Spatial interpolation methods study based on geostatistics for the grasshopper population

Xiaochuang Yao, Dehai Zhu, Sijing Ye, Nan Zhang, Lin Li

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

The ordinary kriging based on the geostatistics provides new methods and tools for the research on the biological population, such as the characteristics of spatial variability and spatial distribution pattern. However, the smoothing effect of ordinary kriging is a well-known dangerous effect associated with this estimation technique, and the result cannot accurately reflect the heterogeneity of spatial distribution of biological populations. In order to address this problem with kriging estimates for the grasshopper population, a precision post-processing method was used in this paper. Based on the sampling points in the September 2012, 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.

Original languageEnglish (US)
Pages (from-to)645-650
Number of pages6
JournalSensor Letters
Volume12
Issue number3-5
DOIs
StatePublished - Jan 1 2014

Keywords

  • Geostatistics
  • Grasshopper population
  • Ordinary kriging
  • Smoothing effect

Fingerprint Dive into the research topics of 'Spatial interpolation methods study based on geostatistics for the grasshopper population'. Together they form a unique fingerprint.

Cite this