Flight elevation and water clarity affect the utility of unmanned aerial vehicles in mapping stream substratum

Effective conservation and management of fish populations usually require an assessment of their habitats. The spatial extent largely depends on the species’ space use, ranging from just a few metres in the Devil's Hole pupfish Cyprinodon diabolis Wales to thousands of kilometres in Chinook salmon Oncorhynchus tshawytscha (Walbaum). This variation ultimately dictates which methods are suitable to obtain information about their habitat use. Direct measurements of habitat features are usually preferred but may be unattainable for wide‐ranging species. Across such large spatial expanses, fisheries biologists are increasingly using remote sensing techniques (Dauwalter, Fesenmyer, Bjork, Leasure, & Wenger, 2017). Aerial, satellite and spectral imagery are readily available for most of the earth's surface and provide valuable information on the physical environment in and around aquatic habitats (Dauwalter et al., 2017). However, for certain applications the spatial and temporal resolution and photographic quality of satellite and aerial images can be a limiting factor. Unmanned aerial vehicles (UAVs or drones) may provide a flexible bridge between remote sensing techniques and on‐the‐ground mapping (Hodgson, Baylis, Mott, Herrod, & Clarke, 2016). The use of UAVs may be particularly useful when one requires more detailed images than what is available from aerial photography, and across larger spatial extents than is feasible to cover on the ground (Tyler et al., 2018). UAVs are increasingly being used in studying aquatic organisms and have recently been employed in quantifying jellyfish aggregations in marine habitats (Schaub et al., 2018) and identifying individual taimen Hucho taimen Pallas in Mongolian rivers (Tyler et al., 2018). Here, an application of UAV‐borne aerial videography to map spawning habitat of adfluvial brown trout Salmo trutta L. in Southern Norway is reported. Brown trout shows strong preferences for the physical characteristics of a spawning site, whereby substratum size, water depth and current velocity are the three main variables most frequently used to describe habitat selection. Of these variables, substratum size is frequently used as a proxy for the combined characteristics because it is easy to quantify in the field. In viewing the substratum on video, it was hypothesised that footage quality, and hence the utility of UAV videography, depends upon water clarity, weather conditions and flight elevation (see candidate models in Table 1). To obtain high‐resolution data over relatively large distances, a commercially available DJI Phantom 4 Pro quadcopter was used, equipped with a 20 Mb digital video camera with a 2.8–11 varifocal lens with a maximum 84° field of vision (DJI, Shenzhen, China). This permitted the recording of continuous footage of the rivers and to play back the video for assessment of substrate size distributions. Flights took place when there was sufficient light to show the details in the substratum, and a polarised filter in combination with a UV filter was used to cut surface glare. Observations of the substratum were only possible in relatively shallow reaches, which characterise typical spawning depths of brown trout (≈50–150 cm deep in this study), where water clarity permitted light penetration and where surface turbulence or wind did not obstruct the view. All footage was recorded in September and October 2017. Despite efforts to optimise the conditions for footage quality, there were variable light and weather conditions during the recordings, and the rivers varied in terms of water clarity, amounts of mosses and aquatic vegetation and dominant substratum sizes. To quantify the conditions that controlled the perceived quality of the footage (the response variable), eight fisheries scientists were asked to score a 20‐s sample of the footage from 32 rivers on a five‐ point Likert scale, with an emphasis on whether they could discern the composition of the substratum (minimum–maximum diameters