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Context: Satellite products of fractional vegetation cover are often used to manage rangelands. However, they frequently miss the details of heterogeneous landscapes. The use of unoccupied aerial systems (UAS) to produce high spatial resolution rangeland fractional cover maps could fill that gap at local scales. Objectives: We evaluated the capabilities of UAS imagery for mapping rangeland fractional vegetation cover in sagebrush steppe communities of the Northern Great Basin, USA. Methods: We applied segmentation and machine learning models for image classification, and established regression functions with field-measured herbaceous cover and multiple spectral indices to quantify herbaceous fraction in bare/herbaceous mixed polygons. Finally, we conducted a correlation analysis to compare UAS-derived rangeland fractional cover with satellite-derived products. Results: Overall classification accuracies for the UAS-derived rangeland fractional cover maps were high (89–98%). Modified Soil Adjusted Vegetation Index was the most important spectral index for predicting photosynthetic classes and including Brightness Index in a multiple index approach improved classification of shadows and bare ground. Regression models effectively estimated herbaceous fractions within bare/herbaceous mixed polygons with high accuracy (R2 = 0.71–0.88). UAS-derived rangeland fractional cover estimates captured within-site variability, while satellite-derived products did not, specifically for herbaceous and litter. Conclusions: This study demonstrated a workflow using UAS and intensive ground sampling for estimating rangeland fractional cover in sagebrush communities. We found a disagreement between UAS-derived and satellite-derived fractional cover products at two sagebrush communities in the Northern Great Basin. We recommend the application of UAS when estimating rangeland fractional cover at local scales. [ABSTRACT FROM AUTHOR]

Context: Plant communities are undergoing compositional changes that affect ecosystem function. These changes are not always uniform across the landscape due to heterogenous topographic and edaphic conditions. To predict areas most at risk of change, it is necessary to identify the landscape drivers affecting plant abundance. Objectives: Annual plants are increasing across the western USA, largely driven by non-native annual invasions. Here, we quantified change in annual plant abundance and identified landscape factors contributing to that change over the past 35 years. Methods: We focused on Willamette Valley (Oregon) grasslands because they represent a new example in this phenomenon. To understand the spatiotemporal patterns of annual plant abundances between 1986 and 2020, we combined a remote-sensing vegetation cover dataset from the rangeland analysis platform with gridded soils data and topographic variables. We determined the rate of change in percent cover for each 30 × 30 m pixel and regressed cover against heat load, soil depth and sand content for > 5975 hectares to determine areas most sensitive to rising annual cover. Results: We found a tendency toward increasing annual cover, with a median gain of + 15% cover among significantly increasing pixels. However, change was uneven across the landscape, with annual cover increasing markedly in areas with high heat load and shallower soils. Conclusions: We identified steep, south-facing slopes as being particularly sensitive to rising annual cover. Annual plant invasions may be lagging in this region compared to elsewhere in the western USA, but trends here suggest it may just be a matter of time. [ABSTRACT FROM AUTHOR]