数字土壤质地制图方法比较——以黑河张掖地区为例

土壤质地类型是陆面过程模型、水文模型和大气模型的重要输入参数。在黑河流域中游地区选取一研究区,利用200个实测土壤剖面样点资料及13类环境因子组合,分别根据基于支持向量机、决策树和模糊逻辑的理论方法进行土壤质地预测制图。结果表明：支持向量机方法对训练点和验证点的测试精度都偏低,分别为90%和55%；基于决策树方法对训练点的验证精度最高,达到98%,但其对验证点的预测准确率为57%；基于模糊逻辑方法的综合评价精度最好,对验证点的预测精度达到64%,对训练样点的验证精度为74%。从制图结构来说,支持向量机方法弱化了类型环境因子的预测性能,减小了连续环境因子分布的空间差异性,生成的土壤质地图结构相对简单,精度偏低；基于决策树方法对未知环境因子组合预测具有不确定性,生成土壤图斑块相对破碎；模糊逻辑方法可弥补决策树方法的一些不足,生成的土壤质地图既较好地保留了斑块结构的完整性,也较好描述了土壤类型与典型环境因子之间的相关性。因此针对研究区数据特点,采用基于决策树和模糊逻辑相结合的土壤制图方法更有优势。

Comparison Analysis on Digital Soil Texture Mapping in an area of Zhangye, Heihe River Basin

Soil texture is a key input parameter for the land surface process models,hydrological models and atmospheric models.Many digital soil mapping methods based on the soil-landscape model concept have been widely studied and applied.The general methods include the decision tree algorithm,the support vector machine approach and the fuzzy logic theory.In this paper,a study was conducted to compare the above three different soil mapping methods in an area of Zhangye of Heihe River Basin by integrated 200 ground m...