Accuracy Assessment of a Uav-Based Landslide Monitoring System

Abstract

ABSTRACT: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.

On the Use of Unmanned Aerial Systems for Environmental Monitoring

“…James and Robson [38] highlighted how unresolved elements of the camera model (lens distortion) can propagate as errors in UAS-DEMs (derived digital elevation models), and how this can be addressed by incorporating oblique images. Other studies have highlighted the importance of flight line configurations [39], as well as minimizing image blur [34]. There is a need to consolidate this evidence to develop best practice guidance for optimizing UAS SfM (structure-from-motion) measurement quality, whilst maintaining ease of use and accessibility.…”

“…This task is often referred to as registration, and is conventionally dependent on establishing ground control points (GCPs) which are fixed by a higher-order control method (usually Global Navigation Satellite System-GNSS or Global Positioning System). A number of studies have examined the effect of GCP networks (number and distribution) in UAS surveys, showing that significant errors are expected in SfM-based products where GCPs are not adopted [39,40]. Nevertheless, systematic DEM error can Remote Sens.…”

UAS-Based Change Detection of the Glacial and Proglacial Transition Zone at Pasterze Glacier, Austria

“…The general steps and specific settings of processing were as follows: after creating a sparse dense cloud, a dense point cloud was generated using medium quality mode and so-called aggressive depth filtering. The latter means that minor surface details are filtered out [31]. According to [8], the reduced quality mode during dense point cloud generation does not considerably affect the DEM accuracy but leads to a reduced processing time.…”

“…According to [8], the reduced quality mode during dense point cloud generation does not considerably affect the DEM accuracy but leads to a reduced processing time. References [8,31] showed that aggressive filtering reduces minor surface details. In our case we do not focus on single surface spots and thus a smoothed DEM is acceptable.…”

Cultural Heritage Through Time: A Case Study at Hadrian’s Wall, United Kingdom

“…The latter facilitates autonomous UAV flights using pre-programmed flight plan parameters. Peppa et al (2016) provides further details and specifications of the UAV.…”