Lightweight UAV digital elevation models and orthoimagery for environmental applications: data accuracy evaluation and potential for river flood risk modelling. Issue 8 (19th May 2017)

• Record Type: Journal Article
• Authors: Coveney, Seamus; Roberts, Kenneth
• Is Part Of: International journal of remote sensing. Volume 38:Issue 8/10(2017)
• Page Start: 3159
• Page End: 3180
• Publication Date: 2017-05-19
• Subjects: Remote sensing -- Periodicals; Télédétection -- Périodiques; 621.3678
• DOI: 10.1080/01431161.2017.1292074
• Languages: English
• ISSNs: 0143-1161

Abstract

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.