As part of the USGS Changing Arctic Ecosystem (CAE) Initiative, the Alaska Science Center is conducting research to assess the distribution and nutrient value of halophytic graminoid “grazing lawn” habitat across the Arctic Coastal Plain (ACP) of Alaska. These grazing lawns are important habitat for Pacific Black Brant and Lesser Snow and Greater White Fronted Geese. The timing of the resource’s seasonal nutrient abundance as related to peak hatch and molting periods is thought to be crucial to reproductive and migratory success. Climate change will likely alter the seasonal nutrient abundance and general distribution of this habitat across the ACP, with unknown consequences for geese populations. During 5 years of research (culminating in 2015), CAE’s Avian and Vegetative Phenology Project has used data from physical vegetation samples, field spectrometer readings, and satellite imagery to establish a tight statistical relationship between the Normalized Difference Vegetation Index (NDVI) derived from spectral profiles, nutrient abundance, and nutrient availability in halophytic graminoid grazing lawns. Statistically significant correlations allow for the use of a “best fit” mathematical model established in one location (or time) to predict nutrient abundance using NDVI readings from another location (or time). This method affords the interpretation of satellite-derived NDVI in terms of nutrient abundance across broad areas once the habitat is mapped, but consideration of scale is key. This research further demonstrates the ability to predict the timing of peak nutrient availability using a seasonal NDVI timeline from both high (spectrometer, WorldView-2) and low (eMODIS) resolution data; it correlates with the period of most rapid increase in NDVI value. As part of the ongoing Arctic Coastal Plain Salt Marsh Habitat Dynamics Project, scientists are using WorldView-2 imagery to create vegetation maps of the arctic coastal zone (~20 km inland) from Oliktok Point to west of Point Barrow (~1,000 km of coastline) with a focus on identifying all grazing lawn habitat. This vegetation map will be integrated with lidar data to create a model that will associate habitat types with elevation allowing predictions of habitat change due to coastal subsidence, driven by permafrost thaw and inundation as the climate warms.