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Time Series Analysis of Landslide Dynamics Using an Unmanned Aerial Vehicle (UAV)

Remote Sensing · 2015 · Vol. 7(2) · pp. 1736–1757
Darren TurnerArko LucieerS.M. de Jong

Abstract

In this study, we used an Unmanned Aerial Vehicle (UAV) to collect a time series of high-resolution images over four years at seven epochs to assess landslide dynamics. Structure from Motion (SfM) was applied to create Digital Surface Models (DSMs) of the landslide surface with an accuracy of 4–5 cm in the horizontal and 3–4 cm in the vertical direction. The accuracy of the co-registration of subsequent DSMs was checked and corrected based on comparing non-active areas of the landslide, which minimized alignment errors to a mean of 0.07 m. Variables such as landslide area and the leading edge slope were measured and temporal patterns were discovered. Volumetric changes of particular areas of the landslide were measured over the time series. Surface movement of the landslide was tracked and quantified with the COSI-Corr image correlation algorithm but without ground validation. Historical aerial photographs were used to create a baseline DSM, and the total displacement of the landslide was found to be approximately 6630 m3. This study has demonstrated a robust and repeatable algorithm that allows a landslide’s dynamics to be mapped and monitored with a UAV over a relatively long time series.

Landslides and related hazardsRemote Sensing and LiDAR Applications3D Surveying and Cultural HeritageLandslideSeries (stratigraphy)Remote sensingEnvironmental scienceComputer scienceGeologyGeomorphology

Funding

  • University of Tasmania
Citations
424
FWCI
164.47
field-weighted impact
References
30
Percentile
100%
vs. same field & year
Citations per year
References
The blur effect: perception and estimation with a new no-reference perceptual blur metric
Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2007 · 599 citations
A method for registration of 3-D shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence · 1992 · 17,826 citations
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