Robotic photosieving from low‐cost multirotor sUAS: a proof‐of‐concept

Abstract

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.

Quantification of bedform dynamics and bedload sediment flux in sandy braided rivers from airborne and satellite imagery

“…One advantage of using UAVs that has been demonstrated herein is that because they are relatively simple and inexpensive to deploy, they provide an excellent tool for elucidating the temporal morphodynamics of rivers. To date, most studies have highlighted the improvement in spatial resolution that UAV imagery provides, such as for habitat mapping (Woodget et al, 2017) or detection of grain size variation (Carbonneau et al, 2018). The results described herein for a sand-bed river, where sediment is being transported at most flow stages, demonstrates unambiguously that in optimal conditions, bedform dynamics can be quantified at hourly timescales and at both low and near-bankfull stages.…”

“…To date, most studies have highlighted the improvement in spatial resolution that UAV imagery provides, such as for habitat mapping (Woodget et al, 2017) or detection of grain size variation (Carbonneau et al, 2018). To date, most studies have highlighted the improvement in spatial resolution that UAV imagery provides, such as for habitat mapping (Woodget et al, 2017) or detection of grain size variation (Carbonneau et al, 2018).…”

“…The use of UAVs and aerial images has improved our ability to quantify the spatial organisation of river relief and bed roughness (Williams et al, 2014;Dietrich, 2016;James et al, 2017;Carbonneau et al, 2018), to track morphological change over short time periods (Lane et al, 1996(Lane et al, , 2010Palmsten et al, 2015) and to infer sediment transport rates (Lane et al, 1995;Brasington et al, 2003;Vericat et al, 2017). However, a number of outstanding challenges remain.…”

Remote sensing of river corridors: A review of current trends and future directions

“…Traditional image‐based methods relate image texture to grain size (Carbonneau, Bergeron, & Lane, ; Graham, Rice, & Reid, ). More recent methods exploit SfM topography with high‐resolution imagery (0.0015‐m pixel size) from low flight heights (Langhammer, Lendzioch, Miřijovský, & Hartvich, ) and through relationships between roughness and in field grain‐size measurements (Carbonneau, Bizzi, & Marchetti, ; Woodget & Austrums, ). Work by Woodget, Fyffe, and Carbonneau () demonstrated how image texture on a series of individual images outperformed orthomosaics and SfM roughness measures.…”

Introducing <i>PebbleCounts</i>: a grain-sizing tool for photo surveys of dynamic gravel-bed rivers

“…Catchment-average erosion rates from the area, based on cosmogenic nuclide inventories, suggest rates on the order of 0.6-1 mm year −1 (Bookhagen and Strecker, 2012), with large variability during the Pleistocene and Holocene (Tofelde et al, 2017). The region is frequently affected by extreme hydrometeorological events that lead to flooding and drainage-pattern rearrangement (Castino et al, 2016(Castino et al, , 2017.…”