Journal of Pattern Recognition Research

Chiropteran monitoring has become an important public concern given that wind turbines pose the threat of injury or death to bats through direct impact or barotraumas. Such monitoring therefore requires robust methodology to assess the local density, temporal activity, and diversity of bats. This work develops machine learning based monitoring approach for nocturnal flight activity of bats. It consists of Ultrasound Acoustic Monitoring System (UAMS) and thermal-Infrared Imaging Monitoring System (IIMS), the former for identification of bat echolocation calls and the latter for assessing flight characteristics. Supervised and unsupervised machine learning techniques were used for UAMS and IIMS, respectively. The proposed methodology was tested with real data collected during 2011 spring and fall migration around Lake Erie in Ohio. The research will be helpful for biologists and decision makers to rapidly but effectively asses bat density, activity, and diversity within natural areas or proposed wind development sites.