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Lookup NU author(s): Dr Maria-Valasia PeppaORCiD,
Professor Jon Mills,
Professor Philip Moore,
Dr Pauline Miller
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
Landslides are hazardous events with often disastrous consequences. Monitoring landslides with observations of high spatio-temporal resolution can help mitigate such hazards. Mini unmanned aerial vehicles (UAVs) complemented by structure-from-motion (SfM) photogrammetry and modern per-pixel image matching algorithms can deliver a time-series of landslide elevation models in an automated and inexpensive way. This research investigates the potential of a mini UAV, equipped with a Panasonic Lumix DMC-LX5 compact camera, to provide surface deformations at acceptable levels of accuracy for landslide assessment. The study adopts a selfcalibrating bundle adjustment-SfM pipeline using ground control points (GCPs). It evaluates misalignment biases and unresolved systematic errors that are transferred through the SfM process into the derived elevation models. To cross-validate the research outputs, results are compared to benchmark observations obtained by standard surveying techniques. The data is collected with 6 cm ground sample distance (GSD) and is shown to achieve planimetric and vertical accuracy of a few centimetres at independent check points (ICPs). The co-registration error of the generated elevation models is also examined in areas of stable terrain. Through this error assessment, the study estimates that the vertical sensitivity to real terrain change of the tested landslide is equal to 9 cm.
Author(s): Peppa MV, Mills JP, Moore P, Miller PE, Chambers JE
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences
Year of Conference: 2016
Print publication date: 01/01/2016
Acceptance date: 02/04/2016
Date deposited: 19/12/2017
Publisher: Copernicus Gesellschaft MBH