Implementing real-time acoustic monitoring for conservation: A case study using edge computing and satellite transmission

Abstract

Passive acoustic monitoring is widely used in conservation but often limited by delayed results due to manual retrieval and post-processing. To explore the feasibility of real-time monitoring in remote landscapes, we conducted field evaluations of a prototype automated surveying unit (ASU). The ASU integrated onboard convolutional neural network-based sound classification with satellite data transmission for near real-time species detection. We deployed ASUs alongside conventional autonomous recording units at eight sites to survey for northern spotted owls (Strix occidentalis caurina) and marbled murrelets (Brachyramphus marmoratus) in old-growth forests of the Pacific Northwest, USA. We compared detection rates, background noise levels, and operational performance. ASUs successfully transmitted detection summaries and system status, though the elevated background noise in ASU recordings reduced detection rates relative to conventional units. These preliminary findings suggest that real-time autonomous monitoring systems demonstrate potential to support time-sensitive conservation efforts, while highlighting current limitations and areas for improvement.