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Across agricultural areas of the Prairie Pothole Region (PPR), floral resources are primarily found on public grasslands, roadsides, and private grasslands used as pasture or enrolled in federal conservation programs. Little research has characterized the availability of flowers across the region or identified the primary stakeholders managing lands supporting pollinators. We explored spatial and temporal variability in flower abundance and richness across multiple grassland categories (i.e. general grassland, conservation grassland, and engineered pollinator habitat) in the PPR from 2015 to 2018 and used these data to estimate the number of flowering stems present across the region on private and public land holdings. Both flowering plant abundance and richness were greatest on engineered pollinator habitat, but this land category encompassed < 0.01% of the total grassland area in the PPR. There was a steady decrease in flower abundance over the growing season across all land categories. We detected considerable variation in flower abundance and richness across grassland categories, indicating that not all natural or semi-natural covers provide similar value to pollinators. At a landscape scale, large land holdings such as privately-owned grasslands and Conservation Reserve Program lands contributed the greatest number of flowers by an order of magnitude, though these lands collectively did not support the greatest abundance of flowers per unit area. Our research depicts spatial and temporal variation in pollinator resources across the region. Further, our research will assist managers and policy makers in understanding the role of public and private lands and conservation programs in supporting pollinators. [ABSTRACT FROM AUTHOR]

Remotely monitoring changes in central U.S. grasslands is challenging because these landscapes tend to respond quickly to disturbances and changes in weather. Such dynamic responses influence nutrient cycling, greenhouse gas contributions, habitat availability for wildlife, and other ecosystem processes and services. Traditionally, coarse-resolution satellite data acquired at daily intervals have been used for monitoring. Recently, the harmonized Landsat-8 and Sentinel-2 (HLS) data increased the temporal frequency of the data. Here we investigated if the increased data frequency provided adequate observations to characterize highly dynamic grassland processes. We evaluated HLS data available for 2016 to (1) determine if data from Sentinel-2 contributed to an improvement in characterizing landscape processes over Landsat-8 data alone, and (2) quantify how observation frequency impacted results. Specifically, we investigated into estimating annual vegetation phenology, detecting burn scars from fire, and modeling within-season wetland hydroperiod and growth of aquatic vegetation. We observed increased sensitivity to the start of the growing season (SOST) with the HLS data. Our estimates of the grassland SOST compared well with ground estimates collected at a phenological camera site. We used the Continuous Change Detection and Classification (CCDC) algorithm to assess if the HLS data improved our detection of burn scars following grassland fires and found that detection was considerably influenced by the seasonal timing of the fires. The grassland burned in early spring recovered too quickly to be detected as change events by CCDC; instead, the spectral characteristics following these fires were incorporated as part of the ongoing time-series models. In contrast, the spectral effects from late-season fires were detected both by Landsat-8 data and HLS data. For wetland-rich areas, we used a modified version of the CCDC algorithm to track within-season dynamics of water and aquatic vegetation. The addition of Sentinel-2 data provided the potential to build full time series models to better distinguish different wetland types, suggesting that the temporal density of data was sufficient for within-season characterization of wetland dynamics. Although the different data frequency, in both the spatial and temporal dimensions, could cause inconsistent model estimation or sensitivity sometimes; overall, the temporal frequency of the HLS data improved our ability to track within-season grassland dynamics and improved results for areas prone to cloud contamination. The results suggest a greater frequency of observations, such as from harmonizing data across all comparable Landsat and Sentinel sensors, is still needed. For our study areas, at least a 3-day revisit interval during the early growing season (weeks 14–17) is required to provide a >50% probability of obtaining weekly clear observations. [ABSTRACT FROM AUTHOR]