Wildlife monitoring is vital to conservation efforts and the prevention of animal-related negative impacts on human activities and ecosystems. The use of Unmanned Aerial Vehicles (UAVs) enables data collection with no harm to wildlife and in difficult field conditions. This study proposes a method of detecting hoofed animals in UAV-acquired thermal images, addressing the challenges of low-resolution thermal imaging and the presence of other heated objects hindering simple temperature analysis and image segmentation. The proposed method uses machine learning algorithms and is designed to work with a limited size of training dataset. The method consists of an initial segmentation step that detects potential animals based on thermal and geometrical signatures, followed by classification using a Balanced Random Forest (BRF) algorithm. One of the key aspects of the proposed method is the use of geometric and thermal features along with multi-scale Convolutional Neural Network (CNN) extracted feature representations in BRF. The benefit of the BRF is its speed, little requirement regarding the amount of training data, and its capacity to work with an imbalanced number of objects in different classes. The dataset was collected during two UAV flights over a fenced enclosure with wild hoofed animals. The proposed approach showed high efficiency, achieving an overall accuracy of 90%. These results confirm the feasibility of UAV-based animal detection based solely on thermal images collected during the day and showing many other heated objects. The method provides a solution for wildlife monitoring, with potential adaptability to different species and further applications.