Abstract Techniques that disaggregate complex soil-terrain polygons from legacy maps are becoming more relevant, as cost effective highly detailed soil information is required to advise agriculture, hydrology, ecology, engineering, and a variety of other disciplines. Disaggregation involves the spatial placement of individual soil classes from soil legacy polygons which have multiple soil classes, while specifying the approximate proportion of each soil class and verbally or diagrammatically explaining their distribution in the landscape. One of the most common disaggregation approaches is known as DSMART (“Disaggregation and Harmonisation of Soil Map Units through Resampled Classification Trees”). However, DSMART is computationally intensive and has many parameters that must be optimised. This study aimed to address these drawbacks including input map selection, feature selection, and resample size optimisation. The research site was selected in the upper reaches of the Mvoti river catchment covering 317 k m 2 in KwaZulu Natal province, South Africa. The catchment consists of 20 soil-terrain polygons drawn at a 1:250,000 scale from the South African Land Type Survey (LTS). First, the optimal input map derived from landform elements (geomorphons) was selected through a spatially resampled Cramer’s V test to determine the association between the legacy polygons (proportion of terrain) and the geomorphon units. This was done for five different aggregated geomorphons with different parameters. Second, three feature selection algorithms (FSAs) were embedded into DSMART to determine if the algorithms could improve accuracy and computational efficiency. Third, the FSAs were compared using 25, 50, 100, and 200 resamples per polygon. The results indicate that the Cramer’s V test is a rapid method to determine the optimal input map. All FSAs achieved a significantly greater accuracy then when disaggregating the original legacy polygons and were more computationally efficient than when using all 52 covariates. This study has implications when disaggregating large and small datasets by improving computational efficiency while maintaining an acceptable accuracy.

中文翻译：

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土壤遗留数据的输入图和特征选择

摘要 从遗留地图中分解复杂的土壤地形多边形的技术变得越来越重要，因为需要具有成本效益的高度详细的土壤信息来为农业、水文学、生态学、工程和各种其他学科提供建议。分解涉及具有多个土壤类别的土壤遗留多边形中各个土壤类别的空间布局，同时指定每个土壤类别的大致比例并口头或图解说明它们在景观中的分布。最常见的分解方法之一被称为 DSMART（“通过重采样分类树对土壤图单元进行分解和协调”）。然而，DSMART 是计算密集型的，并且有许多必须优化的参数。本研究旨在解决这些缺点，包括输入地图选择、特征选择和重采样大小优化。研究地点选择在南非夸祖鲁纳塔尔省 317 公里 2 的姆沃蒂河流域上游。集水区由南非土地类型调查 (LTS) 以 1:250,000 比例绘制的 20 个土壤地形多边形组成。首先，通过空间重采样 Cramer's V 检验选择从地形元素（地形体）导出的最佳输入地图，以确定遗留多边形（地形比例）和地形体单元之间的关联。这是针对具有不同参数的五种不同的聚合地貌来完成的。第二，三种特征选择算法 (FSA) 被嵌入到 DSMART 中，以确定这些算法是否可以提高准确性和计算效率。第三，每个多边形使用 25、50、100 和 200 次重采样来比较 FSA。结果表明，Cramer's V 检验是一种确定最佳输入图的快速方法。所有 FSA 在分解原始遗留多边形时都实现了显着更高的准确度，并且比使用所有 52 个协变量时计算效率更高。这项研究在通过提高计算效率同时保持可接受的准确性来分解大型和小型数据集时具有意义。所有 FSA 在分解原始遗留多边形时都实现了显着更高的准确度，并且比使用所有 52 个协变量时计算效率更高。这项研究在通过提高计算效率同时保持可接受的准确性来分解大型和小型数据集时具有意义。所有 FSA 在分解原始遗留多边形时都实现了显着更高的准确度，并且比使用所有 52 个协变量时计算效率更高。这项研究在通过提高计算效率同时保持可接受的准确性来分解大型和小型数据集时具有意义。