In the last decade, small unmanned aerial vehicles (UAVs/drones) have become increasingly popular in the airborne observation of large areas for many purposes, such as the monitoring of agricultural areas, the tracking of wild animals in their natural habitats, and the counting of livestock. Coupled with deep learning, they allow for automatic image processing and recognition. The aim of this work was to detect and count the deer population in northwestern Serbia from such images using deep neural networks, a tedious process that otherwise requires a lot of time and effort. In this paper, we present and compare the performance of several state-of-the-art network architectures, trained on a manually annotated set of images, and use it to predict the presence of objects in the rest of the dataset. We implemented three versions of the You Only Look Once (YOLO) architecture and a Single Shot Multibox Detector (SSD) to detect deer in a dense forest environment and measured their performance based on mean average precision (mAP), precision, recall, and F1 score. Moreover, we also evaluated the models based on their real-time performance. The results showed that the selected models were able to detect deer with a mean average precision of up to 70.45% and a confidence score of up to a 99%. The highest precision was achieved by the fourth version of YOLO with 86%, as well as the highest recall value of 75%. Its compressed version achieved slightly lower results, with 83% mAP in its best case, but it demonstrated four times better real-time performance. The counting function was applied on the best-performing models, providing us with the exact distribution of deer over all images. Yolov4 obtained an error of 8.3% in counting, while Yolov4-tiny mistook 12 deer, which accounted for an error of 7.1%.

Among numerous global problems, one of the most significant is air pollution. In this paper, unwashed (U) and water-washed (W) needles of two conifers—European larch and Douglas fir—were used to assess their capacity for the retention and accumulation of heavy metals. The needle samples were used to represent the atmospheric deposition of heavy metals located on the surface of the needles. The sampled European larch and Douglas fir plantations were situated at three locations in Serbia: a least polluted (Kučevo), a moderately polluted (Avala), and a very polluted (Lazarevac) site. The content of five heavy metals (Ni, Cu, Co, Cd, Pb) was investigated in the study. The concentration of cadmium (Cd) was higher in the European larch needles compared to Douglas fir, while the differences in the content of the other heavy metals between the species studied were insignificant. For both species, the following trend applied with respect to the heavy metal content in their needles: Ni ˃ Cu ˃ Co ˃ Pb ˃ Cd. Based on the results obtained, we deduced that the concentrations of all investigated heavy metals at all three locations for both species were within the allowed limits, except for nickel (Ni) content, which was over the predicted limit values for both species in the highly polluted area (Lazarevac). A PCA (principal component analysis) undertaken suggests that European larch has a greater ability to accumulate Co than Douglas fir on sites contaminated with heavy metals. The predictive foliar metal accumulation index (MAI) value was slightly higher in Douglas fir (4.14) than in European larch (3.76); therefore, the results suggest that this species would be a good planting choice, particularly in urban and industrial environments.

The oak forests in the area of the Special Nature Reserve ?Obedska bara? are extremely valuable and unique at the national and European level, which is why they are recognized as the fundamental value of the reserve. The local name of the oldest complex of oak forests in the special nature reserve is ?Debela gora rainforest? and it has been placed in the first degree of protection regime. Given that it is included in the first level protection regime, the priority goal of forest management is the preservation of biological diversity, which implies the absence of any management treatments. As a result of this approach, there is a loss of oaks and other species with higher demands for light, therefore other species such as hornbeam appear and take over, which is subject of analysis in this paper. Given that the replacement of species is not the goal of forest management in areas under the first degree of protection, this paper proposes forest management protective measures aimed at the restoration and revitalization of areas that could be implemented with the consent of the competent institutions.