实时准确地监测草地植物种类和覆盖面积对草原物种多样性研究和生态环境的可持续发展具有重要意义.草地植物在花期具有独特的光谱特征,相比营养生长期,通过花更容易识别物种.花期是遥感识别物种的关键时期.本文利用2008年8月和2010年7月呼伦贝尔草原上麻花头、棉闭铁线莲、冷蒿、莲子菜、黄花菜、有斑百合和细叶百合七种花以及裸土的实测高光谱数据,通过其冠层光谱的特征分析和参量化,找出了各物种之间的光谱差异,且得到了参量化特征识别方法,经验证,当花在样方中的覆盖度大于1o％时,识别方法的精度在90％以上.在此基础上,采用线性解混模型,计算样方中各种花的覆盖度,与实际数据相比较,误差在4％左右,证明了线性解混模型用于估算草原上花覆盖度的可行性.%Monitoring grassland species and area real-timely and accurately is of great significance in species diversity research, as well as in sustainable development of ecosystem. Flowers have their own unique spectral characteristics. Compared with the nutrient stage, species are more easily identified by florescence. So, florescence is a critical period for identification. In the present paper, spectral differences among such flowers as Galium verum Linn. , Hemerocallis citrina Baroni, Serratula centau-roides Linn. , Clematis hexapetala Pall. , Lilium concolor var. Pulchellum, Lilium pumilum and Artemisia frigida Willd. Sp. PL were found, along with identification methods, by analyzing canopies spectra and parametrizing characteristics. Verification results showed that when the coverage of flowers was greater than 10%, the accuracy of identification methods would be higher than 90%. On this basis, linear unmixing model was adopted to calculate the area of flowers in quadrates. Results showed that linear unmixing model was an effective method for estimating the coverage of flowers in grassland because the accuracy was about 4%.
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