平原丘陵过渡带土壤有机碳空间分布及环境影响

为定量分析景观过渡带中土壤有机碳空间分布及环境影响,将环境因子纳入空间自回归模型与地理加权回归模型分析比较,并以普通最小二乘回归模型作对照.结果表明:土壤性质指标中,容重及有效铁与土壤有机碳存在极显著相关关系;地形及区位因子中,纬度、高程、坡度、粗糙度等稳定性因素与土壤有机碳存在极显著相关关系;土壤有机碳的局部集聚性多发生在核心景观过渡带;空间回归模型的拟合优度均优于普通最小二乘回归模型,估计值的空间自相关变化趋势与实测值一致,残差的空间模式显著减弱;能够灵活调整权函数与带宽的地理加权回归模型能够更好地分析土壤有机碳的空间变异.模型评价方面,GWR-1和GWR-2的残差平方和较OLS分别降低了20.717%和8.799%; SLM、SEM、GWR-1、GWR-2的AIC值较OLS分别减小了5.108、5.391、19.887和11.751.除本身存在的空间自相关外,模型中土壤性质指标及环境因子能大幅解释土壤有机碳的异质性.本研究引入辅助变量,运用空间回归模型分析了平原丘陵过渡带土壤有机碳的空间变异,可为生态恢复、环境变化指示及研究区典型柑橘区的区划提供依据.

The spatial variability of soil organic carbon in plain-hills transition belt and its environmental impact

In this study, the relationships between environmental variables and SOC were analysed using spatial autoregression (SAR) and geographically weighted regression (GWR) to quantitatively model the spatial variability of SOC and its environmental impact, comparing with ordinary least square (OLS) regression. The results demonstrated strong correlations between SOC and auxiliary variables. As to soil properties, bulk density and available iron played significant roles. As to topography factors and location factors, latitude, elevation, slope and roughness were the most important affecting factors. Local clustering of soil organic carbon occurred mostly on core transition zone. Plus, SAR model has a better goodness of fit than OLS regression and its estimated value showed a similar trend with the observated values of SOC. Additionally, weak spatial patterns were detected after modeling. Thanks to the flexibility to adjust the weighting function and the bandwidth, GWR model has a better detection of spatial variability of SOC than the others. On model assessment, the residual sum of squares of GWR-1 and GWR-2 were reduced by 20.717% and 8.799%, comparing with OLS’s, respectively; the AIC values of SLM、SEM、GWR-1、GWR-2 were reduced by 5.108、5.391、19.88 and 11.751, respectively. In addition to the spatial autocorrelation, soil properties and environmental factors can significantly explain the heterogeneity of SOC.The auxiliary variables and spatial regression model used here indicated the variability of SOC may propose a certain basis for further exploring the synergies and quantitative analysis of SOC. This study may pave a way for ecological restoration, indicating changes in the environment and the planning of the typical citrus area.