Dugongs (Dugong dugon) are seagrass specialists distributed in shallow coastal waters in tropical and subtropical seas. The area and distribution of the dugongs’ feeding trails, which are unvegetated winding tracks left after feeding, have been used as an indicator of their feeding ground utilization. However, current ground-based measurements of these trails require a large amount of time and effort. Here, we developed effective methods to observe the dugongs’ feeding trails using unmanned aerial vehicle (UAV) images (1) by extracting the dugong feeding trails using deep neural networks. Furthermore, we demonstrated two applications as follows; (2) extraction of the daily new feeding trails with deep neural networks and (3) estimation the direction of the feeding trails. We obtained aerial photographs from the intertidal seagrass bed at Talibong Island, Trang Province, Thailand. The F1 scores, which are a measure of binary classification model’s accuracy taking false positives and false negatives into account, for the method (1) were 89.5% and 87.7% for the images with ground sampling resolutions of 1 cm/pixel and 0.5 cm/pixel, respectively, while the F1 score for the method (2) was 61.9%. The F1 score for the method (1) was high enough to perform scientific studies on the dugong. However, the method (2) should be improved, and there remains a need for manual correction. The mean area of the extracted daily new feeding trails from September 12–27, 2019, was 187.8 m2 per day (n = 9). Total 63.9% of the feeding trails was estimated to have direction within a range of 112.5° and 157.5°. These proposed new methods will reduce the time and efforts required for future feeding trail observations and contribute to future assessments of the dugongs’ seagrass habitat use.

Monitoring the movement of small animals is a fundamental aspect of ecological studies as well as spatially explicit conservation and management. However, this remains a challenging task especially in mountainous terrains. Although drone-based radiotelemetry (DRT) is employed to localize animals, its application in mountainous terrains is limited by the collision risks associated with undulating terrains as well as the obstruction of signals by dense vegetation and steep slopes. We addressed these challenges by generating fine-scale three-dimensional maps and moving vertically mounted directional antennas in a double grid pattern, scanning both in longitudinal and latitudinal grids. This new DRT system was helpful in localizing four adult Japanese toads ( Bufo japonicus) living in hiding places typical of mountainous terrains. All toads were located within 1–60 days of being released. Transmitter signals were detected within two consecutive flights (three flights in one case). Instances of transmitter detection were significantly biased when the drone was facing either direction of the double-grid path, indicating that the double-grid pattern had reduced detection failure. The absolute localization error ( n = 48) of 22.4 ± 21.0 m (44.8 ± 42% of the transmitter-to-receiver distance) was lower than that reported in a previous study conducted in a similar mountainous terrain.