广义线性地统计模型在典型亚热带丘陵区数字土壤制图中的应用
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1.中山大学地理科学与规划学院;2.广西壮族自治区林业科学研究院

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国家自然科学基金项目(42071062、41771246)


Application of Generalized Linear Geostatistical Model for Digital Soil Mapping in A Typical Subtropical Hilly Area
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Affiliation:

1.School of Geography and Planning, Sun Yat-sen University;2.Forestry Research Institute, Guangxi Zhuang Autonomous Region

Fund Project:

The National Natural Science Foundation of China (Nos. 42071062, 41771246)

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    摘要:

    数字土壤制图(DSM)在当前的应用越来越广泛,其方法主要包括环境相关模型、空间自相关模型,以及这些模型的混合模型。理论上,混合模型相对单一模型具有明显的优势。广义线性地统计模型(GLGM)也是一种混合模型,相对于最常用的混合模型——回归克里格(RK),又具有能加入随机效应来解决土壤变异的非平稳性等优势。然而,GLGM因计算繁琐等缺点,在国内外应用较少。本文以广西南宁高峰林场内一小面积(3.03km2)丘陵为研究区,以14个地形因子为预测变量,使用GLMM及其与普通克里格(OK)相结合的模型(GLGM),对土壤有机碳(SOC)、pH、黏粒和阳离子交换量(CEC)的空间分布进行预测,并与常用的多元线性回归(MLR)、地理加权回归(GWR)、回归森林(RF)、普通克里格(OK)、RK和广义可加模型(GAM)进行比较。结果表明:GLMM在预测黏粒上准确度较高;GLMM和GLGM在预测CEC上准确度中等;GLMM和GLGM在预测SOC和pH上准确度较低。综合线性回归模型的调整决定系数、块金效应和全局莫兰指数,本文认为,当土壤属性与环境变量具有较低的线性回归调整决定系数(即小于5%)、土壤属性具有较弱的空间自相关性(即块金效应大于71%)和较强的局部空间变异(即全局莫兰指数小于0.09)时,GLMM和GLGM具有较高的适用性,例如本文中的黏粒。反之,GLMM和GLGM的适用性不好,例如本文中的SOC和pH。鉴于土壤空间变异的高度异质性和多尺度性,本文认为GLMM和GLGM具有较好的应用前景。但是,在未来的研究中还需要进一步研究如何提高GLMM和GLGM的模拟效率。

    Abstract:

    【Objective】Digital Soil Mapping (DSM) is receiving more attention and becoming widely used. Its methods mainly include environmental correlation-based models, spatial auto-correlation based models, and a mixture of these two kinds of models. The mixed model is expected to be advantageous over the single models. A generalized linear geostatistical model (GLGM) is a kind of mixed model. Compared with the commonly used mixed model, i.e., regression kriging (RK), GLGM has advantages such as having random effects to account for the non-stationarity of soil variability. However, GLGM is seldomly used due to its major disadvantages, i.e., complicated computation. 【Method】In this study, within a small hilly area (3.03 km2) in Gaofeng Forest of Nanning, Guangxi, generalized linear mixed model (GLMM) and its combination with ordinary kriging (OK), i.e., GLGM, were used to predict the spatial distribution of soil organic carbon (SOC), pH, clay and cation exchange capacity (CEC). Performances of the two models were then compared with commonly used models, including multivariable linear regression (MLR), geographically weighted regression (GWR), regression forest (RF), OK, RK and generalized additive model (GAM). 【Result】The results showed that GLMM had higher accuracy in predicting clay, while GLMM and GLGM had medium accuracy in predicting CEC. Further, GLMM and GLGM had lower accuracy in predicting SOC and pH. 【Conclusion】Based on the adjusted R2 of the linear regression model, nugget effect and global Moran’s I, it is concluded that GLMM and GLGM are appropriate when there is a low adjusted R2 of linear soil-landscape regression (less than 5%), weak spatial auto-correlation of soil (nugget-to-sill ratio large than 71%), and strong local spatial variability of soil (Moran’s I less than 0.09), e.g., clay in this paper. Otherwise, GLMM and GLGM are not appropriate, e.g., for SOC and pH in this paper. For the high spatial heterogeneity and multi-scale variability of soil, we think that GLMM and GLGM are promising for DSM, although more studies are needed to improve the efficiency of GLMM and GLGM modelling.

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郝辰恺,孙孝林,王会利.广义线性地统计模型在典型亚热带丘陵区数字土壤制图中的应用[J].土壤学报,,[待发表]
Hao chenkai, Sun xiaolin, Wang huili. Application of Generalized Linear Geostatistical Model for Digital Soil Mapping in A Typical Subtropical Hilly Area[J]. Acta Pedologica Sinica,,[In Press]

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