Vegetation types restrict soil structure and heterogeneous processes of elements, which result in difference in spatial distribution of soil nutrients. In this study, the differences in contents of soil nutrients, TN, TP, TK, and soil organic matter (SOM) among different vegetation types were analyzed, and the accuracy of ordinary kriging, regression model and regression model based on vegetation type in predicting soil nutrients was compared. The results showed that, TN, TK and SOM were significantly (P<0.05) correlated to vegetation type, and TP had no significant correlation with vegetation type (P=0.390). TN and SOM had significant difference between shrubbery and arable land. TK had significant difference between arbor and scrub-grassland, shrubbery and arable land, and scrub-grassland and arable land, respectively. In a non-continuous typical small karst catchment, because of high spatial heterogeneity of terrain, the accuracy of multivariate linear regression model based on the real terrain factors of various points was considerably higher than that of ordinary kriging prediction method considering the locations of the known points and prediction points. Moreover, the regression model based on vegetation type improved the prediction accuracy of the TN.
植被类型制约着土壤结构和元素的异质化过程,致使土壤养分空间分布存在差异性.本文研究了典型喀斯特小流域不同植被类型间土壤养分(全氮TN、全磷TP、全钾TK、有机质SOM)含量分布的差异性,分析比较了普通克里金、回归模型、基于植被类型的回归模型对土壤养分预测的精度.结果表明: TN、TK、SOM与植被类型显著相关(P<0.05),TP与植被类型无显著相关(P=0.390),且TN和SOM在灌木林与耕地之间的差异性显著,TK在乔木林与灌草丛、灌木林与耕地、灌草丛与耕地间的含量差异皆显著;非连续的典型喀斯特小流域地形因子空间异质性较高,基于各样点间真实地形因子的多元线性回归预测模型精度优于基于已知点和预测点位置信息的普通克里金预测方法,且基于植被类型的回归预测模型提高了TN的预测精度.
Keywords: Kriging; regression model; soil nutrient; spatial prediction; vegetation type.