Automatically Counting Florida Manatees (Trichechus manatus latirostris) from Drone Images Using Object-Based Image Analysis

Florida manatees (Trichechus manatus latirostris) require frequent and extensive surveys to inform conservation efforts. Crewed aircraft surveys can be costly, dangerous, and logistically complex. Unoccupied aerial systems (UASs) can assist with these issues. While manual review of UAS imagery can be time- and labor-intensive, automated detection of manatees in aerial survey footage can help. We present an object-based image analysis workflow for the automated detection and count of Florida manatees in Google Earth Engine, a free platform for research that allows for scripts and imagery sharing. Training and testing datasets were built from randomly extracted image frames from two stationary, unoccupied aerial system videos over thermal refugia. The workflow captured most manatees (93.98 to 95.62% recall; 4.38 to 6.03% false negative rate), but also counted many objects as manatees incorrectly (4.24 to 14.77% precision; 998.40 to 3,885.54% false positive over the detectable rate). Sun glint, mud plumes, and water close to shore were common causes of false positives. While the automated count was too high, the workflow lays markers over each detection, allowing for quick manual review for more accurate (semi-automated) counts. This study is an early step in automated detection tools for Florida manatees in a cloud-based platform. Future efforts could explore other platforms or may improve this workflow by including new classes for confounding objects.