Remote sensing technologies are rapidly evolving, offering transformative opportunities for biodiversity science at scales from local to global. Advances in airborne and spaceborne sensors enable us to capture Earth surface and vegetation properties at unprecedented spectral, spatial, and temporal resolutions. Coupled with in situ data, species models, and artificial intelligence, these innovations open pathways to monitor biodiversity across structure, function and composition with great consistency and coverage.

Substantial progress is expected in the coming decade as next-generation Earth Observation missions, multi-sensor integration, and algorithm development converge. These advances promise more precise measurements of essential biodiversity variables such as ecosystem extent, structure and condition. Yet key challenges remain in translating electromagnetic signals into biologically meaningful metrics, scaling from field plots to global extents, integrating multi-source datasets while accounting for uncertainty, and aligning products with ecological theory, conservation practice, and global policy frameworks.

This session invites contributions that showcase how novel remote sensing and AI methods support biodiversity research and conservation. We particularly encourage studies that link remote sensing with in situ data, develop scalable approaches, advance ecological modelling to predict biodiversity change and its drivers, and demonstrate monitoring frameworks combining remote sensing, in situ networks and novel methods. By uniting advances in sensing technology and biodiversity science, the session will highlight how remote sensing can help contribute to the Kunming-Montreal Global Biodiversity Framework, the SDGs, and other international targets.