Global biodiversity is declining at unprecedented rates, yet the tools available to monitor and protect ecosystems remain limited by constraints in power, connectivity, and accessibility. We present SPARROW, a hardware and software open-source platform that integrates solar energy, edge artificial intelligence, and satellite communication to enable continuous, autonomous biodiversity monitoring in remote environments. Each SPARROW node combines a low-power Graphics Processing Unit (GPU) with modular visual, acoustic, and environmental sensors, performing on-device deep learning inference and transmitting summarized results through Low-Earth-Orbit (LEO) satellite or Global System for Mobile Communications (GSM) networks. We deployed SPARROW across tropical, temperate, and montane ecosystems in Colombia, Peru, Tanzania, and the United States, where it sustained 24/7 operation under variable environmental conditions and collected more than two million images and acoustic recordings in the first 190 days. The system demonstrated robust real-time classification and adaptive power management, achieving full autonomy without on-site human intervention. By integrating renewable energy, on-edge AI, and open-source design, SPARROW lowers the technical and financial barriers to ecological monitoring and establishes a scalable foundation for a distributed, intelligent network of sensors, an emerging "Internet of Living Things" for planetary biodiversity monitoring.