Addressing environmental and atmospheric challenges for capturing high-precision thermal infrared data in the field of astro-ecology

Using thermal infrared detectors mounted on drones, and applying techniques from astrophysics, we hope to support the field of conservation ecology by creating an automated pipeline for the detection and identification of certain endangered species and poachers from thermal infrared data. We test part of our system by attempting to detect simulated poachers in the field. Whilst we find that we can detect humans hiding in the field in some types of terrain, we also find several environmental factors that prevent accurate detection, such as ambient heat from the ground, absorption of infrared emission by the atmosphere, obscuring vegetation and spurious sources from the terrain. We discuss the effect of these issues, and potential solutions which will be required for our future vision for a fully automated drone-based global conservation monitoring system

Optimising observing strategies for monitoring animals using drone-mounted thermal infrared cameras

The proliferation of relatively affordable off-the-shelf drones offers great opportunities for wildlife monitoring and conservation. Similarly the recent reduction in cost of thermal infrared cameras also offers new promise in this field, as they have the advantage over conventional RGB cameras of being able to distinguish animals based on their body heat and being able to detect animals at night. However, the use of drone-mounted thermal infrared cameras comes with several technical challenges. In this paper we address some of these issues, namely thermal contrast problems due to heat from the ground, absorption and emission of thermal infrared radiation by the atmosphere, obscuration by vegetation, and optimizing the flying height of drones for a best balance between covering a large area and being able to accurately image and identify animals of interest. We demonstrate the application of these methods with a case study using field data, and make the first ever detection of the critically endangered riverine rabbit (Bunolagus monticularis) in thermal infrared data. We provide a web-tool so that the community can easily apply these techniques to other studies (http://www.astro.ljmu.ac.uk/~aricburk/uav_calc/)

Knowledge creation and transfer in the Beacon Scheme: improving services through sharing good practice

This article examines the extent to which processes of knowledge creation, transfer and application through sharing learning are effective in improving local public services. We examine the impact of the Beacon Scheme as a means of achieving service and corporate improvement. The research findings suggest that the acquisition of knowledge by learning network members and the transfer of learning leading to change in working practices and corporate culture is effective where the local authority culture is receptive to the impact of knowledge and where key political and managerial actors operate collaboratively. Quantitative and qualitative indicators of learning leading to service and corporate improvement are analysed. Learning through Beacons is shown to be effective but is more modest than its potential and might be enhanced in three policy areas. These are: improvement of learning links between central and local government; greater consideration of processes of knowledge acquisition and application by receivers of learning as much as disseminators of learning; greater emphasis on learning from the innovation element within the scheme