AbstractGovernments are increasingly turning to renewable energy sources to meet growing energy demands. Wind energy is currently one of the most technologically advanced, cheapest and reliable forms of renewable energy and so is a key component in government renewable energy strategies. However, wind energy development can have negative environmental impacts and so, to ensure sustainable development, it is necessary that impacts on the environment are adequately assessed. The impacts of wind farms on birds have been of particular concern. In this thesis I have highlighted how bird flight characteristics and habitat use can be better quantified and the consequences this may have for the assessment of the impacts of wind energy developments on birds.
Assessing the risk of collision between birds and wind turbines is a pre-requisite for the sustainable development of wind energy and, in many countries, is a legislative requirement under the Environmental Impact Assessment (EIA) process. Collisions between birds and turbines are often predicted using theoretical models requiring information on flight activity, particularly flight height and speed. Methods recommended by statutory nature conservation bodies for the collection of such flight data include observer-based visual estimates of flight height and literature-derived values of flight speed. Data derived from such methods have been found to be inaccurate and imprecise when compared to more empirical, sensor-based methods. Therefore, I developed a framework to highlight how the tools and technologies already applied to the collection of bird flight, such as telemetry and radar, may be most usefully applied given the requirements of the EIA process and, in so doing, aim to help update guidance on bird flight measurement. I also recommend a more behaviour-based approach to collision risk estimation and highlight how data for such estimation can be collected within the framework.
A quantitative demonstration of the improvement in bird flight measurements is necessary if empirical, sensor-based methods are to be incorporated into the consenting process. As such, I validated the height and step-length measurements of the ornithodolite, a tool based on a pair of binoculars with inbuilt laser rangefinder, digital magnetic compass and inclinometer, which has been applied to collection of bird flight data for estimating collision
risk. This was undertaken using structures of known values and GPS technology, already applied to the collection of bird flight data. Variation in ornithodolite height measurements was observed with increasing height of the UAV target at a given distance but this effect was less pronounced at distances further from the UAV target. Similarly, step-length measurements recorded by the ornithodolite varied with the azimuth interval between consecutive altitude and longitudes. This effect was variable for different distances between the ornithodolite operator and the target. Additionally, I assessed how the ornithodolite performed when collecting data from a moving target and in a field set-up likely to be experienced during data collection for EIA. There was an upper threshold of detection for
flight characteristics, including speed, tortuosity and height. The resolution of ornithodolite data was also reduced in the presence of ‘clutter’ or non-target objects in the tracking vicinity. These results highlight how useful the ornithodolite might be in collecting bird flight data for the EIA process and more generally.
|Date of Award||5 May 2021|
|Supervisor||Elizabeth Masden (Supervisor) & Ben Wilson (Supervisor)|