Ground-control networks for image based surface reconstruction: an investigation of optimum survey designs using UAV derived imagery and structure-from-motion photogrammetry

Tonkin, T.N. and Midgley, N.G. ORCID: 0000-0003-0076-1785, 2016. Ground-control networks for image based surface reconstruction: an investigation of optimum survey designs using UAV derived imagery and structure-from-motion photogrammetry. Remote Sensing, 8 (9), p. 786. ISSN 2072-4292

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Abstract

The use of small UAV (Unmanned Aerial Vehicle) and Structure-from-Motion (SfM) with Multi-View Stereopsis (MVS) for acquiring survey datasets is now commonplace, however, aspects of the SfM-MVS workflow require further validation. This work aims to provide guidance for scientists seeking to adopt this aerial survey method by investigating aerial survey data quality in relation to the application of ground control points (GCPs) at a site of undulating topography (Ennerdale, Lake District, UK). Sixteen digital surface models (DSMs) were produced from a UAV survey using a varying number of GCPs (3-101). These DSMs were compared to 530 dGPS spot heights to calculate vertical error. All DSMs produced reasonable surface reconstructions (vertical root-mean-square-error (RMSE) of <0.2 m), however, an improvement in DSM quality was found where four or more GCPs (up to 101 GCPs) were applied, with errors falling to within the suggested point quality range of the survey equipment used for GCP acquisition (e.g., vertical RMSE of <0.09 m). The influence of a poor GCP distribution was also investigated by producing a DSM using an evenly distributed network of GCPs, and comparing it to a DSM produced using a clustered network of GCPs. The results accord with existing findings, where vertical error was found to increase with distance from the GCP cluster. Specifically vertical error and distance to the nearest GCP followed a strong polynomial trend (R2 = 0.792). These findings contribute to our understanding of the sources of error when conducting a UAV-SfM survey and provide guidance on the collection of GCPs. Evidence-driven UAV-SfM survey designs are essential for practitioners seeking reproducible, high quality topographic datasets for detecting surface change.

Item Type: Journal article
Publication Title: Remote Sensing
Creators: Tonkin, T.N. and Midgley, N.G.
Publisher: MDPI
Date: 21 September 2016
Volume: 8
Number: 9
ISSN: 2072-4292
Identifiers:
NumberType
10.3390/rs8090786DOI
Divisions: Schools > School of Animal, Rural and Environmental Sciences
Depositing User: Jonathan Gallacher
Date Added: 23 Sep 2016 11:07
Last Modified: 09 Oct 2017 09:46
URI: http://irep.ntu.ac.uk/id/eprint/28611

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