The possibility of turning fiber-optic cables into environmental sensors has paved the way for a new paradigm of observations in geosciences, enabling spatially dense measurements of strain, temperature, and environmental parameters along fibers. The potential to exploit existing fiber-optic infrastructures deployed for telecommunications further increases the applicability of these techniques, enabling environmental monitoring in harsh or remote areas where the deployment of standard instruments is unfeasible. This rapidly evolving technology has already demonstrated significant potential for high-resolution observations in poorly instrumented environments such as volcanic flanks, geothermal fields, ocean bottoms, and glaciers. Moreover, the dense distribution of fiber networks in urban areas opens new opportunities for transforming smart-city applications and infrastructure monitoring.

While the benefits of fiber-optic sensing for conventional monitoring are becoming clear, real-time processing of these data could further enhance their societal impact, with promising implications for the early warning of hazardous phenomena. In volcanic settings, real-time analysis of low-frequency strain can provide valuable information on ongoing eruptive activity, while fiber-optic cables deployed near seismogenic sources could increase lead times for earthquake early-warning systems. At the same time, the growing frequency of extreme meteorological events associated with climate change—such as flash floods or avalanches—calls for monitoring systems capable of rapidly detecting potentially devastating phenomena. Real-time urban monitoring may also contribute to improving the viability and efficiency of smart cities, where fiber-optic sensing could play a key role.

We encourage contributions on operative or potential applications of fiber optic technologies for real-time monitoring in various natural, urban or industrial environments. Contributions highlighting the integration of conventional sensors into fiber-optic networks (e.g. SMART cables), as well as focusing on the challenges of real time processing are welcome. We also strongly encourage contributions that leverage artificial intelligence and machine learning for real-time monitoring, including automated analysis and decision-support systems for early warning applications.