Spatial scale transformation-based estimation model for fresh grass yield: a case study of the Xilingol Grassland, Inner Mongolia, China.

Abstract

Estimating the grass yield of a grassland area is of vital theoretical and practical significance for determining grazing capacity and maintaining ecological balance. Due to the spatial inconsistency between sampling and remote sensing data, improving the accuracy of fresh grass yield (FGY) estimation based on remote sensing is difficult. Using vegetation coverage at different spatial scales, this paper proposes a spatial scale transformation (SST)-based estimation model for FGY adopting normalized difference vegetation index (NDVI) as its estimation factor, using the grassland in Xilingol League, Inner Mongolia, as the study area. Results showed that the SST-based FGY estimation model was able to greatly improve estimation precision; the relative estimation error (REE) of the estimation models constructed using linear with intercept zero (linear-0) and power functions were 18.16% and 18.35%, respectively. The estimation models constructed using linear-0 and power functions were employed to estimate the grass yield of the grassland in Xilingol League, and the total FGYs estimated were 8.777 × 1010kg and 8.583 × 1010kg, respectively. The two models obtained roughly the same estimates, but there were significant differences between them in the spatial distributions of FGY per unit. Taking net primary productivity (NPP) as an example, the effectiveness of other remote sensing data as estimation factors was further verified, and the results showed that SST-based estimation for FGY also effectively improved the estimation accuracy of grass yield.