Identification of salmon redds using RPV‐based imagery produces comparable estimates to ground counts with high inter‐observer variability

Abstract

An integral part of population monitoring within fisheries is ground‐based surveys of fish redds. Remotely piloted vehicles or drones (RPVs) could provide a complementary method but need verification due to a host of methodological differences. To compare methods, we counted summer Chinook redds (Oncorhynchus tshawytscha) (~6 m2 in size) using RPVs and compared them to ground‐based counts in the Wenatchee River (WA, USA). We found individual aerial counts were many times twice the corresponding ground counts. We also found large inter‐observer variability among aerial counters. The coefficient of variation among multiple aerial counts were 37%, 38%, and 50% across three sites, which are comparable to published variation in ground counts. We attribute inter‐observer variability to inherent uncertainties in redd identification similar to ground counting, and importantly, we did not see evidence that the clarity of substrate in the image influenced observer bias. Overall, our data suggest that redd counting using RPVs is an effective method, particularly in high‐density spawning locations. We conclude that RPV imagery accurately identifies redds in a clear, relatively wide (60 m) river, but suggest continuing research into increasing precision, limiting observer variability, and assessing the accuracy across methods and locations.

Evaluating a Novel Approach to Detect the Vertical Structure of Insect Damage in Trees Using Multispectral and Three-Dimensional Data from Drone Imagery in the Northern Rocky Mountains, USA

“…Collecting images from above with a consistent ~nadir view angle at low altitudes enables the drone-based sensor to capture details of top-down damage in individual tree crowns that cannot be viewed from the ground and are too fine grained to be detected using satellite or airborne sensors [68]. Multiple observers can subsequently review the same set of images to quantify variability or bias among observers [69]. Automated classification and the ability to archive or re-analyze imagery (as opposed to single field or aerial observations) make the monitoring workflow more reproducible [69] as well as scalable to provide a more even coverage of larger areas, with potential to quantify change over time [68,69].…”

“…Multiple observers can subsequently review the same set of images to quantify variability or bias among observers [69]. Automated classification and the ability to archive or re-analyze imagery (as opposed to single field or aerial observations) make the monitoring workflow more reproducible [69] as well as scalable to provide a more even coverage of larger areas, with potential to quantify change over time [68,69].…”

Remote sensing of salmonid spawning sites in freshwater ecosystems: The potential of low-cost UAV data

“…For example, the use of UAVs have been essential in monitoring breeding colonies of seabird populations [ 31 ], characterising sensitive habitats such as juvenile fish nursery grounds [ 29 ], and supporting monitoring of salmonid spawning nests [ 21 ]. Despite the research on using UAVs to detect salmonid redds, there is still a great need for developing an easy, standardised, and low-cost approach to map entire spawning habitats as current methods either still rely on manual counting [ 27 , 32 ], utilise rather complex methodology [ 33 ] or need specialised equipment [ 34 ].…”