Identification of Bird Species in Large Multi-channel Data Streams Using Distributed Acoustic Sensing

The health of an ecosystem can be challenging to monitor due to the complex nature of environmental systems. Fortunately, the health of a local ecosystem can be inferred by monitoring key species which are indicative of the overall health of the ecosystem. Microphones have emerged as a powerful tool for detecting bird calls of these key indicator species. However, using an array of microphones to monitor a large area requires a power source at each location in addition to sensor telemetry to retrieve the data. Distributed acoustic sensing (DAS) is a promising approach for large scale monitoring as a single hardware system is used to detect signals over large distances. We propose a novel application of DAS to detect avian species for ecological health monitoring. A single DAS interrogator unit and optical fiber can collect tens of kilometers of high frequency acoustic data with the added benefit that DAS does not suffer from time synchronization errors and remote power issues like traditional microphone arrays. This work investigates the performance of DAS when used to detect bird calls, with particular focus on the Great Horned Owl (GHO), an indicator species for prey vulnerability in an ecosystem. By quantifying the performance of several DAS configurations and bird call detection approaches, we demonstrate the potential of DAS for use in ecological health monitoring applications.