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.