Global Land Monitoring

We advance large-scale land monitoring by leveraging next-generation Earth observation missions and derived data products, thereby improving our ability to observe and characterise land surface dynamics.

We develop and apply advanced analytical approaches, including deep learning, artificial intelligence, multi-sensor integration, and change detection. The aim is to build robust, scalable, and efficient tools and models that can support land monitoring across different regions and applications.

We develop and maintain open-source Python toolboxes that enable scalable, cloud-native access to large spaceborne EO archives. These include gediDB and icesat2DB for structured processing and querying of the full GEDI and ICESat-2 ATL08 archives, and EOForestSTAC, a SpatioTemporal Asset Catalogue interface for streaming analysis-ready global forest EO products without local data downloads. These tools are openly licensed and designed for reuse by the broader research community.

We place strong emphasis on user needs, policy relevance, and operational uptake. Our work contributes to global initiatives such as the Global Forest Observations Initiative (GFOI) and supports the translation of scientific results into meaningful information for reporting, planning, and policy processes.

The GLM team brings together expertise across science, data, and policy, with research spanning forest dynamics, agriculture, and land-use change. Current topics include:

• forest regrowth, disturbance, and structure at the global scale;
• biomass estimation and carbon dynamics for MRV from national to global scales;
• agriculture, food security, and land-use carbon footprints; and
• global land cover change and uncertainty assessments.

We are also active in forest age mapping, post-disturbance monitoring, integration of land observations into carbon budgets, and characterising degraded and secondary forests, disturbance drivers, and commodity-driven land-use change.