基于景观及微地形特征的丘陵区土壤属性预测

Predicting soil property in hilly regions by using landscape and multiscale micro-landform features

为探讨小流域尺度丘陵区的高分辨率数字土壤制图方法,通过对景观相分类的探索,配合应用不同尺度的Geomorphons(GM)微地形特征数据构成分类变量组参与高分辨率土壤pH、黏粒含量和阳离子交换量的预测制图,并与传统数字高程模型衍生变量和遥感变量进行组合与比较分析。此外,采用支持向量机、偏最小二乘回归和随机森林3种机器学习模型择优与残差回归克里金复合参与预测模型的构建与评价。结果表明:景观及多尺度微地形分类变量组的应用分别提高小流域尺度丘陵地貌区pH、黏粒含量和阳离子交换量预测精度的18.8%、8.2%和8.7%。包含植被信息的景观相分类图相比土地利用数据有更高的模型贡献度;5 m分辨率的GM微地形分类图相比低分辨率的分类图更适宜高精度的预测制图。黏粒含量使用随机森林复合模型有最高的预测精度,而pH和阳离子交换量则不适宜在随机森林模型的基础上加入残差回归克里金模型。景观-多尺度微地形分类变量、数字高程模型衍生变量和遥感变量三者结合的模型预测表现最佳,表明多元变量在起伏地形区域相比单一数据源能够包含更多的土壤有效信息。由GM数据和地表景观数据组成的景观分类变量组作为主要变量能够解释小流域丘陵区部分土壤属性约40%的空间变异。在同类型土壤预测制图研究中,多分辨率GM及景观分类数据有潜力作为环境变量参与预测模型的构建。

To assess the high-resolution digital soil mapping method for small watersheds in hilly areas,we explored the role of landscape classification and multiscale micro-landform features in predicting soil pH,soil clay content(SCC),and cation exchange capacity(CEC).Geomorphons(GM)terrain classification method was used to create landform units.The traditional digital elevation model(DEM)derivatives and remote sensing variables were employed for different combinations with landscape and micro-landform classification variables,with further compa-rison and analysis being conducted.In addition,three machine learning techniques,including support vector machine(SVM),partial least squares regression(PLSR),and random forest(RF),were used to build prediction models.The best method was then selected,and then combined with regression kriging by modeling spatial structure of the model residuals.The results showed that the application of landscape and multiscale micro-landform classification variables effectively improved the prediction accuracy of pH,SCC,and CEC by 18.8%,8.2%and 8.7%,respectively.The map of landscape classification that contained vegetation coverage information had greater model contribution than land use data.The GM classification map with 5 m resolution was more suitable for high-precision DSM than those with lower resolution.The composite model of RF performed the best in predicting SCC,while the pH and CEC were not suitable for adding the residual regression kriging on the basis of RF model.Finally,the combination of landscape and multiscale micro-landform classification variables,DEM derivatives and remote sensing variables had the highest prediction accuracy for all the three soil properties.This result indicated that multivariable contained more effective soil information than single data source for rolling areas.The landscape variables composed of GM and surface classified data explained about 40%of the spatial variation of tested soil attributes in hilly area.Therefore,multi-resolution GM and landscape classified variables could be included into the construction of prediction model in research of soil mapping.