Carbonneau, P. and Bizzi, S. and Marchetti, G. (2018) 'Robotic photosieving from low-cost multirotor sUAS : a proof-of-concept.', Earth surface processes and landforms., 43 (5). pp. 1160-1166.
Measurement of riverbed material grainsizes 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 due 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.
|Full text:||(AM) Accepted Manuscript|
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|Publisher Web site:||https://doi.org/10.1002/esp.4298|
|Publisher statement:||This is the accepted version of the following article: Carbonneau, P., Bizzi, S. & Marchetti, G. (2018). Robotic photosieving from low-cost multirotor sUAS: A proof-of-concept. Earth Surface Processes and Landforms, which has been published in final form at https://doi.org/10.1002/esp.4298. This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.|
|Date accepted:||15 November 2017|
|Date deposited:||05 January 2018|
|Date of first online publication:||23 January 2018|
|Date first made open access:||23 January 2019|
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