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Fractional vegetation cover (FVC) is an important parameter in studies of ecosystem balance, soil erosion, and climate change. Remote-sensing inversion is a common approach to estimating FVC. However, there is an important gap between ground-based surveys (quadrat level) and remote-sensing imagery (satellite image pixel scale) from satellites. In this study we evaluated that gap with unmanned aerial vehicle (UAV) aerial images of alpine grassland on the Qinghai–Tibetan Plateau (QTP). The results showed that: (1) the most accurate estimations of FVC came from UAV (FVCUAV) at the satellite image pixel scale, and when FVC was estimated using ground-based surveys (FVCground), the accuracy increased as the number of quadrats used increased and was inversely proportional to the heterogeneity of the underlying surface condition; (2) the UAV method was more efficient than conventional ground-based survey methods at the satellite image pixel scale; and (3) the coefficient of determination (R2) between FVCUAV and vegetation indices (VIs) was significantly greater than that between FVCground and VIs (p < 0.05, n = 5). Our results suggest that the use of UAV to estimate FVC at the satellite image pixel scale provides more accurate results and is more efficient than conventional ground-based survey methods.
Chen et al. (Tue,) studied this question.