Wildlife monitoring can be time-consuming and expensive, but the fast-developing technologies of uncrewed aerial vehicles, sensors, and machine learning pave the way for automated monitoring. In this study we trained YOLOv5 neural networks to detect Points of Interest, hare (Lepus europaeus), and roe deer (Capreolus capreolus) in thermal aerial footage and proposed a method to manually assess the parameter mean average precision (mAP), compared to the number of actual false positive and false negative detections in a subsample. This showed that a mAP close to 1 for a trained model does not necessarily mean perfect detection and provided a method to gain insights into the parameters affecting the trained models' precision. Furthermore, we provided a basic, conceptual algorithm for implementing real-time object detection in uncrewed aircraft systems equipped with thermal sensors, with high zoom capabilities, and a laser rangefinder. Real-time object detection is becoming an invaluable complementary tool for the monitoring of cryptic and nocturnal animals with the use of thermal sensors.

Drones equipped with thermal cameras have recently become readily available, broadening the possibilities for monitoring wildlife. The European hare (Lepus europaeus) is a nocturnal mammal that is closely monitored in Denmark due to populations declining since the mid-1900s. The limitations of current population assessment methods, such as spotlight counts and hunting game statistics, could be overcome by relying on drone surveys with thermal imaging for population counts. The aim of this study was to investigate the use of a DJI Mavic 2 Enterprise Advanced drone with thermal imaging as a tool for monitoring the Danish hare population. Multiple test flights were conducted over agricultural areas in Denmark in spring 2022, testing various flight altitudes, camera settings, and recording methods. The test flights were used to suggest a method for identifying and counting hare. The applied use of this methodology was then evaluated through a case survey that had the aim of identifying and counting hare over an agricultural area of 242 ha. Hare could be detected with thermal imaging at flight altitudes up to 80 m and it was possible to fly as low as 40 m without observing direct behavior changes. Thermal images taken at these altitudes also provided enough detail to differentiate between species and animal body size proved to be a good species indicator. The case study confirmed the use of thermal imaging based drone surveys to identify hare and conduct population counts, thus, indicating the suggested methodology as a viable alternative to traditional counting methods.