Li Aidi, Tan Xing, Wu Wei, Liu Hongbin, Zhu Jie
College of Resources and Environment, Southwest University, Chongqing, China.
Chongqing Land Resources and Housing Surveying and Planning Institute, Chongqing, China.
PLoS One. 2017 Sep 6;12(9):e0183742. doi: 10.1371/journal.pone.0183742. eCollection 2017.
Knowledge about the spatial distribution of active-layer (AL) soil thickness is indispensable for ecological modeling, precision agriculture, and land resource management. However, it is difficult to obtain the details on AL soil thickness by using conventional soil survey method. In this research, the objective is to investigate the possibility and accuracy of mapping the spatial distribution of AL soil thickness through random forest (RF) model by using terrain variables at a small watershed scale. A total of 1113 soil samples collected from the slope fields were randomly divided into calibration (770 soil samples) and validation (343 soil samples) sets. Seven terrain variables including elevation, aspect, relative slope position, valley depth, flow path length, slope height, and topographic wetness index were derived from a digital elevation map (30 m). The RF model was compared with multiple linear regression (MLR), geographically weighted regression (GWR) and support vector machines (SVM) approaches based on the validation set. Model performance was evaluated by precision criteria of mean error (ME), mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2). Comparative results showed that RF outperformed MLR, GWR and SVM models. The RF gave better values of ME (0.39 cm), MAE (7.09 cm), and RMSE (10.85 cm) and higher R2 (62%). The sensitivity analysis demonstrated that the DEM had less uncertainty than the AL soil thickness. The outcome of the RF model indicated that elevation, flow path length and valley depth were the most important factors affecting the AL soil thickness variability across the watershed. These results demonstrated the RF model is a promising method for predicting spatial distribution of AL soil thickness using terrain parameters.
了解活动层（AL）土壤厚度的空间分布对于生态建模、精准农业和土地资源管理至关重要。然而，使用传统土壤调查方法难以获取AL土壤厚度的详细信息。本研究旨在通过随机森林（RF）模型，利用小流域尺度的地形变量，探讨绘制AL土壤厚度空间分布的可能性和准确性。从坡地采集的1113个土壤样本被随机分为校准集（770个土壤样本）和验证集（343个土壤样本）。从数字高程图（30米）中提取了包括海拔、坡向、相对坡位、谷深、流程长度、坡高和地形湿度指数在内的七个地形变量。基于验证集，将RF模型与多元线性回归（MLR）、地理加权回归（GWR）和支持向量机（SVM）方法进行了比较。通过平均误差（ME）、平均绝对误差（MAE）、均方根误差（RMSE）和决定系数（R2）等精度标准对模型性能进行了评估。比较结果表明，RF模型优于MLR、GWR和SVM模型。RF模型的ME值为0.39厘米，MAE值为7.09厘米，RMSE值为10.85厘米，R2值更高，为62%。敏感性分析表明，数字高程模型（DEM）的不确定性低于AL土壤厚度。RF模型的结果表明，海拔、流程长度和谷深是影响整个流域AL土壤厚度变化的最重要因素。这些结果表明，RF模型是一种利用地形参数预测AL土壤厚度空间分布的有前景的方法。
