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Identification of Micro-Scale Landforms of Landslides Using Precise Digital Elevation Models

1
Department of Forest Management and Geodesy, Faculty of Forestry, Technical University in Zvolen, 960 53 Zvolen, Slovakia
2
Department of Landscape Planning and Design, Faculty of Ecology and Environmental Sciences, Technical University in Zvolen, 960 53 Zvolen, Slovakia
3
Department of Geography and Geology, Faculty of Natural Sciences, Matej Bel University Banská Bystrica, 974 01 Banská Bystrica, Slovakia
4
Faculty of Forestry and Wood Sciences, Czech University of Life Sciences Prague, 165 00 Prague, Czech Republic
*
Author to whom correspondence should be addressed.
Geosciences 2019, 9(3), 117; https://doi.org/10.3390/geosciences9030117
Received: 16 January 2019 / Revised: 1 March 2019 / Accepted: 4 March 2019 / Published: 7 March 2019
(This article belongs to the Special Issue Mountain Landslides: Monitoring, Modeling, and Mitigation)
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Abstract

An active gully-related landslide system is located in a deep valley under forest canopy cover. Generally, point clouds from forested areas have a lack of data connectivity, and optical parameters of scanning cameras lead to different densities of point clouds. Data noise or systematic errors (missing data) make the automatic identification of landforms under tree canopy problematic or impossible. We processed, analyzed, and interpreted data from a large-scale landslide survey, which were acquired by the light detection and ranging (LiDAR) technology, remotely piloted aircraft system (RPAS), and close-range photogrammetry (CRP) using the ‘Structure-from-Motion’ (SfM) method. LAStools is a highly efficient Geographic Information System (GIS) tool for point clouds pre-processing and creating precise digital elevation models (DEMs). The main landslide body and its landforms indicating the landslide activity were detected and delineated in DEM-derivatives. Identification of micro-scale landforms in precise DEMs at large scales allow the monitoring and the assessment of these active parts of landslides that are invisible in digital terrain models at smaller scales (obtained from aerial LiDAR or from RPAS) due to insufficient data density or the presence of many data gaps. View Full-Text
Keywords: landslides; precise DEM; semi-automatic pixel-based classification; DEM derivatives; costless remote sensing technologies landslides; precise DEM; semi-automatic pixel-based classification; DEM derivatives; costless remote sensing technologies
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Chudý, F.; Slámová, M.; Tomaštík, J.; Prokešová, R.; Mokroš, M. Identification of Micro-Scale Landforms of Landslides Using Precise Digital Elevation Models. Geosciences 2019, 9, 117.

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