pawłuszek, K.; Marczak, S.; Borkowski, A.; Tarolli, P. Multi-Aspect Analysis of Object-Oriented Landslide Detection Based on an Extended Set of LiDAR-Derived Terrain Features. ISPRS Int. J. Geo-Inf. 2019, 8, 321.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map

1
Back to TopTop