闽江河口湿地土壤盐分的空间分异与集聚特征

Spatial variability and agglomeration of soil salinity in Minjiang estuary wetland, Southeast China

探究河口湿地土壤盐分的空间异质性,揭示分异格局下的空间集聚特征,对河口湿地的可持续发展具有重要意义。本文以福州市闽江河口湿地的Landsat 8遥感影像、数字高程模型和地面实测土壤盐分为数据源,利用相关性分析与主成分分析法选取显著性环境因子,去除变量间的共线性,分别采用支持向量机回归克里格法(SVROK)和回归克里格法(RK)分析了土壤盐分空间异质性,在基础上运用空间自相关法定量描述了土壤盐分空间集聚特征。结果表明:通过主成分分析提取出3个主成分,可解释数据总方差的85%,反映植被覆盖、土壤属性和地形状况等综合变化信息,并保留原始变量的大部分信息;土壤盐分及其插值残差的空间变异受结构性因素和随机性因素的影响,采用主成分为自变量所建立的SVROK模型能更为精准地体现土壤盐分"北高南低"的空间异质特征;土壤盐分的Moran I大于0.5,具有显著的空间正相关,空间集聚程度较高,呈现出"高值集聚、低值广布、低值包围高值"的空间集聚特征。

Understanding the spatial variability and agglomeration of soil salinity is of great significance for the sustainable development of estuarine wetland. Landsat 8 OLI remote sensing image, digital elevation mode and soil surface samples of Minjiang estuary wetland of Fuzhou were used as the data sources. The correlation analysis and principal component analysis were combined to select significant environmental variables and to reduce their dimensions. We analyzed the spatial variability of soil salinity with support vector regression ordinary kriging model(SVROK) and regression kri-ging model(RK), and quantified spatial agglomeration of soil salinity by the spatial autocorrelation analysis. The results showed that three principal components(PCs) extracted by the principal component analysis could explain at least 85% of the total variance in the original dataset and reflected the comprehensive information of vegetation cover, soil properties and topography. Both soil salinity and its residuals were affected by structural factors and random factors. The SVROK model based on principal component(PCs) as input variables can more accurately reflect the spatial variability of soil salinity, with a trend of "higher in the north and lower in the south". The Moran’s I of soil salinity was more than 0.5, with significant positive spatial autocorrelation and a higher spatial aggregation degree, displaying the spatial agglomeration characteristics of "high value agglomeration, high value widespread, high value surrounded by low value".