青海三江源区是长江、黄河、澜沧江3大河流的发源地。草地是该区域的主体生态系统,高寒草甸是其主要类型。近30年来,三江源地区草地发生了大面积的退化,不同退化程度的高寒草甸光谱特征是高寒草甸遥感分类和退化监测的重要依据。2009年8月作者在青海省三江源区对高山嵩草、矮嵩草和藏嵩草3种未退化高寒草甸,以及4种不同退化程度的高山嵩草草甸,进行了地面光谱测量和草地样方调查。同时对实测光谱曲线进行了比较,提取和分析了它们在557nm、675nm和760nm处反射率,以及"红边"斜率。结果表明,3种高寒草甸的光谱曲线,以及4种退化程度高寒草甸和未退化高寒草甸的光谱曲线在557nm处的反射率差异较小,在675nm和760nm处的反射率及"红边"斜率存在明显差异,能有效区分高寒草甸,可为高寒草甸遥感自动分类和退化监测提供依据。不同退化程度的高寒草甸地上生物量与其光谱曲线的"红边"斜率和归一化植被指数(NDVI)线性拟合的确定系数分别为0.93和0.87,其相关性较好,可用于高寒草甸地上生物量的估算。本文提取的光谱反射率的"红边"斜率不仅能有效区分3种典型高寒草甸和不同退化程度的高寒草甸,且与高寒草甸地上生物量的关系优于NDVI,对高寒草甸识别分类,退化监测和生物量估算有重要意义。

The ‘Three-River Headwater’ region at Qinghai Province is the source region of Yangtze River, Yellow River and Lancangjiang River. At here, grassland is the predominate ecosystem and alpine meadow is the main grassland type. In recent 30 years, large amount of grassland in this area suffered degradation, and therefore monitoring the alpine meadow degradation is important for grassland protection in the ‘Three-River Headwater’ region. The spectral characteristics of alpine meadow at different degradation levels are valuable information for alpine meadow classification and degradation monitoring through remote sensing. In this study, we measured the ground spectral data and surveyed grass samples in three sorts of alpine meadows at five degradation levels in August 2009 at the 'Three-River Headwaters’ Region, Qinghai Province. Then we extracted and analyzed four characteristic values of the collected spectral data: the reflectance at 557675 and 760 nanometer and the red edge slopes. The result indicates that there are little differences among 557 nanometers reflectance of the three sorts of alpine meadows at five degradation levels, but there are obvious differences among 675 nanometers reflectance, 760 nanometers reflectance and the red edge slopes. We could distinguish the three sorts of alpine meadows at five degradation levels and make effective monitoring according to those differences. We also calculated the normalized differential vegetation index (NDVI) value of alpine meadow at five degradation levels based on their spectral data and related them with above ground biomass in this study. The result suggests that above ground biomass of alpine meadows at different degradation levels are well related to its ‘red edge’ slope and NDVI, with the determination coefficient of 0.93 and 0.87 respectively. Thus, these two parameters are useful for above ground biomass estimation based on remote sensing data. The ‘red edge’ slope of alpine meadows reflectance not only could distinguish the three sorts of alpine meadows at five degradation levels effectively, but also has a better relationship with above ground biomass than NDVI value. Thus, ‘red edge’ slope of alpine meadows reflectance is essential for alpine meadows classification, degradation monitoring and biomass estimation.