A spectral reflectance-based approach to quantification of grassland cover from Landsat TM imagery

In this paper, a reflectance-based method is proposed to accurately quantify percent grass cover from TM data for a semiarid grassland in western China. In situ measured percent grass cover was sampled over 1 m2 plots at 68 sites. Their ground coordinates were logged with a global positioning system (GPS) receiver and their spectral reflectance measured with a spectrometer. Normalized difference vegetation index (NDVI) was derived from both in situ measured spectral reflectance and radiometrically calibrated Landsat Thematic Mapper (TM) bands 3 and 4. It was found that the NDVI derived from in situ measured spectral reflectance was closely correlated with percent grass cover (R2=0.74), but not with its counterpart derived from the satellite image. After standardization of the latter with the former, the TM-derived NDVI bore a close regression relationship with the in situ measured samples (R2=0.74). This relationship enabled the successful quantification of grass cover from the satellite image at an overall accuracy of 89%. This reflectance-based method can be used to reliably quantify grass cover from TM imagery.