Yang Liu

Comprehensive vegetation cover in grasslands is a crucial indicator of grassland health and ecological balance, holding significant importance for scientifically sound grassland management and ecological environment monitoring. Satellite remote sensing inversion methods can provide full coverage for the scientific assessment of comprehensive vegetation cover in a given area. However, the sampling for inversion modeling often relies on conventional field survey methods, which are not only labor-intensive but also subject to high subjectivity, making it difficult to achieve satisfactory modeling results. This paper explores the use of multispectral low-altitude unmanned aerial vehicle (UAV) aerial photographs for sample point positioning, and employs three methods—RGB image clustering extraction, NRG image clustering extraction, and NDVI threshold extraction—to quantitatively estimate grassland comprehensive vegetation cover. The experimental results for Yuanmou County in Yunnan indicate that for areas with very high comprehensive vegetation cover, all three methods cannot achieve high assessment accuracy due to the interference of yellowed vegetation. In regions with lower comprehensive vegetation cover, NRG image clustering extraction and NDVI threshold extraction can achieve higher accuracy, with NRG images being more conducive to visual interpretation, and the NDVI threshold extraction method being simpler and more efficient.