2026-10-07, 15:45–16:00 (Europe/Madrid), Room 18
The Carbon Lab research consortium, Global Pasture Watch, has produced comprehensive global pasture maps at 30-meter resolution covering the period 2000–2022. Generating consistent global information on pasture is statistically challenging due to the need to discriminate among multiple grassland types, management intensities, and closely related land-cover categories. Here, we present the first statistically rigorous global area estimates for the grassland domain, distinguishing six grassland classes: (1) natural and semi-natural permanent grasslands without visible livestock, (2) natural permanent woody vegetation within open woodlands, (3) semi-natural permanent grasslands with visible livestock presence, (4) temporary grasslands linked to other land use, (5) improved permanent grasslands associated with livestock, and (6) improved permanent grasslands associated with other land use, alongside estimates for other land-cover classes.
We describe the sampling design used to validate the 2020 static map and to derive unbiased global and continental area estimates across all grassland classes, based on an initial global sample of 50,000 locations. The design incorporates stratification targeted at rare classes, systematic interpretation protocols, and estimation procedures that explicitly propagate interpreter uncertainty into class-specific area estimates and confidence intervals. Additional temporal samples were drawn to quantify grassland dynamics over the periods 2000–2010, 2010–2015, and 2015–2022.
Particular emphasis was placed on securing sufficient samples for improved and temporary grassland classes, enabling statistically robust estimates of their global extent and change. Together, these results provide the first consistent quantification of the global distribution and dynamics of grassland systems, supporting applications in land-use modelling, biodiversity assessment, carbon-cycle research, and analyses of management intensity and human influence. They also contribute to ongoing discussions around a potential global “pasture peak,” for which emerging evidence suggests a decline beginning around the year 2000.
Open-Earth-Monitor Cyberinfrastructure (Grant agreement ID: 101059548)
Steffen Fritz is a Principal Research Scholar in the Novel Data Ecosystems for Sustainability (NODES) research group at IIASA. He spearheaded the development of the Geo-Wiki visualization and data collection platform, widely used in global and regional projects addressing land cover, land use, cropland distribution, forest management, and drivers of deforestation, integrating contributions from experts and citizen scientists. Fritz has extensive expertise in geospatial analysis, harmonization, and integration of spatial datasets, including uncertainty analysis, crop type mapping, spatial allocation, and biofuel-related data. He has led numerous high-profile initiatives with budgets exceeding €5 million, including ERC-funded projects and Horizon 2020 initiatives. His research interests include Earth observation, machine learning, artificial intelligence, crowdsourcing, volunteered geographic information, and the application of space and geospatial data to food security, vegetation monitoring, and sustainable land-use practices. He holds an MSc in Geographical Information for Development (University of Durham, UK) and a PhD (University of Leeds, UK), and has authored over 160 peer-reviewed publications.