Robotic photosieving from low‐cost multirotor sUAS: a proof‐of‐concept. Issue 5 (23rd January 2018)

• Record Type: Journal Article
• Authors: Carbonneau, P.E.; Bizzi, S.; Marchetti, G.
• Is Part Of: Earth surface processes and landforms. Volume 43:Issue 5(2018)
• Page Start: 1160
• Page End: 1166
• Publication Date: 2018-01-23
• Subjects: sUAS -- drone -- grain size mapping -- fluvial remote sensing; Geomorphology -- Periodicals; 551.4
• DOI: 10.1002/esp.4298
• Languages: English
• ISSNs: 0197-9337

Abstract

Abstract: Measurement of riverbed material grain sizes is now a routine part of fieldwork in fluvial geomorphology and lotic ecology. In the last decade, several authors have proposed remote sensing approaches of grain size measurements based on terrestrial and aerial imagery. Given the current rise of small unmanned aerial system (sUAS) applications in geomorphology, there is now increasing interest in the application of these remotely sensed grain size mapping methods to sUAS imagery. However, success in this area has been limited owing to two fundamental problems: lack of constraint of image scale for sUAS imagery and blurring effects in sUAS images and resulting orthomosaics. In this work, we solve the former by showing that SfM‐photogrammetry can be used in a direct georeferencing (DG) workflow (i.e. with no ground validation) in order to predict image scale within margins of 3%. We then propose a novel approach of robotic photosieving of dry exposed riverbed grains that relies on near‐ground images acquired from a low‐cost sUAS and which does not require the presence of ground control points or visible scale objects. We demonstrate that this absence of scale objects does not affect photosieving outputs thus resulting in a low‐cost and efficient sampling method for surficial grains. Copyright © 2017 John Wiley & Sons, Ltd. Abstract : We present a novel approach to grain size sampling: robotic photosieving. Our approach uses drones to collet near‐ground imagery. At altitudes below 10 m, individual clasts are visible in imagery thus opening the path for automated photosieving methods. We show how the additional use of a recently developed workflow for the direct georeferencing of drone‐based photogrammetric surveys allows robotic photosieving to operate remotely without the need for any ground validation or direct site access.