Coveney, S. and Roberts, K. (2017) Lightweight UAV digital elevation models and orthoimagery for environmental applications: data accuracy evaluation and potential for river flood risk modelling. International Journal of Remote Sensing, 38(8-10), pp. 3159-3180. (doi: 10.1080/01431161.2017.1292074)
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137558.pdf - Accepted Version 3MB |
Abstract
Digital elevation models (DEMs) generated from unmanned aerial vehicle (UAV) photogrammetry offer opportunities for on-demand DEM production in environmental modelling and flood risk prediction applications. The DEM and orthoimage accuracies that can be achieved using lightweight UAV on-board sensors only, are compared with cases where progressively higher numbers of Global Navigation Satellite System (GNSS) referenced ground targets are utilized. Unacceptably large 95% planimetric orthoimage errors of 5.22 m (root mean square error (RMSE) 3.27 m), and DEM 95% elevation errors of 5.03 m (RMSE 2.2 m) are observed when using the on-board positioning and orientation sensors only. Introducing GNSS ground control points (GCPs) in increasing numbers progressively and substantially improves data accuracy. Remarkably small xy orthoimage errors of 0.076 m (RMS) and DEM elevation errors of 0.08 m (RMS) are achieved using 1 GCP for every 2 ha of ground area and utilizing more GCPs produced more or less identical results. These accuracies compare very favourably with the best commercial airborne survey DEMs, suggesting strong potential for the application of lightweight UAV photogrammetric DEMs in local environmental modelling and flood risk prediction applications. The potential of these DEMS for flood prediction is subsequently assessed and demonstrated by comparison with published flood risk maps and flood depth data, and by cross-comparing the outputs of the UAV DEM flood model predictions.
| Item Type: | Articles |
|---|---|
| Status: | Published |
| Refereed: | Yes |
| Glasgow Author(s) Enlighten ID: | Roberts, Mr Kenny |
| Authors: | Coveney, S., and Roberts, K. |
| College/School: | College of Science and Engineering > School of Geographical and Earth Sciences |
| Journal Name: | International Journal of Remote Sensing |
| Publisher: | Taylor & Francis |
| ISSN: | 0143-1161 |
| ISSN (Online): | 1366-5901 |
| Published Online: | 17 February 2017 |
| Copyright Holders: | Copyright © 2017 Informa UK Limited, trading as Taylor and Francis Group |
| First Published: | First published in International Journal of Remote Sensing 38(8-10): 3159-3180 |
| Publisher Policy: | Reproduced in accordance with the publisher copyright policy |
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