Cost‐effective non‐metric photogrammetry from consumer‐grade sUAS: implications for direct georeferencing of structure from motion photogrammetry

The declining costs of small Unmanned Aerial Systems (sUAS), in combination with Structure‐from‐Motion (SfM) photogrammetry have triggered renewed interest in image‐based topography reconstruction. However, the potential uptake of sUAS‐based topography is limited by the need for ground control acquired with expensive survey equipment. Direct georeferencing (DG) is a workflow that obviates ground control and uses only the camera positions to georeference the SfM results. However, the absence of ground control poses significant challenges in terms of the data quality of the final geospatial outputs. Notably, it is generally accepted that ground control is required to georeference, refine the camera calibration parameters, and remove any artefacts of optical distortion from the topographic model. Here, we present an examination of DG carried out with low‐cost consumer‐grade sUAS. We begin with a study of surface deformations resulting from systematic perturbations of the radial lens distortion parameters. We then test a number of flight patterns and develop a novel error quantification method to assess the outcomes. Our perturbation analysis shows that there exists families of predictable equifinal solutions of K₁‐K₂ which minimize doming in the output model. The equifinal solutions can be expressed as K₂ = f (K₁) and they have been observed for both the DJI Inspire 1 and Phantom 3 sUAS platforms. This equifinality relationship can be used as an external reliability check of the self‐calibration and allow a DG workflow to produce topography exempt of non‐affine deformations and with random errors of 0.1% of the flying height, linear offsets below 10 m and off‐vertical tilts below 1°. Whilst not yet of survey‐grade quality, these results demonstrate that low‐cost sUAS are capable of producing reliable topography products without recourse to expensive survey equipment and we argue that direct georeferencing and low‐cost sUAS could transform survey practices in both academic and commercial disciplines. Copyright © 2016 John Wiley & Sons, Ltd.     

Monitoring turbidity from above: Deploying small unoccupied aerial vehicles to image in‐stream turbidity

Small unoccupied aerial systems (sUASs) are increasingly applied to study hydrologic processes and water quality. Here, we evaluate a novel application of sUAS to stream turbidity monitoring, with the goal of extending analyses implemented with satellite remote sensing to enable high resolution, rapid collection of turbidity imagery along smaller waterbodies. To accomplish this, we collected multispectral imagery using two sUAS platforms under a range of environmental conditions along a local creek in Syracuse, NY. In addition, we collected in situ turbidity observations immediately after each flight along several transects along the creek, as well as within a clear plume created by a natural spring entering the main channel of the creek. The in situ turbidity values were compared with the mean and standard deviation of several single‐band and multiband indices extracted along similar transects from the sUAS flights. On the basis of data collected across several flights, we found optical metrics obtained from multispectral imagery correlated well with in situ turbidity measurements. Though many optical metrics yielded strong relationships considering only values within the main channel, values associated with the red band were strongly related to turbidity estimates from the main channel as well as lower turbidity values observed in the spring plume. Although there are still limitations of this approach associated with variable field conditions, results from this proof of concept analysis show that sUASs offer a promising avenue for cost‐effective turbidity monitoring.     

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

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.     

Small Unmanned Aerial Systems (sUAS) for environmental remote sensing: challenges and opportunities revisited

Hardin and Jensen (2011) presented six challenges to using small Unmanned Aerial Systems (sUAS) for environmental remote sensing: challenge of the hostile flying environment, challenge of power, challenge of available sensors, challenge of payload weight, challenge of data analysis, and challenge of regulation. Eight years later we revisit each of the challenges in the context of the current sUAS environment. We conclude that technological advances made in the interim (as applied to environmental remote sensing) have either (1) improved practitioner ability to respond to a challenge or (2) decreased the magnitude of the challenge itself. However, relatively short flight time remains a primary challenge to using sUAS in environmental remote sensing.