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Oblique geographic coordinates as covariates for digital soil mapping
Soil ( IF 6.8 ) Pub Date : 2020-07-14 , DOI: 10.5194/soil-6-269-2020
Anders Bjørn Møller , Amélie Marie Beucher , Nastaran Pouladi , Mogens Humlekrog Greve

Decision tree algorithms, such as random forest, have become a widely adapted method for mapping soil properties in geographic space. However, implementing explicit spatial trends into these algorithms has proven problematic. Using x and y coordinates as covariates gives orthogonal artifacts in the maps, and alternative methods using distances as covariates can be inflexible and difficult to interpret. We propose instead the use of coordinates along several axes tilted at oblique angles to provide an easily interpretable method for obtaining a realistic prediction surface. We test the method on four spatial datasets and compare it to similar methods. The results show that the method provides accuracies better than or on par with the most reliable alternative methods, namely kriging and distance-based covariates. Furthermore, the proposed method is highly flexible, scalable and easily interpretable. This makes it a promising tool for mapping soil properties with complex spatial variation.

中文翻译:

斜地理坐标作为数字土壤制图的协变量

诸如随机森林之类的决策树算法已成为一种广泛适用的在地理空间中绘制土壤属性的方法。但是,在这些算法中实现明确的空间趋势已被证明是有问题的。使用xy坐标作为协变量会在地图中产生正交的伪像,而使用距离作为协变量的替代方法可能不灵活且难以解释。相反,我们建议使用沿倾斜角度倾斜的多个轴的坐标,以提供易于理解的方法来获得逼真的预测表面。我们在四个空间数据集上测试该方法,并将其与类似方法进行比较。结果表明,与最可靠的替代方法(克里金法和基于距离的协变量)相比,该方法提供的精度更好或与之相当。此外,所提出的方法是高度灵活的,可扩展的并且易于解释的。这使其成为用于绘制具有复杂空间变化的土壤特性的有前途的工具。
更新日期:2020-08-20
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