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Durham University

Research & business

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Publication details for Dr Patrice Carbonneau

Woodget, A.S., Fyffe, C. & Carbonneau, P.E. (2018). From manned to unmanned aircraft: Adapting airborne particle size mapping methodologies to the characteristics of sUAS and SfM. Earth Surface Processes and Landforms 43(4): 857-870.

Author(s) from Durham

Abstract

Subaerial particle size data holds a wealth of valuable information for fluvial, coastal, glacial and other sedimentological applications. Recently, we have gained the opportunity to map and quantify surface particle sizes at the mesoscale using data derived from small unmanned aerial system (sUAS) imagery processed using structure from motion (SfM) photogrammetry. Typically, these sUAS-SfM approaches have been based on calibrating orthoimage texture or point cloud roughness with particle size. Variable levels of success are reported and a single, robust method capable of producing consistently accurate and precise results in a range of settings has remained elusive. In this paper, we develop an original method for mapping surface particle size with the specific constraints of sUAS and SfM in mind. This method uses the texture of single sUAS images, rather than orthoimages, calibrated with particle sizes normalised by individual image scale. We compare results against existing orthoimage texture and roughness approaches, and provide a quantitative investigation into the implications of the use of sUAS camera gimbals. Our results indicate that our novel single image method delivers an optimised particle size mapping performance for our study site, outperforming both other methods and delivering residual mean errors of 0.02mm (accuracy), standard deviation of residual errors of 6.90mm (precision) and maximum residual errors of 16.50mm. Accuracy values are more than two orders of magnitude worse when imagery is collected by a similar drone which is not equipped with a camera gimbal, demonstrating the importance of mechanical image stabilisation for particle size mapping using measures of image texture.