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Sensors 2017, 17(10), 2378;

Time Series UAV Image-Based Point Clouds for Landslide Progression Evaluation Applications

Department of Earth and Environmental Science, Yarmouk University, Irbid 21163, Jordan
Department of Geomatics Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada
Department of Electrical Engineering, Port-Said University, Port Said 42523, Egypt
Department of Geography, University of Calgary, Calgary, AB T2N 1N4, Canada
Lyles School of Civil Engineering, Purdue University, West Lafayette, IN 47907, USA
Author to whom correspondence should be addressed.
Received: 13 September 2017 / Revised: 11 October 2017 / Accepted: 12 October 2017 / Published: 18 October 2017
(This article belongs to the Special Issue Remote Sensing and GIS for Geo-Hazards and Disasters)
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Landslides are major and constantly changing threats to urban landscapes and infrastructure. It is essential to detect and capture landslide changes regularly. Traditional methods for monitoring landslides are time-consuming, costly, dangerous, and the quality and quantity of the data is sometimes unable to meet the necessary requirements of geotechnical projects. This motivates the development of more automatic and efficient remote sensing approaches for landslide progression evaluation. Automatic change detection involving low-altitude unmanned aerial vehicle image-based point clouds, although proven, is relatively unexplored, and little research has been done in terms of accounting for volumetric changes. In this study, a methodology for automatically deriving change displacement rates, in a horizontal direction based on comparisons between extracted landslide scarps from multiple time periods, has been developed. Compared with the iterative closest projected point (ICPP) registration method, the developed method takes full advantage of automated geometric measuring, leading to fast processing. The proposed approach easily processes a large number of images from different epochs and enables the creation of registered image-based point clouds without the use of extensive ground control point information or further processing such as interpretation and image correlation. The produced results are promising for use in the field of landslide research. View Full-Text
Keywords: change detection; landslide dynamics; 3D dense matching; unmanned aerial vehicle (UAV); image-based registration change detection; landslide dynamics; 3D dense matching; unmanned aerial vehicle (UAV); image-based registration

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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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Al-Rawabdeh, A.; Moussa, A.; Foroutan, M.; El-Sheimy, N.; Habib, A. Time Series UAV Image-Based Point Clouds for Landslide Progression Evaluation Applications. Sensors 2017, 17, 2378.

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