The relevant range of scales for multi-scale contextual spatial modelling
The relevant range of scales for multi-scale contextual spatial modelling
复制标题
DOI：
10.1038/s41598-019-51395-3
复制
发表时间：
2019-10-15
影响因子：
4.6
通讯作者：
Zhu, A-Xing
中科院分区：
文献类型：
--
作者：
Behrens, Thorsten;Rossel, Raphael A. Viscarra;Zhu, A-Xing
空间环境模型残差的空间自相关可能是由于缺少协变量信息。在许多情况下，这种空间自相关可以通过使用来自多个尺度的协变量来解释。在这里，我们提出了一种数据驱动的，客观的和系统的方法，用于导出相关的尺度范围，具有不同的上限和下限，用于机器学习的空间建模，并评估其对建模精度的影响。我们还测试了一种使用变差函数的方法，看看这样一个有效的尺度空间是否可以近似的先验和较小的计算成本。结果表明，具有有效尺度空间的建模可以通过机器学习改进空间建模，并且变差函数的属性与相关尺度范围之间存在很强的相关性。因此，土壤属性的变异函数可以用于上下文空间建模的有效尺度空间的先验近似，因此不仅在地质统计学中是一个重要的分析工具，而且在上下文空间建模中用于分析结构依赖性。
Spatial autocorrelation in the residuals of spatial environmental models can be due to missing covariate information. In many cases, this spatial autocorrelation can be accounted for by using covariates from multiple scales. Here, we propose a data-driven, objective and systematic method for deriving the relevant range of scales, with distinct upper and lower scale limits, for spatial modelling with machine learning and evaluated its effect on modelling accuracy. We also tested an approach that uses the variogram to see whether such an effective scale space can be approximated a priori and at smaller computational cost. Results showed that modelling with an effective scale space can improve spatial modelling with machine learning and that there is a strong correlation between properties of the variogram and the relevant range of scales. Hence, the variogram of a soil property can be used for a priori approximations of the effective scale space for contextual spatial modelling and is therefore an important analytical tool not only in geostatistics, but also for analyzing structural dependencies in contextual spatial modelling.