基于线性光谱混合模型的荒漠草地覆盖度估测

为了解决草地混合像元对草地植被覆盖度监测的影响,该研究以Landsat TM为数据源,探讨线性光谱混合模型进行混合像元分解的关键问题,分析混合光谱模型获得的不同组分分量与植被覆盖度之间的关系,以期建立适合天山北坡荒漠草地覆盖度监测模型。结果表明:通过光谱混合模型获得植被、沙丘以及盐碱化土壤3个基本组分,其中植被组分与覆盖度拟合效果较好(R2=0.62),与植被指数法估测草地覆盖度相比,决定系数R2均高于比值植被指数、归一化植被指数、土壤调节植被指数及修正土壤调整植被指数MSAVI。通过对所获模型精度检验,均方根误差RMSE为1.28,结果较为理想。因此,利用线性光谱混合模型解析混合像元估测天山北坡荒漠草地覆盖度具有可行性。

Assessing vegetation coverage of desert grassland based on linear spectral mixture model

Remote sensing offers a scientific, accurate, rapid technique for predicting plant coverage, but a pixel in remote sensing image includes more information, which will affect grassland classification accuracy and the quantitative development of remote sensing techniques. In this study, Landsat Thematic Mapper (TM) data was used to discuss the key problem of mixed pixel decomposition by using spectral mixture analysis in grassland of the Northern Tianshan Mountains. Four vegetation indexes and fractions derived from spectral mixture analysis, i.e., green vegetation, dune, and saline alkali soil, were calculated and compared with field grassland measures. The results showed that green vegetation had higher correlations with the grass coverage than dune and saline alkali soil. In addition, green vegetation also had higher correlation ( R2=0.62) than the ratio vegetation index (RVI), the normalized difference vegetation index (NDVI), the soil-Adjusted vegetation index (SAVI) and the modified soil-adjusted vegetation index (MSAVI). The results imply that it is feasible for assessing desert grassland coverage in the Northern Tianshan Mountains by using mixture pixel decomposition based on linear spectral mixture model.