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Remote Sens. 2016, 8(2), 95; doi:10.3390/rs8020095

Using an Unmanned Aerial Vehicle-Based Digital Imaging System to Derive a 3D Point Cloud for Landslide Scarp Recognition

1
Department of Geomatics Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada
2
Lyles School of Civil Engineering, Purdue University, West Lafayette, IN 47907, USA
3
Department of Electrical Engineering, Port-Said University, Port Said 42523, Egypt
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Academic Editors: Zhenhong Li, Gonzalo Pajares Martinsanz and Prasad S. Thenkabail
Received: 21 October 2015 / Revised: 9 January 2016 / Accepted: 18 January 2016 / Published: 27 January 2016
(This article belongs to the Special Issue Earth Observations for Geohazards)

Abstract

Landslides often cause economic losses, property damage, and loss of lives. Monitoring landslides using high spatial and temporal resolution imagery and the ability to quickly identify landslide regions are the basis for emergency disaster management. This study presents a comprehensive system that uses unmanned aerial vehicles (UAVs) and Semi-Global dense Matching (SGM) techniques to identify and extract landslide scarp data. The selected study area is located along a major highway in a mountainous region in Jordan, and contains creeping landslides induced by heavy rainfall. Field observations across the slope body and a deformation analysis along the highway and existing gabions indicate that the slope is active and that scarp features across the slope will continue to open and develop new tension crack features, leading to the downward movement of rocks. The identification of landslide scarps in this study was performed via a dense 3D point cloud of topographic information generated from high-resolution images captured using a low-cost UAV and a target-based camera calibration procedure for a low-cost large-field-of-view camera. An automated approach was used to accurately detect and extract the landslide head scarps based on geomorphological factors: the ratio of normalized Eigenvalues (i.e., λ1/λ2 ≥ λ3) derived using principal component analysis, topographic surface roughness index values, and local-neighborhood slope measurements from the 3D image-based point cloud. Validation of the results was performed using root mean square error analysis and a confusion (error) matrix between manually digitized landslide scarps and the automated approaches. The experimental results using the fully automated 3D point-based analysis algorithms show that these approaches can effectively distinguish landslide scarps. The proposed algorithms can accurately identify and extract landslide scarps with centimeter-scale accuracy. In addition, the combination of UAV-based imagery, 3D scene reconstruction, and landslide scarp recognition/extraction algorithms can provide flexible and effective tool for monitoring landslide scarps and is acceptable for landslide mapping purposes. View Full-Text
Keywords: landslides scarps; geomorphology; slope; surface roughness; Semi-Global dense matching (SGM); unmanned aerial vehicles (UAVs) landslides scarps; geomorphology; slope; surface roughness; Semi-Global dense matching (SGM); unmanned aerial vehicles (UAVs)
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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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MDPI and ACS Style

Al-Rawabdeh, A.; He, F.; Moussa, A.; El-Sheimy, N.; Habib, A. Using an Unmanned Aerial Vehicle-Based Digital Imaging System to Derive a 3D Point Cloud for Landslide Scarp Recognition. Remote Sens. 2016, 8, 95.

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