Urban trees play a pivotal role in climate adaptation, yet current monitoring methods – ranging from infrequent field inventories to complex remote sensing – often lack the resolution needed for effective real-time care. This paper introduces a scalable, low-cost sensor network that indexes bioelectrical signals from individual trees with environmental data to understand the bioelectrical “language” of trees themselves. A bridge technology between living and digital systems, it uses two electrodes to detect fluctuations in cambium bioelectric potential. An array of sensors (for temperature, humidity, gas levels, light, and sound) provides contextual data, all transmitted to a cloud platform for continuous logging and eventual machine-learning analysis. In a four-day field deployment on maple and birch trees, preliminary results showed stable operation and proof-of-concept. Future improvements will address battery life, refine circuit design, and explore alternative networking protocols. Integrating electrophysiology with IoT promises richer insights into urban forest health and, with the help of artificial intelligence, can contribute to fundamental knowledge about urban ecosystems.