Time Series Analysis of Landslide Dynamics Using an Unmanned Aerial Vehicle (UAV)
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...
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doaj-d1b572dc824b4821a9a58668c334f1422020-11-24T21:42:12ZengMDPI AGRemote Sensing2072-42922015-02-01721736175710.3390/rs70201736rs70201736Time Series Analysis of Landslide Dynamics Using an Unmanned Aerial Vehicle (UAV)Darren Turner0Arko Lucieer1Steven M. de Jong2School of Land and Food, University of Tasmania, Hobart, TAS 7001, AustraliaSchool of Land and Food, University of Tasmania, Hobart, TAS 7001, AustraliaDepartment of Physical Geography, Utrecht University, P.O. Box 80115, Utrecht 3508 TC, The NetherlandsIn 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.http://www.mdpi.com/2072-4292/7/2/1736landslideUAVCosi-corrdigital elevation model (DEM) |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Darren Turner Arko Lucieer Steven M. de Jong |
spellingShingle |
Darren Turner Arko Lucieer Steven M. de Jong Time Series Analysis of Landslide Dynamics Using an Unmanned Aerial Vehicle (UAV) Remote Sensing landslide UAV Cosi-corr digital elevation model (DEM) |
author_facet |
Darren Turner Arko Lucieer Steven M. de Jong |
author_sort |
Darren Turner |
title |
Time Series Analysis of Landslide Dynamics Using an Unmanned Aerial Vehicle (UAV) |
title_short |
Time Series Analysis of Landslide Dynamics Using an Unmanned Aerial Vehicle (UAV) |
title_full |
Time Series Analysis of Landslide Dynamics Using an Unmanned Aerial Vehicle (UAV) |
title_fullStr |
Time Series Analysis of Landslide Dynamics Using an Unmanned Aerial Vehicle (UAV) |
title_full_unstemmed |
Time Series Analysis of Landslide Dynamics Using an Unmanned Aerial Vehicle (UAV) |
title_sort |
time series analysis of landslide dynamics using an unmanned aerial vehicle (uav) |
publisher |
MDPI AG |
series |
Remote Sensing |
issn |
2072-4292 |
publishDate |
2015-02-01 |
description |
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. |
topic |
landslide UAV Cosi-corr digital elevation model (DEM) |
url |
http://www.mdpi.com/2072-4292/7/2/1736 |
work_keys_str_mv |
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