Satellite derived vegetation indices (VIs) are broadly used in ecological research, ecosystem modeling, and land surface monitoring. The Normalized Difference Vegetation Index (NDVI), perhaps the most utilized VI, has countless applications across ecology, forestry, agriculture, wildlife, biodiversity, and other disciplines. Calculating satellite derived NDVI is not always straight-forward, however, as satellite remote sensing datasets are inherently noisy due to cloud and atmospheric contamination, data processing failures, and instrument malfunction. Readily available NDVI products that account for these complexities are generally at coarse resolution; high resolution NDVI datasets are not conveniently accessible and developing them often presents numerous technical and methodological challenges. We address this deficiency by producing a Landsat derived, high resolution (30 m), long-term (30+ years) NDVI dataset for the conterminous United States. We use Google Earth Engine, a planetary-scale cloud-based geospatial analysis platform, for processing the Landsat data and distributing the final dataset. We use a climatology driven approach to fill missing data and validate the dataset with established remote sensing products at multiple scales. We provide access to the composites through a simple web application, allowing users to customize key parameters appropriate for their application, question, and region of interest.

Implementing strategies to navigate large-scale ecological transitions in grasslands is one of this century's greatest conservation challenges. In the US Great Plains, managing areas impacted by woody transitions have been reactive, short-lived, costly, and ineffective. Along with current technological innovation in rangeland monitoring, the promise of early warning science is to provide managers with sufficient time to be better prepared for novel signals of ecological change. Combining the science of early warning signals and frameworks such as the Resist - Accept - Direct (RAD) can provide land managers with guidelines to identify proactive strategies when facing ecological change. Using this approach, we found that opportunities to resist woody transitions decreased from 84 % to 60 % between 1990 and 2020 over the entire biome but remained highest in the northern and western Great Plains, which contributes to large scale conservation targets. These are key areas to prioritize resist opportunities. In contrast, 11 % of the biome exhibited early warning transition signals across all hierarchical scales by 2020, a fourfold increase since 1990. Lastly by 2020, 30 % of the biome exhibited early warning signals across multiple but not all scales. Here, efforts may be more effective when management is directed to conserve fragmented grassland legacies within a woody-dominated matrix and avoid large-scale monocultures of problematic encroaching woody species. Our multi-scale study indicates that 1) anchoring to the last remaining grassland core areas with no early warning of transitions and 2) strategically investing in these intact grasslands may provide the best results for grassland conservation.