Interpolation methods study based on geostatistics for the grasshopper population

摘要

The smoothing effect of ordinary kriging based on grostatistics is a well-known dangerous effect associated with this estimation technique. In order to solve this problem with kriging estimates for the grasshopper population, a precision post-processing method, proposed by Yamamoto, was used in this paper. Based on the sampling points in the September 2011, spatial distribution pattern of the grasshopper population was simulated by original kriging and Yamamoto's method respectively in the study, and the latter was efficient for the reproduction of histogram and semivariogram of the sampling data. Empirical results demonstrated that the Yamamoto's approach could correct the smoothing effect effectively. Furthermore, the real spatial distributions of the grasshopper population density could be preserved without losing both local and global accuracies.