V-STAR: A Cloud-Based Tool for Satellite Detection and Mapping of Volcanic Thermal Anomalies

In recent years, numerous satellite-based systems have been developed to monitor and study volcanic activity from space. This progress reflects the growing demand for accurate and timely monitoring to reduce volcanic risk. Observing volcanoes from a satellite perspective provides key advantages, enabling continuous data acquisition and near-real-time assessment of volcanic activity. Multispectral sensors operating across various regions of the electromagnetic spectrum can detect thermal anomalies associated with lava flows, pyroclastic flows, ash plumes, and volcanic gases. Traditional hotspot detection techniques based on fixed thresholds often miss subtle anomalies on a global scale. In contrast, advanced machine learning algorithms offer a data-driven alternative. We designed and implemented the V-STAR application (Volcanic Satellite Thermal Anomalies Recognition) on Google Earth Engine (GEE) to leverage cloud computing for processing large geospatial datasets in real time. It employs supervised machine learning, specifically Random Forests, to adapt to evolving volcanic conditions. This enhances the accuracy and responsiveness of volcanic monitoring, offering valuable insights into potential eruptive behavior. Here, we present V-STAR as a robust and accessible tool that integrates satellite data and advanced analytics. Through its intuitive interface, V-STAR provides a comprehensive visualization of key volcanic features. The resulting analyses reveal hidden patterns in thermal data, contributing to improved disaster risk reduction strategies associated with volcanic hazards.