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.

This paper proposes a compact planar Wi-Fi antenna with optimized radiation patterns for small uncrewed aerial vehicle (UAV) applications in both urban and open areas. It is suitable for mounting on the outermost side of the non-metallic wing of small UAVs. It has small dimensions of 16.5 mm (L) by 30.3 mm (W) by 1.6 mm (h), and the measured results of its prototype are in agreement with simulated data. Its impedance bandwidths over the two frequency ranges are 2.11 to 2.58 GHz and 5.06 to 7.5 GHz ( ). At 5.8 GHz, it has stronger radiation below the small UAV to reduce interference from rare-use directions. Its maximum radiations, the directions of the maximum radiation in each elevation plane, are below the UAV and between 14° and 29° from the horizontal plane. At 2.4 GHz, it has quasi-omnidirectional radiation to ensure a stable link in all directions, and its maximum radiations are near the horizontal plane. The optimized radiation patterns at 5.8 and 2.4 GHz can provide more antenna gain when the small UAV flies farther in urban and open areas, respectively. In addition, it has good vertically polarized radiation for long-distance applications.

Remotely sensed data acquired by multispectral sensors can be used to monitor soil moisture (SM) across a larger land area than in situ monitoring alone. Although there have been wide-ranging applications of remote sensing tools in SM estimation on many ecosystems, there is a limited understanding of their accuracy in restored wetlands. The objective of this study was to examine the potential of remotely sensed data from Landsat-9, Sentinel-1A SAR, and Blackswift E2 Uncrewed Aircraft Systems (UAS) in predicting SM in a restored wetland complex in the Gunnison Basin of Colorado. We tested two response variables, gravimetric SM and volumetric SM, to determine which indicator of SM was better predicted with remotely sensed data. We also tested the accuracy of remotely sensed data in predicting SM at different soil depths. Overall, satellite and UAS indices predicted gravimetric SM better than volumetric SM. The Normalized Difference Red Edge (NDRE) index from Blackswift E2 UAS had the best prediction of GSM at the depth of 0 to 5 cm (R2 = 0.86, RMSE = 7.41). Results from the study provide information on the accuracy of remotely sensed data for SM monitoring on restored wetlands.