Improving estimates of fractional vegetation cover based on UAV in alpine grassland on the Qinghai-Tibetan Plateau

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 (R-2) 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.