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Open AccessArticle

UAV and Structure from Motion Approach to Monitor the Maierato Landslide Evolution

1
Research Institute for Geo-Hydrological Protection (CNR-IRPI) Turin, National Research Council of Italy, Torino 10135, Italy
2
Research Institute for Geo-Hydrological Protection (CNR-IRPI) Rende, National Research Council of Italy, Cosenza 87036, Italy
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(6), 1039; https://doi.org/10.3390/rs12061039
Received: 14 February 2020 / Revised: 12 March 2020 / Accepted: 21 March 2020 / Published: 24 March 2020
(This article belongs to the Special Issue Unmanned Aerial Vehicles in Geomatics)
In February 2010 a large landslide affected the Maierato municipality (Calabria, Italy). The landslide, mainly caused by a period of prolonged and intense rainfalls, produced a mass displacement of about 5 million m³ and several damages to farmlands, houses and infrastructures. In the aftermath several conventional monitoring actions were carried out. In the current post emergency phase, the monitoring was resumed by carrying out unmanned aerial vehicles (UAV) flights in order to describe the recent behavior of the landslide and to assess residual risk. Thanks to the potentialities of the structure from motion algorithms and the availability of post emergency reconnaissance photos and a previous 3D dataset, the three-dimensional evolution of the area was computed. Moreover, an experimental multispectral flight was carried out and its results supported the interpretation of local phenomena. The dataset allowed to quantify the elevation losses and raises in several peculiar sectors of the landslide. The obtained results confirm that the UAV monitoring and the structure from motion approach can effectively contribute to manage residual risk in the medium and long term within an integrated geotechnical monitoring network. View Full-Text
Keywords: GIS analysis; large landslide; monitoring; residual risk; RPAS; salvaged datasets GIS analysis; large landslide; monitoring; residual risk; RPAS; salvaged datasets
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MDPI and ACS Style

Godone, D.; Allasia, P.; Borrelli, L.; Gullà, G. UAV and Structure from Motion Approach to Monitor the Maierato Landslide Evolution. Remote Sens. 2020, 12, 1039.

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