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The accurate estimation of aboveground net herbaceous production (ANHP) is crucial in rangeland management and monitoring. Remote and rural rangelands typically lack direct observation infrastructure, making satellite-derived methods essential. When ground data are available, a simple and effective way to estimate ANHP from satellites is to derive the empirical relationship between ANHP and plant-absorbed photosynthetically active radiation (APAR), which can be estimated from the normalized difference vegetation index (NDVI). While there is some evidence that this relationship will differ across rangeland vegetation types, it is unclear whether this relationship will change across grazing management regimes. This study aimed to assess the impact of grazing management on the relationship between ground-observed ANHP and satellite-derived APAR, considering variations in plant communities across ecological sites in the shortgrass steppe of northeastern Colorado. Additionally, we compared satellite-predicted biomass production from the process-based Rangeland Analysis Platform (RAP) model to our empirical APAR-based model. We found that APAR could be used to predict ANHP in the shortgrass steppe, with the relationship being influenced by ecosite characteristics rather than grazing management practices. For each unit of added APAR (MJ m−2 day−1), ANHP increased by 9.39 kg ha−1, and ecosites with taller structured herbaceous vegetation produced, on average, 3.92–5.71 kg ha−1 more ANHP per unit APAR than an ecosite dominated by shorter vegetation. This was likely due to the increased allocation of plant resources aboveground for C3 mid-grasses in taller structured ecosites compared to the C4 short-grasses that dominate the shorter structured ecosites. Moreover, we found that our locally calibrated empirical model generally performed better than the continentally calibrated process-based RAP model, though RAP performed reasonably well for the dominant ecosite. For our empirical models, R2 values varied by ecosite ranging from 0.49 to 0.67, while RAP R2 values ranged from 0.07 to 0.4. Managers in the shortgrass steppe can use satellites to estimate herbaceous production even without detailed information on short-term grazing management practices. The results from our study underscore the importance of understanding plant community composition for enhancing the accuracy of remotely sensed predictions of ANHP.

Rangelands are composed of patchy, highly dynamic herbaceous plant communities that are difficult to quantify across broad spatial extents at resolutions relevant to their characteristic spatial scales. Furthermore, differentiation of these plant communities using remotely sensed observations is complicated by their similar spectral absorption profiles. To better quantify the impacts of land management and weather variability on rangeland vegetation change, we analyzed high resolution hyperspectral data produced by the National Ecological Observatory Network (NEON) at a 6500-ha experimental station (Central Plains Experimental Range) to map vegetation composition and change over a 5-year timescale. The spatial resolution (1 m) of the data was able to resolve the plant community type at a suitable scale and the information-rich spectral resolution (426 bands) was able to differentiate closely related plant community classes. The resulting plant community class map showed strong accuracy results from both formal quantitative measurements (F1 75% and Kappa 0.83) and informal qualitative assessments. Over a 5-year period, we found that plant community composition was impacted more strongly by weather than by the rangeland management regime. Our work displays the potential to map plant community classes across extensive areas of herbaceous vegetation and use resultant maps to inform rangeland ecology and management. Critical to the success of the research was the development of computational methods that allowed us to implement efficient and flexible analyses on the large and complex data.

Characterizing roads is important for conservation since the relationship between road use and ecological impact can vary across species. However, road use is challenging to monitor due to limited data and high spatial-temporal variability, especially for unpaved roads, which often coincide with critical habitats. In this study, we developed and evaluated two methods to characterize off-highway road use across a large management area of grizzly bear (Ursus arctos) habitat using: (1) a ‘network-based’ approach to connect human activity hotspots identified from social media posts and remotely detected disturbances and (2) an ‘image-based’ approach, in which we modeled road surface conditions and travel speed from high spatial resolution satellite imagery trained with crowd-sourced smartphone data. To assess the differences between these approaches and their utility for characterizing roads in the context of habitat integrity, we evaluated how behavioural patterns of global positioning system (GPS)-collared grizzly bears were related to road use characterized by these methods compared to (a) assuming all roads have equal human activity and (b) using a ‘reference’ road classification from a government database. The network- and image-based methods showed similar patterns of road use and grizzly bear response compared to the reference, and all three revealed nocturnal behaviour near high-use roads and better predicted grizzly bear habitat selection compared to assuming all roads had equal human activity. The network- and image-based methods show promise as cost-effective approaches to characterize road use for conservation applications where data is not available.