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长白山低山区森林土壤有机碳及养分空间异质性
引用本文:刘玲,王海燕,戴伟,杨晓娟,李旭.长白山低山区森林土壤有机碳及养分空间异质性[J].生态学杂志,2014,25(9):2460-2468.
作者姓名:刘玲  王海燕  戴伟  杨晓娟  李旭
作者单位:(北京林业大学林学院, 北京 100083)
摘    要:以吉林延边汪清林业局金仓林场境内森林土壤为对象,采用多元线性回归方法和地统计学回归克里格方法,研究了土壤有机碳及养分的垂直分布规律,预测了其空间分布,并对预测结果进行插值.结果表明: 0~60 cm深度土壤有机碳密度为(16.14±4.58) kg·m-2.随土壤深度增加,土壤有机碳含量、有机碳密度以及土壤全N、全P、全K、有效P及速效K含量都呈减小趋势,其中不同土层间土壤有机碳含量、有机碳密度差异显著(P<0.01).0~60 cm土层土壤有机碳含量和碳密度的拟合方程中,地形因子中高程和坡向余弦值是最优的拟合因子,方程的决定系数分别为0.34和0.39(P<0.01).0~20和0~60 cm土层的半方差函数模型分别为高斯模型和指数模型,利用回归克里格插值方法得到土壤有机碳的空间分布图.与普通克里格法相比,回归克里格法的空间预测精度改进了18%~58%.利用回归克里格插值方法预测了土壤全N的空间分布特征.

关 键 词:森林土壤  土壤养分特征  回归克里格  空间异质性

Spatial heterogeneity of soil organic carbon and nutrients in low mountain area of Changbai Mountains.
LIU Ling,WANG Hai-yan,DAI Wei,YANG Xiao-juan,LI Xu.Spatial heterogeneity of soil organic carbon and nutrients in low mountain area of Changbai Mountains.[J].Chinese Journal of Ecology,2014,25(9):2460-2468.
Authors:LIU Ling  WANG Hai-yan  DAI Wei  YANG Xiao-juan  LI Xu
Institution:(College of Fores try, Beijing Forestry University, Beijing 100083, China)
Abstract:Soil samples were collected in Jincang Forest Farm, Wangqing Forestry Bureau to study spatial distribution of soil organic carbon (SOC) and nutrients. Geostatistics was used to predict their spatial distribution in the study area, and the prediction results were interpolated using regression kriging and ordinary kriging. Multiple linear regression was used to study the relationship between SOC and spatial factors. The results showed the SOC density (SOCD) at 0-60 cm was (16.14±4.58) kg·m-2. Soil organic carbon decreased significantly with the soil depth. With the increasing soil depth, total N, total P, total K, available P and readily available K concentrations decreased. Stepwise regression analysis showed that SOC had good correlation with elevation and cosine of aspect, with the determination coefficient of 0.34 and 0.39, respectively (P<0.01). Soil organic carbon at 0-20 cm and 0-60 cm soil layers conformed to Gaussian model and exponential model. Compared with ordinary kriging, the prediction accuracy was improved by 18%-58% using regression kriging. Regression kriging interpolation was also applied to study spatial heterogeneity of soil total N.
Keywords:forest soil  soil nutrient characteristics  regression kriging  spatial heterogeneity  
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