Abstract
Offshore wind farms play a critical role in generating clean energy with minimal greenhouse gas emissions. However, their potential impacts on local populations of seabirds and migratory population of other bird groups raises environmental concerns, especially as increasing numbers of turbines are installed along key migratory routes and in the foraging ranges of seabird colonies (Croll et al., 2022). However, the scale and nature of interactions between birds and offshore wind farms remain uncertain, in part because observations of such interactions are technically and logistically challenging, and no existing monitoring approach is without sampling and or measurement imperfections. Further developments to monitoring technologies are necessary to obtain a more robust evidence base, and to allow scalable monitoring as the number and extent of OWFs continue to grow.
The overall objective of this study was to assess the utility of a Spoor AI system to monitor movements of birds around individual offshore wind turbines at the European Offshore Wind Deployment Centre (EOWDC) in Aberdeen Bay, with a particular focus on assessing the precision and accuracy of generated track reconstructions of individual birds, and the potential to quantify bird flux and to detect avoidance behaviour in the close vicinity of the monitored turbine.
To achieve this, we conducted theoretical (Section 2) and experimental (Sections 3,4) work to assess the measurement accuracy of the system, that is its potential of reliably reconstructing the true positions of imaged seabirds using both single-camera (‘monovision’) and stereo camera approaches.
To be able to conduct the estimation of bird flux, i.e. the true number of birds traversing the airspace of the OWF in a given time interval, we further assessed the sampling characteristics of the system and developed a statistical model to estimate bird density while accounting for both sampling and behavioural characteristics (Section 5).