Experimentally determining optimal conditions for mapping forage fish with RPAS

Abstract

RPAS (Remotely piloted aircraft systems, i.e., drones) present an efficient method for mapping schooling coastal forage fish species that have limited distribution and abundance data. However, RPAS imagery acquisition in marine environments is highly dependent on suitable environmental conditions. Additionally, the size, color and depth of forage fish schools will impact their detectability in RPAS imagery. In this study, we identified optimal and suboptimal coastal environmental conditions through a controlled experiment using a model fish school containing four forage fish-like fishing lures. The school was placed at 0.5 m, 1.0 m, 1.5 m, and 2.0 m depths in a wide range of coastal conditions and then we captured RPAS video imagery. The results from a cluster analysis, principal components, and correlation analysis of RPAS data found that the optimal conditions consisted of moderate sun altitudes (20–40°), glassy seas, low winds (<5 km/h), clear skies (<10% cloud cover), and low turbidity. The environmental conditions identified in this study will provide researchers using RPAS with the best criteria for detecting coastal forage fish schools.