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.Comment: Published in Proceedings of SPIE Astronomical Telescopes and Instrumentation 2018. 8 pages, 3 figure

Detecting ‘poachers’ with drones: Factors influencing the probability of detection with TIR and RGB imaging in miombo woodlands, Tanzania

Conservation biologists increasingly employ drones to reduce poaching of animals. However, there are no published studies on the probability of detecting poachers and the factors influencing detection. In an experimental setting with voluntary subjects, we evaluated the influence of various factors on poacher detection probability: camera (visual spectrum: RGB and thermal infrared: TIR), density of canopy cover, subject distance from the image centreline, subject contrast against the background, altitude of the drone and image analyst. We manually analysed the footage and marked all recorded subject detections. A multilevel model was used to analyse the TIR image data and a general linear model approach was used for the RGB image data. We found that the TIR camera had a higher detection probability than the RGB camera. Detection probability in TIR images was significantly influenced by canopy density, subject distance from the centreline and the analyst. Detection probability in RGB images was significantly influenced by canopy density, subject contrast against the background, altitude and the analyst. Overall, our findings indicate that TIR cameras improve human detection, particularly at cooler times of the day, but this is significantly hampered by thick vegetation cover. The effects of diminished detection with increased distance from the image centreline can be improved by increasing the overlap between images although this requires more flights over a specific area. Analyst experience also contributed to increased detection probability, but this might cease being a problem following the development of automated detection using machine learning

Controversies in the physiological basis of the ‘anaerobic threshold: Implications for cardiopulmonary exercise testing

This article reviews the notion of the ‘anaerobic threshold’ in the context of cardiopulmonary exercise testing. Primarily, this is a review of the proposed mechanisms underlying the ventilatory and lactate response to incremental exercise, which is important to the clinical interpretation of an exercise test. Since such tests are often conducted for risk stratification before major surgery, a failure to locate or justify the existence of an anaerobic threshold will have some implications for clinical practice. We also consider alternative endpoints within the exercise response that might be better used to indicate a patient’s capacity to cope with the metabolic demands encountered both during and following major surgery