What is one major takeaway from your research? 

Grasslands are incredibly challenging to monitor at the global scale because they are so heterogeneous—grasslands in the Serengeti look vastly different from those in the American Great Plains or the Mongolian Steppe. This heterogeneity has contributed to a lack of consistent, high-resolution spatial data for grasslands globally. Our work represents a significant step toward filling this gap. People seem to value the contributions we’re making in this space and the open nature of the data, and we’re excited to continue refining and building on these datasets over time.

Why did you use Landsat in this work? Did it give you any insight that would have been difficult to get otherwise? 

We used Landsat primarily because of its long time series, which allows us to analyze changes in grasslands over two decades. It’s important to have a decent temporal coverage in order to say something about how these areas are changing. We also had some high impact use cases that required a longer time series, such as using the data for Greenhouse Gas Protocol compliant Land Use Change emissions calculations, which benefit from a 20-year lookback period. Additionally, Landsat’s 30-meter spatial resolution strikes a balance – it’s detailed enough to provide actionable insights for local and regional decision-making, yet broad enough to enable global-scale analyses. This level of granularity helps us capture fine-scale dynamics, such as grassland fragmentation, that would be missed by coarser datasets.

Why is medium-resolution grassland monitoring important? 

Medium-resolution grassland monitoring is critical because these landscapes are central to numerous global priorities. Grasslands support biodiversity, provide livelihoods through livestock grazing, act as carbon sinks, and play a role in water regulation. We want the maps we’re creating to serve multiple use cases, including:

• Land-use planning and management: Helping policymakers and stakeholders make informed decisions about how to allocate and preserve grassland areas.
• Supply chain monitoring: Supporting sustainable sourcing efforts by tracking conversion of natural ecosystems.
• Carbon accounting: Contributing to accurate reporting of greenhouse gas emissions and carbon sequestration from grasslands.
• Restoration planning: Identifying degraded areas suitable for restoration efforts.
• Finance for sustainable agriculture and nature-based solutions: Providing data for assessing the impacts and benefits of investments.

Are there any research questions you’re interested in that build off of this work?

Yes! We have several complementary datasets in development that will provide insights into the management, condition, and productivity of grasslands. We would like to combine several of these together into a more finely resolved legend that can say more about grasslands as a land cover and a land use. These data are also being integrated into an ensemble land cover product with other classes. Our ultimate goal is to create datasets that are broadly usable, openly accessible, and adaptable to different local and global contexts. We see this work as just the beginning of a broader effort to improve the monitoring and management of grasslands worldwide, hopefully through continued collaboration and great partnerships.