土壤有机碳(SOC),全氮(TN)和全磷(TP)是反映土壤质量的重要指标,可以有效地评价土壤修复效果。许多研究已经评估了SOC的含量,不同生态系统中的TN和TP。然而,在生态脆弱的沿海地区建设了保护森林以进行生态恢复之后,SOC的空间分布及影响机制,TN和TP含量仍不确定。在这项研究中,SOC的空间异质性及其影响因素,通过传统分析和地统计学分析了表层(0-20cm)土壤中的TN和TP。在沿海地区受保护的森林类型下总共收集了39个土壤样品,包括麻黄(QAC),黑松(PTP),在中国北方的沿海保护区森林中混合PTP和QAC(QP)和板栗(CMB)。结果表明,TN和TP含量定义为中等变化,在不同保护林类型下也表现出显著的变化(P<0.05)。半方差结果表明,SOC,TN和TP均表现出较强的空间依赖类,范围为224米,分别为229米和282米,大于200m的采样尺度。空间预测结果表明,TN和TP含量均出现在CMB中极低值的大面积区域,交叉验证结果表明,利用植被和地形因子作为协变量进行SOC空间预测,TN和TP可以提高预测精度。相关性分析结果表明,SOC和TN的影响因素,和TP是NDVI和地形变化,分别。总的来说,植被和地形因子作为辅助因子可以提高滨海地区造林后土壤C-N-P空间分布预测的准确性。
Soil organic carbon (SOC), total nitrogen (TN) and total phosphorus (TP) are important indicators reflecting soil quality, and they can be used to effectively evaluate the effect of soil remediation. Many studies have evaluated the content of SOC, TN and TP in different ecosystems. However, after constructing protected forests for ecological restoration in the ecologically fragile coastal zone, the spatial distribution and influencing mechanism of SOC, TN and TP content is still uncertain. In this study, the spatial heterogeneity and influencing factors of SOC, TN and TP in surface (0-20 cm) soil were analyzed by traditional analysis and geostatistics. A total of 39 soil samples were collected under the coastal zone protected forest types including Quercus acutissima Carruth (QAC), Pinus thunbergii Parl (PTP), mixed PTP and QAC (QP) and Castanea mollissima BL (CMB) in the coastal zone protected forests in northern China. The results show that SOC, TN and TP content were defined as moderate variation, and they also show significant changes under different protected forest types (P < 0.05). The semivariance results indicate that SOC, TN and TP all exhibited strong spatial dependence class, with Range of 224 m, 229 m and 282 m respectively, which were more than the sampling scale of 200 m. The spatial prediction results showed that SOC, TN and TP content all appear in large areas of extremely low value in CMB, and its cross validation results showed that using vegetation and terrain factors as covariates in the spatial prediction of SOC, TN and TP can improve the prediction accuracy. The results of correlation analysis showed that the influencing factor for SOC and TN, and TP were NDVI and topographical changes, respectively. In general, vegetation and terrain factors as auxiliary factors can improved the accuracy of soil C-N-P spatial distribution prediction after afforestation in coastal zone.