Coverage of living (green) vegetation influences rangeland processes and biodiversity but remains a challenge to quantify at small spatial grain. We describe a technique for rapid airborne (unmanned aerial vehicle) measurements of continuous spatial coverage of living vegetation at a resolution (spatial grain) of 8 cm ground sampling distance. We then applied this technique at the pasture (paddock) scale (tens of hectares) in two contrasting grassland ecosystems (semiarid shortgrass steppe in northeastern Colorado, United States and mesic grassland in central Texas, United States) to determine effects of locally relevant grazing treatments on spatial variability and dependence (pattern) in fractional coverage of green vegetation (fractional vegetation cover; FVC). Site-specific regression models developed using reflectance in visible and near-infrared wavebands explained 90% and 89% of variance in FVC for semiarid and mesic grassland, respectively. Mean FVC was similar among shortgrass steppe pastures differing in grazing treatment (light or heavy grazing by cattle, moderate cattle grazing with prairie dogs present). In contrast, FVC was lower with rotational compared with continuous, year-long grazing in the mesic grassland. Heavy grazing in shortgrass steppe and rotational grazing in mesic grassland increased the spatial uniformity in FVC by reducing spatial variability and increasing spatial dependence in FVC, the latter by increasing the similarity in FVC values among spatially separated patches. Unmanned aerial vehicle–enabled remote sensing provides for FVC at sufficiently small spatial grain to characterize spatial variation in FVC at the pasture scale. Results can be used to evaluate the effectiveness of management actions intended to alter spatial variation in FVC to achieve conservation or diversity objectives. Enhanced capacity to monitor FVC at small spatial grain promotes adaptive management of grasslands.
Fractional vegetation cover